Review Article | Published:

Predicting the future burden of cancer

Nature Reviews Cancer volume 6, pages 6374 (2006) | Download Citation



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.

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.

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We warmly thank Max Parkin, Oxford University, UK, for his comments on an earlier draft of this manuscript.

Author information


  1. Cancer Registry of Norway, Institute of Population-based Research, Montebello, Oslo, 0310, Norway.

    • Freddie Bray
    •  & Bjørn Møller


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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Freddie Bray.



Estimates of the future occurrence (in this case, of cancer) that take one or more of the following into account: population growth, ageing of the population, changes in the rate based on past observations, or other potential changes in rates. Taking into account the latter is sometimes described as a forecast.


The frequency of occurrence in a defined population over a specified period of time.


The number of new deaths from a disease in a defined population within a specified time.

Birth cohort

Component of the population born during a particular period and identified by period of birth to enable incidence or mortality to be recorded as that generation moves through successive age and calendar periods.


Changes over a period of time, usually years or decades in the study of cancer. Although short-term changes might be due to fluctuation, trends over longer periods of time might show more consistent long-term direction.


The 'load' carried by society expressed in terms of an observable fact — for cancer burden, this would be described in terms of cancer incidence or mortality.


The number of people with the disease at a given point in time. Cancer prevalence often refers to the number of people living with cancer and who require some from of care.


The number of new cases of disease in a defined population within a specified time.

Age–period–cohort analyses

Tabulations and analyses of rates by age, period and birth cohort to determine their effects. Often involves use of the age–period–cohort model, which is used when it is reasonable to assume that both period and cohort influence disease risk.

Poisson distribution

A distribution used to describe counts of rare events. The distribution is used in a Poisson regression to model incidence and mortality rates in populations over time.

Prediction intervals

Range of possible future observations that take into account the random variation inherent in the past trends as well as the future prediction.

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