Use of generalized linear mixed models to examine the association between air pollution and health outcomes.
BACKGROUND
Time-series and case-crossover are two techniques that are widely used for assessing the short-term impact of ambient air pollution exposure on health.


METHODS
The generalized linear mixed model (GLMM) methodology is proposed here to study the association between ambient air pollution and health outcomes. Poisson random-effects models are applied to analyze the clustered counts, where the groups of days, determined by the triplet <day of week, month, year>, form the clusters. The proposed technique uses a nested structure for the clusters and allows random-effects for hierarchical factors. A random intercept in the models adjusts for different levels of counts among the clusters. A fixed slope represents a common response to the exposure.


CONCLUSIONS
The obtained results are consistent with those generated by a classical approach (for example the case-crossover technique). The GLMM technique is a valid alternative methodology for studying air health effects.
Szyszkowicz
Mieczysław
M
Air Health Effects Division, Health Canada, Ottawa, Ontario, Canada. mietek_Szyszkowicz@hc-sc.gc.ca
D000397:Air Pollution / Q000009:adverse effects*; D016000:Cluster Analysis; D018592:Cross-Over Studies; D004781:Environmental Exposure / Q000009:adverse effects*; D006304:Health Status; D006801:Humans; D008954:Models, Biological; D016012:Poisson Distribution