Literature DB >> 10303654

The health benefits of prevention: a simulation approach.

L Gunning-Schepers.   

Abstract

In 1986 the Health 2000 Report, a long term health policy document, was presented to the Dutch parliament. This document is part of shift in interest in public health towards health rather than health services planning. There are two interesting features in this shift. The one is the tendency to measure the effectiveness of a policy, and intervention or a technology in terms of health, the outcome rather than the input, output or process. The other is the acceptance that political choices need to be made, since however large the budget for health is, it will always be limited. One of the choices to make will be whether or not to invest in preventive interventions. Preventive interventions can be defined as deliberate changes in the prevalence of risk factors in a population. To be able to weigh the costs and the benefits of such preventive interventions, an estimate will have to be made of their effect on the health of the population. Furthermore changes in risk factor prevalence may also occur autonomously. An estimate of the changes in the health status of the population as a result of these shifts in risk factor prevalence, will be important for the planning of health services and for the setting of realistic targets, as proposed by WHO. Prevent is a tool that will estimate the health effects of changes in risk factor prevalence in a population, as a result of trends or interventions. Its results can either be used directly in health policy making to formulate targets or quantify different scenario's on changes in risk factor prevalence in the future, or its results can be used as input for formal decision making processes such as for instance cost effectiveness studies. In epidemiology an analysis of the distribution of disease incidence and risk factor prevalence in different populations is used to confirm the hypothesis of a causal relationship between risk factor and disease. The strength of the relationship is often expressed as the ratio of incidence between exposed and non exposed, the Incidence Density Ratio (IDR). The importance of a risk factor for the incidence of a certain disease in a population is usually expressed as the Etiologic Fraction (EF), the proportion of the total incidence of the disease that can be attributed to the prevalence of that risk factor in the population. The EF is sometimes used as an indication of the proportion of incidence that could be prevented by the total elimination of that risk factor in the population.(ABSTRACT TRUNCATED AT 400 WORDS)

Mesh:

Year:  1989        PMID: 10303654

Source DB:  PubMed          Journal:  Health Policy        ISSN: 0168-8510            Impact factor:   2.980


  25 in total

1.  How good is the Prevent model for estimating the health benefits of prevention?

Authors:  H Brønnum-Hansen
Journal:  J Epidemiol Community Health       Date:  1999-05       Impact factor: 3.710

2.  Guidance for commissioners on the cost effectiveness of smoking cessation interventions. Health Educational Authority.

Authors:  S Parrott; C Godfrey; M Raw; R West; A McNeill
Journal:  Thorax       Date:  1998-12       Impact factor: 9.139

3.  A health impact assessment model for environmental changes attributable to development projects.

Authors:  M McCarthy; J P Biddulph; M Utley; J Ferguson; S Gallivan
Journal:  J Epidemiol Community Health       Date:  2002-08       Impact factor: 3.710

4.  System dynamics modeling for public health: background and opportunities.

Authors:  Jack B Homer; Gary B Hirsch
Journal:  Am J Public Health       Date:  2006-01-31       Impact factor: 9.308

5.  ARMADA--a computer model of the impact of environmental factors on health.

Authors:  Martin Utley; Steve Gallivan; Jane Biddulph; Mark McCarthy; Jake Ferguson
Journal:  Health Care Manag Sci       Date:  2003-08

Review 6.  Economic evaluation of smoking-cessation therapies: a critical and systematic review of simulation models.

Authors:  Kristian Bolin
Journal:  Pharmacoeconomics       Date:  2012-07-01       Impact factor: 4.981

Review 7.  Differences between studies in reported relative risks associated with smoking: an overview.

Authors:  P J van de Mheen; L J Gunning-Schepers
Journal:  Public Health Rep       Date:  1996 Sep-Oct       Impact factor: 2.792

8.  Modelling the effects of increased physical activity on coronary heart disease in England and Wales.

Authors:  B Naidoo; M Thorogood; K McPherson; L J Gunning-Schepers
Journal:  J Epidemiol Community Health       Date:  1997-04       Impact factor: 3.710

9.  Benefits of smoking cessation for longevity.

Authors:  Donald H Taylor; Vic Hasselblad; S Jane Henley; Michael J Thun; Frank A Sloan
Journal:  Am J Public Health       Date:  2002-06       Impact factor: 9.308

10.  Substantial potential for reductions in coronary heart disease mortality in the UK through changes in risk factor levels.

Authors:  J A Critchley; S Capewell
Journal:  J Epidemiol Community Health       Date:  2003-04       Impact factor: 3.710

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