A Bayesian framework for estimating disease risk due to exposure to uranium mine and mill waste on the Navajo Nation. (27th January 2015)

• Record Type: Journal Article
• Authors: Hund, Lauren; Bedrick, Edward J.; Miller, Curtis; Huerta, Gabriel; Nez, Teddy; Ramone, Sandy; Shuey, Chris; Cajero, Miranda; Lewis, Johnnye
• Is Part Of: Journal of the Royal Statistical Society. Volume 178:Number 4(2015:Oct.)
• Page Start: 1069
• Page End: 1091
• Publication Date: 2015-01-27
• Subjects: Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12099
• Languages: English
• ISSNs: 0964-1998

Abstract

<abstract abstract-type="main" id="rssa12099-abs-0001"> <title>Summary</title> <p>More than 1100 abandoned mines, milling sites and waste piles from the uranium mining period are scattered across the Navajo Nation, resulting in exposures to environmental metals, including uranium. The Diné Network for Environmental Health project began in response to concerns regarding the community health effects of these environmental exposures on chronic disease. The paper presents the results of the initial Diné Network for Environmental Health survey of 1304 individuals living on the Navajo Nation. We examine the relationship between uranium mine waste exposure and kidney disease, diabetes and hypertension. These chronic diseases are found at high prevalences in the study population, present major public health risks and have been linked to metals exposures in other studies. We model the exposure–outcome relationship by using a multivariate model for the three binary responses. We implement a Bayesian multivariate <italic>t</italic>‐model, which has marginal log‐odds ratio parameter interpretations and is computationally efficient. In examining environmental exposures, appropriately adjusting for potential confounders is pivotal to obtaining policy relevant effect estimates. We use Bayesian model averaging to account for uncertainty in the functional form for confounding adjustment within a small set of measured confounders. Using this multivariate framework, we find evidence of associations between these chronic diseases and both historic mining era and legacy mining exposures.</p> </abstract>