Prevalence estimates of major depressive disorder in 27 European countries from the European Health Interview Survey: accounting for imperfect diagnostic accuracy of the PHQ-8
Bibliographic record
Abstract
BACKGROUND: Cut-offs on self-report depression screening tools are designed to identify many more people than those who meet criteria for major depressive disorder. In a recent analysis of the European Health Interview Survey (EHIS), the percentage of participants with Patient Health Questionnaire-8 (PHQ-8) scores ≥10 was reported as major depression prevalence. OBJECTIVE: We used a Bayesian framework to re-analyse EHIS PHQ-8 data, accounting for the imperfect diagnostic accuracy of the PHQ-8. METHODS: The EHIS is a cross-sectional, population-based survey in 27 countries across Europe with 258 888 participants from the general population. We incorporated evidence from a comprehensive individual participant data meta-analysis on the accuracy of the PHQ-8 cut-off of ≥10. We evaluated the joint posterior distribution to estimate the major depression prevalence, prevalence differences between countries and compared with previous EHIS results. FINDINGS: Overall, major depression prevalence was 2.1% (95% credible interval (CrI) 1.0% to 3.8%). Mean posterior prevalence estimates ranged from 0.6% (0.0% to 1.9%) in the Czech Republic to 4.2% (0.2% to 11.3%) in Iceland. Accounting for the imperfect diagnostic accuracy resulted in insufficient power to establish prevalence differences. 76.4% (38.0% to 96.0%) of observed positive tests were estimated to be false positives. Prevalence was lower than the 6.4% (95% CI 6.2% to 6.5%) estimated previously. CONCLUSIONS: Prevalence estimation needs to account for imperfect diagnostic accuracy. CLINICAL IMPLICATIONS: Major depression prevalence in European countries is likely lower than previously reported on the basis of the EHIS survey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".