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.