Introduction

Substantial research has been dedicated to understanding the reasons for the dramatic rise in obesity rates in the U.S. in the last 2 decades. Animal studies and epidemiologic studies in children have suggested that air pollution might contribute to weight gain. This study investigates the association between ambient air pollution and weight gain over 16 years of follow-up (1995-2011) in a large cohort of African-American women in the U.S.

Methods

This study assessed associations of fine particulate matter, ozone, and nitrogen dioxide with weight gain using a linear random effects model. All analyses were conducted in 2015.

Results

There was no statistically significant association between weight change and fine particulate matter (mean weight change over 16 years per interquartile range [2.9 μg/m(3)], 0.12 kg; 95% CI=-0.10, 0.35) and ozone (0.16 kg per interquartile range [6.7 ppb]; 95% CI=-0.11, 0.43). There was a small decrease in weight associated with nitrogen dioxide (-0.50 per interquartile range [9.7 ppb]; 95% CI=-0.77, -0.23).

Conclusions

The results do not provide support for an association of air pollution with weight gain in African-American adult women.

Background

Traffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities.

Methods

Noise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration's Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input.

Results

We found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input.

Conclusions

Ambient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics.

While laboratory studies show that air pollutants can potentiate insulin resistance, the epidemiologic evidence regarding the association of air pollution with diabetes incidence is conflicting. The purpose of the present study was to assess the association of the traffic-related nitrogen dioxide (NO2) with the incidence of diabetes in a longitudinal cohort study of African American women. We used Cox proportional hazards models to calculate hazard ratios and 95% confidence intervals (CI) for diabetes associated with exposure to NO2 among 43,003 participants in the Black Women's Health Study (BWHS). Pollutant levels at participant residential locations were estimated with 1) a land use regression model for participants living in 56 metropolitan areas, and 2) a dispersion model for participants living in 27 of the cities. From 1995 to 2011, 4387 cases of diabetes occurred. The hazard ratios per interquartile range of NO2 (9.7 ppb), adjusted for age, metropolitan area, education, vigorous exercise, body mass index, smoking, and diet, were 0.96 (95% CI 0.88-1.06) using the land use regression model estimates and 0.94 (95% CI 0.80, 1.10) using the dispersion model estimates. The present results do not support the hypothesis that exposure to NO2 contributes to diabetes incidence in African American women.

Lower neighborhood socioeconomic status (SES) is associated with higher cardiovascular disease (CVD) risk. Black women have a higher CVD risk and are more likely to live in poor neighborhoods than white women. We examined the association of neighborhood SES with several CVD biomarkers using data from the Black Women's Health Study (BWHS), a follow-up study of US black women reporting high levels of education and income. Blood specimens of 418 BWHS participants were assayed for C-reactive protein (CRP), hemoglobin A1C (hgA1C), and high-density lipoprotein (HDL) cholesterol. US Census block group data were linked to the women's addresses to reflect neighborhood SES. Multivariable-adjusted mixed linear regression models that adjusted for person-level SES and for cardiovascular risk factors were used to assess CRP, hgA1C, and HDL levels in relation to quintiles of neighborhood SES. Women living in the poorest neighborhoods had the least favorable biomarker levels. As neighborhood SES increased, CRP decreased (P for trend = 0.01), hgA1C decreased (P for trend = 0.07), and HDL increased (P for trend = 0.19). These associations were present within strata of individual educational level. The present findings suggest that neighborhood environments may affect physiological processes within residents independently of individual SES.

Background

Ozone is a ubiquitous air pollutant with increasing concentrations in many populous regions. Toxicological studies show that ozone can cause oxidative stress and increase insulin resistance. These pathways may contribute to metabolic changes and diabetes formation. In this paper, we investigate the association between ozone and incident type 2 diabetes in a large cohort of African American women.

Methods

We used Cox proportional hazards models to calculate hazard ratios (HRs) for incident type 2 diabetes associated with exposure to ozone in a cohort of 45,231 African American women living in 56 metropolitan areas across the United States. Ozone levels were estimated using the U.S. EPA Models-3/Community Multiscale Air Quality (CMAQ) predictions fused with ground measurements at a resolution of 12km for the years 2007-2008.

Results

The HR per interquartile range increment of 6.7ppb of ozone was 1.18 (95% CI 1.04-1.34) for incident diabetes in adjusted models. This association was unaltered in models that controlled for fine particulate matter with diameter <2.5μm (PM_2.5). Associations were modified by nitrogen dioxide (NO₂) levels, such that HRs for ozone levels were larger in areas of lower NO₂.

Conclusions

Our results provide initial evidence of a positive association between O₃ and incident diabetes in African American women. Given the ubiquity of ozone exposure and the importance of diabetes on quality of life and survival, these results may have important implications for the protection of public health.

Background

Clinical studies have shown that exposure to fine particulate matter (PM2.5) can increase insulin resistance and blood pressure. The epidemiologic evidence for an association of PM2.5 exposure with the incidence of type 2 diabetes or hypertension is inconsistent. Even a modest association would have great public health importance given the ubiquity of exposure and high prevalence of the conditions.

Methods

We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes and hypertension associated with exposure to PM2.5 in a large cohort of African American women living in 56 metropolitan areas across the US, using data from the Black Women's Health Study. Pollutant levels were estimated at all residential locations over follow-up with a hybrid model incorporating land use regression and Bayesian Maximum Entropy techniques.

Results

During 1995 to 2011, 4,387 cases of diabetes and 9,570 cases of hypertension occurred. In models controlling for age, questionnaire cycle, and metro area, there were positive associations with diabetes (HR = 1.13, 95% CI = 1.04, 1.24) and hypertension (HR = 1.06, 95% CI = 1.00, 1.12) per interquartile range of PM2.5 (2.9 μg/m). Multivariable HRs, however, were 0.99 (95% CI = 0.90, 1.09) for diabetes and 0.99 (95% CI = 0.93, 1.06) for hypertension.

Conclusions

Our results provide little support for an association of PM2.5 with diabetes or hypertension incidence.

Background

Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM_2.5) is associated with mortality.

Objectives

We compared relative risk estimates of mortality from diseases of the circulatory system for PM_2.5 modeled from RS with that for PM_2.5 modeled using ground-level information.

Methods

We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM_2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM_2.5 with circulatory mortality and ischemic heart disease.

Results

Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 μg/m³ increment in PM_2.5]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 μg/m³ increment in PM_2.5).

Conclusions

We found significant associations between PM_2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.