Promoting sleep in low-income older adults with disabilities: Comparing CAPABLE with a social engagement control.

Sleep plays a vital role in older adults' health. The Community Aging in Place-Advancing Better Living for Elders (CAPABLE) trial, conducted in Maryland between 2012 and 2016, is a 5-month biobehavioral environmental intervention study to reduce functional disabilities in 300 low-income older adults. Individual and environmental factors impacting sleep were addressed in CAPABLE. This secondary data analysis was to test the preliminary effect of CAPABLE on actigraph-measured sleep, compared with a social engagement control in 73 CAPABLE participants with pretest-posttest actigraph data. Participants in this analysis were aged 75.8±7.5 years; 86.3% of them were females and 84.9% were Black/African Americans. Both CAPABLE intervention and social engagement control improved sleep efficiency and reduced sleep onset latency. The effect of CAPABLE on sleep was comparable to social engagement. These findings underline the importance of promoting physical function and maintaining social activity for sleep in low-income older adults with disabilities.

Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment.

BACKGROUND: The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. METHODS: We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. RESULTS: Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. CONCLUSIONS: Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Predicting geographical variation in health-related quality of life.

Goods and services provided by businesses can either promote health or represent an additional risk factor. We assessed the association between business pattern indicators and the prevalence of adult obesity, diabetes, physical inactivity, fair or poor health and frequent physical and mental distress. Data on business types were obtained from the 2013 U.S. Census Bureau County Business Patterns. County health data were obtained from the Centers for Disease Control and Prevention Diabetes Interactive Atlas, Behavior Risk Factor Surveillance System and Fatality Analysis Reporting System. We explored the relationship at county level using the global (Ordinary Least Square regression) and local (Geographically Weighted Regression (GWR)) models in 3108 U.S. counties. Density of full service restaurants and fitness centers was associated with a significant decrease in adult obesity, diabetes, fair or poor health, physical inactivity, physical and mental distress. Conversely, density of payday loan centers was associated with an increase in these adverse health outcomes. However, our GWR models revealed substantial geographical variations in these relationships across the U.S. counties. Better understanding of the association between area-level structures and important health outcomes at the local level is important for developing targeted context-specific policy interventions. Full service restaurants and fitness centers may provide places for people to access higher quality food, socialize and exercise. Conversely, payday loans provide an expensive form of short-term credit and this debt may degrade an individual or family's ability to achieve or maintain health. Our study emphasizes the influence of local built environment characteristics on important health outcomes.

Trajectories in county-level Low Birthweight Rates and Associated Contextual Factors in the United States, 2016-2021.

Introduction: Infants with low birthweight (less than 2500 grams) have greater risk of mortality, long-term neurologic disability and chronic diseases such as diabetes and cardiovascular disease as compared to infants with normal birthweight. This study examined the trajectories of low birthweight rate in the U.S. across the metropolitan and non-metropolitan counties over the time period of 2016-2021 and the associated contextual factors. Methods: This longitudinal study utilized data on 21,759,834 singleton births across 3,108 counties. Data on birthweight and maternal sociodemographic and behavioral characteristics was obtained from the National Center for Health Statistics. A generalized estimating equations model was used to examine the association of county-level contextual variables with low birthweight rates. Results: A significant increase in low birthweight rates was observed across the counties over the duration of the study. Large metro and small metro counties had significantly higher low birthweight rates as compared to non-metro counties. High percentage of Black women, underweight women, age more than 35 years, lack of prenatal care, uninsured population, and high violent crime rate was associated with an increase in low-birth-weight rates. Other contextual characteristics (percentage of married women, American Indian/Alaskan Native women, and unemployed population) differed in their associations with low birthweight rates depending on county metropolitan status. Conclusions: Our study findings emphasize the importance of developing interventions to address geographical heterogeneity in low birthweight burden, particularly for metropolitan areas and communities with vulnerable racial/ethnic and socioeconomic groups.

Cerebrospinal fluid eosinophils in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease.

BACKGROUND: Eosinophils in cerebrospinal fluid (CSF) are an uncommon finding most often associated with parasitic infections, but have also been described in some neuroinflammatory disorders. Eosinophilic infiltration is not thought to be a typical feature of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). We aim to describe the rate of CSF eosinophil positivity in a cohort of pediatric MOGAD patients. METHODS: Single-center retrospective chart review of pediatric MOGAD patients. Clinical and laboratory data was collected from the electronic medical record and analyzed. RESULTS: Of 46 pediatric patients with positive serum myelin oligodendrocyte glycoprotein antibody (MOG-IgG) identified, 38 patients fulfilling internationally proposed MOGAD diagnostic criteria were included for analysis. 6 patients with MOGAD were excluded as no CSF data was available, and 2 patients with positive MOG-IgG but diagnosis more consistent with MS were excluded. Median age was 7.3 years, and 19/38 (50 %) were female. Acute disseminated encephalomyelitis (ADEM) was the most common presenting phenotype (23/38, 61 %), and other phenotypes included optic neuritis (10/38, 26 %), transverse myelitis (3/38, 8 %), and neuromyelitis optica spectrum disorder (NMOSD) (2/38, 5 %). 12 of 36 (33 %) patients with all lumbar puncture (LP) data available had CSF eosinophils present, with eosinophil mean of 3 % and range from 1 % to 18 % of CSF while blood cells. CONCLUSION: CSF eosinophils were present in one third of pediatric MOGAD patients, which is a higher rate than previously reported in either MOGAD or aquaporin-4 antibody positive NMOSD cohorts. Understanding the CSF composition of pediatric MOGAD patients helps to facilitate more prompt diagnosis and treatment and may shed light onto underlying pathologic mechanisms of disease with the goal to inform future therapeutic targets.

Pubertal Suppression in Early Puberty Followed by Testosterone Mildly Increases Final Height in Transmasculine Youth.

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.

Neighborhood location and nutritional resources as a risk factor for congenital heart disease in the fetus.

BACKGROUND: Congenital heart disease (CHD) is the most common birth defect, influenced by maternal health, environmental conditions, and genetics. Maternal health and nutrition, particularly maternal diabetes, is a modifiable risk factor for development of CHD in the fetus. However, the importance of food access during pregnancy on the development of CHD remains unknown. The objective of this study was to investigate the association between maternal neighborhood characteristics, particularly food access, and occurrence of prenatally diagnosed CHD. METHODS: A retrospective case series studied maternal-fetal dyads with prenatally diagnosed CHD between 2019 and 2021 in Washington, DC. Moran's I of maternal addresses evaluated geographic clustering of disease. Negative binomial regression assessed association between census tract demographics and population-adjusted CHD rate. RESULTS: A total of 307 dyads were analyzed. Global Moran's I showed significant CHD clustering (p-value = .004). However, degree of clustering was not clinically meaningful. After adjusting for neighborhood socioeconomic status, residing in food deserts was not a predictor for CHD. However, neighborhoods with a higher percentage of households receiving Supplemental Nutrition Assistance Program (SNAP) benefits were associated with higher rates of conotruncal heart defects (Incident Rate Ratio [IRR] = 1.04, CI = 1.01-1.08) and aggregate CHD (IRR = 1.03, CI = 1.01-1.05). CONCLUSIONS: Neighborhood location and food access were not associated with CHD. However, increased enrollment in SNAP was associated with higher rates of CHD. The association between CHD and SNAP benefits warrants further exploration. Understanding food access and maternal nutrition may illuminate disparities in the burden of CHD.

Food insecurity gaps in the Supplemental Nutrition Assistance Program based on disability status.

BACKGROUND: Households including someone with disabilities experience disproportionately high food insecurity rates and likely face disproportionate barriers accessing Supplemental Nutrition Assistance Program (SNAP) benefits. OBJECTIVE: This article aims to examine the role of SNAP with regard to food insecurity disparities based on disability status. METHODS: Modified Poisson regression models examined food insecurity risk based on disability status (household includes no one with disabilities vs. those with work-limiting disabilities or non-work-limiting disabilities) among 2018 Survey of Income and Program Participation households eligible for SNAP (income ≤130% of the poverty threshold). Weighted estimates were used to account for the study design and non-response. RESULTS: Households including someone with work-limiting disabilities were more than twice as likely to be food insecure than households including no one with disabilities (PR = 2.16, 95% CI: 1.90, 2.45); households including someone with non-work-limiting disabilities were 65% more likely (PR = 1.65, 95% CI: 1.39, 1.95). However, disparities were more pronounced among households not participating in SNAP (PR = 2.67, 95% CI: 2.22, 3.23 for work-limiting disabilities and PR = 1.86, 95% CI: 1.44, 2.40 for non-work-limiting disabilities) than SNAP-participating households (PR = 1.71, 95% CI: 1.45, 2.03 and PR = 1.46, 95% CI: 1.17, 1.82, respectively). Approximately 4.2 million low-income U.S. households including someone with disabilities are food insecure. Of these, 1.4 million were not participating in SNAP and another 2.8 million households were food insecure despite participating in SNAP. CONCLUSIONS: Access to SNAP benefits is not proportionate to the scale of food insecurity among households that include people with disabilities. Action is needed to strengthen food assistance for those with disabilities.

Assessing the Effect of Augmented Reality on Procedural Outcomes During Ultrasound-Guided Vascular Access.

OBJECTIVE: Augmented reality devices are increasingly accepted in health care, though most applications involve education and pre-operative planning. A novel augmented reality ultrasound application, HoloUS, was developed for the Microsoft HoloLens 2 to project real-time ultrasound images directly into the user's field of view. In this work, we assessed the effect of using HoloUS on vascular access procedural outcomes. METHODS: A single-center user study was completed with participants with (N = 22) and without (N = 12) experience performing ultrasound-guided vascular access. Users completed a venipuncture and aspiration task a total of four times: three times on study day 1, and once on study day 2 between 2 and 4 weeks later. Users were randomized to use conventional ultrasound during either their first or second task and the HoloUS application at all other times. Task completion time, numbers of needle re-directions, head adjustments and needle visualization rates were recorded. RESULTS: For expert users, task completion time was significantly faster using HoloUS (11.5 s, interquartile range [IQR] = 6.5-23.5 s vs. 18.5 s, IQR = 11.0-36.5 s; p = 0.04). The number of head adjustments was significantly lower using the HoloUS app (1.0, IQR = 0.0-1.0 vs. 3.0, IQR = 1.0-5.0; p < 0.0001). No significant differences were identified in other measured outcomes. CONCLUSION: This is the first investigation of augmented reality-based ultrasound-guided vascular access using the second-generation HoloLens. It demonstrates equivalent procedural efficiency and accuracy, with favorable usability, ergonomics and user independence when compared with traditional ultrasound techniques.

Supplemental Nutrition Assistance Program Access and Racial Disparities in Food Insecurity.

Importance: Racially minoritized people experience disproportionately high rates of food insecurity. The Supplemental Nutrition Assistance Program (SNAP) reduces food insecurity. Objective: To evaluate SNAP access with regard to racial disparities in food insecurity. Design, Setting, and Participants: This cross-sectional study used data from the 2018 Survey of Income and Program Participation (SIPP). On the basis of random sampling strategies, 44 870 households were eligible for the SIPP, and 26 215 (58.4%) participated. Sampling weights accounted for survey design and nonresponse. Data were analyzed from February 25 to December 12, 2022. Exposures: This study examined disparities based on household racial composition (entirely Asian, entirely Black, entirely White, and multiple races or multirace based on SIPP categories). Main Outcomes and Measures: Food insecurity during the prior year was measured using the validated 6-item US Department of Agriculture Food Security Survey Module. SNAP participation during the prior year was classified based on whether anyone in the household received SNAP benefits. Modified Poisson regression tested hypothesized disparities in food insecurity. Results: A total of 4974 households that were eligible for SNAP (income ≤130% of the poverty threshold) were included in this study. A total of 218 households (5%) were entirely Asian, 1014 (22%) were entirely Black, 3313 (65%) were entirely White, and 429 (8%) were multiracial or of other racial groups. Adjusting for household characteristics, households that were entirely Black (prevalence rate [PR], 1.18; 95% CI, 1.04-1.33) or multiracial (PR, 1.25; 95% CI, 1.06-1.46) were more likely to be food insecure than entirely White households, but associations differed depending on SNAP participation. Among households that did not participate in SNAP, those that were entirely Black (PR, 1.52; 97.5% CI, 1.20-1.93) or multiracial (PR, 1.42; 97.5% CI, 1.04-1.94) were more likely to be food insecure than White households; however, among SNAP participants, Black households were less likely than White households to be food insecure (PR, 0.84; 97.5% CI, 0.71-0.99). Conclusions and Relevance: In this cross-sectional study, racial disparities in food insecurity were found among low-income households that do not participate in SNAP but not among those that do, suggesting that access to SNAP should be improved. These results also highlight the need to examine the structural and systemic racism in food systems and in access to food assistance that may contribute to disparities.

Diet during the COVID-19 pandemic: An analysis of Twitter data.

In this study, we measured the association between county characteristics and changes in healthy-food, fast-food, and alcohol tweets during the COVID-19 pandemic in the United States. Our analytic dataset consisted of 1,282,316 geotagged tweets that referenced food consumption posted before (63.2%) and during (36.8%) the pandemic and included all US states. We found the share of healthy-food tweets increased by 20.5% during the pandemic compared with pre-pandemic, while fast-food and alcohol tweets decreased by 9.4% and 11.4%, respectively. We also observed that time spent at home and more grocery stores per capita were associated with increased odds of healthy-food tweets and decreased odds of fast-food tweets. More liquor stores per capita was associated with increased odds of alcohol tweets. Our results highlight the potential impact of the pandemic on nutrition and alcohol consumption and the association between the built environment and health behaviors.

The effect of COVID-19 pandemic-related financial challenges on mental health and well-being among US older adults.

BACKGROUND: Despite profound financial challenges during the COVID-19 pandemic, there is a gap in estimating their effects on mental health and well-being among older adults. METHODS: The National Health and Aging Trends Study is an ongoing nationally representative cohort study of US older adults. Outcomes included mental health related to COVID-19 (scores averaged across eight items ranging from one to four), sleep quality during COVID-19, loneliness during COVID-19, having time to yourself during COVID-19, and hopefulness during COVID-19. Exposures included income decline during COVID-19 and financial difficulty due to COVID-19. Propensity score weighting produced covariate balance for demographic, socioeconomic, household, health, and well-being characteristics that preceded the pandemic to estimate the average treatment effect. Sampling weights accounted for study design and non-response. RESULTS: In weighted and adjusted analyses (n = 3257), both income decline during COVID-19 and financial difficulty due to COVID-19 were associated with poorer mental health related to COVID-19 (b = -0.159, p < 0.001 and b = -0.381, p < 0.001, respectively), poorer quality sleep (OR = 0.63, 95% CI: 0.46, 0.86 and OR = 0.42, 95% CI: 0.30, 0.58, respectively), more loneliness (OR = 1.53, 95% CI: 1.16, 2.02 and OR = 2.72, 95% CI: 1.96, 3.77, respectively), and less time to yourself (OR = 0.54, 95% CI: 0.40, 0.72 and OR = 0.37, 95% CI: 0.27, 0.51, respectively) during COVID-19. CONCLUSIONS: Pandemic-related financial challenges are associated with worse mental health and well-being regardless of pre-pandemic characteristics, suggesting that they are distinct social determinants of health for older adults. Timely intervention is needed to support older adults experiencing pandemic-related financial challenges.

Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017-2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10-27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders-controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5-10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients' health by further considering patients' residential environments, which present both risks and resources.

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes.

OBJECTIVES: Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. METHODS: We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non-single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). RESULTS: Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non-single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. CONCLUSIONS: The use of computer vision and big data image sources makes possible national studies of the built environment's effects on health, producing data and results that may inform national and local decision-making.

Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes.

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.

Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter.

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 < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.

Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample.

BACKGROUND: Social 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: Individuals 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. CONCLUSIONS: Twitter-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.

Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images.

Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States.

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

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.