Article Abstract

Online ISSN: 1099-176X    Print ISSN: 1091-4358
The Journal of Mental Health Policy and Economics
Volume 17, Issue 2, 2014. Pages: 51-60
Published Online: 1 June 2014

Copyright © 2014 ICMPE.

Mental Health Care System Optimization from a Health-Economics Perspective: Where to Sow and Where to Reap?

Joran Lokkerbol,¹* Rifka Weehuizen,² Ifigeneia Mavranezouli,³ Cathrine Mihalopoulos,⁴ Filip Smit⁵

¹Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, and Department of Clinical Psychology, EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, Netherlands
²University of Strasbourg Institute for Advanced Study, Strasbourg, France
³National Collaborating Centre for Mental Health, Centre for Outcomes Research and Effectiveness, Research Department of Clinical, Educational & Health Psychology, University College London, London, UK
⁴Deakin Health Economics, Deakin University, Melbourne, Australia
⁵Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, Department of Clinical Psychology, EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, and Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, Netherlands

Background: Health care expenditure (as % of GDP) has been rising in all OECD countries over the last decades. Now, in the context of the economic downturn, there is an even more pressing need to better guarantee the sustainability of health care systems. This requires that policy makers are informed about optimal allocation of budgets. We take the Dutch mental health system in the primary care setting as an example of new ways to approach optimal allocation.

Aims of the Study: To demonstrate how health economic modelling can help in identifying opportunities to improve the Dutch mental health care system for patients presenting at their GP with symptoms of anxiety, stress, symptoms of depression, alcohol abuse/dependence, anxiety disorder or depressive disorder such that changes in the health care system have the biggest leverage in terms of improved cost-effectiveness. Investigating such scenarios may serve as a starting point for setting an agenda for innovative and sustainable health care policies.

Methods: A health economic simulation model was used to synthesize clinical and economic evidence. The model was populated with data from GPs' national register on the diagnosis, treatment, referral and prescription of their patients in the year 2009. A series of `what-if' analyses was conducted to see what parameters (uptake, adherence, effectiveness and the costs of the interventions) are associated with the most substantial impact on the cost-effectiveness of the health care system overall.

Results: In terms of improving the overall cost-effectiveness of the primary mental health care system, substantial benefits could be derived from increasing uptake of psycho-education by GPs for patients presenting with stress and when low cost interventions are made available that help to increase the patients' compliance with pharmaceutical interventions, particularly in patients presenting with symptoms of anxiety. In terms of intervention costs, decreasing the costs of antidepressants is expected to yield the biggest impact on the cost-effectiveness of the primary mental health care system as a whole. These ``target group -- intervention'' combinations are the most appealing candidates for system innovation from a cost-effectiveness point of view, but need to be carefully aligned with other considerations such as equity, acceptability, appropriateness, feasibility and strength of evidence.

Discussion and Limitations: The study has some strengths and limitations. Cost-effectiveness analysis is performed using a health economic model that is based on registration data from a sample of GPs, but assumptions had to be made on how these data could be extrapolated to all GPs. Parameters on compliance rates were obtained from a focus group or were based on mere assumptions, while the clinical effectiveness of interventions were taken from meta-analyses or randomised trials. Effectiveness is expressed in terms of years lived with disability (YLD) averted; indirect benefits such as reduction of lost productivity or lesser pressure on informal caregivers are not taken into account. Whenever assumptions had to be made, we opted for conservative estimates that are unlikely to have resulted in an overly optimistic portrayal of the cost-effectiveness ratios.

Implications for Health Care Provision and Use: The model can be used to guide health care system innovation, by identifying those parameters where changes in the uptake, compliance, effectiveness and costs of interventions have the largest impact on the cost-effectiveness of a mental health care system overall. In this sense, the model could assist policy makers during the first stage of decision making on where to make improvements in the health care system, or assist the process of guideline development. However, the improvement candidates need to be assessed during a second-stage `normative filter', to address considerations other than cost-effectiveness.