title: Validation of a Population-Based Algorithm to Detect Chronic Psychotic Illness.
abstract: OBJECTIVE
  To validate algorithms to detect people with chronic psychotic illness in population-based health administrative databases.
  
  
  METHODS
  We developed 8 algorithms to detect chronic psychotic illness using hospitalization and physician service claims data from administrative health databases in Ontario to identify cases of chronic psychotic illness between 2002 and 2007. Diagnostic data abstracted from the records of 281 randomly selected psychiatric patients from 2 hospitals in Toronto were linked to the administrative data cohort to test sensitivity, specificity, and positive predictive values (PPV) and negative predictive values.
  
  
  RESULTS
  Using only hospitalization data to capture chronic psychotic illness yielded the highest specificity (range 69.9% to 84.7%) and the highest PPV (range 55.2% to 80.8%). Using physician service claims in addition to hospitalization data to capture cases increased sensitivity (range 90.1% to 98.8%) but decreased specificity (range 31.1% to 68.0%) and PPV (range 38.4% to 71.1%).
  
  
  CONCLUSIONS
  Using health administrative data to study population-based outcomes for people with chronic psychotic illness is feasible and valid. Researchers can select case identification methods based on whether a more sensitive or more specific definition of chronic psychotic illness is desired.
authors.affiliation: Director, Health Systems Research, Social and Epidemiological Research, Centre for Addiction and Mental Health, Toronto, Ontario; Lead, Mental Health and Addictions Research Program, Institute for Clinical Evaluative Sciences, Toronto, Ontario; Assistant Professor, Department of Psychiatry and Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario.
authors.forename: Paul
authors.initials: P
authors.lastname: Kurdyak
authors.affiliation: Research Scientist, Provincial System Support Program, Centre for Addiction and Mental Health, Toronto, Ontario; Adjunct Scientist, Institute for Clinical Evaluative Sciences, Toronto, Ontario; Associate Professor, Department of Psychiatry, University of Toronto, Toronto, Ontario.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Lin
authors.affiliation: Analyst, Institute for Clinical Evaluative Sciences, Toronto, Ontario.
authors.forename: Diane
authors.initials: D
authors.lastname: Green
authors.affiliation: Scientist, Women's College Research Institute, Women's College Hospital, Toronto, Ontario; Assistant Professor, Department of Psychiatry and Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario; Adjunct Scientist, Institute for Clinical Evaluative Sciences, Toronto, Ontario.
authors.forename: Simone
authors.initials: S
authors.lastname: Vigod
mesh_terms: D000328:Adult; D000465:Algorithms; D002908:Chronic Disease; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D008875:Middle Aged; D011618:Psychotic Disorders / Q000175:diagnosis*; D012042:Registries / Q000706:statistics & numerical data*; D012559:Schizophrenia / Q000175:diagnosis*; D012680:Sensitivity and Specificity