Background: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment’s health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes. Materials and methods: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments. Results: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps’ API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected. Conclusions: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.

Background: Bicycling is an important form of physical activity in populations. Research assessing the effect of infrastructure on bicycling with high-resolution smartphone data is emerging in several places, but it remains limited in low-bicycling US settings, including the Southeastern US. The Atlanta area has been expanding its bicycle infrastructure, including off-street paved trails such as the Atlanta BeltLine and some protected bike lanes. Methods: Using the generalized synthetic-control method, we estimated effects of five groups of off-street paved trails and protected bike lanes on bicycle ridership in their corresponding areas. To measure bicycling, we used 2 years (October 1, 2016 to September 30, 2018) of monthly Strava data in Atlanta's urban core along with data from 15 on-the-ground counters to adjust for spatiotemporal variation in app use. Results: Considering all infrastructure as one joint intervention, an estimated 1.10 (95% confidence interval [CI]: 0.99, 1.18) times more bicycle-distance was ridden than would have been expected in the same areas had the infrastructure not been built, when defining treatment areas by the narrower of two definitions (defined in text). The Atlanta BeltLine Westside Trail and Proctor Creek Greenway had especially strong effect estimates, e.g., ratios of 1.45 (95% CI: 1.12, 1.86) and 1.55 (1.10, 2.14) under each treatment-area definition, respectively. We estimated that other infrastructure had weaker positive or no effects on bicycle-distance ridden. Conclusions: This study advances research on the topic because of its setting in the US Southeast, simultaneous assessment of several infrastructure groups, and data-driven approach to estimating effects. See video abstract at, http://links.lww.com/EDE/B936.

Objectives: To identify risk factors for and the consequences (several adverse perinatal outcomes) of physician-diagnosed major depression during pregnancy treated in specialised healthcare.Design: A population-based cross-sectional study.Setting: Data were gathered from Finnish health registers for 1996-2010.Participants: All singleton births (n=511 938) for 2002-2010 in Finland.Primary outcome measures: Prevalence, risk factors and consequences of major depression during pregnancy.Results: Among 511 938 women, 0.8% experienced major depression during pregnancy, of which 46.9% had a history of depression prior to pregnancy. After history of depression, the second strongest associated factor for major depression was fear of childbirth, with a 2.6-fold (adjusted OR (aOR=2.63, 95% CI 2.39 to 2.89) increased prevalence. The risk profile of major depression also included adolescent or advanced maternal age, low or unspecified socioeconomic status (SES), single marital status, smoking, prior pregnancy terminations, anaemia and gestational diabetes regardless of a history of depression. Outcomes of pregnancies were worse among women with major depression than without. The contribution of smoking was substantial to modest for small-for-gestational age newborn (<-2 SD below mean birth), low birth weight (<2500 g), preterm birth (<37 weeks) and admission to neonatal intensive care associated with major depression, whereas SES made only a minor contribution.Conclusions: Physician-diagnosed major depression during pregnancy was found to be rare. The strongest risk factor was history of depression prior to pregnancy. Other associated factors were fear of childbirth, low SES, lack of social support and unhealthy reproductive behaviour such as smoking. Outcomes of pregnancies were worse among women with major depression than without. Smoking during pregnancy made a substantial to modest contribution to adverse outcomes associated with depression during pregnancy.

BACKGROUND: Pregnant women less frequently receive COVID-19 vaccination and are at increased risk for adverse pregnancy outcomes from COVID-19. OBJECTIVE: This study aimed to first, describe the vaccination status, treatment, and outcomes of hospitalized, symptomatic pregnant women with COVID-19, and second, estimate whether treatment differs by pregnancy status among treatment-eligible (ie, requiring supplemental oxygen per National Institutes of Health guidelines at the time of the study) women. STUDY DESIGN: From January to November 2021, the COVID-19-Associated Hospitalization Surveillance Network completed medical chart abstraction for a probability sample of 2715 hospitalized women aged 15 to 49 years with laboratory-confirmed SARS-CoV-2 infection. Of these, 1950 women had symptoms of COVID-19 on admission, and 336 were pregnant. We calculated weighted prevalence estimates of demographic and clinical characteristics, vaccination status, and outcomes among pregnant women with symptoms of COVID-19 on admission. We used propensity score matching to estimate prevalence ratios and 95% confidence intervals of treatment-eligible patients who received remdesivir or systemic steroids by pregnancy status. RESULTS: Among 336 hospitalized pregnant women with symptomatic COVID-19, 39.6% were non-Hispanic Black, 24.8% were Hispanic or Latino, and 61.9% were aged 25 to 34 years. Among those with known COVID-19 vaccination status, 92.9% were unvaccinated. One-third (32.7%) were treatment-eligible. Among treatment-eligible pregnant women, 74.1% received systemic steroids and 61.4% received remdesivir. Among those that were no longer pregnant at discharge (n=180), 5.4% had spontaneous abortions and 3.5% had stillbirths. Of the 159 live births, 29.0% were preterm. Among a propensity score–matched cohort of treatment-eligible hospitalized women of reproductive age, pregnant women were less likely than nonpregnant women to receive remdesivir (prevalence ratio, 0.82; 95% confidence interval, 0.69–0.97) and systemic steroids (prevalence ratio, 0.80; 95% confidence interval, 0.73–0.87). CONCLUSION: Most hospitalized pregnant patients with symptomatic COVID-19 were unvaccinated. Hospitalized pregnant patients were less likely to receive recommended remdesivir and systemic steroids compared with similar hospitalized nonpregnant women. Our results underscore the need to identify opportunities for improving COVID-19 vaccination, implementation of treatment of pregnant women, and the inclusion of pregnant women in clinical trials.

Background: Place is critical to our understanding of human immunodeficiency virus (HIV) infections among men who have sex with men (MSM) in the United States. However, within the scientific literature, place is almost always represented by residential location, suggesting a fundamental assumption of equivalency between neighborhood of residence, place of risk, and place of prevention. However, the locations of behaviors among MSM show significant spatial variation, and theory has posited the importance of nonresidential contextual exposures. This focus on residential locations has been at least partially necessitated by the difficulties in collecting detailed geolocated data required to explore nonresidential locations. Objective: Using a Web-based map tool to collect locations, which may be relevant to the daily lives and health behaviors of MSM, this study examines the completeness and reliability of the collected data. Methods: MSM were recruited on the Web and completed a Web-based survey. Within this survey, men used a map tool embedded within a question to indicate their homes and multiple nonresidential locations, including those representing work, sex, socialization, physician, and others. We assessed data quality by examining data completeness and reliability. We used logistic regression to identify demographic, contextual, and location-specific predictors of answering all eligible map questions and answering specific map questions. We assessed data reliability by comparing selected locations with other participant-reported data. Results: Of 247 men completing the survey, 167 (67.6%) answered the entire set of eligible map questions. Most participants (>80%) answered specific map questions, with sex locations being the least reported (80.6%). Participants with no college education were less likely than those with a college education to answer all map questions (prevalence ratio, 0.4; 95% CI, 0.2-0.8). Participants who reported sex at their partner's home were less likely to indicate the location of that sex (prevalence ratio, 0.8; 95% CI, 0.7-1.0). Overall, 83% of participants placed their home's location within the boundaries of their reported residential ZIP code. Of locations having a specific text description, the median distance between the participant-selected location and the location determined using the specific text description was 0.29 miles (25th and 75th percentiles, 0.06-0.88). Conclusions: Using this Web-based map tool, this Web-based sample of MSM was generally willing and able to provide accurate data regarding both home and nonresidential locations. This tool provides a mechanism to collect data that can be used in more nuanced studies of place and sexual risk and preventive behaviors of MSM.

Using a case–control study of patients receiving antiretroviral treatment (ART) in 2010–2012 at McCord Hospital in Durban, South Africa, we sought to understand how residential locations impact patients’ risk of virologic failure (VF). Using generalized estimating equations to fit logistic regression models, we estimated the associations of VF with socioeconomic status (SES) and geographic access to care. We then determined whether neighborhood-level poverty modifies the association between individual-level SES and VF. Automobile ownership for men and having non-spouse family members pay medical care for women remained independently associated with increased odds of VF for patients dwelling in moderately and severely poor neighborhoods. Closer geographic proximity to medical care was positively associated with VF among men, while higher neighborhood-level poverty was positively associated with VF among women. The programmatic implications of our findings include developing ART adherence interventions that address the role of gender in both the socioeconomic and geographical contexts.

BACKGROUND Although the increased prevalence of childhood obesity in the United States has been documented, little is known about its incidence. We report here on the national incidence of obesity among elementary-school children. METHODS We evaluated data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999, a representative prospective cohort of 7738 participants who were in kindergarten in 1998 in the United States. Weight and height were measured seven times between 1998 and 2007. Of the 7738 participants, 6807 were not obese at baseline; these participants were followed for 50,396 person-years. We used standard thresholds from the Centers for Disease Control and Prevention to define “overweight” and “obese” categories. We estimated the annual incidence of obesity, the cumulative incidence over 9 years, and the incidence density (cases per person-years) overall and according to sex, socioeconomic status, race or ethnic group, birth weight, and kindergarten weight. RESULTS When the children entered kindergarten (mean age, 5.6 years), 12.4% were obese and another 14.9% were overweight; in eighth grade (mean age, 14.1 years), 20.8% were obese and 17.0% were overweight. The annual incidence of obesity decreased from 5.4% during kindergarten to 1.7% between fifth and eighth grade. Overweight 5-year-olds were four times as likely as normal-weight children to become obese (9-year cumulative incidence, 31.8% vs. 7.9%), with rates of 91.5 versus 17.2 per 1000 person-years. Among children who became obese between the ages of 5 and 14 years, nearly half had been overweight and 75% had been above the 70th percentile for body-mass index at baseline. CONCLUSIONS Incident obesity between the ages of 5 and 14 years was more likely to have occurred at younger ages, primarily among children who had entered kindergarten overweight.

Background: The increasing focus of population surveillance and research on maternal - and not only fetal and infant - health outcomes is long overdue. The United States maternal mortality rate is higher than any other high-income country, and Georgia is among the highest rates in the country. Severe maternal morbidity (SMM) is conceived of as a "near miss"for maternal mortality, is 50 times more common than maternal death, and efforts to systematically monitor SMM rates in populations have increased in recent years. Much of the current population-based research on SMM has occurred in coastal states or large cities, despite substantial geographical variation with higher maternal and infant health burdens in the Southeast and rural regions. Methods: This population-based study uses hospital discharge records linked to vital statistics to describe the epidemiology of SMM in Georgia between 2009 and 2020. Results: Georgia had a higher SMM rate than the United States overall (189.2 vs. 144 per 10,000 deliveries in Georgia in 2014, the most recent year with US estimates). SMM was higher among racially minoritized pregnant persons and those at the extremes of age, of lower socioeconomic status, and with comorbid chronic conditions. SMM rates were 5 to 6 times greater for pregnant people delivering infants <1500 grams or <32 weeks' gestation as compared with those delivering normal weight or term infants. Since 2015, SMM has increased in Georgia. Conclusion: SMM represents a collection of life-threatening emergencies that are unevenly distributed in the population and require increased attention. This descriptive analysis provides initial guidance for programmatic interventions intending to reduce the burden of SMM and, subsequently, maternal mortality in the US South.

Background: Pregnancy-related mortality in the United States is the greatest among all high-income countries, and Georgia has one of the highest maternal mortality rates—almost twice the national rate. Furthermore, inequities exist in rates of pregnancy-related deaths. In Georgia, non-Hispanic Black women are nearly 3 times more likely to die from pregnancy-related complications than non-Hispanic White women. Unlike health equity, a clear definition of maternal health equity is lacking, overall and in Georgia specifically, but is needed to reach consensus and align stakeholders for action. Therefore, we used a modified Delphi method to define maternal health equity in Georgia and to determine research priorities based on gaps in understanding of maternal health in Georgia. Methods: Thirteen expert members of the Georgia Maternal Health Research for Action Steering Committee (GMHRA-SC) participated in an iterative, consensus-driven, modified Delphi study comprised of 3 rounds of anonymous surveys. In round 1 (web-based survey), experts generated open-ended concepts of maternal health equity and listed research priorities. In rounds 2 (web-based meeting) and 3 (web-based survey), the definition and research priorities suggested during round 1 were categorized into concepts for ranking based on relevance, importance, and feasibility. Final concepts were subjected to a conventional content analysis to identify general themes. Results: The consensus definition of maternal health equity created after undergoing the Delphi method is: maternal health equity is the ultimate goal and ongoing process of ensuring optimal perinatal experiences and outcomes for everyone as the result of practices and policies free of interpersonal or structural bias that tackle current and historical injustices, including social, structural, and political determinants of health impacting the perinatal period and life course. This definition highlights addressing the current and historical injustices manifested in the social determinants of health, and the structural and political structures that impact the perinatal experience. Conclusion: The maternal health equity definition and identified research priorities will guide the GMHRA-SC and the broader maternal health community for research, practice, and advocacy in Georgia.

Background: Recent evidence suggests transmission of Mycobacterium tuberculosis (Mtb) may be characterized by extreme individual heterogeneity in secondary cases (i.e., few cases account for the majority of transmission). Such heterogeneity implies outbreaks are rarer but more extensive and has profound implications in infectious disease control. However, discrete person-to-person transmission events in tuberculosis (TB) are often unobserved, precluding our ability to directly quantify individual heterogeneity in TB epidemiology. Methods: We used a modified negative binomial branching process model to quantify the extent of individual heterogeneity using only observed transmission cluster size distribution data (i.e., the simple sum of all cases in a transmission chain) without knowledge of individual-level transmission events. The negative binomial parameter k quantifies the extent of individual heterogeneity (generally, k < 1 indicates extensive heterogeneity, and as k → ∞ transmission becomes more homogenous). We validated the robustness of the inference procedure considering common limitations affecting cluster size data. Finally, we demonstrate the epidemiologic utility of this method by applying it to aggregate US molecular surveillance data from the US Centers for Disease Control and Prevention. Results: The cluster-based method reliably inferred k using TB transmission cluster data despite a high degree of bias introduced into the model. We found that the TB transmission in the United States was characterized by a high propensity for extensive outbreaks (k = 0.09; 95% confidence interval = 0.09, 0.10). Conclusions: The proposed method can accurately quantify critical parameters that govern TB transmission using simple, more easily obtainable cluster data to improve our understanding of TB epidemiology.