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2022 NATIONAL
AMBULATORY MEDICAL
CARE SURVEY HEALTH
CENTER (NAMCS HC)
COMPONENT TECHNICAL
DOCUMENTATION
For Public Use Data File
Division of Health Care Statistic
Natio aay nter for Health Statistic
Ma va
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Overview Summary
This document provides detailed information and guidance for users of the 2022 National Ambulatory
Medical Care Survey Health Center (NAMCS HC) Component public use data file. As a principal source of
information on health care utilization in the United States, the NAMCS HC Component collects visit data
from a nationally representative sample of U.S. federally qualified health centers (FQHCs) and FQHC
look-alikes through electronic health record (EHR) data submission. The 2022 NAMCS HC Component is
conducted by the National Center for Health Statistics (NCHS) and is a member of the National Health
Care Surveys — a family of surveys which measure health care utilization across a variety of health care
providers and settings.
Section 1 of this document includes information on the scope of the survey, the data sources, and the
confidentiality protections related to the data. Section 2 contains details on the sampling process, data
collection procedures, and weighting methodology used to produce national estimates on health care
utilization. Section 3 provides information on the number of sampled health centers that were eligible to
participate in the NAMCS HC Component and submitted data in 2022. Section 4 details the contents of
the 2022 NAMCS HC Component public use data file and the edits used in the creation of the file.
Section 5 contains an explanation of the procedures used to accurately produce variance estimates.
NCHS presentation standards for proportions, counts, and rates, and their relation to NAMCS HC
Component data, are discussed in Section 6, and the data analysis guidelines are provided in Section 7.
Section 8 provides information on item missingness, and Section 9 provides a comparison of frequencies
between the NAMCS HC Component public use and restricted use data files. Section 10 provides a list of
preferred reporting items for complex sample survey analysis. Section 11 provides further information
on the availability of NAMCS HC Component restricted use data files available in NCHS and Federal
Research Data Centers. Appendix A provides unweighted frequencies for selected variables included on
the public use data file.
Suggested Citation
Technical Documentation: National Center for Health Statistics. Division of Health Care Statistics. 2022
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component Public Use Data File
Documentation, May 2024. Hyattsville, Maryland.
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Data File: National Center for Health Statistics. Division of Health Care Statistics. 2022 National
Ambulatory Medical Care Survey Health Center (NAMCS HC) Component public use data file. 2024.
Hyattsville, Maryland.
Contact Information
Data users can find the latest information about the NAMCS HC Component on our website, at:
https://www.cdc.gov/nchs/ahcd/namcs_ index.htm. If data users have queries about the public use data
file, they may send their question through email to ambcare@cdc.gov, or call us at 301-458-4600. A
response to data user inquiries is generally provided in 1-2 business days.
The National Center for Health Statistics has an ambulatory health care data listserv, where updates and
information about the most recent ambulatory care data (including the NAMCS HC Component) are sent
out. Details on how to subscribe to the NCHS Listserv for ambulatory health care data can be found at:
https://www.cdc.gov/nchs/ahcd/ahcd listserv.htm.
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Contents
Section 1 About the National Ambulatory Medical Care Survey Health Center Component ..............006 6
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Section 2.2 Health Center Frame and Sample Design. ...............:cccccssccccessteeesesseeesenseeeessnueeeesenueeeesennneeenes 8
Section 2.3 NAMCS HC Component Public Use Data File Sample Design .................:cccsscccsessteeesesteeeres 9
Section 2.4 Data Collection Procedures. ............e:ccceccceessecteneeeeeeeessaeeseaaeeeeneeeeaeeseaaeeeeneeeneeseaaeeneaeereeesenas 10
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Section 3 Sample Size, Eligibility, and Response Rate .............cccsssssccecsssssceccsssecccsssscescessseeseesseseseesseees 12
Section: 4 Data: PrOCeSSING iicciissssecccccssdadessctvestsstsdsesssavescoutiveedstiebcccotsstunsscesscccovsedssscusessossssunesvecdessdeseer 13
Section: 4:1. Diagnosis, Data wici.veviscecsescadesseiccccseencecesdeaceccvsascacevavicccdvenacadesdanteccvcaltaceveehinedesentedevdeatebevareceeds 13
SOCtiON: 4:2 PAtiemt Age ies iii cc2. cei sdcedivesdyvareSageed cunsseihastel cnddanavalawtage sae debuste uelnateedcensvaubaadugeeeddnseaseaaiateeddanseas 14
Section: 4.3: Patient SON ss szec: siss2ecciaesenctze vse cecnagsceng caps etiendesees tua esedonanesdene saved anageseestuessedetanescens dees eeanaaeseeatans 14
Section 4.4 Patient Race and Hispanic Ethnicity ...................ccccccccsssceccessneecseseneecssseneeessseeeesseeneeerseeneees 14
Section 4.5 Patient Marital Status .2....0. ccc ceceeeseceeeeeeeeeeecaaeeeeaaeseeeeecaeeesaaeseeaeeseeeeesaeeeeaaeeneaeeeea 14
Section 4.6 Visit Month and Day ..............:ccccccescccseseseecsessneeessseeeessseseeessseneeesseenaecssseseeesseesaeeseneaeenseenaees 14
Section 5 Standard Errors and Variance Estimation .............cssscccccscsssesesseeeeeseceesseeeeeeeeseeeecsseeseeseeseeeaes 15
Section 5.1 Subpopulation Analysis — Subsetting Data.................cccccccccescecesseneecseseneeesssseeersseeeesseenaees 15
SECTION 6 Presentation Standards iaisssesissccrccccssvsacssscessscacnandieccesssnasnsesssdecescvadnsensvesccccecadsesséenencsasnacese 16
Section 7 Data Analysis Guidance sisiiiscissscsscccacssccsssccnveceassctessscsecteasescsdssccssscoasesesesscessvenssestsssecdsscousese 17
Section: 7:1-Visit' Weight). ..ciiieczivcicivegeccudiagedieivateedeudeein ivvveahs cevneeitivisabbiswendett oiweghs ceiecelistestieaeeneencesgnens 17
Section 7.2 Guidance on Weight Normalization ..............::cccccsccccessseecesseneecseeseecssseseeesseeeeeseeeeeenseenaees 18
Section 7.2.1 Normalization Example...............cc:cccccssccccessseeeceesseeeeeenseeeeseseeeesenaeeseseseeseseneeeeneneeeenss 19
Section 7.3.1 Normalization Example Code .............ccccccccssccccecsseeeeeenseeeeeeseeeeseneeeeseneeseeeseeseeeneeeenes 22
Section 7.3 SAS SUDAAN Survey Procedules .............cccccccccsssssccsessneeeseseneeeseseeecsseeseeesseesaeesseenaeessnenaees 23
Section 7.3.1 NEST Statement Variables ..0..........ccecccesceeeeeeeeseeteseeeeeneeeeaeeseaaeeeeneeeneeseaaeeeeaeeseeeeeaas 24
Section 7:4:SAS Survey: Proc@QureS s..:issesssieisssocueeseustdevsacetunvsantacssacetauesstencedscatachvcunnacdescecaveveuntdavencatans 24
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Section 7.6 Stata Survey Procedules .............cccccccsssseccssssseecseseneeessceeeeseseeeeseeeeeessceseeesseesaeeseeeaeenseenaees 26
Sections Survey CONTENE sce iisecsscccesesecsstecsetecsdecesicescteccassiseesctauwistoxsseceesceuseceasesesecscdecrecssscesteeaseepesdese 26
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 8.1 Demographic Item Missingness Rate .................cccccccscccsesssessseeeeeesseesecssaeeseeesseeseaeaeesesesees 26
Section 8.2 Diagnosis Item Missingness Rate. ..............0.cccccccecesscceceeeeeeeeeeaaeaeceeeeeseesaaaeaeeeeeeeeeeeeaeeeeeeeess 27
Section 9 Data: COMPAMiSON sie. .ccsssccesdsseecendecscssecceeescecevccesccosevcesdevcosscceuevccasvsosesccueesadsvessescsuseeesssveesess 29
Section 9.1 Public Use Data Files and Restricted Use Data File ...............cceeeccecceeeseeeeeeeeeeeeeeneeerseeteaas 29
Section 10 Preferred Reporting Items for Complex Sample Survey Analysis (PRICSSA) Checklist for the
2022 NAMCS HC Component Public Use Data File ...............ccssssccccssssccccsssceccssceccenssecceecsssessecssseseaeens 33
Section 11 Research Data Center ...........cccccssessssssesececeesecscseeeeeeseeeeccseseseeeseeeacscseseseeeseseueseeeeseeeseauacsess 34
SECTION 12 RETENCNCES wscssccccedssisesscsecscccsestnesnsascasssssacsanwnabaceedssi disawmansdentssdannawadsccsdéctanmnbeacsdessessananbosess 35
Appendix A Unweighted frequencies for health center ViSits............csssccssssccsssscssccssesccsesccssseseesceeees 36
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 1 About the National Ambulatory Medical Care Survey Health
Center Component
Section 1.1 Background
The National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component is an annual
survey that provides data on health care utilization at health centers in the United States. As a part of
NAMCS, the National Center for Health Statistics (NCHS) began collecting data from health centers in
2006. A separate sample of health centers was drawn in 2012 for NAMCS. In 2021, NCHS redesigned the
NAMCS HC Component to collect visit data from electronic health records (EHRs) from participating
health centers for the entire calendar year.
The NAMCS HC Component collects data on health center visits including information on diagnoses and
patient demographics. The survey aims to provide health trends and outcomes of the U.S. population’s
utilization of health centers in the following ways:
e Provide nationally representative, accurate, and reliable health care data for health centers in
the United States.
e Answer key questions of interest to health care professionals, researchers, and policy makers
about health care quality, use of resources, and disparities of services to population subgroups.
e Monitor national trends in health care topics for which health centers play an important role,
such as mental health and substance use-related care, maternal and child health, and HIV-
related care.
e Contribute to a stronger public health foundation that helps address current and future public
health threats.
In 2022, the entire sample included 324 federally qualified health centers (FQHC) and FQHC look-alikes
in the 50 U.S. states and the District of Columbia that used an EHR system. Out of the entire sample, 104
health centers were included in the primary sample and 220 health centers made up the reserve
sample. Ultimately, 255 health centers were contacted and 64 health centers agreed to participate and
provided visit data from their EHRs. Out of the 64 responding health centers, 26 continued participation
from 2021 and 38 health centers were newly recruited in 2022. For more detailed information regarding
the sample frame, see Section 2.2.
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Overall, 5,640,370 health centers visits were collected from the 64 responding health centers. Of these,
282,017 health center visits were selected to create the 2022 NAMCS HC Component public use data
file.
Section 1.2 Data Sources
The NAMCS HC Component receives data from EHR systems. Participating health centers submit EHR
data, which contain an unlimited number of medical diagnosis and procedure codes, laboratory and
medication data, and unstructured clinical notes. However, the public use data file will only include
diagnosis variables and demographic information. The NAMCS HC Component accepts EHR data in the
format of HL7 CDA® R2 Implementation Guide: National Health Care Surveys Release 1, DSTU Release
1.2 — US Realm (http://www.hl7.org/implement/standards/product_brief.cfm?product_id=385).
However, some EHR vendors are not able to format their data in the HL7 CDA format as specified in the
National Health Care Surveys Implementation Guide. Alternatively, these centers were able to submit
their EHR data as custom extracts, which contained many (but not all) data elements extracted via the
above implementation guide.
Section 1.3 Data Confidentiality
NCHS and its agents take the security and confidentiality of NAMCS HC Component public use data file
very seriously. Strict laws have been implemented to establish minimum Federal standards for
safeguarding the privacy of individually identifiable health information. Assurance of confidentiality is
provided to all health centers according to Section 308(d) of the Public Health Services Act [42 United
States Code 242m (d)]. Strict procedures according to Section 3572 of the Confidential Information
Protection and Statistical Efficiency Act (44 U.S.C. 3561-3583) are utilized to prevent disclosure of
personal identifiable information in NAMCS HC Component data. All information which could identify a
participating health center is confidential and seen only by persons associated with NAMCS HC
Component, and is not disclosed or released to others for any other purpose. Prior to the release of
public use data file, NCHS conducts extensive disclosure risk analysis to minimize the chance of
inadvertent disclosure. As a result, selected characteristics and/or data elements may have been
omitted or masked on the public use data file to minimize the potential risk of disclosure. Masking was
performed in such a way to cause minimal impact on the data. See Section 4: Data Processing for more
information on which data elements in the public use data file were impacted.
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
The protocol for NAMCS HC Component has been approved by the NCHS Research Ethics Review Board
since the survey’s establishment (2006).
Section 2 Methodology
Section 2.1 Brief Overview
The 2022 NAMCS HC Component used a national probability sample of health centers to collect data on
visits to develop the public use data file. The 2022 NAMCS HC Component public use data file sample
was designed to allow for nationally representative estimates of visits at health centers in the United
States.
Section 2.2 Health Center Frame and Sample Design
The 2022 NAMCS HC Component identified a targeted universe of FQHCs and FQHC look-alikes in the 50
U.S. states and the District of Columbia that provide direct ambulatory care and use an EHR system at
one or more delivery sites. Health centers that were fully or partially funded by the Health Resources
and Services Administration (HRSA) were considered for inclusion. Health centers were deemed
ineligible if they:
° Did not have an EHR system
e Did not provide healthcare services to the general U.S. population, or only provided care to
special institutionalized populations such in prisons, nursing homes, homeless shelters, etc.
) Only provided dental services
) Were located on a military installation or outside of the 50 U.S. states and the District of
Columbia
To create the sampling frame and draw the sample, NCHS worked with the HRSA to use a nationally
representative database that contains a list of all health centers in the United States. The database
contained 1,482 health centers for the 2022 NAMCS HC Component. To create the sampling frame from
this database, ineligible health centers were removed. This included 64 health centers that did not meet
the inclusion criteria described above and 149 health centers that were included in the 2021 sample.
This process yielded a sampling frame of 1,269 eligible health centers.
In 2021, a stratified random sample of 50 FQHCs and FQHC look-alikes was drawn as the primary
sample, along with a reserve sample of 100 health centers. The 2022 NAMCS HC Component sample was
expanded to initially add 60 respondent health centers to the 50 respondent health centers from the
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
2021 sample, resulting in 110 FQHCs and FQHC look-alikes making up the 2022 NAMCS HC Component
sample. However, 54 health centers were ultimately fielded due to budget constraints. Due to this, six
randomly selected health centers were removed from the sample in four strata. In 2022, an additional
120 additional health centers were selected for the reserve sample (Williams et al., 2023).
Ultimately 255 health centers were contacted to participate in the 2022 NAMCS HC Component, which
includes 64 respondents and 191 eligible non-respondents. The 64 participating health centers include
26 health centers from the 2021 sample and 38 health centers from the 2022 sample. Weighting was
conducted to produce health center-level and visit-level estimates. Data were collected for 100% of
visits from the sampled health centers via EHR submission.
Section 2.3 NAMCS HC Component Public Use Data File Sample Design
While the NAMCS HC Component restricted use data file includes every health center (HC, visit record
submitted to NAMCS HC Component for the survey year, the 2022 NAMCS HC Component public use
data file consists of a5% sample of NAMCS HC Component visit data. This 5% sample of NAMCS HC
Component records was selected for the public use data file instead of the full listing of records to
decrease disclosure risk and increase efficiency for data users when conducting statistical analyses.
In 2022, the NAMCS HC Component collected 5,640,370 visit records. Stratified systematic sampling was
used to select the public use data file sample of health center visits. A targeted number of records was
determined by taking 5% of the total health center visit records (n=282,017). The sampling interval was
the inverse of the percent of submitted EHRs targeted for inclusion in the subsample. The sampling
interval used to select the public use data file records in the 2022 NAMCS HC Component was 1/0.05, or
20. Within each estimation stratum, participating health centers were randomly ordered. Within each
health center, visits were then sorted by the following variables:
Visit Week > Day of Week
Once sorted, visits were serially numbered in each estimation stratum. Next from the ordered array of
HC; records, visits were selected for the public use data file sample if the assigned “array sequential”
numbers were the nearest integer greater than or equal to:
Ry + Int(EHR), x k
Where:
Ry = random number between 0 and Int(EHR);
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
k=0,1;2, 3.
Int(EHR)r, = sampling interval
Section 2.4 Data Collection Procedures
In 2022, health centers submitted EHR data via two sources, either directly from the health centers’ EHR
system or as a custom extract, as mentioned above in Section 1.2. Once data were collected, several
steps were required for data processing. Specifications for checking, configuring, and transmitting the
data files were developed by NCHS. Once NCHS received the data files they were processed to
harmonize data from the two data sources. All records from participating health centers’ EHRs were
brought into the restricted database, and those records were then collapsed so that a given patient
could only have one record (called a visit in the PUF) per day at a given health center.
Section 2.5 Weighting
Weighting was conducted to produce health center-level and visit-level estimates, and to account for
sampling probabilities and nonresponse. Only visit-level weights are included in the public use data file,
and users are only able to produce visit-level estimates with this file.
Health center-level data were collected via self-completed forms from participating health centers. All
2022 health center visits were collected from the sampled health centers via electronic files of their EHR
system. Participating health centers submitted data for all visits that occurred during the 2022 calendar
year. While the 2022 NAMCS HC Component restricted use data file includes all (100%) of the visit
records sent, the public use data file includes a 5% sample of those records, as described in Section 2.3.
All health center visit data collected for 2022 were used to develop weights. To produce visit-level
weights, health center-level weights were first developed and smoothed. The visit-level weights were
then developed for the restricted use file that includes all visits from participating health centers. These
visit weights were formulated as the final health center weight multiplied first by the health center’s
actual annual number of visits made for medical care followed by a partial non-response adjustment
factor. Visit weights for all visits were then smoothed before they were finalized. Because the public use
data file only contains a 5% sample of all visits submitted in the 2022 NAMCS HC Component, visit
weights for visits included on the public use data file were adjusted accordingly. This ensures that
weighted estimates from the restricted use file and the public use data file sum to approximately the
same total number of weighted visits at health centers in the survey year.
10
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Variance estimation procedures for weighted estimates are described further in Section 5 with coding
examples in Section 7, and comparisons of weighted estimates between the restricted and public use
data files in Section 9.
11
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 3 Sample Size, Eligibility, and Response Rate
All 255 health centers that were contacted for participation were eligible to participate in the survey.
Ultimately, 64 health centers participated in the 2022 NAMCS HC Component yielding a response rate of
25.1%. As described in Section 2.2, 54 health centers in 2022 were added to the 50 health centers
selected in 2021, totaling to 104 health centers in the 2022 NAMCS HC Component sample. With this
target of recruiting and securing 104 health centers to participate in the 2022 NAMCS HC Component,
64 ultimately participated (or 61.5% of this targeted goal) ultimately agreed. A health center was
considered a full respondent if they provided data for at least six months of the survey year. Of the 64
participating health centers that were included in the 2022 NAMCS HC restricted use data file, all
provided at least six months of data. Therefore, all health centers were selected to create the public use
data file. From the 64 health centers, 5% of all records were selected for the public use data file. Overall,
282,017 health center visits were selected. Table 3.1 presents the number of health centers, visits, and
response rates for the 2022 NAMCS HC Component.
Table 3.1 Number of health centers, visits, and unweighted response rates, NAMCS HC Component,
2022
TOTAL
Health Centers Visits Unweighted Response
Rate*
Restricted Use Data File 64 5,640,370 25.1
Public Use Data File 64 282,017 N/A
Note: N/A is not applicable.
*Response Rate was calculated using American Association for Public Opinion Research (AAPOR) Response Rate 1 formula. The
percentage is a calculation of the eligible respondents and partial respondents (N=64) divided by the eligible respondents,
partial respondents, eligible non-responding and not contacted respondents (N=255).
12
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 4 Data Processing
The data included in the public use data file underwent additional processing to prepare them for
release. Suppression rules such as masking were applied for some records to protect patient
confidentiality. Other items were either top-coded or bottom-coded in accordance with NCHS
confidentiality requirements; this is noted for specific data items outlined in this section. Imputation was
not conducted for data elements with missing values prior to creation of the 2022 NAMCS HC
Component public use data file.
Section 4.1 Diagnosis Data
In the 2022 NAMCS HC Component, diagnosis data from participating health centers were submitted in
three different diagnosis coding systems including: International Classification of Diseases, Ninth
Revision, Clinical Modification (ICD-9-CM); International Classification of Diseases, Tenth Revision,
Clinical Modification (ICD-10-CM); and SNOMED Clinical Terms (SNOMED CT). In the creation of a
harmonized and integrated database, the ICD-9-CM and SNOMED CT diagnosis codes were translated to
ICD-10-CM, where applicable. Translation from ICD-9-CM and SNOMED CT to ICD-10-CM was the only
modification to the diagnosis codes. On the public use data file, medical diagnosis codes were limited to
ICD-10-CM diagnosis codes.
An ICD-10-CM code can have a maximum of 7 characters and is organized by chapters from A to Z. For
the 2022 NAMCS HC Component public use data file, ICD-10-CM codes have been truncated to four
characters to minimize disclosure risks. While the codes have been truncated, the diagnosis codes are
never updated or revised to a different code that would result in a change to the original diagnosis for a
visit. To maintain integrity of the data, any codes that appear to be invalid are kept as is.
Duplicate 4-character ICD-10-CM codes were removed for each unique visit on the public use data file.
Although visits collected from health center EHR systems could have had an unlimited number of
diagnosis records, diagnosis codes were limited to 30 unique codes per visit (variables DX1 through
DX30) in the public use data file, which captured 96.6% of diagnoses recorded at visits included on the
public use data file. Rarity of diagnoses was assessed and those deemed rare were truncated to two
characters.
At least one diagnosis code is listed in 62.0% of all visits. Six health centers did not provide any condition
codes that could be translated to ICD-10-CM, therefore do not have any visits that include at least one
13
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
condition code in DX1-DX30. Of the 58 health centers that provide any codes that translated to ICD-10-
CM, 74.4% of their visit have at least one diagnosis code in the public use data file.
Section 4.2 Patient Age
Patient age is present for all visits in the 2022 NAMCS HC Component public use data file. Visits were top
coded to the 99.5" percentile of age, thus visits by patients ages 88 and older were top coded to 88
years.
Section 4.3 Patient Sex
Patient sex is missing in 0.1% of records on the 2022 NAMSC HC Component public use data file.
Section 4.4 Patient Race and Hispanic Ethnicity
Patient race is missing from 24.5% of records on the 2022 NAMCS HC Component public use data file.
Eleven health centers are missing patient race for all visit records. Excluding the 11 health centers with
complete missingness, 17.7% of visits are missing patient race.
Patient ethnicity is missing from 12.9% of records on the 2022 NAMCS HC Component public use data
file. Ten health centers are missing patient ethnicity for all visit records. Excluding the 10 health centers
with complete missingness, 6.6% of visits are missing patient ethnicity.
Section 4.5 Patient Marital Status
Marital status of patients is included in the public use data file but is missing from 20.9% of records
overall. Ten health centers are missing marital status from all visit records. For the remaining 54 health
centers, marital status is missing from 15.2% of visits.
Section 4.6 Visit Month and Day
Exact dates are not provided on the NAMCS HC Component public use data file. Instead, only the month
and day of the week of health center visits are provided.
14
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 5 Standard Errors and Variance Estimation
Standard error is primarily a measure of the sampling variability that occurs by chance because only a
sample of health centers are in NAMCS HC Component, rather than the entire universe of health
centers. Standard errors and other measures of sampling variability are best determined by using a
statistical software package that takes into account the sample designs of surveys to produce such
measures.
See Section 7 for further guidance on how to apply weights and calculate standard errors to generate
national estimates.
Section 5.1 Subpopulation Analysis — Subsetting Data
For data users who may have a subpopulation of interest, such as a particular age group or sex, a
domain analysis must be performed, also known as a subgroup or subpopulation analysis.
For some variance estimation methods, the entire set of data containing the appropriate weights for a
particular survey year must be used to obtain the correct variance estimates. Therefore, it is not
recommended to drop observations from the dataset when subsetting data, as it may affect variance
estimation. Instead, the estimation procedure must indicate which records are in the subgroup of
interest. For example, when examining female patients aged 35 and over, the entire dataset of
examined individuals (both male and female patients of all reported ages) must be read into the
statistical software program.
The STAT and DOMAIN statements in the SAS survey procedure, SUBPOPN in SAS callable SUDAAN, or
comparable statements in other programs (SUBSET in R; subpop or over in Stata) must be used to
indicate the subgroup of interest (i.e., females aged 35 and over).
Depending on the specifications of a data user’s statistical software of choice, an indicator variable
created by the data user prior to the procedure may facilitate the identification of the subgroup in the
procedure statements.
15
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 6 Presentation Standards
Data users should be aware of the reliability of survey estimates, particularly smaller estimates. NCHS
has published standards for the assessment of reliability and presentation of proportions (or
percentages) (https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf) and for the presentation of
rates and counts (https://www.cdc.gov/nchs/data/series/sr_02/sr02-200.pdf). For presentation or
publication of count estimates using data from the NAMCS HC Component, we recommend visit
estimates be rounded to the nearest thousand.
These presentation standards apply to products published by NCHS. If, according to the presentation
standards, an estimate is not reliable, data users should examine the confidence interval carefully
before using the estimate.
16
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 7 Data Analysis Guidance
The following section provides an overview on how data users can derive visit estimates and compute
variances to produce standard errors, using statistical software tools such as SAS, R, and Stata. For the
NAMCS HC Component public use data file, SAS-callable SUDAAN software procedures are used for
survey analysis, however, SAS/STAT software procedures beginning with SURVEY for survey analysis may
also be used. R relies on the “survey” package to conduct survey data analysis whereas Stata, uses the
“svy” command. SAS/SUDAAN, R and Stata users can use these procedures to conduct statistical analysis
on data from the 2022 NAMCS HC Component public use data file. Additionally, this section provides
guidance on normalizing visit weights to account for complete missingness for analytic variables of
interest. The guidance provides data users a framework to implement normalizing weights for data
analysis. Data users should always investigate if there are any variables of interest that have complete
missingness at health centers in the 2022 NAMCS HC Component public use data file.
Section 7.1 Visit weight
The visit weight is a critical component in the process of producing estimates from sample data and its
use should be clearly understood by all data users. The statistics contained on the public use data file
reflect only a sample of visits; a 5% sample of the NAMCS HC Component data collected from
participating health centers, not a complete count of all visits that occurred in the United States. Each
health center’s visit record in the public use data file represents one patient visit in the sample of
282,017 visits. To obtain national estimates from the 5% sample, each record is assigned an inflation
factor called the "visit weight” (variable VISWT in the public use data file).
By aggregating the “visit weights" assigned to the VISWT variable on the 282,017 health center visits for
2022, the data user can obtain the estimated total of 109,087,913 health center visits (standard error of
19,896,515 health center visits) made in the United States in 2022.
Note that estimates of health center visits produced from the 2022 NAMCS HC Component public use
data file may differ somewhat from those estimates produced from the 2022 NAMCS HC Component
restricted use data file. This is because of adjustments required for the public use data files, as part of
the disclosure risk mitigation process. Certain variables were masked on some records for confidentiality
purposes. Other variables were top and/or bottom coded in accordance with NCHS confidentiality
requirements.
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
The table in Section 9 compares aggregate unweighted and weighted data for selected variables
between the 2022 NAMCS HC Component public use data file and restricted use data file.
Section 7.2 Guidance on Weight Normalization
Some health centers did not provide certain data elements for any of their visits in the 2022 data year.
In certain situations, some health centers needed to produce custom extracts of their records to
conform with the format needed for processing as specified in the HL7 CDA Implementation Guide.
Therefore, not all data elements were required of health centers providing custom extracts. In other
situations, even for health centers providing data via the IG, certain variables were incomplete for all
visits at specific health centers.
Regardless of the reason for missingness, data users must identify health centers that have complete
missingness for specific analytic variable(s) of interest, and exclude those health centers’ visits from
analysis. Additionally, if certain health centers’ visits must be excluded, users must normalize the weight
variable (VISWT) so that the sum of weights of visits in the analysis is equal to the sum of weights of all
visits in the 2022 NAMCS HC Component public use data file.
Steps for a complete case analysis:
1. Identify health centers to be included in your analysis:
a. Identify variable(s) required for your analysis
b. Identify health centers that are missing values at ALL visits for at least one variable of
interest from Step 1a
c. Subset all visits from health centers identified with complete missingness for at least
one variable of interest, as identified in Step 1b above.
NOTE: This process does not eliminate all missingness, rather it eliminates complete missingness of a
specific variable for a specific health center. Health centers that are included may still have some visits
with missing information for the variables of interest, but this process removes visits at health centers
that did not provide any information for variables of interest.
2. Normalize weights if only a subset of health centers’ visits is included:
a. Calculate the sum of weights for all visits in the public use data file. In 2022, the sum of
weights (VISWT) is 109,087,913.
b. Calculate the sum of weights for visits at health centers to be included in your analysis.
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
c. Calculate the normalization factor [X] by dividing the sum of weights for all visits in the
survey (from step 2a) by the sum of weights for visits in your analysis (from Step 2b),
and the value of X from this calculation is the factor you will use to normalize your
weights.
i. X= [sum of all visit weights] / [sum of visit weights in your analysis]
1. NOTE: X will always be greater than 1.
d. Create a new weight variable for visits in your analysis by multiplying the original weight
variable by your normalization factor (X).
i. NEW_WT=VISWT * X
e. Use NEW_WT for your analysis in place of VISWT.
NOTE: If you add or subtract variables from your analysis, or you develop a new research question and
analysis, you must conduct these steps again to ensure that you: 1) capture visits from health centers
providing data on your variables of interest, and 2) normalize those visits’ weights accordingly.
Table 7.1 Variables that contain health centers with complete missingness in the 2022 NAMCS HC
public use data file
Variable Name Variable Description HCID_S to exclude
DX1-DX30 Diagnoses 1-30 22, 26, 42, 46, 60, 62
ETHNICITY Patient Hispanic ethnicity 4, 11, 12, 18, 20, 23,
25, 30, 47, 63
MARITAL Marital status 4,11, 12, 18, 20, 23,
25, 30, 47, 63
RACE Patient race 4, 11, 12, 18, 20, 23,
25, 29, 30, 47, 63
RACERETH Combined race and ethnicity variable 4,11, 12, 18, 20, 23,
25, 29, 30, 47, 63
Section 7.2.1 Normalization Example
The example below will showcase the differences in estimates when normalizing the 2022 NAMCS HC
public use data file for visits with a mental health disorder and race as opposed to not normalizing. This
example will provide context on normalizing weights when assessing complete missingness for two
variables on the public use data file (DX1 and RACE).
Before following the steps for a complete case analysis, it is helpful to assess the unweighted and
weighted number of visits for all 64 health centers in the public use data file, as shown in Table 7.2.
There are 282,017 visits in the public use data file representing 109,087,913 health center visits.
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Table 7.2 Weighted and unweighted number of visits in the 2022 NAMCS HC Component public use
data file
Visits at all health centers
(N=64)
Unweighted 282,017
Weighted 109,087,913
In this example, assume the user wants to assess visits with a first-listed diagnosis (DX1) of a mental
health disorder, stratified by race (RACE) using the 2022 NAMCS HC Component PUF. For the purposes
of this example, a mental health disorder was classified as any ICD-10-CM code in the Mental,
Behavioral and Neurodevelopmental disorders chapter (FO1-F99). Please note that in this public use
data file, when DX1 is missing, all DX1-DX30 variables will be missing, so whether assessing first-listed or
any-listed diagnosis, we only need to assess complete missingness for DX1.
First, the user must identify all health centers that have complete missingness in either the race (RACE)
or first-listed diagnosis (DX1) variables (or both) from Table 7.1 above. In 2022, 17 health centers have
complete missingness in the DX1 or RACE variables. Health centers 22, 26, 42, 46, 60, 62 are missing DX1
at all visits. Health centers 4, 11, 12, 18, 20, 23, 25, 29, 30, 47, and 63 are missing RACE at all visits.
Therefore, 47 health centers make up the subset of data to analyze first-listed mental health diagnoses
by race. The normalization factor X should be calculated by dividing the sum of all visit weights
(109,087,913) by the sum of visit weights from the 47 health centers included in this example
(74,065,859). The normalization factor is (109,087,913/74,065,859) or approximately 1.47. The
normalization factor is used to create a new weight variable, which for this example is calculated as
NEW_WT=VISWT*(1.47). After calculating the normalization factor and creating a new weight variable,
the data user should apply the new visit weight variable to the subset of visits at the 47 health centers
included in this example. The total sum of weights in the analytic subset (sum of NEW_WT at HC visits to
be included) should be equal to the total sum of weights for all visits at all health centers in the NAMCS
HC public use data file as shown in Table 7.2.
At the 47 health centers identified for inclusion in this example, we identified visits with a first-listed
mental health ICD-10-CM diagnosis and race information. We then produced unweighted and weighted
estimates (using the normalized NEW_WT variable) of visits with a first-listed mental health diagnoses at
health centers in 2022. These estimates are detailed in Table 7.3 for users to replicate. Please note,
normalization of weights at the subset of visits to be included only impacts the weighted numerator and
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
weighted denominator estimates; the unweighted counts and weighted percentage will not change in
the same subset of visits due to weight normalization.
Table 7.3 Visits with a first-listed mental health diagnosis, with race and diagnosis information, in the
2022 NAMCS HC public use data file
Overall Non-Normalized subset Normalized subset
Subset without Subset with
Analysis Overall Data File Normalization Normalization correctly
implemented implemented
Number of health centers 64 47 47
Unweighted numerator 23,642 21,151 21,151
Unweighted denominator 282,017 211,452 211,452
Weight used VISWT VISWT NEW_WT!?
Weighted numerator 8,958,206 7,888,090 11,617,975
Weighted denominator 109,087,913 74,065,859 109,087,913
Weighted Percent (SE) 8.21 (1.69) 10.65 (1.58) 10.65 (1.58)
1 As described in Section 7.2.1, NEW_WT= VISWT *1.47, where 1.47 is the calculated normalization
factor.
In the first column of Table 7.3, the data is neither subset nor using a normalized visit weight. The
weighted numerator underestimates the weighted number of visits with a first-listed mental health
diagnosis and race, which also results in an underestimated weighted percent. In the second column,
the data is subset to exclude health centers with complete missingness but does not use the normalized
visit weight. This further underestimates the weighted number of visits with a first-listed mental health
diagnosis. Additionally, because of the use of the subset of health centers and a non-normalized visit
weight in the second column, the weighted denominator does not add up to the total number of visits in
the public use data file. The last column displays the correct analysis using the subset of health centers
and the normalized weight variable.
Using a subset of health centers and normalizing their weights produces a higher weighted numerator
than using all health centers and the non-normalized weight or using the subset of health centers and
the non-normalized weight. In the overall analysis in Table 7.3, visits at health centers with complete
missingness for diagnosis data are automatically considered to be non-mental health visits despite not
having enough information to discern whether there was a mental health diagnosis. Consequently, the
overall weighted numerator is an undercount of visits with a first-listed mental health diagnosis at
health centers in the United States.
In short, normalizing weights may produce different estimates when analyzing the 2022 NAMCS HC
Component public use data file depending on the number of health centers that are included in the
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
analysis. Without excluding health centers with complete missingness and subsequently normalizing
visit weights, data users will underreport counts and rates for their analysis of interest. Data users
should consider the full scope of their research question to make decisions on the subset of health
centers to include, and how normalizing visit weights will impact the calculation of estimates. Data users
should reference Table 7.1 to ensure the correct health centers are excluded in their analysis when
normalizing weights in a complete case analysis.
Section 7.3.1 Normalization Example Code
For further assistance in implementing normalization on the 2022 NAMCS HC Component public use
data file, the following SAS code replicates the normalization example described in Section 7.2.1.
*STEP 1;
*Identify the variables of interest for your analysis;
*Research Question: First listed diagnoses of mental health by age and race;
*Variables needed: DX1, RACE;
*In this example you will need to subset the data where DX1 is missing or RACE
is missing according to Table 7.1;
*DX1 is missing where HCID_S in (22, 26, 42, 46, 60, 62);
*RACE is missing where HCID_S in (4, 11, 12, 18, 20, 23, 25, 29, 30, 47, 63);
*STEP 2;
*Calculate two sums:
1. the sum of weights at all HCs in the original datafile and
2. the sum of weights at HCs to be included in your analysis;
*1. Overall sum of weights;
proc sql;
create table sum_total as
select sum(viswt) as sum_total
from /*[full datafile]*/;
quit;
proc print data=sum_total;
run;
*2. Subset sum of weights;
proc sql;
create table sum_subset as
select sum(viswt) as sum_subset
from /*[full datafile]*/
where HCID_S not in (4, 11, 12, 18, 20, 22, 23, 25, 26, 29, 30, 42, 46, 47, 60, 62, 63);
quit;
proc print data=sum_ subset;
run;
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
*STEP 3;
*Create two new variables for your analysis:
1. anormalized weight, using sum_total and sum_subset calculated in step 4 and
2. an inclusion indicator where the record is at a PSU identified in ‘all_ three' from STEP 3 above;
data /*new_datafile*/;
set /*[full datafile]*/;
new_wt=viswt*(/*[value of sum_total]/[value of sum_subset]*/);
if HCID_S notin (4, 11, 12, 18, 20, 22, 23, 25, 26, 29, 30, 42, 46, 47, 60, 62, 63)
then include=1;
else include=2;
if "FO1"<substr(DX1, 1, 3)<"F99"
then mntihith=1;
else mntlhith=0;
run;
*STEP 4;
*Use these two variables (new_wt and include) in all procedures used to produce weighted output for
this analysis;
*Note: this step shows a SUDAAN procedure for setting up a weighted analysis, but an example of a SAS
procedure is provided below in Section 7.4;
*First, sort the data by STRATUM and HCID_S;
proc sort data=/*[new datafile]*/;
by STRATUM_S HCID_S;
run;
*Second, set up your SUDAAN statement as follows (showing a crosstab procedure);
proc crosstab data=[new_datafile] filetype=sas design=wr atlevel1=1 atlevel2=2;
nest STRATUM_S HCID_S / MISSUNIT;
weight new_wt;
subpopn include=1;
class mntlhith; *include analytic indicators/variables to cross;
tables mntlhith; *cross your class variables in the desired order;
output nsum wsum sewsgt totper setot atlev1 atlev2 / filename = /*[output dataset]*/ replace
tablecell=default;
run;
Section 7.3 SAS SUDAAN Survey Procedures
The program below demonstrates how to set up your design and weight variables to produce weighted
estimates using the 2022 NAMCS HC Component public use data file:
PROC (procedure) DATA=(input data set) FILETYPE=SAS DESIGN=WR ATLEVEL1=1 ATLEVEL2=2;
NEST STRATUM_SHCID_S/ MISSUNIT;
*SUBPOPN (variable1) = (value); *Only use subpopn statement if needed;
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
WEIGHT VISWT; *or replace with your normalized weight if required for your analysis;
CLASS (variable2);
TABLES (variable2);
OUTPUT nsum wsum sewsgt totper setot atlev1 atlev2 / FILENAME=[output dataset] REPLACE
TABLECELL=DEFAULT;
RUN;
In the above example, replace the parentheses with the information named in the parentheses. When
health centers are missing a data element for all visits, those health centers’ visits should be excluded
from your analysis. If a subset of health centers’ visits must be excluded due to complete missingness,
replace VISWT with normalized version of the weight, and add a SUBPOPN statement to correctly subset
to health centers’ visits of interest. Refer to Section 7.2 above, for more guidance on weight
normalization to account for complete missingness.
When using SAS-callable SUDAAN software, sort the input data set in the order specified in the NEST
statement, in this case by sampling strata (STRATUM_S) followed by health center identifier (HCID_S)
within STRATUM_S. If software other than SUDAAN is used to approximate the variances, other
statements will be required by that software. The variance variables required by that software are the
same as those include in the above example, which are further explained below in Section 7.3.1.
Section 7.3.1 NEST Statement Variables
The SUDAAN NEST statement for variances at the visit-level is:
NEST STRATUM_S HCID_S/ MISSUNIT;
Where:
STRATUM_S is the scrambled value of the original sampling stratum from which the health
center was selected.
HCID_S is the scrambled identifier for the health center.
Section 7.4 SAS Survey Procedures
The program below demonstrates how to calculate variance estimates using SAS SURVEYFREQ and
SURVEYMEANS procedures:
For categorical variables:
PROC SURVEYFREQ DATA = (input data set);
TABLE VAR1; *Replace “VAR1” with the categorical variable of interest.
CLUSTER HCID_S;
STRATA STRATUM_S;
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
WEIGHT VISWT; *or replace with your normalized weight if required for your analysis;
ODS OUTPUT ONEWAY=(name of output);
RUN;
For continuous variables:
PROC SURVEYMEANS DATA = (input data set);
VAR VAR1; *Replace “VAR1” with the continuous variable of interest.
CLUSTER HCID_S;
STRATA STRATUM_S;
WEIGHT VISWT;
ODS OUTPUT STATISTICS=(name of output);
RUN;
In the above example, replace the parentheses with the information named in the parentheses. When
health centers are missing a data element for all visits, those health centers’ visits should be excluded
from your analysis. If a subset of health centers’ visits must be excluded due to complete missingness,
replace VISWT with normalized version of the weight, and add a DOMAIN statement to correctly subset
to health centers’ visits of interest. Refer to Section 7.2 above, for more guidance on weight
normalization to account for complete missingness.
Section 7.5 R Survey Procedures
The R package “survey” can be utilized for complex survey analysis (https://cran.r-
project.org/web/packages/survey/index.html). The R programs below demonstrate how to install the
survey package, produce visit level weighted estimates, and calculate variance estimates.
install.packages(“survey”)
library(survey)
install.packages(“tidyverse”)
library(tidyverse)
#Using the “survey” package:
{variable name} <- svydesign(
ids = ~ HCID_S,
strata = ~ STRATUM_S,
weights = ~ VISWT,
data={input data frame})
Note: Replace curly brackets {} with the information named in the parenthesis
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 7.6 Stata Survey Procedures
The Stata programs below demonstrate how to use visit weights and calculate variance estimates with
the svyset command (https://www.stata.com/manuals/svysvyset.pdf)
For categorical variables:
/*Set survey design*/
svyset HCID_S [pweight = VISWT], strata(STRATUM_S)
/*Specify one-way tables, change “VAR1” to categorical variable of interest*/
tab VAR1
svy: tab VAR1, count se
svy: tab VAR1, percent
For continuous variables:
/*Set survey design*/
svyset HCID_S, [pweight= VISWT], strata(STRATUM_S)
/*Specify one-way tables, change “VAR1” to continuous variable of interest*/
svy: mean VAR1
Section 8 Survey Content
For the 2022 NAMCS HC Component public use data file, 77 variables were included; 60 (77.9%)
variables include information on medical diagnoses, 8 (10.4%) variables include patient demographic
information, 2 (2.6%) data items include visit information, and 7 (9.1%) variables include weights or
other survey-related information.
Please refer to the 2022 NAMCS HC Component public use data file codebook for detailed information
on the variables, including variable names, variable type, variable descriptions, and variable values.
Additionally, unweighted frequencies for selected variables on the public use data file are available in
Appendix A.
Section 8.1 Demographic Item Missingness Rate
In the 2022 NAMCS HC Component public use data file, four (5.2%) demographic variables had an
unweighted missingness rate that was greater than 5% including RACE, ETHNICITY, RACERETH and
MARITAL.
The variables in the table below had an unweighted item missingness percentage greater than 5%. As
explained in Section 7.2, some health centers contained complete missingness in certain variables. In
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Table 8.1, two denominators are presented to demonstrate missingness. First, is the percent missing
among all visits in all health centers (N=64) in the public use datafile. The second denominator is the
percent missing among all visits in all health centers that do not have complete missingness in the
diagnosis variable (N=54 or N=53).
Table 8.1 Percent missing (unweighted) for demographic variables in the NAMCS HC Component
public use data file with a missingness greater than 5%
Variable Name Variable Description % Missing % Missing*
(all visits)
RACERETH Patient Race and Ethnicity 12.04 4.133
ETHNICITY Patient Hispanic Ethnicity ee 6.587
RACE Patient Race 24.52 17,73"
MARITAL Marital Status 20.92 15,15°
1Denominators vary as percentages exclude health centers with complete missingness.
*N=54 health centers.
3N=53 health centers.
Section 8.2 Diagnosis Item Missingness Rate
In the 2022 NAMCS HC Component public use data file, 60 diagnosis variables (77.9%) had an
unweighted missingness rate that was greater than 5%. It is expected that most of the diagnosis
variables after the first-listed diagnosis variable will have a high missingness percentage as not all visits
are expected to have multiple diagnoses.
The variables in the table below had an unweighted item missingness percentage greater than 5%. As
explained in Section 7.2, some health centers contained complete missingness in certain variables. In
Table 8.2, two denominators are presented to demonstrate missingness. First, is the percent missing
among all visits in all health centers (N=64) in the public use data file. The second denominator is the
percent missing among all visits in all health centers that do not have complete missingness in the
diagnosis variable (N=58).
Table 8.2 Percent missing (unweighted) for diagnoses variables in the NAMCS HC Component public
use data file with a missingness greater than 5%
Variable Name Variable Description % Missing % Missing?
(All visits)
DX1 Diagnosis #1 (ICD-10-CM), diagnosis code 38.05 25.56
DX2 Diagnosis #2 (ICD-10-CM), diagnosis code 56.99 48.32
DX3 Diagnosis #3 (ICD-10-CM), diagnosis code 68.42 62.05
DX4 Diagnosis #4 (ICD-10-CM), diagnosis code 76.22 71.43
DX5 Diagnosis #5 (ICD-10-CM), diagnosis code 81.87 78.22
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
DX6 Diagnosis #6 (ICD-10-CM), diagnosis code 86.37 83.63
DX7 Diagnosis #7 (ICD-10-CM), diagnosis code 89.43 87.30
DX8 Diagnosis #8 (ICD-10-CM), diagnosis code 91.58 89.88
DXx9 Diagnosis #9 (ICD-10-CM), diagnosis code 93.19 91.81
DX10 Diagnosis #10 (ICD-10-CM), diagnosis code 94.35 93.21
DX11 Diagnosis #11 (ICD-10-CM), diagnosis code 95.26 94.31
DX12 Diagnosis #12 (ICD-10-CM), diagnosis code 96.00 95.19
DX13 Diagnosis #13 (ICD-10-CM), diagnosis code 96.55 95.85
DX14 Diagnosis #14 (ICD-10-CM), diagnosis code 96.98 96.37
DX15 Diagnosis #15 (ICD-10-CM), diagnosis code 97.33 96.79
DX16 Diagnosis #16 (ICD-10-CM), diagnosis code 97.62 97.14
DX17 Diagnosis #17 (ICD-10-CM), diagnosis code 97.85 97.42
DX18 Diagnosis #18 (ICD-10-CM), diagnosis code 98.08 97.69
DX19 Diagnosis #19 (ICD-10-CM), diagnosis code 98.27 97.92
DX20 Diagnosis #20 (ICD-10-CM), diagnosis code 98.42 98.10
DX21 Diagnosis #21 (ICD-10-CM), diagnosis code 98.55 98.26
DX22 Diagnosis #22 (ICD-10-CM), diagnosis code 98.68 98.42
DX23 Diagnosis #23 (ICD-10-CM), diagnosis code 98.79 98.54
DX24 Diagnosis #24 (ICD-10-CM), diagnosis code 98.89 98.67
DX25 Diagnosis #25 (ICD-10-CM), diagnosis code 98.98 98.77
DX26 Diagnosis #26 (ICD-10-CM), diagnosis code 99.06 98.87
DX27 Diagnosis #27 (ICD-10-CM), diagnosis code 99.13 98.95
DX28 Diagnosis #28 (ICD-10-CM), diagnosis code 99.19 99.02
DX29 Diagnosis #29 (ICD-10-CM), diagnosis code 99.25 99.10
DX30 Diagnosis #30 (ICD-10-CM), diagnosis code 99.30 99.16
DX_TYPE1 Diagnosis Type #1. Corresponds to Diagnosis #1 52.84 43.34
DX_TYPE2 Diagnosis Type #2. Corresponds to Diagnosis #2 68.28 61.89
DX_TYPE3 Diagnosis Type #3. Corresponds to Diagnosis #3 76.90 72.25
DX_TYPE4 Diagnosis Type #4. Corresponds to Diagnosis #4 82.66 79.17
DX_TYPES Diagnosis Type #5. Corresponds to Diagnosis #5 86.82 84.16
DX_TYPE6 Diagnosis Type #6. Corresponds to Diagnosis #6 89.91 87.87
DX_TYPE7 Diagnosis Type #7. Corresponds to Diagnosis #7 92.27 90.72
DX_TYPE8 Diagnosis Type #8. Corresponds to Diagnosis #8 93.97 92.75
DX_TYPE9 Diagnosis Type #9. Corresponds to Diagnosis #9 95.23 94.26
DX_TYPE10 Diagnosis Type #10. Corresponds to Diagnosis #10 96.14 95.36
DX_TYPE11 Diagnosis Type #11. Corresponds to Diagnosis #11 96.84 96.21
DX_TYPE12 Diagnosis Type #12. Corresponds to Diagnosis #12 97.41 96.88
DX_TYPE13 Diagnosis Type #13. Corresponds to Diagnosis #13 97.81 97.37
DX_TYPE14 Diagnosis Type #14. Corresponds to Diagnosis #14 98.14 97.77
DX_TYPE15 Diagnosis Type #15. Corresponds to Diagnosis #15 98.42 98.10
DX_TYPE16 Diagnosis Type #16. Corresponds to Diagnosis #16 98.65 98.38
DX_TYPE17 Diagnosis Type #17. Corresponds to Diagnosis #17 98.83 98.59
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
DX_TYPE18 Diagnosis Type #18. Corresponds to Diagnosis #18 98.99 98.79
DX_TYPE19 Diagnosis Type #19. Corresponds to Diagnosis #19 99.14 98.96
DX_TYPE20 Diagnosis Type #20. Corresponds to Diagnosis #20 99.24 99.09
DX_TYPE21 Diagnosis Type #21. Corresponds to Diagnosis #21 99.33 99.20
DX_TYPE22 Diagnosis Type #22. Corresponds to Diagnosis #22 99.41 99.30
DX_TYPE23 Diagnosis Type #23. Corresponds to Diagnosis #23 99.48 99.37
DX_TYPE24 Diagnosis Type #24. Corresponds to Diagnosis #24 99.54 99.45
DX_TYPE25 Diagnosis Type #25. Corresponds to Diagnosis #25 99.59 99.50
DX_TYPE26 Diagnosis Type #26. Corresponds to Diagnosis #26 99.63 99.56
DX_TYPE27 Diagnosis Type #27. Corresponds to Diagnosis #27 99.67 99.60
DX_TYPE28 Diagnosis Type #28. Corresponds to Diagnosis #28 99.70 99.64
DX_TYPE29 Diagnosis Type #29. Corresponds to Diagnosis #29 99.74 99.68
DX_TYPE30 Diagnosis Type #30. Corresponds to Diagnosis #30 99.76 99.71
1Denominators exclude health centers with complete missingness for all diagnosis variables (N=58
health centers).
Section 9 Data Comparison
Section 9.1 Public Use Data Files and Restricted Use Data File
Of the 64 participating health centers that were included in the 2022 NAMCS HC Component restricted
use data file, all 64 (100%) were selected to create the public use data file sample. The 2022 public use
data file contains 282,017 health center visits, for a weighted total of 109,087,913 health center visits
(standard error of 19,896,515 health center visits). The 2022 NAMCS HC Component restricted use data
file contains unweighted data from the same 64 health centers that submitted 5,640,370 health center
visits, for a weighted total of 109,088,618 health center visits (standard error of 19,896,579 health
center visits). A comparison of weighted frequencies for health center visits in the public use data file
and restricted use data file is presented in Table 9.1.
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
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National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Table 9.1 Comparison of frequencies for health center visits on the public use data file (weighted n=109,087,913) and
restricted use data file (weighted n=109,088,618) for NAMCS HC Component, 2022
Variable
Age (in years)
Unweighted
Count
Count
Public Use Data file
Weighted
Std. Error
Unweighted
Count
Restricted Use Data File
Count
Weighted
Std. Error
Under 1 6,024 2,288,302 411,698 2.1 122,534 2,308,440 412,527 2.1
1-17 years 45,064 17,211,377 2,923,236 15.8 905,192 17,253,666 2,930,383 15.8
18-44 years 99,228 38,421,536 6,790,823 35.2 1,975,554 38,304,878 6,780,308 35.1
45-64 years 85,517 33,488,962 6,413,249 30.7 1,710,991 33,399,450 6,414,163 30.6
65-74 years 31,066 12,025,463 2,740,872 11.0 620,942 12,081,788 2,733,533 11.1
75 years and 15,116 5,651,169 1,330,611 5.2 305,157 5,739,747 1,333,869 5.3
over
Missing <5 1,104 1,104 0.0 28 649 372 0.0
Sex
Male 105,699 41,028,536 7,177,835 37.6 2,109,098 40,899,996 7,149,913 37.5
Female 176,044 67,937,046 12,722,296 62.3 3,526,088 68,072,537 12,752,804 62.4
Missing 274 122,332 58,309 0.1 5,184 116,085 53,868 0.1
Visit month
January 24,403 9,637,222 1,869,987 8.8 488,037 9,637,161 1,869,843 8.8
February 21,269 8,241,409 1,480,435 7.6 425,460 8,242,383 1,480,504 7.6
March 23,017 9,133,987 1,749,641 8.4 460,333 9,133,975 1,749,420 8.4
April 22,177 8,627,374 1,644,153 7.9 443,632 8,628,944 1,644,429 7.9
May 22,267 8,655,310 1,672,140 7.9 445,243 8,652,907 1,672,003 7.9
June 23,038 8,871,436 1,658,288 8.1 460,800 8,873,100 1,658,351 8.1
July 21,794 8,487,764 1,601,614 7.8 435,793 8,485,961 1,601,677 7.8
August 25,695 9,795,541 1,818,491 9.0 513,933 9,795,745 1,818,375 9.0
September 25,793 9,840,784 1,783,007 9.0 515,871 9,841,231 1,783,090 9.0
October 24,277 9,494,903 1,754,538 8.7 485,482 9,493,768 1,754,615 8.7
November 24,980 9,379,282 1,669,960 8.6 499,563 9,493,768 1,669,787 8.6
December 23,307 8,922,901 1,666,742 8.2 466,223 8,924,503 1,666,919 8.2
Race
AIAN 2,279 881,481 188,471 0.8 46,054 894,900 186,278 0.8
Asian 8,209 3,896,949 1,574,629 3.6 163,968 3,877,577 1,563,474 3.6
Black 44,688 15,481,630 4,944,862 14.2 891,426 15,454,283 4,962,336 14.2
31
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
NHOPI 1,952 964,033 529,511 0.9 41,100 1,010,010 557,207 0.9
White 143,411 54,129,306 9,687,712 49.6 2,869,416 54,192,231 9,698,795 49.7
Other 12,336 4,683,057 1,234,131 4,3 245,558 4,642,998 1,222,285 4.3
Missing? 69,142 29,051,458 10,088,550 26.6 1,382,848 29,016,620 10,032,231 26.6
Ethnicity
Hispanic or 104,395 42,902,358 13,120,788 39.3 2,086,383 42,877,971 13,137,274 39.3
Latino
Not Hispanic or 141,149 52,650,996 9,344,459 48.3 2,822,521 52,618,547 9,336,253 48.2
Latino
Missing? 36,473 13,534,559 2,646,004 12.4 731,466 13,592,101 2,648,372 12.5
1A|| health centers, including the health centers with complete missingness in race and ethnicity were included in the comparison of
frequencies for race and ethnicity. When presenting analysis, data users should follow the normalization guidance provided in
Section 7.2.
NOTE: All estimates provided in this table do not round to the nearest thousandth for comparison purposes. Data users should
round to the nearest thousandth when presenting analyses as indicated in the presentation standards in Section 6.
32
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 10 Preferred Reporting Items for Complex Sample Survey
Analysis (PRICSSA) Checklist for the 2022 NAMCS HC Component
Public Use Data File
Table 10.1 below provides a Preferred Reporting Items for Complex Survey Analysis (PRICSSA) checklist
(Seidenberg, Moser, & West, 2023) for users of the 2022 NAMCS HC Component public use data file.
This information may be helpful to users when analyzing the 2022 NAMCS HC Component public use
data file.
10.1 Preferred Reporting Items for Complex Sample Survey Analysis
Preferred Reporting Items for Description
Complex Sample Survey Analysis
(PRICSSA)
Name of survey National Ambulatory Medical Care Survey Health Center Component
Data collection mode EHR data submission
Target population Federally qualified health centers (FQHCs) and FQHC look-alikes in the 50
U.S. states and the District of Columbia that used an EHR system
Populations excluded Health Centers excluded:
- Indian Health Service Program facilities
- Did not provide healthcare services to the general U.S.
population, or only provided care to special institutionalized
populations such in prisons, nursing homes, homeless
shelters, etc.
- Only provided dental services
- Were located ona military installation or outside of the 50
U.S. states and the District of Columbia
Sample design Stratified systematic sampling
Variance and standard error PSU (HCID_S) and Stratum (STRATUM_S) variables were applied and
estimation Taylor Series Linearization was used to produce design-adjusted standard
errors.
Weighting VISWT, POPVST
Presentation standards Proportions or percentages:
https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf
Rates and counts:
https://www.cdc.gov/nchs/data/series/sr_02/sr02_202.pdf
Unweighted total sample size 282,017 visits
Weighted total sample size 109,087,913 visits
Response rate (unweighted) 25.1%
Location of example code See Section 7
33
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 11 Research Data Center
NCHS operates the Research Data Center (RDC) to allow researchers access to restricted-use data. The
RDC is responsible for protecting the confidentiality of survey respondents, study subjects, and
institutions while providing access to restricted-use data for statistical purposes. The 2022 NAMCS HC
Component restricted use data file, which contains unmasked and additional data from all visits at
participating health centers (N=5,640,370 visits), can be accessed through the Federal and NCHS RDC. In
addition, the 2022 NAMCS HC Component restricted use data file will be linked to other vital and
administrative records such as the National Death Index (NDI), U.S. Housing and Urban Development
(HUD) administrative data, and others. The linked data will both expand the analytic utility of the
NAMCS HC Component data and provide the opportunity to conduct a vast array of studies focused on
the associations between a variety of health factors, health care utilization, housing situations, and
mortality.
For information on how to access the 2022 NAMCS HC Component restricted use data file through the
RDC, please see: https://www.cdc.gov/rdc/b1idatatype/Dt1224a.htm.
34
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Section 12 References
Health Level Seven International. HL7 CDA® R2 Implementation Guide: National Health Care Surveys
(NHCS), R1 STU Release 3.1 - US Realm. Available at:
http://www.hl7.org/implement/standards/product_brief.cfm?product_id=385. Accessed November 29,
2023.
Lumley T. “survey: analysis of complex survey samples.” R package version 4.2. 2023. Available at:
https://cran.r-project.org/web/packages/survey/index.html.
Parker JD, Talih M, Malec DJ, et al. National Center for Health Statistics data presentation standards for
proportions. National Center for Health Statistics. Vital Health Stat 2(175). 2017. Available at:
https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf
Parker JD, Talih M, lrimata KE, Zhang G, Branum AM, Davis D et al. National Center for Health Statistics
data presentation standards for rates and counts. National Center for Health Statistics. Vital Health Stat
2(200). 2023. DOI: https://dx.doi.org/10.15620/cdc:124368.
Seidenberg AB, Moser RP, West BT. Preferred Reporting Items for Complex Sample Survey Analysis
(PRICSSA). J Surv Stat Methodol 11(4). 2023. https://doi.org/10.1093/jssam/smacO040.,
StataCorp. Stata 18 Survey Data Reference Manual. College Station, TX: Stata Press. 2023. Available at:
https://www.stata.com/manuals/svysvyset.pdf.
Williams SN, Ukaigwe J, Ward BW, Okeyode T, Shimizu IM. Sampling procedures for the collection of
electronic health record data from federally qualified health centers, 2021-2022 National Ambulatory
Medical Care Survey. National Center for Health Statistics. Vital Health Stat Series 2(203). 2023. DOI:
https://dx.doi.org/10.15620/cdc:127730.
35
Appendix A Unweighted frequencies for health center visits
Appendix Table A.1. Unweighted frequencies for health center visits on the public use data file,
National Ambulatory Medical Care Survey Health Center Component, 2022 (n=282,017)
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
Variable Description Count %
YEAR Survey year
2022 282,017 100
DAY Day of the week
1 Sunday 1,697 0.6
2 Monday 54,076 19.2
3 Tuesday 60,480 21.5
4 Wednesday 58,219 20.6
5 Thursday 55,532 19.7
6 Friday 47,745 16.9
7 Saturday 4,268 1.5
MONTH Month of visit
1 January 24,403 8.7
2 February 21,269 7.5
3 March 23,017 8.2
4 April 2D ATT 7.9
5 May 22,267 7.9
6 June 23,038 8.2
7 July 21,794 7.7
8 August 25,695 9.1
9 September 25,793 9.2
10 October 24,277 8.6
11 November 24,980 8.9
12 December 23,307 8.3
MARITAL Marital status
-9 Missing 59,010 20.9
D Divorced 14,717 5.2
L Legally Separated 3,567 1.3
M Married 68,275 24.2
O Other 23 0.0
S Single 46,783 16.6
T Domestic Partner 3,700 1.3
U Unmarried 77,297 27.4
W Widowed 8,645 3.1
AGE_GROUP Patient age group
-9 Missing 2 0
1 Less than 18 years 51,088 18.1
2 18-44 years 99,228 35.2
3 45-64 years 85,517 30.3
4 65 years or more 46,182 16.4
ETHNICITY Patient Hispanic ethnicity
-9 Missing 36,473 12.9
1 Hispanic or Latino 104,395 37.0
2 Not Hispanic or Latino 141,149 50.1
RACE Patient race
-9 Missing 69,142 24.5
al AIAN 2,279 0.8
36
National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component
2 Asian 8,209 2.9
3 Black 44,688 15.9
4 NHOPI 1,952 0.7
5 White 143,411 50.9
6 Other 12,336 4.4
RACERETH Patient race and Hispanic ethnicity
-9 Missing 33,963 12.0
1 White 86,824 30.8
2 Black 42,807 15.2
3 Hispanic 103,447 36.7
4 Other 14,976 5.3
SEX Patient sex
-9 Missing 274 0.1
al Male 105,699 37.5
2 Female 176,044 62.4
37
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