Migraine Treatment and Healthcare Resource Utilization in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Migraine poses a significant burden worldwide; however, there is limited evidence as to the burden in Canada. This study examined the treatment patterns, healthcare resource use (HRU), and costs among newly diagnosed or recurrent patients with migraine in Alberta, Canada, from the time of diagnosis or recurrence. METHODS: This retrospective observational study utilized administrative health data from Alberta, Canada. Patients were included in the Total Migraine Cohort if they had: (1) ≥1 International Classification of Diseases diagnostic code for migraine; or (2) ≥1 prescription dispense(s) for triptans from April 1, 2012, to March 31, 2018, with no previous diagnosis or dispensation code from April 1, 2010, to April 1, 2012. RESULTS: The mean age of the cohort (n = 199,931) was 40.0 years and 72.3% were women. The most common comorbidity was depression (19.7%). In each medication class examined, less than one-third of the cohort was prescribed triptans and fewer than one-fifth was prescribed a preventive. Among patients with ≥1 dispense, the mean rate of opioid prescriptions was 4.61 per patient-year, compared to 2.28 triptan prescriptions per patient-year. Migraine-related HRU accounted for 3%-10% of all use. CONCLUSION: Comorbidities and high all-cause HRU were observed among newly diagnosed or recurrent patients with migraine. There is an underutilization of acute and preventive medications in the management of migraine. The high rate of opioid use reinforces the suboptimal management of migraine in Alberta. Migraine management may improve by educating healthcare professionals to optimize treatment strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.003 | 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".