Forecasting mortality, different approaches for different cause of deaths? The cases of lung cancer; influenza, pneumonia, and bronchitis; and motor vehicle accidents

Most of the methods of mortality forecasting have been assessed using performance on overall mortality, and few studies address the issue of identifying the appropriate forecasting models for specific causes of deaths. This study analyses trends and forecasts mortality rates for three major causes of death — lung cancer, influenza-pneumonia-bronchitis, and motor vehicle accidents — using Lee–Carter, Booth–Maindonald–Smith, Age-Period-Cohort, and Bayesian models, to assess how far different causes of death need different forecasting methods. Using data from the Twentieth and Twenty-First Century Mortality databases for England and Wales, results show major differences among the different forecasting techniques. In particular, when linearity is the main driver of past trends, Lee–Carter-based approaches are preferred due to their straightforward assumptions and limited need for subjective judgment. When a clear cohort pattern is detectable, such as with lung cancer, the Age-Period-Cohort model shows the best outcome. When complete and reliable historical trends are available the Bayesian model does not produce better results than the other models.