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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.141 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".