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• W2890116557 abstract "Disease mapping applications generally assume homogeneous regression effects and use random intercepts to account for residual spatial dependence. However, there may be local variation in the association between disease and area risk factors. We consider implications for model fit, estimated regression coefficients, and substantive inferences of allowing spatial variability in impacts of area risk factors. An application to suicide in 6791 English small areas shows that average regression coefficients and substantive inferences (e.g. about relative risk) may be considerably affected by allowing spatially varying predictor effects, while fit is improved." @default.
• W2890116557 created "2018-09-27" @default.
• W2890116557 creator A5001636587 @default.
• W2890116557 date "2018-09-04" @default.
• W2890116557 modified "2023-10-12" @default.
• W2890116557 title "Spatial heterogeneity in Bayesian disease mapping" @default.
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• W2890116557 doi "https://doi.org/10.1007/s10708-018-9920-1" @default.
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