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Measurement error in epidemiologic studies of air pollution based on land-use regression models
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 2245448
Author(s) Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino
Author(s) at UniBasel Künzli, Nino
Year 2013
Title Measurement error in epidemiologic studies of air pollution based on land-use regression models
Journal American journal of epidemiology
Volume 178
Number 8
Pages / Article-Number 1342-6
Keywords air pollution, bias (epidemiology), measurement error, regression analysis

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Publisher Williams and Wilkins
ISSN/ISBN 0002-9262
Full Text on edoc No
Digital Object Identifier DOI 10.1093/aje/kwt127
PubMed ID
ISI-Number WOS:000325759000023
Document type (ISI) Journal Article

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