Predicting the future burden of cancer

Predicting cancer occurrence is a difficult task. However, if predictions are accurate, they can be useful to health planners trying to optimize resources, and to assess the impact of planned interventions. How are predictions made and what are the challenges? Key Points Estimating the future cancer burden (the number of new cancer cases or deaths) is vital both for health planning and the evaluation of interventions or changes in risk factors. The causes behind anticipated changes in the number of future cancer cases can be divided into two main categories: changes in cancer risk, and changes in population growth and ageing. A further factor that can conspire to increase the observed number of cases is increased detection. Predictions of the future cancer burden can be calculated by applying population forecasts to projections of cancer rates. Cancer rates are projected using the assumption that current trends continue into the future. Age, calendar period and birth cohort components of current trends (obtained from age–period–cohort models) might form the basis of projections. High-quality (and preferably long-term) population-based data on cancer incidence or mortality are prerequisites for making sensible predictions. Of equal importance are reliable population forecasts. In establishing prevention strategies, knowledge of the root causes, as well as the number of cancer events, is required for action. If reliable and quantifiable information on specific risk factors or interventions are available, the selected statistical model can be modified to accommodate this. Predictions of future cancer risk are inherently uncertain, and numbers must be interpreted with appropriate caution. As observations in the past do not necessarily hold into the future, predicting future cancer occurrence is fraught with uncertainty. Nevertheless, predictions can aid health planners in allocating resources and allow scientists to explore the consequence of interventions aimed at reducing the impact of cancer. Simple statistical models have been refined over the past few decades and often provide reasonable predictions when applied to recent trends. Intrinsic to their interpretation, however, is an understanding of the forces that drive time trends. We explain how and why cancer predictions are made, with examples to illustrate the concepts in practice.