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Although tobacco smoking, pan chewing and alcohol drinking are important risk factors for head and neck cancer (HNC), the HNC risks conferred by products available in Nepal for these habits are unknown. We assessed the associations of tobacco smoking, chewing habits, and alcohol drinking with HNC risk in Nepal. A case–control study was conducted in Nepal with 549 incident HNC cases and 601 controls. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional logistic regression adjusting for potential confounders. We observed increased HNC risk for tobacco smoking (OR: 1.54; 95% CI: 1.14, 2.06), chewing habits (OR: 2.39; 95% CI: 1.77, 3.23), and alcohol drinking (OR: 1.57; 95% CI: 1.14, 2.18). The population attributable fraction (PAF) was 24.3% for tobacco smoking, 39.9% for chewing habits and 23.0% for alcohol drinking. Tobacco smoking, chewing habits, and alcohol drinking might be responsible for 85.3% of HNC cases. Individuals who smoked tobacco, chewed products and drank alcohol had a 13‐fold increase in HNC risk (OR: 12.83; 95% CI: 6.91, 23.81) compared to individuals who did not have any of these habits. Both high frequency and long duration of these habits were strong risk factors for HNC among the Nepalese with clear dose–response trends. Preventive strategies against starting these habits and support for quitting these habits are necessary to decrease the incidence of HNC in Nepal.

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.

  BACKGROUND: The significance of the timing and chronicity of childhood adversity for depression outcomes later in life is unclear. Identifying trajectories of adversity throughout childhood would allow classification of children according to the accumulation, timing, and persistence of adversity, and may provide unique insights into the risk of subsequent depression. METHODS: Using data from the Avon Longitudinal Study of Parents and Children, we created a composite adversity score comprised of 10 prospectively assessed domains (e.g., violent victimization, inter-parental conflict, and financial hardship) for each of eight time points from birth through age 11.5 years. We used semiparametric group-based trajectory modeling to derive childhood adversity trajectories and examined the association between childhood adversity and depression outcomes at the age of 18 years. RESULTS: Among 9,665 participants, five adversity trajectories were identified, representing stable-low levels (46.3%), stable-mild levels (37.1%), decreasing levels (8.9%), increasing levels (5.3%), and stable-high levels of adversity (2.5%) from birth through late childhood. Approximately 8% of the sample met criteria for probable depression at 18 years and the mean depression severity score was 3.20 (standard deviation = 3.95, range 0-21). The risk of depression in young adulthood was elevated in the decreasing (odds ratio [OR] = 1.72, 95% confidence interval [CI] = 1.19-2.48), increasing (OR = 1.81, 95% CI = 1.15-2.86), and stable-high (OR = 1.80, 95% CI = 1.00-3.23) adversity groups, compared to those with stable-low adversity, when adjusting for potential confounders. CONCLUSIONS: Children in trajectory groups characterized by moderate or high levels of adversity at some point in childhood exhibited consistently greater depression risk and depression severity, regardless of the timing of adversity.

Jones awarded NEH grant to document 100 years of women veteran's war experiences

When is Hope Enough? Hopefulness, Discrimination and Racial Disparities in Physiological Dysregulation

Center for Global Migration Studies panel

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.

New National Institute on Minority Health and Health Disparities grant

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.

Sonalde Desai and Reeve Vanneman study the "Determinants of Maternal and Child Health in India"