An assessment of the mediating role of hypertension in the effect of long-term air pollution exposure on dementia.

Background: Growing evidence links air pollution exposure to the risk of dementia. We hypothesized that hypertension may partially mediate this effect. Methods: We previously documented an association between air pollution and dementia in the Ginkgo Evaluation of Memory Study, a randomized, placebo-controlled trial of 3069 adults ≥75 years across four US sites who were evaluated for dementia every 6 months from 2000-2008. We utilized a two-stage regression approach for causal mediation analysis to decompose the total effect of air pollution on dementia into its natural direct and indirect effect through prevalent hypertension. Exposure to air pollution in the 10 or 20 years before enrollment was assigned using estimates from fine-scale spatial-temporal models for PM2.5, PM10, and NO2. We used Poisson regression models for hypertension and Cox proportional hazard models for time-to-incident all-cause dementia, adjusting for a priori confounders. Results: Participants were free of mild cognitive impairment at baseline (n = 2564 included in analyses); 69% had prevalent hypertension at baseline. During follow-up, 12% developed all-cause dementia (Alzheimer's disease [AD] = 212; vascular dementia with or without AD [VaD/AD mixed] = 97). We did not find an adverse effect of any air pollutant on hypertension. Hypertension was associated with VaD/AD mixed (HR, 1.92 [95% CI = 1.14, 3.24]) but not AD. We did not observe mediation through hypertension for the effect of any pollutant on dementia outcomes. Conclusions: The lack of mediated effect may be due to other mechanistic pathways and the minimal effect of air pollution on hypertension in this cohort of older adults.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach.

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 µg/m3]) compared to the model with all LCM measurements (0.84 [0.9 µg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 µg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 µg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 µg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Racial and Ethnic Differences in All-Cause and Cardiovascular Disease Mortality: The MESA Study.

BACKGROUND: Despite improvements in population health, marked racial and ethnic disparities in longevity and cardiovascular disease (CVD) mortality persist. This study aimed to describe risks for all-cause and CVD mortality by race and ethnicity, before and after accounting for socioeconomic status (SES) and other factors, in the MESA study (Multi-Ethnic Study of Atherosclerosis). METHODS: MESA recruited 6814 US adults, 45 to 84 years of age, free of clinical CVD at baseline, including Black, White, Hispanic, and Chinese individuals (2000-2002). Using Cox proportional hazards modeling with time-updated covariates, we evaluated the association of self-reported race and ethnicity with all-cause and adjudicated CVD mortality, with progressive adjustments for age and sex, SES (neighborhood SES, income, education, and health insurance), lifestyle and psychosocial risk factors, clinical risk factors, and immigration history. RESULTS: During a median of 15.8 years of follow-up, 22.8% of participants (n=1552) died, of which 5.3% (n=364) died of CVD. After adjusting for age and sex, Black participants had a 34% higher mortality hazard (hazard ratio [HR], 1.34 [95% CI, 1.19-1.51]), Chinese participants had a 21% lower mortality hazard (HR, 0.79 [95% CI, 0.66-0.95]), and there was no mortality difference in Hispanic participants (HR, 0.99 [95% CI, 0.86-1.14]) compared with White participants. After adjusting for SES, the mortality HR for Black participants compared with White participants was reduced (HR, 1.16 [95% CI, 1.01-1.34]) but still statistically significant. With adjustment for SES, the mortality hazards for Chinese and Hispanic participants also decreased in comparison with White participants. After further adjustment for additional risk factors and immigration history, Hispanic participants (HR, 0.77 [95% CI, 0.63-0.94]) had a lower mortality risk than White participants, and hazard ratios for Black participants (HR, 1.08 [95% CI, 0.92-1.26]) and Chinese participants (HR, 0.81 [95% CI, 0.60-1.08]) were not significantly different from those of White participants. Similar trends were seen for CVD mortality, although the age- and sex-adjusted HR for CVD mortality for Black participants compared with White participants was greater than all-cause mortality (HR, 1.72 [95% CI, 1.34-2.21] compared with HR, 1.34 [95% CI, 1.19-1.51]). CONCLUSIONS: These results highlight persistent racial and ethnic differences in overall and CVD mortality, largely attributable to social determinants of health, and support the need to identify and act on systemic factors that shape differences in health across racial and ethnic groups.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection.

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 µg/m3 to 0.92 and 1.63 µg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 µg/m3 to 0.79 and 0.88 µg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study.

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Racial Segregation and Respiratory Outcomes among Urban Black Residents with and at Risk of Chronic Obstructive Pulmonary Disease.

Rationale: Racial residential segregation has been associated with worse health outcomes, but the link with chronic obstructive pulmonary disease (COPD) morbidity has not been established.Objectives: To investigate whether racial residential segregation is associated with COPD morbidity among urban Black adults with or at risk of COPD.Methods: Racial residential segregation was assessed using isolation index, based on 2010 decennial census and baseline address, for Black former and current smokers in the multicenter SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), a study of adults with or at risk for COPD. We tested the association between isolation index and respiratory symptoms, physiologic outcomes, imaging parameters, and exacerbation risk among urban Black residents, adjusting for established COPD risk factors, including smoking. Additional mediation analyses were conducted for factors that could lie on the pathway between segregation and COPD outcomes, including individual and neighborhood socioeconomic status, comorbidity burden, depression/anxiety, and ambient pollution.Measurements and Main Results: Among 515 Black participants, those residing in segregated neighborhoods (i.e., isolation index ⩾0.6) had worse COPD Assessment Test score (ß = 2.4; 95% confidence interval [CI], 0.7 to 4.0), dyspnea (modified Medical Research Council scale; ß = 0.29; 95% CI, 0.10 to 0.47), quality of life (St. George's Respiratory Questionnaire; ß = 6.1; 95% CI, 2.3 to 9.9), and cough and sputum (ß = 0.8; 95% CI, 0.1 to 1.5); lower FEV1% predicted (ß = -7.3; 95% CI, -10.9 to -3.6); higher rate of any and severe exacerbations; and higher percentage emphysema (ß = 2.3; 95% CI, 0.7 to 3.9) and air trapping (ß = 3.8; 95% CI, 0.6 to 7.1). Adverse associations attenuated with adjustment for potential mediators but remained robust for several outcomes, including dyspnea, FEV1% predicted, percentage emphysema, and air trapping.Conclusions: Racial residential segregation was adversely associated with COPD morbidity among urban Black participants and supports the hypothesis that racial segregation plays a role in explaining health inequities affecting Black communities.

Contribution of Individual and Neighborhood Factors to Racial Disparities in Respiratory Outcomes.

Rationale: Black adults have worse health outcomes compared with white adults in certain chronic diseases, including chronic obstructive pulmonary disease (COPD).Objectives: To determine to what degree disadvantage by individual and neighborhood socioeconomic status (SES) may contribute to racial disparities in COPD outcomes.Methods: Individual and neighborhood-scale sociodemographic characteristics were determined in 2,649 current or former adult smokers with and without COPD at recruitment into SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study). We assessed whether racial differences in symptom, functional, and imaging outcomes (St. George's Respiratory Questionnaire, COPD Assessment Test score, modified Medical Research Council dyspnea scale, 6-minute-walk test distance, and computed tomography [CT] scan metrics) and severe exacerbation risk were explained by individual or neighborhood SES. Using generalized linear mixed model regression, we compared respiratory outcomes by race, adjusting for confounders and individual-level and neighborhood-level descriptors of SES both separately and sequentially.Measurements and Main Results: After adjusting for COPD risk factors, Black participants had significantly worse respiratory symptoms and quality of life (modified Medical Research Council scale, COPD Assessment Test, and St. George's Respiratory Questionnaire), higher risk of severe exacerbations and higher percentage of emphysema, thicker airways (internal perimeter of 10 mm), and more air trapping on CT metrics compared with white participants. In addition, the association between Black race and respiratory outcomes was attenuated but remained statistically significant after adjusting for individual-level SES, which explained up to 12-35% of racial disparities. Further adjustment showed that neighborhood-level SES explained another 26-54% of the racial disparities in respiratory outcomes. Even after accounting for both individual and neighborhood SES factors, Black individuals continued to have increased severe exacerbation risk and persistently worse CT outcomes (emphysema, air trapping, and airway wall thickness).Conclusions: Disadvantages by individual- and neighborhood-level SES each partly explain disparities in respiratory outcomes between Black individuals and white individuals. Strategies to narrow the gap in SES disadvantages may help to reduce race-related health disparities in COPD; however, further work is needed to identify additional risk factors contributing to persistent disparities.

Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study.

Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors' operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson's r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R2 = 0.96, RMSE = 1.15 µg/m3 for daily averaged PM2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson's r = 0.84) and accuracy (cross-validated R2 = 0.40, RMSE = 4.49 µg/m3). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R2 = 0.74-0.95, RMSE = 2.46-0.84 µg/m3). Applying the Seattle model to the other regions resulted in decreased performance (R2 = 0.67-0.84, RMSE = 3.41-1.67 µg/m3), likely due to differences in meteorological conditions and particle sources. We describean approach to metropolitan region-specific calibration models for low-cost sensors that can be used with cautionfor exposure measurement in epidemiological studies.

Author Correction: Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

In addition to the acknowledgments that were included, the authors wish to add the following: MESA was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute.

The Association of Ambient Air Pollution with Sleep Apnea: The Multi-Ethnic Study of Atherosclerosis.

RATIONALE: Air pollution may influence sleep through airway inflammation or autonomic nervous system pathway alterations. Epidemiological studies may provide evidence of relationships between chronic air pollution exposure and sleep apnea. OBJECTIVES: To determine whether ambient-derived pollution exposure is associated with obstructive sleep apnea and objective sleep disruption. METHODS: We analyzed data from a sample of participants in MESA (Multi-Ethnic Study of Atherosclerosis) who participated in both the Sleep and Air studies. Mean annual and 5-year exposure levels to nitrogen dioxide (NO2) and particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) were estimated at participants' homes using spatiotemporal models based on cohort-specific monitoring. Participants completed in-home full polysomnography and 7 days of wrist actigraphy. We used multivariate models, adjusted for demographics, comorbidities, socioeconomic factors, and site, to assess whether air pollution was associated with sleep apnea (apnea-hypopnea index ≥ 15) and actigraphy-measured sleep efficiency. RESULTS: The participants (n = 1,974) were an average age of 68 (±9) years, 46% male, 36% white, 24% Hispanic, 28% black, and 12% Asian; 48% had sleep apnea and 25% had a sleep efficiency of ≤88%. A 10 ppb annual increase in NO2 exposure was associated with 39% greater adjusted odds of sleep apnea (95% confidence interval [CI], 1.03-1.87). A 5 µg/m3 greater annual PM2.5 exposure was also associated with 60% greater odds of sleep apnea (95% CI, 0.98-2.62). Sleep efficiency was not associated with air pollution levels in fully adjusted models. CONCLUSIONS: Individuals with higher annual NO2 and PM2.5 exposure levels had a greater odds of sleep apnea. These data suggest that in addition to individual risk factors, environmental factors also contribute to the variation of sleep disorders across groups, possibly contributing to health disparities.

Longitudinal Associations of Local Cigarette Prices and Smoking Bans with Smoking Behavior in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Few studies have examined associations of geographically proximal cigarette prices with within-person changes in smoking outcomes or assessed interactions between cigarette prices and smoking bans. METHODS: We linked neighborhood cigarette prices (inflation-adjusted) at chain supermarkets and drug stores and bar/restaurant smoking ban policies to cohort participants (632 smokers from the Multi-Ethnic Study of Atherosclerosis, 2001-2012, baseline mean age 58 years) using geocoded retailer and participant addresses. We used fixed-effects models to investigate associations of within-person changes in price and ban exposures with within-person changes in five smoking outcomes: current smoking, heavy (≥10 cigarettes) smoking, cessation, relapse, and intensity (average number of cigarettes smoked per day, natural log transformed). We assessed intensity associations among all smokers, and heavy (≥10 cigarettes per day) and light (<10) baseline smokers. Finally, we tested interactions between cigarette price and bans. RESULTS: A $1 increase in price was associated with a 3% reduction in risk of current smoking (adjusted risk ratio [aRR]: 0.97; 95% confidence interval [CI] = 0.93, 1.0), a 7% reduction in risk of heavy smoking (aRR: 0.93; CI = 0.87, 0.99), a 20% increase in risk of smoking cessation (aRR: 1.2; CI = 0.99, 1.4), and a 35% reduction in the average number of cigarettes smoked per day by heavy baseline smokers (ratio of geometric means: 0.65; CI = 0.45, 0.93). We found no association between smoking bans and outcomes, and no evidence that price effects were modified by the presence of bans. CONCLUSIONS: Results underscore the importance of local prices, but not hospitality smoking bans, in influencing older adults' smoking behaviors.

Creating a Future for Occupational Health.

Objectives: Economic, social, technical, and political drivers are fundamentally changing the nature of work and work environments, with profound implications for the field of occupational health. Nevertheless, researchers and practitioners entering the field are largely being trained to assess and control exposures using approaches developed under old models of work and risks. Methods: A speaker series and symposium were organized to broadly explore current challenges and future directions for the occupational health field. Broad themes identified throughout these discussions are characterized and discussed to highlight important future directions of occupational health. Findings: Despite the relatively diverse group of presenters and topics addressed, some important cross-cutting themes emerged. Changes in work organization and the resulting insecurity and precarious employment arrangements change the nature of risk to a large fraction of the workforce. Workforce demographics are changing, and economic disparities among working groups are growing. Globalization exacerbates the 'race to the bottom' for cheap labor, poor regulatory oversight, and limited labor rights. Largely, as a result of these phenomena, the historical distinction between work and non-work exposures has become largely artificial and less useful in understanding risks and developing effective public health intervention models. Additional changes related to climate change, governmental and regulatory limitations, and inadequate surveillance systems challenge and frustrate occupational health progress, while new biomedical and information technologies expand the opportunities for understanding and intervening to improve worker health. Conclusion: The ideas and evidences discussed during this project suggest that occupational health training, professional practice, and research evolve towards a more holistic, public health-oriented model of worker health. This will require engagement with a wide network of stakeholders. Research and training portfolios need to be broadened to better align with the current realities of work and health and to prepare practitioners for the changing array of occupational health challenges.

Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Tools to assess time-location patterns related to environmental exposures have expanded from reliance on time-location diaries (TLDs) and questionnaires to use of geospatial location devices such as data-logging Global Positioning System (GPS) equipment. The Multi-Ethnic Study of Atherosclerosis and Air Pollution obtained typical time-location patterns via questionnaire for 6424 adults in six US cities. At a later time (mean 4.6 years after questionnaire), a subset (n=128) participated in high-resolution data collection for specific 2-week periods resulting in concurrent GPS and detailed TLD data, which were aggregated to estimate time spent in various microenvironments. During these 2-week periods, participants were observed to spend the most time at home indoors (mean of 78%) and a small proportion of time in-vehicle (mean of 4%). Similar overall patterns were reported by these participants on the prior questionnaire (mean home indoors: 75%; mean in-vehicle: 4%). However, individual micro-environmental time estimates measured over specific 2-week periods were not highly correlated with an individual's questionnaire report of typical behavior (Spearman's ρ of 0.43 for home indoors and 0.39 for in-vehicle). Although questionnaire data about typical time-location patterns can inform interpretation of long-term epidemiological analyses and risk assessment, they may not reliably represent an individual's short-term experience.

Explaining racial/ethnic differences in all-cause mortality in the Multi-Ethnic Study of Atherosclerosis (MESA): Substantive complexity and hazardous working conditions as mediating factors.

Research on racial/ethnic health disparities and socioeconomic position has not fully considered occupation. However, because occupations are racially patterned, certain occupational characteristics may explain racial/ethnic difference in health. This study examines the role of occupational characteristics in racial/ethnic disparities in all-cause mortality. Data are from a U.S. community-based cohort study (n=6342, median follow-up: 12.2 years), in which 893 deaths (14.1%) occurred. We estimated mortality hazard ratios (HRs) for African Americans, Hispanics, and Chinese Americans compared with whites. We also estimated the proportion of the HR mediated by each of two occupational characteristics, substantive complexity of work (e.g., problem solving, inductive/deductive reasoning on the job) and hazardous conditions (e.g., noise, extreme temperature, chemicals), derived from the Occupational Information Network database (O*NET). Analyses were adjusted for age, sex, nativity, working status at baseline, and study sites. African Americans had a higher rate of all-cause death (HR 1.41; 95% confidence interval [CI]: 1.19-1.66) than whites. Chinese-American ethnicity was protective (HR 0.59, CI: 0.40-0.85); Hispanic ethnicity was not significantly different from whites (HR 0.88; CI: 0.67-1.17). Substantive complexity of work mediated 30% of the higher rate of death for African Americans compared with whites. For other groups, mediation was not significant. Hazardous conditions did not significantly mediate mortality in any racial/ethnic group. Lower levels of substantive complexity of work mediate a substantial part of the health disadvantage in African Americans. This job characteristic may be an important factor in explaining racial health disparities.

Individual and Neighborhood Socioeconomic Status and the Association between Air Pollution and Cardiovascular Disease.

BACKGROUND: Long-term fine particulate matter (PM2.5) exposure is linked with cardiovascular disease, and disadvantaged status may increase susceptibility to air pollution-related health effects. In addition, there are concerns that this association may be partially explained by confounding by socioeconomic status (SES). OBJECTIVES: We examined the roles that individual- and neighborhood-level SES (NSES) play in the association between PM2.5 exposure and cardiovascular disease. METHODS: The study population comprised 51,754 postmenopausal women from the Women's Health Initiative Observational Study. PM2.5 concentrations were predicted at participant residences using fine-scale regionalized universal kriging models. We assessed individual-level SES and NSES (Census-tract level) across several SES domains including education, occupation, and income/wealth, as well as through an NSES score, which captures several important dimensions of SES. Cox proportional-hazards regression adjusted for SES factors and other covariates to determine the risk of a first cardiovascular event. RESULTS: A 5 µg/m3 higher exposure to PM2.5 was associated with a 13% increased risk of cardiovascular event [hazard ratio (HR) 1.13; 95% confidence interval (CI): 1.02, 1.26]. Adjustment for SES factors did not meaningfully affect the risk estimate. Higher risk estimates were observed among participants living in low-SES neighborhoods. The most and least disadvantaged quartiles of the NSES score had HRs of 1.39 (95% CI: 1.21, 1.61) and 0.90 (95% CI: 0.72, 1.07), respectively. CONCLUSIONS: Women with lower NSES may be more susceptible to air pollution-related health effects. The association between air pollution and cardiovascular disease was not explained by confounding from individual-level SES or NSES. Citation: Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. 2016. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environ Health Perspect 124:1840-1847; http://dx.doi.org/10.1289/EHP199.

Air Pollution, Cardiovascular Outcomes, and Social Disadvantage: The Multi-ethnic Study of Atherosclerosis.

BACKGROUND: Social factors may enhance health effects of air pollution, yet empirical support is inconsistent. The interaction of social and environmental factors may only be evident with long-term exposures and outcomes that reflect long-term disease development. METHODS: We used cardiac magnetic resonance imaging data from the Multi-Ethnic Study of Atherosclerosis to assess left-ventricular mass index (LVMI) and left-ventricular ejection fraction (LVEF). We assigned residential concentrations of fine particulate matter (PM2.5), oxides of nitrogen, and nitrogen dioxide in the year 2000 to each participant in 2000 using prediction models. We examined modifying roles of four measures of adversity: race/ethnicity, racial/ethnic residential segregation, and socioeconomic status and psychosocial adversity as composite indices on the association between air pollution and LVMI or LVEF. RESULTS: Compared with whites, blacks showed a stronger adjusted association between air pollution and LVMI. For example, for each 5 µg/m greater PM2.5 level, whites showed a 1.0 g/m greater LVMI (95% confidence interval = -1.3, 3.1), while blacks showed an additional 4.0 g/m greater LVMI (95% confidence interval = 0.3, 8.2). Results were similar for oxides of nitrogen and nitrogen dioxide with regard to black race and LVMI. However, we found no evidence of a modifying role of other social factors or ethnic groups. Furthermore, we found no evidence of a modifying role for any social factors or racial/ethnic groups on the association between air pollution and LVEF. CONCLUSIONS: Our results suggest that racial group membership may modify the association between air pollution and cardiovascular disease.

Distribution and burden of newly detected coronary artery calcium: Results from the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: The transition from no coronary artery calcium (CAC) to detectable CAC is important, as even mild CAC is associated with increased cardiovascular events. We sought to characterize the anatomic distribution and burden of newly detectable CAC over 10-year follow-up. METHODS: We evaluated 3112 participants (mean age, 58 years; 64% female) with baseline CAC = 0 from the Multi-Ethnic Study of Atherosclerosis. Participants underwent repeat CAC testing at different time intervals (between 2-10 years after baseline) per the Multi-Ethnic Study of Atherosclerosis protocol. Among participants who developed CAC on a follow-up scan, we used logistic regression and marginal probability modeling to describe the coronary distribution and burden of new CAC by age, sex, and race after adjustment for cardiovascular risk factors and time to detection. RESULTS: A total of 1125 participants developed detectable CAC during follow-up with a mean time to detection of 6.1 ± 3 years. New CAC was most commonly isolated to 1 vessel (72% of participants), with the left anterior descending artery (44% of total) most commonly affected followed by the right coronary (12%), left circumflex (10%), and left main (6%). These patterns were similar across age, sex, and race. In multivariate models, residual predictors of multivessel CAC (28% of total) included male sex, African American or Hispanic race, hypertension, obesity, and diabetes. At the first detection of CAC >0, burden was usually low with median Agatston CAC score of 7.1 and <5% with CAC scores >100. CONCLUSION: New-onset CAC most commonly involves just 1 vessel, occurs in the left anterior descending artery, and has low CAC burden. New CAC can be detected at an early stage when aggressive preventive strategies may provide benefit.