Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

Background In the U.S., where coronary heart disease (CHD) is the leading cause of mortality, CHD risk assessment is a priority and accurate blood pressure (BP) measurement is essential. Methods Hypertension estimates in the National Longitudinal Study of Adolescent Health (Add Health), Wave IV (2008)—a nationally-representative field study of 15,701 participants aged 24–32—was referenced against NHANES (2007–2008) participants of the same age. We examined discordances in hypertension, and estimated the accuracy and reliability of blood pressure in the Add Health study. Results Hypertension rates (BP ≥ 140/90 mm Hg) were higher in Add Health compared with NHANES (19% versus 4%), but self-reported history was similar (11% versus 9%) among adults aged 24–32. Survey weights and adjustments for differences in participant characteristics, examination time, use of anti-hypertensive medications, and consumption of food/caffeine/cigarettes before blood pressure measurement had little effect on between-study differences in hypertension estimates. Among Add Health participants interviewed and examined twice (full and abbreviated interviews), blood pressure was similar, as was blood pressure at the in-home and in-clinic exams conducted by NHANES III (1988–1994). In Add Health, there was minimal digit preference in blood pressure measurements; mean bias never exceeded 2 mm Hg; and reliability (estimated as intra-class correlation coefficients) was 0.81 and 0.68 for systolic and diastolic BPs, respectively. Conclusions The proportion of young adults in NHANES reporting a history of hypertension was twice that with measured hypertension, whereas the reverse was found in Add Health. Between-survey differences were not explained by digit preference, low validity, or reliability of Add Health blood pressure data, or by salient differences in participant selection, measurement context, or interview content. The prevalence of hypertension among Add Health Wave IV participants suggests an unexpectedly high risk of cardiovascular disease among U.S. young adults and warrants further scrutiny.

No abstract

BackgroundStudies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research.ObjectiveThe aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors.MethodsWe utilized Twitter’s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics.ResultsMachine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic.ConclusionsMachine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lowe...

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.