The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. An update of the Rural-Urban Continuum Codes is planned for mid-2023.

Abstract

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

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

The USDA provides RUCC codes at the county.

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

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

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

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

Methodology

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

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PLURAL (Place-level urban-rural indices) is a framework to create continuous classifications of "rurality" or "urbanness" based on the spatial configuration of populated places. PLURAL makes use of the concept of "remoteness" to characterize the level of spatial isolation of a populated place with respect to its neighbors. There are two implementations of PLURAL, including (a) PLURAL-1, based on distances to the nearest places of user-specified population classes, and (b) PLURAL-2, based on neighborhood characterization derived from spatial networks. PLURAL requires simplistic input data, i.e., the coordinates (x,y) and population p of populated places (villages, towns, cities) in a given point in time. Due to its simplistic input, the PLURAL rural-urban classification scheme can be applied to historical data, as well as to data from data-scarce settings. Using the PLURAL framework, we created place-level rural-urban indices for the conterminous United States from 1930 to 2018. Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. We developed a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. Based on these methods, we constructed indices of urbanness for 30,000 places in the United States from 1930 to 2018. We call these indices the place-level urban-rural index (PLURAL), enabling long-term, fine-grained analyses of urban and rural change in the United States. The method paper has been peer-reviewed and is published in "Landscape and Urban Planning". The PLURAL indices from 1930 to 2018 are available as CSV files, and as point-based geospatial vector data (.SHP). Moreover, we provide animated GIF files illustrating the spatio-temporal variation of the different variants of the PLURAL indices, illustrating the dynamics of the rural-urban continuum in the United States from 1930 to 2018. Apply the PLURAL rural-urban classification to your own data: Python code is fully open source and available at https://github.com/johannesuhl/plural. Data sources: Place-level population counts (1980-2010) and place locations 1930 - 2018 were obtained from IPUMS NHGIS, (University of Minnesota, www.nhgis.org; Manson et al. 2022). Place-level population counts 1930-1970 were digitized from historical census records (U.S. Census Bureau 1942, 1964). References: Uhl, J.H., Hunter, L.M., Leyk, S., Connor, D.S., Nieves, J.J., Hester, C., Talbot, C. and Gutmann, M., 2023. Place-level urban–rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, p.104762. DOI: https://doi.org/10.1016/j.landurbplan.2023.104762 Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0 U.S. Census Bureau (1942). U.S. Census of Population: 1940. Vol. I, Number of Inhabitants. U.S. Government Printing Office, Washington, D.C. U.S. Census Bureau (1964). U.S. Census of Population: 1960. Vol. I, Characteristics of the Population. Part I, United States Summary. U.S. Government Printing Office, Washington, D.C.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

Unpublished data product not for circulation Persistent Poverty tracts*Persistent poverty area and enduring poverty area measures with reference year 2015-2019 are research measures only. The ERS offical measures are updated every ten years. The next updates will use 1960 through 2000 Decennial Census data and 2007-2011 and 2017-2021 5-year ACS estimates. The updates will take place following the Census Bureau release of the 2017-2021 estimates (anticipated December 2022).A reliability index is calculated for each poverty rate (PctPoor) derived using poverty count estimates and published margins of error from the 5-yr ACS. If the poverty rate estimate has low reliability (=3) AND the upper (PctPoor + derived MOE) or lower (PctPoor - derived MOE) bounds of the MOE adjusted poverty rate would change the poverty status of the estimate (high = 20.0% or more; extreme = 40.0% or more) then the county/tract type is coded as "N/A". If looking at metrics named "PerPov0711" and PerPov1519" ERS says: The official measure ending in 2007-11 included data from 1980. The research measure ending in 2015-19 drops 1980 and begins instead with 1990. There were huge differences in geographic coverage of census tracts and data quality between 1980 and 1990, namely "because tract geography wasn’t assigned to all areas of the country until the 1990 Decennial Census. Last date edited 9/1/2022Variable NamesVariable Labels and ValuesNotesGeographic VariablesGEO_ID_CTCensus download GEOID when downloading county and tract data togetherSTUSABState Postal AbbreviationfipsCounty FIPS code, in numericCountyNameArea Name (county, state)TractNameArea Name (tract, county, state)TractCensus Tract numberRegionCensus region numeric code 1 = Northeast 2 = Midwest 3 = South 4 = Westsubreg3ERS subregions 1 = Northeast and Great Lakes 2 = Eastern Metropolitan Belt 3 = Eastern and Interior Uplands 4 = Corn Belt 5 = Southeastern Coast 6 = Southern Coastal Plain 7 = Great Plains 8 = Rio Grande and Southwest 9 = West, Alaska and HawaiiMetNonmet2013Metro and nonmetro county code 0 = nonmetro county 1 = metro countyBeale2013ERS Rural-urban Continuum Code 2013 (counties) 1 = counties in metro area of 1 million population or more 2 = counties in metro area of 250,000 to 1 million population 3 = counties in metro area of fewer than 250,000 population 4 = urban population of 20,000 or more, adjacent to a metro area 5 = urban population of 20,000 or more, not adjacent to a metro area 6 = urban population of 2,500 to 19,999, adjacent to a metro area 7 = urban population of 2,500 to 19,999, not adjacent to a metro area 8 = completely rural or less than 2,500, adjacent to a metro area 9 = completely rural or less than 2,500, not adjacent to a metro areaRUCA_2010Rural Urban Commuting Areas, primary code (census tracts) 1 = Metropolitan area core: primary flow within an urbanized area (UA) 2 = Metropolitan area high commuting: primary flow 30% or more to a UA 3 = Metropolitan area low commuting: primary flow 10% to 30% to a UA 4 = Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC) 5 = Micropolitan high commuting: primary flow 30% or more to a large UC 6 = Micropolitan low commuting: primary flow 10% to 30% to a large UC 7 = Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC) 8 = Small town high commuting: primary flow 30% or more to a small UC 9 = Small town low commuting: primary flow 10% to 30% to a small UC 10 = Rural areas: primary flow to a tract outside a UA or UC 99 = Not coded: Census tract has zero population and no rural-urban identifier informationBNA01Census tract represents block numbering areas; BNAs are small statistical subdivisions of a county for numbering and grouping blocks in nonmetropolitan counties where local committees have not established tracts. 0 = not a BNA tract 1 = BNA tractPoverty Areas MeasuresHiPov60Poverty Rate greater than or equal to 20.0% 1960 (counties only) -1 = N/A 0 = PctPoor60 < 20.0% 1 = PctPoor60 >= 20.0%HiPov70Poverty Rate greater than or equal to 20.0% 1970 -1 = N/A 0 = PctPoor70 < 20.0% 1 = PctPoor70 >= 20.0%HiPov80Poverty Rate greater than or equal to 20.0% 1980 -1 = N/A 0 = PctPoor80 < 20.0% 1 = PctPoor80 >= 20.0%HiPov90Poverty Rate greater than or equal to 20.0% 1990 -1 = N/A 0 = PctPoor90 < 20.0% 1 = PctPoor90 >= 20.0%HiPov00Poverty Rate greater than or equal to 20.0% 2000 -1 = N/A 0 = PctPoor00 < 20.0% 1 = PctPoor00 >= 20.0%HiPov0711Poverty Rate greater than or equal to 20.0% 2007-11 ACS -1 = N/A 0 = PctPoor0711 < 20.0% 1 = PctPoor0711 >= 20.0%HiPov1519Poverty Rate greater than or equal to 20.0% 2015-19 ACS -1 = N/A 0 = PctPoor1519 < 20.0% 1 = PctPoor1519 >= 20.0%ExtPov60Poverty Rate greater than or equal to 40.0% 1960 (counties only) -1 = N/A 0 = PctPoor60 < 40.0% 1 = PctPoor60 >= 40.0%ExtPov70Poverty Rate greater than or equal to 40.0% 1970 -1 = N/A 0 = PctPoor70 < 40.0% 1 = PctPoor70 >= 40.0%ExtPov80Poverty Rate greater than or equal to 40.0% 1980 -1 = N/A 0 = PctPoor80 < 40.0% 1 = PctPoor80 >= 40.0%ExtPov90Poverty Rate greater than or equal to 40.0% 1990 -1 = N/A 0 = PctPoor90 < 40.0% 1 = PctPoor90 >= 40.0%ExtPov00Poverty Rate greater than or equal to 40.0% 2000 -1 = N/A 0 = PctPoor00 < 40.0% 1 = PctPoor00 >= 40.0%ExtPov0711Poverty Rate greater than or equal to 40.0% 2007-11 ACS -1 = N/A 0 = PctPoor0711 < 40.0% 1 = PctPoor0711 >= 40.0%ExtPov1519Poverty Rate greater than or equal to 40.0% 2015-19 ACS -1 = N/A 0 = PctPoor1519 < 40.0% 1 = PctPoor1519 >= 40.0%PerPov90Official ERS Measure: Persistent Poverty 1990: poverty rate >= 20.0% in 1960, 1970, 1980, and 1990 (counties only) May not match previously published versions due to changes in geographic normalization procedures. -1 = N/A 0 = poverty rate not >= 20.0% in 1960, 1970, 1980, and 1990 1 = poverty rate >= 20.0% in 1960, 1970, 1980, and 1990PerPov00Official ERS Measure: Persistent Poverty 2000: poverty rate >= 20.0% in 1970, 1980, 1990, and 2000May not match previously published versions due to changes in geographic normalization procedures. -1 = N/A 0 = poverty rate not >= 20.0% in 1970, 1980, 1990, and 2000 1 = poverty rate >= 20.0% in 1970, 1980, 1990, and 2000PerPov0711Official ERS Measure: Persistent Poverty 2007-11: poverty rate >= 20.0% in 1980, 1990, 2000, and 2007-11May not match previously published versions due to changes in geographic normalization procedures and -1 = N/A application of reliability criteria. 0 = poverty rate not >= 20.0% in 1980, 1990, 2000, and 2007-11 1 = poverty rate >= 20.0% in 1980, 1990, 2000, and 2007-11PerPov1519Research Measure Only: Persistent Poverty 2015-19: poverty rate >= 20.0% in 1990, 2000, 2007-11, and 2015May not match previously published versions due to changes in geographic normalization procedures and -1 = N/A application of reliability criteria. 0 = poverty rate not >= 20.0% in 1990, 2000, 2007-11, and 2015-19 1 = poverty rate >= 20.0% in 1990, 2000, 2007-11, and 2015-19EndurePov0711Official ERS Measure: Enduring Poverty 2007-11: poverty rate >= 20.0% for at least 5 consecutive time periods up-to and including 2007-11 -1 = N/A 0 = Poverty Rate not >=20.0% in 1970, 1980, 1990, 2000, and 2007-11 1 = poverty rate >= 20.0% in 1970, 1980, 1990, 2000, and 2007-11 2 = poverty rate >=20.0% in 1960, 1970, 1980, 1990, 2000, and 2007-11 (counties only)EndurePov1519Research Measure Only: Enduring Poverty 2015-19: poverty rate >= 20.0% for at least 5 consecutive time periods, up-to and including 2015-19 -1 = N/A 0 = Poverty Rate not >=20.0% in 1980, 1990, 2000, 2007-11, and 2015-19 1 = poverty rate >= 20.0% in 1980, 1990, 2000, 2007-11, and 2015-19 2 = poverty rate >= 20.0% in 1970, 1980, 1990, 2000, 2007-11, and 2015-19 3 = poverty rate >=20.0% in 1960, 1970, 1980, 1990, 2000, 2007-11, and 2015-19 (counties only)Additional Notes: *In the combined data tab each variable ends with a 'C' for county and a 'T' for tractThe spreadsheet was joined to Esri's Living Atlas Social Vulnerability Tract Data (CDC) and therefore contains the following information as well: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and TransportationThis feature layer visualizes the 2018 overall SVI for U.S. counties and tracts. Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract.15 social factors grouped into four major themes | Index value calculated for each county for the 15 social factors, four major themes, and the overall rank

Logistic regression model assessing associations between participant Rural-Urban Continuum Code (RUCC) and reported exercise change.

This link contains downloadable data for the Atlas of Rural and Small-Town America which provides statistics by broad categories of socioeconomic factors: People: Demographic data from the American Community Survey (ACS), including age, race and ethnicity, migration and immigration, education, household size, and family composition. Jobs: Economic data from the Bureau of Labor Statistics and other sources, including information on employment trends, unemployment, and industrial composition of employment from the ACS. County classifications: Categorical variables including the rural-urban continuum codes, economic dependence codes, persistent poverty, persistent child poverty, population loss, onshore oil/natural gas counties, and other ERS county typology codes. Income: Data on median household income, per capita income, and poverty (including child poverty). Veterans: Data on veterans, including service period, education, unemployment, income, and other demographic characteristics.

The goal is to predict the rate of heart disease (per 100,000 individuals) across the United States at the county-level from other socioeconomic indicators. The data is compiled from a wide range of sources and made publicly available by the United States Department of Agriculture Economic Research Service (USDA ERS).

There are 33 variables in this dataset. Each row in the dataset represents a United States county, and the dataset we are working with covers two particular years, denoted a, and b We don't provide a unique identifier for an individual county, just a row_id for each row.

The variables in the dataset have names that of the form category_variable, where category is the high level category of the variable (e.g. econ or health). variable is what the specific column contains.

We're trying to predict the variable heart_disease_mortality_per_100k (a positive integer) for each row of the test data set.

Columns

area — information about the county

area_rucc — Rural-Urban Continuum Codes "form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/)

area_urban_influence — Urban Influence Codes "form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/urban-influence-codes/)

econ — economic indicators

econ_economic_typology — County Typology Codes "classify all U.S. counties according to six mutually exclusive categories of economic dependence and six overlapping categories of policy-relevant themes. The economic dependence types include farming, mining, manufacturing, Federal/State government, recreation, and nonspecialized counties. The policy-relevant types include low education, low employment, persistent poverty, persistent child poverty, population loss, and retirement destination." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/county-typology-codes.aspx)

econ_pct_civilian_labor — Civilian labor force, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_unemployment — Unemployment, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_uninsured_adults — Percent of adults without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/) econ_pct_uninsured_children — Percent of children without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/)

health — health indicators

health_pct_adult_obesity — Percent of adults who meet clinical definition of obese (National Center for Chronic Disease Prevention and Health Promotion)

health_pct_adult_smoking — Percent of adults who smoke (Behavioral Risk Factor Surveillance System)

health_pct_diabetes — Percent of population with diabetes (National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation)

health_pct_low_birthweight — Percent of babies born with low birth weight (National Center for Health Statistics)

health_pct_excessive_drinking — Percent of adult population that engages in excessive consumption of alcohol (Behavioral Risk Factor Surveillance System, )

health_pct_physical_inacticity — Percent of adult population that is physically inactive (National Center for Chronic Disease Prevention and Health Promotion)

health_air_pollution_particulate_matter — Fine particulate matter in µg/m³ (CDC WONDER, https://wonder.cdc.gov/wonder/help/pm.html)

health_homicides_per_100k — Deaths by homicide per 100,000 population (National Center for Health Statistics)

health_motor_vehicle_crash_deaths_per_100k — Deaths by motor vehicle crash per 100,000 population (National Center for Health Statistics)

health_pop_per_dentist — Population per dentist (HRSA Area Resource File)

health_pop_per_primary_care_physician — Population per Primary Care Physician (HRSA Area Resource File)

demo — demographics information

demo_pct_female — Percent of population that is female (US Census Population Estimates)

demo_pct_below_18_years_of_age — Percent of population that is below 18 years of age (US Census Population Estimates)

demo_pct_aged_65_years_and_older — Percent of population that is aged 65 years or older (US Census Population Estimates)

dem...

The USRDS is the largest and most comprehensive national ESRD surveillance system in the US (Collins et al., 2015). The USRDS contains data on all ESRD cases in the US through the Medical Evidence Report CMS-2728 which is mandated for all new patients diagnosed with ESRD (Foley and Collins, 2013). Detailed information about the USRDS can be found on their website (http://www.usrds.org). The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data stored as csv files. This dataset is associated with the following publication: Kosnik, M., D. Reif, D. Lobdell, T. Astell-Burt, X. Feng, J. Hader, and J. Hoppin. Associations between access to healthcare, environmental quality, and end-stage renal disease survival time: Proportional-hazards models of over 1,000,000 people over 14 years. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 14(3): e0214094, (2019).

Infant mortality was defined as death before completion of first year of life [1]. We obtained linked birth and infant death data from the U.S. Centers for Disease Control and Prevention for the years 2000–2005, corresponding to the time frame covered by the EQI. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018).

The MarketScan health claims database is a compilation of nearly 110 million patient records with information from more than 100 private insurance carriers and large self-insuring companies. Public forms of insurance (i.e., Medicare and Medicaid) are not included, nor are small (< 100 employees) or medium (1000 employees). We excluded the relatively few (n=6735) individuals over 65 years of age because Medicare is the primary insurance of U.S. adults over 65. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

There has been considerable international study on the etiology of rising mental disorders, such as attention-deficit hyperactivity disorder (ADHD), in human populations. As glyphosate is the most commonly used herbicide in the world, we sought to test the hypothesis that glyphosate use in agriculture may be a contributing environmental factor to the rise of ADHD in human populations. State estimates for glyphosate use and nitrogen fertilizer use were obtained from the U.S. Geological Survey (USGS). We queried the Healthcare Cost and Utilization Project net (HCUPNET) for state-level hospitalization discharge data in all patients for all-listed ADHD from 2007 to 2010. We used rural-urban continuum codes from the USDA-Economic Research Service when exploring the effect of urbanization on the relationship between herbicide use and ADHD. Least squares dummy variable (LSDV) method and within method using two-way fixed effects was used to elucidate the relationship between glyphosate use and all-listed ADHD hospital discharges. We show that a one kilogram increase in glyphosate use, in particular, in one year significantly positively predicts state-level all-listed ADHD discharges, expressed as a percent of total mental disorders, the following year (coefficient = 5.54E-08, p

Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). Prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that _domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each _domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and _domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).

Introduction: Chronic pain is the most common qualifying condition found in states with legal (certified) access to medical cannabis (MC). We assessed the geographic distribution of MC certifications for severe chronic or intractable pain in Pennsylvania (PA) between 2018 to 2024, identifying relationships between community variables with the percentage of adults with a MC certification for pain.

Methods: Using data from the PA Department of Health (PDOH) from 2018 to 2024 (N = 44,645 to 165,740 certifications for pain / year), we mapped Zip codes associated with MC certifications for pain to counties and Zip code tabulation areas (ZCTAs), geographic areas that approximate a standardized representation of zip codes for statistical purposes. The difference between the highest and lowest counties was determined. A linear regression evaluated correlations between community variables and the percentage of adults in geographical areas with a MC certification for pain in 2024.

Results: There was almost a four-fold difference in the percent of adults with a MC certification for pain in the highest (Perry = 2.3%) versus lowest (Tioga = 0.6%) counties in 2024. Bradford and Tioga County had a significantly (p < 0.05) lower percentage certified relative to the county-wide average. There was a significantly higher proportion of certifications for pain in counties with larger population densities of adults (1.76 +/- 0.12%) than counties with smaller population densities (1.38% +/- 0.14%) of adults (t(65) = 4.66, p < 0.001, d = 1.14). At the county level, higher median household income (r(65) = +0.335, p < 0.01), lower median age (r(65) = -0.241, p = 0.0499), and higher Rural-Urban Continuum Code (r(65) = +0.644, p < 0.001) were associated with a greater percentage of adults with an MC certification for pain using simple linear regression. Using a multiple regression model, only Rural-Urban Continuum Code was significantly associated with the percent of adults with an MC certification for pain (p < 0.001). At the ZCTA level, the proportion of non-White individuals, including Hispanics, showed a significant inverse association with the percent of adults with an MC certification for pain (r(1,722) = -0.07, p < 0.01).

Conclusions: This study identified four-fold county level disparities in MC certifications for pain. The association between median household income and MC pain certifications may indicate differences in accessibility of MC based on financial status. Further research may be warranted pending any changes to the legal status or demand for MC.

The aim of this study was to provide a systematic empirical assessment of three basic organizational premises of Community-Oriented Policing (COP). This study constructed a comprehensive data set by synthesizing data available in separate national data sets on police agencies and communities. The base data source used was the 1999 Law Enforcement Management and Administrative Statistics (LEMAS) survey [LAW ENFORCEMENT MANAGEMENT AND ADMINISTRATIVE STATISTICS (LEMAS), 1999 (ICPSR 3079)], which contained data on police organizational characteristics and on adoption of community-oriented policing procedures. The 1999 survey was supplemented with additional organizational variables from the 1997 LEMAS survey [LAW ENFORCEMENT MANAGEMENT AND ADMINISTRATIVE STATISTICS (LEMAS), 1997 (ICPSR 2700)] and from the 1996 Directory of Law Enforcement Agencies [DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1996: UNITED STATES]. Data on community characteristics were extracted from the 1994 County and City Data Book, from the 1996 to 1999 Uniform Crime Reports [UNIFORM CRIME REPORTING PROGRAM DATA. [UNITED STATES]: OFFENSES KNOWN AND CLEARANCES BY ARREST (1996-1997: ICPSR 9028, 1998: ICPSR 2904, 1999: ICPSR 3158)], from the 1990 and 2000 Census Gazetteer files, and from Rural-Urban Community classifications. The merging of the separate data sources was accomplished by using the Law Enforcement Agency Identifiers Crosswalk file [LAW ENFORCEMENT AGENCY IDENTIFIERS CROSSWALK [UNITED STATES], 1996 (ICPSR 2876)]. In all, 23 data files from eight separate sources collected by four different governmental agencies were used to create the merged data set. The entire merging process resulted in a combined final sample of 3,005 local general jurisdiction policing agencies. Variables for this study provide information regarding police organizational structure include type of government, type of agency, and number and various types of employees. Several indices from the LEMAS surveys are also provided. Community-oriented policing variables are the percent of full-time sworn employees assigned to COP positions, if the agency had a COP plan, and several indices from the 1999 LEMAS survey. Community context variables include various Census population categories, rural-urban continuum (Beale) codes, urban influence codes, and total serious crime rate for different year ranges. Geographic variables include FIPS State, county, and place codes, and region.

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

The data were collected during 2013-2014, the last years of the Tobacco Transition Payment Program. The survey included a series of questions about history of innovation, business creation, current state of business activities, business risk taking, entrepreneurial intentions, and community, sociodemographic, family, and personality characteristics (total 229 questions). The survey was mailed to 12,000 household addresses in the state of Kentucky, covering 79 of Kentucky’s 120 counties—56 rural farming counties, 12 mining counties, and 11 urban counties, classified according to the US Department of Agriculture’s Rural-Urban Continuum Codes. For each occupation category of interest (self-employed, farming, neither self-employed nor farming) a random address list for the sample area was generated to be representative of the distribution of occupation by county. Completed surveys were returned by 1481 residents, a response rate of 12.3%; 661 of them reported to have experience with new venture creation. The respondents were highly diverse in terms of age, education, income, and personal and professional experience. A subset of respondents (N = 138) were farmers who participated in the Tobacco Transition Payment Program. The diversity of the respondents and breadth of survey questions allows examining complex relationships between individual, family, business, and community level factors affecting entrepreneurial intentions, actions, and success in rural and urban settings.