The Direct Cost of Managing a Rare Disease: Assessing Medical and Pharmacy Costs Associated with Duchenne Muscular Dystrophy in the United States
Notice bibliographique
Résumé
BACKGROUND: A Duchenne muscular dystrophy (DMD) cohort was identified using a claims-based algorithm to estimate health care utilization and costs for commercially insured DMD patients in the United States. Previous analyses have used broad diagnosis codes that include a range of muscular dystrophy types as a proxy to estimate the burden of DMD. OBJECTIVE: To estimate DMD-associated resource utilization and costs in a sample of patients identified via a claims-based algorithm using diagnosis codes, pharmacy prescriptions, and procedure codes unique to DMD management based on DMD clinical milestones. METHODS: DMD patients were selected from a commercially insured claims database (2000-2009). Patients with claims suggestive of a non-DMD diagnosis or who were aged 30 years or older were excluded. Each DMD patient was matched by age, gender, and region to controls without DMD in a 1:10 ratio (DMD patients n = 75; controls n = 750). All-cause health care resource utilization, including emergency department, inpatient, outpatient, and physician office visits, and all-cause health care costs were examined over a minimum 1-year period. Costs were computed as total health-plan and patient-paid amounts of adjudicated medical claims (in annualized U.S. dollars). RESULTS: The average age of the DMD cohort was 13 years. Patients in the DMD cohort had a 10-fold increase in health care costs compared with controls ($23,005 vs. $2,277, P < 0.001). Health care costs were significantly higher for the DMD cohort across age strata and, in particular, for DMD patients aged 14-29 years ($40,132 vs. $2,746, P < 0.001). CONCLUSIONS: In the United States, resource use and medical costs of DMD are substantial and increase with age. DISCLOSURES: Funding for this study (GHO-10-4441) was provided by GlaxoSmithKline (GSK). Optum was contracted by GSK to conduct the study. Thayer was an employee of Optum Health Economics and Outcomes Research at the time of this study and was not compensated for her participation as an author of this manuscript. Bell is an employee and shareholder of GSK. McDonald has been a consultant for GSK, Sarepta, PTC Therapeutics, Biomarin, and Catabasis on clinical trials regarding Duchenne muscular dystrophy clinical trial design, endpoint selection, and data analysis; Mitobridge for drug development; and Eli Lilly as part of a steering committee for clinical trials. Study concept and design were contributed primarily by Bell, along with Thayer and McDonald. Thayer collected the data, and data interpretation was performed by Thayer and Bell, along with McDonald. The manuscript was written by Thayer and Bell, along with McDonald, and revised by all the authors.
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 | 0,002 | 0,001 |
| 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 ».