Global disparities in immunotherapy clinical trials: A comprehensive analysis of low- and middle-income countries over the past decade.
Notice bibliographique
Résumé
1606 Background: Immunotherapy (IO) has largely impacted cancer treatment over the last decade, yet its accessibility and representation in clinical trials remain severely limited in low- and middle- income countries. We investigated the involvement of these countries in IO cancer trials, analyzing country-specific rates and influencing factors. Methods: Data was obtained from clinicaltrials.gov. Advanced search focused on cancer interventional studies from 12/31/2013 to 01/01/2024 with specific IO treatments. Only completed trials were included. Studies unrelated to immunotherapy or cancer were excluded. Country trial rates were calculated per 100k population. Statistical analysis used Chi-square test for nominal data. Results: Of 1593 trials, 1282 were included in the final analysis. Of world’s 217 countries and regions (World Bank) 72 (33.2%) participated in IO cancer trials in the last decade. Zero country from low-income group was involved in the trials. Only 2.4% (31) of all trials, included lower-middle-income countries (LMICs). Out of 54 LMICs, only 8 were represented: Ukraine, Philippines, India, Guatemala, Egypt, Vietnam, Morocco, and Eswatini (Swaziland). Ukraine led this group with 16 trials, while the remaining 7 were included in less than 10 trials each. Upper-middle-income countries (UMICs) were included in 21.3% (273) of trials. Out of 54 UMICs, 22 were involved in the trials; China led this group (174), followed by Russia (69), Brazil (52), Mexico and Turkey (41 each). Among the top 30 countries with the highest trial participation, none included LMICs, and only 5 were UMICs. Of 81 high-income countries (HICs) 42 participated in IO trials. USA was involved in over 65% of all trials (840), followed by Spain (222), France (193), and Canada (180). However, population-adjusted rates varied, with European HICs like Latvia (2.16), Belgium (1.09), Norway (0.87) among 25 other countries, showing higher rates than USA (0.25). Majority of trials done solely in one country were also from USA (65%), and China (79%). A significant link was found between the economic status of participating countries and the funding source of trials (p<0.00001). Industry funded 28 of 31 (90%) trials in LMICs, indicating a reliance on industry funding in these regions. Industry favored adult-only over pediatric-inclusive trials (p<0.0001). Out of 80 trials (6.24%) including children, 22 were industry-funded. Exclusively pediatric IO trials were only 4 (0.3%) of all IO trials. LMICs participated in 3.75% (3) of all pediatric-inclusive trials. Conclusions: Countries with low economic status and children with cancer from both low- and high-income areas are largely underrepresented in IO cancer trials globally. More inclusive IO trials across age groups and LMICs are vital to implement trial results in real-world settings and close the care gap.
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 | 0,016 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».