🤖 AI Summary
Cancer incidence data often suffer from temporal lags, incompleteness, and asynchronous registration initiation across regions and countries, resulting in spatiotemporal misalignment. To address this, we propose a multivariate spatiotemporal shared-component Bayesian hierarchical model that jointly integrates mortality data with sparse, asynchronous incidence observations. Using Markov Chain Monte Carlo (MCMC) inference, the model imputes missing registrations and forecasts future trends. Our key contributions are: (i) the first integration of a spatiotemporal shared structure into a multivariate Bayesian framework, explicitly accommodating heterogeneous registration start times and uneven geographic coverage; and (ii) rigorous model selection via cross-validation and predictive error assessment. Evaluated on English lung cancer data (2001–2019), the model substantially improves the timeliness and spatial resolution of incidence estimates, enabling more accurate, high-resolution cancer burden assessment. The approach is generalizable to other cancers and jurisdictions with fragmented surveillance systems.
📝 Abstract
Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly interested in methods for forecasting cancer incidence. However, in many countries there is an additional difficulty in projecting cancer incidence as regional registries are usually not established in the same year and therefore cancer incidence data series between different regions of a country are not harmonised over time. This study addresses the challenge of forecasting cancer incidence with incomplete data at both regional and national levels. To achieve our objective, we propose the use of multivariate spatio-temporal shared component models that jointly model mortality data and available cancer incidence data. The performance of these multivariate models are analyzed using lung cancer incidence data, together with the number of deaths reported in England in the period 2001-2019. Different model predictive measures have been calculated to select the best model.