Use of Health Administrative Data to Identify Migraine in Individuals With a Recognized Pregnancy: A Validation Study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Migraine is a common risk factor for adverse perinatal outcomes, showing the importance of studying migraine in pregnancy. Despite the growing use of routinely collected administrative data in health research, the validity of such data to detect migraine in pregnant populations is unestablished. We validated algorithms to identify a history of migraine among pregnant individuals using health administrative data and population-representative self-report data. METHODS: We included N = 8824 females in Ontario, Canada with a documented pregnancy with an estimated conception date from 1 September 2005 to 31 December 2021 who completed the Canadian Community Health Survey (CCHS) within 5 years before conception. We created algorithms using different combinations of diagnostic codes for headache disorders and migraine-specific drug claims with varying lookback periods before conception. We compared their performance to self-reported migraine diagnoses from the CCHS. Measures of validity were sensitivity, specificity, predictive values, and agreement. RESULTS: The prevalence of self-reported migraine from the CCHS was 18% (95% confidence interval [CI]: 16%, 19%). The prevalence using administrative data depended on the definition (range: 2%-25%). All algorithms had high specificity (81.7%-98.9%), while sensitivity varied (6.1%-53.2%). The algorithm requiring ≥2 physician visits or ≥1 hospitalizations or emergency department visits with diagnostic codes International Classification of Diseases, Ninth Revision: 346/International Classification of Diseases, Tenth Revision: G43, with a lifetime lookback, had high specificity (94.0%; 95% CI: 93.1%, 94.8%) and negative predictive value (86.3%; 95% CI: 85.0%, 87.6%) and modest sensitivity (30.4%; 95% CI: 27.3%, 33.6%) and positive predictive value (51.9%; 95% CI: 46.8%, 57.0%). Agreement was fair ( κ = 0.29; 95% CI: 0.25, 0.33). CONCLUSION: Longitudinally linked health administrative data are effective at identifying pregnant individuals with migraine, with high specificity and reasonable sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".