On the PM2.5 -- Mortality Relationship: A Bayesian Dynamic Model for Spatio-Temporal Confounding

Spatial confounding, often regarded as a major concern in epidemiological studies, relates to the difficulty of recovering the effect of an exposure on an outcome when these variables are associated with unobserved factors. This issue is particularly challenging in spatio-temporal analyses, where it has been less explored so far. To study the effects of air pollution on mortality in Italy, we argue that a model that simultaneously accounts for spatio-temporal confounding and for the non-linear form of the effect of interest is needed. To this end, we propose a Bayesian dynamic generalized linear model, which allows for a non-linear association and for a decomposition of the exposure effect into two components. This decomposition accommodates associations with the outcome at fine and coarse temporal and spatial scales of variation. These features, when combined, allow reducing the spatio-temporal confounding bias and recovering the true shape of the association, as demonstrated through simulation studies. The results from the real-data application indicate that the exposure effect seems to have different magnitudes in different seasons, with peaks in the summer. We hypothesize that this could be due to possible interactions between the exposure variable with air temperature and unmeasured confounders.