EXPERTISE, UNDERUSE, AND OVERUSE IN HEALTHCARE * Amitabh Chandra Harvard and the NBER. Douglas O. Staiger Dartmouth and the NBER

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1 PRELIMINARY & INCOMPLETE DO NOT CITE OR DISTRIBUTE EXPERTISE, UNDERUSE, AND OVERUSE IN HEALTHCARE * Amitab Candra Harvard and te NBER Douglas O. Staiger Dartmout and te NBER Version: Marc 27, 2011 Abstract Te reasons for variation in treatment rates across ospitals serving similar patient populations are not well understood. Differences in te use of a treatment across ospitals may be due to greater benefits of treatment in some ospitals (expertise), witolding of beneficial treatment in some ospitals (underuse), or providing armful treatment in oter ospitals (overuse). We develop an empirical model tat can distinguis between tese explanations, based on a beavioral model in wic ospitals coose to treat patients if te benefit from treatment exceeds a ospital-specific tresold. Expertise, underuse, and overuse are identified based on differences across ospitals in bot teir treatment rates and te treatment effect on patient survival. Using data on eart attack treatments, we find tat expertise varies considerably across ospitals, but a substantial amount of variation in treatment is due to overuse. * Tis researc was funded by te National Institute of Aging (NIA) P01 AG We tank Jonatan Skinner and seminar participants at Dartmout for comments tat ave greatly improved our paper. We obtained access to te proprietary data used in tis paper troug a data use agreement between te Centers for Medicare and Medicaid Services (CMS) and Dartmout Medical Scool. Readers wising to use tese data must obtain tem from CMS.

2 I. Introduction An enormous body of literature in economics and medicine as documented variations in te use of medical and surgical treatments across ospitals and regions wit observationally similar patient populations. Generally, iger treatment rates are not found to be associated wit improved satisfaction, survival, or oter patient outcomes. Tese facts ave often been interpreted as evidence of overuse: ig use ospitals or regions are coosing to treat marginal patients wo receive no benefit from te treatment. However, a fundamental problem wit tis explanation is tat it implies tat iger treatment rates sould be associated wit lower benefits to treatment for te average patient getting treatment, wile a number of studies ave found te opposite (Beck et al., 2003; Pilote, McClellan, et al., 2003; Candra and Staiger, 2007). In tis paper, we develop a simple empirical model tat can distinguis between alternative explanations for variations in treatment rates, based on a beavioral model in wic ospitals coose to treat patients if te benefit from treatment exceeds a ospital-specific tresold. In our model, differences in te use of a treatment across ospitals may be due to greater benefits of treatment in some ospitals (expertise), witolding of beneficial treatment in some ospitals (underuse), or providing armful treatment in oter ospitals (overuse). Expertise, underuse, and overuse are identified based on differences across ospitals in bot teir treatment rates and te treatment effect on patient survival. Using data on eart attack treatments, we find tat expertise varies considerably across ospitals, but a substantial amount of variation in treatment is due to overuse. In Section II we develop te teoretical model underlying our analysis. Section III discusses te etiology of eart attacks and teir treatments, and introduces data from te Cooperative Cardiovascular Project (CCP). In Section IV we detail our estimation strategy, paying particular attention to ow te teoretical model developed in Section II will be evaluated using te CCP data. Section V presents results and Section VI concludes. II. Teory A simple model of patient treatment coice guides our empirical work. We assume tat treatment is provided to eac patient wenever te expected benefit from te treatment exceeds a minimal tresold. Tus, in te terminology of Heckman, Urzua and Vytlacil (2006), our model allows for essential eterogeneity were te decision to provide treatment to eac patient is made wit knowledge of teir idiosyncratic response to treatment. Witin tis framework, tere are two ways in wic a patient s ospital could affect treatment coice. First, te expected benefit of treatment for a given patient may vary across ospitals, reflecting eac ospital s idiosyncratic level of expertise in providing te treatment. Second, te minimum tresold for receiving care may vary across ospitals, as determined by local 1

3 incentives and norms at eac ospital. From te patient s point of view, treatment sould be provided wenever te expected benefit from treatment exceeds zero. Terefore, tere is underuse of te treatment in ospitals tat set a minimum benefit tresold above zero, and overuse in ospitals tat set a minimum tresold below zero. Setup of te Model Let B i represent te expected benefit from treatment for patient i at ospital. Benefit is te gain or improvement in survival relative to not receiving a treatment, not te level of survival. We focus on te ealt benefits of te treatment, wic would include any reduction in mortality or morbidity tat was expected from te treatment, e.g. te impact of te treatment on Quality Adjusted Live Years (QALYs). In principal, te benefit could also incorporate te expected impact of te treatment on te patient s medical cost, and capture te ealt benefits net of costs. However, in our application we focus on survival for simplicity. Suppose tat te expected benefit from treatment depends on te ospital s expertise ( ), observable patient caracteristics (X i ) suc as age, medical istory, and lab results, and oter factors tat are known to te medical care provider wen making te treatment decision but unobserved by te econometrician (ε i ): (1) Bi X i i Eac patient receives treatment if te expected benefit from treatment exceeds a minimal tresold (τ ), were te tresold varies across ospitals due to local incentives or norms. Tis corresponds to a simple Roy model of treatment allocation, were a patient receives treatment if te gain from te treatment exceeds a tresold. Assuming tat B i captures te total net benefit to te patient of providing treatment, ten te optimal decision from te patient s perspective would let 0, and provide treatment wenever te benefits to te patient exceed zero. Tere is underuse if 0, since patients wit positive benefits are under te tresold and do not receive treatment. Tere is overuse if 0, since patients wit negative benefits (wo would do better witout treatment) are above te tresold and receive treatment. Tis decision process implies a very simple tobit structure tat determines bot te probability of treatment as well as te expected benefit conditional on being treated (te treatment-on-te-treated parameter). Te probability of receiving treatment is just te probability tat expected benefits exceed te minimum tresold: (2) PrTreatment 1 PrB Pr I, i i i i 2

4 were I i X i Equation (2) igligts tat differences in treatment rates across ospitals, olding all else equal, can be due to eiter differences in ospital expertise ( ) or differences in te treatment tresold ( ) reflecting over or underuse. A ospital may be more likely to provide treatment because of greater expected benefits of treatment 0 or because of a lower benefit tresold for providing care g 0. Conversely, even if treatment rates were te same across ospitals, tere could still be overuse or underuse if, say, ospital s wit greater expected benefits of treatment set a correspondingly iger tresold for providing care 0. Tus, because variation in treatment rates across ospitals masks variation in ospital expertise and ospital treatment tresolds, suc variation cannot by itself say anyting about overuse or underuse. In oter words, wile te conventional wisdom is to interpret variation in ospital treatment rates or spending as saying someting about differences in teir treatment tresold (as in flat of te curve models of provider beavior), tis inference ignores te role of expertise in also explaining tat variation. However, overuse and underuse can be identified separately from ospital expertise if information on te treatment effect among te treated population is available. Te treatment-on-tetreated parameter is defined as: (3) EB Treatment 1 EB I X E I i Noting tat X i I i i (4) EB Treatment 1 gi, i i i were g i i, we can rewrite Equation (3) as: I I E I i i i i i i Equation (4) states tat in te absence of any difference across ospitals in te minimum tresold to receive care, two patients receiving treatment wo ave te same propensity to get te treatment (same I) will ave te same expected benefit from te treatment. Tis relies on te assumption tat g(i) depends only on te index, i.e. we must assume a single-index selectivity model, so tat te truncated mean of te error in equation (4) depends only on te truncation point (I). Tis would not be te case if te distribution of te unobservable factors determining treatment (ε) differed across ospitals, wic in turn would appen if some ospitals were better at measuring tese unobservable factors. Examining equation (4) makes it clear tat ospitals wit larger unobservables will ave larger treatment on te treated effects (wic will look like aving a iger treatment tresold) because of te iger values of conditional error term. Since tere is no evidence to suggest tat tis is not te case, we will maintain te single index assumption for our analyses. i i i i 3

5 Identification Conditional on te Estimated Propensity If te propensity to get te treatment (or equivalently te index I) can be estimated directly from Equation (2), we can identify differences in te minimum tresold from an estimate of te treatment-onte-treated parameter: For patients wit te same propensity, tose treated at a ospital wit a iger minimum tresold (a iger ) will ave a larger treatment effect. Moreover, because g(i i ) goes to zero as te propensity to receive treatment goes to zero (or equivalently as I i gets increasingly negative), we can also identify overuse 0 or underuse 0: Tere is overuse wen te treatment effect for te lowest propensity patients is negative, and underuse wen te treatment effect for te lowest propensity patients remains positive. It is important to note tat our model does not imply tat te treatment-on-te-treated parameter is te same for all patients treated in ospitals wit te same minimum tresold. In fact, Equation (4) implies tat te treatment-on-te-treated effect will tend to be larger among patients tat ave a iger propensity to be treated (since g(i i ) is increasing in I i ). Te treatment effect is te same only for patients wit te same propensity to be treated and treated in ospitals wit te same minimum tresold. Since ospital expertise ( ) raises te propensity to be treated, it will also raise te treatment effect. Te key difference, owever, is tat differences in ospital expertise ave an impact on treatment effects by sifting te propensity to be treated (and, terefore, g(i i )), wile differences in te minimum tresold ave an impact on treatment effects tat is independent of te propensity. Te grapical intuition for our model can be seen in Figure 1. Te expected benefit from treatment (B) is given on te vertical axis, wile te propensity of being treated (wic depends on I) is given on te orizontal axis. Te top curve in Figure 1 represents te treatment-on-te-treated effect for a patient wit a given propensity tat is treated in a ospital wit a ig minimum tresold for treatment ig 0, i.e. it represents EBi Bi gii. Te lower curve represents te same ting for a ospital wit a low minimum tresold low 0. Treatment-on-te-treated approaces te minimum tresold (τ ig or τ low ) for a patient wit a low propensity of being treated (a very negative I), since no patient is ever treated wit a benefit below tis tresold. For a patient wit a ig propensity of being treated (a very positive I), truncation becomes irrelevant and te treatment-on-te-treated effect asymptotes to te unconditional benefit of treatment. However, conditional on a patient s propensity, te treatment effect is always iger in te ospital wit te iger tresold. In tis figure, differences in ospital expertise would sow up as a movement along te curves canging te propensity of patients to be treated (and terefore te treatment-on-te-treated effect), but not affecting treatment effects conditional on propensity. 4

6 Identification Using Joint Distribution of Hospital Random Effects Te above approac identifies differences in te minimum treatment tresold by comparing te benefit from treatment once te propensity to receive treatment as been controlled for. But it is possible tat our ability to control for tis propensity is imperfect-- especially on te dimension of ospital effects in te propensity to receive treatment-- because of small numbers of patients at some ospitals. In some ospitals we ave fewer tan 20 patients, and te resulting imperfection would cause us to overstate te role of treatment tresold differences across ospitals wen in fact tere are actually large differences in ospital expertise. Alternatively, ospital expertise ( ) can be separately identified from te minimum treatment tresold ( ) using estimates of te joint distribution of te ospital effects in equations 2 and 4. Tis second approac requires us to constrain g(.) into a simpler form. To see tis, we take a linear first-order taylor approximation of te function g(.), and combine te ospital-level parameters into random effects to rewrite te equations as: (2 ) Pr Treatment 1 Pr X, were E B i i i (4 ) i i i i Treatment 1 0 1I 0 1 X, were 1 Equation 2 defines te propensity to receive treatment as te sum of a linear combination of patient observables ( X i ) and a ospital random effect ( ) wic captures bot te ospital treatment tresold and ospital expertise. Equation 4 defines te treatment effect parameter as proportional to te same linear combination of observables tat determines treatment ( 1 X i ) plus a different ospital random effect ( ). We can write te original parameters of our model tat captured ospital expertise and te minimum treatment tresold as functions of te parameters in tis transformed model, wit 1 1 and 1. Tus, estimates of te parameters in tis transformed model identify estimates of ospital expertise and te minimum treatment tresold. In particular, te joint distribution of expertise and te minimum tresold can be derived based on estimates of te joint distribution of te random effects (, ) combined wit an estimate of 1. We provide more details of tis strategy in Section IV. III. Heart-Attacks: Biology, Treatments, and Data Heart-Attack Biology and Treatments 5

7 Heart attacks (more precisely, acute myocardial infarction (AMI)) occur wen te eart-muscle (te myocardium) does not receive sufficient oxygen, because of a blockage in one of te coronary arteries wic supply blood to te eart. Te blockage is typically caused by a blood clot tat occurs because of coagulation induced by te rupture of aterosclerotic plaque inside te coronary arteries. Timely trombolytics, wic are also known as fibrinolytics, are administered intravenously and break down blood clots by parmacological means (tese drugs include tissue plasminogen activators, streptokinanse and urokinase). Angioplasty (were a balloon on a cateter is inflated inside te blocked coronary artery to restore blood flow) and trombolytics are two treatments tat are used for immediate reperfusion (opening up te coronary artery). Following te clinical literature, we define a patient to ave received reperfusion if any of tese terapies was provided witin 12 ours of te eart attack. In our data from te mid-1990s, over 90 percent of patients receiving reperfusion received trombolytics. We focus our empirical work on te treatment of AMI for a number of reasons. First, cardiovascular disease, of wic eart attacks are te primary manifestation, is te leading cause of deat in te US. A perusal of te leading medical journals would indicate tat eart attack treatments are constantly being refined, and a large body of trial evidence points to significant terapeutic gains from many of tese treatments. In tis context, variation in treatments across ospitals may directly translate into lost lives, and tere is a ric tradition of studying variation across ospitals in treatments and outcomes after eart attacks. Second, as a consequence of wat is known about eart attack treatments from randomized controlled trials, and more specifically for our setting, te benefits from reperfusion, we are able to assess weter our regression estimates of te benefits from reperfusion are comparable to tose found in te medical literature, or weter tey are confounded by selection-bias. We focus on reperfusion, were our use of cart data allows us to replicate te RCT evidence tat is summarized by te Fibrinolytic Terapy Trialists' Collaborative Group (1994). Cart data provides compreensive documentation on te patient s condition at te time tat te treatment decision is made, and terefore minimizes te possibility tat unobserved clinical factors related to a patient s survival are correlated wit treatment. Tird, because mortality post-ami is ig (mortality rates at 30 days are nearly 20 percent), a well-defined endpoint is available to test te efficacy of eart attack treatments. Tis would not be true if we focused on treatment variation for more cronic conditions suc as diabetes, cronic obstructive pulmonary disease, or artritis. Our fourt reason for focusing on eart attacks is tat it is an acute condition for wic virtually all patients are ospitalized at a nearby ospital and receive some medical care. Moreover, during te acute pase of te eart attack te terapeutic empasis is on maximizing survival, wic is acieved by timely reperfusion, and ospital staff (not patients and teir families) make treatment decisions. Te fact 6

8 tat patients are generally taken to te nearest ospital for immediate treatment, makes it likely tat a patient s ospital coice of ospital is exogenous and not driven by differences across ospitals in expertise or te treatment tresold. Tis feature of eart attack treatments would not be true, for example, of cancer terapies were two clinically identical patients may cose different providers based on teir evaluation of te provider s expertise and standards for providing treatment. Data Because acute myocardial infarction is bot common and serious, it as been te topic of intense scientific and clinical interest. One effort to incorporate evidence-based practice guidelines into te care of eart attack patients, begun in 1992, is te Healt Care Financing Administration's Healt Care Quality Improvement Initiative Cooperative Cardiovascular Project (CCP). Information about more tan 200,000 patients admitted to ospitals for treatment of eart attacks in 1994/1995 was obtained from clinical records. Te CCP is considerably superior to administrative/claims data (of te type used by McClellan et al. (1994)) as it collects cart data on te patients detailed information is provided on laboratory tests, enzyme levels, te location of te myocardial infarction, and te condition of te patient at te time of admission. Detailed clinical data were abstracted from eac patient s cart using a standard protocol. Furter details about te CCP data are available in Marciniak et al. (1998), O Connor et al. (1999), and in te appendix to tis paper. Te coice of sample and variables is identical to wat we used and described in Barnato et al. (2005) and Candra and Staiger (2007, 2010). IV. Estimation Te Propensity to Receive Treatment We use data on eart attack treatments to estimate te key components of our model, using receipt of reperfusion witin 12 ours of te initial eart attack as our treatment. Te propensity to receive treatment (I in te teoretical model) is estimated from a random effect logit model tat regresses weter a patient received reperfusion witin 12 ours of te eart attack on all te CCP risk-adjusters listed in te appendix (X i ) and a random ospital effect (θ ) tat is assumed to be normally distributed: 2 (5) Prreperfusion 1 FX, were ~ N0, i i Were te function F(.) represents te logistic function. Equation 5 is te empirical analog of Equation 2 in our model, were te ospital random effect is te difference between expertise and te tresold ( ) as in Equation 2. Estimating equation 5 (using xtmelogit in Stata 11) yields maximum ˆ and te standard deviation of te ospital random effect ˆ, likeliood estimates of te coefficients 7

9 and posterior estimates of te ospital random effects ˆ. We combine tese to form an estimate of te propensity index for eac patient Iˆ ˆ ˆ. i X i Estimation of Treatment Effects Conditional on te Estimated Propensity We focus on patient survival as te outcome of interest wic captures te key benefits of treatment. Survival is measured as a binary variable tat measures survival to a certain date (e.g. survival to 30 days). Our model, and Equation 4 in particular, suggests tat te effect of reperfusion on survival sould be eterogeneous across patients and ospitals. Tis suggests estimating models of te following form (were F(.) represents te logistic function): (6) Pr Survival 1 Freperfusion X, were gi i i i In equation 6, survival for all patients depends on patient risk adjusters (X i ) and a ospital effect tat captures general skill of te ospital staff (φ ). Te coefficient on reperfusion ( ) is te empirical analog of Equation 4 in our model, and captures te survival benefit of reperfusion. Our model says te effect of reperfusion on survival depends only on a patient s propensity index (I i ) and on te minimum treatment tresold at te ospital (τ ). Equation 6 suggests a simple test for weter te minimum tresold for treating a patient ( ) varies across ospitals. Conditional on a patient s propensity index (I i ), variation across ospitals in te benefit of reperfusion is due to differences in te minimum tresold. Tis minimum tresold sould be negatively correlated wit te ospital random effect from te propensity equation (since i ). Intuitively, according to our model, ospitals wit iger minimum tresolds for treatment sould bot treat fewer patients (conditional on X) and ave iger benefits to treatment (conditional on I). To implement tis test, we use estimates of I i and from te propensity equation (5), and approximate te treatment effect wit a linear function i Iˆ i ˆ, yielding an estimating equation: 0 1 (7) Pr Survival 1 Freperfusio n reperfusio n Iˆ reperfusio n ˆ i i 0 i i 1 i 2 i 2 i i X If ospitals vary in teir expertise ( ) but not in teir minimum tresold for treating a patient ( ), ten our model implies 2 0 ; controlling for patient propensity, te treatment effect is unrelated to te ospital effect in te propensity equation ( ˆ ). Alternatively, if ospitals vary in teir minimum tresold but not in expertise, ten we expect 2 0 ; controlling for patient propensity, te treatment effect is i 8

10 smaller in ospitals wit a ig propensity to treat. If bot expertise and te minimum tresold vary across ospitals, ten we expect 2 0 unless tere is a strong positive association between expertise and te minimum tresold (since te sign of 2 depends on cov(τ,θ) = cov(τ, α-τ) = cov(τ,α)-var(τ)). Equation 7 provides two oter tests of ospital beavior. First, if ospitals coose patients for treatment based on te benefit of te treatment, ten te treatment effect sould be increasing in te patient s propensity index ( 1 0 ). Second, te treatment effect among patients wit a low propensity index is an (upper bound) estimate of eac ospital s minimum tresold, since gi and g(.) approaces zero as te propensity index falls. Terefore, if te treatment effect among low-propensity patients is negative in some ospitals, tis is evidence of overuse ( 0 ). We explore a number of alternative specifications to Equation 7 in order to ceck te robustness of our results. First, in some specification we include ospital fixed effects (using fixed-effect logit models) in order to capture general differences in ospital skill affecting survival of all patients ( ). Second, in some specifications we estimate g(i) non-parametrically wit 100 indicator variables for te percentiles of I interacted wit reperfusion (and included directly in X), tus allowing te return to reperfusion to vary flexibly wit a patient s propensity to receive reperfusion. Finally, we also use semiparametric metods (described in more detail in a future appendix) to flexibly estimate ow te effect of reperfusion on survival varies wit Î i and wit ˆ. i i Joint Estimation of Treatment Effect and Propensity Equations One problem wit Equation 7 is tat te estimates and tests are conditional on estimates of te patient s propensity ( Iˆ i X ˆ i ˆ ) and te ospital s effect in te propensity equation ( ˆ ). In our large sample, te coefficients on patient risk-adjusters ( ˆ ) are consistently estimated and can be tougt of as known (asymptotically). However, te ospital effect ( ˆ ) is a posterior estimate and based on small samples of patients for many ospitals alf of te ospital s in our sample ave fewer tan 20 AMI patients in te sample. Terefore, Îi will only imperfectly control for a patient s propensity, and after conditioning on Î i tere may be remaining variation in treatment effects across ospitals due to expertise (wic was not fully reflected in te estimated propensity). An alternative approac tat avoids tis problem is to estimate te treatment propensity equation and te survival equation jointly, treating te ospital intercept in te propensity equation and te ospital-specific effect of reperfusion in te survival equation as correlated random coefficients (along 9

11 wit te ospital intercept in te survival equation). We implement tis approac by taking a linear approximation to Equation 4 ( gi i 0 1 I i Prreperfusio ni 1 FXi (8) PrSurvival 1 Freperfusio n reperfusio n X i i 0 ) and estimating te following equations jointly: i i reperfusio n X Te tree ospital-level parameters (,, ) are treated as random coefficients tat are distributed as a joint normal wit variance and covariance to be estimated. As discussed in te teory section, ospitallevel expertise ( ) and te minimum tresold ( ) are linear combinations of te ospital intercept in te propensity equation ( ) and te ospital-level coefficient on reperfusion ( ), were 1 1 and 1. Tus, estimates of te joint distribution of expertise and te minimum tresold can be derived from estimates of te joint distribution of te random effects (, ) combined wit an estimate of 1. Equation 8 is a ierarcical logistic model (patients nested witin ospitals) wit random coefficients at te ospital level. To simplify estimation, we estimated te coefficients on te patient-level risk adjusters (β,γ) in a first stage, using simple logit models of eac equation estimated separately (omitting te ospital effects). We ten interacted te first-stage estimate of X iˆ wit reperfusion, and included te estimated indexes ( X ˆ, i X i 1 ˆ ) directly in Equation 8 wit a single coefficient (expected to equal 1) rater tan including all of te X variables individually. Te remaining parameters determining te effect of reperfusion ( 0,1 ) and te variance and covariance of te ospital-level random coefficients were estimated by maximum likeliood using xtmelogit in Stata All of te reported standard errors are conditional on te first-stage estimates ( X ˆ, i X i ˆ ), but any adjustment for using tese generated regressors is likely to be second-order because of te large samples used to estimate te patient-level coefficients in te first-stage logit models. i i V. Results In Table 1 we report some basic caracteristics of our sample overall, and by weter te patient received reperfusion witin 12 ours of admission to te ospital. In our sample, 19% of patients received reperfusion witin 12 ours of admission for a eart attack. Overall, 81% of patients were still alive 30 days after admission, but survival was iger for patients receiving reperfusion (86%) tan for patients wo did not receive reperfusion (80%). However, muc of te difference in survival between tese two groups was due to differences in underlying ealt and pre-existing conditions, rater tan te result of 10

12 reperfusion. Patients receiving reperfusion were younger, and muc less likely to ave pre-existing conditions suc as congestive eart failure, ypertension, diabetes, and dementia. In te appendix table, we report estimates from a random effect logit model predicting reperfusion as a function of te full list of patient risk-adjusters (also listed in te appendix) and a random ospital-level intercept (Equation 5). Tese results were used to form posterior estimates of te ospital random effects ˆ and an estimate of te propensity index for eac patient Iˆ ˆ ˆ. Te i X i coefficients on te patient-level variables are consistent wit te medical literature, wit reperfusion being less likely among patients wit pre-existing conditions and wo are older (based on full age-sex-race interactions not reported in te table), and also depending on te location and severity of te eart attack. Te estimated standard deviation of te ospital effect is 0.44 (Std. Err. = 0.01), wic implies tat a one standard deviation in te ospital effect increases te logodds of receiving reperfusion by 0.44, wic would increase an average patients probability of receiving reperfusion from 19% to 26%. Tus, tere is sizable variation across ospitals in te rate at wic tey provide reperfusion to observationally similar patients. Te model is able to predict muc of te ospital-level variation, wit te posterior prediction of eac ospital s effect on reperfusion aving a standard deviation of 0.30 in our data. In te appendix table we also report estimates from a simple logit model estimating te impact of reperfusion on 30-day survival, controlling for te patient risk-adjusters. Our estimation strategy relies on using te ricness of te CCP data to invoke a `selection on observables assumption to estimate treatment on te treated for reperfusion terapy. We compared te estimates from tis logit model to tose obtained from clinical trials to evaluate te plausibility tis assumption. A summary of nine trials was publised in te journal Lancet by te Fibrinolytic Terapy Trialists' Collaborative Group (FTTCG, 1994). Tis was te same time-period as te CCP data and eac trial evaluated fibrinolytic terapy in eart-attack patients. Across tese nine trials, reperfusion witin 12 ours reduced 35-day mortality from 11.5% to 9.6%, wic implies tat reperfusion reduced te logodds of mortality by Te logit model controlling for te CCP risk-adjusters estimates a nearly identical effect, wit reperfusion increasing te logodds of survival (equivalently reducing te logodds of mortality) by (S.E. = 0.023). We take tis evidence as supporting te case tat tese logit models provide unbiased estimates of te treatment effect. Estimates of Treatment Effects Conditional on te Estimated Propensity In Table 2 we present results from estimating te treatment effect of reperfusion conditional on te estimated propensity using Equation 7: (7) Pr Survival 1 Freperfusio n reperfusio n Iˆ reperfusio n ˆ i i 0 i i 1 i 2 X i 11

13 Recall from te earlier discussion tat te key parameters of interest are tose associated wit reperfusion ( 0, 1, 2 ). Table 2 reports estimates of tese parameters, wic togeter determine te treatment effect: i 0 1I i 2 ˆ ˆ. Coefficients on te patient risk-adjusters are not reported, but similar to tose reported in te appendix table. Te top panel reports coefficients from various specifications using simple logit models (clustering standard errors at te ospital level), wile te bottom panel reports coefficients from te same specifications adding ospital fixed effects (conditional logit models). As a baseline, te first column of Table 2 reports estimates tat do not condition on te patient s propensity, but allow te effect of reperfusion to interact wit te estimated ospital effect from te propensity equation ( ˆ ). Since tis effect is mean zero, te coefficient on reperfusion gives te treatment effect for an average ospital, and is and igly significant in te model witout ospital fixed effects. Te coefficient on te interaction wit te ospital effect from te propensity equation is negative, but not statistically significant. Te results wit ospital fixed effects are very similar. Since tese specifications do not condition on te propensity, te coefficient on te interaction wit te ospital effect (wic is part of te propensity) is biased in te positive direction, and not a strong test of weter ospitals differ in teir minimum treatment tresold. Te second column of Table 2 allows te effect of reperfusion to interact wit te patient s propensity to receive reperfusion (te index, Î i ). To elp wit interpretation, we ave normed te index so tat a value of 0 refers to te average patient receiving reperfusion. Tus, te coefficient on reperfusion is an estimate of te effect of reperfusion on an average patient receiving reperfusion treated at an average ospital. Te coefficient on te interaction of reperfusion wit te propensity index is positive and igly significant in specifications wit or witout ospital fixed effects, implying tat te treatment effect of reperfusion on survival is increasing in te patient s propensity index ( 1 0 ) as predicted by our model. Te coefficient on tis interaction implies tat an increase in te propensity index of one (about one standard deviation of te propensity index in te treated population) is associated wit rougly a doubling of te treatment effect. Tus, it appears tat ospitals are coosing patients for treatment based on te benefit of te treatment, and te eterogeneity in te treatment effect is large relative to te average treatment effect. As expected, te coefficient on te interaction of reperfusion wit te ospital effect from te propensity equation becomes more negative and statistically significant in te specifications tat condition on te propensity index. Te coefficient is similar in column 3, were we non-parametrically control for te interaction of reperfusion wit a set of 100 dummies for eac propensity percentile. In oter words, conditional on a patient s propensity, te treatment effect is smaller in aggressive ospitals 12

14 wit a ig propensity to treat ( 2 0 ). As discussed earlier, tis finding suggests tat ospitals vary in teir minimum tresold, and more aggressive ospitals wit lower minimum tresolds for treatment treat more patients (conditional on X) and ave lower benefits to treatment (conditional on I). Te estimated coefficients suggest tat a one standard deviation increase in te ospital effect from te propensity equation (about 0.3) lowers te return to reperfusion by about As discussed above, te treatment effect for patients wit a low propensity index is an (upper bound) estimate of eac ospital s minimum tresold, and can be used to identify overuse ( 0 ). Based on te estimates from Table 2, we can use i Iˆ i ˆ to calculate te expected treatment effect for patients wit a low propensity index admitted to a given ospital. For example, consider a low propensity patient wo is one standard deviation below average in teir propensity index (about -1). Using estimates from te parametric model witout ospital fixed effects, a low propensity patient at an average ospital would ave a treatment effect near zero ( *0.299=.006). But a low propensity patient at a ospital tat was one standard deviation above average (about 0.3) in its ospital effect from te propensity equation would ave a negative treatment effect ( * *0.3= ). In oter words, for many patients in our sample, our estimates imply overuse (a negative treatment effect). Te parametric model estimated in Table 2 imposes linearity, and may not provide accurate predictions of treatment effects for patients wit low propensity. Terefore, we explore our results nonparametrically in Figures 2-4 (see te appendix for details of ow tis was done). In te left-and panel of figure 2 we plot te estimated survival benefit from reperfusion (and 95% confidence interval) against te ospital effect from te propensity equation using a locally-weigted logit model to estimate te reperfusion effect (controlling non-parametrically for te propensity index as was done in column 3 of Table 2). Te rigt-and panel of Figure 2 is te analogous plot but estimated only for low-propensity patients wose propensity index implied tat tey ad below a 20% probability of receiving reperfusion. Bot plots sow a clear downward slope, wit lower benefit from treatment for patients treated by ospitals wit iger random effects in te propensity equation. Among all patients (te left-and plot), te estimated survival benefit from reperfusion is positive for all ospitals, altoug it is small and not significant in ospitals wit te igest treatment rates (tose 2 standard deviations above average, wit ˆ =0.6). In contrast, among te lowest propensity patients (te rigt-and plot), only ospital s wit te lowest treatment rates are estimated to ave survival benefits from reperfusion tat are near to zero. Te estimated survival benefit from reperfusion is negative and significant in ospitals wit te igest treatment rates, suggesting tat tere is overuse in tese ospitals and, as a result, we were able to identify substantial subsets of patients wo were armed by reperfusion treatment. 13

15 Figure 3 plots te estimated survival benefit from reperfusion against a patient s treatment propensity index for ospital s in te lowest (left-and side) and igest (rigt-and side) terciles of te estimated ospital effect from te propensity equation ( ˆ ). Bot plots sow a strong upward slope, wit iger benefit from treatment for patients wit a iger propensity to receive reperfusion. But at every propensity, te benefits of reperfusion are lower in te top-tercile ospitals. At te lowest propensity levels, te survival benefits from reperfusion are significantly negative for te top-tercile ospitals, suggesting tat tere is overuse among tese ospitals. In te bottom-tercile ospitals, te estimate survival benefits from reperfusion for te lowest propensity patients are less negative and not significantly different from zero, wic is consistent wit appropriate use of reperfusion in tese ospitals. Joint Estimates of Treatment Effect and Propensity Equations In Table 3 we present results from estimating te treatment propensity equation and te survival equation jointly using Equation 8: (8) Pr Pr reperfusio ni 1 FX i Survival 1 Freperfusio n reperfusio n X i i 0 i i reperfusio n X Recall from te earlier discussion tat te key parameters of interest are te standard deviations and correlation of te random ospital intercept in te propensity equation and te ospital-specific effect of reperfusion, and te coefficient ( 1 ) on te interaction between reperfusion and X iˆ. Table 3 reports estimates of tese parameters (along wit te reperfusion parameters in te survival equation), wic (as described earlier) were ten used to construct estimates reported at te bottom of te table of te variance across ospitals in bot expertise and te minimum treatment tresold, along wit teir correlation. We focus our discussion on tese transformed estimates at te bottom of te table, since tey are directly interpretable in te context of our model (Equations 1-4 in Section II). Te estimates in Table 3 suggest tat tere is considerable variation across ospitals in bot expertise (Std. Dev. = 0.451) and te minimum tresold for treatment (Std. Dev. = 0.327). Te standard deviation in expertise captures te difference in te benefit from reperfusion across ospitals for a randomly cosen patient. Because ospitals wit low benefits from reperfusion will select fewer patients for reperfusion (and only te most appropriate), te treatment-on-treated effect will vary less across ospitals. In our model, one can multiply expertise by 1 to get te variation across ospitals in te treatment-on-treated effect. Tus, variation in expertise across ospitals accounts for a standard deviation across ospitals in te observed treatment effect of (0.451*0.276). Tis calculation suggests tat a large portion of te variation across ospitals in te observed treatment effect (wit Std. Dev. = 0.307) is te result of variation in te treatment tresold rater tan expertise. Interestingly, te standard deviation 1 i i 14

16 in te ospital intercept in te reperfusion equation (0.442) is less tan one migt expect because a ospital s minimum treatment tresold is positively correlated wit ospital expertise ospitals wit low expertise also tend to overuse reperfusion, making teir overall reperfusion rate less low tan it would be oterwise. VI. Conclusions Te reasons for variation in treatment rates across ospitals serving similar patient may be due to greater benefits of treatment in some ospitals (expertise), witolding of beneficial treatment in some ospitals (underuse), or providing armful treatment in oter ospitals (overuse). Our empirical results distinguis between tese explanations, using a simple beavioral model in wic ospitals coose to treat patients if te benefit from treatment exceeds a ospital-specific tresold. We distinguised between expertise, underuse, and overuse based on differences across ospitals in bot teir reperfusion rates and te effect of reperfusion on patient survival in a sample of eart attack patients. Our results suggest tat expertise varies considerably across ospitals, but a substantial amount of variation in treatment and treatment effectiveness in our data was due to overuse. 15

17 References Barnato, AE, FL Lucas, D Staiger, A Candra Hospital-Level Racial Disparities in Acute Myocardial Infarction Treatment and Outcomes. Medical Care 43: Candra, Amitab and Jonatan S. Skinner, Geograpy and Racial Healt Disparities in Anderson, Norman B., Rodolfo A. Bulatao, and Barney Coen (Eds) Critical Perspectives: on Racial and Etnic Differences in Healt in Late Life. Wasington D.C.: Te National Academies Press. Candra, Amitab and Douglas O. Staiger Productivity Spillovers in Healtcare: Evidence from te Treatment of Heart Attacks. Journal of Political Economy. Fibrinolytic Terapy Trialists' Collaborative Group Indications for Fibrinolytic Terapy in Suspected Acute Myocardial Infarction: Collaborative Overview of Early Mortality and Major Morbidity Results from all Randomised Trials of More Tan 1000 patients. Lancet 343(8893): Heckman JH, S Urzua, E Vytlacil Understanding Instrumental Variables in Models wit Essential Heterogeneity. Te Review of Economics and Statistics 88(3): Marciniak, TA, EF Ellerbeck, MJ Radford, et al Improving te Quality of Care for Medicare Patients wit Acute Myocardial Infarction: Results from te Cooperative Cardiovascular Project. Journal of te American Medical Association 279: McClellan, Mark, Barbara J. McNeil, Josep P. Newouse "Does More Intensive Treatment of Acute Myocardial Infarction in te Elderly Reduce Mortality? Analysis Using Instrumental Variables." Journal of te American Medical Association 272(11): O Connor, GT, HB Quinton, ND Traven, et al Geograpic Variation in te Treatment of Acute Myocardial Infarction: Te Cooperative Cardiovascular Project. Journal of te American Medical Association 281: Skinner, Jonatan, Amitab Candra, Douglas Staiger, Julie Lee, and Mark McClellan Mortality After Acute Myocardial Infarction in Hospitals tat Disproportionately Treat African Americans. Circulation. 16

18 Appendix Construction of CCP Estimation Sample: Te CCP used bills submitted by acute care ospitals (UB-92 claims form data) and contained in te Medicare National Claims History File to identify all Medicare discarges wit an International Classification of Diseases, Nint Revision, Clinical Modification (ICD-9-CM) principal diagnosis of 410 (myocardial infarction), excluding tose wit a fift digit of 2, wic designates a subsequent episode of care. Te study randomly sampled all Medicare beneficiaries wit acute myocardial infarction in 50 states between February 1994 and July 1995, and in te remaining 5 states between August and November, 1995 (Alabama, Connecticut, Iowa, and Wisconsin) or April and November 1995 (Minnesota); for details see O Connor et al. (1999). Among patients wit multiple myocardial infarction (MIs) during te study period, only te first AMI was examined. Te Claims History File does not reliably include bills for all of te approximately 12% of Medicare beneficiaries insured troug managed care risk contracts, but te sample was representative of te Medicare fee-for-service (FFS) patient population in te United States in te mid-1990s. After sampling, te CCP collected ospital carts for eac patient and sent tese to a study center were trained cart abstracters abstracted clinical data. Abstracted information included elements of te medical istory, pysical examination, and data from laboratory and diagnostic testing, in addition to documentation of administered treatments. Te CCP monitored te reliability of te data by montly random reabstractions. Details of data collection and quality control ave been reported previously in Marciniak et al. (1998). For our analyses, we delete patients wo were transferred from anoter ospital, nursing ome or emergency room since tese patients may already ave received care tat would be unmeasured in te CCP. We transformed continuous pysiologic variables into categorical variables (e.g., systolic BP < 100 mm Hg or > 100 mm Hg, creatinine <1.5, or >2.0 mg/dl) and included dummy variables for missing data. Our coice of variables was based on tose selected by Fiser et al. (2003a,b) and Barnato et al. (2005). Wit te exception of two variables tat are bot measured by blood-tests, albumin and bilirubin (were te rates of missing data were 24 percent), we do not ave a lot of missing data (rates were less tan 3 percent). Included in our model are te following risk-adjusters (APPENDIX TABLES TO BE INCLUDED IN LATER DRAFT): 17

19 Age, Race, Sex (full interactions) previous revascularization (1=y) x old mi (1=y) x cf (1=y) istory of dementia x diabetes (1=y) x ypertension (1=y) x leukemia (1=y) x ef <= 40 (1=y) x metastatic ca (1=y) x non-metastatic ca (1=y) x pvd (1=y) x copd (1=y) x angina (ref=no) x angina missing (ref=no) x terminal illness (1=y) current smoker atrial fibrillation on admission cpr on presentation indicator mi = anterior indicator mi = inferior indicator mi = oter eart block on admission cf on presentation ypotensive on admission ypotensive missing sock on presentation peak ck missing peak ck gt 1000 no-ambulatory (ref=independent) ambulatory wit assistance ambulatory status missing albumin low(ref>=3.0) albumin missing(ref>=3.0) bilirubin ig(ref<1.2) bilirubin missing(ref<1.2) creat 1.5-<2.0(ref=<1.5) creat >=2.0(ref=<1.5) creat missing(ref=<1.5) ematocrit low(ref=>30) ematocrit missing(ref=>30) ideal for CATH (ACC/AHA criteria)

20 Figure 1: How te Expected Benefit from Treatment Varies wit te Propensity to Get Treatment and te Treatment Tresold. E(Benefit Benefit > Hurdle) Underuse and Overuse Hig Treatment Tresold Benefit Low Treatment Tresold τ ig >0 (underuse) 0 Propensity to get Treatment 1 τ low <0 (overuse) Harm Te figure illustrates te relationsip between te expected benefit from treatment, E(B B> τ), on te vertical axis, and te propensity index I (wic determines te propensity of being treated) on te orizontal axis. Te tick curves represent te treatment-on-te-treated effect for a patient wit index I, and approac te minimum tresold (τ) for a patient wit a low propensity of being treated. Te top curve represents a ospital wit a ig treatment tresold (underuse) and te bottom curve represents a ospital wit a low treatment tresold (overuse).

21 Figure 2: Survival Benefit from Reperfusion According to Hospital Effect on Treatment Propensity, All patients (Panel A) and Low-propensity patients (Panel B). All Patients Low Propensity Patients Effect of reperfusion on logodds of 30-day survival Effect of reperfusion on logodds of 30-day survival Hospital effect from propensity equation Hospital effect from propensity equation Te left-and panel plots te estimated survival benefit from reperfusion (and 95% confidence interval) against te ospital effect from te propensity equation using a locally-weigted logit model to estimate te reperfusion effect (controlling non-parametrically for te propensity index as was done in column 3 of Table 2). Te rigt-and panel is te analogous plot estimated only for low-propensity patients wose propensity index implied tat tey ad below a 20% probability of receiving reperfusion. 20

22 Figure 3: Survival Benefit from Reperfusion According to Patient s Treatment Propensity, Low-Treatment-Rate (Panel A) and Hig-Treatment-Rate (Panel B) Hospitals. Hospitals in Lowest Tercile of Hospital Effects from Propensity Equation Hospitals in Higest Tercile of Hospital Effects from Propensity Equation Effect of reperfusion on logodds of 30-day survival Effect of reperfusion on logodds of 30-day survival Patient Treatment Propensity Index Patient Treatment Propensity Index Te figures plot te estimated survival benefit (and 95% confidence intervals) from reperfusion against a patient s treatment propensity index for ospital s in te lowest (left-and side) and igest (rigt-and side) terciles of te estimated ospital effect from te propensity equation 21

23 Table 1: Means of Selected Variables, Overall and By Reperfusion Variable Full Received No Sample Reperfusion Reperfusion w/in 12 ours w/in 12 ours Survival 30 days post-ami 81% 86% 80% Reperfusion w/in 12 ours 19% 100% 0% Age Previous diagnoses: Congestive Heart Failure 22% 7% 25% Hypertension 62% 56% 63% Diabetes 30% 23% 32% Dementia 6% 2% 7% Number of observations 138,957 25, ,081 22

24 Table 2. Logit Estimates of te Effect of Reperfusion on 30-day Survival, Conditional on te Estimated Propensity and te Hospital Effect from te Propensity Equation. Witout Hospital Fixed Effects Not Conditional Conditional Conditional on Propensity on Propensity on Propensity (Parametric) (Non-Parametric) Reperfusion <12 ours non-parametric (0.027) (0.027) Reperfusion <12 ours non-parametric * propensity index (0.018) Reperfusion <12 ours * Hospital effect from (0.078) (0.080) (0.080) propensity equation Wit Hospital Fixed Effects Reperfusion <12 ours non-parametric (0.026) (0.027) Reperfusion <12 ours non-parametric * propensity index (0.018) Reperfusion <12 ours * Hospital effect on (0.074) (0.076) (0.077) treatment propensity Note: Dependent variable is te weter survived to 30 days. Te top panel reports coefficients from various specifications using simple logit models (clustering standard errors at te ospital level), wile te bottom panel reports coefficients from te same specifications wit ospital fixed effects (conditional logit models). Standard errors in parenteses. Models include all CCP risk-adjusters. Column 3 includes 100 percentiles of I interacted wit te receipt of Reperfusion. 23