Diagnostic boundaries, reasoning and depressive disorder, I. Development of a probabilistic morbidity model for public health psychiatry

• Published in: Psychological medicine 1997-07, Vol.27 (4), p.835-845, Article S0033291797005072
• Main Authors: WAINWRIGHT, N. W. J., SURTEES, P. G., GILKS, W. R.
• Format: Article
• Language: English
• Subjects: Adult and adolescent clinical studies; Bayes Theorem; Biological and medical sciences; Confidence Intervals; Databases, Factual; Decision Support Techniques; Depression; Depressive Disorder - classification; Depressive Disorder - diagnosis; Depressive Disorder - epidemiology; Health Surveys; Humans; Likelihood Functions; Medical sciences; Models, Psychological; Models, Statistical; Mood disorders; Odds Ratio; Prevalence; Probability; Psychiatry - methods; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Public Health - methods; Risk Factors; Severity of Illness Index
• ISSN: 0033-2917; 1469-8978
• DOI: 10.1017/S0033291797005072

Background. In recent years diagnostic practice in psychiatry has become increasingly structured in an attempt to standardize definitions of disorders and improve reliability. At the same time there has been an increasing recognition of the need to take account of uncertainty in the process of diagnostic decision making. For the most part, diagnosis is still represented by a binary outcome while this is known to entail a substantial loss of information. Many diagnostic schemes involve, in part, taking thresholds on the numbers of symptoms required from symptom lists. Methods. A model is proposed here, using ideas derived from latent class analysis to permit generalization from these schemes through moving from a binary to a probabilistic measure of psychiatric case status and replacing thresholds with smoothed transitions. Results. An outcome measure is produced where disorder status is expressed in terms of probabilities without changing the meaning of the original measure. Prevalence estimates (using ICD-10 Depressive Episode criteria) are more stable and can be given with increased precision. Conclusions. Disorder status when expressed in this way retains more diagnostic information and provides a useful extension to traditional binary analyses when looking at prevalence and risk factor estimation.