Abstract
INTRODUCTION: The growing availability of electronic health data provides an opportunity to ascertain diagnosis-specific cases via systematic methods for sample recruitment for clinical research and health services evaluation. We developed and implemented a migraine probability algorithm (MPA) to identify migraine from electronic health records (EHR) in an integrated health plan. METHODS: We identified all migraine outpatient diagnoses and all migraine-specific prescriptions for a five-year period (April 2008-March 2013) from the Kaiser Permanente, Northern California (KPNC) EHR. We developed and evaluated the MPA in two independent samples, and derived prevalence estimates of medically-ascertained migraine in KPNC by age, sex, and race. RESULTS: The period prevalence of medically-ascertained migraine among KPNC adults during April 2008-March 2013 was 10.3% (women: 15.5%, men: 4.5%). Estimates peaked with age in women but remained flat for men. Prevalence among Asians was half that of whites. CONCLUSIONS: We demonstrate the feasibility of an EHR-based algorithm to identify cases of diagnosed migraine and determine that prevalence patterns by our methods yield results comparable to aggregate estimates of treated migraine based on direct interviews in population-based samples. This inexpensive, easily applied EHR-based algorithm provides a new opportunity for monitoring changes in migraine prevalence and identifying potential participants for research studies.