The Two Cultures of Prevalence Mapping: Small Area Estimation and Model-Based Geostatistics

TL;DR

This paper compares two main approaches—small area estimation and model-based geostatistics—for prevalence mapping in low-resource settings, highlighting their strengths, limitations, and potential for integration to improve subnational health estimates.

Contribution

It provides a comprehensive review of prevalence mapping methods, discusses their challenges, and suggests ways to improve binary response modeling by integrating survey and spatial statistics insights.

Findings

SAE models incorporate sampling weights and covariates.

MBG models explicitly model spatial effects and use diverse data sources.

Application to Zambia HIV data demonstrates method insights.

Abstract

In low- and middle-income countries (LMICs), accurate estimates of subnational health and demographic indicators are critical for guiding policy and identifying disparities. Many indicators of interest are proportions of binary outcomes and the task of estimating these fractions is often called prevalence mapping. In LMICs, health and vital records data are limited, so prevalence mapping relies on data from household surveys with complex sampling designs. However, estimates are often desired at spatial resolutions at which data are insufficient. We review two families of approaches to prevalence mapping: small area estimation (SAE) methods (from the survey statistics literature) and model-based geostatistics (MBG) methods (from the spatial statistics literature). SAE models can be ``area-level" or ``unit-level" and commonly use area-specific random effects and rely upon high-quality covariate data from administrative sources. Unit-level models for binary responses are relatively underdeveloped. MBG approaches explicitly specify binary response models, incorporate continuous spatial random effects, and leverage alternative data sources, e.g., satellite imagery. SAE methods often address the design by incorporating sampling weights or modeling the sampling mechanism. Two delicate issues arise when using MBG methods. First, aggregating unit level predictions to create area-level summaries requires population-level information that is rarely available. Second, MBG approaches typically assume the sampling design is ignorable. We review both approaches, and argue that binary response models can be improved using insights from both the survey sampling and the spatial statistics literature. We highlight these issues using household survey data from the Zambia 2018 Demographic Health Survey to estimate subnational HIV prevalence for woman aged 15--49.