In May, authorities confirmed an Ebola outbreak centered in the Democratic Republic of the Congo (DRC), which has since grown to become the third largest Ebola outbreak on record, causing more than 2,000 cases and 750 deaths as of July. The State Department has announced the U.S. would provide “$270 million in direct Ebola response funding” and other support to assist the international response. This response from the Trump administration, however, comes amid a changed global and domestic health landscape, marked by reduced U.S. foreign aid funding and the dissolution of USAID, as well as cuts to domestic public health programs.

The latest KFF Health Tracking Poll finds many adults have yet to form an opinion on the U.S. government’s response to the Ebola outbreak in the Democratic Republic of the Congo, though among those with a view, more say the government is falling short than say it is doing enough.

About four in ten adults (38%) say the U.S government is not doing enough to prevent an Ebola outbreak in the U.S., while one in four (23%) say the government is doing enough and 39% say they are not sure. Views are similar when it comes to the U.S. role abroad, with four in ten (40%) saying the government is not doing enough to help fight the outbreak in Africa, while one in five (18%) say the government is doing enough and 42% say they are not sure.

Across partisans, Democrats are more critical of the U.S. government’s Ebola response with majorities saying it is not doing enough to prevent an outbreak in the U.S. (58%) nor to help fight the outbreak in Africa (66%). Notably, about four in ten independents and Republicans say they are not sure if the U.S. is doing enough to prevent an Ebola outbreak in the U.S. and about half of both groups are unsure if the government is doing enough to help fight the outbreak in Africa.

Several years after the height of the COVID-19 pandemic and amid recent changes to federal health agencies and global health engagement during President Trump's second term, the public holds mixed views on whether the U.S. government is prepared for another pandemic or widespread health crisis. A plurality of adults (40%) say the U.S. government is “less prepared” to deal with a pandemic now than it was in 2020, while about a third (34%) say the government is now “more prepared,” and one in four say it is “just as prepared” as it was in 2020. Views are divided across partisan lines. While two-thirds of Democrats (67%) say the U.S. government is now “less prepared,” a majority of Republicans (56%) say the country is now more prepared for a pandemic. About four in ten independents (41%) say the U.S. is “less prepared” for a pandemic compared to 2020, while about a third (32%) say it is “more prepared.”

Republicans and Republican-leaning independents who identify as supporters of the Make America Great Again (MAGA) movement are the most confident; six in ten (61%) of them say the U.S. is “more prepared” to deal with a pandemic now than in 2020, compared to about half (47%) of their non-MAGA counterparts who say the same.

Compared to views shortly before the start of the second Trump administration, the public overall now takes a dimmer view of the U.S. government’s pandemic preparedness, though Republicans’ views have moved in the opposite direction. The share of the public who say the U.S. government is now less prepared to deal with another pandemic than it was in 2020 is up 14 percentage points since January 2025, before the Trump administration made significant changes to U.S. foreign aid efforts (40%, up from 26%). This increase is driven largely by Democrats, two-thirds (67%) of whom now say the U.S. is "less prepared" — an increase of 40 percentage points from early last year. Independents are also now more likely to say the U.S. is “less prepared” (41%, up from 23% in January 2025).

Republicans’ views, on the other hand, have shifted in the opposite direction. The share of Republicans who say the government is "more prepared" for a pandemic has increased 12 percentage points since January 2025 (56%, up from 44%), while fewer Republicans now view the U.S. as “less prepared” (14%, down from 27%).

This KFF Health Tracking Poll/ KFF Tracking Poll on Health Information and Trust was designed and analyzed by public opinion researchers at KFF. The survey was conducted June 25 – June 30, 2026, online and by telephone among a nationally representative sample of 1,321 U.S. adults in English (n=1,238) and in Spanish (n=83). The sample includes 1,015 adults (n=69 in Spanish) reached through the SSRS Opinion Panel either online (n=990) or over the phone (n=25). The SSRS Opinion Panel is a nationally representative probability-based panel where panel members are recruited randomly in one of two ways: (a) Through invitations mailed to respondents randomly sampled from an Address-Based Sample (ABS) provided by Marketing Systems Groups (MSG) through the U.S. Postal Service’s Computerized Delivery Sequence (CDS); (b) from a dual-frame random digit dial (RDD) sample provided by MSG. For the online panel component, invitations were sent to panel members by email followed by up to three reminder emails. 

Another 306 (n=14 in Spanish) adults were reached through random digit dial telephone sample of prepaid cell phone numbers obtained through MSG. Phone numbers used for the prepaid cell phone component were randomly generated from a cell phone sampling frame with disproportionate stratification aimed at reaching Hispanic and non-Hispanic Black respondents. Stratification was based on incidence of the race/ethnicity groups within each frame. Among this prepaid cell phone component, 142 were interviewed by phone and 164 were invited to the web survey via short message service (SMS). 

Respondents in the prepaid cell phone sample who were interviewed by phone received a $15 incentive via a check received by mail or an electronic gift card incentive. Respondents in the prepaid cell phone sample reached via SMS received a $10 electronic gift card incentive. SSRS Opinion Panel respondents received a $5 electronic gift card incentive (some harder-to-reach groups received a $10 electronic gift card). In order to ensure data quality, cases were removed if they failed two or more quality checks: (1) attention check questions in the online version of the questionnaire, (2) had over 30% item non-response, or (3) had a length less than one quarter of the mean length by mode. Based on this criterion, 1 case was removed. 

The combined cell phone and panel samples were weighted to match the sample’s demographics to the national U.S. adult population using data from the Census Bureau’s 2025 Current Population Survey (CPS), September 2023 Volunteering and Civic Life Supplement data from the CPS, and the 2026 KFF Benchmarking Survey with ABS and prepaid cell phone samples. The demographic variables included in weighting for the general population sample are gender, age, education, race/ethnicity, region, civic engagement, frequency of internet use and political party identification. The weights account for differences in the probability of selection for each sample type (prepaid cell phone and panel). This includes adjustment for the sample design and geographic stratification of the cell phone sample, within household probability of selection, and the design of the panel-recruitment procedure. 

The margin of sampling error including the design effect for the full sample is plus or minus 3 percentage points. Numbers of respondents and margins of sampling error for key subgroups are shown in the table below. For results based on other subgroups, the margin of sampling error may be higher. Sample sizes and margins of sampling error for other subgroups are available on request. Sampling error is only one of many potential sources of error and there may be other unmeasured error in this or any other public opinion poll. KFF public opinion and survey research is a charter member of the Transparency Initiative of the American Association for Public Opinion Research. 

This report, Donor Government Funding for HIV in Low- and Middle-Income Countries in 2025, tracks funding levels of the donor governments that collectively provide the bulk of international assistance for AIDS through bilateral programs and contributions to multilateral organizations. The new report, produced as a partnership between KFF and UNAIDS, provides the latest data available on donor funding disbursements based on data provided by governments. It includes their bilateral assistance to low- and middle-income countries and contributions to the Global Fund to Fight AIDS, Tuberculosis and Malaria as well as UNITAID.

Donor government funding for the HIV response in low-and middle-income countries fell substantially in 2025, its largest drop since the donor government funding scale-up to address HIV began, with funding dipping to pre-2008 levels. The decline was driven by reductions in disbursements from the United States, the largest donor to HIV in the world; aggregate funding from all other donors remained flat (even after adjusting for exchange rate fluctuations). This report, which focuses on both bilateral and multilateral funding for HIV provided by donor governments, provides the first analysis of the extent of this decline, comparing funding levels in 2025 to 2024 and examining broader funding trends over the past 15 years. Key findings are as follows:

• Donor government funding for HIV decreased by US$2.1 billion, or 25%, in 2025 compared to the prior year. Disbursements totaled US$6.2 billion in 2025 compared to US$8.3 billion in 2024. This was the lowest level of donor government support since 2007 (US$5.0 billion).^{1, 2, 3}
• Both bilateral and multilateral funding from donor governments decreased. Bilateral funding declined by US$1.5 billion (26%) and multilateral funding declined by US$581 million (23%).
• U.S. disbursements for HIV were US$2.1 billion less in 2025 compared to 2024 (US$4.6 billion compared to US$6.7 billion). The U.S. declines were due to significant changes made by the current U.S. administration to foreign assistance programs, including global health and HIV. At the same time, the U.S. Congress has continued to appropriate funding for HIV at steady levels, meaning that additional funding remains in the U.S. pipeline, although the extent to which those funds will be fully spent remains uncertain.
• While HIV funding provided by all other donor governments was flat in 2025, the longer trend shows a decline, resulting in the U.S. shouldering an increasing share of the funding burden. Excluding the U.S., funding from all other donor governments totaled US$1.6 billion in 2025 compared to US$3.2 billion in 2011, a decrease of nearly 50%, largely due to declining bilateral support from other donor governments.  As a result, the U.S. share of total donor government funding for HIV rose from 59% in 2011 to 74% in 2025 making it increasingly vulnerable to changes in U.S. support as was seen in 2025. 
• Future funding prospects are uncertain. The Organisation for Economic Co-operation and Development (OECD) Development Assistance Committee (DAC), which reported a 23% decrease in official development assistance (ODA) in 2025, has projected ODA will decrease again in 2026. Whether donor governments provide additional funding for HIV in the future, the significant reductions in 2025 have already affected the HIV response. While the U.S., the world’s largest donor for HIV, has begun to speed up obligations – agreements that will result in disbursements in the future, and the U.S. Congress has continued to appropriate funding at prior year levels, the America First Global Health Strategy calls for a significant reduction in funding to countries over a five-year period. As such, the extent to which all appropriated U.S. funds will be spent remains uncertain. In addition, the longer trend shows that funding from most other donor governments is on the decline, having already decreased by 50% since 2011.

Introduction

This report provides the latest available data on donor government resources provided to address HIV in low- and middle-income countries, reporting on disbursements made in 2025. It is part of a collaborative tracking effort between UNAIDS and KFF that began almost 20 years ago, just as new global initiatives were being launched to address the epidemic. The analysis includes data from all 34 members of the Organisation for Economic Co-operation and Development (OECD)’s Development Assistance Committee (DAC), as well as non-DAC members who report data to the DAC. Data are collected directly from donor governments, UNAIDS, the Global Fund, and Unitaid, and supplemented with data from the DAC. Of the 34 DAC members, fifteen provide 98% of total disbursements for HIV; data for these donors are presented individually. For the remaining 19 DAC members, data are provided in aggregate. All totals are presented in current U.S. dollars (amounts are not adjusted for inflation). Totals include both bilateral and multilateral assistance. Multilateral assistance for HIV includes disbursements by donors to the Global Fund and Unitaid, adjusted for an estimated HIV share, and to UNAIDS. Overall trend data are provided for the 2002 to 2025 period). Disaggregated data on bilateral and multilateral amounts are provided starting in 2011 (see methodology for more detail).

The data for 2025 represent one of the first assessments of the financial impact of major changes made by the United States starting last year, including a freezing and then canceling of numerous global HIV projects, eliminating the U.S. Agency for International Development (USAID), and introducing a new approach, the America First Global Health Strategy, with plans to significantly scale-down U.S. support for countries over the next few years.   

Findings

Total Funding

In 2025, donor government funding for HIV through bilateral and multilateral channels totaled US$6.2 billion in current USD.⁴ This is a decrease of US$2.1 billion (25%) compared to 2024 (US$6.2 in 2025 compared to US$8.3 billion in 2024), marking the largest drop since the donor government funding scale-up began and the lowest level of funding since 2007 ($5.0 billion) (See Figure 1 and Table 1). Donor governments accounted for more than one-third of the UNAIDS estimated US$17.6 billion made available from all sources to address HIV in 2025, an 18% decline compared to the total resources available in 2024.^5,6

The decrease in 2025 was due to a decline in disbursements, or payouts, by the United States following the current administration’s actions that fundamentally altered U.S. foreign assistance programs. Collectively, these actions temporarily halted, and then slowed, U.S. HIV disbursements in 2025. Although, these declines in U.S. HIV disbursements were not as steep as overall decreases in U.S. development assistance as reported by the OECD DAC.⁷ At the same time, the U.S. Congress has continued to appropriate funding for HIV at steady levels, meaning that additional funding remains in the U.S. pipeline and could be spent in the future. Still, the extent to which the administration will spend these funds is unknown and the administration’s America First Global Health Strategy calls for significant scale-down in U.S. funding for countries in the future, including an estimated $7.3 billion decline over the next five years, compared to the prior five-year period.

Although there were some fluctuations by other donors, in the aggregate, their HIV funding remained flat in 2025 (US$1.6) compared to 2024. Still, when the U.S. is removed, the longer trend shows that funding for HIV provided by other donor governments has been on the decline and is significantly below funding provided in 2011 (US$3.2 billion), a decrease of nearly 50%, largely due to declining bilateral support from other donor governments (See Figure 2).⁸

Despite the decline, the United States continued to be the largest donor to HIV efforts, providing US$4.6 billion and accounting for 74% of total donor government funding in 2025.⁹ The second largest donor was France (US$280 million, 4%), followed by the U.K. (US$213 million, 3%), Japan (US$202 million, 3%), and Germany (US$200 million, 3%).^10,11

Bilateral Disbursements

Bilateral disbursements for HIV – that is, funding disbursed by a donor on behalf of a recipient country or region – totaled US$4.3 billion in 2025, a decrease of US$1.5 billion (-26%) compared to 2024 (US$5.8 billion). The decline was due to decreased bilateral funding by the U.S., which disbursed US$3.9 billion in 2025, a decline of US$1.5 billion (28%) compared to 2024 (US$5.4 billion). As noted above, while the U.S. Congress has continued to appropriate funding for HIV, and the administration could choose to disburse additional funds in the future, the extent to which this will occur is unknown (See Figure 3).^12,13

When the U.S. is removed, bilateral disbursements from all other donor governments totaled US$373 million in 2025, a slight increase compared to 2024 (US$368 million). Almost all other donor governments either increased slightly (Australia, Canada, Italy, Norway, Spain, and Sweden), or remained flat (Denmark, Ireland, Japan, the Netherlands, and the U.K.); bilateral funding from France and Germany declined. These trends were the same after accounting for exchange rate fluctuations.

Looking more broadly, bilateral funding from these donor governments (excluding the U.S.) declined by US$1.3 billion, or 78%, between 2011 (US$1.7 billion) and 2025 (US$373 million) (See Figure 2).

Multilateral Contributions

Multilateral contributions from donor governments to the Global Fund, Unitaid, and UNAIDS for HIV – funding disbursed by donor governments to these organizations which in turn use some (Global Fund and Unitaid) or all (UNAIDS) of that funding for HIV – totaled US$1.9 billion in 2025 (after adjusting for an HIV share to account for the fact that the Global Fund and Unitaid address other areas). This represents a decrease of US$581 million (23%) compared to 2024 (US$2.5 billion).^14,15  While contributions to the Global Fund accounted for most of this decline - decreasing by US$470 million (21%) between 2025 (US$1.8 billion) and 2024 (US$2.3 billion), funding for UNAIDS was reduced by US$109 million (65%) in 2025 (US$60 million) compared to 2024 (US$169 million) as several donor governments eliminated or significantly reduced support; contributions to Unitaid were flat (US$57 million in 2025 compared to US$59 million in 2024).

The decrease in 2025 multilateral disbursements was primarily driven by the U.S., which provided US$672 million in 2025, a decline of nearly half ($588 million or 47%) the amount provided in 2024 (US$1.3 billion). This decline was due in part to the timing of U.S. payments to the Global Fund (per U.S. policy requirements, U.S. contributions cannot exceed 33% of total contributions to the Global Fund, resulting in significant year-to-year differences depending on the amounts other donors have provided),¹⁶ but also to a reduction in U.S. payments to the Global Fund last year, even where they were matched by other donor contributions. The U.S. also eliminated its support to UNAIDS in 2025 (U.S. funding totaled US$50 million in 2024). As with bilateral support, appropriated funds remain and the administration could choose to provide this funding in the future (See Figure 4).¹⁷

While there were fluctuations among some of the other donor governments, these changes were largely due to the timing of payments to the Global Fund that coincide with pledge periods and donor decisions about when to fulfill pledges. When the U.S. is removed, multilateral funding from all other donor governments totaled US$1.2 billion in 2025, matching the prior year level.

Most donor governments (eleven of the fifteen profiled in 2025) provide the majority of their HIV funding through multilateral organizations. Only Denmark, the Netherlands, the U.K., and the U.S. provide a larger share bilaterally (See Figure 5). While the U.K. provided most of its HIV funding bilaterally in 2025 (and 2024), this was entirely due to the timing of payments to the Global Fund. The U.K. fulfilled almost its entire pledge to the Global Fund for 2023-2025 in 2023 resulting in significantly lower levels of multilateral funding in both 2024 and 2025. In fact, between 2019-2023, most HIV funding from the U.K. was provided through multilateral channels.

Fair Share

There are different ways to measure donor government contributions to HIV, relative to one another. While the U.S. government provides the largest amount of funding for HIV, for example, it also has the largest economy in the world. To assess relative contributions, or “fair share”, two measures were used: ranking by overall funding amount and ranking by funding for HIV per US$1 million GDP, to adjust for the size of donor economies (See Table 2):

• Rank by share of total donor government funding for HIV: By this measure, the U.S. ranked first in 2025, followed by France, the U.K., Japan, and Germany. The U.S. has ranked #1 in absolute funding amounts since tracking efforts began.
• Rank by funding for HIV per US$1 million GDP: By this measure, the Netherlands ranks first, followed by the U.S., Denmark, Norway, and France (See Figure 6).¹⁸

Looking Forward

Future funding prospects are uncertain. While the current administration could choose to disburse additional funds, the extent to which they will do so is unclear. Moreover, the stated strategy of the administration is to reduce, and in some cases eliminate, U.S. funding to countries as these countries take on an increasing level of financial and operational responsibility. More broadly, the OECD DAC reported a 23% decrease in official development assistance (ODA) in 2025 and is projecting further decreases in 2026; while largely driven by the U.S., other donor governments have also reduced their development assistance.

This project represents a collaboration between the Joint United Nations Programme on HIV/AIDS (UNAIDS) and KFF. Data provided in this report were collected and analyzed by UNAIDS and KFF.

Totals presented in this analysis include both bilateral funding for HIV in low- and middle-income countries, core contributions to UNAIDS, and the estimated share of donor government contributions to the Global Fund and Unitaid that are used for HIV. Amounts are based on analysis of data from the 34 donor government members of the Organisation for Economic Co-operation and Development (OECD) Development Assistance Committee (DAC) in 2024 who had reported Official Development Assistance (ODA). Bilateral and multilateral data were collected from multiple sources. Disaggregated bilateral and multilateral data are only available starting from 2011.

Data on gross domestic product (GDP) were obtained from the International Monetary Fund’s World Economic Outlook Database and represent current price data for 2025 (see: https://data.imf.org/en/datasets/IMF.RES:WEO).

Bilateral Funding:

Bilateral funding is defined as any earmarked (HIV-designated) amount, including earmarked non-core (“multi-bi”) contributions to multilateral organizations, such as UNAIDS. Data included in this report represent funding assistance for HIV prevention, care, treatment and support activities, but do not include funding for international HIV research conducted in donor countries (which is not considered in estimates of resource needs for service delivery of HIV-related activities).

The research team collected the latest bilateral funding data directly from twelve governments: Australia, Canada, Denmark, France, Germany, Ireland, Japan, the Netherlands, Norway, Sweden, the United Kingdom, and the United States during the first half of 2026, representing the fiscal year 2025 period. Direct data collection from these donors was desirable because they represent the preponderance of donor government assistance for HIV and the latest official statistics – from the Organisation for Economic Co-operation and Development (OECD) Creditor Reporting System (CRS) (see: http://www.oecd.org/dac/stats/data)  – are from 2024 and do not include all forms of international assistance (e.g., certain funding streams provided by donors, such as HIV components of mixed-purpose grants to non-governmental organizations). Bilateral estimates for Ireland and Japan for 2025 were not available at the time of publication. Prior year bilateral totals for these two donor governments were used as preliminary estimates that will be updated once data are available. Data for all other member governments of the OECD DAC – Austria, Belgium, the Czech Republic, the European Commission, Estonia, Finland, Greece, Hungary, Iceland, Italy, Korea, Lithuania, Luxembourg, New Zealand, Poland, Portugal, the Slovak Republic, Slovenia, Spain, and Switzerland – which collectively accounted for less than 5 percent of bilateral disbursements in each of the past several years, were obtained from the OECD CRS database and are from calendar year 2024.

In 2025, France provided data revising prior year amounts to account for “set-aside” funding (adjusted for an HIV-share) that supports Global Fund related activities. While this funding is considered part of France’s pledge to the Global Fund, it is not counted by the Global Fund as a direct contribution and is instead included under bilateral totals in this analysis. Due to this update, amounts presented in this report will differ from prior reports. The U.K. provided a revised estimate for bilateral funding in 2024 following the release of the previous report “Donor Government Funding for HIV in Low- and Middle-Income Countries in 2024”. The 2024 total for the U.K. has been adjusted in this report.

Where donor governments were members of the European Union (EU), the research team ensured that no double-counting of funds occurred between EU Member State reported amounts and European Commission (EC) reported amounts for international HIV assistance. Figures obtained directly using this approach should be considered as the upper bound estimation of financial flows in support of HIV-related activities.

Reflecting deliberate strategies of integrating HIV activities into other activity sectors, some donors use policy markers to attribute portions of mixed-purpose projects to HIV. This is done, for example, by the Netherlands and the U.K. The bilateral figures submitted by the UK Foreign, Commonwealth & Development Office (FCDO) for the financial year 2025/26 are based on an existing FCDO ‘HIV policy marker’. Denmark also attributes percentages of multipurpose projects to HIV. Canada breaks its mixed-purpose projects into components by percentage. Germany, Norway, and Sweden provided data much more conservatively, consistent with DAC constructs and purpose codes. Apart from targeted HIV/AIDS programs, bilateral health programs mainly focusing on health systems strengthening are also designed to contribute to the HIV response in partner countries.

Bilateral assistance data represent disbursements. A disbursement is the actual release of funds to, or the purchase of goods or services for, a recipient. Disbursements in any given year may include disbursements of funds committed in prior years and in some cases, not all funds committed during a government fiscal year are disbursed in that year. In addition, a disbursement by a government does not necessarily mean that the funds were provided to a country or other intended end-user.

Amounts presented are for the fiscal year period, which varies by country. The U.S. fiscal year runs from October 1-September 30. The fiscal years for Canada, Japan, and the U.K. are April 1-March 31. The Australian fiscal year runs from July 1-June 30. The European Commission, Denmark, France, Germany, Italy, Ireland, the Netherlands, Norway, and Sweden use the calendar year. The OECD uses the calendar year, so data collected from the CRS for other donor governments reflect January 1-December 31. Most UN agencies use the calendar year, and their budgets are biennial.

All data are expressed in current US dollars (USD), unless otherwise noted. Where data were provided by governments in their currencies, they were adjusted by average daily exchange rates to obtain a USD equivalent, based on foreign exchange rate historical data available from the U.S. Federal Reserve (see: http://www.federalreserve.gov/) or the OECD.

Funding totals presented in this analysis should be considered preliminary estimates based on data provided and validated by representatives of the donor governments who were contacted directly.

Multilateral Funding:

Multilateral funding includes core contributions to UNAIDS, as well as contributions to the Global Fund (see: http://www.theglobalfund.org/en/) and Unitaid (see: http://www.unitaid.org/#end). All Global Fund contributions were adjusted to represent 52% of the donor’s core contribution, reflecting the Fund’s reported grant approvals for HIV-related projects to date and includes funding for HIV/TB activities. Unitaid contributions were adjusted to represent 48% of the donor’s core contribution, reflecting Unitaid reported attribution for HIV-related projects.

Data obtained from UNAIDS, the Global Fund, and Unitaid were already adjusted to represent a USD equivalent based on date of receipts.

UNAIDS core contributions reflect amounts received in 2025. In 2024, the Netherlands provided two core contributions to UNAIDS; the first payment was provided for the 2024 contribution, while the second was a prepayment of the 2025 contribution. Global Fund and Unitaid contributions from all governments correspond to amounts received during the 2025 calendar year, regardless of which contributor’s fiscal year such disbursements pertain to.

In addition to contributions supporting the Global Fund’s and Unitaid’s core activities, some donor governments provided significant funding to these multilateral organizations for COVID-related efforts between 2020-2023. These COVID-specific contributions were not included in totals in this analysis. The U.S., for example, provided almost US$1.9 billion in such funding to the Global Fund during 2022. Other than contributions provided by governments to the Global Fund and Unitaid, un-earmarked general contributions to United Nations entities, most of which are membership contributions set by treaty or other formal agreement (e.g., the World Bank’s International Development Association or United Nations country membership assessments), are not identified as part of a donor government’s HIV assistance even if the multilateral organization in turn directs some of these funds to HIV. Rather, these would be considered as HIV funding provided by the multilateral organization, as in the case of the World Bank’s efforts, and are not considered for purposes of this report.

1. Donor government disbursements are a subset of overall international assistance for HIV in low-and-middle-income countries, which also includes funding provided by other multilateral institutions, UN agencies, and foundations. ↩︎
2. UNAIDS estimates that US$17.6 billion was available for HIV from all sources (domestic resources, donor governments, multilaterals, and philanthropic organizations) in 2025, an 18% decline compared to 2024. In addition, while the amounts presented in this analysis include donor contributions to multilateral organizations, the UNAIDS estimate of total available resources for HIV includes the actual disbursements made by multilateral organizations in 2025 rather than the donor government contributions to these entities. ↩︎
3. Between 2020-2023, some donor governments provided COVID-specific emergency contributions to the Global Fund and UNITAID in addition to their contributions for core activities. For the purposes of this report, these COVID-specific amounts have been excluded as they cannot be attributed to a specific area, such as HIV. ↩︎
4. Donor government disbursements are a subset of overall international assistance for HIV in low-and-middle-income countries, which also includes funding provided by other multilateral institutions, UN agencies, and foundations. ↩︎
5. UNAIDS, direct communication, July 2026. ↩︎
6. UNAIDS estimates that US$17.6 billion was available for HIV from all sources (domestic resources, donor governments, multilaterals, and philanthropic organizations) in 2025, an 18% decline compared to 2024. In addition, while the amounts presented in this analysis include donor contributions to multilateral organizations, the UNAIDS estimate of total available resources for HIV includes the actual disbursements made by multilateral organizations in 2025 rather than the donor government contributions to these entities. ↩︎
7. OECD, “A historic decline in foreign aid: Preliminary 2025 ODA data”, April 2026. ↩︎
8. Between 2020-2023, some donor governments provided COVID-specific emergency contributions to the Global Fund and UNITAID in addition to their contributions for core activities. For the purposes of this report, these COVID-specific amounts have been excluded as they cannot be attributed to a specific area, such as HIV. ↩︎
9. U.S. totals represent funding amounts provided through regular appropriations only. In 2021, the U.S. Congress appropriated additional emergency supplemental funding for bilateral HIV activities and for the Global Fund to address the impacts of the COVID-19 pandemic. These emergency supplemental funding amounts are not included in overall U.S. totals. ↩︎
10. In 2025, France provided data revising prior year amounts to account for “set-aside” funding (adjusted for an HIV-share) that supports Global Fund related activities. While this funding is considered part of France’s pledge to the Global Fund, it is not counted by the Global Fund as a direct contribution and is instead included under bilateral totals in this analysis. Due to this update, amounts presented in this report will differ from prior reports. ↩︎
11. Total HIV funding from the Netherlands in 2024 includes two core contributions to UNAIDS; the first payment was provided for the 2024 contribution, while the second was a prepayment of the 2025 contribution. ↩︎
12. KFF, “The U.S. President’s Emergency Plan for AIDS Relief (PEPFAR)”, May 2026. ↩︎
13. U.S. totals represent funding amounts provided through regular appropriations only. In 2021, the U.S. Congress appropriated additional emergency supplemental funding for bilateral HIV activities and for the Global Fund to address the impacts of the COVID-19 pandemic. These emergency supplemental funding amounts are not included in overall U.S. totals. ↩︎
14. Between 2020-2023, some donor governments provided COVID-specific emergency contributions to the Global Fund and UNITAID in addition to their contributions for core activities. For the purposes of this report, these COVID-specific amounts have been excluded as they cannot be attributed to a specific area, such as HIV. ↩︎
15. In 2025, 52% of the Global Fund’s disbursements and 48% of UNITAID’s disbursements were directed to HIV activities. These percentages were applied to the full donor government contributions to these multilateral organizations to calculate the “HIV-share” (see Methodology for additional details). ↩︎
16. The U.S. has had a long-standing legislative requirement that total U.S. contributions to the Global Fund could not exceed 33% of all contributions (see “KFF – The U.S. & The Global Fund to Fight AIDS, Tuberculosis and Malaria”), which results in year-to-year fluctuations in U.S. payouts to the Global Fund depending on when other donors provide funds. However, this requirement technically expired in March when the authorization legislation ended (see “KFF - PEPFAR Reauthorization: Side-by-Side of Legislation Over Time”). ↩︎
17. U.S. totals represent funding amounts provided through regular appropriations only. In 2021, the U.S. Congress appropriated additional emergency supplemental funding for bilateral HIV activities and for the Global Fund to address the impacts of the COVID-19 pandemic. These emergency supplemental funding amounts are not included in overall U.S. totals. ↩︎
18. GDP estimates are from the International Monetary Fund’s (IMF) World Economic Outlook (WEO) Database (accessed July 2026). ↩︎

While millions of people have gained health coverage through Medicaid expansion under the Affordable Care Act (ACA) over the last decade, state decisions not to expand Medicaid continue to leave many without an affordable coverage option. In the 41 states including the District of Columbia that have adopted the expansion, adults with incomes up to 138% of the federal poverty level (FPL) are eligible for Medicaid. Medicaid expansion has led to significant coverage gains, particularly as more states adopted the expansion over the years. However, an estimated 1.2 million uninsured people in the ten states that have still not expanded remain ineligible for affordable coverage because they fall in the coverage gap—their incomes are too high for their states’ Medicaid program but too low to qualify for ACA Marketplace subsidies. Many adults in the coverage gap work or live with someone who works, are disproportionally people of color, and generally do not have dependent children.

The number of adults in the coverage gap is not expected to decline further as Medicaid changes in the 2025 reconciliation law make it less likely any state will newly adopt the expansion. The law eliminated the financial incentive included in the American Rescue Plan Act (ARPA) that was intended to encourage adoption of the expansion by non-expansion states and imposes new financial penalties on expansion states.

More broadly, policy changes in the 2025 reconciliation law and the expiration of enhanced Marketplace premium tax credits are expected to increase the number of uninsured people who do not fall in the coverage gap. Starting in January 2027, or earlier at state option, the 2025 reconciliation law requires all expansion states along with Georgia, Tennessee, and Wisconsin to condition Medicaid eligibility for individuals eligible through the expansion or waiver program on meeting work requirements. This new requirement is expected to result in significant coverage loss among expansion adults, with the Congressional Budget Office estimating that Medicaid work requirements will increase the number of uninsured individuals by 5.3 million over the next ten years. Yet these coverage losses will not increase the number of people in the coverage gap because adults who lose Medicaid because they do not meet or report work requirements continue to remain eligible for the program based on their income even if they lose Medicaid and become uninsured.

Using data from 2024, this brief estimates the number and characteristics of uninsured individuals in the ten non-expansion states who could gain coverage if Medicaid expansion were adopted.

How many people are in the coverage gap?

The coverage gap exists because not all states have adopted the ACA’s Medicaid expansion. The expansion extended Medicaid eligibility to adults ages 19-64 with incomes at or below 138% the federal poverty level (FPL), or $22,025 for an individual in 2026. Medicaid expansion covers both parents and adults without dependent children—who were previously not eligible for Medicaid. The “coverage gap” occurred because a 2012 Supreme Court ruling made Medicaid expansion optional for states, rather than the nationwide requirement Congress originally intended.

Among the ten states that have yet to adopt Medicaid expansion, an estimated 1.2 million adults fall into the coverage gap because they earn too much to qualify for their state’s Medicaid program but not enough to access ACA Marketplace subsidies (Figure 2). Because the Medicaid expansion was intended to be mandatory for all states with Marketplace coverage available for individuals above the Medicaid limit, the minimum eligibility level for subsidies in the Marketplace was set at 100% FPL. When expanding Medicaid effectively became optional, poor adults living in states that decided not to expand were left without an affordable coverage option.

In states that have not expanded Medicaid, eligibility remains limited. The median income limit for parents is 40% FPL in these states, which is $10,928 per year for a family of three in 2026. Texas has the nation’s lowest eligibility threshold for parents at 15% FPL and bars Medicaid access for parents in a family of three earning more than $4,098, or $342 per month. With the exception of Wisconsin and Georgia, which offer coverage though a waiver, non-expansion states do not provide Medicaid coverage to adults under age 65 without dependent children, regardless of income, unless they qualify on the basis of disability (Figure 3). As a result, 78% of adults in the coverage gap are adults without dependent children.

States that have not expanded Medicaid have uninsured rates nearly twice as high as states that have expanded Medicaid (14.5% vs 8.0%). Adults who are uninsured have a harder time accessing care. In 2024, nearly four in ten adults (39%) without health insurance reported delaying or forgoing health care, including physical and mental health services and prescription medication, due to cost compared to 17% of adults with insurance. Uninsured individuals are also less likely than those with insurance to receive services to treat chronic conditions.

Who is in the coverage gap and how many people could gain coverage if all states expanded Medicaid?

Most adults in the coverage gap work or live with someone who works, and they are disproportionately people of color. Six in ten adults in the coverage gap live in a family with a worker, and over four in ten are working themselves (Figure 4). Many are employed in low-wage jobs and often work for employers that do not offer affordable job-based coverage. Over half of workers in the coverage gap (58%) are employed in the service, retail, and construction industries, with common occupations including cashiers, servers, cooks, constructions labors, housekeepers, retails salespeople, and janitors. Because Medicaid eligibility levels for parents are so low in non-expansion states, even part-time work can make them ineligible. Additionally, Hispanic and Black adults make up over half (56%) of people in the coverage gap.

If all remaining states adopted Medicaid expansion, approximately 2.4 million uninsured adults would become eligible for Medicaid. This includes 1.2 million in the coverage gap, who currently have no affordable coverage option, and 1.2 million with incomes between 100% and 138% of the FPL, who are eligible for but not enrolled in Marketplace coverage (Figure 5). For adults eligible for Marketplace coverage, Medicaid would offer an affordable alternative, generally with more comprehensive benefits and lower out-of-pocket costs. While some uninsured adults who are eligible for but not enrolled in Marketplace coverage could qualify for zero- or low-premium Marketplace coverage, the expiration of the temporary enhanced premium tax credits at the end of 2025 increased premiums for most Marketplace enrollees, and fewer adults are now eligible for zero-premium plans.

How will changes in the 2025 reconciliation law affect the overall uninsured population?

Adults in the coverage gap represent a small share (4.5%) of the 26.7 million people ages 0-64 who were uninsured in 2024 (Figure 6). In the coming years, the number of adults in the coverage gap is unlikely to change substantially; however, the number of uninsured people overall is expected to increase. The Congressional Budget Office has estimated that Medicaid and Marketplace policy changes in the 2025 reconciliation law, most notably new Medicaid work requirements for adults enrolled in the Medicaid expansion, along with the expiration of the Marketplace enhanced premium tax credits, will increase the number of people who are uninsured by 14 million over the next decade. While some of the individuals who become uninsured will no longer be eligible for Medicaid or Marketplace coverage, others remain eligible but will lose coverage because they cannot meet work reporting requirements or are no longer able to afford their Marketplace premium.

This analysis uses data from the 2024 American Community Survey (ACS). The ACS provides socioeconomic and demographic information for the United States population and specific subpopulations. Importantly, the ACS provides detailed data on families and households, which we use to determine income and household composition for ACA eligibility purposes.

Medicaid and Marketplaces have different rules about household composition and income for eligibility. The ACS questionnaire captures the relationship between each household resident and one household reference person, but not necessarily each individual to all others. Therefore, prior to estimating eligibility, we implement a series of logical rules based on each person’s relationship to that household reference person in order to estimate the person-to-person relationships of all individuals within a respondent household to one another. We then assess income eligibility for both Medicaid and Marketplace subsidies by grouping individuals into household insurance units (HIUs) and calculate HIU income using the rules for each program. For more detail on how we construct person-to-person relationships, aggregate Medicaid and Marketplace households, and then count income, see the detailed Technical Appendix A.

Undocumented immigrants are ineligible for federally-funded Medicaid and Marketplace coverage. Since ACS data do not directly indicate whether an immigrant is lawfully present, we draw on the methods underlying the 2013 analysis by the State Health Access Data Assistance Center (SHADAC) and the recommendations made by Van Hook et. Al.^1,2 This approach uses the 2023 KFF/LA Times Survey of Immigrants to develop a model that predicts immigration status for each person in the sample.  We apply the model to ACS, controlling to state-level estimates of total undocumented population as well as the undocumented population in the labor force from the Pew Research Center. For more detail on the immigration imputation used in this analysis, see the Technical Appendix B.

Individuals in tax-filing units with access to an affordable offer of Employer-Sponsored Insurance (ESI) are still potentially MAGI-eligible for Medicaid coverage, but they are ineligible for advance premium tax credits in the Health Insurance Exchanges. Since ACS data do not designate policyholders of employment-based coverage nor indicate whether workers hold an offer of ESI, we developed a model that predicts both the policyholder and the offer of ESI based on the Current Population Survey (CPS). Additionally, for families with a Marketplace eligibility level below 250% FPL, we assume any reported worker offer does not meet affordability requirements and therefore does not disqualify the family from Tax Credit eligibility on the Exchanges. For more detail on the offer imputation used in this analysis, see the Technical Appendix C.

As of January 2014, Medicaid financial eligibility for most adults ages 19-64 is based on modified adjusted gross income (MAGI). To determine whether each individual is eligible for Medicaid, we use each state’s reported eligibility levels as of April 2025, updated to reflect 2026 Federal Poverty Levels. Some adults ages 19-64 with incomes above MAGI levels may be eligible for Medicaid through other pathways; however, we only assess eligibility through the MAGI pathway.³

An individual’s income is likely to fluctuate throughout the year, impacting his or her eligibility for Medicaid. Our estimates are based on annual income and thus represent a snapshot of the number of people in the coverage gap at a given point in time. Over the course of the year, a larger number of people are likely to move in and out of the coverage gap as their income fluctuates.

Starting with our estimates of ACA eligibility in 2017, we transferred our core modeling approach from relying on the Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC) to the American Community Survey (ACS). ACS includes a 1% sample of the US population and allows for precise state-level estimates as well as longer trend analyses. Since our methodology excludes a small number of individuals whose poverty status could not be determined, our ACS-based population totals appear slightly below CPS-based totals and some ACS population totals published by the Census Bureau. This difference is in large part attributable to students who reside in college dormitories. Comparing the two survey designs, CPS counts more of these individuals in the household of their parent(s) than ACS does.

Code available at https://github.com/KFFData/CPS-ACS-Analytic-Code

KFF ACA Eligibility Analysis, Technical Appendix A: Household Construction

In KFF’s estimates of eligibility for ACA coverage, income eligibility for both Medicaid and Marketplace subsidies is assessed by grouping people into “health insurance units” (HIUs) and calculating HIU income according to Medicaid and Marketplace program rules. HIUs group people according to how they are counted for eligibility for health insurance, versus grouping people according to who they live with (e.g., “households”) or are related to (e.g., “families”). HIU construction is an important step in assessing income as a share of the federal poverty line (FPL) because it impacts whose income is counted (and thus the total income for the unit) and how many people share that income (and thus the corresponding FPL to use for comparison, since FPL varies by family size). Our HIUs are designed to match ACA eligibility rules for both Medicaid and Marketplaces. Below we describe how we construct HIUs for this analysis. The programming code, written using the statistical computing package R v.4.5.2, is available at https://github.com/KFFData/CPS-ACS-Analytic-Code for people interested in replicating this approach for their own analysis.

Person to Person Relationships

We construct spousal and parent-to-child person-to-person linkage variables within each household of the microdata. The American Community Survey (ACS) includes only the relationship of each person in a household to one central reference person. Using the household reference person's known relationships to all other individuals within each household, we iterate through every pair of individuals present in each household to determine probable person to person links for possible mother, father, and spousal pairs. Our approach to determining probable family interrelationship linkages closely follows the construction documented by IPUMS-USA with the notable exception of unmarried partner relationships.⁴ We intentionally diverge from IPUMS-USA because the presence of an unmarried partner relationship does not impact federal program eligibility. Among individuals designated as married with a spouse present in the household, our constructed spousal pointer matches the IPUMS SPLOC variable 99% of the time in the 2013 microdata. Our construction of mother and father pointers match the IPUMS MOMLOC, POPLOC, MOMLOC2, and POPLOC2 variables for more than 99% of all person-records.

Family Aggregation

Separate from person-to-person linkage variables, we assemble individual records into family units reproducing the Census Bureau's Family Poverty Ratio (POVPIP) variable. Although the Census Bureau does not include a unique family identifier on the ACS microdata, we approximate the groupings used to generate the ACS income-to-poverty ratio variable with the following steps:

1. Both non-relatives of the household reference person (RELP of 11-17) and all individuals in non-family households (HHT of 4-7) are categorized as single-person families.
2. Married couples and other family households without subfamilies (PSF of 0) are categorized into single-family households.
3. Married couples and other family households with subfamilies (PSF of 1) are categorized based on their subfamily number (SFN).

This family identifier is used in estimating family-wide statistics, such as the percent of the uninsured Americans in a family below poverty or the count of Medicaid-enrollees with one or more workers in their family. This family aggregation matches the groupings used to determine the income-to-poverty ratio variable, and estimates of health insurance presented by family poverty categories align with Census Bureau publications based on the ACS.⁵ Since many family members obtain health coverage separately from one another (for example, an elderly parent cohabiting with their working-age child might hold Medicare coverage and Employer Sponsored Insurance, respectively), descriptive statistics focused on family attributes rely on this family identifier but Medicaid and Marketplace eligibility determinations do not.

Overview of KFF-HIUS 

We construct two different HIUs for everyone in the sample: a Medicaid HIU and a Marketplace HIU. We use two HIUs because the rules for counting families and income differ between the two programs. For example, in Medicaid, children with unmarried parents have both parents’ income counted toward their income, whereas under Marketplace rules, only the income of the parent who claims the child on his/her taxes counts. In another example, certain tax dependents (e.g., a parent) are treated differently for Medicaid eligibility than they are for Marketplace eligibility. To account for these rules, we developed an algorithm for sorting people into HIUs. We construct HIUs and HIU incomes separately for each person in a household and take into account the family relationships and income of the other people in the person’s household. People in the same household or in the same family may not have the same HIU composition or income for determining either Medicaid eligibility or eligibility for tax credits.

In simplest terms, the HIU algorithm sorts people into tax filing units. For all people in the data set, the algorithm assesses whether they are likely to be a tax filer themselves and, if so, who they are likely to claim or, if not, who is likely to claim them. It also captures whether someone is neither a tax filer nor claimed as a dependent by someone else. Importantly, the HIU construction considers all relationships for each person within the household. This step is particularly important in correctly classifying people in non-nuclear families (e.g., households with more than one generation, with unmarried partners, or with relatives outside the nuclear family such as an aunt or uncle), which may contain either one or multiple tax filing units.

In counting income for both Medicaid and Marketplace HIUs, we use modified adjusted gross income (MAGI), corresponding to the ACA rules. MAGI differs from total income in that some sources of income (e.g., cash assistance payments from TANF or SSI) do not count toward MAGI. We calculate HIU income as a share of poverty using the Health and Human Services Poverty Guidelines.⁶

For a small number of people, Medicaid HIU income as a share of poverty does not match Marketplace HIU income as a share of poverty due to the different rules between the programs. This analysis first calculates Medicaid HIU and classifies anyone who meets Medicaid eligibility into that category (including most individuals below 138% FPL in the Medicaid expansion states). We then calculate Marketplace HIU; anyone meeting subsidy eligibility is grouped into that category (above Medicaid and also above 100% FPL up to 400% FPL for most individuals). This approach follows the eligibility rules in the ACA, which specify that people are eligible for tax credits only if they are ineligible for Medicaid.

Steps in Calculating KFF-HIUS

Before we group people into HIUs, we first calculate annual MAGI for each respondent. We compare each person’s income to IRS filing requirements for being a tax filer⁷ and for being a qualifying relative claimed by someone else.⁸

We then group people into HIUs. We begin this process by grouping everyone within a household who is related into “cohabitating families.” Cohabitating families include all family relations; they also include unmarried cohabitating partners and relatives of each cohabitating partner.

Within each cohabitating family, we assess whether any individual is eligible to claim any other individual as a tax dependent. People are eligible to claim others as tax dependents if their income is above the IRS filing threshold for a head of household or, if married, for a married couple. People are eligible to be claimed by others if (a) they are a child (under age 19 or, for tax credits, 23 if a full-time student), and someone else in the cohabitating family has at least twice their income, or (b) they are below the limit to be a tax filer, have income below the qualifying relative limit, and someone else in the cohabitating family has at least twice their income. Within each cohabitating family, we assess who is likely to claim whom, using the assumptions that:

• People who are claimed by others are more likely to be claimed by close relatives (e.g., a parent) than by others (e.g., a grandparent).
• Married couples (who file) file jointly
• If more than one person in a cohabitating family is eligible to claim others within that cohabitating family, the wealthiest person claims the eligible dependents.

Once we determine who within the cohabitating family is likely to claim each other, we know the HIU size and are able to apply income rules for the HIU. We apply Medicaid and Marketplace rules for whose income counts in calculating Medicaid HIUs and Marketplace HIUs, respectively.⁹ People who are filers but are not eligible to claim someone else or to be claimed by someone else are an HIU of 1. People who are not filers and are not claimed by filers have their HIU size and income counted according to Medicaid non-filer rules.¹⁰

Inflation Factors

In order to determine ACA eligibility during calendar year 2024, we compared tax filing unit income against the most current premiums available, for open enrollment 2026.¹¹ We relied on the Bureau of Labor Statistics Employment Cost Index (ECI), Private Wages and Salaries to inflate the income of each HIU by approximately 7.0% to align 2024 incomes to 2026 premiums.¹² Since most state Medicaid eligibility determinations through the MAGI pathway are calculated as a percent of HHS Poverty Guidelines for that year and not a fixed dollar amount, inflation was not necessary to assess the Medicaid eligibility of individuals.

After inflating 2024 tax filing unit incomes to match 2026 premiums, we similarly inflated 2024 IRS thresholds for both filing requirements¹³ and for qualifying relative tests¹⁴ by the same factor so that these thresholds aligned with the inflated income amounts.

Limitations

As with any analysis, there are some limitations to our approach due to the level of detail that we can obtain from available survey data. Key limitations to bear in mind include:

• We currently are not able to appropriately group anyone who lives outside the household with a household that claims them as a tax dependent. For example, we are not able to connect students living away from home or children with a non-custodial parent with the people who may be claiming them (and whose income should count to their HIU). We are also not able to determine married people who file separately.
• To group people into tax filing units, we have to make assumptions about how people are likely to file their taxes. We assume that tax filers claim qualifying relatives they are able to claim. We make this assumption based on the fact that Medicaid and Marketplace eligibility rules are determined not by who is actually claimed on the tax return but by who is allowed to be claimed. However, people may sort themselves into different tax filing units than we estimate.

KFF ACA Eligibility Analysis, Technical Appendix B: Immigration Status Imputation

To impute documentation status, we draw on the methods underlying the 2013 analysis by the State Health Access Data Assistance Center (SHADAC) and the recommendations made by Van Hook et. al..^15,16 This approach uses the 2023 KFF/LA Times Survey of Immigrants to develop a model that predicts immigration status for each person in the sample.¹⁷ We apply the model to a second data source, controlling to state-level estimates of total undocumented population as well as the undocumented population in the labor force from the Pew Research Center.¹⁸ Below we describe how we developed the regression model and applied it to the American Community Survey (ACS). We also describe how the model may be applied to other data sets. The programming code, written using the statistical computing package R v.4.5.2, is available at https://github.com/KFFData/CPS-ACS-Analytic-Code for people interested in replicating this approach for their own analysis.

Data Sources

We used the 2023 KFF/LA Times Survey of Immigrants data to build the regression model. The 2023 Survey of Immigrants dataset contains questions on citizenship and legal status at the person level. The KFF/LA Time Survey of Immigrants¹⁹ is a probability-based survey exploring the immigrant experience in the U.S. and draws on three different sampling frames including an address-based sample (ABS), a random digit dial (RDD) sample of pre-paid cell phone numbers, and callbacks to an RDD sample in which the individual did not speak English or Spanish. The survey includes interviews with 3,358 immigrant adults and was offered in ten different languages.

The regression model is designed to be applied to other datasets in order to impute legal immigration status in surveys that do not ask about migration status. The code mentioned above includes programming to apply the model to either the Survey of Income and Program Participation (SIPP) Core files, ACS, or the Current Population Survey (CPS). Because the SIPP Core file contains different survey questions and variable specifications from the ACS and CPS, we create unique regression models to apply the model to each dataset. For the analysis underlying this brief and other KFF estimates of eligibility for ACA coverage, we apply the regression model to the 2013 ACS and then each subsequent year of the ACS.

Due to underreporting of legal immigration status in survey datasets, in imputing immigration status we control to state and national-level estimates of the total undocumented population and also the undocumented population in the labor force from the Pew Research Center. Pew reports these estimates for all states and the District of Columbia.²⁰

Construction of Regression Model

We use the 2023 Survey of Immigrants to create a binomial, dependent variable that identifies a respondent as a potential unauthorized immigrant. The dependent variable is constructed based on the following factors:

1. Respondent was not a United States (US) citizen,
2. Respondent did not have permanent resident status or a valid work or student visa, , and
3. Respondent does not have other indicators that imply legal status.²¹

We use the following independent variables to predict unauthorized immigrant status:

1. Year of US entry,
2. Job industry classification,
3. State of residence,
4. Household Income,
5. Ownership or rental of residence,
6. Number of occupants in the household (< or >= six occupants),
7. Whether all household occupants are related,
8. Health insurance coverage status,
9. Country of birth,
10. Sex, and
11. Ethnicity.

The regression model was sub-populated to remove respondents who could not be considered unauthorized. People who could not be considered unauthorized include people who are US citizens or have other indicators that imply legal status.

Imputing Unauthorized Immigrants in Other Datasets

We use the Pew estimates as targets for the total number of unauthorized immigrants that the imputation generates. We first apply this strategy to the 2013 ACS, which contains health insurance information prior to the ACA's coverage expansions. We stratify the targets by state and the District of Columbia and by participation in the labor force. We impute immigration status within each of these 102 strata.²²

To generate the imputed immigration status variable, we first calculated the probability that each person in the dataset was unauthorized based on the 2023 Survey of Immigrants regression model. Next, we isolated the dataset to each individual stratum described above. Within each stratum, we sampled the data using the probability of being unauthorized for each person. After sampling, we summed the person weights until reaching the Pew population estimate for each stratum. The records that fell within the Pew population estimate were considered to be unauthorized immigrants. We repeated the process of sampling using the probability of being unauthorized and subsequently summing the person weights to reach Pew targets five times, creating five different unauthorized variables per record. These five imputed authorization status variables were then incorporated into a standard multiple imputation algorithm, closely matching the imputed variable analysis techniques used by the Centers for Disease Control and Prevention for the National Health Interview Survey.²³

To easily apply the regression model to other data sets, we created a function that applies this approach to a chosen data set. The function first loads the dataset of choice, then standardizes the data to match the independent variables from the 2023 Survey of Immigrants regression model, and finally applies the multiple imputation to generate a variable for legal immigration status.

KFF ACA Eligibility Analysis, Technical Appendix C: Imputation of Offer of Employer-Sponsored Insurance

An integral part of determining ACA eligibility is assessing whether workers without employer-sponsored insurance (ESI) hold an offer through their workplace that they decline to take up. In most cases, an affordable offer of ESI disqualifies members of the tax filing unit of the worker from receiving subsidized coverage on the ACA Health Insurance Marketplace. The American Community Survey (ACS) does not ask about employer offers of ESI; however, the Current Population Survey Annual Social and Economic Supplement (CPS-ASEC) includes questions about whether each worker received an offer of ESI from his or her employer at the time of interview. We use the CPS-ASEC offer of ESI variable to inform a regression-based multiple imputation of whether each tax filing unit constructed in the ACS had at least one offer at work, and also assess affordability for the employee and, separately, for any potential dependents within the unit. Since the health insurance coverage variables available in the CPS-ASEC 2025 capture sources of coverage at any point during calendar year 2024 (versus at the time of survey, as with the offer rate variable), a subset of sampled individuals had a change in their employer-based coverage status across the two distinct time periods.²⁴ Therefore, among workers who potentially experienced a shift in offer status across the two time periods, we recoded or imputed offer rates in 2024 using the offer status in 2025. After constructing this revised offer variable for workers in CPS, we aggregated the results at the tax filing unit level to create a prediction model to apply to the ACS. Below we describe these recodes and imputation. The programming code, written using the statistical computing package R v.4.5.2, is available at https://github.com/KFFData/CPS-ACS-Analytic-Code for people interested in replicating this approach for their own analysis.

Recoding and Imputing Offer Rate Data in the CPS

As a first step in our analysis, we divided CPS-ASEC survey respondents into five distinct groups:

1. All individuals who did not work during 2024 and also did not hold an offer of ESI in 2025 were assumed not to have an offer in 2024.
2. All individuals who reported being an ESI policyholder (that is, anyone reporting having taken-up their offer of ESI) during 2023 and also reported holding an offer of ESI during early 2025 were assumed to have an offer in 2024.
3. All workers in 2024 who held their own ESI policies during 2023 but then reported not holding an offer during 2025 were re-coded as holding an offer of ESI in 2024.
4. All non-workers during 2024 who reported holding an offer during 2024 were re-coded as not holding an offer of ESI in 2024.
5. Some workers during 2024 who did not report being ESI policyholders but did report holding an offer of ESI during early 2025 were imputed to not have an offer of ESI during 2024.

For many groups, including those in groups (1) and (2) listed above, the offer status did not change across the two time periods. In contrast, we recoded offer status for people in groups (3) and (4): every non-offered worker in group (3), which includes people who held ESI policies in their own name in 2024, were considered to have an offer of ESI in 2024, and offered workers in group (4), which includes people who did not work themselves in 2024, were considered to not have their offer of ESI in 2024. Last, we implemented a probability-based random sample imputation of offers of ESI for people in group (5), described in more detail below. Only a subset of the group was re-coded from holding an offer in 2025 to not holding an offer in 2024.The number of workers selected from this population was equal to the population size of (3) subtracted by the population size of (4), thereby assuming an unchanging offer rate for the total worker population across the period.

Imputing Offer Rates for CPS Respondents with Ambiguous Offer Rate Status

The CPS-ASEC worker-level regression model was designed to be applied to a single dataset where ESI offer status is known at one point in time but not another. The code mentioned above includes programming to apply the model to the Current Population Survey (CPS-ASEC) (for years 2014 on). For the analysis underlying KFF’s current estimates of ACA eligibility, we apply the regression model to workers in the 2025 CPS-ASEC.

• We use the 2025 point-in-time worker offer variable provided by the US Census Bureau²⁵ to create a binomial, dependent variable that identifies a respondent as a recipient of an offer of employer-sponsored insurance at his or her workplace in early 2025. The dependent variable was constructed at the worker-level based on individuals not holding their own ESI policy at time of interview and also reporting an ESI offer or eligibility to be covered that was then voluntarily declined.

We use the following independent variables to predict offer status in 2024 among workers not covered by their own ESI during both 2024 and early 2025 but potentially holding an offer of ESI in 2024:

• Any public coverage,
• Any nongroup coverage,
• Worker earnings among all jobs,
• Full-time versus part-time status,
• Age of worker,
• Work within the construction industry.

The regression model was sub-populated to remove respondents already covered by their own ESI and also to remove non-workers. Since this imputation does not account for the affordability of the offer or whether it meets the minimum value test, we included an assumption that workers in tax filing units with a MAGI below 250% FPL do not hold affordable offers of ESI and therefore might be eligible to purchase subsidized coverage on the Exchanges.²⁶

As mentioned above, we assume an unchanging offer rate for the total worker population across the two time periods. We determined the needed size of the population to impute by subtracting the population of (4) from the population of (3) to ensure an equivalent number of offers were gained and lost. This left only workers who reported holding an offer of ESI during early 2025, since (3) represented a larger count of workers than (4). We then calculated the probability that each worker in the dataset was offered ESI during calendar year 2024 based on our 2025 CPS-ASEC regression model. Next, we selected workers within the potential population (5) using the sampling probabilities resultant from our model.

Construction and Application of ACS Regression Model

For the analysis underlying KFF estimates of ACA eligibility, we construct a prediction model of having an offer of ESI using the 2025 CPS-ASEC and then apply this regression to tax filing units in the 2024 ACS to estimate who has an ESI offer in ACS.

We aggregate the worker offer variables constructed the 2025 CPS-ASEC as described above to create a binomial, dependent variable that identifies each tax filing unit as either holding or not holding an affordable offer of employer-sponsored insurance.

We use the following independent variables to predict offer status among tax filing units:

• Any senior citizen in the household,
• Oldest member of the tax-filing unit,
• Any member of the tax-filing unit has employer-sponsored insurance coverage,
• Any member of the tax-filing unit has nongroup coverage,
• Any uninsured individuals in the tax filing unit,
• Share of adults working full-time and part-time, and
• Highest worker earnings.

Since the imputation of documentation status (discussed in Technical Appendix B) required a multiply-imputed approach, this secondary imputation and subsequent worker sampling was only conducted once per implicate, keeping the number of ACS implicates to five.

1. State Health Access Data Assistance Center. 2013. “State Estimates of the Low-income Uninsured Not Eligible for the ACA Medicaid Expansion.” Issue Brief #35. Minneapolis, MN: University of Minnesota. Available at: http://www.rwjf.org/content/dam/farm/reports/issue_briefs/2013/rwjf404825. ↩︎
2. Van Hook, J., Bachmeier, J., Coffman, D., and Harel, O. 2015. “Can We Spin Straw into Gold? An Evaluation of Immigrant Legal Status Imputation Approaches” Demography. 52(1):329-54. ↩︎
3. Non-MAGI pathways for nonelderly adults include disability-related pathways, such as SSI beneficiary; Qualified Severely Impaired Individuals; Working Disabled; and Medically Needy. We are unable to assess disability status in the ACS sufficiently to model eligibility under these pathways. However, previous research indicates high current participation rates among individuals with disabilities (largely due to the automatic link between SSI and Medicaid in most states, see Kenney GM, V Lynch, J Haley, and M Huntress. “Variation in Medicaid Eligibility and Participation among Adults: Implications for the Affordable Care Act.” Inquiry. 49:231-53 (Fall 2012)), indicating that there may be a small number of eligible uninsured individuals in this group. Further, many of these pathways (with the exception of SSI, which automatically links an individual to Medicaid in most states) are optional for states, and eligibility in states not implementing the ACA expansion is limited. ↩︎
4. Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas, and Matthew Sobek. IPUMS USA: Version 8.0 [dataset]. Minneapolis, MN: IPUMS, 2018. https://doi.org/10.18128/D010.V8.0 For a detailed description of how IPUMS constructs family interrelationships variables, see https://usa.ipums.org/usa/chapter5/chapter5.shtml ↩︎
5. According to the Public Use Microdata Sample (PUMS) documentation, "Estimates generated with PUMS microdata will be slightly different from the pretabulated estimates for the same characteristics published on data.census.gov. These differences are due to the fact that the PUMS files include only about two-thirds of the cases that were used to produce estimates on data.census.gov, as well as additional PUMS edits." ↩︎
6. Medicaid eligibility in 2026 is based on 2026 poverty guidelines, available at: U.S. Department of Health and Human Services, Office of The Assistant Secretary for Planning and Evaluation, Poverty Guidelines. https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines. Tax credit eligibility in 2026 is based on 2025 poverty guidelines, available at: U.S. Department of Health and Human Services, Office of The Assistant Secretary for Planning and Evaluation, 2025 Poverty Guidelines https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references.  ↩︎
7. See Internal Revenue Service, Publication 501, Table 1.2024: Filing Requirements Chart for Most Taxpayers. Available at: https://www.irs.gov/pub/irs-prior/p501--2024.pdf. ↩︎
8. See Internal Revenue Service, Publication 501, Qualifying Relative. Available at: https://www.irs.gov/pub/irs-prior/p501--2024.pdf. ↩︎
9. A detailed explanation of Medicaid and Marketplace income counting rules can be found in Center on Budget and Policy Priorities webinar available at: http://www.healthreformbeyondthebasics.org/wp-content/uploads/2013/08/Income-Definitions-Webinar-Aug-28.pdf. ↩︎
10. A detailed explanation of Medicaid and Marketplace HIU size calculations can be found in the Center on Budget and Policy Priorities webinar available at http://www.healthreformbeyondthebasics.org/wp-content/uploads/2013/08/Household-Definitions-Webinar-7Aug13.pdf. ↩︎
11. This is the same underlying data as the 2026 Health Insurance Marketplace Calculator. Available at: https://www.kff.org/interactive/subsidy-calculator/. ↩︎
12. See Congressional Budget Office, Economic Projections. Available at: https://www.cbo.gov/system/files/2025-09/51135-2025-09-Economic-Projections.xlsx. ↩︎
13. See Internal Revenue Service, Publication 501, Table 1.2024: Filing Requirements Chart for Most Taxpayers. Available at: https://www.irs.gov/pub/irs-prior/p501--2024.pdf. ↩︎
14. See Internal Revenue Service, Publication 501, Qualifying Relative. Available at: https://www.irs.gov/pub/irs-prior/p501--2024.pdf. ↩︎
15. State Health Access Data Assistance Center. 2013. “State Estimates of the Low-income Uninsured Not Eligible for the ACA Medicaid Expansion.” Issue Brief #35. Minneapolis, MN: University of Minnesota. Available at: http://www.rwjf.org/content/dam/farm/reports/issue_briefs/2013/rwjf404825. ↩︎
16. Van Hook, J., Bachmeier, J., Coffman, D., and Harel, O. 2015. “Can We Spin Straw into Gold? An Evaluation of Immigrant Legal Status Imputation Approaches” Demography. 52(1):329-54. ↩︎
17. This data source is a change from previous KFF analyses, which used microdata from the 2008 Panel of the Survey of Income and Program Participation (SIPP) ↩︎
18. This data source is a change from previous KFF analyses, which used estimates from the Department of Homeland Security. ↩︎
19. More information about the survey methods is available at https://www.kff.org/report-section/understanding-the-u-s-immigrant-experience-the-2023-kff-la-times-survey-of-immigrants-methodology/ ↩︎
20. Pew updates these estimates periodically. We use the most recent estimates available at the time of our analysis, and in some cases incorporate estimates received from correspondence with researchers at Pew prior to their publication - however we do not release these numbers ourselves. We draw on Pew directly for all published data and interpolate years missing from their trend. Our analysis uses the year applicable to the year for the data sets to which we apply the regression model. The most recent estimates as of the time of our analysis were: J Passel, J Krogstad. U.S. Unauthorized Immigrant Population Reached a Record 14 Million in 2023. (Pew Research Center), August 2025. Available at: https://www.pewresearch.org/race-and-ethnicity/2025/08/21/u-s-unauthorized-immigrant-population-reached-a-record-14-million-in-2023/. ↩︎
21. Indicators that imply legal status include: (i) respondent entered the US prior to 2000, (ii) respondent is enrolled in Medicare or military health insurance, or (iii) respondent reports Medicaid coverage but resides in a state that does not offer coverage to the undocumented population beyond CHIP’s From-Conception-to-End-of-Pregnancy (FCEP) option. ↩︎
22. For more information, see SHADAC 2013, footnote 1. The table created for this function contains estimates of the undocumented across 2013, 2023, and 2024. ↩︎
23. For more detail, see documentation available at: National Health Interview Survey. 2024 Imputed income technical document. Available at: https://www.cdc.gov/nchs/nhis/documentation/2024-nhis.html. ↩︎
24. For example, anyone who did not work during 2024 who then held an offer of ESI in early 2025 would appear incongruous in our CPS-based eligibility model.  In the other direction, workers covered by health insurance through their own employer in 2024 who lost their offer of ESI during the early months of 2025 (perhaps due to a job change) would also appear incongruous due to the discrepancy across the two time periods. ↩︎
25. Available at: https://www.census.gov/data/datasets/time-series/demo/health-insurance/cps-asec-research-files.html. For more detail about these microdata, see: J. Abramowitz, B. O'Hara.  New Estimates of Offer and Take-up of Employer-Sponsored Insurance (US Census Bureau), 2016.  Available at: https://www.census.gov/library/working-papers/2016/demo/Abramowitz-2016.html. ↩︎
26. For an explanation of affordability, see: KFF. Employer Responsibility Under the Affordable Care Act. February 2024. Available at: https://www.kff.org/infographic/employer-responsibility-under-the-affordable-care-act/. ↩︎