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

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 for reliable weighted estimation. 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, often obtained 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 sources of data such as those arising from satellite imagery. These models are usually studied under a Bayesian framework. SAE methods often address the design by incorporating sampling weights or modeling the sampling mechanism. Two delicate issues arise when using MBG methods for prevalence mapping. First, aggregating unit level predictions to create area-level summaries requires population-level information that is rarely directly available. Second, MBG approaches typically assume the sampling design is ignorable. We review both SAE and MBG approaches to prevalence mapping, 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 different Demographic and Health Surveys, and with various indicators.

This is joint work with Geir-Arne Fuglstad, Peter Gao and Zehang Richard Li.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.