title: Developing and applying a stochastic dynamic population model for chronic obstructive pulmonary disease.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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%.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands. hoogendoorn@bmg.eur.nl
authors.forename: Martine
authors.initials: M
authors.lastname: Hoogendoorn
authors.forename: Maureen P M H
authors.initials: MP
authors.lastname: Rutten-van Mölken
authors.forename: Rudolf T
authors.initials: RT
authors.lastname: Hoogenveen
authors.forename: Maiwenn J
authors.initials: MJ
authors.lastname: Al
authors.forename: Talitha L
authors.initials: TL
authors.lastname: Feenstra
mesh_terms: D000367:Age Factors; D003362:Cost-Benefit Analysis; D018450:Disease Progression; D017048:Health Care Costs; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D011157:Population Dynamics; D029424:Pulmonary Disease, Chronic Obstructive / Q000191:economics / Q000503:physiopathology / Q000628:therapy*; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D012720:Severity of Illness Index; D012737:Sex Factors; D012907:Smoking / Q000009:adverse effects; D016540:Smoking Cessation / Q000379:methods*; D013269:Stochastic Processes