Assessing non-white ethnic participation in type 2 diabetes mellitus randomized clinical trials: A Meta-Analysis
Notice bibliographique
Résumé
Abstract Importance The prevalence of type 2 diabetes mellitus (T2DM) is increasing globally, and the greatest burden is borne by non-white ethnic groups. Randomized clinical trials (RCTs) provide evidence regarding the optimal medical therapy for the treatment of T2DM patients and inform national and international guidelines. However, there are concerns that the enrollment of ethnically diverse people into these trials is limited, which has resulted in a lack of ethnic diversity in RCTs of T2DM. Furthermore, the extent of underrepresentation may differ according to whether a trial is government-funded or industry-funded. Objective To systematically review and meta-analyze the proportion of non-white and white participants relative to their disease burden of T2DM included in large, influential government- and industry-funded RCTs of T2DM pharmacotherapies. Data Sources The PubMed electronic database was searched from January 2000 through January 2021. Study Selection Reports of RCTs of T2DM medications with a total sample size of at least 100 participants published in the year 2000 onwards, in high impact general medical journals (i.e., impact factor >10), were included. Data Extraction and Synthesis Data including the number of participants, proportion of participants by ethnicity, and funding sources, were extracted from trial reports. Main Outcomes and Measures The main outcome was the participation-to-prevalence ratio (PPR), which was calculated for each trial by dividing the percentage of white and non-white participants in the trial by the percentage of white and non-white participants with T2DM for the countries or regions of recruitment represented in each trial. A random-effects meta-analysis was used to generate the pooled PPR and 95% confidence intervals (CI) across study types. A PPR <0.80 indicates underrepresentation and >1.20 indicates overrepresentation. Results A total of 82 trials were included involving 296,964 participants: 14 were government-funded trials, and 68 were industry-funded trials. For government trials, the PPR for white participants was 1.11 (95% CI; 1.00-1.23) and for non-white participants was 0.73 (95% CI:0.62-0.86). Among industry trials, the PPR for white participants was 2.19 (95%CI: 1.91-2.50), and the PPR for non-white participants was 0.33 (95%CI: 0.29-0.38). Heterogeneity was high across all PPRs. Conclusions and Relevance Non-white participants are underrepresented in both government- and industry-funded T2DM trials, compared to white participants. The greatest disparity in ethnic diversity in RCTs is observed for industry-funded trials. Key Points Question What is the representation of non-white participants in type 2 diabetes randomized clinical trials relative to their disease burden? Findings In this meta-analysis, non-white participants are underrepresented in both government-and industry-funded type 2 diabetes randomized trials, compared to white participants. The greatest disparity in ethnic diversity in randomized trials was observed for those funded by industry. Meaning Deliberate strategies to improve recruitment and enrolment of diverse participants proportional to the type 2 diabetes disease burden into industry and government-funded randomized controlled trials are needed to enhance the generalizability of research findings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,364 | 0,791 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,020 | 0,010 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».