Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data
Qianyu Dong1, Yunhan Wu2, Zehang Richard Li3
1Qianyu Dong are with Department of Statistics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA.
Journal of Survey Statistics and Methodology
|April 29, 2026
Summary
This study introduces a practical workflow for creating detailed health prevalence maps using household survey data in low- and middle-income countries (LMICs). It provides guidance on model selection and interpretation, enhancing data analysis accessibility.
Area of Science:
- Geographic epidemiology
- Health informatics
- Statistical modeling
Background:
- Prevalence mapping is crucial for understanding health indicators in low- and middle-income countries (LMICs).
- Limited vital registration data in LMICs necessitates reliance on household surveys.
- Existing methods often lack practical guidance and accessible tools for researchers in low-resource settings.
Purpose of the Study:
- To propose a general workflow for prevalence mapping using household survey data.
- To provide statistical and practical guidance on model choice, evaluation, and interpretation.
- To enhance the accessibility of prevalence mapping tools for researchers in LMICs.
Main Methods:
- Development of a comprehensive workflow for the entire analysis pipeline.
- Emphasis on model selection and interpretation strategies.
- Illustration using a case study on antenatal care visits in Kenya, implemented in the R package surveyPrev.
Main Results:
- A reproducible workflow for prevalence mapping is presented.
- The workflow is demonstrated with a practical case study.
- All reproducible code is provided to facilitate adoption and extension.
Conclusions:
- The proposed workflow offers a standardized approach to prevalence mapping in LMICs.
- The methodology enhances the ability to generate high-resolution health indicator maps.
- The R package surveyPrev and provided code promote data analysis reproducibility and accessibility.
Keywords:
Bayesian hierarchical modelsReproducibilitySmall area estimationSpatial modelsSurvey samplingMore Related Videos
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