Responsiveness to change in health status of the EQ-5D in patients treated for depression and anxiety.

BACKGROUND: The EQ-5D is a commonly used generic measure of health but evidence on its responsiveness to change in mental health is limited. This study aimed to explore the responsiveness of the five-level version of the instrument, the EQ-5D-5 L, in patients receiving treatment for depression and anxiety. METHODS: Patient data (N = 416) were collected at baseline and at end of treatment in an observational study in a Norwegian outpatient clinic. Patients were adults of working age (18-69 years) and received protocol-based metacognitive or cognitive therapy for depression or anxiety according to diagnosis. Responsiveness in the EQ-5D was compared to change in the Beck Depression Inventory-II (BDI-II) and the Beck Anxiety Inventory (BAI). Effect sizes (Cohen's d), Standardised response mean (SRM), and Pearson's correlation were calculated. Patients were classified as "Recovered", "Improved", or "Unchanged" during treatment using the BDI-II and the BAI. ROC analyses determined whether the EQ-5D could correctly classify patient outcomes. RESULTS: Effect sizes were large for the BAI, the BDI-II, the EQ-5D value and the EQ VAS, ranging from d = 1.07 to d = 1.84. SRM were also large (0.93-1.67). Pearson's correlation showed strong agreement between change scores of the EQ-5D value and the BDI-II (rs -0.54) and moderate between the EQ-5D value and the BAI (rs -0.43). The EQ-5D consistently identified "Recovered" patients versus "Improved" or "Unchanged" in the ROC analyses with AUROC ranging from 0.72 to 0.84. CONCLUSION: The EQ-5D showed good agreement with self-reported symptom change in depression and anxiety, and correctly identified recovered patients. These findings indicate that the EQ-5D may be appropriately responsive to change in patients with depression and anxiety disorders, although replication in other clinical samples is needed.

The EQ-5D is a questionnaire that people fill in to report their subjective health. It is often used in clinics or hospitals to better understand how patients are affected by their illnesses, and if their health improves after treatment. For this information to be trustworthy, we need to verify how accurately the EQ-5D measures health for the particular patients we want to use it with. This is often done by comparing EQ-5D scores with scores from other questionnaires. For example, if we want to use the EQ-5D with a group of patients with depression, we compare the scores of the EQ-5D with scores from questionnaires that are commonly used to measure depression symptoms.In this study, we compared the scores of the EQ-5D with scores from questionnaires measuring symptoms of depression and anxiety. Their performances were similar, and the EQ-5D scores could also correctly identify which patients had recovered during treatment. This implies that the EQ-5D can be a useful tool for understanding the impact of depression and anxiety and can help in decision-making regarding these patients.

Changes in depression domains as predictors of return to work in common mental disorders.

BACKGROUND: Depression highly impairs function and reduces quality of life. Therefore, both symptomatic and functional recovery are important treatment goals. Depression consists of several cognitive, somatic, and affective symptom factors that differently affect function. However, it is unclear whether changes in these domains predict return to work (RTW) after treatment. METHODS: Data were collected during treatment from patients on full or partial sick leave reporting depression symptoms (N = 300) at an out-patient clinic. Information on work status was assessed pre- and post-treatment and at 6 months follow-up. Multiple logistic regression was used to investigate if residualized changes in symptom factors predicted full RTW, controlling for gender, education level, and age. RESULTS: Changes (as symptom improvement) in the cognitive, somatic, and affective factor scores each significantly predicted full RTW post-treatment and at follow-up for patients on full and partial sick leave, even after controlling for gender, education level, and age. The change in the somatic factor explained the largest proportion of variance for full work post-treatment in patients on full sick-leave, while change in the cognitive factor explained most unique variance for patients on graded sick leave. LIMITATIONS: The sample consisted of a majority of women with a relatively high level of education. This study should be replicated in more heterogeneous samples. CONCLUSION: Changes in depression symptom domains are significant predictors for RTW work post-treatment. The change in the somatic factor explained the largest proportion of variance in patients on full sick leave and thus may particularly influence RTW after treatment.