Disparities in Clinical Trial Enrollment for Patients with Hematologic Malignancies - a 15-Year Princess Margaret Cancer Centre Experience
Notice bibliographique
Résumé
Background: Clinical trial (CT) participation is critical to advance the management of hematologic malignancies (HM) and is an important indicator of quality of care. Disparities in CT participation are important health inequities that limit the generalizability of study findings. Within Canada, sociodemographic disparities in clinical trial participation for patients with HM are not well understood. Therefore, in this study, we aimed to identify factors associated with CT enrollment of patients with HM at a large comprehensive cancer centre. Methods: We performed a retrospective study of CT enrollment among new patients with HM who were seen in consultation from 2006 to 2019 with follow-up until 2021 at Princess Margaret Cancer Centre in Toronto, Canada. CT enrollment was categorized as a binary outcome at 2-, 5- and 10-year follow-up from the date of initial consultation. Demographic data collected included sex, age at diagnosis, language, distance to hospital, access to a primary care provider, and marginalization dimensions. The 2016 Ontario Marginalization Index was used, capturing area-level data on four key dimensions of marginalization: residential instability, material deprivation, dependency, and ethnic concentration. Univariable and multivariable logistic regression models were used to assess the impact of baseline variables on CT enrollment. Cumulative incidence of trial enrollment was measured using death as the competing event, and competing risk regression analyses were performed to assess associated factors as a sensitivity analysis. Results: A total of 21,286 new HM patients were seen at PM during the study period of whom 1,692 (7.9%) were enrolled in a CT at 2 years from initial consultation, 1,954 (9.2%) at 5 years, and 2,059 (9.7%) at 10 years. The cumulative instance of CT enrollment at 10-years was 2,592 (12.2%), with 2059 patients (79.4%) having enrolled in 1 trial, 411 in 2 trials (15.9%) and 122 in 3 or more trials (4.7%). Of these, 34% were phase I trials, 29% were phase II trials, 23% were phase III/IV trials, and 14% had missing phase data. Most CTs (56%) were industry sponsored. A majority of CT participants had myeloid diseases (1283/2592, 50%), and fewer patients had lymphoma (834/2592, 32%) or myeloma (475/2592, 18%). Most patients enrolled were age 40-69 (66%, n=1357), 16% were <40 years, 18% were >70 years and 58% (n = 1187) were male. Patients enrolled in CT had lower rates of material deprivation (p<0.001) compared to patients not enrolled. Multivariable regression analysis for CT enrollment at 2 years from first visit showed decreased odds of enrollment with increasing age (compared to age <40: age 40-69 years aOR 0.73, 95% CI 0.63-0.84; age >70 years aOR 0.51, 95% CI 0.43-0.60), greater distance from the cancer center (compared to 0-15 kilometers (km):>250km aOR 0.75, 95% CI 0.75-1.00), and higher material deprivation (aOR 0.96, 95% CI 0.92-0.99). There was no significant association with area-level residential instability, dependency or increasing ethnic concentration. The presence of a primary care provider was associated with a higher odds of CT enrollment (aOR 1.23, 95% CI 1.01-1.49). With longer follow-up at 5- and 10-years from first visit, decreased odds of enrollment continued to be seen with increasing age, greater distance from cancer center and higher material deprivation. With 10 years follow-up, increased dependency was the only variable no longer associated with decreased odds of CT enrollment (aOR 1.05, 95% CI 1.01-1.09). Sensitivity analyses with competing risk regression showed consistent findings. Discussion: To our knowledge, this is the largest study assessing disparities in CT enrollment for patients with HM at a Canadian institution. Despite a single payer health system, significant disparities in enrollment were observed across several sociodemographic domains, including age, material deprivation, and geographic distance. Over time, improvements were only seen for patients with increased dependency, and a majority of disparities persisted. Comprehensive plans to increase CT diversity are needed to improve equity in access and generalizability of trial 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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».