Chronic diseases represent 71% of worldwide mortality, with an estimated 41 million deaths in 2016. Cardiovascular diseases, cancer, chronic respiratory diseases are responsible for approximately 80% of the death toll of chronic diseases. Along with mortality comes a huge financial burden on health care systems around the world, especially in highly developed countries, where between 70-90% of the money spent on health care is allocated to chronic disease management.
In his thesis, titled “Economic evaluation of early warning systems for chronic disease management: The Case of Heart Failure”, Fernando Albuquerque de Almeida MSc studied the methodology used in the economic evaluation of early warning systems for chronic disease management. In his study, he focused the decision modelling methods used in this framework.
Decision modelling
Early warning systems, regardless of their target disease, have in common that they are aimed at monitoring patients’ health status through periodically measuring individual patient characteristics in order to anticipate health deterioration and to trigger prompt clinical intervention. Considering this, the possibility to develop a more generic decision model for assessing the cost-effectiveness of early warning systems was explored.
Areas for improvement
A systematic literature review revealed that published models had some variability with regards to their general study characteristics and they displayed satisfactory methodological quality overall. However, the consideration and discussion of any competing theories regarding model structure and disease progression, identification of key parameters and the use of expert opinion, and uncertainty analyses were identified as key areas for improvement in the development of future decision models.
The largest contribution of this thesis for the methodology used in the economic evaluation of early warning systems for chronic disease management was the development and validation of a discrete event simulation model able to model heart failure patients and to account for the impact of individual patient characteristics in their health outcomes. Using this model, we found that home telemonitoring with the addition of a diagnostic algorithm extendedly dominated home telemonitoring and it had an incremental cost-effectiveness ratio of €27,712 per quality-adjusted life year against usual care.
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Fernando Albuquerque de Almeida’s defence of his thesis will take place on October 28th. Click here for more information about the event.
