Literature DB >> 18581234

Predicting the effectiveness of prevention: a role for epidemiological modeling.

Helen L Walls1, Anna Peeters, Christopher M Reid, Danny Liew, John J McNeil.   

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.

Mesh:

Year:  2008        PMID: 18581234     DOI: 10.1007/s10935-008-0143-y

Source DB:  PubMed          Journal:  J Prim Prev        ISSN: 0278-095X


  25 in total

1.  A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk.

Authors:  J J McNeil; A Peeters; D Liew; S Lim; T Vos
Journal:  J Cardiovasc Risk       Date:  2001-02

Review 2.  Explaining risks: turning numerical data into meaningful pictures.

Authors:  Adrian Edwards; Glyn Elwyn; Al Mulley
Journal:  BMJ       Date:  2002-04-06

3.  Epidemiological modelling (including economic modelling) and its role in preventive drug therapy.

Authors:  Kent R Johnson; Marissa N Lassere
Journal:  Med J Aust       Date:  2003-02-17       Impact factor: 7.738

4.  The health care costs of smoking.

Authors:  J J Barendregt; L Bonneux; P J van der Maas
Journal:  N Engl J Med       Date:  1997-10-09       Impact factor: 91.245

5.  Improved allocation of HIV prevention resources: using information about prevention program production functions.

Authors:  Margaret L Brandeau; Gregory S Zaric; Vanda de Angelis
Journal:  Health Care Manag Sci       Date:  2005-02

6.  Pharmaceutical benefits scheme policy: confused and tough on patients.

Authors:  E Doran; D Henry
Journal:  Intern Med J       Date:  2006-04       Impact factor: 2.048

7.  Cost-effectiveness comparisons using "real world" randomized trials: the case of new antidepressant drugs.

Authors:  G Simon; E Wagner; M Vonkorff
Journal:  J Clin Epidemiol       Date:  1995-03       Impact factor: 6.437

8.  Epidemiological modelling of routine use of low dose aspirin for the primary prevention of coronary heart disease and stroke in those aged > or =70.

Authors:  Mark R Nelson; Danny Liew; Melanie Bertram; Theo Vos
Journal:  BMJ       Date:  2005-05-20

9.  The framing effect of relative and absolute risk.

Authors:  D J Malenka; J A Baron; S Johansen; J W Wahrenberger; J M Ross
Journal:  J Gen Intern Med       Date:  1993-10       Impact factor: 5.128

10.  Explaining the decrease in U.S. deaths from coronary disease, 1980-2000.

Authors:  Earl S Ford; Umed A Ajani; Janet B Croft; Julia A Critchley; Darwin R Labarthe; Thomas E Kottke; Wayne H Giles; Simon Capewell
Journal:  N Engl J Med       Date:  2007-06-07       Impact factor: 91.245

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  4 in total

Review 1.  A systematic review of economic evaluations of interventions to tackle cardiovascular disease in low- and middle-income countries.

Authors:  Marc Suhrcke; Till A Boluarte; Louis Niessen
Journal:  BMC Public Health       Date:  2012-01-03       Impact factor: 3.295

2.  Modeling the Burden of Cardiovascular Diseases in Iran from 2005 to 2025: The Impact of Demographic Changes.

Authors:  Masoumeh Sadeghi; Ali Akbar Haghdoost; Abbas Bahrampour; Mohsen Dehghani
Journal:  Iran J Public Health       Date:  2017-04       Impact factor: 1.429

3.  Estimation of myocardial infarction death in Iran: artificial neural network.

Authors:  Mohammad Asghari-Jafarabadi; Kamal Gholipour; Rahim Khodayari-Zarnaq; Mehrdad Azmin; Gisoo Alizadeh
Journal:  BMC Cardiovasc Disord       Date:  2022-10-07       Impact factor: 2.174

Review 4.  Modelling microbial infection to address global health challenges.

Authors:  Meagan C Fitzpatrick; Chris T Bauch; Jeffrey P Townsend; Alison P Galvani
Journal:  Nat Microbiol       Date:  2019-09-20       Impact factor: 17.745

  4 in total

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