NDLI: Misspecification of within-area exposure distribution in ecological Poisson models

Content Provider	Springer Nature Link
Author	Fortunato, Léa Guihenneuc Jouyaux, Chantal Tirmarche, Margot Laurier, Dominique Hémon, Denis
Copyright Year	2007
Abstract	Ecological studies enable investigation of geographic variations in exposure to environmental variables, across groups, in relation to health outcomes measured on a geographic scale. Such studies are subject to ecological biases, including pure specification bias which arises when a nonlinear individual exposure-risk model is assumed to apply at the area level. Introduction of the within-area variance of exposure should induce a marked reduction in this source of ecological bias. Assuming several measurements per area of exposure and no confounding risk factors, we study the model including the within-area exposure variability when Gaussian within-area exposure distribution is assumed. The robustness is assessed when the within-area exposure distribution is misspecified. Two underlying exposure distributions are studied: the Gamma distribution and an unimodal mixture of two Gaussian distributions. In case of strong ecological association, this model can reduce the bias and improve the precision of the individual parameter estimates when the within-area exposure means and variances are correlated. These different models are applied to analyze the ecological association between radon concentration and childhood acute leukemia in France.
Starting Page	341
Ending Page	353
Page Count	13
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	16
Issue Number	3
e-ISSN	15733009
Language	English
Publisher	Springer US
Publisher Date	2007-10-25
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ecological association Ecological bias Robustness Within-area exposure variability Evolutionary Biology Statistics Mathematical Biology in General Ecology
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

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