The Challenges for Pharmacoepidemiologists Identifying Migraine in Electronic Healthcare Data Sources: A Systematic Literature Review

Purpose Ascertaining migraine in electronic healthcare data is challenging because of likely diagnosis underrecording and treatment with over‐the‐counter analgesics, which cannot be used as disease proxies. Algorithm‐identified migraine prevalence may depend on algorithm characteristics and target population. Methods To describe migraine‐identifying algorithms implemented in electronic healthcare data sources and summarize validation results and observed migraine prevalence, we searched PubMed for peer‐reviewed, English‐language, original research articles that identified migraine in adults using electronic algorithms in electronic healthcare data. We summarized algorithms, validation results, and migraine prevalence (PROSPERO: CRD42023491279). Results Of 360 unique titles and abstracts, 50 articles (14%) were selected for full‐text review; of them, 41 articles (82%) were finally included: 16 were studies conducted in Europe, 13 in North America, and 12 in Asia. Sixteen studies (39%) identified migraine only using diagnosis codes, 5 (12%) only treatments, 9 (22%) diagnosis and/or treatment codes, and 11 (27%) diagnosis codes, treatments, and setting (e.g., primary care, specialist consultation). Reported migraine prevalence in the general population ranged between 4% and 17%. Only two studies reported validation results: one identified prevention‐eligible patients with migraine (positive predictive value [PPV] = 97%), and one identified migraine on the basis of calculated probabilities with PPVs between 74% and 92%. Conclusion Finding patients with migraine is feasible in various types of data sources; preferred algorithms vary; algorithm performance is mostly unknown. Identifying chronic migraine or other complex types of migraine requires combining diagnosis codes, treatments, and care settings, which is possible in only some data sources.