Small area estimation in low- and middle-income countries with the sae4health R shiny application

Abstract Accurate subnational estimation of health indicators is critical for public health planning, particularly in low- and middle-income countries, 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 350 demographic and health indicators, based on over 150 demographic and health surveys 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. 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.