Teresa Chahine

Particulate Air Pollution, Oxidative Stress Genes, and Heart Rate Variability in an Elderly Cohort

Background and objectives We have previously shown that reduced defenses against oxidative stress due to glutathione S-transferase M1 (GSTM1) deletion modify the effects of PM2.5 (fine-particulate air pollution of < 2.5 μm in aerodynamic diameter) on heart rate variability (HRV) in a cross-sectional analysis of the Normative Aging Study, an elderly cohort. We have extended this to include a longitudinal analysis with more subjects and examination of the GT short tandem repeat polymorphism in the heme oxygenase-1 (HMOX-1) promoter. Methods HRV measurements were taken on 539 subjects. Linear mixed effects models were fit for the logarithm of HRV metrics—including standard deviation of normal-to-normal intervals (SDNN), high frequency (HF), and low frequency (LF)—and PM2.5 concentrations in the 48 hr preceding HRV measurement, controlling for confounders and a random subject effect. Results PM2.5 was significantly associated with SDNN (p = 0.04) and HF (p = 0.03) in all subjects. There was no association in subjects with GSTM1, whereas there was a significant association with SDNN, HF, and LF in subjects with the deletion. Similarly, there was no association with any HRV measure in subjects with the short repeat variant of HMOX-1, and significant associations in subjects with any long repeat. We found a significant three-way interaction of PM2.5 with GSTM1 and HMOX-1 determining SDNN (p = 0.008), HF (p = 0.01) and LF (p = 0.04). In subjects with the GSTM1 deletion and the HMOX-1 long repeat, SDNN decreased by 13% [95% confidence interval (CI), −21% to −4%], HF decreased by 28% (95% CI, −43% to −9%), and LF decreased by 20% (95% CI, −35% to −3%) per 10 μg/m3 increase in PM. Conclusions Oxidative stress is an important pathway for the autonomic effects of particles.

Sociodemographic and geographic variability in smoking in the U.S.: A multilevel analysis of the 2006–2007 Current Population Survey, Tobacco Use Supplement

Because smoking is a pervasive risk factor for numerous health outcomes, it is essential to understand smoking patterns in different populations and places. Previous studies have described sociodemographic and geographic variation in smoking across the U.S., providing insight to public health scientists aiming to identify high-risk populations and places in the absence of local data. This study extends previous research by considering the influence of a combination of covariates at the U.S. state, core-based statistical area (CBSA), and individual levels, focusing on a time period when smoking prevalence has decreased significantly but disparities remain. We applied multilevel regression to the 2006-2007 Current Population Survey-Tobacco Use Supplement. We conducted a four-level logistic regression model and evaluated fixed and random effects to quantify the contribution of sociodemographic characteristics at the individual level, area poverty at the CBSA level, and indoor smoking legislation and cigarette taxes at the state level, to the probability of smoking at the individual level and the variance in smoking at the state and CBSA levels. Sociodemographic covariates were significant predictors of smoking and explained 67% of variance at the CBSA level alone but only 41% at the state level. Contextual covariates alone, such as indoor smoking legislation and cigarette taxes at the state level and area poverty at the CBSA level, explained a larger proportion of state variance (82%) but individually had modest statistical significance. Our findings emphasize the continued disparities in smoking patterns in the U.S. despite the decrease of smoking prevalence in the past decade; as well as the ongoing necessity to consider associations with both compositional and contextual factors. Patterns of residual variance emphasize the continued need to identify and evaluate appropriate contextual covariates, with more refined geographic resolution than that available from national surveys at present.

Tools available to communities for conducting cumulative exposure and risk assessments.

This paper summarizes and assesses over 70 tools that could aid with gathering information and taking action on environmental issues related to community-based cumulative risk assessments (CBCRA). Information on tool use, development and research needs, was gathered from websites, documents, and CBCRA program participants and researchers, including 25 project officers who work directly with community groups. The tools were assessed on the basis of information provided by project officers, community members, CBCRA researchers, and by case study applications. Tables summarize key environmental issues and tool features: (1) a listing of CBCRA-related environmental issues of concern to communities; (2) web-based tools that map environmental information; (3) step-by-step guidance documents; (4) databases of environmental information; and (5) computer models that simulate human exposure to chemical stressors. All tools described here are publicly available, with the focus being on tools developed by the US Environmental Protection Agency. These tables provide sources of information to promote risk identification and prioritization beyond risk perception approaches, and could be used by CBCRA participants and researchers. The purpose of this overview is twofold: (1) To present a comprehensive, though not exhaustive, summary of numerous tools that could aid with performing CBCRAs; and (2) To use this toolset as a sample of the current state of CBCRA tools to critically examine their utility and guide research for the development of new and improved tools.

Modeling Joint Exposures and Health Outcomes for Cumulative Risk Assessment: The Case of Radon and Smoking

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case example, given its large attributable risk, effect modification due to smoking, and significant variability in radon concentrations and smoking patterns. In spite of this fact, no study to date has estimated geographic and sociodemographic patterns of both radon and smoking in a manner that would allow for inclusion of radon in community-based cumulative risk assessment. In this study, we apply multi-level regression models to explain variability in radon based on housing characteristics and geological variables, and construct a regression model predicting housing characteristics using U.S. Census data. Multi-level regression models of smoking based on predictors common to the housing model allow us to link the exposures. We estimate county-average lifetime lung cancer risks from radon ranging from 0.15 to 1.8 in 100, with high-risk clusters in areas and for subpopulations with high predicted radon and smoking rates. Our findings demonstrate the viability of screening-level assessment to characterize patterns of lung cancer risk from radon, with an approach that can be generalized to multiple chemical and non-chemical stressors.

Modeling geographic and demographic variability in residential concentrations of environmental tobacco smoke using national data sets

Despite substantial attention toward environmental tobacco smoke (ETS) exposure, previous studies have not provided adequate information to apply broadly within community-scale risk assessments. We aim to estimate residential concentrations of particulate matter (PM) from ETS in sociodemographic and geographic subpopulations in the United States for the purpose of screening-level risk assessment. We developed regression models to characterize smoking using the 2006–7 Current Population Survey—Tobacco Use Supplement, and linked these with air exchange models using the 2007 American Housing Survey. Using repeated logistic and log-linear models (n=1000), we investigated whether household variables from the 2000 United States census can predict exposure likelihood and ETS-PM concentration in exposed households. We estimated a mean ETS-PM concentration of 16 μg/m3 among the 17% of homes with non-zero exposure (3 μg/m3 overall), with substantial variability among homes. The highest exposure likelihood was in the South and Midwest regions, rural populations, and low-income households. Concentrations in exposed households were highest in the South and demonstrated a non-monotonic association with income, related to air exchange rate patterns. We provide estimates of ETS-PM concentration distributions for different subpopulations in the United States, providing a starting point for communities interested in characterizing aggregate and cumulative risks from indoor pollutants.

Assessment and Application of National Environmental Databases and Mapping Tools at the Local Level to Two Community Case Studies

Communities are concerned over pollution levels and seek methods to systematically identify and prioritize the environmental stressors in their communities. Geographic information system (GIS) maps of environmental information can be useful tools for communities in their assessment of environmental-pollution-related risks. Databases and mapping tools that supply community-level estimates of ambient concentrations of hazardous pollutants, risk, and potential health impacts can provide relevant information for communities to understand, identify, and prioritize potential exposures and risk from multiple sources. An assessment of existing databases and mapping tools was conducted as part of this study to explore the utility of publicly available databases, and three of these databases were selected for use in a community-level GIS mapping application. Queried data from the U.S. EPAs National-Scale Air Toxics Assessment, Air Quality System, and National Emissions Inventory were mapped at the appropriate spatial and temporal resolutions for identifying risks of exposure to air pollutants in two communities. The maps combine monitored and model-simulated pollutant and health risk estimates, along with local survey results, to assist communities with the identification of potential exposure sources and pollution hot spots. Findings from this case study analysis will provide information to advance the development of new tools to assist communities with environmental risk assessments and hazard prioritization.