Merge branch 'emma-nechamkin/release/score-narwhal' into emma-nechamkin/1849-income-imputation-cleanup

.github/workflows/data-checks.yml

@@ -39,6 +39,7 @@ jobs:
         run: poetry show -v
       - name: Install dependencies
         run: poetry install
-        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        # TODO: investigate why caching layer started failing.
+        # if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
       - name: Run tox
         run: poetry run tox

.github/workflows/deploy_be_staging.yml

@@ -61,7 +61,10 @@ jobs:
           poetry run python3 data_pipeline/application.py score-full-run
       - name: Generate Score Post
         run: |
-          poetry run python3 data_pipeline/application.py generate-score-post -s aws
+          poetry run python3 data_pipeline/application.py generate-score-post
+      - name: Generate Score Geo
+        run: |
+          poetry run python3 data_pipeline/application.py geo-score
       - name: Run Smoketests
         run: |
           poetry run pytest data_pipeline/ -m smoketest
@@ -100,9 +103,6 @@ jobs:
           mkdir -p /usr/local/bin
           cp tippecanoe /usr/local/bin/tippecanoe
           tippecanoe -v
-      - name: Generate Score Geo
-        run: |
-          poetry run python3 data_pipeline/application.py geo-score
       - name: Generate Tiles
         run: |
           poetry run python3 data_pipeline/application.py generate-map-tiles

data/data-pipeline/README.md

@@ -12,11 +12,14 @@
       - [2. Extract-Transform-Load (ETL) the data](#2-extract-transform-load-etl-the-data)
       - [3. Combined dataset](#3-combined-dataset)
       - [4. Tileset](#4-tileset)
+      - [5. Shapefiles](#5-shapefiles)
     - [Score generation and comparison workflow](#score-generation-and-comparison-workflow)
       - [Workflow Diagram](#workflow-diagram)
       - [Step 0: Set up your environment](#step-0-set-up-your-environment)
       - [Step 1: Run the script to download census data or download from the Justice40 S3 URL](#step-1-run-the-script-to-download-census-data-or-download-from-the-justice40-s3-url)
       - [Step 2: Run the ETL script for each data source](#step-2-run-the-etl-script-for-each-data-source)
+        - [Table of commands](#table-of-commands)
+        - [ETL steps](#etl-steps)
       - [Step 3: Calculate the Justice40 score experiments](#step-3-calculate-the-justice40-score-experiments)
       - [Step 4: Compare the Justice40 score experiments to other indices](#step-4-compare-the-justice40-score-experiments-to-other-indices)
     - [Data Sources](#data-sources)
@@ -26,21 +29,27 @@
     - [MacOS](#macos)
     - [Windows Users](#windows-users)
     - [Setting up Poetry](#setting-up-poetry)
-    - [Downloading Census Block Groups GeoJSON and Generating CBG CSVs](#downloading-census-block-groups-geojson-and-generating-cbg-csvs)
+    - [Running tox](#running-tox)
+    - [The Application entrypoint](#the-application-entrypoint)
+    - [Downloading Census Block Groups GeoJSON and Generating CBG CSVs (not normally required)](#downloading-census-block-groups-geojson-and-generating-cbg-csvs-not-normally-required)
+    - [Run all ETL, score and map generation processes](#run-all-etl-score-and-map-generation-processes)
+    - [Run both ETL and score generation processes](#run-both-etl-and-score-generation-processes)
+    - [Run all ETL processes](#run-all-etl-processes)
     - [Generating Map Tiles](#generating-map-tiles)
     - [Serve the map locally](#serve-the-map-locally)
     - [Running Jupyter notebooks](#running-jupyter-notebooks)
     - [Activating variable-enabled Markdown for Jupyter notebooks](#activating-variable-enabled-markdown-for-jupyter-notebooks)
-  - [Miscellaneous](#miscellaneous)
   - [Testing](#testing)
     - [Background](#background)
-    - [Configuration / Fixtures](#configuration--fixtures)
+    - [Score and post-processing tests](#score-and-post-processing-tests)
       - [Updating Pickles](#updating-pickles)
-      - [Future Enchancements](#future-enchancements)
-    - [ETL Unit Tests](#etl-unit-tests)
+      - [Future Enhancements](#future-enhancements)
+    - [Fixtures used in ETL "snapshot tests"](#fixtures-used-in-etl-snapshot-tests)
+    - [Other ETL Unit Tests](#other-etl-unit-tests)
       - [Extract Tests](#extract-tests)
       - [Transform Tests](#transform-tests)
       - [Load Tests](#load-tests)
+    - [Smoketests](#smoketests)
 
 <!-- /TOC -->
 
@@ -234,6 +243,15 @@ If you want to run tile generation, please install TippeCanoe [following these i
 - We use [Poetry](https://python-poetry.org/) for managing dependencies and building the application. Please follow the instructions on their site to download.
 - Install Poetry requirements with `poetry install`
 
+### Running tox 
+
+Our full test and check suite is run using tox. This can be run using commands such 
+as `poetry run tox`.
+
+Each run can take a while to build the whole environment. If you'd like to save time,
+you can use the previously built environment by running `poetry run tox -e lint` 
+which will drastically speed up the process.
+
 ### The Application entrypoint
 
 After installing the poetry dependencies, you can see a list of commands with the following steps:
@@ -487,3 +505,13 @@ See above [Fixtures](#configuration--fixtures) section for information about whe
 These make use of [tmp_path_factory](https://docs.pytest.org/en/latest/how-to/tmp_path.html) to create a file-system located under `temp_dir`, and validate whether the correct files are written to the correct locations.
 
 Additional future modifications could include the use of Pandera and/or other schema validation tools, and or a more explicit test that the data written to file can be read back in and yield the same dataframe.
+
+### Smoketests
+
+To ensure the score and tiles process correctly, there is a suite of "smoke tests" that can be run after the ETL and score data have been run, and outputs like the frontend GEOJSON have been created.
+These tests are implemented as pytest test, but are skipped by default. To run them.
+
+1. Generate a full score with `poetry run python3 data_pipeline/application.py score-full-run`
+2. Generate the tile data with `poetry run python3 data_pipeline/application.py generate-score-post`
+3. Generate the frontend GEOJSON with `poetry run python3 data_pipeline/application.py geo-score`
+4. Select the smoke tests for pytest with `poetry run pytest data_pipeline/tests -k smoketest`

data/data-pipeline/data_pipeline/config.py

@@ -12,6 +12,7 @@ settings = Dynaconf(
 
 # set root dir
 settings.APP_ROOT = pathlib.Path(data_pipeline.__file__).resolve().parent
+settings.DATA_PATH = settings.APP_ROOT / "data"
 settings.REQUESTS_DEFAULT_TIMOUT = 3600
 # To set an environment use:
 # Linux/OSX: export ENV_FOR_DYNACONF=staging

data/data-pipeline/data_pipeline/content/config/csv.yml

@@ -380,3 +380,12 @@ fields:
 - score_name: Income data has been estimated based on neighbor income
   label: Income data has been estimated based on geographic neighbor income
   format: bool
+- score_name: Number of Tribal areas within Census tract
+  label: Number of Tribal areas within Census tract
+  format: int64
+- score_name: Names of Tribal areas within Census tract
+  label: Names of Tribal areas within Census tract
+  format: string
+- score_name: Percent of the Census tract that is within Tribal areas
+  label: Percent of the Census tract that is within Tribal areas
+  format: percentage

data/data-pipeline/data_pipeline/content/config/excel.yml

@@ -384,3 +384,12 @@ sheets:
       - score_name: Income data has been estimated based on neighbor income
         label: Income data has been estimated based on geographic neighbor income
         format: bool
+      - score_name: Number of Tribal areas within Census tract
+        label: Number of Tribal areas within Census tract
+        format: int64
+      - score_name: Names of Tribal areas within Census tract
+        label: Names of Tribal areas within Census tract
+        format: string
+      - score_name: Percent of the Census tract that is within Tribal areas
+        label: Percent of the Census tract that is within Tribal areas
+        format: percentage

data/data-pipeline/data_pipeline/etl/base.py

@@ -7,6 +7,9 @@ from typing import Optional
 import pandas as pd
 
 from data_pipeline.config import settings
+from data_pipeline.etl.score.etl_utils import (
+    compare_to_list_of_expected_state_fips_codes,
+)
 from data_pipeline.etl.score.schemas.datasets import DatasetsConfig
 from data_pipeline.utils import (
     load_yaml_dict_from_file,
@@ -43,7 +46,7 @@ class ExtractTransformLoad:
     APP_ROOT: pathlib.Path = settings.APP_ROOT
 
     # Directories
-    DATA_PATH: pathlib.Path = APP_ROOT / "data"
+    DATA_PATH: pathlib.Path = settings.DATA_PATH
     TMP_PATH: pathlib.Path = DATA_PATH / "tmp"
     CONTENT_CONFIG: pathlib.Path = APP_ROOT / "content" / "config"
     DATASET_CONFIG_PATH: pathlib.Path = APP_ROOT / "etl" / "score" / "config"
@@ -82,6 +85,23 @@ class ExtractTransformLoad:
     # NULL_REPRESENTATION is how nulls are represented on the input field
     NULL_REPRESENTATION: str = None
 
+    # Whether this ETL contains data for the continental nation (DC & the US states
+    # except for Alaska and Hawaii)
+    CONTINENTAL_US_EXPECTED_IN_DATA: bool = True
+
+    # Whether this ETL contains data for Alaska and Hawaii
+    ALASKA_AND_HAWAII_EXPECTED_IN_DATA: bool = True
+
+    # Whether this ETL contains data for Puerto Rico
+    PUERTO_RICO_EXPECTED_IN_DATA: bool = True
+
+    # Whether this ETL contains data for the island areas
+    ISLAND_AREAS_EXPECTED_IN_DATA: bool = False
+
+    # Whether this ETL contains known missing data for any additional
+    # states/territories
+    EXPECTED_MISSING_STATES: typing.List[str] = []
+
     # Thirteen digits in a census block group ID.
     EXPECTED_CENSUS_BLOCK_GROUPS_CHARACTER_LENGTH: int = 13
     # TODO: investigate. Census says there are only 217,740 CBGs in the US. This might
@@ -95,56 +115,56 @@ class ExtractTransformLoad:
     #  periods. https://github.com/usds/justice40-tool/issues/964
     EXPECTED_MAX_CENSUS_TRACTS: int = 74160
 
+    # Should this dataset load its configuration from
+    # the YAML files?
+    LOAD_YAML_CONFIG: bool = False
+
     # We use output_df as the final dataframe to use to write to the CSV
     # It is used on the "load" base class method
     output_df: pd.DataFrame = None
 
     def __init_subclass__(cls) -> None:
-        cls.DATASET_CONFIG = cls.yaml_config_load()
+        if cls.LOAD_YAML_CONFIG:
+            cls.DATASET_CONFIG = cls.yaml_config_load()
 
     @classmethod
-    def yaml_config_load(cls) -> Optional[dict]:
+    def yaml_config_load(cls) -> dict:
         """Generate config dictionary and set instance variables from YAML dataset."""
-        if cls.NAME is not None:
-            # check if the class instance has score YAML definitions
-            datasets_config = load_yaml_dict_from_file(
-                cls.DATASET_CONFIG_PATH / "datasets.yml",
-                DatasetsConfig,
+        # check if the class instance has score YAML definitions
+        datasets_config = load_yaml_dict_from_file(
+            cls.DATASET_CONFIG_PATH / "datasets.yml",
+            DatasetsConfig,
+        )
+
+        # get the config for this dataset
+        try:
+            dataset_config = next(
+                item
+                for item in datasets_config.get("datasets")
+                if item["module_name"] == cls.NAME
             )
+        except StopIteration:
+            # Note: it'd be nice to log the name of the dataframe, but that's not accessible in this scope.
+            logger.error(
+                f"Exception encountered while extracting dataset config for dataset {cls.NAME}"
+            )
+            sys.exit()
 
-            # get the config for this dataset
-            try:
-                dataset_config = next(
-                    item
-                    for item in datasets_config.get("datasets")
-                    if item["module_name"] == cls.NAME
-                )
-            except StopIteration:
-                # Note: it'd be nice to log the name of the dataframe, but that's not accessible in this scope.
-                logger.error(
-                    f"Exception encountered while extracting dataset config for dataset {cls.NAME}"
-                )
-                sys.exit()
-
-            # set some of the basic fields
-            if "input_geoid_tract_field_name" in dataset_config:
-                cls.INPUT_GEOID_TRACT_FIELD_NAME = dataset_config[
-                    "input_geoid_tract_field_name"
-                ]
-
-            # get the columns to write on the CSV
-            # and set the constants
-            cls.COLUMNS_TO_KEEP = [
-                cls.GEOID_TRACT_FIELD_NAME,  # always index with geoid tract id
+        # set some of the basic fields
+        if "input_geoid_tract_field_name" in dataset_config:
+            cls.INPUT_GEOID_TRACT_FIELD_NAME = dataset_config[
+                "input_geoid_tract_field_name"
             ]
-            for field in dataset_config["load_fields"]:
-                cls.COLUMNS_TO_KEEP.append(field["long_name"])
-                setattr(cls, field["df_field_name"], field["long_name"])
 
-                # set the constants for the class
-                setattr(cls, field["df_field_name"], field["long_name"])
-            return dataset_config
-        return None
+        # get the columns to write on the CSV
+        # and set the constants
+        cls.COLUMNS_TO_KEEP = [
+            cls.GEOID_TRACT_FIELD_NAME,  # always index with geoid tract id
+        ]
+        for field in dataset_config["load_fields"]:
+            cls.COLUMNS_TO_KEEP.append(field["long_name"])
+            setattr(cls, field["df_field_name"], field["long_name"])
+        return dataset_config
 
     # This is a classmethod so it can be used by `get_data_frame` without
     # needing to create an instance of the class. This is a use case in `etl_score`.
@@ -289,6 +309,24 @@ class ExtractTransformLoad:
                         f"`{geo_field}`."
                     )
 
+        # Check whether data contains expected states
+        states_in_output_df = (
+            self.output_df[self.GEOID_TRACT_FIELD_NAME]
+            .str[0:2]
+            .unique()
+            .tolist()
+        )
+
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=states_in_output_df,
+            continental_us_expected=self.CONTINENTAL_US_EXPECTED_IN_DATA,
+            alaska_and_hawaii_expected=self.ALASKA_AND_HAWAII_EXPECTED_IN_DATA,
+            puerto_rico_expected=self.PUERTO_RICO_EXPECTED_IN_DATA,
+            island_areas_expected=self.ISLAND_AREAS_EXPECTED_IN_DATA,
+            additional_fips_codes_not_expected=self.EXPECTED_MISSING_STATES,
+            dataset_name=self.NAME,
+        )
+
     def load(self, float_format=None) -> None:
         """Saves the transformed data.
 

data/data-pipeline/data_pipeline/etl/constants.py

@@ -186,6 +186,12 @@ DATASET_LIST = [
         "class_name": "AbandonedMineETL",
         "is_memory_intensive": True,
     },
+    {
+        "name": "tribal_overlap",
+        "module_dir": "tribal_overlap",
+        "class_name": "TribalOverlapETL",
+        "is_memory_intensive": True,
+    },
 ]
 
 CENSUS_INFO = {

data/data-pipeline/data_pipeline/etl/runner.py

@@ -106,6 +106,8 @@ def etl_runner(dataset_to_run: str = None) -> None:
                 # Otherwise, the exceptions are silently ignored.
                 fut.result()
 
+    # Note: these high-memory datasets also usually require the Census geojson to be
+    # generated, and one of them requires the Tribal geojson to be generated.
     if high_memory_datasets:
         logger.info("Running high-memory jobs")
         for dataset in high_memory_datasets:

data/data-pipeline/data_pipeline/etl/score/config/datasets.yml

@@ -289,4 +289,43 @@ datasets:
         field_type: percentage
         include_in_tiles: true
         include_in_downloadable_files: true
-        create_percentile: true
+        create_percentile: true
+  - long_name: "Overlap between Census tract boundaries and Tribal area boundaries."
+    short_name: "tribal_overlap"
+    module_name: "tribal_overlap"
+    input_geoid_tract_field_name: "GEOID10_TRACT"
+    load_fields:
+      - short_name: "tribal_count"
+        df_field_name: "COUNT_OF_TRIBAL_AREAS_IN_TRACT"
+        long_name: "Number of Tribal areas within Census tract"
+        field_type: int64
+        include_in_tiles: true
+        include_in_downloadable_files: true
+        create_percentile: false
+      - short_name: "tribal_percent"
+        df_field_name: "PERCENT_OF_TRIBAL_AREA_IN_TRACT"
+        long_name: "Percent of the Census tract that is within Tribal areas"
+        field_type: float
+        include_in_tiles: true
+        include_in_downloadable_files: true
+        create_percentile: false
+        number_of_decimals_in_output: 6
+      - short_name: "tribal_names"
+        df_field_name: "NAMES_OF_TRIBAL_AREAS_IN_TRACT"
+        long_name: "Names of Tribal areas within Census tract"
+        field_type: string
+        include_in_tiles: true
+        include_in_downloadable_files: true
+  - long_name: "CDC Life Expeectancy"
+    short_name: "cdc_life_expectancy"
+    module_name: "cdc_life_expectancy"
+    input_geoid_tract_field_name: "Tract ID"
+    load_fields:
+    -  short_name: "LLEF"
+       df_field_name: "LIFE_EXPECTANCY_FIELD_NAME"
+       long_name: "Life expectancy (years)"
+       field_type: float
+       include_in_tiles: false
+       include_in_downloadable_files: true
+       create_percentile: false
+       create_reverse_percentile: true

data/data-pipeline/data_pipeline/etl/score/constants.py

@@ -131,6 +131,58 @@ TILES_NATION_THRESHOLD_COUNT = 21
 # 60: American Samoa, 66: Guam, 69: N. Mariana Islands, 78: US Virgin Islands
 TILES_ISLAND_AREA_FIPS_CODES = ["60", "66", "69", "78"]
 TILES_PUERTO_RICO_FIPS_CODE = ["72"]
+TILES_ALASKA_AND_HAWAII_FIPS_CODE = ["02", "15"]
+TILES_CONTINENTAL_US_FIPS_CODE = [
+    "01",
+    "04",
+    "05",
+    "06",
+    "08",
+    "09",
+    "10",
+    "11",
+    "12",
+    "13",
+    "16",
+    "17",
+    "18",
+    "19",
+    "20",
+    "21",
+    "22",
+    "23",
+    "24",
+    "25",
+    "26",
+    "27",
+    "28",
+    "29",
+    "30",
+    "31",
+    "32",
+    "33",
+    "34",
+    "35",
+    "36",
+    "37",
+    "38",
+    "39",
+    "40",
+    "41",
+    "42",
+    "44",
+    "45",
+    "46",
+    "47",
+    "48",
+    "49",
+    "50",
+    "51",
+    "53",
+    "54",
+    "55",
+    "56",
+]
 
 # Constant to reflect UI Experience version
 # "Nation" referring to 50 states and DC is from Census
@@ -327,6 +379,10 @@ TILES_SCORE_COLUMNS = {
     field_names.PERCENT_AGE_UNDER_10: "AGE_10",
     field_names.PERCENT_AGE_10_TO_64: "AGE_MIDDLE",
     field_names.PERCENT_AGE_OVER_64: "AGE_OLD",
+    field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT: "TA_COUNT",
+    field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT: "TA_PERC",
+    
+
 }
 
 # columns to round floats to 2 decimals
@@ -399,5 +455,6 @@ TILES_SCORE_FLOAT_COLUMNS = [
     # that use null to signify missing information in a boolean field.
     field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME,
     field_names.AML_BOOLEAN,
-    field_names.HISTORIC_REDLINING_SCORE_EXCEEDED
+    field_names.HISTORIC_REDLINING_SCORE_EXCEEDED,
+    field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT
 ]

data/data-pipeline/data_pipeline/etl/score/etl_score.py

@@ -15,6 +15,7 @@ from data_pipeline.etl.sources.fsf_flood_risk.etl import (
     FloodRiskETL,
 )
 from data_pipeline.etl.sources.eamlis.etl import AbandonedMineETL
+from data_pipeline.etl.sources.tribal_overlap.etl import TribalOverlapETL
 from data_pipeline.etl.sources.us_army_fuds.etl import USArmyFUDS
 from data_pipeline.etl.sources.nlcd_nature_deprived.etl import NatureDeprivedETL
 from data_pipeline.etl.sources.fsf_wildfire_risk.etl import WildfireRiskETL
@@ -52,6 +53,7 @@ class ScoreETL(ExtractTransformLoad):
         self.nature_deprived_df: pd.DataFrame
         self.eamlis_df: pd.DataFrame
         self.fuds_df: pd.DataFrame
+        self.tribal_overlap_df: pd.DataFrame
 
     def extract(self) -> None:
         logger.info("Loading data sets from disk.")
@@ -148,6 +150,9 @@ class ScoreETL(ExtractTransformLoad):
         # Load FUDS dataset
         self.fuds_df = USArmyFUDS.get_data_frame()
 
+        # Load Tribal overlap dataset
+        self.tribal_overlap_df = TribalOverlapETL.get_data_frame()
+
         # Load GeoCorr Urban Rural Map
         geocorr_urban_rural_csv = (
             constants.DATA_PATH / "dataset" / "geocorr" / "usa.csv"
@@ -359,6 +364,7 @@ class ScoreETL(ExtractTransformLoad):
             self.nature_deprived_df,
             self.eamlis_df,
             self.fuds_df,
+            self.tribal_overlap_df
         ]
 
         # Sanity check each data frame before merging.
@@ -380,7 +386,8 @@ class ScoreETL(ExtractTransformLoad):
         ), "Join against national tract list ADDED rows"
         logger.info(
             "Dropped %s tracts not in the 2010 tract data",
-            pre_join_len - census_tract_df[field_names.GEOID_TRACT_FIELD].nunique()
+            pre_join_len
+            - census_tract_df[field_names.GEOID_TRACT_FIELD].nunique(),
         )
 
         # Now sanity-check the merged df.
@@ -468,12 +475,15 @@ class ScoreETL(ExtractTransformLoad):
             field_names.PERCENT_AGE_UNDER_10,
             field_names.PERCENT_AGE_10_TO_64,
             field_names.PERCENT_AGE_OVER_64,
+            field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT,
+            field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT,
         ]
 
         non_numeric_columns = [
             self.GEOID_TRACT_FIELD_NAME,
             field_names.TRACT_ELIGIBLE_FOR_NONNATURAL_THRESHOLD,
             field_names.AGRICULTURAL_VALUE_BOOL_FIELD,
+            field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT,
         ]
 
         boolean_columns = [
@@ -551,6 +561,9 @@ class ScoreETL(ExtractTransformLoad):
         #     For *Non-Natural Space*, we may only want to include tracts that have at least 35 acreas, I think. This will
         #     get rid of  tracts that we think are aberrations statistically. Right now, we have left this out
         #     pending ground-truthing.
+        #
+        #     For *Traffic Barriers*, we want to exclude low population tracts, which may have high burden because they are
+        #     low population alone. We set this low population constant in the if statement.
 
         for numeric_column in numeric_columns:
             drop_tracts = []
@@ -575,6 +588,22 @@ class ScoreETL(ExtractTransformLoad):
                     f"Dropping {len(drop_tracts)} tracts from Linguistic Isolation"
                 )
 
+            elif numeric_column in [
+                field_names.DOT_TRAVEL_BURDEN_FIELD,
+                field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD,
+            ]:
+                # Not having any people appears to be correlated with transit burden, but also doesn't represent
+                # on the ground need. For now, we remove these tracts from the percentile calculation.ß
+                # Similarly, we want to exclude low population tracts from FEMA's index
+                low_population = 20
+                drop_tracts = df_copy[
+                    df_copy[field_names.TOTAL_POP_FIELD].fillna(0)
+                    <= low_population
+                ][field_names.GEOID_TRACT_FIELD].to_list()
+                logger.info(
+                    f"Dropping {len(drop_tracts)} tracts from DOT traffic burden"
+                )
+
             df_copy = self._add_percentiles_to_df(
                 df=df_copy,
                 input_column_name=numeric_column,

data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py

@@ -41,7 +41,6 @@ class GeoScoreETL(ExtractTransformLoad):
         self.SCORE_CSV_PATH = self.DATA_PATH / "score" / "csv"
         self.TILE_SCORE_CSV = self.SCORE_CSV_PATH / "tiles" / "usa.csv"
 
-        self.DATA_SOURCE = data_source
         self.CENSUS_USA_GEOJSON = (
             self.DATA_PATH / "census" / "geojson" / "us.json"
         )
@@ -60,6 +59,7 @@ class GeoScoreETL(ExtractTransformLoad):
             field_names.GEOID_TRACT_FIELD
         ]
         self.GEOMETRY_FIELD_NAME = "geometry"
+        self.LAND_FIELD_NAME = "ALAND10"
 
         # We will adjust this upwards while there is some fractional value
         # in the score. This is a starting value.
@@ -86,13 +86,22 @@ class GeoScoreETL(ExtractTransformLoad):
         )
 
         logger.info("Reading US GeoJSON (~6 minutes)")
-        self.geojson_usa_df = gpd.read_file(
+        full_geojson_usa_df = gpd.read_file(
             self.CENSUS_USA_GEOJSON,
             dtype={self.GEOID_FIELD_NAME: "string"},
-            usecols=[self.GEOID_FIELD_NAME, self.GEOMETRY_FIELD_NAME],
+            usecols=[
+                self.GEOID_FIELD_NAME,
+                self.GEOMETRY_FIELD_NAME,
+                self.LAND_FIELD_NAME,
+            ],
             low_memory=False,
         )
 
+        # We only want to keep tracts to visualize that have non-0 land
+        self.geojson_usa_df = full_geojson_usa_df[
+            full_geojson_usa_df[self.LAND_FIELD_NAME] > 0
+        ]
+
         logger.info("Reading score CSV")
         self.score_usa_df = pd.read_csv(
             self.TILE_SCORE_CSV,

data/data-pipeline/data_pipeline/etl/score/etl_utils.py

@@ -1,11 +1,19 @@
 import os
 import sys
+import typing
 from pathlib import Path
 from collections import namedtuple
 import numpy as np
 import pandas as pd
 
 from data_pipeline.config import settings
+from data_pipeline.etl.score.constants import (
+    TILES_ISLAND_AREA_FIPS_CODES,
+    TILES_PUERTO_RICO_FIPS_CODE,
+    TILES_CONTINENTAL_US_FIPS_CODE,
+    TILES_ALASKA_AND_HAWAII_FIPS_CODE,
+)
+from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes
 from data_pipeline.utils import (
     download_file_from_url,
     get_module_logger,
@@ -305,3 +313,106 @@ def create_codebook(
     return merged_codebook_df[constants.CODEBOOK_COLUMNS].rename(
         columns={constants.CEJST_SCORE_COLUMN_NAME: "Description"}
     )
+
+
+# pylint: disable=too-many-arguments
+def compare_to_list_of_expected_state_fips_codes(
+    actual_state_fips_codes: typing.List[str],
+    continental_us_expected: bool = True,
+    alaska_and_hawaii_expected: bool = True,
+    puerto_rico_expected: bool = True,
+    island_areas_expected: bool = True,
+    additional_fips_codes_not_expected: typing.List[str] = None,
+    dataset_name: str = None,
+) -> None:
+    """Check whether a list of state/territory FIPS codes match expectations.
+
+    Args:
+        actual_state_fips_codes (List of str): Actual state codes observed in data
+        continental_us_expected (bool, optional): Do you expect the continental nation
+            (DC & states except for Alaska and Hawaii) to be represented in data?
+        alaska_and_hawaii_expected (bool, optional): Do you expect Alaska and Hawaii
+            to be represented in the data? Note: if only *1* of Alaska and Hawaii are
+            not expected to be included, do not use this argument -- instead,
+            use `additional_fips_codes_not_expected` for the 1 state you expected to
+            be missing.
+        puerto_rico_expected (bool, optional): Do you expect PR to be represented in data?
+        island_areas_expected (bool, optional): Do you expect Island Areas to be represented in
+            data?
+        additional_fips_codes_not_expected (List of str, optional): Additional state codes
+            not expected in the data. For example, the data may be known to be missing
+            data from Maine and Wisconsin.
+        dataset_name (str, optional): The name of the data set, used only in printing an
+            error message. (This is helpful for debugging during parallel etl runs.)
+
+    Returns:
+        None: Does not return any values.
+
+    Raises:
+        ValueError: if lists do not match expectations.
+    """
+    # Setting default argument of [] here to avoid mutability problems.
+    if additional_fips_codes_not_expected is None:
+        additional_fips_codes_not_expected = []
+
+    # Cast input to a set.
+    actual_state_fips_codes_set = set(actual_state_fips_codes)
+
+    # Start with the list of all FIPS codes for all states and territories.
+    expected_states_set = set(get_state_fips_codes(settings.DATA_PATH))
+
+    # If continental US is not expected to be included, remove it from the
+    # expected states set.
+    if not continental_us_expected:
+        expected_states_set = expected_states_set.difference(
+            TILES_CONTINENTAL_US_FIPS_CODE
+        )
+
+    # If both Alaska and Hawaii are not expected to be included, remove them from the
+    # expected states set.
+    # Note: if only *1* of Alaska and Hawaii are not expected to be included,
+    # do not use this argument -- instead, use `additional_fips_codes_not_expected`
+    # for the 1 state you expected to be missing.
+    if not alaska_and_hawaii_expected:
+        expected_states_set = expected_states_set.difference(
+            TILES_ALASKA_AND_HAWAII_FIPS_CODE
+        )
+
+    # If Puerto Rico is not expected to be included, remove it from the expected
+    # states set.
+    if not puerto_rico_expected:
+        expected_states_set = expected_states_set.difference(
+            TILES_PUERTO_RICO_FIPS_CODE
+        )
+
+    # If island areas are not expected to be included, remove them from the expected
+    # states set.
+    if not island_areas_expected:
+        expected_states_set = expected_states_set.difference(
+            TILES_ISLAND_AREA_FIPS_CODES
+        )
+
+    # If additional FIPS codes are not expected to be included, remove them from the
+    # expected states set.
+    expected_states_set = expected_states_set.difference(
+        additional_fips_codes_not_expected
+    )
+
+    dataset_name_phrase = (
+        f" for dataset `{dataset_name}`" if dataset_name is not None else ""
+    )
+
+    if expected_states_set != actual_state_fips_codes_set:
+        raise ValueError(
+            f"The states and territories in the data{dataset_name_phrase} are not "
+            f"as expected.\n"
+            "FIPS state codes expected that are not present in the data:\n"
+            f"{sorted(list(expected_states_set - actual_state_fips_codes_set))}\n"
+            "FIPS state codes in the data that were not expected:\n"
+            f"{sorted(list(actual_state_fips_codes_set - expected_states_set))}\n"
+        )
+    else:
+        logger.info(
+            "Data matches expected state and territory representation"
+            f"{dataset_name_phrase}."
+        )

data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py

@@ -2,7 +2,10 @@ import pandas as pd
 import numpy as np
 import pytest
 
-from data_pipeline.etl.score.etl_utils import floor_series
+from data_pipeline.etl.score.etl_utils import (
+    floor_series,
+    compare_to_list_of_expected_state_fips_codes,
+)
 
 
 def test_floor_series():
@@ -70,3 +73,181 @@ def test_floor_series():
         match="Argument series must be of type pandas series, not of type list.",
     ):
         floor_series(invalid_type, number_of_decimals=3)
+
+
+def test_compare_to_list_of_expected_state_fips_codes():
+    # Has every state/territory/DC code
+    fips_codes_test_1 = [
+        "01",
+        "02",
+        "04",
+        "05",
+        "06",
+        "08",
+        "09",
+        "10",
+        "11",
+        "12",
+        "13",
+        "15",
+        "16",
+        "17",
+        "18",
+        "19",
+        "20",
+        "21",
+        "22",
+        "23",
+        "24",
+        "25",
+        "26",
+        "27",
+        "28",
+        "29",
+        "30",
+        "31",
+        "32",
+        "33",
+        "34",
+        "35",
+        "36",
+        "37",
+        "38",
+        "39",
+        "40",
+        "41",
+        "42",
+        "44",
+        "45",
+        "46",
+        "47",
+        "48",
+        "49",
+        "50",
+        "51",
+        "53",
+        "54",
+        "55",
+        "56",
+        "60",
+        "66",
+        "69",
+        "72",
+        "78",
+    ]
+
+    # Should not raise any errors
+    compare_to_list_of_expected_state_fips_codes(
+        actual_state_fips_codes=fips_codes_test_1
+    )
+
+    # Should raise error because Puerto Rico is not expected
+    with pytest.raises(ValueError) as exception_info:
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=fips_codes_test_1,
+            puerto_rico_expected=False,
+        )
+    partial_expected_error_message = (
+        "FIPS state codes in the data that were not expected:\n['72']\n"
+    )
+    assert partial_expected_error_message in str(exception_info.value)
+
+    # Should raise error because Island Areas are not expected
+    with pytest.raises(ValueError) as exception_info:
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=fips_codes_test_1,
+            island_areas_expected=False,
+        )
+    partial_expected_error_message = (
+        "FIPS state codes in the data that were not expected:\n"
+        "['60', '66', '69', '78']\n"
+    )
+    assert partial_expected_error_message in str(exception_info.value)
+
+    # List missing PR and Guam
+    fips_codes_test_2 = [x for x in fips_codes_test_1 if x not in ["66", "72"]]
+
+    # Should raise error because all Island Areas and PR are expected
+    with pytest.raises(ValueError) as exception_info:
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=fips_codes_test_2,
+        )
+    partial_expected_error_message = (
+        "FIPS state codes expected that are not present in the data:\n"
+        "['66', '72']\n"
+    )
+    assert partial_expected_error_message in str(exception_info.value)
+
+    # Missing Maine and Wisconsin
+    fips_codes_test_3 = [x for x in fips_codes_test_1 if x not in ["23", "55"]]
+
+    # Should raise error because Maine and Wisconsin are expected
+    with pytest.raises(ValueError) as exception_info:
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=fips_codes_test_3,
+        )
+    partial_expected_error_message = (
+        "FIPS state codes expected that are not present in the data:\n"
+        "['23', '55']\n"
+    )
+    assert partial_expected_error_message in str(exception_info.value)
+
+    # Should not raise error because Maine and Wisconsin are expected to be missing
+    compare_to_list_of_expected_state_fips_codes(
+        actual_state_fips_codes=fips_codes_test_3,
+        additional_fips_codes_not_expected=["23", "55"],
+    )
+
+    # Missing the continental & AK/HI nation
+    fips_codes_test_4 = [
+        "60",
+        "66",
+        "69",
+        "72",
+        "78",
+    ]
+
+    # Should raise error because the nation is expected
+    with pytest.raises(ValueError) as exception_info:
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=fips_codes_test_4,
+        )
+
+    partial_expected_error_message = (
+        "FIPS state codes expected that are not present in the data:\n"
+        "['01', '02', '04', '05', '06', '08', '09', '10', '11', '12', '13', '15', '16', "
+        "'17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', "
+        "'30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', "
+        "'44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56']"
+    )
+
+    assert partial_expected_error_message in str(exception_info.value)
+
+    # Should not raise error because continental US and AK/HI is not to be missing
+    compare_to_list_of_expected_state_fips_codes(
+        actual_state_fips_codes=fips_codes_test_4,
+        continental_us_expected=False,
+        alaska_and_hawaii_expected=False,
+    )
+
+    # Missing Hawaii but not Alaska
+    fips_codes_test_5 = [x for x in fips_codes_test_1 if x not in ["15"]]
+
+    # Should raise error because both Hawaii and Alaska are expected
+    with pytest.raises(ValueError) as exception_info:
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=fips_codes_test_5,
+            alaska_and_hawaii_expected=True,
+        )
+    partial_expected_error_message = (
+        "FIPS state codes expected that are not present in the data:\n"
+        "['15']\n"
+    )
+    assert partial_expected_error_message in str(exception_info.value)
+
+    # Should work as expected
+    compare_to_list_of_expected_state_fips_codes(
+        actual_state_fips_codes=fips_codes_test_5,
+        alaska_and_hawaii_expected=True,
+        additional_fips_codes_not_expected=["15"],
+    )

data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py

@@ -1,58 +1,139 @@
+import pathlib
 from pathlib import Path
 import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.score.etl_utils import (
+    compare_to_list_of_expected_state_fips_codes,
+)
+from data_pipeline.score import field_names
 from data_pipeline.utils import get_module_logger, download_file_from_url
 
 logger = get_module_logger(__name__)
 
 
 class CDCLifeExpectancy(ExtractTransformLoad):
+    GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+
+    NAME = "cdc_life_expectancy"
+
+    USA_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/US_A.CSV"
+    LOAD_YAML_CONFIG: bool = False
+    LIFE_EXPECTANCY_FIELD_NAME = "Life expectancy (years)"
+    INPUT_GEOID_TRACT_FIELD_NAME = "Tract ID"
+
+    STATES_MISSING_FROM_USA_FILE = ["23", "55"]
+
+    # For some reason, LEEP does not include Maine or Wisconsin in its "All of
+    # USA" file. Load these separately.
+    WISCONSIN_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/WI_A.CSV"
+    MAINE_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/ME_A.CSV"
+
+    TRACT_INPUT_COLUMN_NAME = "Tract ID"
+    STATE_INPUT_COLUMN_NAME = "STATE2KX"
+
+    raw_df: pd.DataFrame
+    output_df: pd.DataFrame
+
     def __init__(self):
-        self.FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/US_A.CSV"
         self.OUTPUT_PATH: Path = (
             self.DATA_PATH / "dataset" / "cdc_life_expectancy"
         )
 
-        self.TRACT_INPUT_COLUMN_NAME = "Tract ID"
-        self.LIFE_EXPECTANCY_FIELD_NAME = "Life expectancy (years)"
-
         # Constants for output
         self.COLUMNS_TO_KEEP = [
             self.GEOID_TRACT_FIELD_NAME,
-            self.LIFE_EXPECTANCY_FIELD_NAME,
+            field_names.LIFE_EXPECTANCY_FIELD,
         ]
 
-        self.raw_df: pd.DataFrame
-        self.output_df: pd.DataFrame
-
-    def extract(self) -> None:
-        logger.info("Starting data download.")
-
-        download_file_name = (
-            self.get_tmp_path() / "cdc_life_expectancy" / "usa.csv"
-        )
+    def _download_and_prep_data(
+        self, file_url: str, download_file_name: pathlib.Path
+    ) -> pd.DataFrame:
         download_file_from_url(
-            file_url=self.FILE_URL,
+            file_url=file_url,
             download_file_name=download_file_name,
             verify=True,
         )
 
-        self.raw_df = pd.read_csv(
+        df = pd.read_csv(
             filepath_or_buffer=download_file_name,
             dtype={
                 # The following need to remain as strings for all of their digits, not get converted to numbers.
                 self.TRACT_INPUT_COLUMN_NAME: "string",
+                self.STATE_INPUT_COLUMN_NAME: "string",
             },
             low_memory=False,
         )
 
+        return df
+
+    def extract(self) -> None:
+        logger.info("Starting data download.")
+
+        all_usa_raw_df = self._download_and_prep_data(
+            file_url=self.USA_FILE_URL,
+            download_file_name=self.get_tmp_path()
+            / "US_A.CSV",
+        )
+
+        # Check which states are missing
+        states_in_life_expectancy_usa_file = list(
+            all_usa_raw_df[self.STATE_INPUT_COLUMN_NAME].unique()
+        )
+
+        # Expect that PR, Island Areas, and Maine/Wisconsin are missing
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=states_in_life_expectancy_usa_file,
+            continental_us_expected=self.CONTINENTAL_US_EXPECTED_IN_DATA,
+            puerto_rico_expected=self.PUERTO_RICO_EXPECTED_IN_DATA,
+            island_areas_expected=self.ISLAND_AREAS_EXPECTED_IN_DATA,
+            additional_fips_codes_not_expected=self.STATES_MISSING_FROM_USA_FILE,
+        )
+
+        logger.info("Downloading data for Maine")
+        maine_raw_df = self._download_and_prep_data(
+            file_url=self.MAINE_FILE_URL,
+            download_file_name=self.get_tmp_path()
+            / "maine.csv",
+        )
+
+        logger.info("Downloading data for Wisconsin")
+        wisconsin_raw_df = self._download_and_prep_data(
+            file_url=self.WISCONSIN_FILE_URL,
+            download_file_name=self.get_tmp_path()
+            / "wisconsin.csv",
+        )
+
+        combined_df = pd.concat(
+            objs=[all_usa_raw_df, maine_raw_df, wisconsin_raw_df],
+            ignore_index=True,
+            verify_integrity=True,
+            axis=0,
+        )
+
+        states_in_combined_df = list(
+            combined_df[self.STATE_INPUT_COLUMN_NAME].unique()
+        )
+
+        # Expect that PR and Island Areas are the only things now missing
+        compare_to_list_of_expected_state_fips_codes(
+            actual_state_fips_codes=states_in_combined_df,
+            continental_us_expected=self.CONTINENTAL_US_EXPECTED_IN_DATA,
+            puerto_rico_expected=self.PUERTO_RICO_EXPECTED_IN_DATA,
+            island_areas_expected=self.ISLAND_AREAS_EXPECTED_IN_DATA,
+            additional_fips_codes_not_expected=[],
+        )
+
+        # Save the updated version
+        self.raw_df = combined_df
+
     def transform(self) -> None:
-        logger.info("Starting DOE energy burden transform.")
+        logger.info("Starting CDC life expectancy transform.")
 
         self.output_df = self.raw_df.rename(
             columns={
-                "e(0)": self.LIFE_EXPECTANCY_FIELD_NAME,
+                "e(0)": field_names.LIFE_EXPECTANCY_FIELD,
                 self.TRACT_INPUT_COLUMN_NAME: self.GEOID_TRACT_FIELD_NAME,
             }
         )

data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py

@@ -1,6 +1,7 @@
+import typing
 import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.utils import get_module_logger, download_file_from_url
 from data_pipeline.score import field_names
 
@@ -8,13 +9,27 @@ logger = get_module_logger(__name__)
 
 
 class CDCPlacesETL(ExtractTransformLoad):
+    NAME = "cdc_places"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+
+    CDC_GEOID_FIELD_NAME = "LocationID"
+    CDC_VALUE_FIELD_NAME = "Data_Value"
+    CDC_MEASURE_FIELD_NAME = "Measure"
+
     def __init__(self):
         self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "cdc_places"
 
         self.CDC_PLACES_URL = "https://chronicdata.cdc.gov/api/views/cwsq-ngmh/rows.csv?accessType=DOWNLOAD"
-        self.CDC_GEOID_FIELD_NAME = "LocationID"
-        self.CDC_VALUE_FIELD_NAME = "Data_Value"
-        self.CDC_MEASURE_FIELD_NAME = "Measure"
+        self.COLUMNS_TO_KEEP: typing.List[str] = [
+            self.GEOID_TRACT_FIELD_NAME,
+            field_names.DIABETES_FIELD,
+            field_names.ASTHMA_FIELD,
+            field_names.HEART_DISEASE_FIELD,
+            field_names.CANCER_FIELD,
+            field_names.HEALTH_INSURANCE_FIELD,
+            field_names.PHYS_HEALTH_NOT_GOOD_FIELD,
+        ]
 
         self.df: pd.DataFrame
 
@@ -22,9 +37,7 @@ class CDCPlacesETL(ExtractTransformLoad):
         logger.info("Starting to download 520MB CDC Places file.")
         file_path = download_file_from_url(
             file_url=self.CDC_PLACES_URL,
-            download_file_name=self.get_tmp_path()
-            / "cdc_places"
-            / "census_tract.csv",
+            download_file_name=self.get_tmp_path() / "census_tract.csv",
         )
 
         self.df = pd.read_csv(
@@ -42,7 +55,6 @@ class CDCPlacesETL(ExtractTransformLoad):
             inplace=True,
             errors="raise",
         )
-
         # Note: Puerto Rico not included.
         self.df = self.df.pivot(
             index=self.GEOID_TRACT_FIELD_NAME,
@@ -65,12 +77,4 @@ class CDCPlacesETL(ExtractTransformLoad):
         )
 
         # Make the index (the census tract ID) a column, not the index.
-        self.df.reset_index(inplace=True)
-
-    def load(self) -> None:
-        logger.info("Saving CDC Places Data")
-
-        # mkdir census
-        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
-        self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
+        self.output_df = self.df.reset_index()

data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py

@@ -25,6 +25,7 @@ class ChildOpportunityIndex(ExtractTransformLoad):
     # Metadata for the baseclass
     NAME = "child_opportunity_index"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    LOAD_YAML_CONFIG: bool = True
 
     # Define these for easy code completion
     EXTREME_HEAT_FIELD: str
@@ -32,6 +33,8 @@ class ChildOpportunityIndex(ExtractTransformLoad):
     IMPENETRABLE_SURFACES_FIELD: str
     READING_FIELD: str
 
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+
     def __init__(self):
         self.SOURCE_URL = (
             "https://data.diversitydatakids.org/datastore/zip/f16fff12-b1e5-4f60-85d3-"

data/data-pipeline/data_pipeline/etl/sources/doe_energy_burden/etl.py

@@ -15,6 +15,7 @@ class DOEEnergyBurden(ExtractTransformLoad):
         + "/DOE_LEAD_AMI_TRACT_2018_ALL.csv.zip"
     )
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    LOAD_YAML_CONFIG: bool = True
 
     REVISED_ENERGY_BURDEN_FIELD_NAME: str
 
@@ -56,8 +57,3 @@ class DOEEnergyBurden(ExtractTransformLoad):
         )
 
         self.output_df = output_df
-
-    def load(self) -> None:
-        logger.info("Saving DOE Energy Burden CSV")
-
-        super().load()

data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py

@@ -16,6 +16,8 @@ class TravelCompositeETL(ExtractTransformLoad):
     NAME = "travel_composite"
     SOURCE_URL = "https://www.transportation.gov/sites/dot.gov/files/Shapefile_and_Metadata.zip"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    LOAD_YAML_CONFIG: bool = True
 
     # Output score variables (values set on datasets.yml) for linting purposes
     TRAVEL_BURDEN_FIELD_NAME: str

data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py

@@ -19,6 +19,24 @@ class AbandonedMineETL(ExtractTransformLoad):
     NAME = "eamlis"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
     AML_BOOLEAN: str
+    LOAD_YAML_CONFIG: bool = True
+
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    EXPECTED_MISSING_STATES = [
+        "10",
+        "11",
+        "12",
+        "15",
+        "23",
+        "27",
+        "31",
+        "33",
+        "34",
+        "36",
+        "45",
+        "50",
+        "55",
+    ]
 
     # Define these for easy code completion
     def __init__(self):

data/data-pipeline/data_pipeline/etl/sources/ejscreen/etl.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.score import field_names
 from data_pipeline.utils import get_module_logger
 
@@ -10,6 +10,10 @@ logger = get_module_logger(__name__)
 class EJSCREENETL(ExtractTransformLoad):
     """Load updated EJSCREEN data."""
 
+    NAME = "ejscreen"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    INPUT_GEOID_TRACT_FIELD_NAME: str = "ID"
+
     def __init__(self):
         self.EJSCREEN_FTP_URL = "https://gaftp.epa.gov/EJSCREEN/2021/EJSCREEN_2021_USPR_Tracts.csv.zip"
         self.EJSCREEN_CSV = (
@@ -52,16 +56,16 @@ class EJSCREENETL(ExtractTransformLoad):
         logger.info("Transforming EJScreen Data")
         self.df = pd.read_csv(
             self.EJSCREEN_CSV,
-            dtype={"ID": str},
+            dtype={self.INPUT_GEOID_TRACT_FIELD_NAME: str},
             # EJSCREEN writes the word "None" for NA data.
             na_values=["None"],
             low_memory=False,
         )
 
         # rename ID to Tract ID
-        self.df.rename(
+        self.output_df = self.df.rename(
             columns={
-                "ID": self.GEOID_TRACT_FIELD_NAME,
+                self.INPUT_GEOID_TRACT_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME,
                 "ACSTOTPOP": field_names.TOTAL_POP_FIELD,
                 "CANCER": field_names.AIR_TOXICS_CANCER_RISK_FIELD,
                 "RESP": field_names.RESPIRATORY_HAZARD_FIELD,
@@ -80,13 +84,4 @@ class EJSCREENETL(ExtractTransformLoad):
                 "PRE1960PCT": field_names.LEAD_PAINT_FIELD,
                 "UST": field_names.UST_FIELD,  # added for 2021 update
             },
-            inplace=True,
-        )
-
-    def load(self) -> None:
-        logger.info("Saving EJScreen CSV")
-        # write nationwide csv
-        self.CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df[self.COLUMNS_TO_KEEP].to_csv(
-            self.CSV_PATH / "usa.csv", index=False
         )

data/data-pipeline/data_pipeline/etl/sources/fsf_flood_risk/etl.py

@@ -14,8 +14,11 @@ class FloodRiskETL(ExtractTransformLoad):
     """ETL class for the First Street Foundation flood risk dataset"""
 
     NAME = "fsf_flood_risk"
+    # These data were emailed to the J40 team while first street got
+    # their official data sharing channels setup.
     SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/fsf_flood.zip"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    LOAD_YAML_CONFIG: bool = True
 
     # Output score variables (values set on datasets.yml) for linting purposes
     COUNT_PROPERTIES: str

data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py

@@ -14,8 +14,13 @@ class WildfireRiskETL(ExtractTransformLoad):
     """ETL class for the First Street Foundation wildfire risk dataset"""
 
     NAME = "fsf_wildfire_risk"
+    # These data were emailed to the J40 team while first street got
+    # their official data sharing channels setup.
     SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/fsf_fire.zip"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    LOAD_YAML_CONFIG: bool = True
+    ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False
 
     # Output score variables (values set on datasets.yml) for linting purposes
     COUNT_PROPERTIES: str

data/data-pipeline/data_pipeline/etl/sources/geo_utils.py

@@ -4,6 +4,7 @@ from pathlib import Path
 from typing import Optional
 from functools import lru_cache
 import geopandas as gpd
+from data_pipeline.etl.sources.tribal.etl import TribalETL
 from data_pipeline.utils import get_module_logger
 from .census.etl import CensusETL
 from data_pipeline.score import field_names
@@ -19,23 +20,48 @@ def get_tract_geojson(
     GEOJSON_PATH = _tract_data_path
     if GEOJSON_PATH is None:
         GEOJSON_PATH = CensusETL.NATIONAL_TRACT_JSON_PATH
-        if not GEOJSON_PATH.exists():
-            logger.debug("Census data has not been computed, running")
-            census_etl = CensusETL()
-            census_etl.extract()
-            census_etl.transform()
-            census_etl.load()
-        else:
-            logger.debug("Loading existing tract geojson")
-    tract_data = gpd.read_file(GEOJSON_PATH, include_fields=["GEOID10"])
-    tract_data.rename(
-        columns={"GEOID10": field_names.GEOID_TRACT_FIELD}, inplace=True
+    if not GEOJSON_PATH.exists():
+        logger.debug("Census data has not been computed, running")
+        census_etl = CensusETL()
+        census_etl.extract()
+        census_etl.transform()
+        census_etl.load()
+    else:
+        logger.debug("Loading existing tract geojson")
+
+    tract_data = gpd.read_file(
+        GEOJSON_PATH,
+        include_fields=["GEOID10"],
+    )
+    tract_data = tract_data.rename(
+        columns={"GEOID10": "GEOID10_TRACT"},
+        errors="raise",
     )
     return tract_data
 
 
+@lru_cache()
+def get_tribal_geojson(
+    _tribal_data_path: Optional[Path] = None,
+) -> gpd.GeoDataFrame:
+    logger.info("Loading Tribal geometry data from Tribal ETL")
+    GEOJSON_PATH = _tribal_data_path
+    if GEOJSON_PATH is None:
+        GEOJSON_PATH = TribalETL().NATIONAL_TRIBAL_GEOJSON_PATH
+    if not GEOJSON_PATH.exists():
+        logger.debug("Tribal data has not been computed, running")
+        tribal_etl = TribalETL()
+        tribal_etl.extract()
+        tribal_etl.transform()
+        tribal_etl.load()
+    tribal_data = gpd.read_file(
+        GEOJSON_PATH,
+    )
+    return tribal_data
+
+
 def add_tracts_for_geometries(
-    df: gpd.GeoDataFrame, _tract_data_path: Optional[Path] = None
+    df: gpd.GeoDataFrame, tract_data: Optional[gpd.GeoDataFrame] = None
 ) -> gpd.GeoDataFrame:
     """Adds tract-geoids to dataframe df that contains spatial geometries
 
@@ -43,8 +69,8 @@ def add_tracts_for_geometries(
 
     Args:
         df (GeoDataFrame): a geopandas GeoDataFrame with a point geometry column
-        _tract_data_path (Path): an override to directly pass a GEOJSON file of
-                              tracts->Geometries, to simplify testing.
+        tract_data (GeoDataFrame): optional override to directly pass a
+            geodataframe of the tract boundaries. Also helps simplify testing.
 
     Returns:
         GeoDataFrame: the above dataframe, with an additional GEOID10_TRACT column that
@@ -52,7 +78,12 @@ def add_tracts_for_geometries(
                       spatial analysis
     """
     logger.debug("Appending tract data to dataframe")
-    tract_data = get_tract_geojson(_tract_data_path)
+
+    if tract_data is None:
+        tract_data = get_tract_geojson()
+    else:
+        logger.debug("Using existing tract data.")
+
     assert (
         tract_data.crs == df.crs
     ), f"Dataframe must be projected to {tract_data.crs}"

data/data-pipeline/data_pipeline/etl/sources/geocorr/etl.py

@@ -1,7 +1,7 @@
 import pandas as pd
 
 from data_pipeline.config import settings
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.utils import (
     get_module_logger,
     unzip_file_from_url,
@@ -11,6 +11,10 @@ logger = get_module_logger(__name__)
 
 
 class GeoCorrETL(ExtractTransformLoad):
+    NAME = "geocorr"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+
     def __init__(self):
         self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "geocorr"
 
@@ -24,6 +28,10 @@ class GeoCorrETL(ExtractTransformLoad):
         self.GEOCORR_PLACES_URL = "https://justice40-data.s3.amazonaws.com/data-sources/geocorr_urban_rural.csv.zip"
         self.GEOCORR_GEOID_FIELD_NAME = "GEOID10_TRACT"
         self.URBAN_HEURISTIC_FIELD_NAME = "Urban Heuristic Flag"
+        self.COLUMNS_TO_KEEP = [
+            self.GEOID_TRACT_FIELD_NAME,
+            self.URBAN_HEURISTIC_FIELD_NAME,
+        ]
 
         self.df: pd.DataFrame
 
@@ -35,13 +43,11 @@ class GeoCorrETL(ExtractTransformLoad):
             file_url=settings.AWS_JUSTICE40_DATASOURCES_URL
             + "/geocorr_urban_rural.csv.zip",
             download_path=self.get_tmp_path(),
-            unzipped_file_path=self.get_tmp_path() / "geocorr",
+            unzipped_file_path=self.get_tmp_path(),
         )
 
         self.df = pd.read_csv(
-            filepath_or_buffer=self.get_tmp_path()
-            / "geocorr"
-            / "geocorr_urban_rural.csv",
+            filepath_or_buffer=self.get_tmp_path() / "geocorr_urban_rural.csv",
             dtype={
                 self.GEOCORR_GEOID_FIELD_NAME: "string",
             },
@@ -50,22 +56,10 @@ class GeoCorrETL(ExtractTransformLoad):
 
     def transform(self) -> None:
         logger.info("Starting GeoCorr Urban Rural Map transform")
+        # Put in logic from Jupyter Notebook transform when we switch in the hyperlink to Geocorr
 
-        self.df.rename(
+        self.output_df = self.df.rename(
             columns={
                 "urban_heuristic_flag": self.URBAN_HEURISTIC_FIELD_NAME,
             },
-            inplace=True,
         )
-
-        pass
-
-        # Put in logic from Jupyter Notebook transform when we switch in the hyperlink to Geocorr
-
-    def load(self) -> None:
-        logger.info("Saving GeoCorr Urban Rural Map Data")
-
-        # mkdir census
-        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
-        self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)

data/data-pipeline/data_pipeline/etl/sources/historic_redlining/etl.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 from data_pipeline.config import settings
 
@@ -8,11 +8,28 @@ logger = get_module_logger(__name__)
 
 
 class HistoricRedliningETL(ExtractTransformLoad):
+    NAME = "historic_redlining"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    EXPECTED_MISSING_STATES = [
+        "10",
+        "11",
+        "16",
+        "23",
+        "30",
+        "32",
+        "35",
+        "38",
+        "46",
+        "50",
+        "56",
+    ]
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    ALASKA_AND_HAWAII_EXPECTED_IN_DATA: bool = False
+    SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/HRS_2010.zip"
+
     def __init__(self):
         self.CSV_PATH = self.DATA_PATH / "dataset" / "historic_redlining"
-        self.HISTORIC_REDLINING_URL = (
-            settings.AWS_JUSTICE40_DATASOURCES_URL + "/HRS_2010.zip"
-        )
+
         self.HISTORIC_REDLINING_FILE_PATH = (
             self.get_tmp_path() / "HRS_2010.xlsx"
         )
@@ -25,13 +42,6 @@ class HistoricRedliningETL(ExtractTransformLoad):
         ]
         self.df: pd.DataFrame
 
-    def extract(self) -> None:
-        logger.info("Downloading Historic Redlining Data")
-        super().extract(
-            self.HISTORIC_REDLINING_URL,
-            self.get_tmp_path(),
-        )
-
     def transform(self) -> None:
         logger.info("Transforming Historic Redlining Data")
         # this is obviously temporary
@@ -57,16 +67,4 @@ class HistoricRedliningETL(ExtractTransformLoad):
                 f"{self.REDLINING_SCALAR} meets or exceeds {round(threshold, 2)}"
             )
 
-        self.df = historic_redlining_data
-
-    def load(self) -> None:
-        logger.info("Saving Historic Redlining CSV")
-        # write selected states csv
-        self.CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df[self.COLUMNS_TO_KEEP].to_csv(
-            self.CSV_PATH / "usa.csv", index=False
-        )
-
-    def validate(self) -> None:
-        logger.info("Validating Historic Redlining Data")
-        pass
+        self.output_df = historic_redlining_data

data/data-pipeline/data_pipeline/etl/sources/hud_housing/etl.py

@@ -1,16 +1,18 @@
 import pandas as pd
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.utils import get_module_logger
 
 logger = get_module_logger(__name__)
 
 
 class HudHousingETL(ExtractTransformLoad):
+    NAME = "hud_housing"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+
     def __init__(self):
-        self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "hud_housing"
         self.GEOID_TRACT_FIELD_NAME = "GEOID10_TRACT"
         self.HOUSING_FTP_URL = "https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip"
-        self.HOUSING_ZIP_FILE_DIR = self.get_tmp_path() / "hud_housing"
+        self.HOUSING_ZIP_FILE_DIR = self.get_tmp_path()
 
         # We measure households earning less than 80% of HUD Area Median Family Income by county
         # and paying greater than 30% of their income to housing costs.
@@ -22,6 +24,14 @@ class HudHousingETL(ExtractTransformLoad):
         self.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD_NAME = (
             "Share of homes with no kitchen or indoor plumbing (percent)"
         )
+        self.COLUMNS_TO_KEEP = [
+            self.GEOID_TRACT_FIELD_NAME,
+            self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME,
+            self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME,
+            self.HOUSING_BURDEN_FIELD_NAME,
+            self.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD_NAME,
+            "DENOM INCL NOT COMPUTED",
+        ]
 
         # Note: some variable definitions.
         # HUD-adjusted median family income (HAMFI).
@@ -234,19 +244,4 @@ class HudHousingETL(ExtractTransformLoad):
             float
         )
 
-    def load(self) -> None:
-        logger.info("Saving HUD Housing Data")
-
-        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
-        # Drop unnecessary fields
-        self.df[
-            [
-                self.GEOID_TRACT_FIELD_NAME,
-                self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME,
-                self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME,
-                self.HOUSING_BURDEN_FIELD_NAME,
-                self.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD_NAME,
-                "DENOM INCL NOT COMPUTED",
-            ]
-        ].to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
+        self.output_df = self.df

data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py

@@ -17,6 +17,8 @@ class NationalRiskIndexETL(ExtractTransformLoad):
     NAME = "national_risk_index"
     SOURCE_URL = "https://hazards.fema.gov/nri/Content/StaticDocuments/DataDownload//NRI_Table_CensusTracts/NRI_Table_CensusTracts.zip"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    LOAD_YAML_CONFIG: bool = True
 
     # Output score variables (values set on datasets.yml) for linting purposes
     RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME: str

data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/README.md

@@ -1,80 +0,0 @@
-# Nature deprived communities data
-
-The following dataset was compiled by TPL using NCLD data. We define as: AREA - [CROPLAND] - [IMPERVIOUS SURFACES].
-
-## Codebook 
-- GEOID10 – Census tract ID
-- SF – State Name
-- CF – County Name
-- P200_PFS – Percent of individuals below 200% Federal Poverty Line (from CEJST source data).
-- CA_LT20 – Percent higher ed enrollment rate is less than 20% (from CEJST source data).
-- TractAcres – Acres of tract calculated from ALAND10 field (area land/meters) in 2010 census tracts.
-    - CAVEAT: Some census tracts in the CEJST source file extend into open water. ALAND10 area was used to constrain percent calculations (e.g. cropland area) to land only.
-- AcresCrops – Acres crops calculated by summing all cells in the NLCD Cropland Data Layer crop classes.
-- PctCrops – Formula: AcresCrops/TractAcres*100.
-- PctImperv – Mean imperviousness for each census tract.
-    - CAVEAT: Where tracts extend into open water, mean imperviousness may be underestimated.  
-- __TO USE__ PctNatural – Formula: 100 – PctCrops – PctImperv.
-- PctNat90 – Tract in or below 10th percentile for PctNatural. 1 = True,  0 = False.
-    - PctNatural 10th percentile = 28.6439%
-- ImpOrCrop – If tract >= 90th percentile for PctImperv OR PctCrops. 1 = True,  0 = False.
-    - PctImperv 90th percentile = 67.4146 %
-    - PctCrops 90th percentile = 27.8116 %
-- LowInAndEd – If tract >= 65th percentile for P200_PFS AND CA_LT20.
-    - P200_PFS 65th percentile = 64.0%
-- NatureDep – ImpOrCrp = 1 AND LowInAndEd = 1.
-
-We added `GEOID10_TRACT` before converting shapefile to csv. 
-
-## Instructions to recreate
-
-### Creating Impervious plus Cropland Attributes for Census Tracts
-
-The Cropland Data Layer and NLCD Impervious layer were too big to put on our OneDrive, but you can download them here:
-   CDL:  https://www.nass.usda.gov/Research_and_Science/Cropland/Release/datasets/2021_30m_cdls.zip
-   Impervious:  https://s3-us-west-2.amazonaws.com/mrlc/nlcd_2019_impervious_l48_20210604.zip
-
-
-#### Crops
-
-Add an attribute called TractAcres (or similar) to the census tracts to hold a value representing acres covered by the census tract.
-Calculate the TractAcres field for each census tract by using the Calculate Geometry tool (set the Property to Area (geodesic), and the Units to Acres).
-From the Cropland Data Layer (CDL), extract only the pixels representing crops, using the Extract by Attributes tool in ArcGIS Spatial Analyst toolbox.
-a. The attribute table tells you the names of each type of land cover. Since the CDL also contains NLCD classes and empty classes, the actual crop classes must be extracted.
-From the crops-only raster extracted from the CDL, run the Reclassify tool to create a binary layer where all crops = 1, and everything else is Null.
-Run the Tabulate Area tool:
-a. Zone data = census tracts
-b. Input raster data = the binary crops layer
-c. This will produce a table with the square meters of crops in each census tract contained in an attribute called VALUE_1
-Run the Join Field tool to join the table to the census tracts, with the VALUE_1 field as the Transfer Field, to transfer the VALUE_1 field (square meters of crops) to the census tracts.
-Add a field to the census tracts called AcresCrops (or similar) to hold the acreage of crops in each census tract.
-Calculate the AcresCrops field by multiplying the VALUE_1 field by 0.000247105 to produce acres of crops in each census tracts.
-a. You can delete the VALUE_1 field.
-Add a field called PctCrops (or similar) to hold the percent of each census tract occupied by crops.
-Calculate the PctCrops field by dividing the AcresCrops field by the TractAcres field, and multiply by 100 to get the percent.
-Impervious
-
-Run the Zonal Statistics as Table tool:
-a. Zone data = census tracts
-b. Input raster data = impervious data raster layer
-c. Statistics type = Mean
-d. This will produce a table with the percent of each census tract occupied by impervious surfaces, contained in an attribute called MEAN
-
-Run the Join Field tool to join the table to the census tracts, with the MEAN field as the Transfer Field, to transfer the MEAN field (percent impervious) to the census tracts.
-
-Add a field called PctImperv (or similar) to hold the percent impervious value.
-
-Calculate the PctImperv field by setting it equal to the MEAN field.
-a. You can delete the MEAN field.
-Combine the Crops and Impervious Data
-
-Open the census tracts attribute table and add a field called PctNatural (or similar). Calculate this field using this equation: 100 – PctCrops – PctImperv . This produces a value that tells you the percent of each census tract covered in natural land cover.
-
-Define the census tracts that fall in the 90th percentile of non-natural land cover:
-a. Add a field called PctNat90 (or similar)
-b. Right-click on the PctNatural field, and click Sort Ascending (lowest PctNatural values on top)
-c. Select the top 10 percent of rows after the sort
-d. Click on Show Selected Records in the attribute table
-e. Calculate the PctNat90 field for the selected records = 1
-f. Clear the selection
-g. The rows that now have a value of 1 for PctNat90 are the most lacking for natural land cover, and can be symbolized accordingly in a map

data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/etl.py

@@ -1,77 +0,0 @@
-# pylint: disable=unsubscriptable-object
-# pylint: disable=unsupported-assignment-operation
-
-import pandas as pd
-from data_pipeline.config import settings
-
-from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class NatureDeprivedETL(ExtractTransformLoad):
-    """ETL class for the Nature Deprived Communities dataset"""
-
-    NAME = "ncld_nature_deprived"
-    SOURCE_URL = (
-        settings.AWS_JUSTICE40_DATASOURCES_URL
-        + "/usa_conus_nat_dep__compiled_by_TPL.csv.zip"
-    )
-    GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
-
-    # Output score variables (values set on datasets.yml) for linting purposes
-    ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME: str
-    TRACT_PERCENT_IMPERVIOUS_FIELD_NAME: str
-    TRACT_PERCENT_NON_NATURAL_FIELD_NAME: str
-    TRACT_PERCENT_CROPLAND_FIELD_NAME: str
-
-    def __init__(self):
-        # define the full path for the input CSV file
-        self.INPUT_CSV = (
-            self.get_tmp_path() / "usa_conus_nat_dep__compiled_by_TPL.csv"
-        )
-
-        # this is the main dataframe
-        self.df: pd.DataFrame
-
-        # Start dataset-specific vars here
-        self.PERCENT_NATURAL_FIELD_NAME = "PctNatural"
-        self.PERCENT_IMPERVIOUS_FIELD_NAME = "PctImperv"
-        self.PERCENT_CROPLAND_FIELD_NAME = "PctCrops"
-        self.TRACT_ACRES_FIELD_NAME = "TractAcres"
-        # In order to ensure that tracts with very small Acreage, we want to create an eligibility criterion
-        # similar to agrivalue. Here, we are ensuring that a tract has at least 35 acres, or is above the 1st percentile
-        # for area. This does indeed remove tracts from the 90th+ percentile later on
-        self.TRACT_ACRES_LOWER_BOUND = 35
-
-    def transform(self) -> None:
-        """Reads the unzipped data file into memory and applies the following
-        transformations to prepare it for the load() method:
-
-        - Renames columns as needed
-        """
-        logger.info("Transforming NCLD Data")
-
-        logger.info(self.COLUMNS_TO_KEEP)
-
-        df_ncld: pd.DataFrame = pd.read_csv(
-            self.INPUT_CSV,
-            dtype={self.INPUT_GEOID_TRACT_FIELD_NAME: str},
-            low_memory=False,
-        )
-
-        df_ncld[self.ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME] = (
-            df_ncld[self.TRACT_ACRES_FIELD_NAME] >= self.TRACT_ACRES_LOWER_BOUND
-        )
-        df_ncld[self.TRACT_PERCENT_NON_NATURAL_FIELD_NAME] = (
-            1 - df_ncld[self.PERCENT_NATURAL_FIELD_NAME]
-        )
-
-        # Assign the final df to the class' output_df for the load method with rename
-        self.output_df = df_ncld.rename(
-            columns={
-                self.PERCENT_IMPERVIOUS_FIELD_NAME: self.TRACT_PERCENT_IMPERVIOUS_FIELD_NAME,
-                self.PERCENT_CROPLAND_FIELD_NAME: self.TRACT_PERCENT_CROPLAND_FIELD_NAME,
-            }
-        )

data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py

@@ -19,6 +19,9 @@ class NatureDeprivedETL(ExtractTransformLoad):
         + "/usa_conus_nat_dep__compiled_by_TPL.csv.zip"
     )
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    LOAD_YAML_CONFIG: bool = True
+    ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False
 
     # Output score variables (values set on datasets.yml) for linting purposes
     ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME: str
@@ -63,7 +66,7 @@ class NatureDeprivedETL(ExtractTransformLoad):
             df_ncld[self.TRACT_ACRES_FIELD_NAME] >= self.TRACT_ACRES_LOWER_BOUND
         )
         df_ncld[self.TRACT_PERCENT_NON_NATURAL_FIELD_NAME] = (
-            1 - df_ncld[self.PERCENT_NATURAL_FIELD_NAME]
+            100 - df_ncld[self.PERCENT_NATURAL_FIELD_NAME]
         )
 
         # Assign the final df to the class' output_df for the load method with rename

data/data-pipeline/data_pipeline/etl/sources/persistent_poverty/etl.py

@@ -2,7 +2,7 @@ import functools
 import pandas as pd
 
 from data_pipeline.config import settings
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.utils import (
     get_module_logger,
     unzip_file_from_url,
@@ -19,6 +19,10 @@ class PersistentPovertyETL(ExtractTransformLoad):
     Codebook: `https://s4.ad.brown.edu/Projects/Diversity/Researcher/LTBDDload/Dfiles/codebooks.pdf`.
     """
 
+    NAME = "persistent_poverty"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+
     def __init__(self):
         self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "persistent_poverty"
 
@@ -75,7 +79,7 @@ class PersistentPovertyETL(ExtractTransformLoad):
     def extract(self) -> None:
         logger.info("Starting to download 86MB persistent poverty file.")
 
-        unzipped_file_path = self.get_tmp_path() / "persistent_poverty"
+        unzipped_file_path = self.get_tmp_path()
 
         unzip_file_from_url(
             file_url=settings.AWS_JUSTICE40_DATASOURCES_URL
@@ -155,14 +159,4 @@ class PersistentPovertyETL(ExtractTransformLoad):
             )
         )
 
-        self.df = transformed_df
-
-    def load(self) -> None:
-        logger.info("Saving persistent poverty data.")
-
-        # mkdir census
-        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
-        self.df[self.COLUMNS_TO_KEEP].to_csv(
-            path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
-        )
+        self.output_df = transformed_df

data/data-pipeline/data_pipeline/etl/sources/tribal/etl.py

@@ -3,6 +3,7 @@ import geopandas as gpd
 import pandas as pd
 
 from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.score import field_names
 from data_pipeline.utils import get_module_logger, unzip_file_from_url
 
 logger = get_module_logger(__name__)
@@ -59,7 +60,10 @@ class TribalETL(ExtractTransformLoad):
         )
 
         bia_national_lar_df.rename(
-            columns={"LARID": "tribalId", "LARName": "landAreaName"},
+            columns={
+                "LARID": field_names.TRIBAL_ID,
+                "LARName": field_names.TRIBAL_LAND_AREA_NAME,
+            },
             inplace=True,
         )
 
@@ -87,7 +91,10 @@ class TribalETL(ExtractTransformLoad):
         )
 
         bia_aian_supplemental_df.rename(
-            columns={"OBJECTID": "tribalId", "Land_Area_": "landAreaName"},
+            columns={
+                "OBJECTID": field_names.TRIBAL_ID,
+                "Land_Area_": field_names.TRIBAL_LAND_AREA_NAME,
+            },
             inplace=True,
         )
 
@@ -113,7 +120,10 @@ class TribalETL(ExtractTransformLoad):
         )
 
         bia_tsa_df.rename(
-            columns={"TSAID": "tribalId", "LARName": "landAreaName"},
+            columns={
+                "TSAID": field_names.TRIBAL_ID,
+                "LARName": field_names.TRIBAL_LAND_AREA_NAME,
+            },
             inplace=True,
         )
 
@@ -136,8 +146,8 @@ class TribalETL(ExtractTransformLoad):
 
         alaska_native_villages_df.rename(
             columns={
-                "GlobalID": "tribalId",
-                "TRIBALOFFICENAME": "landAreaName",
+                "GlobalID": field_names.TRIBAL_ID,
+                "TRIBALOFFICENAME": field_names.TRIBAL_LAND_AREA_NAME,
             },
             inplace=True,
         )

data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/etl.py

@@ -0,0 +1,208 @@
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.sources.geo_utils import (
+    add_tracts_for_geometries,
+    get_tribal_geojson,
+    get_tract_geojson,
+)
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class TribalOverlapETL(ExtractTransformLoad):
+    """Calculates the overlap between Census tracts and Tribal boundaries."""
+
+    # Metadata for the baseclass
+    NAME = "tribal_overlap"
+    GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+    ALASKA_AND_HAWAII_EXPECTED_IN_DATA = True
+    EXPECTED_MISSING_STATES = [
+        # 15 is Hawaii, which has Hawaiian Home Lands, but they are not included in
+        # this dataset.
+        "15",
+        # The following states do not have any federally recognized Tribes in this
+        # dataset.
+        "10",
+        "11",
+        "13",
+        "17",
+        "18",
+        "21",
+        "24",
+        "33",
+        "34",
+        "39",
+        "50",
+        "51",
+        "54",
+    ]
+
+    # A Tribal area that requires some special processing.
+    ANNETTE_ISLAND_TRIBAL_NAME = "Annette Island LAR"
+
+    CRS_INTEGER = 3857
+
+    # Define these for easy code completion
+    def __init__(self):
+        self.COLUMNS_TO_KEEP = [
+            self.GEOID_TRACT_FIELD_NAME,
+            field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT,
+            field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT,
+            field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT,
+        ]
+
+        self.output_df: pd.DataFrame
+        self.census_tract_gdf: gpd.GeoDataFrame
+        self.tribal_gdf: gpd.GeoDataFrame
+
+    @staticmethod
+    def _create_string_from_list(series: pd.Series) -> str:
+        """Helper method that creates a sorted string list (for tribal names)."""
+        str_list = series.tolist()
+        str_list = sorted(str_list)
+        return ", ".join(str_list)
+
+    def extract(self) -> None:
+        self.census_tract_gdf = get_tract_geojson()
+        self.tribal_gdf = get_tribal_geojson()
+
+    def transform(self) -> None:
+        logger.info("Starting tribal overlap transforms.")
+
+        # First, calculate whether tracts include any areas from the Tribal areas,
+        # for both the points in AK and the polygons in the continental US (CONUS).
+        tribal_overlap_with_tracts = add_tracts_for_geometries(
+            df=self.tribal_gdf, tract_data=self.census_tract_gdf
+        )
+
+        tribal_overlap_with_tracts = tribal_overlap_with_tracts.groupby(
+            [self.GEOID_TRACT_FIELD_NAME]
+        ).agg(
+            {
+                field_names.TRIBAL_ID: "count",
+                field_names.TRIBAL_LAND_AREA_NAME: self._create_string_from_list,
+            }
+        )
+
+        tribal_overlap_with_tracts = tribal_overlap_with_tracts.reset_index()
+
+        tribal_overlap_with_tracts = tribal_overlap_with_tracts.rename(
+            columns={
+                field_names.TRIBAL_ID: field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT,
+                field_names.TRIBAL_LAND_AREA_NAME: field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT,
+            }
+        )
+
+        # Second, calculate percentage overlap.
+        # Drop the points from the Tribal data (because these cannot be joined to a
+        # (Multi)Polygon tract data frame)
+        tribal_gdf_without_points = self.tribal_gdf[
+            self.tribal_gdf.geom_type.isin(["Polygon", "MultiPolygon"])
+        ]
+
+        # Switch from geographic to projected CRSes
+        # because logically that's right
+        self.census_tract_gdf = self.census_tract_gdf.to_crs(crs=self.CRS_INTEGER)
+        tribal_gdf_without_points = tribal_gdf_without_points.to_crs(crs=self.CRS_INTEGER)
+
+        # Create a measure for the entire census tract area
+        self.census_tract_gdf["area_tract"] = self.census_tract_gdf.area
+
+        # Performing overlay funcion
+        # We have a mix of polygons and multipolygons, and we just want the overlaps
+        # without caring a ton about the specific types, so we ignore geom type.
+        # Realistically, this changes almost nothing in the calculation; True and False
+        # are the same within 9 digits of precision
+        gdf_joined = gpd.overlay(
+            self.census_tract_gdf,
+            tribal_gdf_without_points,
+            how="intersection",
+            keep_geom_type=False,
+        )
+
+        # Calculating the areas of the newly-created overlapping geometries
+        gdf_joined["area_joined"] = gdf_joined.area
+
+        # Calculating the areas of the newly-created geometries in relation
+        # to the original tract geometries
+        gdf_joined[field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT] = (
+            gdf_joined["area_joined"] / gdf_joined["area_tract"]
+        )
+
+        # Aggregate the results
+        percentage_results = gdf_joined.groupby(
+            [self.GEOID_TRACT_FIELD_NAME]
+        ).agg({field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT: "sum"})
+
+        percentage_results = percentage_results.reset_index()
+
+        # Merge the two results.
+        merged_output_df = tribal_overlap_with_tracts.merge(
+            right=percentage_results,
+            how="outer",
+            on=self.GEOID_TRACT_FIELD_NAME,
+        )
+
+        # Finally, fix one unique error.
+        # There is one unique Tribal area (self.ANNETTE_ISLAND_TRIBAL_NAME) that is a polygon in
+        # Alaska. All other Tribal areas in Alaska are points.
+        # For tracts that *only* contain that Tribal area, leave percentage as is.
+        # For tracts that include that Tribal area AND Alaska Native villages,
+        # null the percentage, because we cannot calculate the percent of the tract
+        # this is within Tribal areas.
+
+        # Create state FIPS codes.
+        merged_output_df_state_fips_code = merged_output_df[
+            self.GEOID_TRACT_FIELD_NAME
+        ].str[0:2]
+
+        # Start by testing for Annette Island exception, to make sure data is as
+        # expected
+        alaskan_non_annette_matches = (
+            # Data from Alaska
+            (merged_output_df_state_fips_code == "02")
+            # Where the Tribal areas do *not* include Annette
+            & (
+                ~merged_output_df[
+                    field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT
+                ].str.contains(self.ANNETTE_ISLAND_TRIBAL_NAME)
+            )
+            # But somehow percentage is greater than zero.
+            & (
+                merged_output_df[field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT]
+                > 0
+            )
+        )
+
+        # There should be none of these matches.
+        if sum(alaskan_non_annette_matches) > 0:
+            raise ValueError(
+                "Data has changed. More than one Alaskan Tribal Area has polygon "
+                "boundaries. You'll need to refactor this ETL. \n"
+                f"Data:\n{merged_output_df[alaskan_non_annette_matches]}"
+            )
+
+        # Now, fix the exception that is already known.
+        merged_output_df[
+            field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT
+        ] = np.where(
+            # For tracts inside Alaska
+            (merged_output_df_state_fips_code == "02")
+            # That are not only represented by Annette Island
+            & (
+                merged_output_df[field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT]
+                != self.ANNETTE_ISLAND_TRIBAL_NAME
+            ),
+            # Set the value to `None` for tracts with more than just Annette.
+            None,
+            # Otherwise, set the value to what it was.
+            merged_output_df[field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT],
+        )
+
+        self.output_df = merged_output_df

data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py

@@ -19,6 +19,9 @@ class USArmyFUDS(ExtractTransformLoad):
     INELIGIBLE_FUDS_COUNT_FIELD_NAME: str
     ELIGIBLE_FUDS_BINARY_FIELD_NAME: str
     GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    LOAD_YAML_CONFIG: bool = True
+
+    ISLAND_AREAS_EXPECTED_IN_DATA = True
 
     def __init__(self):
         self.FILE_URL: str = (

data/data-pipeline/data_pipeline/ipython/check_tribal_count.ipynb

@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8f03fec9-e4a0-4621-b94c-ff6459a42032",
+   "metadata": {},
+   "source": [
+    "This code is to do the one time check described in [ticket 1917]( Run a one-time check on count of federal Indian reservations)\n",
+    "\n",
+    "> We should do a one-time check on the BIA data we import, such that after import that have (for now in our staging environment) 326 federal Indian reservations on our map. The number 326 from https://www.bia.gov/faqs/what-federal-indian-reservation.\n",
+    ">\n",
+    "> If that one-time check to make sure we have all reservations fails, there's a list of federal Indian reservation names that we can use to track down which reservations are not shown on our map and troubleshoot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "76c8eeac-21e0-4ff1-886d-b9cdd6199411",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "module_path = os.path.abspath(os.path.join(\"../..\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aaf46946-4fa7-4744-8cd6-723ce86a0213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from data_pipeline.etl.sources.tribal.etl import TribalETL\n",
+    "from data_pipeline.etl.sources.geo_utils import get_tribal_geojson\n",
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "039d244f-6c97-4b1b-af04-6f879cd2cd86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext lab_black"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0f0b3597-844e-4328-b6c9-ac760301383e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-09-23 11:56:25,551 [data_pipeline.etl.sources.tribal.etl] INFO     Downloading Tribal Data\n",
+      "2022-09-23 11:56:25,552 [data_pipeline.utils] INFO     Downloading https://justice40-data.s3.amazonaws.com/data-sources/BIA_National_LAR_json.zip\n",
+      "2022-09-23 11:56:26,068 [data_pipeline.utils] INFO     Extracting /home/matt/active/justice40-tool/data/data-pipeline/data_pipeline/data/tmp/downloaded-47ce415c-cc72-4e6f-9cbc-7ad833e08813.zip\n",
+      "2022-09-23 11:56:26,190 [data_pipeline.utils] INFO     Downloading https://justice40-data.s3.amazonaws.com/data-sources/Alaska_Native_Villages_json.zip\n",
+      "2022-09-23 11:56:26,290 [data_pipeline.utils] INFO     Extracting /home/matt/active/justice40-tool/data/data-pipeline/data_pipeline/data/tmp/downloaded-7c700e59-83cd-4752-889c-159e58c71154.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "etl = TribalETL()\n",
+    "etl.extract()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "51a5a5c7-c02b-4a6f-9f3d-19868e45d7b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "GEOJSON_BASE_PATH = etl.GEOJSON_BASE_PATH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c4224aeb-6aa5-47fa-9c6f-e2cd9354720d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bia_national_lar_geojson = (\n",
+    "    GEOJSON_BASE_PATH / \"bia_national_lar\" / \"BIA_National_LAR.json\"\n",
+    ")\n",
+    "bia_aian_supplemental_geojson = (\n",
+    "    GEOJSON_BASE_PATH / \"bia_national_lar\" / \"BIA_AIAN_Supplemental.json\"\n",
+    ")\n",
+    "bia_tsa_geojson_geojson = GEOJSON_BASE_PATH / \"bia_national_lar\" / \"BIA_TSA.json\"\n",
+    "alaska_native_villages_geojson = (\n",
+    "    GEOJSON_BASE_PATH / \"alaska_native_villages\" / \"AlaskaNativeVillages.gdb.geojson\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7dd74d24-4f55-45db-bdd9-f4ac945d6a78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bia_national_lar_df = gpd.read_file(bia_national_lar_geojson)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae8dafb7-f997-4977-87da-53f0b4f98a98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "326"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(\n",
+    "    sorted(\n",
+    "        list(\n",
+    "            bia_national_lar_df.LARName.str.replace(r\"\\(.*\\) \", \"\", regex=True).unique()\n",
+    "        )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40849cc4-345b-4658-94ac-498154107e9f",
+   "metadata": {},
+   "source": [
+    "Looking at the main BIA LAR data file, and removing paranthecials (so that `'Acoma (Red Lake) LAR','Acoma LAR'` are counted as a single tribal entry), **we have 326 tribal areas**, which is the number we expected."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/data-pipeline/data_pipeline/ipython/explore_eamlis.ipynb

@@ -2435,7 +2435,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

data/data-pipeline/data_pipeline/ipython/geopandas_speed_test.ipynb

@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0b801f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import pyogrio\n",
+    "from data_pipeline.etl.sources.census.etl import CensusETL\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c4cbab25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken to execute the function using pyogrio is 63.07696199417114\n"
+     ]
+    }
+   ],
+   "source": [
+    "begin = time.time()\n",
+    "census_tract_gdf = gpd.read_file(\n",
+    "    CensusETL.NATIONAL_TRACT_JSON_PATH,\n",
+    "    # Use `pyogrio` because it's vectorized and faster.\n",
+    "    engine=\"pyogrio\",\n",
+    ")\n",
+    "end = time.time()\n",
+    "  \n",
+    "print(\"Time taken to execute the function using pyogrio is\", end-begin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "372ab939",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken to execute the function using include fields is 67.33577013015747\n"
+     ]
+    }
+   ],
+   "source": [
+    "begin2 = time.time()\n",
+    "census_tract_gdf = gpd.read_file(\n",
+    "    CensusETL.NATIONAL_TRACT_JSON_PATH,\n",
+    "    engine=\"fiona\",\n",
+    "    include_fields=[\"GEOID10\"]\n",
+    ")\n",
+    "end2 = time.time()\n",
+    "  \n",
+    "print(\"Time taken to execute the function using include fields is\", end2-begin2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "32fb7d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/lx/xmq8p65j71v9xq2bhsd2j5w40000gp/T/ipykernel_21074/2531126572.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mbegin2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m census_tract_gdf = gpd.read_file(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mCensusETL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNATIONAL_TRACT_JSON_PATH\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"fiona\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0minclude_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"GEOID10\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file\u001b[0;34m(filename, bbox, mask, rows, engine, **kwargs)\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"fiona\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m         return _read_file_fiona(\n\u001b[0m\u001b[1;32m    254\u001b[0m             \u001b[0mpath_or_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         )\n",
+      "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file_fiona\u001b[0;34m(path_or_bytes, from_bytes, bbox, mask, rows, **kwargs)\u001b[0m\n\u001b[1;32m    338\u001b[0m                 )\n\u001b[1;32m    339\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m                 df = GeoDataFrame.from_features(\n\u001b[0m\u001b[1;32m    341\u001b[0m                     \u001b[0mf_filt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"geometry\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m                 )\n",
+      "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/geopandas/geodataframe.py\u001b[0m in \u001b[0;36mfrom_features\u001b[0;34m(cls, features, crs, columns)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mfeature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures_lst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m             \u001b[0;31m# load geometry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__geo_interface__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mfiona/ogrext.pyx\u001b[0m in \u001b[0;36mfiona.ogrext.Iterator.__next__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mfiona/ogrext.pyx\u001b[0m in \u001b[0;36mfiona.ogrext.Iterator._next\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/logging/__init__.py\u001b[0m in \u001b[0;36mdebug\u001b[0;34m(self, msg, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1422\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1424\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1425\u001b[0m         \"\"\"\n\u001b[1;32m   1426\u001b[0m         \u001b[0mLog\u001b[0m \u001b[0;34m'msg % args'\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mseverity\u001b[0m \u001b[0;34m'DEBUG'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "begin2 = time.time()\n",
+    "census_tract_gdf = gpd.read_file(\n",
+    "    CensusETL.NATIONAL_TRACT_JSON_PATH,\n",
+    "    engine=\"fiona\",\n",
+    "    include_fields=[\"GEOID10\"],\n",
+    "    rows=slice(0, 76322, 100),\n",
+    ")\n",
+    "end2 = time.time()\n",
+    "\n",
+    "print(\"Time taken to execute the function using slice is\", end2 - begin2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/data-pipeline/data_pipeline/score/field_names.py

@@ -349,6 +349,17 @@ ELIGIBLE_FUDS_BINARY_FIELD_NAME = (
 )
 ELIGIBLE_FUDS_FILLED_IN_FIELD_NAME = "Is there at least one Formerly Used Defense Site (FUDS) in the tract, where missing data is treated as False?"
 
+# Tribal variables
+TRIBAL_ID = "tribalId"
+TRIBAL_LAND_AREA_NAME = "landAreaName"
+
+# Tribal overlap variables
+COUNT_OF_TRIBAL_AREAS_IN_TRACT = "Number of Tribal areas within Census tract"
+NAMES_OF_TRIBAL_AREAS_IN_TRACT = "Names of Tribal areas within Census tract"
+PERCENT_OF_TRIBAL_AREA_IN_TRACT = (
+    "Percent of the Census tract that is within Tribal areas"
+)
+
 #####
 # Names for individual factors being exceeded
 

data/data-pipeline/data_pipeline/score/score_narwhal.py

@@ -12,7 +12,7 @@ logger = get_module_logger(__name__)
 
 
 class ScoreNarwhal(Score):
-    """Very similar to Score M, at present."""
+    """Score N, aka Narwhal."""
 
     LOW_INCOME_THRESHOLD: float = 0.65
     MAX_COLLEGE_ATTENDANCE_THRESHOLD: float = 0.20

data/data-pipeline/data_pipeline/tests/score/fixtures.py

@@ -215,3 +215,16 @@ def national_tract_df():
         low_memory=False,
         header=None,
     )
+
+
+@pytest.fixture()
+def tribal_overlap():
+    tribal_overlap = (
+        constants.DATA_PATH / "dataset" / "tribal_overlap" / "usa.csv"
+    )
+
+    return pd.read_csv(
+        tribal_overlap,
+        dtype={GEOID_TRACT_FIELD: "string"},
+        low_memory=False,
+    )

data/data-pipeline/data_pipeline/tests/score/test_output.py

@@ -27,11 +27,12 @@ from .fixtures import (
     census_2010_df,
     hrs_df,
     national_tract_df,
+    tribal_overlap,
 )
 
 
 pytestmark = pytest.mark.smoketest
-UNMATCHED_TRACK_THRESHOLD = 1000
+UNMATCHED_TRACT_THRESHOLD = 1000
 
 
 def _helper_test_count_exceeding_threshold(df, col, error_check=1000):
@@ -249,11 +250,21 @@ def test_data_sources(
     census_decennial_df,
     census_2010_df,
     hrs_df,
+    tribal_overlap,
 ):
     data_sources = {
         key: value for key, value in locals().items() if key != "final_score_df"
     }
 
+    # For each data source that's injected via the fixtures, do the following:
+    # * Ensure at least one column from the source shows up in the score
+    # * Ensure any tracts NOT in the data source are NA/null in the score
+    # * Ensure the data source doesn't have a large number of tract IDs that are not
+    #   included in the final score, since that implies the source is using 2020
+    #   tract IDs
+    # * Verify that the data from the source that's in the final score output
+    #   is the "equal" to the data from the ETL, allowing for the minor
+    #   differences that come from floating point comparisons
     for data_source_name, data_source in data_sources.items():
         final = "final_"
         df: pd.DataFrame = final_score_df.merge(
@@ -275,12 +286,12 @@ def test_data_sources(
         ), f"No columns from data source show up in final score in source {data_source_name}"
 
         # Make sure we have NAs for any tracts in the final data that aren't
-        # covered in the  final data
+        # included in the data source
         assert np.all(df[df.MERGE == "left_only"][final_columns].isna())
 
         # Make sure the datasource doesn't have a ton of unmatched tracts, implying it
         # has moved to 2020 tracts
-        assert len(df[df.MERGE == "right_only"]) < UNMATCHED_TRACK_THRESHOLD
+        assert len(df[df.MERGE == "right_only"]) < UNMATCHED_TRACT_THRESHOLD
 
         df = df[df.MERGE == "both"]
 
@@ -293,6 +304,7 @@ def test_data_sources(
                 f"Column {final_column} not equal "
                 f"between {data_source_name} and final score"
             )
+            # For non-numeric types, we can use the built-in equals from pandas
             if df[final_column].dtype in [
                 np.dtype(object),
                 np.dtype(bool),
@@ -301,6 +313,8 @@ def test_data_sources(
                 assert df[final_column].equals(
                     df[data_source_column]
                 ), error_message
+            # For numeric sources, use np.close so we don't get harmed by
+            # float equaity weirdness
             else:
                 assert np.allclose(
                     df[final_column],

data/data-pipeline/data_pipeline/tests/score/test_tiles_smoketests.py

@@ -2,6 +2,7 @@
 from dataclasses import dataclass
 from typing import Optional
 import pandas as pd
+import geopandas as gpd
 import numpy as np
 import pytest
 from data_pipeline.config import settings
@@ -26,6 +27,13 @@ def tiles_df(scope="session"):
     )
 
 
+@pytest.fixture()
+def tiles_geojson_df():
+    return gpd.read_file(
+        settings.APP_ROOT / "data" / "score" / "geojson" / "usa-high.json"
+    )
+
+
 PERCENTILE_FIELDS = [
     "DF_PFS",
     "AF_PFS",
@@ -102,6 +110,19 @@ def test_tract_equality(tiles_df, final_score_df):
     assert tiles_df.shape[0] == final_score_df.shape[0]
 
 
+def is_col_fake_bool(col) -> bool:
+    if col.dtype == np.dtype("float64"):
+        fake_bool = {1.0, 0.0, None}
+        # Replace the nans in the column values with None for
+        # so we can just use issubset below
+        col_values = set(
+            not np.isnan(val) and val or None
+            for val in col.value_counts(dropna=False).index
+        )
+        return len(col_values) <= 3 and col_values.issubset(fake_bool)
+    return False
+
+
 @dataclass
 class ColumnValueComparison:
     final_score_column: pd.Series
@@ -110,16 +131,7 @@ class ColumnValueComparison:
 
     @property
     def _is_tiles_column_fake_bool(self) -> bool:
-        if self.tiles_column.dtype == np.dtype("float64"):
-            fake_bool = {1.0, 0.0, None}
-            # Replace the nans in the column values with None for
-            # so we can just use issubset below
-            col_values = set(
-                not np.isnan(val) and val or None
-                for val in self.tiles_column.value_counts(dropna=False).index
-            )
-            return len(col_values) <= 3 and col_values.issubset(fake_bool)
-        return False
+        return is_col_fake_bool(self.tiles_column)
 
     @property
     def _is_dtype_ok(self) -> bool:
@@ -215,6 +227,49 @@ def test_for_column_fidelitiy_from_score(tiles_df, final_score_df):
     assert not errors, error_message
 
 
+def test_for_geojson_fidelity_from_tiles_csv(tiles_df, tiles_geojson_df):
+    tiles_geojson_df = tiles_geojson_df.drop(columns=["geometry"]).rename(
+        columns={"GEOID10": "GTF"}
+    )
+    assert tiles_df.shape == tiles_geojson_df.shape
+    assert tiles_df["GTF"].equals(tiles_geojson_df["GTF"])
+    assert sorted(tiles_df.columns) == sorted(tiles_geojson_df.columns)
+
+    # Are all the dtypes and values the same?
+    for col_name in tiles_geojson_df.columns:
+        if is_col_fake_bool(tiles_df[col_name]):
+            tiles_df[col_name] = (
+                tiles_df[col_name]
+                .astype("float64")
+                .replace({0.0: False, 1.0: True})
+            )
+        if is_col_fake_bool(tiles_geojson_df[col_name]):
+            tiles_geojson_df[col_name] = (
+                tiles_geojson_df[col_name]
+                .astype("float64")
+                .replace({0.0: False, 1.0: True})
+            )
+        tiles_geojson_df[col_name] = tiles_df[col_name].replace({None: np.nan})
+        error_message = f"Column {col_name} not equal "
+        # For non-numeric types, we can use the built-in equals from pandas
+        if tiles_df[col_name].dtype in [
+            np.dtype(object),
+            np.dtype(bool),
+            np.dtype(str),
+        ]:
+            assert tiles_df[col_name].equals(
+                tiles_geojson_df[col_name]
+            ), error_message
+        # For numeric sources, use np.close so we don't get harmed by
+        # float equaity weirdness
+        else:
+            assert np.allclose(
+                tiles_df[col_name],
+                tiles_geojson_df[col_name],
+                equal_nan=True,
+            ), error_message
+
+
 def test_for_state_names(tiles_df):
     states = tiles_df["SF"].value_counts(dropna=False).index
     assert np.nan not in states

data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py

@@ -16,9 +16,11 @@ from data_pipeline.score import field_names
 
 @contextmanager
 def patch_calculate_tract_adjacency_scores():
-    tract_data = Path(__file__).parent / "data" / "us.geojson"
+    # Use fixtures for tract data.
+    tract_data_path = Path(__file__).parent / "data" / "us.geojson"
+
     get_tract_geojson_mock = partial(
-        get_tract_geojson, _tract_data_path=tract_data
+        get_tract_geojson, _tract_data_path=tract_data_path
     )
     with mock.patch(
         "data_pipeline.score.utils.get_tract_geojson",

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/ME_A.CSV

@@ -0,0 +1,2 @@
+"Tract ID","STATE2KX","CNTY2KX","TRACT2KX","e(0)","se(e(0))","Abridged life table flag"
+23001010100,23,001,010100,72.3,2.2928,3

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/US_A.CSV

@@ -0,0 +1,16 @@
+Tract ID,STATE2KX,CNTY2KX,TRACT2KX,e(0),se(e(0)),Abridged life table flag
+15001021010,15,001,021010,77.4,1.6548,2
+15001021101,15,001,021101,82.5,3.9086,3
+15001021402,15,001,021402,80.4,1.093,2
+15001021800,15,001,021800,79.5,1.132,2
+15003010201,15,003,010201,79.4,1.5261,3
+15007040603,15,007,040603,86.3,2.2285,3
+15007040604,15,007,040604,84.9,2.1995,3
+15007040700,15,007,040700,80.4,0.7571,2
+15009030100,15,009,030100,77.2,1.8736,3
+15009030402,15,009,030402,83.5,1.8267,3
+15009030800,15,009,030800,82.2,1.6251,3
+06027000800,06,007,040500,99.1,3.1415,3
+06069000802,06,001,020100,99.1,3.1415,3
+06061021322,06,007,040300,99.1,3.1415,3
+15009030201,15,009,030201,99.1,3.1415,3

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/WI_A.CSV

@@ -0,0 +1,2 @@
+"Tract ID","STATE2KX","CNTY2KX","TRACT2KX","e(0)","se(e(0))","Abridged life table flag"
+55001950201,55,001,950201,74.5,2.5471,3

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/extract.csv

@@ -0,0 +1,16 @@
+Tract ID,STATE2KX,CNTY2KX,TRACT2KX,e(0),se(e(0)),Abridged life table flag
+15001021010,15,1,21010,77.4000000000,1.6548000000,2
+15001021101,15,1,21101,82.5000000000,3.9086000000,3
+15001021402,15,1,21402,80.4000000000,1.0930000000,2
+15001021800,15,1,21800,79.5000000000,1.1320000000,2
+15003010201,15,3,10201,79.4000000000,1.5261000000,3
+15007040603,15,7,40603,86.3000000000,2.2285000000,3
+15007040604,15,7,40604,84.9000000000,2.1995000000,3
+15007040700,15,7,40700,80.4000000000,0.7571000000,2
+15009030100,15,9,30100,77.2000000000,1.8736000000,3
+15009030402,15,9,30402,83.5000000000,1.8267000000,3
+15009030800,15,9,30800,82.2000000000,1.6251000000,3
+6027000800,6,7,40500,99.1000000000,3.1415000000,3
+6069000802,6,1,20100,99.1000000000,3.1415000000,3
+6061021322,6,7,40300,99.1000000000,3.1415000000,3
+15009030201,15,9,30201,99.1000000000,3.1415000000,3

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/output.csv

@@ -0,0 +1,18 @@
+GEOID10_TRACT,Life expectancy (years)
+15001021010,77.4000000000
+15001021101,82.5000000000
+15001021402,80.4000000000
+15001021800,79.5000000000
+15003010201,79.4000000000
+15007040603,86.3000000000
+15007040604,84.9000000000
+15007040700,80.4000000000
+15009030100,77.2000000000
+15009030402,83.5000000000
+15009030800,82.2000000000
+06027000800,99.1000000000
+06069000802,99.1000000000
+06061021322,99.1000000000
+15009030201,99.1000000000
+23001010100,72.3000000000
+55001950201,74.5000000000

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/transform.csv

@@ -0,0 +1,18 @@
+GEOID10_TRACT,STATE2KX,CNTY2KX,TRACT2KX,Life expectancy (years),se(e(0)),Abridged life table flag
+15001021010,15,1,21010,77.4000000000,1.6548000000,2
+15001021101,15,1,21101,82.5000000000,3.9086000000,3
+15001021402,15,1,21402,80.4000000000,1.0930000000,2
+15001021800,15,1,21800,79.5000000000,1.1320000000,2
+15003010201,15,3,10201,79.4000000000,1.5261000000,3
+15007040603,15,7,40603,86.3000000000,2.2285000000,3
+15007040604,15,7,40604,84.9000000000,2.1995000000,3
+15007040700,15,7,40700,80.4000000000,0.7571000000,2
+15009030100,15,9,30100,77.2000000000,1.8736000000,3
+15009030402,15,9,30402,83.5000000000,1.8267000000,3
+15009030800,15,9,30800,82.2000000000,1.6251000000,3
+06027000800,06,7,40500,99.1000000000,3.1415000000,3
+06069000802,06,1,20100,99.1000000000,3.1415000000,3
+06061021322,06,7,40300,99.1000000000,3.1415000000,3
+15009030201,15,9,30201,99.1000000000,3.1415000000,3
+23001010100,23,1,10100,72.3000000000,2.2928000000,3
+55001950201,55,1,950201,74.5000000000,2.5471000000,3

data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/test_etl.py

@@ -0,0 +1,112 @@
+# pylint: disable=protected-access
+import pathlib
+from unittest import mock
+import requests
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.sources.cdc_life_expectancy.etl import CDCLifeExpectancy
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class TestCDCLifeExpectency(TestETL):
+    """Tests the CDC Life Expectancy ETL.
+
+    This uses pytest-snapshot.
+    To update individual snapshots: $ poetry run pytest
+            data_pipeline/tests/sources/cdc_life_expectancy/test_etl.py::TestClassNameETL::<testname>
+            --snapshot-update
+    """
+
+    _ETL_CLASS = CDCLifeExpectancy
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "US_A.CSV"
+    _SAMPLE_DATA_ZIP_FILE_NAME = None
+    _EXTRACT_TMP_FOLDER_NAME = "CDCLifeExpectanc"
+    _EXTRACT_CSV_FILE_NAME = "extract.csv"
+    _FIXTURES_SHARED_TRACT_IDS = TestETL._FIXTURES_SHARED_TRACT_IDS + [
+        "55001950201",  # WI
+        "23001010100",  # ME
+    ]
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)
+
+    def _setup_etl_instance_and_run_extract(
+        self, mock_etl, mock_paths
+    ) -> ExtractTransformLoad:
+        """Method to setup an ETL instance with proper upstream mocks to run extract.
+        This must be re-implemented in every child class.
+
+        This method can be used by multiple tests that need to run the same fixtures
+        that need these same mocks.
+
+        In order to re-implement this method, usually it will involve a
+        decent amount of work to monkeypatch `requests` or another method that's
+        used to retrieve data in order to force that method to retrieve the fixture
+        data. A basic version of that patching is included here for classes that can use it.
+        """
+
+        with mock.patch(
+            "data_pipeline.utils.requests"
+        ) as requests_mock, mock.patch(
+            "data_pipeline.etl.score.etl_utils.get_state_fips_codes"
+        ) as mock_get_state_fips_codes:
+            tmp_path = mock_paths[1]
+
+            def fake_get(url, *args, **kwargs):
+                file_path = url.split("/")[-1]
+                with open(
+                    self._DATA_DIRECTORY_FOR_TEST / file_path,
+                    "rb",
+                ) as file:
+                    file_contents = file.read()
+
+                response_mock = requests.Response()
+                response_mock.status_code = 200
+                # pylint: disable=protected-access
+                # Return text fixture:
+                response_mock._content = file_contents
+                return response_mock
+
+            requests_mock.get = fake_get
+            mock_get_state_fips_codes.return_value = [
+                x[0:2] for x in self._FIXTURES_SHARED_TRACT_IDS
+            ]
+            # Instantiate the ETL class.
+            etl = self._get_instance_of_etl_class()
+
+            # Monkey-patch the temporary directory to the one used in the test
+            etl.TMP_PATH = tmp_path
+
+            # Run the extract method.
+            etl.extract()
+        return etl
+
+    def test_init(self, mock_etl, mock_paths):
+        etl = self._ETL_CLASS()
+        data_path, _ = mock_paths
+        assert etl.DATA_PATH == data_path
+        assert etl.COLUMNS_TO_KEEP == [
+            "GEOID10_TRACT",
+            "Life expectancy (years)",
+        ]
+        assert etl.INPUT_GEOID_TRACT_FIELD_NAME == "Tract ID"
+        assert etl.LIFE_EXPECTANCY_FIELD_NAME == "Life expectancy (years)"
+
+    def test_get_output_file_path(self, mock_etl, mock_paths):
+        """Tests the right file name is returned."""
+        etl = self._ETL_CLASS()
+        data_path, tmp_path = mock_paths
+
+        output_file_path = etl._get_output_file_path()
+        expected_output_file_path = (
+            data_path / "dataset" / "cdc_life_expectancy" / "usa.csv"
+        )
+        assert output_file_path == expected_output_file_path

data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/census_tract.csv

@@ -0,0 +1,451 @@
+Year,StateAbbr,StateDesc,CountyName,CountyFIPS,LocationName,DataSource,Category,Measure,Data_Value_Unit,Data_Value_Type,Data_Value,Data_Value_Footnote_Symbol,Data_Value_Footnote,Low_Confidence_Limit,High_Confidence_Limit,TotalPopulation,Geolocation,LocationID,CategoryID,MeasureId,DataValueTypeID,Short_Question_Text
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,36.1,,,35.2,36.8,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,13.1,,,12.6,13.6,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1,,,14.4,15.8,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.0,,,80.5,83.4,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,14.2,,,12.8,15.6,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,33.1,,,32.1,34.1,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.4,,,86.1,86.7,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.2,,,15.8,18.5,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,8.6,,,8.1,9.1,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,30.2,,,29.4,31.0,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,24.2,,,22.4,25.9,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.3,,,73.6,74.9,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.9,,,3.8,4.1,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.8,,,15.8,17.7,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6,,,22.4,29.2,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.5,,,24.0,29.1,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.5,,,71.9,73.0,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,26.9,,,25.3,28.4,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,35.8,,,35.2,36.5,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,8.3,,,8.0,8.5,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.5,,,18.9,20.2,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,9.2,,,8.4,10.0,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,COPD,CrdPrv,COPD
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,16.2,,,15.8,16.6,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,17.7,,,14.2,21.6,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.1,,,9.7,10.4,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,74.9,,,72.7,77.0,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,58.1,,,55.6,59.9,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.5,,,59.2,63.6,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,28.5,,,27.8,29.2,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.6,,,4.3,4.9,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.0,,,22.2,24.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.2,,,22.5,23.9,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,3.9,,,3.7,4.2,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,16.6,,,15.2,18.1,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2,,,16.4,18.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.9,,,18.3,19.5,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,35.7,,,31.8,39.8,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2018,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9,,,30.6,33.2,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.2,,,2.1,2.3,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.6,,,8.2,8.9,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0,,,11.1,13.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4,,,81.6,85.2,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,6.9,,,6.6,7.3,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,34.4,,,30.3,38.5,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8,,,28.2,29.4,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.2,,,85.7,86.8,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.2,,,75.6,80.3,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9,,,5.7,6.1,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.2,,,2.0,2.4,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,72.1,,,69.9,74.0,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.0,,,7.8,10.6,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,11.4,,,9.8,13.2,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.4,,,66.4,68.4,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,9.4,,,8.7,10.1,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,70.2,,,69.4,70.9,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.6,,,5.6,10.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,12.3,,,11.0,13.6,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.2,,,3.8,4.8,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,COPD,CrdPrv,COPD
+2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.7,,,18.0,19.4,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.8,,,69.5,74.1,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.5,,,5.9,7.0,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,COPD,CrdPrv,COPD
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,12.3,,,9.6,15.5,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6,,,75.4,79.6,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.3,,,9.0,9.6,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.6,,,23.0,24.3,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,64.8,,,62.9,67.0,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,3.0,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.7,,,83.5,86.0,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.7,,,18.3,19.1,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.5,,,12.9,14.2,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,30.3,,,26.8,34.1,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.4,,,12.7,14.2,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.2,,,18.6,19.9,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0,,,6.8,7.2,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,32.7,,,32.1,33.3,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,87.1,,,86.9,87.2,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9,,,30.7,32.8,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.9,,,17.5,20.4,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6,,,69.8,71.2,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,29.3,,,26.0,32.8,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,71.1,,,70.5,71.7,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.2,,,3.0,3.4,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.7,,,63.5,67.7,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7,,,21.4,24.1,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,13.2,,,11.9,14.5,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2,,,9.8,10.6,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.2,,,29.5,30.9,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.8,,,5.5,6.1,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2,,,28.4,30.0,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.5,,,13.3,15.7,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,73.5,,,73.1,73.8,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.4,,,29.0,29.8,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.4,,,20.4,26.5,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,20.7,,,20.5,20.9,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4,,,11.9,12.9,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3,,,24.0,24.7,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.9,,,63.7,66.2,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9,,,15.6,16.3,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.8,,,10.3,11.3,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.9,,,23.8,25.9,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.0,,,3.0,3.1,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.0,,,14.9,17.2,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,21.1,,,20.8,21.4,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,11.3,,,11.1,11.5,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.3,,,81.3,83.2,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,3.0,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.5,,,73.9,75.1,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8,,,5.5,6.0,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.6,,,14.7,16.5,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.2,,,18.0,18.4,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.3,,,17.4,23.4,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.5,,,10.9,12.2,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8,,,28.6,29.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1,,,9.1,11.3,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,22.6,,,21.1,24.3,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.0,,,13.6,14.3,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.6,,,72.3,72.9,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,32.6,,,31.6,33.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.2,,,82.6,83.9,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.5,,,7.2,10.1,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.8,,,19.1,20.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,17.8,,,17.4,18.3,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.5,,,29.2,29.8,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.8,,,83.7,83.9,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,61.9,,,60.9,63.0,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9,,,5.9,6.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,13.3,,,11.7,15.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2,,,12.9,13.6,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.5,,,82.4,82.6,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.8,,,23.4,24.2,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.2,,,9.0,9.4,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2,,,10.8,11.5,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.4,,,10.0,10.7,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.5,,,19.1,22.0,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,79.8,,,79.7,79.9,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,39.4,,,38.2,41.2,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.6,,,29.1,30.2,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.8,,,29.4,30.2,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,21.7,,,21.3,22.1,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4,,,6.2,6.6,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.2,,,3.1,3.3,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,22.6,,,21.7,23.6,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7,,,10.5,11.0,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.1,,,18.7,19.5,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4,,,10.6,12.1,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2,,,75.3,77.1,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6,,,70.1,71.0,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.7,,,24.8,26.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,21.4,,,19.4,23.6,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.8,,,9.6,9.9,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,12.5,,,11.9,13.1,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.6,,,3.4,3.7,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5,,,63.5,68.0,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2,,,39.7,40.7,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.5,,,3.3,3.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.0,,,3.9,4.2,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8,,,61.3,66.1,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,42.6,,,41.8,43.1,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.1,,,83.0,83.3,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.3,,,26.5,28.1,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.4,,,7.1,7.8,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,25.8,,,24.0,27.7,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,60.7,,,59.6,61.7,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.7,,,17.4,17.9,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.0,,,17.1,19.0,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,GHLTH,CrdPrv,General Health
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1,,,22.7,25.6,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4,,,82.0,84.8,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.4,,,73.9,74.9,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.2,,,3.9,4.5,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.8,,,22.8,26.9,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.9,,,75.6,76.3,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.3,,,17.6,19.0,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.4,,,75.2,75.7,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2,,,19.0,19.4,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2,,,12.7,13.6,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0,,,5.8,6.3,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.0,,,23.3,24.8,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2,,,10.9,11.4,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.6,,,30.2,31.0,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7,,,6.6,6.8,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,19.3,,,17.2,21.4,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2,,,39.3,41.0,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2,,,9.1,11.4,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.7,,,16.2,17.2,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,66.0,,,65.0,67.0,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.2,,,4.9,5.5,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,COPD,CrdPrv,COPD
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.4,,,66.4,68.4,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.8,,,6.6,7.0,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,21.1,,,20.6,21.5,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.0,,,82.5,83.7,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6,,,22.2,22.9,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,75.9,,,74.6,77.2,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.3,,,67.0,69.7,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.8,,,30.2,31.4,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.6,,,73.7,75.4,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3,,,6.2,6.4,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.9,,,27.0,28.8,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8,,,29.4,30.2,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2,,,75.1,77.3,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7,,,3.6,3.8,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,7.7,,,7.2,8.3,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,18.5,,,17.0,20.1,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.8,,,16.1,17.6,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.9,,,6.7,7.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.9,,,8.9,15.0,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6,,,75.5,79.5,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,21.0,,,19.1,23.0,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1,,,15.1,17.0,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,22.9,,,22.1,23.7,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.3,,,6.5,8.2,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7,,,14.4,17.0,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.8,,,2.7,2.9,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7,,,6.6,6.9,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,27.0,,,25.1,28.9,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7,,,10.3,11.1,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2,,,16.7,17.6,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.1,,,8.9,9.3,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,20.4,,,19.8,21.0,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.7,,,22.3,23.1,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.8,,,60.6,63.0,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.7,,,11.0,12.4,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.5,,,3.3,3.6,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.8,,,83.9,85.7,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.0,,,22.7,23.4,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3,,,23.7,25.0,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5,,,64.3,66.8,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,38.7,,,38.0,39.4,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6,,,22.0,23.1,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7,,,21.4,24.2,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.3,,,28.9,29.7,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.5,,,26.6,28.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.0,,,72.4,73.6,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.1,,,13.6,14.7,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.3,,,23.2,25.5,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.5,,,69.7,73.4,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.9,,,13.7,16.2,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3,,,6.1,6.4,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.2,,,68.1,70.1,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.2,,,9.4,13.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.6,,,83.3,83.8,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,70.2,,,69.0,71.4,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.6,,,18.2,18.9,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.6,,,84.8,86.6,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.1,,,5.7,6.6,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.0,,,30.5,31.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,11.0,,,10.4,11.6,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9,,,13.4,14.3,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.3,,,21.7,29.2,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,82.1,,,81.0,83.1,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7,,,14.8,16.7,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.8,,,10.3,11.3,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.1,,,30.3,31.8,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,12.2,,,12.0,12.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.0,,,25.3,28.5,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2,,,28.6,29.8,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.0,,,83.8,86.2,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.6,,,4.3,5.0,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8,,,77.5,78.1,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,6.2,,,5.1,7.5,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,8.9,,,8.5,9.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6,,,23.3,27.9,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.9,,,12.5,13.4,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.9,,,66.6,69.3,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8,,,5.5,6.2,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,20.0,,,19.0,21.0,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,GHLTH,CrdPrv,General Health
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.4,,,80.9,84.0,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.2,,,83.4,85.1,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.3,,,24.6,26.0,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.0,,,67.5,70.4,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.2,,,10.0,10.5,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.2,,,11.5,12.8,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.3,,,21.0,21.6,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.5,,,36.9,38.1,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1,,,8.7,11.6,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1,,,6.2,10.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.5,,,6.3,6.8,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2,,,18.9,19.6,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.9,,,40.1,41.6,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.5,,,15.0,16.0,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5,,,16.4,18.7,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.2,,,14.9,15.6,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.0,,,20.6,21.4,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,27.0,,,26.6,27.4,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,67.1,,,65.3,68.9,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.4,,,25.8,26.9,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,28.1,,,27.6,28.7,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.4,,,6.3,6.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9,,,15.6,16.2,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9,,,82.7,83.0,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.7,,,2.6,2.8,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.8,,,21.5,22.1,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0,,,5.8,6.3,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.5,,,2.5,2.6,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9,,,82.9,83.0,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.5,,,24.9,25.9,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7,,,10.4,11.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.0,,,66.2,69.9,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.3,,,11.6,13.0,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.9,,,8.7,9.2,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.0,,,2.8,3.2,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,19.5,,,18.6,20.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4,,,11.6,13.1,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.1,,,3.0,3.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.8,,,23.8,25.9,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.1,,,22.4,30.0,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.1,,,22.6,23.6,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.3,,,12.0,12.7,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.0,,,22.0,25.9,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4,,,6.1,6.7,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.7,,,23.0,24.3,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,84.2,,,84.1,84.4,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.1,,,14.3,15.8,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7,,,3.5,3.9,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,79.1,,,78.8,79.4,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0,,,22.9,27.3,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9,,,82.5,83.3,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,13.6,,,12.6,14.6,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.3,,,27.9,28.7,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.0,,,8.7,9.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.1,,,20.7,23.7,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.5,,,76.1,78.9,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.7,,,14.2,15.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.2,,,61.5,64.8,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.8,,,3.7,4.0,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2,,,10.0,10.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.4,,,22.7,28.5,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.0,,,81.7,82.4,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5,,,17.9,19.2,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6,,,78.6,80.7,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.1,,,10.3,11.9,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.5,,,23.0,24.0,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,18.4,,,17.3,19.6,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.6,,,9.2,10.1,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.3,,,23.2,29.7,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6,,,70.0,71.4,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.7,,,9.4,10.0,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.6,,,5.1,6.1,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,COPD,CrdPrv,COPD
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.8,,,35.8,37.7,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.9,,,6.2,9.9,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.7,,,26.6,28.7,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.2,,,25.7,26.6,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,80.7,,,78.6,82.6,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.1,,,17.1,19.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1,,,2.9,3.2,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,23.8,,,22.7,24.9,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.2,,,6.4,10.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,24.8,,,22.9,27.0,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0,,,12.5,13.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.5,,,18.3,18.8,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.3,,,9.0,9.5,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.1,,,6.0,6.3,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.2,,,68.0,70.5,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4,,,10.4,12.6,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,27.9,,,27.3,28.5,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.6,,,2.5,2.7,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.1,,,70.8,73.3,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.0,,,21.0,25.0,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.9,,,69.4,70.4,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0,,,11.5,12.5,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0,,,6.8,7.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,2.9,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.2,,,72.9,73.6,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.0,,,29.4,30.6,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8,,,77.5,78.2,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,24.5,,,24.2,24.8,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.4,,,39.6,41.5,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.1,,,73.6,74.6,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,9.6,,,7.8,11.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.8,,,23.3,24.1,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.1,,,81.2,82.9,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.6,,,82.3,83.0,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,73.2,,,71.2,74.9,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,21.3,,,19.5,23.3,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,33.4,,,32.9,34.0,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.7,,,69.9,71.4,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.6,,,12.0,13.1,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.5,,,5.3,5.7,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.0,,,22.2,23.7,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.7,,,22.1,27.4,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.7,,,10.3,11.0,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.5,,,11.8,13.2,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2,,,12.7,13.8,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.2,,,70.8,73.5,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,3.0,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.2,,,14.1,16.4,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.5,,,5.0,6.1,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7,,,3.4,3.9,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.3,,,9.3,11.3,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.3,,,3.2,3.4,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9,,,13.2,14.6,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.2,,,22.7,23.8,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.6,,,14.8,16.5,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.6,,,24.8,26.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.6,,,21.7,23.6,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6,,,76.1,79.2,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.9,,,5.6,6.2,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2,,,9.5,11.0,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.3,,,4.1,4.6,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0,,,4.7,5.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.6,,,9.4,9.8,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.3,,,14.0,16.7,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.5,,,4.2,4.8,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5,,,18.0,18.9,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1,,,21.1,27.2,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.5,,,14.2,14.9,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.4,,,5.3,5.5,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.3,,,8.3,10.2,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,78.5,,,78.0,78.9,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1,,,2.9,3.3,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.5,,,77.1,79.9,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,22.7,,,20.4,25.1,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,48.2,,,47.6,48.9,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1,,,15.1,17.0,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8,,,61.5,65.9,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.8,,,74.3,75.4,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.6,,,10.0,11.3,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5,,,15.9,19.2,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1,,,7.1,9.3,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0,,,4.8,5.3,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.7,,,8.5,8.9,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0,,,12.4,13.5,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.8,,,4.5,5.1,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.2,,,16.8,19.5,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.2,,,73.8,74.6,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,77.1,,,76.5,77.7,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.8,,,37.1,38.6,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2,,,9.5,11.1,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8,,,29.4,30.1,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.4,,,63.1,67.3,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.7,,,67.1,68.2,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,76.2,,,75.8,76.7,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6,,,77.8,81.3,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3,,,23.8,24.7,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.3,,,86.7,88.1,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.1,,,5.8,6.5,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,17.9,,,17.5,18.2,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,26.0,,,25.6,26.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.1,,,75.2,79.0,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.9,,,6.8,7.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.2,,,30.8,31.8,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1,,,14.5,15.7,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.7,,,86.6,88.8,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.3,,,4.2,4.4,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.4,,,35.6,36.9,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.8,,,82.7,82.9,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.2,,,24.6,25.9,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,27.8,,,25.4,30.3,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.3,,,2.3,2.4,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0,,,23.0,27.0,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.2,,,62.8,65.7,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening

data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/extract.csv

@@ -0,0 +1,451 @@
+Year,StateAbbr,StateDesc,CountyName,CountyFIPS,LocationName,DataSource,Category,Measure,Data_Value_Unit,Data_Value_Type,Data_Value,Data_Value_Footnote_Symbol,Data_Value_Footnote,Low_Confidence_Limit,High_Confidence_Limit,TotalPopulation,Geolocation,LocationID,CategoryID,MeasureId,DataValueTypeID,Short_Question_Text
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,36.1000000000,,,35.2000000000,36.8000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,13.1000000000,,,12.6000000000,13.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1000000000,,,14.4000000000,15.8000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.0000000000,,,80.5000000000,83.4000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,14.2000000000,,,12.8000000000,15.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,33.1000000000,,,32.1000000000,34.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.4000000000,,,86.1000000000,86.7000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.2000000000,,,15.8000000000,18.5000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,8.6000000000,,,8.1000000000,9.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,30.2000000000,,,29.4000000000,31.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,24.2000000000,,,22.4000000000,25.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.3000000000,,,73.6000000000,74.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.9000000000,,,3.8000000000,4.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.8000000000,,,15.8000000000,17.7000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6000000000,,,22.4000000000,29.2000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.5000000000,,,24.0000000000,29.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.5000000000,,,71.9000000000,73.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,26.9000000000,,,25.3000000000,28.4000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,35.8000000000,,,35.2000000000,36.5000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,8.3000000000,,,8.0000000000,8.5000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.5000000000,,,18.9000000000,20.2000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,9.2000000000,,,8.4000000000,10.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,COPD,CrdPrv,COPD
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,16.2000000000,,,15.8000000000,16.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,17.7000000000,,,14.2000000000,21.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.1000000000,,,9.7000000000,10.4000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,74.9000000000,,,72.7000000000,77.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,58.1000000000,,,55.6000000000,59.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.5000000000,,,59.2000000000,63.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,28.5000000000,,,27.8000000000,29.2000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.6000000000,,,4.3000000000,4.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.0000000000,,,22.2000000000,24.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.2000000000,,,22.5000000000,23.9000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,3.9000000000,,,3.7000000000,4.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,16.6000000000,,,15.2000000000,18.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2000000000,,,16.4000000000,18.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.9000000000,,,18.3000000000,19.5000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,35.7000000000,,,31.8000000000,39.8000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2018,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9000000000,,,30.6000000000,33.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.2000000000,,,2.1000000000,2.3000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.6000000000,,,8.2000000000,8.9000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0000000000,,,11.1000000000,13.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4000000000,,,81.6000000000,85.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,6.9000000000,,,6.6000000000,7.3000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,34.4000000000,,,30.3000000000,38.5000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8000000000,,,28.2000000000,29.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.2000000000,,,85.7000000000,86.8000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.2000000000,,,75.6000000000,80.3000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9000000000,,,5.7000000000,6.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.2000000000,,,2.0000000000,2.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,72.1000000000,,,69.9000000000,74.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.0000000000,,,7.8000000000,10.6000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,11.4000000000,,,9.8000000000,13.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.4000000000,,,66.4000000000,68.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,9.4000000000,,,8.7000000000,10.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,70.2000000000,,,69.4000000000,70.9000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.6000000000,,,5.6000000000,10.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,12.3000000000,,,11.0000000000,13.6000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.2000000000,,,3.8000000000,4.8000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,COPD,CrdPrv,COPD
+2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.7000000000,,,18.0000000000,19.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.8000000000,,,69.5000000000,74.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.5000000000,,,5.9000000000,7.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,COPD,CrdPrv,COPD
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,12.3000000000,,,9.6000000000,15.5000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6000000000,,,75.4000000000,79.6000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.3000000000,,,9.0000000000,9.6000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.6000000000,,,23.0000000000,24.3000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,64.8000000000,,,62.9000000000,67.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,3.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.7000000000,,,83.5000000000,86.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.7000000000,,,18.3000000000,19.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.5000000000,,,12.9000000000,14.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,30.3000000000,,,26.8000000000,34.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.4000000000,,,12.7000000000,14.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.2000000000,,,18.6000000000,19.9000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0000000000,,,6.8000000000,7.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,32.7000000000,,,32.1000000000,33.3000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,87.1000000000,,,86.9000000000,87.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9000000000,,,30.7000000000,32.8000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.9000000000,,,17.5000000000,20.4000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6000000000,,,69.8000000000,71.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,29.3000000000,,,26.0000000000,32.8000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,71.1000000000,,,70.5000000000,71.7000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.2000000000,,,3.0000000000,3.4000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.7000000000,,,63.5000000000,67.7000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7000000000,,,21.4000000000,24.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,13.2000000000,,,11.9000000000,14.5000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2000000000,,,9.8000000000,10.6000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.2000000000,,,29.5000000000,30.9000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.8000000000,,,5.5000000000,6.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2000000000,,,28.4000000000,30.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.5000000000,,,13.3000000000,15.7000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,73.5000000000,,,73.1000000000,73.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.4000000000,,,29.0000000000,29.8000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.4000000000,,,20.4000000000,26.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,20.7000000000,,,20.5000000000,20.9000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4000000000,,,11.9000000000,12.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,24.0000000000,24.7000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.9000000000,,,63.7000000000,66.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9000000000,,,15.6000000000,16.3000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.8000000000,,,10.3000000000,11.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.9000000000,,,23.8000000000,25.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.0000000000,,,3.0000000000,3.1000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.0000000000,,,14.9000000000,17.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,21.1000000000,,,20.8000000000,21.4000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,11.3000000000,,,11.1000000000,11.5000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.3000000000,,,81.3000000000,83.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,3.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.5000000000,,,73.9000000000,75.1000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8000000000,,,5.5000000000,6.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.6000000000,,,14.7000000000,16.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.2000000000,,,18.0000000000,18.4000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.3000000000,,,17.4000000000,23.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.5000000000,,,10.9000000000,12.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8000000000,,,28.6000000000,29.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1000000000,,,9.1000000000,11.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,22.6000000000,,,21.1000000000,24.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.0000000000,,,13.6000000000,14.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.6000000000,,,72.3000000000,72.9000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,32.6000000000,,,31.6000000000,33.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.2000000000,,,82.6000000000,83.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.5000000000,,,7.2000000000,10.1000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.8000000000,,,19.1000000000,20.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,17.8000000000,,,17.4000000000,18.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.5000000000,,,29.2000000000,29.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.8000000000,,,83.7000000000,83.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,61.9000000000,,,60.9000000000,63.0000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9000000000,,,5.9000000000,6.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,13.3000000000,,,11.7000000000,15.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2000000000,,,12.9000000000,13.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.5000000000,,,82.4000000000,82.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.8000000000,,,23.4000000000,24.2000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.2000000000,,,9.0000000000,9.4000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2000000000,,,10.8000000000,11.5000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.4000000000,,,10.0000000000,10.7000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.5000000000,,,19.1000000000,22.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,79.8000000000,,,79.7000000000,79.9000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,39.4000000000,,,38.2000000000,41.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.6000000000,,,29.1000000000,30.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.8000000000,,,29.4000000000,30.2000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,21.7000000000,,,21.3000000000,22.1000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4000000000,,,6.2000000000,6.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.2000000000,,,3.1000000000,3.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,21.7000000000,23.6000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.5000000000,11.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.1000000000,,,18.7000000000,19.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4000000000,,,10.6000000000,12.1000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2000000000,,,75.3000000000,77.1000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6000000000,,,70.1000000000,71.0000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.7000000000,,,24.8000000000,26.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,21.4000000000,,,19.4000000000,23.6000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.8000000000,,,9.6000000000,9.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,12.5000000000,,,11.9000000000,13.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.6000000000,,,3.4000000000,3.7000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5000000000,,,63.5000000000,68.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2000000000,,,39.7000000000,40.7000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.5000000000,,,3.3000000000,3.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.0000000000,,,3.9000000000,4.2000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8000000000,,,61.3000000000,66.1000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,42.6000000000,,,41.8000000000,43.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.1000000000,,,83.0000000000,83.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.3000000000,,,26.5000000000,28.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.4000000000,,,7.1000000000,7.8000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,25.8000000000,,,24.0000000000,27.7000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,60.7000000000,,,59.6000000000,61.7000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.7000000000,,,17.4000000000,17.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.0000000000,,,17.1000000000,19.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,GHLTH,CrdPrv,General Health
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1000000000,,,22.7000000000,25.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4000000000,,,82.0000000000,84.8000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.4000000000,,,73.9000000000,74.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.2000000000,,,3.9000000000,4.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.8000000000,,,22.8000000000,26.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.9000000000,,,75.6000000000,76.3000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.3000000000,,,17.6000000000,19.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.4000000000,,,75.2000000000,75.7000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2000000000,,,19.0000000000,19.4000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2000000000,,,12.7000000000,13.6000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0000000000,,,5.8000000000,6.3000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.0000000000,,,23.3000000000,24.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2000000000,,,10.9000000000,11.4000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.6000000000,,,30.2000000000,31.0000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7000000000,,,6.6000000000,6.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,19.3000000000,,,17.2000000000,21.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2000000000,,,39.3000000000,41.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2000000000,,,9.1000000000,11.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.7000000000,,,16.2000000000,17.2000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,66.0000000000,,,65.0000000000,67.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.2000000000,,,4.9000000000,5.5000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,COPD,CrdPrv,COPD
+2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.4000000000,,,66.4000000000,68.4000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.8000000000,,,6.6000000000,7.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,21.1000000000,,,20.6000000000,21.5000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.0000000000,,,82.5000000000,83.7000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,22.2000000000,22.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,75.9000000000,,,74.6000000000,77.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.3000000000,,,67.0000000000,69.7000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.8000000000,,,30.2000000000,31.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.6000000000,,,73.7000000000,75.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3000000000,,,6.2000000000,6.4000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.9000000000,,,27.0000000000,28.8000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8000000000,,,29.4000000000,30.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2000000000,,,75.1000000000,77.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7000000000,,,3.6000000000,3.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,7.7000000000,,,7.2000000000,8.3000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,18.5000000000,,,17.0000000000,20.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.8000000000,,,16.1000000000,17.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.9000000000,,,6.7000000000,7.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.9000000000,,,8.9000000000,15.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6000000000,,,75.5000000000,79.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,21.0000000000,,,19.1000000000,23.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1000000000,,,15.1000000000,17.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,22.9000000000,,,22.1000000000,23.7000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.3000000000,,,6.5000000000,8.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7000000000,,,14.4000000000,17.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.8000000000,,,2.7000000000,2.9000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7000000000,,,6.6000000000,6.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,27.0000000000,,,25.1000000000,28.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.3000000000,11.1000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2000000000,,,16.7000000000,17.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.1000000000,,,8.9000000000,9.3000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,20.4000000000,,,19.8000000000,21.0000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.7000000000,,,22.3000000000,23.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.8000000000,,,60.6000000000,63.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.7000000000,,,11.0000000000,12.4000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.5000000000,,,3.3000000000,3.6000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.8000000000,,,83.9000000000,85.7000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.0000000000,,,22.7000000000,23.4000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,23.7000000000,25.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5000000000,,,64.3000000000,66.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,38.7000000000,,,38.0000000000,39.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,22.0000000000,23.1000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7000000000,,,21.4000000000,24.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.3000000000,,,28.9000000000,29.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.5000000000,,,26.6000000000,28.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.0000000000,,,72.4000000000,73.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.1000000000,,,13.6000000000,14.7000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,23.2000000000,25.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.5000000000,,,69.7000000000,73.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.9000000000,,,13.7000000000,16.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3000000000,,,6.1000000000,6.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.2000000000,,,68.1000000000,70.1000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.2000000000,,,9.4000000000,13.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.6000000000,,,83.3000000000,83.8000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,70.2000000000,,,69.0000000000,71.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.6000000000,,,18.2000000000,18.9000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.6000000000,,,84.8000000000,86.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.1000000000,,,5.7000000000,6.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.0000000000,,,30.5000000000,31.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,11.0000000000,,,10.4000000000,11.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9000000000,,,13.4000000000,14.3000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.3000000000,,,21.7000000000,29.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,82.1000000000,,,81.0000000000,83.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7000000000,,,14.8000000000,16.7000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.8000000000,,,10.3000000000,11.3000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.1000000000,,,30.3000000000,31.8000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,12.2000000000,,,12.0000000000,12.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.0000000000,,,25.3000000000,28.5000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2000000000,,,28.6000000000,29.8000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.0000000000,,,83.8000000000,86.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.6000000000,,,4.3000000000,5.0000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8000000000,,,77.5000000000,78.1000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,6.2000000000,,,5.1000000000,7.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,8.9000000000,,,8.5000000000,9.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6000000000,,,23.3000000000,27.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.9000000000,,,12.5000000000,13.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.9000000000,,,66.6000000000,69.3000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8000000000,,,5.5000000000,6.2000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,20.0000000000,,,19.0000000000,21.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,GHLTH,CrdPrv,General Health
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.4000000000,,,80.9000000000,84.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.2000000000,,,83.4000000000,85.1000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.3000000000,,,24.6000000000,26.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.0000000000,,,67.5000000000,70.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.2000000000,,,10.0000000000,10.5000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.2000000000,,,11.5000000000,12.8000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.3000000000,,,21.0000000000,21.6000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.5000000000,,,36.9000000000,38.1000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1000000000,,,8.7000000000,11.6000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1000000000,,,6.2000000000,10.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.5000000000,,,6.3000000000,6.8000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2000000000,,,18.9000000000,19.6000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.9000000000,,,40.1000000000,41.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.5000000000,,,15.0000000000,16.0000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5000000000,,,16.4000000000,18.7000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.2000000000,,,14.9000000000,15.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.0000000000,,,20.6000000000,21.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,27.0000000000,,,26.6000000000,27.4000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,67.1000000000,,,65.3000000000,68.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.4000000000,,,25.8000000000,26.9000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,28.1000000000,,,27.6000000000,28.7000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.4000000000,,,6.3000000000,6.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9000000000,,,15.6000000000,16.2000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9000000000,,,82.7000000000,83.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.7000000000,,,2.6000000000,2.8000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.8000000000,,,21.5000000000,22.1000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0000000000,,,5.8000000000,6.3000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.5000000000,,,2.5000000000,2.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9000000000,,,82.9000000000,83.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.5000000000,,,24.9000000000,25.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.4000000000,11.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.0000000000,,,66.2000000000,69.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.3000000000,,,11.6000000000,13.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.9000000000,,,8.7000000000,9.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.0000000000,,,2.8000000000,3.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,19.5000000000,,,18.6000000000,20.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4000000000,,,11.6000000000,13.1000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.1000000000,,,3.0000000000,3.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.8000000000,,,23.8000000000,25.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.1000000000,,,22.4000000000,30.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.1000000000,,,22.6000000000,23.6000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.3000000000,,,12.0000000000,12.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.0000000000,,,22.0000000000,25.9000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4000000000,,,6.1000000000,6.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.7000000000,,,23.0000000000,24.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,84.2000000000,,,84.1000000000,84.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.1000000000,,,14.3000000000,15.8000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7000000000,,,3.5000000000,3.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,79.1000000000,,,78.8000000000,79.4000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0000000000,,,22.9000000000,27.3000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9000000000,,,82.5000000000,83.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,13.6000000000,,,12.6000000000,14.6000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.3000000000,,,27.9000000000,28.7000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.0000000000,,,8.7000000000,9.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.1000000000,,,20.7000000000,23.7000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.5000000000,,,76.1000000000,78.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.7000000000,,,14.2000000000,15.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.2000000000,,,61.5000000000,64.8000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.8000000000,,,3.7000000000,4.0000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2000000000,,,10.0000000000,10.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.4000000000,,,22.7000000000,28.5000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.0000000000,,,81.7000000000,82.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5000000000,,,17.9000000000,19.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6000000000,,,78.6000000000,80.7000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.1000000000,,,10.3000000000,11.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.5000000000,,,23.0000000000,24.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,18.4000000000,,,17.3000000000,19.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.6000000000,,,9.2000000000,10.1000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.3000000000,,,23.2000000000,29.7000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6000000000,,,70.0000000000,71.4000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.7000000000,,,9.4000000000,10.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.6000000000,,,5.1000000000,6.1000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,COPD,CrdPrv,COPD
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.8000000000,,,35.8000000000,37.7000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.9000000000,,,6.2000000000,9.9000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.7000000000,,,26.6000000000,28.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.2000000000,,,25.7000000000,26.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,80.7000000000,,,78.6000000000,82.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.1000000000,,,17.1000000000,19.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1000000000,,,2.9000000000,3.2000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,23.8000000000,,,22.7000000000,24.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.2000000000,,,6.4000000000,10.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,24.8000000000,,,22.9000000000,27.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0000000000,,,12.5000000000,13.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.5000000000,,,18.3000000000,18.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.3000000000,,,9.0000000000,9.5000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DIABETES,CrdPrv,Diabetes
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.1000000000,,,6.0000000000,6.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.2000000000,,,68.0000000000,70.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4000000000,,,10.4000000000,12.6000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,27.9000000000,,,27.3000000000,28.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.6000000000,,,2.5000000000,2.7000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.1000000000,,,70.8000000000,73.3000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.0000000000,,,21.0000000000,25.0000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.9000000000,,,69.4000000000,70.4000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0000000000,,,11.5000000000,12.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0000000000,,,6.8000000000,7.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,2.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.2000000000,,,72.9000000000,73.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.0000000000,,,29.4000000000,30.6000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8000000000,,,77.5000000000,78.2000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,24.5000000000,,,24.2000000000,24.8000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.4000000000,,,39.6000000000,41.5000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.1000000000,,,73.6000000000,74.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,9.6000000000,,,7.8000000000,11.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.8000000000,,,23.3000000000,24.1000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.1000000000,,,81.2000000000,82.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.6000000000,,,82.3000000000,83.0000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,73.2000000000,,,71.2000000000,74.9000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,DENTAL,CrdPrv,Dental Visit
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,21.3000000000,,,19.5000000000,23.3000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,33.4000000000,,,32.9000000000,34.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,OBESITY,CrdPrv,Obesity
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.7000000000,,,69.9000000000,71.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.6000000000,,,12.0000000000,13.1000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.5000000000,,,5.3000000000,5.7000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.0000000000,,,22.2000000000,23.7000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.7000000000,,,22.1000000000,27.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.3000000000,11.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.5000000000,,,11.8000000000,13.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2000000000,,,12.7000000000,13.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.2000000000,,,70.8000000000,73.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,DENTAL,CrdPrv,Dental Visit
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,3.0000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.2000000000,,,14.1000000000,16.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.5000000000,,,5.0000000000,6.1000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7000000000,,,3.4000000000,3.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,STROKE,CrdPrv,Stroke
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.3000000000,,,9.3000000000,11.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.3000000000,,,3.2000000000,3.4000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9000000000,,,13.2000000000,14.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.2000000000,,,22.7000000000,23.8000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.6000000000,,,14.8000000000,16.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.6000000000,,,24.8000000000,26.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,21.7000000000,23.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,LPA,CrdPrv,Physical Inactivity
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6000000000,,,76.1000000000,79.2000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.9000000000,,,5.6000000000,6.2000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2000000000,,,9.5000000000,11.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.3000000000,,,4.1000000000,4.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0000000000,,,4.7000000000,5.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.6000000000,,,9.4000000000,9.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.3000000000,,,14.0000000000,16.7000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.5000000000,,,4.2000000000,4.8000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,COPD,CrdPrv,COPD
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5000000000,,,18.0000000000,18.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1000000000,,,21.1000000000,27.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREW,CrdPrv,Core preventive services for older women
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.5000000000,,,14.2000000000,14.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DEPRESSION,CrdPrv,Depression
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.4000000000,,,5.3000000000,5.5000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.3000000000,,,8.3000000000,10.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,78.5000000000,,,78.0000000000,78.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1000000000,,,2.9000000000,3.3000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,STROKE,CrdPrv,Stroke
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.5000000000,,,77.1000000000,79.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,22.7000000000,,,20.4000000000,25.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,48.2000000000,,,47.6000000000,48.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1000000000,,,15.1000000000,17.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8000000000,,,61.5000000000,65.9000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.8000000000,,,74.3000000000,75.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.6000000000,,,10.0000000000,11.3000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5000000000,,,15.9000000000,19.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,CSMOKING,CrdPrv,Current Smoking
+2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1000000000,,,7.1000000000,9.3000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0000000000,,,4.8000000000,5.3000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.7000000000,,,8.5000000000,8.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CASTHMA,CrdPrv,Current Asthma
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0000000000,,,12.4000000000,13.5000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,PHLTH,CrdPrv,Physical Health
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.8000000000,,,4.5000000000,5.1000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.2000000000,,,16.8000000000,19.5000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,GHLTH,CrdPrv,General Health
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.2000000000,,,73.8000000000,74.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHECKUP,CrdPrv,Annual Checkup
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,77.1000000000,,,76.5000000000,77.7000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.8000000000,,,37.1000000000,38.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2000000000,,,9.5000000000,11.1000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,ACCESS2,CrdPrv,Health Insurance
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8000000000,,,29.4000000000,30.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.4000000000,,,63.1000000000,67.3000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.7000000000,,,67.1000000000,68.2000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,76.2000000000,,,75.8000000000,76.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,BPMED,CrdPrv,Taking BP Medication
+2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6000000000,,,77.8000000000,81.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,23.8000000000,24.7000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.3000000000,,,86.7000000000,88.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.1000000000,,,5.8000000000,6.5000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,17.9000000000,,,17.5000000000,18.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,BINGE,CrdPrv,Binge Drinking
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,26.0000000000,,,25.6000000000,26.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,OBESITY,CrdPrv,Obesity
+2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.1000000000,,,75.2000000000,79.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,MAMMOUSE,CrdPrv,Mammography
+2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.9000000000,,,6.8000000000,7.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CANCER,CrdPrv,Cancer (except skin)
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.2000000000,,,30.8000000000,31.8000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1000000000,,,14.5000000000,15.7000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,MHLTH,CrdPrv,Mental Health
+2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.7000000000,,,86.6000000000,88.8000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.3000000000,,,4.2000000000,4.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.4000000000,,,35.6000000000,36.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours
+2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.8000000000,,,82.7000000000,82.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening
+2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.2000000000,,,24.6000000000,25.9000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure
+2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,27.8000000000,,,25.4000000000,30.3000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.3000000000,,,2.3000000000,2.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease
+2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0000000000,,,23.0000000000,27.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREM,CrdPrv,Core preventive services for older men
+2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.2000000000,,,62.8000000000,65.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening

data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/output.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,Diagnosed diabetes among adults aged greater than or equal to 18 years,Current asthma among adults aged greater than or equal to 18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years
+06027000800,13.1000000000,10.1000000000,8.6000000000,8.3000000000,14.2000000000,16.8000000000
+06061021322,6.9000000000,8.6000000000,3.9000000000,5.9000000000,9.0000000000,9.4000000000
+06069000802,10.2000000000,9.3000000000,5.8000000000,7.0000000000,13.2000000000,13.4000000000
+15001021010,11.2000000000,11.3000000000,6.9000000000,5.9000000000,12.5000000000,16.7000000000
+15001021101,11.5000000000,10.4000000000,7.7000000000,6.8000000000,10.2000000000,16.0000000000
+15001021402,11.2000000000,9.2000000000,6.0000000000,6.3000000000,11.4000000000,12.4000000000
+15001021800,10.7000000000,9.8000000000,6.4000000000,6.7000000000,10.8000000000,13.2000000000
+15003010201,10.2000000000,12.2000000000,5.5000000000,6.4000000000,10.2000000000,13.0000000000
+15007040603,12.2000000000,9.0000000000,6.1000000000,7.0000000000,11.4000000000,12.3000000000
+15007040604,12.3000000000,9.6000000000,6.4000000000,6.7000000000,11.7000000000,13.2000000000
+15007040700,10.7000000000,8.7000000000,5.0000000000,6.3000000000,10.2000000000,10.8000000000
+15009030100,10.7000000000,10.2000000000,6.0000000000,6.9000000000,11.1000000000,13.0000000000
+15009030201,8.9000000000,9.7000000000,5.0000000000,6.5000000000,9.3000000000,12.4000000000
+15009030402,9.6000000000,8.9000000000,4.8000000000,6.1000000000,10.3000000000,11.0000000000
+15009030800,9.3000000000,9.1000000000,4.3000000000,5.4000000000,10.6000000000,10.7000000000

data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/transform.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,All teeth lost among adults aged >=65 years,Arthritis among adults aged >=18 years,Binge drinking among adults aged >=18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Cervical cancer screening among adult women aged 21-65 years,Cholesterol screening among adults aged >=18 years,Chronic kidney disease among adults aged >=18 years,Chronic obstructive pulmonary disease among adults aged >=18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Current asthma among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Current smoking among adults aged >=18 years,Depression among adults aged >=18 years,Diagnosed diabetes among adults aged greater than or equal to 18 years,Fair or poor self-rated health status among adults aged >=18 years,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",High blood pressure among adults aged >=18 years,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,Mammography use among women aged 50-74 years,Mental health not good for >=14 days among adults aged >=18 years,No leisure-time physical activity among adults aged >=18 years,Obesity among adults aged >=18 years,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening","Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years,Sleeping less than 7 hours among adults aged >=18 years,Stroke among adults aged >=18 years,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,Visits to dentist or dental clinic among adults aged >=18 years,Visits to doctor for routine checkup within the past year among adults aged >=18 years
+06027000800,17.7000000000,28.5000000000,16.2000000000,8.3000000000,82.0000000000,86.4000000000,3.9000000000,9.2000000000,8.6000000000,10.1000000000,14.2000000000,17.2000000000,19.5000000000,13.1000000000,24.2000000000,61.5000000000,36.1000000000,35.8000000000,74.9000000000,15.1000000000,26.9000000000,30.2000000000,26.5000000000,25.6000000000,16.8000000000,33.1000000000,4.6000000000,74.3000000000,58.1000000000,72.5000000000
+06061021322,7.6000000000,18.7000000000,18.9000000000,5.9000000000,83.4000000000,86.2000000000,2.2000000000,4.2000000000,3.9000000000,8.6000000000,9.0000000000,11.4000000000,17.2000000000,6.9000000000,12.3000000000,72.1000000000,23.2000000000,28.8000000000,78.2000000000,12.0000000000,16.6000000000,23.0000000000,35.7000000000,34.4000000000,9.4000000000,31.9000000000,2.2000000000,67.4000000000,71.8000000000,70.2000000000
+06069000802,12.3000000000,23.6000000000,18.7000000000,7.0000000000,84.7000000000,87.1000000000,2.9000000000,6.5000000000,5.8000000000,9.3000000000,13.2000000000,14.5000000000,19.2000000000,10.2000000000,18.9000000000,65.7000000000,30.2000000000,32.7000000000,77.6000000000,13.5000000000,22.7000000000,29.2000000000,30.3000000000,29.3000000000,13.4000000000,31.9000000000,3.2000000000,70.6000000000,64.8000000000,71.1000000000
+15001021010,13.3000000000,23.8000000000,20.7000000000,5.9000000000,83.0000000000,79.8000000000,3.2000000000,7.4000000000,6.9000000000,11.3000000000,12.5000000000,22.6000000000,21.1000000000,11.2000000000,22.9000000000,60.7000000000,30.6000000000,28.8000000000,76.2000000000,17.8000000000,27.3000000000,29.4000000000,18.5000000000,22.6000000000,16.7000000000,42.6000000000,4.0000000000,70.6000000000,61.9000000000,72.6000000000
+15001021101,11.9000000000,25.7000000000,19.1000000000,6.8000000000,83.4000000000,82.5000000000,3.5000000000,7.3000000000,7.7000000000,10.4000000000,10.2000000000,19.3000000000,19.8000000000,11.5000000000,21.0000000000,63.8000000000,32.6000000000,30.8000000000,77.6000000000,15.6000000000,25.8000000000,27.9000000000,20.3000000000,23.4000000000,16.0000000000,39.4000000000,4.2000000000,74.6000000000,65.5000000000,74.5000000000
+15001021402,8.5000000000,21.1000000000,18.2000000000,6.3000000000,82.3000000000,83.8000000000,2.9000000000,5.2000000000,6.0000000000,9.2000000000,11.4000000000,16.1000000000,15.9000000000,11.2000000000,18.0000000000,64.9000000000,29.6000000000,29.8000000000,75.9000000000,13.2000000000,24.9000000000,21.7000000000,21.4000000000,24.8000000000,12.4000000000,40.2000000000,3.6000000000,74.4000000000,68.3000000000,75.9000000000
+15001021800,10.1000000000,22.6000000000,19.2000000000,6.7000000000,83.2000000000,83.1000000000,3.0000000000,5.8000000000,6.4000000000,9.8000000000,10.8000000000,16.8000000000,17.7000000000,10.7000000000,18.3000000000,66.0000000000,29.8000000000,29.5000000000,76.2000000000,14.0000000000,24.0000000000,24.3000000000,20.5000000000,24.1000000000,13.2000000000,40.2000000000,3.7000000000,73.5000000000,67.4000000000,75.4000000000
+15003010201,10.1000000000,22.7000000000,21.3000000000,6.4000000000,87.3000000000,82.9000000000,2.9000000000,5.9000000000,5.5000000000,12.2000000000,10.2000000000,17.5000000000,18.5000000000,10.2000000000,20.0000000000,69.2000000000,31.2000000000,29.8000000000,82.1000000000,15.1000000000,24.8000000000,33.4000000000,25.0000000000,27.0000000000,13.0000000000,48.2000000000,3.5000000000,69.9000000000,72.1000000000,79.1000000000
+15007040603,8.1000000000,25.3000000000,17.9000000000,7.0000000000,82.4000000000,83.6000000000,3.1000000000,5.6000000000,6.1000000000,9.0000000000,11.4000000000,14.9000000000,14.7000000000,12.2000000000,18.2000000000,67.1000000000,31.1000000000,30.0000000000,77.1000000000,12.6000000000,27.0000000000,24.3000000000,26.3000000000,24.1000000000,12.3000000000,36.8000000000,3.7000000000,77.1000000000,68.0000000000,78.5000000000
+15007040604,9.6000000000,25.5000000000,18.5000000000,6.7000000000,82.1000000000,82.8000000000,3.3000000000,5.8000000000,6.4000000000,9.6000000000,11.7000000000,15.6000000000,15.9000000000,12.3000000000,19.5000000000,64.2000000000,31.0000000000,29.3000000000,77.5000000000,13.9000000000,27.7000000000,26.0000000000,25.0000000000,21.3000000000,13.2000000000,37.5000000000,3.8000000000,76.2000000000,65.5000000000,77.8000000000
+15007040700,6.2000000000,22.6000000000,19.2000000000,6.3000000000,84.8000000000,84.2000000000,2.7000000000,4.5000000000,5.0000000000,8.7000000000,10.2000000000,13.6000000000,14.5000000000,10.7000000000,15.7000000000,69.0000000000,27.9000000000,28.3000000000,77.6000000000,12.0000000000,24.3000000000,23.1000000000,27.8000000000,25.6000000000,10.8000000000,36.4000000000,3.1000000000,74.8000000000,72.2000000000,77.8000000000
+15009030100,11.2000000000,23.2000000000,23.0000000000,6.9000000000,85.6000000000,82.6000000000,2.9000000000,6.1000000000,6.0000000000,10.2000000000,11.1000000000,18.4000000000,17.2000000000,10.7000000000,18.1000000000,63.2000000000,28.1000000000,27.0000000000,78.5000000000,14.1000000000,23.8000000000,29.2000000000,22.7000000000,23.0000000000,13.0000000000,40.4000000000,3.7000000000,70.6000000000,67.9000000000,74.2000000000
+15009030201,8.2000000000,23.0000000000,23.5000000000,6.5000000000,87.7000000000,82.9000000000,2.6000000000,5.5000000000,5.0000000000,9.7000000000,9.3000000000,17.5000000000,18.5000000000,8.9000000000,15.7000000000,65.4000000000,25.6000000000,26.4000000000,80.7000000000,13.9000000000,22.1000000000,27.5000000000,26.1000000000,25.3000000000,12.4000000000,37.8000000000,3.1000000000,69.2000000000,73.2000000000,73.0000000000
+15009030402,7.9000000000,20.4000000000,21.0000000000,6.1000000000,85.0000000000,82.9000000000,2.5000000000,4.6000000000,4.8000000000,8.9000000000,10.3000000000,15.3000000000,15.5000000000,9.6000000000,15.2000000000,63.8000000000,25.2000000000,26.2000000000,79.6000000000,12.5000000000,22.7000000000,23.7000000000,25.4000000000,24.7000000000,11.0000000000,38.7000000000,3.0000000000,70.7000000000,71.5000000000,74.1000000000
+15009030800,8.1000000000,18.6000000000,21.8000000000,5.4000000000,84.2000000000,82.0000000000,2.3000000000,4.3000000000,4.3000000000,9.1000000000,10.6000000000,16.1000000000,15.2000000000,9.3000000000,15.1000000000,61.8000000000,23.8000000000,24.5000000000,79.6000000000,12.9000000000,22.6000000000,24.3000000000,24.8000000000,24.0000000000,10.7000000000,40.9000000000,2.8000000000,67.7000000000,70.2000000000,73.2000000000

data/data-pipeline/data_pipeline/tests/sources/cdc_places/test_etl.py

@@ -0,0 +1,25 @@
+import pathlib
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.cdc_places.etl import CDCPlacesETL
+
+
+class TestCDCPlacesETL(TestETL):
+    _ETL_CLASS = CDCPlacesETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "census_tract.csv"
+    _SAMPLE_DATA_ZIP_FILE_NAME = None
+    _EXTRACT_TMP_FOLDER_NAME = "cdc_places"
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)
+
+    def test_sample_data_exists(self):
+        """This will test that the sample data exists where it's supposed to as it's supposed to
+        As per conversation with Jorge, here we can *just* test that the zip file exists.
+        """
+        assert (self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_FILE_NAME).exists()

data/data-pipeline/data_pipeline/tests/sources/child_opportunity_index/test_etl.py

@@ -59,7 +59,7 @@ class TestChildOpportunityIndexETL(TestETL):
 
     def test_get_output_file_path(self, mock_etl, mock_paths):
         """Tests the right file name is returned."""
-        etl = self._ETL_CLASS()
+        etl = self._get_instance_of_etl_class()
         data_path, tmp_path = mock_paths
 
         output_file_path = etl._get_output_file_path()

data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py

@@ -51,7 +51,7 @@ class TestDOEEnergyBurdenETL(TestETL):
 
     def test_get_output_file_path(self, mock_etl, mock_paths):
         """Tests the right file name is returned."""
-        etl = self._ETL_CLASS()
+        etl = self._get_instance_of_etl_class()
         data_path, tmp_path = mock_paths
 
         output_file_path = etl._get_output_file_path()

data/data-pipeline/data_pipeline/tests/sources/dot_travel_composite/data/output.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,DOT Travel Barriers Score
+06061021322,52.3684736425
+06069000802,67.6807523475
+15001021101,65.6905624925
+15001021800,64.6348560575
+15007040603,47.3085751425
+15007040604,48.7634318775
+15007040700,56.8031262775
+15009030201,64.1950173025
+15009030402,50.2530948600
+15009030800,56.1490333775
+15001021010,69.4901838075
+15001021402,53.4854747375
+15003010201,54.7191133125
+15009030100,37.8950511525
+06027000800,38.5533081475

data/data-pipeline/data_pipeline/tests/sources/dot_travel_composite/test_etl.py

@@ -0,0 +1,45 @@
+import pathlib
+import geopandas as gpd
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.dot_travel_composite.etl import (
+    TravelCompositeETL,
+)
+
+
+class TestTravelCompositeETL(TestETL):
+    _ETL_CLASS = TravelCompositeETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "DOT_Disadvantage_Layer_Final_April2022.shp"
+    _SAMPLE_DATA_ZIP_FILE_NAME = "Shapefile_and_Metadata.zip"
+    _EXTRACT_TMP_FOLDER_NAME = "TravelCompositeETL"
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)
+
+    def test_extract_produces_valid_data(self, mock_etl, mock_paths):
+        etl = self._setup_etl_instance_and_run_extract(
+            mock_etl=mock_etl,
+            mock_paths=mock_paths,
+        )
+        df = gpd.read_file(
+            etl.get_tmp_path() / self._SAMPLE_DATA_FILE_NAME,
+            dtype={etl.GEOID_TRACT_FIELD_NAME: str},
+        )
+        assert df.shape[0] == 30
+        assert df.shape[1] == 86
+
+    def test_transform_removes_blank_tracts(self, mock_etl, mock_paths):
+        etl: TravelCompositeETL = self._setup_etl_instance_and_run_extract(
+            mock_etl=mock_etl,
+            mock_paths=mock_paths,
+        )
+        etl.transform()
+        etl.load()
+        df = etl.get_data_frame()
+        assert df.shape[0] == 15
+        assert df.shape[1] == len(etl.COLUMNS_TO_KEEP)

data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py

@@ -65,7 +65,7 @@ class TestAbandondedLandMineETL(TestETL):
         initiliazed correctly.
         """
         # setup
-        etl = self._ETL_CLASS()
+        etl = self._get_instance_of_etl_class()
         # validation
         assert etl.GEOID_FIELD_NAME == "GEOID10"
         assert etl.GEOID_TRACT_FIELD_NAME == "GEOID10_TRACT"
@@ -78,7 +78,7 @@ class TestAbandondedLandMineETL(TestETL):
 
     def test_get_output_file_path(self, mock_etl, mock_paths):
         """Tests the right file name is returned."""
-        etl = self._ETL_CLASS()
+        etl = self._get_instance_of_etl_class()
         data_path, tmp_path = mock_paths
 
         output_file_path = etl._get_output_file_path()
@@ -150,3 +150,10 @@ class TestAbandondedLandMineETL(TestETL):
             assert len(df[etl.GEOID_TRACT_FIELD_NAME]) == len(
                 self._FIXTURES_SHARED_TRACT_IDS
             )
+
+    def test_tract_id_lengths(self, mock_etl, mock_paths):
+        with mock.patch(
+            "data_pipeline.etl.sources.eamlis.etl.add_tracts_for_geometries",
+            new=_fake_add_tracts_for_geometries,
+        ):
+            super().test_tract_id_lengths(mock_etl, mock_paths)

data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/extract.csv

@@ -0,0 +1,16 @@
+OBJECTID,ID,ACSTOTPOP,ACSIPOVBAS,ACSEDUCBAS,ACSTOTHH,ACSTOTHU,ACSUNEMPBAS,MINORPOP,MINORPCT,LOWINCOME,LOWINCPCT,LESSHS,LESSHSPCT,LINGISO,LINGISOPCT,UNDER5,UNDER5PCT,OVER64,OVER64PCT,UNEMP,UNEMPPCT,PRE1960,PRE1960PCT,VULEOPCT,VULSVI6PCT,VULEO,VULSVI6,DISPEO,DISPSVI6,DSLPM,CANCER,RESP,PTRAF,PWDIS,PNPL,PRMP,PTSDF,OZONE,PM25,UST,D_LDPNT_2,D_DSLPM_2,D_CANCR_2,D_RESP_2,D_PTRAF_2,D_PWDIS_2,D_PNPL_2,D_PRMP_2,D_PTSDF_2,D_OZONE_2,D_PM25_2,D_UST_2,STATE_NAME,ST_ABBREV,REGION,P_MINORPCT,P_LWINCPCT,P_LESHSPCT,P_LNGISPCT,P_UNDR5PCT,P_OVR64PCT,P_UNEMP,P_UNEMPPCT,P_LDPNT,P_VULEOPCT,P_VULSVI6PCT,P_VULSVI6,P_DISPSVI6,P_DSLPM,P_CANCR,P_RESP,P_PTRAF,P_PWDIS,P_PNPL,P_PRMP,P_PTSDF,P_OZONE,P_PM25,P_UST,P_LDPNT_D2,P_DSLPM_D2,P_CANCR_D2,P_RESP_D2,P_PTRAF_D2,P_PWDIS_D2,P_PNPL_D2,P_PRMP_D2,P_PTSDF_D2,P_OZONE_D2,P_PM25_D2,P_UST_D2,B_MINORPCT,B_LWINCPCT,B_LESHSPCT,B_LNGISPCT,B_UNDR5PCT,B_OVR64PCT,B_UNEMP,B_UNEMPPCT,B_LDPNT,B_VULEOPCT,B_VULSVI6PCT,B_VULSVI6,B_DISPSVI6,B_DSLPM,B_CANCR,B_RESP,B_PTRAF,B_PWDIS,B_PNPL,B_PRMP,B_PTSDF,B_OZONE,B_PM25,B_UST,B_LDPNT_D2,B_DSLPM_D2,B_CANCR_D2,B_RESP_D2,B_PTRAF_D2,B_PWDIS_D2,B_PNPL_D2,B_PRMP_D2,B_PTSDF_D2,B_OZONE_D2,B_PM25_D2,B_UST_D2,T_MINORPCT,T_LWINCPCT,T_LESHSPCT,T_LNGISPCT,T_UNDR5PCT,T_OVR64PCT,T_UNEMPPCT,T_VULEOPCT,T_LDPNT,T_LDPNT_D2,T_DSLPM,T_DSLPM_D2,T_CANCR,T_CANCR_D2,T_RESP,T_RESP_D2,T_PTRAF,T_PTRAF_D2,T_PWDIS,T_PWDIS_D2,T_PNPL,T_PNPL_D2,T_PRMP,T_PRMP_D2,T_PTSDF,T_PTSDF_D2,T_OZONE,T_OZONE_D2,T_PM25,T_PM25_D2,T_UST,T_UST_D2,AREALAND,AREAWATER,NPL_CNT,TSDF_CNT,Shape_Length,Shape_Area
+4529,6027000800,3054,3009,2337,1420,2067,1443,1218,0.3988212181,1210,0.4021269525,475,0.2032520325,134,0.0943661972,129,0.0422396857,747,0.2445972495,62,0.0429660430,763,0.3691340106,0.4004740853,0.2309005559,1223.0478564307,705.1702977293,135.9429095904,144.8520486255,0.0162608457,20.0000000000,0.2000000000,134.3731709435,0.0000000476,0.0088169702,0.0161739005,0.0231458734,59.8143830065,5.9332945205,0.0271801764,50.1811514356,2.2105466749,2718.8581918080,27.1885819181,18267.0798289539,0.0000064773,1.1986045786,2.1987270931,3.1465173743,8131.3412612630,806.5893205801,3.6949522625,California,CA,9,58.2565807824,70.8357682483,82.0300855712,83.4211514441,22.4791060804,91.4310072487,20.6342392033,44.8003303446,69.4492207493,64.4805710566,73.9747591523,41.2001973366,69.9936559849,0.4881982980,32.2031638835,14.4688811492,33.6358789383,2.7793036790,3.1380644255,0.3541522801,2.0598614138,97.6642425963,3.6388096802,6.3535808084,71.4956721564,59.1319320934,61.5316181718,60.9745786385,62.4689837463,62.0864910202,59.8317854029,59.0710337447,59.2599060994,64.9284478117,62.2619591744,60.9702180540,6,8,9,9,3,10,3,5,7,7,8,5,7,1,4,2,4,1,1,1,1,11,1,1,8,6,7,7,7,7,6,6,6,7,7,7,40% (58%ile),40% (70%ile),20% (82%ile),9% (83%ile),4% (22%ile),24% (91%ile),4% (44%ile),40% (64%ile),0.37 = fraction pre-1960 (69%ile),71%ile,0.0163 ug/m3 (0%ile),59%ile,20 lifetime risk per million (32%ile),61%ile,0.2  (14%ile),60%ile,130 daily vehicles/meters distance (33%ile),62%ile,0.000000048 toxicity-weighted concentration/meters distance (2%ile),62%ile,0.0088 sites/km distance (3%ile),59%ile,0.016 facilities/km distance (0%ile),59%ile,0.023 facilities/km distance (2%ile),59%ile,59.8 ppb (97%ile),64%ile,5.93 ug/m3 (3%ile),62%ile,0.027 facilities/sq km area (6%ile),60%ile,17743852489.0000000000,41257887.0000000000,0,1,969231.5231135677,27404749177.8422279358
+8028,6061021322,20899,20874,13290,6549,6904,9172,9199,0.4401646012,3881,0.1859250743,825,0.0620767494,225,0.0343563903,1429,0.0683764773,2939,0.1406287382,312,0.0340165722,231,0.0334588644,0.3130448377,0.1552546718,6542.3240634282,3244.6673856589,-896.9052371663,-589.6780917541,0.1849562857,30.0000000000,0.5000000000,12.5173455346,0.2667203153,0.0687928975,0.4515663958,0.2027045525,52.7832287582,12.1102756164,0.0258826940,-30.0094307337,-165.8882612555,-26907.1571149896,-448.4526185832,-11226.8727654026,-239.2228476257,-61.7007100657,-405.0122653138,-181.8067747336,-47341.5543077505,-10861.7696239112,-23.2143238368,California,CA,9,61.7694531724,28.3124099080,32.2625612545,63.3138029183,65.9392366308,44.1611446180,92.1063805127,31.2336817151,19.3531578232,52.0599864076,48.1147912182,98.1253263672,8.5598852754,35.4160437794,83.7767623034,95.2520218071,6.7786023570,88.6613290583,53.5138135020,56.0049245976,28.8270859466,89.7745222973,94.2035706464,6.2511191138,43.0185694890,24.7769097248,17.2770098374,9.5647689629,49.9350307593,5.0850465016,20.5837755437,15.4478896201,34.6338200533,14.8104044330,10.3206402564,53.0011626680,7,3,4,7,7,5,10,4,2,6,5,11,1,4,9,11,1,9,6,6,3,9,10,1,5,3,2,1,5,1,3,2,4,2,2,6,44% (61%ile),19% (28%ile),6% (32%ile),3% (63%ile),7% (65%ile),14% (44%ile),3% (31%ile),31% (52%ile),0.033 = fraction pre-1960 (19%ile),43%ile,0.185 ug/m3 (35%ile),24%ile,30 lifetime risk per million (83%ile),17%ile,0.5  (95%ile),9%ile,13 daily vehicles/meters distance (6%ile),49%ile,0.27 toxicity-weighted concentration/meters distance (88%ile),5%ile,0.069 sites/km distance (53%ile),20%ile,0.45 facilities/km distance (56%ile),15%ile,0.2 facilities/km distance (28%ile),34%ile,52.8 ppb (89%ile),14%ile,12.1 ug/m3 (94%ile),10%ile,0.026 facilities/sq km area (6%ile),53%ile,258653359.0000000000,119890.0000000000,0,0,124755.3452199987,427225089.6229769588
+8849,6069000802,3049,3045,2076,955,1119,1493,1247,0.4089865530,747,0.2453201970,307,0.1478805395,31,0.0324607330,240,0.0787143326,468,0.1534929485,93,0.0622906899,390,0.3485254692,0.3271533750,0.1778092173,997.4906403941,542.1403034316,-87.8345013597,-17.2605942492,0.0375346206,20.0000000000,0.2000000000,15.7944927934,,0.0396183204,0.0811927061,0.1674220356,47.0434058824,7.4113546849,0.0102735941,-30.6125607956,-3.2968346872,-1756.6900271942,-17.5669002719,-1387.3013987358,,-3.4798554127,-7.1315208575,-14.7054310128,-4132.0340979390,-650.9726431509,-0.9023760119,California,CA,9,59.1858457424,41.3904741949,69.9513617378,62.0187896062,79.0518001240,52.1216510370,37.3180569516,68.3483551403,67.5701406274,54.3994266601,57.9926859232,26.1831217492,58.7612911558,2.0014414700,32.2031638835,14.4688811492,8.1570460385,,34.5749415665,10.3739430074,25.1131375379,84.5333172848,19.2864164585,4.9410824602,42.8621394303,58.0471933934,56.5430390950,57.0023528116,55.7266348497,,54.6373148803,57.1359685902,54.8116596007,56.2167239668,56.9568759225,56.2801621878,6,5,7,7,8,6,4,7,7,6,6,3,6,1,4,2,1,0,4,2,3,9,2,1,5,6,6,6,6,0,6,6,6,6,6,6,41% (59%ile),25% (41%ile),15% (69%ile),3% (62%ile),8% (79%ile),15% (52%ile),6% (68%ile),33% (54%ile),0.35 = fraction pre-1960 (67%ile),42%ile,0.0375 ug/m3 (2%ile),58%ile,20 lifetime risk per million (32%ile),56%ile,0.2  (14%ile),57%ile,16 daily vehicles/meters distance (8%ile),55%ile,,,0.04 sites/km distance (34%ile),54%ile,0.081 facilities/km distance (10%ile),57%ile,0.17 facilities/km distance (25%ile),54%ile,47 ppb (84%ile),56%ile,7.41 ug/m3 (19%ile),56%ile,0.01 facilities/sq km area (4%ile),56%ile,2987635876.0000000000,3272257.0000000000,1,0,422237.6856758550,4643687820.1565904617
+20324,15001021010,8606,8586,6124,3300,4089,3602,5362,0.6230536835,4430,0.5159562078,425,0.0693990856,36,0.0109090909,315,0.0366023704,1715,0.1992795724,502,0.1393670183,46,0.0112496943,0.5695049456,0.2425333351,4901.1595620778,2087.2418818153,1837.7590471768,508.2966127298,0.0067389217,10.0000000000,0.1000000000,0.1074143214,,0.0027318608,0.0478749209,0.0931096253,,,0.0259838494,20.6742274811,12.3845143014,18377.5904717679,183.7759047177,197.4016409694,,5.0205019537,87.9825690670,171.1130563323,,,47.7520542990,Hawaii,HI,9,74.7108013633,85.0291087110,36.7675143964,39.4832933303,15.2054293702,77.9602931979,98.1974410889,95.8100562593,9.2273848439,81.1726508957,76.5777942789,91.7961653862,85.2496673015,0.0699884723,1.8303662611,1.0748659980,0.5930748980,,0.1022787768,3.9663081684,14.5954101870,,,6.2654376121,66.8695670869,60.7245800447,74.1372134844,71.3832220072,58.5855777989,,62.7945832024,66.7236133386,67.8259227785,,,62.6039374599,8,9,4,4,2,8,11,11,1,9,8,10,9,1,1,1,1,0,1,1,2,0,0,1,7,7,8,8,6,0,7,7,7,0,0,7,62% (74%ile),52% (85%ile),7% (36%ile),1% (39%ile),4% (15%ile),20% (77%ile),14% (95%ile),57% (81%ile),0.011 = fraction pre-1960 (9%ile),66%ile,0.00674 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),74%ile,0.1  (1%ile),71%ile,0.11 daily vehicles/meters distance (0%ile),58%ile,,,0.0027 sites/km distance (0%ile),62%ile,0.048 facilities/km distance (3%ile),66%ile,0.093 facilities/km distance (14%ile),67%ile,,,,,0.026 facilities/sq km area (6%ile),62%ile,151184621.0000000000,0.0000000000,0,0,71817.3979516648,171030272.0024483502
+20327,15001021101,3054,3049,2569,1543,1958,1227,1086,0.3555992141,1450,0.4755657593,159,0.0618917867,30,0.0194426442,92,0.0301244270,909,0.2976424361,144,0.1173594132,33,0.0168539326,0.4155824867,0.2067110446,1269.1889143982,631.2955301209,182.0839675579,70.9772810171,0.0033713587,10.0000000000,0.1000000000,1.7167679255,,0.0025910486,0.2484740667,0.2746856427,,,0.0375389154,3.0688309139,0.6138703733,1820.8396755785,18.2083967558,312.5959152482,,0.4717884163,45.2431438983,50.0158516497,,,6.8352346500,Hawaii,HI,9,53.9485559986,80.7725127831,32.1386664749,50.6977998974,9.0084816593,96.6414513706,60.5643076662,92.9519087655,12.2637725970,66.3100091245,67.7573127614,34.5728724616,65.2982705045,0.0304238852,1.8303662611,1.0748659980,2.1438079687,,0.0856704529,41.7496844562,33.9762800526,,,7.1437776548,61.7192931562,58.8385531222,60.6418036749,60.2753222588,58.6611251941,,59.1844884925,63.7189045047,63.4569851575,,,61.1560978740,6,9,4,6,1,11,7,10,2,7,7,4,7,1,1,1,1,0,1,5,4,0,0,1,7,6,7,7,6,0,6,7,7,0,0,7,36% (53%ile),48% (80%ile),6% (32%ile),2% (50%ile),3% (9%ile),30% (96%ile),12% (92%ile),42% (66%ile),0.017 = fraction pre-1960 (12%ile),61%ile,0.00337 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),60%ile,0.1  (1%ile),60%ile,1.7 daily vehicles/meters distance (2%ile),58%ile,,,0.0026 sites/km distance (0%ile),59%ile,0.25 facilities/km distance (41%ile),63%ile,0.27 facilities/km distance (33%ile),63%ile,,,,,0.038 facilities/sq km area (7%ile),61%ile,106332317.0000000000,11164968.0000000000,0,1,61396.6485753379,132838116.6897320002
+20331,15001021402,3778,3755,2731,1374,1583,1803,3034,0.8030704076,705,0.1877496671,214,0.0783595752,56,0.0407569141,284,0.0751720487,933,0.2469560614,23,0.0127565169,276,0.1743524953,0.4954100374,0.2386774457,1871.6591211718,901.7233898617,526.8383978048,208.5726547229,0.0131608945,10.0000000000,0.1000000000,635.9981128640,,0.0033357209,0.0225482603,0.6278707343,,,0.5088713177,91.8555892572,6.9336645713,5268.3839780481,52.6838397805,335068.2267881447,,1.7573858477,11.8792893273,330.7864116735,,,268.0929496982,Hawaii,HI,9,84.7051677398,28.7308989413,41.8348284107,67.0398022547,74.9080519616,91.8116448026,3.9836481634,4.5613171192,47.5766504879,74.5975822746,75.7504514060,55.7976616902,73.4870488517,0.2827998785,1.8303662611,1.0748659980,72.8760884284,,0.2741900811,0.7727230166,47.2090589343,,,27.1111550629,75.5651255220,59.8939075539,64.0364159199,62.9860278691,78.3054903833,,60.2827950818,60.4356420863,71.4747506314,,,66.1787674526,9,3,5,7,8,10,1,1,5,8,8,6,8,1,1,1,8,0,1,1,5,0,0,3,8,6,7,7,8,0,7,7,8,0,0,7,80% (84%ile),19% (28%ile),8% (41%ile),4% (67%ile),8% (74%ile),25% (91%ile),1% (4%ile),50% (74%ile),0.17 = fraction pre-1960 (47%ile),75%ile,0.0132 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),64%ile,0.1  (1%ile),62%ile,640 daily vehicles/meters distance (72%ile),78%ile,,,0.0033 sites/km distance (0%ile),60%ile,0.023 facilities/km distance (0%ile),60%ile,0.63 facilities/km distance (47%ile),71%ile,,,,,0.51 facilities/sq km area (27%ile),66%ile,41940841.0000000000,6313950.0000000000,0,1,49320.0395726063,54601507.7207057551
+20340,15001021800,5998,5977,4357,2112,2631,3179,4020,0.6702234078,1613,0.2698678267,180,0.0413128299,76,0.0359848485,352,0.0586862287,1411,0.2352450817,241,0.0758100031,441,0.1676168757,0.4700456172,0.2185533706,2819.3336121800,1310.8831165809,684.2794304026,210.4283496771,0.0049503455,10.0000000000,0.1000000000,0.0743045071,,0.0038298946,0.0402733327,0.0410968274,,,0.1071290552,114.6967802385,3.3874195708,6842.7943040262,68.4279430403,50.8450457862,,2.6207181028,27.5582131707,28.1217136206,,,73.3062089025,Hawaii,HI,9,77.3411360526,46.5995830170,19.2008132329,64.3649112351,50.1501995746,89.5998226210,84.7007475319,78.6950985799,46.5822171086,72.0610072717,71.0200691830,75.5958544882,73.5619777633,0.0461211856,1.8303662611,1.0748659980,0.4752886632,,0.3337575583,2.8011103792,5.4042726335,,,11.7520153164,77.1764003076,59.3164619569,65.4523902797,64.1879137098,58.4409328484,,60.9620163260,62.2019174282,62.1288554973,,,63.2369902585,8,5,2,7,6,9,9,8,5,8,8,8,8,1,1,1,1,0,1,1,1,0,0,2,8,6,7,7,6,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),4% (19%ile),4% (64%ile),6% (50%ile),24% (89%ile),8% (78%ile),47% (72%ile),0.17 = fraction pre-1960 (46%ile),77%ile,0.00495 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),65%ile,0.1  (1%ile),64%ile,0.074 daily vehicles/meters distance (0%ile),58%ile,,,0.0038 sites/km distance (0%ile),60%ile,0.04 facilities/km distance (2%ile),62%ile,0.041 facilities/km distance (5%ile),62%ile,,,,,0.11 facilities/sq km area (11%ile),63%ile,365110254.0000000000,37900489.0000000000,0,0,92961.9049100969,459707845.8010936975
+20560,15003010201,4936,4798,3182,1441,1938,2266,3695,0.7485818476,1439,0.2999166319,231,0.0725958517,49,0.0340041638,476,0.0964343598,651,0.1318881686,115,0.0507502207,413,0.2131062951,0.5242492398,0.2305701706,2587.6942476032,1138.0943619037,830.6706662005,232.4850372225,0.0171119880,10.0000000000,0.1000000000,1493.8870892160,,0.0694550700,0.0548137804,0.4080845621,,,0.0995447326,177.0211481635,14.2144264416,8306.7066620049,83.0670666200,1240928.1836273719,,57.6942892272,45.5321994534,338.9838750865,,,82.6888893711,Hawaii,HI,9,81.7629198374,52.7224313484,38.5909198362,63.0702607098,91.9817357703,38.9013315993,48.2935747432,55.4563410918,53.0116816593,77.2470730276,73.8991238733,68.7030659293,74.5973100320,0.5552831600,1.8303662611,1.0748659980,88.8972054263,,53.8623224639,5.1063675682,39.9537688137,,,11.2751492958,80.6790478991,60.9966060167,66.7493132622,65.1696166202,90.1940360178,,78.6444132849,63.7434121326,71.6211763075,,,63.4227438652,9,6,4,7,10,4,5,6,6,8,8,7,8,1,1,1,9,0,6,1,4,0,0,2,9,7,7,7,10,0,8,7,8,0,0,7,75% (81%ile),30% (52%ile),7% (38%ile),3% (63%ile),10% (91%ile),13% (38%ile),5% (55%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),80%ile,0.0171 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1  (1%ile),65%ile,1500 daily vehicles/meters distance (88%ile),90%ile,,,0.069 sites/km distance (53%ile),78%ile,0.055 facilities/km distance (5%ile),63%ile,0.41 facilities/km distance (39%ile),71%ile,,,,,0.1 facilities/sq km area (11%ile),63%ile,66256288.0000000000,7249455.0000000000,0,0,42997.4044651793,85395519.3857139796
+20614,15007040603,2984,2978,2104,1058,2064,1468,2011,0.6739276139,797,0.2676292814,138,0.0655893536,33,0.0311909263,168,0.0563002681,756,0.2533512064,64,0.0435967302,193,0.0935077519,0.4707784477,0.2246647750,1404.8028878442,670.3996884791,342.6152122150,122.9243592959,0.0225796264,10.0000000000,0.1000000000,255.5966484444,,0.0065810172,0.1042895043,0.5200441984,,,0.1610354485,32.0371782740,7.7361234992,3426.1521221502,34.2615212215,87571.2999482384,,2.2547565999,35.7311706322,178.1750534077,,,55.1731943631,Hawaii,HI,9,77.5456369017,46.0970850340,34.4418368771,61.1045870101,46.0008635792,92.7630589707,21.6211577108,45.6650954498,34.4080455626,72.1367523014,72.5017628853,38.1901457794,68.6478207991,0.9188048692,1.8303662611,1.0748659980,48.7907692784,,1.7474241413,15.4267424965,43.8004140051,,,14.6634418792,68.8698351102,60.0337698662,62.2468364816,61.5558275260,68.4712288534,,60.6727330898,62.8928664453,68.0134997705,,,62.8256530053,8,5,4,7,5,10,3,5,4,8,8,4,7,1,1,1,5,0,1,2,5,0,0,2,7,7,7,7,7,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),7% (34%ile),3% (61%ile),6% (46%ile),25% (92%ile),4% (45%ile),47% (72%ile),0.094 = fraction pre-1960 (34%ile),68%ile,0.0226 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),62%ile,0.1  (1%ile),61%ile,260 daily vehicles/meters distance (48%ile),68%ile,,,0.0066 sites/km distance (1%ile),60%ile,0.1 facilities/km distance (15%ile),62%ile,0.52 facilities/km distance (43%ile),68%ile,,,,,0.16 facilities/sq km area (14%ile),62%ile,41255867.0000000000,7041518.0000000000,0,0,36855.9892981643,56378891.3118786365
+20615,15007040604,3529,3458,2370,1187,1625,1757,2203,0.6242561632,1275,0.3687102371,133,0.0561181435,42,0.0353833193,333,0.0943610088,632,0.1790875602,109,0.0620375640,322,0.1981538462,0.4964832002,0.2263194054,1752.0892134182,798.6811814815,495.9027833594,151.2145472028,0.0297040750,10.0000000000,0.1000000000,464.0468169721,,0.0064334940,0.1282189641,0.3810520320,,,0.2277699060,98.2650438411,14.7303334730,4959.0278335940,49.5902783359,230122.1081455294,,3.1903875793,63.5841411863,188.9647632974,,,112.9517303753,Hawaii,HI,9,74.7796778307,65.5138546463,28.4761545436,63.9820945208,90.9928060818,67.6211097498,45.4287763592,68.1087856009,51.0102487547,74.6953085329,72.8443904311,48.6363888652,70.3699602168,1.3729134893,1.8303662611,1.0748659980,64.8054389268,,1.7014251995,20.6071512154,38.9237463430,,,17.5743663328,76.0259182532,61.0699450431,63.7308514802,62.7533582560,75.0094643983,,61.3999056181,65.1045697304,68.3212418487,,,64.0124628341,8,7,3,7,10,7,5,7,6,8,8,5,8,1,1,1,7,0,1,3,4,0,0,2,8,7,7,7,8,0,7,7,7,0,0,7,62% (74%ile),37% (65%ile),6% (28%ile),4% (63%ile),9% (90%ile),18% (67%ile),6% (68%ile),50% (74%ile),0.2 = fraction pre-1960 (51%ile),76%ile,0.0297 ug/m3 (1%ile),61%ile,10 lifetime risk per million (1%ile),63%ile,0.1  (1%ile),62%ile,460 daily vehicles/meters distance (64%ile),75%ile,,,0.0064 sites/km distance (1%ile),61%ile,0.13 facilities/km distance (20%ile),65%ile,0.38 facilities/km distance (38%ile),68%ile,,,,,0.23 facilities/sq km area (17%ile),64%ile,21724894.0000000000,2371158.0000000000,0,1,27760.3117775823,28129042.7970332205
+20616,15007040700,9552,9523,6234,2895,3298,4974,7071,0.7402638191,1980,0.2079176730,309,0.0495668912,95,0.0328151986,772,0.0808207705,1834,0.1920016750,205,0.0412143144,346,0.1049120679,0.4740907460,0.2172310046,4528.5148062585,2074.9905558098,1128.3751689898,322.4823975128,0.0120486502,10.0000000000,0.1000000000,829.6297843840,,0.0062317499,0.2776903565,0.5315584393,,,0.8605507426,118.3801723682,13.5953976825,11283.7516898982,112.8375168990,936133.6481533048,,7.0317517976,313.3389029609,599.7973437979,,,971.0240895638,Hawaii,HI,9,81.2804870193,33.1925490446,24.3539720817,62.2442189921,81.2507708079,74.5290795692,78.3337372056,42.2880946853,36.4589583341,72.4690666385,70.6843205648,91.6638490724,78.6800785920,0.2182262554,1.8303662611,1.0748659980,78.8807149861,,1.6540510210,44.6852775053,44.1677595343,,,35.8025350464,77.4223719690,60.9007569981,69.0968995957,67.2008685709,87.5969776098,,64.0977043448,75.6532891441,75.2184280558,,,72.0462363568,9,4,3,7,9,8,8,5,4,8,8,10,8,1,1,1,8,0,1,5,5,0,0,4,8,7,7,7,9,0,7,8,8,0,0,8,74% (81%ile),21% (33%ile),5% (24%ile),3% (62%ile),8% (81%ile),19% (74%ile),4% (42%ile),47% (72%ile),0.1 = fraction pre-1960 (36%ile),77%ile,0.012 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),69%ile,0.1  (1%ile),67%ile,830 daily vehicles/meters distance (78%ile),87%ile,,,0.0062 sites/km distance (1%ile),64%ile,0.28 facilities/km distance (44%ile),75%ile,0.53 facilities/km distance (44%ile),75%ile,,,,,0.86 facilities/sq km area (35%ile),72%ile,93005151.0000000000,5658877.0000000000,0,1,70950.4293149945,115233329.7073323578
+20624,15009030100,1405,1374,980,467,796,729,1060,0.7544483986,400,0.2911208151,44,0.0448979592,0,0.0000000000,124,0.0882562278,342,0.2434163701,41,0.0562414266,170,0.2135678392,0.5227846069,0.2370232951,734.5123726346,333.0177296537,234.3871433253,75.2419798205,0.0026846006,10.0000000000,0.1000000000,,,0.0046765532,0.0398066625,0.0329594792,,,0.0973247551,50.0575557353,0.6292358547,2343.8714332533,23.4387143325,,,1.0961239415,9.3301699117,7.7252781836,,,22.8116713113,Hawaii,HI,9,82.0728020535,51.0042892937,21.4097431797,22.2149776961,87.3264635475,91.2196103997,10.4895772204,61.9590696975,53.0589655984,77.1174117652,75.3619764353,7.5930221772,65.5506156308,0.0292580509,1.8303662611,1.0748659980,,,0.7827594098,2.7444494654,3.8365691660,,,11.1452377410,71.4770935032,58.8413451490,61.1806956436,60.6714580607,,,59.7579323526,60.0938330974,59.9756316335,,,61.8121208766,9,6,3,3,9,10,2,7,6,8,8,1,7,1,1,1,0,0,1,1,1,0,0,2,8,6,7,7,0,0,6,7,6,0,0,7,75% (82%ile),29% (51%ile),4% (21%ile),0% (22%ile),9% (87%ile),24% (91%ile),6% (61%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),71%ile,0.00268 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),61%ile,0.1  (1%ile),60%ile,,,,,0.0047 sites/km distance (0%ile),59%ile,0.04 facilities/km distance (2%ile),60%ile,0.033 facilities/km distance (3%ile),59%ile,,,,,0.097 facilities/sq km area (11%ile),61%ile,555262221.0000000000,25398369.0000000000,0,0,165450.9181509207,667169893.1947253942
+20625,15009030201,2340,2327,1879,842,1045,1395,992,0.4239316239,623,0.2677266867,62,0.0329962746,0,0.0000000000,150,0.0641025641,554,0.2367521368,133,0.0953405018,97,0.0928229665,0.3458291553,0.1709182144,809.2402234637,399.9486215874,-23.7085570230,-29.3718443271,0.0063521816,10.0000000000,0.1000000000,7.0868595222,,0.0053511202,0.1292001112,0.0908033666,,,0.0098923140,-2.2006985945,-0.1506010605,-237.0855702299,-2.3708557023,-168.0192130960,,-0.1268673373,-3.0631482031,-2.1528167944,,,-0.2345324900,Hawaii,HI,9,60.4734810662,46.1223337837,13.8610651481,22.2149776961,59.2951174656,89.9006101655,56.2295738392,87.6116027815,34.2663836750,57.2620275919,55.1869446757,12.9269845729,57.7176364334,0.0651806253,1.8303662611,1.0748659980,4.6368984700,,1.3707744996,20.8229399202,14.1722671160,,,4.9075810703,55.1341860078,58.6779670685,58.4263245996,58.4806520514,57.3658121936,,58.5702573228,58.1201119303,58.2216584263,,,56.4332033900,7,5,2,3,6,9,6,9,4,6,6,2,6,1,1,1,1,0,1,3,2,0,0,1,6,6,6,6,6,0,6,6,6,0,0,6,42% (60%ile),27% (46%ile),3% (13%ile),0% (22%ile),6% (59%ile),24% (89%ile),10% (87%ile),35% (57%ile),0.093 = fraction pre-1960 (34%ile),55%ile,0.00635 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),58%ile,0.1  (1%ile),58%ile,7.1 daily vehicles/meters distance (4%ile),57%ile,,,0.0054 sites/km distance (1%ile),58%ile,0.13 facilities/km distance (20%ile),58%ile,0.091 facilities/km distance (14%ile),58%ile,,,,,0.0099 facilities/sq km area (4%ile),56%ile,118113265.0000000000,4116462.0000000000,0,0,68639.8224567451,140691933.2772550285
+20629,15009030402,8562,8562,6540,3180,3473,4778,5420,0.6330296660,1535,0.1792805419,294,0.0449541284,39,0.0122641509,397,0.0463676711,1550,0.1810324690,210,0.0439514441,264,0.0760149726,0.4061551039,0.1828214379,3477.5000000000,1565.3171513473,429.7617698603,-5.5554252167,0.0153866969,10.0000000000,0.1000000000,233.6880574427,,0.0055146115,0.6633705951,0.5914191729,,,0.4432670413,32.6683291803,6.6126141119,4297.6176986027,42.9761769860,100430.1931617688,,2.3699691830,285.0913210180,254.1693504667,,,190.4992282028,Hawaii,HI,9,75.2886221409,26.8565530362,21.4439231945,41.5097856648,28.7449442824,68.6690121278,79.3833218173,46.1798708736,30.8409993308,65.1595288992,59.9703175674,82.9721886150,59.7346670413,0.4238414396,1.8303662611,1.0748659980,46.4851154373,,1.4363834459,65.6894718646,46.0793962757,,,25.2542546354,68.9789876320,59.8548490559,63.0853618909,62.2063722143,69.2554009969,,60.7689024131,74.9199434460,69.9134569074,,,65.2067338787,8,3,3,5,3,7,8,5,4,7,6,9,6,1,1,1,5,0,1,7,5,0,0,3,7,6,7,7,7,0,7,8,7,0,0,7,63% (75%ile),18% (26%ile),4% (21%ile),1% (41%ile),5% (28%ile),18% (68%ile),4% (46%ile),41% (65%ile),0.076 = fraction pre-1960 (30%ile),68%ile,0.0154 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),63%ile,0.1  (1%ile),62%ile,230 daily vehicles/meters distance (46%ile),69%ile,,,0.0055 sites/km distance (1%ile),60%ile,0.66 facilities/km distance (65%ile),74%ile,0.59 facilities/km distance (46%ile),69%ile,,,,,0.44 facilities/sq km area (25%ile),65%ile,46066876.0000000000,109238.0000000000,0,1,49929.5140313853,53123180.2369696796
+20639,15009030800,7879,7871,5174,2235,2335,4210,6161,0.7819520244,1091,0.1386100877,195,0.0376884422,3,0.0013422819,594,0.0753902780,1027,0.1303464907,163,0.0387173397,285,0.1220556745,0.4602810560,0.1942216008,3626.5544403507,1530.2719926347,821.9375850282,84.7096204381,0.0169064550,10.0000000000,0.1000000000,575.9991000531,0.0008675195,0.0061499864,1.0347888110,0.5999348163,,,0.0263640121,100.3221463525,13.8960508008,8219.3758502819,82.1937585028,473435.3092760353,0.7130468458,5.0549049971,850.5318163110,493.1089740953,,,21.6695724544,Hawaii,HI,9,83.5697361596,17.7978784355,16.7867123482,22.7576898463,75.1762179508,37.9999341618,67.3605976499,38.4952526223,39.4719140158,71.0499579469,63.9144112870,82.0845801288,66.2238170511,0.5328167647,1.8303662611,1.0748659980,70.4238972397,46.4801242962,1.6311542890,76.8934515139,46.3897571267,,,6.2894452647,76.1966079716,60.9434745816,66.6801583126,65.1083356253,81.3398307482,78.4421389238,62.8183222708,84.3474497827,73.9856189804,,,61.7713841743,9,2,2,3,8,4,7,4,4,8,7,9,7,1,1,1,8,5,1,8,5,0,0,1,8,7,7,7,9,8,7,9,8,0,0,7,78% (83%ile),14% (17%ile),4% (16%ile),0% (22%ile),8% (75%ile),13% (37%ile),4% (38%ile),46% (71%ile),0.12 = fraction pre-1960 (39%ile),76%ile,0.0169 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1  (1%ile),65%ile,580 daily vehicles/meters distance (70%ile),81%ile,0.00087 toxicity-weighted concentration/meters distance (46%ile),78%ile,0.0061 sites/km distance (1%ile),62%ile,1 facilities/km distance (76%ile),84%ile,0.6 facilities/km distance (46%ile),73%ile,,,,,0.026 facilities/sq km area (6%ile),61%ile,141603534.0000000000,11781155.0000000000,0,0,80194.4536675024,176674254.7197769880

data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/output.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,Total population,Air toxics cancer risk,Respiratory hazard index,Diesel particulate matter exposure,PM2.5 in the air,Ozone,Traffic proximity and volume,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Proximity to NPL sites,Wastewater discharge,Percent of households in linguistic isolation,Poverty (Less than 200% of federal poverty line),Individuals over 64 years old,Individuals under 5 years old,Percent pre-1960s housing (lead paint indicator),Leaky underground storage tanks
+06027000800,3054,20.0000000000,0.2000000000,0.0162608457,5.9332945205,59.8143830065,134.3731709435,0.0161739005,0.0231458734,0.0088169702,0.0000000476,0.0943661972,0.4021269525,0.2445972495,0.0422396857,0.3691340106,0.0271801764
+06061021322,20899,30.0000000000,0.5000000000,0.1849562857,12.1102756164,52.7832287582,12.5173455346,0.4515663958,0.2027045525,0.0687928975,0.2667203153,0.0343563903,0.1859250743,0.1406287382,0.0683764773,0.0334588644,0.0258826940
+06069000802,3049,20.0000000000,0.2000000000,0.0375346206,7.4113546849,47.0434058824,15.7944927934,0.0811927061,0.1674220356,0.0396183204,,0.0324607330,0.2453201970,0.1534929485,0.0787143326,0.3485254692,0.0102735941
+15001021010,8606,10.0000000000,0.1000000000,0.0067389217,,,0.1074143214,0.0478749209,0.0931096253,0.0027318608,,0.0109090909,0.5159562078,0.1992795724,0.0366023704,0.0112496943,0.0259838494
+15001021101,3054,10.0000000000,0.1000000000,0.0033713587,,,1.7167679255,0.2484740667,0.2746856427,0.0025910486,,0.0194426442,0.4755657593,0.2976424361,0.0301244270,0.0168539326,0.0375389154
+15001021402,3778,10.0000000000,0.1000000000,0.0131608945,,,635.9981128640,0.0225482603,0.6278707343,0.0033357209,,0.0407569141,0.1877496671,0.2469560614,0.0751720487,0.1743524953,0.5088713177
+15001021800,5998,10.0000000000,0.1000000000,0.0049503455,,,0.0743045071,0.0402733327,0.0410968274,0.0038298946,,0.0359848485,0.2698678267,0.2352450817,0.0586862287,0.1676168757,0.1071290552
+15003010201,4936,10.0000000000,0.1000000000,0.0171119880,,,1493.8870892160,0.0548137804,0.4080845621,0.0694550700,,0.0340041638,0.2999166319,0.1318881686,0.0964343598,0.2131062951,0.0995447326
+15007040603,2984,10.0000000000,0.1000000000,0.0225796264,,,255.5966484444,0.1042895043,0.5200441984,0.0065810172,,0.0311909263,0.2676292814,0.2533512064,0.0563002681,0.0935077519,0.1610354485
+15007040604,3529,10.0000000000,0.1000000000,0.0297040750,,,464.0468169721,0.1282189641,0.3810520320,0.0064334940,,0.0353833193,0.3687102371,0.1790875602,0.0943610088,0.1981538462,0.2277699060
+15007040700,9552,10.0000000000,0.1000000000,0.0120486502,,,829.6297843840,0.2776903565,0.5315584393,0.0062317499,,0.0328151986,0.2079176730,0.1920016750,0.0808207705,0.1049120679,0.8605507426
+15009030100,1405,10.0000000000,0.1000000000,0.0026846006,,,,0.0398066625,0.0329594792,0.0046765532,,0.0000000000,0.2911208151,0.2434163701,0.0882562278,0.2135678392,0.0973247551
+15009030201,2340,10.0000000000,0.1000000000,0.0063521816,,,7.0868595222,0.1292001112,0.0908033666,0.0053511202,,0.0000000000,0.2677266867,0.2367521368,0.0641025641,0.0928229665,0.0098923140
+15009030402,8562,10.0000000000,0.1000000000,0.0153866969,,,233.6880574427,0.6633705951,0.5914191729,0.0055146115,,0.0122641509,0.1792805419,0.1810324690,0.0463676711,0.0760149726,0.4432670413
+15009030800,7879,10.0000000000,0.1000000000,0.0169064550,,,575.9991000531,1.0347888110,0.5999348163,0.0061499864,0.0008675195,0.0013422819,0.1386100877,0.1303464907,0.0753902780,0.1220556745,0.0263640121

data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/transform.csv

@@ -0,0 +1,16 @@
+OBJECTID,GEOID10_TRACT,Total population,ACSIPOVBAS,ACSEDUCBAS,ACSTOTHH,ACSTOTHU,ACSUNEMPBAS,MINORPOP,MINORPCT,LOWINCOME,Poverty (Less than 200% of federal poverty line),LESSHS,LESSHSPCT,LINGISO,Percent of households in linguistic isolation,UNDER5,Individuals under 5 years old,OVER64,Individuals over 64 years old,UNEMP,UNEMPPCT,PRE1960,Percent pre-1960s housing (lead paint indicator),VULEOPCT,VULSVI6PCT,VULEO,VULSVI6,DISPEO,DISPSVI6,Diesel particulate matter exposure,Air toxics cancer risk,Respiratory hazard index,Traffic proximity and volume,Wastewater discharge,Proximity to NPL sites,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Ozone,PM2.5 in the air,Leaky underground storage tanks,D_LDPNT_2,D_DSLPM_2,D_CANCR_2,D_RESP_2,D_PTRAF_2,D_PWDIS_2,D_PNPL_2,D_PRMP_2,D_PTSDF_2,D_OZONE_2,D_PM25_2,D_UST_2,STATE_NAME,ST_ABBREV,REGION,P_MINORPCT,P_LWINCPCT,P_LESHSPCT,P_LNGISPCT,P_UNDR5PCT,P_OVR64PCT,P_UNEMP,P_UNEMPPCT,P_LDPNT,P_VULEOPCT,P_VULSVI6PCT,P_VULSVI6,P_DISPSVI6,P_DSLPM,P_CANCR,P_RESP,P_PTRAF,P_PWDIS,P_PNPL,P_PRMP,P_PTSDF,P_OZONE,P_PM25,P_UST,P_LDPNT_D2,P_DSLPM_D2,P_CANCR_D2,P_RESP_D2,P_PTRAF_D2,P_PWDIS_D2,P_PNPL_D2,P_PRMP_D2,P_PTSDF_D2,P_OZONE_D2,P_PM25_D2,P_UST_D2,B_MINORPCT,B_LWINCPCT,B_LESHSPCT,B_LNGISPCT,B_UNDR5PCT,B_OVR64PCT,B_UNEMP,B_UNEMPPCT,B_LDPNT,B_VULEOPCT,B_VULSVI6PCT,B_VULSVI6,B_DISPSVI6,B_DSLPM,B_CANCR,B_RESP,B_PTRAF,B_PWDIS,B_PNPL,B_PRMP,B_PTSDF,B_OZONE,B_PM25,B_UST,B_LDPNT_D2,B_DSLPM_D2,B_CANCR_D2,B_RESP_D2,B_PTRAF_D2,B_PWDIS_D2,B_PNPL_D2,B_PRMP_D2,B_PTSDF_D2,B_OZONE_D2,B_PM25_D2,B_UST_D2,T_MINORPCT,T_LWINCPCT,T_LESHSPCT,T_LNGISPCT,T_UNDR5PCT,T_OVR64PCT,T_UNEMPPCT,T_VULEOPCT,T_LDPNT,T_LDPNT_D2,T_DSLPM,T_DSLPM_D2,T_CANCR,T_CANCR_D2,T_RESP,T_RESP_D2,T_PTRAF,T_PTRAF_D2,T_PWDIS,T_PWDIS_D2,T_PNPL,T_PNPL_D2,T_PRMP,T_PRMP_D2,T_PTSDF,T_PTSDF_D2,T_OZONE,T_OZONE_D2,T_PM25,T_PM25_D2,T_UST,T_UST_D2,AREALAND,AREAWATER,NPL_CNT,TSDF_CNT,Shape_Length,Shape_Area
+4529,06027000800,3054,3009,2337,1420,2067,1443,1218,0.3988212181,1210,0.4021269525,475,0.2032520325,134,0.0943661972,129,0.0422396857,747,0.2445972495,62,0.0429660430,763,0.3691340106,0.4004740853,0.2309005559,1223.0478564307,705.1702977293,135.9429095904,144.8520486255,0.0162608457,20.0000000000,0.2000000000,134.3731709435,0.0000000476,0.0088169702,0.0161739005,0.0231458734,59.8143830065,5.9332945205,0.0271801764,50.1811514356,2.2105466749,2718.8581918080,27.1885819181,18267.0798289539,0.0000064773,1.1986045786,2.1987270931,3.1465173743,8131.3412612630,806.5893205801,3.6949522625,California,CA,9,58.2565807824,70.8357682483,82.0300855712,83.4211514441,22.4791060804,91.4310072487,20.6342392033,44.8003303446,69.4492207493,64.4805710566,73.9747591523,41.2001973366,69.9936559849,0.4881982980,32.2031638835,14.4688811492,33.6358789383,2.7793036790,3.1380644255,0.3541522801,2.0598614138,97.6642425963,3.6388096802,6.3535808084,71.4956721564,59.1319320934,61.5316181718,60.9745786385,62.4689837463,62.0864910202,59.8317854029,59.0710337447,59.2599060994,64.9284478117,62.2619591744,60.9702180540,6,8,9,9,3,10,3,5,7,7,8,5,7,1,4,2,4,1,1,1,1,11,1,1,8,6,7,7,7,7,6,6,6,7,7,7,40% (58%ile),40% (70%ile),20% (82%ile),9% (83%ile),4% (22%ile),24% (91%ile),4% (44%ile),40% (64%ile),0.37 = fraction pre-1960 (69%ile),71%ile,0.0163 ug/m3 (0%ile),59%ile,20 lifetime risk per million (32%ile),61%ile,0.2  (14%ile),60%ile,130 daily vehicles/meters distance (33%ile),62%ile,0.000000048 toxicity-weighted concentration/meters distance (2%ile),62%ile,0.0088 sites/km distance (3%ile),59%ile,0.016 facilities/km distance (0%ile),59%ile,0.023 facilities/km distance (2%ile),59%ile,59.8 ppb (97%ile),64%ile,5.93 ug/m3 (3%ile),62%ile,0.027 facilities/sq km area (6%ile),60%ile,17743852489.0000000000,41257887.0000000000,0,1,969231.5231135677,27404749177.8422279358
+8028,06061021322,20899,20874,13290,6549,6904,9172,9199,0.4401646012,3881,0.1859250743,825,0.0620767494,225,0.0343563903,1429,0.0683764773,2939,0.1406287382,312,0.0340165722,231,0.0334588644,0.3130448377,0.1552546718,6542.3240634282,3244.6673856589,-896.9052371663,-589.6780917541,0.1849562857,30.0000000000,0.5000000000,12.5173455346,0.2667203153,0.0687928975,0.4515663958,0.2027045525,52.7832287582,12.1102756164,0.0258826940,-30.0094307337,-165.8882612555,-26907.1571149896,-448.4526185832,-11226.8727654026,-239.2228476257,-61.7007100657,-405.0122653138,-181.8067747336,-47341.5543077505,-10861.7696239112,-23.2143238368,California,CA,9,61.7694531724,28.3124099080,32.2625612545,63.3138029183,65.9392366308,44.1611446180,92.1063805127,31.2336817151,19.3531578232,52.0599864076,48.1147912182,98.1253263672,8.5598852754,35.4160437794,83.7767623034,95.2520218071,6.7786023570,88.6613290583,53.5138135020,56.0049245976,28.8270859466,89.7745222973,94.2035706464,6.2511191138,43.0185694890,24.7769097248,17.2770098374,9.5647689629,49.9350307593,5.0850465016,20.5837755437,15.4478896201,34.6338200533,14.8104044330,10.3206402564,53.0011626680,7,3,4,7,7,5,10,4,2,6,5,11,1,4,9,11,1,9,6,6,3,9,10,1,5,3,2,1,5,1,3,2,4,2,2,6,44% (61%ile),19% (28%ile),6% (32%ile),3% (63%ile),7% (65%ile),14% (44%ile),3% (31%ile),31% (52%ile),0.033 = fraction pre-1960 (19%ile),43%ile,0.185 ug/m3 (35%ile),24%ile,30 lifetime risk per million (83%ile),17%ile,0.5  (95%ile),9%ile,13 daily vehicles/meters distance (6%ile),49%ile,0.27 toxicity-weighted concentration/meters distance (88%ile),5%ile,0.069 sites/km distance (53%ile),20%ile,0.45 facilities/km distance (56%ile),15%ile,0.2 facilities/km distance (28%ile),34%ile,52.8 ppb (89%ile),14%ile,12.1 ug/m3 (94%ile),10%ile,0.026 facilities/sq km area (6%ile),53%ile,258653359.0000000000,119890.0000000000,0,0,124755.3452199987,427225089.6229769588
+8849,06069000802,3049,3045,2076,955,1119,1493,1247,0.4089865530,747,0.2453201970,307,0.1478805395,31,0.0324607330,240,0.0787143326,468,0.1534929485,93,0.0622906899,390,0.3485254692,0.3271533750,0.1778092173,997.4906403941,542.1403034316,-87.8345013597,-17.2605942492,0.0375346206,20.0000000000,0.2000000000,15.7944927934,,0.0396183204,0.0811927061,0.1674220356,47.0434058824,7.4113546849,0.0102735941,-30.6125607956,-3.2968346872,-1756.6900271942,-17.5669002719,-1387.3013987358,,-3.4798554127,-7.1315208575,-14.7054310128,-4132.0340979390,-650.9726431509,-0.9023760119,California,CA,9,59.1858457424,41.3904741949,69.9513617378,62.0187896062,79.0518001240,52.1216510370,37.3180569516,68.3483551403,67.5701406274,54.3994266601,57.9926859232,26.1831217492,58.7612911558,2.0014414700,32.2031638835,14.4688811492,8.1570460385,,34.5749415665,10.3739430074,25.1131375379,84.5333172848,19.2864164585,4.9410824602,42.8621394303,58.0471933934,56.5430390950,57.0023528116,55.7266348497,,54.6373148803,57.1359685902,54.8116596007,56.2167239668,56.9568759225,56.2801621878,6,5,7,7,8,6,4,7,7,6,6,3,6,1,4,2,1,0,4,2,3,9,2,1,5,6,6,6,6,0,6,6,6,6,6,6,41% (59%ile),25% (41%ile),15% (69%ile),3% (62%ile),8% (79%ile),15% (52%ile),6% (68%ile),33% (54%ile),0.35 = fraction pre-1960 (67%ile),42%ile,0.0375 ug/m3 (2%ile),58%ile,20 lifetime risk per million (32%ile),56%ile,0.2  (14%ile),57%ile,16 daily vehicles/meters distance (8%ile),55%ile,,,0.04 sites/km distance (34%ile),54%ile,0.081 facilities/km distance (10%ile),57%ile,0.17 facilities/km distance (25%ile),54%ile,47 ppb (84%ile),56%ile,7.41 ug/m3 (19%ile),56%ile,0.01 facilities/sq km area (4%ile),56%ile,2987635876.0000000000,3272257.0000000000,1,0,422237.6856758550,4643687820.1565904617
+20324,15001021010,8606,8586,6124,3300,4089,3602,5362,0.6230536835,4430,0.5159562078,425,0.0693990856,36,0.0109090909,315,0.0366023704,1715,0.1992795724,502,0.1393670183,46,0.0112496943,0.5695049456,0.2425333351,4901.1595620778,2087.2418818153,1837.7590471768,508.2966127298,0.0067389217,10.0000000000,0.1000000000,0.1074143214,,0.0027318608,0.0478749209,0.0931096253,,,0.0259838494,20.6742274811,12.3845143014,18377.5904717679,183.7759047177,197.4016409694,,5.0205019537,87.9825690670,171.1130563323,,,47.7520542990,Hawaii,HI,9,74.7108013633,85.0291087110,36.7675143964,39.4832933303,15.2054293702,77.9602931979,98.1974410889,95.8100562593,9.2273848439,81.1726508957,76.5777942789,91.7961653862,85.2496673015,0.0699884723,1.8303662611,1.0748659980,0.5930748980,,0.1022787768,3.9663081684,14.5954101870,,,6.2654376121,66.8695670869,60.7245800447,74.1372134844,71.3832220072,58.5855777989,,62.7945832024,66.7236133386,67.8259227785,,,62.6039374599,8,9,4,4,2,8,11,11,1,9,8,10,9,1,1,1,1,0,1,1,2,0,0,1,7,7,8,8,6,0,7,7,7,0,0,7,62% (74%ile),52% (85%ile),7% (36%ile),1% (39%ile),4% (15%ile),20% (77%ile),14% (95%ile),57% (81%ile),0.011 = fraction pre-1960 (9%ile),66%ile,0.00674 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),74%ile,0.1  (1%ile),71%ile,0.11 daily vehicles/meters distance (0%ile),58%ile,,,0.0027 sites/km distance (0%ile),62%ile,0.048 facilities/km distance (3%ile),66%ile,0.093 facilities/km distance (14%ile),67%ile,,,,,0.026 facilities/sq km area (6%ile),62%ile,151184621.0000000000,0.0000000000,0,0,71817.3979516648,171030272.0024483502
+20327,15001021101,3054,3049,2569,1543,1958,1227,1086,0.3555992141,1450,0.4755657593,159,0.0618917867,30,0.0194426442,92,0.0301244270,909,0.2976424361,144,0.1173594132,33,0.0168539326,0.4155824867,0.2067110446,1269.1889143982,631.2955301209,182.0839675579,70.9772810171,0.0033713587,10.0000000000,0.1000000000,1.7167679255,,0.0025910486,0.2484740667,0.2746856427,,,0.0375389154,3.0688309139,0.6138703733,1820.8396755785,18.2083967558,312.5959152482,,0.4717884163,45.2431438983,50.0158516497,,,6.8352346500,Hawaii,HI,9,53.9485559986,80.7725127831,32.1386664749,50.6977998974,9.0084816593,96.6414513706,60.5643076662,92.9519087655,12.2637725970,66.3100091245,67.7573127614,34.5728724616,65.2982705045,0.0304238852,1.8303662611,1.0748659980,2.1438079687,,0.0856704529,41.7496844562,33.9762800526,,,7.1437776548,61.7192931562,58.8385531222,60.6418036749,60.2753222588,58.6611251941,,59.1844884925,63.7189045047,63.4569851575,,,61.1560978740,6,9,4,6,1,11,7,10,2,7,7,4,7,1,1,1,1,0,1,5,4,0,0,1,7,6,7,7,6,0,6,7,7,0,0,7,36% (53%ile),48% (80%ile),6% (32%ile),2% (50%ile),3% (9%ile),30% (96%ile),12% (92%ile),42% (66%ile),0.017 = fraction pre-1960 (12%ile),61%ile,0.00337 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),60%ile,0.1  (1%ile),60%ile,1.7 daily vehicles/meters distance (2%ile),58%ile,,,0.0026 sites/km distance (0%ile),59%ile,0.25 facilities/km distance (41%ile),63%ile,0.27 facilities/km distance (33%ile),63%ile,,,,,0.038 facilities/sq km area (7%ile),61%ile,106332317.0000000000,11164968.0000000000,0,1,61396.6485753379,132838116.6897320002
+20331,15001021402,3778,3755,2731,1374,1583,1803,3034,0.8030704076,705,0.1877496671,214,0.0783595752,56,0.0407569141,284,0.0751720487,933,0.2469560614,23,0.0127565169,276,0.1743524953,0.4954100374,0.2386774457,1871.6591211718,901.7233898617,526.8383978048,208.5726547229,0.0131608945,10.0000000000,0.1000000000,635.9981128640,,0.0033357209,0.0225482603,0.6278707343,,,0.5088713177,91.8555892572,6.9336645713,5268.3839780481,52.6838397805,335068.2267881447,,1.7573858477,11.8792893273,330.7864116735,,,268.0929496982,Hawaii,HI,9,84.7051677398,28.7308989413,41.8348284107,67.0398022547,74.9080519616,91.8116448026,3.9836481634,4.5613171192,47.5766504879,74.5975822746,75.7504514060,55.7976616902,73.4870488517,0.2827998785,1.8303662611,1.0748659980,72.8760884284,,0.2741900811,0.7727230166,47.2090589343,,,27.1111550629,75.5651255220,59.8939075539,64.0364159199,62.9860278691,78.3054903833,,60.2827950818,60.4356420863,71.4747506314,,,66.1787674526,9,3,5,7,8,10,1,1,5,8,8,6,8,1,1,1,8,0,1,1,5,0,0,3,8,6,7,7,8,0,7,7,8,0,0,7,80% (84%ile),19% (28%ile),8% (41%ile),4% (67%ile),8% (74%ile),25% (91%ile),1% (4%ile),50% (74%ile),0.17 = fraction pre-1960 (47%ile),75%ile,0.0132 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),64%ile,0.1  (1%ile),62%ile,640 daily vehicles/meters distance (72%ile),78%ile,,,0.0033 sites/km distance (0%ile),60%ile,0.023 facilities/km distance (0%ile),60%ile,0.63 facilities/km distance (47%ile),71%ile,,,,,0.51 facilities/sq km area (27%ile),66%ile,41940841.0000000000,6313950.0000000000,0,1,49320.0395726063,54601507.7207057551
+20340,15001021800,5998,5977,4357,2112,2631,3179,4020,0.6702234078,1613,0.2698678267,180,0.0413128299,76,0.0359848485,352,0.0586862287,1411,0.2352450817,241,0.0758100031,441,0.1676168757,0.4700456172,0.2185533706,2819.3336121800,1310.8831165809,684.2794304026,210.4283496771,0.0049503455,10.0000000000,0.1000000000,0.0743045071,,0.0038298946,0.0402733327,0.0410968274,,,0.1071290552,114.6967802385,3.3874195708,6842.7943040262,68.4279430403,50.8450457862,,2.6207181028,27.5582131707,28.1217136206,,,73.3062089025,Hawaii,HI,9,77.3411360526,46.5995830170,19.2008132329,64.3649112351,50.1501995746,89.5998226210,84.7007475319,78.6950985799,46.5822171086,72.0610072717,71.0200691830,75.5958544882,73.5619777633,0.0461211856,1.8303662611,1.0748659980,0.4752886632,,0.3337575583,2.8011103792,5.4042726335,,,11.7520153164,77.1764003076,59.3164619569,65.4523902797,64.1879137098,58.4409328484,,60.9620163260,62.2019174282,62.1288554973,,,63.2369902585,8,5,2,7,6,9,9,8,5,8,8,8,8,1,1,1,1,0,1,1,1,0,0,2,8,6,7,7,6,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),4% (19%ile),4% (64%ile),6% (50%ile),24% (89%ile),8% (78%ile),47% (72%ile),0.17 = fraction pre-1960 (46%ile),77%ile,0.00495 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),65%ile,0.1  (1%ile),64%ile,0.074 daily vehicles/meters distance (0%ile),58%ile,,,0.0038 sites/km distance (0%ile),60%ile,0.04 facilities/km distance (2%ile),62%ile,0.041 facilities/km distance (5%ile),62%ile,,,,,0.11 facilities/sq km area (11%ile),63%ile,365110254.0000000000,37900489.0000000000,0,0,92961.9049100969,459707845.8010936975
+20560,15003010201,4936,4798,3182,1441,1938,2266,3695,0.7485818476,1439,0.2999166319,231,0.0725958517,49,0.0340041638,476,0.0964343598,651,0.1318881686,115,0.0507502207,413,0.2131062951,0.5242492398,0.2305701706,2587.6942476032,1138.0943619037,830.6706662005,232.4850372225,0.0171119880,10.0000000000,0.1000000000,1493.8870892160,,0.0694550700,0.0548137804,0.4080845621,,,0.0995447326,177.0211481635,14.2144264416,8306.7066620049,83.0670666200,1240928.1836273719,,57.6942892272,45.5321994534,338.9838750865,,,82.6888893711,Hawaii,HI,9,81.7629198374,52.7224313484,38.5909198362,63.0702607098,91.9817357703,38.9013315993,48.2935747432,55.4563410918,53.0116816593,77.2470730276,73.8991238733,68.7030659293,74.5973100320,0.5552831600,1.8303662611,1.0748659980,88.8972054263,,53.8623224639,5.1063675682,39.9537688137,,,11.2751492958,80.6790478991,60.9966060167,66.7493132622,65.1696166202,90.1940360178,,78.6444132849,63.7434121326,71.6211763075,,,63.4227438652,9,6,4,7,10,4,5,6,6,8,8,7,8,1,1,1,9,0,6,1,4,0,0,2,9,7,7,7,10,0,8,7,8,0,0,7,75% (81%ile),30% (52%ile),7% (38%ile),3% (63%ile),10% (91%ile),13% (38%ile),5% (55%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),80%ile,0.0171 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1  (1%ile),65%ile,1500 daily vehicles/meters distance (88%ile),90%ile,,,0.069 sites/km distance (53%ile),78%ile,0.055 facilities/km distance (5%ile),63%ile,0.41 facilities/km distance (39%ile),71%ile,,,,,0.1 facilities/sq km area (11%ile),63%ile,66256288.0000000000,7249455.0000000000,0,0,42997.4044651793,85395519.3857139796
+20614,15007040603,2984,2978,2104,1058,2064,1468,2011,0.6739276139,797,0.2676292814,138,0.0655893536,33,0.0311909263,168,0.0563002681,756,0.2533512064,64,0.0435967302,193,0.0935077519,0.4707784477,0.2246647750,1404.8028878442,670.3996884791,342.6152122150,122.9243592959,0.0225796264,10.0000000000,0.1000000000,255.5966484444,,0.0065810172,0.1042895043,0.5200441984,,,0.1610354485,32.0371782740,7.7361234992,3426.1521221502,34.2615212215,87571.2999482384,,2.2547565999,35.7311706322,178.1750534077,,,55.1731943631,Hawaii,HI,9,77.5456369017,46.0970850340,34.4418368771,61.1045870101,46.0008635792,92.7630589707,21.6211577108,45.6650954498,34.4080455626,72.1367523014,72.5017628853,38.1901457794,68.6478207991,0.9188048692,1.8303662611,1.0748659980,48.7907692784,,1.7474241413,15.4267424965,43.8004140051,,,14.6634418792,68.8698351102,60.0337698662,62.2468364816,61.5558275260,68.4712288534,,60.6727330898,62.8928664453,68.0134997705,,,62.8256530053,8,5,4,7,5,10,3,5,4,8,8,4,7,1,1,1,5,0,1,2,5,0,0,2,7,7,7,7,7,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),7% (34%ile),3% (61%ile),6% (46%ile),25% (92%ile),4% (45%ile),47% (72%ile),0.094 = fraction pre-1960 (34%ile),68%ile,0.0226 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),62%ile,0.1  (1%ile),61%ile,260 daily vehicles/meters distance (48%ile),68%ile,,,0.0066 sites/km distance (1%ile),60%ile,0.1 facilities/km distance (15%ile),62%ile,0.52 facilities/km distance (43%ile),68%ile,,,,,0.16 facilities/sq km area (14%ile),62%ile,41255867.0000000000,7041518.0000000000,0,0,36855.9892981643,56378891.3118786365
+20615,15007040604,3529,3458,2370,1187,1625,1757,2203,0.6242561632,1275,0.3687102371,133,0.0561181435,42,0.0353833193,333,0.0943610088,632,0.1790875602,109,0.0620375640,322,0.1981538462,0.4964832002,0.2263194054,1752.0892134182,798.6811814815,495.9027833594,151.2145472028,0.0297040750,10.0000000000,0.1000000000,464.0468169721,,0.0064334940,0.1282189641,0.3810520320,,,0.2277699060,98.2650438411,14.7303334730,4959.0278335940,49.5902783359,230122.1081455294,,3.1903875793,63.5841411863,188.9647632974,,,112.9517303753,Hawaii,HI,9,74.7796778307,65.5138546463,28.4761545436,63.9820945208,90.9928060818,67.6211097498,45.4287763592,68.1087856009,51.0102487547,74.6953085329,72.8443904311,48.6363888652,70.3699602168,1.3729134893,1.8303662611,1.0748659980,64.8054389268,,1.7014251995,20.6071512154,38.9237463430,,,17.5743663328,76.0259182532,61.0699450431,63.7308514802,62.7533582560,75.0094643983,,61.3999056181,65.1045697304,68.3212418487,,,64.0124628341,8,7,3,7,10,7,5,7,6,8,8,5,8,1,1,1,7,0,1,3,4,0,0,2,8,7,7,7,8,0,7,7,7,0,0,7,62% (74%ile),37% (65%ile),6% (28%ile),4% (63%ile),9% (90%ile),18% (67%ile),6% (68%ile),50% (74%ile),0.2 = fraction pre-1960 (51%ile),76%ile,0.0297 ug/m3 (1%ile),61%ile,10 lifetime risk per million (1%ile),63%ile,0.1  (1%ile),62%ile,460 daily vehicles/meters distance (64%ile),75%ile,,,0.0064 sites/km distance (1%ile),61%ile,0.13 facilities/km distance (20%ile),65%ile,0.38 facilities/km distance (38%ile),68%ile,,,,,0.23 facilities/sq km area (17%ile),64%ile,21724894.0000000000,2371158.0000000000,0,1,27760.3117775823,28129042.7970332205
+20616,15007040700,9552,9523,6234,2895,3298,4974,7071,0.7402638191,1980,0.2079176730,309,0.0495668912,95,0.0328151986,772,0.0808207705,1834,0.1920016750,205,0.0412143144,346,0.1049120679,0.4740907460,0.2172310046,4528.5148062585,2074.9905558098,1128.3751689898,322.4823975128,0.0120486502,10.0000000000,0.1000000000,829.6297843840,,0.0062317499,0.2776903565,0.5315584393,,,0.8605507426,118.3801723682,13.5953976825,11283.7516898982,112.8375168990,936133.6481533048,,7.0317517976,313.3389029609,599.7973437979,,,971.0240895638,Hawaii,HI,9,81.2804870193,33.1925490446,24.3539720817,62.2442189921,81.2507708079,74.5290795692,78.3337372056,42.2880946853,36.4589583341,72.4690666385,70.6843205648,91.6638490724,78.6800785920,0.2182262554,1.8303662611,1.0748659980,78.8807149861,,1.6540510210,44.6852775053,44.1677595343,,,35.8025350464,77.4223719690,60.9007569981,69.0968995957,67.2008685709,87.5969776098,,64.0977043448,75.6532891441,75.2184280558,,,72.0462363568,9,4,3,7,9,8,8,5,4,8,8,10,8,1,1,1,8,0,1,5,5,0,0,4,8,7,7,7,9,0,7,8,8,0,0,8,74% (81%ile),21% (33%ile),5% (24%ile),3% (62%ile),8% (81%ile),19% (74%ile),4% (42%ile),47% (72%ile),0.1 = fraction pre-1960 (36%ile),77%ile,0.012 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),69%ile,0.1  (1%ile),67%ile,830 daily vehicles/meters distance (78%ile),87%ile,,,0.0062 sites/km distance (1%ile),64%ile,0.28 facilities/km distance (44%ile),75%ile,0.53 facilities/km distance (44%ile),75%ile,,,,,0.86 facilities/sq km area (35%ile),72%ile,93005151.0000000000,5658877.0000000000,0,1,70950.4293149945,115233329.7073323578
+20624,15009030100,1405,1374,980,467,796,729,1060,0.7544483986,400,0.2911208151,44,0.0448979592,0,0.0000000000,124,0.0882562278,342,0.2434163701,41,0.0562414266,170,0.2135678392,0.5227846069,0.2370232951,734.5123726346,333.0177296537,234.3871433253,75.2419798205,0.0026846006,10.0000000000,0.1000000000,,,0.0046765532,0.0398066625,0.0329594792,,,0.0973247551,50.0575557353,0.6292358547,2343.8714332533,23.4387143325,,,1.0961239415,9.3301699117,7.7252781836,,,22.8116713113,Hawaii,HI,9,82.0728020535,51.0042892937,21.4097431797,22.2149776961,87.3264635475,91.2196103997,10.4895772204,61.9590696975,53.0589655984,77.1174117652,75.3619764353,7.5930221772,65.5506156308,0.0292580509,1.8303662611,1.0748659980,,,0.7827594098,2.7444494654,3.8365691660,,,11.1452377410,71.4770935032,58.8413451490,61.1806956436,60.6714580607,,,59.7579323526,60.0938330974,59.9756316335,,,61.8121208766,9,6,3,3,9,10,2,7,6,8,8,1,7,1,1,1,0,0,1,1,1,0,0,2,8,6,7,7,0,0,6,7,6,0,0,7,75% (82%ile),29% (51%ile),4% (21%ile),0% (22%ile),9% (87%ile),24% (91%ile),6% (61%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),71%ile,0.00268 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),61%ile,0.1  (1%ile),60%ile,,,,,0.0047 sites/km distance (0%ile),59%ile,0.04 facilities/km distance (2%ile),60%ile,0.033 facilities/km distance (3%ile),59%ile,,,,,0.097 facilities/sq km area (11%ile),61%ile,555262221.0000000000,25398369.0000000000,0,0,165450.9181509207,667169893.1947253942
+20625,15009030201,2340,2327,1879,842,1045,1395,992,0.4239316239,623,0.2677266867,62,0.0329962746,0,0.0000000000,150,0.0641025641,554,0.2367521368,133,0.0953405018,97,0.0928229665,0.3458291553,0.1709182144,809.2402234637,399.9486215874,-23.7085570230,-29.3718443271,0.0063521816,10.0000000000,0.1000000000,7.0868595222,,0.0053511202,0.1292001112,0.0908033666,,,0.0098923140,-2.2006985945,-0.1506010605,-237.0855702299,-2.3708557023,-168.0192130960,,-0.1268673373,-3.0631482031,-2.1528167944,,,-0.2345324900,Hawaii,HI,9,60.4734810662,46.1223337837,13.8610651481,22.2149776961,59.2951174656,89.9006101655,56.2295738392,87.6116027815,34.2663836750,57.2620275919,55.1869446757,12.9269845729,57.7176364334,0.0651806253,1.8303662611,1.0748659980,4.6368984700,,1.3707744996,20.8229399202,14.1722671160,,,4.9075810703,55.1341860078,58.6779670685,58.4263245996,58.4806520514,57.3658121936,,58.5702573228,58.1201119303,58.2216584263,,,56.4332033900,7,5,2,3,6,9,6,9,4,6,6,2,6,1,1,1,1,0,1,3,2,0,0,1,6,6,6,6,6,0,6,6,6,0,0,6,42% (60%ile),27% (46%ile),3% (13%ile),0% (22%ile),6% (59%ile),24% (89%ile),10% (87%ile),35% (57%ile),0.093 = fraction pre-1960 (34%ile),55%ile,0.00635 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),58%ile,0.1  (1%ile),58%ile,7.1 daily vehicles/meters distance (4%ile),57%ile,,,0.0054 sites/km distance (1%ile),58%ile,0.13 facilities/km distance (20%ile),58%ile,0.091 facilities/km distance (14%ile),58%ile,,,,,0.0099 facilities/sq km area (4%ile),56%ile,118113265.0000000000,4116462.0000000000,0,0,68639.8224567451,140691933.2772550285
+20629,15009030402,8562,8562,6540,3180,3473,4778,5420,0.6330296660,1535,0.1792805419,294,0.0449541284,39,0.0122641509,397,0.0463676711,1550,0.1810324690,210,0.0439514441,264,0.0760149726,0.4061551039,0.1828214379,3477.5000000000,1565.3171513473,429.7617698603,-5.5554252167,0.0153866969,10.0000000000,0.1000000000,233.6880574427,,0.0055146115,0.6633705951,0.5914191729,,,0.4432670413,32.6683291803,6.6126141119,4297.6176986027,42.9761769860,100430.1931617688,,2.3699691830,285.0913210180,254.1693504667,,,190.4992282028,Hawaii,HI,9,75.2886221409,26.8565530362,21.4439231945,41.5097856648,28.7449442824,68.6690121278,79.3833218173,46.1798708736,30.8409993308,65.1595288992,59.9703175674,82.9721886150,59.7346670413,0.4238414396,1.8303662611,1.0748659980,46.4851154373,,1.4363834459,65.6894718646,46.0793962757,,,25.2542546354,68.9789876320,59.8548490559,63.0853618909,62.2063722143,69.2554009969,,60.7689024131,74.9199434460,69.9134569074,,,65.2067338787,8,3,3,5,3,7,8,5,4,7,6,9,6,1,1,1,5,0,1,7,5,0,0,3,7,6,7,7,7,0,7,8,7,0,0,7,63% (75%ile),18% (26%ile),4% (21%ile),1% (41%ile),5% (28%ile),18% (68%ile),4% (46%ile),41% (65%ile),0.076 = fraction pre-1960 (30%ile),68%ile,0.0154 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),63%ile,0.1  (1%ile),62%ile,230 daily vehicles/meters distance (46%ile),69%ile,,,0.0055 sites/km distance (1%ile),60%ile,0.66 facilities/km distance (65%ile),74%ile,0.59 facilities/km distance (46%ile),69%ile,,,,,0.44 facilities/sq km area (25%ile),65%ile,46066876.0000000000,109238.0000000000,0,1,49929.5140313853,53123180.2369696796
+20639,15009030800,7879,7871,5174,2235,2335,4210,6161,0.7819520244,1091,0.1386100877,195,0.0376884422,3,0.0013422819,594,0.0753902780,1027,0.1303464907,163,0.0387173397,285,0.1220556745,0.4602810560,0.1942216008,3626.5544403507,1530.2719926347,821.9375850282,84.7096204381,0.0169064550,10.0000000000,0.1000000000,575.9991000531,0.0008675195,0.0061499864,1.0347888110,0.5999348163,,,0.0263640121,100.3221463525,13.8960508008,8219.3758502819,82.1937585028,473435.3092760353,0.7130468458,5.0549049971,850.5318163110,493.1089740953,,,21.6695724544,Hawaii,HI,9,83.5697361596,17.7978784355,16.7867123482,22.7576898463,75.1762179508,37.9999341618,67.3605976499,38.4952526223,39.4719140158,71.0499579469,63.9144112870,82.0845801288,66.2238170511,0.5328167647,1.8303662611,1.0748659980,70.4238972397,46.4801242962,1.6311542890,76.8934515139,46.3897571267,,,6.2894452647,76.1966079716,60.9434745816,66.6801583126,65.1083356253,81.3398307482,78.4421389238,62.8183222708,84.3474497827,73.9856189804,,,61.7713841743,9,2,2,3,8,4,7,4,4,8,7,9,7,1,1,1,8,5,1,8,5,0,0,1,8,7,7,7,9,8,7,9,8,0,0,7,78% (83%ile),14% (17%ile),4% (16%ile),0% (22%ile),8% (75%ile),13% (37%ile),4% (38%ile),46% (71%ile),0.12 = fraction pre-1960 (39%ile),76%ile,0.0169 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1  (1%ile),65%ile,580 daily vehicles/meters distance (70%ile),81%ile,0.00087 toxicity-weighted concentration/meters distance (46%ile),78%ile,0.0061 sites/km distance (1%ile),62%ile,1 facilities/km distance (76%ile),84%ile,0.6 facilities/km distance (46%ile),73%ile,,,,,0.026 facilities/sq km area (6%ile),61%ile,141603534.0000000000,11781155.0000000000,0,0,80194.4536675024,176674254.7197769880

data/data-pipeline/data_pipeline/tests/sources/ejscreen/test_etl.py

@@ -0,0 +1,19 @@
+import pathlib
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.ejscreen.etl import EJSCREENETL
+
+
+class TestEJSCREENETL(TestETL):
+    _ETL_CLASS = EJSCREENETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "EJSCREEN_2021_USPR_Tracts.csv"
+    _SAMPLE_DATA_ZIP_FILE_NAME = "EJSCREEN_2021_USPR_Tracts.csv.zip"
+    _EXTRACT_TMP_FOLDER_NAME = "EJSCREENETL"
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)

data/data-pipeline/data_pipeline/tests/sources/example/etl.py

@@ -21,6 +21,7 @@ class ExampleETL(ExtractTransformLoad):
     LAST_UPDATED_YEAR = 2017
     SOURCE_URL = "https://www.example.com/example.zip"
     GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+    LOAD_YAML_CONFIG: bool = True
 
     def __init__(self):
         self.COLUMNS_TO_KEEP = [

data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py

@@ -2,7 +2,7 @@
 import copy
 import os
 import pathlib
-from typing import Type
+from typing import Type, Optional
 from unittest import mock
 import pytest
 
@@ -11,6 +11,10 @@ import numpy as np
 import pandas as pd
 
 from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
+from data_pipeline.etl.score.constants import (
+    TILES_CONTINENTAL_US_FIPS_CODE,
+    TILES_ALASKA_AND_HAWAII_FIPS_CODE,
+)
 from data_pipeline.tests.sources.example.etl import ExampleETL
 from data_pipeline.utils import get_module_logger
 
@@ -41,7 +45,7 @@ class TestETL:
     # so that we do not have to manually copy the "sample data" when we run the tests.
     _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
     _SAMPLE_DATA_FILE_NAME = "input.csv"
-    _SAMPLE_DATA_ZIP_FILE_NAME = "input.zip"
+    _SAMPLE_DATA_ZIP_FILE_NAME: Optional[str] = "input.zip"
     _EXTRACT_TMP_FOLDER_NAME = "ExampleETL"
 
     # Note: We used shared census tract IDs so that later our tests can join all the
@@ -86,7 +90,25 @@ class TestETL:
         self._DATA_DIRECTORY_FOR_TEST = pathlib.Path(filename).parent / "data"
 
     def _get_instance_of_etl_class(self) -> Type[ExtractTransformLoad]:
-        return self._ETL_CLASS()
+        etl_class = self._ETL_CLASS()
+
+        # Find out what unique state codes are present in the test fixture data.
+        states_expected_from_fixtures = {
+            x[0:2] for x in self._FIXTURES_SHARED_TRACT_IDS
+        }
+
+        # Set values to match test fixtures
+        etl_class.EXPECTED_MISSING_STATES = [
+            x
+            for x in TILES_CONTINENTAL_US_FIPS_CODE
+            + TILES_ALASKA_AND_HAWAII_FIPS_CODE
+            if x not in states_expected_from_fixtures
+        ]
+        etl_class.PUERTO_RICO_EXPECTED_IN_DATA = False
+        etl_class.ISLAND_AREAS_EXPECTED_IN_DATA = False
+        etl_class.ALASKA_AND_HAWAII_EXPECTED_IN_DATA = True
+
+        return etl_class
 
     def _setup_etl_instance_and_run_extract(
         self, mock_etl, mock_paths
@@ -102,24 +124,39 @@ class TestETL:
         used to retrieve data in order to force that method to retrieve the fixture
         data. A basic version of that patching is included here for classes that can use it.
         """
-        with mock.patch("data_pipeline.utils.requests") as requests_mock:
-            zip_file_fixture_src = (
-                self._DATA_DIRECTORY_FOR_TEST / self._SAMPLE_DATA_ZIP_FILE_NAME
-            )
-            tmp_path = mock_paths[1]
 
-            # Create mock response.
-            with open(zip_file_fixture_src, mode="rb") as file:
-                file_contents = file.read()
+        with mock.patch(
+            "data_pipeline.utils.requests"
+        ) as requests_mock, mock.patch(
+            "data_pipeline.etl.score.etl_utils.get_state_fips_codes"
+        ) as mock_get_state_fips_codes:
+            tmp_path = mock_paths[1]
+            if self._SAMPLE_DATA_ZIP_FILE_NAME is not None:
+                zip_file_fixture_src = (
+                    self._DATA_DIRECTORY_FOR_TEST
+                    / self._SAMPLE_DATA_ZIP_FILE_NAME
+                )
+
+                # Create mock response.
+                with open(zip_file_fixture_src, mode="rb") as file:
+                    file_contents = file.read()
+            else:
+                with open(
+                    self._DATA_DIRECTORY_FOR_TEST / self._SAMPLE_DATA_FILE_NAME,
+                    "rb",
+                ) as file:
+                    file_contents = file.read()
             response_mock = requests.Response()
             response_mock.status_code = 200
             # pylint: disable=protected-access
             response_mock._content = file_contents
             # Return text fixture:
             requests_mock.get = mock.MagicMock(return_value=response_mock)
-
+            mock_get_state_fips_codes.return_value = [
+                x[0:2] for x in self._FIXTURES_SHARED_TRACT_IDS
+            ]
             # Instantiate the ETL class.
-            etl = self._ETL_CLASS()
+            etl = self._get_instance_of_etl_class()
 
             # Monkey-patch the temporary directory to the one used in the test
             etl.TMP_PATH = tmp_path
@@ -172,6 +209,16 @@ class TestETL:
 
         assert actual_file_path == expected_file_path
 
+    def test_tract_id_lengths(self, mock_etl, mock_paths):
+        etl = self._setup_etl_instance_and_run_extract(
+            mock_etl=mock_etl, mock_paths=mock_paths
+        )
+        etl.transform()
+        etl.validate()
+        etl.load()
+        df = etl.get_data_frame()
+        assert (df[etl.GEOID_TRACT_FIELD_NAME].str.len() == 11).all()
+
     def test_fixtures_contain_shared_tract_ids_base(self, mock_etl, mock_paths):
         """Check presence of necessary shared tract IDs.
         Note: We used shared census tract IDs so that later our tests can join all the
@@ -203,9 +250,14 @@ class TestETL:
         """This will test that the sample data exists where it's supposed to as it's supposed to
         As per conversation with Jorge, here we can *just* test that the zip file exists.
         """
-        assert (
-            self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_ZIP_FILE_NAME
-        ).exists()
+        if self._SAMPLE_DATA_ZIP_FILE_NAME is not None:
+            assert (
+                self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_ZIP_FILE_NAME
+            ).exists()
+        else:
+            assert (
+                self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_FILE_NAME
+            ).exists()
 
     def test_extract_unzips_base(self, mock_etl, mock_paths):
         """Tests the extract method.
@@ -213,17 +265,18 @@ class TestETL:
         As per conversation with Jorge, no longer includes snapshot. Instead, verifies that the
         file was unzipped from a "fake" downloaded zip (located in data) in a  temporary path.
         """
-        tmp_path = mock_paths[1]
+        if self._SAMPLE_DATA_ZIP_FILE_NAME is not None:
+            tmp_path = mock_paths[1]
 
-        _ = self._setup_etl_instance_and_run_extract(
-            mock_etl=mock_etl,
-            mock_paths=mock_paths,
-        )
-        assert (
-            tmp_path
-            / self._EXTRACT_TMP_FOLDER_NAME
-            / self._SAMPLE_DATA_FILE_NAME
-        ).exists()
+            _ = self._setup_etl_instance_and_run_extract(
+                mock_etl=mock_etl,
+                mock_paths=mock_paths,
+            )
+            assert (
+                tmp_path
+                / self._EXTRACT_TMP_FOLDER_NAME
+                / self._SAMPLE_DATA_FILE_NAME
+            ).exists()
 
     def test_extract_produces_valid_data(self, snapshot, mock_etl, mock_paths):
         """Tests the extract method.

data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/extract.csv

@@ -0,0 +1,17 @@
+GEOID,count_properties,mid_depth_100_year00,mid_depth_100_year30
+6027000800,942,214,215
+6069000802,1131,283,292
+6061021322,1483,100,108
+15001021010,1888,179,186
+15001021101,3463,130,137
+15007040603,1557,152,181
+15007040700,1533,177,191
+15009030100,1658,232,242
+15009030201,6144,431,447
+15001021402,4118,321,329
+15001021800,2813,350,356
+15009030402,3374,852,888
+15009030800,4847,1003,1019
+15003010201,2335,220,227
+15007040604,5364,630,641
+2290000400,1,1,1

data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/output.csv

@@ -0,0 +1,17 @@
+GEOID10_TRACT,Count of properties eligible for flood risk calculation within tract (floor of 250),Count of properties at risk of flood today,Count of properties at risk of flood in 30 years,Share of properties at risk of flood today,Share of properties at risk of flood in 30 years
+06027000800,942,214,215,0.2271762208,0.2282377919
+06069000802,1131,283,292,0.2502210433,0.2581786030
+06061021322,1483,100,108,0.0674308833,0.0728253540
+15001021010,1888,179,186,0.0948093220,0.0985169492
+15001021101,3463,130,137,0.0375397055,0.0395610742
+15007040603,1557,152,181,0.0976236352,0.1162491972
+15007040700,1533,177,191,0.1154598826,0.1245923027
+15009030100,1658,232,242,0.1399276236,0.1459589867
+15009030201,6144,431,447,0.0701497396,0.0727539062
+15001021402,4118,321,329,0.0779504614,0.0798931520
+15001021800,2813,350,356,0.1244223249,0.1265552791
+15009030402,3374,852,888,0.2525192650,0.2631890931
+15009030800,4847,1003,1019,0.2069321230,0.2102331339
+15003010201,2335,220,227,0.0942184154,0.0972162741
+15007040604,5364,630,641,0.1174496644,0.1195003729
+02290000400,250,1,1,0.0040000000,0.0040000000

data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/transform.csv

@@ -0,0 +1,17 @@
+GEOID,count_properties,Count of properties at risk of flood today,Count of properties at risk of flood in 30 years,GEOID10_TRACT,Count of properties eligible for flood risk calculation within tract (floor of 250),Share of properties at risk of flood today,Share of properties at risk of flood in 30 years
+06027000800,942,214,215,06027000800,942,0.2271762208,0.2282377919
+06069000802,1131,283,292,06069000802,1131,0.2502210433,0.2581786030
+06061021322,1483,100,108,06061021322,1483,0.0674308833,0.0728253540
+15001021010,1888,179,186,15001021010,1888,0.0948093220,0.0985169492
+15001021101,3463,130,137,15001021101,3463,0.0375397055,0.0395610742
+15007040603,1557,152,181,15007040603,1557,0.0976236352,0.1162491972
+15007040700,1533,177,191,15007040700,1533,0.1154598826,0.1245923027
+15009030100,1658,232,242,15009030100,1658,0.1399276236,0.1459589867
+15009030201,6144,431,447,15009030201,6144,0.0701497396,0.0727539062
+15001021402,4118,321,329,15001021402,4118,0.0779504614,0.0798931520
+15001021800,2813,350,356,15001021800,2813,0.1244223249,0.1265552791
+15009030402,3374,852,888,15009030402,3374,0.2525192650,0.2631890931
+15009030800,4847,1003,1019,15009030800,4847,0.2069321230,0.2102331339
+15003010201,2335,220,227,15003010201,2335,0.0942184154,0.0972162741
+15007040604,5364,630,641,15007040604,5364,0.1174496644,0.1195003729
+2290000400,1,1,1,02290000400,250,0.0040000000,0.0040000000

data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/test_etl.py

@@ -0,0 +1,22 @@
+import pathlib
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.fsf_flood_risk.etl import FloodRiskETL
+
+
+class TestFloodRiskETL(TestETL):
+    _ETL_CLASS = FloodRiskETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "fsf_flood/flood-tract2010.csv"
+    _SAMPLE_DATA_ZIP_FILE_NAME = "fsf_flood.zip"
+    _EXTRACT_TMP_FOLDER_NAME = "FloodRiskETL"
+    _FIXTURES_SHARED_TRACT_IDS = TestETL._FIXTURES_SHARED_TRACT_IDS + [
+        "02290000400"  # A tract with 1 property
+    ]
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)

data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/extract.csv

@@ -0,0 +1,17 @@
+GEOID,count_properties,burnprob_year00_flag,burnprob_year30_flag
+6027000800,942,31,634
+6069000802,1131,0,264
+6061021322,1483,13,478
+15001021010,1888,62,550
+15001021101,3463,18,192
+15007040603,1557,0,509
+15007040700,1535,0,43
+15009030100,1660,177,968
+15009030201,6144,173,2856
+15001021402,4118,20,329
+15001021800,2814,111,770
+15009030402,3375,7,437
+15009030800,4847,3268,3529
+15003010201,2335,1949,2005
+15007040604,5365,3984,4439
+4003001402,1,1,1

data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/output.csv

@@ -0,0 +1,17 @@
+GEOID10_TRACT,Count of properties eligible for wildfire risk calculation within tract (floor of 250),Count of properties at risk of wildfire today,Count of properties at risk of wildfire in 30 years,Share of properties at risk of fire today,Share of properties at risk of fire in 30 years
+06027000800,942,31,634,0.0329087049,0.6730360934
+06069000802,1131,0,264,0.0000000000,0.2334217507
+06061021322,1483,13,478,0.0087660148,0.3223196224
+15001021010,1888,62,550,0.0328389831,0.2913135593
+15001021101,3463,18,192,0.0051978054,0.0554432573
+15007040603,1557,0,509,0.0000000000,0.3269107258
+15007040700,1535,0,43,0.0000000000,0.0280130293
+15009030100,1660,177,968,0.1066265060,0.5831325301
+15009030201,6144,173,2856,0.0281575521,0.4648437500
+15001021402,4118,20,329,0.0048567266,0.0798931520
+15001021800,2814,111,770,0.0394456290,0.2736318408
+15009030402,3375,7,437,0.0020740741,0.1294814815
+15009030800,4847,3268,3529,0.6742314834,0.7280792243
+15003010201,2335,1949,2005,0.8346895075,0.8586723769
+15007040604,5365,3984,4439,0.7425908667,0.8273998136
+04003001402,250,1,1,0.0040000000,0.0040000000

data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/transform.csv

@@ -0,0 +1,17 @@
+GEOID,count_properties,Count of properties at risk of wildfire today,Count of properties at risk of wildfire in 30 years,GEOID10_TRACT,Count of properties eligible for wildfire risk calculation within tract (floor of 250),Share of properties at risk of fire today,Share of properties at risk of fire in 30 years
+06027000800,942,31,634,06027000800,942,0.0329087049,0.6730360934
+06069000802,1131,0,264,06069000802,1131,0.0000000000,0.2334217507
+06061021322,1483,13,478,06061021322,1483,0.0087660148,0.3223196224
+15001021010,1888,62,550,15001021010,1888,0.0328389831,0.2913135593
+15001021101,3463,18,192,15001021101,3463,0.0051978054,0.0554432573
+15007040603,1557,0,509,15007040603,1557,0.0000000000,0.3269107258
+15007040700,1535,0,43,15007040700,1535,0.0000000000,0.0280130293
+15009030100,1660,177,968,15009030100,1660,0.1066265060,0.5831325301
+15009030201,6144,173,2856,15009030201,6144,0.0281575521,0.4648437500
+15001021402,4118,20,329,15001021402,4118,0.0048567266,0.0798931520
+15001021800,2814,111,770,15001021800,2814,0.0394456290,0.2736318408
+15009030402,3375,7,437,15009030402,3375,0.0020740741,0.1294814815
+15009030800,4847,3268,3529,15009030800,4847,0.6742314834,0.7280792243
+15003010201,2335,1949,2005,15003010201,2335,0.8346895075,0.8586723769
+15007040604,5365,3984,4439,15007040604,5365,0.7425908667,0.8273998136
+4003001402,1,1,1,04003001402,250,0.0040000000,0.0040000000

data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/test_etl.py

@@ -0,0 +1,22 @@
+import pathlib
+from data_pipeline.tests.sources.example.test_etl import TestETL
+from data_pipeline.etl.sources.fsf_wildfire_risk.etl import WildfireRiskETL
+
+
+class TestWildfireRiskETL(TestETL):
+    _ETL_CLASS = WildfireRiskETL
+
+    _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data"
+    _SAMPLE_DATA_FILE_NAME = "fsf_fire/fire-tract2010.csv"
+    _SAMPLE_DATA_ZIP_FILE_NAME = "fsf_fire.zip"
+    _EXTRACT_TMP_FOLDER_NAME = "WildfireRiskETL"
+    _FIXTURES_SHARED_TRACT_IDS = TestETL._FIXTURES_SHARED_TRACT_IDS + [
+        "04003001402"  # A tract with 1 property, also missing a digit
+    ]
+
+    def setup_method(self, _method, filename=__file__):
+        """Invoke `setup_method` from Parent, but using the current file name.
+
+        This code can be copied identically between all child classes.
+        """
+        super().setup_method(_method=_method, filename=filename)

data/data-pipeline/data_pipeline/tests/sources/geocorr/data/extract.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,population_in_rural_areas,population_in_urban_areas,perc_population_in_rural_areas,perc_population_in_urban_areas,urban_heuristic_flag
+06027000800,3378.0000000000,,1.0000000000,,0
+06061021322,2252.0000000000,6510.0000000000,0.2570189454,0.7429810546,1
+06069000802,2007.0000000000,527.0000000000,0.7920284136,0.2079715864,0
+15001021010,7884.0000000000,,1.0000000000,,0
+15001021101,3312.0000000000,219.0000000000,0.9379779099,0.0620220901,0
+15001021402,1532.0000000000,2493.0000000000,0.3806211180,0.6193788820,1
+15001021800,2725.0000000000,3597.0000000000,0.4310344828,0.5689655172,1
+15003010201,827.0000000000,5055.0000000000,0.1405984359,0.8594015641,1
+15007040603,789.0000000000,1755.0000000000,0.3101415094,0.6898584906,1
+15007040604,275.0000000000,2864.0000000000,0.0876075183,0.9123924817,1
+15007040700,814.0000000000,7589.0000000000,0.0968701654,0.9031298346,1
+15009030100,2291.0000000000,,1.0000000000,,0
+15009030201,1982.0000000000,471.0000000000,0.8079902161,0.1920097839,0
+15009030402,419.0000000000,8233.0000000000,0.0484281091,0.9515718909,1
+15009030800,1100.0000000000,5807.0000000000,0.1592587230,0.8407412770,1

data/data-pipeline/data_pipeline/tests/sources/geocorr/data/geocorr_urban_rural.csv

@@ -0,0 +1,16 @@
+GEOID10_TRACT,population_in_rural_areas,population_in_urban_areas,perc_population_in_rural_areas,perc_population_in_urban_areas,urban_heuristic_flag
+06027000800,3378.0,,1.0,,0
+06061021322,2252.0,6510.0,0.25701894544624515,0.7429810545537548,1
+06069000802,2007.0,527.0,0.7920284135753749,0.2079715864246251,0
+15001021010,7884.0,,1.0,,0
+15001021101,3312.0,219.0,0.9379779099405268,0.062022090059473234,0
+15001021402,1532.0,2493.0,0.3806211180124224,0.6193788819875776,1
+15001021800,2725.0,3597.0,0.43103448275862066,0.5689655172413793,1
+15003010201,827.0,5055.0,0.14059843590615437,0.8594015640938456,1
+15007040603,789.0,1755.0,0.31014150943396224,0.6898584905660378,1
+15007040604,275.0,2864.0,0.08760751831793565,0.9123924816820643,1
+15007040700,814.0,7589.0,0.09687016541711294,0.9031298345828871,1
+15009030100,2291.0,,1.0,,0
+15009030201,1982.0,471.0,0.807990216061965,0.19200978393803506,0
+15009030402,419.0,8233.0,0.04842810910772076,0.9515718908922792,1
+15009030800,1100.0,5807.0,0.1592587230346026,0.8407412769653975,1