Projecting cancer incidence rates and case numbers: a probabilistic approach using data from German cancer registries (1999 – 2013)

Projections of cancer incidence rates and case numbers, either stratified or standardized according to age and sex, are of great interest for healthcare planning and research. Historically, these projections have often been based on age-period-cohort and joinpoint regression models assuming Poisson distributed outcomes. A drawback in applications of these two approaches has been that the projections were usually deterministic, i.e. they did not allow statements of uncertainty, which would be desirable for communicating results to health policy decision makers. Although Bayesian age-period-cohort (APC) approaches have been proposed which allow for probabilistic projections, consideration of alternatives may sometimes be warranted or even necessary. This is the case especially in situations where long-term nationwide cancer incidence data for the development oft projection models are not available, which complicates the use of APC models, or where it appears unreasonable to assume age, period and cohort effects. We propose a modeling strategy for projection of vital rates data based on Bayesian Poisson and negative binomial models with linear and (restricted) cubic spline effects of year and age. It is applied to German cancer registry data which is available nationwide from 1999 to 2013 to project stratum-specific and standardized cancer incidence rates along with uncertainty intervals in 2030. We further combine the predictive distributions of incidence rates with probabilistic estimates of population projections in Monte Carlo analyses to obtain estimates of future incident case numbers and corresponding uncertainty intervals. We discuss the benefits and problems of the approach and conclude that its application may facilitate communication of epidemiological research to health policy decision makers.