diff --git a/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py b/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py
index ad58a23d..9527b242 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py
@@ -52,11 +52,11 @@ class CDCPlacesETL(ExtractTransformLoad):
 
         # rename columns to be used in score
         rename_fields = {
-            "Current asthma among adults aged >=18 years": field_names.ASTHMA_FIELD,  # 'Current asthma among adults aged greater than or equal to 18 years'
-            "Coronary heart disease among adults aged >=18 years": field.names.HEART_DISEASE_FIELD,  # "Coronary heart disease among adults aged greater than or equal to 18 years"
-            "Cancer (excluding skin cancer) among adults aged >=18 years": field_names.CANCER_FIELD,  # 'Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years',
-            "Diagnosed diabetes among adults aged >=18 years": field_names.DIABETES_FIELD,  # 'Diagnosed diabetes among adults aged greater than or equal to 18 years',
-            "Physical health not good for >=14 days among adults aged >=18 years": field_names.PHYS_HEALTH_NOT_GOOD_FIELD,  # 'Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years',
+            "Current asthma among adults aged >=18 years": field_names.ASTHMA_FIELD,
+            "Coronary heart disease among adults aged >=18 years": field_names.HEART_DISEASE_FIELD,
+            "Cancer (excluding skin cancer) among adults aged >=18 years": field_names.CANCER_FIELD,
+            "Diagnosed diabetes among adults aged >=18 years": field_names.DIABETES_FIELD,
+            "Physical health not good for >=14 days among adults aged >=18 years": field_names.PHYS_HEALTH_NOT_GOOD_FIELD,
         }
         self.df.rename(
             columns=rename_fields,
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
index 51097cbe..af6b3c48 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
@@ -5,6 +5,7 @@ from data_pipeline.etl.sources.census_acs.etl_utils import (
     retrieve_census_acs_data,
 )
 from data_pipeline.utils import get_module_logger
+from data_pipeline.score import field_names
 
 logger = get_module_logger(__name__)
 
@@ -353,18 +354,29 @@ class CensusACSETL(ExtractTransformLoad):
             + df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE]
         ) / df[self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED]
 
+        # strip columns
+        df = df[self.COLUMNS_TO_KEEP]
+
         # Save results to self.
         self.df = df
 
+        # rename columns to be used in score
+        rename_fields = {
+            "Percent of individuals < 200% Federal Poverty Line": field_names.POVERTY_LESS_THAN_200_FPL_FIELD,
+        }
+        self.df.rename(
+            columns=rename_fields,
+            inplace=True,
+            errors="raise",
+        )
+
     def load(self) -> None:
         logger.info("Saving Census ACS 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.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
 
     def validate(self) -> None:
         logger.info("Validating Census ACS Data")
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs_2010/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs_2010/etl.py
index 05d823a6..ebc98121 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_acs_2010/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs_2010/etl.py
@@ -5,6 +5,7 @@ from data_pipeline.etl.sources.census_acs.etl_utils import (
     retrieve_census_acs_data,
 )
 from data_pipeline.utils import get_module_logger
+from data_pipeline.score import field_names
 
 logger = get_module_logger(__name__)
 
@@ -149,15 +150,6 @@ class CensusACS2010ETL(ExtractTransformLoad):
             + df["C17002_007E"]
         ) / df["C17002_001E"]
 
-        # Save results to self.
-        self.df = df
-
-    def load(self) -> None:
-        logger.info("Saving Census ACS Data")
-
-        # mkdir census
-        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
         columns_to_include = [
             self.GEOID_TRACT_FIELD_NAME,
             self.UNEMPLOYED_FIELD_NAME,
@@ -166,7 +158,7 @@ class CensusACS2010ETL(ExtractTransformLoad):
             self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
         ]
 
-        output_df = self.df[columns_to_include]
+        output_df = df[columns_to_include]
 
         # Add the year to the end of every column, so when it's all joined in the
         # score df, it's obvious which year this data is from.
@@ -178,7 +170,26 @@ class CensusACS2010ETL(ExtractTransformLoad):
                     }
                 )
 
-        output_df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
+        # rename columns to be used in score
+        rename_fields = {
+            "Percent of individuals < 100% Federal Poverty Line in 2010": field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
+        }
+        output_df.rename(
+            columns=rename_fields,
+            inplace=True,
+            errors="raise",
+        )
+
+        # Save results to self.
+        self.df = output_df
+
+    def load(self) -> None:
+        logger.info("Saving Census ACS 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)
 
     def validate(self) -> None:
         logger.info("Validating Census ACS Data")