Background: Extensive geographic variation in adverse health outcomes exists, but global measures ignore differences between adjacent geographic areas, which often have very different mortality rates. We describe a novel application of advanced spatial analysis to 1) examine the extent of differences in mortality rates between adjacent counties, 2) describe differences in risk factors between adjacent counties, and 3) determine if differences in risk factors account for the differences in mortality rates between adjacent counties.
Methods: We conducted a cross-sectional study in Missouri, USA with 2005-2009 age-adjusted all-cause mortality rate as the outcome and county-level explanatory variables from a 2007 population-based survey. We used a multi-level Gaussian model and a full Bayesian approach to analyze the difference in risk factors relative to the difference in mortality rates between adjacent counties.
Results: The average mean difference in the age-adjusted mortality rate between any two adjacent counties was -3.27 (standard deviation = 95.5) per 100,000 population (maximum = 258.80). Six variables were associated with mortality differences: inability to obtain medical care because of cost (β = 2.6), hospital discharge rate (β = 1.03), prevalence of fair/poor health (β = 2.93), and hypertension (β = 4.75) and poverty prevalence (β = 6.08).
Conclusions: Examining differences in mortality rates and associated risk factors between adjacent counties provides additional insight for future interventions to reduce geographic disparities.
Keywords: Bayesian analysis; Neighborhood effects; Spatial statistics.