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A model of toxoplasmosis incidence in the UK: evidence synthesis and consistency of evidence

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  • N. J. Welton
  • A. E. Ades

Abstract

Summary. We present a Bayesian evidence synthesis model combining data on seroprevalence, seroconversion and tests of recent infection, to produce estimates of current incidence of toxoplasmosis in the UK. The motivation for the study was the need for an estimate of current average incidence in the UK, with a realistic assessment of its uncertainty, to inform a decision model for a national screening programme to prevent congenital toxoplasmosis. The model has a hierarchical structure over geographic region, a random‐walk model for temporal effects and a fixed age effect, with one or more types of data informing the regional estimates of incidence. Inference is obtained by using Markov chain Monte Carlo simulations. A key issue in the synthesis of evidence from multiple sources is model selection and the consistency of different types of evidence. Alternative models of incidence are compared by using the deviance information criterion, and we find that temporal effects are region specific. We assess the consistency of the various forms of evidence by using cross‐validation where practical, and posterior and mixed prediction otherwise, and we discuss how these measures can be used to assess different aspects of consistency in a complex evidence structure. We discuss the contribution of the various forms of evidence to estimated current average incidence.

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  • N. J. Welton & A. E. Ades, 2005. "A model of toxoplasmosis incidence in the UK: evidence synthesis and consistency of evidence," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 385-404, April.
    • Handle: RePEc:bla:jorssc:v:54:y:2005:i:2:p:385-404
    DOI: 10.1111/j.1467-9876.2005.00490.x
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    Cited by:

    1. Tazio Vanni & Jonathan Karnon & Jason Madan & Richard White & W. Edmunds & Anna Foss & Rosa Legood, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 35-49, January.
    2. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    3. N. J. Welton & A. E. Ades & J. B. Carlin & D. G. Altman & J. A. C. Sterne, 2009. "Models for potentially biased evidence in meta‐analysis using empirically based priors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 119-136, January.
    4. Sofia Dias & Alex J. Sutton & A. E. Ades & Nicky J. Welton, 2013. "Evidence Synthesis for Decision Making 2," Medical Decision Making, , vol. 33(5), pages 607-617, July.
    5. S. Dias & N. J. Welton & V. C. C. Marinho & G. Salanti & J. P. T. Higgins & A. E. Ades, 2010. "Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 613-629, July.
    6. Turner, Matthew & Lenhart, Suzanne & Rosenthal, Benjamin & Zhao, Xiaopeng, 2013. "Modeling effective transmission pathways and control of the world’s most successful parasite," Theoretical Population Biology, Elsevier, vol. 86(C), pages 50-61.
    7. A. Goubar & A. E. Ades & D. De Angelis & C. A. McGarrigle & C. H. Mercer & P. A. Tookey & K. Fenton & O. N. Gill, 2008. "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 541-580, June.
    8. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35, January.

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