This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows the market potential for an adult to carry medical/hospital/accident insurance in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Market Potential Index and count of adults expected to carry medical/hospital/accident insuranceMarket Potential Index and count of adults expected to carry different types of medical insurance (HMO, PPO, etc)Market Potential Index and count of adults expected to carry insurance from various sources (Medicare, place of work, etc)Esri's 2016 Market Potential (MPI) data measures the likely demand for a product or service in an area. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product or service. An MPI compares the demand for a specific product or service in an area with the national demand for that product or service. The MPI values at the US level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the US average; an index of 80 implies that demand is 20 percent lower than the US average. See Market Potential database to view the methodology statement and complete variable list.Esri's Financial & Insurance Data Collection includes data that measures the likely demand for financial and insurance products and services, including health insurance. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product, activity, or service. See the United States Data Browser to view complete variable lists for each Esri demographics collection.Additional Esri Resources:U.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97. COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Breast Cancer Screening: Percent of women aged 50-74 years who have received a mammogram in the past 2 years. (Source: PLACES Project. Centers for Disease Control and Prevention, 2020.)Cervical Cancer Screening: Percent of women between the ages of 21 and 65 living within a census tract who reported having had a cervical screening test, and the type of test depends on age of female respondent. For those 21-29, the test used is a pap test, whereas for those 30-65 years old, either pap test alone, HPV test alone, or a combination can be recommended. Data does not include women who reported having a hysterectomy. (Source: PLACES Project. Centers for Disease Control and Prevention, 2020.)Colorectal Cancer Screening: Percent of adults aged 50–75 years who have received a fecal occult blood test (FOBT) within the past year, a sigmoidoscopy within the past 5 years and a FOBT within the past 3 years, or a colonoscopy within the past 10 years. (Source: PLACES Project. Centers for Disease Control and Prevention, 2020.)Preventive Care: Percent of men or women 65 years and older who have received all of the following: an influenza vaccination in the past year; a pneumococcal vaccination ever; either a fecal occult blood test (FOBT) within the past year, a sigmoidoscopy within the past 5 years and a FOBT within the past 3 years, or a colonoscopy within the previous 10 years; and (for women only) a mammogram in the past 2 years. (Source: PLACES Project. Centers for Disease Control and Prevention, 2020.)Routine Checkups: Percent of adults who report having been to a doctor for a routine checkup (e.g., a general physical exam, not an exam for a specific injury, illness, condition) in the previous year. (Source: PLACES Project. Centers for Disease Control and Prevention, 2020.)Uninsured: Percent of residents who did not have health insurance. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

Key Table Information.Table Title.VA Health Care by Sex by Age.Table ID.ACSDT1Y2024.B27009.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of ...

This Data set is from the Behavioral Risk Factor Surveillance System survey of the United States. "The Behavioral Risk Factor Surveillance System (BRFSS) is the worlds largest, on-going telephone health survey system, tracking health conditions and risk behaviors in the United States yearly since 1984. Conducted by the 50 state health departments as well as those in the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands with support from the CDC, BRFSS provides state-specific information about issues such as asthma, diabetes, health care access, alcohol use, hypertension, obesity, cancer screening, nutrition and physical activity, tobacco use, and more." (http://www.cdc.gov/brfss/index.htm) Data URL: http://www.cdc.gov/brfss/maps/gis_data.htm All values a percentage from 0-100

Abstract (en): This study comprises the second round of the physician survey component of the Community Tracking Study (CTS) sponsored by the Robert Wood Johnson Foundation. The CTS is a national study designed to track changes in the American health care system and the effects of the changes on care delivery and on individuals. Central to the design of the CTS is its community focus. Sixty sites (51 metropolitan areas and 9 nonmetropolitan areas) were randomly selected to form the core of the CTS and to be representative of the nation as a whole. As in the first round of the physician survey (COMMUNITY TRACKING STUDY PHYSICIAN SURVEY, 1996-1997: UNITED STATES), the second round was administered to physicians in the 60 CTS sites and to a supplemental national sample of physicians. The survey instrument collected information on physician supply and specialty distribution, practice arrangements and physician ownership of practices, physician time allocation, sources of practice revenue, level and determinants of physician compensation, provision of charity care, career satisfaction, physicians' perceptions of their ability to deliver care, views on care management strategies, and various other aspects of physicians' practice of medicine. In addition, primary care physicians (PCPs) were asked to recommend courses of action in response to some vignettes of clinical presentations for which there was no prescribed method of treatment. Dataset 3, the Site and County Crosswalk Data File, identifies the counties that constitute each CTS site. Dataset 4, the Physician Survey Summary File, contains site-level estimates and standard errors of the estimates for selected physician characteristics, e.g., the percentage of physicians who were foreign medical school graduates, the mean age of physicians, and the mean percentage of patient care practice revenue from Medicaid. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Physicians practicing in the 48 states of the contiguous United States who provided direct patient care for at least 20 hours per week and were not federal employees, specialists in fields in which the primary focus was not direct patient care, or graduates of foreign medical schools who were only temporarily licensed to practice in the United States. Residents, interns, and fellows were excluded. The CTS sites were selected using stratified sampling with probability proportional to population size. The supplemental sample, which was selected using stratified random sampling, was included in the survey in order to increase the precision of national estimates. The sample frame was developed by combining lists of physicians from the American Medical Association and the American Osteopathic Association. For both the site and supplemental samples, the sampling design involved randomly selecting physicians who were part of the Round 1 survey and physicians who were not covered by Round 1. Thus, about 58 percent of the Round 2 respondents also participated in Round 1. PCPs were oversampled in the site sample. 2009-02-02 Stata setups produced by ICPSR were added to the collection.2004-02-24 The user guide for the restricted-use version of the main data file has been revised. As noted on the "What's New" page in the guide, there are minor changes to the text related to the recommended SUDAAN parameters.2002-03-01 The user guides for the public- and restricted-use versions of the main data file have been revised. A discussion was added about how to pool data from Round 1 and Round 2 in order to increase sample size. In addition, the data definition statements have been enhanced. Funding insitution(s): Robert Wood Johnson Foundation (29275). computer-assisted telephone interview (CATI) For additional information about this study see the Web site of the Center for Studying Health System Change.

This dataset presents the footprint of the percentage of people who delayed or did not see a medical specialist, GP, get an imaging test or get a pathology test when needed due to cost in the last 12 months. The data spans the financial year of 2016-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are …Show full descriptionThis dataset presents the footprint of the percentage of people who delayed or did not see a medical specialist, GP, get an imaging test or get a pathology test when needed due to cost in the last 12 months. The data spans the financial year of 2016-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. The claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim has been processed by the Department of Human Services. Data are reported for claims processed between 1 July 2016 and 30 June 2017. The data also contains the results from the ABS 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. The Patient Experience Survey is conducted annually by the Australian Bureau of Statistics (ABS) and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patients' spending on Medicare Services data accompanies the Patients' out-of-pocket spending on Medicare services 2016-17 Report. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Patients' out-of-pocket spending on Medicare services Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The data is based on the patient's Medicare enrolment postcode, not where they received the health care service. Most peoples' Medicare enrolment postcode will be their residential postcode. The data excludes pathology and imaging tests conducted in a hospital, and any dental imaging tests. If respondents sought clarification on the definition of a medical specialist, interviewers were instructed to advise that medical specialists provide services which are covered, at least in part, by Medicare (e.g. dermatologists, cardiologists, neurologists and gynaecologists). Imaging tests or diagnostic imaging include all tests that produce images or pictures of the inside of the body in order to diagnose diseases. Tests involve the use of radiant energy, including x-rays, sound waves, radio waves, and radioactive waves and particles that are recorded by photographic films or other types of detectors. Pathology tests refer to laboratory tests that include analysis of specimens such as urine and blood in order to diagnose disease. The survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities). Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings. Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values. NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2018): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

This dataset shows the total amount of State Prison Expenditures for Medical Care, Food expenses, and Utilities in the year 2001. Over a quarter of prison operating costs are for basic living expenses. Prisoner medical care, food service, utilities, and contract housing totaled $7.3 billion, or about 26% of State prison current operating expenses. Inmate medical care totaled $3.3 billion, or about 12% of operating expenditures. Supplies and services of government staff and full-time and part-time managed care and fee-for service providers averaged $2,625 per inmate, or $7.19 per day. By comparison, the average annual health care expenditure of U.S. residents, including all sources in FY 2001, was $4,370, or $11.97 per day. Factors beyond the scope of this report contributed to the variation in spending levels for prisoner medical care. Lacking economies of scale, some States had significantly higher than average medical costs for everyone, and some had higher proportions of inmates whose abuse of drugs or alcohol had led to disease. Also influencing variations in expenditures were staffing and funding of prisoner health care and distribution of specialized medical equipment for prisoner treatment. Food service in FY 2001 cost $1.2 billion, or approximately 4% of State prison operating expenditures. On average nationwide, State departments of correction spent $2.62 to feed inmates each day. Utility services for electricity, natural gas, heating oil, water, sewerage, trash removal, and telephone in State prisons totaled $996 million in FY 2001. Utilities accounted for about 3.5% of State prison operating expenditure. For more information see the url source of this dataset.

This dataset explores the United States Department of Agriculture (USDA) Food and Nutrition Service Program - WIC Program Nutrition Service and Administrative Costs by state for fiscal years 2003-2007. WIC provides Federal grants to States for supplemental foods, health care referrals, and nutrition education for low-income pregnant, breastfeeding, and non-breastfeeding postpartum women, and to infants and children up to age five who are found to be at nutritional risk. * WIC is the common abbreviation for Special Supplemental Nutrition Program for Women, Infants and Children. The above costs include certifying participant eligibility, nutrition education, breastfeeding promotion, health care coordination and referral, drug abuse education, clinic operations, food delivery and warehousing, vendor monitoring, financial management, program integrity, and systems development and operations. Data are subject to revision.