{ "fips": { "description": "5-digit county FIPS code (2-digit state + 3-digit county), zero-padded string", "unit": "code", "year": "2024 geography vintage", "source_name": "US Census Bureau / County Health Rankings", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "Join key for all rows. Sourced from the CHR file's '5-digit FIPS Code' column; spot-checked against known FIPS for LA County (06037), Manhattan/New York County (36061), Harris County TX (48201)." }, "county": { "description": "County (or county-equivalent) name", "unit": "text", "year": "2024", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "" }, "state": { "description": "2-letter USPS state abbreviation", "unit": "text", "year": "2024", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "" }, "state_name": { "description": "Full state name", "unit": "text", "year": "2024", "source_name": "Derived (static FIPS/USPS state name lookup)", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "" }, "county_type_metro": { "description": "1 = metro county, 0 = nonmetro county", "unit": "binary (1/0)", "year": "2023", "source_name": "USDA Economic Research Service, Rural-Urban Continuum Codes 2023", "source_url": "https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/", "caveat": "Derived from USDA Rural-Urban Continuum Code (RUCC) 2023: codes 1-3 = metro -> 1; codes 4-9 = nonmetro -> 0. Blank for 8 counties where RUCC 2023 has no matching FIPS (Connecticut legacy county codes, retired -- see README)." }, "population": { "description": "Total county population", "unit": "count (persons)", "year": "2023 (PLACES) / see caveat", "source_name": "CDC PLACES 2025 release TotalPopulation field (Census Bureau 2023 county population estimate), with County Health Rankings 2024 'Population raw value' used as fallback for the handful of counties absent from PLACES", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "Two different source vintages are blended: PLACES population (Census 2023 vintage) where available, else CHR population estimate (vintage varies, generally 2021-2022). Not itself a PLACES model estimate -- it is CDC's pass-through of the Census county population estimate." }, "alz_prevalence_pct_65plus": { "description": "Alzheimer's/dementia diagnosed prevalence, % of population 65+", "unit": "percent (0-100)", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. Re-investigated 2026-09-27 (see data/county_outcomes_README.md for the full trail): CMS's Mapping Medicare Disparities (MMD) Tool still only exposes this through an interactive web map with per-query CSV export, confirmed to be a client-rendered SPA with no discoverable bulk API; CMS's older county-level Chronic Conditions Excel files (2007-2018, which included Alzheimer's) have since been retired by CMS in favor of the MMD Tool. CDC's Alzheimer's Disease and Healthy Aging Data Portal is confirmed state/national-only (fetched directly). A real peer-reviewed county estimate exists (Dhana et al. 2023, Alzheimer's & Dementia 19:4388-4395), but the per-county numbers are behind a paywall with no public data table found -- using the map image or press-release examples to fill other counties would be fabrication, so it was not used. Do not fill this column with a proxy (e.g. PLACES COGNITION, which measures self-reported cognitive difficulty in the general adult population, not diagnosed Alzheimer's/dementia in the 65+ population) without clearly relabeling it." }, "alz_count_estimate": { "description": "Estimated count of county residents with Alzheimer's/dementia", "unit": "count (persons)", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. Re-investigated 2026-09-27 (see data/county_outcomes_README.md for the full trail): CMS's Mapping Medicare Disparities (MMD) Tool still only exposes this through an interactive web map with per-query CSV export, confirmed to be a client-rendered SPA with no discoverable bulk API; CMS's older county-level Chronic Conditions Excel files (2007-2018, which included Alzheimer's) have since been retired by CMS in favor of the MMD Tool. CDC's Alzheimer's Disease and Healthy Aging Data Portal is confirmed state/national-only (fetched directly). A real peer-reviewed county estimate exists (Dhana et al. 2023, Alzheimer's & Dementia 19:4388-4395), but the per-county numbers are behind a paywall with no public data table found -- using the map image or press-release examples to fill other counties would be fabrication, so it was not used. Do not fill this column with a proxy (e.g. PLACES COGNITION, which measures self-reported cognitive difficulty in the general adult population, not diagnosed Alzheimer's/dementia in the 65+ population) without clearly relabeling it." }, "dementia_mortality_per_100k": { "description": "Age-adjusted death rate, all dementia (ICD-10 F01,F03,G30), per 100,000 population", "unit": "rate per 100,000", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. Re-confirmed 2026-09-27 (see data/county_outcomes_README.md), for two independent reasons: (1) CDC WONDER's own API documentation (wonder.cdc.gov/wonder/help/wonder-api.html) states outright that 'only national data are available for query by the API. Queries for mortality and births statistics from the National Vital Statistics System cannot limit or group results by any location field, such as Region, Division, State or County' -- this is a stated policy, not a fixable parameter mistake. (2) Independently, a live GET and a live hand-built XML POST to https://wonder.cdc.gov/controller/datarequest/D76 today both returned HTTP 403 Akamai 'Access Denied' pages, so even the interactive UI is unreachable by automation. The interactive WONDER web UI does expose county-level mortality (subject to CDC's own <10-death suppression), but only to a human in a browser. A future job with real-browser access to wonder.cdc.gov's UI (or a CDC data-use agreement / NCHS Research Data Center application for a restricted-use county mortality file) could source this manually per county." }, "dementia_deaths_total": { "description": "Total deaths with underlying cause = dementia", "unit": "count (deaths)", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. Re-confirmed 2026-09-27 (see data/county_outcomes_README.md), for two independent reasons: (1) CDC WONDER's own API documentation (wonder.cdc.gov/wonder/help/wonder-api.html) states outright that 'only national data are available for query by the API. Queries for mortality and births statistics from the National Vital Statistics System cannot limit or group results by any location field, such as Region, Division, State or County' -- this is a stated policy, not a fixable parameter mistake. (2) Independently, a live GET and a live hand-built XML POST to https://wonder.cdc.gov/controller/datarequest/D76 today both returned HTTP 403 Akamai 'Access Denied' pages, so even the interactive UI is unreachable by automation. The interactive WONDER web UI does expose county-level mortality (subject to CDC's own <10-death suppression), but only to a human in a browser. A future job with real-browser access to wonder.cdc.gov's UI (or a CDC data-use agreement / NCHS Research Data Center application for a restricted-use county mortality file) could source this manually per county." }, "alz_deaths_total": { "description": "Total deaths with underlying cause = Alzheimer's disease (ICD-10 G30)", "unit": "count (deaths)", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. Re-confirmed 2026-09-27 (see data/county_outcomes_README.md), for two independent reasons: (1) CDC WONDER's own API documentation (wonder.cdc.gov/wonder/help/wonder-api.html) states outright that 'only national data are available for query by the API. Queries for mortality and births statistics from the National Vital Statistics System cannot limit or group results by any location field, such as Region, Division, State or County' -- this is a stated policy, not a fixable parameter mistake. (2) Independently, a live GET and a live hand-built XML POST to https://wonder.cdc.gov/controller/datarequest/D76 today both returned HTTP 403 Akamai 'Access Denied' pages, so even the interactive UI is unreachable by automation. The interactive WONDER web UI does expose county-level mortality (subject to CDC's own <10-death suppression), but only to a human in a browser. A future job with real-browser access to wonder.cdc.gov's UI (or a CDC data-use agreement / NCHS Research Data Center application for a restricted-use county mortality file) could source this manually per county." }, "pct_65plus": { "description": "Percent of population age 65 and older", "unit": "percent (0-100)", "year": "~2018-2022 (ACS vintage embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of ACS 5-year data; not independently re-pulled from ACS 2020-2024 for consistency with other CHR-sourced demographic shares (pct_female fallback) in this file." }, "pct_85plus": { "description": "Percent of population age 85 and older", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. Computed as (male 85+ + female 85+) / total population from table B01001." }, "pct_female": { "description": "Percent of population that is female", "unit": "percent (0-100)", "year": "2020-2024 (ACS) or CHR vintage for the 8 CT counties missing ACS", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B01001; CHR '% Female raw value' used as fallback for the 8 Connecticut counties absent from the ACS pull." }, "pct_white_nh": { "description": "Percent of population that is White alone, Not Hispanic or Latino", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B03002." }, "pct_black": { "description": "Percent of population that is Black or African American alone, Not Hispanic or Latino", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B03002. Excludes Hispanic Black residents (counted under pct_hispanic instead) -- this is the standard non-Hispanic race breakdown, not 'Black alone regardless of ethnicity.'" }, "pct_hispanic": { "description": "Percent of population that is Hispanic or Latino (any race)", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B03002." }, "pct_asian": { "description": "Percent of population that is Asian alone, Not Hispanic or Latino", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B03002." }, "median_age": { "description": "Median age of county population", "unit": "years", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B01002." }, "population_density_per_sqmi": { "description": "Population per square mile of land area", "unit": "persons per square mile", "year": "2023 (population) / 2024 (land area)", "source_name": "Derived: population (see 'population' column) divided by ALAND_SQMI from the 2024 Census Gazetteer file", "source_url": "https://www.census.gov/geographies/reference-files/time-series/geo/gazetteer-files.html", "caveat": "" }, "median_household_income": { "description": "Median household income", "unit": "US dollars (nominal, ACS inflation-adjusted to survey-period dollars)", "year": "2020-2024 (ACS) or CHR vintage for 8 CT counties", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B19013; CHR 'Median Household Income raw value' used as fallback for the 8 Connecticut counties absent from the ACS pull." }, "pct_below_poverty": { "description": "Percent of all people (all ages) with income in the past 12 months below the poverty level", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B17001 (total population poverty status, not just children). Differs from CHR's 'Children in Poverty' measure, which was NOT used here." }, "pct_bachelors_plus": { "description": "Percent of population 25+ with a bachelor's degree or higher", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B15003 (bachelor's + master's + professional + doctorate, divided by population 25+)." }, "unemployment_rate": { "description": "Unemployment rate (unemployed / civilian labor force)", "unit": "percent (0-100)", "year": "2020-2024 (ACS) or CHR vintage for 8 CT counties", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B23025; CHR 'Unemployment raw value' used as fallback for the 8 Connecticut counties absent from the ACS pull. Not seasonally adjusted; differs methodologically from BLS LAUS unemployment rates." }, "pct_uninsured": { "description": "Percent of civilian noninstitutionalized population with no health insurance coverage", "unit": "percent (0-100)", "year": "~2021 (Small Area Health Insurance Estimates vintage embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of Census SAHIE data." }, "pct_no_vehicle": { "description": "Percent of households with no vehicle available", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B08201." }, "pct_single_person_households": { "description": "Percent of households that are a single person living alone (nonfamily householder living alone)", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B11001 (B11001_008 / B11001_001)." }, "pct_living_alone_65plus": { "description": "Percent of the 65+ population living alone", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B09021 (population 65+ 'lives alone' living-arrangement category / total population 65+ in that table's universe)." }, "pct_diabetes": { "description": "Percent of adults 18+ with diagnosed diabetes", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_obesity": { "description": "Percent of adults 18+ with obesity (BMI 30+)", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_physical_inactivity": { "description": "Percent of adults 18+ reporting no leisure-time physical activity", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_smoking": { "description": "Percent of adults 18+ who are current smokers", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_hypertension": { "description": "Percent of adults 18+ with diagnosed high blood pressure", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_depression": { "description": "Percent of adults 18+ ever told they have a depressive disorder", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_binge_drinking": { "description": "Percent of adults 18+ reporting binge drinking", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_fair_poor_health": { "description": "Percent of adults 18+ reporting fair or poor self-rated general health", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "pct_checkup_annual": { "description": "Percent of adults 18+ who report visiting a doctor for a routine checkup within the past year", "unit": "percent (0-100)", "year": "2023 (BRFSS)", "source_name": "CDC PLACES: County Data (GIS-Friendly Format), 2025 release", "source_url": "https://data.cdc.gov/500-Cities-Places/PLACES-County-Data-GIS-Friendly-Format-2025-releas/i46a-9kgh", "caveat": "MODELLED (small-area) estimate, not a direct survey measurement. Derived from BRFSS 2023 self-reported survey responses using a validated multilevel regression and poststratification (MRP) small-area model, not a full county census. Values for counties in Kentucky, Pennsylvania, and Loving County TX are blank in this release -- see README missingness table. Interpret correlations with caution: because these are model outputs (not raw counts), they can be smoother / more spatially autocorrelated than a true measured rate, which can inflate correlations with other smoothed covariates (e.g., other PLACES measures or CHR measures that are also modelled)." }, "primary_care_physicians_per_100k": { "description": "Primary care physicians per 100,000 population", "unit": "rate per 100,000", "year": "~2022 (Area Health Resources Files vintage embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of HRSA Area Health Resources Files; computed here as CHR's population-to-provider ratio raw value inverted and rescaled to per-100k." }, "mental_health_providers_per_100k": { "description": "Mental health providers per 100,000 population", "unit": "rate per 100,000", "year": "~2023 (CMS National Provider Identification vintage embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of CMS NPI file counts; computed here as CHR's population-to-provider ratio raw value inverted and rescaled to per-100k." }, "hospital_beds_per_1000": { "description": "Certified hospital beds per 1,000 population", "unit": "beds per 1,000 population", "year": "2026 snapshot (facility roster) / population as dated above", "source_name": "CMS Provider of Services File - Quality Improvement and Evaluation System (QIES), current quarterly snapshot pulled 2026-09-21", "source_url": "https://data.cms.gov/provider-characteristics/hospitals-and-other-facilities/provider-of-services-file-quality-improvement-and-evaluation-system", "caveat": "Counts only Medicare-certified short-term/critical-access hospitals with active certification (PGM_TRMNTN_CD = 00) in this CMS extract; excludes VA, psychiatric-only, and non-Medicare-certified facilities. Blank means no active Medicare-certified hospital was found in this county in the extract -- it usually means the county genuinely has none (common for small rural counties whose residents use a neighboring county's hospital), but a small share could be extract gaps." }, "nursing_home_beds_per_1000": { "description": "Nursing home / skilled nursing facility beds per 1,000 population", "unit": "beds per 1,000 population", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. The CMS Provider of Services QIES extract used for hospital_beds_per_1000 does not include skilled nursing facilities in this dataset's category codes. CMS's 'Long-Term Care Facility Characteristics' dataset covers SNFs but only has City/State/ZIP, no county FIPS, and would require a ZIP-to-county crosswalk (with the usual many-to-many ZIP/county ambiguity) that was out of scope for this job." }, "pct_65plus_with_disability": { "description": "Percent of population 65+ with any disability", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B18101 (ages 65-74 and 75+, both sexes, 'with a disability' summed over 'total' for those age bands)." }, "physicians_per_1000": { "description": "All physicians (any specialty) per 1,000 population", "unit": "rate per 1,000", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. CHR and PLACES only publish primary-care-physician and mental-health-provider counts (already captured in the two columns above), not an all-specialty physician count. A true all-physician county rate would require HRSA's Area Health Resources File (county) or a CMS NPPES-derived count, neither of which could be pulled and correctly deduplicated by county within this job's scope." }, "pm25_avg_ugm3": { "description": "Average daily fine particulate matter (PM2.5) concentration", "unit": "micrograms per cubic meter (ug/m3)", "year": "~2021 (CDC/EPA Environmental Public Health Tracking vintage embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of CDC/EPA Air Quality System modelled daily PM2.5 concentration estimates -- itself a MODELLED (not directly monitored-everywhere) estimate produced by CDC's Environmental Public Health Tracking Network, since ground monitors do not exist in every county." }, "drinking_water_violations": { "description": "Whether the county had a community water system with a health-based drinking water violation", "unit": "binary (1 = had a violation, 0 = did not)", "year": "~2022 (embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of EPA Safe Drinking Water Information System (SDWIS). This is a binary presence/absence flag, not a violation count or rate." }, "pct_lead_exposure_risk": { "description": "Percent of housing stock / population at elevated risk of lead exposure", "unit": "percent (0-100)", "year": "", "source_name": "NOT SOURCED", "source_url": "", "caveat": "This column is intentionally empty for every row -- NOT SOURCED. No machine-readable, county-level, nationally complete bulk dataset for lead exposure risk could be located within this job's scope (CDC's childhood blood lead surveillance data is state-level and has large reporting gaps; HUD/EPA housing-age-based lead risk indices were not available as a clean bulk download)." }, "social_association_index": { "description": "Number of membership associations (civic, religious, business, political, labor, etc.) per 10,000 population", "unit": "count per 10,000 population", "year": "~2022 (County Business Patterns vintage embedded in CHR 2024)", "source_name": "County Health Rankings & Roadmaps (Univ. of Wisconsin Population Health Institute), 2024 release", "source_url": "https://www.countyhealthrankings.org/health-data/methodology-and-sources/rankings-data-documentation", "caveat": "CHR pass-through of Census County Business Patterns. Already expressed per-10,000; no further conversion applied." }, "pct_veterans": { "description": "Percent of civilian population 18+ who are veterans", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B21001." }, "pct_foreign_born": { "description": "Percent of population that is foreign-born", "unit": "percent (0-100)", "year": "2020-2024", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B05002." }, "pct_broadband": { "description": "Percent of households with a broadband internet subscription of any type", "unit": "percent (0-100)", "year": "2020-2024 (ACS) or CHR vintage for 8 CT counties", "source_name": "US Census Bureau, American Community Survey 5-Year Estimates 2020-2024 (detailed tables), retrieved via the Census Reporter API (a non-key-gated public proxy of the official Census Bureau ACS API)", "source_url": "https://www.census.gov/data/developers/data-sets/acs-5year.html", "caveat": "ACS 5-year estimates are themselves modelled/smoothed over a 5-year rolling window (2020-2024), not a single-year census count; small counties carry wide margins of error not included in this file. Connecticut's 8 legacy counties (FIPS 09001-09015) have no data here because the Census Bureau retired those FIPS codes in favor of 9 new Connecticut planning regions for the 2020-2024 vintage -- see README. From table B28002; CHR 'Broadband Access raw value' used as fallback for the 8 Connecticut counties absent from the ACS pull." }, "norc_bene_cnt_2020": { "description": "Count of Medicare beneficiaries meeting NORC Dementia DataHub inclusion criteria, county total (all ages, all insurance types), 2020 -- the denominator for the two rate columns below", "unit": "count (persons)", "year": "2020", "source_name": "NORC Dementia DataHub (NORC at the University of Chicago), 2020 Public Use File", "source_url": "https://www.dementiadatahub.org", "caveat": "Joined additively from data/county_dementia_outcomes.csv (see that file's meta for full methodology). All-age Medicare beneficiary population, not total county population. Blank where suppressed (true value < 11 per CMS rule) -- see norc_bene_cnt_2020_suppressed; blank is NOT zero. 15 of 3,143 panel counties have no match in the source (11 Alaska boroughs/census areas with no corresponding SSA county code, Kalawao County HI, and all 3 Delaware counties -- Delaware's source row is a state-level total that cannot be attributed to any single county, so it was excluded rather than misattributed; see build script)." }, "norc_bene_cnt_2020_suppressed": { "description": "1 if norc_bene_cnt_2020 was suppressed at the source (blank, true value < 11); 0 if a real value is present; blank if the county had no NORC match at all", "unit": "boolean (0/1) or blank", "year": "2020", "source_name": "Derived from NORC Dementia DataHub suppression flag", "source_url": "https://www.dementiadatahub.org", "caveat": "Distinguishes genuine suppression from no-match: blank in this column means the county had no NORC row at all (see norc_bene_cnt_2020 caveat), not that a value was suppressed." }, "norc_prev_cnt_2020": { "description": "Count of Medicare beneficiaries with prevalent diagnosed AD/ADRD in 2020, county total, broadest NORC case definition (DEMENTIACAT blank = 'All Determinations' = Highly likely + Likely + Possible combined)", "unit": "count (persons)", "year": "2020", "source_name": "NORC Dementia DataHub (NORC at the University of Chicago), 2020 Public Use File", "source_url": "https://www.dementiadatahub.org", "caveat": "DIAGNOSED cases identified from Medicare claims, NOT a modelled disease-prevalence estimate -- diagnosis rates for identical underlying symptoms vary by place, so a county with a low count/rate may under-diagnose rather than have less disease. This is a distinct measurement from the panel's own alz_count_estimate column (which remains NOT SOURCED and untouched by this join). Blank where suppressed (true value < 11); blank is NOT zero. See data/county_dementia_outcomes_meta.json for full case-definition and suppression methodology." }, "norc_prev_cnt_2020_suppressed": { "description": "1 if norc_prev_cnt_2020 was suppressed at the source; 0 if present; blank if the county had no NORC match", "unit": "boolean (0/1) or blank", "year": "2020", "source_name": "Derived from NORC Dementia DataHub suppression flag", "source_url": "https://www.dementiadatahub.org", "caveat": "24 of the 3,128 matched counties are suppressed at prev_cnt." }, "norc_inc_cnt_2020": { "description": "Count of Medicare beneficiaries newly (first-time) diagnosed with AD/ADRD in calendar year 2020, county total, broadest NORC case definition", "unit": "count (persons)", "year": "2020", "source_name": "NORC Dementia DataHub (NORC at the University of Chicago), 2020 Public Use File", "source_url": "https://www.dementiadatahub.org", "caveat": "Single calendar-year first-time-diagnosis count from Medicare claims. Blank where suppressed (true value < 11); blank is NOT zero." }, "norc_inc_cnt_2020_suppressed": { "description": "1 if norc_inc_cnt_2020 was suppressed at the source; 0 if present; blank if the county had no NORC match", "unit": "boolean (0/1) or blank", "year": "2020", "source_name": "Derived from NORC Dementia DataHub suppression flag", "source_url": "https://www.dementiadatahub.org", "caveat": "" }, "norc_mort_cnt_2020": { "description": "Count of all-cause deaths in 2020 among beneficiaries defined as prevalent AD/ADRD cases, county total", "unit": "count (deaths)", "year": "2020", "source_name": "NORC Dementia DataHub (NORC at the University of Chicago), 2020 Public Use File", "source_url": "https://www.dementiadatahub.org", "caveat": "ALL-CAUSE mortality among the diagnosed-prevalent population, NOT deaths with an ICD-10 dementia/Alzheimer's cause-of-death code -- do not conflate with the panel's own (NOT SOURCED) dementia_deaths_total/alz_deaths_total columns, which were defined as cause-of-death counts and remain untouched by this join. Blank where suppressed (true value < 11); blank is NOT zero." }, "norc_mort_cnt_2020_suppressed": { "description": "1 if norc_mort_cnt_2020 was suppressed at the source; 0 if present; blank if the county had no NORC match", "unit": "boolean (0/1) or blank", "year": "2020", "source_name": "Derived from NORC Dementia DataHub suppression flag", "source_url": "https://www.dementiadatahub.org", "caveat": "" }, "norc_dementia_prevalence_rate_2020": { "description": "norc_prev_cnt_2020 / norc_bene_cnt_2020 * 100 -- diagnosed dementia prevalence rate among all-age Medicare beneficiaries", "unit": "percent (0-100)", "year": "2020", "source_name": "Derived from NORC Dementia DataHub counts per the DataHub's own published rate formula (dementiadatahub.org/resources/outcome-measures.html)", "source_url": "https://www.dementiadatahub.org", "caveat": "Blank whenever prev_cnt or bene_cnt is suppressed -- never computed from an imputed numerator or denominator. Denominator is ALL-AGE Medicare beneficiaries (includes under-65 disability/ESRD beneficiaries), not restricted to 65+, so this is NOT directly comparable to the panel's own alz_prevalence_pct_65plus column (which remains NOT SOURCED and untouched) despite the similar name." }, "norc_dementia_incidence_rate_2020": { "description": "norc_inc_cnt_2020 / norc_bene_cnt_2020 * 100 -- diagnosed dementia incidence rate (first-time diagnoses in 2020) among all-age Medicare beneficiaries", "unit": "percent (0-100)", "year": "2020", "source_name": "Derived from NORC Dementia DataHub counts per the DataHub's own published rate formula", "source_url": "https://www.dementiadatahub.org", "caveat": "Blank whenever inc_cnt or bene_cnt is suppressed." }, "norc_dementia_mortality_rate_2020": { "description": "norc_mort_cnt_2020 / norc_prev_cnt_2020 * 100 -- share of diagnosed prevalent dementia cases who died in 2020 (a case-fatality rate among the diagnosed population)", "unit": "percent (0-100)", "year": "2020", "source_name": "Derived from NORC Dementia DataHub counts per the DataHub's own published rate formula (mortality and COVID measures are divided by prev_cnt, not bene_cnt, per the codebook)", "source_url": "https://www.dementiadatahub.org", "caveat": "IMPORTANT: this is NOT a population mortality rate per 100,000 residents and is NOT comparable to the panel's own (NOT SOURCED) dementia_mortality_per_100k column, despite both being 'dementia mortality' -- it is the percent of already-diagnosed prevalent cases who died that year, i.e. a case-fatality rate, per the DataHub's own denominator guidance. Blank whenever mort_cnt or prev_cnt is suppressed." }, "_norc_join_methodology": { "description": "Top-level provenance/methodology note for the 11 norc_* columns above (not itself a data column)", "unit": "n/a", "year": "2020", "source_name": "NORC Dementia DataHub (NORC at the University of Chicago)", "source_url": "https://www.dementiadatahub.org", "caveat": "These are DIAGNOSED dementia cases identified from Medicare fee-for-service and Medicare Advantage claims (2020) -- diagnosis rates for identical underlying symptoms vary considerably by place, so cross-county comparisons partly reflect diagnostic/coding capacity and health-system contact, not purely underlying disease burden. Attribution required by the data use agreement: 'NORC at the University of Chicago. Dementia DataHub Public Use File: 2020 Data. Retrieved by request from dementiadatahub.org.' Case definition used: DEMENTIACAT blank ('All Determinations'), verified by direct arithmetic to equal Highly likely + Likely + Possible summed. Suppression rule: any cell with a direct or inferrable value < 11 is blank in the source (CMS disclosure rule); suppressed cells are left blank here (never treated as zero) and flagged by a companion *_suppressed column. Join: additive left-join on 5-digit FIPS onto the pre-existing panel_county.csv -- every pre-existing row, column, and value is unchanged; 3,128 of 3,143 panel counties matched a NORC county row, 15 did not (11 Alaska boroughs/census areas and Kalawao County HI absent from the NORC file entirely; all 3 Delaware counties excluded because NORC's single Delaware row is a state-level total, not attributable to one county -- see data/county_dementia_outcomes.csv build script). Do not reverse-engineer or publish an estimate of any suppressed cell, per the accepted data use agreement." }, "pct_low_access_to_store_2019": { "description": "Percent of county population living more than 1 mile (urban) / 10 miles (rural) from the nearest supermarket, supercenter or large grocery store", "unit": "percent (0-100)", "year": "2019", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "pct_low_income_low_access_2019": { "description": "Percent of county population that is both low-income and low-access to a supermarket or large grocery store", "unit": "percent (0-100)", "year": "2019", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "pct_seniors_low_access_2019": { "description": "Percent of county population age 65+ living low-access to a supermarket or large grocery store", "unit": "percent (0-100)", "year": "2019", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "grocery_stores_per_1000pop_2020": { "description": "Grocery stores per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "convenience_stores_per_1000pop_2020": { "description": "Convenience stores per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "supercenters_club_stores_per_1000pop_2020": { "description": "Supercenters and club stores per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "specialized_food_stores_per_1000pop_2020": { "description": "Specialized food stores (meat markets, produce markets, etc.) per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "dollar_stores_per_1000pop_2020": { "description": "Dollar stores per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "fastfood_restaurants_per_1000pop_2020": { "description": "Fast-food restaurants per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "fullservice_restaurants_per_1000pop_2020": { "description": "Full-service restaurants per 1,000 population", "unit": "stores per 1,000 population", "year": "2020", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "snap_authorized_stores_per_1000pop_2023": { "description": "SNAP-authorized retail stores per 1,000 population", "unit": "stores per 1,000 population", "year": "2023", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "wic_authorized_stores_per_1000pop_2022": { "description": "WIC-authorized retail stores per 1,000 population", "unit": "stores per 1,000 population", "year": "2022", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "snap_benefits_per_capita_2022": { "description": "Average annual SNAP benefits redeemed per capita", "unit": "US dollars per capita", "year": "2022", "source_name": "USDA Economic Research Service, Food Environment Atlas (2025 release)", "source_url": "https://www.ers.usda.gov/media/5570/food-environment-atlas-csv-files.zip?v=79064", "caveat": "USDA Economic Research Service, Food Environment Atlas 2025 release. Joins on county_fips against panel_county.csv:fips at 3,143/3,143 (100%, including all 8 Connecticut legacy counties -- this file does not have the CT FIPS-scheme problem that affects the CMS/BLS columns added in the same join). Blank cells reflect gaps in USDA's own file (commonly for rare store categories like supercenters in small counties); USDA's public documentation does not distinguish a true zero from a suppressed or unreported cell in this release, so blank is left blank rather than read as zero." }, "num_tracts": { "description": "Number of census tracts in the county", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pop2010_total": { "description": "Total population, 2010 Census (denominator for several shares in this group)", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "ohu2010_total": { "description": "Total occupied housing units, 2010 Census", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_group_quarters_total": { "description": "Total group-quarters population, 2010 Census", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_tracts_urban": { "description": "Percent of the county's census tracts classified as urban", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_access_pop_halfmi": { "description": "Percent of population living low-access to a supermarket/large grocery store (urban: >0.5 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_income_low_access_pop_halfmi": { "description": "Percent of population that is both low-income and low-access (urban: >0.5 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_kids_low_access_halfmi": { "description": "Percent of population under 18 living low-access (urban: >0.5 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_seniors_low_access_halfmi": { "description": "Percent of population 65+ living low-access (urban: >0.5 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_housing_units_no_vehicle_low_access_halfmi": { "description": "Percent of housing units with no vehicle that are also low-access (urban: >0.5 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_housing_units_snap_low_access_halfmi": { "description": "Percent of SNAP-household housing units that are also low-access (urban: >0.5 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_access_pop_1mi": { "description": "Percent of population living low-access to a supermarket/large grocery store (>1 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_income_low_access_pop_1mi": { "description": "Percent of population that is both low-income and low-access (>1 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_kids_low_access_1mi": { "description": "Percent of population under 18 living low-access (>1 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_seniors_low_access_1mi": { "description": "Percent of population 65+ living low-access (>1 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_housing_units_no_vehicle_low_access_1mi": { "description": "Percent of housing units with no vehicle that are also low-access (>1 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_housing_units_snap_low_access_1mi": { "description": "Percent of SNAP-household housing units that are also low-access (>1 mile)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_access_pop_10mi": { "description": "Percent of population living low-access to a supermarket/large grocery store (rural: >10 miles)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_income_low_access_pop_10mi": { "description": "Percent of population that is both low-income and low-access (rural: >10 miles)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_access_pop_20mi": { "description": "Percent of population living low-access to a supermarket/large grocery store (rural: >20 miles)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "pct_low_income_low_access_pop_20mi": { "description": "Percent of population that is both low-income and low-access (rural: >20 miles)", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "tract_lowincome_pop_total": { "description": "Total low-income population summed across the county's tracts", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "tract_kids_pop_total": { "description": "Total population under 18 summed across the county's tracts", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "tract_seniors_pop_total": { "description": "Total population 65+ summed across the county's tracts", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "tract_housing_units_no_vehicle_total": { "description": "Total housing units with no vehicle available, summed across the county's tracts", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "tract_housing_units_snap_total": { "description": "Total SNAP-recipient housing units, summed across the county's tracts", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "poverty_rate_popweighted_avg_pct": { "description": "Population-weighted average tract poverty rate", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "median_family_income_popweighted_avg_usd": { "description": "Population-weighted average tract median family income, US dollars", "unit": "percent (0-100)", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_lila_1and10": { "description": "Number of tracts flagged low-income-and-low-access under the 1-and-10-mile definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_lila_halfand10": { "description": "Number of tracts flagged low-income-and-low-access under the half-and-10-mile definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_lila_1and20": { "description": "Number of tracts flagged low-income-and-low-access under the 1-and-20-mile definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_lila_vehicle": { "description": "Number of tracts flagged low-income-and-low-access under the vehicle-access definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_low_income": { "description": "Number of tracts classified low-income", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_hunv_flag": { "description": "Number of tracts flagged for housing units with no vehicle and low access", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_la1and10": { "description": "Number of tracts flagged low-access under the 1-and-10-mile definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_lahalfand10": { "description": "Number of tracts flagged low-access under the half-and-10-mile definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_la1and20": { "description": "Number of tracts flagged low-access under the 1-and-20-mile definition", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_latracts_half": { "description": "Number of tracts flagged low-access at the half-mile threshold", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_latracts1": { "description": "Number of tracts flagged low-access at the 1-mile threshold", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_latracts10": { "description": "Number of tracts flagged low-access at the 10-mile threshold", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_latracts20": { "description": "Number of tracts flagged low-access at the 20-mile threshold", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "num_tracts_latracts_vehicle20": { "description": "Number of tracts flagged low-access at 20 miles among households with no vehicle", "unit": "count", "year": "2019 data, initial release April 2021", "source_name": "USDA Economic Research Service, Food Access Research Atlas (2019 vintage, county aggregation)", "source_url": "https://www.ers.usda.gov/media/5627/2019-large-retailer-access-map-lram-formerly-known-as-the-food-access-research-atlas-fara-data.zip", "caveat": "USDA Economic Research Service, Food Access Research Atlas, 2019 vintage (initial release April 2021), aggregated from census tract to county. Joins on county_fips against panel_county.csv:fips at 3,141/3,143 (99.9%). The 2 unmatched are Alaska's Chugach (02063) and Copper River (02066) Census Areas, which post-date this 2019 file's pre-split Valdez-Cordova (02261) -- a real, separate join gap from the Connecticut FIPS issue that affects the CMS/BLS columns in this same batch; all 8 Connecticut legacy counties ARE present and populated in this file, left null rather than guessed for the 2 Alaska areas." }, "cms_nh_certified_beds_total": { "description": "Total CMS-certified nursing home beds in the county", "unit": "count", "year": "2026 (August 2026 Care Compare snapshot)", "source_name": "CMS Care Compare, Nursing Home Provider Info", "source_url": "https://data.cms.gov/provider-data/api/1/metastore/schemas/dataset/items/4pq5-n9py", "caveat": "CMS Care Compare, Nursing Home Provider Info, August 2026 snapshot, aggregated to county by facility address (14,691 facilities, name-normalized county match). Joins on county_fips against panel_county.csv at 2,826/3,143 (89.9%). Connecticut is blank under EITHER FIPS scheme (legacy county or post-2022 planning region) -- confirmed by direct set-difference, not assumed: this CMS file has zero Connecticut rows at all, because Connecticut provider addresses no longer carry a legacy county FIPS to build one from. This is a genuine absence in the CMS source, not a fixable join gap -- no crosswalk exists for it, including at the state level (panel_state.csv's cms_nh_* columns are also blank for CT; see data/dataset_expansion.json 'KNOWN LIMITATION' entry). Blank for other non-CT counties this Care Compare extract does not cover, too -- blank is never filled with 0." }, "cms_nh_n_facilities": { "description": "Number of CMS-certified nursing home facilities in the county", "unit": "count", "year": "2026 (August 2026 Care Compare snapshot)", "source_name": "CMS Care Compare, Nursing Home Provider Info", "source_url": "https://data.cms.gov/provider-data/api/1/metastore/schemas/dataset/items/4pq5-n9py", "caveat": "CMS Care Compare, Nursing Home Provider Info, August 2026 snapshot, aggregated to county by facility address (14,691 facilities, name-normalized county match). Joins on county_fips against panel_county.csv at 2,826/3,143 (89.9%). Connecticut is blank under EITHER FIPS scheme (legacy county or post-2022 planning region) -- confirmed by direct set-difference, not assumed: this CMS file has zero Connecticut rows at all, because Connecticut provider addresses no longer carry a legacy county FIPS to build one from. This is a genuine absence in the CMS source, not a fixable join gap -- no crosswalk exists for it, including at the state level (panel_state.csv's cms_nh_* columns are also blank for CT; see data/dataset_expansion.json 'KNOWN LIMITATION' entry). Blank for other non-CT counties this Care Compare extract does not cover, too -- blank is never filled with 0." }, "cms_nh_beds_per_1000_pop": { "description": "CMS-certified nursing home beds per 1,000 population (beds divided by panel_county.csv's own population column)", "unit": "beds per 1,000 population", "year": "2026 (August 2026 Care Compare snapshot)", "source_name": "CMS Care Compare, Nursing Home Provider Info", "source_url": "https://data.cms.gov/provider-data/api/1/metastore/schemas/dataset/items/4pq5-n9py", "caveat": "CMS Care Compare, Nursing Home Provider Info, August 2026 snapshot, aggregated to county by facility address (14,691 facilities, name-normalized county match). Joins on county_fips against panel_county.csv at 2,826/3,143 (89.9%). Connecticut is blank under EITHER FIPS scheme (legacy county or post-2022 planning region) -- confirmed by direct set-difference, not assumed: this CMS file has zero Connecticut rows at all, because Connecticut provider addresses no longer carry a legacy county FIPS to build one from. This is a genuine absence in the CMS source, not a fixable join gap -- no crosswalk exists for it, including at the state level (panel_state.csv's cms_nh_* columns are also blank for CT; see data/dataset_expansion.json 'KNOWN LIMITATION' entry). Blank for other non-CT counties this Care Compare extract does not cover, too -- blank is never filled with 0." }, "cms_ma_total_medicare_beneficiaries": { "description": "Total Medicare beneficiaries (Original Medicare + Medicare Advantage), county total, June 2026", "unit": "count (beneficiaries)", "year": "June 2026", "source_name": "CMS Medicare Monthly Enrollment", "source_url": "https://data.cms.gov/sites/default/files/2026-09/4f178691-06d8-4a77-96ea-43fa911e28b5/Medicare%20Monthly%20Enrollment%20Data_June%202026.csv", "caveat": "CMS Medicare Monthly Enrollment, June 2026 (most recent complete month; source file covers 2013-06 through 2026-06). Joins on county_fips against panel_county.csv at 3,135/3,143 (99.7%); the 8 unmatched are Connecticut. This CMS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column." }, "cms_ma_original_medicare_beneficiaries": { "description": "Original (fee-for-service) Medicare beneficiaries, county total, June 2026", "unit": "count (beneficiaries)", "year": "June 2026", "source_name": "CMS Medicare Monthly Enrollment", "source_url": "https://data.cms.gov/sites/default/files/2026-09/4f178691-06d8-4a77-96ea-43fa911e28b5/Medicare%20Monthly%20Enrollment%20Data_June%202026.csv", "caveat": "CMS Medicare Monthly Enrollment, June 2026 (most recent complete month; source file covers 2013-06 through 2026-06). Joins on county_fips against panel_county.csv at 3,135/3,143 (99.7%); the 8 unmatched are Connecticut. This CMS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column." }, "cms_ma_advantage_beneficiaries": { "description": "Medicare Advantage and other plan beneficiaries, county total, June 2026", "unit": "count (beneficiaries)", "year": "June 2026", "source_name": "CMS Medicare Monthly Enrollment", "source_url": "https://data.cms.gov/sites/default/files/2026-09/4f178691-06d8-4a77-96ea-43fa911e28b5/Medicare%20Monthly%20Enrollment%20Data_June%202026.csv", "caveat": "CMS Medicare Monthly Enrollment, June 2026 (most recent complete month; source file covers 2013-06 through 2026-06). Joins on county_fips against panel_county.csv at 3,135/3,143 (99.7%); the 8 unmatched are Connecticut. This CMS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column." }, "medicare_advantage_penetration_pct": { "description": "Medicare Advantage enrollment as a percent of total Medicare beneficiaries, June 2026 (= advantage_beneficiaries / total_medicare_beneficiaries * 100, from CMS's own published counts)", "unit": "percent (0-100)", "year": "June 2026", "source_name": "CMS Medicare Monthly Enrollment", "source_url": "https://data.cms.gov/sites/default/files/2026-09/4f178691-06d8-4a77-96ea-43fa911e28b5/Medicare%20Monthly%20Enrollment%20Data_June%202026.csv", "caveat": "CMS Medicare Monthly Enrollment, June 2026 (most recent complete month; source file covers 2013-06 through 2026-06). Joins on county_fips against panel_county.csv at 3,135/3,143 (99.7%); the 8 unmatched are Connecticut. This CMS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column." }, "naics62_health_socasst_estabs": { "description": "Number of health care and social assistance establishments (NAICS 62, broad sector), 2024 annual average", "unit": "count", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics62_health_socasst_emplvl_annual_avg": { "description": "Average annual employment level at health care and social assistance establishments (NAICS 62), 2024", "unit": "count", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics62_health_socasst_total_annual_wages": { "description": "Total annual wages paid by health care and social assistance establishments (NAICS 62), 2024, US dollars", "unit": "US dollars", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics62_health_socasst_avg_annual_pay_usd": { "description": "Average annual pay per employee, health care and social assistance establishments (NAICS 62), 2024, US dollars", "unit": "US dollars", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics62_any_ownership_cell_suppressed": { "description": "'Y' if at least one of the 4 ownership-sector cells (Federal/State/Local government, Private) summed into the NAICS 62 county total was BLS-suppressed for confidentiality; blank otherwise", "unit": "flag ('Y' or blank)", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics623_nursing_residential_care_estabs": { "description": "Number of nursing and residential care facility establishments (NAICS 623, a subsector of 62), 2024 annual average", "unit": "count", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics623_nursing_residential_care_emplvl_annual_avg": { "description": "Average annual employment level at nursing and residential care facilities (NAICS 623), 2024", "unit": "count", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics623_total_annual_wages": { "description": "Total annual wages paid by nursing and residential care facilities (NAICS 623), 2024, US dollars", "unit": "US dollars", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics623_avg_annual_pay_usd": { "description": "Average annual pay per employee, nursing and residential care facilities (NAICS 623), 2024, US dollars", "unit": "US dollars", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "naics623_any_ownership_cell_suppressed": { "description": "'Y' if at least one of the 4 ownership-sector cells summed into the NAICS 623 county total was BLS-suppressed for confidentiality; blank otherwise", "unit": "flag ('Y' or blank)", "year": "2024 annual averages", "source_name": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment", "source_url": "https://data.bls.gov/cew/data/files/2024/csv/2024_annual_by_industry.zip", "caveat": "BLS Quarterly Census of Employment and Wages (QCEW), county health-sector employment, 2024 annual averages. Joins on county_fips against panel_county.csv at 3,121/3,143 (99.3%); 22 unmatched = Connecticut's 8 legacy counties plus 14 non-CT counties BLS itself suppresses or omits from this extract (HI 15005; MT 30103; NE 31005, 31007, 31009, 31075, 31085, 31103, 31117; NV 32009, 32011; ND 38087; TX 48173, 48301 -- confirmed by direct set-difference against panel_county.csv, not estimated). This BLS file carries Connecticut's 9 post-2022 planning regions (FIPS 09110, 09120, 09130, 09140, 09150, 09160, 09170, 09180, 09190, 09999) instead of the 8 legacy county FIPS panel_county.csv uses (09001-09015); zero FIPS keys overlap between the two schemes, so all 8 CT counties are blank here at the COUNTY level. No crosswalk was built: Connecticut's 2022 transition split 7 of the 8 legacy counties across 2-3 planning regions each, so a correct county-level correspondence does not exist -- see data/dataset_expansion.json 'KNOWN LIMITATION -- Connecticut county-FIPS mismatch' entry. Real Connecticut figures ARE recovered at the STATE level in panel_state.csv (computed directly from this source file, grouped by the state FIPS prefix '09', which is unambiguous under either scheme) -- use panel_state.csv for a Connecticut figure, not this county column. BLS does not publish a county-level 'Total, all ownerships' row in this file; the *_estabs and *_total_annual_wages / *_avg_annual_pay_usd columns here are a direct sum of the 4 published ownership-sector rows (Federal/State/Local government + Private), so any county flagged by *_any_ownership_cell_suppressed = 'Y' has a total that is a floor, not a complete count. *_estabs and *_emplvl_annual_avg are BLS's own 4-quarter averages, not a point-in-time snapshot; *_total_annual_wages is a true annual sum; *_avg_annual_pay_usd = total_annual_wages / emplvl_annual_avg and inherits that averaging." }, "cms_hospice_facilities_count": { "description": "Count of distinct Medicare-certified hospice facilities (by CCN) headquartered in the county", "unit": "count", "year": "2026 (August 2026 Care Compare snapshot)", "source_name": "CMS Care Compare, Hospice - Provider Data", "source_url": "https://data.cms.gov/provider-data/api/1/metastore/schemas/dataset/items/252m-zfp9", "caveat": "Full national file (453,493 long-format measure-rows, one row per facility x quality-measure) was fetched and deduplicated to 6,669 unique CCNs, then matched to panel_county.csv by (state, normalized County/Parish name). 6,551/6,669 facilities matched (98.2%); the 118 unmatched are genuine CMS data-quality gaps in the source file itself (county field literally '-', 'UNITED STATES', or a state name instead of a county) plus Puerto Rico/Guam/US Virgin Islands facilities outside the continental panel -- left blank, not guessed. Counts skew heavily toward a small number of counties (Los Angeles County CA = 1,160, ~18% of the national total) which matches independently-reported 2023-2024 hospice-licensing surges in Los Angeles/Orange/Maricopa/Clark/Harris/Dallas counties, not a join artifact -- cross-checked against the raw per-county count before treating as plausible." }, "cms_hospice_facilities_per_1000pop": { "description": "cms_hospice_facilities_count per 1,000 county population", "unit": "facilities per 1,000 population", "year": "2026 facility snapshot over panel population", "source_name": "Derived (cms_hospice_facilities_count / population * 1000)", "source_url": "", "caveat": "Denominator is this panel's own 'population' column, not a Care Compare field." }, "cms_home_health_agencies_count": { "description": "Count of Medicare-certified home health agencies located in the county", "unit": "count", "year": "2026 (July 2026 Care Compare snapshot)", "source_name": "CMS Care Compare, Home Health Care Agencies", "source_url": "https://data.cms.gov/provider-data/api/1/metastore/schemas/dataset/items/6jpm-sxkc", "caveat": "This CMS file carries only a ZIP code per agency, no county field, so agencies were mapped ZIP -> county FIPS via the Census Bureau 2020 ZCTA-to-county relationship file (data/sources/census_zcta_county_crosswalk/), choosing the county with the largest land-area overlap for ZCTAs that straddle more than one county. 12,375/12,460 agencies (99.3%) matched to a panel county; the remainder fell in ZCTAs with no panel-county match (territories or ZCTA/county edge cases). This is an approximate ZIP-based assignment, not the agency's legal county of record." }, "cms_home_health_agencies_per_1000pop": { "description": "cms_home_health_agencies_count per 1,000 county population", "unit": "agencies per 1,000 population", "year": "2026 agency snapshot over panel population", "source_name": "Derived (cms_home_health_agencies_count / population * 1000)", "source_url": "", "caveat": "Denominator is this panel's own 'population' column, not a Care Compare field." }, "nurse_practitioners_npi_2024": { "description": "Nurse practitioners, NPI-registered, headcount in the county", "unit": "count", "year": "2024", "source_name": "HRSA Area Health Resources Files (AHRF) 2024-2025, health professions sub-file", "source_url": "https://data.hrsa.gov/DataDownload/AHRF/AHRF_2024-2025_CSV.zip", "caveat": "AHRF field np_npi_24. This is a headcount of NPI-registered nurse practitioners, not an FTE or workload measure. Checked and confirmed absent from this AHRF release: any county-level RN, LPN, or nursing-aide/CNA total, and any geriatrician-specific physician specialty field -- AHRF2025hp.csv's 1,927 columns were searched for RN/nurse/LPN/CNA/aide/geriatric patterns and only NP, PA, and clinical-nurse-specialist NPI counts exist. That is a genuine, checked gap in this federal source, not an oversight here." }, "nurse_practitioners_per_1000pop_2024": { "description": "nurse_practitioners_npi_2024 per 1,000 county population", "unit": "NPs per 1,000 population", "year": "2024 over 2023 population estimate", "source_name": "Derived, HRSA AHRF", "source_url": "https://data.hrsa.gov/DataDownload/AHRF/AHRF_2024-2025_CSV.zip", "caveat": "Denominator reuses population_est_2023 already extracted in data/sources/hrsa_ahrf/county_physician_hospital_supply.csv (same AHRF release), not this panel's population column." }, "physician_assistants_npi_2024": { "description": "Physician assistants, NPI-registered, headcount in the county", "unit": "count", "year": "2024", "source_name": "HRSA Area Health Resources Files (AHRF) 2024-2025, health professions sub-file", "source_url": "https://data.hrsa.gov/DataDownload/AHRF/AHRF_2024-2025_CSV.zip", "caveat": "AHRF field pa_npi_24. Headcount, not FTE." }, "physician_assistants_per_1000pop_2024": { "description": "physician_assistants_npi_2024 per 1,000 county population", "unit": "PAs per 1,000 population", "year": "2024 over 2023 population estimate", "source_name": "Derived, HRSA AHRF", "source_url": "https://data.hrsa.gov/DataDownload/AHRF/AHRF_2024-2025_CSV.zip", "caveat": "Denominator reuses population_est_2023 already extracted in data/sources/hrsa_ahrf/county_physician_hospital_supply.csv (same AHRF release), not this panel's population column." }, "clinical_nurse_specialists_npi_2024": { "description": "Clinical nurse specialists, NPI-registered, headcount in the county", "unit": "count", "year": "2024", "source_name": "HRSA Area Health Resources Files (AHRF) 2024-2025, health professions sub-file", "source_url": "https://data.hrsa.gov/DataDownload/AHRF/AHRF_2024-2025_CSV.zip", "caveat": "AHRF field clin_nurse_spec_npi_24. Headcount, not FTE." }, "clinical_nurse_specialists_per_1000pop_2024": { "description": "clinical_nurse_specialists_npi_2024 per 1,000 county population", "unit": "CNS per 1,000 population", "year": "2024 over 2023 population estimate", "source_name": "Derived, HRSA AHRF", "source_url": "https://data.hrsa.gov/DataDownload/AHRF/AHRF_2024-2025_CSV.zip", "caveat": "Denominator reuses population_est_2023 already extracted in data/sources/hrsa_ahrf/county_physician_hospital_supply.csv (same AHRF release), not this panel's population column." } }