title: Explaining large mortality differences between adjacent counties: a cross-sectional study.
abstract: 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.
  
  
  
  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.
  
  
  
  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).
  
  
  
  Examining differences in mortality rates and associated risk factors between adjacent counties provides additional insight for future interventions to reduce geographic disparities.
authors.affiliation: Department of Epidemiology, Saint Louis University College for Public Health and Social Justice, 3545 Lafayette Avenue, St. Louis, MO, 63104, USA. schootm@slu.edu.|Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, MO, USA. schootm@slu.edu.
authors.forename: M
authors.identifier: 0000-0003-1162-8824
authors.initials: M
authors.lastname: Schootman
authors.affiliation: University of Texas School of Public Health at San Antonio Regional Campus, Department of Biostatistics, San Antonio, TX, USA.
authors.forename: L
authors.initials: L
authors.lastname: Chien
authors.affiliation: Missouri Department of Health and Senior Services, Office of Epidemiology, Jefferson City, MO, USA.
authors.forename: S
authors.initials: S
authors.lastname: Yun
authors.affiliation: Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
authors.forename: S L
authors.initials: SL
authors.lastname: Pruitt
mesh_terms: D001499:Bayes Theorem; D002423:Cause of Death; D003365:Costs and Cost Analysis; D003430:Cross-Sectional Studies; D005260:Female; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D054624:Health Status Disparities; D006801:Humans; D006973:Hypertension / Q000401:mortality*; D008297:Male; D008923:Missouri / Q000453:epidemiology; D010351:Patient Discharge / Q000706:statistics & numerical data; D011203:Poverty / Q000706:statistics & numerical data*; D015995:Prevalence; D012307:Risk Factors; D062206:Spatial Analysis
keywords: Bayesian analysis; Neighborhood effects; Spatial statistics