Small Area Estimation for Disease Prevalence Mapping. (24th July 2020)

• Record Type: Journal Article
• Authors: Wakefield, Jonathan; Okonek, Taylor; Pedersen, Jon
• Is Part Of: International statistical review. Volume 88:Number 2(2020)
• Page Start: 398
• Page End: 418
• Publication Date: 2020-07-24
• Subjects: area‐level models -- Bayesian methods -- complex surveys -- design‐based inference -- direct estimation -- indirect estimation -- model‐based inference -- spatial smoothing -- unit‐level models -- weighting; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12400
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: 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 coronavirus disease 2019 is discussed.