title: A validation study of administrative data algorithms to identify patients with Parkinsonism with prevalence and incidence trends.
abstract: BACKGROUND
  Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard.
  
  
  METHODS
  We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged ≥ 20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms.
  
  
  RESULTS
  We selected, '2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (≥ 65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively.
  
  
  CONCLUSIONS
  Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy.
authors.affiliation: Research Institute, Department of Family and Community Medicine, University of Toronto, Toronto, Ont., Canada.
authors.forename: Debra A
authors.initials: DA
authors.lastname: Butt
authors.forename: Karen
authors.initials: K
authors.lastname: Tu
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Young
authors.forename: Diane
authors.initials: D
authors.lastname: Green
authors.forename: Myra
authors.initials: M
authors.lastname: Wang
authors.forename: Noah
authors.initials: N
authors.lastname: Ivers
authors.forename: Liisa
authors.initials: L
authors.lastname: Jaakkimainen
authors.forename: Robert
authors.initials: R
authors.lastname: Lam
authors.forename: Mark
authors.initials: M
authors.lastname: Guttman
mesh_terms: D000368:Aged; D000465:Algorithms; D016208:Databases, Factual; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D020734:Parkinsonian Disorders / Q000453:epidemiology*; D015995:Prevalence; D012189:Retrospective Studies; D012680:Sensitivity and Specificity