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Small Area Estimation for Disease Prevalence Mapping
Authors:Jonathan Wakefield  Taylor Okonek  Jon Pedersen
Affiliation:1. Department of Biostatistics, University of Washington, Seattle, USA;2. Fafo AVF, Oslo, Norway
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 coronavirus disease 2019 is discussed.
Keywords:area-level models  Bayesian methods  complex surveys  design-based inference  direct estimation  indirect estimation  model-based inference  spatial smoothing  unit-level models  weighting
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