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Predicting occupational diseases
  1. Eva Suarthana1,2,3,
  2. Evert Meijer1,
  3. Diederick E Grobbee2,
  4. Dick Heederik1,2
  1. 1
    IRAS (Institute for Risk Assessment Sciences), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands
  2. 2
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
  3. 3
    Community Medicine Department, Faculty of Medicine, University of Indonesia, Jakarta Pusat, Indonesia
  1. Correspondence to Dr Eva Suarthana, IRAS (Institute for Risk Assessment Sciences), Environmental Epidemiology Division, Utrecht University, PO Box 80178, 3508 TD, Utrecht, The Netherlands; E.Suarthana{at}

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Prediction research is relatively new in the occupational health field,1 2 3 4 although it is well established in clinical medicine.5 6 Prediction models are developed to estimate the individual probability of the presence (diagnostic model) or future occurrence (prognostic model) of an outcome (ie, disease). As an example from clinical practice, Wells and colleagues demonstrated that a diagnostic model (comprised of the patient’s history and physical examination) in combination with impedance plethysmography can safely rule out the presence of deep vein thrombosis. This approach reduced patient health care costs by avoiding expensive venography.5 Assessment of the 10-year risk of coronary heart disease (CHD) using the Framingham scores is a well known example of prognostic prediction.6 Such prediction allows physicians to identify a subset of patients with a higher probability of CHD in whom preventive action should be more effective.

The development of prediction models makes it possible to identify a small number of predictors to provide the best possible knowledge base for diagnosis. These models enable risk groups to be easily identified by quantification of the individual probability of having an occupational disease. Recently, we …

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  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.