P.035 Health System Utilization and Medication Use among Adults with Migraine in Alberta: An observational cohort study using Alberta administrative health data
Bibliographic record
Abstract
Background: Migraine is costly to governments. Despite significant burden, Canada lacks population data regarding migraine prevalence, resource and medication utilization. We sought to characterize the demographics, health resource utilization, and medication use in an adult migraine cohort in Alberta. Methods: Migraine cohort: previously validated case definition of migraine (ICD 10 + dispensation of abortive and/or preventative migraine drug (04/2010-03/2016). Patients over 18 years, followed three years from index date [first dispensation of migraine medication]. Health resource utilization (HRU) assessed by emergency department (ED) visits, hospital admission and physician claims. Medication assessed province-wide dispensation database linkage. Patient demographics and Charlson Comorbidity Index (CCI) included. Results: Over 5 years: 53,333 migraine cases identified (mean age 40.5 years, 79% female). Common comorbidities: hypertension, COPD, diabetes mellitus, cancer, cerebrovascular disease. Mean CCI 0.55 (SD 1.06). Metropolitan patients: 48%, urban 34.6%, rural 17.4%. Initial migraine diagnosis: 46% by GP, 31% in ED. Rural patients present more to ED/hospital for care in 3-year follow-up (IRR 2.95 [2.83, 3.08]). Conclusions: Our migraine case definition is more specific than sensitive and underestimates Alberta’s migraine prevalence. Higher female prevalence as expected. Rurally, migraine care largely occurs in ED/hospital. Study of prevalence, HRU and medications may help inform health policy in Alberta and Canada.
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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 | 0.001 | 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 | 0.001 |
| 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".