Value and Digital-Based Maternal and Child Health: Data, Equity, and Systems Solutions

Maternal and child health (MCH) has achieved notable gains over the last two decades, yet preventable morbidity and mortality persist and are increasingly shaped by metabolic risk, mental health, environmental change, and social inequity. Rapid growth in digital tools—remote monitoring, AI-enabled risk prediction, electronic registries—and the post-pandemic expansion of primary care create new opportunities to deliver timely, person-centred services. However, fragmentation between clinical care, public health, and financing systems limits scale-up, while data quality, algorithmic bias, and privacy challenges hamper trust. There is an urgent need for rigorous, policy-relevant research that connects methods to impact: from causal inference and pragmatic trials to implementation science and economic evaluation across diverse settings.The goal of this Research Topic is to advance practical, equitable, and scalable solutions for maternal and child health by integrating high-quality data, robust methods, and value-based policy design. We seek contributions that: (1) define priority problems (e.g., gestational diabetes, hypertensive disorders, perinatal mental health, vaccination and infectious threats); (2) develop and test predictive and decision-support models with attention to fairness, transparency, and real-world performance; (3) evaluate effectiveness, cost-effectiveness, and budget impact of interventions across the continuum of care (preconception–antenatal–intrapartum–postnatal–early childhood); and (4) translate evidence into implementable strategies in primary care and community settings, especially for underserved populations. By connecting clinicians, data scientists, public health practitioners, and policymakers, this Research Topic will curate a blueprint for delivering measurable health gains and financial protection for women, newborns, children, and families.Suitable themes for manuscripts include, but are not limited to:• Early-life risk stratification and prediction: AI/ML performance, generalizability and fairness in MCH• Maternal metabolic health: prevention and management of GDM, obesity and hypertensive disorders• Perinatal mental health, sleep, and digital phenotyping: screening and stepped-care models• Maternal immunization and infectious disease in pregnancy: neonatal outcomes and surveillance• Health systems and financing innovations for MCH: primary care models, bundled payments, value-based care• Community-based and digital interventions for access, adherence, and continuity• Equity, social determinants, and migration: reducing urban–rural gaps and protecting vulnerable groups• Data infrastructure, registries, interoperability and privacy-by-design in MCH data ecosystems• Economic evaluation, budget impact and affordability analyses for MCH programs and technologies• Implementation science, scale-up strategies and policy translation (learning health systems). Topic editors: Tingting Xu, Yanhui Dong, Wai-kit Ming Source: https://www.frontiersin.org/research-topics/75290/value-and-digital-based-maternal-and-child-health-data-equity-and-systems-solutions