Evaluating Cumulative Impacts: The Value of Epidemiology Jonathan Levy Associate Professor of Environmental Health and Risk Assessment, Harvard School of Public Health CHE Partnership Call Evaluating the Impact of Cumulative Stressors on Health April 19, 2010

Dose-response relationship is dependent on heterogeneity in background exposure, biological susceptibility  important to consider all relevant stressors to which people are exposed NRC, 2009

Example: Joint effect of air pollution and exposure to violence on asthma development Clougherty et al., 2007

Science and Decisions conclusions on cumulative risk NRC committee applauded EPA’s move toward a broader definition, making risk assessment more informative and relevant to decisions and stakeholders. However, in practice, EPA risk assessments often fall short of what is possible and supported by agency guidelines. Little consideration of nonchemical stressors, vulnerability, and background risk factors

The role of epidemiology Most non-chemical stressors (e.g., access to health care, SES) cannot be evaluated in animal studies In spite of this, there is no guidance for using epidemiology in the EPA Framework for Cumulative Risk Assessment, no mention of epidemiology in any EPA pesticide cumulative risk assessments

Levy, 2008 Problem formulation/ planning and scoping Risk management objectives Health outcomes and populations of concern Chemical and non-chemical stressors under consideration (primary and secondary) Selection of epidemiological studies Statistically significant dose-response function? Biases anticipated and reasonably quantifiable? Confounding limited unless stressors that cannot be separated jointly considered in risk management? Development of dose-response (D-R) functions Mixture of concern or sufficiently similar Similar mixture, different vulnerability Whole mixture Components Similar vulnerability, different mixture Different vulnerability, different mixture Characterize relative exposures and population vulnerability Use D-R function directly Adjust D-R function using related evidence or defaults Adjust D-R function using evidence about components Characterize individual D-R functions, interactions, population vulnerability Combine D-R functions across studies using meta-regression when possible Adjust D-R functions to address vulnerability, interactions, effect modification Levy, 2008

Wason et al., 2010

Conclusions Cumulative risk assessment/impact assessment can be a key component of environmental decision-making, but needs to better capture non-chemical stressors Direct epidemiological evidence may be impractical in many settings, but epidemiological insight can be leveraged, and non-chemical stressors can be considered through other risk modeling mechanisms