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A coupled model to value indirect impacts of maternal, newborn and child health interventions
Walsh T, Vogel JP, Makama M, Abeysuriya R, Scott N ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This document describes a mathematical model designed to estimate the impact of maternal, newborn and child health interventions on health outcomes and costs. The model combines 15 interconnected modules covering conditions like anemia, pre-eclampsia, malnutrition, and neonatal infections across pregnancy and early childhood. As an example, the authors show how scaling up standard care and AI-ultrasound diagnostics for pregnant women could prevent 207 million DALYs globally between 2024 and 2040. The tool is intended to help policymakers assess the potential of both established and new interventions.
Key points
- The model includes 15 interconnected modules for pregnant women, newborns and children under 5 covering multiple health conditions
- It captures dynamic interactions between maternal, newborn and child health conditions across different pregnancy trimesters
- In a use case scenario, scaling anemia and pre-eclampsia interventions plus AI-ultrasound to 60% coverage could avert 207 million DALYs globally over 2024-2040
- The tool can be adapted to assess novel interventions and applied at global, regional, national or sub-national levels
- Existing models like Lives Saved Tool (LiST) cannot flexibly integrate novel interventions, which this model addresses
Limitations
This document presents a modeling framework and one illustrative example, not empirical data from actual implementation or validation studies. It cannot demonstrate whether the model's predictions match real-world outcomes when interventions are actually deployed.
Abstract
Background Outcomes associated with maternal, newborn and child health (MNCH) conditions have improved significantly over the past 20 years, however, they are not on track to meet the global SDG targets by 2030. Emerging interventions have the potential to greatly increase access to care, but estimating their impacts is difficult due to complicated interactions between interventions. Models exist to quantify outcomes and costs associated with scaling up interventions related to MNCH (e.g., The Lives saved tool (LiST)), but none have been designed to flexibly integrate novel interventions. This study aimed to develop a model that can estimate the impact of standard-of-care (SOC) and novel interventions on MNCH conditions, and present an example use case. Methods We developed a compartment-based model with 15 interconnected population- and condition-specific modules for pregnant women (in different trimesters), newborns and children <5 years to quantify impacts of bespoke novel interventions. The model included modules for anemia, pre-eclampsia, obstructed labour, hemorrhage, maternal sepsis, preterm, small-for-gestation-age (SGA), respiratory distress syndrome (RDS), neonatal sepsis, birth asphyxia, wasting (neonates and children), stunting and modules that captures neonatal and childhood conditions not captured elsewhere in the model.range of conditions related to MNCH conditions. Results The model captures dynamic interactions between MNCH conditions, including across trimesters, and accounts for the full impact of early detection and prevention of conditions during pregnancy. In an example use case, the model identified that scaling-up standard-of-care and novel diagnostic interventions for anemia and pre-eclampsia plus AI-ultrasound (for early detection of obstructed labour, hemorrhage and birth outcome risks, leading to facility referrals) to 60% could avert 207 million DALYs globally over 2024-2040. Conclusions We have developed a modeling tool which can be flexibly adapted to assess the impacts of novel interventions for MNCH conditions, and can be applied at a global, regional, national or sub-national level. Funding Bill and Melinda Gates Foundation