title: Development and validation of prediction algorithms for major depressive episode in the general population.
abstract: BACKGROUND
  To develop and validate sex specific prediction algorithms for 4-year risk of major depressive episode (MDE) using data from a population-based longitudinal cohort.
  
  
  METHODS
  Household residents from 10 provinces were randomly recruited and interviewed by Statistics Canada. 10,601 participants who were aged 18 years and older and who did not meet the criteria for MDE in the 12 months prior to a baseline interview in 2000/01 were included in algorithm development; data from 7902 participants who were aged 18 and older and who were free of MDE in 2004/05 were used for validation. Validation was also conducted in sub-populations that are of practice and policy importance. MDE was assessed using the World Health Organization's Composite International Diagnostic Interview(CIDI)-Short Form for Major Depression (CIDI-SFMD).
  
  
  RESULTS
  In the training data, the C statistics for algorithms in men was 0.7953 and was 0.7667 for algorithm in women. The algorithms had good predictive power and calibrated well in the development and validation data.
  
  
  CONCLUSIONS
  The data relied on self-report. MDE was assessed with CIDI-SFMD. It was not feasible to validate the algorithms in different populations from different countries.
  
  
  CONCLUSIONS
  More studies are needed to further validate and refine these algorithms. However, the ability of a small number of easily assessed variables to predict MDE risk indicates that algorithms are a promising strategy for identifying individuals in need of enhanced monitoring and preventive interventions. Ultimately, application of algorithms may lead to increased personalization of treatment, and better clinical outcomes.
authors.affiliation: Department of Psychiatry and of Community Health Sciences, Faculty of Medicine, University of Calgary, Calgary, AB, Canada. jlwang@ucalgary.ca
authors.forename: Jian Li
authors.initials: JL
authors.lastname: Wang
authors.forename: Douglas
authors.initials: D
authors.lastname: Manuel
authors.forename: Jeanne
authors.initials: J
authors.lastname: Williams
authors.forename: Norbert
authors.initials: N
authors.lastname: Schmitz
authors.forename: Heather
authors.initials: H
authors.lastname: Gilmour
authors.forename: Scott
authors.initials: S
authors.lastname: Patten
authors.forename: Glenda
authors.initials: G
authors.lastname: MacQueen
authors.forename: Arden
authors.initials: A
authors.lastname: Birney
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D003865:Major Depressive Disorder / Q000209:etiology* / Q000523:psychology; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012307:Risk Factors; D012737:Sex Factors; D012959:Socioeconomic Factors; D055815:Young Adult
keywords: Major depression; Population-based; Prediction algorithms; Sex-specific