Abstract PS5-07: Financial difficulty over time in young adults with breast cancer
Notice bibliographique
Résumé
Abstract Introduction: Although young adults (YA) aged 18-39 represent the minority of breast cancer diagnoses, they are particularly vulnerable to financial hardship. Factors contributing to sustained financial hardship are incompletely understood. Arm morbidity, one such understudied factor and key source of expense, may be particularly salient for YAs given that a high proportion of this demographic presents with aggressive tumor subtypes requiring comprehensive axillary management (a known risk factor for treatment-related lymphatic injury). In this study, we leverage a multi-institutional prospective cohort of YAs to identify patterns of financial hardship over time and characterize factors associated with discrete trajectories hypothesizing that treatment-related arm morbidity would be among the factors predicting long-term financial difficulty. Methods: This analysis utilized data from women ≤ 40 years with newly diagnosed stage 0 to III breast cancer enrolled in The Young Women’s Breast Cancer Study (YWS), a multi-institutional prospective cohort study enrolling from 2006 and 2016 at Dana-Farber Cancer Institute and 12 other academic and community hospitals in the United States and Canada. Patient, disease, and treatment information was obtained from surveys serially collected through 10 years post-diagnosis. Arm morbidity was assessed by asking patients about the degree to which they experienced upper extremity swelling and/or functional limitations using two Likert-scale response items (range: 0-4). Medical record review was used to gather supplemental clinical data. The primary outcome of interest, perceived financial difficulty, was assessed serially using a single Likert-scale response item (range: 0-4) from the CAncer Rehabilitation Evaluation System (CARES) scale. Group-based trajectory modeling classified patterns of financial difficulty from baseline through 10 years post-diagnosis. Multinomial logistic regression identified patient, disease, and treatment characteristics associated with each trajectory. Results: 1008 (78%) of 1297 participants were included. Median age at diagnosis was 36 years (IQR 33-39). The majority of individuals were non-Hispanic (95%), White (88%), college graduates (83%), partnered at baseline (76%), parous (64%), and without comorbidities at enrollment (90%). Patients’ tumors were primarily stage I-II (86%), ER/PR-positive (75%), and HER2-negative (68%). Patients were more frequently treated with mastectomy than breast conservation (p<0.001). Receipt of radiation (62%), chemotherapy (75%), and endocrine therapy (63%) were common. 72% (N=727) of patients reported arm symptoms within 2 years of surgery. Three distinct financial trajectories emerged: 54% had low financial difficulty (Trajectory 1), 30% had mild difficulty that improved (Trajectory 2), and 17% had moderate/severe difficulty peaking several years after diagnosis before improving (Trajectory 3). BMI ≥ 25, undergoing bilateral mastectomy, Hispanic ethnicity, being unemployed at both baseline and 1 year, and arm symptoms were predictive of Trajectory 2 and/or 3 (more financial difficulty). Having a college degree or being partnered were predictive of Trajectory 1 (low financial difficulty). CONCLUSION: This study of YAs with breast cancer identified a subset of patients who experienced a high degree of financial difficulty that persisted into early survivorship before it improved. Targeted interventions to mitigate financial toxicity, including those focused on modifiable factors such as arm symptoms and employability/return to work after cancer, are needed. Citation Format: Sara Myers, Yue Zheng, Kate Dibble, Elizabeth A. Mittendorf, Tari A. King, Kathryn J. Ruddy, Jeffrey M. Peppercorn, Lidia Schapira, Virginia F. Borges, Steven E. Come, Shoshana M. Rosenberg, Ann H. Partridge. Financial difficulty over time in young adults with breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS5-07.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».