The Effect of Increases in HMO Penetration and Changes in Payer Mix on In-hospital Mortality and Treatment Patterns for Acute Myocardial Infarction

Objective: To determine whether changes in health maintenanceorganization (HMO) penetration or payer mix affected inhospitalmortality and treatment patterns of patients with acutemyocardial infarction (AMI).

Study Design: Observational study using patient-level logisticregression analysis and hospital and year fixed effects of data fromthe Agency for Healthcare Research and Quality's Healthcare Costand Utilization Project Nationwide Inpatient Sample, a geographicallydiverse sample of 20% of the hospitalized patients in theUnited States.

Patients and Methods: Discharges of patients (n = 340 064)with a primary diagnosis of acute myocardial infarction who weretreated in general medical or surgical hospitals that contributed atleast 2 years of data to the HealthCare Cost and Utilization ProjectNationwide Inpatient Sample from 1989 to 1996. In-hospital mortalityand rates of cardiac catheterization, angioplasty, or coronaryartery bypass grafting for Medicare patients or non-Medicarepatients were the main outcome measures.

Results: Among Medicare patients, increases in HMO penetrationwere associated with reduced odds of receiving cardiaccatheterization, angioplasty, or coronary artery bypass grafting of3% to 16%, but were not associated with any change in mortalityrisk. Increases in the number of HMOs within a metropolitan statisticalarea, our measure of HMO competition, were associatedwith small but significant increases in the odds of cardiac catheterizationand angioplasty of about 2%. There was no pattern ofchanges in cardiac procedure rates or in-hospitality mortalityamong non-Medicare patients.

Conclusion: Increases in HMO penetration reduced cardiacprocedure rates by statistically significant but small amountsamong Medicare patients with AMI, without affecting mortalityrates.

(Am J Manag Care. 2004;10:505-512)

The growth of managed care as a mechanism tolower healthcare costs has led to concerns thatthis may adversely affect quality of care. Relativereductions in payment may affect the quality of carebecause hospitals have fewer resources with which toprovide services. The overall effect on patient care ofdeclines in reimbursement by a particular insurer comprises2 phenomena: a direct effect on the care ofpatients with that particular insurance, who mayreceive fewer beneficial services, and an indirect orspillover effect on other patients within the same hospital,whose care is altered because of hospital-wideresource constraints.

Direct comparisons of quality of care between healthmaintenance organization (HMO) and fee-for-service(FFS) patients have produced mixed results.^1-3However, commercial HMO penetration has beenshown to have spillover effects on the cost of careamong Medicare FFS patients, as lower costs amongMedicare patients have been observed in areas withhigh commercial HMO penetration.^4,5 In addition, higherHMO and managed care (HMO or preferred providerorganization) penetration rates have been shown to beassociated with reduced access to care,³ less use of costlydiagnostic testing,⁶ and reductions in hospital costgrowth.^7-9 The effect of HMOs on quality of care for hospitalizedpatients could be mediated by several mechanismsbesides reduced availability of resources. Growthin managed care could bring about changes in physicianpractice patterns, system-wide availability of resourcesand new technology, and changes in the mix of servicesavailable to favor more cost-effective technologies andservices.¹⁰

The quality effects for non-HMO patients of theobserved spillover effects of HMOs on hospital costs areunknown. While reductions in hospital cost growthcould lead to fewer resources being available for patientcare and to worse outcomes, HMOs have greater abilityto shop for better quality providers than individuals.Two cross-sectional analyses have examined the associationbetween HMO penetration and quality of carereceived by Medicare FFS patients. Heidenreich et al¹¹used data from 1994 to 1995 on Medicare FFS patientswith acute myocardial infarction (AMI) in a cross-sectionalanalysis of geographic areas with different levelsof HMO penetration and found that higher HMO penetrationwas associated with higher use of beneficialmedications such as &#946;-blockers and aspirin and lowerrates of coronary angiography, but was not associatedwith AMI mortality rates. Mukamel et al¹² examined1990 data for Medicare patients and found a statisticallysignificant but small reduction in 30-day mortalityrisk of roughly 0.15% among patients in the highest(>24.5%) HMO market penetration group relative tothose in the lowest (<10.6%) HMO penetration group.

The cross-sectional design of the Heidenreich andMukamel studies makes inferences about causalityproblematic. Explanations such as a tendency by HMOsto locate in higher cost markets because of greaterpotential for cost savings may explain the observedassociations, as higher cost markets may also have betteroutcomes.¹³ In addition, both of the studies focusonly on the Medicare population, and the effect of managedcare on patient quality may have been more significantin hospitals with greater proportions of patientsyounger than 65 years, particularly if a significant portionof patients is part of a vulnerable population group,such as the uninsured. The role of HMO competition inthis context is also important to explore. Previous workhas demonstrated that, while HMO profits have beenshown to be lower in markets with more competition,HMO market penetration was not independently associatedwith profit rates.¹⁴ This suggests that competitionbetween HMOs may limit the ability of HMOs to reduceprices paid to hospitals and might also reduce thedegree of cost reductions and the potential effect onquality of care.

In this analysis, we use data from 1989 through 1996from the Agency for Healthcare Research and Quality'sHealthcare Cost and Utilization Project NationwideInpatient Sample (HCUP NIS) to examine the associationbetween changes in HMO penetration and changesin outcomes and treatment patterns for all AMI patients.Using discharges from repeated cross-sectional samplesof hospitals makes the selection bias inherent in singlecross-sectional studies less problematic and allows us tocontrol for baseline quality differences among patientsin different hospital markets to examine how outcomesand treatment patterns changed over time in associationwith changes in HMO penetration. In contrast toprevious work, we studied not only Medicare patientsbut also non-Medicare patients younger than 65 years,because the effects of price competition and reducedreimbursement may be most direct in this group.

While it is important to study Medicare and non-Medicare patients, there is some evidence that within agiven marketplace outcomes and treatment patternsmay be similar for all patients, although the price beingpaid for similar services may vary widely.¹⁵ For this reason,we focus on intertemporal changes in mortality andcardiac procedure use for all patients within each of our2 main groupings of patients (the Medicare and non-Medicare groups), as opposed to looking at changes inthese measures for patients with specific insurancetypes. In addition, we examine the role of HMO marketconcentration to determine whether the effects observedare a function of the competitiveness of HMO markets.We adjust for changes in the proportion of uninsured,Medicaid, and Medicare patients, as reimbursement forthese payers has been low relative to FFS indemnity;therefore, increases in the proportion of these low-payingpayers may also have affected the process and outcomesof care delivered within hospitals.

METHODS

Data Sources

Discharges with AMI were chosen for this studybecause AMIs are common, the treatment objectivegenerally is to ensure survival, and the quality of careaffects the likelihood of survival.^16,17 In addition, allpatients with recognized AMI are admitted on an emergencybasis, usually to the nearest hospital. Therefore,there is little opportunity for selection bias to affect thedecision about who gets admitted or risk-adjusted mortalityover time.

International Classification of Diseases, Ninth Revision,Clinical Modification [ICD-9-CM]

The database for this study was constructed from 3primary sources: the HCUP NIS, the Area Resource File,and the HMO Geographical Enrollment Data file (D. R.Wholey, PhD, Division of Health Services Research andPolicy, University of Minnesota, unpublished data,2001). We used data from 1989 to 1996 and includedindividuals who had a principal discharge diagnosis ofAMI ( code 410.XX),excluding subsequent care (code 410.X2) (807 743 observations).These codes were chosen to focus on newAMI admissions, using the same approach taken by theCalifornia Hospital Outcomes Project.¹⁸ We excludedpatients who were not treated in hospitals located withinmetropolitan statistical areas (MSAs) (n = 100 701),as our HMO data were limited to patients in MSAs;patients with a coded primary diagnosis of AMI whowere discharged alive after a length of stay of less than2 days (n = 32 110), because it is likely that thesepatients did not experience an AMI; individuals youngerthan 18 years and older than 85 years (n = 47 483);patients with a length of stay greater than 30 days (n =9105); patients for whom neither discharge alive ordead was coded (n = 636); patients treated in governmentalhospitals (n = 58 210); and patients with agemissing (n = 4071), as age is an important risk adjuster.Because we were not able to determine their ultimateoutcome, we excluded individuals who were transferredto another hospital (52 591 observations). In addition,we excluded patients who went to hospitals at whichuninsurance rates, Medicare rates, or Medicaid rateswere coded as 0% for any year, on the basis that theinsurance variable was likely miscoded (n = 122 359).To use hospital fixed effects to control for unobservedheterogeneity among hospitals, we excluded dischargedpatients who were treated in hospitals for which we hadonly a single year of data (n = 40 413). The final sampleof this unbalanced panel consisted of 340 064 dischargeobservations from 457 hospitals from 1989 to 1996.

The Area Resource File was used to link the individualpatient-level data records from the NIS with MSAcodes. The HMO Geographical Enrollment Data file providedthe commercial HMO penetration and the numberof HMOs within each MSA for each year being studied.

Measure of HMO Penetration and Payer Mix

We used an MSA-level measure of HMO penetrationand the number of HMOs within each MSA as a measureof HMO competition. We used an MSA-level measurerather than hospital-level measures because it is lesslikely to be endogenous, as HMOs might choose to contractwith better quality hospitals but would be unlikelyto make selective contracting decisions on the marketlevel based on differing levels of quality. Using an MSA-levelmeasure of HMO penetration also protects againstpotential hospital-level selection bias, which couldresult from significant changes in admission patternsof patients, with different-than-average AMI mortalityin conjunction with increases in HMO penetration.

Each discharge was grouped into 1 of 4 quartilesbased on the market-level HMO penetration for thathospital in that year: HMO penetration between 0% andless than 9.3% (quartile 1), HMO penetration between9.3% and 14.5% (quartile 2), HMO penetration between14.6% and 21.4% (quartile 3), and HMO penetrationgreater than 21.4% (quartile 4).

We calculated the percentage of uninsured,Medicare, and Medicaid patients in each hospital peryear in the sample using the NIS data.

Measurement of In-hospital Mortalityand Treatment Patterns

ICD-9-CM

The principal outcome measure was death thatoccurred during the initial hospitalization, provided thelength of stay was less than or equal to 30 days. Toexamine changes in treatment patterns, we examinedcardiac catheterizations ( codes 37.21-37.23and 88.55-88.57), coronary artery bypass graft (CABG)procedures (codes 36.11-36.19), and coronary angioplastiesor stents (codes 36.01, 36.02, 36.05, and 36.06)during the first 30 days of the initial hospitalization.

Risk Adjustment

The method for risk adjustment developed byElixhauser and colleagues¹⁹ at the Agency forHealthcare Research and Quality was used, as thismethod has tested well in terms of superior discriminationvs other risk adjustment approaches for use withadministrative data.²⁰ Variables in the risk adjustmentalgorithm include age, sex, peripheral vascular disease,paralysis, other neurological disorders, chronic pulmonarydisease, diabetes mellitus, diabetes mellituswith chronic complications, hypothyroidism, renal failure,liver disease, peptic ulcer disease, AIDS, lymphoma,metastatic cancer, solid tumor withoutmetastasis, rheumatoid arthritis, coagulopathy, obesity,weight loss, fluid and electrolyte disorders, chronicblood loss anemia, deficiency anemias, alcohol abuse,drug abuse, pyschoses, depression, and hypertension.

Statistical Analysis

We used patient-level logistic regressions with hospitaldummy variables to control for unobserved heterogeneityamong hospitals, and year dummy variables tocontrol for intertemporal changes common to all hospitalsin the sample. Hospital-level dummy variables areincluded because, even with the inclusion of many controlvariables, there may be unobservable characteristicsof areas that are not included but that arecorrelated with HMO market share and treatment patternsor in-hospital mortality.

To test for spillover effects, primarily whetherchanges in the proportion of patients with HMO insuranceaffected the outcomes and treatment patterns ofpatients with Medicare insurance, the sample was dividedinto 2 subsamples: Medicare and non-Medicarepatients. We examined treatment patterns and outcomesat the discharge level in both subsamples. Thedependent variables were mortality and 3 cardiacprocedures: catheterization, angioplasty, and coronaryartery bypass graft (CABG). In this way, by examiningthe Medicare subsample, we were able to test whetherthere was a spillover effect from changes in HMO penetration,Medicaid, or the percentage of uninsured on theoutcomes and treatment patterns of the Medicare population.We examined the non-Medicare patient subsampleseparately, as nearly all studies of HMO spillovershave examined Medicare patients only, and this allowedus to examine whether there were changes in outcomesand treatment patterns based on changes in payer mixwithin the non-Medicare population.

Patient-level logistic regression analysis was used toexamine the relationship between changes in HMO penetration,HMO market concentration, and the outcomeand treatments of interest. To control for baseline qualitydifferences among the hospitals, hospital fixed-effectsdummy variables were used. Through the use ofhospital fixed effects, the outcome (death or survival) isinterpreted as a function of within-hospital changes inpayer mix, as variation in time-invariant hospital characteristicsis accounted for by the hospital fixed-effectsdummy variables. Because we used quartiles of HMOpenetration to measure HMO penetration and becauseHMO penetration increased in every market during thestudy, the estimated regression coefficients examine theeffect of interquartile increases in HMO market shareon treatment patterns and in-hospital mortality. Patientcharacteristics such as age, sex, and clinical risk factorswere used as controls. Coronary artery bypass graft surgery,angioplasty, and cardiac catheterization weremodeled as dependent variables in a fashion similar tothat used for mortality. We also controlled for intertemporalchanges common to all hospitals in the sample byusing dummy variables for each year.

To compensate for the fact that our analyses of HMOpenetration were correlated at the MSA level, we adjustedour standard errors for clustering at the MSA level.Analyses were done using SAS software (SAS Institute,Cary, NC), with adjustment of standard errors for clusteringdone using STATA (Stata Corporation,College Station, Tex).

RESULTS

Descriptive Data

The hospitals included in the NISchanged from year to year based on theNIS sampling criteria. In 1989, our sampleincluded 180 hospitals, and in 1996, oursample included 238 hospitals. Our totalsample included 457 hospitals that contributeddata for at least 2 years, whichenabled the use of hospital fixed effects.Mean HMO penetration increased from18.8% in 1989 to 27.5% in 1996. Therewere no large changes in the mean numberof HMOs within each MSA. The percentageof uninsured decreased from 6.0%to 4.1%. Changes in payer mix for each ofthe other classes of payers were small(Table 1). Overall, in-hospital mortality for AMIpatients declined from 14.6% to 9.3% during this period,while cardiac catheterization, angioplasty, and CABGrates rose substantially. Hospitals in the sample in 1989and hospitals in the sample in 1996 were not necessarilythe same hospitals.

Measurement of the HMO effect on changes in outcomesand treatments over time has the potential to bebiased if there are underlying unmeasured differencesin areas that attracted HMOs to a greater degree thanothers. For instance, HMO penetration might be morelikely to increase in areas in which the population isyounger or healthier. To investigate whether there weresystematic differences between markets in which HMOpenetration increased or did not, we comparedobserved patient characteristics in these 2 types of markets(Table 2). There were 96 124 patients in hospitalsthat did not change their quartile of HMO penetrationduring the study. There were 243 940 patients in hospitalsthat experienced interquartile change in HMO penetration.Although there are several statisticallysignificant differences in the proportion of patients withthese comorbidities between these 2 groups, the magnitudeof the differences in all cases is small and does notfavor one group or the other.

Regression Analysis Results

We present results separately for the Medicarepatients and the non-Medicare patients.

Medicare Patients

. Among Medicare patients, wefound extensive evidence for spillover effects fromincreases in HMO penetration, particularly in terms ofchanges in treatment patterns. Odds ratios (ORs) thatwere significantly lower than 1 were found with eachinterquartile increase of HMO penetration for cardiaccatheterization and angioplasty and for patients in hospitalmarkets in which HMO penetration increased intothe highest quartile for CABG (OR, 0.84; 95% confidenceinterval [CI], 0.74-0.95) (Table 3). For each ofthese procedures, the OR stayed the same or decreasedwith increasing levels of HMO penetration, such thatthe ORs were lowest in hospital markets in which HMOpenetration increased into the highest quartile. Themagnitude of these coefficients means that, for everyinterquartile increase in HMO penetration (roughly 5%-7%, depending on the quartile), the odds of a MedicareAMI patient receiving cardiac catheterization decreasedby 12% to 17%, the odds of receiving an angioplastydecreased by 7% to 12%, and the odds of receiving aCABG decreased by 3% to 16%.

There was no evidence that increases in HMO penetrationhad a significant effect on inpatient mortality, aspatients in hospitals that experienced increases fromthe first quartile to the second quartile of HMO penetrationhad a slightly increased odds of mortality (OR,1.07; 95% CI, 1.01-1.13), but patients in hospitals inwhich HMO penetration increased into the third orfourth quartile did not experience an OR of mortalitysignificantly different than 1. Increases in the numberof HMOs within an MSA, our measure of HMO competition,were associated with small but significant increasesin cardiac catheterization (OR, 1.03; 95% CI,1.01-1.04) and angioplasty (OR, 1.02; 95% CI, 1.00-1.04) rates. There were no significant effects of thechange in the number of HMOs on mortality or CABGrates.

Increases in the hospital's proportion of uninsured orMedicaid patients had no statistically significant effecton the mortality risk for Medicare patients or the oddsof receiving cardiac catheterization, angioplasty, orCABG. Increases in the percentage of Medicare patientswere associated with reductions in the odds of mortality(OR, 0.71; 95% CI, 0.53-0.96) and in the odds ofbypass surgery (OR, 0.50; 95% CI, 0.25-0.99), but hadno significant effect on the odds of cardiac catheterizationor angioplasty.

Using fixed effects to control for unobserved heterogeneityin a logistic regression analysis may result inbiased coefficient estimates in cases in which there arefew events per hospital. As a test for robustness and tomitigate the potential for bias, the regression for eachdependent variable was also run using a data subsamplelimited to patients in hospitals with 10 or more occurrencesof each dependent variable. We found no evidenceof biased coefficients in any of the specifiedregression models.

Non-Medicare Patients

. We found less evidence ofchanges in treatment patterns and in-hospital mortalitydue to changes in HMO penetration in the non-Medicaresample. Interquartile changes in HMO penetration hadno significant effect on mortality, cardiac catheterization,or CABG rates. Increases into the third quartile ofHMO penetration were associated with a reduced oddsof angioplasty (OR, 0.90; 95% CI, 0.82-0.99), butincreases into the second or fourth quartile of HMOpenetration were not associated with ORs of angioplastythat were significantly different than 1. Increases inthe number of HMOs within an MSA, our measure ofHMO competition, were associated with a small but statisticallysignificant increase in angioplasty rates (OR,1.02; 95% CI, 1.01-1.04), but no significant change inthe odds of mortality, cardiac catheterization, or bypasssurgery (Table 4).

Increases in the percentage of uninsured had no significanteffect on the odds of mortality, cardiaccatheterization, or angioplasty, but were associatedwith a significant reduction in the odds of all non-Medicare patients receiving bypass surgery (OR, 0.17;95% CI, 0.03-0.84). Increases in the percentage ofMedicaid patients were also associated with significantreductions in the odds of bypass surgery (OR, 0.15; 95%CI, 0.05-0.49), but had no significant effect on the oddsof mortality, cardiac catheterization, or angioplasty.The reduction in the odds of receiving bypass surgeryfrom a 1% increase in the percentage of uninsured orMedicaid patients is large, but given that the baselinerates of each of these is only about 4%, a 1% increaserepresents a potentially substantial additional financialburden. Changes in the percentage of Medicare patientshad no significant effects on any of these measureswithin the non-Medicare population.

There was no evidence of bias in any of the studyvariable coefficients in subsamples of the data that werelimited to patients in hospitals with 10 or more occurrencesof the dependent variable.

DISCUSSION

Our results indicate that increases in HMO penetrationfrom 1989 to 1996 significantly affected treatmentpatterns for Medicare AMI patients within the HCUPNIS, and these effects were largest in the markets inwhich HMO penetration increased into the highestquartile. The relative risk of receiving cardiac procedureswas reduced by about 10% to 15% at hospitalswith increases in HMO penetration into 1 of the 3 upperquartiles. Given the rates at which patients receivedthese procedures, the magnitude of this relativedecrease is about 1% to 2% for CABG, 2% to 3% forangioplasty, and 5% to 7% for cardiac catheterization.We found no evidence of any increased mortality riskfrom increases in HMO penetration. Increases in thenumber of HMOs, suggesting greater HMO competition,were associated with modest increases of approximately2% in the rates of cardiac catheterization (eg, 1.0%,given a baseline rate of about 50%) and angioplasty(0.5%) after AMI. We believe that these estimates areunbiased by cross-sectional differences in case-mix orhospital characteristics, because we used a measurementframework that provides estimates of within-hospitalchanges in response to different degrees of changein MSA-level HMO penetration.

In the non-Medicare population, we found little evidencethat treatment patterns or in-hospital mortalitywas affected by increases in HMO penetration.However, increases in the proportion of uninsuredpatients and Medicaid patients were associated withsignificant reductions in the odds of receiving bypasssurgery for all non-Medicare patients. Because care forsuch patients (to the degree that Medicaid reimbursementdoes not cover marginal costs) is funded out ofhospital margins, these results are not surprising.

Our results on treatment patterns and mortality inthe Medicare population are similar to those found inthe study by Heidenreich et al,¹¹ who showed that higherrates of HMO penetration were associated with lowerrates of coronary angiography (relative risk, 0.93; 95%CI, 0.86-1.01) but no significant differences in AMImortality. We did not observe reductions in mortality,as Mukamel and colleagues¹² did (a small reduction of0.15%). Reductions in cardiac procedure use are consistentwith the literature that has demonstrated reductionsin expenditures in areas with bigger increases inHMO penetration.^4,5,7-10

Given that there were no changes in mortality riskas a result of changes in HMO penetration, the ramificationsof the observed changes in treatment patternsfor quality of care are difficult to assess. From theadministrative data contained in the NIS, we cannotdetermine the appropriateness of the treatments thatindividual AMI patients received. Therefore, we do notknow whether the changes in treatment patterns are ofa beneficial or detrimental nature, as it depends onwhether the procedures received were appropriate andnecessary, or not.

An important limitation of this study is that we studiedonly patients with AMI; therefore, we cannot makeinferences about how outcomes and treatment patternsfor other conditions were affected by changes in HMOpenetration. We were limited to in-hospital measures ofmortality, but the mean length of stay for all AMIpatients decreased to a similar degree in hospitals inthe areas with the biggest and smallest increases inHMO penetration, so these measures would not havebeen biased by differential changes in length of stay. Inaddition, some investigations focused on quality of carehave found results robust to using inpatient deaths ordeaths within 30 days of admission as measures of quality.²¹ The HCUP NIS did not code whether patients wereHMO patients; therefore, we could not directly analyzespillover effects among non-HMO patients younger than65 years or among Medicare patients who were notHMO enrollees. However, we think this was unlikely tohave any significant effect on our results, as the percentageof Medicare HMO patients nationwide wassmall during 1989 to 1996, and Baker¹⁰ demonstratedthat overall HMO market share, as opposed to MedicareHMO market share, predicts spending among Medicarepatients in several different investigations. It would alsobe useful to estimate the managed care effects byincluding preferred provider organization data, but suchdata were not available on a national level for all MSAsat the time of the study. Furthermore, HMOs have beenthe managed care entities that have typically mostaggressively negotiated with providers, exerted influenceover treatment decisions, and attempted to affectthe practice patterns of physicians.¹⁰

The observations we have made with these data arereassuring, as they suggest that increases in HMO penetrationwithin hospitals in the HCUP NIS did not affectthe availability of resources to a sufficient degree tohave a significant adverse effect on AMI mortality.Further work should investigate the effects of changesin payer mix and HMO competition on quality of carefor other diseases, as well as in portions of the countrythat have experienced large increases in the percentageof HMO patients or other changes in payer mix thatcould cause sufficient financial stress within hospitals toaffect the quality of care.

Acknowledgments

We acknowledge helpful suggestions from seminar participantsat the 2002 Association for Public Policy Analysis and ManagementFall Research Conference, November 7-9, 2002, Dallas, Tex;the 2002 Annual Research Meeting of Academy Health, June 23-25, 2002, Washington, DC; the HealthCare Cost and UtilizationProject Partners Meeting, March 2002, Rockville, Md, the WhartonSchool, and the University of Pennsylvania School of Medicine. Wealso acknowledge helpful suggestions from Sean Nicholson, PhD,and Irma Arispe, PhD.

From the Center for Health Equity Research and Promotion, Philadelphia Veterans Hospital, and Department of Medicine, School of Medicine (KGMV), and The Wharton School (KGMV, EB), University of Pennsylvania, Philadelphia; and the National Center for Health Statistics, Hyattsville, Md (EB).

Dr. Volpp is a VA HSR&D Career Development Awardee and a Doris Duke Clinical Scientist Development Award recipient.

This study was funded by the Center for Health Equity Research and Promotion and the Doris Duke Charitable Foundation, New York, NY.

Address correspondence to: Kevin G. M. Volpp, MD, PhD, Department of Medicine, School of Medicine, University of Pennsylvania, 1232 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021. E-mail: volpp70@mail.med.upenn.edu.

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