Deep Learning - Methods to Amplify Epidemiological Data Collection and Analyses.

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.

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.

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.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019-2021 fatal collisions in the USA.

OBJECTIVE: The USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas or single cities. This study aims to quantify associations between built environment characteristics and traffic collisions in the USA. METHODS: Built environment characteristics were derived from Google Street View images and summarised at the census tract level. Fatal traffic collisions were obtained from the 2019-2021 Fatality Analysis Reporting System. Fatal and non-fatal traffic collisions in Washington DC were obtained from the District Department of Transportation. Adjusted Poisson regression models examined whether built environment characteristics are related to motor vehicle collisions in the USA, controlling for census tract sociodemographic characteristics. RESULTS: Census tracts in the highest tertile of sidewalks, single-lane roads, streetlights and street greenness had 70%, 50%, 30% and 26% fewer fatal vehicle collisions compared with those in the lowest tertile. Street greenness and single-lane roads were associated with 37% and 38% fewer pedestrian-involved and cyclist-involved fatal collisions. Analyses with fatal and non-fatal collisions in Washington DC found streetlights and stop signs were associated with fewer pedestrians and cyclists-involved vehicle collisions while road construction had an adverse association. CONCLUSION: This study demonstrates the utility of using data algorithms that can automatically analyse street segments to create indicators of the built environment to enhance understanding of large-scale patterns and inform interventions to decrease road traffic injuries and fatalities.

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.

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.

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.

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-ß) 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-ß receptor II mutations (LDS2) presented with the most severe enamel defects, followed by patients with TGF-ß 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.

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 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.

Does Mother Know Best? Treatment Adherence as a Function of Anticipated Treatment Benefit.

BACKGROUND: We describe bias resulting from individualized treatment selection, which occurs when treatment has heterogeneous effects and individuals selectively choose treatments of greatest benefit to themselves. This pernicious bias may confound estimates from observational studies and lead to important misinterpretation of intent-to-treat analyses of randomized trials. Despite the potentially serious threat to inferences, individualized treatment selection has rarely been formally described or assessed. METHODS: The Moving To Opportunity trial randomly assigned subsidized rental vouchers to low-income families in high-poverty public housing. We assessed the Kessler-6 psychological distress and Behavior Problems Index outcomes for 2,829 adolescents 4-7 years after randomization. Among families randomly assigned to receive vouchers, we estimated probability of moving (treatment), predicted by prerandomization characteristics (c statistic = 0.63). We categorized families into tertiles of this estimated probability of moving, and compared instrumental variable effect estimates for moving on behavior problems index and Kessler-6 across tertiles. RESULTS: Instrumental variable estimated effects of moving on behavioral problems index were most adverse for boys least likely to move (b = 0.93; 95% confidence interval: 0.33, 1.53) compared with boys most likely to move (b = 0.14; 95% confidence interval: -0.15, 0.44; P = 0.02 for treatment × tertile interaction). Effects on Kessler-6 were more beneficial for girls least likely to move compared with girls most likely to move (-0.62 vs. 0.02; interaction; P = 0.03). CONCLUSIONS: Evidence of individualized treatment selection differed by child gender and outcome and should be evaluated in randomized trial reports, especially when heterogeneous treatment effects are likely and nonadherence is common.

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.

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.

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.

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.

The reliability of in-home measures of height and weight in large cohort studies: Evidence from Add Health.

BACKGROUND: With the emergence of obesity as a global health issue an increasing number of major demographic surveys are collecting measured anthropometric data. Yet little is known about the characteristics and reliability of these data. OBJECTIVES: We evaluate the accuracy and reliability of anthropometric data collected in the home during Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health), compare our estimates to national standard, clinic-based estimates from the National Health and Nutrition Examination Survey (NHANES) and, using both sources, provide a detailed anthropometric description of young adults in the United States. METHODS: The reliability of Add Health in-home anthropometric measures was estimated from repeat examinations of a random subsample of study participants. A digit preference analysis evaluated the quality of anthropometric data recorded by field interviewers. The adjusted odds of obesity and central obesity in Add Health vs. NHANES were estimated with logistic regression. RESULTS: Short-term reliabilities of in-home measures of height, weight, waist and arm circumference-as well as derived body mass index (BMI, kg/m2)-were excellent. Prevalence of obesity (37% vs. 29%) and central obesity (47% vs. 38%) was higher in Add Health than in NHANES while socio-demographic patterns of obesity and central obesity were comparable in the two studies. CONCLUSIONS: Properly trained non-medical field interviewers can collect reliable anthropometric data in a nationwide, home visit study. This national cohort of young adults in the United States faces a high risk of early-onset chronic disease and premature mortality.

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.

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.