Economic burden of Huntington disease in Europe and the USA: Results from the Huntington's Disease Burden of Illness study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The prevalence of Huntington disease (HD) has increased over time; however, there is a lack of up-to-date evidence documenting the economic burden of HD by disease stage. This study provides an estimate of the annual direct medical, nonmedical, and indirect costs associated with HD from participants in the Huntington's Disease Burden of Illness (HDBOI) study in five European countries and the USA. METHODS: The HDBOI is a retrospective, cross-sectional study. Data collection was conducted between September 2020 and May 2021. Participants were recruited by their HD-treating physicians and categorized as early stage (ES), mid stage (MS), or advanced stage (AS) HD. Data were collected via three questionnaires: a case report form, completed by physicians who collected health care resource use associated with HD to compute direct medical cost, and optional patient and caregiver questionnaires, which included information used to compute nondirect medical and indirect costs. Country-specific unit cost sources were used. RESULTS: HDBOI cost estimates were €12,663 (n = 2094) for direct medical costs, €2984 (n = 359) for nondirect medical costs, and €47,576 (n = 436) for indirect costs. Costs are higher in patients who are at later stages of disease; for example, direct medical costs estimates were €9220 (n = 846), €11,885 (n = 701), and €18,985 (n = 547) for ES, MS, and AS, respectively. Similar trends were observed for nondirect and indirect costs. Costs show large variations between patients and countries. CONCLUSIONS: Cost estimates from the HDBOI study show that people with HD and their caregivers bear a large economic burden that increases as disease progresses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".