IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0231935.html
   My bibliography  Save this article

A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis

Author

Listed:
  • K C Flórez
  • A Corberán-Vallet
  • A Iftimi
  • J D Bermúdez

Abstract

Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior distribution with an efficient estimate of the marginal likelihood of the data given this parameter. An extension of the model incorporating covariates is also shown. These covariates may incorporate additional information on the problem or they may account for spatial correlation in the data. We illustrate the performance of the proposed model through both a simulation study and a case study of reported cases of varicella in the city of Valencia, Spain.

Suggested Citation

  • K C Flórez & A Corberán-Vallet & A Iftimi & J D Bermúdez, 2020. "A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-17, May.
    • Handle: RePEc:plo:pone00:0231935
    DOI: 10.1371/journal.pone.0231935
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231935
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0231935&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0231935?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. Leonhard Knorr-Held & Günter Raßer, 2000. "Bayesian Detection of Clusters and Discontinuities in Disease Maps," Biometrics, The International Biometric Society, vol. 56(1), pages 13-21, March.
    2. Ronald E. Gangnon & Murray K. Clayton, 2000. "Bayesian Detection and Modeling of Spatial Disease Clustering," Biometrics, The International Biometric Society, vol. 56(3), pages 922-935, September.
    3. Aris Perperoglou & Paul Eilers, 2010. "Penalized regression with individual deviance effects," Computational Statistics, Springer, vol. 25(2), pages 341-361, June.
    4. Kazem Nasserinejad & Joost van Rosmalen & Wim de Kort & Emmanuel Lesaffre, 2017. "Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    5. D. G. T. Denison & C. C. Holmes, 2001. "Bayesian Partitioning for Estimating Disease Risk," Biometrics, The International Biometric Society, vol. 57(1), pages 143-149, March.
    Full references (including those not matched with items on IDEAS)

    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. Minge Xie & Qiankun Sun & Joseph Naus, 2009. "A Latent Model to Detect Multiple Clusters of Varying Sizes," Biometrics, The International Biometric Society, vol. 65(4), pages 1011-1020, December.
    2. Deborah A. Costain, 2009. "Bayesian Partitioning for Modeling and Mapping Spatial Case–Control Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1123-1132, December.
    3. Goepp, Vivien & van de Kassteele, Jan, 2024. "Graph-based spatial segmentation of areal data," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    4. Leonhard Knorr-Held & Günter Raßer & Nikolaus Becker, 2002. "Disease Mapping of Stage-Specific Cancer Incidence Data," Biometrics, The International Biometric Society, vol. 58(3), pages 492-501, September.
    5. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    6. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    7. Ian Dryden & Rahman Farnoosh & Charles Taylor, 2006. "Image segmentation using voronoi polygons and MCMC, with application to muscle fibre images," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(6), pages 609-622.
    8. Dayton M. Lambert & Kevin T. McNamara, 2009. "Location determinants of food manufacturers in the United States, 2000–2004: are nonmetropolitan counties competitive?," Agricultural Economics, International Association of Agricultural Economists, vol. 40(6), pages 617-630, November.
    9. Sam Hui & Eric Bradlow, 2012. "Bayesian multi-resolution spatial analysis with applications to marketing," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 419-452, December.
    10. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    11. David C. Wheeler & Antonio Páez & Jamie Spinney & Lance A. Waller, 2014. "A Bayesian approach to hedonic price analysis," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 663-683, August.
    12. Cato Waeterloos & Peter Conradie & Michel Walrave & Koen Ponnet, 2021. "Digital Issue Movements: Political Repertoires and Drivers of Participation among Belgian Youth in the Context of ‘School Strike for Climate’," Sustainability, MDPI, vol. 13(17), pages 1-19, September.
    13. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    14. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    15. Christian Grovermann & Sylvain Quiédeville & Adrian Muller & Florian Leiber & Matthias Stolze & Simon Moakes, 2021. "Does organic certification make economic sense for dairy farmers in Europe?–A latent class counterfactual analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 1001-1012, November.
    16. Shang, Zuofeng, 2012. "On latent process models in multi-dimensional space," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1259-1266.
    17. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    18. Earl W Duncan & Kerrie L Mengersen, 2020. "Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-28, May.
    19. Singh, Jyotsna & Homem de Almeida Correia, Gonçalo & van Wee, Bert & Barbour, Natalia, 2023. "Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    20. Frasquet, Marta & Ieva, Marco & Ziliani, Cristina, 2021. "Online channel adoption in supermarket retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).

    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:plo:pone00:0231935. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.