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They are one of the four research groups chosen for the data contract

Decades of neglect have left India’s public health system with a very weak arsenal to fight and eliminate contagious diseases

Telephone survey shows high understanding of social distancing, support for lockdown

Cropper discusses a recent working paper that assesses the full benefits and costs of the groundbreaking law’s many programs to protect the environment.

MPRC Special Workshop

Public Beliefs about Housing Affordability as a Key Social Determinant of Health and its Impact on Support for Affordable Housing Policy

Structural Racism and Population Health

Alternative Trajectories in Body Weight, Mental and Cognitive Health among Older Americans: Roles of Genetics and Earlier SES

Race differences in school attendance across the Jim Crow South and its implications for Black-White disparities in cognitive impairment risk among older adults

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.