Operationalising an integrated mortality transition framework for programmatic priority setting across maternal and...

Area of Science:

• Global Health
• Maternal and Newborn Health
• Data Science

Background:

• Achieving Sustainable Development Goals by 2030 requires reducing maternal and newborn mortality and stillbirths.
• Context-specific evidence is vital for addressing local health priorities and needs.
• Integrating local data with global health data provides valuable insights.

Purpose of the Study:

• To introduce an interactive benchmarking tool for maternal and newborn health.
• To contextualize local data within a global maturity model using evidence from over 150 countries.
• To support data-driven decision-making for accelerating progress in maternal and newborn health.

Main Methods:

• Development of an interactive benchmarking tool.
• Utilizing a maturity model based on data from over 150 countries.
• Positioning countries into one of five mortality phases for comparison.

Main Results:

• The tool compares country performance across maternal/newborn health program areas with peers.
• It identifies high-performing "Exemplar" countries for lessons learned.
• The approach has been piloted in select countries to inform evaluations and planning.

Conclusions:

• The benchmarking tool facilitates evidence-based priority setting for maternal and newborn health.
• It helps identify key indicators and subnational regions for targeted interventions.
• This adaptable approach fosters a unified strategy for maternal and newborn health planning, especially in resource-constrained environments.