This statistic shows the percentage of persons under 65 years of age in the U.S. with private health insurance coverage in 2000 and 2019, by ethnicity. In 2019, approximately ** percent of the white U.S. population under 65 years of age had private health insurance coverage.

This statistic shows the percentage of the U.S. population under 65 years of age without health insurance coverage from 1984 to 2019. In 2019, ** percent of the U.S. population under 65 years were without health insurance coverage.

In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the 18th round of the survey (third quarter 2019) include self-reported health status, health insurance coverage, access to and use of health care, out-of-pocket health care costs, health care affordability, awareness of and attitudes toward Medicaid work requirements, health savings accounts, flexible spending accounts, access to behavioral health care and dental care, and attitudes toward proposals to expand health insurance coverage. Additional information collected by the survey includes age, gender, sexual orientation, marital status, education, race, Hispanic origin, United States citizenship, housing type, home ownership, internet access, income, employment status, and employer size.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.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..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in 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.An "-" entry in the estimate column indicates that 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, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

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.

In the period 2019-2023, some ** percent of young people aged between 14 and 24 in Massachusetts had health insurance coverage, the highest number across all U.S. states. On the other hand, Texas, with approximately ** percent, had the lowest share of young adults with health insurance coverage. This statistic depicts the share of young people in the United States who had health insurance from 2019 to 2023, by state.

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: 2015-2019ACS 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 10, 2020National 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. 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.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.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.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..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in 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.An "-" entry in the estimate column indicates that 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, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

United States Health Insurance: Accident and Health: Covered Lives: Comprehensive: Individual data was reported at 17,694,399.000 Person in 2023. This records an increase from the previous number of 13,694,256.000 Person for 2022. United States Health Insurance: Accident and Health: Covered Lives: Comprehensive: Individual data is updated yearly, averaging 13,113,504.000 Person from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 17,694,399.000 Person in 2023 and a record low of 10,541,013.000 Person in 2019. United States Health Insurance: Accident and Health: Covered Lives: Comprehensive: Individual data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG021: Health Insurance: Accident and Health: Number of Covered Lives by Lines of Business.

United States Health Insurance: Accident and Health: Covered Lives: Medicare Supplement data was reported at 13,673,523.000 Person in 2023. This records a decrease from the previous number of 15,172,285.000 Person for 2022. United States Health Insurance: Accident and Health: Covered Lives: Medicare Supplement data is updated yearly, averaging 13,673,523.000 Person from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 21,591,586.000 Person in 2019 and a record low of 11,978,438.000 Person in 2015. United States Health Insurance: Accident and Health: Covered Lives: Medicare Supplement data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG021: Health Insurance: Accident and Health: Number of Covered Lives by Lines of Business.

In 2019, there were approximately *** million persons with private health insurance coverage in the United States. This statistic shows the number of persons under 65 years in the U.S. with private health insurance coverage from 1984 to 2019.

This layer shows health insurance coverage sex and race 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. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black)This layer is symbolized to show the percent of population with no health insurance coverage. 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, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this 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.

Data on Medicaid coverage among persons under age 65 by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health Interview Survey, health insurance supplements (1984, 1989, 1994-1996). Starting with 1997, data are from the family core and the sample adult questionnaires. Data for level of difficulty are from the 2010 Quality of Life, 2011-2017 Functioning and Disability, and 2018 Sample Adult questionnaires. For more information on the National Health Interview Survey, see the corresponding Appendix entry at https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/data/hus/hus19-appendix-508.pdf.

The global commercial medical insurance market is experiencing robust growth, driven by factors such as rising healthcare costs, increasing prevalence of chronic diseases, and expanding health insurance coverage worldwide. A Compound Annual Growth Rate (CAGR) of 5% from 2019-2024 suggests a significant market expansion. The market is segmented by product type (individual & family, group) and application (comprehensive plans, treatment & care, others). Individual and family plans are expected to hold a larger market share compared to group plans, primarily due to increasing individual disposable incomes and a growing preference for customized health insurance solutions. Similarly, comprehensive plans dominate the application segment, owing to their wide coverage and ability to address a broader spectrum of healthcare needs. North America, particularly the United States, currently holds a significant market share due to its advanced healthcare infrastructure and high insurance penetration rates. However, Asia-Pacific is projected to witness substantial growth in the coming years, fuelled by rising middle-class incomes, increasing health awareness, and supportive government policies in countries like China and India. Key players like Anthem, UnitedHealth Group, and others are leveraging technological advancements, such as telehealth and data analytics, to enhance their service offerings and expand their market reach. The market faces challenges such as regulatory changes, increasing fraud and abuse, and the need to manage rising healthcare costs effectively. Despite these challenges, the long-term outlook for the commercial medical insurance market remains positive. The ongoing demand for better healthcare access and coverage, coupled with the introduction of innovative insurance products and services, will continue to propel market expansion. The shift towards value-based care models and the adoption of preventive healthcare initiatives are expected to further shape the market landscape. Competition among existing players is intense, with a focus on product differentiation, strategic partnerships, and geographical expansion. Furthermore, the increasing adoption of digital technologies is expected to improve efficiency and customer experience, leading to improved market penetration and sustained growth. Companies are investing in advanced technologies to enhance claims processing, improve customer service, and offer more personalized health plans.

Snapshot img

Health Insurance Market Size 2025-2029

The health insurance market size is forecast to increase by USD 1,341 billion at a CAGR of 7.3% between 2024 and 2029.

The market experiences robust growth, fueled by the increasing demand for comprehensive coverage due to heightened healthcare awareness and a growing emphasis on preventive health. This trend is further driven by the escalating costs of healthcare services and medical treatments, which underscores the importance of insurance as a financial safeguard. However, market expansion encounters significant challenges. Regulatory hurdles impact adoption, as governments and regulatory bodies implement stringent regulations to ensure affordability and accessibility for consumers. Supply chain inconsistencies, such as disparities in provider networks and reimbursement rates, temper growth potential. This is particularly evident in the rising prevalence of chronic conditions such as cancer, stroke, and kidney failure, which necessitate ongoing medication and hospitalization. Additionally, another trend is the shift towards online sales and digital platforms for purchasing insurance policies and accessing healthcare services.
To capitalize on opportunities and navigate challenges effectively, companies must stay informed of regulatory changes and collaborate with healthcare providers to streamline operations and maintain competitive pricing. By focusing on innovation, transparency, and customer-centric solutions, insurers can differentiate themselves in a competitive landscape and meet the evolving needs of health-conscious consumers.

What will be the Size of the Health Insurance Market during the forecast period?

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In the dynamic market, chronic disease management and mental health coverage have emerged as significant areas of focus. Health insurance networks strive to offer comprehensive solutions, integrating geriatric care, preventive care, and end-of-life care into their offerings. Innovation drives the industry, with wellness programs, home health care, and telemedicine becoming increasingly popular. Compliance with regulations, including those related to maternity care, newborn care, and substance abuse treatment, is crucial.
Specialty care and provider networks continue to shape the landscape, while ethics and claims processing remain critical components of health insurance services. Incorporating mental health coverage into plans and addressing the needs of the aging population are key trends shaping the market.

How is this Health Insurance Industry segmented?

The health insurance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Public
  Private


Type

  Life insurance
  Term insurance


Age Group

  Adults
  Senior citizens
  Minors


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Service Insights

The public segment is estimated to witness significant growth during the forecast period.

In the dynamic market, various entities play crucial roles in shaping its landscape. Public organizations, such as the National Health Service (NHS) in the UK and Medicare in Australia, are leading providers due to increased government involvement in ensuring universal healthcare access. These programs offer comprehensive coverage, affordable premiums, and a focus on preventive care. Collaborations with commercial insurers, legislative frameworks, and investments in healthcare infrastructure further expand their reach. Quality is a top priority, with health insurance advisors and brokers facilitating the selection of plans that best fit businesses and individuals. Prescription drug coverage is a significant consideration, and self-funded health insurance and health reimbursement arrangements offer flexibility for employers.

Group health insurance and individual health insurance provide different solutions for various needs, with portability ensuring continuity. Health insurance cybersecurity and technology are essential, with health insurance portals, virtual care, and telemedicine transforming the industry. Health savings accounts, flexible spending accounts, and out-of-pocket maximums help manage costs. Managed care and employer-sponsored health insurance are common, with health insurance plans catering to diverse needs. Regulations and compliance are critical, with long-term care insurance addressing specific healthcare requirements. Disability insurance and life insurance provide additional coverage, while the marketing and transparency ensure consumer understanding. Point-of-service (POS) plans and dental/vision insurance of

This statistic shows the percentage of the U.S. population under 65 years of age without health insurance coverage from 1984 to 2019, by gender. In 2019, around 13 percent of the male U.S. population under 65 years were without health insurance coverage.

IntroductionIn India, regular monitoring of health insurance at district levels (the most essential administrative unit) is important for its effective uptake to contain the high out of pocket health expenditures. Given that the last individual data on health insurance coverage at district levels in India was in 2016, we update the evidence using the latest round of the National Family Health Survey conducted in 2019-2021.MethodsWe use the unit records of households from the latest round (2021) of the nationally representative National Family Health Survey to calculate the weighted percentage (and 95% CI) of households with at least one member covered by any form of health insurance and its types across socio-economic characteristics and geographies of India. Further, we used a random intercept logistic regression to measure the variation in coverage across communities, district and state. Such household level study of coverage is helpful as it represents awareness and outreach for at least one member, which can percolate easily to the entire household with further interventions.ResultsWe found that only 2/5th of households in India had insurance coverage for at least one of its members, with vast geographic variation emphasizing need for aggressive expansion. About 15.5% were covered by national schemes, 47.1% by state health scheme, 13.2% by employer provided health insurance, 3.3% had purchased health insurance privately and 25.6% were covered by other health insurance schemes (not covered above). About 30.5% of the total variation in coverage was attributable to state, 2.7% to districts and 9.5% to clusters. Household size, gender, marital status and education of household head show weak gradient for coverage under “any” insurance.DiscussionDespite substantial increase in population eligible for state sponsored health insurance and rise in private health insurance companies, nearly 60% of families do not have a single person covered under any health insurance scheme. Further, the existing coverage is fragmented, with significant rural/urban and geographic variation within districts. It is essential to consider these disparities and adopt rigorous place-based interventions for improving health insurance coverage.

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.

These data include PDF files of 1496 health insurance contracts from the year 2019 in the United States. They come from 40 corporations [435 self-insured plans using third party administrators (TPAs)] and health insurance contracts from the individual, small group, and large group markets sold in the states of CA (852 plans) and MI (209 plans). The Excel spreadsheet is our coding sheet for all of these contracts analyzing transgender health care coverage options.

We drew these contracts from AXIACI from Leverage Global Consulting, a proprietary database that contains insurance plan offerings and coverage from private and public insurance market segments. Our use of the proprietary database for the purposes of public policy research and analysis in health insurance is governed by a data use agreement between Leverage and Professor Anna Kirkland.

We use the term “contract” to mean the Summary Plan Documents (SPD), which is the roughly 100-200 page document that an individual gets from their health insurance company describing coverage and exclusions. These are the documents contained here. Other documents include the Member Handbook, Certificate of Coverage, Summary of Benefits and Coverage, Subscribers Contract, medical policies, and drug formulary, all of which are distinct and differently regulated documents.