title: Area variations in health: a spatial multilevel modeling approach.
abstract: Both space and membership in geographically-embedded administrative units can produce variations in health, resulting in geographic clusters of good and poor health. Despite important differences between these two types of dependence, one is easily mistaken for the other, and the possibility that both are at work is commonly ignored. We fit a series of hierarchical and spatially-explicit multilevel models to a U.S. county-level life dataset of life expectancy in 1999 to demonstrate approaches for data analysis and interpretation when multiple sources of area-clustering are present. We demonstrate the methods to detect, interpret, and differentiate evidence of spatial and geographic membership effects and discuss key considerations for analyzing data with spatial or/and membership dimensions. We find evidence that life expectancy is driven by both within-state geographic process, and by spatial processes. We argue that considering spatial and membership processes simultaneously yields valuable insights into the patterning of area variations in health.
authors.affiliation: Department of Society, Human Development and Health, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. marcaya@hsph.harvard.edu
authors.forename: Mariana
authors.initials: M
authors.lastname: Arcaya
authors.forename: Mark
authors.initials: M
authors.lastname: Brewster
authors.forename: Corwin M
authors.initials: CM
authors.lastname: Zigler
authors.forename: S V
authors.initials: SV
authors.lastname: Subramanian
mesh_terms: D003627:Data Interpretation, Statistical; D040362:Geographic Information Systems; D006304:Health Status; D006801:Humans; D008017:Life Expectancy; D008962:Models, Theoretical; D014481:United States / Q000453:epidemiology