Assessment of source-specific health effects has received growing attention in air pollution epidemiology over the past decade. Regardless of inherent uncertainty in the assessment of source-specific exposures, only a handful of previous studies coped with model uncertainty in source apportionment and/or accounted for exposure measurement error in the estimation of health effects, all under normal health outcome models. We present a source-specific health effects evaluation approach within a Bayesian framework that can handle both parameter uncertainty and model uncertainty in source apportionment under Poisson health outcome models for low daily mortality count data. While the use of a Poisson health outcome model is apparently more appropriate for low daily mortality count data for which normal approximation is not justified, it introduces additional complexity in estimating model uncertainty. We handle this complexity by introducing appropriate latent variables. The proposed method is illustrated with simulated data and daily ambient concentrations of the chemical composition of fine particulate matter (PM2.5), weather data, and counts of deaths from pneumonia in older adults (≥65 years of age) in Houston, Texas, from January 2002 to August 2005.
Bibliographical noteFunding Information:
Health Effects Institute, Grant/Award Number: R-82811201; National Research Foundation of Korea, Grant/Award Number: 2016R1A2B4008914
Research described in this article was conducted under contract to the Health Effects Institute, an organization jointly funded by the United States Environmental Protection Agency (Assistance Award No. R-82811201) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of Health Effects Institute, or its sponsors, nor do they necessarily reflect the views and policies of the United States Environmental Protection Agency or motor vehicle and engine manufacturers. Oh's research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2016R1A2B400). The authors are grateful to Dr. Clifford Spiegelman, Dr. Jim Price, Ms. Melanie Hotchkiss, and Dr. Daikwon Han for their help with the acquisition of the Houston PM2.5 speciation data and mortality data. The authors also thank the editor, the associate editor, and the referee for their constructive comments and suggestions that led to significant improvements in the presentation of the article.
Copyright © 2017 John Wiley & Sons, Ltd.
- PM health effects
- exposure measurement error
- model uncertainty
- mortality from pneumonia
- multipollutant approach