Bayesian approach to predicting cancer incidence for an area without cancer registration by using cancer incidence data from nearby areas

Authors

  • Ramon Clèries,

    Corresponding author
    1. Department of Clinical Sciences, University of Barcelona, Barcelona 08907, Spain
    • Cancer Registry of Catalonia - Plan for Oncology of the Catalan Government. IDIBELL, Hospital Duran i Reynals, Catalonia, Spain
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  • Josepa Ribes,

    1. Cancer Registry of Catalonia - Plan for Oncology of the Catalan Government. IDIBELL, Hospital Duran i Reynals, Catalonia, Spain
    2. Department of Clinical Sciences, University of Barcelona, Barcelona 08907, Spain
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  • Maria Buxo,

    1. Epidemiology Unit and Cancer Registry of Girona (UERCG) of the Plan for Oncology of the Catalan Government, Biomedical Research Institute (IdIBGi) Girona 17005, Spain
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  • Alberto Ameijide,

    1. Tarragona Cancer Registry, Foundation Society for Cancer Research & Prevention, Reus 43201, IISPV, Spain
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  • Rafael Marcos-Gragera,

    1. Epidemiology Unit and Cancer Registry of Girona (UERCG) of the Plan for Oncology of the Catalan Government, Biomedical Research Institute (IdIBGi) Girona 17005, Spain
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  • Jaume Galceran,

    1. Tarragona Cancer Registry, Foundation Society for Cancer Research & Prevention, Reus 43201, IISPV, Spain
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  • José Miguel Martínez,

    1. Centro de Investigación en Salud Laboral (CiSAL), Universitat Pompeu Fabra, Barcelona, Spain
    2. Grup de Recerca de Desigualtats en Salut (GREDS / EMCONET), Universitat Pompeu Fabra, Barcelona, Spain
    3. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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  • Yutaka Yasui

    1. School of Public Health, University of Alberta, Edmonton, Alberta, Canada T6G 2T4
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  • Supporting information may be found in the online version of this article.

Ramon Clèries, Cancer Registry of Catalonia - Plan for Oncology of the Catalan Government. IDIBELL, Hospital Duran i Reynals. Av. Gran Via de l'Hospitalet, 199-203 - 1a planta 08908 - L'Hospitalet de Llobregat, Catalonia-Spain.

E-mail: r.cleries@iconcologia.net

Abstract

This paper compares three different methods for performing cancer incidence prediction in an area without a cancer registry under a Bayesian framework, using linear and log-linear age-period models with either age-specific slopes or a common slope across age groups. The three methods assume that a nearby area with a cancer registration has similar incidence and mortality patterns as the area of interest without a cancer registry where the cancer incidence prediction is carried out. The three methods differ in modeling strategies: (i) modeling the incidence rate directly; (ii) modeling the ratio of the number of incident cases to that of mortality cases; and (iii) modeling the difference between the incidence rate and the mortality rate. Strategy (iii) is a new approach in this type of projection. Empirical assessment is made using real data from the cancer registry of Tarragona, Spain, to predict cancer incidence in Girona, Spain, and vice versa. Predictions of short-term (3–4 years) incidence were made for 2001 in Tarragona using observed cancer incidence and mortality data for 1994–1998 from Girona. Short-term predictions were made for 2002 in Girona using Tarragona's 1994–1998 data. Additionally, long-term (10 years) incidence rate predictions were made for 2002 in Girona using data from Tarragona for the period 1985–1992. Our results suggest that extrapolating time-trends of incidence rates minus mortality rates may have the best predictive performance overall. These methods of population-level disease vincidence prediction are highly relevant to health care planning and policy decisions. Copyright © 2012 John Wiley & Sons, Ltd.

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