Most studies of neighborhood effects on health have used the multilevel approach. However, since this methodology
does not incorporate any notion of space, it may not provide optimal epidemiologic information when
modeling variations or when investigating associations between contextual factors and health. Investigating mental
disorders due to psychoactive substance use among all 65,830 individuals aged 40–59 years in 2001 in Malmo¨ ,
Sweden, geolocated at their place of residence, the authors compared a spatial analytical perspective, which builds
notions of space into hypotheses and methods, with the multilevel approach. Geoadditive models provided precise
cartographic information on spatial variations in prevalence independent of administrative boundaries. The multilevel
model showed significant neighborhood variations in the prevalence of substance-related disorders. However,
hierarchical geostatistical models provided information on not only the magnitude but also the scale of neighborhood
variations, indicating a significant correlation between neighborhoods in close proximity to each other. The
prevalence of disorders increased with neighborhood deprivation. Far stronger associations were observed when
using indicators measured in spatially adaptive areas, centered on residences of individuals, smaller in size than
administrative neighborhoods. In neighborhood studies, building notions of space into analytical procedures may
yield more comprehensive information than heretofore has been gathered on the spatial distribution of outcomes.
epidemiologi