Reporting and Representation of Race and Ethnicity in Clinical Trials of Pharmacotherapy for Mental Disorders
Notice bibliographique
Résumé
Importance: Representation of race and ethnicity in randomized clinical trials (RCTs) is critical for understanding treatment efficacy across populations with different racial and ethnic backgrounds. Objective: To examine race and ethnicity representation and reporting across RCTs of pharmacotherapies for mental disorders. Data Sources: PubMed (Medline), Embase (Ovid), APA PsycInfo, and Web of Science were searched until March 1, 2024, to retrieve network meta-analyses including RCTs of pharmacotherapies for International Statistical Classification of Diseases and Related Health Problems, Tenth Revision mental disorders. Study Selection: RCTs that recruited people of any age with a diagnosis of a mental disorder and that tested the efficacy of any pharmacologic intervention vs any control arm. Data Extraction and Synthesis: Random-effects logit-transformed proportion meta-analyses were used to estimate prevalence rates of race and ethnicity groups and their temporal trends across RCTs and to compare US RCT prevalence rates with US Census data. The Preferred Reporting Items for Overviews of Reviews was used to report our review. Main Outcomes and Measures: Reporting of data and percentages of race and ethnicity. The year of publication, type of RCT, geographic location, age group, and sample size were also included. There were no deviations that occurred from the original protocol. Results: Data were obtained from 1683 RCTs (375 120 participants in total). Of these, 1363 (91.7% of participants) included participants aged 18 years or older; 680 RCTs (36.0% of participants) were from the US, 404 (17.1% of participants) were from Europe, and 293 (29.9% of participants) were from multiple geographic locations. Race and ethnicity were reported in 39.2% of RCTs; reporting was the highest in US-based RCTs (58.7%) and lowest in Central and South America (8.7%) and Asia and the Middle East (12.4%). Among participants, 2.7% (95% CI, 2.1%-3.5%) self-reported as Asian, 9.0% (95% CI, 8.1%-10.0%) as Black, 11.0% (95% CI, 9.1%-13.3%) as Hispanic among White, 80.2% (95% CI, 78.8%-81.5%) as White including Hispanic, and 5.8% (95% CI, 5.2%-6.4%) as other race or ethnicity, multiracial, or multiethnic. There was more frequent reporting of race and ethnicity in US RCTs (log odds increased by 0.066 each year) and less frequent reporting in non-US RCTs (log odds increased by 0.023 each year). Studies reporting race and ethnicity did not generally include larger sample sizes (mean sample size, 263.7 [95% CI, 15.0-860.3] participants) compared with those not reporting such data (mean sample size, 196.6 [95% CI, 12.0-601.3] participants), albeit not in all locations. In US RCTs, adults in the other or multiracial and multiethnic category were historically overrepresented, while adults in Asian, Black, Hispanic among White, and White including Hispanic categories were underrepresented; Asian, Black, and Hispanic among White children and adolescents are still currently underrepresented. Conclusions and Relevance: The findings of this meta-analysis suggest that differences in reporting race and ethnicity across geographic locations and underrepresentation of certain racial and ethnic groups in US-based RCTs highlight the need for international guidelines to ensure equitable recruitment and reporting in clinical trials.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,045 | 0,127 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».