sae4health: An R Shiny Application for Small Area Estimation in Low- and Middle-Income Countries
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909811638009856 |
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| author | Wu, Yunhan Dong, Qianyu Xu, Jieyi Li, Zehang Richard Wakefield, Jon |
| author_facet | Wu, Yunhan Dong, Qianyu Xu, Jieyi Li, Zehang Richard Wakefield, Jon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | sae4health: An R Shiny Application for Small Area Estimation in Low- and Middle-Income Countries Wu, Yunhan Dong, Qianyu Xu, Jieyi Li, Zehang Richard Wakefield, Jon Computation 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. |
| title | sae4health: An R Shiny Application for Small Area Estimation in Low- and Middle-Income Countries |
| topic | Computation |
| url | https://arxiv.org/abs/2505.01467 |