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The online version of this article (https://doi.org/10.1007/s11136-018-1936-y) contains supplementary material, which is available to authorized users.
Wellbeing measures have been proposed for inclusion in economic evaluation to measure the effect of depression and compensate for shortcomings of existing multi-attribute utility instruments (MAUIs). The aims of this study were to identify dimensions of health-related quality of life (HRQoL) and wellbeing that are most affected by depression and to examine the extent to which these are captured by MAUIs.
Data were used from the Multi-Instrument Comparison study. Dimensions of HRQoL (SF-36v2 and AQoL-8D dimensions), capability wellbeing (ICECAP-A), and subjective wellbeing (including PWI, SWLS, and IHS) were identified that distinguished most individuals with depression from a healthy public. The extent to which these dimensions explain the content of five existing MAUIs (15D, AQoL-8D, EQ-5D-5L, HUI-3, and SF-6D) was examined using regression analyses. Additionally, the sensitivity of all MAUIs was also assessed towards depression-specific symptoms measured by DASS-21 and K-10.
The sample consisted of 917 individuals with self-reported depression and 1760 healthy subjects. Dimensions that distinguished most individuals with depression from the healthy group (effect size > 2) included AQoL-8D coping, AQoL-8D happiness, AQoL-8D self-worth, ICECAP-A, SF-36 mental health, and SF-36 social functioning. The AQoL-8D was most sensitive to the dimensions above as well as towards the depression-specific measures, the K10, DASS-S, and DASS-D.
This study has shown that psychosocial dimensions of HRQoL have the greatest ability to capture the impact of depression when compared with dimensions of capability wellbeing and SWB. Some MAUIs, such as the AQoL-8D, are sensitive to most distinguishing dimensions of HRQoL and wellbeing, which may obviate the need for supplementary wellbeing instruments.
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- The impact of depression on health-related quality of life and wellbeing: identifying important dimensions and assessing their inclusion in multi-attribute utility instruments
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