Pain severity among Black and White patients with cancer within a remote symptom monitoring program using electronic patient-reported outcomes.
Notice bibliographique
Résumé
138 Background: Black patients with cancer report higher pain intensity for both consistent and breakthrough pain. Remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROs) allows patients with cancer to communicate symptoms between visits. This study evaluated whether there were racial disparities in pain severity reporting among Black and White patients with cancer utilizing RSM with ePROs. Methods: This retrospective study analyzed data from 1453 patients with cancer undergoing treatment who utilized an RSM platform for the completion of weekly PRO-CTCAEs surveys to report pain intensity. Survey data from patients during the initial 6 months following RSM enrollment at the University of Alabama at Birmingham (UAB) and the Mitchell Cancer Institute (MCI) were included. Descriptive statistics were compared using frequencies, percentages, and Cramer’s V for categorical variables. Regression models were adjusted for age, sex, cancer type, and rurality. Results: Data from 1453 patients and 17,722 surveys were analyzed. 454 (31.2%) patients were Black and 999 (68.8%) patients were White. Among 454 baseline surveys completed by Black patients, no pain was reported in 68%, mild pain in 4%, moderate in 12%, and severe in 16%. In comparison, among 999 baseline surveys completed by White patients, no pain was reported in 71%, mild pain in 3%, moderate in 12% and severe in 14% (V=0.03). Among all post-baseline surveys completed by Black patients, no pain was reported in 69% (n=5081), mild pain in 5%, moderate in 13% and severe in 13%. In comparison, among all post-baseline surveys completed by White patients, no pain was reported in 72% (n=11188), mild pain in 4%, moderate in 14% and severe in 10% (V=0.05). In adjusted analysis, severe pain at follow-up was higher amongst Black patients than White patients (OR 1.17; 95% CI 0.87-1.56). Conclusions: Although modestly increased prevalence (3%) in any pain at baseline and severe pain during their 6 months after enrollment in RSM was observed for Black participants compared to Whites, this finding was not significant in adjusted models. This analysis suggests similar pain levels for Black and White patients within RSM programs. RSM can promote proactive reporting and early intervention for pain, which may help to mitigate previously reported disparities in long-term pain for Black individuals.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».