Evaluating uncertainty to strengthen epidemiologic data for use in human health risk assessments
Background: There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. While most epidemiologic studies result in uncertainty, techniques exist to better characterize uncertainty that can be applied to weight-of-evidence evaluations and risk characterization efforts.
Methods: This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, in October 2012 to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytical methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government and industry convened to discuss uncertainty, exposure assessment, and application of analytical methods to address these challenges.
Synthesis: Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. Improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure sufficient information is collected for a thorough uncertainty evaluation. Advanced analytical methods and tools such as directed-acyclic graphs (DAGs) and Bayesian statistical techniques, should be used more routinely as part of an epidemiology analysis.
Conclusions: The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making.