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Pregnancy Rates, by Age for Hispanic Women: United States, 1990-2010","issued":"2015-12-02","keyword":["hispanic women","nchs","pregnancy rate","united states"],"landingPage":"https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","language":["English"],"license":"https://www.usa.gov/government-works","modified":"2026-09-23","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"rights":"public","spatial":"50 states and District of Columbia","temporal":"1990/2010","theme":["National Center for Health Statistics"],"title":"NCHS - Pregnancy Rates, by Age for Hispanic Women: United States, 1990-2010"},"description":"https://www.cdc.gov/nchs/data-visualization/natality-trends/index.htm","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/c4f249cb-39a5-474f-81cf-aaf8a9daa184","harvest_record_raw":"https://catalog.data.gov/harvest_record/c4f249cb-39a5-474f-81cf-aaf8a9daa184/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/hdy7-e2a3","keyword":["hispanic women","nchs","pregnancy rate","united states"],"last_harvested_date":"2026-09-23T21:25:50.842680","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":"NCHS - Pregnancy Rates, by Age for Hispanic Women: United States, 1990-2010","popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"nchs-pregnancy-rates-by-age-for-hispanic-women-united-states-1990-2010","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Health Statistics"],"title":"NCHS - Pregnancy Rates, by Age for Hispanic Women: United States, 1990-2010","type":"dataset"},{"_score":13.8220215,"_sort":[1790198747126,13.8220215,3,"088c4f2a-a053-4fab-8a5e-7007ba955677"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks.\r\n\r\nPotentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths.\r\n\r\nNot all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services.\r\n\r\nMortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map.\r\n\r\nUnderlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10)\r\n\r\nHeart disease (I00-I09, I11, I13, and I20\u2013I51)\r\nCancer (C00\u2013C97)\r\nUnintentional injury (V01\u2013X59 and Y85\u2013Y86)\r\nChronic lower respiratory disease (J40\u2013J47)\r\nStroke (I60\u2013I69)\r\nLocality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget\u2019s 2013 county-based classification scheme.\r\n\r\nBenchmarks are based on the three states with the lowest age and cause-specific mortality rates.\r\n\r\nPotentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths.\r\n\r\nUsers can explore three benchmarks:\r\n\r\n\u201c2010 Fixed\u201d is a fixed benchmark based on the best performing States in 2010.\r\n\u201c2005 Fixed\u201d is a fixed benchmark based on the best performing States in 2005.\r\n\u201cFloating\u201d is based on the best performing States in each year so change from year to year.\r\n \r\nSOURCES\r\n\r\nCDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).\r\n\r\nREFERENCES \r\n\r\n1. Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas \u2013 United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8.\r\n\r\n2. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1\u20137.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/vdpk-qzpr/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/vdpk-qzpr/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/vdpk-qzpr/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/vdpk-qzpr","issued":"2017-01-19","keyword":["cancer","chronic lower respiratory disease","heart disease","stroke","unintentional injury"],"landingPage":"https://www.cdc.gov/nchs/data-visualization/potentially-excess-deaths/","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"theme":["National Center for Health Statistics"],"title":"NCHS - Potentially Excess Deaths from the Five Leading Causes of Death"},"description":"MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks.\r\n\r\nPotentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths.\r\n\r\nNot all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services.\r\n\r\nMortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map.\r\n\r\nUnderlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10)\r\n\r\nHeart disease (I00-I09, I11, I13, and I20\u2013I51)\r\nCancer (C00\u2013C97)\r\nUnintentional injury (V01\u2013X59 and Y85\u2013Y86)\r\nChronic lower respiratory disease (J40\u2013J47)\r\nStroke (I60\u2013I69)\r\nLocality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget\u2019s 2013 county-based classification scheme.\r\n\r\nBenchmarks are based on the three states with the lowest age and cause-specific mortality rates.\r\n\r\nPotentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths.\r\n\r\nUsers can explore three benchmarks:\r\n\r\n\u201c2010 Fixed\u201d is a fixed benchmark based on the best performing States in 2010.\r\n\u201c2005 Fixed\u201d is a fixed benchmark based on the best performing States in 2005.\r\n\u201cFloating\u201d is based on the best performing States in each year so change from year to year.\r\n \r\nSOURCES\r\n\r\nCDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).\r\n\r\nREFERENCES \r\n\r\n1. Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas \u2013 United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8.\r\n\r\n2. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. 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Evaluation of trends over time should compare estimates from year to year (June 2021 and June 2022), rather than month to month, to avoid overlapping time periods. It is important to note that the data represent counts of deaths, and not mortality ratios or rates, which are the standard measure used to compare groups, and therefore should not be used to determine populations at disproportionate risk of drug overdose death.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/8hzs-zshh/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/8hzs-zshh/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/8hzs-zshh/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/8hzs-zshh","issued":"2024-04-15","keyword":["deaths","drug overdose","hhs region","mortality","nchs","nvss","provisional","united states","vsrr"],"landingPage":"https://www.cdc.gov/nchs/nvss/vsrr/provisional-drug-overdose.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"2019-01-01/2026-03-01","theme":["National Center for Health Statistics"],"title":"Provisional drug overdose death counts for specific drugs"},"description":"This data presents counts of provisional drug overdose deaths by selected drugs and U.S. Department of Health and Human Services (HHS) public health regions, based on provisional mortality data from the National Vital Statistics System. 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Evaluation of trends over time should compare estimates from year to year (June 2021 and June 2022), rather than month to month, to avoid overlapping time periods. It is important to note that the data represent counts of deaths, and not mortality ratios or rates, which are the standard measure used to compare groups, and therefore should not be used to determine populations at disproportionate risk of drug overdose death.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/2ff78883-ed8e-4af0-9416-efd114294e62","harvest_record_raw":"https://catalog.data.gov/harvest_record/2ff78883-ed8e-4af0-9416-efd114294e62/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/8hzs-zshh","keyword":["deaths","drug overdose","hhs region","mortality","nchs","nvss","provisional","united states","vsrr"],"last_harvested_date":"2026-09-23T21:25:02.554324","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":22,"publisher":"Centers for Disease Control and Prevention","slug":"provisional-drug-overdose-death-counts-for-specific-drugs","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"Provisional drug overdose death counts for specific drugs","type":"dataset"},{"_score":40.812702,"_sort":[1790198700175,40.812702,4,"79a0c3f5-41d1-4c35-84fa-39daaf5dd7e6"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1Y","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:healthus@cdc.gov"},"description":"Data on cholesterol in adults age 20 and older in the United States, by selected characteristics. Data are from Health, United States. 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Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities.\nNote: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. 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The VEHSS Medicaid MAX dataset was last updated May 2023.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/bwx3-gx66/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/bwx3-gx66/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/bwx3-gx66/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/bwx3-gx66/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/bwx3-gx66/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/bwx3-gx66/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/bwx3-gx66","issued":"2025-05-15","keyword":["claims","diagnosed prevalence","eye exams","max","medicaid","medical diagnoses","screening","service utilization"],"landingPage":"https://www.cdc.gov/visionhealth/vehss/index.html","license":"http://opendatacommons.org/licenses/by/1.0/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.medicaid.gov/medicaid/index.html","https://wwwdev.cdc.gov/visionhealth/vehss/data/claims/medicaid.html"],"theme":["Vision & Eye Health"],"title":"Medicaid Claims (MAX) - Vision and Eye Health Surveillance"},"description":"2016-2019. 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Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data.  For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11\u201312 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see:\n\nRossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. 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For more detail about the nowcasting methods and models, see:\n\nRossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. 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United States death counts and rates include the 50 states, plus the District of Columbia. \n\nDeaths with confirmed or presumed COVID-19, coded to ICD\u201310 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.\n\nData during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.\n\nDeath counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. \n\nThe ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.\n\nRates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).\n\nRate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year.\n\nSub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/dmnu-8erf/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/dmnu-8erf/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/dmnu-8erf/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/dmnu-8erf","issued":"2023-05-10","keyword":["coronavirus","covid-19","death rate","deaths","hhs region","mortality","nchs","nvss","united states","weekly"],"landingPage":"https://data.cdc.gov/d/dmnu-8erf","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"2019-12-29/2023-07-29","theme":["National Center for Health Statistics"],"title":"Provisional COVID-19 death counts and rates, by jurisdiction of residence and demographic characteristics"},"description":"This file contains COVID-19 death counts and rates by jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). 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Some states report deaths on a daily basis, while other states report deaths weekly or monthly. \n\nThe ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.\n\nRates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).\n\nRate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year.\n\nSub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/a560803e-b41e-467e-8eff-0867757aadf5","harvest_record_raw":"https://catalog.data.gov/harvest_record/a560803e-b41e-467e-8eff-0867757aadf5/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/dmnu-8erf","keyword":["coronavirus","covid-19","death rate","deaths","hhs region","mortality","nchs","nvss","united states","weekly"],"last_harvested_date":"2026-09-23T21:24:14.110297","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":15,"publisher":"Centers for Disease Control and Prevention","slug":"provisional-covid-19-death-counts-and-rates-by-jurisdiction-of-residence-and-demographic-c","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"Provisional COVID-19 death counts and rates, by jurisdiction of residence and demographic characteristics","type":"dataset"},{"_score":10.183443,"_sort":[1790198653496,10.183443,2,"f37e3d46-facc-4770-bbf7-b2b89c6e8dda"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"PLACES Public Inquiries","hasEmail":"mailto:places@cdc.gov"},"description":"This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States\u201450 states and the District of Columbia (DC)\u2014at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 27 measures at the place level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ndzg-9nmv/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ndzg-9nmv/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ndzg-9nmv/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ndzg-9nmv/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ndzg-9nmv/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ndzg-9nmv/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/ndzg-9nmv","issued":"2021-09-29","keyword":["behaviors","city","gis","outcomes","place","places","prevalence","prevention","unhealthy"],"landingPage":"http://www.cdc.gov/nccdphp/dph/","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/places/measure-definitions/index.html"],"theme":["500 Cities & Places"],"title":"PLACES: Place Data (GIS Friendly Format), 2020 release"},"description":"This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES project 2020 release in GIS-friendly format. 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The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 27 measures at the place level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/1e3c22ca-d90c-464b-9210-0d037578b340","harvest_record_raw":"https://catalog.data.gov/harvest_record/1e3c22ca-d90c-464b-9210-0d037578b340/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/ndzg-9nmv","keyword":["behaviors","city","gis","outcomes","place","places","prevalence","prevention","unhealthy"],"last_harvested_date":"2026-09-23T21:24:13.496929","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":2,"publisher":"Centers for Disease Control and Prevention","slug":"places-place-data-gis-friendly-format-2020-release","spatial_centroid":null,"spatial_shape":null,"theme":["500 Cities & Places"],"title":"PLACES: Place Data (GIS Friendly Format), 2020 release","type":"dataset"},{"_score":3.0910234,"_sort":[1790198650546,3.0910234,1,"ca8bcd42-1ea1-44d7-9434-0f0cc97c6f84"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"Active Bacterial Core surveillance","hasEmail":"mailto:abcs@cdc.gov"},"description":"ABCs is an ongoing surveillance program that began in 1997. <a href=\"https://www.cdc.gov/abcs/reports-findings/surv-reports.html\">ABCs reports</a> describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year.  The methods, <a href=\"https://www.cdc.gov/abcs/methodology/surv-pop.html\">surveillance areas</a>, and <a href=\"https://www.cdc.gov/abcs/methodology/surv-pop.html\" >laboratory isolate collection areas</a> have changed over time.\n                Additionally, the way missing data are taken into account changed in 2010.  It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method.\n                Given these changes over time, trends should be interpreted with caution.\n<ul><li><a href=\"http://www.cdc.gov/abcs/methodology/index.html\">Methodology</a>\nFind details about surveillance population, case determination, surveillance evaluation, and more. </li> <li> <a href=\"http://www.cdc.gov/abcs/reports-findings/index.html\">Reports and Findings</a>\nGet official interpretations from reports and publications created from ABCs data.\n</li>                </ul>","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/9y49-tura/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/9y49-tura/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/9y49-tura/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/9y49-tura","issued":"2021-06-23","keyword":["abcs","bactfacts"],"landingPage":"https://data.cdc.gov/d/9y49-tura","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"theme":["Public Health Surveillance"],"title":"Active Bacterial Core surveillance (ABCs) Group A Streptococcus"},"description":"ABCs is an ongoing surveillance program that began in 1997. <a href=\"https://www.cdc.gov/abcs/reports-findings/surv-reports.html\">ABCs reports</a> describe the ABCs case definition and the specific methodology used to calculate rates and estimated numbers in the United States for each bacterium by year.  The methods, <a href=\"https://www.cdc.gov/abcs/methodology/surv-pop.html\">surveillance areas</a>, and <a href=\"https://www.cdc.gov/abcs/methodology/surv-pop.html\" >laboratory isolate collection areas</a> have changed over time.\n                Additionally, the way missing data are taken into account changed in 2010.  It went from distributing unknown values based on known values of cases by site to use of multiple imputation using a sequential regression imputation method.\n                Given these changes over time, trends should be interpreted with caution.\n<ul><li><a href=\"http://www.cdc.gov/abcs/methodology/index.html\">Methodology</a>\nFind details about surveillance population, case determination, surveillance evaluation, and more. </li> <li> <a href=\"http://www.cdc.gov/abcs/reports-findings/index.html\">Reports and Findings</a>\nGet official interpretations from reports and publications created from ABCs data.\n</li>                </ul>","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/6e8b3d94-8103-4f57-97c3-f8f0d800429a","harvest_record_raw":"https://catalog.data.gov/harvest_record/6e8b3d94-8103-4f57-97c3-f8f0d800429a/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/9y49-tura","keyword":["abcs","bactfacts"],"last_harvested_date":"2026-09-23T21:24:10.546202","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"active-bacterial-core-surveillance-abcs-group-a-streptococcus","spatial_centroid":null,"spatial_shape":null,"theme":["Public Health Surveillance"],"title":"Active Bacterial Core surveillance (ABCs) Group A Streptococcus","type":"dataset"},{"_score":29.340164,"_sort":[1790198645753,29.340164,1,"c25e573f-4fd4-4e29-a52d-00c4da873434"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"OSHData Support","hasEmail":"mailto:nccdoshinquiries@cdc.gov"},"describedBy":"https://chronicdata.cdc.gov/Policy/SAMHSA-Synar-Reports-Youth-Tobacco-Sales/escb-scz6","description":"1997-2018. Substance Abuse and Mental Health Services Administration (SAMHSA). Synar Reports: Youth Tobacco Sales. Policy \u2013 Youth Tobacco Sales. SAMHSA\u2019s Synar Report on Youth Tobacco Sales presents findings on compliance of the Synar Amendment aimed at decreasing youth access to tobacco, and reviews progress in enforcing State youth tobacco access laws and in reducing the percentage of retailers selling tobacco products to minors.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/escb-scz6/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/escb-scz6/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/escb-scz6/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/escb-scz6/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/escb-scz6/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/escb-scz6/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/escb-scz6","issued":"2023-07-14","keyword":["cdc","office on smoking and health","osh","tobacco","youth tobacco sales"],"landingPage":"https://www.cdc.gov/statesystem/index.html","license":"http://opendatacommons.org/licenses/by/1.0/","modified":"2026-09-23","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://chronicdata.cdc.gov/d/p8tr-pquj"],"theme":["Policy"],"title":"SAMHSA Synar Reports: Youth Tobacco Sales"},"description":"1997-2018. Substance Abuse and Mental Health Services Administration (SAMHSA). Synar Reports: Youth Tobacco Sales. Policy \u2013 Youth Tobacco Sales. SAMHSA\u2019s Synar Report on Youth Tobacco Sales presents findings on compliance of the Synar Amendment aimed at decreasing youth access to tobacco, and reviews progress in enforcing State youth tobacco access laws and in reducing the percentage of retailers selling tobacco products to minors.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/142674fd-31ef-41be-a59a-712b2eae8576","harvest_record_raw":"https://catalog.data.gov/harvest_record/142674fd-31ef-41be-a59a-712b2eae8576/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/escb-scz6","keyword":["cdc","office on smoking and health","osh","tobacco","youth tobacco sales"],"last_harvested_date":"2026-09-23T21:24:05.753672","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"samhsa-synar-reports-youth-tobacco-sales","spatial_centroid":null,"spatial_shape":null,"theme":["Policy"],"title":"SAMHSA Synar Reports: Youth Tobacco Sales","type":"dataset"},{"_score":36.022057,"_sort":[1790198642701,36.022057,13,"1b127d64-381c-4b7c-975d-7d9491513312"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1W","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:healthus@cdc.gov"},"description":"Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. \n\nSOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/9gay-j69q/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/9gay-j69q/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/9gay-j69q/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/9gay-j69q","issued":"2022-04-28","keyword":["adolescent","adult","african americans","age","asian continental ancestry group","child","chronic conditions","continental population groups","european continental ancestry group","health risk factors","health us","hispanic americans","obesity","overweight","poverty","sex"],"landingPage":"https://www.cdc.gov/nchs/hus","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"1988/2019","theme":["National Center for Health Statistics"],"title":"Obesity among children and adolescents aged 2\u201319 years, by selected characteristics: United States"},"description":"Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. \n\nSOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/a99ad66d-6ded-4ef3-9a5f-d655090f3a15","harvest_record_raw":"https://catalog.data.gov/harvest_record/a99ad66d-6ded-4ef3-9a5f-d655090f3a15/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/9gay-j69q","keyword":["adolescent","adult","african americans","age","asian continental ancestry group","child","chronic conditions","continental population groups","european continental ancestry group","health risk factors","health us","hispanic americans","obesity","overweight","poverty","sex"],"last_harvested_date":"2026-09-23T21:24:02.701956","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":13,"publisher":"Centers for Disease Control and Prevention","slug":"obesity-among-children-and-adolescents-aged-219-years-by-selected-characteristics-united-s","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"Obesity among children and adolescents aged 2\u201319 years, by selected characteristics: United States","type":"dataset"},{"_score":15.978856,"_sort":[1790198642289,15.978856,2,"23164155-b1cd-4d7e-a6d3-31b55e723e0d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"CDC Info","hasEmail":"mailto:ddtpubsmailbox@cdc.gov"},"description":"VEHSS Composite Prevalence Estimates\n2017, 2019, 2021, 2022. This dataset contains estimates of the prevalence of visual acuity loss and major eye diseases generated using a Bayesian meta-analytic modeling approach that combines information from multiple data sources to produce comprehensive estimates of prevalence by age, race, and gender at the national, state and county levels. These composite prevalence estimates are the primary surveillance measures developed by the Centers for Disease Control and Prevention\u2019s Vision & Eye Health Surveillance System (VEHSS).  \n\nFor more information about these estimates including summary tables and maps, methods, and links to related publications visit https://www.cdc.gov/visionhealth/vehss/estimates/index.html\nTo view this data in the VEHSS interactive data visualization application, visit https://ddt-vehss.cdc.gov/ and search for \u201cVEHSS Composite Prevalence Estimate\u201d.\n\n\nVisual Acuity Loss:\nVisual acuity loss prevalence estimates represent best-corrected visual acuity in the better-seeing eye and are included in rows where Category=\u2019Measured Visual Acuity\u2019.  Rows with Subgroup = \u2018Any vision loss' represents any impairment or blindness of 20/40 or worse; rows with Subgroup = 'US-defined blindness' refers to the subset of vision loss that is 20/200 or worse.\n\n\nAge Related Macular Degeneration:\nThe age-related macular degeneration (AMD) estimates represent AMD as measured with retinal imaging examination, and are included in rows where Category = \u2018Age Related Macular Degeneration\u2019.  The Subgroup \u2018Vision threatening AMD\u2019 includes patients with geographic atrophy, wet-form AMD, or choroidal neovascularization in either eye. The Subgroup \u2018Non-vision threatening AMD\u2019 includes patients with early or intermediate dry-form AMD defined as retinal pigment epithelium abnormalities or drusen \u2265125 \u00b5m in the worse-affected eye, and do not have vision threatening AMD.\n\n\nDiabetic Retinopathy:\nThe diabetic retinopathy (DR) estimates represent DR as measured with retinal imaging examination, and are included in rows where Category=\u2019Diabetic Eye Diseases\u2019.  The Subgroup \u2018Vision threatening DR\u2019 includes patients with severe non-proliferative DR, proliferative DR, and diabetic macular edema. The Subgroup \u2018Non-vision threatening DR\u2019 is defined as patients with mild-moderate non-proliferative DR or unspecified DR, and do not have vision threatening DR.\n\n\nGlaucoma:\nThe glaucoma estimates represent glaucoma as measured with retinal imaging examination and are included in rows where Category=\u2019Glaucoma\u2019. The Subgroup \u2018Vision affecting glaucoma\u2019 includes people with glaucoma and abnormal visual field. The Subgroup \u2018Non-vision affecting glaucoma\u2019 is defined as people with glaucoma without an abnormal visual field.\n\n\nAge Groups:\nThe VEHSS Composite Prevalence Estimates are available by major age groups (All ages, ages 0-17, 18-39, 40-64, 65-84, 85+) and detailed (5-year) age groups, which are indicated by the text \u201cby detailed age groups\u201d in the \u2018Indicator\u2019 field. \n\n\nPrevalence Data Type:\nThese estimates are also available as crude (Data_Value_Type = \u2018Crude Prevalence\u2019) or adjusted data (Data_Value_Type=\u2019Adjusted Prevalence).  Crude Prevalence is the estimate of the actual number and percentage of people living with each condition.  Adjusted Prevalence estimates are adjusted to match the national population by age, race/ethnicity, and gender.  Adjusted prevalence estimates can be used to help identify disparities in prevalence between geographic areas that are not explained by differences in demographic characteristics.\n\n  \nData Sources:\nData sources for VEHSS Composite Prevalence Estimates include the National Health and Nutrition Examination Survey (NHANES), the American Community Survey (ACS), the National Survey of Children\u2019s Health (NSCH), the Behavioral Risk Factor Surveillance System (BRFSS), Medicare Fee-For-Service claims, the Transformed Medicaid Statistical Information System, MarketScan commercial insurance","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/qeru-k2y2/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/qeru-k2y2/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/qeru-k2y2/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/qeru-k2y2/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/qeru-k2y2/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/qeru-k2y2/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/qeru-k2y2","issued":"2026-08-11","keyword":["blind","blindness","prevalence","vision","visual"],"landingPage":"https://www.cdc.gov/visionhealth/vehss/estimates/amd-prevalence.html","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/vision-health-data/prevalence-estimates/index.html"],"theme":["Vision & Eye Health"],"title":"VEHSS Modeled Estimates"},"description":"VEHSS Composite Prevalence Estimates\n2017, 2019, 2021, 2022. This dataset contains estimates of the prevalence of visual acuity loss and major eye diseases generated using a Bayesian meta-analytic modeling approach that combines information from multiple data sources to produce comprehensive estimates of prevalence by age, race, and gender at the national, state and county levels. These composite prevalence estimates are the primary surveillance measures developed by the Centers for Disease Control and Prevention\u2019s Vision & Eye Health Surveillance System (VEHSS).  \n\nFor more information about these estimates including summary tables and maps, methods, and links to related publications visit https://www.cdc.gov/visionhealth/vehss/estimates/index.html\nTo view this data in the VEHSS interactive data visualization application, visit https://ddt-vehss.cdc.gov/ and search for \u201cVEHSS Composite Prevalence Estimate\u201d.\n\n\nVisual Acuity Loss:\nVisual acuity loss prevalence estimates represent best-corrected visual acuity in the better-seeing eye and are included in rows where Category=\u2019Measured Visual Acuity\u2019.  Rows with Subgroup = \u2018Any vision loss' represents any impairment or blindness of 20/40 or worse; rows with Subgroup = 'US-defined blindness' refers to the subset of vision loss that is 20/200 or worse.\n\n\nAge Related Macular Degeneration:\nThe age-related macular degeneration (AMD) estimates represent AMD as measured with retinal imaging examination, and are included in rows where Category = \u2018Age Related Macular Degeneration\u2019.  The Subgroup \u2018Vision threatening AMD\u2019 includes patients with geographic atrophy, wet-form AMD, or choroidal neovascularization in either eye. The Subgroup \u2018Non-vision threatening AMD\u2019 includes patients with early or intermediate dry-form AMD defined as retinal pigment epithelium abnormalities or drusen \u2265125 \u00b5m in the worse-affected eye, and do not have vision threatening AMD.\n\n\nDiabetic Retinopathy:\nThe diabetic retinopathy (DR) estimates represent DR as measured with retinal imaging examination, and are included in rows where Category=\u2019Diabetic Eye Diseases\u2019.  The Subgroup \u2018Vision threatening DR\u2019 includes patients with severe non-proliferative DR, proliferative DR, and diabetic macular edema. The Subgroup \u2018Non-vision threatening DR\u2019 is defined as patients with mild-moderate non-proliferative DR or unspecified DR, and do not have vision threatening DR.\n\n\nGlaucoma:\nThe glaucoma estimates represent glaucoma as measured with retinal imaging examination and are included in rows where Category=\u2019Glaucoma\u2019. The Subgroup \u2018Vision affecting glaucoma\u2019 includes people with glaucoma and abnormal visual field. The Subgroup \u2018Non-vision affecting glaucoma\u2019 is defined as people with glaucoma without an abnormal visual field.\n\n\nAge Groups:\nThe VEHSS Composite Prevalence Estimates are available by major age groups (All ages, ages 0-17, 18-39, 40-64, 65-84, 85+) and detailed (5-year) age groups, which are indicated by the text \u201cby detailed age groups\u201d in the \u2018Indicator\u2019 field. \n\n\nPrevalence Data Type:\nThese estimates are also available as crude (Data_Value_Type = \u2018Crude Prevalence\u2019) or adjusted data (Data_Value_Type=\u2019Adjusted Prevalence).  Crude Prevalence is the estimate of the actual number and percentage of people living with each condition.  Adjusted Prevalence estimates are adjusted to match the national population by age, race/ethnicity, and gender.  Adjusted prevalence estimates can be used to help identify disparities in prevalence between geographic areas that are not explained by differences in demographic characteristics.\n\n  \nData Sources:\nData sources for VEHSS Composite Prevalence Estimates include the National Health and Nutrition Examination Survey (NHANES), the American Community Survey (ACS), the National Survey of Children\u2019s Health (NSCH), the Behavioral Risk Factor Surveillance System (BRFSS), Medicare Fee-For-Service claims, the Transformed Medicaid Statistical Information System, MarketScan commercial insurance","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/cce2c8fc-f79d-432b-a152-44ed71cb4703","harvest_record_raw":"https://catalog.data.gov/harvest_record/cce2c8fc-f79d-432b-a152-44ed71cb4703/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/qeru-k2y2","keyword":["blind","blindness","prevalence","vision","visual"],"last_harvested_date":"2026-09-23T21:24:02.289638","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":2,"publisher":"Centers for Disease Control and Prevention","slug":"vehss-modeled-estimates","spatial_centroid":null,"spatial_shape":null,"theme":["Vision & Eye Health"],"title":"VEHSS Modeled Estimates","type":"dataset"},{"_score":17.456848,"_sort":[1790198630709,17.456848,0,"8a2c33c8-e710-47a1-9608-581281d5e1bf"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"Alicia Budd, Krista Kniss, and Arielle Colon","hasEmail":"mailto:FluViewSupport@cdc.gov"},"description":"This dataset has been archived and will no longer be updated as of 10/16/2024. For updated data, please refer to the <a href=\"https://gis.cdc.gov/grasp/fluview/main.html\"><b>ILINet State Activity Indicator Map</b></a>.\n\nInformation on outpatient visits to health care providers for respiratory illness referred to as influenza-like illness (ILI) is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. More than 100 million patient visits were reported during the 2022-23 season. Each week, more than 3,000 outpatient health care providers around the country report to CDC the number of patient visits for ILI by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and \u226565 years) and the total number of visits for any reason. A subset of providers also reports total visits by age group. For this system, ILI is defined as fever (temperature of 100\u00b0F [37.8\u00b0C] or greater) and a cough and/or a sore throat. Activity levels are based on the percent of outpatient visits due to ILI in a jurisdiction compared to the average percent of ILI visits that occur during weeks with little or no influenza virus circulation (non-influenza weeks) in that jurisdiction. The number of sites reporting each week is variable; therefore, baselines are adjusted each week based on which sites within each jurisdiction provide data. To perform this adjustment, provider level baseline ILI ratios are calculated for those that have a sufficient reporting history. Providers that do not have the required reporting history to calculate a provider-specific baseline are assigned the baseline ratio for their practice type. The jurisdiction level baseline is then calculated using a weighted sum of the baseline ratios for each contributing provider. \n\nThe activity levels compare the mean reported percent of visits due to ILI during the current week to the mean reported percent of visits due to ILI during non-influenza weeks. The 13 activity levels correspond to the number of standard deviations below, at, or above the mean for the current week compared with the mean during non-influenza weeks. Activity levels are classified as minimal (levels 1-3), low (levels 4-5), moderate (levels 6-7), high (levels 8-10), and very high (levels 11-13). An activity level of 1 corresponds to an ILI percentage below the mean, level 2 corresponds to an ILI percentage less than 1 standard deviation above the mean, level 3 corresponds to an ILI percentage more than 1 but less than 2 standard deviations above the mean, and so on, with an activity level of 10 corresponding to an ILI percentage 8 to 11 standard deviations above the mean. The very high levels correspond to an ILI percentage 12 to 15 standard deviations above the mean for level 11, 16 to 19 standard deviations above the mean for level 12, and 20 or more standard deviations above the mean for level 13. \n\nDisclaimers: \n\nThe ILI Activity Indicator map reflects the intensity of ILI activity, not the extent of geographic spread of ILI, within a jurisdiction. Therefore, outbreaks occurring in a single area could cause the entire jurisdiction to display high or very high activity levels. In addition, data collected in ILINet may disproportionally represent certain populations within a jurisdiction, and therefore, may not accurately depict the full picture of respiratory illness activity for the entire jurisdiction. Differences in the data presented here by CDC and independently by some health departments likely represent differing levels of data completeness with data presented by the health department likely being more complete. \n\nMore information is available on <a href=\"https://gis.cdc.gov/grasp/fluview/main.html\">FluView Interactive</a>.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/6svj-q4zv/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/6svj-q4zv/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/6svj-q4zv/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/6svj-q4zv","issued":"2023-09-26","keyword":["archive","ncird","ncird-id","respiratory-virus-response"],"landingPage":"https://data.cdc.gov/d/6svj-q4zv","license":"https://www.usa.gov/government-works","modified":"2026-09-23","programCode":["009:026"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"US","temporal":"2022-10-08/2024-02-17","theme":["Public Health Surveillance"],"title":"Outpatient Respiratory Illness Activity Map"},"description":"This dataset has been archived and will no longer be updated as of 10/16/2024. For updated data, please refer to the <a href=\"https://gis.cdc.gov/grasp/fluview/main.html\"><b>ILINet State Activity Indicator Map</b></a>.\n\nInformation on outpatient visits to health care providers for respiratory illness referred to as influenza-like illness (ILI) is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. More than 100 million patient visits were reported during the 2022-23 season. Each week, more than 3,000 outpatient health care providers around the country report to CDC the number of patient visits for ILI by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and \u226565 years) and the total number of visits for any reason. A subset of providers also reports total visits by age group. For this system, ILI is defined as fever (temperature of 100\u00b0F [37.8\u00b0C] or greater) and a cough and/or a sore throat. Activity levels are based on the percent of outpatient visits due to ILI in a jurisdiction compared to the average percent of ILI visits that occur during weeks with little or no influenza virus circulation (non-influenza weeks) in that jurisdiction. The number of sites reporting each week is variable; therefore, baselines are adjusted each week based on which sites within each jurisdiction provide data. To perform this adjustment, provider level baseline ILI ratios are calculated for those that have a sufficient reporting history. Providers that do not have the required reporting history to calculate a provider-specific baseline are assigned the baseline ratio for their practice type. The jurisdiction level baseline is then calculated using a weighted sum of the baseline ratios for each contributing provider. \n\nThe activity levels compare the mean reported percent of visits due to ILI during the current week to the mean reported percent of visits due to ILI during non-influenza weeks. The 13 activity levels correspond to the number of standard deviations below, at, or above the mean for the current week compared with the mean during non-influenza weeks. Activity levels are classified as minimal (levels 1-3), low (levels 4-5), moderate (levels 6-7), high (levels 8-10), and very high (levels 11-13). An activity level of 1 corresponds to an ILI percentage below the mean, level 2 corresponds to an ILI percentage less than 1 standard deviation above the mean, level 3 corresponds to an ILI percentage more than 1 but less than 2 standard deviations above the mean, and so on, with an activity level of 10 corresponding to an ILI percentage 8 to 11 standard deviations above the mean. The very high levels correspond to an ILI percentage 12 to 15 standard deviations above the mean for level 11, 16 to 19 standard deviations above the mean for level 12, and 20 or more standard deviations above the mean for level 13. \n\nDisclaimers: \n\nThe ILI Activity Indicator map reflects the intensity of ILI activity, not the extent of geographic spread of ILI, within a jurisdiction. Therefore, outbreaks occurring in a single area could cause the entire jurisdiction to display high or very high activity levels. In addition, data collected in ILINet may disproportionally represent certain populations within a jurisdiction, and therefore, may not accurately depict the full picture of respiratory illness activity for the entire jurisdiction. Differences in the data presented here by CDC and independently by some health departments likely represent differing levels of data completeness with data presented by the health department likely being more complete. \n\nMore information is available on <a href=\"https://gis.cdc.gov/grasp/fluview/main.html\">FluView Interactive</a>.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/25904282-7b9e-4f1b-a393-427ac6e38228","harvest_record_raw":"https://catalog.data.gov/harvest_record/25904282-7b9e-4f1b-a393-427ac6e38228/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/6svj-q4zv","keyword":["archive","ncird","ncird-id","respiratory-virus-response"],"last_harvested_date":"2026-09-23T21:23:50.709933","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":0,"publisher":"Centers for Disease Control and Prevention","slug":"outpatient-respiratory-illness-activity-map","spatial_centroid":null,"spatial_shape":null,"theme":["Public Health Surveillance"],"title":"Outpatient Respiratory Illness Activity Map","type":"dataset"},{"_score":6.6339817,"_sort":[1790198619404,6.6339817,4,"868c4899-750b-4dfb-b337-76420a62aa97"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"Jessica Belser","hasEmail":"mailto:jax6@cdc.gov"},"description":"Data from influenza A virus (IAV) infected ferrets (Mustela putorius furo) provides invaluable information towards the study of novel and emerging viruses that pose a threat to human health. This gold standard animal model can recapitulate many clinical signs of infection present in IAV-infected humans, supports virus replication of human and zoonotic strains without prior adaptation, and permits evaluation of virus transmissibility by multiple modes. While ferrets have been employed in risk assessment settings for >20 years, results from this work are typically reported in discrete stand-alone publications, making aggregation of raw data from this work over time nearly impossible. Here, we describe a dataset of 333 ferrets inoculated with 107 unique IAV, conducted by a single research group (NCIRD/ID/IPB/Pathogenesis Laboratory Team) under a uniform experimental protocol. This collection of ferret tissue viral titer data on a per-individual ferret level represents a companion dataset to \u2018An aggregated dataset of serially collected influenza A virus morbidity and titer measurements from virus-infected ferrets\u2019. However, care must be taken when combining datasets at the level of individual animals (see PMID 40245007 for guidance in best practices for comparing datasets comprised of serially-collected and fixed-timepoint in vivo-generated data).\n\nSee publications using and describing data for more information:\nKieran TJ, Sun X, Tumpey TM, Maines TR, Belser JA. 202X. Spatial variation of infectious virus load in aggregated day 3 post-inoculation respiratory tract tissues from influenza A virus-infected ferrets. Under peer review. \n\nKieran TJ, Sun X, Maines TR, Belser JA. 2025. Predictive models of influenza A virus lethal disease: insights from ferret respiratory tract and brain tissues. Scientific Reports, in press.\n\nBullock TA, Pappas C, Uyeki TM, Brock N, Kieran TJ, Olsen SJ, Davis CD, Tumpey TM, Maines TR, Belser JA. 2025. The (digestive) path less traveled: influenza A virus and the gastrointestinal tract. mBio, in press.\n\nKieran TJ, Sun X, Maines TR, Beauchemin CAA, Belser JA. 2024. Exploring associations between viral titer measurements and disease outcomes in ferrets inoculated with 125 contemporary influenza A viruses. J Virol98: e01661-23. https://doi.org/10.1038/s41597-024-03256-6\n\nRelated dataset:\nKieran TJ, Sun X, Creager HM, Tumpey TM, Maine TR, Belser JA. 2025. An aggregated dataset of serial morbidity and titer measurements from influenza A virus-infected ferrets. Sci Data, 11(1):510. https://doi.org/10.1038/s41597-024-03256-6\n\nhttps://data.cdc.gov/National-Center-for-Immunization-and-Respiratory-D/An-aggregated-dataset-of-serially-collected-influe/cr56-k9wj/about_data\n\nOther relevant publications for best practices on data handling and interpretation: \nKieran TJ, Maines TR, Belser JA. 2025. Eleven quick tips to unlock the power of in vivo data science. PLoS Comput Biol, 21(4):e1012947. https://doi.org/10.1371/journal.pcbi.1012947\n\nKieran TJ, Maines TR, Belser JA. 2025. Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold. PLoS Pathog, 20(8):e1012460. https://doi.org/10.1371/journal.ppat.1012460","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/d9u6-mdu6/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/d9u6-mdu6/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/d9u6-mdu6/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/d9u6-mdu6","issued":"2025-05-16","keyword":["animal model","ferret","flu","in vivo","influenza","ncird","ncird-id","necropsy","pathogenesis","risk assessment","tissue","virus"],"landingPage":"https://data.cdc.gov/d/d9u6-mdu6","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:026"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"theme":["National Center for Immunization and Respiratory Diseases"],"title":"An aggregated dataset of day 3 post-inoculation viral titer measurements from influenza A virus-infected ferret tissues"},"description":"Data from influenza A virus (IAV) infected ferrets (Mustela putorius furo) provides invaluable information towards the study of novel and emerging viruses that pose a threat to human health. This gold standard animal model can recapitulate many clinical signs of infection present in IAV-infected humans, supports virus replication of human and zoonotic strains without prior adaptation, and permits evaluation of virus transmissibility by multiple modes. While ferrets have been employed in risk assessment settings for >20 years, results from this work are typically reported in discrete stand-alone publications, making aggregation of raw data from this work over time nearly impossible. Here, we describe a dataset of 333 ferrets inoculated with 107 unique IAV, conducted by a single research group (NCIRD/ID/IPB/Pathogenesis Laboratory Team) under a uniform experimental protocol. This collection of ferret tissue viral titer data on a per-individual ferret level represents a companion dataset to \u2018An aggregated dataset of serially collected influenza A virus morbidity and titer measurements from virus-infected ferrets\u2019. However, care must be taken when combining datasets at the level of individual animals (see PMID 40245007 for guidance in best practices for comparing datasets comprised of serially-collected and fixed-timepoint in vivo-generated data).\n\nSee publications using and describing data for more information:\nKieran TJ, Sun X, Tumpey TM, Maines TR, Belser JA. 202X. Spatial variation of infectious virus load in aggregated day 3 post-inoculation respiratory tract tissues from influenza A virus-infected ferrets. Under peer review. \n\nKieran TJ, Sun X, Maines TR, Belser JA. 2025. Predictive models of influenza A virus lethal disease: insights from ferret respiratory tract and brain tissues. Scientific Reports, in press.\n\nBullock TA, Pappas C, Uyeki TM, Brock N, Kieran TJ, Olsen SJ, Davis CD, Tumpey TM, Maines TR, Belser JA. 2025. The (digestive) path less traveled: influenza A virus and the gastrointestinal tract. mBio, in press.\n\nKieran TJ, Sun X, Maines TR, Beauchemin CAA, Belser JA. 2024. Exploring associations between viral titer measurements and disease outcomes in ferrets inoculated with 125 contemporary influenza A viruses. J Virol98: e01661-23. https://doi.org/10.1038/s41597-024-03256-6\n\nRelated dataset:\nKieran TJ, Sun X, Creager HM, Tumpey TM, Maine TR, Belser JA. 2025. An aggregated dataset of serial morbidity and titer measurements from influenza A virus-infected ferrets. Sci Data, 11(1):510. https://doi.org/10.1038/s41597-024-03256-6\n\nhttps://data.cdc.gov/National-Center-for-Immunization-and-Respiratory-D/An-aggregated-dataset-of-serially-collected-influe/cr56-k9wj/about_data\n\nOther relevant publications for best practices on data handling and interpretation: \nKieran TJ, Maines TR, Belser JA. 2025. Eleven quick tips to unlock the power of in vivo data science. PLoS Comput Biol, 21(4):e1012947. https://doi.org/10.1371/journal.pcbi.1012947\n\nKieran TJ, Maines TR, Belser JA. 2025. Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold. PLoS Pathog, 20(8):e1012460. https://doi.org/10.1371/journal.ppat.1012460","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/1cda5851-0003-40d9-9cab-3828b4a3664d","harvest_record_raw":"https://catalog.data.gov/harvest_record/1cda5851-0003-40d9-9cab-3828b4a3664d/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/d9u6-mdu6","keyword":["animal model","ferret","flu","in vivo","influenza","ncird","ncird-id","necropsy","pathogenesis","risk assessment","tissue","virus"],"last_harvested_date":"2026-09-23T21:23:39.404994","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":4,"publisher":"Centers for Disease Control and Prevention","slug":"an-aggregated-dataset-of-day-3-post-inoculation-viral-titer-measurements-from-influenza-a-","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Immunization and Respiratory Diseases"],"title":"An aggregated dataset of day 3 post-inoculation viral titer measurements from influenza A virus-infected ferret tissues","type":"dataset"},{"_score":3.1220407,"_sort":[1790198617309,3.1220407,4,"cb91cd08-33fb-49bf-82c3-23af91a3f524"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1W","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Syndromic Surveillance Program","hasEmail":"mailto:nssp@cdc.gov"},"description":"NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined \u2013 by Demographic Category \n\nFor additional information, please see: <a href=\"https://www.cdc.gov/ncird/surveillance/respiratory-illnesses/index.html#companion-guide\"><b>Companion Guide: NSSP Emergency Department Data on Respiratory Illness</b></a>.\n\nUpdated once per week on Fridays.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/7xva-uux8/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/7xva-uux8/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/7xva-uux8/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/7xva-uux8","issued":"2023-09-27","keyword":["coronavirus","covid19","ed","emergency department","flu","influenza","national syndromic surveillance program","ncird","nssp","ophdst","respiratory syncytial","respiratory syncytial virus","respiratory-virus-response","rsv","rvr","virus"],"landingPage":"https://data.cdc.gov/d/7xva-uux8","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:026"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"U.S.","temporal":"2022-11-05/2024-02-17","theme":["Public Health Surveillance"],"title":"NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined \u2013 by Demographic Category"},"description":"NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined \u2013 by Demographic Category \n\nFor additional information, please see: <a href=\"https://www.cdc.gov/ncird/surveillance/respiratory-illnesses/index.html#companion-guide\"><b>Companion Guide: NSSP Emergency Department Data on Respiratory Illness</b></a>.\n\nUpdated once per week on Fridays.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/69d36f5a-72f5-4aad-a775-5624f4560db3","harvest_record_raw":"https://catalog.data.gov/harvest_record/69d36f5a-72f5-4aad-a775-5624f4560db3/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/7xva-uux8","keyword":["coronavirus","covid19","ed","emergency department","flu","influenza","national syndromic surveillance program","ncird","nssp","ophdst","respiratory syncytial","respiratory syncytial virus","respiratory-virus-response","rsv","rvr","virus"],"last_harvested_date":"2026-09-23T21:23:37.309956","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":4,"publisher":"Centers for Disease Control and Prevention","slug":"nssp-emergency-department-visits-covid-19-flu-rsv-combined-by-demographic-category","spatial_centroid":null,"spatial_shape":null,"theme":["Public Health Surveillance"],"title":"NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined \u2013 by Demographic Category","type":"dataset"},{"_score":35.713158,"_sort":[1790198613925,35.713158,7,"b080ac70-1a73-48de-a9da-d8023c31ce82"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"OSHData Support","hasEmail":"mailto:nccdoshinquiries@cdc.gov"},"describedBy":"https://chronicdata.cdc.gov/Healthy-People-2020/Healthy-People-2020-Tobacco-Use-Objectives/hhew-mxbt","description":"U.S. Department of Health and Human Services (HHS). Centers for Disease Control and Prevention (CDC). Healthy People 2020 Tobacco Use Objectives. Healthy People 2020. Healthy People 2020 provides a framework for action to reduce tobacco use to the point that it is no longer a public health problem for the Nation. This dataset includes information related to the Healthy People 2020 Tobacco Use objectives, operational definitions, baselines, and targets. Baseline years may vary by objective. Targets represented correspond to the year 2020.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/hhew-mxbt/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/hhew-mxbt/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/hhew-mxbt/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/hhew-mxbt","issued":"2017-08-23","keyword":["centers for disease control and prevention","department of health and human services","healthy people 2020","leading health indicator","office on smoking and health","osh"],"landingPage":"https://www.cdc.gov/statesystem/index.html","license":"http://opendatacommons.org/licenses/by/1.0/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"theme":["Healthy People 2020"],"title":"Healthy People 2020 Tobacco Use Objectives"},"description":"U.S. Department of Health and Human Services (HHS). Centers for Disease Control and Prevention (CDC). Healthy People 2020 Tobacco Use Objectives. Healthy People 2020. Healthy People 2020 provides a framework for action to reduce tobacco use to the point that it is no longer a public health problem for the Nation. This dataset includes information related to the Healthy People 2020 Tobacco Use objectives, operational definitions, baselines, and targets. Baseline years may vary by objective. Targets represented correspond to the year 2020.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/b3bcac6e-37b0-4caa-a13a-8368db042b82","harvest_record_raw":"https://catalog.data.gov/harvest_record/b3bcac6e-37b0-4caa-a13a-8368db042b82/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/hhew-mxbt","keyword":["centers for disease control and prevention","department of health and human services","healthy people 2020","leading health indicator","office on smoking and health","osh"],"last_harvested_date":"2026-09-23T21:23:33.925513","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":7,"publisher":"Centers for Disease Control and Prevention","slug":"healthy-people-2020-tobacco-use-objectives","spatial_centroid":null,"spatial_shape":null,"theme":["Healthy People 2020"],"title":"Healthy People 2020 Tobacco Use Objectives","type":"dataset"},{"_score":23.538868,"_sort":[1790198609600,23.538868,1,"13d36802-e379-4bda-befb-069b975f3da5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1Y","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:dqs@cdc.gov"},"description":"Data on Low birthweight live births by state in the United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Birth File.  \nSearch, visualize, and download these and other estimates from over 150 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ga7k-kycn/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ga7k-kycn/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/ga7k-kycn/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/ga7k-kycn","issued":"2025-07-24","keyword":["births","nvss"],"landingPage":"https://www.cdc.gov/nchs/dqs/","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"2014/2023","theme":["National Center for Health Statistics"],"title":"DEV DQS NVSS Low birthweight live births, by state: United States."},"description":"Data on Low birthweight live births by state in the United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Birth File.  \nSearch, visualize, and download these and other estimates from over 150 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/9f4ddde6-d304-4abb-8080-f561d37867a8","harvest_record_raw":"https://catalog.data.gov/harvest_record/9f4ddde6-d304-4abb-8080-f561d37867a8/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/ga7k-kycn","keyword":["births","nvss"],"last_harvested_date":"2026-09-23T21:23:29.600991","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"dev-dqs-nvss-low-birthweight-live-births-by-state-united-states","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"DEV DQS NVSS Low birthweight live births, by state: United States.","type":"dataset"},{"_score":51.187714,"_sort":[1790198607673,51.187714,1,"f649140a-517e-453a-bc52-913d1374bb12"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"500 Cities Public Inquiries","hasEmail":"mailto:places@cdc.gov"},"describedBy":"https://chronicdata.cdc.gov/dataset/PLACES-Local-Data-for-Better-Health-ZCTA-Data-2020/qnzd-25i4","description":"This dataset contains model-based ZIP Code tabulation Areas (ZCTA) level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States\u201450 states and the District of Columbia (DC)\u2014at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/fbbf-hgkc/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/fbbf-hgkc/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/fbbf-hgkc/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/fbbf-hgkc/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/fbbf-hgkc/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/fbbf-hgkc/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/fbbf-hgkc","issued":"2021-09-29","keyword":["behaviors","brfss","outcomes","places","prevalence","prevention","unhealthy","zcta","zip code"],"landingPage":"http://www.cdc.gov/nccdphp/dph/","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/places/measure-definitions/index.html"],"theme":["500 Cities & Places"],"title":"PLACES: Local Data for Better Health, ZCTA Data 2020 release"},"description":"This dataset contains model-based ZIP Code tabulation Areas (ZCTA) level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States\u201450 states and the District of Columbia (DC)\u2014at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/0d47d3ae-d451-4d16-9bc1-288c649f7917","harvest_record_raw":"https://catalog.data.gov/harvest_record/0d47d3ae-d451-4d16-9bc1-288c649f7917/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/fbbf-hgkc","keyword":["behaviors","brfss","outcomes","places","prevalence","prevention","unhealthy","zcta","zip code"],"last_harvested_date":"2026-09-23T21:23:27.673903","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"places-local-data-for-better-health-zcta-data-2020-release","spatial_centroid":null,"spatial_shape":null,"theme":["500 Cities & Places"],"title":"PLACES: Local Data for Better Health, ZCTA Data 2020 release","type":"dataset"},{"_score":9.21428,"_sort":[1790198600167,9.21428,72,"8285305b-1249-49bd-b94e-4b3de4db77f0"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:births@cdc.gov"},"describedBy":"https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","description":"This data set contains estimated teen birth rates for age group 15\u201319 (expressed per 1,000 females aged 15\u201319) by county and year.\n\nDEFINITIONS\n\nEstimated teen birth rate: Model-based estimates of teen birth rates for age group 15\u201319 (expressed per 1,000 females aged 15\u201319) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates \u201cborrow strength\u201d across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information\u2014including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables\u2014to provide a stable estimate of the county teen birth rate.\nMedian teen birth rate: The middle value of the estimated teen birth rates for the age group 15\u201319 for counties in a state.\nBayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model.\n\nNOTES\n\nData on the number of live births for women aged 15\u201319 years were extracted from the National Center for Health Statistics\u2019 (NCHS) National Vital Statistics System birth data files for 2003\u20132015 (5).\n\nPopulation estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used.\n\nHierarchical Bayesian space\u2013time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003\u20132015 (1,2,3,4).\n\nThe Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-\u03b1)% Bayesian credible interval for an unknown parameter vector \u03b8 and observed data vector y is a subset C of parameter space \u0424 such that\n1-\u03b1\u2264P({C\u2502y})=\u222bp{\u03b8 \u2502y}d\u03b8,\nwhere integration is performed over the set  and is replaced by summation for discrete components of \u03b8.  The probability that \u03b8 lies in C given the observed data y is at least (1- \u03b1) (6).\n\nCounty borders in Alaska changed, and new counties were formed and others were merged, during 2003\u20132015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003\u20132015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005\u20132007 bridged-race population file geographies (7).","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/3h58-x6cd/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/3h58-x6cd/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/3h58-x6cd/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/3h58-x6cd","isPartOf":"NCHS - Teen Birth Rates for Age Group 15-19 in the United States by County","issued":"2018-02-07","keyword":["county teen birth trends","county trends on teen births","nchs","teen births","u.s. teen birth rate","united states"],"landingPage":"https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","language":["English"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["http://onlinelibrary.wiley.com/doi/10.1111/rssa.12266/abstract","http://www.cdc.gov/nchs/data/nvss/bridged_race/County_Geography_Changes.pdf","http://www.cdc.gov/nchs/nvss/dvs_data_release.htm","http://www.sciencedirect.com/science/article/pii/S1877584516300442","https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf","https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","https://www.cdc.gov/nchs/nvss/bridged_race.htm"],"rights":"public","spatial":"50 states and District of Columbia","temporal":"2003/2020","theme":["National Center for Health Statistics"],"title":"NCHS - Teen Birth Rates for Age Group 15-19 in the United States by County"},"description":"This data set contains estimated teen birth rates for age group 15\u201319 (expressed per 1,000 females aged 15\u201319) by county and year.\n\nDEFINITIONS\n\nEstimated teen birth rate: Model-based estimates of teen birth rates for age group 15\u201319 (expressed per 1,000 females aged 15\u201319) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates \u201cborrow strength\u201d across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information\u2014including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables\u2014to provide a stable estimate of the county teen birth rate.\nMedian teen birth rate: The middle value of the estimated teen birth rates for the age group 15\u201319 for counties in a state.\nBayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model.\n\nNOTES\n\nData on the number of live births for women aged 15\u201319 years were extracted from the National Center for Health Statistics\u2019 (NCHS) National Vital Statistics System birth data files for 2003\u20132015 (5).\n\nPopulation estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used.\n\nHierarchical Bayesian space\u2013time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003\u20132015 (1,2,3,4).\n\nThe Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-\u03b1)% Bayesian credible interval for an unknown parameter vector \u03b8 and observed data vector y is a subset C of parameter space \u0424 such that\n1-\u03b1\u2264P({C\u2502y})=\u222bp{\u03b8 \u2502y}d\u03b8,\nwhere integration is performed over the set  and is replaced by summation for discrete components of \u03b8.  The probability that \u03b8 lies in C given the observed data y is at least (1- \u03b1) (6).\n\nCounty borders in Alaska changed, and new counties were formed and others were merged, during 2003\u20132015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003\u20132015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005\u20132007 bridged-race population file geographies (7).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/5101f6b6-8705-42f8-9b2d-9633cf2ceb27","harvest_record_raw":"https://catalog.data.gov/harvest_record/5101f6b6-8705-42f8-9b2d-9633cf2ceb27/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/3h58-x6cd","keyword":["county teen birth trends","county trends on teen births","nchs","teen births","u.s. teen birth rate","united states"],"last_harvested_date":"2026-09-23T21:23:20.167907","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":"NCHS - Teen Birth Rates for Age Group 15-19 in the United States by County","popularity":72,"publisher":"Centers for Disease Control and Prevention","slug":"nchs-teen-birth-rates-for-age-group-15-19-in-the-united-states-by-county","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Health Statistics"],"title":"NCHS - Teen Birth Rates for Age Group 15-19 in the United States by County","type":"dataset"},{"_score":67.053856,"_sort":[1790198598285,67.053856,3,"46d9c44f-35ec-46cd-b246-b7f2bc0236e9"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"PLACES Public Inquiries","hasEmail":"mailto:places@cdc.gov"},"describedBy":"https://chronicdata.cdc.gov/dataset/PLACES-Local-Data-for-Better-Health-Place-Data-202/eav7-hnsx","description":"This dataset contains model-based place (incorporated and census-designated places) estimates. PLACES covers the entire United States\u201450 states and the District of Columbia\u2014at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018\u20132022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at  www.cdc.gov/places.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/sd8v-uq83/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/sd8v-uq83/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/sd8v-uq83/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/sd8v-uq83/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/sd8v-uq83/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/sd8v-uq83/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/sd8v-uq83","issued":"2025-12-04","keyword":["behaviors","brfss","city","disability","health","outcomes","place","places","prevalence","prevention","risk","status"],"landingPage":"http://www.cdc.gov/nccdphp/dph/","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-23","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/places/measure-definitions/index.html"],"theme":["500 Cities & Places"],"title":"PLACES: Local Data for Better Health, Place Data 2024 release"},"description":"This dataset contains model-based place (incorporated and census-designated places) estimates. PLACES covers the entire United States\u201450 states and the District of Columbia\u2014at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018\u20132022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. 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Data deaths by place of death are available in this dataset https://data.cdc.gov/NCHS/d/4va6-ph5s.\n\nDeaths involving  COVID-19, pneumonia and influenza reported to NCHS by place of death and state, United States.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/uggs-hy5q/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/uggs-hy5q/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/uggs-hy5q/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/uggs-hy5q","issued":"2020-05-01","keyword":["coronavirus","covid-19","deaths","influenza","monthly","mortality","nchs","nvss","place of death","pneumonia","provisional","puerto rico","state","united states","yearly"],"landingPage":"https://www.cdc.gov/nchs/covid19/covid-19-mortality-data-files.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States, Puerto Rico","theme":["National Center for Health Statistics"],"title":"Provisional COVID-19 Deaths by Place of Death and State"},"description":"Effective June 28, 2023, this dataset will no longer be updated. 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The PLACES project is the expansion of the original 500 Cities project and covers the entire United States\u201450 states and the District of Columbia (DC)\u2014at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/4ai3-zynv/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/4ai3-zynv/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/4ai3-zynv/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/4ai3-zynv/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/4ai3-zynv/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/4ai3-zynv/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/4ai3-zynv","issued":"2021-09-29","keyword":["behaviors","brfss","census tract","outcomes","places","prevalence","prevention","unhealthy"],"landingPage":"http://www.cdc.gov/nccdphp/dph/","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-09-23","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/places/measure-definitions/index.html"],"theme":["500 Cities & Places"],"title":"PLACES: Local Data for Better Health, Census Tract Data 2020 release"},"description":"This dataset contains model-based census tract-level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States\u201450 states and the District of Columbia (DC)\u2014at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/25fda189-d347-45b1-9c1f-066ba61d1ade","harvest_record_raw":"https://catalog.data.gov/harvest_record/25fda189-d347-45b1-9c1f-066ba61d1ade/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/4ai3-zynv","keyword":["behaviors","brfss","census tract","outcomes","places","prevalence","prevention","unhealthy"],"last_harvested_date":"2026-09-23T21:22:06.400030","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"places-local-data-for-better-health-census-tract-data-2020-release","spatial_centroid":null,"spatial_shape":null,"theme":["500 Cities & Places"],"title":"PLACES: Local Data for Better Health, Census Tract Data 2020 release","type":"dataset"},{"_score":2.6165829,"_sort":[1790198518363,2.6165829,5,"a0136558-f4fa-4d9b-ba7e-aa5c913390ef"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015.\r\n\r\nDeaths are classified using the International Classification of Diseases, Tenth Revision (ICD\u201310). Drug-poisoning deaths are defined as having ICD\u201310 underlying cause-of-death codes X40\u2013X44 (unintentional), X60\u2013X64 (suicide), X85 (homicide), or Y10\u2013Y14 (undetermined intent).\r\n\r\nEstimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011\u20132015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.\r\n\r\nEstimate does not meet standards of reliability or precision. Death rates are flagged as \u201cUnreliable\u201d in the chart when the rate is calculated with a numerator of 20 or less.\r\n\r\nDeath rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD\u201310 codes for unintentional poisoning as R99, \u201cOther ill-defined and unspecified causes of mortality\u201d (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.\r\n\r\nSmoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3\u20135). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999\u20132015. These annual county-level estimates \u201cborrow strength\u201d across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/pbkm-d27e/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/pbkm-d27e/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/pbkm-d27e/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/pbkm-d27e","issued":"2016-01-07","keyword":["county","deaths","drug poisoning","mortality","nchs","united states"],"landingPage":"https://www.cdc.gov/nchs/data-visualization/drug-poisoning-mortality/","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","theme":["National Center for Health Statistics"],"title":"NCHS - Drug Poisoning Mortality by County: United States"},"description":"This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015.\r\n\r\nDeaths are classified using the International Classification of Diseases, Tenth Revision (ICD\u201310). Drug-poisoning deaths are defined as having ICD\u201310 underlying cause-of-death codes X40\u2013X44 (unintentional), X60\u2013X64 (suicide), X85 (homicide), or Y10\u2013Y14 (undetermined intent).\r\n\r\nEstimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011\u20132015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.\r\n\r\nEstimate does not meet standards of reliability or precision. Death rates are flagged as \u201cUnreliable\u201d in the chart when the rate is calculated with a numerator of 20 or less.\r\n\r\nDeath rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD\u201310 codes for unintentional poisoning as R99, \u201cOther ill-defined and unspecified causes of mortality\u201d (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.\r\n\r\nSmoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3\u20135). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999\u20132015. These annual county-level estimates \u201cborrow strength\u201d across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/eaeaaf54-3fad-4b37-9bd5-17e21c6e13de","harvest_record_raw":"https://catalog.data.gov/harvest_record/eaeaaf54-3fad-4b37-9bd5-17e21c6e13de/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/pbkm-d27e","keyword":["county","deaths","drug poisoning","mortality","nchs","united states"],"last_harvested_date":"2026-09-23T21:21:58.363059","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":5,"publisher":"Centers for Disease Control and Prevention","slug":"nchs-drug-poisoning-mortality-by-county-united-states-4ff5a","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"NCHS - Drug Poisoning Mortality by County: United States","type":"dataset"},{"_score":2.5728388,"_sort":[1790198515065,2.5728388,10,"1d0e3642-16bf-430e-93b4-8c9b498dbd5a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.\n\nEstimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected.\n\nEstimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/xkkf-xrst/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/xkkf-xrst/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/xkkf-xrst/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/xkkf-xrst","issued":"2020-04-29","keyword":["all causes","coronavirus","covid-19","deaths","excess deaths","mortality","nchs","nvss","provisional","puerto rico","state","united states","weekly"],"landingPage":"https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States, Puerto Rico","theme":["National Center for Health Statistics"],"title":"Excess Deaths Associated with COVID-19"},"description":"Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.\n\nEstimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected.\n\nEstimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/0b03bb8a-4c94-4a6f-8dc8-7b3b50a5e95b","harvest_record_raw":"https://catalog.data.gov/harvest_record/0b03bb8a-4c94-4a6f-8dc8-7b3b50a5e95b/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/xkkf-xrst","keyword":["all causes","coronavirus","covid-19","deaths","excess deaths","mortality","nchs","nvss","provisional","puerto rico","state","united states","weekly"],"last_harvested_date":"2026-09-23T21:21:55.065583","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":10,"publisher":"Centers for Disease Control and Prevention","slug":"excess-deaths-associated-with-covid-19","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Health Statistics"],"title":"Excess Deaths Associated with COVID-19","type":"dataset"},{"_score":14.395256,"_sort":[1790198512623,14.395256,2,"227c3e28-5b39-4249-a033-327c7082eb39"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Provisional deaths involving coronavirus disease 2019 (COVID-19) reported to NCHS by time-period (week, month, year), HHS region, race and Hispanic origin, and age group (0-24, 25-64, 65+ years) for 2020-2021.\n\nUnited States death counts include the 50 states, plus the District of Columbia and New York City. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. 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For more detail, see the Technical Notes.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/u6jv-9ijr/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/u6jv-9ijr/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/u6jv-9ijr/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/u6jv-9ijr","issued":"2020-05-28","keyword":["alzheimer disease","cancer","causes of death","cerebrovascular disease","chronic lower respiratory disease","circulatory disease","deaths","dementia","diabetes","heart failure","hypertensive disease","influenza","ischemic heart disease","mortality","nchs","nvss","pneumonia","provisional","puerto rico","renal failure","respiratory disease","septicemia","state","united states","weekly"],"landingPage":"https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States, Puerto Rico","theme":["National Center for Health Statistics"],"title":"Weekly Counts of Death by Jurisdiction and Select Causes of Death"},"description":"Effective September 27, 2023, this dataset will no longer be updated. 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Estimated numbers of deaths due to these other causes of death could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19 (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems). Deaths with an underlying cause of death of COVID-19 are not included in these estimates of deaths due to other causes. Deaths due to external causes (i.e. injuries) or unknown causes are excluded. 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HRR is determined by county of occurrence. Weekly weighted counts of deaths from all causes and due to COVID-19 are provided by HRR overall and for decedents 65 years and older. The weighted counts by HRRs are based on published methods for aggregating county-level data to HRRs. More detail about aggregating to HRRs from counties can be found in the following: https://github.com/Dartmouth-DAC/covid-19-hrr-mapping\nhttps://dartmouthatlas.org/covid-19/hrr-mapping/","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/mqmc-4b9n/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/mqmc-4b9n/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/mqmc-4b9n/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/mqmc-4b9n","issued":"2021-05-12","keyword":["all causes","coronavirus","covid-19","deaths","hospital referral region","mortality","nchs","nvss","provisional","united states","weekly"],"landingPage":"https://www.cdc.gov/nchs/covid19/covid-19-mortality-data-files.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"2015-01-04/2021-07-03","theme":["National Center for Health Statistics"],"title":"AH Provisional COVID-19 Deaths by Hospital Referral Region"},"description":"Provisional count of deaths involving coronavirus disease 2019 (COVID-19) in the United States by week of death and by hospital referral region (HRR). 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More detail about aggregating to HRRs from counties can be found in the following: https://github.com/Dartmouth-DAC/covid-19-hrr-mapping\nhttps://dartmouthatlas.org/covid-19/hrr-mapping/","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/be029304-b9e2-4884-a4c3-081a0ed4652a","harvest_record_raw":"https://catalog.data.gov/harvest_record/be029304-b9e2-4884-a4c3-081a0ed4652a/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/mqmc-4b9n","keyword":["all causes","coronavirus","covid-19","deaths","hospital referral region","mortality","nchs","nvss","provisional","united states","weekly"],"last_harvested_date":"2026-09-23T21:20:36.285968","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"ah-provisional-covid-19-deaths-by-hospital-referral-region","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"AH Provisional COVID-19 Deaths by Hospital Referral Region","type":"dataset"},{"_score":15.103861,"_sort":[1790198435393,15.103861,1,"a78489ec-29c0-4b8b-85a1-bb29c4eca0df"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"CDC-INFO","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Mandated reporting of Weekly Aggregate Case and Death Count data among dialysis patients and dialysis facility staff (healthcare personnel or HCP) in the United States was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. 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This dataset will contain weekly aggregate data from January 1, 2021, through May 10, 2023, and will remain publicly available.\n\nThis archived public use dataset contains reported COVID-19 case and death data per week for all states and territories, along with weekly totals for the entire United States, throughout the given timeframe.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/2d0c1a3f-90d2-40d6-8ca4-225c58b459ae","harvest_record_raw":"https://catalog.data.gov/harvest_record/2d0c1a3f-90d2-40d6-8ca4-225c58b459ae/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/n5qs-vw3x","keyword":["coronavirus","covid","covid cases","covid deaths","covid-19","dialysis","hemodialysis","sars-cov-2"],"last_harvested_date":"2026-09-23T21:20:35.393945","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"weekly-united-states-covid-19-cases-and-deaths-among-dialysis-patients-archived","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["Case Surveillance"],"title":"Weekly United States COVID-19 Cases and Deaths among Dialysis Patients - ARCHIVED","type":"dataset"},{"_score":5.972189,"_sort":[1790198428218,5.972189,3,"48e87195-7da3-4a14-97c2-c07d5fdba956"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1D","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"NCIRD","hasEmail":"mailto:iisinfo@cdc.gov"},"description":"This site provides historical data beginning June 22, 2022, for the visualization presented on <a href=\"https://covid.cdc.gov/covid-data-tracker/#vaccinations\" target=\"_blank\">COVID-19 Data Tracker\u2019s \u201cVaccinations in the United States\u201d</a> site titled \u201cPrimary Series Completion, Booster Dose Eligibility, and Booster Dose Receipt by Age, United States\u201d.\n\n<b >Fully Vaccinated / Completed Primary Series:</b> <ul><li> For surveillance purposes, COVID Data Tracker counts people as being \"fully vaccinated\" or as having \"completed a primary series\" if they received two doses on different days (regardless of time interval) of the two-dose mRNA series or received one dose of a single-dose vaccine. When the vaccine manufacturer is not reported, the recipient is considered fully vaccinated with two doses.</li></ul><b>First Booster Dose:\u202f </b><ul><li>For surveillance purposes, the count and percentage of people who received a first booster dose includes anyone who is fully vaccinated and has received another dose of COVID-19 vaccine since August 13, 2021. This includes people who received a first booster dose and people who received an additional primary series dose as this metric does not distinguish if the recipient is <b><a href='https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/immuno.html' target=\"_blank\">\u202fimmunocompromised and received an additional dose</a></b>.</li><li><b>   First booster dose eligibility: </b><ul><li>CDC counts people as being <b>\"\u202f<a href=\"https://www.cdc.gov/coronavirus/2019-ncov/vaccines/booster-shot.html\" target=\"_blank\">eligible for a first booster dose</a>\"</b> if it has been at least 5 months since completion of a Pfizer-BioNTech or Moderna primary series or at least 2 months since receipt of a Janssen (Johnson & Johnson) singe-dose vaccine. </li></ul></li></ul>\n<b>Second Booster Dose:</b><ul><li>For surveillance purposes, the count and percentage of people who received a second booster dose includes anyone who is fully vaccinated and has received two subsequent doses of COVID-19 vaccine since August 13, 2021. This includes people who received two booster doses and people who received one additional dose and one booster dose.\u202f </li><li>The count of people who received a second booster dose and the percentage of people with a first booster who received a second booster dose does not account for whether a person is <b><a href='https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/immuno.html'  target=\"_blank\">immunocompromised or time interval since first booster dose</a></b>. </li><li><b>Second booster dose eligibility: </b><ul><li>CDC counts people as being\u202f <b>\"<a href=\"https://www.cdc.gov/coronavirus/2019-ncov/vaccines/booster-shot.html\" target=\"_blank\"><b>eligible for a second booster dose</b></a>\"\u202f</b> if it has been at least 4 months since receiving a first booster dose. </ul></li></li><li><b>Limitations to counting people with a second booster dose: </b>\n<ul><li>Due to the aggregate vaccination record reporting method used by Idaho for its residents under the age of 18 years and by Texas for all its residents, CDC counts all 4th doses received by these populations as a second booster dose. This includes immunocompromised individuals who received a three-dose primary series and only one booster dose. This limitation may lead to an undercount of people who received the single-dose J&J/Janssen vaccine as their primary series and two booster doses.</li></ul>\n</li></ul>Data represents all vaccine partners including jurisdictional partner clinics, retail pharmacies, long-term care facilities, dialysis centers, Federal Emergency Management Agency and Health Resources and Services Administration partner sites, and federal entity facilities.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/3pbe-qh9z/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/3pbe-qh9z/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/3pbe-qh9z/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/3pbe-qh9z","issued":"2022-07-25","keyword":["administration","age","coronavirus","covid-19","demographics","ethnicity","immunization","izdl","race","sex","vaccination"],"landingPage":"https://data.cdc.gov/d/3pbe-qh9z","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/coronavirus/2019-ncov/vaccines/distributing/about-vaccine-data.html"],"spatial":"US","temporal":"2020-12-13/2023-05-12","theme":["Vaccinations"],"title":"COVID-19 Primary Series Completion, Booster Dose Eligibility, and Booster Dose Receipt by Age, United States\u00a0"},"description":"This site provides historical data beginning June 22, 2022, for the visualization presented on <a href=\"https://covid.cdc.gov/covid-data-tracker/#vaccinations\" target=\"_blank\">COVID-19 Data Tracker\u2019s \u201cVaccinations in the United States\u201d</a> site titled \u201cPrimary Series Completion, Booster Dose Eligibility, and Booster Dose Receipt by Age, United States\u201d.\n\n<b >Fully Vaccinated / Completed Primary Series:</b> <ul><li> For surveillance purposes, COVID Data Tracker counts people as being \"fully vaccinated\" or as having \"completed a primary series\" if they received two doses on different days (regardless of time interval) of the two-dose mRNA series or received one dose of a single-dose vaccine. When the vaccine manufacturer is not reported, the recipient is considered fully vaccinated with two doses.</li></ul><b>First Booster Dose:\u202f </b><ul><li>For surveillance purposes, the count and percentage of people who received a first booster dose includes anyone who is fully vaccinated and has received another dose of COVID-19 vaccine since August 13, 2021. This includes people who received a first booster dose and people who received an additional primary series dose as this metric does not distinguish if the recipient is <b><a href='https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/immuno.html' target=\"_blank\">\u202fimmunocompromised and received an additional dose</a></b>.</li><li><b>   First booster dose eligibility: </b><ul><li>CDC counts people as being <b>\"\u202f<a href=\"https://www.cdc.gov/coronavirus/2019-ncov/vaccines/booster-shot.html\" target=\"_blank\">eligible for a first booster dose</a>\"</b> if it has been at least 5 months since completion of a Pfizer-BioNTech or Moderna primary series or at least 2 months since receipt of a Janssen (Johnson & Johnson) singe-dose vaccine. </li></ul></li></ul>\n<b>Second Booster Dose:</b><ul><li>For surveillance purposes, the count and percentage of people who received a second booster dose includes anyone who is fully vaccinated and has received two subsequent doses of COVID-19 vaccine since August 13, 2021. This includes people who received two booster doses and people who received one additional dose and one booster dose.\u202f </li><li>The count of people who received a second booster dose and the percentage of people with a first booster who received a second booster dose does not account for whether a person is <b><a href='https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/immuno.html'  target=\"_blank\">immunocompromised or time interval since first booster dose</a></b>. </li><li><b>Second booster dose eligibility: </b><ul><li>CDC counts people as being\u202f <b>\"<a href=\"https://www.cdc.gov/coronavirus/2019-ncov/vaccines/booster-shot.html\" target=\"_blank\"><b>eligible for a second booster dose</b></a>\"\u202f</b> if it has been at least 4 months since receiving a first booster dose. </ul></li></li><li><b>Limitations to counting people with a second booster dose: </b>\n<ul><li>Due to the aggregate vaccination record reporting method used by Idaho for its residents under the age of 18 years and by Texas for all its residents, CDC counts all 4th doses received by these populations as a second booster dose. 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Settings currently include inpatient and emergency departments (ED). Additionally, the NHCS contributes data that may inform public health emergencies as the survey is designed to capture emerging diseases and viruses that require hospitalizations, including COVID-19 encounters. The 2020 - 2023 NHCS are not yet fully operational so it is important to note that these data are not nationally representative.\n\nThe data are from 26 hospitals submitting inpatient and 26 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from March 18, 2020-December 26, 2023.  Even though the data are not nationally representative, they can provide insight on the impact of COVID-19 on various types of hospitals throughout the country. This information is not available in other hospital reporting systems. The NHCS data from these hospitals can show results by a combination of indicators related to COVID-19, such as length of inpatient stay, in-hospital mortality, comorbidities, and intubation or ventilator use. NHCS data allow for reporting on patient conditions and treatments within the hospital over time.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/q3t8-zr7t/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/q3t8-zr7t/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/q3t8-zr7t/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/q3t8-zr7t","issued":"2020-12-15","keyword":["covid-19","emergency department","hospital encounters","inpatient","intubation","mortality","respiratory illness","screenings","ventilator use"],"landingPage":"https://www.cdc.gov/nchs/covid19/nhcs.htm","language":["en-US"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"2020-03-18/2023-12-26","theme":["National Center for Health Statistics"],"title":"COVID-19 Hospital Data from the National Hospital Care Survey"},"description":"The National Hospital Care Survey (NHCS) collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings currently include inpatient and emergency departments (ED). Additionally, the NHCS contributes data that may inform public health emergencies as the survey is designed to capture emerging diseases and viruses that require hospitalizations, including COVID-19 encounters. The 2020 - 2023 NHCS are not yet fully operational so it is important to note that these data are not nationally representative.\n\nThe data are from 26 hospitals submitting inpatient and 26 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from March 18, 2020-December 26, 2023.  Even though the data are not nationally representative, they can provide insight on the impact of COVID-19 on various types of hospitals throughout the country. This information is not available in other hospital reporting systems. The NHCS data from these hospitals can show results by a combination of indicators related to COVID-19, such as length of inpatient stay, in-hospital mortality, comorbidities, and intubation or ventilator use. 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Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf).","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s54h-bixi/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s54h-bixi/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s54h-bixi/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/s54h-bixi","isPartOf":"NCHS - Natality Measures for Females by Hispanic Origin Subgroup: United States","issued":"2015-12-02","keyword":["birth","birth rate","ethnicity","fertility rate","hispanic origin","nchs","united states"],"landingPage":"https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","language":["English"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf","https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_01.pdf","https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13-508.pdf"],"rights":"public","spatial":"50 states and District of Columbia","temporal":"1989/2018","theme":["National Center for Health Statistics"],"title":"NCHS - Natality Measures for Females by Hispanic Origin Subgroup: United States"},"description":"This dataset includes live births, birth rates, and fertility rates by Hispanic origin of mother in the United States since 1989. \n\nNational data on births by Hispanic origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; New Hampshire and Oklahoma in 1990; and New Hampshire in 1991 and 1992. Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/2b663ff8-8c93-45cd-ae42-bbf7af92bffc","harvest_record_raw":"https://catalog.data.gov/harvest_record/2b663ff8-8c93-45cd-ae42-bbf7af92bffc/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/s54h-bixi","keyword":["birth","birth rate","ethnicity","fertility rate","hispanic origin","nchs","united states"],"last_harvested_date":"2026-09-23T21:20:18.099023","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":"NCHS - Natality Measures for Females by Hispanic Origin Subgroup: United States","popularity":6,"publisher":"Centers for Disease Control and Prevention","slug":"nchs-natality-measures-for-females-by-hispanic-origin-subgroup-united-states","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Health Statistics"],"title":"NCHS - Natality Measures for Females by Hispanic Origin Subgroup: United States","type":"dataset"},{"_score":19.867897,"_sort":[1790198417851,19.867897,19,"ea3b01eb-b99f-40c4-86c6-361d445f8d46"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:births@cdc.gov"},"describedBy":"https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","description":"This dataset includes live births, birth rates, and fertility rates by race of mother in the United States since 1960. \n\nData availability varies by race and ethnicity groups. All birth data by race before 1980 are based on race of the child. Since 1980, birth data by race are based on race of the mother. For race, data are available for Black and White births since 1960, and for American Indians/Alaska Native and Asian/Pacific Islander births since 1980. Data on Hispanic origin are available since 1989. Teen birth rates for specific racial and ethnic categories are also available since 1989. From 2003 through 2015, the birth data by race were based on the \u201cbridged\u201d race categories (5). Starting in 2016, the race categories for reporting birth data changed; the new race and Hispanic origin categories are: Non-Hispanic, Single Race White; Non-Hispanic, Single Race Black; Non-Hispanic, Single Race American Indian/Alaska Native; Non-Hispanic, Single Race Asian; and, Non-Hispanic, Single Race Native Hawaiian/Pacific Islander (5,6). Birth data by the prior, \u201cbridged\u201d race (and Hispanic origin) categories are included through 2018 for comparison.\n\nSOURCES\n\nNCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/).\n\nREFERENCES\n\n1. National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf.\n\n2. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf.\n\n3. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I\u2013Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf.\n\n4. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.\n\n5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf.\n\n6. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/89yk-m38d/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/89yk-m38d/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/89yk-m38d/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/89yk-m38d","isPartOf":"NCHS - Natality Measures for Females by Race and Hispanic Origin: United States","issued":"2015-12-02","keyword":["birth rates","births","ethnicity","fertility rates","hispanic origin","nchs","race","united states"],"landingPage":"https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm","language":["English"],"license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"references":["https://www.cdc.gov/nchs/data/misc/usvss.pdf","https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf","https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_01.pdf","https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13-508.pdf","https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf","https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf"],"rights":"public","spatial":"50 states and District of Columbia","temporal":"1960/2018","theme":["National Center for Health Statistics"],"title":"NCHS - Natality Measures for Females by Race and Hispanic Origin: United States"},"description":"This dataset includes live births, birth rates, and fertility rates by race of mother in the United States since 1960. \n\nData availability varies by race and ethnicity groups. All birth data by race before 1980 are based on race of the child. Since 1980, birth data by race are based on race of the mother. For race, data are available for Black and White births since 1960, and for American Indians/Alaska Native and Asian/Pacific Islander births since 1980. Data on Hispanic origin are available since 1989. Teen birth rates for specific racial and ethnic categories are also available since 1989. From 2003 through 2015, the birth data by race were based on the \u201cbridged\u201d race categories (5). Starting in 2016, the race categories for reporting birth data changed; the new race and Hispanic origin categories are: Non-Hispanic, Single Race White; Non-Hispanic, Single Race Black; Non-Hispanic, Single Race American Indian/Alaska Native; Non-Hispanic, Single Race Asian; and, Non-Hispanic, Single Race Native Hawaiian/Pacific Islander (5,6). Birth data by the prior, \u201cbridged\u201d race (and Hispanic origin) categories are included through 2018 for comparison.\n\nSOURCES\n\nNCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/).\n\nREFERENCES\n\n1. National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf.\n\n2. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf.\n\n3. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I\u2013Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf.\n\n4. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.\n\n5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf.\n\n6. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/1382ed40-f9a7-40d6-ab9f-6a93638dfca9","harvest_record_raw":"https://catalog.data.gov/harvest_record/1382ed40-f9a7-40d6-ab9f-6a93638dfca9/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/89yk-m38d","keyword":["birth rates","births","ethnicity","fertility rates","hispanic origin","nchs","race","united states"],"last_harvested_date":"2026-09-23T21:20:17.851843","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":"NCHS - Natality Measures for Females by Race and Hispanic Origin: United States","popularity":19,"publisher":"Centers for Disease Control and Prevention","slug":"nchs-natality-measures-for-females-by-race-and-hispanic-origin-united-states","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Health Statistics"],"title":"NCHS - Natality Measures for Females by Race and Hispanic Origin: United States","type":"dataset"},{"_score":18.148422,"_sort":[1790198417400,18.148422,1,"f3679390-dffd-4c7c-be93-7f94dd2d116c"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.\n\nCounty data on race and Hispanic origin is available for counties with more than 100 COVID-19 deaths.\u00a0Deaths are cumulative from the week ending January 4, 2020 to the most recent reporting week, and based on county of occurrence. Data is provisional. \n\nUrban-rural classification is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties (https://www.cdc.gov/nchs/data_access/urban_rural.htm).","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/k8wy-p9cg/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/k8wy-p9cg/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/k8wy-p9cg/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/k8wy-p9cg","issued":"2020-06-24","keyword":["all causes","coronavirus","county","covid-19","deaths","hispanic origin","mortality","nchs","nvss","provisional","race","united states"],"landingPage":"https://www.cdc.gov/nchs/covid19/covid-19-mortality-data-files.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","theme":["National Center for Health Statistics"],"title":"Provisional COVID-19 Deaths by County, and Race and Hispanic Origin"},"description":"Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.\n\nCounty data on race and Hispanic origin is available for counties with more than 100 COVID-19 deaths.\u00a0Deaths are cumulative from the week ending January 4, 2020 to the most recent reporting week, and based on county of occurrence. Data is provisional. \n\nUrban-rural classification is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties (https://www.cdc.gov/nchs/data_access/urban_rural.htm).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/25fa87f6-66db-43fc-9c4a-ddf4390c44cb","harvest_record_raw":"https://catalog.data.gov/harvest_record/25fa87f6-66db-43fc-9c4a-ddf4390c44cb/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/k8wy-p9cg","keyword":["all causes","coronavirus","county","covid-19","deaths","hispanic origin","mortality","nchs","nvss","provisional","race","united states"],"last_harvested_date":"2026-09-23T21:20:17.400053","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"provisional-covid-19-deaths-by-county-and-race-and-hispanic-origin","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"Provisional COVID-19 Deaths by County, and Race and Hispanic Origin","type":"dataset"},{"_score":2.5487738,"_sort":[1790198415710,2.5487738,1,"4eb239a9-7b2e-419c-9c96-478e027e6abd"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Provisional counts of deaths in the United States by age group, sex, and race/ethnicity, from March-July 2020. The dataset includes cumulative provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s9qn-46pq/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s9qn-46pq/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s9qn-46pq/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/s9qn-46pq","issued":"2021-02-01","keyword":["age","age group","all causes","coronavirus","covid-19","deaths","hispanic origin","mortality","nchs","nvss","provisional","race","sex","united states"],"landingPage":"https://www.cdc.gov/nchs/covid19/covid-19-mortality-data-files.htm","license":"https://www.usa.gov/government-works","modified":"2026-09-23","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"temporal":"2020-03-01/2020-07-31","theme":["National Center for Health Statistics"],"title":"AH Provisional COVID-19 Deaths By Race, Age, and Sex from 3/1/2020 to 7/31/2020"},"description":"Provisional counts of deaths in the United States by age group, sex, and race/ethnicity, from March-July 2020. The dataset includes cumulative provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/de00b5c7-da8d-49b6-bb02-9942f3965aea","harvest_record_raw":"https://catalog.data.gov/harvest_record/de00b5c7-da8d-49b6-bb02-9942f3965aea/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/s9qn-46pq","keyword":["age","age group","all causes","coronavirus","covid-19","deaths","hispanic origin","mortality","nchs","nvss","provisional","race","sex","united states"],"last_harvested_date":"2026-09-23T21:20:15.710156","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"ah-provisional-covid-19-deaths-by-race-age-and-sex-from-3-1-2020-to-7-31-2020","spatial_centroid":null,"spatial_shape":null,"theme":["National Center for Health Statistics"],"title":"AH Provisional COVID-19 Deaths By Race, Age, and Sex from 3/1/2020 to 7/31/2020","type":"dataset"},{"_score":39.240986,"_sort":[1790198399509,39.240986,6,"ff54b7da-e9d2-4445-bf44-33bff29d7cd5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1Y","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"National Center for Health Statistics","hasEmail":"mailto:healthus@cdc.gov"},"description":"Data on overweight and obesity among adults aged 20 and over in the United States, by selected characteristics, including sex, age, race, Hispanic origin, and poverty level. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Health and Nutrition Examination Survey.\nSearch, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/be57-s94j/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/be57-s94j/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/be57-s94j/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/be57-s94j","issued":"2025-03-06","keyword":["adults","black or african american","body mass index","body weight","chronic conditions","health risk factors","health us","hispanic or latino","men","mexican","obesity","older persons","overweight","poverty","white","women"],"landingPage":"https://www.cdc.gov/nchs/hus","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"temporal":"1988/2018","theme":["National Center for Health Statistics"],"title":"DEV DQS Normal weight, overweight, and obesity among adults aged 20 and over, by selected characteristics: United States"},"description":"Data on overweight and obesity among adults aged 20 and over in the United States, by selected characteristics, including sex, age, race, Hispanic origin, and poverty level. 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Source: Centers for Medicare & Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Expenditure Accounts, National health expenditures. \nSearch, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s57w-7gbe/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s57w-7gbe/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/s57w-7gbe/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/s57w-7gbe","issued":"2021-11-17","keyword":["bea","cms","gdp","government administration of health insurance","gross domestic product","health care","health consumption expenditures","health consumption spending","health insurance","health us","national health expenditures","national health expenditures accounts","national health spending","nhea","personal health care spending","sdoh-access-to-health-care","sdoh-source-of-health-care","sdoh-use-of-health-care","sdoh-workplace"],"landingPage":"https://www.cdc.gov/nchs/hus","license":"https://www.usa.gov/government-works","modified":"2026-09-22","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","temporal":"1960/2024","theme":["National Center for Health Statistics"],"title":"DEV DQS National health spending: United States"},"description":"National health spending in the United States. Data are from Health, United States. Source: Centers for Medicare & Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Expenditure Accounts, National health expenditures. \nSearch, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/cdf60539-1c1b-47dd-9699-055c70a9d1c1","harvest_record_raw":"https://catalog.data.gov/harvest_record/cdf60539-1c1b-47dd-9699-055c70a9d1c1/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/s57w-7gbe","keyword":["bea","cms","gdp","government administration of health insurance","gross domestic product","health care","health consumption expenditures","health consumption spending","health insurance","health us","national health expenditures","national health expenditures accounts","national health spending","nhea","personal health care spending","sdoh-access-to-health-care","sdoh-source-of-health-care","sdoh-use-of-health-care","sdoh-workplace"],"last_harvested_date":"2026-09-23T21:19:54.316150","organization":{"aliases":["US","dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"2c2fc21f-21d0-4450-af01-cf8c69b44156","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png","name":"U.S. Department of Health & Human Services","organization_type":"Federal Government","slug":"hhs"},"parent_identifier":null,"popularity":1,"publisher":"Centers for Disease Control and Prevention","slug":"dev-dqs-national-health-spending-united-states","spatial_centroid":{"lat":34.4819914,"lon":-101.6218762},"spatial_shape":{"coordinates":[[[[-124.733253,24.544245],[-124.733253,49.388611],[-66.954811,49.388611],[-66.954811,24.544245],[-124.733253,24.544245]]]],"type":"MultiPolygon"},"theme":["National Center for Health Statistics"],"title":"DEV DQS National health spending: United States","type":"dataset"},{"_score":66.209915,"_sort":[1790198390098,66.209915,35,"649cc6c7-4340-46f7-8252-ea6b5b143ac9"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"PLACES Public Inquiries","hasEmail":"mailto:places@cdc.gov"},"describedBy":"https://chronicdata.cdc.gov/dataset/PLACES-Local-Data-for-Better-Health-County-Data-20/swc5-untb","description":"This dataset contains model-based county estimates. 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Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimate data, and American Community Survey 2018\u20132022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. 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