Counting the Cost: The Hidden Financial Realities of Neuromuscular Disease Through Patient and Family Perspectives
Bibliographic record
Abstract
INTRODUCTION: Neuromuscular diseases (NMDs) impose multifaceted challenges on individuals and their families, often resulting in significant medical and non-medical expenses. While cost-of-illness (COI) studies provide valuable quantitative data, few explore the lived experience of financial strain. This study aims to identify the complex, often hidden, financial impacts experienced by individuals with NMDs and their families. METHODS: We conducted a qualitative study involving four virtual semi-structured focus groups, with 58 participants (76% patients and 24% caregivers). Participants were recruited from Muscular Dystrophy Canada's database and had previously completed the national BIND COI survey. Participants shared firsthand accounts of direct non-medical costs, psychosocial burdens and opportunity costs, highlighting hidden expenses, substantial out-of-pocket costs, and the broader financial and emotional toll on families. Thematic analysis of the transcripts of the discussions was performed using an inductive approach, guided by a rare-disease-specific socio-economic burden framework. RESULTS: Four key themes emerged: informational costs (lack of awareness/support for navigating financial resources), time-related costs (time spent advocating for supports), opportunity costs (loss of income or career advancement), and costs to independence (emotional toll and out-of-pocket costs for assistive devices and home modifications). CONCLUSION: This study identified four interconnected categories of hidden costs for people with NMDs and their families: informational burdens, administrative and advocacy demands, employment-related opportunity costs, and reduced independence tied to out-of-pocket spending on equipment and home modifications. These findings reveal how financial and emotional pressures accumulate beyond what traditional COI estimates capture. Greater attention to these costs is critical for fostering equitable and sustainable healthcare systems. PATIENT OR PUBLIC CONTRIBUTION: This study was designed and conducted in partnership with individuals living with NMDs and caregivers. Three trained patient and family research partners contributed to the development of the focus group guide and interpretation of results. All participants contributed their lived experiences to inform and validate key findings. Their input was central to the design, analysis and preparation of this manuscript, ensuring alignment with community priorities.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".