Abstract B034: Sex and Racial Disparities in Time to Treatment for Early-Onset Colorectal Cancer Across a Four-Level Rurality Classification in the United States
Notice bibliographique
Résumé
Abstract Background: Adults with early-onset colorectal cancer (EOCRC, diagnosed at age < 50 years) often face diagnostic delays, making timely treatment essential for optimal outcomes. Evidence shows that male patients and racial/ethnic minorities, especially those in socioeconomically disadvantaged areas, are more likely to experience treatment delays. However, limited studies have explored how sex, racial, and geographic disparities influence treatment timeliness. This study addresses that gap by examining time to treatment across three post-diagnosis intervals, while accounting for a four-level rurality classification. Methods: We conducted a retrospective cohort analysis using the 2006–2020 Incidence Data with Census Tract Attributes from the Surveillance, Epidemiology, and End Results Program. The primary exposures included sex, race (White, Black, Hispanic, American Indian/Alaska Native [AI/AN], and Asian/Pacific Islander [Asian/PI]), and rurality (all urban, mostly urban, mostly rural, all rural). The outcome was time to treatment, categorized as initiation within 30, 60, or 90 days from diagnosis. Patients were censored if treatment was not initiated or occurred beyond the specified timeframes. Cox proportional hazards models were used, adjusting for sociodemographic, clinical factors, and diagnosis year. Multiple imputation addressed missing treatment time data (14.3%, n = 11,312). Results: Among 79,090 EOCRC patients, the average time to treatment was 20 days (SD = 32.4; IQR = 30), the shortest in mostly rural areas (17.8 days), followed by all rural (18.3 days), mostly urban (19.1 days), and all urban areas (20.7 days) (p < 0.001). In the imputed model, male patients were 5% less likely to initiate treatment across all time intervals compared to females (p < 0.05). Hispanic and Asian/PI patients were 4% (95% CI: 0.93–0.99) and 7% (95% CI: 0.91–0.95) less likely, respectively, to receive treatment within 90 days. Conversely, patients residing in non–fully urban areas were 9%–12% more likely to receive treatment across all timeframes (p < 0.05). Stratified analyses further showed that male patients in all urban areas were consistently about 5% less likely to initiate treatment (p < 0.05). Black (HR: 0.95; 95% CI: 0.92–0.98), Hispanic (HR: 0.93; 95% CI: 0.91–0.95) and Asian/PI patients (HR: 0.96; 95% CI: 0.93–0.99) patients in fully urban areas were less likely to receive treatment within 90 days, with similar patterns observed at 30 and 60 days. Conclusions: Although most patients (∼88%) initiated treatment within 30 days of diagnosis, our findings reveal persistent-albeit modest-inequities in access. Male, Hispanic, Asian/PI, and Black patients were slightly more likely to experience delays, particularly beyond 90 days. Those patients in fully urban areas also faced greater delays, suggesting potential strain on urban healthcare systems and highlighting the need for further investigation. These insights can inform targeted interventions to improve timely care for male, racial minorities, and urban populations affected by EOCRC. Citation Format: Meng-Han Tsai, Steven Coughlin, Kenneth J. Vega. Sex and Racial Disparities in Time to Treatment for Early-Onset Colorectal Cancer Across a Four-Level Rurality Classification in the United States [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B034.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».