Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research.

BACKGROUND: Neighbourhood quality has been connected with an array of health issues, but neighbourhood research has been limited by the lack of methods to characterise large geographical areas. This study uses innovative computer vision methods and a new big data source of street view images to automatically characterise neighbourhood built environments. METHODS: A total of 430 000 images were obtained using Google's Street View Image API for Salt Lake City, Chicago and Charleston. Convolutional neural networks were used to create indicators of street greenness, crosswalks and building type. We implemented log Poisson regression models to estimate associations between built environment features and individual prevalence of obesity and diabetes in Salt Lake City, controlling for individual-level and zip code-level predisposing characteristics. RESULTS: Computer vision models had an accuracy of 86%-93% compared with manual annotations. Charleston had the highest percentage of green streets (79%), while Chicago had the highest percentage of crosswalks (23%) and commercial buildings/apartments (59%). Built environment characteristics were categorised into tertiles, with the highest tertile serving as the referent group. Individuals living in zip codes with the most green streets, crosswalks and commercial buildings/apartments had relative obesity prevalences that were 25%-28% lower and relative diabetes prevalences that were 12%-18% lower than individuals living in zip codes with the least abundance of these neighbourhood features. CONCLUSION: Neighbourhood conditions may influence chronic disease outcomes. Google Street View images represent an underused data resource for the construction of built environment features.

Twitter-derived measures of sentiment towards minorities (2015-2016) and associations with low birth weight and preterm birth in the United States.

INTRODUCTION: The objective of this study was to investigate the association between state-level publicly expressed sentiment towards racial and ethnic minorities and birth outcomes for mothers who gave birth in that state. METHODS: We utilized Twitter's Streaming Application Programming Interface (API) to collect 1,249,653 tweets containing at least one relevant keyword pertaining to a racial or ethnic minority group. State-level derived sentiment towards racial and ethnic minorities were merged with data on all 2015 U.S. births (N=3.99 million singleton births). RESULTS: Mothers living in states in the lowest tertile of positive sentiment towards racial/ethnic minorities had greater prevalences of low birth weight (+6%), very low birth weight (+9%), and preterm birth (+10%) compared to mothers living in states in the highest tertile of positive sentiment, controlling for individual-level maternal characteristics and state demographic characteristics. Sentiment towards specific racial/ethnic groups showed a similar pattern. Mothers living in states in the lowest tertile of positive sentiment towards blacks had an 8% greater prevalence of low birth weight and very low birth weight, and a 16% greater prevalence of preterm birth, compared to mothers living in states in the highest tertile. Lower state-level positive sentiment towards Middle Eastern groups was also associated with a 4-13% greater prevalence of adverse birth outcomes. Results from subgroup analyses restricted to racial/ethnic minority mothers did not differ substantially from those seen for the full population of mothers. CONCLUSIONS: More negative area-level sentiment towards blacks and Middle Eastern groups was related to worse individual birth outcomes, and this is true for the full population and minorities.

Twitter-derived neighborhood characteristics associated with obesity and diabetes.

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.

Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015-2016.

OBJECTIVES: To leverage geotagged Twitter data to create national indicators of the social environment, with small-area indicators of prevalent sentiment and social modeling of health behaviors, and to test associations with county-level health outcomes, while controlling for demographic characteristics. METHODS: We used Twitter's streaming application programming interface to continuously collect a random 1% subset of publicly available geo-located tweets in the contiguous United States. We collected approximately 80 million geotagged tweets from 603 363 unique Twitter users in a 12-month period (April 2015-March 2016). RESULTS: Across 3135 US counties, Twitter indicators of happiness, food, and physical activity were associated with lower premature mortality, obesity, and physical inactivity. Alcohol-use tweets predicted higher alcohol-use-related mortality. CONCLUSIONS: Social media represents a new type of real-time data that may enable public health officials to examine movement of norms, sentiment, and behaviors that may portend emerging issues or outbreaks-thus providing a way to intervene to prevent adverse health events and measure the impact of health interventions.