The Healthcare Cost of Migraine: A Retrospective Cohort Study from Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: The economic burden of migraine is substantial; determining the cost that migraine imposes on the Canadian healthcare system is needed. METHODS: Administrative data were used to identify adults living with migraine, including chronic migraine (CM) and episodic migraine (EM), and matched controls in Alberta, Canada. One- and two-part generalized linear models with gamma distribution were used to estimate direct healthcare costs (hospitalization, emergency department, ambulatory care, physician visit, prescription medication; reported in 2022 Canadian dollars) of migraine during a 1-year observation period (2017/2018). RESULTS: = 301,506), with a predicted annual incremental cost of $2,806 (95% CI: $2,664, $2,948) per person. The predicted annual incremental cost of CM and EM was $5,059 (95% CI: $4,836, $5,283) and $669 (95% CI: $512, $827) per person, respectively, compared with matched controls. All healthcare cost categories were greater for migraine (overall, CM and EM) compared with matched controls, with prescription medication the primary cost driver (incremental cost - overall: $1,381 [95% CI: $1,234, $1,529]; CM: $2,057 [95% CI: %1,891, $2,223]; EM: $414 [95% CI: $245, $583] per person per year). CONCLUSION: Persons living with migraine had greater direct healthcare costs than those without. With an estimated migraine prevalence of 8.3%-10.2%, this condition may account for an additional $1.05-1.29 billion in healthcare costs per year in Alberta. Strategies to prevent and effectively manage migraine and associated healthcare costs are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".