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About the Platform

Purpose of SAE4Health

Achieving health-related Sustainable Development Goals (SDGs) requires reliable data not only at the national level, but also at finer geographic scales. Yet producing accurate estimates at the subnational level in low- and middle-income countries (LMICs) often demands advanced statistical modeling and complex software implementation — resources that are typically out of reach for the local policymakers and practitioners who know the health systems best.

SAE4Health is a capacity-building initiative developed by researchers at the University of Washington to bridge this gap. It brings together training, statistical methodology, and accessible tools to make small area estimation (SAE) usable by all audiences. At the center is the user-friendly sae4health R Shiny application, which enables hands-on modeling and visualization of subnational health estimates — without requiring any programming expertise.

The sae4health app draws on statistical methods implemented in the surveyPrev R package and supports both DHS-based (DHS version) and MICS-based (MICS version) workflows. Users can use household survey data and generate SAE-based estimates across different administrative levels through an interactive interface.

SAE4Health is designed not only to produce actionable subnational estimates, but also to build local capacity in statistical reasoning, enabling users to better understand, interpret, and apply model-based results in real-world policy and planning contexts.

Target Audience

SAE4Health is designed for institutions and individuals involved in subnational health data analysis and decision-making, including but not limited to:

  • Ministries of Health using data for planning and monitoring.
  • National Statistics Offices responsible for producing official health estimates.
  • Subnational policymakers at the district or provincial level.
  • Researchers and universities applying SAE methods in training and public health research.
  • Health program implementers who use local estimates to guide interventions.

Core Objectives

SAE4Health supports country-led efforts to strengthen subnational health data use through:

  • Capacity building: Train national teams to apply SAE methods and interpret results for planning and policy.

  • Data-informed planning: Support the development of strategies guided by model-based estimates across diverse health indicators.

  • Knowledge exchange: Facilitate cross-country learning and build a network of technical experts for sustained use.

Methodological Details

The statistical approach behind SAE4Health is centered on advanced SAE methodology, as described in the methods section. There you’ll find an overview of the modeling framework, estimation strategy, and how uncertainty is quantified across geographic levels.