IntroductionCancer is a significant public health issue in Iran, and its incidence has been on the rise in recent years. The objective of this study is to predict the incidence of total cancer in Iran using a Bayesian age-period-cohort (APC) model.MethodsUtilizing age-period-cohort modeling, this study assessed the multifaceted effects of age, period, and cohort on cancer incidence during the period spanning 2005 to 2017. Key metrics, including the net drift (representing the overall annual percentage change), local drift (indicating annual percentage changes within specific age groups), and longitudinal age curves (depicting expected age-specific rates over time), were computed. Moreover, the evaluation encompassed an analysis of period and cohort relative risks. To project the future age-standardized incidence rates of cancers from 2018 to 2027, Bayesian age-period-cohort analysis integrated nested Laplace approximations.ResultThe age-standardized incidence rate and the absolute number of cancer cases in Iran showed an upward trend. The net drift was 1.79% (95% confidence interval, CI: 0.87% to 2.72%) for males and 3.31% (95% CI: 2.49% to 4.14%) for females. Local drifts remained consistently above zero for all age groups from 2005 to 2017, except for the under-5 age group in both males and females, and the 45–49 and 50–54 age groups in females. After accounting for period deviations, the risk of cancer incidence exhibited an exponential increase with age for both sexes. Based on the Bayesian age-period-cohort analysis, it is estimated that there will be around 210,701 new cancer cases in 2027. Moreover, the Age-Standardized Rate (ASR) for cancer is anticipated to reach 240.32 per 100,000 by 2027. The forecasts indicate a rise in cancer incidence rates across all age groups for both males and females.ConclusionThis study underscores the urgency of implementing targeted preventive strategies aligned with demographic shifts and lifestyle factors. Emphasizing the role of robust cancer registries, it advocates for continuous monitoring to inform evidence-based interventions.
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