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A model-driven approach to better identify older people at risk of depression

Published online by Cambridge University Press:  17 September 2019

Chiara Gennaro ,
Omar Paccagnella Open the ORCID record for Omar Paccagnella [Opens in a new window]  and
Paola Zaninotto
Chiara Gennaro
Affiliation:
Palladio Group S.p.A., Dueville, Italy
Omar Paccagnella*
Affiliation:
Department of Statistical Sciences, University of Padua, Padua, Italy
Paola Zaninotto
Affiliation:
Department of Epidemiology and Public Health, University College London, London, UK
*
*Corresponding author. Email: [email protected]
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Abstract

Depression in later life is one of the most common mental disorders. Several instruments have been developed to detect the presence or the absence of certain symptoms or emotional disorders, based on cut-off points. However, the use of a cut-off does not allow identification of depression sub-types or distinguish between mild and severe depression. As a result, depression may be under- or over-diagnosed in older people. This paper aims to apply a model-driven approach to classify individuals into distinct sub-groups, based on different combinations of depressive and emotional conditions. This approach is based on two distinct statistical solutions: first, a latent class analysis is applied to the items collected by the depression scale and, according to the final model, the probability of belonging to each class is calculated for every individual. Second, a factor analysis of these classes is performed to obtain a reduced number of clusters for easy interpretation. We use data collected through the EURO-D scale in a large sample of older individuals, participants of the sixth wave of the Survey of Health, Ageing and Retirement in Europe. We show that by using such a model-based approach it is possible to classify individuals in a more accurate way than the simple dichotomisation ‘depressed’ versus ‘non-depressed’.

Keywords

ageingEURO-D scalefactor analysislatent class analysisSHARE
Type
Article
Information
Ageing & Society , Volume 41 , Issue 2 , February 2021 , pp. 339 - 361
Copyright
Copyright © Cambridge University Press 2019

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