title: Estimating the effects of environmental exposures using a weighted mean of monitoring stations.
abstract: The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.
authors: School of Public Health & Institute of Health and Biomedical Innovation, Queensland University of Technology, QLD 4059, Australia. a.barnett@qut.edu.edu
authors: A G
authors: AG
authors: Barnett
authors: A C A
authors: AC
authors: Clements
authors: P
authors: P
authors: Vaneckova
mesh_terms: D000397:Air Pollution / Q000009:adverse effects / Q000706:statistics & numerical data*; D001315:Australia / Q000453:epidemiology; D001499:Bayes Theorem; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000379:methods*; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D009026:Mortality; D052638:Particulate Matter / Q000009:adverse effects*; D062206:Spatial Analysis; D013696:Temperature
chemical_list: D052638:Particulate Matter