Q C Nguyen1, H Meng2, D Li3, S Kath4, M McCullough5, D Paul4, P Kanokvimankul2, T X Nguyen6, F Li4. 1. Department of Health, Kinesiology, and Recreation, College of Health, University of Utah, Salt Lake City, United States. Electronic address: quynh.nguyen@health.utah.edu. 2. Department of Health, Kinesiology, and Recreation, College of Health, University of Utah, Salt Lake City, United States. 3. Center for Systems Integration and Sustainability, Michigan State University, East Lansing, United States. 4. School of Computing, University of Utah, Salt Lake City, United States. 5. Department of Geography, University of Utah, Salt Lake City, United States. 6. Department of Epidemiology and Biostatistics, UCSF School of Medicine, San Francisco, United States.
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.
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.
Authors: Nancy L Keating; A James O'Malley; Joanne M Murabito; Kirsten P Smith; Nicholas A Christakis Journal: Cancer Date: 2011-01-24 Impact factor: 6.860
Authors: Joseph J Deferio; Scott Breitinger; Dhruv Khullar; Amit Sheth; Jyotishman Pathak Journal: J Am Med Inform Assoc Date: 2019-08-01 Impact factor: 4.497
Authors: Kelly J Thomas Craig; Nicole Fusco; Thrudur Gunnarsdottir; Luc Chamberland; Jane L Snowdon; William J Kassler Journal: Online J Public Health Inform Date: 2021-12-24
Authors: Thu T Nguyen; Hsien-Wen Meng; Sanjeev Sandeep; Matt McCullough; Weijun Yu; Yan Lau; Dina Huang; Quynh C Nguyen Journal: Comput Human Behav Date: 2018-08-09
Authors: Thu T Nguyen; Shaniece Criss; Amani M Allen; M Maria Glymour; Lynn Phan; Ryan Trevino; Shrikha Dasari; Quynh C Nguyen Journal: Int J Environ Res Public Health Date: 2019-05-18 Impact factor: 3.390
Authors: Christina Mair; Jessica Frankeberger; Paul J Gruenewald; Christopher N Morrison; Bridget Freisthler Journal: Curr Epidemiol Rep Date: 2019-09-13
Authors: Karen D Davis; Nima Aghaeepour; Andrew H Ahn; Martin S Angst; David Borsook; Ashley Brenton; Michael E Burczynski; Christopher Crean; Robert Edwards; Brice Gaudilliere; Georgene W Hergenroeder; Michael J Iadarola; Smriti Iyengar; Yunyun Jiang; Jiang-Ti Kong; Sean Mackey; Carl Y Saab; Christine N Sang; Joachim Scholz; Marta Segerdahl; Irene Tracey; Christin Veasley; Jing Wang; Tor D Wager; Ajay D Wasan; Mary Ann Pelleymounter Journal: Nat Rev Neurol Date: 2020-06-15 Impact factor: 42.937