Short-term air pollution levels and sickle cell disease hospital encounters in South Carolina: A case-crossover analysis.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder and symptoms may be sensitive to environmental stressors. Although it has been hypothesized that exposure to outdoor air pollution could trigger acute SCD events, evidence is limited. METHODS: We obtained SCD administrative data on hospital encounters in South Carolina from 2002 to 2019. We estimated outdoor air pollutant (particulate matter<2.5 µm (PM2.5), ozone (O3), and PM2.5 elemental carbon (EC) concentrations at residential zip codes using spatio-temporal models. Using a random bi-directional, fixed-interval case-crossover study design, we investigated the relationship between air pollution exposure over 1-, 3-, 5-, 9-, and14-day periods with SCD hospital encounters. RESULTS: We studied 8410 patients with 144,129 hospital encounters. We did not observe associations among all patients with SCD and adults for PM2.5, O3, and EC. We observed positive associations among children for 9- and 14-day EC (OR: 1.05 (95% confidence interval (CI): 1.02, 1.08) and OR: 1.05 (95% CI: 1.02, 1.09), respectively) and 9- and 14-day O3 (OR: 1.04 (95%CI: 1.00, 1.08)) for both. CONCLUSIONS: Our findings suggest that short-term (within two-weeks) levels of EC and O3 and may be associated with SCD hospital encounters among children. Two-pollutant model results suggest that EC is more likely responsible for effects on SCD than O3. More research is needed to confirm our findings.

Impacts of long-term ambient particulate matter and gaseous pollutants on circulating biomarkers of inflammation in male and female health professionals.

BACKGROUND: Systemic inflammation may serve as a biological mechanism linking air pollution to poor health but supporting evidence from studies of long-term pollutant exposure and inflammatory cytokines is inconsistent. OBJECTIVE: We studied associations between multiple particulate matter (PM) and gaseous air pollutants and pro- and anti-inflammatory cytokines within two nationwide cohorts of men and women. METHODS: Data were obtained from 16,151 women in the Nurses' Health Study and 7,930 men in the Health Professionals' Follow-up Study with at least one measure of circulating adiponectin, C-Reactive Protein (CRP), Interleukin-6 (IL-6) or soluble tumor necrosis-factor receptor-2 (sTNFR-2). Exposure to PM with aerodynamic diameter ≤2.5, 2.5-10, and ≤10 µm (PM2.5, PM2.5-10, PM10) and nitrogen dioxide (NO2) was estimated using spatio-temporal models and were linked to participants' addresses at the time of blood draw. Averages of the 1-, 3-, and 12-months prior to blood draw were examined. Associations between each biomarker and pollutant were estimated from linear regression models adjusted for individual and contextual covariates. RESULTS: In adjusted models, we observed a 2.72% (95% CI: 0.43%, 5.95%), 3.11% (-0.12%, 6.45%), and 3.67% (0.19%, 7.26%) increase in CRP associated with a 10 µg/m3 increase in 1-, 3-, and 12- month averaged NO2 in women. Among men, there was a statistically significant 5.96% (95% CI: 0.07%, 12.20%), 6.99% (95% CI: 0.29%, 14.15%), and 8.33% (95% CI: 0.35%, 16.94%) increase in CRP associated with a 10 µg/m3 increase in 1-, 3-, and 12-month averaged PM2.5-10, respectively. Increasing PM2.5-10 was associated with increasing IL-6 and sTNFR-2 among men over shorter exposure durations. There were no associations with exposures to PM2.5 or PM10, or with adiponectin. Findings were robust to sensitivity analyses restricting to disease-free controls and non-movers. CONCLUSIONS: Across multiple long-term pollutant exposures and inflammatory markers, associations were generally weak. Focusing on specific pollutant-inflammatory mechanisms may clarify pathways.

The relationship between traffic-related air pollution exposures and allostatic load score among youth with type 1 diabetes in the SEARCH cohort.

OBJECTIVE: We investigated the effects of chronic exposures to particulate and traffic-related air pollution on allostatic load (AL) score, a marker of cumulative biological risk, among youth with type 1 diabetes. RESEARCH DESIGN AND METHODS: Participants were drawn from five clinical sites of the SEARCH for Diabetes in Youth (SEARCH) study (n = 2338). Baseline questionnaires, anthropometric measures, and a fasting blood test were taken at a clinic visit between 2001 and 2005. AL was operationalized using 10 biomarkers reflecting cardiovascular, metabolic, and inflammatory risk. Annual residential exposures to PM2.5 and proximity to heavily-trafficked major roadways were estimated for each participant. Poisson regression models adjusted for sociodemographic and lifestyle factors were conducted for each exposure. RESULTS: No significant associations were observed between exposures to PM2.5 or proximity to traffic and AL score, however analyses were suggestive of effect modification by race for residential distance to heavily-trafficked major roadways (p = 0.02). In stratified analyses, residing <100, 100-<200 and 200-<400 m compared to 400 m or more from heavily-trafficked major roadways was associated with 11%, 26% and 14% increases in AL score, respectively (95% CIs: -4, 29; 9, 45; -1, 30) for non-white participants compared to 6%, -2%, and -2% changes (95% CIs: -2, 15; -10, 7; -8, 6) for white participants. CONCLUSIONS: Among this population of youth with type 1 diabetes, we did not observe consistent relationships between chronic exposures to particulate and traffic-related air pollution and changes in AL score, however associations for traffic-related pollution exposures may differ by race/ethnicity and warrant further examination.

Relationship of leukaemias with long-term ambient air pollution exposures in the adult Danish population.

BACKGROUND: Few population-based epidemiological studies of adults have examined the relationship between air pollution and leukaemias. METHODS: Using Danish National Cancer Registry data and Danish DEHM-UBM-AirGIS system-modelled air pollution exposures, we examined whether particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2) and ozone (O3) averaged over 1, 5 or 10 years were associated with adult leukaemia in general or by subtype. In all, 14,986 adult cases diagnosed 1989-2014 and 51,624 age, sex and time-matched controls were included. Separate conditional logistic regression models, adjusted for socio-demographic factors, assessed exposure to each pollutant with leukaemias. RESULTS: Fully adjusted models showed a higher risk of leukaemia with higher 1-, 5- and 10-year-average exposures to PM2.5 prior to diagnosis (e.g. OR per 10 µg/m3 for 10-year average: 1.17, 95% CI: 1.03, 1.32), and a positive relationship with 1-year average BC. Results were driven by participants 70 years and older (OR per 10 µg/m3 for 10-year average: 1.35, 95% CI: 1.15-1.58). Null findings for younger participants. Higher 1-year average PM2.5 exposures were associated with higher risks for acute myeloid and chronic lymphoblastic leukaemia. CONCLUSION: Among older adults, higher risk for leukaemia was associated with higher residential PM2.5 concentrations averaged over 1, 5 and 10 years prior to diagnosis.

Long-term exposure to particulate air pollution and brachial artery flow-mediated dilation in the Old Order Amish.

BACKGROUND: Atmospheric particulate matter (PM) has been associated with endothelial dysfunction, an early marker of cardiovascular risk. Our aim was to extend this research to a genetically homogenous, geographically stable rural population using location-specific moving-average air pollution exposure estimates indexed to the date of endothelial function measurement. METHODS: We measured endothelial function using brachial artery flow-mediated dilation (FMD) in 615 community-dwelling healthy Amish participants. Exposures to PM < 2.5 µm (PM2.5) and PM < 10 µm (PM10) were estimated at participants' residential addresses using previously developed geographic information system-based spatio-temporal models and normalized. Associations between PM exposures and FMD were evaluated using linear mixed-effects regression models, and polynomial distributed lag (PDL) models followed by Bayesian model averaging (BMA) were used to assess response to delayed effects occurring across multiple months. RESULTS: Exposure to PM10 was consistently inversely associated with FMD, with the strongest (most negative) association for a 12-month moving average (- 0.09; 95% CI: - 0.15, - 0.03). Associations with PM2.5 were also strongest for a 12-month moving average but were weaker than for PM10 (- 0.07; 95% CI: - 0.13, - 0.09). Associations of PM2.5 and PM10 with FMD were somewhat stronger in men than in women, particularly for PM10. CONCLUSIONS: Using location-specific moving-average air pollution exposure estimates, we have shown that 12-month moving-average estimates of PM2.5 and PM10 exposure are associated with impaired endothelial function in a rural population.

The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses' Health Study and the impact of measurement-error correction.

BACKGROUND: Long-term exposure to particulate matter less than 2.5 µm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality. METHODS: We followed 108,767 members of the Nurses' Health Study (NHS) 2000-2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 µg/m(3) increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error. RESULTS: Increasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 µg/m(3) increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 µg/m(3) increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates). CONCLUSIONS: These findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible.

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.

BACKGROUND: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. METHODS: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM(2.5-10)) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). RESULTS: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988-1998 and 1999-2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988-1998 and 1999-2007) and PM(2.5-10) (CV R2=0.46 and 0.52 for 1988-1998 and 1999-2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999-2007). CONCLUSIONS: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM(2.5-10) with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.

Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states.

BACKGROUND: Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters. METHODS: In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared. RESULTS: Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters. CONCLUSIONS: Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.

Relationships between the built environment and walking and weight status among older women in three U.S. States.

There are few studies of built environment associations with physical activity and weight status among older women in large geographic areas that use individual residential buffers to define environmental exposures. Among 23,434 women (70.0 ± 6.9 yr; range = 57-85) in 3 states, relationships between objective built environment variables and meeting physical activity recommendations via walking and weight status were examined. Differences in associations by population density and state were explored in stratified models. Population density (odds ratio [OR] =1.04 [1.02, 1.07]), intersection density (ORs = 1.18-1.28), and facility density (ORs = 1.01-1.53) were positively associated with walking. Density of physical activity facilities was inversely associated with overweight/obesity (OR = 0.69 [0.49, 0.96]). The strongest associations between facility density variables and both outcomes were found among women from higher population density areas. There was no clear pattern of differences in associations across states. Among older women, relationships between accessible facilities and walking may be most important in more densely populated settings.

Perceptions of mindfulness practices as a support for individuals managing caregiving responsibilities and chronic disease: A qualitative study.

OBJECTIVES: Explore the lived experience of individuals managing and/or caregiving for someone with a chronic disease and their perceptions of developing a mindfulness program for stress reduction. METHODS: Sixteen participants with chronic disease and/or caregivers participated. Participants completed eligibility screening, demographic questionnaires, and semi-structured interviews (30-60 min each) online or by phone. Interviews (n = 16) were audio recorded, transcribed, and analyzed using thematic analysis and NVivo® 12. Survey data were analyzed using SPSS® 28. RESULTS: Four themes emerged: (a) Chronic disease management and stress-perspectives on life's stressors; (b) Stress reduction techniques/perceptions of mindfulness-knowledge and implementation of stress reduction practices and familiarity with mindfulness; (c) Mindfulness program acceptability, barriers, and facilitators-interest, barriers, and facilitators to attending; (d) Mindfulness program structure-logistics to increase access and appeal to diverse audiences. DISCUSSION: Mindfulness has the potential for addressing the complexities of stress associated with disease management. Targeting mindfulness programs for populations with chronic disease management and caregiving responsibilities should include: Consideration of group formats with participation limited to this population, structuring programs to overcome barriers (i.e., culturally appropriate location), and equipping members of the community being served as instructors to ensure culturally relevant instruction.

Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study.

BACKGROUND: European ecologic studies suggest higher socioeconomic status is associated with higher incidence of type 1 diabetes. Using data from a case-control study of diabetes among racially/ethnically diverse youth in the United States (U.S.), we aimed to evaluate the independent impact of neighborhood characteristics on type 1 diabetes risk. Data were available for 507 youth with type 1 diabetes and 208 healthy controls aged 10-22 years recruited in South Carolina and Colorado in 2003-2006. Home addresses were used to identify Census tracts of residence. Neighborhood-level variables were obtained from 2000 U.S. Census. Multivariate generalized linear mixed models were applied. RESULTS: Controlling for individual risk factors (age, gender, race/ethnicity, infant feeding, birth weight, maternal age, number of household residents, parental education, income, state), higher neighborhood household income (p = 0.005), proportion of population in managerial jobs (p = 0.02), with at least high school education (p = 0.005), working outside the county (p = 0.04) and vehicle ownership (p = 0.03) were each independently associated with increased odds of type 1 diabetes. Conversely, higher percent minority population (p = 0.0003), income from social security (p = 0.002), proportion of crowded households (0.0497) and poverty (p = 0.008) were associated with a decreased odds. CONCLUSIONS: Our study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk. Further research is needed to understand mechanisms underlying the influence of neighborhood context.

Defining and Intervening on Cumulative Environmental Neurodevelopmental Risks: Introducing a Complex Systems Approach.

BACKGROUND: The combined effects of multiple environmental toxicants and social stressor exposures are widely recognized as important public health problems contributing to health inequities. However cumulative environmental health risks and impacts have received little attention from U.S. policy makers at state and federal levels to develop comprehensive strategies to reduce these exposures, mitigate cumulative risks, and prevent harm. An area for which the inherent limitations of current approaches to cumulative environmental health risks are well illustrated is children's neurodevelopment, which exhibits dynamic complexity of multiple interdependent and causally linked factors and intergenerational effects. OBJECTIVES: We delineate how a complex systems approach, specifically system dynamics, can address shortcomings in environmental health risk assessment regarding exposures to multiple chemical and nonchemical stressors and reshape associated public policies. DISCUSSION: Systems modeling assists in the goal of solving problems by improving the "mental models" we use to make decisions, including regulatory and policy decisions. In the context of disparities in children's cumulative exposure to neurodevelopmental stressors, we describe potential policy insights about the structure and behavior of the system and the types of system dynamics modeling that would be appropriate, from visual depiction (i.e., informal maps) to formal quantitative simulation models. A systems dynamics framework provides not only a language but also a set of methodological tools that can more easily operationalize existing multidisciplinary scientific evidence and conceptual frameworks on cumulative risks. Thus, we can arrive at more accurate diagnostic tools for children's' environmental health inequities that take into consideration the broader social and economic environment in which children live, grow, play, and learn. https://doi.org/10.1289/EHP7333.

Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes.

BACKGROUND: There is increasing interest in the study of place effects on health, facilitated in part by geographic information systems. Incomplete or missing address information reduces geocoding success. Several geographic imputation methods have been suggested to overcome this limitation. Accuracy evaluation of these methods can be focused at the level of individuals and at higher group-levels (e.g., spatial distribution). METHODS: We evaluated the accuracy of eight geo-imputation methods for address allocation from ZIP codes to census tracts at the individual and group level. The spatial apportioning approaches underlying the imputation methods included four fixed (deterministic) and four random (stochastic) allocation methods using land area, total population, population under age 20, and race/ethnicity as weighting factors. Data included more than 2,000 geocoded cases of diabetes mellitus among youth aged 0-19 in four U.S. regions. The imputed distribution of cases across tracts was compared to the true distribution using a chi-squared statistic. RESULTS: At the individual level, population-weighted (total or under age 20) fixed allocation showed the greatest level of accuracy, with correct census tract assignments averaging 30.01% across all regions, followed by the race/ethnicity-weighted random method (23.83%). The true distribution of cases across census tracts was that 58.2% of tracts exhibited no cases, 26.2% had one case, 9.5% had two cases, and less than 3% had three or more. This distribution was best captured by random allocation methods, with no significant differences (p-value > 0.90). However, significant differences in distributions based on fixed allocation methods were found (p-value < 0.0003). CONCLUSION: Fixed imputation methods seemed to yield greatest accuracy at the individual level, suggesting use for studies on area-level environmental exposures. Fixed methods result in artificial clusters in single census tracts. For studies focusing on spatial distribution of disease, random methods seemed superior, as they most closely replicated the true spatial distribution. When selecting an imputation approach, researchers should consider carefully the study aims.

Sociodemographic and Environmental Determinants of Indoor Versus Outdoor Active Play Among Children Living in the Washington, DC Area.

BACKGROUND: Sociodemographic and environmental factors play important roles in determining both indoor and outdoor play activities in children. METHODS: The Built Environment and Active Play Study assessed neighborhood playability for children (7-12 y), based on parental report of their children's active play behaviors, neighborhood characteristics, and geographic locations. Simple logistic regression modeling tested the associations between sociodemographic characteristics and the frequency of and access to venues for indoor and outdoor play. RESULTS: Children of higher socioeconomic status were almost 3 times more likely to live more than a 30-minute walk from indoor recreational facilities compared with their less affluent peers (odds ratio [OR] = 2.9; 95% confidence interval [CI], 1.2-6.8). Non-Hispanic black children were less likely to live more than 30 minutes from indoor facilities (OR = 0.21; 95% CI, 0.08-0.57) and more were likely to engage in indoor activity (OR = 3.40; 95% CI, 1.17-9.88) than were white children. Boys were substantially more likely to play outdoors at a playing fields compared with girls (OR = 5.37; 95% CI, 2.10-13.69). CONCLUSIONS: Findings from this study could be used to enhance indoor and outdoor activity spaces for children and to reduce disparities in access to such spaces.

Accelerometer and GPS Data to Analyze Built Environments and Physical Activity.

Purpose: Most built environment studies have quantified characteristics of the areas around participants' homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.

Case-crossover analysis of short-term particulate matter exposures and stroke in the health professionals follow-up study.

BACKGROUND: Stroke is a leading cause of morbidity and mortality in the United States. Associations between short-term exposures to particulate matter (PM) air pollution and stroke are inconsistent. Many prior studies have used administrative and hospitalization databases where misclassification of the type and timing of the stroke event may be problematic. METHODS: In this case-crossover study, we used a nationwide kriging model to examine short-term ambient exposure to PM10 and PM2.5 and risk of ischemic and hemorrhagic stroke among men enrolled in the Health Professionals Follow-up Study. Conditional logistic regression models were used to obtain estimates of odds ratios (OR) and 95% confidence intervals (CI) associated with an interquartile range (IQR) increase in PM2.5 or PM10. Lag periods up to 3 days prior to the stroke event were considered in addition to a 4-day average. Stratified models were used to examine effect modification by patient characteristics. RESULTS: Of the 727 strokes that occurred between 1999 and 2010, 539 were ischemic and 122 were hemorrhagic. We observed positive statistically significant associations between PM10 and ischemic stroke (ORlag0-3 = 1.26; 95% CI: 1.03-1.55 per IQR increase [14.46 µg/m3]), and associations were elevated for nonsmokers, aspirin nonusers, and those without a history of high cholesterol. However, we observed no evidence of a positive association between short-term exposure to PM and hemorrhagic stroke or between PM2.5 and ischemic stroke in this cohort. CONCLUSIONS: Our study provides evidence that ambient PM10 may be associated with higher risk of ischemic stroke and highlights that ischemic and hemorrhagic strokes are heterogeneous outcomes that should be treated as such in analyses related to air pollution.

Inflammation and acute traffic-related air pollution exposures among a cohort of youth with type 1 diabetes.

BACKGROUND: Evidence remains equivocal regarding the association of inflammation, a precursor to cardiovascular disease, and acute exposures to ambient air pollution from traffic-related particulate matter. Though youth with type 1 diabetes are at higher risk for cardiovascular disease, the relationship of inflammation and ambient air pollution exposures in this population has received little attention. OBJECTIVES: Using five geographically diverse US sites from the racially- and ethnically-diverse SEARCH for Diabetes in Youth Cohort, we examined the relationship of acute exposures to PM2.5 mass, Atmospheric Dispersion Modeling System (ADMS)-Roads traffic-related PM concentrations near roadways, and elemental carbon (EC) with biomarkers of inflammation including interleukin-6 (IL-6), c-reactive protein (hs-CRP) and fibrinogen. METHODS: Baseline questionnaires and blood were obtained at a study visit. Using a spatio-temporal modeling approach, pollutant exposures for 7 days prior to blood draw were assigned to residential addresses. Linear mixed models for each outcome and exposure were adjusted for demographic and lifestyle factors identified a priori. RESULTS: Among the 2566 participants with complete data, fully-adjusted models showed positive associations of EC average week exposures with IL-6 and hs-CRP, and PM2.5 mass exposures on lag day 3 with IL-6 levels. Comparing the 25th and 75th percentiles of average week EC exposures resulted in 8.3% higher IL-6 (95%CI: 2.7%,14.3%) and 9.8% higher hs-CRP (95%CI: 2.4%,17.7%). We observed some evidence of effect modification for the relationships of PM2.5 mass exposures with hs-CRP by gender and with IL-6 by race/ethnicity. CONCLUSIONS: Indicators of inflammation were associated with estimated traffic-related air pollutant exposures in this study population of youth with type 1 diabetes. Thus youth with type 1 diabetes may be at increased risk of air pollution-related inflammation. These findings and the racial/ethnic and gender differences observed deserve further exploration.

Development of outcome-specific criteria for study evaluation in systematic reviews of epidemiology studies.

INTRODUCTION AND OBJECTIVE: Systematic review tools that provide guidance on evaluating epidemiology studies are receiving increasing attention and support because their application facilitates improved quality of the review, consistency across reviewers, and transparency for readers. The U.S. Environmental Protection Agency's Integrated Risk Information System (IRIS) Program has developed an approach for systematic review of evidence of health effects from chemical exposures that includes structured approaches for literature search and screening, study evaluation, data extraction, and evidence synthesis and integration. This approach recognizes the need for developing outcome-specific criteria for study evaluation. Because studies are assessed at the outcome level, a study could be considered high quality for one investigated outcome, and low quality for another, due to differences in the outcome measures, analytic strategies, how relevant a certain bias is to the outcome, and how the exposure measure relates to the outcome. The objective of this paper is to illustrate the need for outcome-specific criteria in study evaluation or risk of bias evaluation, describe the process we used to develop the criteria, and summarize the resulting criteria. METHODS: We used a process of expert consultation to develop several sets of outcome-specific criteria to guide study reviewers, improve consistency, and ensure consideration of critical issues specific to the outcomes. The criteria were developed using the following domains: outcome assessment, exposure measurement (specifically timing of exposure in relation to outcome; other exposure measurement issues would be addressed in exposure-specific criteria), participant selection, confounding, analysis, and sensitivity (the study's ability to detect a true effect or hazard). RESULTS: We discuss the application of this process to pregnancy-related outcomes (preterm birth, spontaneous abortion), other reproductive-related outcomes (male reproductive hormones, sperm parameters, time to pregnancy, pubertal development), chronic disease (diabetes, insulin resistance), and acute or episodic conditions (asthma, allergies), and provide examples of the criteria developed. For each outcome the most influential methodological considerations are highlighted including biological sample collection and quality control, sensitivity and specificity of ascertainment tools, optimal timing for recruitment into the study (e.g., preconception, specific trimesters), the etiologically relevant window for exposure assessments, and important potential confounders. CONCLUSIONS: Outcome-specific criteria are an important part of a systematic review and will facilitate study evaluations by epidemiologists with experience in evaluating studies using systematic review methods who may not have extensive discipline-specific experience in the outcomes being reviewed.

Chronic particulate exposure, mortality, and coronary heart disease in the nurses' health study.

Adverse health effects of exposures to acute air pollution have been well studied. Fewer studies have examined effects of chronic exposure. Previous studies used exposure estimates for narrow time periods and were limited by the geographic distribution of pollution monitors. This study examined the association of chronic particulate exposures with all-cause mortality, incident nonfatal myocardial infarction, and fatal coronary heart disease (CHD) in a prospective cohort of 66,250 women from the Nurses' Health Study in northeastern US metropolitan areas. Nonfatal outcomes were assessed through self-report and medical record review and fatalities through death certificates and medical record review. During follow-up (1992-2002), 3,785 deaths and 1,348 incident fatal CHD and nonfatal myocardial infarctions occurred. In age- and calendar-time-adjusted models, 10-microg/m(3) increases in 12-month average exposures to particulate matter <10 microm in diameter were associated with increased all-cause mortality (16%, 95% confidence interval: 5, 28) and fatal CHD (43%, 95% confidence interval: 10, 86). Adjustment for body mass index and physical activity weakened these associations. Body mass index and smoking modified the association between exposure to particulate matter <10 microm in diameter and fatal CHD. In this population, increases in such exposures were associated with increases in all-cause and CHD mortality. Never smokers with higher body mass indexes were at greatest risk of fatal CHD.