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Spatial heterogeneity in Bayesian disease mapping

Bibliographic Data

ID11983232
AuthorsPeter Congdon (0000-0003-1934-9205, Queen Mary University of London, corresponding author)
Year2018
Volume84
Issue5
Pages1303-1316
Publication date2018-09-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueGeoJournal (JOURNAL)
Journal identifiersISSN: 0343-2521 • E-ISSN: 1572-9893
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s10708-018-9920-1
OpenAlexW2890116557
LanguageEN
Citations received3
References cited26

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

Bayesian probability · Econometrics · Geography · Homogeneous · Linear regression · Meta-analysis · Random effects model · Regression · Regression analysis · Residual · Spatial dependence · Spatial heterogeneity · Spatial variability · Statistics · Data-Driven Disease Surveillance · Mathematics · Medicine · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference

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Unique citing works3
Citations per year0.43
Citation span2019 - 2022 (4)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 3

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