The Appalachian Regional Commission designates certain counties and tracts as “distressed areas” in order to identify and monitor the economic status of areas within Appalachia. The designations are based on an index that compares economic indicators of Appalachian counties and tracts to national averages. The index employs three-year average unemployment rates, per capita market incomes, and poverty rates to rank every county in the nation. Counties are designated as distressed, at-risk, transitional, competitive, or attainment, based on this rank, where distressed counties rank as mostly economically depressed (worst 10% economic performance in the nation) and attainment the most economically strong (best 10% economic performance in the nation). In addition to all census tracts located in distressed counties, census tracts in at-risk or transitional counties are considered “distressed areas” if they have a median family income 67% or less than the national average and a poverty rate 150% of the national average or greater.
Distressed areas are updated annually using the most recently available American Community Survey five-year estimates. Distressed areas for 2012 are presented at the 2000 census tract boundaries, and 2013-2022 areas are presented at the 2010 boundaries, 2023 areas are presented at the 2020 boundaries, and 2024-2025 areas are presented at the 2022 boundaries.
Black Knight estimates number of sales of existing single-family homes, condominiums, and mobile homes for U.S. zip codes. The estimates are based on deed transaction filings from county recorders offices’ public-record filings.
Percent changes are calculated by PolicyMap.
Personal income consists of income received in return for provision of labor, land, and capital used in current production, as well as other income such as personal current transfer receipts. It is the sum of wages and salaries, supplements to wages and salaries, proprietors’ income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, less contributions for government social insurance plus the adjustment for residence.
The Bureau of Labor Statistics’ Local Area Unemployment Statistics (LAUS) program produces monthly and annual employment, unemployment, and labor force data for Census regions and divisions, States, counties, metropolitan areas, and many cities, by place of residence. PolicyMap contains county and state counts of people employed, unemployed, and in the labor force, as well as the unemployment rate. The annual values presented in PolicyMap are annual averages for the years listed as provided by the BLS. The concepts and definitions used by LAUS come from the Current Population Survey (CPS), the household survey that is the official measure of the labor force for the nation. According to this definition, employed persons include people who did any paid work as employees, worked in their own business or farm, or did unpaid work of 15 or more hours in an establishment owned by a relative. Unemployed persons include people who had no employment but were available for and seeking employment. People in the labor force are all those people classified as employed or unemployed. The labor force does not include military (active duty) and institutionalized persons. Every April, the Bureau of Labor Statistics re-releases revised data for the previous five years. PolicyMap’s data reflects these revisions. In 2015, the BLS changed the methodology of their model, with changes relating to structural differences, real-time benchmarking, smoothed seasonal adjustment, and treatment of outliers. At the state-level data, this methodology was used to re-estimate all data back to 1976 (PolicyMap displays data for 2000 and later). At sub-state geographies (city, county, CBSA, metropolitan division), the new methodology was only used to re-estimate data going back to January 2010. For this reason, comparisons between data before and after this change are not advised. Certain sub-state areas have the revised methodology re-estimated back to 1990: New York City (back to 1976), the Los Angeles-Long Beach-Glendale metropolitan division, the Chicago-Naperville-Arlington Heights, IL metropolitan division; the Cleveland-Elyria, OH metropolitan area; the Detroit-Warren-Dearborn, MI metropolitan area; the Miami-Miami Beach-Kendall, FL metropolitan division; and the Seattle-Bellevue-Everett, WA metropolitan division. Note that the previous methodology disaggregated data from the state level; because the state methodology is being re-estimated for before 2010, sub-state level data is indirectly affected. Data for Connecticut counties is unavailable prior to 2020 due to boundary changes introduced by BLS. Data for Connecticut is available 2020 onward at the new county-equivalent, planning region boundaries.
The annual data contains percent change calculations on the number of people in the labor force and employed, as well as a change in percent calculation of the unemployment rate. These were calculated by PolicyMap.
The Bureau of Labor Statistics’ gathers data on employment and wages from state workforce agencies and compiles it under the Quarterly Census of Employment and Wages (QCEW) program. The data are derived from quarterly tax reports submitted by employers to State workforce agencies under State Unemployment Insurance (UI) laws; and from Federal agencies under the Unemployment Compensation for Federal Employees (UCFE) program. PolicyMap displays only private sector employment and wage data.
PolicyMap displays QCEW annual location quotient data for both private and public sector employment. BLS uses location quotients, or LQs, to see where occupations or industries are concentrated in the United States. LQs measure how concentrated jobs in a particular industry is in a specific area compared to the national average. If an LQ is equal to 1, then the industry has the same share of its area employment as it does in the nation. An LQ greater than 1 indicates an industry with a greater share of the local area employment than is the case nationwide. For example, in Philadelphia County, the LQ for employment for Educational Services in the private sector is roughly 4. Educational Services makes up 8% of jobs in Philadelphia, compared to about 2% nationally. The concentration of employment in Philadelphia County in Private Educational Services is about 4 times that of the concentration of employment for Educational Services in the U.S. This industry makes up a larger share of the Philadelphia County employment total than it does for the nation as a whole. For more information on QCEW LQ calculations, visit https://www.bls.gov/cew/about-data/location-quotients-explained.htm
Names, locations, and financial data for a large dataset of non-profit entities. Data is sourced from the Internal Revenue Service’s data on Form 990 filings. Organizations file Form 990, or Returns for Organziations Exempt from income tax under particular sections of the Internal Revenue Code. This form is used to report required information such as total expenses, total assets, number of employees, and other information. Some locations (i.e, a given address or set of latitude and lonngitude coordinates) have more than one Employer Identification Number (EIN); this likely represents organizations that have multiple chapters, each with their own EIN, that use a single organization name and address.
The CDC’s PLACES program is an expansion of the small area health estimates that they created for their 500 Cities program starting in 2015. The 500 Cities program used statistical techniques to produce estimates for various health outcomes or risk factors at small areas for the largest 500 Cities in the United States. The PLACES program expands these estimates across the country, and makes the data available for Census tracts, ZCTAs, Census places (called Cities on PolicyMap), and Counties. These estimates were created using the CDC’s Behavioral Risk Factor Surveillance System (BRFSS), the Decennial Census, and estimates from the American Community Survey.
The CDC publishes both crude and age-adjusted estimates. Many of the chronic health conditions and risk factors estimated in this dataset have a strong correlation with age. This means that areas with much older populations may have deceptively high crude rates of illness. Age-adjusted estimates correct for the different age profiles of different geographies, which makes it easier to understand whether certain health conditions are truly worse in certain areas. Crude rates are available at the Census tract, ZCTA, City, and County levels, and Age-adjusted rates are available at the City and County levels.
Equitable Recovery Program: The Community Development Financial Institutions Fund awards Community Development Financial Institutions (CDFIs) grants for economic recovery from the COVID-19 pandemic. The CDFI Fund designates census tracts as eligible for these grants based on the following criteria: (a) are census tracts that (i) demonstrate “severe impact” of the COVID-19 pandemic, and (ii) have a median income at or below 120% of the Area Median Income, and (iii) are CDFI Investment Areas; or (b) are Native Areas.
Severe COVID Impact/Low Community Resilience designation is given to census tracts that demonstrate any of the following: severe mortality (being in the highest tercile of the number of deaths per 100,000 people, according to reported cumulative mortality for the period from April 1, 2020 to March 31, 2021), severe change in unemployment or low community resilience (based on data from the US Census Bureau Community Resilience Estimates Program). CDFI Investment Areas are geographies which have a population poverty rate of at least 20% (including Persistent Poverty Counties, which are counties that have had 20% of more of their populations living in poverty over the past 30 years),or have unemployment rates of at least 1.5 times the national or other criteria listed by CDFI Fund.
The Community Development Financial Institution (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC) and Bank Enterprise Award (BEA) programs, and supports and invests in Community Development Financial Institutions. For information about the NMTC, please see entry, below. The CDFI Fund maintains a list of Census Tracts and their program eligibility or designation, based on income, poverty and unemployment data provided by the Census Bureau’s 2016-2020 American Community Survey (ACS) for 2020 census tracts. For more on these programs users should consult the CDFI Fund website directly: www.cdfifund.gov .
The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC). PolicyMap has performed calculations on various data sources in order to map eligibility and threshold requirements established by the CDFI Fund for Part II (Community Impact) of the NMTC Allocation Application. The NMTC Allocation Application data on PolicyMap is available as follows. Note that the latest eligibility criteria use Census American Community Survey (ACS) 2016-2020 estimates.
CDFI Fund New Markets Tax Credit NMTC Eligibility NMTC Eligible Census tracts include those that have either (1) Median Family Income at or below 80% of Area Median Income (AMI) in the period of 2016-2020 or (2) Poverty Rate of 20% or greater in the period of 2016-2020. PolicyMap provides a map of those eligible Census tracts (“Eligible Tracts”), as well as the underlying data used to create that map in the (“Eligibility Criteria”). PolicyMap also provides the underlying data without the NMTC thresholds (“Tract Family Income as % of AMI” and “Poverty”).
Severe Distress, Deep Distress, High Migration Rural County, and Non-Metropolitan Meeting the NMTC Severe Distress, Deep Distress, High Migration Rural County, or Non-Metropolitan criteria is based on whether or not a given Census tract meets basic NMTC Eligibility, plus one of the following factors:
Severe Distress : having a median family income at or below 60% of AMI in the period of 2016-2020; having a poverty rate at or above 30% in the period of 2016-2020; having an unemployment rate of at least 1.5 times the national unemployment rate in the period of 2016-2020;
Deep Distress : having a median family income at or below 40% of AMI in the period of 2016-2020; having a poverty rate at or above 40% in the period of 2016-2020; having an unemployment rate of at least 2.5 times the national unemployment rate in the period of 2016-2020;
High Migration Rural County : having median family income at or below 85 percent of the applicable AMI in the period of 2016-2020; being in any county during the 20-year period ending with the year in which the most recent census was conducted, with a net out-migration of inhabitants from the county of at least 10 percent of the population of the county at the beginning of such period; high migration rural counties qualify as low-income communities;
Non-Metropolitan : being in a county that is not part of a metropolitan statistical area.
*The median family income threshold for NMTC, more specifically, is: Census tracts with, if located within a non-Metropolitan Area, median family income at or below 60% of statewide median family income or, if located within a Metropolitan Area, median family income at or below 60% of the greater of the statewide median family income or the Metropolitan Area median family income.
Other Criteria for NMTC: Other criteria can include two of the following: meeting NMTC Heavy Distress requirements; being located within: an SBA Designated HUB Zone, a Medically Underserved Area (MUA), a Census tract within which a Brownfield is located, a HOPE VI Redevelopment Area, a Federal Native Area, an Appalachian Regional Commission or Delta Regional Authority Area, a Colonias Area, a State or Local Economic Zone (such as TIF or KOZ), a FEMA Disaster Area, or a ERS/USDA Food Desert. Please note that the data on PolicyMap do not take into account the following, due to unavailability of data: HOPE VI Redevelopment Areas, Federal Native Areas, Colonias Areas, State or Local Economic Zones, and FEMA Disaster Areas. Included in this submenu are the data for each of the available factors that constitute the Secondary Criteria for NMTC Severely Distressed.
Because any of these data sources may have been updated since the production of these calculations, users should verify eligibility directly with the CDFI Fund. Information in PolicyMap does not include HOPE VI Redevelopment Areas, Federal Native Areas, Colonias Areas, or State or Local Economic Zones. FEMA Disaster Areas are accessible under Federal Guidelines.
NMTC Eligibility and Qualified Opportunity Zones A joint dataset that includes both NMTC eligibility and designated Qualified Opportunity Zones. Census tracts labeled as “Designated OZ” are census tracts that have been nominated and designated as a Qualified Opportunity Zone (QOZ), according to the CDFI Fund and census tracts labeled as “NMTC Eligible” are census tracts that meet the CDFI Fund’s New Markets Tax Credit (NMTC) eligibility for CY 2018. For more information see the directory entry for Qualified Opportunity Zones .
The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, provides funding to CDFIs and qualified non-profit housing organizations through the Capital Magnet Fund. Areas of Economic Distress and Rural Areas are among the selection criteria used to determine eligibility. Additionally, Areas of High Housing Need and Metropolitan Areas were among the past selection criteria used to determine eligibility and are also available on PolicyMap. The CDFI Fund considers a tract to be an Area of Economic Distress in 2024 if it meets at least one of the following criteria: (i) At least 20% of Very-Low Income households spend more than half of their income on housing; or (ii) The tract is a Low-Income Housing Tax Credit qualified census tract; or (iv) Greater than 20 percent of households have incomes below the poverty rate with a rental vacancy rate of at least 10 percent; or (iv) Greater than 20 percent of households have incomes below the poverty rate with a homeownership vacancy rate of at least 10 percent; or (v) Tract is an underserved rural area as defined in the CMF Interim Rule. FHFA’s Duty to Serve regulation defines “rural area” as: (i) A census tract outside of a metropolitan statistical area, as designated by the Office of Management and Budget; or (ii) A census tract in a metropolitan statistical area, as designated by the OMB, that is: (A) Outside of the MSA’s Urbanized Areas as designated by the U.S. Department of Agriculture’s (USDA) Rural-Urban Commuting Area Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA’s RUCA Code #2. FHFA’s Duty to Serve regulation defines “high opportunity areas” as: (1) tracts designated by the Department of Housing and Urban Development (HUD) as a “Difficult Development Area” (DDA) during any year covered by an Enterprise’s Underserved Markets Plan or in the year before a Plan’s effective date, whose poverty rate falls below 10% (for metropolitan areas) or 15% (for non-metropolitan areas). Or (2) an area designated by a state or local Qualified Allocation Plan (QAP) as a high opportunity area whose poverty falls below 10% (for metropolitan areas) or 15% (for non-metropolitan areas). High opportunity areas are used to determine eligibility for extra credit under Duty to Serve. Low-Income Area means a census tract in which the median income does not exceed 80 percent of the median income for the area in which such census tract or block numbering area is located. For a census tract or block numbering area located within a Metropolitan Area, the median family income shall be at or below 80 percent of the Metropolitan Area median family income or the national Metropolitan Area median family income, whichever is greater. In the case of a census tract located outside of a Metropolitan Area, the median family income shall be at or below 80 percent of the statewide Non-Metropolitan Area median family income or the national Non-Metropolitan Area median family income, whichever is greater.
For more information about the Capital Magnet Fund data, see the CDFI Fund’s website here .
The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the Capital Magnet Fund (CMF). CMF, established in 2008 and appropriated in 2010, is a competitive grant program to attract private capital for affordable housing development. CMF dollars are available to CDFIs and nonprofit housing developers who are active in affordable housing development. In FY 2012-2013, CMF awardees used the funds to finance 8,049 affordable rental units and 922 homeowner-occupied homes. CMF dollars may be used for the following purposes: loan loss reserves, revolving loan funds, affordable housing funds, or risk-sharing loans; economic development activities or community service facilities (day-care centers, workforce development centers, health care clinics) that support affordable housing as part of an overall community revitalization strategy. CMF grants must be matched at least 10:1 with other funding sources. One hundred percent of housing-eligible project costs must be used to finance units for households with income below 120% of area median income (AMI); and 51% of costs must be used for households with income below 80% of AMI. Rental housing projects have more specific requirements; please see the CDFI Fund website for more information.
CMF grantees track the use of funds though periodic financial and project reports submitted to the CDFI Fund.
The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, supports and invests in Community Development Financial Institutions through the CDFI Program and Native American CDFI Assistance Program. CDFIs are financial institutions that provide products and services in economically distressed target markets. The CDFI Fund certifies CDFIs through an application process on a rolling basis, depending on the type of institution. Not all CDFIs are certified, but certification is a requirement for some federal program funding. All PO Boxes were excluded from geocoding. The remaining addresses geocoded by PolicyMap had a 95.5% match rate. Data on certified CDFI locations are updated twice annually.
For CDFI transactions that span multiple census tracts or counties, medians are calculated using the total project cost while aggregations are calculated by dividing the total transaction cost by the number of census tracts or counties involved. Transaction or project counts at smaller geographies may not match larger geography counts given the double counting of split transactions and projects across census tracts and counties. Subcategories of transactions, such as CDFI Investments by Borrower Type, may not sum to the total amount of CDFI investments in a given area.
The Community Development Financial Institutions (CDFI) Fund, a division of the U.S. Department of the Treasury, collects data from Community Development Entities (CDEs) based on information submitted through the New Markets Tax Credit (NMTC) program. NMTC awards are allocated to CDEs investing in operating businesses and real estate projects located in Low-Income Communities (LICs). This dataset is an aggregated collection of these projects, totaling the number, project type, and dollar value of investments reported from 2010 through 2019. Calculations were conducted by PolicyMap to create summary values based on geography and by project type. Total values were aggregated to state and zip code based on the address provided for the transaction. The CDFI Fund provided 2000 or 2010 census tracts associated with each transaction. Dollar values for transactions in multiple tracts were averaged across all tracts associated with the project. The total number of transactions for census tracts in an area may not be equivalent to totals by state and zip code. NMTC investments are also aggregated to CDEs, using the list of certified CDEs made available by the CDFI Fund. Only entities certified as CDEs may receive NMTC allocations. PolicyMap geocoded 276 CDEs with New Markets transactions as reported from 2005 – 2012, and was able to locate 100% of the addresses on a map.
CDE Locations are no longer updated by the source. PolicyMap will remove this data in 2020 unless the dataset is updated or another source is found.
The Community Development Financial Institutions (CDFI) Fund is a division of the U.S. Department of Treasury. Per the Consolidated Appropriations Act of 2012, funding was provided for several CDFI Fund programs (Bank Enterprise Award Program, CDFI Program, Healthy Food Financing Initiative, and Native American CDFI Assistance Program) on condition that a minimum of 10% of the projects served must be in persistent poverty counties. The legislation defines a persistent poverty county as any county that has had 20 percent or more of its population living in poverty for the past 30 years as measured by the U.S. Census Bureau. Based on this criteria, the CDFI Fund used data from the 1990 and 2000 decennial censuses, and the 2016-2020 American Community Survey to determine qualifying counties.
The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, is now using a priority point system for scoring its applications. Applicants are awarded up to 5 “priority points” for their commitment to serve communities facing the highest levels of distress.
The score is tabulated using various distress indicators, which are also mapped on PolicyMap. These indicators are: poverty rates, median household income, unemployment rates, home foreclosures and high-cost mortgages.
Once designated, Qualified Opportunity Zones (QOZ) can receive substantial tax breaks for long term investments to low-income neighborhoods. State governors can nominate up to twenty five percent or twenty five total, whichever is larger, low-income community (LIC) census tracts for QOZ designation. The current QOZ designations in PolicyMap show those tracts nominated and designated as QOZs in June 2018. Additionally, the QOZ designations in PolicyMap reflect rural area status as defined in IRS Notice 2025-50, as of October 2025. In this notice, a “Rural Area” is defined as any area other than (i) a city or town that has a population of greater than 50,000 inhabitants, and (ii) any urbanized area contiguous and adjacent to a city or town with more than 50,000 inhabitants. Census tracts are considered LICs if the tract has either (1) a median family income at or below 80% of Area Median Income (AMI) or (2) a poverty rate of 20% or greater as determined with the 2011-2015 Census American Community Survey data. LIC eligibility also includes select qualified high migration tracts, low-population tracts within Empowerment Zones, and territorial census tracts that meet the LIC qualifications.
Up to five percent of tracts that are nominated for the QOZ can be qualified non-LIC as long as they are contiguous to a LIC and have a median family income that is not greater than 125 percent of the adjacent LIC. Eligible contiguous non-LIC tracts that are contiguous to a LIC in a different state are also eligible for nomination if the adjacent state nominates the LIC QOZ.
Opportunity Insights, a team of researchers and policy analysts based at Harvard University, conducted a longitudinal study of economic and social conditions of adults based on where they were raised. One of the products of this study was a dataset published at https://opportunityinsights.org/ . This dataset includes average household earnings and incarceration rates for adults who were raised in low income households in a given tract, county, or commuting zone.
The researchers used demographic data from the 2000 and 2010 Census short forms, combined with data from the 2000 Census long form and 2015 American Community Survey. They linked this Census data with tax returns from 1989, 1994, 1995, and 1998 to 2015. By combining all this data at the person level, they were able to match people who were born from 1978 to 1983 with the census tracts where they were born and raised, and with the household earnings of their parents. They used this longitudinal dataset to calculate the incarceration rate (per 100 people) for people raised in households with incomes less than the 25th percentile based on whether they were in jail or prison on April 1, 2010, the reference date of the 2010 Census for two genders—men and women—and three racial and ethnic groups—Black, Hispanic, and White. Average household income was also calculated for this age cohort and parental income group using the income data from the tax returns. Incarceration rates and average incomes were prorated based on how much time the person spent in a given tract or county in their youth, and information was suppressed for areas with fewer than 20 children. Some noise was also infused into the source data to preserve privacy.
PolicyMap calculated the diversity index and predominant race/ethnicity data layers using Census’ American Community Survey estimates. For both the diversity index and predominant race/ethnicity, PolicyMap used a total of 8 non-overlapping racial and ethnic categories provided by the US Census Bureau. These included the ethnic category Hispanic and the following 7 Non-Hispanic racial categories: White, African American, American Indian or Alaska native, Asian, Native Hawaiian or Pacific Islander, some other race, and two or more races. For predominant race, PolicyMap used 7 non-overlapping racial categories: White, African American, American Indian or Alaska native, Native Hawaiian or Pacific Islander, some other race, and two or more races.
The diversity index reflects the probability that any two people chosen at random from a given study area (e.g., block group) are of different races or ethnicities. An index value of 0 indicates complete homogeneity (i.e., an area’s entire population belonging to one racial or ethnic group), while the maximum index value represents complete heterogeneity (i.e., each racial or ethnic group constituting an equal proportion of an area’s population). The maximum value is calculated as one minus the reciprocal of the number of racial or ethnic groups. For example, with 3 racial or ethnic groups, the index value reflecting complete diversity would be 1-(1/3) or 67%. Articulated further, with 3 racial groups of equal proportions (i.e., complete diversity), the index equation becomes 1 – (0.33^2 + 0.33^2 + 0.33^2) = 67%. Given 8 racial or ethnic categories, the maximum value of the index displayed on PolicyMap is 87.5%.
With the diversity index data layer, lower index values between 0 and 20 suggest more homogeneity and higher index values above 50 suggest more heterogeneity. Racial and ethnic diversity can be indicative of economic and behavioral patterns. For example, racially and ethnically homogenous areas are sometimes representative of concentrated poverty or concentrated wealth. They could also be indicative of discriminatory housing policies or other related barriers.
The predominant racial or ethnic group is calculated as the racial or ethnic group constituting the highest proportion of the population in a given geography. A “tie” was noted in cases where the more than one racial or ethnic group shared the highest percent of population, rounded to the nearest 0.1 percent.
PolicyMap calculated Theil’s H index of segregation using the U.S. Census Bureau’s American Commmunity Survey. For this index, PolicyMap used a total of 8 non-overlapping racial and ethnic categories provided by the US Census Bureau. These categories included the ethnic category Hispanic and the following 7 Non-Hispanic racial categories: White, African American, American Indian or Alaska native, Asian, Native Hawaiian or Pacific Islander, some other race, and two or more races. Theil’s H is an index ranging from 0 to 1 that estimates the extent to which racial and ethnic groups are evenly distributed in a sub-area as compared to a larger area. Values approaching 0 suggest that sub-areas have a composition similar to the larger area (i.e., even distribution, less segregation) and values approaching 1 suggest that the racial and ethnic composition of sub-areas within a larger area deviates from the larger area (i.e., non-uniform distribution, more segregation). On PolicyMap, sub-areas are defined at the level of the Census block and are compared to the following larger areas: block groups, tracts, counties and Core-Based Statistical Areas (CBSAs), which are an approximation of metropolitan areas. The calculation of Theil’s H is based on the methodology presented in the report, “The Multigroup Entropy Index” by John Iceland (2004). This methodology involves calculating the entropy, a measure of diversity, for each sub-area and larger area and calculating the population-weighted deviation in entropy values across all sub-areas within each larger area.
Geographies for which no data or limited data were provided or for which the population was less than 10 are represented as having “Insufficient Data.”
The tract-level persistent poverty data layer on PolicyMap was created by PolicyMap using poverty data from the 2000 and 2010 censuses and the 2008-2012 and 2015-2019 American Community Surveys, as provided by Brown University’s Longitudinal Tract Database (LTDB). In determining persistent poverty tracts, PolicyMap applied the same definition that the Community Development Financial Institutions (CDFI) Fund uses in determining persistent poverty county status, which is to assume a persistent poverty tract to be any tract that has had 20 percent of more of its population living in poverty over the past 30 years.
PolicyMap downloaded the 2000, 2010, 2008-2012, 2015-2019 data at the 2010 tract boundaries from the LTDB . The LTDB, developed by a research team including John Logan (Brown University), Zengwang Xu (University of Wisconsin, Milwaukee), and Brian Stults (Florida State University), provides public-use tools to create estimates within 2010 tract boundaries for tract-level Census data that are available as far back as 1970. For a detailed explanation of the LTDB team’s methodology for harmonizing data over this period, please see their website . Further explanation of the methodology can also be found in the Professional Geographer’s article entitled “ Interpolating US Decennial Census Tract Data from as Early as 1970 to 2010: A Longitudinal Tract Database ,” which was authored by Loan, Xu, and Stults.
Business Dynamics Statistics (BDS) provides data over time on openings and closings of businesses, as well as detailed information about those businesses. Data comes from the Longitudinal Business Database . Data provided on PolicyMap includes number and percent of firms of various sizes and ages. This can be used to see the activity of small businesses and startups, as well as larger and established businesses. Firms included in our calculations include all which have at least one active physical establishment in a given area. (If one firm has two establishments in two different areas, it will be counted once in each location.) Firm age is calculated by assigning an initial age according to the age of the oldest establishment that is part of the firm when it is created. (If a firm is created out of a merger or acquisition, its age is based on the age of the oldest establishment in the original firm or acquisition.) Firm size is calculated by the average employment in the current and previous year.
BDS data is available at the state and CBSA level. To view CBSA data, select “Metro Area” from the “Shaded by” menu in the map legend.
County Business Pattern Data (CBP) is an annual series that provides economic data by industry. The data describe the number and type of jobs that are located in any given place. This is different from describing the occupations of people living in the same area. CBP covers most of the country’s economic activity. The series excludes data on self-employed individuals, employees of private households, railroad employees, agricultural production employees, and most government employees. CBP data are extracted from the Business Register, the Census Bureau’s file of all known single and multi-establishment companies. The Company Organization Survey (annual) and Economic Censuses (every five years) provide individual establishment data for multi-location firms. Data for single-location firms are obtained from various surveys conducted by the Census Bureau, such as the Economic Censuses, the Annual Survey of Manufacturers, and Current Business Surveys, as well as from administrative records of the Internal Revenue Service, the Social Security Administration, and the Bureau of Labor Statistics. Jobs in the CBP data are reported by North American Industry Classification System (NAICS, pronounced “Nakes”) categories. NAICS is the standard for use by Federal statistical agencies in classifying business establishments for the collection, analysis, and publication of statistical data related to the national business economy. NAICS is run through the Office of Management and Budget (OMB), and, in 1997, replaced the Standard Industrial Classification (SIC) system. Business establishments self-assign their NAICS code based on the primary economic activities in which they engage. CBP data is given using two different methodologies. At the county, CBSA, state, and nation level, values are given for the number of employees in a given industry. These values are infused with noise added by the Census in order to avoid disclosing data that would be identifiable to a specific employer. In addition, some values are withheld by the Census to avoid disclosing data for individual companies. In these cases, the Census provides a range within which the value falls, but these are not included on PolicyMap, and are shown as Insufficient Data.
On PolicyMap, the data is also given as an “estimate”. For each industry, at every geography, (including Zip code) CBP provides values for the number of establishments that fall in various ranges of number of employees. These ranges are 1-4 employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, 500-999, and 1000 or more. There are additional higher ranges at the county and CBSA level. PolicyMap takes the midpoint of each range (so, for 10-19 it would be 14.5) and multiplies that by the number of establishments. Establishments with 1,000 or more employees are given a value of 1,750. This number for each range is added together to get the estimate. Though this number is less precise, its advantages are that there are no longer suppressions (so there is better coverage on the map), and ZIP code data is included.
Demographic data for 2000 is from the U.S. Bureau of the Census’ Summary File 3 (SF3). This dataset is derived from the longer version (“long form”) of the household survey that takes place every ten years. SF3 data include information on housing conditions as well as characteristics of the household and its members. Demographic data for 2009-2013, 2014-2018, and 2019-2023 is from the U.S. Bureau of the Census’ American Community Survey (ACS). This survey replaced the long form from the Decennial Census in 2010. Rather than distributing both a short survey and the long form in 2010, the U.S. Census Bureau instead distributed the short survey as the Decennial Census. Beginning in 2000, the U.S. Census Bureau began administering the new ACS Survey, which is comprised of many of the questions from the old Census long form. With the release of the 2005-2009 ACS data, the ACS data includes small geographic estimates. The ACS data provides demographic, social, economic and housing characteristic estimates on a rolling basis (from 2009-2013, 2014-2018, and 2019-2023), whereas the 2010 and 2020 Decennial Census provides counts of the population and their basic characteristics (sex, age, race, Hispanic origin, and homeowner status) as a snapshot in time. The move from the long form on the Decennial Census to the ACS format allows data consumers to enjoy annually updated detailed population characteristics, rather than having to wait for the Decennial Census data release. The ACS differs from the Decennial Census in that it is not an enumeration (complete count) of the population, however. Instead, the Census Bureau collects ACS data from a sample of the population, and it provides a margin of error for every ACS estimate. Margins of error are not shown on PolicyMap, but users are encouraged to visit the Census’ website with questions about ACS estimates shown on PolicyMap. PolicyMap displays the 2009-2013 and 2014-2018 ACS data and the 2010 SF1 data using the Census’ 2010 geographic file boundaries. PolicyMap shows the 2000 SF3 data using the Census’ 2000 geographic file boundaries. PolicyMap shows the 2020 and 2019-2023 Census data using the Census’ 2020 geography file boundaries. Because TIGER file boundaries across different years are not identical, users will likely see differences in boundary areas when toggling from data at different geographic file boundaries. For places, counties and county subdivisions, PolicyMap employed a Census-provided bridge table in order to calculate percent changes. PolicyMap also employed a Census-provided bridge table to relate 2000 census tracts to 2010 census tracts for percent change calculations. Because the Census has not yet provided relationship files for 2010 to 2020 boundaries or 2000 to 2020, PolicyMap created equivalent relationship files, see the Census Geography Division section for more details. In the case of block groups, PolicyMap created a bridge table to relate the 2000 Census SF3 data to the 2010 Census boundaries. The bridge table was created by first allocating 2000 block counts to their respective 2010 blocks using a family, household, or population multiplier. Then, after employing the Census-provided 2000 block to 2010 block table, PolicyMap summed the 2000 block estimates to 2010 Census boundary block groups. For percent change calculations for medians, PolicyMap calculated 2000 medians at the 2010 Census boundaries by creating component buckets of values using the Census 2000 count data at the 2010 Census boundaries and deriving the median from those counts. Data is shown at the Zip Code Tabulation Area (ZCTA) level. ZCTAs are based on US Postal Service ZIP Codes, but they are not identical, and in many cases, may be significantly different. Though ZIP Codes change continuously, ZCTAs are set in 2010 and 2020, and remain unchanged throughout the decade. Data shown for ZCTAs represent the information in the ZCTA area, not the ZIP Code. More information on ZCTAs can be found here: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html . Demographic data for 2010 is from the U.S. Bureau of the Census’ Summary File 1 (SF1). This dataset comprises what previously was referred to as the shorter version (“short form”) of the household survey that takes place every ten years. The SF1 data represents the count of every resident in the United States, mandated by Article I, Section 2 of the Constitution. These counts determine the number of seats per state in the U.S. House of Representatives. It is also used to distribute federal funds at the sub-state level. SF1 data includes information about population, age, race, ethnicity, household composition, home ownership and housing unit occupancy. The American Community Survey (ACS) has replaced the previous Census’ long form. Census 2010 data, therefore, constitutes only a fraction of the indicators previously released as Census Decennial data. Demographic data for 2020 is from the U.S. Bureau of the Census’ Decennial Demographic and Housing Characteristics file (DHC). The DHC provides detailed data on age, sex, race, Hispanic or Latino origin, household and family composition, group quarters population, and housing characteristics such as occupancy and tenure. For the 2020 Census a new form of privacy protection was introduced called differential privacy. This will introduce some noise into the 2020 decennial data that may look different than the previous privacy technique called “swapping” that was employed for the 2010 decennial data. No noise was introduced to the state counts since those are used for apportionment of state representatives. Data on specific languages spoken at home was not released at local geographies after 2011-2015, so data for that time frame is shown.
For clarification of Census variable definitions please refer to this list of subject definitions: https://www.census.gov/programs-surveys/cps/technical-documentation/subject-definitions.html.
Most of the boundary files on PolicyMap come directly from the Geography Division of the U.S. Census. Block Group, Tract, County, State, County Subdivision, Place, Core-based Statistical Area (CBSA), Metropolitan Division, Congressional District, and Zip Code Tabulation Area (ZCTA) boundary files are all publicly available TIGER files. See: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html . Every ten years, following the Decennial Census, there are major updates to many of the Census boundaries based on a new population count. Additionally, there are minor updates annually reflecting changes to local geographies (for example, in 2014, the county-equivalent Bedford City, Virginia, was combined with its adjacent county). These minor updates also reflect more accurate surveying techniques by the Census. Because of the significant changes that were made to underlying geography files in 2010, PolicyMap displays different data at Census 2000, 2010, and 2020 boundaries. In some cases, a single dataset will display some years of data at the different vintages. For example, Census 2000 data is shown at the 2000 Census boundaries while the most recent American Community Survey (ACS) data is displayed at the 2010 boundaries. In cases where a single dataset relies on multiple boundary files, percent changes from 2000 to 2010 are calculated using relationship files and other materials provided by the Census. For more, see: https://www.census.gov/geographies/reference-files.html . In most cases the materials provided by the Census were adequate to make the calculations. However, the process for calculating Block Group percent changes was more complex because the Census does not provide Block Group relationship files. PolicyMap relied on the Census Block relationship files for these calculations using a family, household, or population multiplier to crosswalk the 2000 data to 2010 boundaries to make the calculation. Percent changes that involve both 2000 and 2010 vintages are all displayed using 2010 Census boundary files. For additional information on percent change calculations please see data directory entries for individual datasets. In cases where a single dataset relies on multiple boundary files, percent changes from 2010 to 2020 and 2000 to 2020 are calculated using relationship files created by PolicyMap from Census TIGER boundaries. These PolicyMap relationship files match census boundaries across different vintages (such as 2000 to 2020) as equivalent if both boundaries contain 98% or more of the area for an individual boundary that shares the same unique identification code (often called a FIPS code). Change over time was also calculated across boundaries if each boundary vintage was comprised of more than 50% of both vintages. PolicyMap has the following geographies with distinct boundaries for the 2000, 2010, and 2020 vintages: block group, tract, county, county subdivision, and place. The state boundaries do not have substantial changes from 2000 to 2010 or from 2010 to 2020 (changes are simply related to improved surveying), so only the latest 2020 state file is loaded in PolicyMap. Core-based Statistical Area (CBSAs, or “Metro Areas” on PolicyMap) and Metropolitan Division boundary files have been released on a different schedule than the other boundaries, as they are delineated by the Office of Management and Budget (OMB). Significant changes were made in 2003, 2013, and 2019. PolicyMap uses 2010 CBSAs (which are based on the 2003 OMB delineation), 2013 CBSAs, and 2019 CBSAs. CBSA encompasses both metropolitan and micropolitan areas. For additional information on metro areas and their historical and current delineations please see: https://www.census.gov/programs-surveys/metro-micro.html . Places (identified as “Cities” on PolicyMap) are defined by the Census as either incorporated places or Census Designated Places (CDPs). Incorporated places are legally established to provide governmental functions for a concentration of people. An incorporated place is usually a city, town, village, or borough, but can take other forms. CDPs are statistical entities created by the Census “to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located”. For more on the difference between incorporated areas and CDPs, see: https://www.census.gov/geo/reference/gtc/gtc_place.html The vintages of Place boundaries used in PolicyMap are 2009, 2010, and 2020. The 2009 cities are labeled on PolicyMap as 2000 for the sake of consistency and ease of use, because they are used for Census 2000 data. Zip Code Tabulation Areas (ZCTAs) are based on US Postal Service ZIP Codes, but they are not identical, and in many cases, may be significantly different. Though ZIP Codes change continuously, ZCTAs are set in 2010 and 2020, and remain unchanged throughout the decade. The congressional districts from 2004 to 2020 in the boundary menu reflect the congressional boundaries changing to redistricting in the 108th, 112th, 113th, 115th, 116th, and 117th sessions. On PolicyMap, election data displays the 110th Congress boundaries for 108th, 109th, 110th, 111th, and 112th Congresses, which are years 2004 to 2012. Congressional sessions 113th is for 2014, 2016 maps to 115th, and 2018 maps to 116th Congress. The boundary menu maps the 117th congressional boundaries with updated districts in North Carolina for the 2020 election cycle. The North Carolina boundaries are from the North Carolina General Assembly, which became law in 2019. The vintage – or year – of the boundary used can be found in several places throughout the site. On the Maps page, the boundary type and boundary year is shown in the legend once data is loaded on the map. There is also an option to overlay additional 2000, 2010, and 2020 boundaries by clicking on the “Boundaries” menu. When you search for a geography using the Set Location Bar, a dropdown menu will appear allowing you to select the vintage of the boundary you would like to see. If the boundary for the area of your search has changed, you can select either the 2000, 2010, or 2020 boundary definition. If the boundary has not changed there will only be one item in the dropdown labeled “2000 and 2010 and 2020 boundary”.
PolicyMap ZIP codes are licensed from Precisely. See: Precisely ZIP Code Boundaries.
The LEHD Origin-Destination Employment Statistics (LODES) datasets are released at the Census block level in a series of state-based files available for download here: http://lehd.ces.census.gov/data/#lodes . This is the same data as what is available on Census’ OnTheMap application: http://onthemap.ces.census.gov/ . The Longitudinal Employer-Household Dynamics (LEHD) program is part of the Center for Economic Studies at the U.S. Census Bureau. The LEHD program combines federal, state and Census Bureau data on employers and employees under the Local Employment Dynamics (LED) Partnership. Under the LED partnership, states share Unemployment Insurance earnings data and the Quarterly Census of Employment and Wages (QCEW) data with the Census. Census combines these data with additional federal administrative data, Census data, and surveys. The LEHD program also creates a partially synthetic dataset on workers’ residential patterns, offering a dynamic link showing where people live and where they work. LED was built state by state, and a handful of state-year combinations are not available. These include Alaska (2017-2021), Arizona (2002, 2003), Arkansas (2002, 2019-2021), the District of Columbia (2002-2009), Massachusetts (2002-2010), Mississippi (2002 , 2003, 2019-2021), New Hampshire (2002), Puerto Rico (all years), and the U.S. Virgin Islands (all years). Data on the resident workforce exists for these locations but are not included on PolicyMap due to their incompleteness. The resident workforce values for these missing state-year combinations only show residents who live in these states but work elsewhere. For more on the LED Partnership see: http://lehd.ces.census.gov/state_partners/ . Federal employment is not counted in state Unemployment Insurance data, and as a result federal employment was not included in LEHD until 2010. Data shown by default on PolicyMap does not contain federal employment. However, additional variables in the legend allow users to see data with federal employment included for years 2010 onwards. In order to calculate percent changes in employment and workforce numbers, PolicyMap subtracted federal employment from certain indicators (as noted) from 2010 to the most recent years available. Some demographic variables were introduced in 2009, including race, ethnicity, educational attainment, and sex. Because of the incomparability of the data between 2009 and 2010 with the introduction of Federal employment, PolicyMap chose to begin mapping these additional indicators in 2010.
LODES data are released at the 2020 Census tabulation block geographies and PolicyMap aggregated up to the larger geographies using Census provided relationship tables. PolicyMap displays all LEHD data at the TIGER 2010 boundary geographies, except CBSAs, which are shown at 2019 geographies, and congressional districts, which are shown at the 116 th congressional districts.
The U.S. Census Bureau’s Manufacturing, Mining, and Construction Statistics Division provides annual and monthly estimates of housing units authorized by building permit officials. Data are available at the county, state, CBSA, and national geographies. These estimates are aggregated and imputed from reports submitted by local permit-issuing offices. Most permit-issuing offices are municipalities, and the rest are counties, townships or towns. 9,000 out of the 20,000 permit-issuing places submit the Form C-404 report “Report of Building or Zoning Permits Issued and Local Public Construction” on a monthly basis. The other places are surveyed only annually. The 9,000 surveyed monthly include: all permit-issuing places in the 75 Metropolitan Areas (MAs) with the largest number of permits (as of 2002); all permit-issuing places in states with limited numbers of permit-issuing places; permit-issuing places with special data reporting arrangements. The rest of the sample is stratified by state. Prior to 2005, monthly counts of building permits were based on a different sample of 8,500 out of 19,000 permit-issuing agencies. As a result, comparisons of building permit issuances between 2004 and 2005 should be made very cautiously. If a building permit report is not received for a given month or year, the missing data are either obtained from the Survey of Use of Permits (SUP) or imputed. The SUP is an annual survey of a smaller sample of permit-issuing areas that gathers data on housing construction, completion, sales, and characteristics of new housing. Monthly state and national data are estimates based on the sample data collected from the 9,000 permit-issuing agencies. For information on standard errors associated with these estimates see: http://www.census.gov/construction/bps/ . Monthly county data are counts rather than estimates, and are therefore reported only for those counties where every permit office issues monthly reports. Annual data are obtained by summing monthly data reporters. If permit-issuing agencies submit both monthly and annual reports, the annual count is used. The annual building permit data on PolicyMap is unadjusted data.
Building permit data will not accurately reflect construction activity in those areas where building permits are not issued. Nationally, only roughly 2 percent of housing starts are issued in areas not requiring permits, however this varies greatly state to state and region to region.
The United States Census Bureau’s Planning Database (PDB) was designed as an aid for survey and census planning purposes. It contains information on the mail return rate for the 2010 census, self-response rate for the 2013-2017 ACS, and Low Response Score, all of which may be helpful in targeting areas for a higher response rate in future counts and surveys. In the 2010 census, households that did not return their census form were contacted through a non-response follow-up (NRFU), which utilized various methods to count these households. Non-response follow-ups require more resources than mail returns, so this data can be used to strategically increase mail returns in certain areas.
PolicyMap calculated predominant tenure using Census’ American Community Survey estimates to identify whether most occupied housing units in a given geography are owner-occupied or renter-occupied. For race and ethnicity breakdowns, the measures are calculated separately for occupied housing units with householders who identify as Hispanic or Latino, and for the following seven Census racial groups reported alone: White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, Some Other Race, and Two or More Races (Tables B25003A–I). For example, “Predominant Tenure – Asian” indicates whether owner-occupied or renter-occupied units are more common among occupied housing units with a householder who is Asian. Predominant tenure demographics rankings, calculated across all occupied housing units, show the racial and ethnic composition within the overall predominant tenure category for each geography. Data are sourced from the U.S. Census Bureau’s American Community Survey 5-Year Estimates (Tables B25003 and B25003A–I). At PolicyMap, predominance is determined by ranking the percentage of owner-occupied and renter-occupied households within a given geography; the category with the higher percentage is reported as predominant. Percent change compares the count of occupied housing units in the predominant tenure category between two ACS 5-year periods. It is only calculated when the same tenure type remains predominant across both periods, since counts of owner-occupied and renter-occupied units cannot be directly compared when the predominant category shifts. The measure calculates the growth or decline in the number of owner-occupied or renter-occupied units within a stable tenure category over time. It also indicates if the predominant housing tenure type has flipped, when owner- and renter-occupied units are tied, or when a racial or ethnic group is newly measurable or falls below the minimum threshold. A suppression threshold of 50 occupied housing units is applied to reduce noise and ensure more reliable comparisons across geographies and racial and ethnic groups. Migration for race and ethnicity groups is only flagged when there were at least 50 occupied housing units with householders in a given or previous time frame, to avoid overestimating and to account for margins of error in the data.
The U.S. Census provides annual survey data on public school finances. Data is available at school district geographies. It includes the following indicators: student enrollment, total elementary-secondary revenue, total revenue from federal sources, total revenue for Title I, total revenue for children with disabilities, total revenue for child nutrition act, total revenue from state sources, total revenue from local sources, total elementary-secondary expenditures. Rates calculated by PolicyMap (such as revenue per student) were suppressed for school districts with 0 students in the given year.
The Census’ Small Area Income & Poverty Estimates (SAIPE) dataset provides more current estimates of selected income and poverty statistics than the most recent decennial census. Estimates are created for states, counties, and school districts, depending on the data. This dataset mainly serves administrators of federal programs who need current statistics on the demonstrated need of places.
The Census Bureau identifies urban areas (UAs) following every decennial census. For the 2020 decennial census, UAs are defined as a densely settled core of census blocks that encompasses at least 2,000 housing units or has a population of at least 5,000. UAs also include adjacent territory containing non-residential urban land uses. The Rural designation encompasses all areas not included within an UA. The Urban and Rural Classifications from the Census Bureau are released at the Census Block level. The indicators in PolicyMap are represented at the Census Block Group and Census Tract Level. Areas are identified as urban if a specified threshold of land area sits within the Census designated UA – 50 percent threshold or 80 percent threshold. These indicators were developed by PolicyMap through a spatial join of Block Group and Census Tract boundaries to Census UA boundaries.
The data presents cancer incidence rates and number of new cases of cancer by type per year in state and county level geographies. The data is available by race/ethnicity and cancer type. This data is collected by the CDC from public health surveillance systems by using either their published reports or public use files. Many state departments of health publish state-specific cancer data. This data may be more recent or may provide more detail than the data published nationally. For all data years, rates and counts are suppressed if fewer than 16 cases were reported in a specific category, such as cancer type, race and/or ethnicity, age, and state. This suppression is to ensure confidentiality and reliability of rate estimates.
For 2017-2021 data, Indiana did not meet publication criteria and was excluded from the analysis. County data are not available from Kansas because state legislation and regulations prohibit the release of county-level data to outside entities.
For 2016-2020 data, Indiana and Nevada did not meet publication criteria and was excluded from the analysis. County data are not available from Kansas and Minnesota because state legislation and regulations prohibit the release of county-level data to outside entities. County data from Virginia are suppressed due to incomplete data. Due to a coding issue reported by North Dakota and Wisconsin, state- and county-specific counts and rates by race and ethnicity are not presented for North Dakota. For Wisconsin, state- and county-specific counts and rates are not presented for Hispanic persons. These data for all races and ethnicities are included in national rates.
For 2015-2019 data, Nevada did not meet publication criteria and was excluded from the analysis. County data are not available from Kansas and Minnesota because of state legislation and regulations which prohibit the release of county-level data to outside entities. In addition, Kansas opted not to present state- and county-specific Asian and Pacific Islander counts and rates for these years. The national rates presented include data for Kansas. Please visit U.S. Cancer Statistics data website for detailed technical documentation.
The chronic condition prevalence index was created by following the CDC’s guidelines for creating estimates of high prevalence of multiple chronic diseases. Twelve chronic diseases were included: obesity, hypertension, high cholesterol, coronary heart disease, chronic obstructive pulmonary disease, asthma, chronic kidney disease, diabetes, cancer (excluding skin cancer), depression, stroke, and arthritis. A composite score was calculated by ordering each disease estimate and assigning a score of 0 to ZCTAs in the bottom 25th percentile, 1 for ZCTAs in the middle (25th to 75th percentiles) or 2 to those in the top 25th percentile. These scores were summed so each ZCTA has a score range of 0-24. This data was then rated as “lowest” if it was in the bottom 25th percentile, “moderate” if in the middle percentile (25-75 percentile) and “highest” if in the top 25th percentile.
The predominant chronic conditons was calculated from CDC’s PLACES data. The same twelves chronic conditions that went into the chronic condition prevalence index were also used. The predominant chronic condition in a given geographic area is the one that accounts for the highest percentage of individuals that experience that chronic condition.
The CDC posts daily data updates on COVID-19 vaccination administration and state distribution, assembled from data reported by state public health agencies and offices. Healthcare providers report doses administered to federal, state, territorial, and local agencies within 72 hours of administration, but there could be an additional lag before data reaches the CDC. Each entity may use various reporting methods, including immunization information systems, Vaccine Administration Management System, which supports temporary, mobile, and satellite clinics, in addition to direct submissions. The CDC’s data might differ from state systems and dashboards due to unfound duplicates during the agency’s data consolidation or due to varying reporting practices. Visit https://covid.cdc.gov/covid-data-tracker/#vaccinations to see data validation and collection protocols. Doses delivered and administered for U.S. States, D.C., and Puerto Rico are cumulative counts of COVID-19 vaccine doses reported to the Federal COVID Vaccine Operation delivered since December 14, 2020. Doses delivered to the U.S. Virgin Islands, Palau, Micronesia, Marshall Islands, Guam, American Samoa, and Northern Marianas Islands include those marked as shipped in CDC’s Vaccine Tracking System (VTrckS) since December 13, 2020. Doses delivered and administered in a state or territory also include those delivered and administered in pharmacies and the Federal Pharmacy Partnership for Long-Term Care (LTC) Program in the jurisdiction as reported in VTrckS. Doses administered are attributed to the jurisdiction in which the vaccine was administered. People receiving one or more doses represent the total number of people who have received at least one vaccine dose. People receiving two doses represent the number of people who have received a second dose of the vaccine. The number of people receiving one or more doses and the number of people receiving two doses was determined based on CDC’s information by state, territorial, and local public health agencies and federal entities on dose number, administration date, recipient I.D, and date of submission. A dose number was determined for nearly all reported doses administered; some missing data for dose number resulted in people receiving one or more doses and people receiving two doses not equaling the total doses. Rates per 100,000 represent the number of total doses delivered, the number of total doses administered, the number of people receiving one or more doses, and people receiving two doses per 100,000 residents of all ages. These metrics use the U.S. Census Bureau Annual Estimates of the Resident Population for the United States and Puerto Rico, 2019. 2018 U.S. Census Bureau population estimates and estimates from the CIA World Factbook are used for American Samoa, Federated States of Micronesia, Guam, Northern Mariana Islands, Republic of Palau, Marshall Islands, and U.S. Virgin Islands. Emergency Use Authorization has been granted for using the Pfizer-BioNTech vaccine among persons aged 16 and older and using the Moderna vaccine among persons aged 18 and older. Therefore, vaccine use is limited among those under age 18, representing approximately 22% of the U.S. population. Jurisdictions may use more targeted population counts for the denominators in their rate calculations (i.e., persons over 18 or over 16 years old), resulting in values different from those reported on the CDC COVID Tracker. In some limited circumstances, people might receive vaccinations outside of their state or territory of residency. These rates currently account for vaccinations occurring in the jurisdiction where the vaccination was administered.
Occasionally, states will overreport or underreport vaccine administration and distribution causing “Insufficient Data” to appear on the map for Change in number of people who are fully COVID-19 vaccinated, and Percent change in people who are fully COVID-19 vaccinated indicators.
The Centers for Disease Control (CDC) Flu Activity & Surveillance System dataset provides estimates of flu activity for states and territories of the U.S. Flu activity indicators are a measure of the proportion of visits to healthcare providers for influenza-like illness (ILI) symptoms. Estimates are collected from public health facilities participating in the Outpatient Influenza-like Illness Surveillance Network (ILINet). These data may disproportionately represent certain populations within a state; for instance, a severe flu outbreak in one city or region may cause the statewide activity level to be High, even if flu activity is low or minimal in other areas throughout the state. State health departments may have more geographically precise information available; contact information for these departments is available in FluView. Geographic spread of influenza is reported directly to CDC by state epidemiologists. This is a measure of how much of each state is affected by flu, and is not a measure of the severity of influenza activity. Weekly data and state and local surveillance information are available at the CDC Influenza Surveillance website. ILI activity and geographic spread measures are provided weekly. To obtain seasonal values, PolicyMap calculated the average of the numerical activity levels for all weeks ending in a given season. Flu season is defined as the period beginning in October and ending in May. Only states with at least 24 weeks of activity per season are included in these calculations.
New York City reports flu data to CDC separately from New York State; as such, New York State flu activity and geographic spread measures do not take New York City into account.
The Heat and Health Index (HHI) helps identify communities where people are most likely to feel the effects of heat on their health, in order to build towards a healthier and more heat-resilient future for all. The (HHI) is a national tool that incorporates historical temperature, heat-related illness, and community characteristics data at the ZIP code or ZCTA level to identify areas most likely to experience negative health outcomes from heat and help communities prepare for heat in a changing climate. Each ZCTA has a single ranking for the overall HHI and rankings for individual components so that users can make informed decisions to prepare for and prevent the negative health impacts from heat in their communities. The HHI consists of 25 data indicators of heat and health vulnerability to help communities prepare for warming temperatures in a changing climate. These indicators are grouped into 4 modules.
Data in the HHI come from the Centers for Disease Control and Prevention (CDC), the National Emergency Medical Services Information System (NEMSIS), the United States Census Bureau, the Multi-Resolution Land Characteristics Consortium (MRLC), and the Environmental Protection Agency (EPA).
The Centers for Disease Control (CDC) dataset provides the number of births, the number and percent of infants born with birth weight under 2,500 ounces (low birthweight), the number and percent of infants born with birth weight under 1,500 ounces (very low birthweight), the number and percent of infants born vaginally or by cesarean section, the number and percent of births where prenatal care began during the first trimester and the number and percent of births where prenatal care was received in only the third trimester or not at all,. the number and percent of births where the number of prenatal visits met the recommendations from the American College of Obstetricians and Gynecologists (ACOG) (expected number of visits is 8–14 for a term pregnancy), the number and percent of births by mothers with specified maternal health condition (eclampsia, gestational diabetes, pre-pregnancy diabetes, gestational hypertension, pre-pregnancy hypertension). The CDC only reports numbers of births for counties with populations exceeding 100,000. The CDC also provides numbers and rates for mothers under age 20. Additionally, this dataset includes the number and percent of births to mothers under the age of 20, with break outs for mother under age 18 and mothers 18 and 19 for select years. Data on prenatal care is only available for counties with populations of 100,000 or more. PolicyMap has suppressed data for geographies where .4% or more of the birth characteristics, prenatal care, or maternal health condition indicators were unknown to the CDC.
Beginning in 2007, data are reported from the 2003 U.S. standard Certificate of Live Birth, with additions of information on birth anomalies and several less variables related to maternal risk factors than the previous 2003 revision.
The Centers for Disease Control (CDC) dataset provides the number of infant deaths, and the rate of deaths to infants for every 1000 live births by maternal residents of the US. The CDC only reports numbers of births for counties with populations of 100,000 or more and number and rate of infant deaths for counties with populations of 250,000 or more. It suppresses the rate where there are fewer than 20 deaths reported. Adult mortality data are taken from the National Center for Health Statistics’ Compressed Mortality file as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. The Compressed mortality file provides the number and rate of deaths, by age group and cause of death as reported through the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Data on PolicyMap represent deaths from Alzheimer’s disease, cancer, coronary heart disease, chronic lower respiratory disease, COVID-19, stroke, and chronic lower respiratory disease among those aged 45 or older, from 2000 through 2015. Deaths from homicide, suicide, motor vehicle traffic, and accidental injury for all age groups. These causes have topped the CDC’s list of leading causes of death since 2005. Underlying cause-of-death is indicated on the death certificate by the physician. The National Center for Health Statistics determines one cause of death when more than one cause or condition is entered by the physician. PolicyMap shows mortality data from 2000 through 2021. Adults ages 35 and older are used as a base category for deaths from disease because these age groups represent most of the deaths from the four leading causes. Rates are calculated per 100,000 population 35 and over in the source data using population estimates based on 2000 and 2010 U.S. Census counts. The CDC’s National Center for Health Statistics released an estimated model of drug overdose data in its Data Visualization Gallery. Smoothed crude death rate estimates were generated using Hierarchical Bayesian models with spatial and temporal random effects. Bayesian hierarchical modeling “borrows strength” across geographic areas and allows estimates to be generated for counties that have small populations. Updated county-level estimates now include point estimates rather than estimate ranges. The CDC adds a disclaimer to this dataset that in certain states and years, for example New Jersey (2009) and West Virginia (2005, 2009), the rates may be lower than expected due to a large number of unresolved cases or misclassification of ICD-10 codes. More information on the CDC’s methodology is available here . Opioid and narcotic poisoning data comes from the CDC’s Multiple Cause of Death files. Drug overdose deaths were classified using the Tenth Revision (ICD-10) of the International Classification of Disease underlying-cause-of-death codes for drug poisonings (overdose): X40-44 (unintentional), X60-64 (suicide), X85 (homicide), and Y10–Y14 (undetermined intent). The types of opioid involved in drug overdose deaths were classified following the ICD-10 codes: and T40.1 (heroin), T40.2 (natural and semisynthetic opioids), T40.3 (methadone), and T40.4 (synthetic opioids, other than methadone). The category for all opioid overdoses includes all these categories (T40.1, T40.2, T40.3, and T40.4). T40.0 (opium) was not included since fewer than 10 people are reported each year as having died from opium overdose in the nation. Deaths involving multiple types of opioids are recorded in each applicable category, therefore the US totals may include overcounting. Heroin is an illegally-made semi-synthetic opioid derived from morphine. “Natural and semisynthetic opioids” is a category of prescription opioids, which includes natural opioid analgesics (codeine, morphine, etc.) and semi-synthetic opioid analgesics (hydrocodone, hydromorphone, oxycodone, and oxymorphone), but excludes heroin. Methadone is a prescribed synthetic opioid used to treat moderate to severe pain, and also withdrawal symptoms in those addicted to heroin or other narcotics. “Synthetic opioids, other than methadone” is a category of opioids commonly available by prescription and includes drugs such as fentanyl and tramadol, but excludes methadone. The CDC does not differentiate between deaths from pharmaceutical fentanyl and illegally-made fentanyl, and deaths from both forms are included in the data. While medically not considered a narcotic, cocaine is legally classified as such and is included in the CDC’s definition of narcotics along with opioids. The types of narcotics involved in drug overdose deaths were classified following the ICD-10 codes: T40.6 (other and unspecified narcotics), and T40.5 (cocaine). The category for all narcotics overdoses includes T40.1, T40.2, T40.3, T40.4, T40.5 and T40.6. The methods used to classify deaths on death certificates may lead to a significant undercount of opioid-related deaths, which could inaccurately portray the severity of this public health problem. Because of reporting discrepancies and nonspecific language, it is likely that national statistics underestimate by a substantial fraction the amount of opioid analgesic- and heroin-related deaths. Additionally, the degree of underestimation varies based on states’ death certification systems. For more information undercounting opioid-related deaths visit https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547584/ . To provide context for a given area, it is helpful to also look at how many overdose deaths are recorded with no additional drug information. These were classified according to the ICD-10 code of T50.9 (other or unspecified drugs).
For more information on the data visit https://wonder.cdc.gov/mcd-icd10.html .
The National Center for Health Statistics (NCHS) released the results of their Small-area Life Expectancy Estimates Project in September of 2018. For this project, NCHS calculated abridged life tables at the Census tract level. An “abridged life table” is a series of estimates of life expectancy for people of different ages grouped into multi-year cohorts. To create these abridged life tables, NCHS worked with the National Vital Statistics System to geocode residences recorded on death certificates from 2010 to 2015 (inclusive). The Department of Housing and Urban Development Geocode Center performed the geocoding. Maine and Wisconsin are excluded from this study because they did not have geocoded death certificates for 2010. The life table calculations also required Census tract level population estimates. NCHS worked with the Census Bureau to create a set of custom 6-year population estimates for the years 2010–2015 using data from the 2010 Census and the 2011–2015 ACS for use in this project. The NCHS used information on the location of residence and age of the deceased combined with the population estimates to build age patterns or “schedules of mortality” for 4,639 “model tracts.” Model tracts met two criteria—each had a population over 5,000 and one or more recorded deaths in each age group during the 6-year window. Using schedules of mortality from the model tracts, the NCHS developed statistical models to predict death rates based on demographic, socioeconomic, and geographic variables. They used these models to fill in data for tracts with smaller populations, and for tracts with age groups that had “missing deaths” or no recorded deaths within an age group. For the model tracts, reported values for all age ranges are calculated directly from the death certificates and population estimates. For the tracts with some missing deaths, values predicted by the statistical models are used for the age groups that had no recorded deaths, and observed values are used for all other age ranges. For some tracts with low populations, all values are based on predictions from the statistical models. See the complete documentation of the methodology here: https://www.cdc.gov/nchs/data/series/sr_02/sr02_181.pdf .
The Centers for Disease Control and Prevention HIV Incidence and Case Surveillance Branch provides the number of estimated active HIV infection cases among people aged 13 and older from state and local health departments. These data are available for states and counties. These data represent the place of residence at earliest HIV diagnosis; duplicate records from different states are reconciled by the source. Some states without confidential name-based HIV infection reporting have elected not to release state and/or county-level data.
CDC provides estimates on new HIV diagnoses for a given year. HIV data for a given year presented on PolicyMap represent the number of confirmed diagnoses of HIV infection or infection classified as stage 3 (AIDS) confirmed by laboratory analysis as of December 31 of that year and reported to the CDC by June 30 of the following year. These data may not be limited to new infections (i.e., incidence); rather, these data represent new diagnoses, as the date of infection may vary by individual. Estimates are statistically-adjusted values based upon actual case counts reported to CDC by state and local health departments. The CDC suppressed values in areas with fewer than 5 reported cases and/or population less than 100 as well as in the county with the lowest population in states where only one other county’s data were suppressed.
Data on the number of new cases of chlamydia, gonorrhea, and syphilis reported each year, and the rate of new STD cases reported for every 100,000 residents, by state and county are available from CDC. Data are based on cases of STDs reported to state and local health departments. Data is reported by both public and private agencies, such as STD clinics, counseling/testing sites, drug treatment clinics, family planning clinics, and private physicians. The CDC collects data from regional jurisdictions, and publishes the data in an annual report, which can be downloaded here: https://www.cdc.gov/std/stats/default.htm .
Syphilis is presented as a combined sum of cases classified in either primary or secondary stages of the disease. Other categories of syphilis – not included in the data – are latent (without symptoms), tertiary (late stage), and congenital (transferred from mother to child). Primary and secondary forms of the disease are the most infectious and therefore important when considering the risk of transfer and spread of disease.
Some variability in the amount of in the amount of reporting may exist across the country. Chlamydia, gonorrhea, and syphilis are considered Nationally Notifiable , which means that regional jurisdictions provide information to the CDC on a voluntary basis. A nationally notifiable disease is not necessarily reportable by law within a given state. Because of incomplete diagnosis and reporting, the number of STD cases reported is less than the actual number of cases occurring. The level of consistency may vary between local jurisdictions, reporting agencies, and reporting years. In some areas, reporting from public sources is thought to be more complete than reporting from private sources.
Incidence rates were calculated by the CDC using total population as the denominator. The population denominators used to compute these rates for the 50 states and the District of Columbia were based on the U.S. Census Bureau population estimates utilizing the OMB compliant race categories. Each rate was calculated by dividing the number of cases for the calendar year by the population for that calendar year and then multiplying the number by 100,000 Ten-year percent change variables for incidence of STDs and five-year percent change for HIV incidence were calculated by PolicyMap.
Starting with 2019 data, STI data presented in the AtlasPlus follows the 2017 Council of State and Territorial Epidemiologists (CSTE) data re-release rules. DSTDP suppresses data at any geographic level based on the following 2 conditions:
Opioid prescription rates per 100 persons, reported by the CDC, are calculated using population estimates from the Population Estimates Program, U.S. Census Bureau. Opioid prescriptions, including buprenorphine formulations commonly prescribed for treating pain (Belbuca and Butrans), codeine, fentanyl, hydrocodone, hydromorphone, methadone, morphine, oxycodone, oxymorphone, propoxyphene, tapentadol, and tramadol, were identified using National Drug Codes.Cough and cold formulations containing opioids and buprenorphine, an opioid partial agonist used for treatment of opioid use disorder as well as for pain, are not included. In addition, methadone dispensed through methadone treatment programs is not included. Source for all CDC prescription dispensing data comes from IQVIA Xponent (previously know as QuintilesIMS). IQVIA Xponent is based on a sample of approximately 56,500 retail (non-hospital) pharmacies, which dispense nearly 93% of all retail prescriptions in the United States. For this database, a prescription is a new or refilled prescription dispensed at a retail pharmacy in the sample and paid for by commercial insurance, Medicaid, Medicare, cash or its equivalent, and other third-party coverage. This database does not include mail-order prescriptions. Geographic location is based on the location of the prescriber. For the calculation of dispensing rates, numerators are the projected total number of prescriptions dispensed annually at the state, county, or national level. Annual resident population denominators were obtained from the U.S. Census Bureau. After a steady increase in overall opioid prescribing rates from 2006, total opioid prescriptions peaked in 2012 at 255 million and a rate of 81.3 prescriptions per 100 people. The rate does not represent the percent of the population receiving opioid prescriptions. Since an individual may receive multiple prescriptions in a year, many counties have rates that are greater than 100 prescriptions per 100 persons. Counties displayed as having insufficient data may indicate counties with no retail pharmacies, counties where no retail pharmacies were sampled, or counties where the prescription volume was erroneously attributed to an adjacent, more populous county according to the sampling rules used.
This data differs from the data shown in the July 2017 issue of CDC Vital Signs, which featured different facets of opioid prescribing from 2006 to 2015. For more information visit https://www.cdc.gov/vitalsigns/opioids/index.html .
Social Vulnerability encompasses demographic and socioeconomic factors—such as poverty, limited access to transportation, and crowded housing—that make certain communities more susceptible to hazards and stressors. These stressors may include natural or human-made disasters, like tornadoes or chemical spills, as well as disease outbreaks, such as COVID-19. The CDC/ATSDR Social Vulnerability Index (SVI), managed by the Geospatial Research, Analysis & Services Program (GRASP), is a place-based index, database, and mapping tool designed to identify and quantify socially vulnerable communities. This index helps public health officials and local planners better prepare for and respond to emergencies, aiming to reduce human suffering, economic loss, and health inequities. Over time, the CDC/ATSDR SVI has evolved, and it strongly discourages comparisons across different versions of the database. Each database calculates percentile scores by ranking census tracts relative to others within the same year, so scores from different years are not directly comparable. Adjustments to the SVI categories have been made periodically to reflect updates in U.S. Census Bureau data and incorporate the latest research.
The current SVI uses 16 U.S. Census variables from the 5-year American Community Survey (ACS) to identify communities that may need support during or after disasters. These variables are grouped into four themes representing major areas of social vulnerability, which are then combined into a single measure. The GRASP program assigned each geography a percentile ranking for each variable and calculated an overall score for each category by summing these percentiles. Each of the four themes is then assigned a percentile ranking, which contributes to the overall Social Vulnerability Index. Based on the overall SVI score, communities are classified into four vulnerability levels: Low, Low to Moderate, Moderate to High, and High, dividing all tracts or counties into quantiles. A percentile ranking represents the proportion of tracts (or counties) with equal or lower vulnerability than a given tract or county. For example, a ranking of 0.85 signifies that 85% of tracts (or counties) in the state or nation are less vulnerable, while 15% are more vulnerable. Find more information on the CDC’s Social Vulnerability Index at https://svi.cdc.gov/ .
The Centers for Medicare and Medicaid Services’ Chronic Conditions Data Warehouse contains claims information for persons enrolled in the Medicare fee-for-service (FFS) program. Only information for beneficiaries enrolled in both Part A and Part B is included; information for beneficiaries who have died during the study year is included. Non-FFS Medicare beneficiaries are those with partial Part A and/or Part B coverage and people and who were enrolled in Parts A and B Medicare and Medicare Advantage plan. Medicare Part A (hospital insurance) and Part B (medical insurance) cover individuals ages 65 and over who are receiving Social Security, people who have received disability benefits for at least two years, people who have amyotrophic lateral sclerosis (Lou Gehrig’s disease) and receive disability benefits, and people who have end-stage renal disease (permanent kidney failure) and receive maintenance dialysis or a kidney transplant. Individuals with Medicare Advantage (Part C) and Medicare Prescription Drug Plan (Part D) coverage are not represented in the data.
Chronic health condition data is based on CMS administrative enrollment and claims data for Medicare fee-for-service beneficiaries. A Medicare beneficiary is considered to have a chronic condition if there is a CMS claim indicating that the beneficiary received a service or treatment for that specific condition. Beneficiaries may have more than one of the chronic conditions listed.
In March 2022, the CMS Chronic Conditions Warehouse released an updated algorithm for their Chronic Condition indicators. Due to this algorithm update, some Chronic Conditions indicators may experience significant changes to prevalence rates (i.e., percentages). This update applies to 2021 data and forward. For more information, please visit CMS Mapping Medicare Disparities Tool Technical Documentation. Please note that Alcohol Use Disorder and Drug Use Disorder conditions are available for years 2007-2018, and 2021 due to CMS’s data availability.
CMS demographics, spending, and service utilization data available on PolicyMap comes from CMS’s Fee-for-Service Geographic Variation Public Use File (FFS GV PUF). This public use file is based primarily on information from CMS’s Chronic Conditions Data Warehouse.
In May 2024, CMS released 2022 data for the FFS GV PUF and updated historic data for years 2014-2021. This is due to CMS using different classification systems to define physician services between the 2014-2022 and 2007-2013 data years. For more information on methodology changes, please visit the FFS GV PUF Technical Documentation May 2024 Update.
All dollar amounts in this data set are standardized by CMS to adjust for factors that result in different payment rates for the same service, including local variations in wages and payments Medicare makes to hospitals to advance program goals (including training doctors). The standardized values represent what Medicare would have paid in the absence of those adjustments. Because the state of Maryland is exempt from reporting special payments to Medicare, costs in Maryland were standardized using different factors than the nationwide model.
Centers for Medicare and Medicaid Services’ (CMS) opioid prescription claims include information about Medicare and Medicaid prescriptions. CMS opioid claims and prescribing rates are available for all opioids as well as extended-release and long-acting (ER/LA) opioid formulations. Prescription numbers and rates for ER/LA opioids are also counted within the “all opioids” indicators. ER/LA opioids are designed to deliver more stable dosing for chronic pain patients and reduce dosage frequency but may pose a higher risk of overdose when misused. Certain methadone types are considered ER/LA. For a list of opioids included see https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/OpioidDrugList.zip. . Medicare Opioid Prescriptions CMS Medicare opioid prescription claims come from Medicare Part D claims prescribed by health care providers, as collected from Part D Prescriber public use files. Medicare opioid prescribing rates were calculated as the rate of opioid prescription claims per 100 total Medicare Part D prescription claims, including both prescriptions and refills. Medicare opioid prescription data includes claims for beneficiaries enrolled in Medicare Advantage Prescription Drug Plans and stand-alone Prescription Drug plans, but does not include prescriptions for patients on Medicaid, those with commercial insurance, or self-pay patients. Approximately 70% of Medicare beneficiaries have Medicare prescription drug coverage either from a Part D plan or a Medicare Advantage Plan offering Medicare prescription drug coverage. In 2017, Medicare Part D spending was $155 billion, while U.S. retail prescription drug spending was around $333 billion. Due to data redactions for geographies with 10 or fewer claims, county and Zip code Medicare opioid claim totals may not add up to state totals or may be lower than the true program totals. Medicaid Opioid Prescriptions CMS Medicaid opioid prescription claims come from claims where a portion was paid through Medicaid, as reported through the Medicaid State Drug Utilization Data. Medicaid opioid prescribing rates were calculated as the rate of opioid prescription claims per 100 total prescription claims for which Medicaid paid a portion. CMS Medicaid opioid prescription data includes claims prescribed through Fee-For-Service (FFS) programs and Manage Care Organizations (MCO). In 2017, Medicaid spending on prescription drugs was $68 billion, while U.S. retail prescription drug spending was around $333 billion.
As of the 2025 data release, Medicaid opioid claims are available at the zip, county, and state level. However, data may be sparse for smaller geographies due to CMS data processing methods. County-and zip-level measures presented in this tool were aggregated from the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF). In order to be included in this tool, state-level TAF data had to meet quality checks for each data year. Due to known data quality issues in prior years, TAF data is presented only for data years 2016 and onward. In order to protect the privacy of beneficiaries, suppression is applied to the data. At each aggregated geographic level, any derived opioid claim counts between 1 and 10 are suppressed (replaced with missing a.k.a. Insufficient Data values). Secondary suppression is applied in cases where a single subgroup category is primary suppressed.For example, if the Fee-for-Service opioid claim count for a given state is primary suppressed but the Managed Care opioid claim count is not primary suppressed, then the Managed Care opioid claim count for that state must be secondary suppressed to prevent disclosure of the Fee-for-Service opioid claim count suppressed value. Secondary suppression prevents backing into a primary suppressed value by using the values from the other opioid claim counts such as total opioid claim count. These claims include all types of opioids.
The Hospital Compare dataset is part of a data repository maintained by the Centers for Medicare & Medicaid Services (CMS), focusing on the quality of care at over 4,500 Medicare-certified hospitals (including acute care hospitals, critical access hospitals (CAHs), children’s hospitals, and hospital outpatient departments) across the country. The dataset was created in collaboration with organizations representing consumers, doctors, hospitals, employers, accrediting organizations, and other federal agencies, as part of an overall effort to improve patient safety and care. The Hospital Compare dataset on PolicyMap includes data on:
This dataset is available on PolicyMap as point data based on hospital location, and can be viewed upon clicking each respective point. The CMS Hospital Compare data was joined by PolicyMap to hospital locations using data from HRSA. HRSA hospital location data can be found here .
The Medicare chronic condition prevalence index rating was created by following the CDC’s guidelines for creating estimates of high prevalence of multiple chronic diseases. Nineteen CMS Medicare chronic diseases were included: Alzheimer’s disease, arthritis, asthma, atrial fibrillation, autism spectrum disorder, cancer (excluding skin), COPD, depression, diabetes, heart disease, heart failure, hepatitis (chronic viral B & C), high cholesterol, hypertension (high blood pressure), HIV/AIDS, chronic kidney disease, osteoporosis, schizophrenia, and stroke. These county-level chronic disease prevalence estimates come from the CMS medicare chronic conditions data set. A composite score was calculated by ordering each disease estimate and assigning a score of 0 to counties in the bottom 25th percentile, 1 for counties in the middle (25th to 75th percentiles) or 2 to those in the top 25th percentile. These scores were summed so each county has a score range of 0-24. This data was then rated as “lowest” if it was in the bottom 25th percentile, “moderate” if in the middle percentile (25-75 percentile) and “highest” if in the top 25th percentile.
The Centers for Medicare & Medicaid Services’ national downloadable file contains individual-level data on medical providers who accept Medicare assignment. Data in the national downable file is collected from providers’ Medicare enrollment records and includes information on provider credentials, sub-specialties, practice locations, and whether Medicare assignment is accepted as payment in full. PolicyMap has aggregated this data to create facility-level points that group providers practicing the same sub-specialty at the same location. If a provider practices at multiple locations, they are counted at each applicable location.
In addition, PolicyMap has incorporated data from CMS’ PY 2022 Clinician Public Reporting: Overall MIPS Performance dataset to include facility-level information on the MIPS scores of providers at a location. CMS calculates MIPS scores at both the individual and group levels; PolicyMap uses only group-level scores to better reflect the collective performance of providers at a facility. When multiple groups practice at the same location, their scores are averaged.
Each point on the map is labeled with either the name of the facility where the providers are located or, when there is only a single provider, the provider’s name. When providers of different sub-specialties practice at the same facility, multiple points may appear at the same location.
The Civil Rights Data Collection (CRDC) contains wide-ranging data on equity among public schools. The data is based on sc