Significance: Monitoring vital signs for laboring women and their fetuses is foundational to the delivery of obstetrical care; however, monitoring platforms for pregnancy have undergone little innovation over the last several decades with many low-income settings lacking basic access. We report a new time-synchronized, flexible, and wireless sensor system applicable across the entire continuum of antepartum and postpartum care that provides continuous, comprehensive, and noninvasive monitoring (heart rate, respiratory rate, and pulse oxygenation) compatible with a wide range of mobile devices. This system offers advanced features such as continuous blood pressure, uterine electrohysterography, and automated body position classification. We further demonstrate the performance of this new system among pregnant individuals ( n = 576) in both high-resource settings and low-resource care settings.

Importance: Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device.Objective: To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography.Design, Setting, and Participants: This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the “ground truth” GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks’ to 27 6/7 weeks’ gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard).Main Outcomes and Measures: The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method’s estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days.Results: In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, −0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index.Conclusions and Relevance: Between 14 and 27 weeks’ gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people.Trial Registration: ClinicalTrials.gov Identifier: NCT05433519

Annually, around 44 million abortions are induced worldwide. Safe termination of pregnancy (TOP) services can reduce maternal mortality, but induced abortion is illegal or severely restricted in many countries. All abortions, particularly unsafe induced abortions, may require post-abortion care (PAC) services to treat complications and prevent future unwanted pregnancy. We used a signal-function approach to look at abortion care services and illustrated its utility with secondary data from Zambia. We refined signal functions for basic and comprehensive TOP and PAC services, including family planning (FP), and assessed functions currently being collected via multi-country facility surveys. We then used the 2005 Zambian Health Facility Census to estimate the proportion of 1369 health facilities that could provide TOP and PAC services under three scenarios. We linked facility and population data, and calculated the proportion of the Zambian population within reach of such services. Relevant signal functions are already collected in five facility assessment tools. In Zambia, 30 % of facilities could potentially offer basic TOP services, 3.7 % comprehensive TOP services, 2.6 % basic PAC services, and 0.3 % comprehensive PAC services (four facilities). Capability was highest in hospitals, except for FP functions. Nearly two-thirds of Zambians lived within 15 km of a facility theoretically capable of providing basic TOP, and one-third within 15 km of comprehensive TOP services. However, requiring three doctors for non-emergency TOP, as per Zambian law, reduced potential access to TOP services to 30 % of the population. One-quarter lived within 15 km of basic PAC and 13 % of comprehensive PAC services. In a scenario not requiring FP functions, one-half and one-third of the population were within reach of basic and comprehensive PAC respectively. There were huge urban-rural disparities in access to abortion care services. Comprehensive PAC services were virtually unavailable to the rural population. Secondary data from facility assessments can highlight gaps in abortion service provision and coverage, but it is necessary to consider TOP and PAC separately. This approach, especially when combined with population data using geographic coordinates, can also be used to model the impact of various policy scenarios on access, such as requiring three medical doctors for non-emergency TOP. Data collection instruments could be improved with minor modifications and used for multi-country comparisons.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery ( r = 0.65), maternal age ( r = 0.59), gravidity ( r = 0.56), and BMI ( r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Mentoring is beneficial to mentors, mentees, and their institutions, especially in low- and middle-income countries (LMICs), that are faced with complex disease burdens, skills shortages, and resource constraints. Mentoring in global health research can be enhanced by defining key competencies, to enable the skill set required for effective mentoring, determine training needs for local research mentors, and facilitate institutional capacity building to support mentors. The latter includes advocating for resources, institutional development of mentoring guidelines, and financial and administrative support for mentoring. Nine core global health research mentoring competencies were identified: maintaining effective communication; aligning expectations with reasonable goals and objectives; assessing and providing skills and knowledge for success; addressing diversity; fostering independence; promoting professional development; promoting professional integrity and ethical conduct; overcoming resource limitations; and fostering institutional change. The competencies described in this article will assist mentors to sharpen their cognitive skills, acquire or generate new knowledge, and enhance professional and personal growth and job satisfaction. Similarly, the proposed competencies will enhance the knowledge and skills of mentees, who can continue and extend the work of their mentors, and advance knowledge for the benefit of the health of populations in LMICs.

Zambia’s maternal mortality ratio was estimated at 398/100,000 live births in 2014. Successful aversion of deaths is dependent on availability and usability of signal functions for emergency obstetric and neonatal care. Evidence of availability, usability and quality of signal functions in urban settings in Zambia is minimal as previous research has evaluated their distribution in rural settings. This survey evaluated the availability and usability of signal functions in private and public health facilities in Lusaka District of Zambia. A descriptive cross sectional study was conducted between November 2014 and February 2015 at 35 public and private health facilities. The Service Availability and Readiness Assessment tool was adapted and administered to overall in-charges, hospital administrators or maternity ward supervisors at health facilities providing maternal and newborn health services. The survey quantified infrastructure, human resources, equipment, essential drugs and supplies and used the UN process indicators to determine availability, accessibility and quality of signal functions. Data on deliveries and complications were collected from registers for periods between June 2013 and May 2014. Of the 35 (25.7% private and 74.2% public) health facilities assessed, only 22 (62.8%) were staffed 24 h a day, 7 days a week and had provided obstetric care 3 months prior to the survey. Pre-eclampsia/ eclampsia and obstructed labor accounted for most direct complications while postpartum hemorrhage was the leading cause of maternal deaths. Overall, 3 (8.6%) and 5 (14.3%) of the health facilities had provided Basic and Comprehensive EmONC services, respectively. All facilities obtained blood products from the only blood bank at a government referral hospital. The UN process indicators can be adequately used to monitor progress towards maternal mortality reduction. Lusaka district had an unmet need for BEmONC as health facilities fell below the minimum UN standard. Public health facilities with capacity to perform signal functions should be upgraded to Basic EmONC status. Efforts must focus on enhancing human resource capacity in EmONC and improving infrastructure and supply chain. Obstetric health needs and international trends must drive policy change.