Methods for Modeling Exposures and Health Risks from Combined Chemical and Non-Chemical Stressors

1. Methods for Modeling Exposures and Health Risks from Combined Chemical and Non-Chemical Stressors Jon Levy Chair and Professor, Department of Environmental Health Boston University School of Public Health Northeast Superfund Research Program Meeting Session: Analyzing the Health Effects of Complex Mixtures of Chemical and Non-Chemical Environmental Stressors April 4, 2019

2. Premise • “Analyzing the health effects of complex mixtures of chemical and non-chemical environmental stressors” requires: • (Epidemiological) studies that evaluate the health effects of complex combinations of stressors AND • Exposure models that characterize multi-stressor exposures for the population of interest

3. Example #1: New Bedford (MA) • Population: 95,000 • Harbor designated Superfund site in 1982 (PCB contamination) • Other pollutant sources • hazardous waste sites • heavy industry • nearby major roadways • Elevated rates of poverty, low birth weight, blood lead, smoking, special education programs, inadequate prenatal care, etc.

4. Exposure prediction model • Input data: New Bedford Cohort (NBC) • Longitudinal birth cohort, 788 newborns enrolled 1993-1998 • Extensive prospective questionnaire data (pregnancy smoking, alcohol, diet, sociodemographics, etc.) • Biomarker measures of prenatal PCBs, OC pesticides and metals, childhood blood lead • Models • ΣPCB4, DDE, HCB, Pb and Hg using generalized additive models (GAMs) • Multivariable smooth term: latitude and longitude of residential address, infant year of birth, and maternal age at birth • Predictors common to NBC and key data resources for community-wide applications (e.g., Pregnancy to Early Life Longitudinal Data System, Census)

5. Estimates of the percent change in cord serum ΣPCB4, DDE, and HCB in the NBC for parametric terms in the generalized additive exposure models

6. Continued: Estimates of the percent change in cord serum ΣPCB4, DDE, and HCB in the NBC for parametric terms in the generalized additive exposure models

7. Epidemiological analysis • Exposures: • Chemical stressors (Pb, ΣPCB4, DDE, Hg) • Non-chemical stressors (maternal characteristics, HOME score) • Outcome: • Self-reported self esteem • Drawn from Behavioral Assessment System for Children, 2nd Edition (BASC-2) • Semi-parametric additive models with multivariate loess smooth term for mixtures of continuous predictors + categorical variables • Results mapped by predicting for all possible combinations of 2 variables in the mixture, holding other variables constant

8. Association between adolescent self-reported self-esteem and mixture of log cord blood Pb, maternal depression symptom severity, and home environment quality Log Cord Blood Pb Log Cord Blood Pb Maternal depression Maternal depression Self-esteem values for high HOME score Self-esteem values for low HOME score

9. Example #2: CRESSH • Center for Research on Environmental and Social Stressors in Housing Across the Life Course • Focus on Massachusetts • All births 2000-2015 • All deaths 2000-2015 • Participants in Children’s HealthWatch study (caretakers of children < 4 years of age recruited from Boston Medical Center)

10. Massachusetts Georeferenced Database GIS derived variables Public databases Healthcare facilities Roads Airport Grocery stores Housing assessor’s Remote sensing Pollution sources Massachusetts Transportation Green space LiDAR (1m) PM2.5 (Shi 2016) Meteorology Wind Precipitation Thermal (90 ft) NDVI (0.3 m) Temperature

11. Epidemiological analysis • Study population: • Births in urban block groups in MA, 2001-2011 • Exposures: • Candidate chemical/non-chemical stressors: PM2.5, PM2.5 summer infiltration, walkability index, noise at night, NDVI within 500 m, temperature, multiple area-level sociodemographic and SES variables • Controlled for season and year of birth, maternal race, age, parity, smoking, gestational or chronic diabetes, gestational age and the newborn’s sex • Outcome: • Birth weight • Approach for variable selection: Adaptive LASSO • Regression shrinkage and selection approach that applies a penalty to the components’ regression coefficients • Adaptive LASSO uses weights for penalizing different coefficients • Non modifiable maternal confounders were forced into the model

12. Maternal exposures selected as significant predictors of birth weight, important in fetal growth and development Walkability Greenness Nighttime noise • Temperature • PM2.5 0 Economically integrated +1 High income isolation -1 Low income isolation Economic residential segregation

13. Strengths and limitations • Exposure methods • Exposure regression models leverage measurements, akin to LUR modeling, but relevant input data and predictors not always available • GIS data resources ideal for numerous non-chemical stressors but significant processing required and chemical stressor data limited • Epidemiological methods • GAMs provide flexible modeling approach and ability to visualize non-linear interactions for mixtures, but challenging for numerous stressors and blend of continuous and categorical variables • LASSO allows for variable selection among a large set but does not independently resolve issues of complex non-linear interactions

14. Conclusions • Modeling health risks from combined chemical/non-chemical stressors requires both robust exposure models and corresponding/coordinated epidemiological analyses • Exposure models can leverage biomarker measurements, satellite data, or other public data resources to characterize community-scale patterns • Methods such as GAMs, LASSO, and BKMR and WQSR provide alternative strategies for multi-stressor epidemiology, where the “right” approach depends on sample size, context, dose-response shape, etc.

15. Acknowledgments • Collaborators: Roxana Khalili, Patricia Fabian, Susan Korrick, Veronica Vieira, Antonella Zanobetti, Maayan Yitshak-Sade, Kevin Lane • Funding: NIH/NIEHS P42ES007381, P42 ES005947, R01 ES014864, NIH/NIMHD P50MD010428, US EPA RD-836156