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Stat Med. 2012 May 10;31(10):978-87. doi: 10.1002/sim.4463. Epub 2012 Jan 11.

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

Author information

1
Cancer Registry of Catalonia - Plan for Oncology of the Catalan Government, IDIBELL, Hospital Duran i Reynals, Catalonia, Spain. 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-incidence prediction are highly relevant to health care planning and policy decisions.

PMID:
22237653
DOI:
10.1002/sim.4463
[Indexed for MEDLINE]

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