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Enregistrement W3211765601 · doi:10.1182/blood-2021-151271

Cost of Care of Rare Hematologic Disorders in the United States: Call to Action for Policy Supporting Pharmacologic Innovation

2021· article· en· W3211765601 sur OpenAlexaboutno aff
Pedro Andreu Perez, Gina Cioffi, Jenny Karam, Caroline Child, Fernando Tricta, Giacomo Chiesi

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePorphyrin Metabolism and Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineIndirect costsDiseaseThalassemiaSpecialtyHealth carePediatricsIntensive care medicineFamily medicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract BACKGROUND: Rare diseases (RD) present a societal concern because of lack of treatment availability and difficulty developing new treatments. Even when treatment options exist, there are considerable barriers to diagnosis and access to specialty care. OBJECTIVES: To estimate direct (attributable to patient care), indirect (patients' and caregivers' loss of productivity), and mortality-related costs of 5 rare hematologic disorders (atypical hemolytic uremic syndrome [aHUS], acute intermittent porphyria, acquired aplastic anemia, beta thalassemia major, and sickle cell disease) and evaluate burden of care, both when treatment is available and when no treatment exists. We compared these costs with mass market (MM) diseases, including a common hematologic disorder, deep vein thrombosis (DVT), to highlight the need to better serve RD individuals. METHODS: We evaluated peer-reviewed published articles and databases (e.g., Orphanet, the Genetic and Rare Disease Information Center, NORD, NIH), conducted interviews with patient advocacy groups (e.g., Global Gene, the EveryLife Foundation for Rare Diseases, NORD) and key opinion leaders (e.g., Penn Blood Disorders Center), and referred to the US Bureau of Labor Statistics and Medi-Span Price Rx. We performed a statistical analysis to confirm that the sample size of patients covered in our disease selection was significant. In-depth analyses were performed to assess the per patient per year (PPPY) direct, indirect, and mortality costs associated with the 5 disorders, as well as costs for MM diseases, including DVT. While treatments exist for each of these 5 rare disorders, there are no universal curative options. Cost data for MM diseases, including DVT, were derived from literature reviews. RESULTS: The economic burden of rare hematologic disorders in the US is considerable. For most diseases, treatment costs account for the majority of total direct costs (52-90%). Indirect and mortality costs account for 4% and 22% of the total burden cost, respectively, but mortality costs vary widely (4-74% of total costs). Highest overall direct cost observed was for aHUS ($530k) due to challenging diagnosis, persistent treatment, and poor prognosis. Productivity loss is 2 hours/week for patients and 2-4 hours/week for caregivers. The life expectancy of aHUS patients is ~60 years, but if untreated this may be shortened to ~35 years. The lowest overall direct cost was for beta thalassemia major ($69k). The majority of direct costs are split between treatment costs and medical procedures. Patient productivity loss is estimated to be >3.5 weeks of work loss/year in the 60% of patients who require bimonthly transfusions. Caregiver burden constitutes 9.2 hours/week of work loss. New therapies are likely to offset mortality costs in the future. Although the direct, indirect, and mortality costs of these 5 disorders are high (average total cost $228k), the burden of cost is higher in all scenarios if treatments did not exist (60% increase in overall cost). As would be expected under the "no treatment" scenarios, the direct costs attributed to each disease decreased, but indirect and mortality costs increased. Value of treatment is demonstrated by decreases in PPPY indirect costs. When no treatments were available, the range for productivity loss was ~$33k to $61k for patients and ~$25k to $61k for caregivers, compared with ~$3k to $22k for patients and ~$4k to $5k for caregivers when treatments were available. The average PPPY costs of MM diseases for which treatments are available, including DVT, are estimated to be between $6k to $27k for direct costs, $10k to $16k for indirect costs, and $3k to $24k for mortality costs. In comparison with MM diseases, including DVT, the 5 rare disorders had average direct costs of ~$169k (a 6.25- to 26-fold increase), indirect costs of ~$9k (a modest decrease), and mortality costs of ~$50k (a 2.1- to 15-fold increase). CONCLUSIONS: These scenario analyses demonstrate that RD therapies generate positive economic value. Further, analysis shows that RD pose a greater social economic burden than MM diseases. This information can be utilized to further efforts by the RD community for increased governmental investment in RD treatment, diagnosis, and access. Disclosures Andreu Perez: Chiesi Global Rare Diseases: Other: PA is a full-time employee IQVIA. The employer of PA received consulting fees from Chiesi Global Rare Diseases for this analysis. Cioffi: Chiesi Global Rare Diseases: Current Employment. Karam: Chiesi Global Rare Diseases: Other: JK is a full-time employee IQVIA. The employer of JK received consulting fees from Chiesi Global Rare Diseases for this analysis. Child: Chiesi Global Rare Diseases: Other: CC is a full-time employee IQVIA. The employer of CC received consulting fees from Chiesi Global Rare Diseases for this analysis. Tricta: Chiesi Canada Corp: Current Employment. Chiesi: Chiesi Farmaceutici SpA: Current 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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,240

Scores Codex et Gemma par catégorie

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

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 Opus0,025
Tête enseignante GPT0,345
Écart entre enseignants0,319 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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
Publié2021
Routes d'admission1
Résumé présentoui

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