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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.

We inquire how early in childhood children most at risk for problematic patterns of internalizing and externalizing behaviors can be accurately classified. Yearly measures of anxiety/depressive symptoms and aggressive behaviors (ages 6–13;  n  = 334), respectively, are used to identify behavioral trajectories. We then assess the degree to which limited spans of yearly information allow for the correct classification into the elevated, persistent pattern of the problem behavior, identified theoretically and empirically as high-risk and most in need of intervention. The true positive rate (sensitivity) is below 70% for anxiety/depressive symptoms and aggressive behaviors using behavioral information through ages 6 and 7. Conversely, by age 9, over 90% of the high-risk individuals are correctly classified (i.e., sensitivity) for anxiety/depressive symptoms, but this threshold is not met until age 12 for aggressive behaviors. Notably, the false positive rate of classification for both high-risk problem behaviors is consistently low using each limited age span of data (< 5%). These results suggest that correct classification into highest risk groups of childhood problem behavior is limited using behavioral information observed at early ages. Prevention programming targeting those who will display persistent, elevated levels of problem behavior should be cognizant of the degree of misclassification and how this varies with the accumulation of behavioral information. Continuous assessment of problem behaviors is needed throughout childhood in order to continually identify high-risk individuals most in need of intervention as behavior patterns are sufficiently realized.

Background: While alcohol consumption is pervasive in the country of Georgia, the extent of alcohol consumption among pregnant women is yet to be examined. The goal of this study is to examine prevalence and correlates of alcohol consumption during pregnancy in Georgia. Methods: Using data from the World Health Organization’s Stepwise approach to noncommunicable disease risk factor surveillance in Georgia, this study examined prevalence and sociodemographic correlates of alcohol use among pregnant women in Georgia. The study sample of reproductive age (18-45) women was drawn from the STEPS, which is a large and nationally representative survey of adults with a 95% participation rate. Frequencies, multivariate analyses and related statistics were computed to describe and study associations among the target population and the odds of alcohol consumption during pregnancy. Results: Only 66 individuals in the sample were pregnant. About 13% of pregnant women consumed alcohol in the past 30 days and nearly 70% of them engaged in binge drinking on at least one occasion. Pregnant women who were young, married, homemakers, living in two-member households and in the lowest bracket of monthly income had the highest likelihood of consuming alcohol and binge drinking. The study results were statistically significant (p< .05). Conclusions: This study reveals the magnitude of alcohol consumption and binge drinking among reproductive age women in Georgia. This study also shows prevalence and correlates of alcohol consumption during pregnancy in Georgia. The results identify characteristics of women who are most likely to use alcohol during pregnancy. Given that, alcohol use is a modifiable behavioral risk factor, the findings in this study provide the foundation for evidence-based prevention strategies that target pregnant and reproductive age women.

To review blockchain lessons learned in 2018 and near-future predictions for blockchain in healthcare, Blockchain in Healthcare Today (BHTY) asked the world's blockchain in healthcare experts to share their insights. Here, our internationally-renowned BHTY peer-review board discusses their major predictions. Based on their responses, presented in detail below, ten major themes (Table ) for the future of blockchain in healthcare will emerge over the 12 months.

HIGHLIGHTS: RWJF Health & Society Scholars (HSS) program outcomes evaluated. HSS alumni have higher scholarly productivity and impact than control group. HSS alumni are more engaged in population health research than controls. HSS alumni and controls are similar on other outcome measures. Training programs can be evaluated with adequate attention to selection bias.