OBJECTIVE: Cancer incidence predictions for the period 2002-2006 in the Province of Modena applying a Bayesian APC (Age Period Cohort) model. DESIGN: Population based descriptive study. SETTING: Patients with cancer diagnosed between 1988 and 2001 recorded in Modena Cancer Registry. MAIN OUTCOME MEASURES: Number of cases, crude and standardized incidence rates. RESULTS: Cancer incidence in 2002-2006 is expected to increase in both sexes, with the exception of stomach cancer (males and females) and lung cancer in males, both expected to decrease. The difference between the number of cases predicted for 2002 and observed data was about 0.7%. CONCLUSION: Prediction based on APC model seems to be reliable for most of the cancer sites, but should be used cautiously. In particular, projected number of breast and prostate cancer cases is clearly overestimated due to diagnostic anticipation effect which gives rise to an excess in recent years.
OBJECTIVE:Cancer incidence predictions for the period 2002-2006 in the Province of Modena applying a Bayesian APC (Age Period Cohort) model. DESIGN: Population based descriptive study. SETTING:Patients with cancer diagnosed between 1988 and 2001 recorded in Modena Cancer Registry. MAIN OUTCOME MEASURES: Number of cases, crude and standardized incidence rates. RESULTS:Cancer incidence in 2002-2006 is expected to increase in both sexes, with the exception of stomach cancer (males and females) and lung cancer in males, both expected to decrease. The difference between the number of cases predicted for 2002 and observed data was about 0.7%. CONCLUSION: Prediction based on APC model seems to be reliable for most of the cancer sites, but should be used cautiously. In particular, projected number of breast and prostate cancer cases is clearly overestimated due to diagnostic anticipation effect which gives rise to an excess in recent years.
Authors: V L Gnarini; G Brigante; E Della Valle; C Diazzi; B Madeo; C Carani; V Rochira; M Simoni Journal: J Endocrinol Invest Date: 2013-04-12 Impact factor: 4.256