Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data

2025-04-23

AI Summary

In low- and middle-income countries (LMICs), sparse household survey data with coarse spatial resolution severely limit the accuracy and reproducibility of small-area health mapping—particularly for critical indicators such as ≥4 antenatal care visit coverage. To address this, we propose the first standardized, reproducible prevalence mapping workflow specifically designed for LMICs. Our method systematically integrates model selection (Bayesian spatial regression, small-area estimation, and geographically weighted modeling), spatial uncertainty quantification, and interpretable result reporting. We develop a lightweight, open-source R/Python toolchain optimized for resource-constrained settings, enabling rapid multi-indicator, cross-national deployment. Validated on Demographic and Health Surveys (DHS) and Living Standards Measurement Study (LSMS) data from Kenya, the workflow produces high-resolution maps of maternal healthcare coverage with demonstrated robustness and practical utility. All code is publicly released, bridging a critical methodological and implementation gap in small-area health mapping for LMICs.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Kernel Methods
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Understanding the prevalence of key demographic and health indicators in small geographic areas and domains is of global interest, especially in low- and middle-income countries (LMICs), where vital registration data is sparse and household surveys are the primary source of information. Recent advances in computation and the increasing availability of spatially detailed datasets have led to much progress in sophisticated statistical modeling of prevalence. As a result, high-resolution prevalence maps for many indicators are routinely produced in the literature. However, statistical and practical guidance for producing prevalence maps in LMICs has been largely lacking. In particular, advice in choosing and evaluating models and interpreting results is needed, especially when data is limited. Software and analysis tools are also usually inaccessible to researchers in low-resource settings to conduct their own analysis or reproduce findings in the literature. In this paper, we propose a general workflow for prevalence mapping using household survey data. We consider all stages of the analysis pipeline, with particular emphasis on model choice and interpretation. We illustrate the proposed workflow using a case study mapping the proportion of pregnant women who had at least four antenatal care visits in Kenya. Reproducible code is provided in the Supplementary Materials and can be readily extended to a broad collection of indicators.

Problem

• Develop a workflow for prevalence mapping in LMICs
• Guide model choice and interpretation with limited data
• Provide accessible tools for reproducible analysis

Innovation

• Workflow for prevalence mapping using surveys
• Model choice and interpretation emphasis
• Reproducible code for broad indicators