Using Secondary Data to Evaluate Diverse Groups of Chemical and Nonchemical Stressors in Cumulative Risk Assessment Amanda M. Evans 1 Glenn E. Rice 2, Linda K. Teuschler 2, J. Michael Wright 2 1 Oak Ridge Institute for Science and Education 2 U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment

Acknowledgments This project was supported in part by an appointment to the Research Participation Program at the National Center for Environmental Assessment, Office of Research and Development, U.S. EPA, administered by the ORISE through an interagency agreement between the U.S. Department of Energy and U.S. EPA Disclaimer The views expressed herein are those of the authors and do not necessarily reflect of the views or policies of the U.S. EPA. 10 December 2013SRA 2013 Baltimore2

Secondary Data for CRA Data Needed: Exposure estimates of chemical and nonchemical stressors Vulnerability considerations (e.g., demographic, lifestyle, socioeconomic)  Unlikely to have everything in same dataset 10 December 20133SRA 2013 Baltimore

Sound Levels Ldn (dB) Secondary Data Analysis for CRA Issues Combining Heterogeneous Data 1.Dissimilar Variables 2.Spatial Misalignment (Seto et al, 2007)(Evans et al, 2013) 10 December 20134SRA 2013 Baltimore

5 December 2011 SRA 2011 Annual Meeting-Charleston, South Carolina 5 HI for hearing impairment due to noise and total VOC exposure Maximum population subgroup – specific HI Minimum population subgroup- specific HI Noise Exposure dBA

Sound & VOC Case Study: Overview Study Aim Characterize combined exposures to sound and volatile organic compounds (VOCs) in an urban community Data sources VOCs: nationally representative, individual-level personal air exposures ( NHANES) Sound: city-wide, street-level modeled sound map (Seto et al., 2007) Local: block group-level race-gender-education population (US Census), state-level race-gender smoking rates (CA Tobacco Survey) 10 December 20136SRA 2013 Baltimore

Sound & VOC Case Study: Dissimilar Variables Extrapolating local VOC exposure estimates VOCs: 36 race-gender-education-smoking specific VOC exposures ( NHANES) Local: – race-gender-education specific population estimates (US Census) – race-gender smoking rates (CA Tobacco Survey)  Can only use variables common to both datasets – May lose important data – May not be generalizable to all local populations 10 December 20137SRA 2013 Baltimore

Sound & VOC Case Study: Dissimilar variables Extrapolating local VOC exposure estimates from NHANES 1.May lose important data (e.g., time activity data ) – Examine potential effect using sensitivity analyses (e.g., the average decrease in noise exposure across all populations due to spending 90% of time indoors) 2.May not be generalizable to all local populations – Differential race/ethnicity categorization across datasets leads to loss of information on important local subpopulations (e.g., Asian/Pacific Islander in CA from Census data) 10 December 20138SRA 2013 Baltimore

Sound & VOC Case Study: Spatial Misalignment Different Scales of Exposures VOCs: Subpopulation- weighted block group-level VOC estimates Sound: Census Block Group (area-level) 10 December 20139SRA 2013 Baltimore (Evans et al, 2013)

Sound & VOC Case Study: Spatial Misalignment No true joint exposure distribution – uncertain if populations identified as highly exposed for VOCs also have high sound exposures – used the Hazard Index approach to examine combined hearing impairment hazard Aggregation leads to a loss in data – relationship between variables may vary by scale 10 December SRA 2013 Baltimore

Not just limitations…  Limited availability of population-specific integrated data for multiple stressors Epidemiological studies are resource and time intensive Secondary data is readily available and require fewer resources  Secondary data analysis is an efficient starting point for screening-level CRAs 10 December SRA 2013 Baltimore Reveal data gaps Relative importance of each stressor