title: Prevalence of migraine in a diverse community--electronic methods for migraine ascertainment in a large integrated health plan.
abstract: BACKGROUND
  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.
authors.affiliation: Sutter Health, Research, Development and Dissemination, USA Kaiser Permanente, Division of Research, USA pressmar@sutterhealth.org.
authors.forename: Alice
authors.initials: A
authors.lastname: Pressman
authors.affiliation: Sutter Health, Research, Development and Dissemination, USA Kaiser Permanente, Division of Research, USA.
authors.forename: Alice
authors.initials: A
authors.lastname: Jacobson
authors.affiliation: Sutter Health, Research, Development and Dissemination, USA.
authors.forename: Roderick
authors.initials: R
authors.lastname: Eguilos
authors.affiliation: University of California, San Francisco, Department of Neurology, USA.
authors.forename: Amy
authors.initials: A
authors.lastname: Gelfand
authors.affiliation: Kaiser Permanente, Division of Research, USA.
authors.forename: Cynthia
authors.initials: C
authors.lastname: Huynh
authors.affiliation: Kaiser Permanente, Division of Research, USA.
authors.forename: Luisa
authors.initials: L
authors.lastname: Hamilton
authors.affiliation: Kaiser Permanente, Division of Research, USA University of California, San Francisco, Department of Medicine, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Avins
authors.affiliation: Kaiser Permanente, Walnut Creek, Department of Neurology, USA.
authors.forename: Nandini
authors.initials: N
authors.lastname: Bakshi
authors.affiliation: Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, USA.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Merikangas
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D002648:Child; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008881:Migraine Disorders / Q000453:epidemiology*; D015995:Prevalence; D055815:Young Adult
keywords: Migraine; electronic health record (EHR); electronic medical record (EMR); headache; pain; prevalence