In epidemiological studies of air pollution and public health, estimating the health impact of exposure to air pollution may be hindered by the unknown functional form of the exposure-outcome association and by unmeasured confounding factors that are linked to both exposure and outcome. These challenges are especially relevant in spatio-temporal analyses, where their joint exploration remains limited. To study the effects of fine particulate matter on mortality among elderly people in Italy, we propose a Bayesian spatial dynamic generalized linear model that captures the non-linear exposure-outcome association and decomposes the exposure effect across fine and coarse spatio-temporal scales of variation. Together, these features allow reducing the spatio-temporal confounding bias and recovering the shape of the association, as demonstrated through simulation studies. The real-data analysis reveals a clear temporal pattern in the exposure effect, with peaks during summer months. We argue that this finding may be due to interactions of particulate matter with air temperature and unmeasured confounders.

翻译：在空气污染与公共卫生的流行病学研究中，评估空气污染暴露对健康的影响可能受到以下因素的阻碍：暴露-结局关联的未知函数形式，以及与暴露和结局均相关的未测量混杂因素。这些挑战在时空分析中尤为突出，而对其进行的联合探索仍然有限。为研究意大利老年人中细颗粒物对死亡率的影响，我们提出了一种贝叶斯空间动态广义线性模型。该模型能够捕捉非线性的暴露-结局关联，并将暴露效应分解到精细和粗糙的时空变异尺度上。模拟研究表明，这些特征共同作用有助于减少时空混杂偏倚并还原关联形态。实际数据分析揭示了暴露效应中存在明显的时间模式，其峰值出现在夏季月份。我们认为这一发现可能与颗粒物与气温及未测量混杂因素之间的相互作用有关。