The initiatives to reduce racial disparities in health care utilization and outcomes have been motivated by observed treatment differences from nationally-aggregated data. Current policies, fueled by highly publicized evidence of physician discrimination in referral patterns 34
, call upon providers to treat blacks and whites equally. However, there has been less attention paid to the way that systematic differences in environments, access, and provider quality might influence these disparities 17-20
. In this paper we show that because blacks and whites tend to go to different hospitals for AMI care, unobserved differences across hospitals may play a role in observed racial disparities. These differences may be hospital practice or “quality” effects, proxies for local socioeconomic effects, or both. By using a method that allowed us to compare the treatment of black and white patients admitted to the same hospital, we demonstrated that for lower intensity medical treatments under the immediate purview of the admitting hospital, within-hospital racial disparities were smaller than aggregated estimates or absent altogether. In contrast, for 30-day high-intensity cardiac procedures that require follow-up and may not be under the direct control of the admitting hospital, within-hospital disparities were in some cases even larger than aggregate risk-adjusted estimates. Blacks’ within-hospital risk-adjusted survival advantage after AMI was larger than aggregate risk-adjusted estimates.
Our analysis of the same data used by previous researchers to explore racial disparities in AMI treatment took the analysis two steps further than most 6, 8, 12-15, 35
and one step further than more recent work 36
by adjusting for unobserved similarities that may exist among patients treated by the same hospital and for individual hospital effects that may actually correlate with race. First, our risk-adjusted model followed recent trends in health services research methods encouraging adjustments for provider-level clustering 24, 37–39
. Specifically, this model adjusted the standard errors on patient-level regression coefficients to account for the fact that the patients treated by particular hospitals may be more similar
in measured and unmeasured characteristics than patients treated at different hospitals. This approach is superior to simple logistic regression models that do not include any information about provider or that enter summary hospital characteristics in patient-level regressions. However, the model relies upon the assumption that the distribution of similar patients into one hospital versus another is independent of hospital characteristics. This assumption would be violated if patient characteristics systematically varied by hospital type. We hypothesized that blacks may systematically be admitted to lower quality hospitals. To address this hypothesis, our risk- and hospital-adjusted fixed-effects model allowed for patient characteristics to be correlated with hospital. Furthermore, unlike specifications that assume the systematic differences in hospitals that blacks and whites use are a linear function of the percent of white patients in the hospital 20, 40
, our hospital fixed-effect model is not so constrained. This is important if all observable and unobservable measures of hospital quality are not summarized by percent white. For example, one can imagine that rural community hospitals in Appalachia that see mostly white AMI patients may have quality and resource limitations that are similar to rural community hospitals in the South that see mostly black AMI patients.
Although our analyses provide different quantitative conclusions regarding disparities by focusing at the hospital level, where actual treatment decisions are made, we emphasize that we are not making an effort to “explain away” racial treatment disparities for AMI or other conditions. The crude national figures prove that they exist. Rather, we are making an effort to better understand these disparities, and, in so doing, focus potential interventions. For example, because the disparity between blacks and whites decreased for lower intensity medical treatments with hospital adjustment, we can conclude that blacks, on average, went to hospitals that provided less of this evidence-based care. Because the disparity for 30-day cardiac procedures increased, this means that blacks, on average, went to hospitals that provided more of these services. Using hospitals with lower compliance with evidence-based medical treatments may increase mortality risk, but using hospitals with higher rates of invasive cardiac procedures (if they are used for patients with appropriate indications for treatment) may be protective. Thus, these indicators of AMI treatment suggest that blacks on average went to hospitals with lower quality medical treatment but higher quality surgical treatment; a more complex picture than we initially hypothesized. Regardless, though, if risk-adjusted mortality is the ultimate quality measure of interest, our data suggest that blacks went to lower-quality hospitals because blacks’ survival advantage would be even larger if they went to the same hospitals as whites. Indeed, if the 8,286 black patients in this cohort had been treated at the same hospitals in the same proportions as their white counterparts, 55 fewer men and 68 fewer women would have died by 1 year after their AMI. Initiatives targeted at hospitals that disproportionately serve black patients could simultaneously address quality deficiencies for all patients in the hospital and potentially decrease national health care disparities. Furthermore, by focusing at the hospital level, researchers might explore mediators of decreased surgical treatment rates among blacks and try to explain the paradoxical risk-adjusted medium-term survival advantage among blacks.
The current study has strengths as well as limitations. The primary strength is that it is based upon the CCP database, which offers a nationally-representative sample with rich clinical data for use in risk-adjustment models and information regarding prescription drugs. Limitations include the age of the data, which prohibits generalization to current practice, although it does allow direct comparison to other studies using the same data, and the fact that younger patients are not included in the cohort. Also, we cannot discern whether the hospital effect is a “quality” effect, a socioeconomic effect, or both. Our analysis only distinguishes whether a given provider appeared to treat blacks and whites differently (potential provider discrimination), but it does not address larger issues of cultural discrimination that lead to residential segregation and differential access to high quality hospitals. Importantly, our analysis doesn’t explain why some within-hospital differences by race do exist, particularly for “invasive” and expensive procedures. Fundamentally different mechanisms may play a role in lower rates of reperfusion among blacks than in lower rates of cardiac procedures such as catheterization, PCI, and CABG that rely on more complex processes of referral and follow-up. Finally, our findings may not extend to racial disparities in treatments and outcomes observed for other conditions or in other settings.
In summary, utilization of different hospitals by blacks and whites contributed substantially to observed treatment disparities. Policy interventions aimed at reducing treatment disparities should consider focused, provider-level efforts in addition to current national initiatives. Future research should focus on the mediators of these hospital-level effects, better understanding why within-hospital differences persist for invasive procedures (particularly for treatments requiring follow-up after initial hospitalization), and on explaining the paradoxical medium-term survival advantage of black patients despite their use of lower quality hospitals.