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Small Area Estimation for Disease Prevalence Mapping

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  • Jonathan Wakefield
  • Taylor Okonek
  • Jon Pedersen

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.

Suggested Citation

  • Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:2:p:398-418
    DOI: 10.1111/insr.12400
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    References listed on IDEAS

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    1. Steve Gutreuter & Ehimario Igumbor & Njeri Wabiri & Mitesh Desai & Lizette Durand, 2019. "Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
    2. repec:nas:journl:v:115:y:2018:p:3529-3537 is not listed on IDEAS
    3. Geir-Arne Fuglstad & Daniel Simpson & Finn Lindgren & Håvard Rue, 2019. "Constructing Priors that Penalize the Complexity of Gaussian Random Fields," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 445-452, January.
    4. Jon Pedersen & Jing Liu, 2012. "Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories," PLOS Medicine, Public Library of Science, vol. 9(8), pages 1-13, August.
    5. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    6. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    7. Skinner, Chris J. & Wakefield, Jon, 2017. "Introduction to the design and analysis of complex survey data," LSE Research Online Documents on Economics 76991, London School of Economics and Political Science, LSE Library.
    8. Lynn M. R. Ybarra & Sharon L. Lohr, 2008. "Small area estimation when auxiliary information is measured with error," Biometrika, Biometrika Trust, vol. 95(4), pages 919-931.
    9. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    10. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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