Analysis of racial differences in hospital stays in the presence of geographic confounding. (August 2019)

• Record Type: Journal Article
• Authors: Davis, Melanie L.; Neelon, Brian; Nietert, Paul J.; Burgette, Lane F.; Hunt, Kelly J.; Lawson, Andrew B.; Egede, Leonard E.
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 30(2019)
• Publication Date: 2019-08
• Subjects: spatial data analysis -- propensity score matching -- health disparities; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2019.100284
• Languages: English
• ISSNs: 1877-5845

Abstract

Abstract: Using recent methods for spatial propensity score modeling, we examine differences in hospital stays between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes. We augment a traditional patient-level propensity score model with a spatial random effect to create a matched sample based on the estimated propensity score. We then use a spatial negative binomial hurdle model to estimate differences in both hospital admissions and inpatient days. We demonstrate that in the presence of unmeasured geographic confounding, spatial propensity score matching in addition to the spatial negative binomial hurdle outcome model yields improved performance compared to the outcome model alone. In the motivating application, we construct three estimates of racial differences in hospitalizations: the risk difference in admission, the mean difference in number of inpatient days among those hospitalized, and the mean difference in number of inpatient days across all patients (hospitalized and non-hospitalized). Results indicate that non-Hispanic black veterans with type 2 diabetes have a lower risk of hospital admission and a greater number of inpatient days on average. The latter result is especially important considering that we observed much smaller effect sizes in analyses that did not incorporate spatial matching. These results emphasize the need to address geographic confounding in health disparity studies.