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.

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.

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.

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.

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.

Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal.

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

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.

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.

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.

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.

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.

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.

Implementation of a Family Support Grant to Subsidize Caregiving Needs and Support Attendance at American Society for Nutrition's Annual Professional Scientific Conference.

Attendance at professional society meetings facilitates networking, collaboration, and success in academic/scientific fields. Insufficient funds, support, or resources for caregiving can inhibit attendance for parents/caretakers, who may become professionally disadvantaged by not attending professional society conferences. The American Society for Nutrition (ASN) offered a family support grant for caregiving needs during the annual conference (maximum: $750); however, the perceived impact of caregiving funds on attendance outcomes is unknown. The objective of this study was to assess the need of family support for attendance to the ASN annual conference among applicants and to assess recipients' experience and usage of funds. Applicants completed a pre-conference survey assessing requested funds, out-of-pocket caregiving expenses to attend the meeting, the influence of receiving the grant on attendance, and additional factors. Recipients completed a post-conference survey assessing use of the funds and impact of the grant on attending/participating. Grant applications (n = 110) were majority women, aged 26-45 y, married, at the trainee or assistant professor level, from diverse racial/ethnic backgrounds, and with parenting noted as the primary responsibility. Thirty-seven percent of applicants were currently lactating or expressing milk. The average amount requested was $650 US dollars, and >60% of respondents indicated plans to use funds to bring a family member/friend to the conference. Seventy-seven percent of respondents indicated that receiving the grant would influence their attendance. The post-conference survey (n = 25) indicated that recipients felt that receiving the grant was helpful in attending the conference (92%), specifically attending scientific sessions (96%) and poster sessions (80%). Recipients indicated the grant helped them network with attendees (88%), visit the exhibitor hall (72%), and participate in career development activities (64%). The ASN family support grant aided attendance and supported recipients' participation in conference activities, particularly early-career women who are parents, with the goal of supporting diversity and inclusivity in scientific/academic fields. This trial was registered at www.clinicaltrials.gov as NCT03432585.

Enhancing Quality Measurement With Clinical Information: A Use Case of Body Mass Index Change Among Children Taking Second Generation Antipsychotics.

OBJECTIVE: We sought to examine the extent to which body mass index (BMI) was available in electronic health records for Florida Medicaid recipients aged 5 to 18 years taking Second-Generation Antipsychotics (SGAP). We also sought to illustrate how clinical data can be used to identify children most at-risk for SGAP-induced weight gain, which cannot be done using process-focused measures. METHODS: Electronic health record (EHR) data and Medicaid claims were linked from 2013 to 2019. We quantified sociodemographic differences between children with and without pre- and post-BMI values. We developed a linear regression model of post-BMI to examine pre-post changes in BMI among 4 groups: 1) BH/SGAP+ children had behavioral health conditions and were taking SGAP; 2) BH/SGAP- children had behavioral health conditions without taking SGAP; 3) children with asthma; and 4) healthy children. RESULTS: Of 363,360 EHR-Medicaid linked children, 18,726 were BH/SGAP+. Roughly 4% of linked children and 8% of BH/SGAP+ children had both pre and post values of BMI required to assess quality of SGAP monitoring. The percentage varied with gender and race-ethnicity. The R2 for the regression model with all predictors was 0.865. Pre-post change in BMI differed significantly (P < .0001) among the groups, with more BMI gain among those taking SGAP, particularly those with higher baseline BMI. CONCLUSION: Meeting the 2030 Centers for Medicare and Medicaid Services goal of digital monitoring of quality of care will require continuing expansion of clinical encounter data capture to provide the data needed for digital quality monitoring. Using linked EHR and claims data allows identifying children at higher risk for SGAP-induced weight gain.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research.

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Association between atopic dermatitis and race from infancy to early childhood: a retrospective cohort study.

BACKGROUND: Atopic dermatitis (AD) is a common pediatric skin condition with significant morbidity. It is unclear what factors contribute to racial differences in disease prevalence. METHODS: A single-site, retrospective cohort study of infants born from June 1, 2011, to April 30, 2017, was performed. RESULTS: Of the 4016 infants included, 39.2% (n = 1574) were Black, 38.5% (n = 1543) White (non-Hispanic), 7.1% (n = 286) Hispanic, 5.3% (n = 213) Asian, 6.5% (n = 262) "other" race, 3.4% (n = 135) multiracial, and 0.1% (n = 3) not reported. Prevalence of AD differed by race, with 37.0% (n = 583) of Black, 25.8% (n = 55) of Asian, 24.1% (n = 69) of Hispanic, 23.0% (n = 31) of multiracial, 19.1% (n = 50) of "other" race, and 17.9% (n = 276) of White patients diagnosed (P < 0.0001). Delivery mode, NICU stay, and gestational age were all significantly associated with race. In modeling AD with logistic regression, race was significantly associated with the development of AD (P < 0.0001, OR Black = 2.6 [2.2-3.2], OR Asian = 1.6 [1.1-2.2], OR Hispanic = 1.4 [1.0-1.9], OR multiracial 1.4 [0.91-2.2], OR "other" 0.97 [0.67-1.4], and OR White 1.0). CONCLUSIONS: Racial differences in rates of AD arise early in life. Diagnosis is associated with race rather than delivery mode, insurance type, and gestational age. Further investigation into these disparities and interventions to mitigate them should focus on infancy and early childhood.

Metabolomic Profiling of Biological Reference Materials using a Multiplatform High-Resolution Mass Spectrometric Approach.

The number of metabolomics studies have increased dramatically in recent years, spanning from basic/mechanistic research to the identification and validation of clinical biomarkers. Developments in analyte separation techniques and the growth of databases are largely responsible for the rapid growth of metabolomics, although broad differences in analytical workflows can result in difficulty when comparing data across studies. The establishment of baseline metabolomics data for human reference materials using complementary/orthogonal data acquisition strategies can help to alleviate some of these challenges. To this end, we report nontargeted semiquantitative metabolomics data for 22 commercially available materials including plasma (healthy, diabetic, hypertriglyceridemic, African-American), serum (female, male, pregnant, among others), feces (meconium, vegan, omnivore), urine (smokers' and nonsmokers'), breast milk, saliva, and vaginal fluid, using ultrahigh-performance liquid chromatography-tandem mass spectrometry in positive and negative electrospray ionization, as well as gas chromatography-electron ionization-mass spectrometry. Significant differences were observed in the metabolomic fingerprints between all sample types. Post hoc comparisons between relevant sample types support the relevance of these materials and the validity of nontargeted strategies in global metabolomics. As the number and variety of reference materials continues to increase, it is imperative that their adoption is matched. The results of this study may inform future biomedical research by highlighting several metabolites across matrixes and treatments/states that could serve as clinical biomarkers or important biochemical pathway intermediates. Furthermore, our work can serve as a metric for systems suitability, quality assurance, and quality control across the community via the dissemination of high-quality and publicly available annotated metabolomics data.

Acceptability of Research and Health Care Visits During the COVID-19 Pandemic: Cross-sectional Survey Study.

BACKGROUND: The COVID-19 pandemic has had a widespread impact on attendance in biomedical research and health care visits. OBJECTIVE: This study aimed to identify when and how American adults might feel comfortable about resuming in-person research and health care visits. METHODS: Cross-sectional questionnaire data were collected from 135 adults (age: median 48 years; women: n=113, 83.7%; White participants: n=92, 68.2%) who were engaged in health-related research. RESULTS: More than half of the respondents (65/122, 53.3%) felt that the COVID-19 pandemic positively affected their desire to participate in research. Although 73.6% (95/129) of respondents also indicated a willingness to attend in-person health care visits while Centers for Disease Control and Prevention (CDC) guidelines are implemented, 85.8% (109/127) indicated a willingness to attend in-person, outdoor visits, and 92.2% (118/128) reported a willingness to attend drive-through visits (with CDC guidelines implemented during both visit types). Videoconferencing was the most preferred format for intervention visits; however, adults over the age of 65 years preferred this format less than younger adults (P=.001). CONCLUSIONS: Researchers and clinicians should continue to provide opportunities for continuing the conduction of remote-based interventions while enforcing CDC guidelines during in-person visits.

Perspectives of Pregnant and Breastfeeding Women on Participating in Longitudinal Mother-Baby Studies Involving Electronic Health Records: Qualitative Study.

BACKGROUND: Electronic health records (EHRs) hold great potential for longitudinal mother-baby studies, ranging from assessing study feasibility to facilitating patient recruitment to streamlining study visits and data collection. Existing studies on the perspectives of pregnant and breastfeeding women on EHR use have been limited to the use of EHRs to engage in health care rather than to participate in research. OBJECTIVE: The aim of this study is to explore the perspectives of pregnant and breastfeeding women on releasing their own and their infants' EHR data for longitudinal research to identify factors affecting their willingness to participate in research. METHODS: We conducted semistructured interviews with pregnant or breastfeeding women from Alachua County, Florida. Participants were asked about their familiarity with EHRs and EHR patient portals, their comfort with releasing maternal and infant EHR data to researchers, the length of time of the data release, and whether individual research test results should be included in the EHR. The interviews were transcribed verbatim. Transcripts were organized and coded using the NVivo 12 software (QSR International), and coded data were thematically analyzed using constant comparison. RESULTS: Participants included 29 pregnant or breastfeeding women aged between 22 and 39 years. More than half of the sample had at least an associate degree or higher. Nearly all participants (27/29, 93%) were familiar with EHRs and had experience accessing an EHR patient portal. Less than half of the participants (12/29, 41%) were willing to make EHR data available to researchers for the duration of a study or longer. Participants' concerns about sharing EHRs for research purposes emerged in 3 thematic domains: privacy and confidentiality, transparency by the research team, and surrogate decision-making on behalf of infants. The potential release of sensitive or stigmatizing information, such as mental or sexual health history, was considered in the decisions to release EHRs. Some participants viewed the simultaneous use of their EHRs for both health care and research as potentially beneficial, whereas others expressed concerns about mixing their health care with research. CONCLUSIONS: This exploratory study indicates that pregnant and breastfeeding women may be willing to release EHR data to researchers if researchers adequately address their concerns regarding the study design, communication, and data management. Pregnant and breastfeeding women should be included in EHR-based research as long as researchers are prepared to address their concerns.

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.