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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.

  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.

There is a growing recognition of social media data as being useful for understanding local area patterns. In this study, we sought to utilize geotagged tweets—specifically, the frequency and type of food mentions—to understand the neighborhood food environment and the social modeling of food behavior. Additionally, we examined associations between aggregated food-related tweet characteristics and prevalent chronic health outcomes at the census tract level. We used a Twitter streaming application programming interface (API) to continuously collect ~1% random sample of public tweets in the United States. A total of 4,785,104 geotagged food tweets from 71,844 census tracts were collected from April 2015 to May 2018. We obtained census tract chronic disease outcomes from the CDC 500 Cities Project. We investigated associations between Twitter-derived food variables and chronic outcomes (obesity, diabetes and high blood pressure) using the median regression. Census tracts with higher average calories per tweet, less frequent healthy food mentions, and a higher percentage of food tweets about fast food had higher obesity and hypertension prevalence. Twitter-derived food variables were not predictive of diabetes prevalence. Food-related tweets can be leveraged to help characterize the neighborhood social and food environment, which in turn are linked with community levels of obesity and hypertension.

Purpose: Reproductive autonomy is associated with educational attainment, advanced employment, and wellbeing. While U.S. Latinas use contraception to control their own childbearing and have reported a desire to do so, they often use it inconsistently and have the lowest rates of contraceptive use of any group. Reasons previously cited for why Latinas do not use contraception compared with non-Latino white women include lack of access, lack of knowledge, language barriers, emphasis on large families, machismo, and religiosity. These reasons are often overly simplistic and can lead to widespread generalizations about Latinas. Methods: Using focus groups and semistructured interviews from November 2014 through June 2015, this study describes the family planning perspectives and experiences of 16 Latinas living in Baltimore and recruited from two federally qualified health centers. A social determinant of health framework was used to guide identification of important concepts and explain findings. Results: Results demonstrated that respondents reported contraceptive agency and claimed autonomy over their bodies; described a sense of responsibility and often expressed caution about having families too large to care for; expressed educational and career aspirations; and perceived contraception as critical for the postponement of childbearing to achieve their goals. Conclusion: The patient/provider encounter should include communication that recognizes all patient preferences and lived experiences to support vulnerable and/or marginalized Latinas in their desires to control their own childbearing and life choices.

This study examined the changes in sleep duration (total sleep time, night‐time sleep and daytime naps) after retirement transitions in China using a panel dataset of the China Health and Retirement Longitudinal Study in 2011, 2013 and 2015 with a total of 48,458 respondents. Linear regression analysis with generalized estimating equations was employed to examine the changes in sleep duration after transitions between different types of employment status. After controlling for the confounders, the results showed that the retired population and the population working in agricultural sectors slept 8.02 ( p  < .01) and 5.19 ( p  < .01) minutes longer than the population working in non‐agricultural sectors, respectively. Employment transition also had significant effects on sleep duration. Transition from non‐agricultural sectors to retirement increased total sleep time by 13.58 ( p  < .01) minutes and also raised the probability of daytime naps by 18% (OR = 1.18,  p  < .01). Transition from agricultural employment to retirement did not significantly affect the total sleep time, but significantly increased the probability of daytime naps (OR = 1.12,  p  = .02). Reentering the non‐agricultural sectors for the retirees did not significantly affect night‐time sleep, but decreased the probability of daytime naps (OR = 0.73,  p  < .01) and daytime nap duration (by 5.26 min,  p  = .01). In conclusion, people in China increased their sleep duration after transitions to retirement, but the magnitudes were much smaller than those in Western countries. Differences may be attributed to an abundant amount of Chinese people working in agricultural sectors, the high volume of retired people reentering the work force and the large proportion of people in China that had daytime naps at baseline.

Abstract Purpose: The purpose of this study was to identify subgroups of sexual and gender minority (SGM) youth who are most vulnerable to tobacco use. Methods: We analyzed data from a national nonprobability sample of 11,192 SGM youth (ages 13–17). Age of cigarette initiation and current use were modeled using Cox proportional hazard and binomial regression. Sexual and gender identities were explanatory variables and the models were adjusted for ethnoracial identity and age. Results: Approximately 7\% of the sample reported current smoking. Cisgender and transgender boys had higher odds of current smoking compared with cisgender and transgender girls (adjusted odds ratio [AOR] = 1.86; 95\% confidence interval [CI]: 1.56–2.21). Pansexual-identified youth had higher odds of smoking (AOR = 1.33; 95\% CI: 1.05–1.70) compared with gay/lesbian youth independent of gender identity. Pansexual-identified cisgender boys had the highest smoking prevalence (21.6\%). Predicted probabilities were higher among transgender boys across all sexual identities, except asexual. The hazard of smoking at a younger age was greater for transgender boys compared with cisgender boys (adjusted hazard ratio [AHR] = 1.67; 95\% CI: 1.43–1.94) as well as for bisexual (AHR = 1.12; 95\% CI: 1.01–1.24) and pansexual (AHR = 1.17; 95\% CI: 1.03–1.33) youth compared with those who identified as gay or lesbian. Conclusions: These findings suggest that transgender boys may be at higher risk for early and current cigarette use regardless of their sexual identity, whereas smoking varied more widely for youth across different sexual identities. The findings suggest that specific subgroups of SGM youth require focused attention in tobacco control research and practice.

BACKGROUND: Using a population-based sample of youth, we examined rates of cigarette use and trends in cigarette use disparities between heterosexual youth and 3 subgroups of sexual minority youth (SMY) (ie, lesbian or gay, bisexual, and unsure) from 2005 to 2015. METHODS: Data are from 6 cohorts of the Youth Risk Behavior Survey, a national, biennial, school-based survey of ninth- to 12th-grade students in the United States (n = 404 583). Sex-stratified analyses conducted in 2017 examined trends in 2 cigarette-related behaviors: lifetime cigarette use and heavy cigarette use (20+ days in the past 30). RESULTS: Disparities in lifetime cigarette use between lesbian and heterosexual girls were statistically smaller in 2015 relative to 2005 (adjusted odds ratio [aOR] 0.29; 95% confidence interval [CI] 0.12–0.75; P = .011). Sexual orientation disparities in heavy use were narrower for bisexual boys in 2015 compared with 2005 (aOR 0.39; 95% CI 0.17–0.90; P = .028). Girls and boys unsure of their sexual identity had wider disparities in heavy use in 2015 (aOR 3.85; 95% CI 1.39–11.10; P = .009) relative to 2005 (aOR 2.44; 95% CI 1.22–5.00; P = .012). CONCLUSIONS: SMY remain at greater risk for cigarette-related behaviors despite greater acceptance of lesbian, gay, and bisexual people in the United States. Focused policies and programs aimed at reducing rates of SMY cigarette use are warranted, particularly for youth questioning their sexual identity.

Scientific evidence regarding sexual minority populations has generally come from studies based on two types of samples: community-derived samples and probability samples. Probability samples are lauded as the gold standard of population research for their ability to represent the population of interest. However, while studies using community samples lack generalizability, they are often better able to assess population-specific concerns (e.g., minority stress) and are collected more rapidly, allowing them to be more responsive to changing population dynamics. Given these advantages, many sexual minority population studies rely on community samples. To identify how probability and community samples of sexual minorities are similar and different, we compared participant characteristics from two companion samples from the  Generations Study , each designed with the same demographic profile of U.S. sexual minority adults in mind. The first sample was recruited for a national probability survey, whereas the second was recruited for a multicommunity sample from four U.S. cities. We examined sociodemographic differences between the samples. Although there were several statistical differences between samples, the effect sizes were small for sociodemographic characteristics that defined the sample inclusion criteria: sex assigned at birth, race/ethnicity, and age cohort. The samples differed across other characteristics: bisexual respondents, respondents with less education, and those living in non-urban areas were underrepresented in the community sample. Our findings offer insights for recruiting community samples of sexual minority populations and for measuring sexual identity on probability surveys. They also bolster confidence in well-designed community samples as sources for data on sexual minority populations.

MPRC Special Workshop

Abstract: Public health and built environment design have a long-intertwined history of promoting a healthy quality of life. They emerged with the common goal of preventing infectious disease outbreaks in urban areas and improving occupants’ living conditions. In recent years, however, the two disciplines have become less engaged and with each developing a distinct focus. To respond to this disconnection, a systematic review was conducted to identify the connection and divergence between public health and built environment design and planning. This paper aims to establish a context for understanding the connections, synergies, and divergence between public health and built environment design disciplines. Further, the four main health factors in the built environment are identified and explained: physical, physiological, biological, and psychological factors. Finally, future trends to reconnect public health with build environment design are then outlined.