Access to diagnostics, therapies, and advanced care for lymphoma in Latin America: A multicenter, survey-based study
Bibliographic record
Abstract
Abstract Background: The management of lymphomas has been revolutionized by the integration of advanced diagnostics, immunochemotherapy, targeted agents, and cellular therapies such as CAR-T cells. These advances have significantly improved survival in high-income countries. However, in Latin America (LATAM), a region composed mainly of low- to middle-income countries, access to these therapies continues to face profound disparities.Despite recognition of these challenges, data on the availability of infrastructure, personnel, diagnostic tools, and innovative therapies across LATAM lymphoma centers remain scarce. We aimed to evaluate the availability of diagnostics, treatments, and transplant capacity across LATAM lymphoma centers via a collaborative regional survey. Methods: We conducted a cross-sectional digital survey, coordinated through the Grupo de Estudio Latinoamericano de Linfoproliferativos (GELL)—a regional collaborative network dedicated to advancing the care of lymphoid malignancies in LATAM. The survey targeted hematologists actively involved in the management of lymphoma patients. A structured electronic questionnaire was developed using REDCap and iteratively reviewed after validation from experts in the GELL to ensure its relevance and applicability. The tool explored infrastructure, human resources, drug access, transplant, and cellular therapy capacity. The survey was disseminated during July 2025 through GELL networks with the goal of retrieving one response per center, duplicates were eliminated. A descriptive analysis of the responses was carried out. Results: A total of 73 hematology centers from 16 Latin American countries completed the survey: Bolivia (n=15), Mexico (n=13), Argentina (n=7), Colombia (n=6), Cuba (n=5), Brazil (n=4), Guatemala (n=4), Uruguay (n=4), Paraguay (n=3), Peru (n=3), Venezuela (n=3), Chile (n=2), Ecuador (n=2), Honduras (n=1), El Salvador (n=1), and the Dominican Republic (n=1). Most centers were public institutions (n = 39, 53%), followed by private (n = 27, 37%), and academic centers (n = 7, 9.6%). The median number of hematologists per center was 4 (range, 2-10). Most centers identified as hematology clinics within a hospital (n=45) or independent from one (n=6), while 12 had only a single office and 10 had multiple offices inside a hospital. Laboratory support was heterogeneous: n=41 (56%) had in-house pathology services, 17 (23%) were outsourced, and 15 (21%) had no access to immunohistochemistry. Time to pathology report was ≥3 weeks in 56%. Access to PET-CT was available in 30 (41%) of the centers. Molecular testing for lymphomas was scarce, with only 13 (18%) reporting the availability of PCR for clonality assessment and 11 (15%) having access to next-generation sequencing. Regarding drug access, partial or restricted access to rituximab was reported in 27 centers (35.8%). Unavailability of drugs was reported for the following drugs in varying proportions: brentuximab vedotin (n=22, 30%), polatuzumab vedotin (n=16, 22%), tafasitamab (n=58, 79%), glofitamab (n=51, 70%), epcoritamab (n=47, 64%), and checkpoint inhibitors (n=18, 25%). Access to advanced therapies was reported as highly restricted: brentuximab vedotin (n=16, 31%), polatuzumab vedotin (n=37, 65%), tafasitamab (n=12, 80%), glofitamab (n=16, 73%), epcoritamab (n=19, 72.7%). CAR-T cell therapy was available in 6 centers (8.2%), with only 4 centers (5.7%) currently developing an institutional CAR-T cell program. The 41% (n=30) of the centers perform autologous transplantation, and 33% (n=24) allogeneic, most of them exclusively inpatient (83%, n=25). A median of 28 (IQR 8-50) of autologous HCT and 16 (IQR 10-30) allogeneic transplants are performed among respondent centers. Almost half (45%, n = 18) of the centers not currently offering HCT had plans to develop a HCT unit. Clinical trials are performed in 25 centers (35%), primarily industry sponsored trials (21, 84%). Conclusion: Our findings highlight striking disparities in access to diagnostics, treatments, and advanced therapeutic modalities across Latin America. CAR-T therapy, transplant capabilities, and access to novel drugs remain highly restricted and unevenly distributed. Additionally, most centers lack participation in clinical trials. These findings underscore the importance of collaborative efforts through networks such as GELL in building capacity, advocating for equitable access, and promoting regionally relevant research in Latin America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".