Costs of Illness of Spinal Muscular Atrophy: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The objective of our study was to conduct a systematic literature review of estimates of costs of illness of spinal muscular atrophy (SMA). METHODS: We searched MEDLINE (through PubMed), CINAHL, Embase, Web of Science, National Health Service Economic Evaluation Database, and the National Health Service Health Technology Assessment Database for studies published from inception up until 31 August, 2020, reporting direct medical, direct non-medical, and/or indirect costs of any phenotype of SMA. Two reviewers independently screened records for eligibility, extracted the data, and assessed studies for risk of bias using the Newcastle-Ottawa Scale. Costs were adjusted and converted to 2018 US dollars. RESULTS: The search identified 14 studies from eight countries (Australia, France, Germany, Italy, Spain, Sweden, the UK, and the USA). The mean per-patient annual direct medical cost of illness was estimated at between $3320 (SMA type III, Italy) and $324,410 (SMA type I, USA), mean per-patient annual direct non-medical cost between $25,880 (SMA types I-III, Spain) and $136,800 (SMA type I, Sweden), and mean per-patient annual indirect cost between $9440 (SMA type I, Germany) and $74,910 (SMA type II, Australia). Most studies exhibited a risk of bias. CONCLUSIONS: The current body of evidence of costs of illness of SMA is relatively scarce and characterized by considerable variability across geographical settings and disease phenotypes. Our review provides data pertaining to the economic impact of SMA, which is of particular relevance in light of emerging treatments and ongoing research in this field, and underscores the substantial unmet medical need in this patient population.
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".