Review

Predicting the future burden of cancer

  • Nature Reviews Cancer volume 6, pages 6374 (2006)
  • doi:10.1038/nrc1781
  • Download Citation
Published online:

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.

  • Subscribe to Nature Reviews Cancer for full access:

    $265

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

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

  2. 2.

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

  3. 3.

    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).

  4. 4.

    & Grappling with cancer — defeatism versus the reality of progress. N. Engl. J. Med. 337, 931–934 (1997).

  5. 5.

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

  6. 6.

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

  7. 7.

    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.

  8. 8.

    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).

  9. 9.

    , , & 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).

  10. 10.

    & 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.

  11. 11.

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

  12. 12.

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

  13. 13.

    & Progress against cancer? N. Engl. J. Med. 314, 1226–1232 (1986).

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 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).

  18. 18.

    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).

  19. 19.

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

  20. 20.

    & Cancer Scenarios: an Aid to Planning Cancer Services in Scotland in the Next Decade. (The Scottish Executive, Edinburgh, 2001). Innovative 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.

  21. 21.

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

  22. 22.

    Cancer in Scotland:Sustaining Change. Cancer Incidence Projections for Scotland (2001–2020). An Aid to Planning Cancer Services (NHS Scotland, Edinburgh, 2004).

  23. 23.

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

  24. 24.

    , , & At least one in seven cases of cancer is caused by smoking. Global estimates for 1985. Int. J. Cancer 59, 494–504 (1994).

  25. 25.

    , , & Global cancer statistics, 2002. CA Cancer J. Clin. 55, 74–108 (2005).

  26. 26.

    , , , & 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).

  27. 27.

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

  28. 28.

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

  29. 29.

    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).

  30. 30.

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

  31. 31.

    , & A mathematical model for the age distribution of cancer in man. Int. J. Cancer 4, 93–112 (1969).

  32. 32.

    , & There is no such thing as ageing, and cancer is not related to it. IARC Sci. Publ. 43–53 (1985).

  33. 33.

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

  34. 34.

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

  35. 35.

    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).

  36. 36.

    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).

  37. 37.

    , , , & Cancer Incidence in Five Continents (International Agency for Research on Cancer Scientific Publications, Lyon, 2002).

  38. 38.

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

  39. 39.

    , & 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.

  40. 40.

    , , I & Cancer Incidence and Mortality in England and Wales: Trends and Risk Factors (Oxford University Press, Oxford, 2001).

  41. 41.

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

  42. 42.

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

  43. 43.

    , & Do the predictions for cancer incidence come true? Experience from Finland. Cancer 57, 2454–2458 (1986).

  44. 44.

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

  45. 45.

    , & A simple non-linear model in incidence prediction. Statist. Med. 16, 2297–2309 (1997).

  46. 46.

    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).

  47. 47.

    & Cancer Incidence Projections for Area and Rural Health Services in New South Wales. (NSW Cancer Council, Sydney, 1998).

  48. 48.

    New Zealand Ministry of Health. Cancer in New Zealand: Trends and Projections. (New Zealand Government, Wellington, 2002).

  49. 49.

    , & Prediction of lung cancer mortality in four Central European countries, 1990–2009. Neoplasma 45, 60–67 (1998).

  50. 50.

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

  51. 51.

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

  52. 52.

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

  53. 53.

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

  54. 54.

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

  55. 55.

    , , , & The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050. Br. J. Cancer 92, 587–593 (2005).

  56. 56.

    , , & Continuing increase in mesothelioma mortality in Britain. Lancet 345, 535–539 (1995).

  57. 57.

    , , , & Projections of asbestos-related disease 1980–2009. J. Occup. Med. 25, 409–425 (1983).

  58. 58.

    , & Lung cancer rate predictions using generalized additive models. Biostatistics. 6, 576–589 (2005).

  59. 59.

    Healthy aging. Br. Med. J. 315, 1090–1096 (1997).

  60. 60.

    , , & GLOBOCAN 2002: Cancer Incidence, Mortality and Prevalence Worldwide (IARC, Lyon, 2004).

  61. 61.

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

  62. 62.

    , & The changing global patterns of female breast cancer incidence and mortality. Breast Cancer Res. 6, 229–239 (2004).

  63. 63.

    , & 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.

  64. 64.

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

  65. 65.

    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).

  66. 66.

    , & Evaluation of the increase in breast cancer incidence in relation to mammography use. J. Natl Cancer Inst. 82, 1546–1552 (1990).

  67. 67.

    , & 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).

  68. 68.

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

  69. 69.

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

  70. 70.

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

  71. 71.

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

  72. 72.

    , & Do the predictions for cancer incidence come true? Experience from Finland. Cancer 57, 2454–2458 (1986).

  73. 73.

    , & 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.

  74. 74.

    , & Trends in mortality from cervical cancer in the Nordic countries: association with organised screening programmes. Lancet 1, 1247–1249 (1987).

  75. 75.

    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).

  76. 76.

    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).

  77. 77.

    , , & Effect of type of screening laboratory on population-based occurrence of cervical lesions in Finland. Int. J. Cancer 99, 732–736 (2002).

  78. 78.

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

  79. 79.

    , , & Projections to the end of the century of mortality from major cancer sites in Italy. Tumori 76, 420–428 (1990).

  80. 80.

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

  81. 81.

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

  82. 82.

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

  83. 83.

    & Age–period–cohort models: statistical inference in the Lexis diagram.

Download references

National Cancer Institute

  1. breast cancer

    • cervical cancer

      • colon cancer

        • lung cancer

          • mesothelioma

            • prostate cancer

              Acknowledgements

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

              Author information

              Affiliations

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

                • Freddie Bray
                •  & Bjørn Møller

              Authors

              1. Search for Freddie Bray in:

              2. Search for Bjørn Møller in:

              Competing interests

              The authors declare no competing financial interests.

              Corresponding author

              Correspondence to Freddie Bray.

              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.