Global trends in antidepressant, atypical antipsychotic, and benzodiazepine use: A cross-sectional analysis of 64 countries
Bibliographic record
Abstract
OBJECTIVE: To describe the trends in use of antidepressants (ADs), atypical antipsychotics (AAPs), and benzodiazepines (BZDs) among high-, middle-, and low-income countries. METHODS: A cross-sectional time-series analysis by country from July 2014 to December 2019 utilizing IQVIA's Multinational Integrated Data Analysis database was conducted. Population-controlled rates of use were calculated in number of standard units of medications per drug class per population size. The United Nations' 2020 World Economic Situation and Prospects was used to group countries into high-, middle-, and low-income. Percent change in rates of use per drug class was calculated from July 2014 to July 2019. Linear regression analyses were conducted to assess the predictability of percent change in use utilizing a country's baseline rate of use per drug class and economic status as predictor variables. RESULTS: A total of 64 countries were included: 33 high-, 6 middle-, and 25 low-income. Average baseline rates of use for ADs in high-, middle-, and low-income countries were 2.15, 0.35, and 0.38 standard units per population size, respectively. For AAPs, rates were 0.69, 0.15, and 0.13, respectively. For BZDs, rates were 1.66, 1.46, and 0.33, respectively. Average percent changes in use for ADs by economic status were 20%, 69%, and 42%, respectively. For AAPs, they were 27%, 78%, and 69%, respectively. For BZDs, they were -13%, 4%, and -5%, respectively. Some associations were found demonstrating that as a country's economic status increases, percent change of AD (p = 0.916), AAP (p = 0.23), and BZD (p = 0.027) use decreases. Similarly, as baseline rate of use for ADs and AAPs increases, percent change in use decreases with p-values of 0.026 and 0.054, respectively. For BZDs, as baseline rate of use increases, percent change in use increases (p = 0.038). CONCLUSIONS: High-income countries have a higher rate of treatment utilization compared to low- and middle-income countries (LMICs) with treatment utilization increasing in all countries of interest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".