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Disease clusters: should they be investigated, and, if so, when and how?

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  • Paul Elliott
  • Jon Wakefield

Abstract

Individual cluster reports are subject to several difficulties in interpretation. Although they rarely lead to new aetiological insights, a public health response to delineate the size and extent of any excess risk may be warranted. Further investigation, where merited, should usually include an examination of data for different areas and/or different time periods. A statistical evaluation of disease clusters is often secondary to a detailed appreciation of issues such as the availability and quality of data, confounding and bias in the selection of areas for study.

Suggested Citation

  • Paul Elliott & Jon Wakefield, 2001. "Disease clusters: should they be investigated, and, if so, when and how?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 3-12.
    • Handle: RePEc:bla:jorssa:v:164:y:2001:i:1:p:3-12
    DOI: 10.1111/1467-985X.00180
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    Cited by:

    1. Longford, Nicholas T., 2010. "Small area estimation with spatial similarity," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1151-1166, April.
    2. Riccardo Borgoni & Francesco C. Billari, 2002. "Bayesian spatial analysis of demographic survey data: an application to contraceptive use at first sexual intercourse," MPIDR Working Papers WP-2002-048, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Riccardo Borgoni & Francesco Billari, 2003. "Bayesian spatial analysis of demographic survey data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 8(3), pages 61-92.
    4. Nicholas Longford, 2008. "Small-area estimation with spatial similarity," Economics Working Papers 1105, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2009.

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