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Comparing algorithms for deriving psychosis diagnoses from longitudinal administrative clinical records |
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| Authors: | Grant Sara Luming Luo Vaughan J. Carr Alessandra Raudino Melissa J. Green Kristin R. Laurens Kimberlie Dean Martin Cohen Philip Burgess Vera A. Morgan |
| Affiliation: | 1. Mental Health and Drug and Alcohol Office, NSW Ministry of Health, Sydney, Australia 2. Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, Australia 3. School of Population Health, University of Queensland, Brisbane, Australia 4. Research Unit for Schizophrenia Epidemiology, School of Psychiatry, University of New South Wales, Sydney, Australia 5. Schizophrenia Research Institute, Sydney, Australia 6. Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King’s College London, London, UK 7. Faculty of Medicine, School of Psychiatry, University of New South Wales, Sydney, Australia 8. Justice Health and Forensic Mental Health Network, Sydney, NSW, Australia 9. School of Medicine and Public Health University of Newcastle, Newcastle, Australia 10. Neuropsychiatric Epidemiology Research Unit, School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Perth, Australia |
| Abstract: | PurposeRegisters derived from administrative datasets are valuable tools in psychosis research, but diagnostic accuracy can be problematic. We sought to compare the relative performance of four methods for assigning a single diagnosis from longitudinal administrative clinical records when compared with reference diagnoses.MethodsDiagnoses recorded in inpatient and community mental health records were compared to research diagnoses of psychotic disorders obtained from semi-structured clinical interviews for 289 persons. Diagnoses were derived from administrative datasets using four algorithms; ‘At least one’ diagnosis, ‘Last’ or most recent diagnosis, ‘Modal’ or most frequently occurring diagnosis, and ‘Hierarchy’ in which a diagnostic hierarchy was applied. Agreements between algorithm-based and reference diagnoses for overall presence/absence of psychosis and for specific diagnoses of schizophrenia, schizoaffective disorder, and affective psychosis were examined using estimated prevalence rates, overall agreement, ROC analysis, and kappa statistics.ResultsFor the presence/absence of psychosis, the most sensitive and least specific algorithm (‘At least one’ diagnosis) performed best. For schizophrenia, ‘Modal’ and ‘Last’ diagnoses had greatest agreement with reference diagnosis. For affective psychosis, ‘Hierarchy’ diagnosis performed best. Agreement between clinical and reference diagnoses was no better than chance for diagnoses of schizoaffective disorder. Overall agreement between administrative and reference diagnoses was modest, but may have been limited by the use of participants who had been screened for likely psychosis prior to assessment.ConclusionThe choice of algorithm for extracting a psychosis diagnosis from administrative datasets may have a substantial impact on the accuracy of the diagnoses derived. An ‘Any diagnosis’ algorithm provides a sensitive measure for the presence of any psychosis, while ‘Last diagnosis’ is more accurate for specific diagnosis of schizophrenia and a hierarchical diagnosis is more accurate for affective psychosis. |
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