Panel Paper: The Impact of Hospital Market Consolidation on Care for Low-Income Populations: Evidence from New York Medicaid

Friday, November 8, 2019
I.M Pei Tower: Majestic Level, Majestic Ballroom (Sheraton Denver Downtown)

*Names in bold indicate Presenter

Sunita Desai1, Sherry Glied1 and Jacob Wallace2, (1)New York University, (2)Yale University

The past decade has seen rapid consolidation in the health care delivery system. However, there has been limited research examining the impacts of consolidation on care for low-income populations, particularly the Medicaid-insured. Following consolidation, non-profit hospitals might use surplus revenue to expand care for low-income populations. On the other hand, consolidation could increase hospital bargaining power with private insurers, resulting in renewed focus on private patients at the expense of Medicaid patients. Given this theoretical ambiguity, we empirically study the impact of hospital mergers on care for Medicaid beneficiaries using data from New York State (NY). NY is an ideal setting for this study because it has experienced substantial consolidation and has a large low-income population insured by Medicaid.

Our primary empirical strategy leverages changes in hospital market concentration with respect to inpatient admissions for privately insured patients, as measured by hospital-level Herfindahl-Hirschmann Index (HHI). Our analysis uses several data sets: the American Hospital Association (AHA) Annual Survey identifies mergers and hospital characteristics which is merged to inpatient data which supplies details on all inpatient admissions across payer groups in NY. This data is supplemented with NY Medicaid enrollment and claims data.

The primary outcomes, which are at the hospital-year level, are the log-transformed number and proportion of admissions for Medicaid patients overall, across major diagnostic categories (MDCs) disproportionately experienced by Medicaid patients, and diagnostic categories used heavily by both Medicaid and privately insured patients. In secondary analyses, we will examine impacts on quality of care for Medicaid patients, including 30-day readmission rates.

We estimate the following hospital-year level model: E(Outcome_ist)=B_0+B_1*HHI_ist+B_2X_ist+B_3 D_st+Alpha_t+Alpha_i

where Outcome_ist denotes the outcome for hospital i in market in year ; HHI_ist denotes a hospital-level measure of market concentration in which the weighted sum of MDC-ZIP code level HHIs are computed for each hospital with weights reflecting the share of a hospitals admissions within each MDC-ZIP code category; X_ist are time-varying hospital-level characteristics; D_st are market characteristics, including the number of Medicaid enrollees living in the hospital’s county in that year; Alpha_t denotes year fixed effects to account for overall trends in the outcomes over the course of the study period; and Alpha_i denotes hospital fixed effects to control for time-invariant hospital characteristics. Standard errors are be clustered at the market level, which are defined as counties. The coefficient of interest will estimate the average effect of a change in a hospital’s market concentration on changes in admissions for Medicaid patients.

We find evidence that greater concentration is associated with relative decreases in the number of Medicaid patients admitted to a hospital and in the proportion of admissions for Medicaid patients. In particular, our preliminary results suggest that moving from a perfectly competitive market to a monopoly market leads to a 60% (p<0.05) reduction in admissions for Medicaid patients. Results are similar across Medicaid-intensive (drug/substance abuse, mental health) and other MDCs. Our findings, which suggest that increases in hospital consolidation lead to reduced provision of inpatient care for Medicaid patients, have important antitrust and policy implications.