Skip to main content

Advertisement

Log in

Predicting the Effectiveness of Prevention: A Role for Epidemiological Modeling

  • Original Paper
  • Published:
The Journal of Primary Prevention Aims and scope Submit manuscript

Abstract

It is well known that the current combination of aging populations and advances in health technology is resulting in burgeoning health costs in developed countries. Prevention is a potentially important way of containing health costs. In an environment of intense cost pressures, coupled with developments in disease prevention and health promotion, it is increasingly important for decision-makers to have a systematic, coordinated approach to the targeting and prioritization of preventive strategies. However, such a systematic approach is made difficult by the fact that preventive strategies need to be compared over the long term, in a variety of populations, and in real life settings not found in most trials. Information from epidemiological models can provide the required evidence base. In this review, we outline the role of epidemiological modeling in this context and detail its application using examples. Editors’ Strategic Implications: Policymakers and researchers will benefit from this description of the utility of epidemiological modeling as a means of generating translational evidence that helps to prioritize data-based prevention approaches and bridge the gap between clinical research and public health practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Barendregt, J. J., Bonneux, L., & Van Der Maas, P. J. (1997). The health care costs of smoking. New England Journal of Medicine, 337, 1052–1057.

    Article  PubMed  CAS  Google Scholar 

  • Beir, V. M. (2001). On the state of the art: Risk communication to the public. Reliability Engineering and System Safety, 71, 139–150.

    Article  Google Scholar 

  • Brandeau, M., Zaric, G., & De Angelis, V. (2005). Improved allocation of HIV prevention resources: Using information about prevention program production functions. Health Care Management Science, 8, 19–28.

    Article  PubMed  Google Scholar 

  • Doran, E., & Henry, D. (2006). Pharmaceutical benefits scheme policy: Confused and tough on patients. Internal Medicine Journal, 36, 211–213.

    Article  PubMed  CAS  Google Scholar 

  • Edwards, A., Elwyn, G., & Mulley, A. (2002). Explaining risks: Turning numerical data into meaningful pictures. British Medical Journal, 324, 827–830.

    Article  PubMed  Google Scholar 

  • Ford, E. S., Ajani, U. A., Croft, J. B., Critchely, J., Labarthe, D. R., Kottke, T. E., et al. (2007). Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. New England Journal of Medicine, 356, 2388.

    Article  PubMed  CAS  Google Scholar 

  • Franco, O. H., Peeters, A., Looman, C. W., & Bonneux, L. (2005). Cost effectiveness of statins in coronary heart disease. Journal of Epidemiology and Community Health, 59, 927–933.

    Article  PubMed  Google Scholar 

  • Fries, J. F. (1980). Aging, natural death, and the compression of morbidity. New England Journal of Medicine, 303, 130–135.

    PubMed  CAS  Google Scholar 

  • Haddix, A., Teutsch, S. M., & Corso, P. S. (2003). Prevention effectiveness: A guide to decision analysis and economic evaluation, (2nd ed., pp. 264). Oxford: Oxford University Press. ISBN: 0-19-514897-5.

  • Jackson, R. (2001). Are the new lipid management guidelines good for Australia’s health? Medical Journal of Australia, 175, 452–453.

    PubMed  CAS  Google Scholar 

  • Johnson, K., & Lassere, M. (2003). Letters—Epidemiological modelling (including economic modelling) and its role in preventive drug therapy. Medical Journal of Australia, 178, 188.

    PubMed  Google Scholar 

  • Liew, D., McNeil, J., Peeters, A., Lim, S., & Vos, T. (2002). Epidemiological modelling (including economic modelling) and its role in preventive drug therapy. Medical Journal of Australia, 177, 364–367.

    PubMed  Google Scholar 

  • Lim, S., Vos, T., Peeters, A., Liew, D., & McNeil, J. (2001). Cost-effectiveness of prescribing statins according to pharmaceutical benefits scheme criteria. Medical Journal of Australia, 175, 459–464.

    PubMed  CAS  Google Scholar 

  • Malenka, D. J., Baron, J. A., Johansen, S., Wahrenberger, J. W., & Ross, J. (1993). The framing effect of relative and absolute risk. Journal of General Internal Medicine, 8, 543–548.

    Google Scholar 

  • Mamun, A. A., Peeters, A., Barendregt, J. J., Willekens, F., Nusselder, W. J., & Bonneux, L. (2004). Smoking decreases the duration of life lived with and without cardiovascular disease: A life course analysis of the Framingham heart study. European Heart Journal, 25, 409–415.

    Article  PubMed  Google Scholar 

  • Marshall, T. (2003). Coronary heart disease prevention: Insights from modelling incremental cost effectiveness. British Medical Journal, 327, 1264–1275.

    Article  PubMed  Google Scholar 

  • Mathers, C. D., & Loncar, D. (2005). Updated projections of global mortality and burden of disease, 2002–2030: Data sources, methods and results. Evidence and information for policy. Geneva: World Health Organization.

    Google Scholar 

  • McEwan, P., Peters, J. R., Bergenheim, K., & Currie, C. J. (2006). Evaluation of the costs and outcomes from changes in risk factors in type 2 diabetes using the Cardiff stochastic simulation cost-utility model (DiabForecaster). Current Medical Research Opinion, 22, 121–129.

    Article  Google Scholar 

  • McNeil, J., Peeters, A., Liew, D., Lim, S., & Vos, T. (2001). A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk. Journal of Cardiovascular Risk, 8, 31–37.

    Article  PubMed  CAS  Google Scholar 

  • Murray, C. J. L., & Lopez, A. D. (1997). Mortality by cause for eight regions of the world: Global burden of disease study. The Lancet, 349, 1269–1276.

    Article  CAS  Google Scholar 

  • Nelson, M., Liew, D., Bertram, M., & Vos, T. (2005). Epidemiological modelling of routine use of low dose aspirin for the primary prevention of coronary heart disease and stroke in those aged ≥70. British Medical Journal, 327, 1–6.

    Google Scholar 

  • Peeters, A., Bonneaux, L., Barendregt, J. J., & Mackenbach, J. P. (2003). Improvements in treatment of coronary heart disease and cessation of stroke mortality rate decline. American Stroke Association, 34, 1610–1614.

    Google Scholar 

  • Pike, R., Keech, A., & Simes, R. (2003). Clinical trials research in the new millennium: The International Clinical Trials Symposium, Sydney, 21–23 October 2002. Medical Journal of Australia, 178, 316–317.

    PubMed  Google Scholar 

  • Reid, C., Meehan, A., Owen, A., Wlodarczyk, J., Robb, T., Hardy, M., et al. (2006). The Bosentan patient registry. In Proceedings of the National Medicines Symposium, Canberra, AU.

  • Simes, R. (2002). Clinical trails and “real-world” medicine. Medical Journal of Australia, 177, 410–411.

    PubMed  Google Scholar 

  • Simon, G., Wagner, E., & Vonkorff, M. (1995). Cost-effectiveness comparisons using “real-world” randomized trials: The case of new antidepressant drugs. Journal of Clinical Epidemiology, 48, 363–373.

    Article  PubMed  CAS  Google Scholar 

  • Stocks, N., Allan, J., & Mansfield, P. R. (2005). Management of hyperlipidaemia. Australian Family Physician, 34, 447–453.

    PubMed  Google Scholar 

  • Unal, B., Capewell, S., & Critchley, J. A. (2006). Coronary heart disease policy models: A systematic review. BMC Public Health, 6, 213.

    Article  PubMed  Google Scholar 

  • Zhang, P., Engelgau, M. M., Norris, S. L., Gregg, E. W., & Narayan, K. M. V. (2004). Application of economic analysis to diabetes and diabetes care. Annals of Internal Medicine, 140, 972–977.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Helen L. Walls.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Walls, H.L., Peeters, A., Reid, C.M. et al. Predicting the Effectiveness of Prevention: A Role for Epidemiological Modeling. J Primary Prevent 29, 295–305 (2008). https://doi.org/10.1007/s10935-008-0143-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10935-008-0143-y

Keywords

Navigation