Introduction

The Temporary Protected Status (TPS) program was established in 1990 and allows the Secretary of Homeland Security to designate a country for TPS if there is an ongoing armed conflict, environmental disaster,  epidemic, or other conditions that may “temporarily prevent the country’s nationals from returning safely.” Eligible individuals from TPS designated countries can receive TPS, which protects them from deportation and allows them to work in the U.S. for temporary, extendable periods.

The Trump administration has carried out numerous immigration policy changes focused on increasing immigration enforcement and reducing immigration into the country, including seeking to end TPS designations for many countries. Further, under the 2025 reconciliation law, TPS holders will lose access to subsidized ACA Marketplace coverage starting January 1, 2027, and Medicare starting no later than January 4, 2027, while they already are ineligible for Medicaid and the Children’s Health Insurance Program (CHIP). This brief provides an overview of the TPS program, recent changes to TPS designations announced by the Trump administration, and potential implications of loss of TPS for individuals on health and health care. It includes KFF analysis of Congressional Research Service reports to assess changes in the number of individuals and countries with TPS designations over time and of 2024 American Community Survey (ACS) data to estimate the number of noncitizen immigrant workers likely to have TPS, who could be impacted by the elimination of TPS designations.

According to federal data, as of March 2025 (the latest data available), nearly 1.3 million individuals from 17 countries had TPS.  As of March 2026, the Trump administration has ended or attempted to end TPS designations for 13 of 17 countries with active TPS designations at the time he took office, which could impact over one million TPS holders. However, implementation of some of these changes is subject to ongoing litigation. Individuals who lose TPS lose their work authorization and become at risk for deportation, which may negatively impact their health and access to health coverage and care. Moreover, termination of TPS designations could negatively impact the U.S. economy and workforce by putting hundreds of thousands of immigrant workers at risk of deportation. Immigrants likely to have TPS from 16 of the 17 countries for which data are available made up about 740,000 workers ages 18 and older in the U.S. as of 2024, including about 53,000 workers in the health care industry.

Overview of the TPS Program

The TPS program was established under the Immigration Act of 1990 to allow eligible immigrants from designated countries to live and work in the U.S. The program is administered by U.S. Citizenship and Immigration Services (USCIS) within the Department of Homeland Security (DHS). DHS has the authority to designate a country for TPS for periods of 6 to 18 months and can extend these periods if conditions in the country continue to prevent its nationals from returning safely, such as due to armed conflict or environmental disasters. TPS provides immigrants with employment authorization and protection from deportation but it does not provide a pathway to citizenship. A TPS holder can only obtain permanent status by separately qualifying for another immigration status, such as lawful permanent residence through a family-based or employment-based visa petition.

As of March 2025, close to 1.3 million noncitizen immigrants from 17 countries had TPS with five countries, Venezuela, Haiti, El Salvador, Ukraine, and Honduras, accounting for approximately 97% of all recipients. The remaining 12 countries include over 39,000 individuals with TPS. The number of people with TPS has grown in recent years, from under 500,000 in 2017 to about 1.3 million in 2025, driven by new country designations, particularly for Venezuela and Ukraine, as well as redesignations for countries like Haiti that continue to face ongoing humanitarian crises (Figure 1).

Recent Changes to TPS Designations

The Trump administration has ended or attempted to end TPS designations for 13 of 17 countries that had designations when he took office, which could impact about one million TPS holders, but implementation is subject to ongoing litigation (Appendix Table 1). As of March 31, 2026, termination of TPS designations for Afghanistan, Cameroon, Honduras, Nepal, Nicaragua, and certain Venezuelan TPS holders had already taken effect, impacting close to 320,000 TPS holders from these countries. Termination of TPS designation for Yemen is expected to take effect in May 2026, and termination of TPS designation as well as work authorization for remaining Venezuelan TPS holders is expected to take effect in October 2026, which could impact over 350,000 immigrants from these countries. Further, the Trump administration has taken steps to end TPS designations for Burma (Myanmar), Ethiopia, Haiti, Somalia, South Sudan, and Syria, but these terminations were on hold as of March 31, 2026, due to court challenges. If allowed to proceed, these terminations could lead to an additional 330,000 immigrants losing TPS status.

Potential Implications of Loss of TPS

Individuals who lose TPS lose their work authorization and become at risk of deportation, which may negatively impact their access to health coverage and care. The Trump administration’s attempts to end TPS designations for 13 of 17 countries put a vast majority of TPS holders at risk of losing their status and becoming subject to deportation as well as losing access to health coverage. As of 2026, TPS holders are eligible for subsidized ACA and Medicare coverage if they meet other program eligibility requirements. They are not eligible for Medicaid and CHIP. TPS holders who lose their status would become undocumented and lose access to any federally funded coverage. Under longstanding policy, undocumented immigrants are ineligible for all forms of federally funded health coverage including Medicaid, CHIP, Medicare, and coverage through the ACA Marketplace. Further, loss of TPS would result in immigrants losing their work authorization, leading to employment loss and, consequently, potential loss of access to employer-sponsored health coverage. At the same time, lost income due to job loss may make it difficult for impacted immigrants to afford health care. Based on KFF analysis of ACS data, as of 2024, over four in ten (44%) of likely TPS workers 18 and older from 16 of 17 countries with active TPS designations at the time had employer-sponsored health coverage compared to 45% of other noncitizen workers and 70% of U.S. citizen workers 18 and older.

Loss of TPS may also negatively affect people’s health by increasing their immigration-related worries and making them more reluctant to access health care. People losing TPS status become undocumented, putting them at risk for deportation and likely increasing their immigration-related worries. Data from the 2025 KFF Survey of Immigrants show that over three in four (77%) of likely undocumented immigrants say they have experienced negative health impacts due to immigration-related worries since January 2025, and about half (48%) say that they have avoided seeking medical care since January 2025 due to immigration-related concerns.

Termination of TPS designations may also have negative impacts on the U.S. workforce, which included about 740,000 likely TPS workers overall as of 2024. KFF analysis of ACS data show that, as of 2024, noncitizen immigrants likely to have TPS from 16 of the 17 countries with active designations at the time made up about 740,000 workers in the U.S., including about 53,000 health care workers. These include noncitizen immigrants ages 18 and older who were born in a country with an active TPS designation as of December 2024 and whose year of entry in the U.S. is on or before the most recent continuous residence requirement for their country as specified by USCIS (see methods for more details). Data for immigrants from South Sudan were not available separately in ACS. Among noncitizen immigrants ages 18 and older likely to have TPS from the 16 countries with data available, about three in four (74%) report being employed compared to about two-thirds (67%) of other noncitizen immigrants and about six in ten (62%) U.S. citizens ages 18 and older.

Appendix

CMS recently finalized policies as part of the 2027 Medicare Advantage final rule that both enhance consumer protections and roll back changes to the Medicare Advantage program that were intended to protect consumers. These changes have gotten less attention than payment issues and changes to the star ratings system, which also affect plan payments, but could have implications for Medicare beneficiaries (See Table 1):

• CMS will enhance some consumer protections by requiring Medicare Advantage plans to post eligibility criteria for Special Supplemental Benefits for the Chronically Ill (SSBCI), making it easier for prospective enrollees to assess their eligibility for these benefits, which include food and produce, pest control, and transportation for non-medical needs, among others. CMS also added guardrails for debit cards issued by plans to administer supplemental benefits, so enrollees can better understand how to use these cards to obtain their benefits and also to prevent the purchase of non-covered items.
• CMS rolled back some changes to the Medicare Advantage program that were intended to protect consumers, including rescinding a requirement that plans notify enrollees of unused supplemental benefits mid-year, as well as eliminating a number of marketing requirements, such as provisions aimed at increasing the separation between marketing activities from educational events and a prohibition on the use of superlatives in marketing materials, and removes the State Health Insurance Assistance Programs (SHIPs) from the list of resources that brokers must offer to beneficiaries for further information during sales calls.
• In addition, CMS did not finalize a proposal that would have modified a special enrollment period to make it easier for enrollees to switch coverage if one of their providers is no longer part of their Medicare Advantage’s plan network.

CMS Finalized a Few Changes to the Medicare Advantage Program That Enhance Some Consumer Protections

Improving SSBCI Eligibility Transparency. Medicare Advantage plans offer supplemental benefits to Medicare Advantage plan enrollees, such as dental, vision, and hearing, which are considered primarily health related (e.g., preventing or treating an illness). Beginning in 2020, Medicare Advantage plans have also been able to offer supplemental benefits that are not primarily health related for chronically ill beneficiaries, known as Special Supplemental Benefits for the Chronically Ill (SSBCI). These benefits include food and produce, general supports for living (i.e. assistance with housing and utilities), pest control, and transportation for non-medical needs, among others. To receive these benefits, Medicare Advantage enrollees must have one or more comorbid and medically complex chronic conditions that meet all of the following criteria:

• is life threatening or significantly limits the overall health or function of the enrollee;
• has a high risk of hospitalization or other adverse health outcomes; and
• requires intensive care coordination.

Additionally, Medicare Advantage plan must determine that the benefit has a reasonable expectation of improving or maintaining the health or overall function of the chronically ill enrollee.

The final rule requires Medicare Advantage plans to post on their websites the eligibility criteria they use to determine whether an enrollee qualifies for SSBCI to increase transparency for potential enrollees, including both the criteria for meeting the “chronically ill” definition as well as the specific criteria for each benefit. Previously, plans were not required to post eligibility criteria publicly. CMS noted that in response to a prior rule, they had received many comments requesting that plans post their specific SSBCI criteria on a public-facing website. CMS expects this change will provide greater transparency for Medicare Advantage enrollees and improve their ability to assess whether they are eligible for these benefits and make an informed decision when they are deciding whether to enroll in a plan.

Moreover, CMS added regulatory language to ensure plans apply the eligibility criteria to Medicare Advantage enrollees in an objective and consistent manner. The rule clarifies that Medicare Advantage plans must verify all statutory criteria for "chronically ill" status through an objective process such as a health risk assessment or a claims review, rather than allowing self-attestation alone.

Enhancing Guardrails for Debit Cards that Administer Supplemental Benefits. Medicare Advantage plans are permitted to use debit cards to administer supplemental benefits, such as helping cover the cost of dental or vision services, the purchase of over-the-counter products, or the purchase of food and produce at participating retailers. CMS requires that Medicare Advantage plans administer these benefits in a way that ensures the debit card only be used towards plan-covered items and services. CMS noted, however, that enrollees frequently express confusion about what can be purchased with their plan debit card, and that stakeholders have raised concerns that these cards could be used to purchase items that are not covered by Medicare Advantage plans, particularly at large retailers. CMS also indicated that debit cards may be subject to fraud in the absence of stronger guardrails applied to non-covered items.

The final rule codifies existing regulations and adds requirements regarding the use of debit cards for supplemental benefits. Beginning in 2027, Medicare Advantage plans that choose to use debit cards to administer supplemental benefits must provide cards that are electronically linked to plan-covered benefits through a real-time identification mechanism that verifies eligibility at the point of sale. CMS states that real-time verification will ensure ease of access to benefits, increase transparency, and help eliminate fraud by preventing unauthorized purchases of non-covered items. Plans are also required to provide instructions to enrollees on how to use the debit card, provide customer support service to enrollees who have questions about how to use the debit card, and maintain an alternative reimbursement process for circumstances where enrollees are not able to use their debit card. CMS explained that it expects these changes will make Medicare Advantage enrollees more aware of their debit card benefits and how to use them.

The rule does not finalize a proposed change that would have prohibited marketing materials from listing the dollar value of supplemental benefits or the method by which these benefits are administered (e.g., debit cards or “Medicare flex cards”). In the proposal, CMS raised concerns with marketing tactics related to debit cards, including that some Medicare Advantage plans had been marketing the debit cards in inaccurate and misleading ways, using terms like "flex card" with an enticing dollar value attached to them, which might imply enrollees will automatically receive unrestricted spending money just by enrolling in the plan. However, CMS declined to finalize this proposal, citing concerns that this change would reduce informed decision-making before beneficiaries enroll in a plan.

CMS Also Rolled Back Changes to the Medicare Advantage Program That Were Intended to Protect Consumers

Mid-Year Supplemental Benefits Notice Rescinded. Medicare Advantage plans offer an array of supplemental benefits, but there is little data yet available to examine how frequently enrollees are using the benefits available to them. Medicare beneficiaries often highlight the availability of extra benefits as a reason they choose to enroll in Medicare Advantage plans, and CMS has also observed beneficiaries make enrollment decisions on these benefits, but that enrollees are often unaware of the benefits available to them and are not using them. The April 2024 final rule required Medicare Advantage plans to send enrollees a mid-year notice, between June 30 and July 31 of each plan year, listing any supplemental benefits the enrollee had not yet used during the first six months of the year, which was set to take effect January 1, 2026.

CMS rescinded this requirement before it took effect, citing several reasons: more recent survey data showing that 70 percent of Medicare Advantage enrollees reported using at least one supplemental benefit in the past year, which CMS suggests means beneficiaries are aware of these benefits, (though CMS notes there are still data gaps on utilization of these benefits); the administrative and financial burden on plans, particularly on smaller Medicare Advantage plans; and that this information is duplicative of information in the Annual Evidence of Coverage document that is already sent to enrollees. They also note this recission is consistent with the administration’s priorities to reduce unnecessary regulatory burdens, laid out in its Executive Order, Unleashing Prosperity Through Deregulation.

Marketing Requirements Rolled Back. CMS regulates how Medicare Advantage insurers, as well as agents, brokers, and other third parties who sell Medicare Advantage plans may communicate with beneficiaries. In recent years, CMS has documented patterns of aggressive and misleading marketing behavior, based on reports from state insurance commissioners, State Health Insurance Assistance Programs (SHIPs), and beneficiary advocacy groups, and has made a number of changes in prior rules to provide additional oversight of Medicare Advantage plan marketing. The recent final rule eliminates many of these provisions, with the stated goal of streamlining regulatory requirements for agents and brokers, and making the services offered by these groups more accessible to beneficiaries.

• Limitations on Marketing at Educational Events Rolled Back: CMS requires that Medicare Advantage insurers, agents, and brokers clearly distinguish between educational and marketing events, and prohibits the discussion of specific plan costs or benefits at events promoted as educational. The April 2023 final rule reinforced this separation by prohibiting the collection of scope of appointment forms at educational events, requiring a 48-hour waiting period between the collection of scope of appointment forms and personal marketing appointments, and requiring a 12-hour waiting period between educational and marketing events at the same location. These provisions were intended to prevent beneficiaries from feeling pressured into attending marketing events or making coverage decisions on the spot when seeking out educational information.
  
  The current final rule rolls back these provisions, citing stakeholder feedback that waiting periods create unnecessary delays and may be burdensome to beneficiaries who must travel for multiple events and appointments that could otherwise take place in a single session. Agents and brokers may now collect scope of appointment forms at educational events, and may conduct a personal marketing appointment at any point afterwards, with no waiting period. Further, educational and marketing events may now be held back-to-back in the same location, provided that beneficiaries are notified of the transition and offered the opportunity to leave if they prefer. CMS noted that some commenters opposed these changes due to concern that they may leave beneficiaries more vulnerable to aggressive sales tactics and may blur the line between educational and marketing information.
• Prohibition on Use of Superlatives in Marketing Materials Eliminated: The final rule eliminates certain requirements around the language used in marketing materials, such as a prohibition on the use of superlatives (e.g., “best” or “most”) without supporting documentation. CMS first introduced this requirement in the April 2023 final rule, citing concern that these claims may be misleading when taken out of context, and may encourage beneficiaries to enroll in a plan based on information that is misrepresented or misunderstood. The current rule revises this stance, stating that existing CMS requirements already prohibit the use of misleading or inaccurate claims in marketing materials, while the prohibition on superlatives represents an undue burden for insurers, agents, and brokers that does not meaningfully expand on these other protections.
• Mandatory Disclaimer Requirements Modified: CMS requires that brokers and other third parties who represent multiple Medicare Advantage insurers begin all sales calls with a mandatory disclaimer stating that they do not represent every plan available in the area and providing beneficiaries with a list of resources they may reach out to for further information. CMS introduced this requirement in the May 2022 final rule to ensure that beneficiaries had access to complete, unbiased information about their coverage options, as many brokers only represent a subset of available plans and may have a financial incentive to steer beneficiaries towards some plans over others. The current final rule preserves this requirement, but allows the disclaimer to be provided later in the call as long as it is stated before any discussion of specific plan benefits, rather than in the first minute of the call as previously required.
  
  Notably, the rule also removes the State Health Insurance Assistance Programs (SHIPs) from the list of resources that must be included in the disclaimer, now limited to official CMS resources such as 1-800-MEDICARE and Medicare.gov. SHIPs are federally-funded, state-based programs that offer free, unbiased counseling and education to Medicare beneficiaries. This change prompted criticism from some commentors, who noted that 1-800-MEDICARE is not generally equipped to provide the same level of in-depth counseling or local information that SHIP counselors are trained to provide. However, CMS states that SHIP volunteers may not always have the expertise to help beneficiaries navigate increasingly complex Medicare Advantage options and that the standardized training and 24/7 availability of customer service representatives at 1-800-MEDICARE make it a more appropriate resource in this context, while also noting that 1-800-MEDICARE may still refer callers to their local SHIP on a case-by-case basis.

CMS Declined to Finalize a Proposal to Streamline the Medicare Advantage Special Enrollment Period for Provider Terminations

Medicare Advantage plans have networks of providers, and beneficiaries must see providers in their plan’s network or potentially pay higher cost sharing. KFF analysis has shown that Medicare Advantage enrollees have access to about half of the physicians available to traditional Medicare beneficiaries in their area, on average. Medicare beneficiaries say having access to their preferred providers is an important factor when selecting their Medicare coverage. With this in mind, the Trump administration recently launched a new provider search tool on the Medicare plan finder to help beneficiaries identify if their doctors are in a plan’s network, though it experienced issues during its initial rollout.

While Medicare beneficiaries may select plans based on access to their preferred doctors and hospitals, providers can leave Medicare Advantage networks at any time during the year, potentially disrupting coverage for plan enrollees. Currently, a special enrollment period (SEP) for Significant Change in Provider Network allows Medicare Advantage enrollees to switch plans or return to traditional Medicare when CMS determines there were "significant" changes to their plan's provider network – for example, the termination of a contract with a large hospital system. When CMS makes that determination, Medicare Advantage plans must send a separate notice to affected enrollees explaining their SEP eligibility to select different coverage, including guaranteed issue rights to purchase a Medigap policy regardless of pre-existing conditions.

CMS proposed to eliminate the significance determination, making the SEP available to any "affected enrollee" of a provider termination, defined as someone assigned to, currently receiving care from, or having received care within the past three months from a terminated provider. Rather than waiting for CMS to review and approve a significance finding, plans would include SEP eligibility information in the standard provider termination notice already sent to enrollees. Enrollees could then attest directly to the plan that they meet the affected enrollee definition and are eligible for a special enrollment period to change their Medicare coverage.

This proposal would have put the decision in the hands of Medicare beneficiaries – allowing them to decide whether a provider termination was significant enough to warrant switching coverage, rather than waiting for that determination from CMS. However, CMS declined to finalize this proposal and did not explain its rationale for its decision. CMS does note that this topic generated broad interest and may be addressed in further rulemaking.

For many years, KFF polling has found that the high cost of health care is a burden on U.S. families, and that health care costs factor into decisions about insurance coverage and care seeking. These costs also rank as the top financial worry for adults and their families. This data note summarizes recent KFF polling on the public’s experiences with health care costs. Main takeaways include:

• Just under half of U.S. adults say it is difficult to afford health care costs, and about three in ten say they or a family member in their household had problems paying for health care in the past 12 months. Hispanic adults, young adults, and the uninsured are particularly likely to report problems affording health care in the past year.
• The cost of health care can lead some to put off needed care. About one-third (36%) of adults say that in the past 12 months they have skipped or postponed getting health care they needed because of the cost. Notably three in four (75%) uninsured adults under age 65 say they went without needed care because of the cost.
• The cost of prescription drugs prevents some people from filling prescriptions. About four in ten (43%) U.S. adults say they have not taken their medication as prescribed in the past year due to costs. This includes three in ten who say they have taken an over-the-counter drug instead of getting a prescription filled (31%), a quarter (27%) who have not filled a prescription, and one in five (19%) who have cut pills in half or skipped doses of medicine because of the cost. Larger shares of lower-income, uninsured, women, Black, and Hispanic adults report taking these measures.
• Health care debt is a burden for a large share of Americans. In 2022, about four in ten adults (41%) reported having debt due to medical or dental bills including debts owed to credit cards, collections agencies, family and friends, banks, and other lenders to pay for their health care costs, with disproportionate shares of Black and Hispanic adults, women, parents, those with low incomes, and uninsured adults saying they have health care debt.
• Those who are covered by health insurance are not immune to the burden of health care costs. Almost four in ten insured adults under the age of 65 (38%) worry about affording their monthly health insurance premium and large shares of adults with employer-sponsored insurance (ESI) and those with Marketplace coverage rate their insurance as “fair” or “poor” when it comes to their monthly premium and to out-of-pocket costs to see a doctor.
• Notable shares of adults say they are worried about affording medical costs such as the cost of health care services (including the cost of health insurance and out-of-pocket costs for things like office visits and prescription drugs). About two-thirds of adults say they are either “very worried” (30%) or “somewhat worried” (34%) about being able to afford the cost of health care for themselves and their families.

Difficulty Affording Medical Costs

Many U.S. adults have trouble affording health care costs. While lower income and uninsured adults are the most likely to report this, those with health insurance and those with higher incomes are not immune to the high cost of medical care. Just under half of U.S. adults say that it is very or somewhat difficult for them to afford their health care costs (44%). Uninsured adults under age 65 are much more likely to say affording health care costs is difficult (82%) compared to those with health insurance coverage (42%). Additionally, a slight majority of Hispanic adults (55%) and half of Black adults (49%) report difficulty affording health care costs compared to about four in ten White adults (39%). Adults in households with annual incomes under $40,000 are more likely than adults in households with higher incomes to say it is difficult to afford their health care costs. (Source: KFF Health Tracking Poll: May 2025)

When asked specifically about problems paying for health care in the past year, about three in ten (28%) adults say they or a family member in their household had problems paying for care, rising to four in ten among Hispanic adults (41%) and young adults ages 18 to 29 (40%). Among those under age 65, six in ten (59%) uninsured adults report problems paying for health care in the past year, about twice the share of insured adults who say the same (30%). (Source: KFF Health Tracking Poll: November 2025)

The cost of care can also lead some adults to skip or delay seeking services, with one-third (36%) of adults saying that they have skipped or postponed getting needed health care in the past 12 months because of the cost. Women are more likely than men to say they have skipped or postponed getting health care they needed because of the cost (38% vs. 32%). Adults ages 65 and older, most of whom are eligible for health care coverage through Medicare, are much less likely than younger age groups to say they have not gotten health care they needed because of cost.

Three-quarters of uninsured adults say they have skipped or postponed getting the health care they needed due to cost. Having health insurance, however, does not offer ironclad protection as about four in ten adults with insurance (37%) still report not getting health care they needed due to cost. (Source: KFF Health Tracking Poll: May 2025)

Skipping care due to costs can have notable health impacts. Nearly two in ten adults (18%) report that their health got worse because they skipped or delayed getting care. Among adults under age 65, those who are uninsured are twice as likely as those with health coverage to say that their health worsened due to skipped or postponed care (42% vs. 20%). About four times as many adults under age 65 (23%) say their health got worse after skipping or postponing care as adults ages 65 and older (6%), most of whom have Medicare coverage. (Source: KFF Health Tracking Poll: May 2025)

A 2022 KFF report found that people who already have debt due to medical or dental care are disproportionately likely to put off or skip medical care. Half (51%) of adults currently experiencing debt due to medical or dental bills say in the past year, cost has been a probititor to getting the medical test or treatment that was recommended by a doctor. (Source: KFF Health Care Debt Survey: Feb.-Mar. 2022)

Prescription Drug Costs

The high cost of prescription drugs also leads some people to cut back on their medications in various ways. About three in ten adults (31%) say in the past 12 months they have taken an over-the-counter drug instead of getting a prescription filled because of cost concerns, while about a quarter (27%) say they have not filled a prescription and about one in five (19%) say they have cut pills in half or skipped doses of medicine due to cost. Overall, four in ten (43%) adults report taking at least one of these measures in the past year, continuing an upward trend from a third (33%) in 2025 and about three in ten (31%) in July 2023.

Larger shares of adults in households with lower and middle incomes report resorting to these cost-saving prescription medication solutions compared to those with higher incomes. About half of adults in households with annual incomes under $40,000 (52%) or between $40,000 and $90,000 (47%) say they have not taken their medication as prescribed due to the cost in the last year, compared to three in ten adults in households with incomes of $90,000 or more. Uninsured adults under the age of 65 are more likely than their counterparts to also report not taking their medicine as prescribed due to costs (58% compared to 43% of insured adults under age 65). Additionally, half of women (49%) say they have taken any of these prescription medication measures compared to about a third (36%) of men. Over half (55%) of Black adults say the same, compared to half (47%) of Hispanic adults and four in ten White adults. (Source: KFF Health Tracking Poll: March 2026)

Health Insurance Cost Ratings

Health insurance provides some financial protection, but premiums and out-of-pocket costs can still present a financial burden for many individuals. Overall, most insured adults rate their health insurance as “excellent” or “good” when it comes to the amount they have to pay out-of-pocket for their prescriptions (61%), the amount they have to pay out-of-pocket to see a doctor (53%), and the amount they pay monthly for insurance (54%). However, at least three in ten rate their insurance as “fair” or “poor” on each of these metrics, and affordability ratings vary depending on the type of coverage people have.

Adults who have private insurance through employer-sponsored insurance or Marketplace coverage are more likely than those with Medicare or Medicaid to rate their insurance negatively when it comes to their monthly premium, the amount they have to pay out of pocket to see a doctor, and their prescription co-pays. About one in four adults with Medicare give negative ratings to the amount they have to pay each month for insurance and to their out-of-pocket prescription costs, while about one in five give their insurance a negative rating when it comes to their out-of-pocket costs to see a doctor.

Medicaid enrollees are less likely than those with other coverage types to give their insurance negative ratings on these affordability measures (Medicaid does not charge monthly premiums in most states, and copays for covered services, where applied, are required to be nominal). (Source: KFF Survey of Consumer Experiences with Health Insurance)

Health Care Debt

In June 2022, KFF released an analysis of the KFF Health Care Debt Survey, a companion report to the investigative journalism project on health care debt conducted by KFF Health News and NPR, Diagnosis Debt. This project found that health care debt is a wide-reaching problem in the United States and that 41% of U.S. adults currently have some type of debt due to medical or dental bills from their own or someone else’s care, including about a quarter of adults (24%) who say they have medical or dental bills that are past due or that they are unable to pay, and one in five (21%) who have bills they are paying off over time directly to a provider. One in six (17%) report debt owed to a bank, collection agency, or other lender from loans taken out to pay for medical or dental bills, while similar shares say they have health care debt from bills they put on a credit card and are paying off over time (17%). One in ten report debt owed to a family member or friend from money they borrowed to pay off medical or dental bills.

While four in ten U.S. adults have some type of health care debt, disproportionate shares of lower income adults, the uninsured, Black and Hispanic adults, women, and parents report current debt due to medical or dental bills.

Vulnerabilities and Worries About Health Care and Long-Term Care Costs

Health care costs remain at the top of the list of people’s financial worries, with nearly two-thirds (64%) saying they are at least somewhat worried about affording the cost of health care, including the cost of health insurance and out-of-pocket costs for things like office visits and prescription drugs for themselves and their families. This includes three in ten who say they are “very worried” about affording health care. A similar share of adults is “very worried” about affording gas and transportation costs (29%), up from about one in six (17%) in January. This follows a nationwide increase in gasoline prices since the U.S. conflict with Iran began in February. About one in five adults say they are “very worried” about affording food and groceries (23%), rent or mortgage (21%) or monthly utilities (21%).

Notably, just under nine in ten (85%) uninsured adults under age 65 say they are worried about affording the cost of health care, though a large share of insured adults is also worried (64%). Health care costs are a top household worry across insurance types and across partisans. (Source: KFF Health Tracking Poll: April 2026)

Many U.S. adults may be one unexpected medical bill from falling into debt. About half of U.S. adults say they would not be able to pay an unexpected medical bill that came to $500 out of pocket. This includes one in five (19%) who would not be able to pay it at all, 5% who would borrow the money from a bank, payday lender, friends or family to cover the cost, and one in five (21%) who would incur credit card debt in order to pay the bill. Women, those with lower household incomes, Black and Hispanic adults are more likely than their counterparts to say they would be unable to afford this type of bill. (Source: KFF Health Care Debt Survey: Feb.-Mar. 2022)

Among older adults, the costs of long-term care and support services are also a concern. Almost six in ten (57%) adults 65 and older say they are at least “somewhat anxious” about affording the cost of a nursing home or assisted living facility if they needed it, and half say they feel anxious about being able to afford support services such as paid nurses or aides. These concerns also loom large among those between the ages of 50 and 64, with more than seven in ten saying they feel anxious about affording residential care (73%) and care from paid nurses or aides (72%) if they were to need these services. See The Affordability of Long-Term Care and Support Services: Findings from a KFF Survey for a deeper dive into concerns about the affordability of nursing homes and support services.

Introduction

Artificial intelligence (AI) is increasingly being integrated into health care, including but not limited to diagnosis and treatment plans, drug development, prediction of health risks and outcomes, health monitoring, and medical imaging. AI can also automate aspects of health care including data processing and administrative tasks, reimbursement decisions, patient interactions, and clinical decision-making. Additionally, individuals are increasingly using AI for health information and advice.

While there has been an increase in funding for and use of AI in health care in recent years, public opinion on AI’s role in providing accurate health information remains mixed. Further, there are concerns that AI may lead to job losses and reduce personalized human-based interactions. Moreover, AI can exacerbate health disparities if the underlying data on which models are built are biased and/or not inclusive. Alternatively, some suggest that AI may help mitigate disparities if it is carefully designed. This brief examines the implications of the growing use of AI for disparities in health and health care and discusses factors that can help reduce AI-related bias in health care.

Growing Use of AI in Health Care

AI tools are becoming increasingly integrated into various aspects of the health care system. For example, hospitals report using AI or predictive models as both administrative tools to perform tasks such as patient scheduling, billing, and medical coding, and as clinician-facing tools to predict health risks and outcomes among patients. A 2025 survey conducted across 16 states found that eight in ten (84%) health insurers report using AI or machine learning for fraud detection, utilization management, and prior authorization, among other uses. Health systems also report using AI to “limit claim denials and streamline prior authorization processes.”

The public also is increasingly using AI for health information and advice although many have limited trust in the reliability of AI tools. According to OpenAI data from 2026, more than 40 million people globally turn to ChatGPT daily for health information. The data also show that AI chatbots are becoming an important source of information for health insurance and billing advice, with users asking between 1.6 and 1.9 million questions per week regarding plan comparisons, claims, billing, and coverage. Further, a 2026 KFF survey finds that about a third (32%) of adults say they use AI chatbots for health information or advice (Figure 1). However, two-thirds (67%) of adults overall say they trust AI tools or chatbots “not too much” or “not at all” to provide reliable health information, and about three in four (77%) say the same regarding information about mental health and emotional well-being. While rates of use for and trust in AI for physical health advice are similar across racial and ethnic groups, Black and Hispanic adults are more likely than their White counterparts to report using AI for mental health advice and Black adults (29%) are somewhat more likely than White adults (20%) to say they trust AI tools or chatbots to provide reliable information about mental health and emotional well-being “a great deal” or “a fair amount.”

Impact of AI on Disparities in Health and Health Care

As the use of AI in health care grows, research suggests that AI models can exacerbate racial and ethnic health disparities. A 2024 systematic review of 30 studies over a ten-year time period (from 2013 to 2023) that assessed instances of racial bias perpetuated by AI and machine learning algorithms in health care found a significant association between AI utilization and an exacerbation of racial disparities in health and health care outcomes. These disparities included longer waiting times for appointments, lower rates of success in predicting mental health outcomes, and underdiagnosis of health conditions, particularly for Black and Hispanic people compared to other groups. For example:

• One study found that a machine learning algorithm used for creating patient appointment schedules led to Black patients experiencing 33% longer wait times than other patients. This was due to the model using socioeconomic indicators such as employment status, zip code, insurance type, and past no-show rates, which are correlated with race, to create appointment schedules.
• Another study found that a widely used algorithm to guide health care decisions assigned Black patients the same level of risk as White patients even though Black patients were sicker. The algorithm used health care costs as an imperfect proxy for illness, since less money is spent on Black patients who have an equivalent level of need due to inequities in access to care. The authors suggest that addressing this disparity would significantly increase the share of Black patients receiving additional care.
• In diagnostics, AI models may underperform on patients with darker skin because training datasets are more likely to collect data from lighter skinned patients.

In the systematic review, the authors identified four primary and interrelated causes for AI-perpetuated disparities including: biased underlying datasets, historical and systemic biases that can be encoded into AI when it is trained on these data, algorithmic design bias, and biased application and/or deployment of AI.

These AI-related racial and ethnic disparities also extend into mental health diagnosis and treatment recommendations. For example, language-based AI models underperformed on predicting depression severity for Black patients as compared to White patients since the two groups use different types of language to express depression symptoms and AI is often primarily trained on language used by White patients given that there is more data available on White patients since they make up a larger share of the population. However, researchers found that even models trained exclusively on the depression-related social media language used by Black individuals performed poorly at predicting depression severity in the group while models trained with the same social media data on White individuals performed well at predicting that group’s depression severity. The authors suggest that this could be due to other factors beyond language, such as paralinguistic features like speech rate or tone, serving as better predictors for depression severity among Black individuals. A separate study found that several AI models made inferior treatment recommendations for Black mental health patients when the patient’s race was explicitly or implicitly mentioned, likely due to biases embedded in the data on which these models are trained. An AI model used for suicide prediction also performed worse for Black patients, with researchers finding that it successfully detected 62% of suicides among White patients but only 10% among Black patients.

Research has found that the use of race in clinical algorithms may also impact the reliability of AI tools for certain groups since they are often trained on these algorithms. AI models are often trained on clinical algorithms used to predict diagnoses and treatments, which in some cases have historically used race as a factor and resulted in worse outcomes for some groups. One of the most well-known examples of this practice is the use of separate measures of kidney function (i.e., estimated glomerular filtration rates, eGFRs) for Black patients compared to non-Black patients, which resulted in many Black patients not receiving a kidney transplant. Another study found that removing the use of race from spirometry, a test used to measure lung function, would increase the number of Black people who would qualify for lung disease diagnosis and disability payments. Further, a 2019 study found that an algorithm used to predict the likelihood of safely having a Vaginal Birth after Cesarean Delivery (VBAC) incorrectly predicted a lower likelihood of success for VBAC for Black and Hispanic women than White women, which led to doctors performing more cesarian deliveries on Black and Hispanic women than White women. A growing number of organizations and health care institutions have recently moved to remove race from these algorithms. However, to the extent AI is trained on algorithms or results from algorithms that use race as a factor, AI could perpetuate these racial biases.

Research also shows that AI models may promote racial and ethnic health misinformation, leading to misdiagnosis or delayed care. A study of multiple AI chatbots found instances of the tools promoting "race-based medicine" and false claims about race such as difference in skin thickness between Black and White patients. Further, all AI chatbots included in the study incorrectly stated that Black men’s and women’s normal lung function tends to be lower than their White counterparts’, reflecting its training on the underlying race-biased algorithm to calculate lung function.

Reducing AI-Related Bias in Health Care 

If carefully designed, AI has the potential to help address disparities. For example, AI-driven decision support tools can be used to identify and correct real-time clinician bias, particularly during high-stress periods when "cognitive load" often leads to disparities in documentation and diagnosis. By automating administrative tasks such as scheduling and billing, AI could help reduce staff burnout at safety-net hospitals, which disproportionately treat underserved groups. AI can also be used to identify the social determinants that drive health inequities through the analysis of large amounts of population data, which can then help guide interventions to address disparities. AI can also help identify disparities in health outcomes that might otherwise go unrecognized. For example, in a recent study, researchers used machine learning to identify excess deaths due to COVID-19 that were unrecognized in official mortality reports and found that these unrecognized deaths occurred disproportionately among people of color, those with lower educational attainment, and those with lower household incomes, among other factors.

Careful design and inclusive data collection; a diverse workforce; and a focus on ethical considerations, transparency, and a collaborative approach are factors that may help mitigate AI biases in health care. Identification and mitigation of biases during AI models’ development, as well as continuous monitoring and inclusion of more representative data over time, can help to address AI-related bias in health care. Further, having a diverse and representative data science workforce and training AI developers to recognize biases in algorithm development also play an important role in developing equitable AI models. Developing and enforcing ethical standards for AI in health care that inform how AI models and algorithms will be designed to help reduce bias and discrimination and establishing accountability in the creation and use of those algorithms may also help to reduce algorithmic bias. Further, collaborating with a wide range of stakeholders, such as health care workers, policymakers, community members, and ethicists when developing AI tools can offer a broader and more nuanced understanding of the impact of AI on health disparities.

Researchers and other experts have increased their focus on the creation of frameworks and coalitions to help guide equitable use of AI in health care. In 2023, the Coalition for Health AI released guidance for the implementation of AI tools that centers equity, fairness, and ethics. The guidance includes recommendations on developing a common set of principles to guide the development and use of AI tools and a coalition or advisory board to help ensure equity and facilitate trustworthiness in health-related AI. In early 2024, experts in health, medicine, technology, and policy issued a call for “ongoing dialogue and ethical commitment from all stakeholders” to ensure that AI in health care is inclusive following a series of discussions at the 2023 Responsible AI for Social and Ethical Healthcare (RAISE) international symposium. In 2024, the Council of Medical Specialty Societies and the Doris Duke Foundation created the Encoding Equity alliance, whose aims are to identify the incorrect use of race in clinical algorithms and guidelines, design “accurate and equitable decision tools”, and collect and disseminate evidence on the use of AI in health care to promote health equity.

While there has been increasing activity at the state-level to regulate AI in health care, the Trump administration has prioritized deregulation of AI, reduced or eliminated equity requirements for AI in health care, and is challenging state regulations that impose strict anti-bias requirements.  President Trump issued Executive Order (EO) 4148 in January 2025 that rescinded a number of Biden administration EOs, including those related to equitable use of AI in health care. He replaced those EOs with EO 14179, which shifts focus away from “equity” mandates and “algorithmic fairness” and towards “minimally burdensome” requirements to encourage innovation. While numerous states have recently introduced or enacted legislation related to AI in health care, the Trump administration is challenging state laws that impose strict bias audits or transparency requirements for AI via EO 14365 issued in December 2025. Under the EO, the Department of Justice created an AI Litigation Task Force in January 2026 to challenge states with AI laws found to be inconsistent with federal policy. The EO also directs the Secretary of Commerce to restrict federal grant money, specifically the Broadband Equity Access and Deployment (BEAD) Program funds, in states with “onerous” AI laws. For example, Colorado passed the “Consumer Protections for Artificial Intelligence” law in 2024, which among other things, requires health care providers and health insurers to take steps to prevent algorithmic discrimination. However, implementation of the law has been postponed due to legal challenges.

Recent news reports indicate HHS decided to initially delay publication of, and then not publish, a CDC-led study that estimates the effectiveness of the COVID-19 vaccine over the past winter season. The study had already passed internal reviews by CDC scientific and editorial teams, and its methodology, as indicated by a reportedly leaked copy of the manuscript, is one that is widely used by researchers around the world for studying respiratory virus vaccine effectiveness. Only a few weeks prior, CDC had published a study on influenza vaccine effectiveness using the same methodological approach, and the approach has also been used by CDC in similar studies of COVID-19 vaccine effectiveness in prior years. National Institutes of Health Director and acting Director of the Centers for Disease Control and Prevention (CDC) Jay Bhattacharya wrote in an op-ed that the decision not to publish the study was due to concerns about its methodology, and that CDC was “upholding its responsibility to ensure that every piece of information it shares is rigorously validated, accurate and worthy of public trust” through additional review of the study prior to publication. To provide context for public discussion of the unpublished study, this policy brief provides an overview of the methodological approach in question – the test-negative case control design – and discusses some of its strengths and limitations in assessing vaccine effectiveness for respiratory diseases like influenza and COVID-19.

What is vaccine effectiveness and how do scientists estimate it?

CDC defines vaccine effectiveness (VE) as “how well vaccination works under real-world conditions to protect people against health outcomes such as symptomatic illness, hospitalization, and death.” This contrasts with “vaccine efficacy,” which refers to how well a vaccine works in ideal, controlled conditions such as a clinical trial. At a basic level, a VE study compares health outcomes (like illness or hospitalization) and vaccination status in different groups of people, in order to generate an estimate of the level of protection that a vaccine provides against those health outcomes. Researchers typically calculate the magnitude of the difference – the level of protection – between groups as a percentage (e.g., a vaccine can have a VE of 50% against illness if that illness was 50% less common in the vaccinated group compared to the unvaccinated group). There are several epidemiological approaches for conducting these types of studies, including “observational” approaches such as cohort studies that follow groups of vaccinated and unvaccinated people over time and measure how frequently health outcomes occur in each group, and case-control studies that identify groups of people with a particular health outcome (cases) and those without that outcome (controls) and determine the frequency of vaccination in each group. A common observational approach used to study VE for respiratory illnesses is a type of case-control study called the “test-negative” design. An “experimental” design such as a randomized controlled trial (where participants are randomized into vaccinated and unvaccinated groups and the vaccine is compared to a placebo) of an existing, FDA approved vaccine is generally considered unethical, meaning observational approaches such as the test-negative design are preferable when assessing VE for vaccines already widely used and shown to be safe and effective.   

What is the test-negative study design?

In recent years the “test-negative” design has become the most prevalent approach for studying and monitoring VE of COVID-19 and influenza vaccines in “real world” situations. The test-negative design is a version of the case-control approach where researchers define a clinical outcome of interest (such as influenza-like-illness or respiratory disease symptoms), identify people seeking health care who present with these same symptoms, then test individuals for the pathogen of interest (such as influenza virus or COVID-19). Those that test positive for the pathogen are cases and those that test negative are controls, and researchers can then compare how many in each group were vaccinated. With this information at hand, researchers can determine VE, which is calculated as 1 minus the odds ratio (the odds of vaccination in cases over the odds of vaccination in controls).  For example, in a hypothetical study with 200 participants, 100 were test positive for infection (cases) and 100 were test-negative (controls). When vaccination status is checked, it is distributed across groups as shown in Table 1.

In this case, the odds of vaccination among cases is 30/70 = 0.43, and in controls it is 60/40 = 1.5.  The odds ratio would be 0.43/1.5 = 0.29. Therefore VE in this hypothetical study would be (1 – 0.29) x 100% = 71%, indicating that the vaccine was about 71% effective at preventing the outcome among the vaccinated compared to the unvaccinated.   

Researchers will also typically use statistical techniques to control for differences between groups in other characteristics, such as age, comorbidities, and prior infection history.  The as-yet unpublished CDC study of COVID-19 VE and the recently published study of influenza VE use the test-negative approach. It is also the approach often used to estimate VE in respiratory disease research networks for influenza and COVID-19 in the United Kingdom, Australia, Canada, and across Europe.

What are the strengths and weaknesses of this approach?

The test-negative approach, which relies on people seeking care and health facilities for identifying cases and controls, has become the dominant study design for several reasons. It is often easier to conduct a test-negative design compared to other approaches because both cases and controls are identified in the same location presenting with similar symptoms, whereas other approaches might require seeking controls from the community to match with cases. It’s oftenefficient because it uses existing surveillance and diagnostic testing infrastructure at health care facilities. The test-negative approach also reduces the likelihood of a common bias in case-control designs, which is the potential for differences in health care-seeking behavior between cases and controls. That is, vaccinated people may systematically differ from unvaccinated people in ways that affect healthcare-seeking behavior, which creates the potential for biased VE estimates when community controls are used.

However, there are also methodological issues and potential biases that can occur with test-negative designs. Care must be taken in defining and correctly identifying persons with the illness or health outcome of interest, so that consistency is maintained over time and potentially across multiple locations. It is important to identify study participants systematically according to pre-defined criteria. The quality and consistency of the tests used is very important, as some types of tests (such as rapid diagnostic tests) are less sensitive/specific compared to others (such as RT-PCR or viral culture); lower quality tests can lead to misclassification of cases vs. controls. In addition, in situations where widespread or mandatory testing is in place (for example, when all incoming patients in a facility are tested for COVID-19, as was common for a period during the pandemic), then asymptomatic individuals could be classified as cases even though they may differ in important ways from other cases that are symptomatic or have more severe disease. There are also potential concerns about how to handle prior infections and prior vaccinations because if the groups differ in a systematic way in these areas it can introduce a bias to the study. For COVID-19 studies, in particular, VE estimates unadjusted for infection history can underestimate VE. There are other potential biases that exist in test negative designs as well. Table 2 presents a summary of key strengths and weaknesses of the test-negative design for studying COVID-19 and influenza.

As with all epidemiological studies, poor study design, inconsistent implementation, and failure to take into consideration important biases and confounders can lead to misleading results. However, when well-designed and implemented with consistency and attention to detail, test-negative study designs can produce accurate estimates of vaccine effectiveness. In fact, studies have shown that effective test-negative designs can produce results highly consistent with randomized trials (the “gold-standard” of epidemiological study designs) when compared directly. The reportedly leaked manuscript of the disputed COVID-19 vaccine study indicates methods that are in line with previously published VE studies of influenza and COVID-19 vaccines, suggesting that the current controversy could be a result of increased scrutiny of COVID-19 vaccine effectiveness studies at this particular moment.