Abstract
Neighborhood characteristics are increasingly connected with health outcomes. Social processes affect health through the maintenance of social norms, stimulation of new interests, and dispersal of knowledge. We created zip code level indicators of happiness, food, and physical activity culture from geolocated Twitter data to examine the relationship between these neighborhood characteristics and obesity and diabetes diagnoses (Type 1 and Type 2). We collected 422,094 tweets sent from Utah between April 2015 and March 2016. We leveraged administrative and clinical records on 1.86 million individuals aged 20 years and older in Utah in 2015. Individuals living in zip codes with the greatest percentage of happy and physically-active tweets had lower obesity prevalence—accounting for individual age, sex, nonwhite race, Hispanic ethnicity, education, and marital status, as well as zip code population characteristics. More happy tweets and lower caloric density of food tweets in a zip code were associated with lower individual prevalence of diabetes. Results were robust in sibling random effects models that account for family background characteristics shared between siblings. Findings suggest the possible influence of sociocultural factors on individual health. The study demonstrates the utility and cost-effectiveness of utilizing existing big data sources to conduct population health studies.
Highlights
Where we live, including the social, political, economic, and built environment, impacts health and creates health disparities
The aim of this study is to investigate associations between neighborhood characteristics and chronic disease, while accounting for potentially confounding individual and zip code level census characteristics
Each tweet was divided into tokens using the Stanford Tokenizer[45], an open access software tool that divides text into tokens, which roughly correspond to “words.” Below, we briefly describe algorithms we utilized to create variables for happiness, and references to food and physical activity from tweets
Summary
Where we live, including the social, political, economic, and built environment, impacts health and creates health disparities. Communities with higher happiness levels have residents with lower rates of obesity, hypertension, and suicide as well as increased life expectancy[24,25,26,27,28,29] Health behaviors, such as, healthy food consumption, health screening, smoking, alcohol consumption, drug use and poor sleep patterns have been observed to spread through social networks[30,31,32,33]. We create zip code level indicators of community happiness and social modeling of diet and physical activity from tweets (utilizing the location information of where the tweet was sent) We test these sociocultural contextual factors as predictors of individual level health outcomes. Combining Twitter data with other sources can allow for the investigation of geographic disparities in health outcomes
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