A dynamic structural equation approach to estimate the short-term effects of air pollution on human health.

Detailed knowledge on the effects of air pollutants on human health is a prerequisite for the development of effective policies to reduce the adverse impact of ambient air pollution. However, measuring the effect of exposure on health outcomes is an extremely difficult task as the health impact of air pollution is known to vary over space and over different exposure periods. In general, standard approaches aggregate the information over space or time to simplify the study but this strategy fails to recognize important regional differences and runs into the well-known risk of confounding the effects. However, modelling directly with the original, disaggregated data requires a highly dimensional model with the curse of dimensionality making inferences unstable; in these cases, the models tend to retain many irrelevant components and most relevant effects tend to be attenuated. The situation clearly calls for an intermediate solution that does not blindly aggregate data while preserving important regional features. We propose a dimension-reduction approach based on latent factors driven by the data. These factors naturally absorb the relevant features provided by the data and establish the link between pollutants and health outcomes, instead of forcing a necessarily high-dimensional link at the observational level. The dynamic structural equation approach is particularly suited for this task. The latent factor approach also provides a simple solution to the spatial misalignment caused by using variables with different spatial resolutions and the state-space representation of the model favours the application of impulse response analysis. Our approach is discussed through the analysis of the short-term effects of air pollution on hospitalization data from Lombardia and Piemonte regions (Italy).