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. Methodology ssiisiisssscscesssccscssssciccssssccstessscscssssavseestsscctessssecdesssscccesssccessssseacesssacccessaesieestsacsees 8 
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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 
Section: 7:5:R:SUrVeY PrOCECUIeS ::.:..52.0s205e2icssetecieiecheteseeueivasuauoes ceveoiiute, galleiapgeisa sda penlsiepboieeiacthtieesnedes 25 
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 


20 


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 


21 


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 


30 


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