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California faces several serious direct and indirect climate exposures that can adversely affect public health, some of which are already occurring. The public health burden now and in the future will depend on atmospheric greenhouse gas concentrations, underlying population vulnerabilities, and adaptation efforts. Here, we present a structured review of recent literature to examine the leading climate risks to public health in California, including extreme heat, extreme precipitation, wildfires, air pollution, and infectious diseases. Comparisons among different climate-health pathways are difficult due to inconsistencies in study design regarding spatial and temporal scales and health outcomes examined. We find, however, that the current public health burden likely affects thousands of Californians each year, depending on the exposure pathway and health outcome. Further, while more evidence exists for direct and indirect proximal health effects that are the focus of this review, distal pathways (e.g., impacts of drought on nutrition) are more uncertain but could add to this burden. We find that climate adaptation measures can provide significant health benefits, particularly in disadvantaged communities. We conclude with priority recommendations for future analyses and solution-driven policy actions.

BackgroundBoth air pollution and noise exposures have separately been shown to affect cognitive impairment. Here, we examine how air pollution and noise exposures interact to influence the development of incident dementia or cognitive impairment without dementia (CIND).MethodsWe used 1,612 Mexican American participants from the Sacramento Area Latino Study on Aging conducted from 1998 to 2007. Air pollution (nitrogen dioxides, particulate matter, ozone) and noise exposure levels were modeled with a land-use regression and via the SoundPLAN software package implemented with the Traffic Noise Model applied to the greater Sacramento area, respectively. Using Cox proportional hazard models, we estimated the hazard of incident dementia or CIND from air pollution exposure at the residence up to 5-years prior to diagnosis for the members of each risk set at event time. Further, we investigated whether noise exposure modified the association between air pollution exposure and dementia or CIND.ResultsIn total, 104 incident dementia and 159 incident dementia/CIND cases were identified during the 10 years of follow-up. For each ∼2 µg/m3 increase in time-varying 1- and 5-year average PM2.5 exposure, the hazard of dementia increased 33% (HR = 1.33, 95%CI: 1.00, 1.76). The hazard ratios for NO2-related dementia/CIND and PM2.5-related dementia were stronger in high-noise (≥65 dB) exposed than low-noise (

BackgroundRecently, it has been suggested that environmental exposures from traffic sources including noise may play a role in cognitive impairment in the elderly. The objective of the study was to investigate the association between local traffic-related noise pollution and incident dementia or cognitive impairment without dementia (CIND) during a 10-year follow-up period.Methods1612 Mexican-American participants from the Sacramento Area Latino Study on Aging (SALSA) were followed every 12-15 months via home visits from 1998 to 2007. We used the SoundPLAN software package to estimate noise originating from local traffic with the input of Annual Average Daily Traffic (AADT) data from Metropolitan Planning Organizations (MPO) based on geocoded residential addresses at baseline (1998-1999). We estimated the risks of incident dementia or CIND from 24-hour and nighttime noise exposure using Cox proportional hazard models.ResultsDuring the follow-up, we identified 159 incident dementia or CIND cases in total. Per 11.6 dB (interquartile range width) increase in 24-hour noise, the hazard of developing dementia or CIND increased (hazard ratio = 1.3 [1.0, 1.6]) during follow-up; estimates were slightly lower (hazard ratio = 1.2 [0.97, 1.6]) when adjusting for modeled local air pollution exposure from traffic sources. Overall, the risk of dementia/CIND was elevated when 24-hour and nighttime noise were higher than 75 and 65 dB respectively. See video Abstract: http://links.lww.com/EDE/B728.ConclusionsIn our study, traffic-related noise exposure was associated with increased risk of dementia or CIND in elderly Mexican-Americans. Future studies taking into account other noise sources and occupational noise exposure before retirement are needed.

BackgroundAmbient air pollution is a modifiable risk factor for cardiovascular disease, yet uncertainty remains about the size of risks at lower levels of fine particulate matter (PM2.5) exposure which now occur in the USA and elsewhere.MethodsWe investigated the relationship of ambient PM2.5 exposure with cause-specific cardiovascular disease mortality in 565 477 men and women, aged 50 to 71 years, from the National Institutes of Health-AARP Diet and Health Study. During 7.5 x 106 person-years of follow up, 41 286 cardiovascular disease deaths, including 23 328 ischaemic heart disease (IHD) and 5894 stroke deaths, were ascertained using the National Death Index. PM2.5 was estimated using a hybrid land use regression (LUR) geostatistical model. Multivariate Cox regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CI).ResultsEach increase of 10  μg/m3 PM2.5 (overall range, 2.9-28.0  μg/m3) was associated, in fully adjusted models, with a 16% increase in mortality from ischaemic heart disease [hazard ratio (HR) 1.16; 95% CI 1.09-1.22] and a 14% increase in mortality from stroke (HR 1.14; CI 1.02-1.27). Compared with PM2.5 exposure

Cognitive impairment has been linked to traffic-related air pollution and noise exposure as well as to metabolic syndrome or some of its individual components. Here, we investigate whether the presence of metabolic dysfunction modifies associations between air pollution or noise exposures and incident dementia or cognitive impairment without dementia (CIND).MethodsFor 1,612 elderly Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) followed for up to 10 years, we estimated residential-based local traffic-related exposures relying on the California Line Source Dispersion Model version 4 (CALINE4) for nitrogen oxides (NOx) and the SoundPLAN software package (Version 8.0; NAVCON, Fullerton, CA) that implements the Federal Highway Administration Traffic Noise Model (TNM) for noise, respectively. We used Cox proportional hazard models to estimate the joint effects of NOx or noise exposures and obesity, hyperglycemia, or low high-density lipoprotein (HDL) cholesterol.ResultsThe risk of developing dementia/CIND among participants with hyperglycemia who also were exposed to high levels of NOx (≥3.44 parts per billion [ppb] [75th percentile]) or noise (≥65 dB) was 2.4 (1.4, 4.0) and 2.2 (1.7, 3.9), respectively. For participants with low HDL-cholesterol, the estimated hazard ratios for dementia/CIND were 2.5 (1.4, 4.3) and 1.8 (1.0, 3.0) for those also exposed to high levels of NOx (≥3.44 ppb) or noise (≥65 dB), respectively, compared with those without metabolic dysfunction exposed to low traffic-related air pollution or noise levels.ConclusionsExposure to traffic-related air pollution or noise most strongly increases the risk of dementia/CIND among older Mexican-Americans living in California who also exhibit hyperglycemia or low HDL-cholesterol.

BackgroundPrevious studies suggested that air pollutants may increase the incidence of metabolic syndrome, but the potential impact from traffic sources is not well-understood. This study aimed to investigate associations between traffic-related nitrogen oxides (NOx) or noise pollution and risk of incident metabolic syndrome and its components in an elderly Mexican-American population.MethodsA total of 1,554 Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) cohort were followed from 1998 to 2007. We used anthropometric measures and biomarkers to define metabolic syndrome according to the recommendations of the Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP ATP III). Based on participants' residential addresses at baseline, estimates of local traffic-related NOx were generated using the California Line Source Dispersion Model version 4 (CALINE4), and of noise employing the SoundPLAN software package. We used Cox regression models with calendar time as the underlying time scale to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of air pollution or noise with metabolic syndrome or its components.ResultsEach per unit increase of traffic-related NOx (2.29 parts per billion (ppb)) was associated with a 15% (HR = 1.15, 95% CI: 1.04-1.28) lower level of high-density lipoprotein cholesterol (HDL-cholesterol), and each 11.6 decibels (dB) increase in noise increased the risk of developing metabolic syndrome by 17% (HR = 1.17, 95% CI: 1.01-1.35).ConclusionPolicies aiming to reduce traffic-related air pollution and noise might mitigate the risk of metabolic syndrome and its components in vulnerable populations.

Environmental triggers is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, novel technologies, precision medicine and pragmatic clinical research. The Challenges in IBD research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the environmental triggers section is focused on the main research gaps in elucidating causality of environmental factors in IBD. Research gaps were identified in: 1) epidemiology of exposures; 2) identification of signatures of biological response to exposures; and 3) mechanisms of how environmental exposures drive IBD. To address these gaps, the implementation of longitudinal prospective studies to determine disease evolution and identify sub-clinical changes in response to exposures is proposed. This can help define critical windows of vulnerability and risk prediction. In addition, systems biology analysis and in silico modeling were proposed as approaches to integrate the IBD exposome for the identification of biological signatures of response to exposures, and to develop prediction models of the effects of environmental factors in driving disease activity and response to therapy. This research could lead to identification of biomarkers of exposures and new modalities for therapeutic intervention. Finally, hypothesis-driven mechanistic studies to understand gene-environment interactions and to validate causality of priority factors should be performed to determine how environment influences clinical outcomes.

Purpose of reviewA number of studies over the past two decades have suggested that type 2 diabetes mellitus (T2DM) patients are at an increased risk of Alzheimer's disease (AD). Several common molecular pathways to cellular and metabolic dysfunction have been implicated in the etiology of both diseases. Here, we review the emerging evidence from observational studies that investigate the relationship between T2DM and AD, and of shared environmental risk factors, specifically air pollution and pesticides, associated with both chronic disorders.Recent findingsParticulate matter and traffic-related air pollution have been widely associated with T2DM, and multiple studies have associated exposures with AD or cognitive function. Organochlorine (OC) and organophosphate (OP) pesticides have been associated with T2DM in multiple independent populations. Two populations have observed increased risks for OC and OP exposures and AD. Other studies, limited in exposure assessment, have reported increased risk of AD with any pesticide exposure assessments. This may suggest shared pathogenic pathways between environmental risk factors, T2DM, and AD. Research focusing on exposures related to both T2DM and AD could provide new disease insights on shared mechanisms and help shape innovative preventative measures and policy decisions.

BackgroundLittle is known about the long-term health effects of ambient ultrafine particles (

BackgroundThe International Agency for Research on Cancer classified both outdoor air pollution and airborne particulate matter as carcinogenic to humans (Group 1) for lung cancer. There may be associations with cancer at other sites; however, the epidemiological evidence is limited.ObjectiveThe aim of this study was to clarify whether ambient air pollution is associated with specific types of cancer other than lung cancer by examining associations of ambient air pollution with nonlung cancer death in the Cancer Prevention Study II (CPS-II).MethodsAnalysis included 623,048 CPS-II participants who were followed for 22 y (1982-2004). Modeled estimates of particulate matter with aerodynamic diameter

BackgroundOzone 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.MethodsWe 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.ResultsThe 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

BackgroundRemote 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 (PM2.5) is associated with mortality.ObjectivesWe compared relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information.MethodsWe 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 PM2.5 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 PM2.5 with circulatory mortality and ischemic heart disease.ResultsEstimates 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 PM2.5]. 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 PM2.5).ConclusionsWe found significant associations between PM2.5 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.

BackgroundIncome, air pollution, obesity, and smoking are primary factors associated with human health and longevity in population-based studies. These four factors may have countervailing impacts on longevity. This analysis investigates longevity trade-offs between air pollution and income, and explores how relative effects of income and air pollution on human longevity are potentially influenced by accounting for smoking and obesity.MethodsCounty-level data from 2,996 U.S. counties were analyzed in a cross-sectional analysis to investigate relationships between longevity and the four factors of interest: air pollution (mean 1999-2008 PM2.5), median income, smoking, and obesity. Two longevity measures were used: life expectancy (LE) and an exceptional aging (EA) index. Linear regression, generalized additive regression models, and bivariate thin-plate smoothing splines were used to estimate the benefits of living in counties with higher incomes or lower PM2.5. Models were estimated with and without controls for smoking, obesity, and other factors.ResultsModels which account for smoking and obesity result in substantially smaller estimates of the effects of income and pollution on longevity. Linear regression models without these two variables estimate that a $1,000 increase in median income (1 μg/m(3) decrease in PM2.5) corresponds to a 27.39 (33.68) increase in EA and a 0.14 (0.12) increase in LE, whereas models that control for smoking and obesity estimate only a 12.32 (20.22) increase in EA and a 0.07 (0.05) increase in LE. Nonlinear models and thin-plate smoothing splines also illustrate that, at higher levels of income, the relative benefits of the income-pollution tradeoff changed-the benefit of higher incomes diminished relative to the benefit of lower air pollution exposure.ConclusionsHigher incomes and lower levels of air pollution both correspond with increased human longevity. Adjusting for smoking and obesity reduces estimates of the benefits of higher income and lower air pollution exposure. This adjustment also alters the tradeoff between income and pollution: increases in income become less beneficial relative to a fixed reduction in air pollution-especially at higher levels of income.

IntroductionSubstantial 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.MethodsThis 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.ResultsThere 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).ConclusionsThe results do not provide support for an association of air pollution with weight gain in African-American adult women.

BackgroundSurvivors of acute myocardial infarction (AMI) are at increased risk of dying within several hours to days following exposure to elevated levels of ambient air pollution. Little is known, however, about the influence of long-term (months to years) air pollution exposure on survival after AMI.ObjectiveWe conducted a population-based cohort study to determine the impact of long-term exposure to fine particulate matter ≤ 2.5 μm in diameter (PM2.5) on post-AMI survival.MethodsWe assembled a cohort of 8,873 AMI patients who were admitted to 1 of 86 hospital corporations across Ontario, Canada in 1999-2001. Mortality follow-up for this cohort extended through 2011. Cumulative time-weighted exposures to PM2.5 were derived from satellite observations based on participants' annual residences during follow-up. We used standard and multilevel spatial random-effects Cox proportional hazards models and adjusted for potential confounders.ResultsBetween 1999 and 2011, we identified 4,016 nonaccidental deaths, of which 2,147 were from any cardiovascular disease, 1,650 from ischemic heart disease, and 675 from AMI. For each 10-μg/m3 increase in PM2.5, the adjusted hazard ratio (HR10) of nonaccidental mortality was 1.22 [95% confidence interval (CI): 1.03, 1.45]. The association with PM2.5 was robust to sensitivity analyses and appeared stronger for cardiovascular-related mortality: ischemic heart (HR10 = 1.43; 95% CI: 1.12, 1.83) and AMI (HR10 = 1.64; 95% CI: 1.13, 2.40). We estimated that 12.4% of nonaccidental deaths (or 497 deaths) could have been averted if the lowest measured concentration in an urban area (4 μg/m3) had been achieved at all locations over the course of the study.ConclusionsLong-term air pollution exposure adversely affects the survival of AMI patients.CitationChen H, Burnett RT, Copes R, Kwong JC, Villeneuve PJ, Goldberg MS, Brook RD, van Donkelaar A, Jerrett M, Martin RV, Brook JR, Kopp A, Tu JV. 2016. Ambient fine particulate matter and mortality among survivors of myocardial infarction: population-based cohort study. Environ Health Perspect 124:1421-1428; http://dx.doi.org/10.1289/EHP185.

BackgroundFine particulate matter (PM2.5) air pollution exposure has been identified as a global health threat. However, the types and sources of particles most responsible are not yet known.ObjectivesWe sought to identify the causal characteristics and sources of air pollution underlying past associations between long-term PM2.5 exposure and ischemic heart disease (IHD) mortality, as established in the American Cancer Society's Cancer Prevention Study-II cohort.MethodsIndividual risk factor data were evaluated for 445,860 adults in 100 U.S. metropolitan areas followed from 1982 through 2004 for vital status and cause of death. Using Cox proportional hazard models, we estimated IHD mortality hazard ratios (HRs) for PM2.5, trace constituents, and pollution source-associated PM2.5, as derived from air monitoring at central stations throughout the nation during 2000-2005.ResultsAssociations with IHD mortality varied by PM2.5 mass constituent and source. A coal combustion PM2.5 IHD HR = 1.05 (95% CI: 1.02, 1.08) per microgram/cubic meter, versus an IHD HR = 1.01 (95% CI: 1.00, 1.02) per microgram/cubic meter PM2.5 mass, indicated a risk roughly five times higher for coal combustion PM2.5 than for PM2.5 mass in general, on a per microgram/cubic meter PM2.5 basis. Diesel traffic-related elemental carbon (EC) soot was also associated with IHD mortality (HR = 1.03; 95% CI: 1.00, 1.06 per 0.26-μg/m3 EC increase). However, PM2.5 from both wind-blown soil and biomass combustion was not associated with IHD mortality.ConclusionsLong-term PM2.5 exposures from fossil fuel combustion, especially coal burning but also from diesel traffic, were associated with increases in IHD mortality in this nationwide population. Results suggest that PM2.5-mortality associations can vary greatly by source, and that the largest IHD health benefits per microgram/cubic meter from PM2.5 air pollution control may be achieved via reductions of fossil fuel combustion exposures, especially from coal-burning sources.CitationThurston GD, Burnett RT, Turner MC, Shi Y, Krewski D, Lall R, Ito K, Jerrett M, Gapstur SM, Diver WR, Pope CA III. 2016. Ischemic heart disease mortality and long-term exposure to source-related components of U.S. fine particle air pollution. Environ Health Perspect 124:785-794; http://dx.doi.org/10.1289/ehp.1509777.

BackgroundNumerous studies have examined associations between air pollution and pregnancy outcomes, but most have been restricted to urban populations living near monitors.ObjectivesWe examined the association between pregnancy outcomes and fine particulate matter in a large national study including urban and rural areas.MethodsAnalyses were based on approximately 3 million singleton live births in Canada between 1999 and 2008. Exposures to PM2.5 (particles of median aerodynamic diameter ≤ 2.5 μm) were assigned by mapping the mother's postal code to a monthly surface based on a national land use regression model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Generalized estimating equations were used to examine the association between PM2.5 and preterm birth (gestational age < 37 weeks), term low birth weight (< 2,500 g), small for gestational age (SGA; < 10th percentile of birth weight for gestational age), and term birth weight, adjusting for individual covariates and neighborhood socioeconomic status (SES).ResultsIn fully adjusted models, a 10-μg/m(3) increase in PM2.5 over the entire pregnancy was associated with SGA (odds ratio = 1.04; 95% CI 1.01, 1.07) and reduced term birth weight (-20.5 g; 95% CI -24.7, -16.4). Associations varied across subgroups based on maternal place of birth and period (1999-2003 vs. 2004-2008).ConclusionsThis study, based on approximately 3 million births across Canada and employing PM2.5 estimates from a national spatiotemporal model, provides further evidence linking PM2.5 and pregnancy outcomes.

Numerous observational studies have assessed the association between ambient air pollution and chronic disease incidence, but there is no uniform approach to create an exposure metric that captures the variability in air pollution through time and determines the most relevant exposure window. The purpose of the present study was to assess ways of modeling exposure to air pollution in relation to incident hypertension. We simulated data on incident hypertension to assess the performance of six air pollution exposure metrics, using characteristics from the Black Women's Health Study. Each metric made different assumptions about how to incorporate time trends in pollutant data, and the most relevant window of exposure. We use observed values for particulate matter ≤ 2.5 microns (PM2.5) for this cohort to create the six exposure metrics and fit Cox proportional hazards models to the simulated data using the six metrics. The optimal exposure metric depends on the underlying association between PM2.5 and disease, which is unknown. Metrics that incorporate exposure information from multiple years tend to be more robust and suffer from less bias. This study provides insight into factors that influence the metric used to quantifying exposure to PM2.5 and suggests the need for careful sensitivity analyses.

BackgroundClinical 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.MethodsWe 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.ResultsDuring 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.ConclusionsOur results provide little support for an association of PM2.5 with diabetes or hypertension incidence.

BackgroundFew studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up.ObjectiveWe investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians.MethodsWe assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects' residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2.ResultsPM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2.ConclusionsIn this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2.CitationCrouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180-1186; http://dx.doi.org/10.1289/ehp.1409276.

The independent and joint effects of within- and between-city contrasts in air pollution on mortality have been investigated rarely. To examine the differential effects of between- versus within-city contrasts in pollution exposure, we used both ambient measurements and land use regression models to assess associations with mortality and exposure to nitrogen dioxide (NO2) among ~735,600 adults in 10 of the largest Canadian cities. We estimated exposure contrasts partitioned into within- and between-city contrasts, and the sum of these as overall exposures, for every year from 1984 to 2006. Residential histories allowed us to follow subjects annually during the study period. We calculated hazard ratios (HRs) adjusted for many personal and contextual variables. In fully-adjusted, random-effects models, we found positive associations between overall NO2 exposures and mortality from non-accidental causes (HR per 5 p.p.b.: 1.05; 95% confidence interval (CI): 1.03-1.07), cardiovascular disease (HR per 5 p.p.b.: 1.04; 95% CI: 1.01-1.06), ischaemic heart disease (HR per 5 p.p.b.: 1.05; 95% CI: 1.02-1.08) and respiratory disease (HR per 5 p.p.b.: 1.04; 95% CI: 0.99-1.08), but not from cerebrovascular disease (HR per 5 p.p.b.: 1.01; 95% CI: 0.96-1.06). We found that most of these associations were determined by within-city contrasts, as opposed to by between-city contrasts in NO2. Our results suggest that variation in NO2 concentrations within a city may represent a more toxic mixture of pollution than variation between cities.

BackgroundChildhood body mass index (BMI) and obesity prevalence have been associated with exposure to secondhand smoke (SHS), maternal smoking during pregnancy, and vehicular air pollution. There has been little previous study of joint BMI effects of air pollution and tobacco smoke exposure.MethodsInformation on exposure to SHS and maternal smoking during pregnancy was collected on 3,318 participants at enrollment into the Southern California Children's Health Study. At study entry at average age of 10 years, residential near-roadway pollution exposure (NRP) was estimated based on a line source dispersion model accounting for traffic volume, proximity, and meteorology. Lifetime exposure to tobacco smoke was assessed by parent questionnaire. Associations with subsequent BMI growth trajectory based on annual measurements and attained BMI at 18 years of age were assessed using a multilevel modeling strategy.ResultsMaternal smoking during pregnancy was associated with estimated BMI growth over 8-year follow-up (0.72 kg/m2 higher; 95% CI: 0.14, 1.31) and attained BMI (1.14 kg/m2 higher; 95% CI: 0.66, 1.62). SHS exposure before enrollment was positively associated with BMI growth (0.81 kg/m2 higher; 95% CI: 0.36, 1.27) and attained BMI (1.23 kg/m2 higher; 95% CI: 0.86, 1.61). Growth and attained BMI increased with more smokers in the home. Compared with children without a history of SHS and NRP below the median, attained BMI was 0.80 kg/m2 higher (95% CI: 0.27, 1.32) with exposure to high NRP without SHS; 0.85 kg/m2 higher (95% CI: 0.43, 1.28) with low NRP and a history of SHS; and 2.15 kg/m2 higher (95% CI: 1.52, 2.77) with high NRP and a history of SHS (interaction p-value 0.007). These results suggest a synergistic effect.ConclusionsOur findings strengthen emerging evidence that exposure to tobacco smoke and NRP contribute to development of childhood obesity and suggest that combined exposures may have synergistic effects.

Exposure to air pollution during pregnancy has been linked to the risk of childhood cancer, but the evidence remains inconclusive. In the present study, we used land use regression modeling to estimate prenatal exposures to traffic exhaust and evaluate the associations with cancer risk in very young children. Participants in the Air Pollution and Childhood Cancers Study who were 5 years of age or younger and diagnosed with cancer between 1988 and 2008 were had their records linked to California birth certificates, and controls were selected from birth certificates. Land use regression-based estimates of exposures to nitric oxide, nitrogen dioxide, and nitrogen oxides were assigned based on birthplace residence and temporally adjusted using routine monitoring station data to evaluate air pollution exposures during specific pregnancy periods. Logistic regression models were adjusted for maternal age, race/ethnicity, educational level, parity, insurance type, and Census-based socioeconomic status, as well as child's sex and birth year. The odds of acute lymphoblastic leukemia increased by 9%, 23%, and 8% for each 25-ppb increase in average nitric oxide, nitrogen dioxide, and nitrogen oxide levels, respectively, over the entire pregnancy. Second- and third-trimester exposures increased the odds of bilateral retinoblastoma. No associations were found for annual average exposures without temporal components or for any other cancer type. These results lend support to a link between prenatal exposure to traffic exhaust and the risk of acute lymphoblastic leukemia and bilateral retinoblastoma.

BackgroundChronic exposure to traffic-related air pollution (TRAP) may contribute to premature mortality, but few studies to date have addressed this topic.ObjectivesIn this study we assessed the association between TRAP and mortality in Toronto, Ontario, Canada.MethodsWe collected nitrogen dioxide samples over two seasons using duplicate two-sided Ogawa passive diffusion samplers at 143 locations across Toronto. We calibrated land use regressions to predict NO2 exposure on a fine scale within Toronto. We used interpolations to predict levels of particulate matter with aerodynamic diameter < or = 2.5 microm (PM(2.5)) and ozone levels. We assigned predicted pollution exposures to 2,360 subjects from a respiratory clinic, and abstracted health data on these subjects from medical billings, lung function tests, and diagnoses by pulmonologists. We tracked mortality between 1992 and 2002. We used standard and multilevel Cox proportional hazard models to test associations between air pollution and mortality.ResultsAfter controlling for age, sex, lung function, obesity, smoking, and neighborhood deprivation, we observed a 17% increase in all-cause mortality and a 40% increase in circulatory mortality from an exposure contrast across the interquartile range of 4 ppb NO2. We observed no significant associations with other pollutants.ConclusionsExposure to TRAP was significantly associated with increased all-cause and circulatory mortality in this cohort. A high prevalence of cardiopulmonary disease in the cohort probably limits inference of the findings to populations with a substantial proportion of susceptible individuals.

Low cost, personal air pollution sensors may reduce exposure measurement errors in epidemiological investigations and contribute to citizen science initiatives. Here we assess the validity of a low cost personal air pollution sensor. Study participants were drawn from two ongoing epidemiological projects in Barcelona, Spain. Participants repeatedly wore the pollution sensor − which measured carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO 2 ). We also compared personal sensor measurements to those from more expensive instruments. Our personal sensors had moderate to high correlations with government monitors with averaging times of 1-h and 30-min epochs (r ~ 0.38–0.8) for NO and CO, but had low to moderate correlations with NO 2 (~0.04–0.67). Correlations between the personal sensors and more expensive research instruments were higher than with the government monitors. The sensors were able to detect high and low air pollution levels in agreement with expectations (e.g., high levels on or near busy roadways and lower levels in background residential areas and parks). Our findings suggest that the low cost, personal sensors have potential to reduce exposure measurement error in epidemiological studies and provide valid data for citizen science studies. [ABSTRACT FROM AUTHOR]

Exposure to green space has been associated with better physical and mental health. Although this exposure could also influence cognitive development in children, available epidemiological evidence on such an impact is scarce. This study aimed to assess the association between exposure to green space and measures of cognitive development in primary school children. This study was based on 2,593 school children in the second to fourth grades (7-10 y) of 36 primary schools in Barcelona, Spain (2012-2013). Cognitive development was assessed as 12-mo change in developmental trajectory of working memory, superior working memory, and inattentiveness by using four repeated (every 3 mo) computerized cognitive tests for each outcome. We assessed exposure to green space by characterizing outdoor surrounding greenness at home and school and during commuting by using high-resolution (5 m × 5 m) satellite data on greenness (normalized difference vegetation index). Multilevel modeling was used to estimate the associations between green spaces and cognitive development. We observed an enhanced 12-mo progress in working memory and superior working memory and a greater 12-mo reduction in inattentiveness associated with greenness within and surrounding school boundaries and with total surrounding greenness index (including greenness surrounding home, commuting route, and school). Adding a traffic-related air pollutant (elemental carbon) to models explained 20-65% of our estimated associations between school greenness and 12-mo cognitive development. Our study showed a beneficial association between exposure to green space and cognitive development among school children that was partly mediated by reduction in exposure to air pollution. [ABSTRACT FROM AUTHOR]

Background The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors. Methods We quantified spatial clustering of BMI among 15,854 adults with diabetes from the Diabetes Study of Northern California (DISTANCE) cohort using the Global and Local Moran's I spatial statistic. As a null model, we assessed the amount of clustering when BMI values were randomly assigned. To evaluate predictors of spatial clustering, we estimated two linear models to estimate BMI residuals. First we included individual factors (demographic and socioeconomic characteristics). Then we added contextual factors (neighborhood deprivation, food environment) that may be associated with BMI. We assessed the amount of clustering that remained using BMI residuals. Results Global Moran's I indicated significant clustering of extreme BMI values; however, after accounting for individual socioeconomic and demographic characteristics, there was no longer significant clustering. Twelve percent of the sample clustered in extreme high or low BMI clusters, whereas, only 2.67% of the sample was clustered when BMI values were randomly assigned. After accounting for individual characteristics, we found clustering of 3.8% while accounting for neighborhood characteristics resulted in 6.0% clustering of BMI. After additional adjustment of neighborhood characteristics, clustering was reduced to 3.4%, effectively accounting for spatial clustering of BMI. Conclusions We found substantial clustering of extreme high and low BMI values in Northern California among adults with diabetes. Individual characteristics explained somewhat more of clustering of the BMI values than did neighborhood characteristics. These findings, although crosssectional, may suggest that selection into neighborhoods as the primary explanation of why individuals with extreme BMI values live close to one another. Further studies are needed to assess causes of extreme BMI clustering, and to identify any community level role to influence behavior change. [ABSTRACT FROM AUTHOR]

Abstract: Nitrogen dioxide (NO2), a surrogate measure of traffic-related air pollution (TRAP), has been associated with incident childhood asthma. Timing of exposure and atopic status may be important effect modifiers. We collected cross-sectional data on asthma outcomes from Toronto school children aged 5–9years in 2006. Lifetime home, school and daycare addresses were obtained to derive birth and cumulative NO2 exposures for a nested case–control subset of 1497 children. Presence of other allergic disease (a proxy for atopy) was defined as self-report of one or more of doctor-diagnosed rhinitis, eczema, or food allergy. Generalized estimating equations were used to adjust for potential confounders, and examine hypothesized effect modifiers while accounting for clustering by school. In children with other allergic disease, birth, cumulative and 2006 NO2 were associated with lifetime asthma (OR 1.46, 95% CI 1.08–1.98; 1.37, 95% CI 1.00–1.86; and 1.60, 95% CI 1.09–2.36 respectively per interquartile range increase) and wheeze (OR 1.44, 95% CI 1.10–1.89; 1.31, 95% CI 1.02–1.67; and 1.60, 95% CI 1.16–2.21). No or weaker effects were seen in those without allergic disease, and effect modification was amplified when a more restrictive algorithm was used to define other allergic disease (at least 2 of doctor diagnosed allergic rhinitis, eczema or food allergy). The effects of modest NO2 levels on childhood asthma were modified by the presence of other allergic disease, suggesting a probable role for allergic sensitization in the pathogenesis of TRAP initiated asthma. [Copyright &y& Elsevier]

Few studies have investigated associations between nonoccupational exposure to ambient volatile organic compounds and lung cancer. We conducted a case-control study of 445 incident lung cancers and 948 controls (523 hospital, 425 general population) in Toronto, Ontario, Canada, between 1997 and 2002. Participants provided information on several risk factors, including tobacco use, secondhand exposure to cigarette smoke, obesity, and family history of cancer. Exposure to benzene, hydrocarbons, and nitrogen dioxide was estimated using land-use regression models. Exposures were linked to residential addresses to estimate exposure at the time of interview, 10 years before interview, and across past residences (time-weighted average). Logistic regression was used to estimate adjusted odds ratios. Analyses involving the population-based controls found that an interquartile-range increase in the time-weighted average benzene concentration (0.15 µg/m3) across previous residences was associated with lung cancer (odds ratio = 1.84, 95% confidence interval: 1.26, 2.68). Similarly, an interquartile-range increase in the time-weighted average nitrogen dioxide concentration (4.8 ppb) yielded an odds ratio of 1.59 (95% confidence interval: 1.19, 2.12). Our study suggests that long-term exposure to ambient volatile organic compounds and nitrogen dioxide at relatively low concentrations is associated with lung cancer. Further work is needed to evaluate joint relationships between these pollutants, smoking, and lung cancer. [ABSTRACT FROM PUBLISHER]

Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000–2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants. [ABSTRACT FROM PUBLISHER]

Few studies have examined associations of birth outcomes with toxic air pollutants (air toxics) in traffic exhaust. This study included 8,181 term low birth weight (LBW) children and 370,922 term normal-weight children born between January 1, 1995, and December 31, 2006, to women residing within 5 miles (8 km) of an air toxics monitoring station in Los Angeles County, California. Additionally, land-use-based regression (LUR)-modeled estimates of levels of nitric oxide, nitrogen dioxide, and nitrogen oxides were used to assess the influence of small-area variations in traffic pollution. The authors examined associations with term LBW (≥37 weeks’ completed gestation and birth weight <2,500 g) using logistic regression adjusted for maternal age, race/ethnicity, education, parity, infant gestational age, and gestational age squared. Odds of term LBW increased 2%–5% (95% confidence intervals ranged from 1.00 to 1.09) per interquartile-range increase in LUR-modeled estimates and monitoring-based air toxics exposure estimates in the entire pregnancy, the third trimester, and the last month of pregnancy. Models stratified by monitoring station (to investigate air toxics associations based solely on temporal variations) resulted in 2%–5% increased odds per interquartile-range increase in third-trimester benzene, toluene, ethyl benzene, and xylene exposures, with some confidence intervals containing the null value. This analysis highlights the importance of both spatial and temporal contributions to air pollution in epidemiologic birth outcome studies. [ABSTRACT FROM PUBLISHER]

Parks and green space areas are important to human health for psychological and physiological reasons. There have been few evaluations of access to green space on mortality. This paper describes a cohort study of approximately 575,000 adults, 35 years of age and older, who resided in 10 urban areas in Ontario, Canada, between 1982 and 1986. Individuals were identified from income tax filings, and vital status was determined up to December 31, 2004 through record linkage to the Canadian Mortality Data Base. Place of residence was defined by postal code data that were extracted from income tax filings. Urban green space was defined by Landsat satellite retrievals with the Normalized Difference Vegetation Index and this was assigned to individuals'' place of residence at inception into the cohort using both a 30m grid cell and a 500m buffer. The proportional hazards model was used to estimate rate ratios (RRs) and their corresponding 95% confidence intervals (CI) for selected underlying causes of death. The rate ratios were adjusted for income, marital status, ambient air pollution, and contextual neighborhood characteristics. About 187,000 subjects died during follow-up. An increase in the interquartile range of green space, using a 500m buffer, was associated with reduced non-accidental mortality (RR=0.95, 95% CI=0.94–0.96). Reductions in mortality with increased residential green space were observed for each underlying cause of death; the strongest association was found for respiratory disease mortality (RR=0.91, 95% CI=0.89–0.93). Risk estimates were essentially unchanged after adjusting for ambient air pollution. Our study suggests that green space in urban environments was associated with long-term reduction in mortality although this finding should be interpreted cautiously as this association may be influenced by residual confounding of sociodemographic and lifestyle factors. Further research is needed to: confirm these findings, better understand the relationships between access to green space and behavioral risk factors for mortality, and identify what green space characteristics may confer the greatest health benefit. [Copyright &y& Elsevier]

Background: Numerous studies have linked criteria air pollutants with adverse birth outcomes, but there is less information on the importance of specific emission sources, such as traffic, and air toxics. Objectives: We used three exposure data sources to examine odds of term low birth weight (LBW) in Los Angeles, California, women when exposed to high levels of traffic-related air pollutants during pregnancy. Methods: We identified term births during 1 June 2004 to 30 March 2006 to women residing within 5 miles of a South Coast Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. Pregnancy period average exposures were estimated for air toxics, including polycyclic aromatic hydrocarbons (PAHs), source-specific particulate matter < 2.5 µm in aerodynamic diameter (PM2.5) based on a chemical mass balance model, criteria air pollutants from government monitoring data, and land use regression (LUR) model estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of term LBW (< 2,500 g) were examined using logistic regression. Results: Odds of term LBW increased approximately 5% per interquartile range increase in entire pregnancy exposures to several correlated traffic pollutants: LUR measures of NO, NO2, and NOx, elemental carbon, and PM2.5 from diesel and gasoline combustion and paved road dust (geological PM2.5). Conclusions: These analyses provide additional evidence of the potential impact of traffic-related air pollution on fetal growth. Particles from traffic sources should be a focus of future studies. INSET: CORRECTION. [ABSTRACT FROM AUTHOR]

Background: Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma. Objectives: We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Methods: Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthmaand wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric. Results: Asthma risk increased with modeled traffic-related pollution exposure from roadways nearhomes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO2 measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO2 and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO2 was attenuated (HR 1.37; 95% CI, 0.69-2.71). Conclusions: Traffic-related pollution exposure at school and homes may both contribute to the development of asthma. [ABSTRACT FROM AUTHOR]

Predicting chronic exposure to air pollution at the intra-urban scale has been recognized as a priority area of research for environmental epidemiology. Exposure assessment models attempt to predict and proxy for individuals' personal exposure to ambient air pollution, and there are no studies to date that explicitly attempt to compare and cross-validate personal exposure concentrations with pollutants modeled at the intra-urban level using methods such as interpolated surfaces and land-use regression (LUR) models. This study aimed to identify how well personal exposure to NO2 (nitrogen dioxide) can be predicted from ambient exposure measurements and intra-urban exposure estimates using LUR and what other factors contribute to predicting variations in personal exposure beyond measured pollutant levels within home. Personal, indoor and outdoor NO2 were measured in a population of older adults (>65 yr old) living in Hamilton, Canada. Our results show that personal NO2 was most strongly associated with contemporaneously collected indoor and outdoor concentrations of NO2. Predicted NO2 exposures from intra-urban LUR models were not associated with personal NO2, whereas interpolated surfaces of particulates and ozone were modestly associated. Combinations of variables that best predicted personal NO2 variability were derived from time-activity diaries, interpolated surfaces of ambient particulate pollutants, and a citywide temporally matched average of NO2. The nonsignificant associations between personal NO2 and the modeled ambient NO2 concentrations suggest that observed associations between NO2 generated by LUR models and health effects are probably not produced by NO2, but by other pollutants that follow a similar spatial pattern. [ABSTRACT FROM AUTHOR]

Abstract: Land use regression (LUR) has emerged as an effective means of estimating exposure to air pollution in epidemiological studies. We created the first LUR models of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NO X ) for the complex megalopolis of Los Angeles (LA), California. Two-hundred and one sampling sites (the largest sampling design to date for LUR estimation) for two seasons were selected using a location-allocation algorithm that maximized the potential variability in measured pollutant concentrations and represented populations in the health study. Traffic volumes, truck routes and road networks, land use data, satellite-derived vegetation greenness and soil brightness, and truck route slope gradients were used for predicting NO X concentrations. A novel model selection strategy known as “ADDRESS” (A Distance Decay REgression Selection Strategy) was used to select optimized buffer distances for potential predictor variables and maximize model performance. Final regression models explained 81%, 86% and 85% of the variance in measured NO, NO2 and NO X concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87–91%. Remote sensing-derived variables were significantly correlated with NO X concentrations, suggesting these data are useful surrogates for modeling traffic-related pollution when certain land use data are unavailable. Our study also demonstrated that reactive pollutants such as NO and NO2 could have high spatial extents of influence (e.g., >5000m from expressway) and high background concentrations in certain geographic areas. This paper represents the first attempt to model traffic-related air pollutants at a fine scale within such a complex and large urban region. [Copyright &y& Elsevier]

Background: The growing interest in the effects of contextual environments on health outcomes has focused attention on the strengths and weaknesses of alternate contextual unit definitions for use in multilevel analysis. The present research examined three methods to define contextual units for a sample of children already enrolled in a respiratory health study. The Inclusive Equal Weights Method (M1) and Inclusive Sample Weighted Method (M2) defined communities using the boundaries of the census blocks that incorporated the residences of the CHS participants, except that the former estimated socio-demographic variables by averaging the census block data within each community, while the latter used weighted proportion of CHS participants per block. The Minimum Bounding Rectangle Method (M3) generated minimum bounding rectangles that included 95% of the CHS participants and produced estimates of census variables using the weighted proportion of each block within these rectangles. GIS was used to map the locations of study participants, define the boundaries of the communities where study participants reside, and compute estimates of socio-demographic variables. The sensitivity of census variable estimates to the choice of community boundaries and weights was assessed using standard tests of significance. Results: The estimates of contextual variables vary significantly depending on the choice of neighborhood boundaries and weights. The choice of boundaries therefore shapes the community profile and the relationships between its components (variables). Conclusion: Multilevel analysis concerned with the effects of contextual environments on health requires careful consideration of what constitutes a contextual unit for a given study sample, because the alternate definitions may have differential impact on the results. The three alternative methods used in this research all carry some subjectivity, which is embedded in the decision as to what constitutes the boundaries of the communities. The Minimum Bounding Rectangle was preferred because it focused attention on the most frequently used spaces and it controlled potential aggregation problems. There is a need to further examine the validity of different methods proposed here. Given that no method is likely to capture the full complexity of human-environment interactions, we would need baseline data describing people's daily activity patterns along with expert knowledge of the area to evaluate our neighborhood units. [ABSTRACT FROM AUTHOR]

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM 2.5 ) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM 2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM 2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM 2.5 and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM 2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM 2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 ( N =1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM 2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM 2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM 2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. [ABSTRACT FROM AUTHOR]