Disparities in Clinical Trial Enrollment for Patients with Hematologic Malignancies - a 15-Year Princess Margaret Cancer Centre Experience
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".