Disparities in Clinical Trial Participation Among Medicare Beneficiaries with Hematologic Malignancies from 2006 to 2019: A SEER-Medicare Analysis
Notice bibliographique
Résumé
Introduction Representative participation in clinical trials (CTs) has been identified as an important dimension of health equity, particularly for older adults. Prior studies have suggested a lack of representativeness amongst CTs for hematologic malignancies (HM), but these findings have relied on synthesized participation data from published trials, which lack comprehensive reporting of participant race and ethnicity. Further, the lack of individual data has limited the multi-level evaluation of patient demographic, geographical, and disease-related factors contributing to CTs for older adults with HM. The purpose of this study is to systematically examine CT disparities among older adults with HM who are Medicare beneficiaries using national population-based data. Methods This retrospective cohort study was conducted using the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database. We identified all patients aged ≥65 yearswith a new diagnosis of lymphoma (Hodgkin lymphoma [HL], non-Hodgkin lymphoma [NHL], chronic lymphocytic leukemia [CLL]), acute leukemias, chronic myeloid neoplasms, and multiple myeloma (MM) between 2006 and 2018 (end of follow-up December 2019). The main outcome was CT participation, defined as the presence of a Medicare claim for the delivery of CT services (ICD9/10 code V70.7/Z00.6, or either of HCPCS modifiers “Q0” and “Q1”). Cumulative incidence was used to estimate the incidence of CT participation while accounting for the competing risk of death using Fine-Gray subdistribution hazard model, reported as adjusted hazard ratios (aHRs) with their 95% confidence intervals (CIs). Covariates included patient factors (age, sex, income, education, race, ethnicity, distance from NCI center), treatment status, and comorbidities. Participation was assessed overall and by HM subtype. Results The study cohort (N=54,121) was 50% female with a median age of 78 years (y) (IQR 72-84). The cumulative incidence of unadjusted CT participation at 1, 2, and 5 y was 2.7% (95% CI 2.5-2.8%), 3.5% (95% CI 3.4-3.7%), and 4.3 (95% CI 4.1-4.4%), respectively. Overall, reduced odds of participation were observed in patients who were older (compared to 66-69 yrs: age 70-74, aHR 0.79, 95% CI 0.71-0.88; age 75-79, aHR 0.63, 95% CI 0.56-0.70; age 80-84, aHR 0.41, 95% CI 0.36-0.46; age 85+, aHR 0.21, 95% CI 0.17-0.24; p<0.001), female (aHR 0.79, 95% CI 0.74-0.86, p<0.001), Black (compared to White, aHR 0.73, 95% CI 0.59-0.90, p<0.001), had greater comorbidities (cardiovascular aHR 0.85, 95% CI 0.72-0.98, p=0.025; pulmonary aHR 0.76, 95% CI 0.68-0.85, p<0.001; renal aHR 0.67, 95% CI 0.59-0.76, p<0.001), and lived ≥ 12.5 km from the nearest NCI center (compared to < 12.5km: 12.5-49 km, aHR 0.89, 95% CI 0.81-0.98, p=0.02; 50-249 km, aHR 0.84, 95% CI 0.74-0.95, p=0.004; 250+ km, aHR 0.64, 95% CI 0.48-0.86, p=0.003) . Treatment received within 1-year of diagnosis was associated with CT participation (aHR 2.2, 95% CI 1.96-2.42, p<0.001). Across other HM subtypes, consistent patterns of disparities were observed for age, sex, and comorbidities, but not for race and ethnicity or geography. Compared to white participants, Black individuals showed lower participation in indolent NHL (aHR 0.22, 95% CI 0.07-0.70, p=0.010), acute leukemias (aHR 0.57, 95% CI 0.33-0.99, p=0.044) and MM (aHR 0.54, 95% CI 0.36-0.81, p=0.003) trials, but not other subtypes.Distance to NCI centers was associated with lower odds of participation among patients with lymphoid malignancies (compared to < 12.5km: indolent NHL 50-249 km aHR 0.72, 95% CI 0.53-0.99, p=0.040; aggressive NHL 50-249 km aHR 0.74, 95% CI 0.61-0.91, p=0.005; 12.5 - 49 km aHR 0.79, 95% CI 0.62-0.99, p=0.047), but not others. Conclusions Amongst older adults with HM malignancies, significant sociodemographic underrepresentation was observed amongst CT participants compared to the population affected, and these disparities persisted across the duration of illness. These disparities compromise the generalizability of trial results and reflect reduced access to trials for patients from vulnerable populations. These data identify several targets for future research and intervention to improve equitable access to innovative therapies across diverse patient demographics. Differences in the patterns of underrepresentation HM subtypes suggest that a disease-specific approach to addressing these disparities may be needed.
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 | 0,007 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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) | 0,002 | 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 ».