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

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’.

Home confinement and mental health problems during the Covid-19 pandemic among the population aged 50 and older: A gender perspective

“…Considering that depression is one of the most common mental disorders in later life ( Gennaro et al, 2019 ) and the fact that it is a highly disruptive and costly condition ( Acciai & Hardy, 2017 ), the mental health of older individuals confined to the home should be a priority for policy-makers. In this regard, measures requiring home confinement should take into account the mental health risks involved and, when introduced, should be accompanied by mental health interventions, particularly for men aged 50 and over.…”

Depression deterioration of older adults during the first wave of the COVID-19 outbreak in Europe

“…The final sample was composed of 37,475 individuals living in 17 European countries (Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Israel, Italy, Luxembourg, Poland, Slovenia, Spain, Sweden and Switzerland). It comprised all respondents identified in any category of the depression classification carried out by Gennaro et al (2021) on the SHARE wave 6 data (see Section 3.3.2 ) who completed the COVID-19 survey.…”

“…Using SHARE wave 6 data on the EURO-D scale, Gennaro et al (2021) investigated the heterogeneity of depressive symptoms amongst older people and identified meaningful sub-groups of depressive symptom profiles. They introduced a model-based approach (consisting of a Latent Class Analysis followed by a Factor Analysis based on the individual probabilities of belonging to each class) for classifying individuals in a more accurate way than the simple dichotomisation of ‘depressed/non-depressed’.…”

“…SHARE is the first cross-country harmonised ageing survey that collects detailed information to study the health-related and socio-economic impacts of the COVID-19 outbreak. Furthermore, to investigate the role of past mental health problems, we take advantage of SHARE datasets that include multiple items to measure depression; we utilise the depression classification developed by Gennaro et al (2021) based on the same SHARE respondents interviewed in 2015 and that attributed each individual to a depressive group. We use this refined classification of mental problems amongst older individuals to explore the relationship between belonging to a certain pre-pandemic depressive group and reporting depression-related disorders five years later at the time of COVID-19.…”