Social Susceptibility to Multiple Air Pollutants in Cardiovascular Disease.

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research identified increased effects of air pollution in lower socioeconomic position (SEP) communities, where pollution exposures are also often higher. The complex spatial confounding between air pollution and SEP makes it very challenging, however, to disentangle the impacts of these very different exposure types and to accurately assess their interactions. The specific causal components (i.e., specific social stressors) underlying this SEP-related susceptibility remain unknown, because there are myriad pathways through which poverty and/or lower-SEP conditions may influence pollution susceptibility - including diet, smoking, coexposures in the home and occupational environments, health behaviors, and healthcare access. Growing evidence suggests that a substantial portion of SEP-related susceptibility may be due to chronic psychosocial stress - given the known wide-ranging impacts of chronic stress on immune, endocrine, and metabolic function - and to a higher prevalence of unpredictable chronic stressors in many lower-SEP communities, including violence, job insecurity, and housing instability. As such, elucidating susceptibility to pollution in the etiology of CVD, and in the risk of CVD events, has been identified as a research priority. This interplay among social and environmental conditions may be particularly relevant for CVD, because pollution and chronic stress both impact inflammation, metabolic function, oxidative stress, hypertension, atherosclerosis, and other processes relevant to CVD etiology. Because pollution exposures are often spatially patterned by SEP, disentangling their effects - and quantifying any interplay - is especially challenging. Doing so, however, would help to improve our ability to identify and characterize susceptible populations and to improve our understanding of how community stressors may alter responses to multiple air pollutants. More clearly characterizing susceptible populations will improve our ability to design and target interventions more effectively (and cost-effectively) and may reveal greater benefits of pollution reduction in susceptible communities, strengthening cost-benefit and accountability analyses, ultimately reducing the disproportionate burden of CVD and reducing health disparities. METHODS: In the current study, we aimed to quantify combined effects of multiple pollutants and stressor exposures on CVD events, using a number of unique datasets we have compiled and verified, including the following. 1. Poverty metrics, violent crime rates, a composite socioeconomic deprivation index (SDI), an index of racial and economic segregation, noise disturbance metrics, and three composite spatial factors produced from a factor analysis of 27 community stressors. All indicators have citywide coverage and were verified against individual reports of stress and stressor exposure, in citywide focus groups and surveys. 2. Spatial surfaces for multiple pollutants from the New York City (NYC) Community Air Survey (NYCCAS), which monitored multiple pollutants year-round at 150 sites and used land use regression (LUR) modeling to estimate fine-scale (100-m) intra-urban spatial variance in fine particles (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). 3. Daily data and time-trends derived from all U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitors in NYC for 2005-2011, which we combined with NYCCAS surfaces to create residence- and day-specific spatiotemporal exposure estimates. 4. Complete data on in- and out-patient unscheduled CVD events presented in NYC hospitals for 2005-2011 (n = 1,113,185) from the New York State (NYS) Department of Health's Statewide Planning and Research Cooperative System (SPARCS). In the study, we quantified relationships between multiple pollutant exposures and both community CVD event rates and individual risk of CVD events in NYC and tested whether pollution-CVD associations varied by community SEP and social stressor exposures. We hypothesized (1) that greater chronic community-level SEP, stressor, and pollution exposures would be associated with higher community CVD rates; (2) that spatiotemporal variations in multiple pollutants would be associated with excess risk of CVD events; and (3) that pollution-CVD associations would be stronger in communities of lower SEP or higher stressor exposures. RESULTS: To first understand the separate and combined associations with CVD for both stressors and pollutants measured at the same spatial and temporal scale of resolution, we used ecological cross-sectional models to examine spatial relationships between multiple chronic pollutant and stressor exposures and age-adjusted community CVD rates. Using census-tract-level annual averages (n = 2,167), we compared associations with CVD rates for multiple pollutant concentrations and social stressors. We found that associations with community CVD rates were consistently stronger for social stressors than for pollutants, in terms of both magnitude and significance. We note, however, that this result may be driven by the relatively greater variation (on a proportional basis) for stressors than for pollutants in NYC. We also tested effect modification of pollutant-CVD associations by each social stressor and found evidence of stronger associations for NO2, PM2.5, and wintertime SO2 with CVD rates, particularly across quintiles of increasing community violence or assault rates (P trend < 0.0001). To examine individual-level associations between spatiotemporal exposures to multiple pollutants and the risk of CVD events, across multiple lag days, we examined the combined effects of multiple pollutant exposures, using spatiotemporal (day- and residence-specific) pollution exposure estimates and hospital data on individual CVD events in case-crossover models, which inherently adjust for nontime-varying individual confounders (e.g., sex and race) and comorbidities. We found consistent significant relationships only for same-day pollutant exposures and the risk of CVD events, suggesting very acute impacts of pollution on CVD risk. Associations with CVD were positive for NO2, PM2.5, and SO2, as hypothesized, and we found inverse associations for O3 (a secondary pollutant chemically decreased ["scavenged"] by fresh emissions that, in NYC, displays spatial and temporal patterns opposite those of NO2). Finally, to test effect modification by chronic community social stressors on the relationships between spatiotemporal pollution measures and the risk of CVD events, we used individual-level case-crossover models, adding interaction terms with categorical versions of each social stressor. We found that associations between NO2 and the risk of CVD events were significantly elevated only in communities with the highest exposures to social stressors (i.e., in the highest quintiles of poverty, socioeconomic deprivation, violence, or assault). The largest positive associations for PM2.5 and winter SO2 were generally found in the highest-stressor communities but were not significant in any quintile. We again found inverse associations for O3, which were likewise stronger for individuals living in communities with greater stressor exposures. CONCLUSIONS: In ecological models, we found stronger relationships with community CVD rates for social stressors than for pollutant exposures. In case-crossover analyses, higher exposures to NO2, PM2.5, and SO2 were associated with greater excess risk of CVD events but only on the case day (there were no consistent significant lagged-day effects). In effect-modification analyses at both the community and individual level, we found evidence of stronger pollution-CVD associations in communities with higher stressor exposures. Given substantial spatial confounding across multiple social stressors, further research is needed to disentangle these effects in order to identify the predominant social stressors driving this observed differential susceptibility.

Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis.

UNLABELLED: Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations. PRACTICAL IMPLICATIONS: By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.

A longitudinal analysis of the efficacy of environmental interventions on asthma-related quality of life and symptoms among children in urban public housing.

In an environmental intervention study in public housing, we examined monthly Juniper Paediatric Asthma Quality of Life (QOL) Questionnaires for 51 children. Longitudinal analysis and spline models were used to identify time periods with significant improvements in QOL to inform judgments about causality. We found significant improvements in QOL, with moderate improvements before environmental interventions, increased rates of improvement immediately after, and reduced rates more than 5 months post-intervention. Effect modification analyses identified high-risk subpopulations and emphasized the importance of environmental, social, and economic conditions. Our results demonstrate the value of longitudinal techniques in evaluating the benefits of environmental interventions for asthma.