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Your Environment. Your Health.

Publication Detail

Title: Does exposure prediction bias health-effect estimation?: The relationship between confounding adjustment and exposure prediction.

Authors: Cefalu, Matthew; Dominici, Francesca

Published In Epidemiology, (2014 Jul)

Abstract: In environmental epidemiology, we are often faced with 2 challenges. First, an exposure prediction model is needed to estimate the exposure to an agent of interest, ideally at the individual level. Second, when estimating the health effect associated with the exposure, confounding adjustment is needed in the health-effects regression model. The current literature addresses these 2 challenges separately. That is, methods that account for measurement error in the predicted exposure often fail to acknowledge the possibility of confounding, whereas methods designed to control confounding often fail to acknowledge that the exposure has been predicted. In this article, we consider exposure prediction and confounding adjustment in a health-effects regression model simultaneously. Using theoretical arguments and simulation studies, we show that the bias of a health-effect estimate is influenced by the exposure prediction model, the type of confounding adjustment used in the health-effects regression model, and the relationship between these 2. Moreover, we argue that even with a health-effects regression model that properly adjusts for confounding, the use of a predicted exposure can bias the health-effect estimate unless all confounders included in the health-effects regression model are also included in the exposure prediction model. While these results of this article were motivated by studies of environmental contaminants, they apply more broadly to any context where an exposure needs to be predicted.

PubMed ID: 24815302 Exiting the NIEHS site

MeSH Terms: Air Pollutants/adverse effects; Bias*; Confounding Factors (Epidemiology)*; Data Interpretation, Statistical; Environmental Exposure/adverse effects; Environmental Exposure/statistics & numerical data*; Environmental Health/methods; Humans

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