Patients With Head and Neck Cancer and High Health Care Costs
Notice bibliographique
Résumé
Importance: The care for a small subset of patients is responsible for a disproportionately large share of health care expenditures. Head and neck cancer is associated with significant health care costs due to complex treatment regimens and long-term sequelae. Given this high baseline cost, identifying patients with high care costs within a population with cancer might help inform interventions to optimize resource allocation. Objective: To characterize patients with head and neck cancer with the highest health care costs during the first year after diagnosis. Design, Setting, and Participants: A population-based, retrospective cohort study was conducted using administrative data from the Institute for Clinical and Evaluative Sciences in Ontario, Canada, and included adults diagnosed with head and neck cancer between January 2007 and October 2020 (identified from the provincial cancer registry) with a full 1.5-year follow-up from the date of diagnosis to the date of death or October 31, 2021. The total 1-year health care costs were estimated using a patient-level algorithm and were collected in 2020 Canadian dollar values. The main analyses were performed in April 2023 and a sensitivity analysis was performed in April 2025. Main Outcomes and Measures: High health care costs (>75th percentile) during the first year after a head and neck cancer diagnosis. Predictors of high health care costs were identified using a multivariable logistic regression model. Results: The cohort included 13 795 patients (mean age, 63.2 [SD, 11.7] years and 3452 [25.0%] were female), 3448 (25%) of whom had high health care costs. Cancer stage was the strongest predictor of high health care costs. Compared with patients with stage I cancer, those with stage II cancer had 2-fold greater odds for high health care costs (odds ratio [OR], 3.14 [95% CI, 2.56-3.84]), those with stage III cancer had 5-fold greater odds for high health care costs (OR, 6.08 [95% CI, 4.99-7.41]), and those with stage IV cancer had 8-fold greater odds for high health care costs (OR, 8.94 [95% CI, 7.43-10.80]). Receiving multiple treatment modalities also was associated with greater odds for high-cost care. Conclusions and Relevance: This cohort study found that more advanced disease stage and receiving multiple treatment modalities were the strongest predictors of high-cost care among patients diagnosed with head and neck cancer. Prioritizing research and implementation of screening programs, earlier cancer diagnoses, and effective treatment deescalation strategies might mitigate a significant portion of these high costs.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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 ».