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.

{"definition": "9-level classification of counties by metro-nonmetro status, location, and urban size", "availableYears": "2000", "name": "Rural-urban continuum code, 2003", "units": "Classification", "shortName": "RuralUrbanContinuumCode2003", "geographicLevel": "County", "dataSources": "U.S. Department of Agriculture, Economic Research Service, using data from the U.S. Census Bureau"}

© RuralUrbanContinuumCode2003 This layer is sourced from gis.ers.usda.gov.

Urban-Rural Classifications by USDA, Economic Research Service - http://www.ers.usda.gov/topics/rural-economy-population/rural-classifications.aspxCode Urban Influence Description1 Large Metro >1M Population2 Small Metro <1M Population3 Micropolitan Near Large Metro4 Rural Near Large Metro5 Micropolitan Near Small Metro6 Rural Near Small Metro, town >2.5K7 Rural Near Small Metro, town <2.5K8 Micropolitan Not Near Metro9 Rural Near Micropolitan, town >2.5K10 Rural Near Micropolitan, town <2.5K11 Rural Not Near Metro or Micro, town >2.5K12 Rural Not Near Metro or Micro, town <2.5Khttp://www.ers.usda.gov/data-products/urban-influence-codes/documentation.aspx#.UYKQ2kpZRvYCode Rural-Urban Continuum Description1 Metro Area >1M Population2 Metro Area >250K-1M Population3 Metro Area <250K Population4 Urban >20K Near Metro5 Urban >20K Not Near Metro6 Urban 2.5K-20K Near Metro7 Urban 2.5K-20K Not Near Metro8 Rural, Near Metro9 Rural, Not Near Metrohttp://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx#.UYJuVEpZRvY

The counties comprising Appalachia, based on the Appalachian Regional Commission (https://www.arc.gov/appalachian-counties-served-by-arc), plus the counties that fall within a 10-mile buffer of the ARC counties, with 2010 RUCA codes joined. The original source of the counties shapefile was the U.S. Census Bureau's 2020 Cartographic Boundary Files. The original source of the data was the USDA ERS (https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx), averaged from the tract level to the county level using the FIPS code.

The Rural-Urban Continuum Codes (RUCC), developed by the U.S. Department of Agriculture's Economic Research Service (ERS), classify U.S. counties by their level of urbanization and proximity to metropolitan areas. Counties are categorized as metropolitan or nonmetropolitan, with further divisions based on population size, urbanization level, and adjacency to metro regions. The RUCC provides a detailed framework that supports research and policy analysis in areas such as public health, sociology, regional planning, and economic development. It is widely used for identifying rural-urban disparities and integrates Census data, aligning with Office of Management and Budget (OMB) metro delineations for consistent updates. Its nuanced stratification is particularly valuable in studies like the Alzheimer's Disease Neuroimaging Initiative (ADNI), which explore the social determinants of health.

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).

This project introduces researchers and other users to the Index of Relative Rurality (IRR), a continuous, multi-dimensional, and scalable measure for characterizing the rurality of areas or regions in the United States. First proposed by Waldorf in 2006,[1] and later operationalized by Waldorf & Kim,[2,3] the IRR is an alternative to categorical measures such as the Rural-Urban Continuum Codes (RUCC), Rural-Urban Commuting Areas (RUCA), and Frontier and Remote (FAR) Codes, or binary classifications researchers derive from them. We are distributing these data because IRR values for some of these US geographies have not been available previously, and because we want to clearly and fully document the data sources and methods necessary to calculate the IRR. 1. Waldorf, B.S., 2006. A continuous multi-dimensional measure of rurality: Moving beyond threshold measures. Accessed 3/26/2025 at https://ageconsearch.umn.edu/record/21383?v=pdf. 2. Waldorf, B. and Kim, A., 2015. Defining and measuring rurality in the US: From typologies to continuous indices. In Commissioned paper presented at the Workshop on Rationalizing Rural Area Classifications, Washington, DC. Accessed 3/26/2025 at http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf. 3. Kim, A. and Waldorf, B., 2023. The Index of Relative Rurality (IRR): US County Data for 2020. Accessed 3/26/2025 at https://zenodo.org/records/7675745. DOI: 10.5281/zenodo.7675745

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

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).

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

The US Environmental Protection Agency's (EPA) Center for Public Health and Environmental Assessment (CPHEA) Public Health & Environmental Systems Division (PHESD) is currently engaged in research aimed at developing a measure that estimates overall environmental quality at the census tract level for the United States. This work is being conducted as an effort to learn more about how various environmental factors simultaneously contribute to health disparities in low-income and minority populations, and to better estimate the total environmental and social context to which humans are exposed. This work contains the finalized Environmental Quality Index (EQI), as a single index combining variables from each of the associated domains for the 2006-2010 census tract level EQI: air, water, land, built environment, and sociodemographic environment as well as EQI for census tract stratified by Rural Urban Continuum Code (RUCA) as determined by a reclassification based off urbancity and commuting flow initially proposed in Urban-Rural Residence and the Occurrence of Cleft Lip and Cleft Palate in Texas, 1999-2003 published in Annals of Epidemiology (Messer, et al, 2010, https://pubmed.ncbi.nlm.nih.gov/20006274/); RUCA initially was 10 classifications made by USDA Economic Research Service composed of: RUCA 1 Metropolitan Core Area, RUCA 2 Metropolitan High Commuting Area, RUCA 3 Metropolitan Low Commuting Area, RUCA 4 Micropolitan Area Core, RUCA 5, Micropolitan High Commuting, RUCA 6 Micropolitan Low Commuting Area, RUCA 7 Small Town Core, RUCA 8 Small Town High Commuting Area, RUCA 9 Small Town Low Commuting, RUCA 10 Rural Areas (https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/). RUCA 1 remained it's own class, RUCA 2 remained it's own class, RUCA 3, 4, 5, 6 were combined and conveyed as RUCA 3, RUCA 7, 8, 9 were combined and now conveyed as RUCA 4 and RUCA 10 became RUCA 5 in the new classification. Within the new classification RUCA 1 is Urban Core, RUCA 2 is Suburban Area, RUCA 3 is Micropolitan Area, RUCA 4 is Small Town Area and RUCA 5 is Rural Area (Messer, et al, 2010). This dataset contains the finalized variables chosen to represent the overall environment within in a single Principal Component Analysis (PCA); data sources are: EPA's CMAQ: The Community Multiscale Air Quality Modeling System (http://www.https://www.epa.gov/cmaq/), the National-Scale Air Toxics Assessment (http://www.epa.gov/nata/), the U.S. Geological Survey Estimates of Water Use in the U.S. for 2010 (https://water.usgs.gov/watuse/data/2010/), the U.S. Drought Monitor Data (http://droughtmonitor.unl.edu/), “Estimated Annual Agricultural Pesticide Use for Counties of the Conterminous United States” data for pesticide use (https://www.usgs.gov/data/estimated-annual-agricultural-pesticide-use-counties-conterminous-united-states-2013-17-ver-20), CropScape (https://nassgeodata.gmu.edu/CropScape), EPA Facility Registry Service (https://www.epa.gov/frs/geospatial-data-download-service), Dun and Bradstreet North American Industry Classification System (NAICS) codes(http://www.dnb.com); National Land Cover Database (NCDL) (https://www.mrlc.gov/), United States Census (http://www2.census.gov) and ESRI Crime Report (https://doc.arcgis.com/en/esri-demographics/data/crime-indexes.htm).

IntroductionBased on questions about impairments and activity limitations, the American Community Survey shows that roughly 13% of the U.S. population is experiencing disability. As most people live in households with other persons, this study explores disability at the household level. Considering the literature on household decision-making, solidarity, and capabilities in disability, this analysis of the household context of disability takes into account residential settings, household composition, and urban–rural differences.MethodThe 2015–2019 ACS Public Use Microdata Sample (PUMS), which shows persons with disability (PwD) and persons without disability (PwoD), also indicates household membership, used here to separately identify PwoD as those living in households with persons with disability (PwoD_HHwD) and those in households without any household member with disability (PwoD_HHwoD). Relationship variables reveal the composition of households with and without disabilities. An adaption of Beale's rural–urban continuum code for counties is used to approximate rural–urban differences with ACS PUMS data.ResultsSolo living is two times as common among persons with disability than among persons without disability, and higher in rural than urban areas. In addition to 43 million PwD, there are another 42 million PwoD_HHwD. Two times as many persons are impacted by disability, either of their own or that of a household member, than shown by an analysis of individual-level disability. For family households, differences in the composition of households with and without disabilities are considerable with much greater complexities in the makeup of families with disability. The presence of multiple generations stands out. Adult sons or daughters without disability play an important role. Modest urban–rural differences exist in the composition of family households with disability, with a greater presence of multigenerational households in large cities.DiscussionThis research reveals the much wider scope of household-level disability than indicated by disability of individuals alone. The greater complexity and multigenerational makeup of households with disability imply intergenerational solidarity, reciprocity, and resource sharing. Household members without disability may add to the capabilities of persons with disabilities. For the sizeable share of PwD living solo, there is concern about their needs being met.

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.

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 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 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.