Marc Peterson

Background: Endometrial cancer is the most common gynecologic cancer in the US. Endometrial cancer survivors may experience changes in microbiome due to cancer treatment and other factors. The human microbiome plays a crucial role in maintaining the proper functioning of the body. A more diverse microbiome often indicates a healthier gut environment, while lower vaginal microbiome diversity, specifically a Lactobacillus -dominant vagitype, is associated with more favorable health outcomes. The objectives of this pilot study were to evaluate potential variation in stool and vaginal microbiome communities and to assess the feasibility and acceptability of self-sampling among endometrial cancer survivors. Methods: Endometrial cancer survivors (N = 50) enrolled in the Carolina Endometrial Cancer Study, a cohort of women diagnosed with endometrial cancer, were mailed Genotek vaginal swab and stool self-collection kits. Self-reported questionnaires assessed information on survivors’ demographics, sexual and bowel function, and perspectives on the self-sampling processes. Tumor characteristics and cancer treatment information were assessed from medical records. Microbiota profiles were characterized by bacteria 16S rRNA amplicon sequencing. Results: Overall, 48 vaginal swabs and 47 stool samples were obtained. Alpha (Shannon p = 0.04) and beta (Bray-Curtis p = 0.004) diversity of vaginal microbiome samples varied by cancer treatment, with higher microbial diversity after chemotherapy or radiation compared to surgery alone. In the surgery only group, 63% of samples were Lactobacillus -dominant compared to 17% among the chemotherapy or radiation group. Stool microbiome diversity did not vary by cancer treatment status. No statistically significant differences in alpha or beta diversity were observed in either vaginal or stool microbiome communities across racial subgroups or by sexual or bowel function. Conclusion: Self-collection of stool and vaginal microbiome samples is feasible and acceptable in cancer survivors. Our results suggest that radiation and chemotherapy for endometrial cancer may decrease the abundance of beneficial Lactobacillus and increase less favorable vaginal microbial diversity among endometrial cancer survivors.

Background: Each year, nearly 300,000 women and 5 million fetuses or neonates die during childbirth or shortly thereafter, a burden concentrated disproportionately in low- and middle-income countries. Identifying women and their fetuses at risk for intrapartum-related morbidity and death could facilitate early intervention. Methods: The Limiting Adverse Birth Outcomes in Resource-Limited Settings (LABOR) Study is a multi-country, prospective, observational cohort designed to exhaustively document the course and outcomes of labor, delivery, and the immediate postpartum period in settings where adverse outcomes are frequent. The study is conducted at four hospitals across three countries in Ghana, India, and Zambia. We will enroll approximately 12,000 women at presentation to the hospital for delivery and follow them and their fetuses/newborns throughout their labor and delivery course, postpartum hospitalization, and up to 42 days thereafter. The co-primary outcomes are composites of maternal (death, hemorrhage, hypertensive disorders, infection) and fetal/neonatal adverse events (death, encephalopathy, sepsis) that may be attributed to the intrapartum period. The study collects extensive physiologic data through the use of physiologic sensors and employs medical scribes to document examination findings, diagnoses, medications, and other interventions in real time. Discussion: The goal of this research is to produce a large, sharable dataset that can be used to build statistical algorithms to prospectively stratify parturients according to their risk of adverse outcomes. We anticipate this research will inform the development of new tools to reduce peripartum morbidity and mortality in low-resource settings.

Background: Each year, nearly 300,000 women and 5 million fetuses or neonates die during childbirth or shortly thereafter, a burden concentrated disproportionately in low- and middle-income countries. Identifying women and their fetuses at risk for intrapartum-related morbidity and death could facilitate early intervention. Methods: The Limiting Adverse Birth Outcomes in Resource-Limited Settings (LABOR) Study is a multi-country, prospective, observational cohort designed to exhaustively document the course and outcomes of labor, delivery, and the immediate postpartum period in settings where adverse outcomes are frequent. The study is conducted at four hospitals across three countries in Ghana, India, and Zambia. We will enroll approximately 12,000 women at presentation to the hospital for delivery and follow them and their fetuses/newborns throughout their labor and delivery course, postpartum hospitalization, and up to 42 days thereafter. The co-primary outcomes are composites of maternal (death, hemorrhage, hypertensive disorders, infection) and fetal/neonatal adverse events (death, encephalopathy, sepsis) that may be attributed to the intrapartum period. The study collects extensive physiologic data through the use of physiologic sensors and employs medical scribes to document examination findings, diagnoses, medications, and other interventions in real time. Discussion: The goal of this research is to produce a large, sharable dataset that can be used to build statistical algorithms to prospectively stratify parturients according to their risk of adverse outcomes. We anticipate this research will inform the development of new tools to reduce peripartum morbidity and mortality in low-resource settings.

Background Ultrasound is indispensable to gestational age estimation, and thus to quality obstetric care, yet high equipment cost and need for trained sonographers limit its use in low-resource settings. Methods From September 2018 through June 2021, we recruited 4,695 pregnant volunteers in North Carolina and Zambia and obtained blind ultrasound sweeps (cineloops) of the gravid abdomen alongside standard fetal biometry. We trained a neural network to estimate gestational age from the sweeps and, in three test sets, assessed performance of the model and biometry against previously established gestational age. Results In our main test set, model mean absolute error (MAE) was 3.9 days (standard error [SE] 0.12) vs. 4.7 days (SE 0.15) for biometry (difference -0.8 days; 95% CI -1.1, -0.5; pConclusions Our model estimated gestational age more accurately from blindly obtained ultrasound sweeps than did trained sonographers performing fetal biometry. These results presage a future where all pregnant people – not just those in rich countries – can access the diagnostic benefits of sonography.

Researchers have linked neighborhood food availability to the overall frequency of using food outlets without noting if those outlets were within or outside of participants’ neighborhoods. We aimed to examine the association of neighborhood restaurant and food store availability with frequency of use of neighborhood food outlets, and whether such an association was modified by neighborhood street connectivity using a large and diverse population-based cohort of middle-aged U.S. adults. We used self-reported frequency of use of fast food restaurants, sit-down restaurants, and grocery stores in respondents’ home neighborhoods using data from the Coronary Artery Risk Development in Young Adults study Year 20 exam in 2005–2006 (n = 2860; Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA) and geographically matched GIS-measured neighborhood-level food resource, street, and U.S. Census data. We used mixed-effects logistic regression to examine the associations of the GIS-measured count of neighborhood fast food restaurants, sit-down restaurants, and grocery stores with self-reported frequency of using neighborhood restaurants and food stores and whether such associations differed by GIS-measured neighborhood street connectivity among those who perceived at least one such food outlet. In multivariate analyses, we observed a positive association between the GIS-measured count of neighborhood sit-down restaurants (OR = 1.02, 95% CI 1.00–1.04) and the self-reported frequency of using neighborhood sit-down restaurants. We observed no statistically significant association between GIS-measured count of neighborhood fast food restaurants and self-reported frequency of using neighborhood fast food restaurants, nor did we observe a statistically significant association between GIS-measured count of neighborhood grocery stores and self-reported frequency of using neighborhood grocery stores. We observed inverse associations between GIS-measured neighborhood street connectivity and the self-reported frequencies of using neighborhood fast food restaurants (OR = 0.42, 95% CI 0.26–0.68) and grocery stores (OR = − 2.26, 95% CI − 4.52 to − 0.01). Neighborhood street connectivity did not modify the association between GIS-measured neighborhood restaurant and food store count and the self-reported frequency of using neighborhood restaurants and food stores. Our findings suggest that, for those who perceived at least one sit-down restaurant in their neighborhood, individuals who have more GIS-measured sit-down restaurants in their neighborhoods reported more frequent use of sit-down restaurants than those whose neighborhoods contain fewer such restaurants. Our results also suggest that, for those who perceived at least one fast food restaurant in their neighborhood, individuals who live in neighborhoods with greater GIS-measured street connectivity reported less use of neighborhood fast food restaurants than those who live in neighborhoods with less street connectivity. The count of neighborhood sit-down restaurants and the connectivity of neighborhood street networks appear important in understanding the use of neighborhood food resources.

We studied the effect of key development and expansion of an off-road multipurpose trail system in Minneapolis, Minnesota between 2000 and 2007 to understand whether infrastructure investments are associated with increases in commuting by bicycle. We used repeated measures regression on tract-level (N = 116 tracts) data to examine changes in bicycle commuting between 2000 and 2008–2012. We investigated: 1) trail proximity measured as distance from the trail system and 2) trail potential use measured as the proportion of commuting trips to destinations that might traverse the trail system. All analyses (performed 2015–2016) adjusted for tract-level sociodemographic covariates and contemporaneous cycling infrastructure changes (e.g., bicycle lanes). Tracts that were both closer to the new trail system and had a higher proportion of trips to destinations across the trail system experienced greater 10-year increases in commuting by bicycle. Proximity to off-road infrastructure and travel patterns are relevant to increased bicycle commuting, an important contributor to overall physical activity. Municipal investment in bicycle facilities, especially off-road trails that connect a city’s population and its employment centers, is likely to lead to increases in commuting by bicycle.

While increasing evidence suggests an association between physical infrastructure in neighborhoods and health outcomes, relatively little research examines how neighborhoods change physically over time and how these physical improvements are spatially distributed across populations. This paper describes the change over 25 years (1985–2010) in bicycle lanes, off-road trails, bus transit service and parks, and spatial clusters of changes in these domains relative to neighborhood sociodemographics in four US cities that are diverse in terms of geography, size, and population. Across all four cities we identified increases in bicycle lanes, off-road trails, and bus transit service, with spatial clustering in these changes that related to neighborhood sociodemographics. Overall, we found evidence of positive changes in physical infrastructure commonly identified as supportive of physical activity. However, the patterning of infrastructure change by sociodemographic change encourages attention to the equity in infrastructure improvements across neighborhoods.

Background: Neighborhood transportation infrastructure and public recreational facilities are theorized to improve the activity, weight, and cardiometabolic profiles of individuals living in close proximity to these resources. However, owing to data limitations, there has not been adequate study of the influence of timing and placement of new infrastructure on health over time. Methods: This protocol details methods of the Four Cities Study to perform retrospective field audits in order to capitalize on existing longitudinal health data from the Coronary Artery Risk Development in Young Adults (CARDIA) Study. We developed and verified measures of recreation facilities (trails, parks) and transportation infrastructure (bus, light rail, bicycle parking, bicycle paths) in Birmingham, AL, Chicago, IL, Minneapolis, MN, and Oakland, CA. We identify introductions, renovations, and closures between 1985-2010 to develop measures of facility and infrastructure change. Ultimately, these data were linked to CARDIA sites’ respondents’ geographic locations over the 25-year study period to examine associations with health behaviors and outcomes. Results: Data available for retrospective audits was inconsistent by city, primarily due to record-keeping differences. We found large increases in bicycle infrastructure, with the exception of Birmingham, AL. Excluding the addition of a new rail line in Minneapolis, MN, few changes occurred in bus service, rail, and parks. Conclusion: Our method represents innovation toward the collection of retrospective neighborhood data for use in longitudinal analyses. The data produced give insight into the way neighborhood infrastructure has changed over time and the potential relationship between these changes and health behaviors.

The literature on food stores, neighborhood poverty, and race/ethnicity is mixed and lacks methods of accounting for complex spatial and temporal clustering of food resources. We used quarterly data on supermarket and convenience store locations from Nielsen TDLinx (Nielsen Holdings N.V., New York, New York) spanning 7 years (2006–2012) and census tract-based neighborhood sociodemographic data from the American Community Survey (2006–2010) to assess associations between neighborhood sociodemographic characteristics and food store distributions in the Metropolitan Statistical Areas (MSAs) of 4 US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and San Francisco, California). We fitted a space-time Poisson regression model that accounted for the complex spatial-temporal correlation structure of store locations by introducing space-time random effects in an intrinsic conditionally autoregressive model within a Bayesian framework. After accounting for census tract–level area, population, their interaction, and spatial and temporal variability, census tract poverty was significantly and positively associated with increasing expected numbers of supermarkets among tracts in all 4 MSAs. A similar positive association was observed for convenience stores in Birmingham, Minneapolis, and San Francisco; in Chicago, a positive association was observed only for predominantly white and predominantly black tracts. Our findings suggest a positive association between greater numbers of food stores and higher neighborhood poverty, with implications for policy approaches related to food store access by neighborhood poverty.