Racial segregation and disparities in health care delivery: conceptual model and empirical assessment.
OBJECTIVE
This study examines two dimensions of racial segregation across hospitals, using a disease for which substantial disparities have been documented.


METHODS
Black (n=32,289) and white (n=244,042) patients 67 years and older admitted for acute myocardial infarction during 2004-2005 in 105 hospital markets were identified from Medicare data. Two measures of segregation were calculated: Dissimilarity (i.e., dissimilar distribution by race across hospitals), and Isolation (i.e., racial isolation within hospitals). For each measure, markets were categorized as having low, medium, or high segregation.


METHODS
The relationship of hospital segregation to residential segregation and other market characteristics was evaluated. Cox proportional hazards regression was used to evaluate disparities in the use of revascularization within 90 days by segregation level.


RESULTS
Agreement of segregation category based on Dissimilarity and Isolation was poor (kappa=0.12), and the relationship of disparities in revascularization to segregation differed by measure. The hazard of revascularization for black relative to white patients was lowest (i.e., greatest disparity) in markets with low Dissimilarity, but it was unrelated to Isolation.


CONCLUSIONS
Significant racial segregation across hospitals exists in many U.S. markets, although the magnitude and relationship to disparities depends on definition. Dissimilar distribution of race across hospitals may reflect divergent cultural preferences, social norms, and patient assessments of provider cultural competence, which ultimately impact utilization.
Sarrazin
Mary S Vaughan
MS
Center for Research in Innovative Implementation Strategies for Practice (CRIISP), Iowa City VA Medical Center, Iowa City, Iowa 52246, USA. Mary-vaughan-sarrazin@uiowa.edu
Campbell
Mary E
ME
Richardson
Kelly K
KK
Rosenthal
Gary E
GE
D000368:Aged; D044383:Black People / Q000706:statistics & numerical data*; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D006278:Medicare; D008962:Models, Theoretical; D009203:Myocardial Infarction / Q000628:therapy*; D011287:Prejudice; D017064:Process Assessment, Health Care; D016016:Proportional Hazards Models; D011787:Quality of Health Care / Q000706:statistics & numerical data; D014481:United States; D044465:White People / Q000706:statistics & numerical data*; D001741:Black or African American