Extending multilevel spatial models to include spatially varying coefficients. (November 2019)

• Record Type: Journal Article
• Authors: Janko, Mark; Goel, Varun; Emch, Michael
• Is Part Of: Health & place. Volume 60(2019:Nov.)
• Publication Date: 2019-11
• Subjects: Health/medical geography -- Spatially-varying coefficients -- Multilevel models -- Bayesian statistics -- Disease ecology; Health -- Social aspects -- Periodicals; Health services accessibility -- Periodicals; Public health -- Periodicals; Political planning -- Periodicals; Social medicine -- Periodicals; Epidemiology -- Periodicals; Health Policy -- Periodicals; Health Services Accessibility -- Periodicals; Public Health -- Periodicals; Public Policy -- Periodicals; Sociology, Medical -- Periodicals; Épidémiologie -- Périodiques; Politique sanitaire -- Périodiques; Santé, Services de -- Accessibilité -- Périodiques; Health services accessibility; Health -- Social aspects; Political planning; Public health; Social medicine; Periodicals; 613
• DOI: 10.1016/j.healthplace.2019.102235
• Languages: English
• ISSNs: 1353-8292

Abstract

Abstract: Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo. Highlights: Multilevel spatially-varying coefficient models are well-suited to health geography. Multilevel models with spatially varying coefficients are formally presented in a Bayesian framework. These models allow geographers to work on spatially varying relationships between exposures and health outcomes.