ArticleApplied health economics and health policy2023
An Item-Response Mapping from General Health Questionnaire Responses to EQ-5D-3L Using a General Population Sample from England.
Article in Applied health economics and health policy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping to estimate health state utility: a systematic review of development and limitations.Health and quality of life outcomes · 2025Pooled it
- Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.Health and quality of life outcomes · 2025Article
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1 author.
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Abstract
backgroundThe 12-item General Health Questionnaire (GHQ-12) is widely used to measure mental health and well-being; however, it is not possible to estimate values on the full health = 1, dead = 0 scale used to construct quality-adjusted life-years (QALYs) from GHQ-12 responses as it is not preference-based.
objectiveThe aim of this study was to create an item-response mapping between GHQ-12 and EQ-5D-3L health states, for which several value sets exist.
methodsData from the 2012 Health Survey for England with complete GHQ-12 and EQ-5D-3L descriptive system responses were used for analysis. Data were split 70/30 into estimation/test samples. Four modelling approaches, with EQ-5D-3L levels on each dimension as dependent variables and GHQ-12 responses as independent variables were assessed: non-parametric, simple ordered logit (OL), extended OL, and least absolute shrinkage and selection operator (LASSO). Approaches were assessed using Akaike and Bayesian information criteria, predictive accuracy measured using root mean squared error (RMSE), and simplicity.
resultsA total of 8114 responses became 6924 after discarding missing values, with 4847 used in estimation and 2077 used for testing. LASSO had a better model fit on the pain/discomfort dimension, but no model had markedly superior predictive accuracy. The non-parametric approach was chosen for the mapping algorithm based on simplicity. Predicted and observed EQ-5D-3L values for the test sample had a correlation of 0.488. Prediction accuracy was better for GHQ-12 scores below 20 than scores above 20.
conclusionThe mapping allows EQ-5D-3L responses to be predicted using GHQ-12 responses, which may be useful in estimating utility values and QALYs. An R script and Microsoft Excel spreadsheet are provided to facilitate calculations.
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