Analysis of racial differences in hospital stays in the presence of geographic confounding

Spat Spatiotemporal Epidemiol. 2019 Aug:30:100284. doi: 10.1016/j.sste.2019.100284. Epub 2019 Jul 5.


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.

Keywords: health disparities; propensity score matching; spatial data analysis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Black or African American / statistics & numerical data*
  • Confounding Factors, Epidemiologic
  • Diabetes Mellitus, Type 2* / ethnology
  • Diabetes Mellitus, Type 2* / therapy
  • Female
  • Healthcare Disparities / statistics & numerical data*
  • Humans
  • Length of Stay / statistics & numerical data*
  • Male
  • Middle Aged
  • Propensity Score
  • Spatial Analysis
  • United States / epidemiology
  • Veterans / statistics & numerical data*
  • White People / statistics & numerical data*