Abstract
This cross-sectional study evaluates medical and educational debt among the US health care workforce and explores factors associated with higher debt burdens.
Introduction
Millions of individuals in the US incur educational and medical debts annually, and health care workers may be particularly vulnerable. Extensive training requirements may lead to high student debt among some health care workers, while nonprofessional health workers may be at risk for medical debt due to low wages and poor benefits. Reports have highlighted hospitals’ aggressive debt collection actions, including suing their own employees.1
Studies of health care workers’ debts have focused primarily on physician student loan debt.2 We evaluated the educational and medical debt of the US health care workforce and factors associated with higher debt burdens.
Methods
This cross-sectional study analyzed publicly available data from the 2018-2021 Survey of Income and Program Participation (SIPP), a nationally representative longitudinal household panel. The Mass General Brigham institutional review board determined this study was exempt from review. We followed the STROBE reporting guideline.
We compared the medical and educational debts of health care workers (including physicians, registered nurses, nursing aides and assistants, and others; n = 8018, weighted N = 12.6 million) with those of other workers (n = 53 346, weighted N = 83.2 million). To examine the effects of sexism, racism, and other systems of oppression on the distribution of debts, we tabulated the frequency and amount of health care workers’ debts stratified by sex, self-identified race and ethnicity, income, education, occupation, health care industry subsector, unionization status, health insurance, and hospitalization in the previous year. Logistic regression models were fitted to estimate the associations between medical and educational indebtedness and these variables. We used SIPP-provided weights and survey procedures to account for the sample design. Data analyses were performed between April 28, 2023, and May 17, 2024, using SAS, version 9.4 (SAS Institute Inc).
Results
The weighted sample of health care workers included 76.5% women and 23.5% men; 56.4% were 44 years or younger. Health care workers held more medical debt than other workers (13.9% [95% CI, 13.0%-14.8%] vs 11.1% [95% CI, 10.8%-11.4%]). Mean (SE) medical debt among all health care workers was $1567 ($261), totaling $19.8 billion nationally. Medical debt was more common among female vs male (15.3% [95% CI, 14.2%-16.4%] vs 9.3% [95% CI, 7.7%-10.9%]) and Black compared with White (19.7% [95% CI, 16.8%-22.6%] vs 13.1% [95% CI, 12.0%-14.3%]) health care workers. Medical debt was associated with sex, income, education level, working in home health and nursing home care, lacking health insurance, and recent hospitalization (Table 1).
Table 1. Medical Debt Among US Health Care Workers, 2018-2021a.
| Characteristic | Weighted % of all health care workers | Health care workers with any medical debt, % (95% CI) | Medical debt, mean (SE), $b | Adjusted OR of holding medical debt (95% CI)c |
|---|---|---|---|---|
| Sex | ||||
| Male | 23.5 | 9.3 (7.7-10.9) | 1697 (716) | 1 [Reference] |
| Female | 76.5 | 15.3 (14.2-16.4) | 1527 (240) | 1.36 (1.09-1.70) |
| Race and ethnicity | ||||
| Asian | 7.1 | 4.5 (2.3-6.6) | 805 (695) | 0.41 (0.23-0.72) |
| Black | 17.0 | 19.7 (16.8-22.6) | 2152 (655) | 1.15 (0.93-1.44) |
| Hispanic | 14.3 | 14.2 (11.9-16.4) | 1011 (130) | 0.83 (0.66-1.04) |
| White | 58.5 | 13.1 (12.0-14.3) | 1626 (371) | 1 [Reference] |
| Otherd | 3.1 | 17.0 (11.8-22.3) | 1543 (555) | 1.18 (0.77-1.81) |
| Age group, y | ||||
| ≤44 | 56.4 | 14.0 (12.8-15.3) | 1270 (312) | 1 [Reference] |
| 45-64 | 36.5 | 14.4 (12.8-15.9) | 2047 (503) | 1.10 (0.93-1.31) |
| ≥65 | 7.1 | 10.3 (8.0-12.6) | 1456 (698) | 0.76 (0.57-1.02) |
| Household income, % of FPL | ||||
| <100 | 4.0 | 18.1 (13.6-22.6) | 2965 (1542) | 1 [Reference] |
| 100-199 | 10.7 | 22.4 (18.9-25.8) | 1233 (384) | 1.35 (0.92-1.97) |
| 200-299 | 13.7 | 22.2 (19.1-25.3) | 2423 (650) | 1.64 (1.12-2.41) |
| 300-399 | 14.8 | 16.5 (14.0-18.9) | 1412 (497) | 1.21 (0.82-1.78) |
| ≥400 | 56.8 | 9.3 (8.4-10.3) | 1364 (402) | 0.81 (0.56-1.16) |
| Education level | ||||
| High school or less | 24.0 | 18.8 (16.8-20.8) | 1923 (591) | 1 [Reference] |
| Some college | 32.9 | 18.7 (16.9-20.6) | 2179 (453) | 1.10 (0.91-1.32) |
| Bachelor’s degree | 24.4 | 9.2 (7.8-10.5) | 1183 (623) | 0.62 (0.49-0.80) |
| Graduate degree | 18.7 | 5.5 (4.0-6.9) | 537 (349) | 0.42 (0.30-0.60) |
| Occupation | ||||
| Nursing aides and assistants | 24.2 | 18.7 (16.7-20.8) | 2252 (583) | 1 [Reference] |
| Registered nurses | 17.0 | 12.1 (9.8-14.3) | 1956 (809) | 1.05 (0.79-1.41) |
| Physicians | 4.7 | 3.0 (0.6-5.2) | 151 (63) | 0.62 (0.27-1.46) |
| Othere | 54.2 | 13.3 (11.9-14.7) | 1262 (321) | 1.10 (0.91-1.34) |
| Health care industry subsector | ||||
| Hospital | 36.2 | 11.1 (9.7-12.6) | 1121 (306) | 1 [Reference] |
| Nursing home | 16.1 | 19.7 (17.1-22.3) | 2639 (819) | 1.41 (1.11-1.81) |
| Office or clinic | 17.9 | 10.7 (8.8-12.5) | 1581 (787) | 0.97 (0.74-1.29) |
| Home health care | 10.9 | 21.8 (18.7-24.9) | 1808 (331) | 1.65 (1.25-2.18) |
| Unionization status | ||||
| Not union member | 88.5 | 14.2 (13.2-15.1) | 1544 (275) | 1 [Reference] |
| Union member | 11.5 | 12.1 (9.5-14.7) | 1724 (655) | 0.91 (0.68-1.22) |
| Health insurance status | ||||
| Had continuous health insurance | 95.9 | 13.5 (12.6-14.4) | 1539 (261) | 1 [Reference] |
| Uninsured for all or part of the year | 4.1 | 23.5 (18.7-28.2) | 2206 (1263) | 1.56 (1.18-2.07) |
| Hospitalized during year of analysis | ||||
| No | 90.6 | 12.3 (11.4-13.2) | 1232 (234) | 1 [Reference] |
| Yes | 9.0 | 30.3 (26.4-34.2) | 3550 (681) | 2.78 (2.22-3.48) |
| All health care workers | 100 | 13.9 (13.0-14.8) | 1567 (261) | NA |
Abbreviations: FPL, federal poverty level; NA, not applicable; OR, odds ratio.
All dollar amounts are in 2021 dollars (adjusted using the Consumer Price Index). Due to missing data, some percentages may not sum to 100. The CIs were derived from replicate weights using the Fay modified balanced repeated replication method.
Includes all individuals (with and without debt) in the given subgroup.
Derived from a single model that included all health care workers and adjusted for sex, race and ethnicity, age, household income, education level, occupation, health care industry subsector, unionization status, health insurance status, and hospitalization during the year of analysis.
Includes all individuals not identifying as Asian alone, Black alone, or White alone, or as being of Hispanic, Latino, or Spanish origin.
Includes all individuals working in the health care industry (using Survey of Income and Program Participation industry codes [SIPP]) but not having a SIPP code corresponding to working as a physician, nurse, or aide; this includes phlebotomists, medical records specialists, physical therapists, paramedics and emergency medical technicians, janitors and cleaners who work in a health care setting, and others.
Educational indebtedness was more frequent among health care workers than other workers (26.7% [95% CI, 25.6%-27.9%] vs 16.5% [95% CI, 16.1%-16.9%]). Mean (SE) educational debt among all health care workers was $10 642 ($449), totaling $134.4 billion nationally. Educational debt was more common among Black health care workers than White health care workers and less common among older and lower-income workers and those with lower education levels (Table 2).
Table 2. Educational Debt Among US Health Care Workers, 2018-2021a.
| Characteristic | Weighted % of all health care workers | Health care workers with any educational debt, % (95% CI) | Educational debt, mean (SE), $b | Adjusted OR of holding educational debt (95% CI)c |
|---|---|---|---|---|
| Sex | ||||
| Male | 23.5 | 25.4 (23.2-27.7) | 12 550 (1181) | 1 [Reference] |
| Female | 76.5 | 27.1 (25.8-28.5) | 10 056 (477) | 1.11 (0.96-1.29) |
| Race and ethnicity | ||||
| Asian | 7.1 | 19.7 (16.0-23.4) | 9046 (1692) | 0.50 (0.39-0.65) |
| Black | 17.0 | 31.6 (28.3-34.8) | 12 650 (1420) | 1.57 (1.28-1.92) |
| Hispanic | 14.3 | 25.0 (22.0-28.0) | 6912 (760) | 1.02 (0.84-1.24) |
| White | 58.5 | 26.4 (24.8-28.0) | 10 890 (639) | 1 [Reference] |
| Otherd | 3.1 | 30.9 (24.6-37.3) | 15 880 (3682) | 1.26 (0.87-1.84) |
| Age group, y | ||||
| ≤44 | 56.4 | 35.8 (34.0-37.6) | 14 246 (730) | 1 [Reference] |
| 45-64 | 36.5 | 17.0 (15.5-18.5) | 6887 (599) | 0.35 (0.31-0.41) |
| ≥65 | 7.1 | 4.6 (3.0-6.3) | 1296 (390) | 0.08 (0.05-0.11) |
| Household income, % of FPL | ||||
| <100 | 4.0 | 18.7 (13.4-23.6) | 7381 (2492) | 1 [Reference] |
| 100-199 | 10.7 | 23.2 (19.4-27.1) | 5991 (784) | 1.49 (1.00-2.22) |
| 200-299 | 13.7 | 27.4 (24.1-30.7) | 9558 (1153) | 1.80 (1.23-2.64) |
| 300-399 | 14.8 | 27.3 (24.2-30.4) | 9034 (874) | 1.60 (1.10-2.32) |
| ≥400 | 56.8 | 27.6 (26.0-29.3) | 12 433 (702) | 1.50 (1.05-2.15) |
| Education level | ||||
| High school or less | 24.0 | 6.3 (5.9-6.7) | 1819 (300) | 1 [Reference] |
| Some college | 32.9 | 27.9 (25.6-30.1) | 5812 (387) | 2.99 (2.39-3.73) |
| Bachelor’s degree | 24.4 | 33.0 (30.5-35.5) | 10 447 (709) | 4.31 (3.35-5.55) |
| Graduate degree | 18.7 | 36.7 (33.8-39.7) | 30 678 (2006) | 6.05 (4.71-7.76) |
| Occupation | ||||
| Nursing aides and assistants | 24.2 | 23.2 (20.6-35.7) | 5430 (703) | 1 [Reference] |
| Registered nurses | 17.0 | 34.9 (31.8-38.0) | 11 939 (916) | 1.11 (0.88-1.40) |
| Physicians | 4.7 | 28.0 (22.6-33.3 | 33 141 (4411) | 0.75 (0.52-1.09) |
| Othere | 54.2 | 25.7 (24.1-27.2) | 10 613 (610) | 0.84 (0.70-1.01) |
| Health care industry subsector | ||||
| Hospital | 36.2 | 29.8 (27.8-30.9) | 13 657 (962) | 1 [Reference] |
| Nursing home | 16.1 | 22.1 (19.0-25.2) | 6375 (990) | 0.87 (0.70-1.08) |
| Office or clinic | 17.9 | 26.4 (23.5-29.3) | 11 653 (1356) | 0.96 (0.78-1.18) |
| Home health care | 10.9 | 19.7 (16.4-23.0) | 5154 (938) | 0.83 (0.64-1.08) |
| All health care workers | 100.0 | 26.7 (25.6-27.9) | 10 642 (449) | NA |
Abbreviations: FPL, federal poverty level; NA, not applicable; OR, odds ratio.
All dollar amounts are in 2021 dollars (adjusted using the Consumer Price Index). Due to missing data, some percentages may not sum to 100. The CIs and SEs were derived from replicate weights using the Fay modified balanced repeated replication method.
Including all individuals (with and without debt) in the given subgroup.
Derived from a single model that included all health care workers and was adjusted for sex, race and ethnicity, age, household income, education level, occupation, and health care industry subsector.
Includes all individuals not identifying as Asian alone, Black alone, or White alone or as being of Hispanic, Latino, or Spanish origin.
Includes all individuals working in the health care industry (using Survey of Income and Program Participation industry codes [SIPP]) but not having a SIPP code corresponding to working as a physician, nurse, or aide; this includes phlebotomists, medical records specialists, physical therapists, paramedics and emergency medical technicians, janitors and cleaners who work in a health care setting, and others.
Discussion
US health care workers are more likely than other workers to carry medical and educational debt, collectively owing more than $150 billion. We found that medical debt was more prevalent among women, home health and nursing home personnel, uninsured individuals, and those with recent hospitalization. Educational debts disproportionately burdened Black workers and younger workers and those with higher education.
Educational and medical debts are associated with adverse health outcomes2,3 and may limit workers’ professional mobility; reduce workforce diversity; and discourage personnel from entering lower-paying fields, eg, public health or primary care.2,4,5 Health care workers indebted to their employers may be less able to address patient safety concerns or protect themselves from workplace abuses.6
Self-reported data in the SIPP may be subject to recall bias. The SIPP’s top coding of dollar amounts (which varies by debt type and year) may have led to an underestimation of debt size. No information on the creditor initiating the debt was available, precluding analyses of whether workers were indebted to their employer.
These findings suggest that US health care workers bear substantial educational and medical debts. Further research should assess the effect of such debts on the health care workforce and patient care.
Data Sharing Statement
References
- 1.AFL-CIO, National Nurses United, Coalition for a Humane Hopkins . Taking Neighbors to Court: Johns Hopkins Hospital Medical Debt Lawsuits. National Nurses United; 2019:35. Accessed April 24, 2023. https://www.nationalnursesunited.org/sites/default/files/nnu/documents/Johns-Hopkins-Medical-Debt-report.pdf
- 2.Pisaniello MS, Asahina AT, Bacchi S, et al. Effect of medical student debt on mental health, academic performance and specialty choice: a systematic review. BMJ Open. 2019;9(7):e029980. doi: 10.1136/bmjopen-2019-029980 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lippert AM, Houle JN, Walsemann KM. Student debt and cardiovascular disease risk among U.S. adults in early mid-life. Am J Prev Med. 2022;63(2):151-159. doi: 10.1016/j.amepre.2022.02.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Applegate JM, Janssen MA. Job mobility and wealth inequality. Comput Econ. 2022;59(1):1-25. doi: 10.1007/s10614-020-10064-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kang Y, Ibrahim SA. Debt-free medical education—a tool for health care workforce diversity. JAMA Health Forum. 2020;1(12):e201435. doi: 10.1001/jamahealthforum.2020.1435 [DOI] [PubMed] [Google Scholar]
- 6.Department of Labor seeks court order to stop Brooklyn staffing agency from demanding employees stay 3 years or repay wages. US Department of Labor. March 20, 2023. Accessed April 25, 2023. https://www.dol.gov/newsroom/releases/sol/sol20230320
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Sharing Statement
