Socioeconomic burden of cystic fibrosis in Canada
Bibliographic record
Abstract
BACKGROUND: Cost of illness studies are important tools to summarise the burden of disease for individuals, the healthcare system and society. The lack of standardised methods for reporting costs for cystic fibrosis (CF) makes it difficult to quantify the total socioeconomic burden. In this study, we aimed to comprehensively report the socioeconomic burden of CF in Canada. METHODS: The total cost of CF in Canada was calculated by triangulating information from three sources (Canadian CF Registry, customised Burden of Disease survey and publicly available information). A prevalence-based, bottom-up, human capital approach was applied, and costs were categorised into four perspectives (ie, healthcare system, individual/caregiver, variable (ie, medicines) and society) and three domains (ie, direct, indirect and intangible). All costs were converted into 2021 Canadian dollars (CAD) and adjusted for inflation. The cost of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies was excluded. RESULTS: The total socioeconomic burden of CF in Canada in 2021 across the four perspectives was $C414 million. Direct costs accounted for two-thirds of the total costs, with medications comprising half of all direct costs. Out-of-pocket costs to individuals and caregivers represented 18.7% of all direct costs. Indirect costs representing absenteeism accounted for one-third of the total cost. CONCLUSION: This comprehensive cost of illness study for CF represents a community-oriented approach describing the socioeconomic burden of living with CF and serves as a benchmark for future studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".