Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology.

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

Evaluating deciduous tree leaves as biomonitors for ambient particulate matter pollution in Pittsburgh, PA, USA.

Fine particulate matter (PM2.5) air pollution varies spatially and temporally in concentration and composition and has been shown to cause or exacerbate adverse effects on human and ecological health. Biomonitoring using airborne tree leaf deposition as a proxy for particulate matter (PM) pollution has been explored using a variety of study designs, tree species, sampling strategies, and analytical methods. In the USA, relatively few have applied these methods using co-located fine particulate measurements for comparison and relying on one tree species with extensive spatial coverage, to capture spatial variation in ambient air pollution across an urban area. Here, we evaluate the utility of this approach, using a spatial saturation design and pairing tree leaf samples with filter-based PM2.5 across Pittsburgh, Pennsylvania, with the goal of distinguishing mobile and stationary sources using PM2.5 composition. Co-located filter and leaf-based measurements revealed some significant associations with traffic and roadway proximity indicators. We compared filter and leaf samples with differing protection from the elements (e.g., meteorology) and PM collection time, which may account for some variance in PM source and/or particle size capture between samples. To our knowledge, this study is among the first to use deciduous tree leaves from a single tree species as biomonitors for urban PM2.5 pollution in the northeastern USA.

Violent crime and socioeconomic deprivation in shaping asthma-related pollution susceptibility: a case-crossover design.

BACKGROUND: The objective of this study was to quantify and compare the relative influence of community violent crime and socioeconomic deprivation in modifying associations between ozone and emergency department (ED) visits for asthma among children. METHODS: We used a spatiotemporal case-crossover analysis for all New York City EDs for the months May-September from 2005 to 2011 from a statewide administrative ED dataset. The data included 11 719 asthmatic children aged 5-18 years, and the main outcome measure was percentage of excess risk of asthma ED visit based on Cox regression analysis. RESULTS: Stronger ozone-asthma associations were observed for both elevated crime and deprivation (eg, on lag day 2, we found 20.0% (95% CI 10.2% to 30.6 %) and 21.0% (10.5% to 32.5%) increased risk per 10 ppb ozone, for communities in the highest vs lowest quartiles of violent crime and deprivation, respectively). However, in varied models accounting for both modifiers, only violence retained significance. CONCLUSIONS: The results suggest stronger spatiotemporal ozone-asthma associations in communities of higher violent crime or deprivation. Notably, violence was the more consistent and significant modifier, potentially mediating a substantial portion of socioeconomic position-related susceptibility.

Hybrid land use regression modeling for estimating spatio-temporal exposures to PM2.5, BC, and metal components across a metropolitan area of complex terrain and industrial sources.

Land use regression (LUR) modeling has become a common method for predicting pollutant concentrations and assigning exposure estimates in epidemiological studies. However, few LUR models have been developed for metal constituents of fine particulate matter (PM2.5) or have incorporated source-specific dispersion covariates in locations with major point sources. We developed hybrid AERMOD LUR models for PM2.5, black carbon (BC), and steel-related PM2.5 constituents lead, manganese, iron, and zinc, using fine-scale air pollution data from 37 sites across the Pittsburgh area. These models were designed with the aim of developing exposure estimates for time periods of interest in epidemiology studies. We found that the hybrid LUR models explained greater variability in PM2.5 (R2 = 0.79) compared to BC (R2 = 0.59) and metal constituents (R2 = 0.34-0.55). Approximately 70% of variation in PM2.5 was attributable to temporal variance, compared to 36% for BC, and 17-26% for metals. An AERMOD dispersion covariate developed using PM2.5 industrial emissions data for 207 sources was significant in PM2.5 and BC models; all metals models contained a steel mill-specific PM2.5 emissions AERMOD term. Other significant covariates included industrial land use, commercial and industrial land use, percent impervious surface, and summed railroad length.

Methods for Evaluating the Combined Effects of Chemical and Nonchemical Exposures for Cumulative Environmental Health Risk Assessment.

Cumulative risk assessment (CRA) has been proposed as a means of evaluating possible additive and synergistic effects of multiple chemical, physical and social stressors on human health, with the goal of informing policy and decision-making, and protecting public health. Routine application of CRA to environmental regulatory and policy decision making, however, has been limited due to a perceived lack of appropriate quantitative approaches for assessing combined effects of chemical and nonchemical exposures. Seven research projects, which represented a variety of disciplines, including population health science, laboratory science, social sciences, geography, statistics and mathematics, were funded by the US Environmental Protection Agency (EPA) to help address this knowledge gap. We synthesize key insights from these unique studies to determine the implications for CRA practice and priorities for further research. Our analyses of these seven projects demonstrate that the necessary analytical methods to support CRA are available but are ultimately context-dependent. These projects collectively provided advancements for CRA in the areas of community engagement, characterization of exposures to nonchemical stressors, and assessment of health effects associated with joint exposures to chemical and psychosocial stressors.

It's not easy assessing greenness: A comparison of NDVI datasets and neighborhood types and their associations with self-rated health in New York City.

Growing evidence suggests that exposure to greenness benefits health, but studies assess greenness differently. We hypothesize greenness-health associations vary by exposure assessment method. To test this, we considered four vegetation datasets (three Normalized Difference Vegetation Index datasets with different spatial resolutions and a finely-resolved land cover dataset), and six aggregation units (five radial buffer sizes and self-described neighborhoods) of each dataset. We compared associations of self-rated health and these metrics of greenness among a sample of New York City residents. Associations with self-rated health varied more by aggregation unit than by vegetation dataset; larger buffers and self-described neighborhoods showed more positive associations. Researchers should consider spatial exposure misclassification in future greenness and health research.

Spatial Patterns in Rush-Hour vs. Work-Week Diesel-Related Pollution across a Downtown Core.

Despite advances in monitoring and modelling of intra-urban variation in multiple pollutants, few studies have attempted to separate spatial patterns by time of day, or incorporated organic tracers into spatial monitoring studies. Due to varying emissions sources from diesel and gasoline vehicular traffic, as well as within-day temporal variation in source mix and intensity (e.g., rush-hours vs. full-day measures), accurately assessing diesel-related air pollution within an urban core can be challenging. We allocated 24 sampling sites across downtown Pittsburgh, Pennsylvania (2.8 km²) to capture fine-scale variation in diesel-related pollutants, and to compare these patterns by sampling interval (i.e., "rush-hours" vs. "work-week" concentrations), and by season. Using geographic information system (GIS)-based methods, we allocated sampling sites to capture spatial variation in key traffic-related pollution sources (i.e., truck, bus, overall traffic densities). Programmable monitors were used to collect integrated work-week and rush-hour samples of fine particulate matter (PM2.5), black carbon (BC), trace elements, and diesel-related organics (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), in summer and winter 2014. Land use regression (LUR) models were created for PM2.5, BC, total elemental carbon (EC), total organic carbon (OC), elemental (Al, Ca, Fe), and organic constituents (total PAHs, total hopanes), and compared by sampling interval and season. We hypothesized higher pollution concentrations and greater spatial contrast in rush-hour, compared to full work-week samples, with variation by season and pollutant. Rush-hour sampling produced slightly higher total PM2.5 and BC concentrations in both seasons, compared to work-week sampling, but no evident difference in spatial patterns. We also found substantial spatial variability in most trace elements and organic compounds, with comparable spatial patterns using both sampling paradigms. Overall, we found higher concentrations of traffic-related trace elements and organic compounds in rush-hour samples, and higher concentrations of coal-related elements (e.g., As, Se) in work-week samples. Mean bus density was the strongest LUR predictor in most models, in both seasons, under each sampling paradigm. Within each season and constituent, the bus-related terms explained similar proportions of variance in the rush-hour and work-week samples. Rush-hour and work-week LUR models explained similar proportions of spatial variation in pollutants, suggesting that the majority of emissions may be produced during rush-hour traffic across downtown. Results suggest that rush-hour emissions may predominantly shape overall spatial variance in diesel-related pollutants.

Is All Urban Green Space the Same? A Comparison of the Health Benefits of Trees and Grass in New York City.

Living near vegetation, often called "green space" or "greenness", has been associated with numerous health benefits. We hypothesized that the two key components of urban vegetation, trees and grass, may differentially affect health. We estimated the association between near-residence trees, grass, and total vegetation (from the 2010 High Resolution Land Cover dataset for New York City (NYC)) with self-reported health from a survey of NYC adults (n = 1281). We found higher reporting of "very good" or "excellent" health for respondents with the highest, compared to the lowest, quartiles of tree (RR = 1.23, 95% CI = 1.06-1.44) but not grass density (relative risk (RR) = 1.00, 95% CI = 0.86-1.17) within 1000 m buffers, adjusting for pertinent confounders. Significant positive associations between trees and self-reported health remained after adjustment for grass, whereas associations with grass remained non-significant. Adjustment for air pollutants increased beneficial associations between trees and self-reported health; adjustment for parks only partially attenuated these effects. Results were null or negative using a 300 m buffer. Findings imply that higher exposure to vegetation, particularly trees outside of parks, may be associated with better health. If replicated, this may suggest that urban street tree planting may improve population health.

Spatial variation in diesel-related elemental and organic PM2.5 components during workweek hours across a downtown core.

Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km2), cross-stratifying to disentangle source impacts (i.e., truck density, bus route frequency, total traffic density). For buses, outbound and inbound trips per week were summed by route and a kernel density was calculated across sites. Programmable monitors collected fine particulate matter (PM2.5) samples specific to workweek hours (Monday-Friday, 7 am-7 pm), summer and winter 2013. Integrated filters were analyzed for black carbon (BC), elemental carbon (EC), organic carbon (OC), elemental constituents, and diesel-related organic compounds [i.e., polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes]. To our knowledge, no studies have collected this suite of pollutants with such high sampling density, with the ability to capture spatial patterns during specific hours of interest. We hypothesized that we would find substantial spatial variation for each pollutant and significant associations with key sources (e.g. diesel and gasoline vehicles), with higher concentrations near the center of this small downtown core. Using a forward stepwise approach, we developed seasonal land use regression (LUR) models for PM2.5, BC, total EC, OC, PAHs, hopanes, steranes, aluminum (Al), calcium (Ca), and iron (Fe). Within this small domain, greater concentration differences were observed in most pollutants across sites, on average, than between seasons. Higher PM2.5 and BC concentrations were found in the downtown core compared to the boundaries. PAHs, hopanes, and steranes displayed different spatial patterning across the study area by constituent. Most LUR models suggested a strong influence of bus-related emissions on pollution gradients. Buses were more dominant predictors compared to truck and vehicular traffic for several pollutants. Overall, we found substantial variation in diesel-related concentrations in a very small downtown area, which varied across elemental and organic components.

Framework for using deciduous tree leaves as biomonitors for intraurban particulate air pollution in exposure assessment.

Fine particulate matter (PM2.5) air pollution, varying in concentration and composition, has been shown to cause or exacerbate adverse effects on both human and ecological health. The concept of biomonitoring using deciduous tree leaves as a proxy for intraurban PM air pollution in different areas has previously been explored using a variety of study designs (e.g., systematic coverage of an area, source-specific focus), deciduous tree species, sampling strategies (e.g., single day, multi-season), and analytical methods (e.g., chemical, magnetic) across multiple geographies and climates. Biomonitoring is a low-cost sampling method and may potentially fill an important gap in current air monitoring methods by providing low-cost, longer-term urban air pollution measures. As such, better understanding of the range of methods, and their corresponding strengths and limitations, is critical for employing the use of tree leaves as biomonitors for pollution to improve spatially resolved exposure assessments for epidemiological studies and urban planning strategies.

Ambient Fine Particulate Matter, Nitrogen Dioxide, and Preterm Birth in New York City.

BACKGROUND: Recent studies have suggested associations between air pollution and various birth outcomes, but the evidence for preterm birth is mixed. OBJECTIVE: We aimed to assess the relationship between air pollution and preterm birth using 2008-2010 New York City (NYC) birth certificates linked to hospital records. METHODS: We analyzed 258,294 singleton births with 22-42 completed weeks gestation to nonsmoking mothers. Exposures to ambient fine particles (PM2.5) and nitrogen dioxide (NO2) during the first, second, and cumulative third trimesters within 300 m of maternal address were estimated using data from the NYC Community Air Survey and regulatory monitors. We estimated the odds ratio (OR) of spontaneous preterm (gestation < 37 weeks) births for the first- and second-trimester exposures in a logistic mixed model, and the third-trimester cumulative exposures in a discrete time survival model, adjusting for maternal characteristics and delivery hospital. Spatial and temporal components of estimated exposures were also separately analyzed. RESULTS: PM2.5 was not significantly associated with spontaneous preterm birth. NO2 in the second trimester was negatively associated with spontaneous preterm birth in the adjusted model (OR = 0.90; 95% CI: 0.83, 0.97 per 20 ppb). Neither pollutant was significantly associated with spontaneous preterm birth based on adjusted models of temporal exposures, whereas the spatial exposures showed significantly reduced odds ratios (OR = 0.80; 95% CI: 0.67, 0.96 per 10 µg/m3 PM2.5 and 0.88; 95% CI: 0.79, 0.98 per 20 ppb NO2). Without adjustment for hospital, these negative associations were stronger. CONCLUSION: Neither PM2.5 nor NO2 was positively associated with spontaneous preterm delivery in NYC. Delivery hospital was an important spatial confounder. CITATION: Johnson S, Bobb JF, Ito K, Savitz DA, Elston B, Shmool JL, Dominici F, Ross Z, Clougherty JE, Matte T. 2016. Ambient fine particulate matter, nitrogen dioxide, and preterm birth in New York City. Environ Health Perspect 124:1283-1290; http://dx.doi.org/10.1289/ehp.1510266.

Spatial patterning in PM2.5 constituents under an inversion-focused sampling design across an urban area of complex terrain.

Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during "frequent inversion" hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling.

Spatial variation in inversion-focused vs 24-h integrated samples of PM2.5 and black carbon across Pittsburgh, PA.

A growing literature explores intra-urban variation in pollution concentrations. Few studies, however, have examined spatial variation during "peak" hours of the day (e.g., rush hours, inversion conditions), which may have strong bearing for source identification and epidemiological analyses. We aimed to capture "peak" spatial variation across a region of complex terrain, legacy industry, and frequent atmospheric inversions. We hypothesized stronger spatial contrast in concentrations during hours prone to atmospheric inversions and heavy traffic, and designed a 2-year monitoring campaign to capture spatial variation in fine particles (PM2.5) and black carbon (BC). Inversion-focused integrated monitoring (0600-1100 hours) was performed during year 1 (2011-2012) and compared with 1-week 24-h integrated results from year 2 (2012-2013). To allocate sampling sites, we explored spatial distributions in key sources (i.e., traffic, industry) and potential modifiers (i.e., elevation) in geographic information systems (GIS), and allocated 37 sites for spatial and source variability across the metropolitan domain (~388 km(2)). Land use regression (LUR) models were developed and compared by pollutant, season, and sampling method. As expected, we found stronger spatial contrasts in PM2.5 and BC using inversion-focused sampling, suggesting greater differences in peak exposures across urban areas than is captured by most integrated saturation campaigns. Temporal variability, commercial and industrial land use, PM2.5 emissions, and elevation were significant predictors, but did not more strongly predict concentrations during peak hours.

Area-level socioeconomic deprivation, nitrogen dioxide exposure, and term birth weight in New York City.

Numerous studies have linked air pollution with adverse birth outcomes, but relatively few have examined differential associations across the socioeconomic gradient. To evaluate interaction effects of gestational nitrogen dioxide (NO2) and area-level socioeconomic deprivation on fetal growth, we used: (1) highly spatially-resolved air pollution data from the New York City Community Air Survey (NYCCAS); and (2) spatially-stratified principle component analysis of census variables previously associated with birth outcomes to define area-level deprivation. New York City (NYC) hospital birth records for years 2008-2010 were restricted to full-term, singleton births to non-smoking mothers (n=243,853). We used generalized additive mixed models to examine the potentially non-linear interaction of nitrogen dioxide (NO2) and deprivation categories on birth weight (and estimated linear associations, for comparison), adjusting for individual-level socio-demographic characteristics and sensitivity testing adjustment for co-pollutant exposures. Estimated NO2 exposures were highest, and most varying, among mothers residing in the most-affluent census tracts, and lowest among mothers residing in mid-range deprivation tracts. In non-linear models, we found an inverse association between NO2 and birth weight in the least-deprived and most-deprived areas (p-values<0.001 and 0.05, respectively) but no association in the mid-range of deprivation (p=0.8). Likewise, in linear models, a 10 ppb increase in NO2 was associated with a decrease in birth weight among mothers in the least-deprived and most-deprived areas of -16.2g (95% CI: -21.9 to -10.5) and -11.0 g (95% CI: -22.8 to 0.9), respectively, and a non-significant change in the mid-range areas [ß=0.5 g (95% CI: -7.7 to 8.7)]. Linear slopes in the most- and least-deprived quartiles differed from the mid-range (reference group) (p-values<0.001 and 0.09, respectively). The complex patterning in air pollution exposure and deprivation in NYC, however, precludes simple interpretation of interactive effects on birth weight, and highlights the importance of considering differential distributions of air pollution concentrations, and potential differences in susceptibility, across deprivation levels.

Ambient Fine Particulate Matter, Nitrogen Dioxide, and Hypertensive Disorders of Pregnancy in New York City.

BACKGROUND: Previous studies suggested a possible association between fine particulate matter air pollution (PM2.5) and nitrogen dioxide (NO2) and the development of hypertensive disorders of pregnancy, but effect sizes have been small and methodologic weaknesses preclude firm conclusions. METHODS: We linked birth certificates in New York City in 2008-2010 to hospital discharge diagnoses and estimated air pollution exposure based on maternal address. The New York City Community Air Survey provided refined estimates of PM2.5 and NO2 at the maternal residence. We estimated the association between exposures to PM2.5 and NO2 in the first and second trimester and risk of gestational hypertension, mild preeclampsia, and severe preeclampsia among 268,601 births. RESULTS: In unadjusted analyses, we found evidence of a positive association between both pollutants and gestational hypertension. However, after adjustment for individual covariates, socioeconomic deprivation, and delivery hospital, we did not find evidence of an association between PM2.5 or NO2 in the first or second trimester and any of the outcomes. CONCLUSIONS: Our data did not provide clear evidence of an effect of ambient air pollution on hypertensive disorders of pregnancy. Results need to be interpreted with caution considering the quality of the available exposure and health outcome measures and the uncertain impact of adjusting for hospital. Relative to previous studies, which have tended to identify positive associations with PM2.5 and NO2, our large study size, refined air pollution exposure estimates, hospital-based disease ascertainment, and little risk of confounding by socioeconomic deprivation, does not provide evidence for an association.

Identifying Perceived Neighborhood Stressors Across Diverse Communities in New York City.

There is growing interest in the role of psychosocial stress in health disparities. Identifying which social stressors are most important to community residents is critical for accurately incorporating stressor exposures into health research. Using a community-academic partnered approach, we designed a multi-community study across the five boroughs of New York City to characterize resident perceptions of key neighborhood stressors. We conducted 14 community focus groups; two to three in each borough, with one adolescent group and one Spanish-speaking group per borough. We then used systematic content analysis and participant ranking data to describe prominent neighborhood stressors and identify dominant themes. Three inter-related themes regarding the social and structural sources of stressful experiences were most commonly identified across neighborhoods: (1) physical disorder and perceived neglect, (2) harassment by police and perceived safety and (3) gentrification and racial discrimination. Our findings suggest that multiple sources of distress, including social, political, physical and economic factors, should be considered when investigating health effects of community stressor exposures and psychological distress. Community expertise is essential for comprehensively characterizing the range of neighborhood stressors that may be implicated in psychosocial exposure pathways.

Engaging Communities in Research on Cumulative Risk and Social Stress-Environment Interactions: Lessons Learned from EPA's STAR Program.

Studies have documented cumulative health effects of chemical and nonchemical exposures, particularly chronic environmental and social stressors. Environmental justice groups have advocated for community participation in research that assesses how these interactions contribute to health disparities experienced by low-income and communities of color. In 2009, the U.S. Environmental Protection Agency issued a request for research applications (RFA), "Understanding the Role of Nonchemical Stressors and Developing Analytic Methods for Cumulative Risk Assessments." Seven research projects were funded to help address this knowledge gap. Each engaged with communities in different ways. We describe the community engagement approaches of the seven research projects, which ranged from outreach through shared leadership/participatory. We then assess the experiences of these programs with respect to the community engagement goals of the RFA. We present insights from these community engagement efforts, including how the grants helped to build or enhance the capacity of community organizations in addition to contributing to the research projects. Our analysis of project proposals, annual grantee reports, and participant observation of these seven projects suggests guidelines for the development of future funding mechanisms and for conducting community-engaged research on cumulative risk involving environmental and social stressors including: 1) providing for flexibility in the mode of community engagement; 2) addressing conflict between research timing and engagement needs, 3) developing approaches for communicating about the uniquely sensitive issues of nonchemical stressors and social risks; and 4) encouraging the evaluation of community engagement efforts.

Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures.

BACKGROUND: Recent toxicological and epidemiological evidence suggests that chronic psychosocial stress may modify pollution effects on health. Thus, there is increasing interest in refined methods for assessing and incorporating non-chemical exposures, including social stressors, into environmental health research, towards identifying whether and how psychosocial stress interacts with chemical exposures to influence health and health disparities. We present a flexible, GIS-based approach for examining spatial patterns within and among a range of social stressors, and their spatial relationships with air pollution, across New York City, towards understanding their combined effects on health. METHODS: We identified a wide suite of administrative indicators of community-level social stressors (2008-2010), and applied simultaneous autoregressive models and factor analysis to characterize spatial correlations among social stressors, and between social stressors and air pollutants, using New York City Community Air Survey (NYCCAS) data (2008-2009). Finally, we provide an exploratory ecologic analysis evaluating possible modification of the relationship between nitrogen dioxide (NO2) and childhood asthma Emergency Department (ED) visit rates by social stressors, to demonstrate how the methods used to assess stressor exposure (and/or consequent psychosocial stress) may alter model results. RESULTS: Administrative indicators of a range of social stressors (e.g., high crime rate, residential crowding rate) were not consistently correlated (rho = - 0.44 to 0.89), nor were they consistently correlated with indicators of socioeconomic position (rho = - 0.54 to 0.89). Factor analysis using 26 stressor indicators suggested geographically distinct patterns of social stressors, characterized by three factors: violent crime and physical disorder, crowding and poor access to resources, and noise disruption and property crimes. In an exploratory ecologic analysis, these factors were differentially associated with area-average NO2 and childhood asthma ED visits. For example, only the 'violent crime and disorder' factor was significantly associated with asthma ED visits, and only the 'crowding and resource access' factor modified the association between area-level NO2 and asthma ED visits. CONCLUSIONS: This spatial approach enabled quantification of complex spatial patterning and confounding between chemical and non-chemical exposures, and can inform study design for epidemiological studies of separate and combined effects of multiple urban exposures.