Burden of Episodic Migraine, Chronic Migraine, and Medication Overuse Headache in Alberta
Bibliographic record
Abstract
OBJECTIVE: To describe demographic and clinical characteristics, healthcare resource use, costs, and treatment patterns in three migraine cohorts. METHODS: This retrospective observational study using administrative data examined patients with episodic migraine (EM), chronic migraine (CM) (without medication overuse headache [MOH]), and medication overuse headache in Alberta, Canada. Migraine patients were identified between 2012 and 2018 based on ≥ 1 diagnostic codes or triptan prescription. Patients with CM were defined using parameter estimates of a logistic regression model, and MOH was defined as patients with an average of ≥ 15 supply days covered of acute medications. EM was defined as patients without CM or MOH. Study outcomes were summarized using descriptive statistics. RESULTS: Patients with EM (n = 144,574), CM (n = 27,283), and MOH (n = 11,485) were included. Higher rates of healthcare use and costs were observed for CM (mean [SD] all-cause cost: ($12,693 [40,664]) and MOH ($16,611.5 [$38,748]) versus episodic migraine ($4,251 [$40,637]). Across all cohorts, opioids were the most dispensed acute medication (range across cohorts: 31.7%-89.8%), while antidepressants and anticonvulsants were the most dispensed preventive medication. Preventative medication classes were used by a minority of patients in each cohort, except anticonvulsants, where 50% of medication overuse patients had a dispensation. CONCLUSIONS: Patients with CM and MOH have a greater burden of illness compared to patients with EM. The overutilization of acute medication, particularly opioids, and the underutilization of preventive medications highlight an unmet need to more effectively manage migraine.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".