Changes in Hospitalizations at US Safety-Net Hospitals Following Medicaid Expansion | Health Disparities | JAMA Network Open | JAMA Network
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Table 1.  Baseline Demographic Characteristics of Study Participantsa
Baseline Demographic Characteristics of Study Participantsa
Table 2.  Percentage Point Change in Safety-Net Hospitalizations Associated With Medicaid Expansiona
Percentage Point Change in Safety-Net Hospitalizations Associated With Medicaid Expansiona
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    Research Letter
    Health Policy
    June 30, 2021

    Changes in Hospitalizations at US Safety-Net Hospitals Following Medicaid Expansion

    Author Affiliations
    • 1Boston University School of Medicine, Boston, Massachusetts
    • 2Section of General Internal Medicine, Boston Medical Center, Boston, Massachusetts
    • 3Boston Medical Center, Boston, Massachusetts
    • 4Boston University School of Public Health, Boston, Massachusetts
    • 5Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
    JAMA Netw Open. 2021;4(6):e2114343. doi:10.1001/jamanetworkopen.2021.14343

    Studies of Medicaid expansion under the Patient Protection and Affordable Care Act (ACA)1,2 have had conflicting findings regarding safety-net hospital (SNH) utilization and have not examined racial/ethnic differences in SNH use. We used data with a larger number of states; substantial racial/ethnic minority populations, including nearly 83% of the national Hispanic population; and with a longer period of observation. We hypothesized that inpatient utilization among patients with lower socioeconomic status and among those who belong to racial/ethnic minority groups would change differentially in Medicaid expansion states, as patients who previously did not have insurance might transfer care from SNHs to non-SNHs, which have more resources and greater access to specialty care.3


    The institutional review boards at Wake Forest and Boston University Schools of Medicine approved this study and granted a waiver of informed consent because it would not be feasible to obtain consent from the hospitalized patients whose data appear in this study. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.

    We used 2012 to 2017 all-payer inpatient discharge data4,5 from 11 Medicaid expansion states (Arkansas, Arizona, California, Colorado, Iowa, Illinois, Kentucky, New Jersey, New York, Oregon, and Pennsylvania; with the exception of 2017 data for Arkansas and New York) and 6 nonexpansion states (Florida, Georgia, North Carolina, Texas, Virginia, and Wisconsin) (eMethods and eFigure in the Supplement) and classified hospitals that appeared in the top quartile of Medicaid and uninsured discharges in 2012 in each state as SNHs. We grouped Medicaid-covered and uninsured hospitalizations for each quarter in each state into younger (age 26-64 years; target beneficiaries of expansion) and older (age ≥65 years) adults. Our outcome was the percentage of uninsured or Medicaid-insured hospitalizations in an SNH, overall and among subgroups by race/ethnicity and zip code–level poverty, based on the federal poverty level. We analyzed race data to capture unmeasured social factors (eg, structural racism, racial discrimination) and to identify disparities in utilization of safety-net hospitals. While race was not routinely self-identified in our data, race and ethnicity are separately identified in virtually all the states, which is the preferred approach. We used the combined race/ethnicity indicator developed by the Agency of Healthcare Research and Quality. Across all 17 states, the percentage of observations with missing data on race/ethnicity from 2012 to 2016 ranged from 0% to 5.8%, with a median of 1.7%.

    We used a 3-way difference-in-differences study design, contrasting the change in the percentage of uninsured or Medicaid-insured hospitalizations to an SNH from the pre- to postexpansion periods between (1) expansion and nonexpansion states and (2) younger and older adults. Comparison of younger and older adults within geographic areas allows adjustment for unobserved temporal changes in patterns of hospital utilization. We estimated linear regression models with state-level fixed effects.6 We conducted analyses with Stata version 16.1 (StataCorp). Significance was set at P < .05, and all tests were 2-tailed. We provide further methodological details in the eMethods in the Supplement.


    Overall, there were 60 632 753 discharges in the sample, with 42 343 336 (69.8%) among patients aged 65 years or older. We assumed that these patients would be covered by Medicare. Therefore, there were 18 289 417 Medicaid-covered and uninsured patient discharges in the sample (10 855 111 [59.4%] in Medicaid expansion states; 7 434 306 [40.6%] in nonexpansion states). Baseline demographic characteristics of study participants are shown in Table 1. The mean (SD) age in expansion states and nonexpansion states was 43.6 (11.7) years and 43.1 (11.6) years, respectively. Overall, in expansion states there were 6 336 747 discharges (58.4%) among female patients and 4 779 860 (44.0%) among White patients. In nonexpansion states, there were 4 440 908 discharges (59.7%) among female patients and 3 332 668 (44.8%) among White patients. At baseline, among discharges for patients aged 26 to 64 years in the expansion states, the mean (SD) proportion of uninsured or Medicaid-insured discharges at a SNH was higher among Black patients (38.6% [12.1]) and Hispanic patients (39.2% [10.5]) relative to White patients (22.6% [6.7]), and in zip codes with higher poverty levels (Table 2).

    The observed trend in the percentage of uninsured or Medicaid-insured hospitalizations at an SNH showed no systematic changes in the expansion and nonexpansion states. We found no significant change in uninsured or Medicaid-insured hospitalizations at an SNH associated with Medicaid expansion. Likewise, we found no significant change among all subgroups by race/ethnicity and zip code–level poverty (Table 2).


    In this study, hospital utilization patterns suggested that there was socioeconomic, racial, and ethnic segregation in US SNHs. Counter to our hypothesis, increased insurance coverage in ACA Medicaid expansion states did not lead to changes in the hospitals where patients with lower socioeconomic status received care and did not decrease racial and ethnic segregation. It is possible that patients are satisfied with the care they receive and benefit from services that may be unavailable in other settings, such as assistance with insurance, interpretation, and case management. However, it is also possible that the persistence of structural racism and residential segregation prevents patients from transferring care to non-SNHs. A limitation of this study is that unmeasured state policy changes may explain our findings. Extending health insurance coverage alone appears insufficient to reduce hospital segregation by race/ethnicity or socioeconomic status. Future research should identify factors that underlie the use of SNHs among patients with low income.

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    Article Information

    Accepted for Publication: April 17, 2021.

    Published: June 30, 2021. doi:10.1001/jamanetworkopen.2021.14343

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Lasser KE et al. JAMA Network Open.

    Corresponding Author: Karen E. Lasser, MD, MPH, Boston Medical Center, 801 Massachusetts Ave, Boston, MA 02118 (

    Author Contributions: Dr Hanchate 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: Lasser, Paasche-Orlow, Hanchate.

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

    Drafting of the manuscript: Lasser, Hanchate.

    Critical revision of the manuscript for important intellectual content: Liu, Lin, Paasche-Orlow, Hanchate.

    Statistical analysis: Liu, Lin, Hanchate.

    Obtained funding: Hanchate.

    Administrative, technical, or material support: Hanchate.

    Supervision: Lasser, Paasche-Orlow, Hanchate.

    Conflict of Interest Disclosures: Dr Lasser reported receiving grants from National Institute on Minority Health and Health Disparities during the conduct of the study. Ms Liu reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Hanchate reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Paasche-Orlow reported receiving grants from National Institute on Minority Health and Health Disparities during the conduct of the study. No other disclosures were reported.

    Funding/Support: This research was supported by grant R01MD011594 from the National Institutes to Health to Dr Hanchate.

    Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent the views of the National Institutes of Health, Wake Forest School of Medicine, Boston University School of Medicine, or Boston Medical Center.

    Additional Contributions: The authors acknowledge receipt of the state inpatient discharge data from the Agency of Healthcare Research and Quality, the California Office of Statewide Health Planning and Development, the Illinois Department of Public Health, the Pennsylvania Health Care Cost Containment Council, the Texas Department of State Health Services, and the Virginia Health Information; these agencies, their agents and staff, bear no responsibility or liability for the results of the analysis, which are solely the opinion of the authors.

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