Country-specific psychopharmacological risk of reporting suicidality comparing 38 antidepressants and lithium from the FDA Adverse Event Reporting System, 2017–2023
Bibliographic record
Abstract
Background: The United States Food and Drug Administration (FDA) maintains a black-box warning for antidepressants warning of an increased risk of suicidality in children and young adults that is based on proprietary clinical trial data from study sponsors that were submitted for regulatory approval. This article aimed to assess whether the black-box warning for antidepressants is still valid today using recent drug safety data. Methods: Post-marketing adverse drug event data were obtained from the US FDA's Adverse Event Reporting System (FAERS) for the years 2017 through 2023. Logistic regression analysis was conducted using the case versus non-case methodology and adjusted for gender, age group, drug role (primary drug, secondary drug, interacting drug, and concomitant drug), initial FDA reporting year, reporter country, and a drug*gene*age group interaction. Results: In the multivariate analysis, compared to fluoxetine and patients aged 25 to 64 years, children [adjusted reporting odds ratio (aROR) = 7.38, 95% CI, 6.02-9.05] and young adults (aROR = 3.49, 95% CI, 2.65-4.59) were associated with an increased risk of reporting suicidality, but not for the elderly (aROR = 0.76, 95% CI, 0.53-1.09). Relative to fluoxetine, esketamine was associated with the highest rate of reporting suicidality in children (aROR = 3.20, 95% CI, 2.25-4.54); however, esketamine was associated with a lower risk of reporting suicidality in young adults (aROR = 0.59, 95% CI, 0.41-0.84), but not significantly in the elderly (aROR = 0.77, 95% CI, 0.48-1.23). For country-specific findings, relative to the USA, the Slovak Republic, India, and Canada had the lowest risk of reporting suicidality. For the overall study population, desvenlafaxine (aROR = 0.61, 95% CI, 0.46-0.81) and vilazodone (aROR = 0.56, 95% CI, 0.32-0.99) were the only two antidepressants associated with a reduced risk of reporting suicidality. Conclusion: This study shows that with recent antidepressant drug safety data, the US FDA's black-box warning for prescribing antidepressants to children and young adults is valid today in the USA. However, relative to the USA, 15 countries had a significantly lower risk of reporting suicidality, while 16 countries had a higher risk of reporting suicidality from 38 antidepressants and lithium.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".