USE OF GENERALIZED LINEAR MIXED MODELS TO EXAMINE THE ASSOCIATION BETWEEN AIR POLLUTION AND HEALTH OUTCOMES



Abstract
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. Materials and 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. Results and 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