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Medical Debt in the US, 2009-2020

Educational Objective
To understand the prevalence and distribution of medical debt in the US.
1 Credit CME
Key Points

Question  What is the total amount and distribution of medical debt in collections in the US?

Findings  In this retrospective analysis of credit reports for a nationally representative 10% panel of individuals, an estimated 17.8% of individuals in the US had medical debt in collections in June 2020 (reflecting care provided prior to the COVID-19 pandemic). Medical debt was highest among individuals who lived in the South and in zip codes in the lowest income deciles and became more concentrated in lower-income communities in states that did not expand Medicaid.

Meaning  This study provides an estimate of the amount of medical debt in collections in the US based on consumer credit reports from January 2009 to June 2020, reflecting care delivered prior to the COVID-19 pandemic, and suggests that the amount of medical debt was highest among individuals living in the South and in lower-income communities, although further study is needed regarding debt related to COVID-19.

Abstract

Importance  Medical debt is an increasing concern in the US, yet there is limited understanding of the amount and distribution of medical debt, and its association with health care policies.

Objective  To measure the amount of medical debt nationally and by geographic region and income group and its association with Medicaid expansion under the Affordable Care Act.

Design, Setting, and Participants  Data on medical debt in collections were obtained from a nationally representative 10% panel of consumer credit reports between January 2009 and June 2020 (reflecting care provided prior to the COVID-19 pandemic). Income data were obtained from the 2014-2018 American Community Survey. The sample consisted of 4.1 billion person-month observations (nearly 40 million unique individuals). These data were used to estimate the amount of medical debt (nationally and by geographic region and zip code income decile) and to examine the association between Medicaid expansion and medical debt (overall and by income group).

Exposures  Geographic region (US Census region), income group (zip code income decile), and state Medicaid expansion status.

Main Outcomes and Measures  The stock (all unpaid debt listed on credit reports) and flow (new debt listed on credit reports during the preceding 12 months) of medical debt in collections that can be collected on by debt collectors.

Results  In June 2020, an estimated 17.8% of individuals had medical debt (13.0% accrued debt during the prior year), and the mean amount was $429 ($311 accrued during the prior year). The mean stock of medical debt was highest in the South and lowest in the Northeast ($616 vs $167; difference, $448 [95% CI, $435-$462]) and higher in poor than in rich zip code income deciles ($677 vs $126; difference, $551 [95% CI, $520-$581]). Between 2013 and 2020, the states that expanded Medicaid in 2014 experienced a decline in the mean flow of medical debt that was 34.0 percentage points (95% CI, 18.5-49.4 percentage points) greater (from $330 to $175) than the states that did not expand Medicaid (from $613 to $550). In the expansion states, the gap in the mean flow of medical debt between the lowest and highest zip code income deciles decreased by $145 (95% CI, $95-$194) while the gap increased by $218 (95% CI, $163-$273) in the nonexpansion states.

Conclusions and Relevance  This study provides an estimate of the amount of medical debt in collections in the US based on consumer credit reports from January 2009 to June 2020, reflecting care delivered prior to the COVID-19 pandemic, and suggests that the amount of medical debt was highest among individuals living in the South and in lower-income communities. However, further study is needed regarding debt related to COVID-19.

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Article Information

Corresponding Author: Neale Mahoney, PhD, Stanford University, 579 Jane Stanford Way, Stanford, CA 94305 (nmahoney@stanford.edu).

Accepted for Publication: May 13, 2021.

Author Contributions: Dr Mahoney had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Administrative, technical, or material support: Yin.

Supervision: Mahoney, Yin.

Conflict of Interest Disclosures: None reported.

Funder/Support: Dr Mahoney was supported by internal research funds from the University of Chicago (where he was a faculty member through June 2020) and Stanford University (where he has been a faculty member since July 2020). Dr Wong was supported by grant T32-AG000186 from the National Institute on Aging. The credit data used in this study were provided by TransUnion, a global information solutions company, through a relationship with the Kilts Center for Marketing at the University of Chicago Booth School of Business.

Role of the Funder/Sponsor: No funder/sponsor had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation of the manuscript; and decision to submit the manuscript for publication. TransUnion had the right to review the research before dissemination to ensure it accurately describes TransUnion data, does not disclose confidential information, and does not contain material it deems to be misleading or false regarding TransUnion, TransUnion’s partners, affiliates or customer base, or the consumer lending industry.

Additional Contributions: We thank Xuyang Xia, BA (research assistant at Stanford University), for making substantial contributions to the data analysis and who was compensated.

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