期刊
出版社
MDPI
DOI: 10.3390/ijerph19063467
关键词
asbestos-related diseases; ovarian cancer; pleural cancer; Bayesian shared spatial models; mortality; surveillance; epidemiology
资金
- Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro (INAIL), Rome, Italy [BRiC 55/2019]
This study uses Bayesian models to explore the geographical ecological association between ovarian cancer and malignant mesothelioma, and finds evidence of a shared risk factor between the two diseases.
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.
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