Ali Online Article Fall 2009

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RESEARCHVolume 2 Issue 2 | Fall 2009

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Consolidation and Quality:

An examination o the efect o hospital

consolidation on the quality o care over timeTariq Nazir Ali*

*Harvard College 09

Te study o hospital consolidation and its eect on the qua lity o patient care has been o great interest in both theeconomic and legal communities as consolidation activity surged in the mid-1990s. Previous studies have eitherbeen inconclusive or concluded that hospital mergers and acquisitions detrimentally impact qual ity. Tis studyexamines hospital care beore and aer consolidation rom 1993 to 1998 using patient data rom 14 states. Usinginpatient mortality and length o stay or CHF patients as indicators o quality, the study incorporates time lagvariables to test or any time variance in the eect o hospital consolidation on the quality o patient care. Initially,in the rst year post-merger, hospital consolidation results in an initial increase in inpatient mortality and has a

negligible eect on length o stay. In subsequent years, there is a signicant decrease in inpatient mortal ity andlength o stay, both indicating an improvement in quality o care. Tese results seem to counter the conclusions oexisting literature and thus invite urther study.

Introduction

Consolidation o hospitals and other providers in the health careindustry occurred at a dizzying pace during the 1990s. With onlyten hospital mergers and acquisit ions documented nationally in 1991,the number increased within a ew years by a actor o nine (ownet al. 2006), peaking in the mid-nineties. Hospital consolidationshave been attributed to the organizational desires to cut operation-al costs and increase revenue (Dranove and Lindrooth 2003). Withmergers increasing prices by more than 5% over competitors (Vita etal. 2001), hospital mergers have allen under close political scrutinyas an antitrust legal issue, as its social benet to the community isquestioned. As reected in Figure 1, with the increase in mergers andacquisitions, the hospital market concentration, as reected by theHerndahl-Herschman Index (HHI), steadily increased, reectingless competition and possibly resulting in lower quality o care. Con-sequently, many states enacted specic restrictions and barriers tohospital consolidation, requiring proo o a resulting public benet.Starting rom 1996, the state o Montana requires all merging hospi-tals to obtain a Certicate o Public Advantage (COPA) by providingproo that the proposed merger results in lower health care costs,improved access to health care or greater quality o care than would

occur without a merger (Montana Department o Justice). Statesas well as the Federal rade Commission have brought several an-titrust cases against hospital networks, including the notable recentAugust 6, 2007 ruling against the Evanston Northwestern Health-care networks acquisition o Highland Park Hospital in Illinois asanticompetitive and unlawul. One o the arguments cited the lacko proo o an improvement in the quality o care (Bingham 2007).

Consequently, by setting the quality level o care as one o the threebenchmarks or the legality o hospital mergers, the relation betweenhospital consolidation and its eect on the quality o care has becomenot only o great interest to the health care industry, but also to thelegal community. However, very ew empirical studies have been per-

ormed on the impact o consolidation on quality. One such study isthat o Ho and Hamilton (2000) that compared the beore and aereects on quality o 140 hospital mergers and acquisitions in Cali-ornia between 1992 and 1995. Focusing on inpatient mortality orheart attack and stroke patients, 90-day readmission rates or heartattack patients, and early discharge or newborns as quality indica-tors, the study ound no signicant eects on inpatient mortality andan increase in readmission rates and early discharges o newborns.

However, the papers conclusions can be considered weak, as ac-

knowledged by the authors. Te lack a signicant eect on inpatientmortalities with the merged hospitals is attributed to high standarderrors, and thus, an eect on inpatient mortality cannot be ruled out.In examining mergers that have occurred in a two-year window, thestudy ails to account or any variance in the eect o consolidationover time. Without a standardized observation point aer a merger, aninconsistency is ormed; the eect o a merger in 1993 is not observeduntil two years later while a merger in 1994 is observed aer a year.

Te ocus o this paper is to build on the work done by Ho andHamiltons study o hospital mergers and acquisitions and their e-ect on the quality o patient care. wo specic aims o this paper,addressing the questions le by Ho and Hamiltons study, are rst, toobserve any possible time variance in the mergers eect on the qual-ity o care, and second, to test the external validity o their ndings

by expanding the study to across 14 states. Using inpatient mortal-ity and the length o stay o patients diagnosed with chronic heartailure (CHF), dened as DRG 127, as the two quality indicators,patient outcomes beore and aer hospital mergers and acquisitionsbetween the 1993 and 1997 are compared. Te results o the studyshow that hospital consolidations had initially a detrimental eecton inpatient mortality o CHF patients and a statistically insigni-cant eect on their length o stay in the rst year post-merger, but insubsequent years they lowered the impatient mortality by up to 5.8percentage points and reduced hospital length o stay by up to 0.657daysboth indicating an improvement in the quality o patient care.


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RESEARCH Volume 2 Issue 2 | Fall 2009

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Existing Literature

Exhaustive studies have been perormed on the eects o consoli-dation in the health care industry on prices, costs, and efciency. Asindicated in Danove and Lindroths (2003) study, merged hospitalsexperienced a 5% decrease in costs in the rst year aer the merger.However, several studies have shown that such mergers and acquisi-tions do not necessarily lead to lower costs; instead, price increases

because o increases in market power. In Krishnan and Krishnan(2001), hospital acquisitions were ound to result in increased rev-enue per patient and increased operating margins compared to non-merging hospitals but not lower operating costs. Similarly, ound inthe own et al. (2006) study, because o consolidations, average HMOpremiums in 2001 were estimated to have been 3.2% higher than hadthere not been any mergers since the early 1990s. Te authors ur-ther that the resulting increase in premiums have led to a cumula-tive loss o $42.2 bil lion in consumer surplus between 1990 and 2001.

In addition to the Ho and Hamilton (2000) study, several stud-ies have shown no signicant eect on the quality o care with hos-pital consolidation or decreased hospital competition. Shortell andHughes (1988) ound no signicant association between hospitalcompetition and inpatient mortality, while Kessler and McClellan

(2000) ound that competition during that time period (pre-1990) ledto an increase in health costs and higher quality o care and that inthe subsequent period, competition limited adverse health outcomesand reduced costs. In a 2002 study, Sari substantiated the importanceo hospital competition with the study o in-hospital complications,revealing that higher hospital market share and concentration areassociated with lower quality o care. And more specically, Sch-neider (2008) evaluates the inclusion o the quality benchmark inantitrust law that is aimed at hospital mergers. Revealed through astudy o coronary artery bypass gra surgeries in Caliornian hos-pitals, hospital competition is linked with lower risk-adjusted mo-rality rates. Tereore, as all previous studies have shown, there isno noticeable benet, and maybe even a detriment, to the qual-ity o care rom hospital mergers and acquisitions. However, as alsonoted by many o the aorementioned studies, the scope o theirstudies have been relatively limited by size or location (such as onhospitals in only one state), and thereore, there is not a signicantamount o literature testing the external validity o these ndings.

Accounting or time variance in the eect o hospital mergers andacquisitions is not present in many studies. In the ew studies that doaccount or time variance, the inconsistency o the eect o hospitalmergers on various indicators is revealed. Gro et al.s (2007) studyo the eects o mergers on efciency concludes that while there is nosignicant eect on efciency aer the rst year o a merger, the sec-ond year reveals a signicant increase in efciency. Te inconsistencyin eects o mergers thereore substantiates the suspicions o the Hoand Hamilton study by underlining the importance o accounting orthe time variance. Tereore, an examination o the eects o hospitalmergers on quality o care necessitates the accounting o time vari-ance or a more complete analysis on the impact on patient outcomes.

Data and Summary Statistics

Te primary goals o this study are to test the external validityo Ho and Hamiltons analysis and to account or time variance inthe impact o mergers on quality o care. Tus, or this study, patientdata rom hospitals in 14 states and during a six-year timerame rom1993-1998 are analyzed. Included in the ourteen states are Calior-nia, Pennsylvania and Floridathree states known or exceptional

hospital consolidation.1 o maximize the inclusion o merging hos-pitals in sample data, 1993 to 1998 was chosen as the time window; asindicated in Figure 1, it was the peak o hospital consolidation activity.Patient Data

Data on patients or the study came rom the Nationwide Inpa-tient Sample (NIS), a part o the Healthcare Cost and UtilizationProject (HCUP), sponsored by the Agency o Healthcare Researchand Quality. Obtained or the years o 1993, 1997, and 1998, theannual NIS data is approximately a 20 percent sample o the UScommunity hospitals, averaging about 1000 hospitals with nearly7.1 million patient records. For the purpose o this study, overlapo hospitals across the yearly NIS datasets is necessary, and there-ore, out o the approximately 1000 hospitals, 590 hospitals are in-corporated into the study sample. O the 590 hospitals, all patientstreated or CHF were included in the study, yielding a total sample

o 344,946 CHF patients over the course o the study. Because o asignicant change in the data structure o the NIS annual survey in1998, there is a limited overlap in the number o hospitals betweenthe 1997 and 1998 NIS data; CHF patient data drops by a actor othree in 1998 rom 131,780 and 156,315 data points rom 1993 and1997, respectively to only 56,586 data points in the 1998 data sample.Hospital Data

Inormation on hospitals studied was also drawn rom the NISsurvey. In addition to providing basic hospital eatures, certainhospital weights such as the number o hospital beds, urbanity, to-tal discharges and geographical controls are also provided. Data onhospital mergers during the observed window between 1993 and1997 were also derived rom the NIS survey. wo hospital iden-tiers in the NIS survey dier in the eect that a hospital merger

or acquisition has on them. One identier linked to the AmericanHospital Association (AHA) changes aer a hospital consolida-tion as dened by the AHA. Meanwhile the other hospital identi-er, the HCUPs hospital data source number, does not get aectedby mergers or acquisitions. By exploiting the dierence in the waythese identiers respond to consolidations, a list o hospital merg-ers and acquisitions was derived by examining any change in eachhospitals identiers rom 1993 to 1997. Any change would thereoreindicate that merger activity did occur during the three year win-

Figure 1 graphs the total number of horizontal mergers, acquisitions,and system expansions (we refer to this collective consolidation activity

as M&A) across populous metropolitan statistical areas (MSAs) from

1990 to 2003

Source: American Hospital Association and authors calculations

1Complete list o states: AZ, CA, CO, C, FL, IL. IA, MD, MA, NJ, NY, PA,

WA, and WI


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dow. Consequently, all types o mergers and acquisitions will beexamined and not dierentiated in this study, as is done in Ho andHamilton (2000). Out o the 590 hospitals analyzed in this study, 173were determined to have undergone a hospital merger or acquisition.Merger Activity

Te specic year o the hospital consolidation during the threeyear window (1994-1996) was determined through the use o theAmerican Hospital Associations annually published HospitalGuide, which provides characteristics o each hospital in the nation.By tracking changes in each o the 173 merged hospitals prole,

health system afliation, or date o entry into a health system, theyear o consolidation was derived. As shown in able 1, 54 hospi-tals consolidated in 1994, 72 in 1995, and 47 in 1996, providing uswith 31,895, 53,839 and 34,773 CHF patient data points, respectively.Quality Indicators

For the purpose o this study, inpatient mortality and length ohospital stay o CHF patients are used as measurements o qual-ity o care. Te use o inpatient mortality as a quality indicatorhas been used in several o the aorementioned studies, includ-ing Ho and Hamilton, as an indicator o quality o patient care. Itis assumed that holding all else equal, i a rise in the rate o inpa-tient mortality o CHF patients is associated with hospital merg-ers and acquisitions, then one can conclude that consolidationin the health care industry leads to lower quality patient care.

Te length o stay (LOS) is another well-used measure o quality opatient care. While Ho and Hamilton do use LOS as an indicator, theirinterpretation o the association with quality o care, with particularocus on newborn LOS, is dierent rom that o this paper. While theyposit that an increase in the length o stay is associated with a betterquality o care, in this study, using the ndings o Boles and Clement(1995) as a guide, it will be assumed that an increase length o staywill be linked to a decrease in quality o patient care. With a longerstay in the hospital, a patient took longer to regain enough healthto be discharged. Also, a longer hospital stay may imply that a pa-tient had a greater chance o being exposed to hospital complications.

Using NIS data rom 1993 as pre-merger data or comparisonwith 1997 and 1998 statistics as post-merger data, a time trend and

an association o quality indicators with merged hospitals are appar-ent (able 2). For all hospitals, we see a clear decrease in the aver-age length o stay in days or patients admitted or CHF across timewith the mean being 6.85, 5.36, and 5.15 or 1993, 1997, and 1998,respectively. Similarly, the percent o inpatient mortality or CHFpatients also decreased or all hospitals across time with 6.78%,5.00% and 4.67% mortality in 1993, 1997 and 1998, respectively.

In able 2, the mean length o stay and inpatient mortality is

also subdivided in regards to patients who are rom the 173 hospi-tals that are classied as having consolidated in either 1994, 1995,or 1996, and patients that are rom hospitals that did not undergoany consolidation. A signicant portion o the patient data pointsin the study sample are drawn rom hospitals that do not un-dergo any orm o consolidation; 229,757 data points are drawnrom non-consolidated hospitals and 115,046 points are drawnrom hospitals that consolidated through merger/acquisition.

An apparent association between the two quality indicators andmerger exists since both indicators are higher or the hospitals thatconsolidate than those that do not. Te mean lengths o stay or hos-pitals that do not consolidate are 6.77, 5.30, and 5.17 or the threeyears whereas the mean lengths o stay or consolidating hospitalsare 7.00, 5.45, and 5.08. Similarly, or the percentage o inpatient

mortality, non-consolidating hospitals have inpatient mortality rateo 6.67%, 4.89%, and 4.61%, whereas consolidating hospitals have6.99%, 5.77%, and 4.93%. Te dierence between consolidatingand non-consolidating hospitals in 1993 is statistically signicantor both quality indicators: mean length o stay (t=-5.14, p=0.00)and rate o inpatient mortality (t=-2.099, p=0.036). Tat the asso-ciation is also apparent in 1993, prior to the mergers, suggests thatthere are actors in the merged hospitals that are tied to the highmean length o stay and rate o inpatient mortality or CHF pa-tients. Tereore, these summary statistics illustrate the need or thisstudy to account not only or time trends but also or the apparentinitial associations between consolidating hospitals and the qual-ity indicators. Tus, in this study, a dierence-in-dierences mul-tivariate regression analysis will be perormed to control or time

trends as well as or hospital eects o whether or not the CHF pa-tient data is rom a hospital classied as a merger/acquisition or not.

Emperical Framework

o understand the eect o hospital mergers and acquisitions onthe quality o patient care, an approach similar to that employed byHo and Hamilton is used. 1993 is set as a pre-merger date while 1997and 1998 are years examining the eects o hospital consolidationpost-merger. Hospitals that consolidate between 1994 and 1996 areo ocus to this study, thereore allowing us to isolate the eects othe merger/acquisition on any changes in the quality o patient care.

In order to incorporate time variance into the study, a method

Year o Merger/Aquisition

1994 1995 1996

Number o Hospitals 54 72 47

Sample Size 31895 53839 34773

Table 1 Summary o Hospital Mergers/Acquisitions per yearMerged/Acquired Hospitals calculated and year of acquisition/ merger

determined as described in section III. Sample size corresponds to num-ber of patient records from corresponding hospital. Size includes data

from 1993, 1997, and 1998.

All Hospitals No Consolidation Mergers and Aquisitions

Congestive Heart Failure Paients 1993 1997 1998 1993 1997 1998 1993 1997 1998

Mean Length o Stay 6.85 5.36 5.15 6.77 5.30 5.17 7.00 5.45 5.08

Percent Inpatient Death 6.78 5.00 4.67 6.67 4.89 4.61 6.99 5.77 4.93

Sample Size 131780 156437 56586 87309 97011 45437 44471 59426 11149

Table 2 Summary o Quality Indicators, by status o hospital activity during 1994, 1995, and 1996Mean Length of Stay in terms of days with minimum=0. Sample size of patient records restricted to those with DRG=127 (Conges-

tive Heart Failure) and to hospitals present in both the years 1993 and 1997 of the NIS data. Sample size of 1998 is signicantly

smaller because of limited overlap in hospitals in the HCUP NIS data for 1997 and 1998.


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similar to that employed by Gro et al. (2007) in their analysis othe impact o merger activity on hospital efciency is used. By iden-tiying in which year the 173 identied hospitals consolidated andwhen the impact is observed (1997 or 1998) or each data point, theperiod o time post-merger can be taken into account and grouped.Tus all hospitals that have merged in 1996 are not observed untilone year later in the 1997 data or two years later in the 1998 data.For those that consolidated in 1995, there is a two year gap beore

the mergers impact on length o stay or inpatient mortality is ob-served in the 1997 data and a three year gap beore observed inthe 1998 data. Te dierences between when the hospitals mergedand when its impact is observed in the quality indicators is notonly controlled or in this ramework but is also advantageous inunderstanding the time variance in the eect o consolidation.

Tus, by having two post-merger years data we are able to extrapo-late the impact o a hospital merger up to our years post merger. Tosethat merge in 1996 and are observed in 1997 are grouped together re-ecting the eect o hospital consolidation aer one year. Tose thatmerge in 1995 and are observed in 1997 are grouped with those thatmerge in 1996 and are observed in 1998 to analyze the eect o hospi-tal consolidation aer two years. Similarly, those that merge in 1994and are observed in 1997 are grouped with those that merge in 1995

and are observed in 1998 to analyze the eect o consolidation threeyears post-merger. And lastly, those that merge in 1994 and are ob-served in the 1998 data are grouped together to reect the impact oconsolidation on the quality indicators our years aer consolidation.

Tis method assumes that the nature o a hospital merger isnot unique to what year it occurs in. While there are consider-ations regarding anti-trust laws that were passed in the mid-1990sthat might possibly have ormed barriers to consolidation, theyhave no impact on this particular study; the causes o a merger isnot o ocus in this study as it does not aect the quality indicators.Similarly, any concerns that a hospital merging in a later year ex-periences a higher HHI/hospital market concentration also do notaect the ocus o this study or bias the results on the quality in-dicators because hospital market concentration is controlled or in

the perormed regression analysis. Tereore, the assumption thatthe nature o a hospital merger is not unique to what year it occursin sufciently holds or the purpose o this ramework and study.

Tereore, or each quality indicator, the proposed analyti-cal ramework is the same. Allowing the output Y to represent ei-ther the predicted length o stay or CHF patients or the prob-ability o inpatient mortality, we can allow Y to be calculated as

Y=B1mergedyr+B2 yr97+B3 yr98+B4merged+B5yr2+B6yr3+B7yr4+B8X+B9Hosp+ (1)

where yr97and yr98 are binary variables (with data rom pre-merger 1993 as the omitted dummy variable) indicating which yearthe patient data is rom, with B2 and B3 reecting the eect and cap-

turing the time trends on the estimation o the quality indicators. Tevariable merged classies whether the patient data is rom one o theidentied 173 hospitals that consolidated, and thereore captures anyeect that any o the actors unique to that group, even prior to con-solidation, may have on the estimation o the quality indicators. Tisis necessitated as seen in able 2 where even prior to consolidation,the 173 identied hospitals had a statistically signicant dierence(higher) mean length o stay and rate o inpatient mortality o CHFpatients in 1993 than their non-consolidating counterparts.

In the above-proposed equation,Xrepresents various patient con-trol variables such as age, race, and gender, as included in the ob-tained NIS data. In previous quality o care studies (Ho and Hamil-

ton 2000; Schneider 2008), patient comorbidities were also utilized tocontrol and isolate the eect o mergers on quality o care; however,because o limitations in the obtained data, comorbidities were notobtained and thus, not controlled or in this study. Te variable Hosprepresents the aggregate o hospital specic controls provided by theHCUP NIS survey, including the number o beds in the hospital, lo-calization (either urban or rural), the number o total discharges, andthe continental region that it is located in. It is important to account

or these characteristics because there are quite possibly signicantassociations between quality and the urbanity o hospitals (wherehospitals in an urban/municipal location tend to be more equippedand thus generate better patient outcomes than those in rural loca-tions) or between quality and regional dierences. It is also assumedthat the number o hospital beds and o total discharges are linked toquality o care; more beds and more patients treated suggest a moredeveloped and advanced hospital, which may lead to better patientoutcomes. Tus, these three controls may help eliminate any bias inthe estimation o the quality indicators. A patient ow Herndahl-Hirschman Index (HHI) is also included in the hospital controls toaccount or the hospital market concentration. A HHI patient ow in-dex was chosen over HSA or MSA as a denition o the market basedon the arguments posited by Williams et al. (2006) and because with

the regional and urbanity characteristics already being controlledor, patient ow better denes the hospital market concentration.Tereore, the variable o interest in this study is that o mergedyr,

which is the interaction o both yr97/yr98 and merged when theCHF patient data comes rom one o the 173 identied hospitals thatconsolidated and rom data post-merger (either rom 1997 or 1998).With the inclusion o merged controlling or any group eects and

yr97andyr98 controlling or any time trends, this interaction termisolates the impact o hospital consolidation (as captured in the coe-cient B1) on the quality indicators. With the inclusion o lags yr2,

yr3, and yr4, the interaction term more specically reects the im-pact o hospital mergers and acquisition on the quality indicators oneyear aer consolidation. Te dummy time lagsyr2,yr3, andyr4 aredened by grouping the hospitals in those categories depending on

when the hospitals merged and rom which post-merger data year(1997 or 1998) the patient data is drawn rom. Tus, to calculate theeect on the quality indicator two years aer consolidation, coef-cients omergedyrandyr2B1 and B5are summed, and likewiseor three years aer and our years aer. Te statistical signicanceand the sign o these coefcients are o true interest o this study asthese ndings will shed light on what the impact o hospital merg-ers and acquisitions have (i any) on the quality o patient care.

Emperical Results

Multivariate regressions o Equation 1 were perormed orboth quality indicators, length o stay and inpatient mortal-ity, or CHF patients. Because o the binary nature o inpa-

tient mortality, probit regression analysis was perormed orestimating inpatient mortality. OLS regression analysis wasperormed or the estimation o length o stay CHF patients.CHF Inpatient Mortality

As shown in the regression analysis o data in able 3, two re-gressions were perormed in the estimation o inpatient mortality othose with CHF. With 276,965 data points, regression (1) is a probitregression without the time lags yr2, yr3, yr4, and regression (2) iswith the time lags. As predicted and exemplied in the data sum-mary in able 2, the coefcients o the dummy variables capturingwhether or not the data is rom 1997 or 1998 are highly signicantwith a negative coefcients o 16.0 and 17.2 percentage points (15.9


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or 17 in reg. 2) decreases in the probability o an inpatient death,holding all else equal.2 Tis reects the apparent decreasing trendin inpatient mortality rates that is shown in able 2. Similarly, thecoefcient o the merged hospital dummy variableindicatingthat the patient record was rom one o the 173 consolidating hos-pitalsis shown to be highly signicant in both regressions with apositive coefcient o a 2.7 percentage points (4.3 in reg. 2) increase

in inpatient mortality, holding all else equal. Tis nding agreeswith the summary statistics shown in able 2, where the inpatient

2Te large negative coefcient o the year dummy variables is countered by

positive coefcients o controls variables such as urbanity and patient ow HHI,(coefcients o .04 a nd .25 respectively) to result in a more reasonable aggregateeect that produces the low percentages o inpatient mortality as reected inable 2.

Dependent variable CHF Inpatient Mortality CHF Length o Stay

(1) (2) (3) (4)

Probit Probit OLS OLS

Merged Hospital * Years Post Merger(Mergedyr)

0.00683(.01713)

0.07829**(.02457)

-0.17286*(.08669)

0.17048(.111073)

Patient Record is rom 1997 -0.16014**(.01045)

-0.15904**(.01053)

-1.28796**(.02677)

-1.28483**(.026852)

Patient Record is rom 1998 -0.17288**(.01252)

-0.16966**(.012617)

-1.36935**(.03178)

-1.34803**(.031366)

Patient Record is rom Hospital thatMerged

0.02744*(.01276)

0.04368**(.013603)

0.32368**(.04301)

0.40112**(.046067)

Second Year aer Merger __ -0.07254**(.022334)

__ -0.34839**(.06458)

Tird Year aer Merger __ -0.12670**(.03024)

__ -0.57360**(.16912)

Fourth Year aer Merger __ -0.13662**(.05603)

__ -0.82725**(.14851)

Controls or Patient (Age, Race, Sex) Yes Yes Yes Yes

Controls or Hospital (Bedsize, Ur-

banity, Region, otal Discharges)

Yes Yes Yes Yes

Patient ow HHI Yes Yes Yes Yes

Intercept -2.23828**(.03905)

-2.2395**(.03912)

3.91103**(.14165)

3.90544**(.14247)

F-statistics testing the hypothesis that the population coefcients on the indicated regressors are all zero

Mergedyr, Second Year aer Merger __ 12.45**(.0020)

__ 14.81**(.0000)

Mergedyr, Tird Year aer Merger __ 17.94**(.0001)

__ 5.81**(.0030)

Mergedyr, Fourth Year aer Merger __ 11.99**(.0035)

__ 19.73**(.0000)

Regression summary statistics

(Pseudo) R2 0.0143 0.0144 0.0113 0.0114

n 276965 276965 277690 277690

Table 3 Diference in Diference estimates or CHF inpatient mortality and Length o StayNotes: Heteroskedasticity-robust standard errors are given in parentheses under estimated coefcients, and p-values are given in paren-

theses under F- statistics. The F-statistics are heteroskedasticity-robust. Coefcients are signicant at the +10%, *5%, **1% signicance

level. The controls for patient include Age, Race and Sex. Controls for Hospitals include Hospital Bedsize, Urbanity and Region. Patient

ow used as HHI/Hospital Market Share.

*Age= given in years;

*Race= (1) white, (2) black, (3) Hispanic, (4) Asian or Pacic Islander, (5) Native American, (6) other;

* Sex= (1) male, (2) female;

*Hospital Bedsize- (1) small, (2) medium, (3) large

* Urbanity= (0) Rural, (1) Urban

* Region- (1) Northeast, (2) Midwest, (3) South, (4) West

*2nd Year after merger= Hospitals that merged in 1995 in data collected in 1997, Hospitals that merged in 1996 in data collected in 1998.

*3rd Year after merger= Hospitals that merged in 1994 in data collected in 1997, Hospitals that merged in 1995 in data collected in 1998.

*4th Year after merger= Hospitals that merged in 1994 in data collected in 1998.


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mortality rates o merged hospitals were ound to be statistically sig-nicantly higher than the rates rom non-consolidating hospitals.In both regressions (1) and (2), patient and hospital controls wereincluded. Most were ound to be statistically signicant controls.

In regression (1), time variance is ignored and thus the act thatthe 173 consolidating hospitals merged or were acquired at di-erent points in the three year window is ignored. Consequently,we obtain a positive but statistically insignicant coefcient o

the interaction term mergedyrthe regressor o interest. Tere-ore, similar to all previous studies done on this matter, we end upwith a similar result o an inconclusive but possibly increasing e-ect on inpatient mortality by hospital mergers. Also, similar tothe results achieved by Ho and Hamilton, the lack o signicancein the coefcient could be due to the large standard error o 0.017.

When taking into account the time variance with the inclusion othe lags (yr2, yr3, and yr4), we reach a dierent and more signicantconclusion. Te interaction term, mergedyr is now highly statistical lysignicant at the 1% level with a positive coefcient o a 7.8 percent-age point increase in inpatient mortality i a hospital consolidates,holding all else equal. Tis result also conrms conclusions made inexisting literature that hospital mergers and acquisitions lead to de-creases in the quality o care. However, in this regression, because o

the inclusion o lag variables, mergedyr represents only the eect oa merger/acquisition aer the rst year. All three lags, accounting ortime variance o the eects o hospital consolidation were ound to behighly signicant at the 1% level as reected in the results o the F-tests in able 3. Consequently, aer the second year post-merger, theeect on inpatient mortality is reduced rom 7.82 to 0.56 percentagepoints. Inpatient mortality is actually reduced by hospital mergers inits third year by 4.8 percentage points and by 5.8 percentage points inits ourth year. Tese results counter the conclusions reached by exist-ing literature; while it conrms their ndings that initia lly, in the rstyear post-merger inpatient mortality may increase due to consolida-tion, aer the third year, the eect o hospital consolidation reducesinpatient mortality by 4.8-5.8 percentage points, holding all else equal.CHF Length o Stay

Similar to the results o the inpatient mortality, the eect o hos-pital mergers and acquisitions on the length o stay o CHF patientsis signicant and varies across the years post-consolidation. Againtwo OLS regressions were perormed using Equation 1 where regres-sion (3) is without the lag variables yr2, yr3, and yr4, and regression(4) is with them. Te dummy variables that control or whether thepatient data is rom 1997 or 1998 as well as the dummy control vari-able on whether or not the patient data is rom one o the 173 identi-ed consolidating hospitals are ound to be highly signicant at the1% level, capturing the general time trends as well as any state e-ects rom unknown actors in the 173 merging hospitals. Te signso the coefcientssimilar to the regressions o CHF inpatient mor-tality also are in line with the trends in able 2, where the 1997and 1998 year coefcients are negative (approx. -1.3), reecting a

trend o decreasing the length o stay. Tere is a positive coefcientor merged hospitals (0.32 or .40 in regression (4)), reecting the sta-tistically signicantly higher mean length o stay o patients romconsolidating hospitals than those rom non-consolidating hospi-tals, holding all else equal. In both regressions (3) and (4), patientand hospital controls such as the number o beds in the hospital,urbanity, region, total discharges, and patient ow HHI were alsoincluded. Most were ound to be statistically signicant controls.

Similar to regression (1) or inpatient mortality, regression(3) ignores any time variance o the eects o hospital consolida-tion by excluding the lag variables. Te interaction term mergedyris again the regressor o interestisolating the impact o hospi-

tal consolidation on the length o stay o CHF patients. Te coef-cient is ound to be negative and statistically signicant at the 5%level with the impact o a hospital consolidating associated with areduction o the length o stay by .17 days, holding all else equal.

When taking into account the time variance with the inclu-sion o the lags, we reach a similar conclusion that the eect on thelength o stay o CHF patients is decreased by hospital consolida-tion. Te incorporation o the lag variables results in the mergedyr

regressor becoming positive and statistically insignicant. With thismergedyr now reecting the eect o hospital consolidation one yearpost-merger, this result reects that aer only one year o consolida-tion, there is no signicant eect rom consolidation on the lengtho stay o CHF patients. All three lag variables that account or thetime variance were, however, revealed to be highly signicant atthe 1% level as reected in the F-tests in able 3. While there is apossibly positive but statistically insignicant eect o mergers onlength o stay in the rst year aer consolidation, hospital merg-ers lead to a decrease in length o stay by 0.178 days two years post-mergers. Length o stay is reduced by hospital mergers in its thirdyear post-consolidation by 0.403 days and reduced by 0.657 days inits ourth year, holding all else equal. Tese results urther counterthe conclusions o other studies by revealing that holding all else

equal, hospital mergers and acquisitions result in signicant reduc-tions o the length o stay aer the second year, post-consolidation.

Conclusions

Tis paper sought to expand the study o the eects o hospi-tal mergers and acquisitions on the quality o patient care. Severalstudies including that o Ho and Hamilton (2000) have posited thatthe consolidation has negligible or detrimental eects on the qual-ity o care. By expanding the study o hospitals across 14 statesand observing hospital activity and patient outcomes rom 1993-1998, this study urthered the discussion by testing the external

validity o data rom a singl state and by accounting or the time.Te results o the study counter the conclusions reached by pre-

vious studies by showing that while hospital mergers and acquisi-tions initially have a detrimental eect on inpatient mortality oCHF patients and a statistically insignicant eect on their length ostay in the rst year post-merger, in subsequent years they lower theimpatient mortality by 5.8 percentage points and reduced hospitallength o stay by 0.657 daysboth indicating an improvement in thequality o patient care. One possible explanation or the peculiarityo the rst year post-merger in comparison to the observed eectsin the subsequent years is that initially, the hospital, organization-ally, is in a state o restructuring, and thus, quality o care may dip.

Tere are several caveats to these results o this study. Te methodto identiy hospitals that consolidated during the three-year windowwas done through several data manipulations and derivations, andnot by a source explicitly identiy ing mergers and acquisitions. Tere-

ore, the derivation o the 173 hospitals is susceptible to inconsisten-cies in the data or misclassications. Similarly, the process o iden-tiying the year o consolidation through the examination o whenthe prole o a hospital changes in the American Hospital Associa-tions Hospital Guide is also susceptible to inconsistencies and indi-rect changes in classication and measurement by the AHA over theyears. Lastly, this process o identiying mergers and acquisitions didnot allow or the classication o consolidation by type, as is done byHo and Hamilton (2000), which subdivided consolidating hospitalsinto those that merge, are acquired, and are acquired rom one healthsystem to another. Consequently, any variances between the types oconsolidation on the eect o quality o care are not distinguished.


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As raised by Ho and Hamilton in their study, the use o in-patient mortality as a quality indicator necessitates caution orwith earlier discharges caused by consolidationas seen in theresults o the analysis o length o stay or CHF patientsthe in-patient mortality o patients might be reduced because o censor-ing o deaths that occur soon aer discharge that would otherwisehave been counted as an inpatient mortality had the length o staynot been reduced. Tis would cause the data and the regression

analysis to become biased in attributing more o the decrease ininpatient mortality to the impact o consolidation than it truly is.Lastly, there remains a possibility o omitted variable bias in

the regressions. While the regressions were controlled or hospitalcharacteristics such as hospital bed size, urbanity, region, total dis-charges, and patient ow HHI, and by patient statistics such as age,race and gender, several other actors such as patient comorbiditieswere not included due to limitations in the available NIS data. Ad-ditionally, unobservable variances in time eects across hospitals arealso not controlled or in the regression analysis, endangering theresults to biases. It should be noted however, that hospital controlssuch as hospital bed size or total discharges were not controlled overtime and thereore any changes to a hospitals bed size over time isattributed to the hospital consolidation and thus, is included as part

o the hypothesized instigator on changes in the quality o care.Implications o these ndings are signicant as they add crucialinsight into the heated discussion surrounding hospital consolidationand antitrust law. As aorementioned, legal and economic ascinationaround hospital consolidation and quality o patient care was spurredby the use o quality o patient care as one o three benchmarks byseveral state antitrust laws discerning the legality o hospital merg-ers. With the results reached in this study, the common presumptionthat hospital mergers are a detriment to patient care is challengedand invites urther necessary study into this topic o consolidation.

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