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Detecting anxiety and depression in hospitalised elderly patients using a brief inventory

Published online by Cambridge University Press:  16 April 2020

P. Huber ,
R. Mulligan ,
A. Mackinnon ,
T. Nebuloni-French  and
J.P. Michel
P. Huber
Affiliation:
University Hospital of Geneva, Route de Mon-Idée, 1226Geneva, Switzerland
R. Mulligan
Affiliation:
University Hospital of Geneva, Route de Mon-Idée, 1226Geneva, Switzerland
A. Mackinnon
Affiliation:
Biostatistics and Psychometric Unit, Mental Health Research Institute, Department of Psychological Medicine, Monash University Parkville, Australia
T. Nebuloni-French
Affiliation:
University Hospital of Geneva, Route de Mon-Idée, 1226Geneva, Switzerland
J.P. Michel
Affiliation:
University Hospital of Geneva, Route de Mon-Idée, 1226Geneva, Switzerland
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Summary

This study validated a French language version of an inventory designed to detect symptoms of depression and anxiety [Goldberg et al, 1987] in a sample of elderly French-speaking inpatients at risk for one of these disorders. Latent trait analysis was used to replicate the structure of the symptoms in the inventory, and receiver operating characteristic analysis was used to assess the performance of the inventory as a screening measure for Major Depressive Episode and Generalised Anxiety Disorder according to DSM-IV criteria. Reflecting the ascertainment of individuals in the sample as being at risk for a disorder, prevalence of individual symptoms was high although the general structure of the inventory was found to be comparable to that found in samples of both community elderly and younger medical patients. ROC analyses showed that the subscales of inventory performed satisfactorily as screening measures for anxiety or depression but lacked specificity for each disorder. In addition to providing further evidence for the utility of this inventory to detect general psychiatric distress in elderly persons, this study provides a valid means of detecting symptoms of depression and anxiety in French speaking groups.

Keywords

agedaged, 80-and-overgeriatric assessmentanxiety-diagnosisdepression diagnosis
Type
Original article
Information
European Psychiatry , Volume 14 , Issue 1 , March 1999 , pp. 11 - 16
Copyright
Copyright © Elsevier, Paris 1999

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References

Delong, ERDelong, DMClarke-Pearson, DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44 : 837–845.CrossRefGoogle ScholarPubMed
Fernandez, FLevy, JKLachar, BLSmall, GW.The management of depression and anxiety in the elderly. J Clin Psychiatry 1995 : 56 suppl 2 : 20–29.Google ScholarPubMed
Fraser, C. NOHARM II: An IBM PC computer program for fitting both unidimensional and multidimensional normal ogive models of latent trait theory. Unpublished computer program manual. Armidale, Australia: Centre for Behavioural Studies, University of New England.Google Scholar
Goldberg, DPBridges, KDuncan-Jones, PGrayson, D. Dimensions of neuroses seen in primary-care settings. Psychol Med 1987 : 17 : 461–470.CrossRefGoogle ScholarPubMed
Goldberg, DBridges, KDuncan-Jones, PGrayson, D. Detecting anxiety and depression in general medical settings. Br Med J 1988 : 297 : 897–899.CrossRefGoogle ScholarPubMed
Hambleton, RKSwaminathan, H. Item Response Theory. Dordrecht: Kluwer Nijhoff; 1985.10.1007/978-94-017-1988-9CrossRefGoogle Scholar
Hanley, JAMcNeil, BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982: 143: 29–36.CrossRefGoogle Scholar
Henderson, ASJorm, AFMackinnon, AChristensen, HScott, LRKorten, A.Eet alThe prevalence of depressive disorders and the distribution of depressive symptoms in later life: a survey using Draft ICD-10 and DSM-III-R. Psychol Med 1993 : 23 : 719–729.CrossRefGoogle ScholarPubMed
Lord, FMNovick, MR. Statistical theories of mental test scores. Readings: Wesley; 1968.Google Scholar
Mackinnon, AChristensen, HJorm, AFHenderson, ASScott, LRKorten, AE. A latent trait analysis of an inventory designed to detect symptoms of anxiety and depression using an elderly community sample. Psychol Med 1994: 24 : 977–986.CrossRefGoogle ScholarPubMed
Margolis, RL. Nonpsychiatrist house staff frequently misdiagnose psychiatric disorders in General Hospital inpatients. Psychosomatics 1994 : 35 : 485–491.CrossRefGoogle ScholarPubMed
McDonald, RP. Normal-ogive multidimensional model. In: Van der Linden, WJHambleton, RK eds. Handbook of Modern Item Response Theory. Springer: New York 1997.Google Scholar
Murphy, JMBerwick, DMWeinstein, MCBorus, JFBudman, SHKlerman, GL. Performance of screening and diagnostic tests: application of receiver operating characteristic analysis. Arch Gen Psychiatry 1987 : 44 : 550–555.CrossRefGoogle ScholarPubMed
Shrout, PEFleiss, JLReliability and case detection. In: Wing, J.KBebbington, PRobins, L.N eds. What is a Case? London: Grant McIntyre Ltd; 1981. p.117–128.Google Scholar
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