sae4health: An R Shiny Application for Small Area Estimation in Low- and Middle-Income Countries

TL;DR

sae4health is an accessible R Shiny app that provides real-time small area health estimates for LMICs, utilizing Bayesian methods to support public health planning without requiring advanced statistical skills.

Contribution

The paper introduces a user-friendly, browser-based tool that generates small area health estimates from DHS data using Bayesian inference, expanding access to complex statistical methods.

Findings

Provides estimates for over 150 health indicators across 60 countries.

Enables real-time, interactive visualization of health data.

Accessible without programming or statistical expertise.

Abstract

Accurate subnational estimation of health indicators is critical for public health planning, particularly in low- and middle-income countries (LMICs), where data and analytic tools are often limited. sae4health is an open-access Shiny application (https://rsc.stat.washington.edu/sae4health/) that generates small area estimates for more than 150 demographic and health indicators, based on over 150 Demographic and Health Surveys (DHS) from 60 countries. The platform offers both area- and unit-level models with spatial random effects, implemented through fast Bayesian inference using Integrated Nested Laplace Approximation (INLA). The app is fully browser-based and requires no data input, programming skills, or statistical modeling expertise, making advanced methods accessible to a wide range of users. Estimates are processed in real time and presented as interactive maps, tables, and downloadable reports. A companion website (https://sae4health.stat.uw.edu) provides documentation and methodological background to support the app. Together, these resources enhance access to subnational health data and facilitate the use of DHS surveys for evidence-based decision making.