Bayesian benchmark dose analysis for inorganic arsenic in drinking water associated with bladder and lung cancer using epidemiological data

Abstract

Abundant epidemiological evidence has shown that there is a strong causal relationship between long-term exposure to inorganic arsenic (iAs) through drinking water and a few types of cancer (e.g., lung and bladder cancer). Traditionally, a linear low-dose extrapolation assumption was applied in risk assessment for iAs which resulted in a relatively conservative cancer risk estimate. Growing biological evidence suggests that the mode of action of iAs-induced cancer follows a threshold process (e.g., sufficient concentration of trivalent arsenic is required to disrupt normal cellular function). In this study, we applied the benchmark dose (BMD) methodology to model the relationship between the relative risk of bladder and lung cancer and the iAs concentration in drinking water using the high-quality epidemiological data reported in recently published papers, with a special focus on the low exposure range (i.e., <150 μg/L). Because of its biological plausibility and statistical flexibility, the Hill model has been chosen to model the data under a Bayesian framework. A Bayesian hierarchal model together with a bootstrap method for exposure estimation were applied to quantify uncertainty from various sources, including the within-study, between-study, and exposure uncertainties. Dose-response assessment results obtained from a number of alternative model structures and methods consistently demonstrate a threshold type dose-response curve with a threshold in the range between 40–60 μg/L of iAs concentration in drinking water. The BMD for iAs in drinking water associated with 0.1 % increase in relative risk of bladder cancer is 42.2 μg/L (BMDL 39.2 μg/L); for 0.05 % increase, the BMD is 41.6 μg/L (BMDL 38.6 μg/L). For lung cancer, the two counterpart BMD estimates are 57.0 μg/L (BMDL 43.6 μg/L) and 55.7 μg/L (BMDL 42.5 μg/L) for 0.1 % and 0.05 % increase, respectively. These analyses provide additional statistical support for a non-linear dose response for cancer risk from inorganic arsenic which may have important policy implications.