Geographic Barriers Limit CAR T-Cell Therapy Access in US Medicare Patients With DLBCL
Key Clinical Summary
- Only 5.4% of Medicare patients with third-line or later diffuse large B-cell lymphoma (DLBCL) received chimeric antigen receptor (CAR) T-cell therapy between 2017 and 2020.
- Probability of CAR T receipt decreased by 6.2% for every 10 miles farther from an authorized treatment center (ATC).
- Reducing travel distances in states with poor access could increase CAR T use by 37.6% and yield an estimated 105 additional life years annually.
CAR T-cell therapy has demonstrated curative potential in relapsed/refractory DLBCL, yet real-world access disparities persist. In a study published in Blood Advances, researchers analyzed SEER-Medicare data from 2007 to 2020 to quantify how geographic and socioeconomic factors affect CAR T utilization and outcomes among Medicare patients receiving third-line or later therapy.
Study Findings
Among 62 489 Medicare beneficiaries aged ≥65 years diagnosed with DLBCL between 2007 and 2019, 15 673 received treatment, and 2241 progressed to third-line or later therapy between 2017 and 2020. Of these, only 122 patients (5.4%) received CAR T therapy.
Patients receiving CAR T were younger (median age 70 vs 73 years) and more likely to live in higher-income areas. Multivariate analysis showed that CAR T use was less likely among patients with more comorbidities (OR 0.904; P = .001) and among those residing in counties with lower median household income (OR 1.176; P = .004).
Geographic distance was a major determinant. Patients receiving CAR T traveled a median of 38 miles for treatment compared with 12 miles for other third-line therapies. The probability of receiving CAR T decreased by 6.2% for every additional 10 miles from the nearest ATC. At an average distance of 171 miles, the predicted probability of CAR T receipt was 3.8%, compared with 9.1% at 30 miles.
States categorized as “poor-access” had an average distance to the nearest ATC of 104.4 miles versus 34.2 miles in “better-access” states. If distances in poor-access states were reduced to match better-access states, CAR T use would increase from 6.6% to 9.1%, a 37.6% relative increase (P < .001). In the 3 lowest-access states, CAR T use could increase by 277.7%.
Using survival data from ZUMA-1 (50.5% two-year survival with CAR T vs 12.0% without), the authors estimated that improved access could generate 105 additional life years annually among third-line or later Medicare patients.
Clinical Implications
For oncology leaders and pathway committees, the findings underscore distance to an authorized treatment center as a quantifiable and modifiable barrier to CAR T access in relapsed/refractory DLBCL. Travel burden not only influences referral patterns but directly affects treatment probability.
Expanding ATC distribution, particularly in low-income or geographically remote states, could meaningfully improve equity and survival outcomes. However, the authors caution that scaling CAR T programs requires infrastructure, staffing, regulatory compliance, and quality oversight.
Payers should note that although CAR T acquisition costs are covered, nonmedical costs—travel, lodging, caregiver burden, and potential inpatient reimbursement gaps—may disproportionately affect underserved populations. Policies supporting nurse navigation, telemedicine integration, and sustainable reimbursement models may mitigate disparities.
“Reducing travel distance could increase the number of patients receiving CAR-Ts by 37.6% in the United States, increasing patient overall survival,” wrote the authors.
Conclusion
In US Medicare patients with relapsed/refractory DLBCL, geographic distance and socioeconomic status significantly shape CAR T access and survival outcomes. Addressing structural barriers to authorized treatment centers may be essential for equitable diffusion of cellular therapies in oncology practice.
Reference
Chung AP, Shafrin JT, Vadgama S, et al. Inequalities in CAR T-cell therapy access for US patients with relapsed/refractory DLBCL: a SEER-Medicare data analysis. Blood Adv. 2025;9(18):4727-4735. doi:10.1182/bloodadvances.2024015634
