Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Predicting the future burden of cancer

Nature Reviews Cancer volume 6, pages 63–74 (2006)Cite this article

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.

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Observed and predicted incidence rates of male lung cancer in Finland.
Figure 2: Effect of availability of data and flexibility of analytical modelling on prediction strategies.
Figure 3: Population growth and ageing in the world regions, 2000–2050.
Figure 4: Estimated numbers of new cases of cancer in the world, 1975–2050.
Figure 5: Projected changes in breast cancer incidence in women and prostate cancer incidence in men worldwide, 2002–2010.
Figure 6: Observed trends and predictions of colon cancer in men in the Nordic countries, and the age distribution of the underlying male population from 1995 to 2020.
Figure 7: Possible impact of breast cancer screening and the trends of breast cancer incidence in women in the Nordic countries.

Similar content being viewed by others

References

  1. Andvord K. F. What can we learn by following the development of tuberculosis from one generation to another? Norsk Magasin Laegevidenskap 91, 642–660 (1930).

    Google Scholar 

  2. Frost, W. H. The age selection of mortality from tuberculosis in successive decades. Am. J. Hyg. 30, 91–96 (1939).

    Google Scholar 

  3. Macmahon, B. in Trends in Cancer Incidence Causes and Practical Implications (ed. Magnus, K.) 77 (The International Union Against Cancer and The Norwegian Cancer Society, Oslo, 1982).

    Google Scholar 

  4. Kramer, B. S. & Klausner, R. D. Grappling with cancer — defeatism versus the reality of progress. N. Engl. J. Med. 337, 931–934 (1997).

    Article  CAS  Google Scholar 

  5. Armstrong, B. K. The role of the cancer registry in cancer control. Cancer Causes Control 3, 569–579 (1992).

    Article  CAS  Google Scholar 

  6. Hakulinen, T. & Hakama, M. Predictions of epidemiology and the evaluation of cancer control measures and the setting of policy priorities. Soc. Sci. Med. 33, 1379–1383 (1991).

    Article  CAS  Google Scholar 

  7. Hakulinen, T. The future cancer burden as a study subject. Acta Oncologica 35, 665–670 (1996). The author, a longstanding authority on predictions, sets out the objectives and difficulties that need to be confronted, and provides examples of the use of predictions.

    Article  CAS  Google Scholar 

  8. Hakulinen, T. et al. in Evaluating Effectiveness of Primary Prevention of Cancer (eds Hakama, M., Beral, V., Cullen, V. & Parkin, D. M.) 133–148 (International Agency for Research on Cancer Scientific Publications, Lyon, 1990).

    Google Scholar 

  9. Salonen, J. T., Puska, P., Kottke, T. E. & Tuomilehto, J. Changes in smoking, serum cholesterol and blood pressure levels during a community-based cardiovascular disease prevention program — the North Karelia Project. Am. J. Epidemiol. 114, 81–94 (1981).

    Article  CAS  Google Scholar 

  10. Hakulinen, T. & Pukkala, E. Future incidence of lung cancer: forecasts based on hypothetical changes in the smoking habits of males. Int. J. Epidemiol. 10, 233–240 (1981). One of the first papers to apply scenario-based predictions to test the impact of starting and stopping smoking on future lung cancer burden.

    Article  CAS  Google Scholar 

  11. Brown, C. C. & Kessler, L. G. Projections of lung cancer mortality in the United States: 1985–2025. J. Natl Cancer Inst. 80, 43–51 (1988).

    Article  CAS  Google Scholar 

  12. Moller, B. et al. The influence of mammographic screening on national trends in breast cancer incidence. Eur. J. Cancer Prev. 14, 117–128 (2005).

    Article  CAS  Google Scholar 

  13. Bailar, J. C. 3rd & Smith, E. M. Progress against cancer? N. Engl. J. Med. 314, 1226–1232 (1986).

    Article  Google Scholar 

  14. Doll, R. Are we winning the fight against cancer? An epidemiological assessment. EACR — Muhlbock memorial lecture. Eur. J. Cancer 26, 500–508 (1990).

    Article  CAS  Google Scholar 

  15. WHO Regional Office for Europe. Targets for Health for all. European Health for all Series Number 1 (Copenhagen, 1985).

  16. European Commission. Europe Against Cancer Programme: Proposal for an Action Plan, 1987 to 1989 (Official Journal C 50, 26 Feb 1987).

  17. The Expert Advisory Group on Cancer. A Policy Framework for Commissioning Cancer Services: a Report by the Expert Advisory Group on Cancer to the Chief Medical Officers of England and Wales (1995). http://www.dh.gov.uk/assetRoot/04/01/43/66/04014366.pdf

  18. Sharp, L. et al. Will the Scottish Cancer Target for the year 2000 be met? The use of cancer registration and death records to predict future cancer incidence and mortality in Scotland. Br. J. Cancer 73, 1115–1121 (1996).

    Article  CAS  Google Scholar 

  19. Boyle, P. et al. Measuring progress against cancer in Europe: has the 15% decline targeted for 2000 come about? Ann. Oncol. 14, 1312–1325 (2003).

    Article  CAS  Google Scholar 

  20. Black, R. J. & Stockton, D. Cancer Scenarios: an Aid to Planning Cancer Services in Scotland in the Next Decade. (The Scottish Executive, Edinburgh, 2001). http://www.scotland.gov.uk/library3/health/csatp-00.aspInnovative study that asked professionals working across the cancer spectrum for their expert opinion on how cancer prevention and treatment could affect a given set of cancer incidence and mortality predictions.

    Google Scholar 

  21. Moller, B. et al. Prediction of cancer incidence in the Nordic countries up to the year 2020. Eur. J. Cancer Prev. 11 (Suppl.), S1–S96 (2002).

    PubMed  Google Scholar 

  22. Stockton, D. Cancer in Scotland:Sustaining Change. Cancer Incidence Projections for Scotland (2001–2020). An Aid to Planning Cancer Services (NHS Scotland, Edinburgh, 2004). http://www.scotland.gov.uk/Publications/2004/12/20257/46697

    Google Scholar 

  23. Hakama, M. Projection of cancer incidence: experiences and some results in Finland. World Health Stat. Q. 33, 228–240 (1980).

    CAS  PubMed  Google Scholar 

  24. Parkin, D. M., Pisani, P., Lopez, A. D. & Masuyer, E. At least one in seven cases of cancer is caused by smoking. Global estimates for 1985. Int. J. Cancer 59, 494–504 (1994).

    Article  CAS  Google Scholar 

  25. Parkin, D. M., Bray, F., Ferlay, J. & Pisani, P. Global cancer statistics, 2002. CA Cancer J. Clin. 55, 74–108 (2005).

    Article  Google Scholar 

  26. Yamaguchi, N., Mizuno, S., Akiba, S., Sobue, T. & Watanabe, S. A 50-year projection of lung cancer deaths among Japanese males and potential impact evaluation of anti-smoking measures and screening using a computerized simulation model. Jpn. J. Cancer Res. 83, 251–257 (1992).

    Article  CAS  Google Scholar 

  27. Holford, T. R. The estimation of age, period and cohort effects for vital rates. Biometrics 39, 311–324 1983).

    Article  CAS  Google Scholar 

  28. Hobcraft, J., Mencken, J. & Preston, S. in Cohort Analysis in Social Research: Beyond the Identification Problem (eds Mason, W. M. & Fienberg, S. E.) 89–135 (Springer–Verlag, New York, 1985).

    Book  Google Scholar 

  29. Hakulinen, T. et al. Cancer in Finland in 1954–2008. Incidence, Mortality and Prevalence by Region. Cancer Society of Finland Publication Number 42 (Finnish Cancer Registry and Finnish Foundation for Cancer Research, Helsinki, 1989).

    Google Scholar 

  30. Armitage, P. & Doll, R. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br. J. Cancer 8, 1–12 (1954).

    Article  CAS  Google Scholar 

  31. Cook, P. J., Doll, R. & Fellingham, S. A. A mathematical model for the age distribution of cancer in man. Int. J. Cancer 4, 93–112 (1969).

    Article  CAS  Google Scholar 

  32. Peto, R., Parish, S. E. & Gray, R. G. There is no such thing as ageing, and cancer is not related to it. IARC Sci. Publ. 43–53 (1985).

  33. Clayton, D. & Schifflers, E. Models for temporal variation in cancer rates. II: age–period–cohort models. Stat. Med. 6, 469–481 (1987).

    Article  CAS  Google Scholar 

  34. Moller, B. et al. Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches. Stat. Med. 22, 2751–2766 (2003).

    Article  Google Scholar 

  35. Tiwari, R. C. et al. A new method of predicting US and state-level cancer mortality counts for the current calendar year. CA Cancer J. Clin. 54, 30–40 (2004).

    Article  Google Scholar 

  36. Saxen, E. A. in Trends in Cancer Incidence Causes and Practical Implications. (ed. Magnus, K.) 5–16 (The International Union Against Cancer and The Norwegian Cancer Society, Oslo, 1982).

    Google Scholar 

  37. Parkin, D. M., Whelan, S. L., Ferlay, J., Teppo, L. & Thomas, D. B. Cancer Incidence in Five Continents (International Agency for Research on Cancer Scientific Publications, Lyon, 2002).

    Google Scholar 

  38. Doll, R. & Peto, R. The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. J. Natl Cancer Inst. 66, 1191–1308 (1981).

    Article  CAS  Google Scholar 

  39. Muir, C. S., Fraumeni, J. F. Jr. & Doll, R. The interpretation of time trends. Cancer Surveys 19/20, 5–21 (1994). Unsurpassed account of the numerous artefacts that need to be considered when deciphering time trends of cancer.

    Google Scholar 

  40. Swerdlow, A., Dos, S. S., I & Doll, R. Cancer Incidence and Mortality in England and Wales: Trends and Risk Factors (Oxford University Press, Oxford, 2001).

    Book  Google Scholar 

  41. Boyle, P. Relative value of incidence and mortality data in cancer research. Recent Results Cancer Res. 114, 41–63 (1989).

    Article  CAS  Google Scholar 

  42. Teppo, L., Hakulinen, T. & Saxen, E. The prediction of cancer incidence in Finland for the year 1980 by means of cancer registry material. Ann. Clin. Res. 6, 122–125 (1974).

    CAS  PubMed  Google Scholar 

  43. Hakulinen, T., Teppo, L. & Saxen, E. Do the predictions for cancer incidence come true? Experience from Finland. Cancer 57, 2454–2458 (1986).

    Article  CAS  Google Scholar 

  44. Hakulinen, T & Dyba, T. Precision of incidence predictions based on poisson distributed observations. Statist. Med. 13, 1513–1523 (1994).

    Article  CAS  Google Scholar 

  45. Dyba, T., Hakulinen, T. & Paivarinta L. A simple non-linear model in incidence prediction. Statist. Med. 16, 2297–2309 (1997).

    Article  CAS  Google Scholar 

  46. Engeland, A. et al. Prediction of cancer incidence in the Nordic countries up to the years 2000 and 2010. A collaborative study of the five Nordic Cancer Registries. APMIS 101 (suppl.), 5–123 (1993).

    Google Scholar 

  47. Coory, M. & Armstrong, B. K. Cancer Incidence Projections for Area and Rural Health Services in New South Wales. (NSW Cancer Council, Sydney, 1998). http://www.cancercouncil.com.au/html/research/researchreports/ projections/downloads/projections.pdf

    Google Scholar 

  48. New Zealand Ministry of Health. Cancer in New Zealand: Trends and Projections. (New Zealand Government, Wellington, 2002). http://www.moh.govt.nz/moh.nsf/ea6005dc347e7bd44c2566a40079ae6f/ 8e1d731682cab3d9cc256c7e00764a23?OpenDocument

  49. Kubik, A., Plesko, I. & Reissigova, J. Prediction of lung cancer mortality in four Central European countries, 1990–2009. Neoplasma 45, 60–67 (1998).

    CAS  PubMed  Google Scholar 

  50. Reissigova, J., Luostarinen, T., Hakulinen, T. & Kubik, A. Statistical modelling and prediction of lung cancer mortality in the Czech and Slovak Republics, 1960–1999. Int. J. Epidemiol. 23, 665–672 (1994).

    Article  CAS  Google Scholar 

  51. Pierce, J. P., Thurmond, L. & Rosbrook, B. Projecting international lung cancer mortality rates: first approximations with tobacco-consumption data. J. Natl Cancer Inst. Monogr. 45–49 (1992).

  52. Hakulinen, T., Pukkala, E. & Laara, E. Proceedings of the 5th World Conference on Smoking and Health. 1, 706–718 1986 (Winnipeg, Canada, 1983).

    Google Scholar 

  53. Weiss, W. Predictions of lung cancer mortality: the dangers of extrapolation. Arch. Environ. Health 28, 114–117 (1974).

    Article  CAS  Google Scholar 

  54. Berry, G. Prediction of mesothelioma, lung cancer, and asbestosis in former Wittenoom asbestos workers. Br. J. Ind. Med. 48, 793–802 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Hodgson, J. T., McElvenny, D. M., Darnton, A. J., Price, M. J. & Peto, J. The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050. Br. J. Cancer 92, 587–593 (2005).

    Article  CAS  Google Scholar 

  56. Peto, J., Hodgson, J. T., Matthews, F. E. & Jones, J. R. Continuing increase in mesothelioma mortality in Britain. Lancet 345, 535–539 (1995).

    Article  CAS  Google Scholar 

  57. Walker, A. M., Loughlin, J. E., Friedlander, E. R., Rothman, K. J. & Dreyer, N. A. Projections of asbestos-related disease 1980–2009. J. Occup. Med. 25, 409–425 (1983).

    CAS  PubMed  Google Scholar 

  58. Clements, M. S., Armstrong, B. K. & Moolgavkar, S. H. Lung cancer rate predictions using generalized additive models. Biostatistics. 6, 576–589 (2005).

    Article  Google Scholar 

  59. Khaw, K. T. Healthy aging. Br. Med. J. 315, 1090–1096 (1997).

    Article  CAS  Google Scholar 

  60. Ferlay, J., Bray, F., Pisani, P. & Parkin, D. M. GLOBOCAN 2002: Cancer Incidence, Mortality and Prevalence Worldwide (IARC, Lyon, 2004).

    Google Scholar 

  61. United Nations. World Population Prospects: the 2002 Revision. Volume 1: Comprehensive Tables (United Nations, New York, 2003).

  62. Bray, F., McCarron, P. & Parkin, D. M. The changing global patterns of female breast cancer incidence and mortality. Breast Cancer Res. 6, 229–239 (2004).

    Article  Google Scholar 

  63. Parkin, D. M., Bray, F. I. & Devesa, S. S. Cancer burden in the year 2000. The global picture. Eur. J. Cancer 37 (Suppl.), S4–S66 (2001). Comprehensive overview and discussion of geographical and temporal variations of common cancers worldwide, with a section describing the impact of demographic changes on future cancer burden.

    Article  Google Scholar 

  64. Potter, J. D. & Hunter, D. in Textbook of Cancer Epidemiology (eds Adami, H. O., Hunter, D. & Trichopolous, D.) 188–211 (Oxford University Press, Oxford, 2002).

    Google Scholar 

  65. Svensson, E. et al. Trends in colorectal cancer incidence in Norway by gender and anatomic site: an age–period–cohort analysis. Eur. J. Cancer Prev. 11, 489–495 (2002).

    Article  CAS  Google Scholar 

  66. White, E., Lee, C. Y. & Kristal, A. R. Evaluation of the increase in breast cancer incidence in relation to mammography use. J. Natl Cancer Inst. 82, 1546–1552 (1990).

    Article  CAS  Google Scholar 

  67. Wun, L. M., Feuer, E. J. & Miller, B. A. Are increases in mammographic screening still a valid explanation for trends in breast cancer incidence in the United States? Cancer Causes Control 6, 135–144 (1995).

    Article  CAS  Google Scholar 

  68. Quinn M. J. et al. Cancer mortality trends in the EU and acceding countries up to 2015. Ann. Oncol. 14, 1148–1152 (2003).

    Article  CAS  Google Scholar 

  69. Giles, G. G. & Amos, A. Evaluation of the organised mammographic screening programme in Australia. Ann. Oncol. 14, 1209–1211 (2003).

    Article  CAS  Google Scholar 

  70. Bashir, S. A. & Esteve, J. Projecting cancer incidence and mortality using Bayesian age-period-cohort models. J. Epidemiol. Biostat. 6, 287–296 (2001).

    Article  CAS  Google Scholar 

  71. Hakulinen, T. & Dyba, T. Precision of incidence predictions based on poisson distributed observations. Statist. Med. 13, 1513–1523 (1994).

    Article  CAS  Google Scholar 

  72. Hakulinen, T., Teppo, L. & Saxén, E. Do the predictions for cancer incidence come true? Experience from Finland. Cancer 57, 2454–2458 (1986).

    Article  CAS  Google Scholar 

  73. Moller, B., Weedon-Fekjaer, H. & Haldorsen, T. Empirical evaluation of prediction intervals for cancer incidence. BMC Med. Res. Methodol. 5, 21 (2005). Illustrative example of how a comprehensive set of predictions can be made on the basis of long-term cancer registry data.

    Article  Google Scholar 

  74. Laara, E., Day, N. E. & Hakama, M. Trends in mortality from cervical cancer in the Nordic countries: association with organised screening programmes. Lancet 1, 1247–1249 (1987).

    Article  CAS  Google Scholar 

  75. Bray, F. et al. Trends in cervical squamous cell carcinoma incidence in 13 European countries: changing risk and the effects of screening. Cancer Epidemiol. Biomarkers Prev. 14, 677–686 (2005).

    Article  Google Scholar 

  76. Anttila, A. et al. Effect of organised screening on cervical cancer incidence and mortality in Finland, 1963–1995: recent increase in cervical cancer incidence. Int. J. Cancer 83, 59–65 (1999).

    Article  CAS  Google Scholar 

  77. Nieminen, P., Hakama, M., Tarkkanen, J. & Anttila, A. Effect of type of screening laboratory on population-based occurrence of cervical lesions in Finland. Int. J. Cancer 99, 732–736 (2002).

    Article  CAS  Google Scholar 

  78. Osmond, C. Using age, period and cohort models to estimate future mortality rates. Int. J. Epidemiol. 14, 124–129 (1985).

    Article  CAS  Google Scholar 

  79. Negri, E., la Vecchia, C., Decarli, A. & Boyle, P. Projections to the end of the century of mortality from major cancer sites in Italy. Tumori 76, 420–428 (1990).

    Article  CAS  Google Scholar 

  80. Kiemeney, L. et al. Kidney cancer mortality in The Netherlands, 1950–94: prediction of a decreasing trend. J. Epidemiol. Biostat. 4, 303–311 (1999).

    CAS  PubMed  Google Scholar 

  81. Berzuini, C. & Clayton, D. Bayesian Analysis of survival on multiple time scale. Statist. Med. 13, 823–838 (1994).

    Article  CAS  Google Scholar 

  82. Bray, I., Brennan, P. & Boffetta, P. Projections of alcohol- and tobacco-related cancer mortality in Central Europe. Int. J. Cancer 87, 122–128 (2000).

    Article  CAS  Google Scholar 

  83. Carstensen, B. & Keiding, N. Age–period–cohort models: statistical inference in the Lexis diagram. http://staff.pubhealth.ku.dk/~bxc/APC/notes.pdf

Download references

Acknowledgements

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

Author information

Authors and Affiliations

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

    Freddie Bray & Bjørn Møller

Authors
  1. Freddie Bray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bjørn Møller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Freddie Bray.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

DATABASES

National Cancer Institute

breast cancer

cervical cancer

colon cancer

lung cancer

mesothelioma

prostate cancer

FURTHER INFORMATION

Age–period–cohort models, Spring 2004

Cancer in New Zealand: Trends and Projections

Cancer Scenarios: an Aid to Planning Cancer Services in Scotland in the Next Decade

CANCERMondial

GLOBOCAN 2002

Programs in R

Programs in Stata

Glossary

Predictions

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.

Rates

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

Mortality

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.

Trends

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.

Burden

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.

Prevalence

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.

Incidence

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.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bray, F., Møller, B. Predicting the future burden of cancer. Nat Rev Cancer 6, 63–74 (2006). https://doi.org/10.1038/nrc1781

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc1781

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing