Hospital mergers and acquisitions: does market consolidation harm patients?

Ž .Journal of Health Economics 19 2000 767–791www.elsevier.nlrlocatereconbase

Hospital mergers and acquisitions: does marketconsolidation harm patients?

Vivian Ho a,), Barton H. Hamilton b

a School of Public Health, UniÕersity of Alabama, Birmingham, Birmingham, AL, USAb Olin School of Business, Washington UniÕersity in St. Louis, Campus Box 1133,

One Brookings DriÕe, St. Louis, MO 63130-4899, USA

Received 1 July 1998; received in revised form 1 March 2000; accepted 31 March 2000

Abstract

Debate continues on whether consolidation in health care markets enhances efficiency orinstead facilitates market power, possibly damaging quality. We compare the quality ofhospital care before and after mergers and acquisitions in California between 1992 and1995. We analyze inpatient mortality for heart attack and stroke patients, 90-day readmis-sion for heart attack patients, and discharge within 48 h for normal newborn babies. Recentmergers and acquisitions have not had a measurable impact on inpatient mortality, althoughthe associated standard errors are large. Readmission rates and early discharge increased insome cases. The adverse consequences of increased market power on the quality of carerequire further substantiation. q 2000 Elsevier Science B.V. All rights reserved.

JEL classification: Ill; L41

Keywords: Mergers; Acquisitions; Quality

1. Introduction

Competitive pressures have led to mergers among all types of providers in thehealth care industry, including hospitals, HMOs, nursing homes and diagnostic

) Corresponding author. Tel.: q1-205-975-0532; fax: q1-205-936-3367.Ž .E-mail address: [email protected] V. Ho .

0167-6296r00r$ - see front matter q2000 Elsevier Science B.V. All rights reserved.Ž .PII: S0167-6296 00 00052-7

( )V. Ho, B.H. HamiltonrJournal of Health Economics 19 2000 767–791768

Ž .laboratories Lutz, 1996 . Recently, the number of mergers and acquisitions in thehospital sector has been particularly high. In 1998, 144 consolidations involved

Ž .298 hospitals and approximately 44,000 beds Monroe, 1999 .The recent flurry of hospital consolidation has generated interest in determining

its impact on prices, costs, and quality of care. While the previous literature hasanalyzed the impact of mergers on prices and costs1, no general industrialorganization studies or health care studies explicitly examine the impact ofmergers and acquisitions on product quality. Yet, federal antitrust agencies, aswell as patient and consumer groups, have voiced concern that these mergers havenegative implications for the quality of health care. For instance, in three recentlyproposed hospital mergers, the Justice Department and the Federal Trade Commis-sion have argued that the merger of two hospitals would decrease competition and

Žtherefore reduce quality of care in the local market United States of America,Plaintiff v. Mercy Health Services and Finley Tri-States Health Group, 1994;

.Rather, 1997; Vandewater, 1998 . This paper tests this hypothesis by analyzing theimpact of hospital mergers and acquisitions on inpatient outcomes.

In fact, the structure of pricing in the hospital sector suggests that the strategiceffect of mergers may reveal itself in the form of quality rather than price effects.A large proportion of patients admitted for hospital treatment are Medicarepatients, for whom hospitals are reimbursed a fixed price, based on their Diagnosis

Ž .Related Group DRG regardless of the amount of care provided to patients. Inaddition, health insurance companies may use these fixed prices as a guide fornegotiating the prices they will pay to hospitals to cover the care of privatelyinsured patients. Thus, if prices for treating many patients are fixed, hospitals mayattempt to lower quality in order to maximize profits. The ability to do so may befacilitated in the case of hospital mergers or acquisitions, which reduce the need tocompete on the basis of product quality.

Even when national hospital systems acquire independent hospitals withoutchanging local market concentration, concerns arise regarding the consequencesfor patient care. Hospital staff have argued that acquisition by a for-profit hospitalsystem leads to reductions in nursing staff, a shift towards employment oflower-paid employees, and reductions in expenditures on hospital supplies that

Ž .harm the care of patients Lagnado et al., 1997; McGinley, 1998; Burkhart, 1997 .Such allegations and anecdotes are no substitute for an objective study of

whether hospital mergers and acquisitions harm patient care. This study comparespatient outcomes in hospitals before and after mergers and acquisitions that haveoccurred in California between 1992 and 1995. We focus our study on heart attackand stroke patients, two leading causes of death and disability in the US, as well asearly discharge for normal newborn babies. Our results yield no evidence thatmergers and acquisitions have increased inpatient mortality. These results must be

1 Ž . Ž .See, for example, Connor and Feldman 1998 and Dranove 1998 .

( )V. Ho, B.H. HamiltonrJournal of Health Economics 19 2000 767–791 769

interpreted with caution, because the standard errors for the mortality estimates arelarge. Thus, we cannot rule out the possibility that AtrueB detrimental effects ofconsolidation on mortality could readily be three times the estimated coefficientsin our results. Moreover, some hospital consolidation leads to higher readmissionrates for heart attack patients and faster discharge for normal newborns.

The results have important policy implications for the expanding role of marketforces in the US health care industry. Policy makers disagree on the extent towhich government intervention is needed to regulate ongoing consolidation amonghospitals and other health care providers. As stated in a recent review, the keyquestion facing enforcers of antitrust policy in health care markets is whether thesechanges enhance efficiency and quality or instead facilitate collusion and market

Ž .power Haas-Wilson and Gaynor, 1997 . This paper contributes to the debate byanalyzing the impact of these consolidations on quality.

Section 2 reviews the existing literature on health care mergers and acquisi-tions. Section 3 describes the data and provides descriptive statistics. Sections 4and 5 describe the econometric framework and the results. Section 6 concludes.

2. Existing literature on health care mergers and acquisitions

It is important to distinguish how hospital quality may be affected by hospitalmergers versus hospital acquisitions. Independent hospitals involved in mergerstend to be in close geographic proximity, and therefore are more likely to raiseconcerns regarding increased market power. In contrast, hospitals that are acquiredmay be participating in consolidation across markets that have no customeroverlap. For instance, some hospitals in our California database were acquired byan out-of-state hospital system. Such hospital acquisitions may not lead toincreased local market concentration, but may affect the quality of health care forreasons we discuss below.

The industrial organization literature concludes that a wide array of outcomesregarding market share, prices, and costs are possible in cases of Amerger for

Ž . 2oligopolyB Berry and Pakes, 1993; Baker, 1999 . Consolidation potentiallyaffects market variables for all hospitals in a local market, not just those engaged

Ž .in mergers and acquisitions. For instance, Salant et al. Salant et al., 1983 havepointed out that expansion of output by rival firms can render a merger unprof-itable. Mergers may also generate cost savings that encourage the merged firm tolower prices. Thus, theoretical models suggest that merging firms may lead tohigher market concentration; but they may not be able to exercise market power.The direction and magnitude of the impact of hospital mergers and acquisitions oneconomic variables and health care outcomes must be determined empirically.

2 This literature does not provide theoretical models of the impact of mergers on product quality.


We first review the empirical literature on hospital mergers. In the hospitalsector, most empirical research has focused not on the potential detrimental effectsof consolidation, but on the potential operating efficiencies resulting from hospital

Žmergers Connor and Feldman, 1998; Dranove, 1998; Dranove and Shanley,. Ž .1995 . Connor and Feldman 1998 report modest cost savings associated with

mergers, with evidence that these costs savings vanish in more concentratedmarkets. The result may reflect the finding that scale economies exist only forsmall hospitals, so that efficiency gains are unlikely to be realized in the recent

Ž .mergers challenged by federal antitrust agencies Dranove, 1998 .Ž .Gaynor and Haas-Wilson 1999 summarize the literature on the relationship

Ž .between hospital consolidation and price. They note that Connor et al. 1997found that merging hospitals exhibit smaller percentage price increases thannon-merging hospitals. This finding supports the hypothesis that the efficiencygains of hospital mergers outweigh their anti-competitive effects. However, thisresult is reversed in concentrated markets; where merging hospitals display higher

Ž .percentage price increases. Moreover, Krishnan 1999 finds that merged hospitalsincrease prices for those services where the merger raises market share. Thecombined conclusions of the above studies of costs and prices suggest that hospitalmergers raise price–cost margins in concentrated markets.

We next consider the empirical literature on hospital acquisitions. Dranove andŽ .Shanley 1995 hypothesize that local multi-hospital systems gain reputation

benefits from standardizing product offerings and quality. The reputation benefitsthese systems enjoy over non-system hospitals yield higher price–cost margins.However, acquisitions do not necessarily imply an increase in local market power.For instance, some of the hospitals in our sample were acquired by hospitalsystems with headquarters outside of California. Nevertheless, those multi-statesystems that did acquire hospitals in California during our study period oftenacquired multiple hospitals in the state, perhaps with the intent of achieving localmarket power. Even where hospital acquisition does not increase local marketconcentration, one might argue that transfer of control from a local hospital boardto a more distant one could alter a hospital’s behavior. Hospitals with strong ties tothe local community may trade off profit maximization in favor of other goalssuch as quantity or quality maximization. In contrast, non-locally based systemsŽ .particularly for-profit systems acquiring non-profit hospitals may focus more onprofit maximization. Thus, while the literature on the effects of hospital acquisi-tions is limited, there are reasons to believe that such consolidation may raiseprice–cost margins as well.

Although much of the previous literature suggests that market consolidationleads to higher price–cost margins, none of these studies explicitly examined theconsolidation’s impact on hospital quality. Yet Gaynor and Haas-Wilson note thatit is essential that studies of the economic effects of market power include ananalysis of their impact on quality. The same factors that imply higher price–costmargins after merger or acquisition may also lead to lower quality after consolida-


tion. On the other hand, Gaynor and Haas-Wilson note that higher hospital pricesmay simply reflect better quality. While this argument runs counter to thehypothesized effects of increased market concentration, it cannot be ruled outgiven the role that non-price competition is believed to play in health careŽ .Dranove et al., 1992; Luft et al., 1986 .

Moreover, the structure of pricing in the hospital sector suggests that thestrategic impact of mergers may occur in the form of quality as well as priceeffects. Because a substantial proportion of hospitals’ revenues are based onMedicare reimbursement rates3, market consolidation may enable providers tocompromise quality, with less concern for its impact on patient demand or price.Empirical evidence showing that costs decline only slightly or remain constantafter mergers contradicts this reasoning. Yet, federal antitrust agencies and con-sumers strongly voice their concerns regarding the potential detrimental impact ofhospital consolidation on hospital quality. Thus, an examination of the impact ofhospital mergers and acquisitions on patient outcomes will provide a morecomplete picture of market consolidation and social welfare, and contributeobjective information to an antitrust issue which is hotly debated in the publicarena.

3. Data and summary statistics

We chose to analyze the California market, where a great deal of hospitalconsolidation has occurred, and where patient discharge data is readily available.The analysis requires data on the characteristics of hospitals and the timing ofhospital mergers or acquisitions if they occurred, as well as information on thecare of hospital patients who were treated in the same time period.

Ž .Hospital Data. The American Hospital Association AHA Annual Survey ofHospitals provides characteristics of California hospitals from 1991 to 1995. Eachyear of the survey records hospital mergers that occurred since the previoussurvey. These mergers represent two or more corporations coming together into asingle surviving entity, although both physical facilities may continue to treatpatients after the merger.

The AHA survey also lists each individual hospital’s membership in a multi-hospital system, if applicable.4 This information, as well as data in the annualHospital Acquisition Report published by Irving Levin Associates was used to

3 For instance, approximately 60% of heart attack patients and 75% of stroke patients in this studyare covered by Medicare.

4 The AHA Data Base defines a multi-hospital health care system as two or more hospitals owned,leased, sponsored, or contract managed by a central organization.


identify acquisitions of individual hospitals by health care systems, and acquisi-tions of systems by other systems.5

Patient Data. Data on patients come from the California Office of StatewideŽ .Health Planning and Development OSHPD discharge data, Version A for the

years 1991 to 1996. The OSHPD database contains standardized information fromhospital discharge abstracts for all patients admitted to California hospitals. Allpatients admitted to an acute care hospital in California between January, 1991 and

Ž .March, 1996 with a primary diagnosis of acute myocardial infarction heart attackor stroke, and normal newborn babies are included in the sample.6 Approximately4% of patients in the sample were missing information on length of stay and wereexcluded from the sample. In addition, due to size constraints, we analyze a 50%random sample of newborn babies. This yielded a sample of 256,193 heart attack

Ž . Ž .patients in 461 hospitals , 268,506 stroke patients in 476 hospitals , and 510,572Ž .newborn babies in 335 hospitals over the course of the analysis.

3.1. Trends in California mergers and acquisitions

Hospital mergers and acquisitions occur in a variety of forms, which wedistinguish in our analysis. Between 1992 and 1995, there were 21 independentCalifornia hospitals involved in mergers recorded by the AHA. Over this sametime period, 54 independent hospitals were acquired by a hospital system. Prior tothis time period, many hospitals already belonged to a hospital system; andbetween 1992 and 1995, many of these hospitals were again involved in hospitaltransactions. In fact, over this time period, 65 hospitals that already belonged to ahospital system were then acquired by another system. The following analysisdistinguishes between acquisitions of independent hospitals versus hospitals be-longing to a system that was then acquired by another system. Because the lattergroup already belonged to a system, any potential impact of acquisition on localmarket variables may have already occurred. Thus, the impact of acquisition onquality of care for hospitals already belonging to a system is hypothesized to besmaller than that for independent hospitals which are acquired.

3.2. Measuring hospital quality

The primary goal of this paper is to determine whether the quality of patientcare declines after a hospital is merged or acquired. The measures of quality in this

5 Acquisitions of individual hospitals by health care systems, or acquisition of systems by othersystems for 1992 and 1993 were identified based on changes in health care system ID listed betweenthe 1991–1992 surveys and the 1992–1993 AHA surveys. For subsequent years, hospital acquisitionswere defined based on hospital transactions listed in The Hospital Acquisition Report published byIrving Levin Associates, which began tracking transactions in 1994.

6 Acute myocardial infarction patients are those with ICD-9 codes 410.0–410.1 and 410.3–410.9 asa primary diagnosis. Stroke patients have ICD-9 codes 430, 431, 434.00, 434.10, 434.90, or 436 as aprimary diagnosis. Normal newborn babies are discharged with DRG code 391.


study are inpatient mortality, readmission rates, and early discharge of newborns.Inpatient mortality is an important and objective measure of hospital quality. Cet.par., an increase in inpatient mortality associated with mergers and acquisitionsimplies that consolidation reduces quality and harms patient care. One maysurmise that inpatient mortality is a relatively insensitive quality measure; patientwell being or satisfaction may rise or fall substantially, with no accompanyingchange in mortality. However, past research has found that price declines associ-

Žated with the introduction of DRG reimbursement another significant change in.economic incentives facing hospitals had a significant detrimental impact on

Ž .inpatient mortality Cutler, 1995 . Therefore, it seems reasonable to hypothesizethat mergers and acquisitions may affect inpatient mortality as well.

Use of inpatient mortality as an outcome measure is complicated by the factthat this variable is censored by live discharge. For example, the probability ofdying within 30 days for heart attack patients may be equal for merged andunmerged hospitals. However, inpatient mortality rates could appear lower formerged hospitals even if true rates were similar, because they tend to dischargetheir patients earlier than non-merging hospitals do. We will control for thiscensoring of inpatient mortality using Cox proportional hazards models to examinethe hazard rate for inhospital death, with live discharge treated as a censoredobservation.

It would be useful to validate the inpatient mortality results over a longer timeperiod, such as 90-day mortality. However, the OSHPD datasets do not provideinformation on mortality after discharge. Moreover, mortality after discharge ismore difficult to attribute to hospital care, because it is also affected by post-dis-charge outpatient care, which may or may not depend on which hospital thepatient was initially treated in. Nevertheless, given the relatively short timehorizon over which inpatient mortality is observable, we also examine 90-dayreadmission rates for heart attack patients as a measure of health care quality.7 Inthe medical literature, higher readmission rates are often assumed to indicate lowerquality hospital care.

Finally, if shorter hospital stays decrease the amount of care patients receive,then an association between mergers and acquisitions and declining length of staymay also imply a reduction in quality of care. This argument is particularlycompelling for the case of early discharge for newborn babies. Concern that earlydischarge endangers the health of mothers and newborns led to the passage of the

7 We consider readmissions to any California hospital for which the primary diagnosis is AMI, oldAMI, congestive heart failure, or ischemic heart disease. We exclude admissions occurring within 30days of discharge because they are more likely to represent the treatment of the AMI itself, rather thanlater complications. Similar results were found using 6-month readmission rates and readmission forany reason. Results are available from the authors upon request. Less clinical research has beenconducted on readmission following stroke. Therefore, we are reluctant to use readmission for strokepatients as a measure of quality of care.


Mothers’ Health Protection Act of 1996, which mandates coverage of a 48-hhospital stay. Previous studies provide conflicting evidence on the impact of early

Ždischarge on health outcomes for newborns Edmonson et al., 1997; Gazmararian.et al., 1997; Liu et al., 1997 . However, given the absence of information on

patient outcomes after discharge in our data, we use discharge within 48 h as aprocess measure of quality in the period preceding this legislation.8

3.3. DescriptiÕe eÕidence on the impact of mergers and acquisitions on hospitalquality

To provide a first look at the relationship between M&As and outcomes, weexamine descriptive statistics on patients who were treated in hospitals before andafter mergers or acquisitions took place. Table 1 describes outcomes for patientsadmitted to California hospitals for two selected years in our data set, 1991 and1995.9 The patients are subdivided according to whether or not they were treatedin a hospital that merged with another hospital, an independent hospital acquiredby a hospital system, or a hospital belonging to a system that was acquired byanother hospital system. For instance, the column subtitled AMergedB presentsinformation on patients who were admitted in 1991 to a hospital that subsequentlymerged with another hospital between 1992 and 1994; the column then character-izes patients who were admitted in 1995 to one of these same hospitals. Inpatientmortality and length of stay are reported for heart attack and stroke patients. Wealso report 90-day readmission rates for heart attack patients. Length of stay andthe rate of early discharge are reported for newborn babies.

The descriptive statistics illustrate the reasoning underlying the regressionspecification we choose in the forthcoming analysis. First, it seems reasonable tocompare inpatient mortality in hospitals before versus after merger or acquisitionto assess the impact of consolidation on patient outcomes. For example, heartattack patients treated in hospitals that merged after 1991 had an inpatient

8 The OSHPD data does not provide information on time of birth, so that our early dischargedefinition is an approximation of discharge within 48 h. The OSHPD data sets length of stay equal to 1for babies born and discharged on the same day, as well as for babies discharged 1 day after birth.Therefore, our early discharge measure includes all babies discharged between 0 and 24 h after birth, aswell as some of the babies discharged between 25 and 48 h of birth. A baby born at 0300 h one dayand discharged at 1500 h the next day has a length of stay equal to 1 in the OSHPD data, and thereforeis identified as an early discharge. However, a baby born at 2100 h one day and discharged at 0900 h 2days hence is not an early discharge, even though her length of stay was actually 36 h as well.Assuming that the amount of mismeasurement for early discharge is equal across all hospital types,then we can accurately assess the impact of M&As on changes in early discharge patterns.

9 The descriptive statistics only examine information on patients admitted to hospitals in 1991 and1995, in order to simplify the before-and-after comparison of mergers and acquisitions. We do not useinformation on patients from 1996 in the descriptive statistics, because we only have information onpatients admitted through March of this year. The regression analysis will utilize data on patientsadmitted in all years.

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Table 1Patient outcomes, by mergerracquisition status of treating hospital

Consolidation in 1992, 1993, or 1994

All No Consolidation Merged Independent acquired System acquired

Patients admitted inr 1991 1995 1991 1995 1991 1995 1991 1995 1991 1995Babies born in

Heart attack patientsMean length of stay 7.46 5.79 7.54 5.82 6.85 6.30 7.32 5.56 6.27 5.16% died 0.096 0.078 0.096 0.077 0.097 0.093 0.10 0.081 0.086 0.079

a90-Day readmission 0.094 0.085 0.095 0.085 0.087 0.083 0.084 0.084 0.089 0.083Sample Size 44046 50805 38553 44165 1293 1305 2668 3523 1532 1812

Stroke patientsMean length of stay 11.00 7.98 11.19 8.17 9.19 6.31 10.60 6.55 7.70 6.42% died 0.117 0.102 0.115 0.100 0.136 0.107 0.132 0.117 0.107 0.103Sample size 47719 55566 42194 49272 1495 1453 2663 3026 1367 1815

NewbornsMean length of stay 1.84 1.55 1.84 1.55 1.81 1.55 1.81 1.49 1.80 1.49% discharged within 1 day 0.49 0.65 0.49 0.65 0.52 0.65 0.51 0.67 0.52 0.69Sample size 122,833 108,459 112,001 97,144 3061 3637 4233 4447 3538 3231

aReadmission rates are calculated based on rehospitalization with a primary diagnosis of acute myocardial infarction, old AMI, congenstive heart failure, orischemic heart disease for those patients discharged alive. We exclude admissions occurring within 30 days of discharge.


Ž .mortality rate of 9.7% in 1991 prior to merger , versus a mortality rate of 9.3% in1995. The decreases in mortality in 1995 versus 1991 for all hospitals that mergedor were acquired over the time period would lead one to conclude that consolida-tion tends to reduce inhospital death.

However, between 1991 and 1995, inpatient mortality rates also declined forŽ .hospitals that did not undergo consolidation from 9.6% to 7.7% . Therefore,

inhospital death rates for hospitals that did not undergo consolidation between1991 and 1995 can be used as a control group when assessing the impact ofmergers and acquisitions on outcomes. Comparison of outcomes within hospitalsbefore and after consolidation provides an estimate of the direct effect of mergersand acquisitions; while comparison relative to hospitals that did not consolidateaccounts for changes in outcomes that would have occurred if consolidation didnot take place. The analysis follows a Adifferences-in-differencesB frameworkŽ .Heckman et al., 1999 for comparing hospital outcomes, although the unit ofobservation in the regressions will be the patient.

This approach is complicated though, by a selection problem; namely, hospitalsthat merge or are acquired may differ systematically from those that do notconsolidate, even prior to consolidation. Note that in 1991, the mortality rate forheart attack patients discharged alive from hospitals that did not consolidateŽ .9.6% is higher than that observed for hospitals involved in system acquisitionsŽ .8.6% . Systematic differences also arise among stroke patients. In 1991, patientsin independent hospitals that later merged or were acquired had higher inpatient

Ž .mortality rates 13.6% and 13.2% versus hospitals which did not subsequentlyŽ .merge or undergo acquisition 11.5% . If one does not account for systematic

differences in mortality in merging or acquired hospitals both before and afterconsolidation, then one may incorrectly attribute these pre-existing differences to amerger or acquisition effect. Section 4 will discuss the identification of the effectof mergers and acquisitions on outcomes controlling for systematic differences inquality that may exist in consolidating hospitals.

The descriptive statistics do not account for censoring of the patient outcomedata for stroke and heart attack patients. For example, between 1991 and 1995inpatient mortality declined for all stroke patients. However, the overall decline ininpatient mortality between 1991 and 1995 does not necessarily represent animprovement in quality. Descriptive statistics on length of stay provided in Table 1reveal that overall length of stay for stroke patients fell from 11 to 8 days between1991 and 1995. Because patients were staying 3 days less on average in 1995,there was less time to observe inpatient mortality. This data censoring will also becontrolled for in the regression model to follow.

An appendix available from the first author’s website10 provides furtherdescriptive statistics on patients treated in hospitals in 1991 and 1995. A number

10 http:rrlhcwww.soph.uab.edurhcoprho.htmrjhe appendix.pdf.–


of differences exist in patient case mix and insurance status by mergerracquisitiontype and year that must be controlled for when assessing the impact of hospitalconsolidation on patient outcomes. There is a noticeable increase in the number ofcomorbidities reported for heart attack and stroke patients admitted in 1991 versus1995. This increase may be due to DRG creep; as competition in the health careindustry becomes more aggressive hospitals have an incentive to report morecomorbidities per patient, which will increase their payments under the MedicareDRG reimbursement system.

The percentage of normal newborns who are white is more than 10 percentagepoints higher in independent hospitals acquired by systems than in hospitals thatdid not consolidate. In addition, the number of births covered by private insuranceis higher among this group of acquired hospitals than in other facilities. Thesedifferences in case mix by mergerracquisition status will be controlled for in thefollowing multivariate regression framework.

4. Empirical framework

Let y denote the quality measure of interest for patient i admitted to hospitali h t

h in year t, such as whether the patient was readmitted within 90 days after theinitial discharge, or whether a newborn was discharged early. Suppose that yiht

can be written as

y sa MA qu qX bqgVOL q´ , 1Ž .i h t ht h iht ht i ht

where MA is a vector of three dummy variables indicating whether a patient hasht

been admitted to a hospital after the hospital had: merged; been acquired; or beenpart of a system acquired by another hospital system.11 The parameter a repre-sents the effect of merger or acquisition on outcomes, relative to expectedoutcomes if the admitting hospital had not consolidated.

The previous literature on hospital consolidation suggests that mergers andacquisitions lead to higher price–cost margins, although the reasons underlying

Žincreased price–cost margins may differ e.g. market power for local hospitalmergers, reputation benefits, distancing from local community interests, or market

.power for hospital acquisitions . Correspondingly, we hypothesize that the effectof hospital consolidation on outcomes will differ by consolidation type. However,given the absence of previous research on the relation between consolidation andquality, we do not hypothesize whether the impact of consolidation will be largerfor mergers versus acquisitions. But the impact of acquisition on quality of carefor hospitals already belonging to a system is hypothesized to be smaller than thatfor independent hospitals which are acquired.

11 Less than five hospitals consolidated more than once during the sample period. For these hospitals,we measured the effects of the second consolidation on quality.


The data in Table 1 suggest that there are fixed differences across hospitals,such as location, that may be correlated with merger status. For example, hospitalsthat merged or were acquired had inpatient mortality rates that differed fromnon-consolidating hospitals even prior to the consolidation. To account for these

Ž .time invariant differences between hospitals, Eq. 1 includes a vector of hospital-specific intercepts denoted by u .12 Because the regressions include hospitalh

indicators, all characteristics that do not change over the sample period will becaptured by these controls. Therefore, we do not include hospital-level variablesfor other factors such as non-profitrfor-profit status, which affect outcomes butwhich are also unchanging over the sample period.

In general changes in hospital performance after acquisition may result from anaccompanying change in ownership status — from non-profit to for-profit. In thesample over the period of our study, the overwhelming majority of acquisitions donot involve a change in ownership status. Non-profits acquire non-profits, andfor-profits acquire for-profits. Thus, although changes in ownership status afterconsolidation remains an important policy issue, it is not one we can address withthis sample of hospital patients.

The vector X consists of patient characteristics including age, gender, race,i h t

and number of comorbidities to control for the fact that M&A effects may reflectsystematic differences in observed case mix across hospital types. Because of thepotential for DRG creep, we estimate the model with and without the comorbidityvariables. In addition, X also includes year dummies to control for time trendsi h t

that are evident in Table 1. An indicator variable for patients admitted as transfersfrom other acute care hospitals is also included as an explanatory variable in theregressions for heart attack patients. These patients are often transferred tofacilities with more advanced care such as angioplasty or open heart surgery, andtherefore may differ systematically in health status from other patients. Finally, toinvestigate the possibility that changes in outcomes represent a volume effect, thenumber of patients treated with the same primary diagnosisrDRG in the hospital

Ž .during the year in which the patient was admitted VOL is also included as aht

covariate.13

Because hospitals involved in consolidation often had different proportions ofprivate insurance or Medicare patients, or altered their mix of Medicare andprivate insurance patients after consolidation, we also estimate the model includingindicators for primary payment source. To examine whether outcomes after merger

12 For most mergers, both facilities continued to operate after consolidation. Therefore, separate fixedeffects were included in the regressions throughout the sample period for each hospital.

13 The volume variable will capture the association between number of patients treated and outcomesfor any one hospital. However, it will not capture the benefits of increased volume across hospitalswhich may result from merger or acquisition. This latter effect will be reflected in the merger andacquisition dummy variables.


Ž .and acquisition differ by payment source, we then estimate Eq. 1 separately forŽ .Medicare or Medi-Cal in the case of newborns and private insurance patients.

Previous analyses of the impact of hospital consolidation on prices and costssuggest that consolidating hospitals may only be able to exercise market power in

Ž .concentrated markets Connor et al., 1997 . We will determine whether ourfindings change when we allow the effects of hospital mergers and acquisitions tovary by local market concentration. We define local hospital markets using the

Ž . Ž .Health Service Areas HSA formed by Makuc et al. 1991 for the entirecoterminous United States. This method was also used by Connor and FeldmanŽ .1998 in their analysis of hospital mergers.

Makuc et al. identified HSAs based on travel patterns between counties byMedicare beneficiaries for routine hospital care. Cluster analysis was used tocombine counties linked by high border crossing into HSAs. Information on eachpatient’s county of residence in our data was used to assign each patient to anHSA. The patients in our sample resided in 27 different California HSAs. Thelowest number of hospitals in an HSA in a given year was one, and the largestnumber of hospitals in an HSA in a given year was 157.

Ž .The Herfindahl Hirschman Index HHI , equal to the sum of squared marketshares of all hospitals in a local market, was constructed for each HSA and year.The lowest HHI for an HSA among heart attack patients in a given year was0.012. The highest market concentration for an HSA in a given year was 1.0. Themean HHI for all heart attack patients in the sample was 0.090.

To assess the importance of market concentration for health outcomes, we addthe HHI as an explanatory variable to the specification described above. If mergersand acquisitions only affect the quality of care by increasing local marketconcentration, then the coefficient on the HHI and its corresponding coefficientwill capture part of this effect. However, the market power that any consolidatinghospitals will be able to exercise will depend on the size, cost structure, andcasemix of the facilities involved in merger or acquisition. Therefore, we also addinteraction terms of the merger and acquisition dummy variables and the HHI tothe above regression. These variables capture systematic variations in the ability ofconsolidating hospitals to exercise market power at any given level of marketconcentration and thus change quality after merger or acquisition. For ease ofinterpretation, we examine the results of these analyses only after we havediscussed the results of the simpler specification without market concentrationeffects.

4.1. Adapting the framework to surÕiÕal data

Ž .Eq. 1 is easily estimated by standard limited-dependent variable models whenthe outcome is a binary measure such as 90-day readmission, or newborn earlydischarge. However, investigating the impact of M&As on inpatient mortalityrequires estimation of a survival model for time until death in hospital, withcensoring for live patient discharges. For example, suppose that mergers lead to


shorter lengths of stay, but have no effect on inhospital mortality conditional uponlength of stay. In this case, a simple logit regression of mortality on merger statuswould erroneously lead one to conclude that hospital mergers reduce mortality,since inhospital deaths are less likely to be observed for patients with shorterlengths of stay. Consequently, the empirical framework must allow for thepotential non-independence between length of stay and inpatient mortality.

The impact of mergers and acquisitions is therefore estimated using thefollowing likelihood function:

ntd iLs l m exp y l u du , 2Ž . Ž . Ž .Ł H

0is1

Ž .where the hazard function l m is the probability that the patient dies in hospitalafter m days in hospital, conditional upon remaining in the hospital for at least mdays. Because the likelihood function yields the probability of death in hospital onday m given survival up to day m, the estimated inpatient mortality hazard rate isestimated accounting for the fact that length of stay varies across patients.

Ž .The hazard function l m is parameterized to depend upon the MA , Xht iht ,

and VOL vectors. We follow a common approach and adopt a proportionalht

hazards specification, so that<l m MA , X ,VOL sexp a MA qX bqgVOL l m ,Ž . Ž .Ž .i h t ht i ht ht ht i ht ht 0 h iht

3Ž .Ž .where l m represents the hospital-specific baseline transition intensity func-0 h

tion. Measured characteristics thus shift the transition intensity above or below itsbaseline. While a wide variety of parametric assumptions may be made for thebaseline hazard, misspecification of the functional form may lead to biasedparameter estimates. Consequently, we estimate the model using Cox’s PartialMaximum Likelihood estimator, which does not require a specification for thebaseline hazard to obtain estimates of b. While we could include hospital-specific

Ž .indicators in Eq. 3 , we adopt a more general specification of hospital specificŽ .baseline transition intensities l , which allows not only the intercept, but also0 h

the shape of the baseline hazard to differ by hospital to account for fixedŽ .differences across hospitals Kalbfleisch and Prentice, 1980 .

5. Empirical results

This section describes the estimated effects of mergers and acquisitions on eachmeasure of hospital quality. Unlike the descriptive statistics presented in Section 3,data from all the years 1991–1996 are used in this analysis.

5.1. The impact of mergers and acquisitions on inpatient mortality

We first examine the impact of mergers and acquisitions on inpatient mortalityfor heart attack and stroke patients. The Cox proportional hazard estimates of Eq.


Ž .3 for various specifications of our model are presented in Table 2. The regressionis estimated using data on all patients regardless of discharge status, with survivaltreated as a censored variable for patients discharged alive. Positive coefficientsindicate that an increase in the variable implies an increase in the conditionalprobability of dying on a given day.

The first four rows of Table 2 reveal that none of the merger or acquisitionvariables is precisely estimated. Thus, consolidation has no tangible impact oninpatient mortality for either heart attack or stroke patients. Although there is noevidence here that mergers and acquisitions affect patient outcomes, a number ofother explanatory variables behave as hypothesized. Larger patient volume isassociated with a lower conditional probability of inpatient death for heart attack

Žpatients, as is a lower number of patient comorbidities. Previous studies Luft et.al., 1979 attribute the volume effect to learning economies in performing coro-

nary artery bypass grafts and angioplasties. In contrast, hospital volume does notaffect inpatient mortality for stroke patients. This may reflect the fact that stroke ismore often treated through medical management rather than invasive surgery. Inaddition, a larger number of comorbidities appears to be associated with a reducedinpatient mortality hazard rate among stroke patients.14

In order to investigate potential differences in consolidation effects by insur-ance status, we re-estimated the Cox regressions separately for Medicare andprivate insurance patients. Again, we found that mergers and acquisitions do notaffect the conditional probability of inpatient mortality for either insurance type.15

The descriptive statistics discussed in Section 3 suggested a propensity forincreased coding of comorbidities over time, which may have been greater forhospitals which merged or were acquired. Therefore, we re-estimated the specifi-cations in Table 2, excluding the number of comorbidities as an explanatoryvariable.16 Again, the results are consistent with the hypothesis that mergers andacquisitions do not affect inpatient mortality.

The apparent lack of a tangible effect of mergers and acquisitions on inpatientmortality may be due to an insufficient sample size of patients in consolidatinghospitals. Among heart attack patients, 3.1% were in hospitals that merged, 6.6%were in independent hospitals that were acquired, and 4.5% were in hospitalsystems acquired by other systems. These small samples can lead to relativelylarge standard errors. For example, for heart attack patients the merger of twoindependent hospitals has a standard error of 0.065. To find a significant effect of

14 Ž .The raw data reveals that stroke patients who die have more comorbidities 4.76 than thoseŽ .discharged alive 4.38 . However, stroke patients who die also have substantially longer lengths of stay

Ž .than patients discharged alive 16.8 versus 10.2 days . Thus, the Cox regression estimates reflect thefact that sicker stroke patients are less likely to die on any giÕen day given that they are still in hospital— because they are likely to stay much longer than healthier patients, then die in hospital.

15 The results are available from the authors upon request.16 The results are available from the authors upon request.


Table 2Cox proportional hazards estimates for inpatient mortality )

Heart attack patients Stroke patients

Ž . Ž .Merger 0.022 0.339 y0.010 y0.171Ž . Ž .Independent acquired y0.031 y0.712 0.014 0.373Ž . Ž .System acquired y0.010 y0.216 y0.020 y0.508Ž . Ž .Patient volume y0.0006 y2.625 y0.0002 y1.067Ž . Ž .Comorbidities 0.149 47.011 y0.027 y10.297Ž .Transfers y0.364 y14.415 –Ž . Ž .Medi-Cal y0.007 y0.217 y0.164 y6.143Ž . Ž .Private insurance y0.294 y8.595 y0.159 y5.707Ž . Ž .Self-pay 0.141 2.511 0.230 6.151Ž . Ž .Indigent y0.493 y4.531 y0.590 y7.460Ž . Ž .Other payment y0.153 y1.759 y0.084 y1.146

) t-statistics are in parentheses. Ns256,193 heart attack patients, Ns268,506 stroke patients.Each model also contains year dummies, indicators for ages 0–29, 30–39, 40–44, 45–49, . . . , 85–89,90q, gender, black, Hispanic, Asian and other races. The excluded category for insurance dummyvariables is Medicare. Each model also includes hospital specific baseline hazards.

these mergers on inpatient mortality, the parameter estimate would be twice thestandard error, or 0.130. Thus, we cannot rule out the possibility that the AtrueBeffect may indeed be positive, but smaller than 0.130. Even for heart attackpatients in independent hospitals acquired by systems, which is the most preciselyestimated M&A effect in Table 3, we cannot rule out true effects up to three times

Table 3Linear probability estimates for readmission within 90 days for heart attack patients)

Coefficient estimates

All patients Medicare Private insurance

Ž . Ž . Ž .Merger 0.017 3.122 0.014 1.813 0.025 2.731Ž . Ž . Ž .Independent acquired 0.009 2.280 0.013 2.512 y0.005 y0.674Ž . Ž . Ž .System acquired 0.007 1.763 0.006 1.151 0.009 1.305Ž . Ž . Ž .Patient volume y0.00001 y0.733 0.000003 0.117 y0.00003 y1.244

Ž . Ž . Ž .Comorbidities 0.004 12.997 0.004 10.216 0.003 5.389Ž . Ž . Ž .Length of stay y0.0007 y6.890 y0.001 y6.855 y0.0005 y2.016Ž . Ž . Ž .Transfers y0.002 y0.945 0.002 0.753 y0.003 y1.019Ž .Medi-Cal 0.017 5.859Ž .Private insurance y0.018 y7.339Ž .Self-pay y0.023 y6.027Ž .Indigent y0.012 y2.256Ž .Other payment y0.018 y3.377

) t-statistics are in parentheses. Ns234,375 patients; 135,635 Medicare, 68,814 Private Insurance.Each model also contains year dummies, indicators for ages 0–29, 30–39, 40–44, 45–49, . . . , 85–59,

Ž90q, gender, black, Hispanic, Asian and other races. Readmissions are subsequent admissions to any.California acute care hospital for heart failure, acute myocardial infarction, or ischemic heart disease.


the magnitude of the estimated coefficient of y0.031. Thus, the impact of mergersand acquisitions on inpatient mortality may be too small to detect with the givensample sizes.

5.2. The impact of mergers and acquisitions on readmissions

Table 3 contains estimates of the determinants of readmission within 90 daysfor heart attack patients who were discharged alive. These estimates were derivedfrom a linear probability model, where the dependant variable equals 1 if thepatient is readmitted within 90 days and 0 otherwise.17 Column 1 indicates thatreadmission rises after two types of hospital consolidation. Treatment in a mergedhospital increases the probability of readmission 1.7 percentage points, whiletreatment in an independent hospital acquired by a system raises readmission ratesby 0.9 percentage points. Acquisition of a hospital belonging to a system acquiredby another system also increases the probability of readmission, although the

Ž . Ž .effect is smaller 0.7 percentage points and less precisely estimated ts1.76 .Table 1 shows that hospitals which later merged and independent hospitals whichwere later acquired had readmission rates of 8.7% and 8.4%, respectively, in 1991.Thus, the estimates in Table 3 suggest that consolidation raises the probability ofreadmission for heart attack patients by at least 10% in each of these cases. Thus,when readmission is used as a quality measure, mergers and most acquisitions dohave a detrimental impact on quality.

The estimated impact of mergers and acquisitions on readmission rates isderived assuming that trends in hospitals prior to consolidation or trends inhospitals which did not consolidate serve as an appropriate control group. Butgiven that the chosen quality measures are changing rapidly for all hospitals overthis period, one may be concerned that omitted variables that are correlated withthe timing of mergers and acquisitions in the data may be driving the results. Inorder to check for potential omitted variables, we re-ran the specification forreadmission rates including interaction effects for each year and the 27 HSAsincluded in the dataset. Both the magnitude and the precision of the estimates ofthe effects of merger or acquisition on readmission rates remained virtually thesame.18 We also re-ran a specification including interactions between year and

Ž .for-profit versus non-profit status. In this case, the effects of hospital mergers

17 Because of the large sample size and the inclusion of hospital-specific indicators, the readmissionand early discharge regressions were estimated using a linear probability model. Using a subset of theobservations, no difference was found between linear probability estimates and those derived from alogit model.

18 The same interaction effects were added to the inpatient mortality regressions to check for omittedvariables. Again the standard errors for the M&A coefficients were large, so that the effects wereimprecisely estimated.


and acquisition by independent hospitals remains unchanged. However, the impactof acquisition of a hospital system by another system on readmission rates drops to

Ž . Ž .0.3% versus 0.7% , and the t-statistic falls to 0.696 versus 1.763 . Therefore,omitted variables may explain higher readmission rates for hospital systemsacquired by other systems.

When consolidation effects are estimated separately by insurance status, thedetrimental impact of mergers on readmission is tangible for both Medicare andprivate insurance patients. However, only readmission rates for independenthospitals acquired by a system appear higher for Medicare patients.19 This findingis consistent with the hypothesis that quality may more likely be compromised forMedicare patients whose care is covered by fixed price reimbursement.

5.3. The impact of mergers and acquisitions on early discharge for newborns

Ž .Table 4 contains regression estimates of Eq. 1 for the probability of dischargewithin 48 h for normal newborns. Column 1 indicates that being born in a hospitalsystem acquired by another hospital system increases the probability of earlydischarge by 3.2 percentage points. The rate of early discharge for newborns was52% in these hospitals prior to acquisition in 1991. Therefore, the estimatedimpact associated with consolidation in this case represents a 6% increase in theprobability of early discharge. We had hypothesized that the impact of consolida-tion on the quality of care would be smaller for hospitals already belonging to asystem. However, the estimates for newborns do not support this hypothesis.

To check for potential omitted variables, we re-estimated the regression firstincluding interactions between year and HSA, then including interactions betweenyear and for-profit status. In both cases, the impact of hospital systems being

Ž .acquired on early discharge rises slightly to 3.7% and 3.8%, respectively , and theŽ .coefficient remains precisely estimated ts7.776 and 7.466, respectively . The

coefficients on the other merger and acquisition variables remain insignificant.Therefore, the measurable changes in early discharge are robust.

Recall that the majority of babies delivered are covered by either privateinsurance or Medi-Cal. The estimates in Column 1 indicate that private insuranceincreases the probability of discharge within 48 h relative to Medi-Cal coverage.Columns 2 and 3 contain separate regression estimates for Medi-Cal and privateinsurance babies. In this case only babies covered by Medi-Cal who were born inhospitals which consolidated experienced an early discharge effect. Hospitalmergers increased the probability of early discharge by 2.2 percentage points;while acquisition of a hospital system by another system raised the probability of

19 Further tests showed that the impact of mergers on readmissions was not significantly different forMedicare versus private insurance patients. Moreover, readmission rates for privately insured patientsin independent hospitals acquired by a system were significantly lower than for Medicare patients.


Table 4Linear probability estimates for discharge within 48 hours for newborns)

One-day discharges

All patients Medi-Cal Private insurance

Ž . Ž . Ž .Merger y0.001 y0.178 0.022 2.232 y0.005 y0.758Ž . Ž . Ž .Independent acquired y0.003 y0.813 y0.010 y1.572 y0.004 y0.905Ž . Ž . Ž .System acquired 0.032 10.030 0.071 15.090 0.0001 0.020Ž . Ž . Ž .Ceasarean section y0.736 y558.846 y0.677 y318.162 y0.782 y454.297Ž . Ž . Ž .Comorbidities y0.075 y66.301 y0.074 y42.597 y0.073 y46.905Ž .Private insurance 0.041 29.461Ž .Self-pay 0.098 38.655Ž .Indigent 0.053 5.207Ž .Other payment 0.046 10.411

) t-statistics are in parentheses. Ns510,572 babies; 228,779 Medi-Cal, 249,045 Private Insurance.Each model also contains year dummies, and indicators for black, Hispanic, Asian and other races.

early discharge by 7.1 percentage points for Medi-Cal patients. Re-estimating theequations in Table 4 with the number of comorbidities excluded as a regressor didnot change any of these conclusions.

5.4. The impact of mergers and acquisitions in concentrated markets

In the preceding analysis, we found several instances where we could not detectŽan effect of hospital mergers and acquisitions on hospital quality particularly for

.inpatient mortality . Yet, detrimental effects may only be evident in the subgroupof hospital consolidations that occurred in concentrated markets. We thereforere-examine the impact of hospital mergers and acquisitions on patient outcomesincluding measures of local market concentration.

We report results of the regressions for the determinants of AMI readmissionsand early discharge of newborns modified to account for market concentration inTable 5. The table contains coefficient estimates for the merger and acquisitiondummy variables, the HHI, and the interaction of the hospital consolidationdummies with the HHI. The remaining explanatory variables are the same as theones used previously.20 Test statistics for the joint significance of each indicator

20 Because the standard errors for the consolidation effects in the inpatient mortality regressions wererelatively large, it was unlikely that the inclusion of market concentration effects would modify theseresults. Therefore, these results are not reported in Table 5. However, we did add market concentrationeffects to the inpatient regressions, and we still found that hospital mergers and acquisitions had nomeasurable effect on the mortality hazard rates. Parameter estimates for the entire regressions areavailable from the authors upon request.


Table 5Estimates including market concentration measures)

90-Day readmission Early discharge ofheart attack patients newborns

Ž . Ž .Merger 0.020 2.840 y0.022 y3.176Ž . Ž .Independent acquired 0.008 1.723 y0.004 y0.735Ž . Ž .System acquired 0.006 1.262 0.028 7.291Ž . Ž .Merger=HHI y0.046 y0.612 0.319 3.218

Ž . Ž .Independent acquired=HHI 0.004 0.104 0.014 0.365Ž . Ž .System acquired=HHI 0.006 0.181 0.086 2.000Ž . Ž .HHI y0.059 y0.741 0.324 7.721

Joint significance tests F-statistic P-value F-statistic P-value

Merger 3.62 0.013 22.76 P -0.001Independent acquired 1.91 0.126 20.11 P -0.001System acquired 1.22 0.302 55.19 P -0.001

) t-statistics are in parentheses. Sample sizes and additional variables are the same as those reportedin Tables 2–4. Joint significance tests provide test statistics for the null hypothesis that the coefficienton each individual merger or acquisition dummy variable, the HHI, and the interaction between thesetwo variables are jointly equal to 0.

variable for merger or acquisition, the HHI, and their interaction terms arereported at the bottom of the table.

We first examine the impact of market concentration on conclusions regarding90-day readmission for heart attack patients. The analysis in Table 3 revealed thathospital mergers, acquisitions of independent hospitals, and acquisition of systemhospitals all increased the probability of 90-day readmission. However, in Table 5,only the coefficient on the hospital merger dummy variable remains preciselyestimated. Inclusion of market concentration measures reduces the precision of theestimates relating consolidation to poor outcomes based on readmission data.

We also examine the link between market concentration and the impact ofhospital consolidation on early discharge of newborns. In Table 4, we found thatacquisition of hospital systems by other systems increased the probability of earlydischarge. In Table 5 we found that increased market concentration as measuredby the HHI increases the probability of early discharge. Moreover, the coefficientson the HHI, merger, system acquisition, and their interaction terms are jointlysignificantly different from 0.

For any given newborn, the probability of early discharge associated withhospital consolidation is computed by summing the coefficient on the appropriatehospital consolidation dummy and the coefficient on its interaction term multipliedby the HHI for the admitting hospital. The marginal impact of increased marketconcentration on the probability of early discharge after hospital consolidationappears to be substantial. The entire lowest quartile of deliveries in the sample


occurred in markets with an HHI of approximately 0.015.21 The estimates in Table5 indicate that a merger of independent hospitals in a competitive market such asthis reduces the probability of early discharge by 1.7 percentage points. However,

Žan independent hospital in a market with an HHI of 0.1 the approximate sample.mean increases the probability of early discharge by 1 percentage point. Likewise,

acquisition of a system hospital in a market with an HHI of 0.1 raises theprobability of early discharge 3.7 percentage points. At the 90th percentile of the

Ž .HHI distribution 0.18 , the probability of early discharge rises by 3.5 percentagepoints for newborns in merged hospitals; and by 4.3 percentage points fornewborns in acquired system hospitals.22 Note that the estimated effects for thesetwo acquisition types are larger than comparable estimates in Table 4, which didnot control for market concentration. Thus, for the case of early discharge ofnewborns, hospitals in markets with higher concentration do appear to exercisemarket power by lowering quality after consolidation.

5.5. Summary of estimation results

In summary, the regression results indicate that mergers and acquisitions haveno measurable impact on inpatient mortality, although the standard errors associ-ated with these estimates are large. However, mergers and acquisition of anindependent hospital increase readmission rates for heart attack patients. Inaddition, some hospital acquisitions lead to early discharge of normal newborns.These effects for newborns are particularly notable for consolidating hospitals thatoperate in highly concentrated markets. We had originally hypothesized that theimpact of consolidation might be larger for Medicare patients versus otherpatients, and for independent hospitals versus hospitals already belonging to asystem. Although we found evidence consistent with both hypotheses in somecases, neither hypothesis held for the majority of quality measures.

6. Conclusion

This paper provides a first look at the impact of hospital mergers andacquisitions on patient outcomes and the quality of inpatient care for patientsadmitted to California hospitals between 1991 and 1996, a location and period thathas seen a substantial amount of M&A activity. We find no evidence that mergersand acquisitions measurably affect inpatient mortality. However, some types of

21 Most of these patients are in Los Angeles county.22 These figures may in fact be a lower bound of the effects of market consolidation on readmissions,

because cet. par., the HHI also rises after merger or acquisition. However, this marginal effect appearsto be small. For the sample of newborns in cases where consolidation occurred, markets with hospitalmergers experienced the greatest increase in HHI between 1991 and 1995; but the increase in the HHIwas only 0.02.


hospital consolidation are associated with increased readmission rates for heartattack patients, and increased likelihood of early discharge for normal newborns.

The effects of mergers and acquisitions on quality identified in this study differby type of consolidation. For example, acquisition of independent hospitals raisesreadmission rates for heart attack patients, but does not lead to earlier discharge ofnewborn babies. In contrast, acquisition of hospital systems by another systemleads to earlier discharge of newborn babies, but does not raise readmission ratesfor heart attack patients. Thus, increases in consolidation may not always compro-mise the quality of patient care. The extent to which different types of hospitalconsolidations change the quality of care is likely to depend on both demand andcost factors, which can vary by merger and acquisition type and patient popula-tion. Further analysis with data on revenues by patient mix, managed carepenetration, and hospital costs will help to explain the findings in this paper.

Some important caveats to our analysis remain. The descriptive statisticsindicated that patient outcomes differed between consolidating and non-consolidat-ing hospitals and across different types of mergers and acquisitions even in 1991,prior to when consolidation occurred. We controlled for these initial differences inoutcomes in our analysis by using hospital-specific strata in the Cox regressionsand fixed effects in the linear probability models. However, the results in thispaper reflect the change in patient outcomes after consolidation, conditional on ahospital’s decision to merge or be acquired. Determining the factors that lead ahospital to merge or be acquired remains an interesting issue, which is worthy offuture research.

We were not able to detect a detrimental impact of hospital mergers andacquisitions on inpatient mortality. These results are valid even if average lengthsof stay in hospital have fallen over time, so that patients discharged alive are onaverage more unstable when they leave the hospital. The econometric frameworkestimates the hazard of dying in hospital on a given day, given survival in hospitalup until that day, so that shorter lengths of stay for live discharges are accountedfor when estimating the impact of hospital consolidation on inpatient mortality.These results should be interpreted with caution, given that relatively small

Ž .proportions approximately 5% of heart attack and stroke patients were treated ineach type of merged or acquired hospital. The resulting standard errors are largeenough that we cannot rule out the possibility that AtrueB detrimental effects ofconsolidation on mortality could readily be three times the estimated coefficientsin our results. Analyses with data from multiple states are necessary to investigatethis issue further.

Although the regressions included controls for patient health status, unobserv-able differences in patient health status may have biased the results. We tested for

Ž .differences in age, number of comorbidities, and race white versus non-whiteafter consolidation for each of the three patient groups in this study. Only somepatient characteristics were significantly different before versus after consolida-tion, and the direction of the difference varied by merger or acquisition type and


patient group. Therefore, it is unlikely that unobservable differences in patientcasemix systematically biased the findings up or down.

Another weakness of this analysis is that it cannot identify hospitals that maystrategically discharge patients just before the end of life. Identification of suchbehavior requires information on changes in patients’ health status throughouttheir stay in hospital. Such detailed information is usually only available throughdetailed chart abstraction for a smaller sample of patients and hospitals than wasused in this study.

A preliminary means of investigating this issue is to note that strategic behaviorby hospitals to discharge patients near death would suggest that sicker patientswould have a lower hazard of death in hospital. However, our estimates indicatethat a higher number of comorbidities increases the hazard of death in hospital.We also interacted the merger and acquisition dummy variables with the numberof comorbidities to determine whether hazards by health status differed afterconsolidation; but these interaction terms were all insignificant.23 While thisanalysis is coarse, the results suggest that finding no evidence that hospitalconsolidation harms inpatient mortality is not due to strategic decisions byhospitals to discharge patients just before death.24 Future analyses that are able toprovide combined data on inpatient mortality, readmission rates, and post-dis-charge mortality will be more informative on this issue.

We have chosen to analyze only the quality implications of hospital mergersand acquisitions. Analysis of both price and quality are necessary to determinehow market consolidation affects social welfare. However, the California OSHPDdischarge data base is a poor source for examining price–cost margins, because

Ž .only information on patient charges as opposed to actual prices paid is listed.Nevertheless, only a handful of papers exist that analyze the price effects ofhospital consolidation; and none of these studies examines patient outcomes.Therefore, this study yields useful initial findings on the implications of consolida-tion for product quality. Gaynor and Haas-Wilson have suggested that higherhospital prices after consolidation that have been noted in prior studies may reflectbetter quality. In contrast, we find no evidence that hospital consolidation lowersmortality rates, readmission rates, or the probability of early discharge fornewborns. Thus, the rents which consolidating hospitals have been observed toachieve through higher prices may not be dissipated by increases in quality. Futurestudies that can simultaneously evaluate both price and quality changes associatedwith hospital mergers and acquisitions are needed to confirm these findings.

23 The results are available from the authors upon request.24 Likewise, this analysis addresses concerns that the hazard of being discharged alive from hospital

is negatively related to a patient’s mortality hazard. Hospitals are less likely to discharge patients athigh risk of death. However, the insignificant coefficients on the interaction terms between the numberof comorbidities and the consolidation dummies suggest that such behavior does not change aftermerger or acquisition.


Previous studies suggest that consolidation may affect price, cost, or quality forall hospitals in a local market, not just those involved in merger or acquisition.Estimating a more general model to account for this possibility is beyond thescope of this paper. We chose to focus our analysis on the quality effects of thosehospitals directly involved in consolidation, because these are the facilities that arecurrently of greatest concern to policy makers. Nevertheless, the range of qualitymeasures analyzed in this paper is fairly narrow. We examined two process

Ž .measures readmission rates and early discharge and one outcome measureŽ .inpatient mortality of hospital quality. Further analyses of a broader array ofquality measures and a larger set of diagnoses are necessary to substantiate ourfindings.

Debate continues on whether consolidation in health care markets enhancesefficiency and quality or instead facilitates collusion and market power. Theresults in this paper are consistent with the hypothesis that recent mergers andacquisitions have not had a unilateral detrimental impact on the quality of patientcare. Concerns regarding the adverse consequences of increased market power onquality should not be ignored; but further investigation with detailed clinical datais necessary to substantiate these concerns.

Acknowledgements

We are grateful to Richard Boylan, Bill Encinosa, David Eisenstadt, DanaGoldman, Martin Gaynor, Robert Helms, and seminar participants at the 1998Econometric Society Meetings, the American Enterprise Institute 1998 HealthPolicy Discussion Series, the University of Minnesota Institute for Health ServicesResearch, and RAND for their comments on this paper. David Cutler, JosephNewhouse, and two anonymous referees also provided many helpful suggestions.Vivian Ho acknowledges the support of the John M. Olin Foundation FacultyFellowship program.

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Hospital mergers and acquisitions: does market consolidation harm patients?

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