{"id":6783,"date":"2022-07-08T15:23:17","date_gmt":"2022-07-08T19:23:17","guid":{"rendered":"http:\/\/chirblog.org\/?p=6783"},"modified":"2022-07-08T15:23:44","modified_gmt":"2022-07-08T19:23:44","slug":"june-research-roundup-reading-2","status":"publish","type":"post","link":"https:\/\/chirblog.org\/june-research-roundup-reading-2\/","title":{"rendered":"June Research Roundup: What We&#8217;re Reading"},"content":{"rendered":"<p>It\u2019s finally summer, and during the latest heat wave, the CHIR team cooled off with new health policy research. In June, we reviewed studies on improving race and ethnicity data collection in health insurance marketplaces, the value of health savings accounts, and variation in medical debt accumulation across the U.S.<\/p>\n<p>RAND Health Care,<a href=\"https:\/\/aspe.hhs.gov\/reports\/imputation-race-ethnicity-marketplace-enrollment-data\"> Imputation of Race and Ethnicity in Health Insurance Marketplace Enrollment Data, 2015-2022 Open Enrollment Periods<\/a>, HHS Office of the Assistant Secretary for Planning and Evaluation (ASPE), June 13, 2022. Because Affordable Care Act (ACA) marketplace enrollees are not required to report their race and ethnicity when signing up for a marketplace plan, the federal marketplace is missing race and ethnicity data for around one-third of applicants. ASPE contracted with RAND researchers to develop a statistical method to impute missing race and ethnicity data for consumers who selected a marketplace plan on HealthCare.gov during annual open enrollment periods (OEP) between 2015 and 2022.<\/p>\n<p><em>What it Finds<\/em><\/p>\n<ul>\n<li>Across the 2015 and 2022 marketplace OEPs, race and ethnicity data was missing for 32.5 percent of HealthCare.gov marketplace enrollees, reaching a low of 26.3 percent in 2019 and a high of 38.7 percent in 2022.<\/li>\n<li>Researchers were able to fill in data gaps using data self-reported by enrollees in a prior year, reducing the share of enrollees with missing data to 23.5 percent. Using census data, researchers calculated the probability that an individual belonged to a certain racial and ethnic group based on name and address.\n<ul>\n<li>Racial and ethnic distribution of enrollees from self-reported data differed from that of the imputed data for enrollees with missing information. Enrollees who self-reported race and ethnicity were more likely to be white and less likely to be Black or Hispanic, compared to the racial and ethnic distribution of the imputed data.<\/li>\n<\/ul>\n<\/li>\n<li>Researchers estimated that during the 2022 OEP on HealthCare.gov, 51 percent of enrollees were white; 25.3 percent were Hispanic\/Latino; 12.7 percent were Black; 8.5 percent were Asian American, Native Hawaiian, and\/or Pacific Islander; 1.9 percent were multiracial; and 0.7 percent were American Indian or Alaska Native.\n<ul>\n<li>However, the imputation model was less reliable for American Indian, Alaska Native, and multiracial groups.<\/li>\n<\/ul>\n<\/li>\n<li>Individuals across all race and ethnic groups were enrolled in a marketplace plan for an average of 4.10 years during the study period. Individuals identified as American Indian, Alaska Native, Black, and multiracial had slightly shorter average enrollment durations, making it more difficult to replace missing data with self-reported information from a different plan year.<\/li>\n<\/ul>\n<p><em>Why it Matters<\/em><br \/>\nImproving race and ethnicity data collection is a <a href=\"https:\/\/www.commonwealthfund.org\/blog\/2022\/improving-race-and-ethnicity-data-collection-first-step-furthering-health-equity-through\">critical first step<\/a> for health insurance marketplaces seeking to advance health equity. By augmenting self-reported data, ASPE and RAND provide a more comprehensive picture of the racial and ethnic makeup of marketplace enrollment. The analysis suggests that Black and Hispanic\/Latino individuals are likely undercounted in enrollment data. Some state-based marketplaces are <a href=\"https:\/\/www.commonwealthfund.org\/publications\/issue-briefs\/2022\/apr\/what-four-states-are-doing-advance-health-equity-marketplace\">exploring strategies<\/a> to fill these gaps in demographic data, such as leveraging insurers to obtain missing information from enrollees. ASPE and RAND\u2019s model is another tool that can inform efforts to reduce inequities in coverage access.<\/p>\n<p>Sherry A. Glied, Dahlia K. Remler, and Mikaela Springsteen, <a href=\"https:\/\/www.healthaffairs.org\/doi\/abs\/10.1377\/hlthaff.2021.01954?journalCode=hlthaff\">Health Savings Accounts No Longer Promote Consumer Cost-Consciousness<\/a>, Health Affairs, June 2022. Using National Health Interview Survey (NHIS) data from 2007-2018, authors examined the value of Health Savings Accounts (HSAs) with respect to changing trends in the health insurance market. HSAs were established to allow enrollees in certain high deductible health plans to pay for health care expenses with pre-tax dollars. Proponents of HSAs anticipated that eligible consumers would be incentivized to select high deductible plans and would be more \u201ccost conscious\u201d when spending their HSA dollars.<\/p>\n<p><em>What it Finds<\/em><\/p>\n<ul>\n<li>HSAs have grown in popularity since their inception, and higher-income workers are more likely to use HSAs, and make higher contributions.\n<ul>\n<li>By 2018, 17.6 percent of adults ages 22-64 with private health insurance had HSAs, up from 3.9 percent in 2007.<\/li>\n<li>In 2014, roughly 16 percent of workers with incomes over $200,000 reported making HSA contributions, which averaged $4,716, compared to 5 percent of workers with incomes between $30,000-$50,000, whose contributions were roughly one-third as high.<\/li>\n<\/ul>\n<\/li>\n<li>In the private insurance market, HSA plans and non-HSA plans have seen more similar cost-sharing over time, but HSA-eligible enrollees have a leg up due to the tax-favored account.\n<ul>\n<li>The average deductible amount doubled in non-HSA employer-sponsored plans from 2007 to 2018, rising closer to the minimum deductible permitted for an HSA-qualified plan; consequently, many more workers covered by employer-sponsored insurance are in high deductible health plans but are not eligible for an HSA.<\/li>\n<li>Out-of-pocket maximums have also increased for both plan types, such that more Americans are facing out-of-pocket maximums of at least $3,000 regardless of whether they qualify for an HSA.<\/li>\n<li>Between 2007-2012, private insurance enrollees with HSAs experienced difficulty affording doctor visits and prescription drugs. However, between 2013-2018, HSA beneficiaries faced fewer financial barriers and in fact became more likely to have seen a doctor in the last year than their non-HSA counterparts.<\/li>\n<\/ul>\n<\/li>\n<li>A recent study found that an employer switching health coverage offerings from plans with lower deductibles to only high deductible plans with HSAs did not see an overall decline in health care spending\u2014instead more spending was tax-free.<\/li>\n<li>In 2020, there was $12 billion in forgone federal taxes associated with HSAs. Because HSA expenses are concentrated among higher-income workers, researchers characterize the HSA tax structure as \u201chighly regressive.\u201d<\/li>\n<\/ul>\n<p><em>Why it Matters<\/em><br \/>\nEvaluation of health care payment structures must account for changing market conditions. Although HSAs were originally intended to encourage cost-consciousness and efficiency in the private insurance market, in recent years, HSAs have disproportionately benefited higher-income workers who are not facing financial barriers to care and subsequently not incentivized to reduce their spending. This study suggests that the policy has not led to cost savings, and instead provides a regressive tax break for higher-income people. Policymakers should consider other paths to reduce health care costs.<\/p>\n<p>Fredric Blavin, Breno Braga, and Anuj Gangopadhyaya, <a href=\"https:\/\/www.urban.org\/research\/publication\/which-county-characteristics-predict-medical-debt\">Which County Characteristics Predict Medical Debt?<\/a>, Urban Institute, June 2022. Using credit bureau data from August 2021, researchers examine which U.S. counties have the largest share of people with medical debt in collections, and how county-level socioeconomic and health factors impact medical debt.<\/p>\n<p><em>What it Finds<\/em><\/p>\n<ul>\n<li>Researchers find that medical debt is concentrated in southern states, with 99 out of the 100 counties with the highest level of medical debt in collections located in the South.<\/li>\n<li>Out of the 100 counties with the highest levels of medical debt, 79 were in states that have not expanded Medicaid under the ACA.<\/li>\n<li>Residents of the ten counties with the highest rates of medical debt in the country are more likely to be uninsured compared to the national average.<\/li>\n<li>Prevalence of chronic conditions in a county is the strongest predictor of levels of medical debt in collections. Over 24 percent of Medicare beneficiaries in Nolan County, Texas (in the top ten counties with the highest medical debt) have six or more chronic conditions, compared to 18 percent of the total Medicare population.<\/li>\n<li>Rates of medical debt in collections are higher in counties with greater shares of uninsured, low-income, Hispanic, or Black populations.\n<ul>\n<li>The ten counties with highest medical debt have lower average household incomes (around $49,000) compared to the national average income of roughly $89,000.<\/li>\n<li>Populations in four out of the ten counties with the highest medical debt are over 25 percent Hispanic, and non-Hispanic Black populations comprise over 30 percent of the other six counties.<\/li>\n<\/ul>\n<\/li>\n<li>A county\u2019s average income and racial composition is less predictive of medical debt levels when accounting for health factors like chronic conditions, suggesting that health status could contribute to these observed disparities. Correlation between county health status and medical debt could result from higher demands for medical care in counties with poorer population health. This could create a cycle where people with medical debt face barriers to basic needs like food and housing, and subsequently face poorer health outcomes.<\/li>\n<\/ul>\n<p><em>Why it Matters<\/em><br \/>\nMillions of adults in the United States are <a href=\"https:\/\/www.healthsystemtracker.org\/brief\/the-burden-of-medical-debt-in-the-united-states\/?_hsmi=206419781&amp;_hsenc=p2ANqtz--WzYOIE57I9PxBF7ZODhNDm2u-auyKBn4l-RihNCCoKrdE8vl4AURlKdnoTrZXS_SdNOE3Y1HpVNcNLO59rEc5WQBDLw&amp;utm_campaign=KFF-2022-Health-Costs&amp;utm_medium=email&amp;utm_content=206419781&amp;utm_source=hs_email\">burdened<\/a> with medical debt, which often leads to foregoing medical care, financial instability, and increased risk of bankruptcy. This study highlights where medical debt is most prevalent and identifies factors associated with medical debt, serving as a roadmap for stakeholders seeking to alleviate the burden and corroborating other studies showing medical debt\u2019s disproportionate impact on <a href=\"https:\/\/chirblog.org\/new-data-show-medical-debt-disproportionately-affects-vulnerable-populations\/\">vulnerable populations<\/a>. These findings also suggest that policy solutions should account for the cyclical interplay of health and socioeconomic factors that affect medical debt.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>It\u2019s finally summer, and during the latest heat wave, the CHIR team cooled off with new health policy research. In June, we reviewed studies on improving race and ethnicity data collection in health insurance marketplaces, the value of health savings accounts, and variation in medical debt accumulation across the U.S.<\/p>\n","protected":false},"author":36,"featured_media":509,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8,1],"tags":[239,574,674,248,782,316],"_links":{"self":[{"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/posts\/6783"}],"collection":[{"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/users\/36"}],"replies":[{"embeddable":true,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/comments?post=6783"}],"version-history":[{"count":4,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/posts\/6783\/revisions"}],"predecessor-version":[{"id":6792,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/posts\/6783\/revisions\/6792"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/media\/509"}],"wp:attachment":[{"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/media?parent=6783"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/categories?post=6783"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/chirblog.org\/wp-json\/wp\/v2\/tags?post=6783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}