Comparative analysis: Cost-effectiveness of best performing SMIs

Main objectives:

  • To rank self-management interventions (SMIs) according to their relative cost-effectiveness
  • To express the cost-effectiveness of SMIs in terms of cost per quality adjusted life year (QALY) to enable decision makers to compare the return on investment in terms of health and productivity gains for SMIs versus other investments in health
  • To incorporate data of the comparative effectiveness of SMIs (WP5) into a cost-effectiveness model to estimate how SMIs perform compared to other (sometimes pharmacological) interventions over the lifetime of a patient
  • To describe the impact of introducing SMIs on the health care budget of different member states


The cost-effectiveness analysis combines effectiveness data from the NMA with cost data from separate literature reviews. First, a conceptual model is developed. This will be achieved through a literature search and expert interviews. Important choices between model types such as discrete-event models, Markov models, and patient level simulation models are discussed. These decisions will be based on good practice guidelines from pharmacoeconomics communities of medical decision-making and the International Society for Pharmacoeconomics and Outcomes Research. After developing a conceptual model, we model the natural course of each of the four study conditions (including background and disease-specific mortality). Costs of SMIs are calculated from a societal, healthcare, and patient perspective using country-specific reference prices and are added to the model. We integrate the effectiveness of the SMIs using the results of the NMA into the cost-effectiveness models. Finally, we define a ranking of the most cost-effective SMIs with inclusion of uncertainty factors. We prepare the integration of the results into the decision-making tools to ensure that users are able to list the most cost-effective interventions for each condition. Furthermore, a budget impact model is developed that will supplement the forecast scenario with estimates of SMI uptake (market share) and the prevalence and incidence of each condition in the relevant member states.

Leading partner: Institute for Medical Technology Assessment (iMTA), The Netherlands

Other partners involved:

  • Fundación Avedis Donabedian (FAD), Spain
  • Netherlands Institute for Health Services Research (Nivel), The Netherlands
  • Fundació Privada Institut de Recerca de l’Hospital de la Santa Creu i Sant Pau (IR-HSCSP), Spain
  • University of Ioannina (UOI), Greece