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BackgroundSocial media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. ObjectiveThis study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. MethodsWe collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter’s Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. ResultsIndividuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI –1.10 to –0.20) and 0.85 kg/m2 (95% CI –1.48 to –0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively. ConclusionsTwitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16-29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10-26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12-20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names "Ahmaud Arbery," "Breonna Taylor," and "George Floyd" were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019-September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4-9 to 33.66% in June 1-7) (p

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.

Context Treatment for transmasculine youth (TMY) can involve testosterone treatment and is sometimes preceded by gonadotropin-releasing hormone agonist (GnRHa) treatment for puberty blockade. GnRHas can increase final height in birth-assigned females with central precocious puberty. Maximizing final adult height (FAH) is an important outcome for many TMY. Objective Our objective was to determine how GnRHa treatment before testosterone impacts FAH. Methods Retrospective cohort study at 5 US transgender health clinics. Participants were 32 TMY treated with GnRHas in early to midpuberty before testosterone (GnRHa + T group) and 62 late/postpubertal TMY treated with testosterone only (T-only group). Results The difference between FAH minus midparental target height (MPTH) was +2.3 ± 5.7 cm and −2.2 ± 5.6 cm in the GnRHa + T and T-only groups, respectively (P <.01). In the GnRHa + T group, FAH was 1.8 ± 3.4 cm greater than predicted adult height (PAH) (P <.05) and FAH vs initial height (IH) z-score was 0.5 ± 1.2 vs 0.16 ± 1.0 (P <.05). After adjusting for patient characteristics, each additional month of GnRHa monotherapy increased FAH by 0.59 cm (95% CI 0.31, 0.9 cm), stage 3 breast development at start of GnRHa was associated with 6.5 cm lower FAH compared with stage 2 (95% CI −10.43, −2.55), and FAH was 7.95 cm greater in the GnRHa + T group than in T-only group (95% CI −10.85, −5.06). Conclusion Treatment with GnRHa in TMY in early puberty before testosterone increases FAH compared with MPTH, PAH, IH, and TMY who only received testosterone in late/postpuberty. TMY considering GnRHas should be counseled that GnRHas may mildly increase their FAH if started early. [ABSTRACT FROM AUTHOR]