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On the PM2.5–mortality association: a Bayesian model for spatio-temporal confounding

Carlo Zaccardi et al.

Journal of the Royal Statistical Society. Series C: Applied Statistics2026https://doi.org/10.1093/jrsssc/qlag004article
AJG 3
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1093/jrsssc/qlag004

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@article{carlo2026,
  title        = {{On the PM2.5–mortality association: a Bayesian model for spatio-temporal confounding}},
  author       = {Carlo Zaccardi et al.},
  journal      = {Journal of the Royal Statistical Society. Series C: Applied Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/jrsssc/qlag004},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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