2013 Progress Report: Effects-Based Cumulative Risk Assessment in a Low-Income Urban Community near a Superfund Site

EPA Grant Number: R834577
Title: Effects-Based Cumulative Risk Assessment in a Low-Income Urban Community near a Superfund Site
Investigators: Levy, Jonathan , Ezzati, Majid , French, Robert , Fuentes, Freddie , Hammitt, James K. , Korrick, Susan A. , Rosario, Maria , Sexton, Ken , Subramanian, S V
Current Investigators: Levy, Jonathan , Ayala, Arlene , Fabian, Maria Patricia , French, Robert , Korrick, Susan A. , Peters, Junenette , Pina, Jordan , Rosario, Maria
Institution: Boston University , Channing Laboratory , NorthStar Learning Centers
Current Institution: Boston University , NorthStar Learning Centers
EPA Project Officer: Hahn, Intaek
Project Period: September 1, 2010 through February 28, 2014 (Extended to February 28, 2015)
Project Period Covered by this Report: September 1, 2012 through August 31,2013
Project Amount: $749,662
RFA: Understanding the Role of Nonchemical Stressors and Developing Analytic Methods for Cumulative Risk Assessments (2009) RFA Text |  Recipients Lists
Research Category: Health Effects , Human Health Risk Assessment , Health


The study's primary objective is to develop and apply novel statistical and analytical methods for cumulative risk assessment, focusing on a low-income community (New Bedford, MA) living near a Superfund site. We are focusing on specific neurobehavioral and cardiovascular outcomes, –ADHD-like behavior and blood pressure – and we are considering contributions of multiple chemical stressors to these outcomes in the presence of significant non-chemical stressors. We are leveraging data from public databases and an ongoing birth cohort study in the New Bedford area to predict exposures as a function of individual and neighborhood characteristics. Those exposures are then linked with dose-response functions for key chemical and non-chemical stressors, providing health risk characterization with geospatial and demographic variability. The models will be built and outputs will be communicated to community partners in a manner that informs future studies or intervention strategies.

Progress Summary:

Progress has been made in four major areas over the past year. We completed structural equation models linking demographic and geographic predictors with exposures measured within the New Bedford Cohort (NBC). Based on our literature review and preliminary modeling, we focused on Pb, Hg, PCB, and ppDDE as chemical stressors influencing ADHD-like behavior. For predictors, we focused initially on covariates available from the NBC, but for any covariates that would not be available from public datasets, we linked NBC-specific predictors to equations that could be populated with publicly available data. For example, our initial regression model for cord serum PCBs included child date of birth, maternal age, country of birth, smoking during pregnancy, previous lactation, income, and multiple aspects of diet. However, only maternal age, birth country, and income could be extracted from public data resources. We therefore constructed the structural equation model to include predictors of smoking, previous lactation, and food consumption. Models have been completed for PCBs, Pb, Hg, and ppDDE, as well as Home Observation for Measurement of the Environment (HOME) scores as proxies of stress.
For blood pressure, we used data from the National Health and Nutrition Examination Survey (NHANES) 1999-2008 for adults ages 20 years and older to investigate the effect of chemical and non-chemical stressor on blood pressure. We identified multiple candidate chemical stressors from a literature review - arsenic, BPA, cadmium, lead, mercury, PCB, PCDD and PCDF –but ultimately focused on cadmium, lead, and PCB given the results of preliminary regression models. We then constructed structural equation models to simultaneously evaluate predictors of chemical stressor exposures and predictors of four different blood pressure outcomes. Predictive models of chemical and non-chemical exposures illustrated the complexity of the multi-stressor exposure environment. Lead was most significantly elevated among older men with current/past smoking. Smokers and older individuals also had high blood cadmium levels, but exposures were higher among women. PCB exposure was very strongly associated with increasing age, consistent with its persistence, and was also positively associated with lipid levels.  We also developed predictive models for BMI, lipid levels and menopausal status, as other non-chemical risk factors could be ascertained directly from public datasets. In our structural equation models, lead appears to have greater influence on the blood pressure measures that are important predictors in younger populations (i.e., diastolic blood pressure and mean arterial pressure), with PCBs having greater influence on the blood pressure measures more important in later life (i.e., systolic blood pressure and pulse pressure).
Linking the above regression models to New Bedford requires extensive individual-level or cross-tabulated data with high geographic resolution. However, these data are not available because of the need to limit potential identifiability. As such, to estimate cumulative exposures and risks in New Bedford, we needed to generate synthetic microdata that reasonably represented attributes of New Bedford relevant to chemical and non-chemical stressor exposures. We first applied probabilistic reweighting using simulated annealing to data from the 2006-2010 American Community Survey, combining 9,135 microdata samples from the New Bedford area with census tract-level constraints for individual and household characteristics. We then evaluated the synthetic microdata using goodness-of-fit tests and by examining spatial patterns of microdata fields not used as constraints. As a demonstration, we developed a multivariable regression model predicting smoking behavior as a function of individual-level microdata fields using New Bedford-specific data from the 2006-2010 Behavioral Risk Factor Surveillance System, linking this model with the synthetic microdata to predict demographic and geographic smoking patterns in New Bedford.
Our simulation produced microdata representing all 94,944 individuals living in New Bedford in 2006-2010. Variables in the synthetic population matched the constraints well at the census tract level (e.g., ancestry, gender, age, education, household income) and reproduced the census-derived spatial patterns of non-constraint microdata. Smoking in New Bedford was significantly associated with numerous demographic variables found in the microdata, with estimated tract-level smoking rates varying from 20% (95% CI: 17%, 22%) to 37% (95% CI: 30%, 45%). We were therefore able to show that our simulation approach could successfully create geographically-resolved individual-level microdata that can be used in community-wide exposure and risk assessment studies.
Finally, we developed and implemented a community survey to evaluate exposure patterns and predictors in current New Bedford, which could be compared with patterns from the NBC determined in the mid-1990s. The survey was designed collaboratively between academic and community partners, ensuring valuable information for future exposure models and community outreach efforts. Between August and December 2012, our community partner (Northstar) implemented surveys at a variety of community settings as well as on-line, regularly evaluating demographic and geographic patterns in the surveys to focus outreach efforts in subsequent weeks. A total of 382 surveys were successfully completed, covering all neighborhoods of New Bedford and reflecting the demographic diversity of the city (72% had income under $40,000, 26% were Hispanic, 27% were Cape Verdean, and 20% Portuguese/Azorian). In our core analyses, we found that demographic predictors of food consumption and other behaviors were generally consistent with the prior NBC models, providing reassurance regarding their application, while offering insights about populations (e.g., Hispanics) who were underrepresented in the NBC.


Future Activities:

In the final year of our project, we plan for a few key activities. We will focus on integration of the synthetic census with the structural equation models, yielding the final estimates of exposures and cumulative risks. Beyond the manuscripts submitted to date, we anticipate additional manuscripts to be prepared and submitted, focusing on the results of the cumulative risk assessments for ADHD-like behavior and for hypertension, as well as conceptual approaches for synthesizing epidemiological findings in a multi-stressor environment. We will also engage in extensive outreach and communication efforts in New Bedford as our publications are accepted and our findings are in place.

Journal Articles:

No journal articles submitted with this report: View all 20 publications for this project

Supplemental Keywords:

ADHD, blood pressure, cadmium, cumulative risk assessment, lead, mercury, non-chemical stressor, PCBs, Superfund     

Relevant Websites:

BU professor undertakes New Bedford-wide public health study Exit
New website to address cumulative harm on communities and the environment Exit
NorthStar Learning Centers Exit

Progress and Final Reports:

Original Abstract
  • 2010 Progress Report
  • 2011 Progress Report
  • 2012 Progress Report
  • 2014
  • Final Report