Sheryl Magzamen

Do Psychosocial Stress and Social Disadvantage Modify the Association Between Air Pollution and Blood Pressure? The Multi-Ethnic Study of Atherosclerosis

Researchers have theorized that social and psychosocial factors increase vulnerability to the deleterious health effects of environmental hazards. We used baseline examination data (2000-2002) from the Multi-Ethnic Study of Atherosclerosis. Participants were 45-84 years of age and free of clinical cardiovascular disease at enrollment (n = 6814). The modifying role of social and psychosocial factors on the association between exposure to air pollution comprising particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) and blood pressure measures were examined using linear regression models. There was no evidence of synergistic effects of higher PM2.5 and adverse social/psychosocial factors on blood pressure. In contrast, there was weak evidence of stronger associations of PM2.5 with blood pressure in higher socioeconomic status groups. For example, those in the 10th percentile of the income distribution (i.e., low income) showed no association between PM2.5 and diastolic blood pressure (b = -0.41 mmHg; 95% confidence interval: -1.40, 0.61), whereas those in the 90th percentile of the income distribution (i.e., high income) showed a 1.52-mmHg increase in diastolic blood pressure for each 10-µg/m(3) increase in PM2.5 (95% confidence interval: 0.22, 2.83). Our results are not consistent with the hypothesis that there are stronger associations between PM2.5 exposures and blood pressure in persons of lower socioeconomic status or those with greater psychosocial adversity.

Understanding Socioeconomic and Racial Differences in Adult Lung Function

RATIONALE The contribution of socioeconomic factors to racial differences in the distribution of lung function is not well understood. OBJECTIVES We investigated the contribution of socioeconomic factors to racial differences in FEV₁ using statistical tools that allow for examination across the population distribution of FEV₁. METHODS We compared FEV₁ for white and African-American participants (aged 20-80 yr) in NHANES III with greater than or equal to two acceptable maneuvers to a restricted sample following the routine exclusion criteria used to derive population reference equations. Ordinary least squares and quantile regression analyses using spirometric, anthropometric, and socioeconomic data (high school completion) were performed separately by sex for both data sets. MEASUREMENTS AND MAIN RESULTS In the entire sample with acceptable spirometry (n ¼ 9,658), high school completion was associated with a mean 69.13-ml increase in FEV₁ for males (P , 0.05) and a mean 50.75-ml increase in FEV₁ for females (P , 0.01). In quantile regression analysis, we observed a significant racial difference in the association of high school completion with FEV₁ among both sexes that varied across the distribution; college completion was associated with an additional increase in FEV₁ for white males (70.36-250.76 ml) and white females (57.87-317.77 ml). Routine exclusion criteria differentially excluded individuals by age, race, and education. In the restricted sample (n ¼ 2,638), the association with high school completion was not significant. CONCLUSIONS High school completion is associated with racially patterned improvements in the FEV₁ of adults in the general population. The application of routine exclusion criteria leads to underestimates of the role of high school completion on FEV₁.

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1-Nitropyrene and Other Nitroarenes

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitropyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R(2) of 0.87 and cross-validated R(2) of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods.

Spatial and Temporal Estimates of Population Exposure to Wildfire Smoke during the Washington State 2012 Wildfire Season Using Blended Model, Satellite, and In-Situ Data

Abstract In the western U.S., smoke from wild and prescribed fires can severely degrade air quality. Due to changes in climate and land management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of air pollutants in the western U.S. Hence, there is a need to develop a quantitative understanding of wildfire‐smoke‐specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools have been used in past studies to assess exposure to wildfire smoke: in situ measurements, satellite‐based observations, and chemical‐transport model (CTM) simulations. Each of these exposure‐estimation tools has associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge‐regression model and a geographically weighted ridge‐regression model. We evaluate the performance of the three individual exposure‐estimate techniques and the two blended techniques by using leave‐one‐out cross validation. We find that predictions based on in situ monitors are more accurate for this particular fire season than the CTM simulations and satellite‐based observations because of the large number of monitors present; therefore, blending provides only marginal improvements above the in situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.

Quantile regression in environmental health: Early life lead exposure and end-of-grade exams.

Conditional means regression, including ordinary least squares (OLS), provides an incomplete picture of exposure-response relationships particularly if the primary interest resides in the tail ends of the distribution of the outcome. Quantile regression (QR) offers an alternative methodological approach in which the influence of independent covariates on the outcome can be specified at any location along the distribution of the outcome. We implemented QR to examine heterogeneity in the influence of early childhood lead exposure on reading and math standardized fourth grade tests. In children from two urban school districts (n=1,076), lead exposure was associated with an 18.00 point decrease (95% CI: -48.72, -3.32) at the 10th quantile of reading scores, and a 7.50 point decrease (95% CI: -15.58, 2.07) at the 90th quantile. Wald tests indicated significant heterogeneity of the coefficients across the distribution of quantiles. Math scores did not show heterogeneity of coefficients, but there was a significant difference in the lead effect at the 10th (β=-17.00, 95% CI: -32.13, -3.27) versus 90th (β=-4.50, 95% CI: -10.55, 4.50) quantiles. Our results indicate that lead exposure has a greater effect for children in the lower tail of exam scores, a result that is masked by conditional means approaches.

Early lead exposure (<3 years old) prospectively predicts fourth grade school suspension in Milwaukee, Wisconsin (USA).

School suspensions are associated with negative student outcomes. Environmental lead exposure increases hyperactivity and sensory defensiveness, two traits likely to increase classroom misbehavior and subsequent discipline. Childhood Blood Lead Level (BLL) test results categorized urban fourth graders as exposed (2687; lifetime max BLL 10-20 µg/dL) or unexposed (1076; no lifetime BLL ≥5 µg/dL). Exposed children were over twice as likely as unexposed children to be suspended (OR=2.66, 95% CI=[2.12, 3.32]), controlling for covariates. African American children were more likely to be suspended than white children, but lead exposure explained 23% of the racial discipline gap. These results suggest that different rates of environmental lead exposure may contribute to the racial discipline gap.

Moderate lead exposure and elementary school end-of-grade examination performance

PURPOSE This study investigated the association between moderate lead poisoning in early childhood with performance on a comprehensive set of end-of-grade examinations at the elementary school level in two urban school districts. METHODS Children born between 1996 and 2000 who resided in Milwaukee or Racine, WI, with a record of a blood lead test before the age of 3 years were considered for the analysis. Children were defined as exposed (blood lead level ≥10 and <20 μg/dL) or not exposed (BLL < 5 μg/dL). Parents of eligible children were mailed surveys to consent to participation and elicit information on potential confounders. On consent, children were matched to educational records for fourth grade Wisconsin Knowledge and Concepts Examinations. Seemingly unrelated regression was used to evaluate the relation between scaled scores on all sections of the examination (math, reading, language arts, science, and social studies) with exposure status, controlling for demographics, social status indicators, health indicators, and district-based poverty indicators. RESULTS A total of 1133 families responded to the survey and consented to have educational records released; 43% of children were considered exposed. After controlling for demographic and socioeconomic covariates, lead exposure was associated with significantly lower scores in all sections of the Wisconsin Knowledge and Concepts Examinations (range: science, β = -5.21, P = .01; reading, β = -8.91, P = .003). Children who were black, had a parent with less than a high-school education, and were classified by parents as having less than excellent health had significantly lower performance on all examination components. CONCLUSIONS Children with moderate lead poisoning in early childhood performed significantly lower on all components of elementary school end-of-grade examinations compared with unexposed children. Household level social status and childhood health indicators partially explain decreased examination scores.

Global burden of mortalities due to chronic exposure to ambient PM2.5 from open combustion of domestic waste

Uncontrolled combustion of domestic waste has been observed in many countries, creating concerns for air quality; however, the health implications have not yet been quantified. We incorporate the Wiedinmyer et al (2014 Environ. Sci. Technol. 48 9523–30) emissions inventory into the global chemical-transport model, GEOS-Chem, and provide a first estimate of premature adult mortalities from chronic exposure to ambient PM2.5 from uncontrolled combustion of domestic waste. Using the concentration-response functions (CRFs) of Burnett et al (2014 Environ. Health Perspect. 122 397–403), we estimate that waste-combustion emissions result in 270 000 (5th–95th: 213 000–328 000) premature adult mortalities per year. The confidence interval results only from uncertainty in the CRFs and assumes equal toxicity of waste-combustion PM2.5 to all other PM2.5 sources. We acknowledge that this result is likely sensitive to choice of chemical-transport model, CRFs, and emission inventories. Our central estimate equates to 9% of adult mortalities from exposure to ambient PM2.5 reported in the Global Burden of Disease Study 2010. Exposure to PM2.5 from waste combustion increases the risk of premature mortality by more than 0.5% for greater than 50% of the population. We consider sensitivity simulations to uncertainty in waste-combustion emission mass, the removal of waste-combustion emissions, and model resolution. A factor-of-2 uncertainty in waste-combustion PM2.5 leads to central estimates ranging from 138 000 to 518 000 mortalities per year for factors-of-2 reductions and increases, respectively. Complete removal of waste combustion would only avoid 191 000 (5th–95th: 151 000–224 000) mortalities per year (smaller than the total contributed premature mortalities due to nonlinear CRFs). Decreasing model resolution from 2° × 2.5° to 4° × 5° results in 16% fewer mortalities attributed to waste-combustion PM2.5, and over Asia, decreasing resolution from 0.5° × 0.666° to 2° × 2.5° results in 21% fewer mortalities attributed to waste-combustion PM2.5. Owing to coarse model resolution, our global estimates of premature mortality from waste-combustion PM2.5 are likely a lower bound.

Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions

Abstract Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical‐weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM2.5) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time‐stratified case‐crossover design. Hospital admissions aggregated by ZIP code were linked with population‐weighted daily average concentrations of smoke PM2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF‐Chem) model, a kriged interpolation of PM2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF‐Chem, satellite observations of aerosol optical depth, and kriged PM2.5. A 10 μg/m3 increase in GWR smoke PM2.5 was associated with an 8% increased risk in asthma‐related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019–1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM2.5 exposure method: a 10 μg/m3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026–1.145) and not significant using WRF‐Chem (OR: 0.986, 95%CI: 0.931–1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

Traffic-related air pollution and childhood acute leukemia in Oklahoma

BACKGROUND While many studies have evaluated the association between acute childhood leukemia and environmental factors, knowledge is limited. Ambient air pollution has been classified as a Group 1 carcinogen, but studies have not established whether traffic-related air pollution is associated with leukemia. The goal of our study was to determine if children with acute leukemia had higher odds of exposure to traffic-related air pollution at birth compared to controls. METHODS We conducted a case-control study using the Oklahoma Central Cancer Registry to identify cases of acute leukemia in children diagnosed before 20 years of age between 1997 and 2012 (n=307). Controls were selected from birth certificates and matched to cases on week of birth (n=1013). Using a novel satellite-based land-use regression model of nitrogen dioxide (NO2) and estimating road density based on the 2010 US Census, we evaluated the association between traffic-related air pollution and childhood leukemia using conditional logistic regression. RESULTS The odds of exposure to the fourth quartile of NO2 (11.19-19.89ppb) were similar in cases compared to controls after adjustment for maternal education (OR: 1.08, 95% CI: 0.75, 1.55). These estimates were stronger among children with acute myeloid leukemia (AML) than acute lymphoid leukemia, with a positive association observed among urban children with AML (4th quartile odds ratio: 5.25, 95% confidence interval: 1.09, 25.26). While we observed no significant association with road density, male cases had an elevated odds of exposure to roads at 500m from the birth residence compared to controls (OR: 1.39, 95% CI: 0.93, 2.10), which was slightly attenuated at 750m. CONCLUSIONS Although we observed no association overall between NO2 or road density, this was the first study to observe an elevated odds of exposure to NO2 among children with AML compared to controls suggesting further exploration of traffic-related air pollution and AML is warranted.