We know that depression is bad for a person’s health-related quality of life; but how bad?
That’s an important question because when it comes to resource allocation decisions, the value of treatments for depression have to be compared with treatments for a whole host of other diseases. The NHS operates within a budget after all. This is why NICE calls in the economists.
NICE’s favourite type of evaluation in health technology assessments is something called cost-utility analysis. Here utility refers to a person’s health-related quality of life, which is measured on a 0-1 scale and summed up over time to generate quality-adjusted life years (QALYs). NICE usually want these data (along with cost data) to be plugged into a model that extrapolates effects beyond what a trial is normally able to capture. This means that the choice of parameters for the model (such as the health impact of a disease) plays a crucial role in determining whether or not a treatment is recommended by NICE.
When economists build models, they generally turn to the literature to find their parameters. A new systematic review and meta-analysis of utility values for adults with unipolar depression could therefore have important implications for future NICE decisions relating to depression. It suggests that current approaches adopted by economists could lead to dodgy results, and it isn’t entirely the fault of the economists.
Methods
The authors conducted a systematic review, searching all the usual databases: EMBASE, MEDLINE, PsycINFO. The search included disease-related terms like ‘unipolar depression’ and ‘major depressive disorder’, as well as utility-related terms like ‘utility score’ and ‘EQ-5D’ (a widely used measure).
The authors subsequently carried out a meta-analysis to pool reported utility values. Papers were included in the meta-analysis if the reported utility values were categorised by disease severity in the form mild/moderate/severe. The authors divided the papers into two groups; those eliciting utility values directly, and those eliciting them indirectly. Put simply, direct valuations ask respondents how they would value depression-related health states, while indirect valuations apply weights to health states based on values elicited from the public. Whether we should use public or patient values is an ongoing debate.
Results
The search identified 420 references. Ultimately, 35 papers were included in the final review and 6 were included in the meta-analysis. Mean pooled utility values are shown in the table below, categorised by disease severity and elicitation method.
Mild depression | Moderate depression | Severe depression | |
Direct elicitation | 0.69 | 0.52 | 0.27 |
Indirect elicitation | 0.56 | 0.45 | 0.25 |
For health state utility values, a score of 1 represents ‘full health’, while a score of 0 is a health state of equivalent value to being dead.
Discussion
Utility values like this do not communicate the nature or true health burden of depression. However, it’s clear that they reflect the substantial negative impact of depression at all levels of severity. It’s also clear that the mild/moderate/severe categories can be used to differentiate between the size of effect on health-related quality of life amongst patients. The pooled values for mild depression are consistent with values observed in various chronic physical health problems, while severe depression is far worse.
The primary purpose of this study is to provide economists with more robust figures with which to parameterise their models. However, there are also implications for the wider research community.
The review finds that the majority of studies do not classify depression based on whether it is mild, moderate or severe. While such an approach may not hold value in some primary research, the review highlights its potential value for future secondary research. This study lost a lot of data in the meta-analysis due to the non-categorisation of disease severity. Wherever possible, primary research should report severity of depression. Furthermore, practitioners should seek to reach consensus on the best classifications of severity to use.
The review also highlights the potential for huge differences in cost-effectiveness results based on whether direct or indirect elicitation methods are used. NICE’s preferred elicitation method is to use indirect valuation, and in particular the EQ-5D. One implication of this study is that while using indirect methods would attach a greater value to complete remission, they would attach a lesser value than direct methods to reductions in severity. This suggests that public valuation may underestimate the value of some treatments. The debate over which method to use takes place almost exclusively amongst economists, yet practitioners and mental health researchers are better informed about the nature of depression. Their engagement is crucial if the best approach is to be adopted.
Links
Mohiuddin S, Payne K. Utility values for adults with unipolar depression: systematic review and meta-analysis. Medical Decision Making 2014, 34, 666-85. [PubMed]
RT @Mental_Elf: Health effects of depression: keeping economists’ models on track http://t.co/Ek8ggVL0GO
Health effects of depression: keeping economists’ models on track: Health Economist Christopher Sampson report… http://t.co/7cpqMElBeX
Health effects of depression: keeping economists’ models on track. http://t.co/qwNB4tWM5r
Today we report on a new systematic review & meta-analysis of health utility values for depression by @HealthEcon_MCR http://t.co/Fqw73E8BEI
Health effects of depression: keeping economists’ models on track http://t.co/6yewBnsuuj
New blog post by me over at The @Mental_Elf talking about how economists measure the health impact of depression http://t.co/Ysf1gJG4iX
Health effects of depression: keeping economists on track http://t.co/Q1NN7YxYHO
Are @NICEcomms using the best available data when evaluating treatments for depression? http://t.co/Fqw73E8BEI
Mental Elf: Health effects of depression: keeping economists’ models on track http://t.co/IIxr7C69Iy
How do @NICEcomms value the health effects of depression in technology assessments? @ChrisSampson87 explains http://t.co/Fqw73E8BEI
For health economics it’s important to use categories like #psychiatricdiagnosis in mental health http://t.co/QgAYG0ydjS
Hampshire Healthcare Library Service liked this on Facebook.
Systematic review highlights the importance of classifying depression as mild, moderate or severe in primary research http://t.co/Fqw73E8BEI
Today’s @Mental_Elf blog on how we should plan primary studies with eye on secondary analysis http://t.co/FfieJcyHQ5 #trials #data
also interdisciplinary engagement – economists debates shld include #mentalhealth professionals http://t.co/FfieJcyHQ5 by @ChrisSampson87
Health effects of depression: keeping economists’ models on track via @Mental_Elf #depression http://t.co/kvmholSr6S
@Time4Recovery @Mental_Elf Some very interesting connections made in that article, certainly things worth considering!
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When allocating resources to treating depression, whose views matter most: patients’ or the public’s? http://t.co/Fqw73E8BEI
A new meta-analysis provides utility values for use in #HealthEconomics models for unipolar #depression http://t.co/Fqw73E8BEI
Don’t miss – Health effects of depression: keeping economists’ models on track http://t.co/Ek8ggVL0GO
Another story that convinces me I’ll die before I retire #depressingtweetoftheday http://t.co/ZuF3jY7Ql0
Health effects of depression: keeping economists on track – The Mental Elf http://t.co/iBG5Yd7oA8
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