Disparities in clinical trial participation among older adult Medicare beneficiaries with hematologic malignancies from 2006 to 2019: A SEER-Medicare analysis.

INTRODUCTION

Hematologic malignancies (HMs) encompass a diverse group of blood cancers that collectively make up the sixth most common cancer globally. 1 Over the past 2 decades, the HM treatment landscape has evolved rapidly, with novel therapies for HM accounting for 42% of all Food and Drug Administration (FDA) oncology drug approvals between 2000 and 2016. 2 These innovations have significantly improved outcomes, with survival increasing by 10%–20%. 3 , 4 , 5 Clinical trials (CTs) have been central to these advances, 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 but fewer than 5% of cancer patients enroll, raising concerns about their generalizability, especially among underrepresented groups. 12

Although the National Institutes of Health (NIH) and FDA mandate inclusion of minority groups and subgroup reporting, disparities continue to be reported, likely due to insufficient enforcement. 18 For example, only 63% of FDA oncology approvals (2008–2018) reported race, and just 25% included race‐specific analyses. 15 In another dramatic example of underrepresentation, African Americans, who account for 20% of multiple myeloma (MM) cases, represented only 4.5% of trial participants in FDA new drug applications between 2003 and 2007. 19

Although racial and ethnic disparities in CT participation have received attention, their intersection with age‐related disparities is less studied, despite older adults accounting for 60% of cancer diagnoses. 12 , 19 Older patients face distinct barriers, including comorbidities, polypharmacy, and physical or cognitive impairments that may limit eligibility or willingness to participate. 20 , 21 , 22 Logistical challenges such as transportation, frequent visit schedules, and limited social support also impede access. 20 , 21 , 23 , 24 Collectively, these disparities in access to CTs may be an important contributor to the inferior disease outcomes reported among older adults and ethnic minorities. 5 , 25 , 26 , 27

Organizations including the National Cancer Institute (NCI), American Society of Clinical Oncology (ASCO) Friends of Cancer Research, and American Society of Hematology (ASH) have called for improved trial access and greater inclusion. 28 , 29 For HM trials, prior studies often rely on trial‐level data, which lack detailed patient‐level demographics and show inconsistent reporting by age and race. 16 , 30 The 2001 National Coverage Determination, which authorized Medicare payment for routine care costs in CTs, provides an important opportunity to use administrative data to assess real‐world participation. 31 As HM treatments increasingly shift toward targeted and immune‐based therapies, patient‐level analyses are essential to understand and address access disparities.

This study leverages the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database to estimate CT participation among older adults newly diagnosed with HMs and to identify factors associated with enrollment. We also evaluate participation among patients receiving active treatment, representing the most trial‐eligible subset.

MATERIALS AND METHODS

Study design and setting

This is a retrospective observational cohort study. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. The study was deemed exempt by the Harvard T.H. Chan School of Public Health institutional review board.

Data sources

This study used data from the SEER–Medicare linked database. 32 SEER collects data from 17 US cancer registries, representing approximately 30% of the US population. 33 Medicare claims data include Part A (inpatient, skilled nursing, hospice, and home health), Part B (outpatient and physician services, including infused/injected drugs), and Part D (prescription medications), with coding via ICD‐9/10, Healthcare Common Procedure Coding System (HCPCS), and National Drug Codes (NDC). Over 95% of patients aged 65 and older with a HM in SEER are linked to their Medicare claims, enabling comprehensive longitudinal analysis. 34

Study population

Using the 2022 SEER‐Medicare linked database, we identified patients aged ≥66 years diagnosed with lymphoma, leukemia, or MM between 2006 and 2018, with follow‐up through 2019. Patients were required to have continuous enrollment in Medicare fee‐for‐service Parts A and B from 12 months before diagnosis until death or end of follow‐up. Individuals with Medicare through a managed care organization, prior or subsequent cancer diagnoses (within 5 years before or 1 year after HM diagnosis), or missing demographic data (race or ZIP code [1.8%]) were excluded. HM diagnoses and subtypes were classified using SEER site recode variables based on International Classification of Diseases (ICD)‐O‐3 codes aligned with the 2008 World Health Organization classification (Supporting Information S1 : Tables S1 and S2). 35

Outcome

The primary outcome was CT participation after incident diagnosis, identified using ICD‐9 code V70.7 or ICD‐10 code Z00.6 (“Examination of participant in clinical trial”), or HCPCS modifiers Q0 (investigational service in a CT) and Q1 (routine service in a CT) during the study period. Under Centers for Medicare & Medicaid Services policy, these codes must include an associated National Clinical Trial (NCT) identifier for claims to be valid, supporting the specificity for CT participation. Moreover, these codes have been used in prior SEER–Medicare studies and previous analyses have reported that NCT identifiers present in Medicare claims were appropriate for the corresponding tumor type in 96% of cases. 36 , 37 , 38

Variables

Patient‐level variables included demographics (age, sex, race/ethnicity, and year of diagnosis), comorbidities (diabetes, cardiovascular disease, cerebrovascular, pulmonary disease, renal, and liver disease), socioeconomic indicators, and geographic factors. HM subtypes were grouped into indolent non‐Hodgkin lymphoma (NHL) (including chronic lymphocytic leukemia [CLL]), aggressive lymphomas (including Hodgkin lymphoma [HL]), MM, indolent myeloid malignancies (e.g., myeloproliferative neoplasms [MPN], myelodysplastic syndrome [MDS]), and acute leukemias (myeloid [AML] and lymphoblastic [ALL]) (Supporting Information S1 : Tables S1 and S2). Treatment within 1 year of diagnosis was identified through medication names and chemotherapy regimens in Medicare Parts A and D, and HCPCS codes in Part B (Supplemental document 2). 39 , 40 , 41 , 42 , 43 , 44 Additional details are provided in the Supplement Methods.

Statistical analysis

Data from the year of diagnosis were analyzed using descriptive statistics, with the distribution of characteristics compared through standardized mean differences between trial participants and nontrial participants.

The primary outcome was CT participation. Univariate and multivariable cumulative incidence function (CIF) models were used to estimate the incidence of CT participation while accounting for the competing risk of death using a Fine‐Gray subdistribution hazard model, reported as adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs). Unadjusted CIF curves were compared using the log‐rank test and the Wilcoxon‐Breslow‐Gehan test. Multivariable models adjusted for age, sex, census tract income, census tract educational attainment, race and ethnicity, distance from nearest NCI center, prior treatment, cancer sequence, health care utilization, and comorbidities. Models for lymphoid malignancies also adjusted for stage. Subgroup analyses were conducted for HM subtypes defined above. Distance categories were defined as the following intervals: 0–12.49 miles, 12.5–49.9 miles, 50–249 miles, and 250 miles or more, as supported by large‐scale analyses of cancer registry and administrative data in the United States. 45 , 46 , 47 , 48

Sensitivity analyses were conducted using logistic regression to estimate the predicted probabilities of CT participation at 1‐year, 2‐year, and 5‐year intervals, adjusting for the covariates listed above. These time points were selected to account for attrition at clinically relevant intervals and the differing biology and treatment course that may influence study participation. Sensitivity analyses also restricted the cohort to patients who received active treatment.

For all analyses, a two‐tailed p value <.05 was deemed statistically significant. Analyses were performed using STATA version 16.

RESULTS

Study population and baseline characteristics

The cohort comprised 53,919 patients ≥66 years old diagnosed with a HM between 2006 and 2018 (Table 1 ). This cohort included 11,954 patients (22%) with aggressive histology lymphomas, 20,232 patients (38%) with indolent lymphomas including CLL, 9672 patients (18%) with acute leukemias, 3918 patients (7%) with indolent myeloid malignancies, and 8143 patients (6%) with MM. The median age was 78 years (interquartile range [IQR], 72–84), 50% were male, and 60% resided <50 miles from the nearest NCI center. Most patients were White (86%), with 5.5% Black, 3.1% Asian/Pacific Islander, and 5.2% Hispanic.

Only 4.5% ( n = 2403) participated in CTs, and participants were more likely to be younger, male, White, urban‐dwelling, live closer to NCI center, and have higher income and education levels (Table 1 ).

Cumulative incidence of CT participation

The cumulative incidences of CT participation at 1, 2, and 5 years after adjusting for the competing risk of death was 2.7%, 3.5%, and 4.3%, respectively (Table 2 ). Overall, participation in CTs was low, with notable variations across demographic, geographic, and treatment factors discussed below (Table 2 details unadjusted and aHR; Figure 1 shows forest plot of aHRs).

Demographic representation in CTs

Age, sex, and income were key factors influencing CT participation, with older patients progressively less likely to participate. Compared to patients 66–69 years old, patients 70–74 years old had an aHR of 0.79 (95% CI, 0.71–0.88), and those 80–84 years old and 85+ years old had aHRs of 0.41 (95% CI, 0.36–0.46) and 0.21 (95% CI, 0.17–0.24), respectively (Figure 2A ). Females were also less likely to participate than males (aHR, 0.79; 95% CI, 0.73–0.86) (Figure 2B ). Higher income was associated with higher CT participation, with those in the highest income tercile having a 67% higher likelihood (aHR, 1.67; 95% CI, 1.43–1.96). Dual‐eligible patients (Medicare/Medicaid) was associated with lower CT participation (aHR, 0.48; 95% CI, 0.28–0.84) (Figure 2E ). These findings were consistent across HM subtypes (Supporting Information S1 : Tables S1–S5).

Geographic disparities in CT participation

Geographic distance to treatment was also associated with CT participation (Figure 2C ). In the overall cohort, the aHR for distance > 12.5 miles from an NCI center was 0.89 (95% CI, 0.81‐0.98), aHR for distance > 50 miles was 0.84 (95% CI, 0.74‐0.95), and aHR for > 250 miles was 0.64 (95% CI, 0.48‐0.86).

Racial and ethnic disparities in CT participation

Black patients were less likely to participate in CTs compared to White patients (Figure 2D ) (aHR, 0.73; 95% CI, 0.59–0.90). Across HM subtypes, these findings were consistent for indolent lymphoma (aHR, 0.59; 95% CI, 0.37–0.97), acute leukemia (aHR, 0.57; 95% CI, 0.33–0.99), and MM (aHR, 0.56; 95% CI, 0.37–0.83) (Supporting Information S1 : Tables S1–S5).

No consistent association of Asian/Pacific Islander patients and Hispanic ethnicity with CT participation was observed.

Comorbidities

The association between comorbidities and CT participation varied (Supporting Information S1 : Figure S1). Although diabetes (aHR, 1.02; 95% CI, 0.93–1.12) and liver disease (aHR, 0.86; 95% CI, 0.68–1.08) were not associated with CT participation, renal disease was associated with lower CT participation across HM subtypes (aHR, 0.66; 95% CI, 0.59–0.76). For aggressive lymphoma and acute leukemia, pulmonary disease was associated with lower trial participation (aHR, 0.68; 95% CI, 0.52–0.89). Cardiovascular (aHR, 0.80; 95% CI, 0.67–0.96) and cerebrovascular disease (aHR 0.77; 95% CI, 0.59–0.99) were associated with lower CT participation in acute leukemia trials but no other HMs. Other comorbidities had variable associations with CT participation (Supporting Information S1 : Tables S1–S5).

Treatment

Patients who underwent treatment within 1 year of diagnosis were more likely to participate in CTs (aHR, 2.17; 95% CI, 1.95–2.41). This association was consistent across all HM subtypes (Supporting Information S1 : Tables S1–S5).

Sensitivity analysis

The unadjusted and adjusted logistic regression to estimate the predicted odds of CT participation at 1, 2, and 5 years following diagnosis are shown in Supporting Information S1 : Table S7. The findings were similar to the primary analysis and support the cumulative hazard regression analysis results.

For the restricted cohort of 36,566 patients who received treatment within 1 year of diagnosis, 2009 (5.5%) participated in CTs. The demographics of this stratified sample were similar to the full sample, with a median age of 77 (IQR, 72–83), 48% were male, and 87% were predominantly White. The factors associated with lower odds of CT participation were unchanged from the primary analysis (Supporting Information S1 : Table S6).

DISCUSSION

This study of 53,919 older Medicare beneficiaries with HMs found a low adjusted CT participation rate of 4.3%, which is lower than the reported rates of 6%–8% among adult cancer patients overall, supporting underrepresentation of older patients in HM CTs. 14 , 37 , 49 , 50 These low rates were observed despite prior reports suggesting that patients with HM are more likely to participate in CTs compared with those with solid tumors, 51 further highlighting the low participation observed here. Moreover, significant CT participation disparities were observed across sociodemographic groups: increasing age, female sex, Black race, lower income, and greater distance from NCI‐designated cancer centers. Our study makes an important contribution to the literature by using patient‐level data and providing the first nationally representative estimates of CT participation among older adults with HM.

One of the most significant findings of our analysis was the large age‐related decrement in CT participation. Recent work by our group and others indicates that age is more consistently reported in clinical trials than race or ethnicity, suggesting that the underrepresentation of older adults in our study is not due to a lack of reporting. 16 , 30 , 52 , 53 We found that patients aged 80 and above had nearly 60% lower participation rates compared to those 66–69 years old, consistent with previous studies. 54 , 55 , 56 This age‐related disparity is especially concerning, as HMs are primarily diagnosed in older adults, yet they have lower survival rates than younger patients. 57 , 58 , 59 , 60 Although this disparity is multifactorial, trial underrepresentation likely exacerbates the issue. 56 , 61 , 62 , 63 , 64 , 65 Age‐related disparities in trial participation pose a dual challenge: older patients have limited access to potentially beneficial treatments, and trial data from younger patients may not be generalizable to older patients, particularly when considering age‐specific treatment benefits and toxicities.

Encouragingly, recent HM trial designs have sought to expand age eligibility criteria by focusing on older patients, and incorporating geriatric assessments to help identify the most suitable participants. 57 , 66 , 67 , 68 This approach has shown promising results in other disease areas. For example, a comparison of Alliance for Clinical Trials in Oncology breast cancer trials demonstrated that 26% of participants in trials focused on older adults aged 75 and older, compared to <10% in trials with no age specification. 69 , 70 However, broadening eligibility based on age alone may not significantly increase CT participation among older adults if the distinct barriers facing this population (e.g., functional and mobility limitations, transportation challenges, and the complexity of trial procedures) are not addressed. 20 , 21 , 22 , 23 , 24 To that end, a 2020 pilot study revealed that trial navigators who supported participants with logistics, housing, transportation, and referrals to vital support services improved participation and retention in trials. 71

Our analysis also showed a 16% lower CT participation rate among females compared to males. Although caregiving burden is more common among younger females, a national survey of caregiving by the American Association for Retired Persons found that 61% of caregivers are women, and 19% of caregivers are ≥65 years of age. 72 The survey also found that women were, on average, providing more higher‐intensity care than men do. This intersectionality of age with the more frequent and more intensive caregiving role that disproportionately falls on women may have contributed to the sex disparities in this study of older adults with HM. To that end, strategies that address logistical barriers to participation may be especially effective in increasing participation of older females. For example, decentralized trials, which enable remote CT participation and leverage collaboration between community‐based and urban academic centers, have been shown to decrease the logistical burdens associated with traveling to an academic center to participate in a trial. 73 , 74

Previous studies have further highlighted comorbidities as a common reason for exclusion in solid cancer trials. 14 , 65 Within lung cancer, broadening eligibility criteria for CTs to include patients with other malignancies and a creatinine clearance (CrCl) ≥30 mL/minute expanded the eligible pool by up to 88%. 75 However, data on this issue in HM trials remains limited. One study in lymphoma using retrospective cohort data found that 25% of lymphoma patients were excluded due to organ function, underscoring the need for improved study designs to enhance generalizability. 75 Our study adds to this literature by showing that patients with these comorbidities are less likely to participate in CTs. Although it remains unclear whether these patients were explicitly excluded from trials or if other factors contributed to their nonparticipation, our findings are hypothesis‐generating and warrant further investigation in HM trials.

Consistent with prior research, 47 , 64 , 76 , 77 we also found that patients living farther from NCI centers and those with lower socioeconomic status were less likely to participate in CTs. A recent study by Casey et al. 16 using trial level data from ClinicalTrials.gov found that clinical trial sites were clustered in the Northeast, Southeast, and eastern side of the Midwest regions, and counties with the highest mortality from HM had few or no CT sites within a 60‐minute driving radius. It requires significant time, resources, and medical literacy for patients to identify available CTs outside of the hospitals where they have been receiving care. To mitigate some of these barriers, The Leukemia & Lymphoma Society developed a free, national, telephone‐based, nurse‐led navigation service called the Clinical Trial Support Center (CTSC), which works to connect patients with trial opportunities and offers resources for travel and insurance navigation. 78 In an evaluation of this program over a 2‐year period from 2017 to 2019, 16.1% of the 906 patients served ultimately enrolled in a CT, supporting the utility of strategic partnerships in addressing modifiable barriers.

We also found a 30% lower CT participation rate in Black patients compared to White patients. In contrast to prior studies, however, no significant association between Hispanic ethnicity and trial participation was found, which may be due to differences in data sources; previous studies often focused on specific clinical trials, 16 , 30 , 79 whereas our analysis used national, individual‐level data. Nevertheless, barriers to Black patient participation in CTs are multifaceted, and factors such as limited access to academic centers, unconscious bias, and greater social needs (e.g., transportation, broader health system inequities, and cultural mistrust) have all been previously identified as contributors to disparities in accessing care. 16 , 30 , 80 , 81 , 82 , 83 In a 2020 study of nonfederal acute care and cancer hospitals, most cancer‐related services were less likely to be available at hospitals serving Black patients and had a nearly 50% lower odds of receiving core oncology services. When care is received at under‐resourced hospitals, it can often be difficult to access CTs that are typically conducted at major academic institutions. 9 , 84 , 85

Overall, evidence‐based solutions to reduce these disparities among older adults with HM remain to be fully established. Recent initiatives such as the FDA’s Project PRAGMATICA aim to address these gaps by simplifying eligibility criteria, enabling flexible trial delivery, and streamlining outcome measurements. 86 Specifically, the project promotes the elimination of arbitrary upper age limits, inclusion of patients with common comorbidities, and acceptance of polypharmacy. Additional pragmatic elements include minimizing protocol complexity, reducing the number of required study visits, and allowing for remote data collection, which may address transportation and physical limitations common in this population. This pragmatic approach is currently being implemented in the PRAGMATICA LUNG study ( NCT05633602 ) and, if successful, could be adapted for HM trials to support broader participation. Additionally, the FDA’s Project Optimus, which focuses on optimizing dose selection for oncology therapies, has the potential to expand trial access by including older adults and patients with comorbidities.

In addition to system‐level strategies, patient‐level reservations among older adults warrant targeted interventions, including concerns about side effects, caregiving demands, limited time during clinical encounters, and mistrust that may be particularly pronounced among minority older adults. 20 , 22 , 87 , 88 , 89 Simplified consent processes, patient navigators, tailored and linguistically appropriate recruitment materials, and direct clinician engagement with community partnership have been identified as evidence‐based strategies to build trust and promote informed decision‐making. 20 , 21 These approaches have been successfully applied in other fields. For example, the AHEAD 3‐45 Alzheimer’s trial achieved enrollment of underrepresented older adults (55–80 years old) through partnerships with community nursing organizations to co‐develop and co‐deliver culturally and linguistically adapted educational sessions at venues that were identified as appropriate by the community organizations. 90 Recruitment of participants from communities living near these venues was conducted through culturally appropriate informational flyers, person‐to‐person communication, and the leveraging of both social and professional networks. This combination of community‐based partnerships and culturally tailored outreach resulted in substantial minority enrollment and could be applied to HM trials to lower barriers and enhance equitable participation. The goal of such interventions is to promote fairness through voluntary, informed decision‐making that reflects patient preferences, values, and goals of care and respects patient autonomy.

Our study has several limitations. The use of administrative codes to determine trial participation may underestimate the true proportion of patients enrolled in CTs, particularly if claims were not submitted or coded consistently across institutions. Claims data lack detailed clinical information, such as tumor characteristics, performance status, functional status, and psychosocial factors, all of which could influence eligibility and participation in CTs. We were unable to determine the specific reasons for nonparticipation, which is an important area for future research. We could not quantify or match the proportion of patients who met eligibility criteria but did not participate in trials, nor could we identify how many patients were screened and subsequently found to be ineligible or the reasons for their ineligibility. Although we accounted for geographic distance to NCI centers, regional variations in access to care, physician practices, and trial availability may have influenced participation, and these factors were not fully captured. Our study also did not assess patient preferences or decision‐making processes. Last, our findings may not be generalizable to younger, non‐Medicare populations.

In conclusion, our study highlights the low overall participation in CTs among older adults with HMs, with significant sociodemographic disparities observed. Additionally, we found that patients with cardiovascular, pulmonary, and renal diseases, which are commonly seen among patients with HM due to the advanced median age at diagnosis, were associated with lower CT participation. Using patient‐level data from a cohort representative of those encountered in routine clinical practice, our findings address gaps in the existing literature and provide valuable insights to guide the development of targeted interventions.

AUTHOR CONTRIBUTIONS

Inna Y. Gong : Conceptualization; visualization; data curation; methodology; formal analysis; writing–original draft; and writing—review and editing. Mark J. Soto : Formal analysis; visualization; data curation; software; and writing—review and editing. Bahar Rafinejad‐Farahani : Data curation and writing—review and editing. Joseph M. Unger : Methodology and writing—review and editing. Rena M. Conti : Conceptualization; methodology; formal analysis; funding acquisition; and writing—review and editing. Carmen E. Guerra : Methodology and writing—review and editing. Amit Oza : Writing—review and editing. Meredith Rosenthal : Conceptualization; methodology; formal analysis; project administration; supervision; funding acquisition; writing—review and editing; and writing—original draft. Danielle Rodin : Conceptualization; methodology; formal analysis; project administration; supervision; funding acquisition; writing—original draft; and writing—review and editing.

CONFLICT OF INTEREST STATEMENT

Rena M. Conti reports consulting fees from Greylock McKinnon and Associates and Keystone Consulting; grant and/or contract funding from the Laura and John Arnold Foundation, the Leukemia and Lymphoma Society, and the National Institute on Drug Abuse; and stock options with Quantile Heath. Carmen E. Guerra reports fees for professional activities from Guardant Health, Janssen Pharmaceuticals, Natera, and Roche; grant and/or contract funding from Genentech; and stock in Beam Therapeutics, CRISPR, Editas, and Intellia Therapeutics. Amit Oza reports consulting fees from AbbVie, AstraZeneca Canada, Bristol‐Myers Squibb Canada, GlaxoSmithKline, and Morphosys; and grant and/or contract funding from AstraZeneca Canada and Bristol‐Myers Squibb Canada. Meredith Rosenthal reports grant and/or contract funding from the Leukemia and Lymphoma Society. The other authors declare no conflicts of interest.

Supporting information

Supporting Information S1

ACKNOWLEDGMENTS

The authors would like to thank members of the study advisory committee for their insights and contributions: Drs. Holly Fernandez Lynch, Aisha Lofters, and Tom Ehrlich. Inna Y. Gong was supported by the Eliot‐Philipson Clinician Scientist Program, Clinician Investigator Program, and the Hold’em for Life Fellowship, at University of Toronto. This study was supported by the Lymphoma and Leukemia Society (LLS).

Gong IY, Soto MJ, Rafinejad‐Farahani B, et al. Disparities in clinical trial participation among older adult Medicare beneficiaries with hematologic malignancies from 2006 to 2019: a SEER–Medicare analysis. Cancer. 2025;e70204. doi: 10.1002/cncr.70204

Part of this manuscript was presented at the American Society of Hematology 66th Annual Meeting held in December 2024.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from SEER‐Medicare. Restrictions apply to the availability of these data, which were used under license for this study. Therefore, individual data will not be shared. Further information on their policies can be found here: https://health caredelivery.cancer.gov/seermedicare/aboutdata/policies.html . However, we have provided the codes used to identify the cohort and the clinical trial claims in the supplementary appendix.

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Associated Data

Supplementary Materials

Supporting Information S1

Data Availability Statement

The data that support the findings of this study are available from SEER‐Medicare. Restrictions apply to the availability of these data, which were used under license for this study. Therefore, individual data will not be shared. Further information on their policies can be found here: https://health caredelivery.cancer.gov/seermedicare/aboutdata/policies.html . However, we have provided the codes used to identify the cohort and the clinical trial claims in the supplementary appendix.