title: Development and validation of a prediction algorithm for the onset of common mental disorders in a working population.
abstract: Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population.
  
  
  
  We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement.
  
  
  
  Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.
authors.affiliation: 1 Mental Health Policy Unit, Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia.|2 Public Health Agency of Barcelona, Barcelona, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Fernandez
authors.affiliation: 1 Mental Health Policy Unit, Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia.
authors.forename: Luis
authors.initials: L
authors.lastname: Salvador-Carulla
authors.affiliation: 3 Brain and Mind Centre, Central Clinical School, Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia.
authors.forename: Isabella
authors.initials: I
authors.lastname: Choi
authors.affiliation: 4 School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia.
authors.forename: Rafael
authors.initials: R
authors.lastname: Calvo
authors.affiliation: 5 School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.|6 Black Dog Institute, Randwick, NSW, Australia.
authors.forename: Samuel B
authors.initials: SB
authors.lastname: Harvey
authors.affiliation: 3 Brain and Mind Centre, Central Clinical School, Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Glozier
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001315:Australia / Q000453:epidemiology; D004651:Employment / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis* / Q000453:epidemiology; D008875:Middle Aged; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods* / Q000592:standards; D012307:Risk Factors; D012737:Sex Factors; D055815:Young Adult
keywords: Common mental disorders; prevention; risk algorithm