Bayesian area–age–period–cohort model with carcinogenesis age effects in estimating cancer mortality

Objective: Area–age–period–cohort (AAPC) model has been widely used in studying the spatial and temporal pattern of disease incidence and mortality rates. However, lack of biological plausibility and ease of interpretability on temporal components especially for age effects are generally the weakness of AAPC models. We develop a Bayesian AAPC model where carcinogenesis age effect is incorporated to explain age effects from the underlying disease process. An autoregressive prior structure and an arbitrary linear constraint are used to solve the nonidentifiability issues. Methods: Two multistage carcinogenesis models are employed to derive the hazard functions to substitute the age effects in the AAPC models. The Iowa county-wide lung cancer mortality data are used for the model fitting and Deviance Information Criteria (DIC) is used for model comparison. Results: Our study shows that conventional AAPC model (DIC = 19,231.30), AAPC model with Armitage–Doll age effect (DIC = 19,233.00) and with two-stage clonal expansion (TSCE) age effect (DIC = 19,234.70) achieved the similar DIC values which indicated consistent model fitting among three models. The spatial pattern shows that the high spatial effects are clustered in the south of Iowa and also in largely populated areas. The lung cancer mortality rate is continuously declining by birth cohorts while increasing by the calendar period until 2000–2004. The age effects show an increasing pattern over time which can be easily explained by Armitage–Doll carcinogenesis model since we assume a log-linear relationship between age and hazard function. Conclusions: Our finding suggests that the proposed Bayesian AAPC model can be used to replace the conventional AAPC model without affecting model performance while providing a more biological sound approach from the underlining disease process.