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Enregistrement W4389235092 · doi:10.1182/blood-2023-173609

Assessing the Impact of Marginalization on Survival for Patients Undergoing Autologous Stem Cell Transplant in Ontario, Canada

2023· article· en· W4389235092 sur OpenAlexaffabout
Adam Suleman, Sho Podolsky, Ning Liu, Kelvin Chan, Sumedha Arya, Lisa K. Hicks, Matthew C. Cheung, Anca Prica

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Financial Impacts of Cancer
Établissements canadiensPrincess Margaret Cancer CentreSt. Michael's HospitalCanadian Blood ServicesHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesUniversity Health NetworkUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicinePopulationEthnic groupMultiple myelomaSocioeconomic statusInternal medicineProportional hazards modelGerontologyDemographyOncologyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Background Autologous hematopoietic stem cell transplants (ASCT) are potentially life-saving interventions used to treat hematologic disorders. However, it is unclear if socioeconomic disparities impact the patient benefit from ASCT. Recent evidence suggests lower rates of ASCT for patients living in rural settings and for ethnic minority groups. Few population-based studies have assessed comprehensive indices of marginalization as predictors of outcomes after ASCT. In a publicly funded healthcare setting, it is crucial to understand if various aspects of marginalization interact to ultimately impact survival for patients undergoing ASCT. Methods We performed a retrospective population-based study using administrative healthcare databases from Ontario, Canada. Patients were included if they had undergone auto-SCT for lymphoma or myeloma between 2010 and 2022. The Ontario Marginalization Index (ON-Marg), created using Canadian census data, explores four key aspects of marginalization: residential instability (referring to housing instability), material deprivation (referring to individual and community abilities to access basic material needs), dependency (referring to lack of income from employment), and ethnic concentration (referring to individuals who are recent immigrants or belonging to a visible minority group). The primary outcome was 2-year overall survival (OS) from time of transplant to death or end of the study period stratified by ON-Marg quintiles. Multivariable Cox regression analyses were used to identify baseline characteristics associated with OS. Results A total of 1886 patients underwent ASCT for lymphoma and 2842 patients underwent ASCT for myeloma. The median age of patients undergoing ASCT for lymphoma was 56 years (IQR 44-63) and 64% of patients were male. 22% of patients had high aggregated diagnosis group (ADG) comorbidity scores and 15% had a prior history of cancer. 2-year OS for patients in the fifth quintile of marginalization of the ethnic concentration index was 80.4% (95% CI 75.9-84.1%), compared to 72.3% (95% CI 67.5-76.5%) in the first quintile ( Figure 1A). This survival advantage (HR 0.71, 95% CI 0.52-0.99) persisted after adjusting for age, comorbidity burden, and distance to hospital ( Table 1). Patients who lived 150-200 km from the transplant center had a lower risk of death compared to patients who lived within 50km of the hospital (HR 0.53, 95% CI 0.30-0.94). 2-year OS was not significantly different for patients in quintile 5 of marginalization of the dependency index compared to quintile 1 (HR 1.25, 95% CI 0.93-1.67), with similarly no significant difference across residential instability or material deprivation. The median age of patients undergoing ASCT for myeloma was 61 years (IQ 55-66), and 25% of patients had high ADG comorbidity scores. OS was not significantly different across all domains of marginalization, with a 2-year OS of 88.8% (95% CI 85.8-91.2%) in quintile 1 of ethnic deprivation and 87.5% (95% CI 84.7-89.8%) in quintile 5 ( Figure 1B). Higher comorbidity scores were associated with an increased risk of death compared to lower comorbidity scores (HR 2.17, 95% CI 1.58-2.98), as shown in Table 1. Living 150-200 km from the transplant center was associated with worse OS (HR 1.74, 95% CI 1.24-2.45). Conclusion This is one of the first studies to examine the effect of marginalization on outcomes after ASCT in a publicly-funded healthcare system. For patients undergoing ASCT for lymphoma, patients in quintile 5 of ethnic concentration had improved 2-year OS compared to patients in quintile 1. One possible explanation is the healthy immigrant effect, whereby immigrant patients are healthier than their Canadian-born counterparts. Cultural factors may also have a protective role. Patients undergoing ASCT for lymphoma who lived farther from the transplant center had a lower risk of death; these patients may be more highly selected based on fitness and disease biology to be referred for transplant compared to patients who live within close proximity. For patients undergoing ASCT for myeloma, 2-year OS was not affected by ethnic concentration quintile. The decision for referral for ASCT for myeloma is not as subjective, with ASCT commonly used in the first-line setting for eligible patients regardless of distance, which likely accounts for this difference. Further work is needed to ensure that all eligible patients receive ASCT.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,249

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,003
Études des sciences et des technologies0,0020,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,238
Écart entre enseignants0,206 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission2
Résumé présentoui

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