Neighborhood built environment, obesity, and diabetes: A Utah siblings study

Background

This study utilizes innovative computer vision methods alongside Google Street View images to characterize neighborhood built environments across Utah.

Methods

Convolutional Neural Networks were used to create indicators of street greenness, crosswalks, and building type on 1.4 million Google Street View images. The demographic and medical profiles of Utah residents came from the Utah Population Database (UPDB). We implemented hierarchical linear models with individuals nested within zip codes to estimate associations between neighborhood built environment features and individual-level obesity and diabetes, controlling for individual- and zip code-level characteristics (n = 1,899,175 adults living in Utah in 2015). Sibling random effects models were implemented to account for shared family attributes among siblings (n = 972,150) and twins (n = 14,122).

Results

Consistent with prior neighborhood research, the variance partition coefficients (VPC) of our unadjusted models nesting individuals within zip codes were relatively small (0.5%-5.3%), except for HbA1c (VPC = 23%), suggesting a small percentage of the outcome variance is at the zip code-level. However, proportional change in variance (PCV) attributable to zip codes after the inclusion of neighborhood built environment variables and covariates ranged between 11% and 67%, suggesting that these characteristics account for a substantial portion of the zip code-level effects. Non-single-family homes (indicator of mixed land use), sidewalks (indicator of walkability), and green streets (indicator of neighborhood aesthetics) were associated with reduced diabetes and obesity. Zip codes in the third tertile for non-single-family homes were associated with a 15% reduction (PR: 0.85; 95% CI: 0.79, 0.91) in obesity and a 20% reduction (PR: 0.80; 95% CI: 0.70, 0.91) in diabetes. This tertile was also associated with a BMI reduction of -0.68 kg/m2 (95% CI: -0.95, -0.40).

Conclusion

We observe associations between neighborhood characteristics and chronic diseases, accounting for biological, social, and cultural factors shared among siblings in this large population-based study.

Experiences of racism in the U.S. - A perspective from Asian & Pacific Islander, Black, Latina, and Middle Eastern women

Introduction

Racism is a critical social determinant of health because it can have a direct impact on health and well-being, as well as infiltrate systems, policies, and practices. Few studies have explored the similarities and differences of experiences with racism and health between different minoritized groups. The objective of this paper is to examine how racism influences life experiences from the perspectives of Asian & Pacific Islander, Black, Latina, and Middle Eastern women.

Methods

Eleven online racially/ethnically homogeneous focus groups with a total of 52 participants were conducted in the U.S., with representation from the North, South, and West coast. The online focus groups were recorded, transcribed, and two were translated into English (from Vietnamese and Spanish). The data was coded through NVivo and analyzed through a series of team meetings to establish themes.

Results

Participants reported experiences of racism and discrimination, including physical and verbal personal attacks. They shared the role of microaggressions in their daily life, along with the ubiquitous anti-Black sentiment discussed in every group. Our participants discussed the complexities of intersectionality in their experience of discrimination, specifically regarding immigration status, language spoken, and gender. Participants also reported the role of direct racism and vicarious racism (e.g., the experiences with racism of friends or family, awareness of racist incidents via the news) in affecting their mental health. Some effects were fear, stress, anxiety, depression, and self-censoring. For participants in the Black and Latina focus groups, mental health stressors often manifested into physical issues.

Discussion

Understanding the nuances in experiences across racial/ethnic groups is beneficial in identifying potential interventions to prevent and address racism and its negative health impacts.

Road Traffic Injuries and the Built Environment in Bogotá, Colombia, 2015-2019: A Cross-Sectional Analysis

Nine in 10 road traffic deaths occur in low- and middle-income countries (LMICs). Despite this disproportionate burden, few studies have examined built environment correlates of road traffic injury in these settings, including in Latin America. We examined road traffic collisions in Bogotá, Colombia, occurring between 2015 and 2019, and assessed the association between neighborhood-level built environment features and pedestrian injury and death. We used descriptive statistics to characterize all police-reported road traffic collisions that occurred in Bogotá between 2015 and 2019. Cluster detection was used to identify spatial clustering of pedestrian collisions. Adjusted multivariate Poisson regression models were fit to examine associations between several neighborhood-built environment features and rate of pedestrian road traffic injury and death. A total of 173,443 police-reported traffic collisions occurred in Bogotá between 2015 and 2019. Pedestrians made up about 25% of road traffic injuries and 50% of road traffic deaths in Bogotá between 2015 and 2019. Pedestrian collisions were spatially clustered in the southwestern region of Bogotá. Neighborhoods with more street trees (RR, 0.90; 95% CI, 0.82-0.98), traffic signals (0.89, 0.81-0.99), and bus stops (0.89, 0.82-0.97) were associated with lower pedestrian road traffic deaths. Neighborhoods with greater density of large roads were associated with higher pedestrian injury. Our findings highlight the potential for pedestrian-friendly infrastructure to promote safer interactions between pedestrians and motorists in Bogotá and in similar urban contexts globally.

Vigilance and Protection: How Asian and Pacific Islander, Black, Latina, and Middle Eastern Women Cope with Racism

BACKGROUND

Research is needed to fully investigate the differential mechanisms racial and ethnic groups use to deal with ongoing intersectional racism in women's lives. The aim of this paper was to understand how Asian American and Pacific Islander, Black, Latina, and Middle Eastern women experience racism-from personal perceptions and interactions to coping mechanisms and methods of protection.

METHODS

A purposive sample of 52 participants participated in 11 online racially/ethnically homogeneous focus groups conducted throughout the USA. A team consensus approach was utilized with codebook development and thematic analysis.

RESULTS

The findings relate to personal perceptions and interactions related to race and ethnicity, methods of protection against racism, vigilant behavior based on safety concerns, and unity across people of color. A few unique concerns by group included experiences of racism including physical violence among Asian American Pacific Islander groups, police brutality among Black groups, immigration discrimination in Latina groups, and religious discrimination in Middle Eastern groups. Changes in behavior for safety and protection include altering methods of transportation, teaching their children safety measures, and defending their immigration status. They shared strategies to help racial and ethnic minorities against racism including mental health resources and greater political representation. All racial and ethnic groups discussed the need for unity, solidarity, and allyship across various communities of color but for it to be authentic and long-lasting.

CONCLUSION

Greater understanding of the types of racism specific groups experience can inform policies and cultural change to reduce those factors.

Exploring Psychosocial and Structural Syndemic Effects as Predictors of HIV Risk Behaviors Among Black Women (HPTN 064)

Background: Syndemic models have been used in previous studies exploring HIV-related outcomes; however, these models do not fully consider intersecting psychosocial (e.g., substance use, depressive symptoms) and structural factors (unstable housing, concentrated housing vacancy) that influence the lived experiences of women. Therefore, there is a need to explore the syndemic effects of psychosocial and structural factors on HIV risk behaviors to better explain the multilevel factors shaping HIV disparities among black women. Methods: This analysis uses baseline data (May 2009-August 2010) from non-Hispanic black women enrolled in the HIV Prevention Trials Network 064 Women's Seroincidence Study (HPTN 064) and the American Community Survey 5-year estimates from 2007 to 2011. Three parameterizations of syndemic factors were applied in this analysis a cumulative syndemic index, three syndemic groups reflecting the level of influence (psychosocial syndemic group, participant-level structural syndemic group, and a neighborhood-level structural syndemic group), and syndemic factor groups. Clustered mixed effects log-binomial analyses measured the relationship of each syndemic parameterization on HIV risk behaviors in 1,347 black women enrolled in HPTN 064. Results: A higher syndemic score was significantly associated with increased prevalence of unknown HIV status of the last male sex partner (adjusted prevalence ratio (aPR) = 1.07, 95% confidence interval or CI 1.04-1.10), involvement in exchange sex (aPR = 1.17, 95% CI: 1.14-1.20), and multiple sex partners (aPR = 1.07, 95% CI: 1.06-1.09) in the last 6 months. A dose-response relationship was observed between the number of syndemic groups and HIV risk behaviors, therefore, being in multiple syndemic groups was significantly associated with increased prevalence of reporting HIV risk behaviors compared with being in one syndemic group. In addition, being in all three syndemic groups was associated with increased prevalence of unknown HIV status of the last male sex partner (aPR = 1.67, 95% CI: 1.43-1.95) and multiple sex partners (aPR = 1.53, 95% CI: 1.36-1.72). Conclusions: Findings highlight syndemic factors influence the lived experiences of black women.

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.

Rosie, a Health Education Question-and-Answer Chatbot for New Mothers: Randomized Pilot Study

BACKGROUND

Stark disparities exist in maternal and child outcomes and there is a need to provide timely and accurate health information.

OBJECTIVE

In this pilot study, we assessed the feasibility and acceptability of a health chatbot for new mothers of color.

METHODS

Rosie, a question-and-answer chatbot, was developed as a mobile app and is available to answer questions about pregnancy, parenting, and child development. From January 9, 2023, to February 9, 2023, participants were recruited using social media posts and through engagement with community organizations. Inclusion criteria included being aged ≥14 years, being a woman of color, and either being currently pregnant or having given birth within the past 6 months. Participants were randomly assigned to the Rosie treatment group (15/29, 52% received the Rosie app) or control group (14/29, 48% received a children's book each month) for 3 months. Those assigned to the treatment group could ask Rosie questions and receive an immediate response generated from Rosie's knowledgebase. Upon detection of a possible health emergency, Rosie sends emergency resources and relevant hotline information. In addition, a study staff member, who is a clinical social worker, reaches out to the participant within 24 hours to follow up. Preintervention and postintervention tests were completed to qualitatively and quantitatively evaluate Rosie and describe changes across key health outcomes, including postpartum depression and the frequency of emergency room visits. These measurements were used to inform the clinical trial's sample size calculations.

RESULTS

Of 41 individuals who were screened and eligible, 31 (76%) enrolled and 29 (71%) were retained in the study. More than 87% (13/15) of Rosie treatment group members reported using Rosie daily (5/15, 33%) or weekly (8/15, 53%) across the 3-month study period. Most users reported that Rosie was easy to use (14/15, 93%) and provided responses quickly (13/15, 87%). The remaining issues identified included crashing of the app (8/15, 53%), and users were not satisfied with some of Rosie's answers (12/15, 80%). Mothers in both the Rosie treatment group and control group experienced a decline in depression scores from pretest to posttest periods, but the decline was statistically significant only among treatment group mothers (P=.008). In addition, a low proportion of treatment group infants had emergency room visits (1/11, 9%) compared with control group members (3/13, 23%). Nonetheless, no between-group differences reached statistical significance at P<.05.

CONCLUSIONS

Rosie was found to be an acceptable, feasible, and appropriate intervention for ethnic and racial minority pregnant women and mothers of infants owing to the chatbot's ability to provide a personalized, flexible tool to increase the timeliness and accessibility of high-quality health information to individuals during a period of elevated health risks for the mother and child.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06053515; https://clinicaltrials.gov/study/NCT06053515.

A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021

BACKGROUND

Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This article provides practical guidance on using social media data for measuring public sentiment.

METHODS

We describe the main steps of such research, including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from 1 January 2011 to 31 December 2021 using Twitter's Application Programming Interface. We restricted analyses to US tweets in English using one or more 90 race-related keywords. We used a Support Vector Machine, a supervised machine learning model, for sentiment analysis.

RESULTS

The proportion of tweets referencing racially minoritized groups that were negative increased at the county, state, and national levels, with a 16.5% increase at the national level from 2011 to 2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared with all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group, such as the killing of George Floyd regarding sentiment of posts referencing Black people, discussions of the border crisis near the 2018 midterm elections and anti-Latinx sentiment, and the emergence of COVID-19 and anti-Asian sentiment.

CONCLUSIONS

This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.A supplemental digital video is available at http://links.lww.com/EDE/C91.

Racism During Pregnancy and Birthing: Experiences from Asian and Pacific Islander, Black, Latina, and Middle Eastern Women

BACKGROUND

Despite persistent racial disparities in maternal health in the USA, there is limited qualitative research on women's experiences of discrimination during pregnancy and childbirth that focuses on similarities and differences across multiple racial groups.

METHODS

Eleven focus groups with Asian American and Pacific Islander (AAPI), Black, Latina, and Middle Eastern women (N = 52) in the USA were conducted to discuss the extent to which racism and discrimination impact pregnancy and birthing experiences.

RESULTS

Participants across groups talked about the role of unequal power dynamics, discrimination, and vulnerability in patient-provider relationships. Black participants noted the influence of prior mistreatment by providers in their healthcare decisions. Latinas expressed fears of differential care because of immigration status. Middle Eastern women stated that the Muslim ban bolstered stereotypes. Vietnamese participants discussed how the effect of racism on mothers' mental health could impact their children, while Black and Latina participants expressed constant racism-related stress for themselves and their children. Participants recalled better treatment with White partners and suggested a gradient of treatment based on skin complexion. Participants across groups expressed the value of racial diversity in healthcare providers and pregnancy/birthing-related support but warned that racial concordance alone may not prevent racism and emphasized the need to go beyond "band-aid solutions."

CONCLUSION

Women's discussions of pregnancy and birthing revealed common and distinct experiences that varied by race, skin complexion, language, immigration status, and political context. These findings highlight the importance of qualitative research for informing maternal healthcare practices that reduce racial inequities.

Practical Guidance for the Development of Rosie, a Health Education Question-and-Answer Chatbot for New Mothers

Communities of color experience higher maternal and infant mortality, as well as a host of other adverse outcomes, during pregnancy and postpartum. To address this, our team is developing a free, user-friendly, question-answering chatbot called Rosie. Chatbots have gained significant popularity due to their scalability and success in individualizing resources. In recent years, scientific communities and researchers have started recognizing this technology's potential to inform communities, promote health outcomes, and address health disparities. The development of Rosie is an interdisciplinary project, with teams focused on the technical build of the application (app), the development of machine learning models, and community outreach, making Rosie a chatbot built with the input from the communities it aims to serve. From June to October 2022, more than 20 demonstration sessions were conducted in Washington, District of Columbia, Maryland, and Virginia, where a total of 109 pregnant women and new mothers of color could interact with Rosie. Results from the live demonstrations showed that 75% of mothers searched for maternity and baby-related information at least once a week and more than 90% of participants expressed the likelihood to use the app. Most of the participants inquired about their baby's development, nutrition for babies, and identifying and addressing the causes of certain symptoms and conditions, accounting for about 80% of the total questions asked. Mother-related questions in the community demonstrations were mainly about pregnancy. The high level of interest in the chatbot is a clear indication of the need for more resources. Rosie aims to help close the racial gap in maternal and infant health disparities by providing new mothers with easy access to reliable health information.

Multi-Task Classification for Improved Health Outcome Prediction Based on Environmental Indicators

This paper aims to address the challenges associated with evaluating the impact of neighborhood environments on health outcomes. Google street view (GSV) images provide a valuable tool for assessing neighborhood environments on a large scale. By annotating the GSV images with labels indicating the presence or absence of specific neighborhood features, we can develop classifiers capable of automatically analyzing and evaluating the environment. However, the process of labeling GSV images to analyze and evaluate the environment is a time-consuming and labor-intensive task. To overcome these challenges, we propose using a multi-task classifier to enhance the training of classifiers with limited supervised GSV data. Our multi-task classifier utilizes readily available, inexpensive online images collected from Flickr as a related classification task. The hypothesis is that a classifier trained on multiple related tasks is less likely to overfit to small amounts of training data and generalizes better to unseen data. We leverage the power of multiple related tasks to improve the classifier's overall performance and generalization capability. Here we show that, with the proposed learning paradigm, predicted labels for GSV test images are more accurate. Across different environment indicators, the accuracy, F1 score and balanced accuracy increase up to 6 % in the multi-task learning framework compared to its single-task learning counterpart. The enhanced accuracy of the predicted labels obtained through the multi-task classifier contributes to a more reliable and precise regression analysis determining the correlation between predicted built environment indicators and health outcomes. The R2 values calculated for different health outcomes improve by up to 4 % using multi-task learning detected indicators.

Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021

BACKGROUND

Large racial and ethnic disparities in adverse birth outcomes persist. Increasing evidence points to the potential role of racism in creating and perpetuating these disparities. Valid measures of area-level racial attitudes and bias remain elusive, but capture an important and underexplored form of racism that may help explain these disparities. Cultural values and attitudes expressed through social media reflect and shape public norms and subsequent behaviors. Few studies have quantified attitudes toward different racial groups using social media with the aim of examining associations with birth outcomes.

OBJECTIVE

We used Twitter data to measure state-level racial sentiments and investigate associations with preterm birth (PTB) and low birth weight (LBW) in a multiracial or ethnic sample of mothers in the United States.

METHODS

A random 1% sample of publicly available tweets from January 1, 2011, to December 31, 2021, was collected using Twitter's Academic Application Programming Interface (N=56,400,097). Analyses were on English-language tweets from the United States that used one or more race-related keywords. We assessed the sentiment of each tweet using support vector machine, a supervised machine learning model. We used 5-fold cross-validation to assess model performance and achieved high accuracy for negative sentiment classification (91%) and a high F1 score (84%). For each year, the state-level racial sentiment was merged with birth data during that year (~3 million births per year). We estimated incidence ratios for LBW and PTB using log binomial regression models, among all mothers, Black mothers, racially minoritized mothers (Asian, Black, or Latina mothers), and White mothers. Models were controlled for individual-level maternal characteristics and state-level demographics.

RESULTS

Mothers living in states in the highest tertile of negative racial sentiment for tweets referencing racial and ethnic minoritized groups had an 8% higher (95% CI 3%-13%) incidence of LBW and 5% higher (95% CI 0%-11%) incidence of PTB compared to mothers living in the lowest tertile. Negative racial sentiment referencing racially minoritized groups was associated with adverse birth outcomes in the total population, among minoritized mothers, and White mothers. Black mothers living in states in the highest tertile of negative Black sentiment had 6% (95% CI 1%-11%) and 7% (95% CI 2%-13%) higher incidence of LBW and PTB, respectively, compared to mothers living in the lowest tertile. Negative Latinx sentiment was associated with a 6% (95% CI 1%-11%) and 3% (95% CI 0%-6%) higher incidence of LBW and PTB among Latina mothers, respectively.

CONCLUSIONS

Twitter-derived negative state-level racial sentiment toward racially minoritized groups was associated with a higher risk of adverse birth outcomes among the total population and racially minoritized groups. Policies and supports establishing an inclusive environment accepting of all races and cultures may decrease the overall risk of adverse birth outcomes and reduce racial birth outcome disparities.

Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities

We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public's exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.

Solidarity and strife after the Atlanta spa shootings: A mixed methods study characterizing Twitter discussions by qualitative analysis and machine learning

Background

On March 16, 2021, a white man shot and killed eight victims, six of whom were Asian women at Atlanta-area spa and massage parlors. The aims of the study were to: (1) qualitatively summarize themes of tweets related to race, ethnicity, and racism immediately following the Atlanta spa shootings, and (2) examine temporal trends in expressions hate speech and solidarity before and after the Atlanta spa shootings using a new methodology for hate speech analysis.

Methods

A random 1% sample of publicly available tweets was collected from January to April 2021. The analytic sample included 708,933 tweets using race-related keywords. This sample was analyzed for hate speech using a newly developed method for combining faceted item response theory with deep learning to measure a continuum of hate speech, from solidarity race-related speech to use of violent, racist language. A qualitative content analysis was conducted on random samples of 1,000 tweets referencing Asians before the Atlanta spa shootings from January to March 15, 2021 and 2,000 tweets referencing Asians after the shooting from March 17 to 28 to capture the immediate reactions and discussions following the shootings.

Results

Qualitative themes that emerged included solidarity (4% before the shootings vs. 17% after), condemnation of the shootings (9% after), racism (10% before vs. 18% after), role of racist language during the pandemic (2 vs. 6%), intersectional vulnerabilities (4 vs. 6%), relationship between Asian and Black struggles against racism (5 vs. 7%), and discussions not related (74 vs. 37%). The quantitative hate speech model showed a decrease in the proportion of tweets referencing Asians that expressed racism (from 1.4% 7 days prior to the event from to 1.0% in the 3 days after). The percent of tweets referencing Asians that expressed solidarity speech increased by 20% (from 22.7 to 27.2% during the same time period) (p < 0.001) and returned to its earlier rate within about 2 weeks.

Discussion

Our analysis highlights some complexities of discrimination and the importance of nuanced evaluation of online speech. Findings suggest the importance of tracking hate and solidarity speech. By understanding the conversations emerging from social media, we may learn about possible ways to produce solidarity promoting messages and dampen hate messages.

Leveraging 13 million responses to the U.S. COVID-19 Trends and Impact Survey to examine vaccine hesitancy, vaccination, and mask wearing, January 2021-February 2022

BACKGROUND

The urgency of the COVID-19 pandemic called upon the joint efforts from the scientific and private sectors to work together to track vaccine acceptance and prevention behaviors.

METHODS

Our study utilized individual responses to the Delphi Group at Carnegie Mellon University U.S. COVID-19 Trends and Impact Survey, in partnership with Facebook. We retrieved survey data from January 2021 to February 2022 (n = 13,426,245) to examine contextual and individual-level predictors of COVID-19 vaccine hesitancy, vaccination, and mask wearing in the United States. Adjusted logistic regression models were developed to examine individual and ZIP code predictors of COVID-19 vaccine hesitancy and vaccination status. Given the COVID-19 vaccine was rolled out in phases in the U.S. we conducted analyses stratified by time, January 2021-May 2021 (Time 1) and June 2021-February 2022 (Time 2).

RESULTS

In January 2021 only 9% of U.S. Facebook respondents reported receiving the COVID-19 vaccine, and 45% were vaccine hesitant. By February 2022, 80% of U.S. Facebook respondents were vaccinated and only 18% were vaccine hesitant. Individuals who were older, held higher educational degrees, worked in white collar jobs, wore a mask most or all the time, and identified as white and Asian had higher COVID-19 vaccination rates and lower vaccine hesitancy across Time 1 and Time 2. Essential workers and blue-collar occupations had lower COVID vaccinations and higher vaccine hesitancy. By Time 2, all adults were eligible for the COVID-19 vaccine, but blacks and multiracial individuals had lower vaccination and higher vaccine hesitancy compared to whites. Those 55 years and older and females had higher odds of wearing masks most or all the time. Protective service, construction, and installation and repair occupations had lower odds of wearing masks. ZIP Code level percentage of the population with a bachelors' which was associated with mask wearing, higher vaccination, and lower vaccine hesitancy.

CONCLUSION

Associations found in earlier phases of the pandemic were generally found to also be present later in the pandemic, indicating stability in inequities. Additionally, inequities in these important outcomes suggests more work is needed to bridge gaps to ensure that the burden of COVID-19 risk does not disproportionately fall upon subgroups of the population.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong

Background: Domestic workers, flight crews, and sailors are three vulnerable population subgroups who were required to travel due to occupational demand in Hong Kong during the COVID-19 pandemic. Objective: The aim of this study was to explore the social networks among three vulnerable population subgroups and capture temporal changes in their probability of being exposed to SARS-CoV-2 via mobility. Methods: We included 652 COVID-19 cases and utilized Exponential Random Graph Models to build six social networks: one for the cross-sectional cohort, and five for the temporal wave cohorts, respectively. Vertices were the three vulnerable population subgroups. Edges were shared scenarios where vertices were exposed to SARS-CoV-2. Results: The probability of being exposed to a COVID-19 case in Hong Kong among the three vulnerable population subgroups increased from 3.38% in early 2020 to 5.78% in early 2022. While domestic workers were less mobile intercontinentally compared to flight crews and sailors, domestic workers were 1.81-times in general more likely to be exposed to SARS-CoV-2. Conclusions: Vulnerable populations with similar ages and occupations, especially younger domestic workers and flight crew members, were more likely to be exposed to SARS-CoV-2. Social network analysis can be used to provide critical information on the health risks of infectious diseases to vulnerable populations.

Leveraging 13 million responses to the Facebook COVID-19 Trends and Impact Survey to examine vaccine hesitancy, vaccination, and mask wearing, January 2021-February 2022

Background: The urgency of the COVID-19 global pandemic called upon the joint efforts from the scientific and private sectors to work together to track vaccine acceptance, prevention behaviors, and symptoms. Methods: Our study utilized individual responses to the Facebook’s COVID-19 Trends and Impact Survey from January 2021 to February 2022 (n=13,426,245) to examine contextual and individual-level predictors of COVID-19 vaccine hesitancy, vaccination, and mask wearing. Adjusted logistic regression models were developed to examine individual and zip code predictors of COVID-19 vaccine hesitancy and vaccination status. Given the COVID vaccine was rolled out in phases in the U.S. we conducted analyses stratified by time, January 2021-May 2021 (Time 1) and June 2021-February 2022 (Time 2). Results: On January 2021 only 9% of Facebook respondents reported receiving the COVID-19 vaccine, and 45% were vaccine hesitant. By February 2022, 80% of respondents were vaccinated and only 18% were vaccine hesitant. Individuals who were older, held higher educational degrees, worked in white collar jobs, wore a mask most of the time or some of the time, and identified as white and Asian had higher COVID-19 vaccination rates and lower vaccine hesitancy across Time 1 and Time 2. COVID vaccinations were lower among essential workers and blue-collar occupations (OR=0.31-0.40) including those in food preparation and serving, construction, installation and repair, transportation, and production in Time 1. In Time 2, these disparities attenuated but were still present (OR-0.36-0.64). For these same occupation groups, vaccine hesitancy was higher (OR=1.88-2.30 in Time 1) and (OR=2.05-2.80 in Time 2). By Time 2, all adults were eligible for the COVID-19 vaccine, but blacks (OR=0.71; 95% CI: 0.70-0.72) and multiracial (OR=0.47; 95% CI: 0.47-0.48) individuals had lower vaccination and higher vaccine hesitancy compared to whites. Conclusions: Associations found in earlier phases of the pandemic were generally found to also be present later in the pandemic, indicating stability in inequities. Additionally, inequities in these important outcomes suggests more work is needed to bridge gaps to ensure that the burden of COVID-19 risk does not disproportionately fall upon subgroups of the population.

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.

The diagnostic odyssey of autism: a cross-sectional study of 3 age cohorts of children from the 2016-2018 National Survey of Children's Health

BACKGROUND

Autism prevalence has increased rapidly in recent years, however, nationally representative estimates on the ages of first identification and intervention are out of date. Objectives: (1) To estimate the ages at which children with autism receive their first diagnosis, intervention plan, and developmental services; and (2) To evaluate differences in ages at events by birth cohort and sociodemographic characteristics.

METHODS

Using cross-sectional data from the 2016-2018 National Survey of Children's Health (NSCH), we examined associations via linear regression among a sample of 2303 children aged 2-17 years old, who had ever been diagnosed with autism and either (1) ever had a plan for special education or early intervention, or (2) ever received special services to meet developmental needs. Exposures included age cohort, child, household and healthcare provider characteristics.

RESULTS

Most children in the study sample (n = 2303) were over age 6 years, male, of non-Hispanic white race/ethnicity and had mild/moderate autism. Mean ages (years) at first diagnosis was 4.56 (SE = 0.13); first plan was 4.43 (SE = 0.11); and first services was 4.10 (SE = 0.11). After adjustment for exposures and survey year, the middle childhood cohort was 18 months older at first intervention (β = 1.49, 95% CI, 1.18-1.81), and adolescents were 38 months older at first diagnosis (β = 3.16, 95% CI, 2.72-3.60) compared to those in early childhood. Younger ages at events were observed among: Hispanic/Latinx as compared to white children, those with moderate or severe symptoms as compared to mild symptoms, and children who received their diagnosis from a specialist as compared to psychologists or psychiatrists.

CONCLUSIONS

Children with autism receive their first diagnosis, intervention plans and developmental services at younger ages than they had in the past. Future research is needed to identify the mechanisms for these improvements in early identification and intervention to accelerate additional progress.

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.

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.

Twitter-Characterized Sentiment Towards Racial/Ethnic Minorities and Cardiovascular Disease (CVD) Outcomes

Sentiments towards racial/ethnic minorities may impact cardiovascular disease (CVD) through direct and indirect pathways. In this study, we assessed the association between Twitter-derived sentiments towards racial/ethnic minorities at state-level and individual-level CVD-related outcomes from the 2017 Behavioral Risk Factor Surveillance System (BRFSS). Outcomes included hypertension, diabetes, obesity, stroke, myocardial infarction (MI), coronary heart disease (CHD), and any CVD from BRFSS 2017 (N = 433,434 to 433,680 across outcomes). A total of 30 million race-related tweets were collected using Twitter Streaming Application Programming Interface (API) from 2015 to 2018. Prevalence of negative and positive sentiment towards racial/ethnic minorities were constructed at the state level and merged with CVD outcomes. Poisson regression was used, and all the models adjusted for individual-level demographics as well as state-level demographics. Individuals living in states with the highest level of negative sentiment towards racial/ethnic minorities had 11% higher prevalence of hypertension (PR 1.11, 95% CI 1.08, 1.14), 15% higher prevalence of diabetes (PR 1.15, 95% CI 1.08, 1.22), 14% higher prevalence of obesity (PR 1.14, 95% CI 1.10, 1.18), 30% higher prevalence of stroke (PR 1.30, 95% CI 1.16, 1.46), 14% higher prevalence of MI (PR 1.14, 95% CI 1.03, 1.25), 9% higher prevalence of CHD (PR 1.09, 95% CI 1.00, 1.19), and 16% higher prevalence of any CVD outcomes (PR 1.16, 95% CI 1.09, 1.24). Conversely, Twitter-derived positive sentiment towards racial/ethnic minorities was associated with a lower prevalence of CVD outcomes. Programs and policies that promote racially inclusive environments may improve population health.

Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19

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.

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.

The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study

Background

In the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level.

Objective

This study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes.

Methods

We utilized Twitter’s Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ≤2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models.

Results

The accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8% greater, 95% CI 4%-13%) and preterm birth (8% greater, 95% CI 0%-14%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities.

Conclusions

A negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people.

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.

Do changes in neighborhood social context mediate the effects of the moving to opportunity experiment on adolescent mental health?

This study investigated whether changes in neighborhood context induced by neighborhood relocation mediated the impact of the Moving to Opportunity (MTO) housing voucher experiment on adolescent mental health. Mediators included participant-reported neighborhood safety, social control, disorder, and externally-collected neighborhood collective efficacy. For treatment group members, improvement in neighborhood disorder and drug activity partially explained MTO's beneficial effects on girls' distress. Improvement in neighborhood disorder, violent victimization, and informal social control helped counteract MTO's adverse effects on boys' behavioral problems, but not distress. Housing mobility policy targeting neighborhood improvements may improve mental health for adolescent girls, and mitigate harmful effects for boys.

Severity of oro-dental anomalies in Loeys-Dietz syndrome segregates by gene mutation

Background Loeys-Dietz syndrome (LDS), an autosomal dominant rare connective tissue disorder, has multisystemic manifestations, characterised by vascular tortuosity, aneurysms and craniofacial manifestations. Based on the associated gene mutations along the transforming growth factor-beta (TGF-β) pathway, LDS is presently classified into six subtypes. Methods We present the oro-dental features of a cohort of 40 patients with LDS from five subtypes. Results The most common oro-dental manifestations were the presence of a high-arched and narrow palate, and enamel defects. Other common characteristics included bifid uvula, submucous cleft palate, malocclusion, dental crowding and delayed eruption of permanent teeth. Both deciduous and permanent teeth had enamel defects in some individuals. We established a grading system to measure the severity of enamel defects, and we determined that the severity of the enamel anomalies in LDS is subtype-dependent. In specific, patients with TGF-β receptor II mutations (LDS2) presented with the most severe enamel defects, followed by patients with TGF-β receptor I mutations (LDS1). LDS2 patients had higher frequency of oro-dental deformities in general. Across all five subtypes, as well as within each subtype, enamel defects exhibited incomplete penetrance and variable expression, which is not associated with the location of the gene mutations. Conclusion This study describes, in detail, the oro-dental manifestations in a cohort of LDS, and we conclude that LDS2 has the most severely affected phenotype. This extensive characterisation, as well as some identified distinguishing features can significantly aid dental and medical care providers in the diagnosis and clinical management of patients with this rare connective tissue disorder.

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.

Oral health-related quality of life in Loeys-Dietz syndrome, a rare connective tissue disorder: an observational cohort study

BACKGROUND

Loeys-Dietz syndrome (LDS) is a rare connective tissue disorder whose oral manifestations and dental phenotypes have not been well-characterized. The aim of this study was to explore the influence of oral manifestations on oral health-related quality of life (OHRQoL) in LDS patients.

MATERIAL AND METHODS

LDS subjects were assessed by the craniofacial team at the National Institutes of Health Clinical Center Dental Clinic between June 2015 and January 2018. Oral Health Impact Profile (OHIP-14) questionnaire, oral health self-care behavior questionnaire and a comprehensive dental examination were completed for each subject. OHRQoL was assessed using the OHIP-14 questionnaire with higher scores corresponding to worse OHRQoL. Regression models were used to determine the relationship between each oral manifestation and the OHIP-14 scores using a level of significance of p ≤ 0.05.

RESULTS

A total of 33 LDS subjects (51.5% female) aged 3-57 years (19.6 ± 15.1 years) were included in the study. The OHIP-14 scores (n = 33) were significantly higher in LDS subjects (6.30 [SD 6.37]) when compared to unaffected family member subjects (1.50 [SD 2.28], p < 0.01), and higher than the previously reported scores of the general U.S. population (2.81 [SD 0.12]). Regarding oral health self-care behavior (n = 32), the majority of LDS subjects reported receiving regular dental care (81%) and maintaining good-to-excellent daily oral hygiene (75%). Using a crude regression model, worse OHRQoL was found to be associated with dental hypersensitivity (β = 5.24; p < 0.05), temporomandibular joints (TMJ) abnormalities (β = 5.92; p < 0.01), self-reported poor-to-fair oral health status (β = 6.77; p < 0.01), and cumulation of four or more oral manifestations (β = 7.23; p < 0.001). Finally, using a parsimonious model, self-reported poor-to-fair oral health status (β = 5.87; p < 0.01) and TMJ abnormalities (β = 4.95; p < 0.01) remained significant.

CONCLUSIONS

The dental hypersensitivity, TMJ abnormalities, self-reported poor-to-fair oral health status and cumulation of four-or-more oral manifestations had significant influence on worse OHRQoL. Specific dental treatment guidelines are necessary to ensure optimal quality of life in patients diagnosed with LDS.

Social media captures demographic and regional physical activity

Objectives

We examined the use of data from social media for surveillance of physical activity prevalence in the USA.

Methods

We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention.

Results

The association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes.

Conclusions

The regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.

Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study

Background

Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine health care trends. However, infoveillance, while very effective in identifying many real-world health trends, may miss opportunities that use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends.

Objective

The purpose of this research is to (1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes and (2) report on real-world use of continuous glucose monitoring (CGM) on Instagram.

Methods

This exploratory mixed methods study examined all photographs posted on Instagram that were identified with the hashtag #dexcom over a 3-month period—December 2016 to February 2017. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM device wear and CGM failure and were analyzed for emerging themes.

Results

A total of 2923 photographs were manually screened; 12.08% (353/2923) depicted a photograph with a CGM site location. The majority (225/353, 63.7%) of the photographs showed a CGM site in an off-label location, while 26.2% (92/353) were in an FDA-approved location (ie, abdomen) and 10.2% (36/353) were in an unidentifiable location. There were no significant differences in the number of likes or comments based on US Food and Drug Administration (FDA) approval. Five themes emerged from the analysis of original posts (N=353) and corresponding comments (N=2364): (1) endorsement of CGM as providing a sense of well-being; (2) reciprocating information, encouragement, and support; (3) reciprocating CGM-related frustrations; (4) life hacks to optimize CGM use; and (5) sharing and learning about off-label CGM activity.

Conclusions

Our results indicate that individuals successfully used CGM in off-label locations, posting photos of these areas with greater frequency than of the abdomen, with no indication of sensor failure. While these photographs only capture a snapshot in time, these posts can be used to inform providers and industry leaders of real-world trends in CGM use. Additionally, there were instances in which sensors were worn beyond the FDA-approved 7-day period; however, they represented the minority in this study.

Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US

Objective: Describe variation in sentiment of tweets using race-related terms and identify themes characterizing the social climate related to race. Methods: We applied a Stochastic Gradient Descent Classifier to conduct sentiment analysis of 1,249,653 US tweets using race-related terms from 2015–2016. To evaluate accuracy, manual labels were compared against computer labels for a random subset of 6600 tweets. We conducted qualitative content analysis on a random sample of 2100 tweets. Results: Agreement between computer labels and manual labels was 74%. Tweets referencing Middle Eastern groups (12.5%) or Blacks (13.8%) had the lowest positive sentiment compared to tweets referencing Asians (17.7%) and Hispanics (17.5%). Qualitative content analysis revealed most tweets were represented by the categories: negative sentiment (45%), positive sentiment such as pride in culture (25%), and navigating relationships (15%). While all tweets use one or more race-related terms, negative sentiment tweets which were not derogatory or whose central topic was not about race were common. Conclusion: This study harnesses relatively untapped social media data to develop a novel area-level measure of social context (sentiment scores) and highlights some of the challenges in doing this work. New approaches to measuring the social environment may enhance research on social context and health.

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density. At the county level, greater presence of highways was related to lower chronic diseases and premature mortality. Areas characterized by street view images as 'rural' (having limited infrastructure) had higher obesity, diabetes, fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the census tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015⁻2018

There is a growing recognition of social media data as being useful for understanding local area patterns. In this study, we sought to utilize geotagged tweets—specifically, the frequency and type of food mentions—to understand the neighborhood food environment and the social modeling of food behavior. Additionally, we examined associations between aggregated food-related tweet characteristics and prevalent chronic health outcomes at the census tract level. We used a Twitter streaming application programming interface (API) to continuously collect ~1% random sample of public tweets in the United States. A total of 4,785,104 geotagged food tweets from 71,844 census tracts were collected from April 2015 to May 2018. We obtained census tract chronic disease outcomes from the CDC 500 Cities Project. We investigated associations between Twitter-derived food variables and chronic outcomes (obesity, diabetes and high blood pressure) using the median regression. Census tracts with higher average calories per tweet, less frequent healthy food mentions, and a higher percentage of food tweets about fast food had higher obesity and hypertension prevalence. Twitter-derived food variables were not predictive of diabetes prevalence. Food-related tweets can be leveraged to help characterize the neighborhood social and food environment, which in turn are linked with community levels of obesity and hypertension.

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.

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.

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.

Disparities in digital reporting of illness: A demographic and socioeconomic assessment

Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelor's degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.

THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE

With the booming of new technologies, biomedical science has transformed into digitalized, data intensive science. Massive amount of data need to be analyzed and interpreted, demand a complete pipeline to train next generation data scientists. To meet this need, the transinstitutional Big Data to Knowledge (BD2K) Initiative has been implemented since 2014, complementing other NIH institutional efforts. In this report, we give an overview the BD2K K01 mentored scientist career awards, which have demonstrated early success. We address the specific trainings needed in representative data science areas, in order to make the next generation of data scientists in biomedicine.

The effects of a housing mobility experiment on participants' residential environments

We used the Moving to Opportunity (MTO) housing experiment to inform how housing choice vouchers and housing mobility policies can assist families living in high-poverty areas to make opportunity moves to higher quality neighborhoods, across a wide range of neighborhood attributes. We compared the neighborhood attainment of the three randomly-assigned MTO treatment groups (Low Poverty voucher, Section 8 voucher, Control group) at 1997 and 2002 locations (4-7 years after baseline), by using survey reports, and by linking residential histories to numerous different administrative and population-based datasets. Compared to controls, families in Low-Poverty and Section 8 groups experienced substantial improvements in neighborhood conditions across diverse measures, including economic conditions, social systems (e.g., collective efficacy), physical features of the environment (e.g., tree cover) and health outcomes. The Low-poverty voucher group moreover achieved better neighborhood attainment compared to Section 8. Treatment effects were largest for New York and Los Angeles. We discuss the implications of our findings for expanding affordable housing policy.

Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity

Background

Studies 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.

Objective

The 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.

Methods

We 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.

Results

Machine 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.

Conclusions

Machine 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 lower happiness and fewer healthy behaviors.

Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity

OBJECTIVES

Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York.

METHODS

We utilize 2.8 million tweets collected between February-August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics.

RESULTS

For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths.

CONCLUSIONS

Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities-information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.

Racial Disparities in Access to Care Under Conditions of Universal Coverage

BACKGROUND

Racial disparities in access to regular health care have been reported in the U.S., but little is known about the extent of disparities in societies with universal coverage.

PURPOSE

To investigate the extent of racial disparities in access to care under conditions of universal coverage by observing the association between race and regular access to a doctor in Canada.

METHODS

Racial disparities in access to a regular doctor were calculated using the largest available source of nationally representative data in Canada--the Canadian Community Health Survey. Surveys from 2000-2010 were analyzed in 2014. Multinomial regression analyses predicted odds of having a regular doctor for each racial group compared to whites. Analyses were stratified by immigrant status--Canadian-born versus shorter-term immigrant versus longer-term immigrants--and controlled for sociodemographics and self-rated health.

RESULTS

Racial disparities in Canada, a country with universal coverage, were far more muted than those previously reported in the U.S. Only among longer-term Latin American immigrants (OR=1.90, 95% CI=1.45, 2.08) and Canadian-born Aboriginals (OR=1.34, 95% CI=1.22, 1.47) were significant disparities noted. Among shorter-term immigrants, all Asians were more likely than whites, and among longer-term immigrants, South Asians were more like than whites, to have a regular doctor.

CONCLUSIONS

Universal coverage may have a major impact on reducing racial disparities in access to health care, although among some subgroups, other factors may also play a role above and beyond health insurance.

Heterogeneous Effects of Housing Vouchers on the Mental Health of US Adolescents

OBJECTIVES

To assess the mental health effects on adolescents of low-income families residing in high-poverty public housing who received housing vouchers to assist relocation.

METHODS

We defined treatment effects to compare 2829 adolescents aged 12 to 19 years in families offered housing vouchers versus those living in public housing in the Moving to Opportunity experiment (1994-1997; Boston, MA; Baltimore, MD; Chicago, IL; Los Angeles, CA; New York, NY). We employed model-based recursive partitioning to identify subgroups with heterogeneous treatment effects on psychological distress and behavior problems measured in 2002. We tested 35 potential baseline treatment modifiers.

RESULTS

For psychological distress, Chicago participants experienced null treatment effects. Outside Chicago, boys experienced detrimental effects, whereas girls experienced beneficial effects. Behavior problems effects were null for adolescents who were aged 10 years or younger at baseline. For adolescents who were older than 10 years at baseline, violent crime victimization, unmarried parents, and unsafe neighborhoods increased adverse treatment effects. Adolescents who were older than 10 years at baseline without learning problems or violent crime victimization, and whose parents moved for better schools, experienced beneficial effects.

CONCLUSIONS

Health effects of housing vouchers varied across subgroups. Supplemental services may be necessary for vulnerable subgroups for whom housing vouchers alone may not be beneficial.

Cross-national comparison of socioeconomic inequalities in obesity in the United States and Canada

INTRODUCTION

Prior cross-national studies of socioeconomic inequalities in obesity have only compared summary indices of inequality but not specific, policy-relevant dimensions of inequality: (a) shape of the socioeconomic gradient in obesity, (b) magnitude of differentials in obesity across socioeconomic levels and, (c) level of obesity at any given socioeconomic level. We use unique data on two highly comparable societies - U.S. and Canada - to contrast each of these inequality dimensions.

METHODS

Data came from the 2002/2003 Joint Canada/U.S. Survey of Health. We calculated adjusted prevalence ratios (APRs) for obesity (compared to normal weight) by income quintile and education group separately for both nations and, between Canadians and Americans in the same income or education group.

RESULTS

In the U.S., every socioeconomic group except the college educated had significant excess prevalence of obesity. By contrast in Canada, only those with less than high school were worse off, suggesting that the shape of the socioeconomic gradient differs in the two countries. U.S. differentials between socioeconomic levels were also larger than in Canada (e.g., PR quintile 1 compared to quintile 5 was 1.82 in the U.S. [95 % CI: 1.52-2.19] but 1.45 in Canada [95 % CI: 1.10-1.91]). At the lower end of the socioeconomic gradient, obesity was more prevalent in the U.S. than in Canada.

CONCLUSIONS

Our results suggest there is variation between U.S. and Canada in different dimensions of socioeconomic inequalities in obesity. Future research should examine a broader set of nations and test whether specific policies or environmental exposures can explain these differences.

Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided.