Anxiolytic Medication Use in Low‐ Middle‐ and High‐Income Countries: A World Mental Health Surveys Report
Bibliographic record
Abstract
ABSTRACT Background Anxiolytic medications, particularly benzodiazepines, are widely prescribed, giving impetus to long‐standing debates about how often these agents should be employed in clinical practice. There are, however, few cross‐country studies of the pharmacoepidemiology of these agents. We report on the frequency of anxiolytic medication use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries. Methods Face‐to‐face interviews with community samples totaling n = 49,919 respondents in the World Health Organization World Mental Health (WMH) Surveys asked about anxiolytic medication use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses to all respondents. Results A weighted 5.6% ( n = 4079) of respondents reported anxiolytic medication use within the past 12 months; the vast majority comprised benzodiazepine use, and use was highest amongst respondents with a subthreshold major depressive episode (MDE) (25.2%) and a 12‐month MDE (19.8%). Rates were significantly higher in high‐income countries (HICs) than low‐ and middle‐income countries (LMICs) (8.5% vs. 2.2%, χ 2 1 = 559.6, p < 0.001). Short‐acting benzodiazepines and z‐drugs were most commonly used for sleep (66.5% and 85.5%), while intermediate‐acting benzodiazepines and long‐acting benzodiazepines were most commonly used either for sleep (37.9% and 30.1%) or anxiety (33.3% and 32.0%). Across all conditions, anxiolytic medications were reported as very effective by 55.7% of users and somewhat effective by an additional 32.2% of users, with similar proportions in HICs and LMICs. Negative predictors of high perceived effectiveness were a 12‐month MDE and taking anxiolytic medication for comorbid anxiety and depression. Conclusion These data do not definitely answer the question of how often benzodiazepines should be prescribed in clinical practice, but they usefully inform discussions of how to optimize their use. It is noteworthy that anxiolytic medications, particularly benzodiazepines, are largely prescribed for anxiety and sleep, and that they are widely perceived to be either very or somewhat effective by users. However, more targeted prescription of these agents may be necessary; in particular antidepressant intervention should be prioritized in the pharmacotherapy of major depressive disorder.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".