In 2023, Alabama and Michigan had the highest rate of Medicare Advantage (MA) penetration, meaning that ** percent of Medicare beneficiaries in these three states were enrolled in MA plans rather than traditional Medicare plans. The national average was ** percent that year. This statistic depicts the leading 10 U.S. states by percentage of Medicare beneficiaries enrolled in a Medicare Advantage plan in 2024.

In 2021, among employees eligible for employer-sponsored health insurance the share of employees enrolled in a high-deductible health plan (HDHP) varied across all states, from as low as **** percent in Hawaii to a high of **** percent in Maine. This statistic represents the share of employees enrolled in high-deductible health plan (HDHP) through their employers in the United States in 2021, by state.

In 2024, 48 percent of employees were enrolled in preferred provider organization (PPO) plans through their employers. PPO plans had the highest market share among the four common types of health plans offered by employers in the United States. This statistic displays the market share of types of employer-sponsored health insurance (ESI) plans in the United States in 2024, by enrollment

In the state of California, there were approximately *** million children enrolled in Medicaid and CHIP insurance plans in March 2025. Additionally, Texas, New York, and Florida all had more than *** million children enrolled in the programs. How many people are enrolled in Medicaid/CHIP? State Medicaid programs provide medical coverage to millions of Americans, including children, pregnant women, and parents. The Children’s Health Insurance Program (CHIP) was introduced in 1997 to help uninsured children who were previously not eligible for Medicaid. The total number of individuals enrolled in Medicaid and CHIP was approximately **** million in May 2021, and California has the largest state program. How is income eligibility determined? The Affordable Care Act established a new methodology to assess income eligibility for Medicaid and CHIP. The adoption of the Modified Adjusted Gross Income (MAGI) methodology helped to align eligibility rules that previously varied nationwide. In general, an individual’s eligibility is now determined by their MAGI and where it falls in relation to the federal poverty level (FPL). For Medicaid and CHIP plans across all states in 2021, the median upper income eligibility level for children was *** percent of the FPL.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. As of August 2024, CMS is no longer releasing the “HealthCare.gov” metrics. Historical data between July 2023-July 2024 will remain available. The “HealthCare.gov Transitions” metrics, which are the CAA, 2023 required metrics, will continue to be released. Sources: HealthCare.gov application and policy data through May 5, 2024, and T-MSIS Analytic Files (TAF) through March 2024 (TAF version 7.1 with T-MSIS enrollment through the end of March 2024). Data include consumers in HealthCare.gov states where the first unwinding renewal cohort is due on or after the end of reporting month (state identification based on HealthCare.gov policy and application data). State data start being reported in the month when the state's first unwinding renewal cohort is due. April data include Arizona, Arkansas, Florida, Indiana, Iowa, Kansas, Nebraska, New Hampshire, Ohio, Oklahoma, South Dakota, Utah, West Virginia, and Wyoming. May data include the previous states and the following new states: Alaska, Delaware, Georgia, Hawaii, Montana, North Dakota, South Carolina, Texas, and Virginia. June data include the previous states and the following new states: Alabama, Illinois, Louisiana, Michigan, Missouri, Mississippi, North Carolina, Tennessee, and Wisconsin. July data include the previous states and Oregon. All HealthCare.gov states are included in this version of the report. Notes: This table includes Marketplace consumers who: 1) submitted a HealthCare.gov application on or after the start of each state’s first reporting month; and 2) who can be linked to an enrollment record in TAF that shows Medicaid or CHIP enrollment between March 2023 and the latest reporting month. Cumulative counts show the number of unique consumers from the included population who had a Marketplace application submitted or a HealthCare.gov Marketplace policy on or after the start of each state’s first reporting month through the latest reporting month. Net counts show the difference between the cumulative counts through a given reporting month and previous reporting months. The data used to produce the metrics are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. For example, the April 2023 reporting period extends from Monday, April 3 through Sunday, April 30. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. Data do not represent Marketplace consumers who had a confirmed Medicaid/CHIP loss. Future reporting will look at coverage transitions for people who lost Medicaid/CHIP. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data on 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in December 2023, data from December 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through November 2023. The report may include negative 'net counts,' which reflect that there were cumulatively fewer counts from one month to the next. Wyoming has negative ‘net counts’ for most of its metrics in March 2024, including 'Marketplace Consumers with Previous M

As of 2023, UnitedHealth Group had a share of ** percent in the U.S. health insurance market. Elevance health (Anthem) had the second-largest health insurance market share, covering ** percent of the market. The top five largest insurance companies represented around ** percent of the total U.S. market share in the health insurance industry. Health insurance market in the U.S. The United States does not have a universal healthcare system for its citizens. In the U.S. most individuals depend on employer-sponsored health coverage for their healthcare needs. Private health insurance dominates the market as it provides group and non-group policies. Public health insurance offers coverage under federal programs, Medicare and Medicaid/CHIP are the most popular ones. The U.S. health insurance industry has witnessed significant changes in the last decade, with increased spending by private insurance, expanded coverage through the ACA, and a growing Medicare Advantage market. Medicare Advantage market Medicare Advantage plans give Medicare beneficiaries the option of receiving benefits from private plans rather than from the traditional Medicare program. UnitedHealthcare, part of UnitedHealth Group, is the largest U.S. health insurance company by total membership. In 2023, Medicare Advantage provided coverage to ** million Americans, among which some *** million Medicare Advantage (MA) beneficiaries were enrolled in a plan from the UnitedHealth Group Inc.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. Sources: HealthCare.gov application and policy data through October 6, 2024, HealthCare.gov inbound account transfer data through November 7, 2024, and T-MSIS Analytic Files (TAF) through July 2024 (TAF version 7.1). The table includes states that use HealthCare.gov. Notes: This table includes Marketplace consumers who submitted a HealthCare.gov application from March 6, 2023 - October 6, 2024 or who had an inbound account transfer from April 3, 2023 - November 7, 2024, who can be linked to an enrollment record in TAF that shows a last day of Medicaid or CHIP enrollment from March 31, 2023 - July 31, 2024. Beneficiaries with a leaving event may have continuous coverage through another coverage source, including Medicaid or CHIP coverage in another state. However, a beneficiary that lost Medicaid or CHIP coverage and regained coverage in the same state must have a gap of at least 31 days or a full calendar month. This table includes Medicaid or CHIP beneficiaries with full benefits in the month they left Medicaid or CHIP coverage. ‘Account Transfer Consumers Whose Medicaid or CHIP Coverage was Terminated’ are consumers 1) whose full benefit Medicaid or CHIP coverage was terminated and 2) were sent by a state Medicaid or CHIP agency via secure electronic file to the HealthCare.gov Marketplace in a process referred to as an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. 'Marketplace Consumers Not on Account Transfer Whose Medicaid or CHIP Coverage was Terminated' are consumers 1) who applied at the HealthCare.gov Marketplace and 2) were not sent by a state Medicaid or CHIP agency via an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. Marketplace consumers counts are based on the month Medicaid or CHIP coverage was terminated for a beneficiary. Counts include all recent Marketplace activity. HealthCare.gov data are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. HealthCare.gov data are through Sunday, October 6. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: The percentages for the 'Marketplace Consumers Not on Account Transfer whose Medicaid or CHIP Coverage was Terminated' data record group are marked as not available (NA) because the full population of consumers without an account transfer was not available for this report. Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data about 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in November 2023, data from November 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through October 2023. APTC: Advance Premium Tax Credit; CHIP: Children's Health Insurance Program; QHP: Qualified Health Plan; NA: Not Available

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 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..The 2022 American Community Survey (ACS) data generally reflect the March 2020 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 2020 Census 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:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

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, 2017-2021 American Community Survey 5-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..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 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 delineation lists 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:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Health Insurance Exchange Market Outlook

According to our latest research, the global health insurance exchange market size stood at USD 7.2 billion in 2024. Driven by regulatory reforms, increasing digital adoption, and a growing emphasis on accessible healthcare coverage, the market is expected to expand at a robust CAGR of 8.7% from 2025 to 2033. By the end of the forecast period, the health insurance exchange market is projected to reach USD 15.3 billion by 2033. This growth trajectory is strongly supported by government initiatives to enhance healthcare access, rising consumer awareness, and the ongoing digital transformation within the insurance sector.

One of the primary growth factors propelling the health insurance exchange market is the increasing demand for transparent, affordable, and easily accessible health insurance plans. As healthcare costs continue to rise globally, individuals and organizations are seeking more efficient platforms to compare, select, and purchase insurance products. Health insurance exchanges, both public and private, provide a centralized marketplace where consumers can evaluate plan features, premiums, and coverage options, fostering greater competition among insurers and ultimately driving down costs. Additionally, the integration of advanced technologies such as artificial intelligence, big data analytics, and cloud computing has significantly improved the user experience, streamlined administrative processes, and enhanced the overall efficiency of these exchanges.

Another significant driver is the evolving regulatory landscape, particularly in developed markets such as North America and Europe. Governments are actively promoting health insurance exchanges as a means to achieve universal coverage, reduce the uninsured population, and simplify the enrollment process. The implementation of policies like the Affordable Care Act (ACA) in the United States has set a precedent for other regions, encouraging the establishment of similar frameworks. Furthermore, the COVID-19 pandemic has underscored the importance of robust healthcare infrastructure and insurance coverage, prompting a surge in enrollment through exchanges and accelerating digital transformation across the sector.

Employer-driven demand is also fueling the growth of the health insurance exchange market. As businesses strive to offer competitive employee benefits and comply with regulatory mandates, they are increasingly turning to private exchanges to customize health plans, manage costs, and provide greater choice to their workforce. Small and medium-sized enterprises, in particular, benefit from the flexibility and scalability offered by these platforms. Additionally, health insurance exchanges are expanding their service offerings to include ancillary products such as dental, vision, and wellness programs, further enhancing their value proposition and attracting a broader customer base.

From a regional perspective, North America continues to dominate the health insurance exchange market, accounting for a significant share of global revenues. This leadership is attributed to well-established public and private exchange platforms, high insurance penetration, and proactive government policies. However, Asia Pacific is emerging as a lucrative market, driven by rapid urbanization, increasing healthcare expenditure, and ongoing digitalization efforts. Europe is also witnessing steady growth, supported by regulatory harmonization and rising demand for cross-border health insurance solutions. Latin America and the Middle East & Africa, while still in the nascent stages, are expected to register healthy growth rates as awareness and adoption of health insurance exchanges increase.

Type Analysis

The health insurance exchange market is broadly segmented by type into public and private exchanges. Public exchanges are government-sponsored platforms designed to facilitate the purchase of health insurance plans, particularly for individuals and small businesses. These exchanges h

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-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..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 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 delineation lists 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 2020 Census 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:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-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..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 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 delineation lists 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 2020 Census 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:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..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..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Medicare Advantage (MA) growth and enrollment increases have not bolstered HMO providers. In addition to losing ground in employer-based coverage—dropping from 24.0% in 2014 to 20.3% in 2019—HMOs, Medicare Advantage (MA) HMO share has fallen to 56.0% in 20224, from 58% in 2023. The pandemic temporarily reduced private insurance enrollment because of heightened unemployment. While labor force participation, growth in the number of businesses and increases in federal funding for Medicare and Medicaid staved off more significant declines, revenue is expected to fall at a CAGR of 0.3% to $236.9 billion by 2025, but with a positive 2.2% increase in 2025 alone. Technology and AI revolutionize HMOs' cost structures and reimbursement processes, enhancing efficiency and reducing costs. Telemedicine reduces in-person consultation expenses, while AI improves diagnostic accuracy and administrative tasks. Predictive analytics minimize treatment expenditures, benefiting HMOs. However, AI's role in reimbursements has sparked disputes over denials, creating tension between providers and payers. As providers invest in AI to negotiate effectively, the U.S. House urges CMS to evaluate AI use in MA plans to ensure fair coverage decisions. Despite the controversy, AI provides smaller HMOs competitive advantages through personalized care plans and innovative services. Alternative plans will more effectively compete with larger insurers and the host of plans (PPO, POS, HDHPs) that substitute in various ways. With increasing concentration and competition from large, well-known insurers that benefit from economies of scale and scope, smaller HMOs may need to focus on a particular market segment -- Medicare, employer, or individual Medicare Advantage offers comprehensive care packages that target older adults' needs. Customizable plans with wellness and mental health support align with employer priorities, while telemedicine and competitive pricing attract younger individuals. Advanced analytics enable tailored offerings, ensuring engagement across demographics. Amid technological advances and diverse consumer demands, HMOs can strengthen market positions by balancing flexibility, cost control and personalized care offerings. With new strategies and growth in favorable economic conditions -- the number of businesses, employees and federal funding -- revenue is expected to climb at a CAGR of 1.1% to an estimated $250.7 billion in 2030, with profit increasing.

California has more Medicaid and CHIP enrollees than any other state in the United States. As of April 2023, approximately ** million Americans were enrolled in the Medicaid health insurance programs in California, which accounted for approximately ** percent of the total number of Medicaid enrollees nationwide (**** million). Blow to Medicaid expansion plans California is one of many states that has expanded its Medicaid program under the Affordable Care Act (ACA) to encourage more low-income adults to sign up for health coverage. One of the original aims of the ACA was to limit some of the variations in state Medicaid programs, but the Supreme Court ruled that the expansion should be optional. Governors of the states that did not expand said they were concerned about long-term costs. California is the leading state for Medicaid expenditure, spending approximately **** billion U.S. dollars in FY2020. Health coverage for children The Children’s Health Insurance Program (CHIP) was created as a complement to Medicaid, expanding the reach of government-funded health coverage to more children in low-income families. As of May 2021, over **** million children were enrolled in Medicaid/CHIP programs in California, more than any other state. As of January 2021, the median Medicaid/CHIP eligibility level for children was *** percent of the federal poverty level.

Although premium prices continue to grow, macroeconomic volatility resulted in a slight contraction of revenue. Profitability was bolstered by cost efficiencies associated with high levels of enrollment for individual providers. However, slight fluctuations in the percent of the population enrolled in vision plans, coupled with a spike in unemployment in 2020, caused revenue to shrink at a CAGR of 0.1% to $60.0 billion over the five years to 2024, including an estimated 2.3% jump in 2024 alone. Vision insurance providers provide coverage for routine eye exams and discounted pricing for eyeglasses, contact lenses and refractive surgery. While vision insurance is distinct from health insurance, many companies provide comprehensive policies that include both health and vision plans, causing a high correlation between health and vision insurance trends. Providers have continued to pursue efforts to consolidate because of the benefits of economies of scale, although this trend was disrupted due to the pandemic, forcing many providers to stay small. A consistent trend of integration with other types of health insurance providers provided further respite against dampened consumer confidence. While smaller providers have continued to grow in an effort to capture growing market share, larger companies continue to benefit from network effects and substantial resources. For example, major player Davis Vision Inc.'s parent company, Versant Health Inc., was acquired by major health insurer MetLife Inc. in January 2021, highlighting the capital advantages larger providers have in staving off larger losses. Moving forward, revenue is expected to rise at a CAGR of 2.7% to $68.7 billion over the five years to 2029. The primary drivers of growth will be the Patient Protection and Affordable Care Act (PPACA) and improving macroeconomic variables, which collectively expand the number of individuals with access to eye care. For example, the number of Medicare beneficiaries is anticipated to rise to 79.0 million in 2030, according to the latest data available from the AARP public policy institute. This is expected to cause Medicare payments to optometrists and ophthalmologists to continue growing. Consistent growth in employment will likely drive demand from the private sector, with employees increasingly seeing vision insurance as an essential benefit.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..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..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Provide the first national description across the US of variations in healthcare measures in 2018 among Medicaid beneficiaries with schizophrenia. Adult beneficiaries with ≥2 diagnoses for schizophrenia, and continuous enrollment with consistent geographical data in all of 2018 were identified from Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) data for 45 of 50 states. Antipsychotic (AP) utilization rates, including long-acting injectable APs (LAIs), quality metrics, and all-cause healthcare resource utilization and costs for claims submitted to Medicaid were reported nationally and by state. Pearson correlation evaluated associations between LAI utilization and total healthcare costs at state and county levels. Across the US 688,437 patients with schizophrenia were identified. The AP utilization rate was 51% (state range: 24–77%), while the LAI utilization rate was 13% (range: 4–26%). The proportion of patients adherent to any AP was 56% (range: 19–73%). Within 30 days post-discharge from an inpatient admission, 22% (range: 8–58%) of patients had an outpatient visit, and 12% (range: 4–48%) had a readmission. The proportion of patients with ≥1 inpatient admission and ≥1 emergency room visit was 34% (range: 19–82%) and 45% (range: 20–70%). Per-patient-per-year total healthcare costs averaged $32,920 (range: $717–$93,972). At the county level, a weak negative correlation was observed between LAI utilization and total healthcare costs. This study included Medicaid beneficiaries enrolled with pharmacy and medical benefits, including beneficiaries dually eligible for Medicare; results cannot be generalized to the overall schizophrenia population or those with other payer coverage. In 2018, half of beneficiaries with schizophrenia did not submit any claims for APs to Medicaid, nearly half had an emergency room visit, and one-third had an inpatient admission. Moreover, healthcare measures varied considerably across states. These findings may indicate unmet treatment needs for Medicaid beneficiaries with schizophrenia. Schizophrenia is a severe mental disorder that poses a large health, social, and cost burden to patients and society. While treatment with antipsychotic medications can reduce the number of relapses and hospitalizations, many patients do not adhere to treatment, which can lead to poor symptom control and further use of healthcare services. Interestingly, these measures of schizophrenia care seem to vary across US states. Therefore, we ran the first study to describe the regional differences in antipsychotic use, measures of quality of care, healthcare use, and healthcare costs among Medicaid-insured patients across the US in 2018. Our results showed that only half of patients used antipsychotics in 2018 (with a range of 24–77% across states) and the proportion of patients adherent to antipsychotic treatment was low (range of 19–73%). Additionally, nearly half of all patients had an emergency room visit (range of 20–70%), and one-third had an inpatient admission (range of 19–82%). These findings highlight large variations in antipsychotic use, performance measures, and healthcare use, possibly due to regional differences in unmet needs in schizophrenia care for Medicaid-insured patients in the US. Since use of inpatient and emergency room services was consistently high in specific states or regions, and yearly healthcare costs per patient varied from $717–$93,972 (mean = $32,920), there may be a particularly high burden in certain areas of the country where patients with schizophrenia may potentially be experiencing multiple relapses. Further research is needed to identify policies that may help narrow these regional differences.

Medicaid is an important public health insurance for individuals with a low income, those that are pregnant, disabled or are children. It was projected that by 2020 there would be approximately **** million Medicaid enrollees. By 2027 that number is expected to increase to ** million individuals covered.

Medicaid in the focus

Medicaid has recently been in the news for several reasons. A proposed Medicaid expansion was announced with the implementation of the Affordable Care Act in 2010. According to the expansion, all states were given the option to expand Medicaid programs to help provide insurance coverage to millions of U.S. Americans. As of 2019, ** states have accepted federal funding to expand their Medicaid programs. Medicaid, after Medicare and private insurance, provides a significant proportion of the total health expenditures in the United States. In general, Medicaid expenditure, like the number of enrollees, has been growing over time.

Medicaid demographics

A significant proportion of Medicaid enrollees in the U.S. are children and low-income adults. Despite children accounting for most of the enrollees in the Medicaid program, the largest percentage of expenditures for Medicaid is dedicated to those enrolled as a disabled individual. Expenditures for the program also vary regionally. The states with the highest Medicaid expenditures include California, New York and Texas, to name a few.

Medicare Advantage Market Outlook

The global Medicare Advantage market size stood at USD 512.3 billion in 2024, according to our latest research, and is projected to exhibit a robust CAGR of 6.8% during the forecast period, reaching USD 943.5 billion by 2033. This strong growth trajectory is driven primarily by the expanding elderly demographic, increasing chronic disease prevalence, and ongoing healthcare reforms aimed at improving accessibility and affordability. The Medicare Advantage market is witnessing substantial transformation as payers, providers, and regulators adapt to evolving consumer expectations and regulatory frameworks, making it a focal point for innovation and investment in the global healthcare landscape.

A key growth factor propelling the Medicare Advantage market is the rapidly aging global population, especially in developed countries such as the United States, Japan, and several European nations. As life expectancy rises and the proportion of individuals aged 65 and above increases, there is a corresponding surge in demand for comprehensive and flexible healthcare coverage. Medicare Advantage plans, which offer additional benefits beyond traditional Medicare, such as vision, dental, and wellness programs, are increasingly favored by this demographic. The growing prevalence of chronic diseases among the elderly, including diabetes, cardiovascular disorders, and respiratory illnesses, further underscores the need for integrated care solutions, positioning Medicare Advantage as a critical pillar in the global health insurance ecosystem.

Another significant driver is the ongoing shift towards value-based care models, which prioritize patient outcomes and cost efficiency over service volumes. Medicare Advantage plans are strategically designed to align with these models, offering coordinated care, preventive services, and disease management programs that reduce hospitalizations and improve quality of life. The integration of advanced digital health technologies, such as telemedicine, remote patient monitoring, and data analytics, is enhancing the ability of insurers and providers to deliver personalized, proactive care. This digital transformation not only streamlines administrative processes but also empowers beneficiaries to make informed decisions, thereby boosting enrollment and retention rates in the Medicare Advantage market.

Regulatory support and policy reforms are also catalyzing market expansion. Governments and regulatory bodies, particularly in the United States, are implementing measures to promote competition, increase plan flexibility, and enhance consumer protections within the Medicare Advantage framework. These initiatives are encouraging private insurers to innovate and diversify their offerings, resulting in a broader array of plan types and benefit structures. The introduction of Special Needs Plans (SNPs) tailored to individuals with specific health conditions or socioeconomic challenges exemplifies the market’s responsiveness to diverse beneficiary needs. Overall, the interplay of demographic, technological, and regulatory forces is fostering a dynamic and competitive Medicare Advantage market poised for sustained growth.

From a regional perspective, North America dominates the Medicare Advantage market, accounting for the largest share due to its mature healthcare infrastructure, high awareness levels, and favorable policy environment. However, emerging markets in Asia Pacific and Latin America are exhibiting rapid growth, fueled by increasing healthcare expenditures, rising insurance penetration, and demographic shifts. Europe is also witnessing steady adoption, particularly in countries with aging populations and supportive regulatory frameworks. While market maturity and growth rates vary across regions, the global outlook for Medicare Advantage remains overwhelmingly positive, with significant opportunities for expansion and innovation in both developed and developing economies.

Plan Type Analysis

The Medicare Advantage market is segm