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Center for Global Migration Studies panel

Kennth Leonard investigates knowledge-practice gaps; emphasizes importance of indigenous solutions

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"

Erich Battistin examines a relatively understudied early-childhood intervention for low birth-weight infants using new policy parameter

Faculty Associate Frauke Kreuter's project, an R01 funded by the National Institute of Mental Health, seeks to develop and assess new statistical methods

Utilization of cost-effective essential preventive health services increased after the implementation of the Affordable Care Act’s (ACA) provision that non-grandfathered private insurers provide cost-effective preventive services without cost sharing in 2010. Little is known, however, whether this change is also observed among Asians in the US. We examined patterns of preventive services utilization among Asian subgroups relative to non-Latino whites (whites) after the implementation of the ACA’s preventive services provisions. Using 2013–2016 Medical Expenditure Panel Survey data, we examined utilization trends in preventive services among Asian Indians, Chinese, Filipinos, and other Asians relative to whites. We also ran logistic regression models to estimate the likelihood of having received each of the seven essential preventive services (routine checkups, flu vaccinations, cholesterol screenings, blood pressure checkups, Papanicolaou “pap” tests, mammograms, and colorectal cancer screenings). Compared to whites, Asians had higher rates of utilization of routine checkups, cholesterol screenings, and flu vaccinations, but they had lower utilization rates of blood pressure checkups, pap tests, and mammograms. The patterns of preventive services utilization differed across the Asian subgroups. All Asian subgroups, except for Filipinos, were less likely to have pap tests or mammograms than whites. Moreover, we observed a decreasing trend in having pap tests, mammograms, or colorectal cancer screenings among all Asian subgroups between 2013 and 2016. Our findings suggest that there are low cancer screening rates across Asian subgroups. This indicates the need for programs tailored to specific Asian subgroups to improve cancer screening.

Students with chronic health conditions miss more school days than their peers and are at increased risk for performing worse on standardized tests and not completing a high school degree. University-based researchers, state government leaders, and a local county school system collaborated to use existing health and academic data to (1) evaluate the strength of the relationship between health status and school performance (absenteeism, grades) and (2) describe the health status of students who are chronically absent. Analyses included descriptive statistics, chi-square tests, negative binomial regression models, and estimated marginal means. The most common health conditions among the 3,663 kindergarten through Grade 12 students were ADD (attention deficit disorder)/ADHD (attention deficit hyperactivity disorder), asthma, migraine headaches, mental health conditions, and eczema/psoriasis/skin disorders. After controlling for covariates, having asthma or a mental health diagnosis was positively associated with absences; and having an ADD/ADHD or mental health diagnosis was negatively associated with GPA (grade point average). Chronically absent students had significantly lower GPAs, and a higher number of health conditions than other students. The success of this demonstration project encourages strengthening existing collaborations and establishing new multidisciplinary partnerships to analyze existing data sources to learn more about the relationship between student health and academic achievement. Moreover, connecting health status to academic achievement might be a chief tactic for advocating for additional resources to improve the care and management of chronic disease conditions among students.