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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Bayesian hierarchical modelsReproducibilitySmall area estimationSpatial modelsSurvey sampling

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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.