1. 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

2. 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

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

4. 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

5. 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

6. 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

7. Wason et al., 2010

8. 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