Recruitment and retention of participants with obesity into a longitudinal birth cohort: the Breastfeeding and Early Child Health (BEACH) study.

BACKGROUND: The study aimed to evaluate how maternal pre-pregnant body mass index (BMI) impacts participant recruitment and retention. METHODS: Participants were enrolled in a longitudinal study between 30 and 36 weeks of pregnancy as having normal weight (pre-pregnant BMI ≥ 18.5 and <25 kg/m2) or obesity (pre-pregnant BMI ≥ 30.0 kg/m2). Recruitment channels included Facebook, email, newspaper, phone calls, radio advertisements, flyers, and word-of-mouth. The stages of recruitment included eligibility, consent, and completion. Pearson's chi-square tests were used to evaluate the relationship between BMI and enrollment outcomes. RESULTS: Recruitment yielded 2770 total prospective participants. After screening, 141 individuals were eligible, 83 consented, and 60 completed the study. Facebook was the most successful method for identifying eligible pregnant patients with obesity, while a higher percentage of participants recruited through word-of-mouth and flyers consented to the study. Pre-pregnant BMI was significantly associated with the stage of recruitment completed by the participant (p = 0.04), whereby individuals eligible for the study with obesity were less likely to consent and complete study visits. CONCLUSION: We demonstrated that maternal obesity was significantly associated with enrollment outcomes in a longitudinal birth cohort study. This study showed that pre-pregnancy BMI influenced study participation. Therefore, tailored recruitment strategies to enhance the recruitment and enrollment of individuals with obesity in maternal-infant health research may be necessary.

Lipid Testing among Youth in Medicaid: The Role of Social Determinants of Health and Providers.

OBJECTIVE: To assess recent temporal trends in guideline-compliant pediatric lipid testing, and to examine the influence of social determinants of health (SDoH) and provider characteristics on the likelihood of testing in youth. STUDY DESIGN: In this observational, multiyear cross-sectional study, we calculated lipid testing prevalence by year among 268 627 12-year olds from 2015 through 2019 who were enrolled in Florida Medicaid and eligible for universal lipid screening during age 9 to 11, and 11 437 22-year olds (2017-2019) who were eligible for screening during age 17-21. We compared trends in testing prevalence by SDoH and health risk factors at two recommended ages and modeled the associations between patient characteristics and provider type on lipid testing using generalized estimating equations. RESULTS: Testing among 12-year olds remained low between 2015 through 2019 with the highest prevalence in 2015 (8.0%) and lowest in 2017 (6.7%). Screening compliance among 22-year olds was highest in 2017 (21.1%) and fell to 17.8% in 2019. Hispanics and non-Hispanic Blacks in both age groups had about 2%-3% lower testing prevalence than non-Hispanic Whites. Testing in 12-year olds was 12.3% vs 7.7% with and without obesity, and 14.4% vs 7.6% with and without antipsychotic use. Participants who saw providers who were more likely to prescribe lipid testing were more likely to receive testing (OR = 2.3, 95% CI 2.0-2.8, P < .001). CONCLUSIONS: Although lipid testing prevalence was greatest among high-risk children, overall prevalence of lipid testing in youth remains very low. Provider specialty and choices by individual providers play important roles in improving guideline-compliant pediatric lipid testing.

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study.

BACKGROUND:  Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and the presence of excessive proteins in the urine. Due to its complexity, the prediction of preeclampsia onset is often difficult and inaccurate. OBJECTIVE:  This study aimed to create quantitative models to predict the onset gestational age of preeclampsia using electronic health records. METHODS:  We retrospectively collected 1178 preeclamptic pregnancy records from the University of Michigan Health System as the discovery cohort, and 881 records from the University of Florida Health System as the validation cohort. We constructed 2 Cox-proportional hazards models: 1 baseline model using maternal and pregnancy characteristics, and the other full model with additional laboratory findings, vitals, and medications. We built the models using 80% of the discovery data, tested the remaining 20% of the discovery data, and validated with the University of Florida data. We further stratified the patients into high- and low-risk groups for preeclampsia onset risk assessment. RESULTS:  The baseline model reached Concordance indices of 0.64 and 0.61 in the 20% testing data and the validation data, respectively, while the full model increased these Concordance indices to 0.69 and 0.61, respectively. For preeclampsia diagnosed at 34 weeks, the baseline and full models had area under the curve (AUC) values of 0.65 and 0.70, and AUC values of 0.69 and 0.70 for preeclampsia diagnosed at 37 weeks, respectively. Both models contain 5 selective features, among which the number of fetuses in the pregnancy, hypertension, and parity are shared between the 2 models with similar hazard ratios and significant P values. In the full model, maximum diastolic blood pressure in early pregnancy was the predominant feature. CONCLUSIONS:  Electronic health records data provide useful information to predict the gestational age of preeclampsia onset. Stratification of the cohorts using 5-predictor Cox-proportional hazards models provides clinicians with convenient tools to assess the onset time of preeclampsia in patients.

A systematic review of contaminants in donor human milk.

Donor human milk (DHM) from a milk bank is the recommended feeding method for preterm infants when the mother's own milk (MOM) is not available. Despite this recommendation, information on the possible contamination of donor human milk and its impact on infant health outcomes is poorly characterised. The aim of this systematic review is to assess contaminants present in DHM samples that preterm and critically ill infants consume. The data sources used include PubMed, EMBASE, CINAHL and Web of Science. A search of the data sources targeting DHM and its potential contaminants yielded 426 publications. Two reviewers (S. T. and D. L.) conducted title/abstract screening through Covidence software, and predetermined inclusion/exclusion criteria yielded 26 manuscripts. Contaminant types (bacterial, chemical, fungal, viral) and study details (e.g., type of bacteria identified, study setting) were extracted from each included study during full-text review. Primary contaminants in donor human milk included bacterial species and environmental pollutants. We found that bacterial contaminants were identified in 100% of the papers in which bacterial contamination was sought (16 papers) and 61.5% of the full data set (26 papers), with the most frequently identified genera being Staphylococcus (e.g., Staphylococcus aureus and coagulase-negative Staphylococcus) and Bacillus (e.g., Bacillus cereus). Chemical pollutants were discovered in 100% of the papers in which chemical contamination was sought (eight papers) and 30.8% of the full data set (26 papers). The most frequently identified chemical pollutants included perfluoroalkyl substances (six papers), toxic metal (one paper) and caffeine (one paper). Viral and fungal contamination were identified in one paper each. Our results highlight the importance of establishing standardisation in assessing DHM contamination and future studies are needed to clarify the impact of DHM contaminants on health outcomes.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.

BACKGROUND: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW: This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW: We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Assessment of human milk in the era of precision health.

PURPOSE OF REVIEW: Precision health provides an unprecedented opportunity to improve the assessment of infant nutrition and health outcomes. Breastfeeding is positively associated with infant health outcomes, yet only 58.3% of children born in 2017 were still breastfeeding at 6 months. There is an urgent need to examine the application of precision health tools that support the development of public health interventions focused on improving breastfeeding outcomes. RECENT FINDINGS: In this review, we discussed the novel and highly sensitive techniques that can provide a vast amount of omics data and clinical information just by evaluating small volumes of milk samples, such as RNA sequencing, cytometry by time-of-flight, and human milk analyzer for clinical implementation. These advanced techniques can run multiple samples in a short period of time making them ideal for the routine clinical evaluation of milk samples. SUMMARY: Precision health tools are increasingly used in clinical research studies focused on infant nutrition. The integration of routinely collected multiomics human milk data within the electronic health records has the potential to identify molecular biomarkers associated with infant health outcomes.

Perspectives of pregnant and breastfeeding women on longitudinal clinical studies that require non-invasive biospecimen collection - a qualitative study.

BACKGROUND: Investigation of the microbiome during early life has stimulated an increasing number of cohort studies in pregnant and breastfeeding women that require non-invasive biospecimen collection. The objective of this study was to explore pregnant and breastfeeding women's perspectives on longitudinal clinical studies that require non-invasive biospecimen collection and how they relate to study logistics and research participation. METHODS: We completed in-depth semi-structured interviews with 40 women who were either pregnant (n = 20) or breastfeeding (n = 20) to identify their understanding of longitudinal clinical research, the motivations and barriers to their participation in such research, and their preferences for providing non-invasive biospecimen samples. RESULTS: Perspectives on research participation were focused on breastfeeding and perinatal education. Participants cited direct benefits of research participation that included flexible childcare, lactation support, and incentives and compensation. Healthcare providers, physician offices, and social media were cited as credible sources and channels for recruitment. Participants viewed lengthy study visits and child protection as the primary barriers to research participation. The barriers to biospecimen collection were centered on stool sampling, inadequate instructions, and drop-off convenience. CONCLUSION: Women in this study were interested in participating in clinical studies that require non-invasive biospecimen collection, and motivations to participate center on breastfeeding and the potential to make a scientific contribution that helps others. Effectively recruiting pregnant or breastfeeding participants for longitudinal microbiome studies requires protocols that account for participant interests and consideration for their time.

Duration of neonatal intensive care unit exposure associated with decreased risk of atopic dermatitis.

BACKGROUND/OBJECTIVES: Premature infants have lower rates of atopic dermatitis (AD) compared with full-term infants, though little is known about the factors contributing to this association. We explored the infant and environmental factors that may contribute to the association between prematurity and atopic dermatitis, including mode of delivery, birthweight, gestation, and duration of stay in the neonatal intensive care unit (NICU). METHODS: This was a single-center retrospective study. Independent samples t tests or chi-square tests were used to compare groups on continuous and categorical variables, respectively. Logistic regression then examined the association of the predictor variables with AD. RESULTS: Four thousand sixteen mother-infant dyads were included. Infants had a higher risk of developing AD if they were delivered vaginally (P = .013), did not stay in the NICU (P < .001), had a longer gestation (P = .001), or had a higher birthweight (P = .002). In modeling atopic dermatitis with the predictor variables, only NICU length of stay remained significantly associated with a lower risk of AD (P = .004). CONCLUSION: Infants had a lower risk of developing AD if they had a longer stay in the NICU.

Implications of the vaginal microbiome and potential restorative strategies on maternal health: a narrative review.

The vaginal microbiome undergoes dramatic shifts before and throughout pregnancy. Although the genetic and environmental factors that regulate the vaginal microbiome have yet to be fully elucidated, high-throughput sequencing has provided an unprecedented opportunity to interrogate the vaginal microbiome as a potential source of next-generation therapeutics. Accumulating data demonstrates that vaginal health during pregnancy includes commensal bacteria such as Lactobacillus that serve to reduce pH and prevent pathogenic invasion. Vaginal microbes have been studied as contributors to several conditions occurring before and during pregnancy, and an emerging topic in women's health is finding ways to alter and restore the vaginal microbiome. Among these restorations, perhaps the most significant effect could be preterm labor (PTL) prevention. Since bacterial vaginosis (BV) is known to increase risk of PTL, and vaginal and oral probiotics are effective as supplemental treatments for BV prevention, a potential therapeutic benefit exists for pregnant women at risk of PTL. A new method of restoration, vaginal microbiome transplants (VMTs) involves transfer of one women's cervicovaginal secretions to another. New studies investigating recurrent BV will determine if VMTs can safely establish a healthy Lactobacillus-dominant vaginal microbiome. In most cases, caution must be taken in attributing a disease state and vaginal dysbiosis with a causal relationship, since the underlying reason for dysbiosis is usually unknown. This review focuses on the impact of vaginal microflora on maternal outcomes before and during pregnancy, including PTL, gestational diabetes, preeclampsia, and infertility. It then reviews the clinical evidence focused on vaginal restoration strategies, including VMTs.

Higher Intake of PUFAs Is Associated with Lower Total and Visceral Adiposity and Higher Lean Mass in a Racially Diverse Sample of Children.

BACKGROUND: Polyunsaturated fatty acids (PUFAs) are associated with protection from obesity-related phenotypes in adults; however, the relation between reported intake of PUFAs with body-composition outcomes in children remains unknown. OBJECTIVE: Our objective was to examine how self-reported intakes of PUFAs, including total, n-6 (ω-6), and n-3 (ω-3) PUFAs and ratios of n-6 to n-3 PUFAs and PUFAs to saturated fatty acids (SFAs), are associated with measures of adiposity and lean mass (LM) in children. We hypothesized that higher self-reported intakes of PUFAs and the ratio of PUFAs to SFAs would be positively associated with LM and negatively associated with total adiposity. METHODS: Body composition and dietary intake were measured in a racially diverse sample of 311 children (39% European American, 34% African American, and 27% Hispanic American) aged 7-12 y. Body composition and abdominal fat distribution were measured by dual-energy X-ray absorptiometry and computed tomography scans, respectively. Self-reported dietary intakes (including total PUFAs, n-3 PUFAs, n-6 PUFAs, and SFAs) were assessed by using two 24-h recalls. Independent-sample t tests and multiple linear regression analyses were conducted. RESULTS: Total PUFA intake was positively associated with LM (P = 0.049) and negatively associated with percentage of body fat (%BF; P = 0.033) and intra-abdominal adipose tissue (IAAT; P = 0.022). A higher ratio of PUFAs to SFAs was associated with higher LM (P = 0.030) and lower %BF (P = 0.028) and IAAT (P = 0.048). Intakes of n-3 and n-6 PUFAs were positively associated with LM (P = 0.017 and P = 0.021, respectively), and the ratio of n-6 to n-3 PUFAs was negatively associated with IAAT (P = 0.014). All results were independent of biological, environmental, and genetic covariates. CONCLUSIONS: Our results show that a higher self-reported intake of PUFAs and a higher ratio of PUFAs to SFAs are positively associated with LM and negatively associated with visceral adiposity and %BF in a healthy cohort of racially diverse children aged 7-12 y. This trial was registered at clinicaltrials.gov as NCT00726778.

Call for Improved Fourth Trimester Care After Stillbirth.

Women who experience stillbirths are at increased risk for severe maternal morbidity and mortality, which makes the postpartum period a critical time in which to address health conditions and prevent complications. However, research on the health care needs of women who experience stillbirths is scarce, and these women are often excluded from research on the postpartum period. Therefore, the purpose of this commentary is to identify gaps in the research on postpartum care after stillbirth, explain why current fourth trimester care guidelines in the United States are inadequate, and advocate for nursing research and practice to improve understanding of health care needs in the fourth trimester.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Classifying early infant feeding status from clinical notes using natural language processing and machine learning.

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.

Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.

Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study's outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.

Evidence for novel genetic loci associated with metabolic traits in Yup'ik people.

OBJECTIVES: To identify genomic regions associated with fasting plasma lipid profiles, insulin, glucose, and glycosylated hemoglobin in a Yup'ik study population, and to evaluate whether the observed associations between genetic factors and metabolic traits were modified by dietary intake of marine derived omega-3 polyunsaturated acids (n-3 PUFA). METHODS: A genome-wide linkage scan was conducted among 982 participants of the Center for Alaska Native Health Research study. n-3 PUFA intake was estimated using the nitrogen stable isotope ratio (δ(15) N) of erythrocytes. All genotyped SNPs located within genomic regions with LOD scores > 2 were subsequently tested for individual SNP associations with metabolic traits using linear models that account for familial correlation as well as age, sex, community group, and n-3 PUFA intake. Separate linear models were fit to evaluate interactions between the genotype of interest and n-3 PUFA intake. RESULTS: We identified several chromosomal regions linked to serum apolipoprotein A2, high density lipoprotein-, low density lipoprotein-, and total cholesterol, insulin, and glycosylated hemoglobin. Genetic variants found to be associated with total cholesterol mapped to a region containing previously validated lipid loci on chromosome 19, and additional novel peaks of biological interest were identified at 11q12.2-11q13.2. We did not observe any significant interactions between n-3 PUFA intake, genotypes, and metabolic traits. CONCLUSIONS: We have completed a whole genome linkage scan for metabolic traits in Native Alaskans, confirming previously identified loci, and offering preliminary evidence of novel loci implicated in chronic disease pathogenesis in this population.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

Association between early life antibiotic exposure and development of early childhood atopic dermatitis.

Background: Atopic dermatitis (AD) is a chronic, inflammatory skin disease commonly onset during infancy. Objective: We examine the association between pre-and postnatal antibiotic exposure and the development of AD. Methods: A retrospective, observational study analyzed 4106 infants at the University of Florida from June 2011 to April 2017. Results: Antibiotic exposure during the first year of life was associated with a lower risk of AD. The association was strongest for exposure during the first month of life. There were no significant differences in the rates of AD in infants with or without exposure to antibiotics in months 2 through 12, when examined by month. Antibiotic exposure during week 2 of life was associated with lower risk of AD, with weeks 1, 3, and 4 demonstrating a similar trend. Limitations: Retrospective data collection from a single center, use of electronic medical record, patient compliance with prescribed medication, and variable follow-up. Conclusions: Early life exposures, such as antibiotics, may lead to long-term changes in immunity. Murine models of atopic dermatitis demonstrate a "critical window" for the development of immune tolerance to cutaneous microbes. Our findings suggest that there may also be a "critical window" for immune tolerance in human infants, influenced by antibiotic exposure.

Maternal gut microbiota in the postpartum Period: A Systematic review.

Studies have demonstrated the importance of the gut microbiota during pregnancy, and there is emerging literature on the postpartum maternal gut microbiota. The primary objective of this paper was to synthesize the literature on the postpartum gut microbiome composition and diversity measured in stool samples from healthy mothers of predominantly term infants. The secondary objectives were (1) to identify biological and environmental factors that influence postpartum maternal gut microbiota and (2) to assess health conditions and clinical intermediate measures associated with postpartum gut microbiota changes in all mothers. Electronic searches were conducted November 9, 2020 and updated July 25, 2021 without publication time limits on PubMed, Embase, CINHAL, Scopus, Cochrane Library, BioArchives, and OpenGrey.eu. Primary research on maternal gut microbiota in the postpartum (up to one year after childbirth) were eligible. Postpartum gut microbiota comparisons to pregnancy or non-pregnancy gut microbiota were of interest, therefore, studies examining these in addition to the postpartum were included. Studies were excluded if they were only conducted in animals, infants, pregnancy, or microbiome of other body locations (e.g., vaginal). Data extraction of microbial composition and diversity were completed and synthesized narratively. Studies were assessed for risk of bias. A total of 2512 articles were screened after deduplication and 27 were included in this review. Of the 27 included studies, 22 addressed the primary objective. Firmicutes was the predominant phylum in the early (<6 weeks) and late postpartum (6 weeks to 1 year). In early postpartum, Bacteroides was the predominant genus. Findings from longitudinal assessments of alpha and beta diversity from the early to the late postpartum varied. Nineteen of the 27 studies assessed biological and environmental factors influencing the postpartum gut microbial profile changes. Timing of delivery, probiotic supplementation, triclosan exposure, and certain diets influenced the postpartum gut microbiota. Regarding health conditions and intermediate clinical measures assessed in 8 studies; inflammatory bowel disease, postpartum depression, early-onset preeclampsia, gestational diabetes, excessive gestational weight gain, and anthropometric measures such as body mass index and waist-to-hip ratio were related to gut microbiota changes. There is limited data on the maternal postpartum gut microbiota and how it influences maternal health. We need to understand the postpartum maternal gut microbiome, establish how it differs from non-pregnancy and pregnancy states, and determine biological and environmental influencers. Future research of the gut microbiome's significance for the birthing parent in the postpartum could lead to a new understanding of how to improve maternal short and long-term health.

Peripartum women's perspectives on research study participation in the OneFlorida Clinical Research Consortium during COVID-19 pandemic.

Introduction: The COVID-19 pandemic created an unprecedented need for population-level clinical trials focused on the discovery of life-saving therapies and treatments. However, there is limited information on perception of research participation among perinatal populations, a population of particular interest during the pandemic. Methods: Eligible respondents were 18 years or older, were currently pregnant or had an infant (≤12 months old), and lived in Florida within 50 miles of sites participating in the OneFlorida Clinical Research Consortium. Respondents were recruited via Qualtrics panels between April and September 2020. Respondents completed survey items about barriers and facilitators to participation and answered sociodemographic questions. Results: Of 533 respondents, most were between 25 and 34 years of age (n = 259, 49%) and identified as White (n = 303, 47%) and non-Hispanic (n = 344, 65%). Facebook was the most popular social media platform among our respondents. The most common barriers to research participation included poor explanation of study goals, discomforts to the infant, and time commitment. Recruitment through healthcare providers was perceived as the best way to learn about clinical research studies. When considering research participation, "myself" had the greatest influence, followed by familial ties. Noninvasive biological samples were highly acceptable. Hispanics had higher positive perspectives on willingness to participate in a randomized study (p = 0.009). Education (p = 0.007) had significant effects on willingness to release personal health information. Conclusion: When recruiting women during the pregnancy and postpartum periods for perinatal studies, investigators should consider protocols that account for common barriers and preferred study information sources. Social media-based recruitment is worthy of adoption.

Perinatal Health Outcomes Across Rural and Nonrural Counties Within a Single Health System Catchment.

Background: Perinatal health outcomes are influenced by a variety of socioeconomic, behavioral, and economic factors that reduce access to health services. Despite these observations, rural communities continue to face barriers, including a lack of resources and the fragmentation of health services. Objective: To evaluate patterns in health outcomes, health behaviors, socioeconomic vulnerability, and sociodemographic characteristics across rural and nonrural counties within a single health system catchment area. Methods: Socioeconomic vulnerability metrics, health care access as determined by licensed provider metrics, and behavioral data were obtained from FlHealthCHARTS.gov and the County Health Rankings. County-level birth and health data were obtained from the Florida Department of Health. The University of Florida Health Perinatal Catchment Area (UFHPCA) was defined as all Florida counties where ≥5% of all infants were delivered at Shands Hospital between June 2011 and April 2017. Results: The UFHPCA included 3 nonrural and 10 rural counties that represented more than 64,000 deliveries. Nearly 1 in 3 infants resided in a rural county, and 7 out of 13 counties did not have a licensed obstetrician gynecologist. Maternal smoking rates (range 6.8%-24.8%) were above the statewide rate (6.2%). Except for Alachua County, breastfeeding initiation rates (range 54.9%-81.4%) and access to household computing devices (range 72.8%-86.4%) were below the statewide rate (82.9% and 87.9%, respectively). Finally, we found that childhood poverty rates (range 16.3%-36.9%) were above the statewide rate (18.5%). Furthermore, risk ratios suggested negative health outcomes for residents of counties within the UFHPCA for each measure, except for infant mortality and maternal deaths, which lacked sample sizes to adequately test. Conclusions: The health burden of the UFHPCA is characterized by rural counties with increased maternal death, neonatal death, and preterm birth, as well as adverse health behaviors that included increased smoking during pregnancy and lower levels of breastfeeding relative to nonrural counties. Understanding perinatal health outcomes across a single health system has potential to not only estimate community needs but also facilitate planning of health care initiatives and interventions in rural and low-resource communities.