Developing and applying a stochastic dynamic population model for chronic obstructive pulmonary disease
Value in Health , Volume 14 - Issue 8 p. 1039- 1047
To develop a stochastic population model of disease progression in chronic obstructive pulmonary disease (COPD) that includes the effects of COPD exacerbations on health-related quality of life, costs, disease progression, and mortality and can be used to assess the effects of a wide range of interventions. The model is a multistate Markov model with time varying transition rates specified by age, sex, smoking status, COPD disease severity, and/or exacerbation type. The model simulates annual changes in COPD prevalence due to COPD incidence, exacerbations, disease progression (annual decline in the forced expiratory volume in 1 second as percentage of the predicted value), and mortality. The main outcome variables are quality-adjusted life years, total exacerbations, and COPD-related health care costs. Exacerbation-related input parameters were based on quantitative meta-analysis. All important model parameters are entered into the model as probability distributions. To illustrate the potential use of the model, costs and effects were calculated for 3-year implementation of three different COPD interventions, one pharmacologic, one on smoking cessation, and one on pulmonary rehabilitation using a time horizon of 10 years for reporting outcomes. Compared with minimal treatment the cost/quality-adjusted life year was €8,300 for the pharmacologic intervention, €10,800 for the smoking cessation therapy, €8,700 for the combination of the pharmacologic intervention and the smoking cessation therapy, and €17,200 for the pulmonary rehabilitation program. The probability of the interventions to be cost-effective at a ceiling ratio of €20,000 varied from 58% to 100%. The COPD model provides policy makers with information about the long-term costs and effects of interventions over the entire chain of care, from primary prevention to care for very severe COPD and includes uncertainty around the outcomes.
|, , , ,|
|Value in Health|
|Organisation||Institute for Medical Technology Assessment (iMTA)|
Hoogendoorn, M, Rutten-van Mölken, M.P.M.H, Hoogenveen, R.T, Al, M.J, & Feenstra, T.L. (2011). Developing and applying a stochastic dynamic population model for chronic obstructive pulmonary disease. Value in Health, 14(8), 1039–1047. doi:10.1016/j.jval.2011.06.008