Systematic Literature Review of the Economic Burden and Cost of Illness in Patients with Myelofibrosis
Notice bibliographique
Résumé
Introduction: Myelofibrosis (MF) is a rare bone marrow cancer classified as a myeloproliferative neoplasm in which bone marrow is replaced by fibrous (scar) tissue, impairing the production of normal blood cells. MF has a global incidence of approximately 0.58 new cases per 100,000 person-years, with many patients experiencing short survival (approximately 6 years). Most patients with MF are found to have either intermediate-2 or high-risk MF, as per their prognostic score (International Prognostic Scoring System [IPSS] or Dynamic IPSS). The economic impact of MF has been studied in individual real-world settings, each of which may have limited generalizability; however, the holistic economic burden of MF is not well understood. The objective of this systematic literature review (SLR) was to describe economic evidence for patients with MF including cost and resource use data. Methods: A SLR was conducted in Embase®, MEDLINE®, the National Health Service Economic Evaluation Database (NHS EED), and the American Economic Association (AEA) EconLit® to identify evidence published from database inception to July 2018. Conference proceedings and bibliographies were also searched. Studies were included if they were published in the English language and reported economic burden associated with adult patients with MF. The evidence was not restricted by any country or time limits. Two reviewers assessed each citation against predefined eligibility criteria, with discrepancies reconciled by a third independent reviewer. All the extracted data were quality checked by a second independent reviewer. A descriptive qualitative analysis was conducted to identify the patterns of economic burden in MF across different countries. Results: A total of 771 potentially relevant abstracts were identified and screened, of which 23 studies were included in the final analysis. Eleven studies reported cost data only, 10 studies reported both cost and resource use data, and 2 studies reported on the budget impact of treatment for MF. Eight of the included studies were conducted in the USA, 2 each in the UK, Canada, and Ireland, and the remaining 9 reported data from other countries. Eight of the included studies reported total MF costs, 4 studies reported productivity losses related to employment, and 3 studies reported indirect costs related to productivity and informal care. The remaining studies reported cost-effectiveness data for the treatment of MF. Of the 5 studies that reported categorical costs, 3 reported that outpatient costs were the major driver of costs, followed by inpatient costs. Among the studies conducted in the USA, total medical healthcare costs associated with MF ranged from USD 21,000 to USD 66,000 per patient. Three European studies reported that the annual productivity losses per patient ranged from EUR 7,774 to EUR 11,000, with total annual productivity losses as high as EUR 217,975. Two US studies compared the total MF-related healthcare costs with age- and sex-matched controls; costs were significantly higher in the MF cohort compared with matched controls (P < 0.05), especially for inpatient costs, outpatient costs, and pharmacy costs (Figure). Four studies, with a majority of the MF patients aged > 50 years, reported that 20-60% of the patients were absent from work, with a mean of 6.2 hours of work missed in the past 7 days. Among the hospitalized patients, 3 studies reported that the median length of stay for patients with MF ranged from 2.5 to 6.6 days, with 46% of patients utilizing emergency room visits and services. Conclusions: MF is associated with significant economic burden and work productivity loss to the health system, patients, and their families. Sustained efforts to develop more effective treatments are required in order to reduce the economic burden associated with MF and help patients and physicians improve disease management. Disclosures Tang: Celgene Corporation: Employment, Equity Ownership. Taneja:BresMed Health Solutions Ltd: Employment. Rajora:BresMed Health Solutions Ltd: Employment. Patel:BresMed: Employment.
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 | 0,012 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,018 | 0,017 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».