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

Bibliographic Data

ID11199040
AuthorsChiara Gennaro, Omar Paccagnella (0000-0002-6961-3839, University of Padua, corresponding author), Paola Zaninotto (0000-0003-3036-0499, University College London)
Year2021
Volume41
Issue2
Pages339-361
Publication date2021-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAgeing and Society (JOURNAL)
Journal identifiersISSN: 0144-686X • E-ISSN: 1469-1779
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0144686x19001077
OpenAlexW2974664223
LanguageEN
Citations received4
References cited40

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’

Class (philosophy) · Cognition · Depression (economics) · Depressive symptoms · Geography · Identification (biology) · Interpretation (philosophy) · Latent class model · Mental health · Psychiatry · Scale (ratio) · Statistics · Artificial Intelligence · Clinical Psychology · Computer Science · Employment and Welfare Studies · Health disparities and outcomes · Mathematics · Psychological Well-being and Life Satisfaction · Psychology

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