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(2011)Journal ArticleObjective: To present scoring rules for predicting DSM-IV mental illness in the past 12 month using the Kessler Psychological Distress Scale in the Australian population. Method: The method described in Kessler et al., [1] was closely followed using data from the 2007 Australian Survey of Mental Health and Wellbeing. A series of 93 nested logistic regression models were generated and compared to estimate the predicted probabilities of mental illness for each survey respondent using scores on the Kessler Psychological Distress Scale. The best model was selected using information theoretic criteria. Concordance between the predicted probabilities of mental illness generated by the best models with DSM-IV defined mental illness, assessed using the Composite International Diagnostic Interview, was determined using receiver operating characteristic analysis. Results: The best fitting models were found to contain the quadratic form of the Kessler Psychological Distress Scale (both 6 item and 10 item versions). Age was found to be significant in the model predicting mood, anxiety, and substance use with serious impairment using the 6 item version whilst age and gender was found to be significant in the model for the 10 item version. The concordance between the predicted probabilities of mood, anxiety, and substance use with serious impairment generated from the best models and DSM-IV mood, anxiety, and substance use with serious impairment was within an acceptable level for both versions. Results were similar when predicting DSM-IV mood, anxiety, and substance use without seriousness indicators and DSM-IV anxiety and depression. The performance of predicted probabilities was then examined in various subpopulations of the Australian population. Conclusions: Using a logistic regression model, the Kessler Psychological Distress Scale can be used to generate predicted probabilities of mental illness with an acceptable level of agreement in Australian-based population studies where it is not feasible to conduct a comprehensive assessment.
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(2010)Journal ArticleBackground: Large community based epidemiological surveys have consistently identified high co-morbidity between major depressive episode (MDE) and generalized anxiety disorder (GAD). Some have suggested that this co-morbidity may be artificial and the product of the current diagnostic system. Due to the added direct and indirect costs associated with co-morbidity it is important to investigate if methods of diagnostic classification are artificially increasing the level of observed co-morbidity. Methods: The item response theory log-likelihood ratio procedure was used to test for differential item functioning of MDE symptoms between respondents with and without a diagnosis of GAD in the 2001-2002 National Epidemiological Survey on Alcohol and Related Conditions. Results: The presence of GAD significantly increased the chances of reporting any symptom of MDE with odds ratios ranging from 2.54 to 3.60. However, there was no indication of significant differential item functioning of MDE symptoms in respondents with and without GAD. Conclusions: The lack of any significant differential item functioning indicates that cases with GAD do not present with a distinct MDE symptom profile, one that is consistent with the endorsement of symptoms that are conceptually similar in nature between the two disorders, compared to cases without GAD. This does not support the hypothesis that co-morbidity between MDE and GAD is artificially inflated due to similar symptom criteria required by the current diagnostic system. Instead, MDE and GAD may be thought of as two distinct diagnostic entities that frequently co-occur due to a shared underlying trait.
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(2008)Journal ArticleObjectives: It has been previously argued that the methodology employed by the Composite International Diagnostic Interview version 2.1 to assess the substance induced and general medical condition exclusion criteria are inadequate. As a result prevalence estimates generated from epidemiological studies using this interview may be underestimated. The purpose of the current study was to examine the substance induced and general medical condition exclusion criteria in the Australian National Survey for Mental Health and Well-being and determine the impact they have on prevalence estimates of the common mental disorders. Method: Data from the 1997 Australian National Survey of Mental Health and Wellbeing were analyzed. Frequencies were generated as an indication of how many respondents believed their psychiatric symptoms were always due to a substance or general medical condition. New DSM-IV prevalence estimates were calculated ignoring the application of the substance induced and general medical condition exclusion criteria and compared to standard DSM-IV prevalence estimates. Results: The effect of the substance induced and general medical condition exclusion criteria on final prevalence rates were minimal with around a 0.1% increase when the exclusions were ignored. This equates to a relative difference ranging from no difference for Generalized Anxiety Disorder to an increase of 12% of the base prevalence estimate for Agoraphobia. Conclusions: In surveys that use the Composite International Diagnostic Interview version 2.1 the substance induced and general medical condition exclusion criteria have a minor impact on determining final case definition in the majority of mental disorders.
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(2012)Journal ArticleDiagnostic instruments must be relatively free from respondent burden and cost effective to administer whilst remaining faithful to the psychiatric nomenclature. It seems logical to develop short form alternatives to rather lengthy and complicated diagnostic interviews to facilitate large scale data collection. The current study examines one method, signal detection theory, for developing a short form interview based on the Composite International Diagnostic Interview version 3.0. The method was able to retain the smallest number of items to predict a lifetime and 30 day DSM-IV diagnosis for ten disorders. Concordance analyses between the full form and the short form modules, demonstrated an excellent level of agreement in the whole sample and various subsamples of the Australian population as well as in an international comparison sample of the U.S. population. The good concordance between the long form and the short form demonstrates the ability of signal detection theory to assist in the development of valid short forms, which could replace lengthy diagnostic interviews when the aim is to reduce respondent burden and overall research costs.
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(2013)Journal Article
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(2013)Journal ArticleBackground The link between heroin use and crime has been well established; however, there has been little opportunity to examine this relationship longitudinally. This study examines the relationship between static and dynamic predictors of criminal involvement, and the degree to which changes in dynamic risk factors moderate the risk of criminal involvement over time. Method Data was collected as part of the Australian Treatment Outcome Study, a 3-year longitudinal study of 615 people with heroin dependence conducted in Sydney, Australia. Past-month criminal involvement (property crime, drug dealing, fraud, violent crime), demographic, drug use and mental health characteristics were assessed at each interview. Results Criminal involvement was consistently and independently predicted by lack of wage/salary as a main source of income, (OR 2.17), meeting diagnostic criteria for anti-social personality disorder (OR 1.91) and major depression (OR 1.41), screening positive for borderline personality disorder (OR 1.47), male sex (OR 1.44), a criminal history (OR 1.33), greater severity of dependence (OR 1.21), more extensive heroin use (OR 1.09), and younger age (OR 0.96) over the 3-year period. Conclusions These findings provide strong evidence of the robust nature of the association between more extensive heroin use, severity of dependence, the co-occurrence of mental health conditions, and an individual’s capacity for employment, and criminal involvement. Interventions aimed at increasing an individual’s employability and improving mental health in particular, may reduce the risk of criminal involvement among people with heroin dependence.