Resource Utilization and Costs in the Care of Patients with Hematologic Malignancies
Notice bibliographique
Résumé
Background: Patients with hematologic malignancies (HMs) make up a significant portion of the healthcare cost burden. To help understand how patients are managed and to guide allocation of public funds, the utilization of healthcare resources in various phases of disease should be investigated. We examined resource utilization and cost patterns during the pre-diagnosis, treatment, follow-up, and end-of-life phases of patients with specific HMs. Methods: We used the Cancer Care Ontario to identify patients with a diagnosis of HMs, including diffuse large B-cell lymphoma (DLBCL) and Hodgkin lymphoma (HL) between 2006 and 2014. For phase-based analysis, we defined the following four phases of care: Pre-diagnosis: 90 days prior to Ontario Cancer Registry (OCR) diagnosis to index dateInitial treatment: index date to 6 months from OCR diagnosis dateFollow-up: from end of treatment phase to beginning of end-of-life (or completion of cohort follow-up)End-of-life: last 6 months of life We characterized resource utilization by determining overall and disaggregated health system costs for DLBCL and HL. Descriptive statistics were used to characterize the population, with continuous variables presented as medians (with interquartile ranges); costs ($CAN 2014) and resource utilization were normalized to 30-days and presented as means +/- standard deviation. Results: A total of 35,556 patients were diagnosed between 2006 and 2014 with a HM. Of these, 15% were diagnosed with DLBCL, and 7% with HL. There were 5,392 patients diagnosed with DLBCL, [53% male, median age 64 years (IQR 53-74)]. The median follow-up was 1,903 days (IQR 1,194-2,882) from diagnosis, and the median age at death was 72 (IQR 61-82). Mean overall 30-day cost was $1,175 (±2,267) in pre-diagnosis; $9,166 (±5,581) during initial treatment; $1,462 (±2,756) during follow-up; and $7,965 (±7,104) during end-of-life. Mean 30-day cost of cancer medication was 80.20 (±454.98) in pre-diagnosis, 3,115.03 (±1,235.76) during initial treatment, 232.48 (±711.93) during follow-up, and 304.63 (±694.28) during end-of-life. There were 2,367 patients with HL [53% male, median age 37 years (IQR 26-53)]. The median follow-up was 2,419 days (IQR 1,581-3,318) from diagnosis, and the median age at death was 62 (IQR 44-75). In HL, mean overall 30-day cost in Canadian dollars was $813 (±1,541) in pre-diagnosis, $5,473 (±3,485) during initial treatment, $895 (±1,619) during follow-up, and $8,678 (±9,086) during end-of-life. Mean 30-day cost of cancer medication was 91.08 (±368.25) in pre-diagnosis, 1,131.91 (±765.85) during initial treatment, 102.86 (±341.69) during follow-up, and 371.05 (±1,612.73) during end-of-life. Conclusions: Total cost per 30 days was highest in the initial treatment phase and at end-of-life. The highest costs are generally associated with inpatient care across all phases. This suggests that patients require significantly more health care resources during end-of-life and while on active treatment. Results can be used to guide allocation of health care dollars to ensure appropriate patient care throughout a patient's cancer journey. Disclosures No relevant conflicts of interest to declare.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».