Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia
An Area of the Po Valley
Bibliographic Data
| ID | 17801038 |
|---|---|
| Authors | Leonardo Trivelli (0000-0001-9800-9887, University of Pavia), Paola Borrelli (0000-0001-9389-627X, University of Pavia), Ennio Cadum (0000-0003-1959-5385, Agenzia Regionale per la Protezione Ambientale del Piemonte), Enrico Pisoni (0000-0001-5484-5744, Joint Research Centre), Simona Villani (0000-0003-2517-6515, University of Pavia, corresponding author) |
| Year | 2021 |
| Volume | 18 |
| Issue | 2 |
| Pages | 658-658 |
| Publication date | 2021-01-14 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18020658 |
| PMID | 33466700 |
| OpenAlex | W3120248093 |
| Language | EN |
| References cited | 37 |
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
Akaike information criterion · Bayesian inference · Bayesian information criterion · Bayesian probability · Econometrics · Geography · Information Criteria · Model selection · Physical geography · Spatial analysis · Statistics · Air Quality and Health Impacts · Environmental Justice and Health Disparities · Mathematics · Urban Transport and Accessibility
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| Highly cited | No |
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