IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v88y2020i2p398-418.html
   My bibliography  Save this article

Small Area Estimation for Disease Prevalence Mapping

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/insr.12400
    Download Restriction: no
    File URL: https://libkey.io/10.1111/insr.12400?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    3. 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.
    4. 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.
    5. repec:nas:journl:v:115:y:2018:p:3529-3537 is not listed on IDEAS
    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. 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.
    8. 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.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chien-Chou Chen & Guo-Jun Lo & Ta-Chien Chan, 2022. "Spatial Analysis on Supply and Demand of Adult Surgical Masks in Taipei Metropolitan Areas in the Early Phase of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-12, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    2. Andre Python & Andreas Bender & Marta Blangiardo & Janine B. Illian & Ying Lin & Baoli Liu & Tim C.D. Lucas & Siwei Tan & Yingying Wen & Davit Svanidze & Jianwei Yin, 2022. "A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 202-218, January.
    3. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.
    4. Paige, John & Fuglstad, Geir-Arne & Riebler, Andrea & Wakefield, Jon, 2022. "Bayesian multiresolution modeling of georeferenced data: An extension of ‘LatticeKrig’," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    5. Márcio Poletti Laurini, 2017. "A spatial error model with continuous random effects and an application to growth convergence," Journal of Geographical Systems, Springer, vol. 19(4), pages 371-398, October.
    6. Luis A. Barboza & Shu Wei Chou Chen & Marcela Alfaro Córdoba & Eric J. Alfaro & Hugo G. Hidalgo, 2023. "Spatio‐temporal downscaling emulator for regional climate models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    7. Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
    8. Marc Francke & Alex Van de Minne, 2021. "Modeling unobserved heterogeneity in hedonic price models," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(4), pages 1315-1339, December.
    9. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    10. Sheyla Rodrigues Cassy & Samuel Manda & Filipe Marques & Maria do Rosário Oliveira Martins, 2022. "Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique," IJERPH, MDPI, vol. 19(10), pages 1-15, May.
    11. Xavier Barber & David Conesa & Antonio López-Quílez & Joaquín Martínez-Minaya & Iosu Paradinas & Maria Grazia Pennino, 2021. "Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach," Mathematics, MDPI, vol. 9(4), pages 1-12, February.
    12. Jorge Sicacha-Parada & Diego Pavon-Jordan & Ingelin Steinsland & Roel May & Bård Stokke & Ingar Jostein Øien, 2022. "A Spatial Modeling Framework for Monitoring Surveys with Different Sampling Protocols with a Case Study for Bird Abundance in Mid-Scandinavia," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 562-591, September.
    13. Peter A. Gao & Hannah M. Director & Cecilia M. Bitz & Adrian E. Raftery, 2022. "Probabilistic Forecasts of Arctic Sea Ice Thickness," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 280-302, June.
    14. Nicoletta D’Angelo & Antonino Abbruzzo & Giada Adelfio, 2021. "Spatio-Temporal Spread Pattern of COVID-19 in Italy," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    15. Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.
    16. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    17. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    18. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    19. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    20. Yuan Yan & Eva Cantoni & Chris Field & Margaret Treble & Joanna Mills Flemming, 2023. "Spatiotemporal modeling of mature‐at‐length data using a sliding window approach," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:istatr:v:88:y:2020:i:2:p:398-418. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.