Cost-utility of of a personalized intervention based on the level and risk profile to prevent major depression in general practice

Abstract

Introduction: In Western Societies, depression is viewed as a major and increasing public health issue, as it causes high levels of distress for those who experience it and their relatives, as well as considerable financial costs to society. Hence, a lot of efforts are being made in order to increase the access to treatment of depression. However, despite this investment the prevalence of mental disorders has not changed. The prevalence of diseases is a combination of incidence (new cases) and duration. So, in order to reduce its prevalence interventions need to focus not only on a condition’ s duration (i.e. trough treatment) but also on reducing the number of new cases (primary prevention) or relapses (secondary prevention. Estimating overall risk across a range of vulnerability and protective risk factors followed by health promotion / risk profile targeting interventions or recommendations is widely accepted as the basis of much primary health carebased prevention programs in developed countries for cardiovascular and metabolic diseases. Following this idea, our team developed the first risk algorithm for predicting the onset of depression (PredictD). The predictD-algorithm provides a quantification of the overall risk of depression as well as information on risk factors for each patient that could guide prevention. This allows us to develop interventions tailored for level of risk and specific risk factors present in each patient. This type of prevention can be called prevention.In this oral communication we will present this new intervention, as well as preliminary data on its efficiency (cost-utility). Method: The ‘predictD-CCRT-study’ is a Spanish multicenter randomized controlled trial, conducted between November 2010August 2012, with cluster assignment by Primary Health Center (PHC) and two parallel arms, conducted. A total of 70 PHC, 140 primary care physicians and 3,326 patients participated in the study. We performed an economic evaluation, from a Government and Health perspective, using as an outcome the cost by Quality of Life-Adjusted Years (QALYs) gained buy the intervention. Results: Cost-utility acceptability curves indicated that the probability of cost-utility at a willingness-to-pay of 30,000 euros (around $50,000) per QALY gained was higher than 95%, both from a Government and Health perspective. Discussion: Preliminary results suggested that our personalized intervention, delivered by general practitioners was efficient. Lessons Learned: It is possible to prevent depression integrating personalized interventions in the primary health care setting, at a very low cost World Congress on Integrated Care 2014, Sydney, November 23-26, 2014. International Journal of Integrated Care – Volume 14, 8 December – URN:NBN:NL:UI:10-1-116615– http://www.ijic.org/ Limitations: It is expected that those who participated in the trial had greater psychosocial and prevention orientation in their professional practice. Therefore, if the “predictD-CCRT-intervention” was applied across the overall primary health care setting, its effect would probably be smaller. In addition, the cost of a primary health care visit in Spain is very low (around 20 euros per visit). Suggestions for future research: Future studies will need to focus on the active components of the intervention; and on its wide-scale implementation.