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  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.

They are one of the four research groups chosen for the data contract

Access to new dataset opens up research opportunities

The past several decades have witnessed growing geographic disparities in life expectancy within the United States, yet the mortality experience of U.S. cities has received little attention. We examine changes in men’s life expectancy at birth for the 25 largest U.S. cities from 1990 to 2015, using mortality data with city of residence identifiers. We reveal remarkable increases in life expectancy for several U.S. cities. Men’s life expectancy increased by 13.7 years in San Francisco and Washington, DC, and by 11.8 years in New York between 1990 and 2015, during which overall U.S. life expectancy increased by just 4.8 years. A significant fraction of gains in the top-performing cities relative to the U.S. average is explained by reductions in HIV/AIDS and homicide during the 1990s and 2000s. Although black men tended to see larger life expectancy gains than white men in most cities, changes in socioeconomic and racial population composition also contributed to these trends.

Men's life expectancy has seen "remarkable increase" for several U.S. cities

Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.

Purpose:  Using representative school-based data and community-level primary data, we investigated how environmental factors (e.g., school and community climate) might be protective against substance use behaviors among a vulnerable population of adolescents. Methods:  We analyzed a sample of 2678 sexual minority adolescents using a combination of student-level data (British Columbia Adolescent Health Survey) and primary community-level data (assessing lesbian, gay, bisexual, transgender, and queer [LGBTQ]-specific community and school environments). Using multilevel logistic regression models, we examined associations between lifetime substance use (alcohol, illegal drugs, marijuana, nonmedical use of prescription drugs, and smoking) and community-level predictors (community and school LGBTQ supportiveness). Results:  Above and beyond student characteristics (e.g., age and years living in Canada), sexual minority adolescents residing in communities with more LGBTQ supports (i.e., more supportive climates) had lower odds of lifetime illegal drug use (for boys and girls), marijuana use (for girls), and smoking (for girls). Specifically, in communities with more frequent LGBTQ events (such as Pride events), the odds of substance use among sexual minority adolescents living in those communities was lower compared with their counterparts living in communities with fewer LGBTQ supports. Conclusions:  The availability of LGBTQ community-level organizations, events, and programs may serve as protective factors for substance use among sexual minority adolescents. In particular, LGBTQ-supportive community factors were negatively associated with substance use, which has important implications for our investment in community programs, laws, and organizations that advance the visibility and rights of LGBTQ people.

Maternal postpartum depression has been shown to be one of the main predictors of externalizing and internalizing behaviors in toddlers and adolescents. Research suggests that presence of such behaviors can be observed as early as infancy. The current study uses longitudinal data from 247 mothers to examine the relationship between postpartum depressive symptoms at 8 weeks and the infant's externalizing and internalizing behaviors at 12 months. In unadjusted linear regression models, there were associations between postpartum depressive symptoms and infant externalizing behaviors (β=0.082, SE=0.032, p=0.012) and internalizing behaviors (β=0.111, SE=0.037, p=0.003). After controlling for potential confounding factors, including maternal age, race, education, home ownership, smoking status in the postpartum period, marital status, parenting stress, and happiness from becoming a parent, the associations between postpartum depressive symptoms and infant externalizing (β=0.051, SE=0.034, p=0.138) and internalizing behaviors (β=0.077, SE=0.040, p=0.057) were reduced and became non-significant. Furthermore, in these models the total amount of variance explained was 17.2% (p<0.0001) for externalizing behaviors and 10.5% (p<0.01) for internalizing behaviors; the only significant predictor of externalizing behaviors was maternal age (β=-0.074, SE=0.030, p=0.014), and of internalizing behaviors was white non-Hispanic ethnicity (β=-1.33, SE=0.378, p=0.0005). A combined effect of the confounding factors seems to explain the finding of no significant independent association between postpartum depressive symptoms and infant externalizing and internalizing behaviors.

Jessica N. Fish, Family Science, investigates sexual-orientation-related disparities in mental, behavioral, and physical health

Health Contextualized: Inequalities in Physiological Function at the Intersection of Race, Skin Color, and Place