- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
Hidden Markov models to classify depression and estimate transitions across health states
- Francesca Bassi
- Omar Paccagnella
- Silvia Pileggi
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.13
Depression is one of the most common mental health disorders. It is characterised by a great heterogeneity of symptoms and leads to a significant deterioration in quality of life. Population ageing, combined with the increasing prevalence of depression in older age groups, has made this disorder an increasingly important public health problem. The aim of this work is to study the dynamics and the evolution of depression over time among elderly Italians. To this aim, we analyse data from the Survey of Health, Ageing and Retirement in Europe (SHARE). The final sample is composed of 1322 old Italian people, interviewed in the fifth, sixth, eighth and ninth waves of SHARE (2013, 2015, 2019 and 2022). Based on the latent nature of the variable of interest, the data are analysed with Hidden Markov Models (HMMs): they are able to identify different behavioural profiles that are not directly observed, allowing to identify groups of individuals with similar behavioural patterns over time from a set of observed variables. The best fitting model defines four latent classes with a first-order Markov chain and response probabilities constant over time; gender and age are active covariates for the initial state and transitions. These classes identify the elderly group with “no risk of depression”, “mild depression”, “moderate depression” and “severe depression”, respectively. As expected, we observed gender differences in levels and some trajectories. In particular, the transition from severe depression to a better state was more difficult after the Covid-19 pandemic. Recognising structural changes in the mental health of the elderly in the post-pandemic period may be useful to support targeted interventions for the population most at risk, optimising prevention or treatment.
- Keywords:
- Ageing,
- Covid-19,
- Depression,
- Latent variable,
- State dependence,
University of Padua, Italy - ORCID: 0000-0002-3257-7029
University of Padua, Italy - ORCID: 0000-0002-6961-3839
University of Padua, Italy
- Beekman, A.T., Copeland, J.R., Prince, M.J. (1999). Review of community prevalence of depression in later life. British Journal of Psychiatry, 174, pp. 307–311. DOI: 10.1192/bjp.174.4.307
- Blazer, D.G. (2003). Depression in late life: review and commentary. The Journals of Gerontology: Series A, 58(3), pp. 249–265. DOI: 10.1093/gerona/58.3.m249
- Börsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., Schaan, B., Stuck, S., Zuber, S. (2013). Data resource profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiology, 42(4), pp. 992–1001. DOI: 10.1093/ije/dyt088
- Bruce, M.L., Leaf, P.J. (1989). Psychiatric disorders and 15-month mortality in a community sample of older adults. American Journal of Public Health, 79(6), pp. 727–730. DOI: 10.2105/ajph.79.6.727
- Cole, M.G., Dendukuri, N. (2003). Risk factors for depression among elderly community subjects: a systematic review and meta-analysis. American Journal of Psychiatry, 160(6), pp. 1147–1156. DOI: 10.1176/appi.ajp.160.6.1147
- Dennerstein, L., Astbury, J., Morse, C. (1993). Psychosocial and mental health aspects of women’s health. World Health Statistics Quarterly, 46(4), pp. 234–236.
- Dewey, M., Prince, M. (2005). Mental health, in A. Börsch-Supan, A. Brugiavini, H. Jürges, J. Mackenbach, J. Siegrist, and G. Weber (eds.). Health, Ageing and Retirement in Europe – First Results from the Survey of Health, Ageing and Retirement in Europe, Mannheim Research Institute for the Economics of Aging (MEA), Mannheim, (DE), pp. 108–117.
- Heun, R., Hein, S. (2005). Risk factors of major depression in the elderly. European Psychiatry, 20(3), pp.199–204. DOI: 10.1016/j.eurpsy.2004.09.036
- Kessler, R.C. (2003). Epidemiology of women and depression. Journal of Affective Disorders, 74(1), pp. 5–13. DOI: 10.1016/s0165-0327(02)00426-3
- Prince, M., Reischies, F., Beekman, A.T., Fuhrer, R., Jonker, C., Kivelä, S.L., Lawlor, B., Lobo, A., Magnusson, H., Fichter, I., van Oyen, H., Roelands, M., Skoog, I., Turrina, C., Copeland, J.R. (1999). Development of the EURO-D scale – A European Union initiative to compare symptoms of depression in 14 European centres. British Journal of Psychiatry, 174, pp. 330–338. DOI: 10.1192/bjp.174.4.330
- Ross, S.M. (2014). Introduction to Probability Models. Academic Press, San Diego (CA). DOI: 10.1016/b978-0-12-407948-9.00001-3
- SHARE-ERIC (2024a). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 5. Release version: 9.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w5.900 DOI: 10.6103/SHARE.w5.900
- SHARE-ERIC (2024b). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 6. Release version: 9.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w6.900 DOI: 10.6103/SHARE.w6.900
- SHARE-ERIC (2024c). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 8. Release version: 9.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w8.900 DOI: 10.6103/SHARE.w8.900
- SHARE-ERIC (2024d). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 9. Release version: 9.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w9.900 DOI: 10.6103/SHARE.w9.900
- Vermunt, J.K., Tran, B., Magidson, J. (2008). Latent class models in longitudinal research, in S. Menard (ed.). Handbook of Longitudinal Research: Design, Measurement, and Analysis, Elsevier, San Diego (CA), pp. 373-385. DOI: 10.4135/9781412986311.n10
- Yaka, E., Keskinoglu, P., Ucku, R., Yener, G.G., Tunca, Z. (2014). Prevalence and risk factors of depression among community dwelling elderly. Archives of gerontology and geriatrics, 59(1), pp. 150–154. DOI: 10.1016/j.archger.2014.03.014
Informazioni sul capitolo
Titolo del capitolo
Hidden Markov models to classify depression and estimate transitions across health states
Autori
Francesca Bassi, Omar Paccagnella, Silvia Pileggi
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.13
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series