Real-World Healthcare Utilization and Costs in Migraine Patients in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: A comprehensive understanding of the burden of migraine in Canada is needed to inform clinicians, clinical care and policymakers. This study assessed real-world healthcare resource utilization (HCRU) and costs of patients with episodic migraine (EM) and chronic migraine (CM) in Ontario, Canada. METHODS: This study utilized administrative databases from the Institute for Clinical Evaluative Sciences (ICES) containing publicly funded health services records for the covered population of Ontario. Patients ≥26 years old with a migraine diagnosis between January 2013 and December 2017 were selected. EM and CM were inferred in eligible patients based on previously studied predictors. Cases were matched with non-migraine controls and followed for two years. RESULTS: 452,431 patients with migraine, 117,655 patients inferred with EM and 24,763 patients inferred with CM were selected and matched to controls. 39.4% of the inferred EM and 69.3% of the inferred CM subpopulations had ≥1 claims of preventive medications. Migraine-specific acute medications were underutilized (EM: 1.0%, CM: 3.3%), and high proportions of patients utilized opioids (EM: 38.8%, CM: 64.9%). Mean all-cause two-year costs per patient for the overall migraine population and inferred EM and CM subpopulations were $7,486 (CAD), $11,908 (CAD) and $24,716 (CAD), respectively. The two-year incremental all-cause cost of migraine to the Ontario public payer was $1.1 billion (CAD). CONCLUSION: Migraine poses a significant unmet need and burden on the Canadian healthcare system. These results demonstrate a gap between real-world care and recommendations from treatment guidelines, emphasizing the need for improved awareness and expanded access to more effective treatment options.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".