21 datasets found
  1. A

    ‘Health Insurance Coverage’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Health Insurance Coverage’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-health-insurance-coverage-1c87/88f5e0a9/?iid=002-220&v=presentation
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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Health Insurance Coverage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hhs/health-insurance on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    The Affordable Care Act (ACA) is the name for the comprehensive health care reform law and its amendments which addresses health insurance coverage, health care costs, and preventive care. The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 by President Barack Obama and was amended by the Health Care and Education Reconciliation Act on March 30, 2010.

    Content

    This dataset provides health insurance coverage data for each state and the nation as a whole, including variables such as the uninsured rates before and after Obamacare, estimates of individuals covered by employer and marketplace healthcare plans, and enrollment in Medicare and Medicaid programs.

    Acknowledgements

    The health insurance coverage data was compiled from the US Department of Health and Human Services and US Census Bureau.

    Inspiration

    How has the Affordable Care Act changed the rate of citizens with health insurance coverage? Which states observed the greatest decline in their uninsured rate? Did those states expand Medicaid program coverage and/or implement a health insurance marketplace? What do you predict will happen to the nationwide uninsured rate in the next five years?

    --- Original source retains full ownership of the source dataset ---

  2. SHIP Uninsured ED Visits 2008-2017

    • healthdata.gov
    • opendata.maryland.gov
    • +4more
    application/rdfxml +5
    Updated Apr 8, 2025
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    opendata.maryland.gov (2025). SHIP Uninsured ED Visits 2008-2017 [Dataset]. https://healthdata.gov/State/SHIP-Uninsured-ED-Visits-2008-2017/9vyt-u9tn/data
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    csv, tsv, application/rssxml, json, xml, application/rdfxmlAvailable download formats
    Dataset updated
    Apr 8, 2025
    Dataset provided by
    opendata.maryland.gov
    Description

    This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024

    Uninsured ED Visits - This indicator shows the percentage of persons without health (medical) insurance who seek care through the Emergency Department. People without health insurance are more likely to be in poor health than the insured. Lack of health insurance can result in increased visits to the emergency department and decreased routine care visits with a primary care provider.

  3. uninsured state

    • gis-for-racialequity.hub.arcgis.com
    Updated May 10, 2017
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    Urban Observatory by Esri (2017). uninsured state [Dataset]. https://gis-for-racialequity.hub.arcgis.com/datasets/UrbanObservatory::uninsured-state
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    Dataset updated
    May 10, 2017
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    This layer shows the percentage of people without health insurance in the U.S. by state and county, from American Community Survey 5-year estimates: 2011-2015 (Table GCT2701). The map switches from state data to county data as the map zooms in. The national average was 13.0%, down from approximately 20% in 2005.A person’s ability to access health services has a profound effect on every aspect of his or her health. Many Americans do not have a primary care provider (PCP) or health center where they can receive regular medical services. People without medical insurance are more likely to lack a usual source of medical care, such as a PCP, and are more likely to skip routine medical care due to costs, increasing their risk for serious and disabling health conditions. When they do access health services, they are often burdened with large medical bills and out-of-pocket expenses. Increasing access to both routine medical care and medical insurance are vital steps in improving the health of all Americans.

  4. Claims Reimbursement to Health Care Providers and Facilities for Testing,...

    • data.cdc.gov
    • data.virginia.gov
    • +2more
    Updated Mar 3, 2022
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    HHS ASPA (2022). Claims Reimbursement to Health Care Providers and Facilities for Testing, Treatment, and Vaccine Administration of the Uninsured [Dataset]. https://data.cdc.gov/Administrative/Claims-Reimbursement-to-Health-Care-Providers-and-/rksx-33p3
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    application/rssxml, csv, xml, application/rdfxml, tsv, application/geo+json, kmz, kmlAvailable download formats
    Dataset updated
    Mar 3, 2022
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    HHS ASPA
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    The COVID-19 Claims Reimbursement to Health Care Providers and Facilities for Testing, Treatment, and Vaccine Administration for the Uninsured Program provides reimbursements on a rolling basis directly to eligible health care entities for claims that are attributed to the testing, treatment, and or vaccine administration of COVID-19 for uninsured individuals. The program funding information is as follow:

    TESTING The American Rescue Plan Act (ARP) which provided $4.8 billion to reimburse providers for testing the uninsured; the Families First Coronavirus Response Act (FFCRA) Relief Fund, which includes funds received from the Public Health and Social Services Emergency Fund, as appropriated in the FFCRCA (P.L. 116-127) and the Paycheck Protection Program and Health Care Enhancement Act (P.L. 116-139) (PPPHCEA), which each appropriated $1 billion to reimburse health care entities for conducting COVID-19 testing for the uninsured.

    TREATMENT & VACCINATION The Provider Relief Fund, which includes funds received from the Public Health and Social Services Emergency Fund, as appropriated in the Coronavirus Aid, Relief, and Economic Security (CARES) Act (P.L. 116-136), provided $100 billion in relief funds. The PPPHCEA appropriated an additional $75 billion in relief funds and the Coronavirus Response and Relief Supplemental Appropriations (CRRSA) Act (P.L. 116-260) appropriated another $3 billion. Within the Provider Relief Fund, a portion of the funding from these sources will be used to support healthcare-related expenses attributable to the treatment of uninsured individuals with COVID-19 and vaccination of uninsured individuals. To learn more about the program, visit: https://www.hrsa.gov/CovidUninsuredClaim

    This dataset represents the list of health care entities who have agreed to the Terms and Conditions and received claims reimbursement for COVID-19 testing of uninsured individuals, vaccine administration and treatment for uninsured individuals with a COVID-19 diagnosis.

    For Provider Relief Fund Data - https://data.cdc.gov/Administrative/HHS-Provider-Relief-Fund/kh8y-3es6

  5. d

    Department of Social Services - People Served by Town and Ethnicity,...

    • catalog.data.gov
    Updated Mar 14, 2025
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    data.ct.gov (2025). Department of Social Services - People Served by Town and Ethnicity, 2015-2024 [Dataset]. https://catalog.data.gov/dataset/department-of-social-services-people-served-by-town-and-ethnicity-2015-2021
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    Dataset updated
    Mar 14, 2025
    Dataset provided by
    data.ct.gov
    Description

    This dataset includes the number of people enrolled in DSS services by town and by ethnicity from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

  6. A

    Indicators of Health Insurance Coverage at the Time of Interview

    • data.amerigeoss.org
    • healthdata.gov
    • +5more
    csv, json, rdf, xml
    Updated Jul 20, 2022
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    United States (2022). Indicators of Health Insurance Coverage at the Time of Interview [Dataset]. https://data.amerigeoss.org/dataset/indicators-of-health-insurance-coverage-at-the-time-of-interview-fdba3
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    rdf, xml, json, csvAvailable download formats
    Dataset updated
    Jul 20, 2022
    Dataset provided by
    United States
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

    The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

  7. ACS Health Insurance Coverage Variables - Centroids

    • coronavirus-resources.esri.com
    • covid-hub.gio.georgia.gov
    • +5more
    Updated Dec 7, 2018
    + more versions
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    Esri (2018). ACS Health Insurance Coverage Variables - Centroids [Dataset]. https://coronavirus-resources.esri.com/maps/7c69956008bb4019bbbe67ed9fb05dbb
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    Dataset updated
    Dec 7, 2018
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state centroids. 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 count and 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.

  8. d

    DSS Medical Benefit Plan Participation by Month CY 2012-2025

    • catalog.data.gov
    • data.ct.gov
    Updated Jul 19, 2025
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    data.ct.gov (2025). DSS Medical Benefit Plan Participation by Month CY 2012-2025 [Dataset]. https://catalog.data.gov/dataset/dss-medical-benefit-plan-participation-by-month-cy-2012-2020
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    Dataset updated
    Jul 19, 2025
    Dataset provided by
    data.ct.gov
    Description

    In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits under a medical benefit plan in that calendar year and month. A recipient may have received benefits from multiple plans in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.\ NOTE: On February 14 2019, the enrollment

  9. O

    DSS - People Served by Town and Type of Assistance (TOA) by Month - CY...

    • data.ct.gov
    • catalog.data.gov
    application/rdfxml +5
    Updated Jul 14, 2025
    + more versions
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    Department of Social Services (2025). DSS - People Served by Town and Type of Assistance (TOA) by Month - CY 2023-2025 [Dataset]. https://data.ct.gov/Health-and-Human-Services/DSS-People-Served-by-Town-and-Type-of-Assistance-T/g7bd-zbqw
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    xml, application/rssxml, csv, tsv, json, application/rdfxmlAvailable download formats
    Dataset updated
    Jul 14, 2025
    Dataset authored and provided by
    Department of Social Services
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly.

    As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. The data represents number of active recipients who received benefits from a type of assistance (TOA) in that calendar year and month. A recipient may have received benefits from multiple TOAs in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) For privacy considerations, a count of zero is used for counts less than five.

    The methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.

  10. O

    Department of Social Services - People Served by Town and Type of Assistance...

    • data.ct.gov
    application/rdfxml +5
    Updated Mar 14, 2025
    + more versions
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    Department of Social Services (2025). Department of Social Services - People Served by Town and Type of Assistance (TOA), 2015-2024 [Dataset]. https://data.ct.gov/Health-and-Human-Services/Department-of-Social-Services-People-Served-by-Tow/2fq6-ae4m
    Explore at:
    application/rssxml, json, csv, application/rdfxml, xml, tsvAvailable download formats
    Dataset updated
    Mar 14, 2025
    Dataset authored and provided by
    Department of Social Services
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset includes the number of people enrolled in DSS services by town and by type of assistance (TOA) from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here.

    About this data

  11. For privacy considerations, a count of zero is used for counts less than five.
  12. A recipient is counted in all towns where that recipient resided in that year.
  13. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly.
  14. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021.

    Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately.

    2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively.

    Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016.

    1. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.

    On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged.

    On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year.

    On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

  • a

    Health Insurance Coverage - States 2015-2019

    • covid19-uscensus.hub.arcgis.com
    Updated Mar 19, 2021
    + more versions
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    US Census Bureau (2021). Health Insurance Coverage - States 2015-2019 [Dataset]. https://covid19-uscensus.hub.arcgis.com/datasets/health-insurance-coverage-states-2015-2019
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    Dataset updated
    Mar 19, 2021
    Dataset authored and provided by
    US Census Bureau
    Area covered
    Description

    This layer shows Health Insurance Coverage. This is shown by 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 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: 2015-2019ACS Table(s): B27010, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 10, 2021National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS): About the SurveyGeography & ACSTechnical Documentation News & 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. 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: Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes.
    All of these are rendered in this dataset as null (blank) values.

  • c

    Health Insurance

    • data.clevelandohio.gov
    Updated Aug 21, 2023
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    Cleveland | GIS (2023). Health Insurance [Dataset]. https://data.clevelandohio.gov/datasets/ClevelandGIS::health-insurance/explore
    Explore at:
    Dataset updated
    Aug 21, 2023
    Dataset authored and provided by
    Cleveland | GIS
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Area covered
    Description

    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: 2019-2023
    ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)

    The United States Census Bureau's American Community Survey (ACS):
    This 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 2022 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 Rico
    • Census 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.

  • A

    ‘DSS Medical Benefit Plan Participation CY 2012-2020’ analyzed by Analyst-2

    • analyst-2.ai
    Updated May 1, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘DSS Medical Benefit Plan Participation CY 2012-2020’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-dss-medical-benefit-plan-participation-cy-2012-2020-4b36/f562a623/?iid=000-869&v=presentation
    Explore at:
    Dataset updated
    May 1, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘DSS Medical Benefit Plan Participation CY 2012-2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b1ce5c21-c2c0-4a18-b065-ffba938f95a5 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly.

    As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. The data represents number of active recipients who received benefits under a medical benefit plan in that calendar year. A recipient may have received benefits from multiple plans in the same year; if so that recipient will be included in multiple categories in this dataset (counted more than once.) For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. NOTE: On 11/30/2018 the counts were revised because of a change in the way active recipients were counted in one source system.

    --- Original source retains full ownership of the source dataset ---

  • O

    Department of Social Services - People Served by Town and Medical Benefit...

    • data.ct.gov
    application/rdfxml +5
    Updated Mar 14, 2025
    + more versions
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    Department of Social Services (2025). Department of Social Services - People Served by Town and Medical Benefit Plan, 2015-2024 [Dataset]. https://data.ct.gov/Health-and-Human-Services/Department-of-Social-Services-People-Served-by-Tow/jv24-edbx
    Explore at:
    csv, json, xml, application/rssxml, application/rdfxml, tsvAvailable download formats
    Dataset updated
    Mar 14, 2025
    Dataset authored and provided by
    Department of Social Services
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset includes the number of people enrolled in DSS services by town and by medical benefit plan from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here.

    About this data

  • For privacy considerations, a count of zero is used for counts less than five.
  • A recipient is counted in all towns where that recipient resided in that year.
  • Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly.
  • Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021.

    Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately.

    2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively.

    Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016.

    1. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.

    On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged.

    On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year.

    On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

  • A

    ‘Disproportionate Share Hospital (DSH) Payments - Annual Reporting...

    • analyst-2.ai
    Updated Jan 26, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Disproportionate Share Hospital (DSH) Payments - Annual Reporting Requirements’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-disproportionate-share-hospital-dsh-payments-annual-reporting-requirements-fd87/6a5761ef/?iid=004-651&v=presentation
    Explore at:
    Dataset updated
    Jan 26, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Disproportionate Share Hospital (DSH) Payments - Annual Reporting Requirements’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ded26022-a55d-4b07-8c9f-9c23edc7e81d on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    Federal law requires that state Medicaid programs make Disproportionate Share Hospital (DSH) payments to qualifying hospitals that serve a large number of Medicaid and uninsured individuals. State-specific annual DSH reports are posted as submitted by states based on their availability.

    For more information, visit https://www.medicaid.gov/medicaid/finance/dsh/index.html.

    --- Original source retains full ownership of the source dataset ---

  • n

    Hospital Admission Data from the Agency for HealthCare Research and Quality...

    • cmr.earthdata.nasa.gov
    Updated Apr 20, 2017
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    (2017). Hospital Admission Data from the Agency for HealthCare Research and Quality (AHRQ) [Dataset]. https://cmr.earthdata.nasa.gov/search/concepts/C1214136020-SCIOPS
    Explore at:
    Dataset updated
    Apr 20, 2017
    Time period covered
    Jan 1, 1970 - Present
    Description

    The Agency for Healthcare Research and Quality (AHRQ, formerly the Agency for Health Care Policy and Research) maintains the Healthcare Cost and Utilization Project (HCUP). HCUP is a Federal-State-industry partnership to build a standardized, multi-State health data system. AHRQ has taken the lead in developing HCUP databases, Web-based products, and software tools and making them available for restricted access public release.

    HCUP comprises a family of administrative longitudinal databases-including State-specific hospital-discharge databases and a national sample of discharges from community hospitals.

    HCUP databases contain patient-level information compiled in a uniform format with privacy protections in place. * The Nationwide Inpatient Sample (NIS) includes inpatient data from a national sample (about 20% of U.S. community hospitals) including roughly 7 million discharges from about 1,000 hospitals. It is the largest all-payer inpatient database in the U.S.; data are now available from 1988-1998. The NIS is ideal for developing national estimates, for analyzing national trends, and for research that requires a large sample size. * The State Inpatient Databases (SID) cover individual data sets in community hospitals from 22 participating States that represent more than half of all U.S. hospital discharges. The data have been translated into a uniform format to facilitate cross-State comparisons. The SID are particularly well-suited for policy inquiries unique to a specific State, studies comparing two or more States, market area research, and small area variation analyses.

    • The State Ambulatory Surgery Databases (SASD) contain data from ambulatory care encounters in 9 participating States. The SASD capture surgeries performed on the same day in which patients are admitted and released form hospital- affiliated ambulatory surgery sites. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within market areas and States.
    • The project's newest restricted access public release is the Kids' Inpatient Database (KID), containing hospital inpatient stays for children 18 years of age and younger. Researchers and policymakers can use the KID to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The KID is the only all-payer inpatient care database for children in the U.S. It contains data from approximately 1.9 million hospital discharges for children. The data are drawn from 22 HCUP 1997 State Inpatient Databases and include a sample of pediatric general discharges from over 2,500 U.S. community hospitals (defined as short-term, non-Federal, general and specialty hospitals, excluding hospital units of other institutions). A key strength of the KID is that the large sample size enables analyses of both common and rare conditions; uncommon treatments, and organ transplantation. The KID also includes charge information on all patients, regardless of payer, including children covered by Medicaid, private insurance, and the uninsured.

      HCUP also contains powerful, user-friendly software that can be used with both HCUP data and with other administrative databases. The AHRQ has developed three powerful software tools Quality Indicators (QIs), Clinical Classification Software (CCS) and HCUPnet. See more on the agency's webpages.

  • disproportionate-share-hospital-dsh-payments-annua

    • huggingface.co
    Updated Jan 24, 2019
    + more versions
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    Department of Health and Human Services (2019). disproportionate-share-hospital-dsh-payments-annua [Dataset]. https://huggingface.co/datasets/HHS-Official/disproportionate-share-hospital-dsh-payments-annua
    Explore at:
    Dataset updated
    Jan 24, 2019
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    Department of Health and Human Services
    Description

    Disproportionate Share Hospital (DSH) Payments - Annual Reporting Requirements

      Description
    

    Federal law requires that state Medicaid programs make Disproportionate Share Hospital (DSH) payments to qualifying hospitals that serve a large number of Medicaid and uninsured individuals. State-specific annual DSH reports are posted as submitted by states based on their availability. For more information, visit https://www.medicaid.gov/medicaid/finance/dsh/index.html.… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/disproportionate-share-hospital-dsh-payments-annua.

  • l

    COVID-19 Vulnerability and Recovery Index

    • data.lacounty.gov
    • geohub.lacity.org
    • +1more
    Updated Aug 5, 2021
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    County of Los Angeles (2021). COVID-19 Vulnerability and Recovery Index [Dataset]. https://data.lacounty.gov/maps/covid-19-vulnerability-and-recovery-index
    Explore at:
    Dataset updated
    Aug 5, 2021
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

    The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

    The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

    *Zip Code data has been crosswalked to Census Tract using HUD methodology

    Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

    Indicator

    ACS Table/Years

    Numerator

    Denominator

    Non-US Citizen

    B05001, 2019-2023

    b05001_006e

    b05001_001e

    Below 200% FPL

    S1701, 2019-2023

    s1701_c01_042e

    s1701_c01_001e

    Overcrowded Housing Units

    B25014, 2019-2023

    b25014_006e + b25014_007e + b25014_012e + b25014_013e

    b25014_001e

    Essential Workers

    S2401, 2019-2023

    s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

    s2401_c01_001

    Seniors 75+ in Poverty

    B17020, 2019-2023

    b17020_008e + b17020_009e

    b17020_008e + b17020_009e + b17020_016e + b17020_017e

    Uninsured

    S2701, 2019-2023

    s2701_c05_001e

    NA, rate published in source table

    Single-Parent Households

    S1101, 2019-2023

    s1101_c03_005e + s1101_c04_005e

    s1101_c01_001e

    Unemployment

    S2301, 2019-2023

    s2301_c04_001e

    NA, rate published in source table

    The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

    Indicator

    Years

    Definition

    Denominator

    Asthma Hospitalizations

    2017-2019

    All ICD 10 codes under J45 (under Principal Diagnosis)

    American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

    Gun Injuries

    2017-2019

    Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

    American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

    Heart Disease Hospitalizations

    2017-2019

    ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

    American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

    Diabetes (Type 2) Hospitalizations

    2017-2019

    All ICD 10 codes under E11 (under Principal Diagnosis)

    American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

    For more information about this dataset, please contact egis@isd.lacounty.gov.

  • d

    DSS Assistance Type Participation by Month CY 2012-2025

    • catalog.data.gov
    • data.ct.gov
    Updated Jul 19, 2025
    + more versions
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    data.ct.gov (2025). DSS Assistance Type Participation by Month CY 2012-2025 [Dataset]. https://catalog.data.gov/dataset/dss-assistance-type-participation-by-month-cy-2012-2020
    Explore at:
    Dataset updated
    Jul 19, 2025
    Dataset provided by
    data.ct.gov
    Description

    In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits of a certain assistance type in that calendar year and month. A recipient may have received benefits of multiple types in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enro

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    Connecticut Department of Social Services - People Served - CY 2012-2024

    • catalog.data.gov
    • data.ct.gov
    Updated May 17, 2025
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    data.ct.gov (2025). Connecticut Department of Social Services - People Served - CY 2012-2024 [Dataset]. https://catalog.data.gov/dataset/connecticut-department-of-social-services-people-served-cy-2012-2019
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    Dataset updated
    May 17, 2025
    Dataset provided by
    data.ct.gov
    Area covered
    Connecticut
    Description

    This report provides information at the state and town level of people served by the Connecticut Department of Social Services for the Calendar Years 2012-2024 by demographics (gender, age-groups, race, and ethnicity) at the state and town level by Medical Benefit Plan (Husky A-D, Husky limited benefit, MSP and Other Medical); Assistance Type (Cash, Food, Medical, Other); and Program (CADAP, CHCPE, CHIP, ConnTRANS, Medicaid, Medical, MSP, Refugee Cash, Repatriation, SAGA, SAGA Funeral, SNAP, Social Work Services, State Funded Medical, State Supplement, TFA). NOTE: On March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients has changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This change in methodology causes a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged.

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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Health Insurance Coverage’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-health-insurance-coverage-1c87/88f5e0a9/?iid=002-220&v=presentation

    ‘Health Insurance Coverage’ analyzed by Analyst-2

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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Health Insurance Coverage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hhs/health-insurance on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    The Affordable Care Act (ACA) is the name for the comprehensive health care reform law and its amendments which addresses health insurance coverage, health care costs, and preventive care. The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 by President Barack Obama and was amended by the Health Care and Education Reconciliation Act on March 30, 2010.

    Content

    This dataset provides health insurance coverage data for each state and the nation as a whole, including variables such as the uninsured rates before and after Obamacare, estimates of individuals covered by employer and marketplace healthcare plans, and enrollment in Medicare and Medicaid programs.

    Acknowledgements

    The health insurance coverage data was compiled from the US Department of Health and Human Services and US Census Bureau.

    Inspiration

    How has the Affordable Care Act changed the rate of citizens with health insurance coverage? Which states observed the greatest decline in their uninsured rate? Did those states expand Medicaid program coverage and/or implement a health insurance marketplace? What do you predict will happen to the nationwide uninsured rate in the next five years?

    --- Original source retains full ownership of the source dataset ---

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