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. 2016 Sep 20;14(1):134.
doi: 10.1186/s12955-016-0535-2.

Assessing outcomes for cost-utility analysis in mental health interventions: mapping mental health specific outcome measure GHQ-12 onto EQ-5D-3L

Marie Lindkvist  1   2 , Inna Feldman  3
Affiliations

Assessing outcomes for cost-utility analysis in mental health interventions: mapping mental health specific outcome measure GHQ-12 onto EQ-5D-3L

Marie Lindkvist et al. Health Qual Life Outcomes. .

Abstract

Background: Many intervention-based studies aiming to improve mental health do not include a multi-attribute utility instrument (MAUI) that produces quality-adjusted life-years (QALYs) and it limits the applicability of the health economic analyses. This study aims to develop 'crosswalk' transformation algorithm between a measure for psychological distress General Health Questionnaire (GHQ-12) and MAUI EuroQoL (EQ-5D-3L).

Methods: The study is based on a survey questionnaire sent to a random sample in four counties in Sweden in 2012. The survey included GHQ-12 and EQ-5D instruments, as well as a question about self-rated health. The EQ-5D index was calculated using the UK and the Swedish tariff values. Two OLS models were used to estimate the EQ-5D health state values using the GHQ-12 as exposure, based on the respondents (n = 17, 101) of two counties. The algorithms were applied to the data from two other counties, (n = 15, 447) to check the predictive capacity of the models.

Results: The final models included gender, age, self-rated health and GHQ-12 scores as a quantitative variable. The regression equations explained 40 % (UK tariff) and 46 % (Swedish tariff) of the variances. The model showed a satisfying predictive capacity between the observed and the predicted EQ-5D index score, with Pearson correlation = 0.65 and 0.69 for the UK and Swedish models, respectively.

Conclusion: The algorithms developed in this study can be used to determine cost-effectiveness of services or interventions that use GHQ-12 as a primary outcome where the utility measures are not collected.

Keywords: Mapping; Mental health; Preference-based measures; Quality of life; Utilities.

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Figures

Fig. 1
Fig. 1
Population survey samplings results used in the study
Fig. 2
Fig. 2
Observed and Predicted values of EQ-5D and EQ-5D-SW with 95 % error bars against GHQ-12 (Validation sample)
See this image and copyright information in PMC

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