Neighborhood disadvantage and racial disparities in colorectal cancer incidence: a population-based study in Louisiana.

PURPOSE: Colorectal cancer (CRC) continues to demonstrate racial disparities in incidence and survival in the United States. This study investigates the role of neighborhood concentrated disadvantage in racial disparities in CRC incidence in Louisiana. METHODS: Louisiana Tumor Registry and U.S. Census data were used to assess the incidence of CRC diagnosed in individuals 35 years and older between 2008 and 2012. Neighborhood concentrated disadvantage index (CDI) was calculated based on the PhenX Toolkit protocol. The incidence of CRC was modeled using multilevel binomial regression with individuals nested within neighborhoods. RESULTS: Our study included 10,198 cases of CRC. Adjusting for age and sex, CRC risk was 28% higher for blacks than whites (risk ratio [RR] = 1.28; 95% confidence interval [CI] = 1.22-1.33). One SD increase in CDI was associated with 14% increase in risk for whites (RR = 1.14; 95% CI = 1.10-1.18) and 5% increase for blacks (RR = 1.05; 95% CI = 1.02-1.09). After controlling for differential effects of CDI by race, racial disparities were not observed in disadvantaged areas. CONCLUSION: CRC incidence increased with neighborhood disadvantage and racial disparities diminished with mounting disadvantage. Our results suggest additional dimensions to racial disparities in CRC outside of neighborhood disadvantage that warrants further research.

The Social Determinants of Health Core: Taking a Place-Based Approach.

INTRODUCTION: There is growing recognition that health disparities research needs to incorporate social determinants in the local environment into explanatory models. In the transdisciplinary setting of the Mid-South Transdisciplinary Collaborative Center (TCC), the Social Determinants of Health (SDH) Core developed an approach to incorporating SDH across a variety of studies. This place-based approach, which is geographically based, transdisciplinary, and inherently multilevel, is discussed. METHODS: From 2014 through 2016, the SDH Core consulted on a variety of Mid-South TCC research studies with the goal of incorporating social determinants into their research designs. The approach used geospatial methods (e.g., geocoding) to link individual data files with measures of the physical and social environment in the SDH Core database. Once linked, the method permitted various types of analysis (e.g., multilevel analysis) to determine if racial disparities could be explained in terms of social determinants in the local environment. RESULTS: The SDH Core consulted on five Mid-South TCC research projects. In resulting analyses for all the studies, a significant portion of the variance in one or more outcomes was partially explained by a social determinant from the SDH Core database. CONCLUSIONS: The SDH Core approach to addressing health disparities by linking neighborhood social and physical environment measures to an individual-level data file proved to be a successful approach across Mid-South TCC research projects.

Misspecification of the effect of race in fixed effects models of health inequalities.

The purpose of this study is to characterize the different results obtained when analyzing health inequalities data in which individuals are nested within their neighborhoods and a single level model is used to characterize risk rather than a multilevel model. The inability of single level models to characterize between neighborhood variance in risk may affect the level of risk attributed to black race if blacks are differentially distributed in high risk neighborhoods. The research replicates in Los Angeles an approach applied by a different group of researchers in Massachusetts (Subramanian, Chen, Rehkopf, Waterman, & Krieger, 2005). Single level and multilevel models were used to analyze Los Angeles County, California, US all-cause mortality data for the years 1989-1991, modeled as 29,936 cells (deaths and population denominators cross-tabulated by age, gender, and race/ethnicity) nested within 1552 census tracts. Overall blacks had 1.27 times the risk of mortality compared to whites. However, multilevel models demonstrated considerable between census tract variance in mortality for both blacks and whites which was partially explained by neighborhood poverty. Comparing the results of equivalent single level and multilevel models, the mortality odds ratio for blacks compared to the white reference group reversed itself, indicating greater risk for blacks in the single level model and lower risk in the multilevel model. Adding an area based socioeconomic measure (ABSM) to the single level model reduced but did not remove the discrepancy. Predictions of mortality risk for the interaction of race and age group demonstrate that all single level models exaggerated the mortality risk associated with black race. We conclude that characterizing health inequalities in mortality for blacks using single level models, which do not account for the cross level interaction created by the greater likelihood of black residence in neighborhoods where the risk of mortality is greater regardless of race, can exaggerate the risk of mortality attributable to the individual level effects of black race.