Social media indicators of the food environment and state health outcomes. (July 2017)

• Record Type: Journal Article
• Authors: Nguyen, Q.C.; Meng, H.; Li, D.; Kath, S.; McCullough, M.; Paul, D.; Kanokvimankul, P.; Nguyen, T.X.; Li, F.
• Is Part Of: Public health. Volume 148(2017)
• Page Start: 120
• Page End: 128
• Publication Date: 2017-07
• Subjects: Social determinants -- Social media -- Geography -- Food and nutrition -- Chronic disease -- Data mining; Public health -- Periodicals; Public health -- Periodicals; Electronic journals; 362.1
• DOI: 10.1016/j.puhe.2017.03.013
• Languages: English
• ISSNs: 0033-3506

Abstract

Abstract: Objectives: Contextual factors can influence health through exposures to health-promoting and risk-inducing factors. The aim of this study was to (1) build, from geotagged Twitter and Yelp data, a national food environment database and (2) to test associations between state food environment indicators and health outcomes. Study design: This is a cross-sectional study based upon secondary analyses of publicly available data. Methods: Using Twitter's Streaming Application Programming Interface (API), we collected and processed 4, 041, 521 food-related, geotagged tweets between April 2015 and March 2016. Using Yelp's Search API, we collected data on 505, 554 unique food-related businesses. In linear regression models, we examined associations between food environment characteristics and state-level health outcomes, controlling for state-level differences in age, percent non-Hispanic white, and median household income. Results: A one standard deviation increase in caloric density of food tweets was related to higher all-cause mortality (+46.50 per 100, 000), diabetes (+0.75%), obesity (+1.78%), high cholesterol (+1.40%), and fair/poor self-rated health (2.01%). More burger Yelp listings were related to higher prevalence of diabetes (+0.55%), obesity (1.35%), and fair/poor self-rated health (1.12%). More alcohol tweets and Yelp bars and pub listings were related to higher state-level binge drinking and heavy drinking, but lower mortality and lower percent reporting fair/poor self-rated health. Supplemental analyses with county-level social media indicators and county health outcomes resulted in finding similar but slightly attenuated associations compared to those found at the state level. Conclusions: Social media can be utilized to create indicators of the food environment that are associated with area-level mortality, health behaviors, and chronic conditions. Highlights: We constructed indicators of food type and caloric density of foods from tweets. We utilized Yelp data to track popularity of cuisines. High caloric foods were associated with higher mortality and chronic conditions. Cafes were associated with lower mortality and lower chronic conditions. Alcohol tweets and bars were linked with lower mortality but higher excessive drinking.