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Predicting the Effectiveness of Prevention: a Role for Epidemiological Modeling

Overview
Journal J Prim Prev
Publisher Springer
Date 2008 Jun 27
PMID 18581234
Citations 5
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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.

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References
1.
Edwards A, Elwyn G, Mulley A . Explaining risks: turning numerical data into meaningful pictures. BMJ. 2002; 324(7341):827-30. PMC: 1122766. DOI: 10.1136/bmj.324.7341.827. View

2.
Murray C, Lopez A . Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet. 1997; 349(9061):1269-76. DOI: 10.1016/S0140-6736(96)07493-4. View

3.
McEwan P, Peters J, Bergenheim K, Currie C . Evaluation of the costs and outcomes from changes in risk factors in type 2 diabetes using the Cardiff stochastic simulation cost-utility model (DiabForecaster). Curr Med Res Opin. 2006; 22(1):121-9. DOI: 10.1185/030079906X80350. View

4.
Lim S, Vos T, Peeters A, Liew D, McNeil J . Cost-effectiveness of prescribing statins according to pharmaceutical benefits scheme criteria. Med J Aust. 2002; 175(9):459-64. DOI: 10.5694/j.1326-5377.2001.tb143676.x. View

5.
Nelson M, Liew D, Bertram M, Vos T . Epidemiological modelling of routine use of low dose aspirin for the primary prevention of coronary heart disease and stroke in those aged > or =70. BMJ. 2005; 330(7503):1306. PMC: 558207. DOI: 10.1136/bmj.38456.676806.8F. View