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Enregistrement W3095662261 · doi:10.1182/blood-2020-136365

Healthcare Utilization and Costs Associated with Different Treatment Protocols for Newly Diagnosed Childhood Acute Lymphoblastic Leukemia: A Population-Based Study

2020· article· en· W3095662261 sur OpenAlexaffabout
Sumit Gupta, Nicole Mittman, Petros Pechlivanoglou, Qing Li, Uma H. Athale, Mylène Bassal, Vicky R. Breakey, Paul Gibson, Mariana Silva, Veda Zabih, Jason D. Pole, Rinku Sutradhar

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensOccupational Cancer Research CentreUniversity of TorontoQueen's UniversityMcMaster Children's HospitalLondon Health Sciences CentreMcMaster UniversityChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenPediatric Oncology GroupSickKids FoundationKingston General HospitalCanadian Agency for Drugs and Technologies in Health
Organismes subventionnairesnon disponible
Mots-clésMedicinePopulationCancer registryHealth careCohortCancerCogPediatricsInternal medicineEmergency medicineEnvironmental health

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Though cooperative trial groups use different treatment protocols for newly diagnosed childhood acute lymphoblastic leukemia (ALL), all achieve high cure rates. The healthcare utilization and costs associated with different treatment strategies have not been rigorously compared. Minimizing utilization and costs may increase quality of life and decrease health system burden. We compared Children's Oncology Group (COG) and Dana-Farber Cancer Institute (DFCI)-based treatment. METHODS: We identified all children diagnosed with ALL in pediatric cancer centers in Ontario, Canada between 2002 and 2012 through the Pediatric Oncology Group of Ontario Networked Information System (POGONIS), a provincial pediatric cancer registry. Detailed data on demographics, disease risk factors (e.g. cytogenetics, minimal residual disease), treatment (e.g. treatment protocol, start and end date of each therapy phase) and events (relapse/progression, death, second cancer) were captured via chart abstraction. Treatment protocols were categorized as either based on COG or DFCI trials. Linkage to population-based health services databases identified all outpatient and emergency department (ED) visits, hospitalizations, and physician billings. Healthcare utilization-associated costs were determined through validated costing algorithms. Chemotherapy-associated costs were calculated separately using local prices. All administered doses of asparaginase (ASNase), including E. Coli, PEG-ASNase, and Erwinia ASNase were recorded. Event-free survival (EFS), overall survival (OS), healthcare utilization rates, and costs were compared between COG and DFCI-treated patients while adjusting for demographics and disease-factors using appropriate regression models. Healthcare-associated costs, ASNase costs, and total chemotherapy costs (2018 Canadian dollars) were compared. RESULTS: The study cohort included 802 patients, 146 (18.2%) of whom were treated on DFCI-based protocols. Median follow-up did not differ between between COG and DFCI patients; nor did EFS or OS. When adjusted for all demographic and disease-related variables, COG patients experienced significantly higher rates of ED visits [rate ratio (RR) 1.3, 95% confidence interval (95CI) 1.1-1.5; p=0.01]. Neither hospitalization rates nor rates of inpatient days differed between the two groups of patients. However, rates of outpatient visits were 60% higher among DFCI patients (RR 1.6, 95CI 1.5-1.7; p<0.0001). The median healthcare-associated cost in the first 5 years following initial diagnosis was $193,700 among COG patients [interquartile range (IQR) 149,200-272,700] compared to $288,000 among DFCI patients (IQR 233,300-407,300; p<0.01), mainly attributable to the cost associated with outpatient visits. In adjusted analyses, DFCI-associated costs were 70% higher (RR 1.7, 95CI 1.5-1.9; p<0.0001). The median ASNase-related cost was similar between COG and DFCI patients [$21,100, IQR 14,800-35,400 vs. $19,900, IQR 15,600-39,900; p=0.91]. The median total chemotherapy cost was higher among COG patients ($29,100, IQR 20,300-50,300 vs. $22,400, IQR 17,300-42,600; p<0.001]. However, ASNase and total chemotherapy costs were highest in DFCI patients treated with PEG-ASNase instead of E. Coli ASNase (N=36), reflecting contemporary practice. Among such patients, median ASNase-related costs were $45,200 (IQR 7,000-68,500) and median total chemotherapy costs were $48,000 (IQR 11,000-73,400) (p=0.004 and p=0.09 vs. COG patients). CONCLUSIONS: Though COG and DFCI protocols are associated with equivalent EFS and OS, patterns of healthcare utilization differ with the former associated with a 30% increase in the rate of ED visits and the latter associated with a 60% increase in the rate of outpatient visits. Overall, healthcare utilization-associated costs were increased in DFCI-treated patients. Though ASNase costs historically did not differ, the shift to PEG-ASNase is associated with higher ASNase and total chemotherapy costs on DFCI protocols. Decreases in PEG-ASNase cost and the ability to administer intravenous or intramuscular chemotherapy at home would decrease overall healthcare utilization and costs, and mitigate differences between COG and DFCI protocols. These results can inform efforts to decrease burden on both families and health systems. 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,107
Score d'incertitude au seuil0,212

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,004
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,038
Tête enseignante GPT0,321
Écart entre enseignants0,283 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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