Small Area Estimation of Health Outcomes

TL;DR

This paper reviews small area estimation methods, emphasizing Bayesian spatial models and auxiliary data use, with applications to health outcomes like HIV prevalence and potential relevance to COVID-19.

Contribution

It provides a comprehensive overview of design-based and model-based SAE approaches, highlighting Bayesian spatial models and covariate integration for health data analysis.

Findings

Bayesian spatial models improve health outcome estimates in small areas.

Inclusion of auxiliary covariate data enhances inference accuracy.

Application to HIV prevalence demonstrates practical utility.

Abstract

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.