Misspecification of within-area exposure distribution in ecological Poisson models View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-09

AUTHORS

Léa Fortunato, Chantal Guihenneuc-Jouyaux, Margot Tirmarche, Dominique Laurier, Denis Hémon

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. More... »

PAGES

341-353

References to SciGraph publications

  • 1991-03. Bayesian image restoration, with two applications in spatial statistics in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • 2002. Modeling the Impact of Traffic-Related Air Pollution on Childhood Respiratory Illness in CASE STUDIES IN BAYESIAN STATISTICS VOLUME V
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