The Direct Cost of Managing a Rare Disease: Assessing Medical and Pharmacy Costs Associated with Duchenne Muscular Dystrophy in the United States
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".