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A focus of research on short interpregnancy intervals (IPI) has been on young disadvantaged women whose births are likely to be unintended. Later initiation of family formation in the United States and other high-income countries points to the need to also consider a woman’s attributes indicative of readiness for purposefully accelerated family formation achieved through short IPIs. We test for whether factors indicating “reproductive readiness”—including being married, being older, and having just had a first birth or a birth later than desired—predict a woman’s non-use of contraception in the postpartum months. We also test for whether this contraceptive non-use results explicitly from wanting to become pregnant again. The data come from the 2012–2015 Pregnancy Risk Assessment Monitoring System, representing women who recently gave birth in any of 35 U.S. states and New York City ( N  = 120,111). We find that these reproductive-readiness factors are highly predictive of women’s postpartum non-use of contraception because of a stated desire to become pregnant and are moderately predictive of contraceptive non-use without an explicit pregnancy intention. We conclude that planning for, or ambivalently risking, a short IPI is a frequent family-formation strategy for women whose family formation has been delayed. This is likely to become increasingly common as family formation in the United States is initiated later in the reproductive life course.

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.

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county  health outcomes , controlling for county-level demographics, economics, and  population density . At the county level, greater presence of highways was related to lower chronic diseases and  premature mortality . Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity,  diabetes , fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the  census  tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

Objectives: We examined the use of data from social media for surveillance of physical activity prevalence in the USA. Methods: We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1382 284 geotagged physical activity tweets from 481146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention. Results: The association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes. Conclusions: The regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.

Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter’s streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

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.

This study investigated whether changes in neighborhood context induced by neighborhood relocation mediated the impact of the Moving to Opportunity (MTO) housing voucher experiment on adolescent mental health. Mediators included participant-reported neighborhood safety, social control, disorder, and externally-collected neighborhood collective efficacy. For treatment group members, improvement in neighborhood disorder and drug activity partially explained MTO's beneficial effects on girls' distress. Improvement in neighborhood disorder, violent victimization, and informal social control helped counteract MTO's adverse effects on boys' behavioral problems, but not distress. Housing mobility policy targeting neighborhood improvements may improve mental health for adolescent girls, and mitigate harmful effects for boys.

Adolescent girls and young women (AGYW) in Uganda are at risk of early sexual debut, unwanted pregnancy, violence, and disproportionally high HIV infection rates, driven in part by transactional sex. This paper examines the extent to which AGYW’s participation in transactional sex is perceived to be coerced. We conducted 19 focus group discussions and 44 in-depth interviews using semi-structured tools. Interviews were audio recorded, and transcribed verbatim. Data were analysed using a thematic analysis. While AGYW did not necessarily use the language of coercion, their narratives describe a number of coercive aspects in their relationships. First, coercion by force as a result of “de-toothing” a man (whereby they received money or resources but did not wish to provide sex as “obligated” under the implicit “terms” of the relationships). Second, they described the coercive role that receiving resources played in their decision to have sex in the face of men’s verbal insistence. Finally, they discussed having sex as a result of coercive economic circumstances including poverty, and because of peer pressure to uphold modern lifestyles. Support for income-generation activities, microfinance and social protection programmes may help reduce AGYW’s vulnerability to sexual coercion in transactional sex relationships. Targeting gender norms that contribute to unequal power dynamics and social expectations that obligate AGYW to provide sex in return for resources, critically assessing the meaning of consensual sex, and normative interventions building on parents’ efforts to ascertain the source of their daughters’ resources may also reduce AGYW’s vulnerability to coercion.

Purpose:  Sexual minorities experience signi fi cant health disparities across a variety of mental, behav ioral, and physical health indicators. Yet, an understanding of the etiology and progression of sexual minority health disparities across the lifespan is limited. Methods:  We used the U.S. National Epidemiologic Survey of Alcohol and Related Conditions III to  evaluate the association between sexual minority status and seven past-year health outcomes (alcohol  use disorder, tobacco use disorder, drug use disorder, major depressive episode, generalized anxiety  disorder, sexually transmitted infection, and cardiovascular conditions). To do this, we used unadjusted  and adjusted logistic regression among our study sample (n ¼ 30,999; aged 18 e 65 years) and time- varying effect models to evaluate how sexual orientation differences in these outcomes vary across  adulthood. Results:  Relative to heterosexuals, sexual minorities had elevated odds of past-year alcohol use disorder  and drug use disorder across all ages (18 e 65 years) although the magnitude of the disparity varies by  age. Sexual minorities were also more likely to experience major depressive episode, generalized anxiety  disorder, tobacco use disorder, sexually transmitted infection, and cardiovascular disease, but only at  speci fi c ages. Conclusions:  Sexual minority health disparities vary appreciably across the adult lifespan, thus eluci dating critical periods for focused prevention efforts.

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.