A closer look at the rural-urban health disparities: Insights from four major diseases in the Commonwealth of Virginia. (September 2015) - Zeng, Di; You, Wen; Mills, Bradford; Alwang, Jeffrey; Royster, Michael; Anson-Dwamena, Rexford
Abstract: Health disparities are increasingly recorded in literature, but are much less understood in a rural-urban context. This study help bridges this gap through investigation of four major diseases in the Commonwealth of Virginia: cancer, stroke, cardiovascular disease and chronic obstructive pulmonary disease. We utilize a unique inpatient hospital discharge billing dataset, and construct average patient counts at ZIP-code level over 2006–2008 where covariates from alternative sources are merged (806 ZIP-code areas, 190 urban, 616 rural). Count data regressions are first fitted to identify possible regional-level factors that affect disease incidences. A system of equations with rural-urban specification are then estimated via seemingly unrelated regression techniques to account for possible associations among these diseases and correlations of errors, which is followed by disease-specific nonlinear Blinder-Oaxaca decompositions that compare the respective explanatory powers of observed characteristics and unobserved mechanisms. Results suggest that regional-level factors are significantly correlated with health outcomes in both rural and urban areas. The unknown mechanisms behind these linkages are different between rural and urban areas, and explain even larger proportions of the observed disparities. These findings confirm the role of regional-level factors in generating rural-urban health disparities, and call for further investigations of the causal mechanisms of such disparities that remain largely unknown. Highlights: We analyze rural-urban health disparities from a unique disease incidence perspective. Four major diseases are considered: cancer, stroke, cardiovascular disease and COPD. Count data regressions identify regional-level factors affecting disease incidences. Correlations among patient counts are addressed in seemingly unrelated regressions. Nonlinear Blinder-Oaxaca decompositions imply the disparities are largely unknown.