The Burden of Illness of Migraine in Canada: New Insights on Humanistic and Economic Cost
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to characterize the burden of illness of migraine in Canada. The primary objective was to estimate the annual direct medical resource use and associated costs in migraine patients who failed at least two prophylactic therapies for migraine. METHODS: Adults with at least four migraine days per month and who had failed at least two prophylactic migraine therapies were included. Participation in a clinical trial within 12 months of enrollment was the sole exclusionary criterion. Patient demographic and clinical characteristics, migraine-related treatment and medical history, and direct medical resource utilization were collected through a retrospective medical chart review. Data on patient characteristics, lifestyle factors, treatments, medical resource utilization, out-of-pocket expenses, and indirect costs were collected through a cross-sectional patient survey. The patient survey also included validated patient-reported outcome instruments to assess migraine impact on quality of life and work productivity loss. RESULTS: In total, 287 migraine patients were included. The mean time since migraine diagnosis was 14.3 years and patients experienced a mean of 14.1 migraine days per month. The total estimated annual cost of chronic migraine (CM) was $25,669 per patient, while the annual total costs for high-frequency episodic and low-frequency episodic migraine (EM) were estimated to be $24,885 and $15,651, respectively. CONCLUSION: Migraine is associated with moderate to severe disability. This results in substantial economic burden, directly from healthcare costs such as prescription medications and indirectly through lost work productivity. We also observed that patients with high-frequency EM experience significant burden, similar to that observed for patients with CM.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".