Abstract
Background: Asbestos exposure is a recognized risk factor for ovarian cancer and malignant mesothelioma. There are reports in the literature of geographical ecological associations between the occurrence of these two diseases. Our aim was to further explore this association by applying advanced Bayesian techniques to a large population (10 million people). Methods: We specified a series of Bayesian hierarchical shared models to the bivariate spatial distribution of ovarian and pleural cancer mortality by municipality in the Lombardy Region (Italy) in 2000–2018. Results: Pleural cancer showed a strongly clustered spatial distribution, while ovarian cancer showed a less structured spatial pattern. The most supported Bayesian models by predictive accuracy (widely applicable or Watanabe–Akaike information criterion, WAIC) provided evidence of a shared component between the two diseases. Among five municipalities with significant high standardized mortality ratios of ovarian cancer, three also had high pleural cancer rates. Wide uncertainty was present when addressing the risk of ovarian cancer associated with pleural cancer in areas at low background risk of ovarian cancer. Conclusions: We found evidence of a shared risk factor between ovarian and pleural cancer at the small geographical level. The impact of the shared risk factor can be relevant and can go unnoticed when the prevalence of other risk factors for ovarian cancer is low. Bayesian modelling provides useful information to tailor epidemiological surveillance.
Original language | English |
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Article number | 3467 |
Journal | International Journal of Environmental Research and Public Health |
Volume | 19 |
Issue number | 6 |
DOIs | |
Publication status | Published - Mar 1 2022 |
Keywords
- Asbestos-related diseases
- Bayesian shared spatial models
- Epidemiology
- Mortality
- Ovarian cancer
- Pleural cancer
- Surveillance
ASJC Scopus subject areas
- Pollution
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis