Study Population

We used 2007 to 2016 data from Irving Levin Associates on hospital mergers and acquisitions.1 For each transaction, we determined consummation dates (if different from announcement dates) from Web searches and used the categorization by Irving Levin Associates of involved hospitals as acquirers or acquired. We used a database of health systems that was constructed from several sources, including Provider Enrollment, Chain, and Ownership System and Internal Revenue Service data, to determine whether involved hospitals were part of systems and to identify member hospitals. (For details on the study population, quality measures, and the statistical analysis, see the Supplementary Appendix, available with the full text of this article at NEJM.org.) We focused on transactions consummated in the years 2009 through 2013 to analyze performance on a consistent set of measures for 2 or 3 years before and 3 or 4 years after the transaction year. We used transaction data from the full period of 2007 through 2016 to remove other hospitals acquired in 2007 and 2008 or 2014 through 2016 from the control group (detailed below). Because data on process measures were not consistently available after 2014, we limited analysis of those measures to transactions in 2009 through 2011.

Our study sample included short-term acute care hospitals with at least 25 beds and at least 100 fee-for-service Medicare admissions in each year for which performance data were available. Because data were, by definition, present for hospitals involved in transactions at least until the transaction year, for each performance measure we excluded hospitals with missing data in pretransaction years to establish consistent inclusion criteria for acquired and control hospitals (Table S1 in the Supplementary Appendix).

The primary treatment group included hospitals acquired during the period of 2009 through 2013 (including all members of acquired systems). The control group included all other hospitals that were not acquired in the period of 2007 through 2016, were not located within 5 miles of an acquired hospital (i.e., potential local competitors), and were not acquirers located in the same state as the hospitals that they acquired (i.e., in-state acquirers). We excluded local competitors and in-state acquirers from the control group to reduce potential bias from effects of diminished local competition for patients or diminished system-level competition for inclusion in insurers’ state hospital networks. (The latter effects from commercial negotiations could spill over onto care in Medicare.) In secondary analyses, we estimated the effects of acquisitions on the performance of local competitors and in-state acquirers.

We performed two subgroup analyses to evaluate whether acquisition effects were different for hospitals acquired by a hospital (or system) in the same state (61% of acquisitions) or for hospitals acquired by a hospital (or system) with high or low quality, which we defined as performance in the top or bottom quartile in the year before acquisition. Previous research has shown that acquirers are able to increase prices after same-state acquisitions20; if there is also quality competition among systems for inclusion in insurer networks, same-state transactions may cause quality to deteriorate. Conversely, acquisition by higher-performing acquirers might yield improvements.

Study Variables

Quality Measures

Using publicly available data from Medicare Hospital Compare,21 we assessed hospital performance on clinical-process and patient-experience measures. The process measures included seven measures related to cardiac, pneumonia, and perioperative care that were consistently reported from 2007 through 2014 (Table S2). The patient-experience measures included five items from the Hospital Consumer Assessment of Healthcare Providers and Systems survey that were aggregated to the hospital level and consistently reported from 2007 through 2016 (Table S3). For each hospital in each year, we computed a composite score for the clinical-process measures and for the patient-experience measures equal to the average of z scores for each component measure with nonmissing data.

The composite measures constituted two of four prespecified primary outcomes. The other two primary outcomes were the all-cause rate of readmission within 30 days after discharge and the rate of death within 30 days after admission.

Hospital and Patient Characteristics

Using data from the Centers for Medicare and Medicaid Services Provider of Services file, we assessed the following hospital characteristics at baseline: size (number of beds), teaching status, ownership type, and location in a rural area (yes or no). Using admission-level data derived from Medicare claims and Master Beneficiary Summary Files, we computed hospital and year-specific means for total admissions for Medicare patients and the following characteristics of admitted patients: age, sex, race or ethnic group (percent non-Hispanic white), disability as the original reason for Medicare eligibility, dual eligibility for Medicare and Medicaid, Medicare diagnosis-related group payment weight,22 and number of conditions recorded in the Chronic Conditions Data Warehouse.23

Statistical Analysis

We used linear regression to estimate the extent to which post-transaction changes in performance for acquired hospitals differed from concurrent changes for control hospitals in the same state. Specifically, we modeled hospital performance on each outcome during the study period as a function of hospital indicators (to control for time-invariant hospital predictors of performance), the hospital-level case-mix variables described above (to control for changes in measurable patient characteristics), indicators for each state-by-year combination (to control for state-specific trends), a term to remove the transaction year from the estimation (treating it as a transition year), and terms estimating the differential change in performance for acquired hospitals from the pretransaction period to each post-transaction year. For a given post-transaction year, the differential change represents the difference between the observed performance for acquired hospitals and their expected performance if the pretransaction difference had remained unchanged in the post-transaction period (i.e., the estimated effect of acquisition). Equivalently, the model compared the average difference between acquired hospitals and control hospitals during the pretransaction period with the difference in each post-transaction year.

Because performance data were not available beyond the third post-transaction year for transactions in 2013, and because acquisition effects could grow over time, we prespecified differential changes in the third post-transaction year as our primary outcomes. We adjusted for testing of the four primary outcomes using the Hochberg procedure.24 In all analyses, we weighted observations according to the baseline bed count at each hospital. We used robust variance estimation to account for clustering within states.

We prespecified analyses to assess the plausibility of the key assumption of our difference-in-differences analysis — that the pretransaction difference between control hospitals and acquired hospitals would have remained constant in the absence of the acquisitions. First, we compared changes in outcomes between acquired hospitals and control hospitals during the pretransaction period. A significant differential change from the pretransaction period to the post-transaction period would not be clearly attributable to the transaction if a differential change in the pretransaction period presented evidence of an alternative explanation. Second, we tested for differential changes from the pretransaction period to the post-transaction period in the characteristics of patients served by acquired hospitals as compared with control hospitals, and we compared estimates of acquisition effects with and without adjustment for patient characteristics. Similar changes and minimal effects of adjustment would reduce concern regarding confounding effects of acquisitions on the mix of patients served by hospitals. In secondary analyses, we substituted local competitors or in-state acquirers for acquired hospitals in the analysis to estimate potential effects on these groups of nonacquired hospitals.