Racial and Ethnic Disparities in the Surgical Treatment of Acute Myocardial Infarction: The

Transcription

1 Racial and Ethnic Disparities in the Surgical Treatment of Acute Myocardial Infarction: The Role of Hospital and Physician Effects Abstract Many studies document disparities between blacks and whites in the treatment of acute myocardial infarction (AMI) upon controlling for patient demographic factors and comorbid conditions. Other studies provide evidence of disparities between Hispanics and whites in cardiac care. Such disparities may be explained by differences in the hospitals where minority and non-minority patients obtain treatment and by differences in the traits of physicians who treat minority and non-minority patients. We used Florida hospital inpatient discharge data to estimate models of cardiac catheterization, percutaneous transluminal coronary angioplasty (PTCA), and coronary artery bypass grafting (CABG) in Medicare fee-for-service patients age 65 and up. Controlling for hospital fixed effects does not explain black-white disparities in cardiac treatment, but largely explains Hispanic-white disparities. Controlling for physician fixed effects accounts for some extent of the racial disparities in treatment and entirely explains the ethnic disparities in treatment. Short Running Title Racial and Ethnic Disparities in the Surgical Treatment of AMI Keywords Race and ethnicity, disparities, cardiac care, acute myocardial infarction, hospital, physician 1

2 INTRODUCTION Racial and ethnic disparities, often defined as utilization differences that exist upon controlling for patient age, gender, access-related factors, and health conditions, have been identified in dozens of studies on the treatment of heart disease (Ford & Cooper, 1995; Kressin & Peterson, 2001; Lillie-Blanton, Rushing, Ruiz, Mayberry, & Boone, 2002; Geiger, 2003). 1 Recent studies continue to report racial disparities in diagnostic tests and treatments for heart disease (Bertoni et al., 2005; Jha, Fisher, Li, Orav, & Epstein, 2005; Vaccarino et al., 2005; Basu & Mobley, 2008; Brown, Ross, Lopez, Thornton, & Kiros, 2008; Cram, Bayman, Popescu, & Vaughan-Sarrazin, 2009). Additional recent studies also document significant Hispanic-white disparities in heart disease treatment (Bertoni et al., 2005; Cram et al., 2009; Guzman et al., 2012). These findings are of particular importance since minorities are at greater risk than nonminorities for cardiovascular disease (Heron et al., 2009; Mensah & Brown, 2007), the leading cause of death in the United States. Racial and ethnic disparities in heart disease treatment may be explained in part by differences in the hospitals where minority and non-minority patients obtain treatment (Liu et al., 2006; Groeneveld, Kruse, Chen, & Asch, 2007; Weeks & Fisher, 2008; Ehlenbach et al., 2009; Popescu, Nallamothu, Vaughan-Sarrazin, & Cram, 2010). Black patients are more likely than whites to be treated in hospitals that are of lower quality (Popescu, Cram, & Vaughan-Sarrazin, 2011), provide lower volume (Liu et al., 2006), and have less technologically-advanced facilities (Cram, Bayman, Popescu, & Vaughan-Sarrazin, 2010). High-proportion Hispanic hospitals are more likely to be for-profit, have lower nurse-to-patient ratios, and have lower quality measures than low-proportion hospitals (Jha, Orav, Zheng, & Epstein, 2008). If hospital traits such as quality, volume, staffing levels, and facility availability are associated with the propensity to use 2

3 certain treatments for heart disease, then accounting for these traits in models of procedure use could help to explain the estimated black-white or Hispanic-white gaps in surgical utilization. Likewise, physician traits may explain some of the observed racial and ethnic disparities in cardiac care utilization. Among patients admitted to hospitals with acute myocardial infarction (AMI), black patients are less likely to be treated by attending physicians who are cardiologists (Funk, Ostfeld, Chang, & Lee, 2002) than are white patients; AMI patients treated by cardiologists are more likely to undergo invasive coronary procedures such as angiography (Funk et al., 2002) and revascularization (Jollis et al., 1996; Ayanian, Guadagnoli, McNeil, & Cleary, 1997; Chen, Radford, Wang, & Krumholz, 2000). Physician practice style, quality, and other traits may also affect the tendency to employ certain cardiac treatments, and these traits may differ in physicians who treat minority patients compared to those who treat whites. Mukamel, Murthy, & Weimer (2000) and Castellanos et al. (2011) found that minority patients who undergo bypass surgery are more likely than whites to be treated by surgeons with higher risk-adjusted mortality rates, and Bach, Pham, Schrag, Tate, & Hargraves (2004) found that blacks are more likely than whites to be seen by physicians who report difficulties providing their patients with access to high-quality specialists and diagnostic imaging. Additional studies show that yet other physician traits are also related to treatment decisions: patients treated by male physicians are more likely to undergo catheterization after AMI than those treated by female physicians (Rathore et al., 2001) and board-certified physicians have higher rates of clinical guideline recommended therapies following AMI (Chen, Rathore, Wang, Radford, & Krumholz, 2006). If black or Hispanic patients are seen more often by physicians who are less likely to employ invasive procedures in the treatment of AMI, then adjusting for physician traits could help to explain observed racial and ethnic disparities in treatment. 3

4 New Contribution This study makes two contributions. First, we build on prior studies by investigating whether the addition of hospital fixed effects to regression models of invasive treatments for heart disease can explain racial and ethnic disparities in treatment. Hospital fixed effects control for all persistent hospital-specific traits, including those that are unobserved by the researcher, and have been employed by multiple studies (Barnato, Lucas, Staiger, Wennberg, & Chandra, 2005; Barnato et al., 2006). We contribute to the literature by examining a longer and more recent time period (1997 to 2005) than prior work and by examining both black-white and Hispanic-white differences in the treatment of AMI. Ethnic disparities in cardiac care have received relatively less attention than racial disparities (Lillie-Blanton et al., 2002). More attention is beneficial since Hispanics represent the largest minority group and one of the fastest growing populations in the U.S. (U.S. Census Bureau, 2011). Second, we also estimate models that include fixed effects for attending physicians, those physicians with the primary and final responsibility for the patient s medical care and treatment. Since patients choose their physicians and physicians can to some extent select which patients they treat, this mutual selection process may lead to non-random matches between attending physicians and patients. Further, this process may result in matches where minority patients are treated by physicians with different traits on average than the physicians treating non-minority patients. We add physician fixed effects to treatment models to examine whether the control of fixed physician traits helps to explain racial and ethnic disparities in treatment. Physician fixed effects are able to control for all fixed physician traits such as specialty, gender, board certification, and practice style, including those that are unobserved to the researcher. To the best of our knowledge, our study is the first to incorporate physician fixed effects in a model that 4

5 estimates the associations between black race and Hispanic ethnicity and the likelihood of undergoing invasive cardiac treatments. We build on a small number of studies that include physician fixed effects in models of other surgical procedures (such as Katz et al. (2010) on mastectomy) and in models of process and quality of care (e.g., Brookhart et al. (2006) on osteoporosis screening, Sequist et al. (2008) on diabetes care, and Huesch (2011) on CABG mortality). Conceptual Framework The conceptual framework underlying our analysis is that decisions to use surgical procedures in the treatment of individuals with cardiovascular disease are affected by patientspecific factors, physician-specific factors, and healthcare system factors, as described in a model presented by Kressin & Petersen (2001). Patient factors include age, sex, race and ethnicity, and socioeconomic status. Factors influencing the physician include patient clinical characteristics as well as physician specialty, training, and certification. Finally, factors associated with the healthcare system might include hospital ownership (non-profit, for-profit, government), teaching status, quality, volume, and facility availability, and broader measures of the local health system such as practice patterns or physicians per capita in the community. Our study examines two main research questions. First, we examine how the inclusion of hospital fixed effects affects both black-white and Hispanic-white disparities in models of three surgical treatments of AMI. We hypothesize that including hospital fixed effects will explain some extent of racial and ethnic disparities given the evidence that black and Hispanic patients are more likely than whites to be treated in hospitals marked by lower quality, lower volume, and less technologically-advanced facilities. Our second research question examines the effect of including attending physician fixed effects in models that estimate racial and ethnic disparities in 5

6 the surgical treatment of AMI. If minority patients are more likely than whites to be seen by physicians who are less likely to employ invasive procedures in the treatment of AMI, then this could explain some of the observed black-white or Hispanic-white disparities in cardiac care. METHODS Data We used data on hospital inpatient discharges from 1997 to 2005 from the Florida Agency for Health Care Administration (AHCA). To construct the sample and study variables, we used information from the discharge record on the patient s race/ethnicity, age, gender, as well as the principal payer, admission source, the principal diagnosis and up to nine secondary diagnoses, and the principal procedure and up to nine other procedures. We used the patient s county of residence to merge each discharge record to county-level measures of socioeconomic status, such as county-level household median income and poverty rates, which serve as proxies for patient socioeconomic status since personal income data are not available. County-level socioeconomic data were obtained from the U.S Census Bureau. We linked the discharge records to other sources of data to construct healthcare system factors. We used the hospital identification code to merge various hospital-level traits, such as ownership type, number of acute beds, rural location, and teaching status, to each discharge record. For each year in the sample, hospital traits were obtained from annual issues of the Florida Hospital Beds and Services List (AHCA, various years). We also merged countylevel measures of physician supply constructed from counts of active non-federal physicians in patient care from the Area Resource File and population data from the Census Bureau. We constructed controls for factors that influence physician decision making. Since patient clinical characteristics affect physician decisions about treatment, we constructed 6

7 indicators of several comorbidities from the discharge record, as described below. We also employed information on the attending physician s identification code to model physician fixed effects. A particular strength of the Florida discharge data is that the attending physician license is reported on each discharge record in a consistent manner over time from 1997 to Study Sample We restricted our focus to black, white, and Hispanic patients with a principal diagnosis of an initial episode of care for AMI. The determination of initial episode of care for AMI was based on the presence of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) codes 410.x1 in the principal diagnosis field. We selected only those patients admitted to the hospital from the emergency room since patients admitted through physician referrals or hospital transfers may differ in the propensity to undergo surgical treatment or in the determination of hospitals/physicians. About three-quarters of initial AMI discharges are admitted through the emergency room. We also restricted the sample to patients aged 65 and over at the time of admission and patients for whom the principal payer was traditional fee-for-service Medicare. These restrictions yielded a more homogenous set of patients in which to examine the role of hospital and physician fixed effects. By examining patients with the same type of insurance coverage, we sidestep the effects of different provider payment policies on treatment decisions, and by selecting only age-eligible Medicare patients, we sidestep the effects of younger age and/or disability on treatment decisions in Medicare patients under 65. A small number of patients who resided in a state other than Florida were excluded, and we excluded less than 0.15% of AMI discharges where the attending physician was a resident, unlicensed medical doctor in training, or physician assistant as opposed to a medical doctor or osteopathic physician. Finally, to avoid 7

8 the effects of outlier physicians with low numbers of AMI admissions, we excluded cases where the attending physician was listed on fewer than 30 total discharges over the sample period. The restrictions have the following effects on sample size. We started with a total of 444,840 AMI discharges between 1997 and 2005 where the patient was a Florida resident and the attending physician was a licensed medical doctor or osteopathic physician. Restricting the sample to whites, blacks, and Hispanics resulted in a loss of 15,717 observations. Of the remaining 429,123 patients, 73.6% were admitted through the ER, and for just over half (52%) of these, Medicare fee-for-service was the principal payer. Of the 165,045 observations, we dropped less than 7% who were less than age 65. The restriction that attending physicians be associated with a minimum of 30 discharges of any patient type or admission source reduced the sample by 33,557 observations, and missing data on covariates resulted in a modest loss of 160 observations. We also excluded 457 observations from the small number of hospitals (three) that experienced changes in their rural status. 3 The final sample consisted of 119,386 observations. Study Variables We defined dependent variables for three surgical procedures used in the treatment of AMI based on the presence of specific ICD-9 procedure codes in any of the ten procedure code fields on the discharge record (the principal procedure and up to nine other procedures). Cardiac catheterization is identified by ICD-9 procedure codes and ; percutaneous transluminal coronary angioplasty (PTCA) is identified with codes , and coronary artery bypass grafting (CABG) is identified with codes The key explanatory variables of interest, the patient s race and ethnicity, are identified from a combined race/ethnicity field included on the discharge record. An indicator for black race equals 1 if the patient is non-hispanic black, and 0 otherwise, and an indicator for Hispanic ethnicity equals 1 if 8

9 the patient is either white Hispanic or black Hispanic, and 0 otherwise. The omitted category is non-hispanic white. We employed various control variables in our multivariate analysis. Patient sex was measured with an indicator variable for female, and we defined a continuous measure of age in years. To account for nonlinearities in the relationship between age and procedure use we also included age squared. We controlled for comorbid conditions using a set of indicator variables defined from diagnosis codes reported in the nine secondary diagnoses fields on the discharge record. These include indicators for a prior myocardial infraction, peripheral vascular disease, dementia, chronic pulmonary disease, rheumatologic disease, mild liver disease, moderate or severe liver disease, diabetes, chronic diabetes, hemiplegia or paraplegia, and renal disease. These controls were defined using the ICD-9 diagnosis codes for each condition as in Deyo et al. (1992). We proxied for patient socioeconomic status with dummies for quartiles in the distribution of annual real median household income in the patient s county of residence and with indicator variables representing the fraction of persons in poverty by county and year. Observable hospital traits included measures of hospital ownership, teaching status, rural status (defined by the state legislature), and bed size. We also included the ratio of active non-federal physicians in patient care to 1,000 residents by county-year. Finally, we included indicator variables for the year of discharge to account for trends in procedure use. Statistical Analysis We used Stata/SE software version 12.0 to analyze the data. We compared means of dependent and explanatory variables across samples of whites and blacks and whites and Hispanics using t-tests (for continuous variables) and chi-squared tests (for binary indicators). In our multivariate analysis, we first estimated the linear probability model illustrated by Equation 9

10 (1) for each of our three dependent variables: (1) y icht =β 0 +β 1 Black+β 2 Hispanic+β 3 *X it +β 4 *H ht +β 5 *C ct +λ t +ε it The dependent variable y icht indicates whether individual i living in county c treated in hospital h in year t received a particular surgical procedure used in the treatment of AMI. We estimated the equation for three separate dependent variables representing cardiac catheterization, PTCA, and CABG. Black and Hispanic are binary variables indicating whether individual i is black or Hispanic; X it is a set of patient characteristics including age, gender, and eleven indicator variables for whether individual i has any of the comorbid conditions shown in Table 1. Hospital characteristics H ht include time-invariant rural status and time-varying teaching status, ownership status (non-profit and for-profit indicators with publicly-owned being the omitted category), and the number of acute care beds. 5 County characteristics C ct include indicators for quartiles in the distribution of real median household income (the omitted category is the lowest quartile), and indicators for whether the percent of households at or below 100% of the federal poverty line is from 10% to less than 12.5%, from 12.5% to less than 15%, or 15% or more (the omitted category is less than 10%), and the number of physicians per 1,000 county residents. 6 Model (1) also controls for year fixed effects (λ t ), which are dummy variables absorbing all time-varying factors that are common to all AMI patients in Florida in a given year, such as potential nationwide changes in practice style and technology advancement. Finally, ε it is the mean-zero idiosyncratic error term. β 1 and β 2 are our coefficients of interest. 7 We then estimated a set of linear hospital fixed effects models as shown by Equation (2): (2) y ichpt =δ 1 Black+δ 2 Hispanic+δ 3 *X it +δ 4 *H ht +δ 5 *C ct +α h +λ t +ε it The hospital fixed effects are represented by α h. These models also control for all of the patient characteristics (X it ), county characteristics (C ct ), and year fixed effects (λ t ), as defined in 10

11 Equation (1). We also include a set of hospital characteristics (H ht ), as in Equation (1), but unlike Equation (1) we do not include the rural indicator in Equation (2). This is because the hospital fixed effects α h absorb all time-invariant hospital characteristics, including rural status. Examples of other time-invariant traits picked up by the hospital fixed effects include hospital management style and efficacy, the nurse-to-patient ratio, the number and/or quality of competitors in the local market, the conditions of devices and equipment, the capital-to-labor ratio, and, in particular, hospitals potential preferences for types of patients or the possibility that a given hospital is used by patients with certain characteristics that are relevant to the outcomes of interest. 8 The power of hospital fixed effects is that they control for all timeinvariant hospital factors affecting all patients admitted to a given hospital, including factors unobservable to the researcher. The coefficients of interest in Equation (2) are δ 1 and δ 2 ; as in the baseline models, we report the marginal effects of the explanatory variables. We next estimated a similar set of linear fixed effects models without the hospital fixed effects but with physician fixed effects, as shown by Equation (3): (3) y ichpt =α 1 Black+α 2 Hispanic+α 3 *X it +α 4 *H ht +α 5 *C ct +θ p +λ t +ε it The physician fixed effects are represented by θ p. Equation (3) controls for the full set of patient, hospital, and county characteristics, as well as year fixed effects. In addition, it controls for both the time-invariant and time-variant hospital characteristics (rural status, as well as ownership, teaching status, and bed size) because physicians treat patients in multiple hospitals (in other words, physicians are not nested within a hospital). Importantly, the physician fixed effects capture all time-invariant physician characteristics that are unobservable to the researcher and similarly affect the treatment of all the patients seen by a given physician. These include, for example, the physicians race, gender, educational background, experience, skill, and the 11

12 physicians potential preference for certain types of patients or the possibility that physicians may be preferred by patients of certain characteristics. In this model, the coefficients of interest are α 1 and α 2, and again, marginal effects are reported. In all regressions, we adjusted the standard errors of the reported coefficients for the clustering of observations; we did so by hospital in the baseline and the hospital fixed effect models and by physician in the physician fixed effect models. Clustering adjusts for potential correlations in the error terms among patients treated in the same hospital or by the same doctor. After estimating the three sets of models, we compared the marginal effects for black race and Hispanic ethnicity obtained from the linear probability models without fixed effects to those obtained from the linear fixed effects models to ascertain whether control for the hospital or the attending physician can explain black-white and Hispanic-white disparities in cardiac care utilization. Since our goal is to examine how the inclusion of hospital fixed effects or physician fixed effects changes the size of the marginal effects of race and ethnicity, linear probability models offer several conceptual and computational advantages over nonlinear models. 9 Importantly, fixed effects logit models (also known as conditional logit models) do not allow researchers to obtain marginal effects because they do not estimate the group-specific intercepts which are necessary for calculating the marginal effects (Wooldridge, 2010, pp ). 10 Finally, we examined the robustness of our findings to a number of variations in our empirical analysis. First, we estimated our models using alternate measures of two dependent variables since prior studies sometimes differ in their definition of cardiac catheterization and revascularization. We defined an alternate measure of cardiac catheterization which used codes and excluded codes (similar to Hall, DeFrances, Williams, Golosinskiy, & Schwartzman, 2010) and we broadened the measure of PTCA to all types of PCI with the 12

13 addition of codes 36.07, and (similar to Epstein, Polsky, Yang, Yang, & Groeneveld, 2011). Second, we estimated our models using larger samples of patients admitted both though the emergency department and through other means and patients treated by physicians who treat any number of patients in the sample period. Third, we excluded large teaching hospitals (those with more than 300 beds) and residents of Miami-Dade county. Large teaching hospitals might differ from other hospitals in the assignment of attending physician. We excluded Miami-Dade since it accounts for a large share of Hispanic and black residents in the state. Finally, we estimated our models on subsets of years in the study period ( and ). RESULTS Descriptive Statistics As shown in the first three rows of Table 1, the full sample consists of 103,899 whites (87%), 5,556 blacks (4.7%), and 9,931 Hispanics (8.3%). The unadjusted rates of all three surgical procedures show evidence of black-white differences and Hispanic-white differences in cardiac catheterization, PTCA and CABG. Across all years, rates of cardiac catheterization are 41.1% for whites, 36.2% for blacks, and 29.5% for Hispanics. Rates of PTCA are 16.9% for whites, 14.2% for blacks, and 13.3% for Hispanics, and rates of CABG are 6.1% for whites, 4.5% for blacks, and 4.5% for Hispanics. For all three procedures, the black-white and Hispanic-white differences in unadjusted rates are statistically significant according to chi-squared tests. These differences exist in almost every year of the study period, although black-white differences in cardiac catheterization and CABG and the Hispanic-white difference in CABG decrease over time. Utilization rates for the individual procedures generally increase over time. [Table 1 about here] The remaining rows in Table 1 show significant differences in patient and hospital 13

14 characteristics by race and ethnicity. For example, black patients are on average slightly younger than white patients, and black and Hispanic patients are more likely than whites to be female. Black and Hispanic patients are less likely than whites to have had a prior AMI listed among the other diagnoses on the discharge records, but they are more likely than whites to have diabetes. Compared to whites, black and Hispanic patients are more likely to be treated in teaching hospitals, larger hospitals, and hospitals with a rural designation. Several racial and ethnic differences emerge in the ownership of the hospital where patients are seen. Black and Hispanic patients are less likely than whites to be treated at non-profit hospitals. Hispanics are more likely than whites to be seen at for-profit hospitals and less likely than whites to be seen at public hospitals, while blacks are more likely than whites to use public hospitals. To illustrate the differences between the use of hospital fixed effects and physician fixed effects in our models, we report descriptive statistics on the numbers of hospitals and physicians in our sample. In the full estimation sample, a total of 202 unique hospitals and 3,955 unique physicians are represented; at the average hospital, there are 47 different physicians treating AMI patients. Thus the hospital fixed effects capture the provider effect at a more aggregated level than do the physician fixed effects. Further, while some physician fixed effects capture differences within the hospital (because multiple physicians may see patients in the same hospital), in most cases, the effects are common to patients treated at different hospitals by the same physician because physicians are not nested within hospitals. In our dataset, two-thirds (69%) of physicians treat patients at more than one hospital; the average physician treats patients at 2.4 hospitals and the number of hospitals per physician ranges from 1 to 14. The distinctions between hospital and physician fixed effects apply to white, black and Hispanic subsamples of patients. White patients are treated at all 202 hospitals, while black and 14

15 Hispanic patients are treated at slightly fewer hospitals (190 and 173 respectively). In terms of physicians, white patients are treated by 3,859 physicians, whereas black and Hispanic patients are treated by 1,977 and 1,665 physicians respectively. Most physicians (66%) treat patients from more than one racial or ethnic group. Among physicians who treat minority patients, almost all also treat non-minority patents: all but seven of the physicians who treat black patients also treat whites, and 96% of the physicians who treat Hispanic patients also treat whites. 11 As in the full sample, physicians are observed at multiple hospitals for all three groups of patients. About two-thirds (65%) of physicians treating whites, one-fifth (20%) of physicians treating blacks, and one-third (33%) of physicians treating Hispanics are observed at more than one hospital in the dataset. Regression Analysis Table 2 reports the results from estimating Equation (1), which specifies the association between black race and Hispanic ethnicity and surgical procedure use while controlling for patient traits and observable hospital traits, but without controlling for hospital or physician fixed effects. These models serve as our baseline specification and we refer to the estimated effects of race and ethnicity in this specification as disparities in treatment. We report marginal effects for all variables included in each model. The key findings from this set of regressions are three-fold. First, black-white disparities in all three surgical procedures persist upon controlling for patient age, gender, and comorbidities, county-based proxies for socioeconomic status, and observable hospital characteristics. Black race lowers the likelihood of undergoing cardiac catheterization by 8.5 percentage points, and lowers the likelihood of revascularization by 2.7 percentage points (for CABG) to 6.6 percentage points (for PTCA), relative to white race. Second, Hispanic ethnicity similarly is associated with a significantly lower incidence of cardiac catheterization 15

16 and PTCA compared to white race. Hispanics are 7.2 percentage points less likely to undergo PTCA and 10.5 percentage points less likely to undergo cardiac catheterization. The Hispanicwhite disparity in CABG remains sizeable (1.4 percentage points relative to a mean of 6 percent), and the estimate is statistically significant at the 10% level. Third, we find that certain hospital traits have significant associations with procedure use. For example, patients treated at larger hospitals (those with more acute care beds) are more likely to undergo surgical treatment for AMI, and relative to patients treated at public hospitals, patients treated at for-profit or non-profit hospitals are more likely to undergo catheterization or angioplasty. [Table 2 about here] Before turning to the fixed effects models, we note that the associations between procedure use and several of the control variables are as expected. Female patients are less likely to receive each of the treatments, consistent with previous research by Redberg (2005). In most instances, the presence of a comorbid condition significantly decreases the chance of receiving one of the three cardiac procedures, as expected. Patients treated in rural hospitals are less likely to undergo cardiac catheterization. Table 3 presents the main results of our analysis and allows us to address the main study questions. Panel A of Table 3 reproduces the marginal effect estimates for black race and Hispanic ethnicity from Table 2, allowing for an easy comparison of the black-white and Hispanic-white disparities in the baseline model to those from models that include fixed effects. Panel B reports the marginal effects from hospital fixed effects models and Panel C reports the marginal effects from physician fixed effects models. In Panel C, the models include the full set of controls reported in Table 2, including year indicator variables; in Panel B, we include all of these controls except the rural indicator variable. For ease of presentation, the coefficients of the 16

17 controls are not shown but are available upon request. [Table 3 about here] A comparison of Panels A and B in Table 3 answers our first study question, which asks how the inclusion of hospital fixed effects in models of surgical procedure use affects both black-white and Hispanic-white disparities in the surgical treatment for cardiac care. We expect that including hospital fixed effects will lessen these disparities given the evidence that minority patients are more likely than whites to be treated in lower quality hospitals with less technologically-advanced facilities. Panel B of Table 3 offers limited support for this hypothesis in regard to racial disparities. For both catheterization and CABG, there is a slight decline in the coefficient of Black race upon the inclusion of hospital fixed effects. In contrast, the Hispanic-white disparity is largely explained by the inclusion of hospital fixed effects. In the case of cardiac catheterization and CABG, the inclusion of hospital fixed effects renders the Hispanic coefficient statistically insignificant. In all three models, the magnitude of the Hispanic marginal effect is greatly reduced; it falls by 96% for cardiac catheterization and by 81% for PTCA, and it changes sign in the model of CABG surgery. Our results show that, in general, the inclusion of hospital fixed effects helps to explain disparities in cardiac care, but important differences exist between blacks and Hispanics. The marginal effects of race in the within-hospital models are only somewhat smaller than the race effects overall, while the marginal effect of Hispanic ethnicity is greatly reduced, and in the case of cardiac catheterization and CABG, rendered statistically insignificant by including hospital fixed effects. A comparison of results shown in Panels A and C answers our second study question concerning the effect of adjusting for attending physician fixed effects. For all three procedures, the results show that the inclusion of physician fixed effects accounts for a large portion of the 17

18 black-white disparity in procedure use. For example, in the baseline model, black patients had an 8.5 percentage point lower chance of undergoing cardiac catheterization compared to whites. Adding physician fixed effects to this model reduces the gap to 5.9 percentage points (or by 31%). Similarly, the addition of physician fixed effects to the model for CABG procedure use lowers the black marginal effect from 2.7 percentage points to 1.7 percentage points (a drop of 37%). The decline in the racial disparity in PTCA upon control of physician fixed effects is smaller, going from 6.6 to 5.2 percentage points (a decline of 21%). This change is still notable given that the inclusion of hospital fixed effects does not account for the race gap. We see a similar pattern from a comparison of the Hispanic coefficient across Panels A and C; namely, controlling for attending physician fixed effects reduces the Hispanic-white disparities, and does so to a somewhat greater degree than the control of hospital fixed effects in Panel B. For cardiac catheterization, the Hispanic coefficient falls from a significant negative 10.5 percentage points to nearly zero; for PTCA the Hispanic coefficient falls from a significant negative 7.2 percentage points to an insignificant negative 0.7 percentage points (a drop of 90%). In the case of CABG, the Hispanic coefficient changes sign to a positive value, albeit very small and highly insignificant. Our results are robust to the specification changes described earlier. We obtain similar results using slightly different definitions for two dependent variables and including patients admitted from all sources or treated by all medical or osteopathic physicians. Excluding large teaching hospitals or residents of Miami-Dade County also yields results similar to our main analysis, as does using data from shorter time periods within our 12-year sample ( and ). The results of these robustness checks are available from the authors upon request. DISCUSSION AND CONCLUSION 18

19 The findings of this study help to identify hospital and physician-level sources of racial and ethnic disparities in the surgical treatment of AMI. We find that disparities in procedure use between Hispanics and whites in our sample are largely explained by differences in the hospitals where patients seek treatment. Hospital traits may matter greatly for Hispanics because, as Jha et al. (2008) note, hospital care for elderly Hispanics is highly concentrated, with 90 percent of elderly Hispanic patients being treated at 25 percent of Americans hospitals, hospitals that on average were of lower quality (p. 531). Similar patterns within Florida may explain why much of the Hispanic-white disparity is accounted by hospital fixed effects. Our findings also are consistent with prior evidence that hospital fixed or random effects explain some portion of disparities in the treatment of AMI (Hasnain-Wynia et al., 2010) or in other types of healthcare, such as ICU use (Barnato et al., 2006) and reperfusion therapy (Bradley et al., 2004). In contrast, we find that disparities between black and white patients are not explained by hospital traits. This suggests that black patients are not systematically sorted to those Florida hospitals with a lower propensity to use intensive surgical treatments for AMI. Instead, physician fixed effects explain more of the black-white disparities in utilization than do hospital fixed effects. For example, about one-third of the black-white gap in CABG is explained by physician fixed effects, but less than 5% of the gap is explained by hospital fixed effects. Thus, the sorting of patents to physicians explains a sizeable portion of racial disparities in treatment. On average, blacks appear more likely than whites to be treated by physicians who have a low propensity to employ surgical treatment in the use of cardiac care. Nonetheless, withinphysician differences by race remain and are statistically significant, just smaller. The importance of physician effects in explaining disparities in healthcare has also been noted in by Rodriguez, von Glahn, Grembowski, Rogers, & Safran (2008) who estimated physician fixed 19

20 effects models of patient experiences. They found that physician fixed effects largely explained the differences in patient experience between minority and non-minority patients. While we find that physician fixed effects also explain ethnic disparities in utilization, we find that the largest reduction is driven by the sorting of Hispanics to different hospitals. Regardless, when we examine differences within-hospitals or differences within-physicians, treatment differences between whites and Hispanics are not statistically significant. Several limitations of our analysis should be noted. Like other studies that employ administrative data, 12 we are unable to include complete information on patients prior medical histories or patient risk factors such as smoking and we also lack data on individual-level measures of socioeconomic status. We addressed this by including comorbid conditions, by focusing on a more homogenous population (seniors with Medicare fee-for-service), and by controlling for county-level socioeconomic measures. However, comorbidities and county-level socioeconomic status may be insufficient controls, and if there are correlations between unobserved SES and health traits and race/ethnicity, we may be over- or under- estimating race/ethnic disparities depending on that nature of these underlying correlations. Our findings are also limited to the Medicare population age 65 and older and may not generalize to preretirees. Further, our study is limited to discharge data from Florida hospitals, so questions about representativeness may arise. It is reassuring that recent data from the Dartmouth Atlas of Healthcare show that for Medicare fee-for-service enrollees aged 65 to 99, national rates of CABG, PTCA/PCI and cardiac catheterization are quite similar to rates in Florida, for both blacks and non-blacks. For cardiac catheterization, for example, the Florida rate for blacks was 18.2 per 1,000 persons, and the national rate was 18 per 1,000 persons, and the rate for non- 20

21 blacks in Florida was 20.3 per 1,000, close to the national rate of 19 per 1,000 (Dartmouth Atlas of Healthcare, 2011). That said, Florida Hispanics face different socioeconomic and political circumstances than minorities nationwide. Nearly a third of Hispanics in Florida are of Cuban origin, compared to only 3.5% of Hispanics nationwide (U.S. Census Bureau, 2001). U.S. immigration has also long-favored Cubans seeking residency and citizenship, and compared to all U.S. Hispanics, Cubans have higher levels of educational attainment and median family income (U.S. Census Bureau, 2004). Thus, the circumstances faced by Hispanics in our sample may be better than those of Hispanics nationwide. Our findings suggest that attempts to lessen ethnic disparities in care should focus on the facilities available in Hispanic areas, perhaps by improving hospital quality and access to revascularization facilities. In the case of black-white disparities in cardiac care treatment, our results suggest a different strategy. Rather than focusing solely on hospital traits, policymakers and practitioners might consider the traits of physicians who treat patients admitted through the emergency room with AMI. Our results show that differences between physicians who treat white and black patients may explain about 21-37% of the difference in treatment propensity. Beyond that, our results suggest that it would be beneficial to policymakers and practitioners if future research were to examine the sources of within-hospital and within-physician differences and effective ways to target them in order to address racial disparities in cardiac care. 21

22 ENDNOTES 1 According to Geiger (2003), racial and ethnic disparities in surgical and medical treatments of AMI and coronary artery disease have been the focus of almost 200 publications since The Florida Agency for Health Care Administration (AHCA) performs extensive auditing and validation of all variables in the inpatient discharge data. For the attending physician ID variable in particular, the AHCA verifies the IDs against state physician licensure information; if a discharge record has a missing or invalid ID, the record is sent back to the hospital for further review and correction. The Florida AHCA also checks the overlap between operating physician ID and attending physician ID for each hospital and flags those hospitals where more than 75% of the discharge records have the same operating and attending physician ID. These hospitals must verify that the accuracy of the data and rule out any errors. 3 We drop these hospitals because the hospital fixed effects models that we describe below control for all time-invariant hospital specific traits and because rural is time invariant for the remaining 199 hospitals in the sample. In a separate robustness check, we include these three hospitals; estimates of the race and ethnicity effects are almost identical. 4 These definitions are the same as those used in Bertoni et al. (2005) for cardiac catheterization and PTCA and Epstein et al. (2011), Cram et al. (2008), and Cram, Bayman, Popescu, & Vaughan-Sarrazin (2010) for CABG. 5 We use data on the hospital s statutory rural designation, which is determined by the state of Florida. As noted earlier, only three hospitals in our sample underwent changes in the statutory rural designation during the sample period and we excluded these three hospitals from our 22

23 analysis. Twelve hospitals in the sample switched teaching status, and 18 hospitals changed ownership status. The number of acute beds changed for 125 hospitals in the sample. 6 Note that median household income controls for the income level in the county and the percent poverty serves as a proxy for inequality in the income distribution. 7 We also estimated the logit model equivalent of Equation (1). Since the marginal effect estimates from this model (available upon request) are similar to those from Equation (1), we use linear probability models as our main analysis models throughout the paper. 8 Note that we perform a patient-level analysis with hospital fixed effects; that is, the fixed effects are at a level that is more aggregated than the unit of analysis. This analysis captures all of the time-invariant hospitals factors that are faced by all AMI patients admitted to the same hospital and that may affect the treatment decision in the same way for all admitted patients. 9 Other drawbacks to nonlinear models are the stronger functional and distributional assumptions of logit and probit models compared to linear probability models (Wooldridge, 2010, pp ), and, in the case of the unconditional logit model, the incidental parameters problem (Wooldridge 2010, p. 495). The main drawback of the linear probability models is, of course, the potential of generating a predicted probability larger than one or smaller than zero. But given that we are not interested in predicting probabilities per se, we do not consider this drawback as a serious limitation, especially given that our main variables of interest and the majority of our control variables are indicator variables (Wooldridge 2010, pp ). 10 Fixed effects logit models estimate odds ratios conveniently; however, marginal effects have a clearer behavioral interpretation and also are measured in the metric of direct interest the probability of the outcome event occurring. To illustrate, let the probability of outcome D (say 23

24 hand-washing) occurring under an intervention (say a large-scale public campaign on the benefits of hand-washing) be p 1 and the probability of D occurring with no intervention be p 0. If p 1 =0.8 and p 0 =0.2, the marginal effect of the intervention is 0.6 while the odds ratio is 16 and the relative risk is 4. All three estimates might lead to the conclusion that the intervention substantially changed the probability that the outcome of interest occurred. If, however, p 1 = and p 0 =0.0002, then the marginal effect is , which is a small difference from a public policy point-of-view; this might lead researchers and policy makers to conclude that the funding for the intervention is better used elsewhere. But the odds ratio is now and the relative risk remains 4. Both may lead to the conclusion that the intervention effect is large the intervention is warranted. For an in-depth discussion of the advantages of marginal effects relative to odds ratios and relative risks, see Lance (2008). 11 Black and Hispanic discharges are not evenly distributed across attending physicians, but large numbers of physicians are still involved in treating the bulk of minority patients. For example, about half of all discharges involving black patients are among 323 or 16% of the total number of physicians treating black patients, and about two-thirds of all discharges involving Hispanic patients are among 231 or 14% of the total number of physicians treating Hispanic patients. 12 Examples include Epstein et al. (2011), Cram et al. (2010), and Brown et al. (2008). 24

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