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Health Care Affordability Across States: National Findings from CHESS

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Category: Resource

Beth Beaudin-Seiler, PhD; Alexandra Allen, MSPPM; Chris Duke, PhD; Emily Zitek, MS

Altarum’s Consumer Healthcare Experience State Survey (CHESS) is a multi-state, population-based survey initiative designed to capture how individuals experience, navigate, and pay for health care. Since its inception in 2018, CHESS has been fielded in sixty-three waves across thirty-seven unique states, producing insights for states on the rates of health care affordability burdens, health care affordability worry, and trouble navigating the health care system occurring in their state. Most importantly, CHESS produces insights into the support for various health care policies that states could take to address health care affordability issues.

Recently the Altarum team developed a cumulative dataset that enables both state-specific insights and cross-state comparisons. State surveys were assessed for survey instrument consistency and recency; 14 met comparability criteria and were retained for analysis, with remaining states under continued review for future inclusion. Drawing on this14-state analytical sample representing approximately 19,444 respondents, this dataset reflects one of the largest and most comprehensive efforts to systematically document consumer experiences with health care affordability, access, and financial burden at the state level. For further details on the development of the cumulative CHESS National data set see Appendix A.

Across the full history of CHESS, dozens of survey waves have been conducted at the state level, collectively forming a robust and expanding evidence base. The survey maintains a consistent core set of questions, allowing for comparability over time and across geographies, while also incorporating state-specific priorities where needed. This structure enables CHESS to function both as a state policy tool and as a national dataset capable of identifying structural patterns in the health care system.

Background

In each state survey wave, CHESS findings are reported in briefs that describe how often respondents experienced key affordability challenges. These measures include skipping needed care, rationing prescription medications, worrying about affording health care now or in the future, and experiencing financial strain from medical costs. Financial strain includes outcomes such as depleting savings, being unable to pay for basic needs such as food, housing, or utilities because of medical bills, and being contacted by a collection’s agency about medical debt. CHESS also reports whether respondents experienced any health care affordability burden overall, which includes skipping or rationing care, experiencing financial strain from medical costs, or going without health insurance in the past year due to cost. Findings are typically analyzed by demographic characteristics, including race, sex, age, insurance type, and disability status. The cumulative dataset strengthens these analyses by increasing the overall sample size and allowing more reliable reporting for smaller population groups that may not have had sufficient sample sizes within an individual state survey, such as uninsured respondents.

A central strength of the CHESS National data set is its ability to reveal consistent themes that persist across states, regardless of differences in population size, political context, or economic conditions. Analysis of the national data set demonstrates that challenges related to health care costs and affordability are not isolated or context-specific, they are systemic and widely shared. Across multiple analytical domains; including demographic patterns, insurance coverage dynamics, and medical debt relative to income, the same underlying pressures emerge repeatedly. Populations facing the greatest affordability burdens include lower-income households, individuals enrolled in Medicaid, younger working-age adults, and those with limited ability to navigate complex insurance systems.

CHESS National Data Findings

Any Affordability Burden

Health care is expensive in the U.S. The U.S. spends substantially more on health care per person than other high income countries. However, this higher spending does not consistently translate into better population health outcomes, such as longer life expectancy or lower rates of avoidable illness and death.1,2,3 Health care costs continue to rise faster than wages and household income,4,5 placing growing pressure on household budgets. In the CHESS National data set, findings verify what individual state findings have led us to believe that health care affordability burden is both widespread and unevenly distributed, affecting more than seven in ten respondents (71.2%). The burden is not confined to a single population group; rather, it is broadly experienced across demographic, socioeconomic, and geographic dimensions, with pronounced disparities among younger adults, lower-income households, individuals with disabilities, and certain racial and ethnic groups.

Affordability challenges are particularly acute among Hispanic (82.4%) and Black (80.6%) respondents, as well as individuals with lower incomes (80.6% for <$50K) and those reporting multiple disabilities (87.4%), highlighting persistent inequities in financial access to care. Younger adults (ages 18–44) face the greatest burden (85.6%), while older adults (65+) report substantially lower rates (41.5%), likely reflecting differences in coverage stability and benefit design.

Insurance coverage plays a critical role in shaping affordability outcomes. While uninsured individuals experience the highest burden (92.5%), elevated burden persists even among insured populations, particularly those in Marketplace (83.6%) and Medicaid (77.7%) coverage, suggesting that coverage alone does not guarantee financial protection. State-level variation further indicates that policy and market conditions influence affordability, with burden ranging from approximately 60% to over 85% across states. Notably, affordability burden is consistent across political affiliations, underscoring its broad, cross-partisan relevance and reinforcing its importance as a shared policy priority.

Figure I. Table of Any Affordability Burden by Respondent Characteristics

When comparing state differences in experiencing affordability burdens in health care, residents in Idaho (85.5%), Tennessee (77.7%), and Kansas (77.1%) were most likely to experience any affordability burden, while those in Connecticut (60.8%), New York (61.5%), and Ohio (64.0%) were least likely to skip care. States showed significant differences with a moderate effect size of .1447.

Figure II. Table of Any Affordability Burden by State

Skipping Care

Skipping or delaying health care and rationing medication due to cost is a serious and well-documented public health crisis with measurable, life-threatening consequences. The downstream effects are severe: the CDC reports that poor medication adherence causes between 30 and 50 percent of chronic disease treatment failures, and nonadherence is associated with greater risk for hypertension, elevated blood sugar in diabetics, dyslipidemia, and extended hospitalizations in patients with cardiovascular disease.6

Beyond individual outcomes, patients who cannot take their medicines as prescribed run the risk of seeing their conditions deteriorate, prompting costly acute care episodes — effectively shifting expenses rather than reducing them. Nearly two in ten adults report that their health got worse directly because they skipped or delayed needed care.7 Vulnerable populations bear a disproportionate burden. One in six patients with diabetes in the U.S. reports rationing or abandoning medications to save costs. For people with disabilities, rationing strategies such as cutting back on medications or forgoing other essential needs have serious implications for their health, health care utilization, and overall quality of life.8 Cost-driven avoidance of health care does not save money in any meaningful sense; it defers and amplifies harm, increases system-wide costs, and in many cases, costs lives.

Findings from the CHESS National data set suggest that more than two in three Americans (68.7%) have skipped health care. The largest demographic differences were by age, with more than four out of five of those ages 18-44 skipping health care (83.2%), compared to two out of five (39.3%) over 65 to skipping health care. Other large differences included insurance type, with 87.5% of those with no insurance skipping care, and 81.9% of those with a marketplace plan skipping care, compared to 50.8% on Medicare skipping care.

Income also showed large differences, with 77.8% of those with household incomes below $50k skipping care compared to 55.8% of those with incomes above $150k skipping care. Household disability status showed large differences, with 84.3% of households with two or more disabilities present skipping care, compared to 63.5% of those with no household disabilities skipping care. Other significant differences with smaller effect sizes include race and education. Respondent sex and political lean showed no significant differences. Full results of these comparisons are shown in the table below.

Figure III. Table of Skipping Health Care by Respondent Characteristics

When comparing state differences in skipping health care, residents in Idaho (84.3%), Oregon (75.7%), and Tennessee (75.0%) were most likely to skip care, while those in Connecticut (57.7%), New York (60.1%), and Ohio (61.4%) were least likely to skip care. States showed significant differences with a moderate effect
size of .1441.

Figure IV. Table of Skipping Health Care, by State

Financial Burden

In 2024, nearly 100 million Americans owed over $220 billion in medical debt.9 Medical debt is largely driven by unaffordable bills: many Americans with private coverage must pay thousands of dollars in out-of-pocket expenses to get care, including increasingly high premiums, deductibles, co-insurance, and copayments.10,11,12 This lack of affordability is reflected in national survey findings showing that many medical debts stem from one-time or short-term medical expenses that are often unexpected,13 and that roughly half of adults say they could not pay a $500 unexpected medical bill, and would either have to incur debt to pay it or would not be able to pay the bill at all.14 These factors contribute to the rising amount of medical debt that many Americans face, which negatively impacts long-term financial security and ability to afford care in the future.

Findings from the CHESS National data set show that more than two in five Americans (39.6%) have experienced financial burden with health care. Like with skipping health care, the largest demographic difference was by age, with more than half of those ages 18-44 having financial burdens (53.1%), compared to just one in seven (14.4%) for those over 65. Other large differences included disability type, with 64.5% of those with two or more household disabilities reporting financial burden, compared to 31.5% for households without disabilities. Insurance type also had large differences, with those on marketplace plans most likely to report financial burdens (56.0%) and those on Medicare least likely to report financial burdens (24.5%). Other significant differences included income and education. Sex and political lean were significantly different, but the effect sizes were very small and the differences slight.

Figure V. Table of Financial Burden by Respondent Characteristics

When comparing state differences in experiencing financial burden with health care, residents in Idaho (49.3%), Oregon (48.3%), and Tennessee (47.7%) were most likely to experience financial burden, while those in New York (27.8%), Ohio (32.4%), and Rhode Island (32.6%) were least likely to experience a financial burden with health care. States showed significant differences with a moderate effect size of .1304.

Figure VI. Table of Health Care Financial Burden, by State

Health Care Worries

Other national surveys show that worry about affording health care is widespread across the U.S., even among insured adults.15,16 Findings from the CHESS National data set support these findings regardless of insurance type, age or income for health care services both now and in the future. The most common worries were losing health insurance (65.7%), health insurance becoming too expensive (42.7%), and being unable to pay medical costs for a serious illness or accident (41.8%).

Nearly four out of five (78.7%) expressed at least one health care worry. The largest demographic difference was by age, with 82.7% of those ages 45 to 64 having a health care worry, compared to 66.1% for those over 65. Other large differences included disability status, with 90.6% of households with two or more disabilities present reporting a health care worry, compared to 75.2% of those with no household disabilities. All respondent characteristics showed some significant differences, including household income, sex, insurance type, and education. Both race/ethnicity and political lean were significant, but the effect sizes were small.

Figure VII. Table of Health Care Worries by Respondent Characteristics

When comparing state differences in worrying about health care, residents in Oregon (85.0%), New Hampshire 83.3%), and Colorado (82.4%) were most likely to worry about health care, while those in Virginia (71.7%), Connecticut (73.8%), and Tennessee (75.5%) were least likely to worry about health care. States showed significant differences with a small effect size of .0870.

Figure VIII. Table of Health Care Worries, by State

Discussion and Implications for Federal Policy

Importantly, these findings are not confined to any single political or geographic context. Instead, they point to a broad-based affordability problem that cuts across traditional divisions, indicating that health care costs function as a unifying issue affecting households in fundamentally similar ways across the country. Recent federal policy changes are expected to impact these affordability challenges over the next several years.

Federal Policy Impacts on Medicaid Enrollees

Among Medicaid enrollees, recent changes under H.R. 1 add new conditions, eligibility rules, and administrative requirements which may make it more difficult to obtain and retain Medicaid coverage. These include community engagement/work reporting requirements, increased eligibility redetermination, tightened enrollment rules, reductions in retroactive coverage, delayed implementation of enrollment simplification rules, and cost-sharing requirements. In addition, many people who lose Medicaid coverage may not be able to successfully enroll in Marketplace plans or other coverage even when eligible.17

For example, individuals disenrolled from Medicaid for failing to meet new work or reporting requirements are prohibited from receiving subsidized Marketplace coverage until they demonstrate compliance or for 12 months, creating a coverage gap when income would otherwise qualify them for Marketplace assistance.18,19 While exemptions to some of these new requirements exist for certain individuals, such as parents of young children and caregivers for relatives with disabilities, many still could lose coverage because of misinformation, system errors, and administrative burdens in part due to the rapid implementation timeline.20,21

As a result, Medicaid enrollees who lose or struggle to maintain consistent coverage due to these federal changes are expected to go uninsured, increasing their chances of going without care due to cost and, if they do receive care, being burdened with high medical bills they cannot afford to pay. As we have seen from the CHESS national data those without any health insurance coverage experience the highest rates of any health care affordability burden. And even among those who do maintain coverage, even small levels of cost-sharing for people with lower incomes is associated with reduced use of care, worse health outcomes, and increased financial burden.22 The net effect of these changes is higher churn in and out of Medicaid, lower overall coverage, and a greater risk of affordability challenges and medical debt for consumers.

Recent federal policy changes are also reducing or freezing funding for Medicaid programs, causing states to consider coverage cuts that may further exacerbate health care affordability burdens and worry about losing insurance. H.R.1 included roughly $1 trillion in cuts to the health care system, the majority coming from Medicaid, and many states are responding by considering cutting coverage, optional benefits, eligibility, and provider reimbursements. For example, H.R.1 prohibits the implementation of new provider taxes across states (including local-government taxes in states that have expanded Medicaid) and gradually phases down existing provider taxes in expansion states.23 In response, states are considering capping or eliminating voluntary services, such as adult dental Medicaid benefits and home and community based services for seniors and people with disabilities.24,25

In a separate but related move, the Center for Medicare and Medicaid Services has also begun freezing or holding funding for Medicaid and home and community-based services based on allegations of waste, fraud, and abuse. The resulting administrative and financial demands on state governments may result in further cuts to these programs and reductions in access to care and living supports for low-income people, seniors, and people with disabilities.26 These state responses to federal policy changes and funding reductions are expected to compound issues of decreased affordability and access to care for Medicaid enrollees.27

As seen in the CHESS national data Medicaid enrollees and respondents having or living with someone that has two or more disabilities reported the highest rates of worry about losing their health insurance. Recent policy changes give this even more texture; state and federal governments can restrict or cut Medicaid coverage at any time based on budgets or political will. In the wake of Medicaid cuts and changes under HR1, enrollees may be aware of this public discourse and its potential impact on their coverage.

Federal Policy Impacts on Marketplace Enrollees

In addition, recent federal changes have already begun reducing the affordability and accessibility of Affordable Care Act Marketplace plans. H.R. 1 includes extensive restructuring of subsidies and Marketplace rules, including: elimination of subsidies for several classes of lawfully present immigrants, enrollment period restrictions, an end to tax credits for income-based special enrollment periods, requiring enrollees to file taxes to reconcile credits, and eliminating automatic reenrollment.28 All of these changes are expected to decrease access and increase costs for consumers.29

At the same time, the separate expiration of enhanced Advanced Premium Tax Credits at the end of 2025 is expected to cause millions of people to lose health coverage. This impact comes first from loss of subsidies, with premiums more than doubling on average and people who paid nothing in premiums now having to pay hundreds of dollars a year. In addition, further coverage losses are expected as a result of risk pool deterioration, increased obstacles to enrollment and retention, limiting coverage options for some immigrants, and increasing medical debt and uncompensated care among newly uninsured or underinsured people.30,31,32,33

Beyond coverage losses, even enrollees that maintain coverage may face higher cost-sharing and risk of going without care due to cost. In response to rising premiums, some enrollees may instead opt for coverage that has a lower monthly premium but with higher deductibles, coinsurance, and copayments. As a result, while technically maintaining coverage, they may still go without necessary care or ration medications due to the higher cost-sharing obligations.34,35

Tracking consumers’ experiences of health care access and affordability is essential as H.R. 1 is implemented because many of the law’s impacts can create obstacles that are not fully captured by coverage data alone, such as delays in care, confusion navigating new requirements, skipped medications, or increased medical debt.36 The CHESS national data shows Marketplace enrollees reported higher levels on measures of direct financial burden due to health care costs than those without health insurance. As we move into the next few years of federal policy implementation, it will be important to continue monitoring Medicaid and Marketplace enrollee experiences with not just losing or gaining coverage, but also with issues affording or accessing care with coverage or while dealing with coverage churn.

Support for State Health Care Affordability Policies

This convergence of experiences in the CHESS national data set has meaningful implications for policymaking. CHESS data demonstrate that while individuals may differ in how they perceive the health care system, there is substantial bipartisan alignment in the underlying financial challenges they face. As a result, policies aimed at improving affordability such as reducing out-of-pocket costs, addressing medical debt, improving price transparency, and strengthening consumer navigation supports have the potential to resonate across political lines.

The CHESS national data showed a high level of support for government-led policy solutions related to health care affordability, access, pricing, and spending. This was the case across different policy types, including price transparency, price interventions, consolidation and competition, coverage and access, health care spending, prescription drugs, facility fees and site neutral payments, reimbursement rates for providers, and workforce and program spending. This level of support was present across political parties—at least 80% of Republican, Democrat, and Independent respondents supported the vast majority of health care policy solutions.

Many of the policies that would impact the health care affordability issues highlighted in the CHESS have strong bipartisan support. For example:

  • Skipping care could be addressed by lowering the cost of care and treatment for people with chronic conditions like diabetes, liver disease or cancer (89-94% support across political affiliation);
  • Rationing medication could be addressed by setting standard prices for drugs to make them more affordable (88-93% support across political affiliation) and caping the out-of-pocket costs for life-saving medications like insulin (90-94% support)
  • Financial burdens could be addressed by preventing hospitals from charging more for routine health care services that are most often provided in doctor’s offices (90-92% support across political affiliation) and prohibiting extra fees that aren’t connected to the cost of care, such as charging facility fees at clinics miles away from the hospital system (90-93% support)
    • Worry about affording care or losing insurance would be partly addressed by expanding health insurance options so that everyone can afford quality coverage (87-95% support across political affiliation) and creating an affordable state-based health insurance plan that any resident can purchase regardless of income or employer coverage status (83-93% support)

There was also bipartisan support for broader health care system reforms that indirectly affect health care affordability, including limiting or preventing health care mergers that could reduce competition and increase health care prices (82-87% support across political affiliation) and setting limits on health care spending growth and penalizing payers and providers that fail to curb excessive spending growth (81-87% support).

Lastly, respondents have shown bipartisan support for policies that would reduce the challenges anticipated by recent federal policy changes, including making it easier for people to apply for and renew their Medicaid coverage (82-87% support across political affiliation), increasing reimbursement rates to incentivize health care professionals to participate in Medicaid (82-90% support), and funding home and community-based programs for people with disabilities (86-93% support).

Taken together, the CHESS national data set provides a compelling, evidence-based foundation for action. By documenting consistent affordability challenges across states and populations, and by highlighting the shared nature of these burdens across political affiliations, CHESS underscores that health care affordability is both a pervasive and broadly recognized problem—one that is well-positioned for cross-partisan policy solutions.

Appendix A: Methodology for CHESS National Data Set

Overview

The CHESS (Consumer Health Experience Survey System) National Dataset was constructed by integrating survey data collected independently across 16 U.S. states as part of the CHESS project. Each state survey was administered separately, with state-specific sampling, weighting, and reporting. The national dataset compiles these state-level files into a single analytical frame to enable cross-state comparisons, national benchmarks, and subgroup analyses that individual state samples cannot support reliably.

Data Integration

Over the eight years since the inception of the CHESS survey, the instrument has been adapted in response to client requests, emerging policy priorities, and insights gained from earlier administrations — resulting in survey versions that vary in question wording, item inclusion, and response category structure. Due to these adaptations, the national dataset was built from the most recent results following the most recent significant survey modification, ensuring the greatest degree of comparability across states. This yielded a 16-state sample, which was subsequently reduced to 14 states after a formal statistical outlier analysis identified Washington D.C. and New Mexico as sufficiently distinct from the remaining states to risk distorting national estimates. All findings in this report are drawn from that 14-state analytical sample of approximately 19,444 weighted respondents.

File Construction

Harmonized state files were appended into a single national dataset through careful datafile comparisons resulting in a single cohesive national codebook. Variations between survey instruments were reconciled through recoding and renaming of variables where applicable. A state identifier variable was retained throughout to allow state-level filtering and subgroup analysis. The appended file was then reviewed for structural integrity:

  • Total record counts verified against expected state-level Ns
  • Variable distributions checked for unexpected values or coding inconsistencies introduced during merge procedures

Post-Stratification Weighting

The national dataset was weighted according to national distributions. These weights adjust the sample to match known population distributions on demographic and insurance coverage margins. The weighting procedure used the following target variables:

  • Age × sex cross-classification
  • Race and ethnicity (Hispanic/Latino; White Non-Hispanic; Black/African American; other)
  • Educational attainment
  • Insurance coverage type (employer-sponsored; Medicaid; Medicare; marketplace/individual; uninsured; other)

Weighting was performed at the national level in accordance to the state level weighting procedure applied for each state specific CHESS project. Initial state level weights are maintained, in addition to the national weight, in the dataset to allow for state level weighting for future analysis as needed.

Weighting note: The CHESS national dataset is not a probability sample and should not be described as such. It is a multi-state weighted convenience sample. National estimates are appropriate for benchmarking and pattern identification but should be interpreted with this limitation in mind.

Outlier Analysis

Before conducting multi-state analyses, a formal statistical outlier analysis was performed to identify any states whose survey response patterns differed systematically from the 16-state aggregate to a degree that would distort national estimates. Two states — Washington D.C. and New Mexico — were excluded on the basis of this analysis.

Outlier detection used a key subset of survey items appearing consistently across all 16 state datasets. For each item, a state-level mean (or proportion) was calculated using the national post-stratification weights. Four complementary detection methods were then applied using a combination of human-in-the-loop individual analysis coupled with Claude AI for confirmation:

AI Assisted Summary Analyses

Three separate analyses were initially performed producing crosstab tables for all survey items across demographic patterns, employment-by-insurance structure, and medical debt relative to household income. Medical debt relative to household income was produced using midpoint mean computations of the collected categorical data. Each set of crosstabs was examined for statistically significant differences across analytical frameworks. A thematic analysis was then performed upon each framework utilizing Claude AI and confirmed with human-in-the-loop instruction and review. An iterative review process was applied revealing a series of overarching themes and patterns within each framework. While these analyses revealed significant trends and patterns at an individual level, combining all three analyses into one produced findings previously unobserved in any single analysis alone enabling the development of the billing-to-debt pipeline framework.

A systematic QC process was applied at every step and each figure reported maps directly to the initial output tables produced. All analyses were produced using SPSS Version 28 or SAS 9.4. Significance testing reported a 95% confidence interval (two-tailed, α = 0.05) and weighting was performed using SAS SUDANN.

What This Dataset Makes Possible

The CHESS national dataset enables analyses that individual state datasets cannot support. With approximately 19,444 weighted respondents across 14 states, the sample is large enough to produce stable estimates for small demographic subgroups and to run models examining how coverage type, income, race, literacy, and care avoidance interact simultaneously.

Predictive Modeling

Survey-weighted logistic regression can estimate the probability of experiencing medical financial burden as a function of multiple predictors simultaneously. The recommended approach is SAS PROC SURVEYLOGISTIC, which handles post-stratification weights natively and produces design-corrected standard errors and odds ratios.

National Benchmarks

The compiled dataset establishes stable national reference values for key health care experience measures — financial burden, care avoidance, provider disrespect, insurance literacy, and medical debt as a share of income — against which individual states, health systems, or programs can be compared. These benchmarks improve in precision and representativeness as additional state datasets are incorporated.

Longitudinal Tracking

Incorporation of applicable survey data collected on previous iterations of the CHESS survey as well as annual or biennial re-administration across participating states would allow measurement of change over time at the national level. The dataset is particularly well-positioned to track whether key gaps — in insurance literacy, financial burden by coverage type, and care avoidance — widen or narrow in response to policy changes such as Medicaid expansion, No Surprises Act implementation, or ACA marketplace shifts.

Cross-State and Sub-State Comparisons

With a sufficiently large pooled sample, the dataset supports comparison of specific states against the national average on targeted measures, and examination of regional patterns (e.g., Medicaid expansion vs. non-expansion states) that are invisible in single-state analyses.

Intersectional Equity Analysis

The pooled sample is large enough to support stable estimates for intersectional subgroups — such as Black/African American Medicaid enrollees, lower-income TRICARE households, or Hispanic/Latino respondents without a high school diploma — that would produce unreliable estimates in any single state dataset. The Medicaid-race-disrespect intersection documented in the Key Findings report is an example of an intersectional finding that was only visible at the national sample size.

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