Global Economic Burden of Spinal Muscular Atrophy: A Systematic Literature Review
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is a rare inherited neuromuscular disease classified into four main subtypes and characterized by severe muscle weakness and loss of motor function. Its high mortality rates, high treatment costs, and lengthy care requirements place a heavy burden on patients, caregivers, and the healthcare system. This study aims to explore the economic burden of SMA subtypes by analyzing costs, healthcare resource use, and loss of productivity for patients and their caregivers. We conducted a systematic literature review, searching for studies published since 2010 via Medline, Embase, Google Scholar, and gray literature databases. We extracted data concerning costs, healthcare resources, and productivity losses among SMA subtypes. The quality of the included studies was assessed using the Newcastle-Ottawa Scale and the Quality of Health Economic Studies tools. We retrieved 55 studies from 32 countries with economic data variation due to the study design, location, and SMA subtype. The weighted average annual cost for an SMA patient was US$109,906 with the highest costs observed in type 1 patients, who incurred direct medical costs without disease-modifying treatments of US$187,88. The non-medical costs accounted for US$109,379 per patient, along with frequent hospitalizations and high caregiver productivity losses, requiring 2,947 hours of caregiving annually. The direct and indirect costs of SMA are substantial. The necessity for standardized approaches to evaluate and analyze the economic impact across various SMA subtypes is highlighted by the heterogeneity of the data. In order to control the financial burden of SMA, governments and healthcare systems can benefit from these insights to develop policies aimed at improving financial sustainability and patient support.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".