%0 Journal Article %1 BroennumHansen1999 %A Brønnum-Hansen, H %D 1999 %J Journal of epidemiology and community health %K CauseofDeath CohortStudies ComputerSimulation HealthPromotion Humans LogisticModels Models Mortality Mortality:trends Prevalence Probability RiskFactors Statistical TimeFactors %N 5 %P 300-5 %T How good is the Prevent model for estimating the health benefits of prevention? %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1756876&tool=pmcentrez&rendertype=abstract %V 53 %X STUDY OBJECTIVE: Prevent is a public health model for estimating the effect on mortality of changes in exposure to risk factors. When the model is tested by simulating a development that has already taken place, the results may differ considerably from the actual situation. The purpose of this study is to test the Prevent model by applying it to a synthetic cohort in which the development is unaffected by concealed factors. DESIGN: A micro-simulation model was developed to create basic data for Prevent and give "exact" results as to changes in risk factor prevalences and mortality. The estimates of Prevent simulations were compared with the "exact" results. MAIN RESULTS: Modelling one risk factor related to a cause specific mortality gave fairly similar results by the two methods. Including two risk factors Prevent tends slightly to overestimate the health benefits of prevention. CONCLUSIONS: The differences between the "exact" mortality and the Prevent estimates will be small for realistic scenarios and Prevent provide reasonable estimates of the health benefits of prevention. %@ 0143-005X

@article{BroennumHansen1999, abstract = {STUDY OBJECTIVE: Prevent is a public health model for estimating the effect on mortality of changes in exposure to risk factors. When the model is tested by simulating a development that has already taken place, the results may differ considerably from the actual situation. The purpose of this study is to test the Prevent model by applying it to a synthetic cohort in which the development is unaffected by concealed factors. DESIGN: A micro-simulation model was developed to create basic data for Prevent and give "exact" results as to changes in risk factor prevalences and mortality. The estimates of Prevent simulations were compared with the "exact" results. MAIN RESULTS: Modelling one risk factor related to a cause specific mortality gave fairly similar results by the two methods. Including two risk factors Prevent tends slightly to overestimate the health benefits of prevention. CONCLUSIONS: The differences between the "exact" mortality and the Prevent estimates will be small for realistic scenarios and Prevent provide reasonable estimates of the health benefits of prevention.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Brønnum-Hansen, H}, biburl = {https://www.bibsonomy.org/bibtex/2e7d3cb1c7153f2de42d309b6930aa6f9/jepcastel}, city = {Danish Institute for Clinical Epidemiology, Copenhagen O, Denmark.}, interhash = {c91ecfdc0a05130bed1d4b1f4fcdf943}, intrahash = {e7d3cb1c7153f2de42d309b6930aa6f9}, isbn = {0143-005X}, issn = {0143-005X}, journal = {Journal of epidemiology and community health}, keywords = {CauseofDeath CohortStudies ComputerSimulation HealthPromotion Humans LogisticModels Models Mortality Mortality:trends Prevalence Probability RiskFactors Statistical TimeFactors}, month = {5}, note = {4322<m:linebreak></m:linebreak>LR: 20061115; PUBM: Print; JID: 7909766; ppublish;<m:linebreak></m:linebreak>Risc atribuïble}, number = 5, pages = {300-5}, pmid = {10396537}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {How good is the Prevent model for estimating the health benefits of prevention?}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1756876&tool=pmcentrez&rendertype=abstract}, volume = 53, year = 1999 }