title: Migraineurs were reliably identified using administrative data.
abstract: BACKGROUND
  Migraine is a common and important source of pain and disability in society. Accurately identifying such people using routinely collected health data would be beneficial for health services research.
  
  
  OBJECTIVE
  Externally validate a previously published method to identify migraineurs using health administrative data; and determine if a better model can be derived using data-mining techniques.
  
  
  METHODS
  Migraine status was determined for Ontarians participating in a population-based, cross-sectional survey. Consenting participants were linked to population-based health administrative data to identify age, sex, and coded diagnoses. Discrimination and calibration measures were used to appraise the models. A de novo technique we term "double threshold analysis" was used to determine optimal lower and upper expected probabilities to identify migraine status in the newly derived model.
  
  
  RESULTS
  A total of 1,01,114 people (mean age 46 years, 46% male) were included in the study, of which 11,314 (11.2%) had migraines. Using data-driven parameter estimates, the previous model to identify migraineurs had adequate discrimination (c-statistic 0.707 [95% CI 0.701-0.712]) and calibration (Hosmer-Lemeshow [H-L] statistic 20.8). A new model that included diagnostic code scores for physician visits, emergency visits, and hospitalizations with nonlinear terms for age and interactions significantly improved the model (c-statistic 0.724 [0.716-0.733], 16.4). Categorizing all people with a predicted migraine probability less than 10% or greater than 90% as without and having the disease, respectively, resulted in a sensitivity of 3.1%, a specificity of 99.96%, and a positive predictive value of 81.0% while capturing 57.0% of the cohort and 29.3% of migraineurs.
  
  
  CONCLUSIONS
  A previously derived model to identify migraineurs was improved using data-mining techniques permitting accurate cohort identification using routinely collected health administrative data.
authors.affiliation: Department of Medicine/School of Epidemiology, Public Health, and Preventive Medicine, University of Ottawa, 451 Smyth Road, Ottawa ON K1N 6N5, Canada; Ottawa Hospital Research Institute; ICES uOttawa. Electronic address: carlv@ohri.ca.
authors.forename: Carl
authors.initials: C
authors.lastname: van Walraven
authors.affiliation: Department of Medicine/School of Epidemiology, Public Health, and Preventive Medicine, University of Ottawa, 451 Smyth Road, Ottawa ON K1N 6N5, Canada.
authors.forename: Ian
authors.initials: I
authors.lastname: Colman
mesh_terms: D000328:Adult; D003430:Cross-Sectional Studies; D057225:Data Mining / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D006306:Health Surveys / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008881:Migraine Disorders / Q000175:diagnosis* / Q000453:epidemiology; D009864:Ontario / Q000453:epidemiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Administrative data; Claims signature model; Data mining; Migraine; Multivariable logistic regression; Validation