Reported adverse drug reactions in women and men: Aggregated evidence from globally collected individual case reports during half a century
Bibliographic record
Abstract
BACKGROUND: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Reports on differences in reporting patterns between women and men exist nationally. The goal of the present study was to assess the global evidence on spontaneous post-marketing ADR reporting differences between reports for women and men. METHODS: We analysed data collected within VigiBase, the WHO global database of individual case safety reports, between 1967-2 January 2018. VigiBase contains more than 18 million reports from the 131 member countries of the WHO Programme for International Drug Monitoring. FINDINGS: Of the reports with information on sex, 9,056,566 (60.1%) concerned female and 6,012,804 (39.9%) male children and adults. More female ADR reports were submitted in all regions of the world and by all types of reporters. A higher proportion of female reports was seen in all age groups from the age group 12-17 years and older. The largest difference was observed in the age group of 18-44 years and could not be explained by hormonal contraceptive use. The proportion of serious and fatal reports was higher for male reports. INTERPRETATION: Global post marketing surveillance data on spontaneous reports indicate that women, from puberty and onwards and especially in their reproductive years, report more ADRs than men. However, there is a higher proportion of serious and fatal ADRs among male reports. Our results suggest important underlying sex-related differences in ADRs. These findings highlight the importance of considering sex throughout the entire life-cycle of drug development and surveillance and understanding the underlying reasons for reporting ADRs.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".