Modelling stillbirth mortality reduction with the Lives Saved Tool

Area of Science:

• Global health
• Maternal and child health
• Epidemiological modeling

Background:

• Stillbirths represent a significant global health burden, with millions of babies stillborn annually.
• The Every Newborn Action Plan aims to reduce stillbirths to ≤12 per 1000 by 2030.
• Effective planning tools are crucial for countries to implement interventions and achieve this target.

Purpose of the Study:

• To describe the methodology for modeling stillbirth reduction interventions within the Lives Saved tool (LiST).
• To review evidence for intervention effectiveness and assumptions regarding their impact on stillbirths.
• To identify areas for future refinement of the LiST stillbirth model.

Main Methods:

• Adapted the general LiST intervention modeling approach for stillbirths.
• Reviewed evidence on the effectiveness of interventions aimed at reducing stillbirths.
• Presented assumptions on the proportion of stillbirths potentially benefiting from interventions.

Main Results:

• High-quality data for all model parameters are not yet available.
• Stillbirths are modeled using an attributable fraction approach based on timing (antepartum/intrapartum).
• Eight of 35 identified interventions are currently modeled, including childbirth care, malaria prevention, and management of hypertensive disorders and diabetes.

Conclusions:

• Effective interventions exist and can be modeled using the LiST tool to reduce stillbirths.
• Improving data quality for stillbirths is essential.
• Continuous refinement of the LiST tool is necessary for reliable policy and programming support.