The Impact of Federal COVID-19 Provider Relief Funds on Patients, Hospitals, and Disparities

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 1R36HS029440-01

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2023.0
    2024.0
  • Known Financial Commitments (USD)

    $43,137
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    . Jason Buxbaum
  • Research Location

    United States of America
  • Lead Research Institution

    HARVARD MEDICAL SCHOOL
  • Research Priority Alignment

    N/A
  • Research Category

    Policies for public health, disease control & community resilience

  • Research Subcategory

    Policy research and interventions

  • Special Interest Tags

    N/A

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Project Summary/Abstract The outbreak of COVID-19 in 2020 imposed extreme demands on the US medical system. Congress responded with $178 billion in emergency relief to be shared among hospitals, physicians, and other providers. However, little is known about the impact of these funds on inpatient capacity, patient experience, patient mortality, or closure and consolidation. The impact of funding on racial/ethnic disparities is also unknown. I aim to address these gaps in knowledge. I propose to exploit a natural experiment made possible by how the US Department of Health and Human Services (HHS) distributed $34 billion in COVID-19 relief funds for hospitals. These funds, awarded to safety- net hospitals and hospitals with high numbers of COVID-19 cases early in the pandemic, were allocated using formulas with inflexible thresholds. Using regression discontinuity methods, I will compare outcomes at hospitals barely missing the criteria for funding with hospitals barely surpassing the criteria for funding. I will extend the common regression discontinuity design to accommodate the multi-variable, multi-cutoff formulas adopted by HHS for fund allocation. Results will have broad policy relevance in several respects, irrespective of whether I detect statistically significant effects. Findings will speak to the advisability of channeling finite resource to the acute care system when public health conditions next overwhelm capacity. Evaluation of this relationship between funding and capacity will speak directly to AHRQ's focus on improving safety, quality, and access. The research will increase understanding of the trade-off between quality and affordability, which can in turn inform decisions around cost containment. In addition, the research will contribute to understanding of the relationship between hospital funding, hospital closures, and competition-reducing consolidation. Closures and consolidation represent perennial challenge to access and affordability - areas of key focus for AHRQ. Finally, findings will speak to the extent that politically viable, "color-blind" policies can reduce disparities across racial and ethnic lines. In so doing, findings can inform the tactics used by policymakers and advocates to reduce healthcare inequities