Bayesian Hierarchical Models as Tools to Evaluate the Association Between Groundwater Quality and the Occurrence of Type 2 Diabetes in Rural Saskatchewan, Canada

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Abstract

There is growing interest in the role of environmental exposures in the development of diabetes. Previous studies in rural Saskatchewan have raised concerns over drinking water contaminants, including arsenic, which has been identified as a possible risk factor for diabetes. Using administrative health and water-quality surveillance data from rural Saskatchewan, an ecological study design was used to investigate associations between concentrations of arsenic, water health standards and aesthetic objectives, and the incidence and prevalence of diabetes. Mixtures of contaminants measured as health standards or as aesthetic objectives were summarized using principal component (PC) analysis. Associations were modeled using Bayesian hierarchical models incorporating both spatial and unstructured random effects, standardized for age and sex, and adjusted for socioeconomic factors and a surrogate measure for smoking rates. Arsenic was not associated with an increased risk of diabetes. For private wells, having groundwater arsenic concentrations in the highest quintile was associated with decreased cumulative diabetes incidence for 2010–2012 (risk ratio [RR] = 0.854, 95% credible interval [CrI] 0.761–0.958) compared with the lowest quintile, a result inconsistent with other studies. For public water supplies, having a first PC score for health standards (primarily summarized selenium, nitrate, and lead) in the third quintile (RR = 1.101, 95% CrI 1.019–1.188), fourth quintile (RR = 1.088, 95% CrI 1.003–1.180), or fifth quintile (RR = 1.115, 95% CrI 1.026–1.213) was associated with an increase in 2010 diabetes prevalence compared with the first quintile. An increase in the PC scores for the third aesthetic objective in private wells (characterized primarily by iron and manganese) was associated with decreased diabetes incidence, although a meaningful dose–response relationship was not evident. No other associations between PC scores for health standards or aesthetic objectives from public or private water supplies and diabetes were identified.

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McLeod, L., Bharadwaj, L., Epp, T. Y., & Waldner, C. L. (2019). Bayesian Hierarchical Models as Tools to Evaluate the Association Between Groundwater Quality and the Occurrence of Type 2 Diabetes in Rural Saskatchewan, Canada. Archives of Environmental Contamination and Toxicology, 76(3), 375–393. https://doi.org/10.1007/s00244-018-00588-4

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