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Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley

Author

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  • Leonardo Trivelli

    (Unit of Biostatistics and Clinical Epidemiology, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy)

  • Paola Borrelli

    (Unit of Biostatistics and Clinical Epidemiology, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy
    Laboratory of Biostatistics, Department of Medical, Oral and Biotechnological Sciences, University “G. d’Annunzio” Chieti-Pescara, 66100 Chieti, Italy)

  • Ennio Cadum

    (Environmental Health Unit, Agency for Health Protection, 27100 Pavia, Italy)

  • Enrico Pisoni

    (European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy)

  • Simona Villani

    (Unit of Biostatistics and Clinical Epidemiology, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy)

Abstract

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe–Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified.

Suggested Citation

  • Leonardo Trivelli & Paola Borrelli & Ennio Cadum & Enrico Pisoni & Simona Villani, 2021. "Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley," IJERPH, MDPI, vol. 18(2), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:658-:d:480156
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    References listed on IDEAS

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    1. Zirong Ye & Li Xu & Zi Zhou & Yafei Wu & Ya Fang, 2018. "Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China," IJERPH, MDPI, vol. 15(1), pages 1-18, January.
    2. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
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    Cited by:

    1. Domenica Matranga & Filippa Bono & Laura Maniscalco, 2021. "Statistical Advances in Epidemiology and Public Health," IJERPH, MDPI, vol. 18(7), pages 1-5, March.

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