BACKGROUND: Ascertaining migraine in electronic health care data is challenging because of likely diagnosis underrecording, and treatment with over-the-counter analgesics, which cannot be used as disease proxies. Migraine prevalence will depend on the characteristics of the migraine-identifying algorithm (eg, diagnosis codes, treatments or combinations of both) and health care data source used.
OBJECTIVES: To describe migraine-identifying algorithms implemented in observational studies that use health care administrative claims, electronic medical records or other data sources and any related validation efforts, and to summarize the prevalence of migraine observed in those studies.
METHODS: This systematic literature review searched PubMed in November 2023 for peer-reviewed, English-language, original research articles published since 2013 that identified migraine in adults using electronic algorithms in electronic health care data. Two reviewers independently screened articles for inclusion; data was extracted by one researcher and checked by the other. The study was registered with PROSPERO (CRD42023491279).
RESULTS: The literature search identified 630 unique articles. After reviewing titles and abstracts, 50 (8%) were selected for full-text review; of them, 41 (82%) were finally included. Among these 41 articles, 18 (44%) identified migraine using only diagnosis codes, 3 (7%) used only migraine treatments, 11 (27%) treatments or diagnosis codes, and 9 (22%) combinations of diagnosis codes and treatments in different setting (e.g., primary care, hospital discharge, specialist). Among these studies, 16 were conducted in Europe, 13 in US or Canada, and 12 in Asia. The most common diagnosis codes to ascertain migraine were ICD-9 346.xx and ICD-10 G43.xx. Triptans were the most frequently drugs used to identify migraine (ATC: N02CC). Where reported, the prevalence of migraine in the general population ranged between 4% and 17%. Algorithms combining diagnosis codes, treatments and additional conditions including setting were able to identify specific types of migraine or cases of treatment-resistant migraine. Only 2 studies reported validation results, one that identified prevention-eligible migraine patients (PPV = 97%), and one that identified migraine based on a calculated probability algorithm, with PPVs between 68% and 92% depending on the diagnostic threshold and the gold-standard used.
CONCLUSIONS: Most of the studies included in this systematic review identified migraine using diagnosis codes. Only studies that included a combination of diagnosis codes and treatments in different settings identified specific migraine types and reported results on validation. Prevalence varied according to the type of data source.
