The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census. The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences. The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences. An update of the Rural-Urban Commuting Area Codes is planned for late 2013.

Abstract

Rural Urban Continuum Codes (RUCC) Source: U.S. Department of Agriculture, Economic Research Service. (January 2024). Rural-Urban Continuum Codes.

The 2013 data and the 2023 data were downloaded from the USDA website on October 28, 2025 and are available here: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes

The USDA provides RUCC codes at the county.

Rural Urban Commuting Area Codes (RUCA) Source: U.S. Department of Agriculture, Economic Research Service. 2020 Rural-Urban Commuting Area Codes, July 2025.

The 2010 data were downloaded from the USDA website on August 23, 2023.
The 2020 data were downloaded from the USDA website on October 20, 2025.

All tables can be accessed directly from the website: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/

The USDA provides RUCA values for zip codes as well as Census Tracts. See website for additional documentation.

Methodology

The following table was taken from the USDA website: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation

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This dataset was created for the Appalachia Ohio GIS Collaborative Hub by taking the 2020 Census TIGER/Line Tract boundaries and ZIP Code boundaries, filtered for Ohio, and joining them to the 2020 USDA Rural Urban Commuting Area (RUCA) Codes tables downloaded from the USDA. RUCA codes are a classification scheme allowing for flexible, census tract and ZIP code delineation of rural and urban areas throughout the United States and its territories. There are two layers in this dataset, census tracts and ZIP codes. By default they are symbolized by the Primary RUCA code. Both layers include Primary and Secondary RUCA codes. The census tract layer additionally includes the Urban Area Cluster associated with a tract, the Urban Core Type, primary and secondary commuting destinations, population, and population density. More detail about attributes can be found in the description for each layer.2020 Rural-Urban Commuting Area (RUCA) CodesThe USDA, Economic Research Service’s (ERS) Rural-Urban Commuting Area (RUCA) codes are a classification scheme allowing for flexible, census tract delineation of rural and urban areas throughout the United States and its territories. RUCA codes were designed to address a major limitation associated with county-based classifications; they are often too large to accurately delineate boundaries between rural and urban areas. The more geographically-detailed information provided by RUCA codes can be used to improve rural research and policy—such as addressing concerns that remote, rural communities in large metropolitan counties are not eligible for some rural assistance programs.The RUCA codes consist of two levels. The primary RUCA codes establish urban cores and the census tracts that are the most economically integrated with those cores through commuting. The secondary RUCA codes indicate whether a census tract has a strong secondary connection (through commuting) to an even larger urban core. This two-level structure provides flexibility in combining levels to meet varying definitional needs and preferences. The RUCA codes were created using census tract data and were subsequently adapted to ZIP codes.The tables used for the joins were the USDA 2020 Rural-Urban Commuting Area Codes, census tracts table and the 2020 Rural-Urban Commuting Area Codes, ZIP codes table. Both were marked as last updated 7/31/2025, and are available for download from https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes. Tables used for join were downloaded 9/25/2025.

The table RUCA 2020 Census Tract is part of the dataset USDA RUCA RUCC Codes, available at https://stanford.redivis.com/datasets/fq6p-db90f3nvv. It contains 85528 rows across 27 variables.

Census tracts with 4, 5, 6 and 10 tier classifications. We'll be adding 2020 data when its available from the USDA or the Census.From Asnake Hailu,The schemes shared in the RUCAGuide.pdf are DOH modified layers, prepared merely for epidemiological purposes [I.e., to delineate geography for a comprehensive epidemiologic assessment, describing rural-urban differences in demographics, health outcomes, risk factors, access to services, and the like.] Those are not as such rural/urban designation tools for census block areas, nor for any of the other geography categories. The files with the DOH modified layers are available at https://doh.wa.gov/public-health-healthcare-providers/rural-health/data-maps-and-other-resources under the sub-county level: Zip Code and Census Tract sub-heading.Please note: those files are essentially a decade old. We were anticipating to update our core products that are on our website, if and when the Federal Office of Rural Health and Policy (FORHP) produces a newer version of RUCA codes based on census 2020. The FORHP customarily contracts with a university for that task. We are three years away from 2020, except there is no update posted on the webpage I am familiar to get the original RUCA delineations. Here is a path where I go to check for the newer version: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/

2010 Rural-Urban Commuting Area Codes (revised 7/3/2019) , joined to SD, SPA, and CSA as of Dec. 2023.Data from https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/. Downloaded 1/9/2024.Primary RUCA Codes, 20101 Metropolitan area core: primary flow within an urbanized area (UA)2 Metropolitan area high commuting: primary flow 30% or more to a UA3 Metropolitan area low commuting: primary flow 10% to 30% to a UA4 Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC)5 Micropolitan high commuting: primary flow 30% or more to a large UC6 Micropolitan low commuting: primary flow 10% to 30% to a large UC7 Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC)8 Small town high commuting: primary flow 30% or more to a small UC9 Small town low commuting: primary flow 10% to 30% to a small UC10 Rural areas: primary flow to a tract outside a UA or UC99 Not coded: Census tract has zero population and no rural-urban identifier informationSecondary RUCA Codes, 20101 Metropolitan area core: primary flow within an urbanized area (UA)1No additional code1.1Secondary flow 30% to 50% to a larger UA2 Metropolitan area high commuting: primary flow 30% or more to a UA2No additional code2.1Secondary flow 30% to 50% to a larger UA3 Metropolitan area low commuting: primary flow 10% to 30% to a UA3No additional code4 Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC)4No additional code4.1Secondary flow 30% to 50% to a UA5 Micropolitan high commuting: primary flow 30% or more to a large UC5No additional code5.1Secondary flow 30% to 50% to a UA6 Micropolitan low commuting: primary flow 10% to 30% to a large UC6No additional code7 Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC)7No additional code7.1Secondary flow 30% to 50% to a UA7.2Secondary flow 30% to 50% to a large UC8 Small town high commuting: primary flow 30% or more to a small UC8No additional code8.1Secondary flow 30% to 50% to a UA8.2Secondary flow 30% to 50% to a large UC9 Small town low commuting: primary flow 10% to 30% to a small UC9No additional code10 Rural areas: primary flow to a tract outside a UA or UC10No additional code10.1Secondary flow 30% to 50% to a UA10.2Secondary flow 30% to 50% to a large UC10.3Secondary flow 30% to 50% to a small UC99 Not coded: Census tract has zero population and no rural-urban identifier informationData Sources:Population data for census tracts, by urban-rural components, 2010:U.S. Census Bureau, Census of Population and Housing, 2010. Summary File 1, FTP download: https://www.census.gov/census2000/sumfile1.htmlAssignment of census tracts to specific urban areas or to rural status was completed using ESRI's ArcMap software and Census Bureau shape files:U.S. Census Bureau. Tiger/Line Shapefiles, Census Tracts and Urban Areas, 2010: https://www.census.gov/programs-surveys/geography.htmlCensus tract commuting flows, 2006-2010:U.S. Census Bureau, American Community Survey 2006-2010 Five-year estimates. Special Tabulation: Census Transportation Planning Products, Part 3, Worker Home-to-Work Flow Tables. https://www.fhwa.dot.gov/planning/census_issues/ctpp/data_products/2006-2010_table_list/sheet04.cfmTract-to-tract commuting flow files were constructed from ACS data as part of a special tabulation for the Department of Transportation—the Census Transportation Planning Package. To derive estimates for small geographic units such as census tracts, information collected annually from over 3.5 million housing units was combined across 5 years (2006-2010). As with all survey data, ACS estimates are not exact because they are based on a sample. In general, the smaller the estimate, the larger the degree of uncertainty associated with it.

The table RUCA 2020 ZipCode is part of the dataset USDA RUCA RUCC Codes, available at https://stanford.redivis.com/datasets/fq6p-db90f3nvv. It contains 41146 rows across 6 variables.

This dataset contains measures of the urban/rural characteristics of each census tract in the United States. These include proportions of urban and rural population, population density, rural/urban commuting area (RUCA) codes, and RUCA-based four- and seven- category urbanicity scales. A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38606/versions/V1

This dataset contains two measures designed to be used in tandem to characterize United States census tracts, originally developed for use in stratified analyses of the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network. The first measure is a 2010 tract-level community type categorization based on a modification of Rural-Urban Commuting Area (RUCA) Codes that incorporates census-designated urban areas and tract land area, with five categories: higher density urban, lower density urban, suburban/small town, rural, and undesignated (McAlexander, et al., 2022). The second measure is a neighborhood social and economic environment (NSEE) score, a community-type stratified z-score sum of 6 US census-derived variables, with sums scaled between 0 and 100, computed for the year 2000 and 2010. A tract with a higher NSEE z-score sum indicates more socioeconomic disadvantage compared to a tract with a lower z-score sum. Analysts should not compare NSEE scores across LEAD community types, as values have been computed and scaled within community type.

FHFA's Duty to Serve regulation defines "rural area" as: (i) A census tract outside of an MSA as designated by the Office of Management and Budget (OMB); or (ii) A census tract in an MSA as designated by OMB that is: (A) Outside of the MSA’s Urbanized Areas as designated by the U.S. Department of Agriculture’s (USDA) Rural-Urban Commuting Area (RUCA) Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA’s RUCA Code #2; or (B) A colonia census tract that does not satisfy paragraphs (i) or (ii)(A) of this definition. This data contains both the specific geographies which meet the Rural Areas definition and also the areas defined as “high-needs rural regions”.

The table RUCC 2023 County Codes is part of the dataset USDA RUCA RUCC Codes, available at https://stanford.redivis.com/datasets/fq6p-db90f3nvv. It contains 3235 rows across 6 variables.

AZ 2020 Census Tracts, RUCA codes and categories, and selected ACS 2020 5 yr estimate data used with DES Services map. US Census data fields include population and households, race/ethnicity, FPL, Medicaid means tested under 65, Spanish speaking households, no vehicle, 1 vehicle, no Internet, no computer

This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees who received a well-child visit paid for by Medicaid or CHIP, overall and by five subpopulation topics: age group, race and ethnicity, urban or rural residence, program type, and primary language. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, except where otherwise noted. Enrollees in Guam, American Samoa, and the Northern Mariana Islands are not included. Results include enrollees with comprehensive Medicaid or CHIP benefits for all 12 months of the year and who were younger than age 19 at the end of the calendar year. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the primary language subpopulation topic exclude select states with data quality issues with the primary language variable in TAF. Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Medicaid and CHIP enrollees who received a well-child visit in 2020." Enrollees are identified as receiving a well-child visit in the year according to the Line 6 criteria in the Form CMS-416 reporting instructions. Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to a program type subpopulation based on the CHIP code and eligibility group code that applies to the majority of their enrolled-months during the year (Medicaid-Only Enrollment; M-CHIP and S-CHIP Enrollment). Enrollees are assigned to a primary language subpopulation based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by urban or rural residence. Results are shown overall; by state; and by four subpopulation topics: scope of Medicaid and CHIP benefits, race and ethnicity, disability-related eligibility category, and managed care participation. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands who were enrolled for at least one day in the calendar year, except where otherwise noted. Enrollees in Guam, American Samoa, and the Northern Mariana Islands are not included. Results shown overall (where subpopulation topic is "Total enrollees") and for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the race and ethnicity, disability category, and managed care participation subpopulation topics only include Medicaid and CHIP enrollees with comprehensive benefits. Results shown for the disability category subpopulation topic only include working-age adults (ages 19 to 64). Results for states with TAF data quality issues in the year have a value of "Unusable data." Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Rural Medicaid and CHIP enrollees in 2020." Enrollees are assigned to an urban or rural category based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF. Enrollees are assigned to the comprehensive benefits or limited benefits subpopulation according to the criteria in the "Identifying Beneficiaries with Full-Scope, Comprehensive, and Limited Benefits in the TAF" DQ Atlas brief. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to a disability category subpopulation using their latest reported eligibility group code and age in the year (Medicaid enrollees who qualify for benefits based on disability in 2020). Enrollees are assigned to a managed care participation subpopulation based on the managed care plan type code that applies to the majority of their enrolled-months during the year (Enrollment in CMC Plans). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

Fleet diversification and increases in energy efficiency continue to weaken the revenue-generating ability of motor fuels taxes (colloquially, “gas taxes”), which are a large source of funding for transportation projects. While alternative funding schemes are necessary, consensus amongst policymakers is lacking and public acceptance of changes to the gas tax is low. We surveyed residents of Vermont, Maine, and New Hampshire to gauge understanding of and support for a mileage fee and a flat fee as potential replacements for the gas tax. Throughout the survey, respondents were provided information and learning opportunities to “myth bust” common misconceptions about the gas tax and the potential policy alternatives. We find that, before education, respondents knew very little about how the current gas tax works and showed minimal support for the proposed policy alternatives. Post-education, support for mileage fees increased by 11%, and the impact of the education was statistically significant in increasing policy support. Additional regression models revealed that while perceptions of fairness may not be easily changed with education in a survey format, presenting respondents with personalized cost estimates was a highly effective way to increase policy support. Overall, we find responding to common public concerns with up-to-date and non-biased information within a relatively simple learning experience can cause substantial changes in policy support. Our findings offer an avenue to understand how support for gas tax alternatives varies amongst different groups of people and the role that education can play in increasing policy support in the face of widespread misconceptions. Methods We created an internet-based survey to gather public opinion on a $0.015 per mile travelled fee (mileage fee) and a $220 per year per vehicle fee (flat fee) to replace state gas taxes in Vermont, New Hampshire, and Maine. The survey was fielded between May 6th and June 3rd of 2022 using Qualtrics paid survey panelists. Each state was surveyed to ensure 210 usable responses per state. A total of 658 complete responses were collected. In the survey, respondents were presented with voting opportuntities (Do you support replacing the gas tax with a mileage fee? Do you support replacing the gas tax with a flat fee?), followed by educational treatments. The order was as follows: Voting Opportunity 1, Education Treatment 1 (respondents presented with personalized cost estimates for each type of fee based on their provided vehicle information), Voting Opportunity 2, Educational Treatment 2 (respondents watched an educational video developed for the purposes of this research discussing mileage fee privacy and mileage collection options as well as the equity / fairness of a gas tax compared to mileage fees and flat fees as is currently understood in the transportation funding / policy literature), Voting Opportunity 3, Reflection / Comment section, Demographics. Respondents provided zip codes in the demographics section. These were spatially intersected with USDA RUCA codes to create a community-type variable. The RUCA codes were then aggregated to a smaller set of variables for modelling as shown below.

RUCA Code

Description

Aggregated RUCA Codes

1

Metropolitan area core: primary flow within urbanized area

Area core

2

Metropolitan area high commuting: primary flow 30% or more to a UA

High commuting

3

Metropolitan area low commuting: primary flow 10% to 30% to a UA

Rural

4

Micropolitan area core: primary flow within an urban cluster of 10,000 to 49,999 (large UC)

Area core

5

Micropolitan area high commuting: primary flow 30% or more to a large UC

High commuting

6

Micropolitan area low commuting: primary flow 10% to 30% to a large UC

Rural

7

Small town core: primary flow within an urban cluster of 2,500 to 9,999 (UC)

Area core

8

Small town high commuting: primary flow 30% or more to a UC

High commuting

9

Small town low commuting: primary flow 10% to 30% to a UC

Rural

10

Rural areas: primary flow to a tract outside a UA or UC

Rural

Respondents provided responses to 15 questions about various attitudes and beliefs using a 5-point Likert scale. Common factor analysis with the primary axis method (a maximum likelihood approach) in the R psych package was used to create a reduced number of variables that capture a latent and broader set of attitudes and beliefs held by respondents. A parallel analysis scree plot was used to identify the number of factors and an orthogonal (varimax) rotation was used to develop final factor loadings. Factor scores were estimated for each respondent using the Thurston method (a regression approach) in the R psych package and used in our regression modeling. For any additional questions, feel free to contact the researchers (Clare Nelson and Gregory Rowangould) at clare.nelson@uvm.edu or gregory.rowangould@uvm.edu.

Variations in any telehealth use by race/ethnicity, RUCA, zip-code level poverty & broadband access among pediatric Medicaid enrollees, stratification by gender.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by primary language spoken (English, Spanish, and all other languages). Results are shown overall; by state; and by five subpopulation topics: race and ethnicity, age group, scope of Medicaid and CHIP benefits, urban or rural residence, and eligibility category. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands who were enrolled for at least one day in the calendar year, except where otherwise noted. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and select states with data quality issues with the primary language variable in TAF are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown overall (where subpopulation topic is "Total enrollees") exclude enrollees younger than age 5 and enrollees in the U.S. Virgin Islands. Results for states with TAF data quality issues in the year have a value of "Unusable data." Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Primary language spoken by the Medicaid and CHIP population in 2020." Enrollees are assigned to a primary language category based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to the comprehensive benefits or limited benefits subpopulation according to the criteria in the "Identifying Beneficiaries with Full-Scope, Comprehensive, and Limited Benefits in the TAF" DQ Atlas brief. Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

ZIP Code business counts data for Maptitude mapping software are from Caliper Corporation and contain aggregated ZIP Code Business Patterns (ZBP) data and Rural-Urban Commuting Area (RUCA) data.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees who received mental health (MH) or substance use disorder (SUD) services, overall and by six subpopulation topics: age group, sex or gender identity, race and ethnicity, urban or rural residence, eligibility category, and primary language. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, ages 12 to 64 at the end of the calendar year, who were not dually eligible for Medicare and were continuously enrolled with comprehensive benefits for 12 months, with no more than one gap in enrollment exceeding 45 days. Enrollees who received services for both an MH condition and SUD in the year are counted toward both condition categories. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and select states with TAF data quality issues are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the primary language subpopulation topic exclude select states with data quality issues with the primary language variable in TAF. Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Medicaid and CHIP enrollees who received mental health or SUD services in 2020." Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to a sex or gender identity subpopulation using their latest reported sex in the calendar year. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Enrollees are assigned to a primary language subpopulation based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.