Εκτίμηση των παραγόντων που προσδιορίζουν τα επίπεδα και πρότυπα νοσηρότητας σε ευπαθείς πληθυσμιακές ομάδες στην Ελλάδα και σε άλλες ευρωπαϊκές χώρες με έμφαση στην ψυχική υγεία

Σεραφετινίδου, Ελένη

Doctoral Thesis

Author

Σεραφετινίδου, Ελένη

Date

2020-02-10

Abstract

Depression is a mental illness that can cause many problems to the individuals suffering from it, to their families and the society. By 2020 depression is projected to become the second most prevalent disease in the world, with the World Health Organization (WHO) ranking it as the fourth most prevalent disease and predicting that by 2030 it will be the most important one. Depression has been shown to be strongly correlated with morbidity, use of health care services, and increased mortality, while it has also been linked to a decline in patients' well-being and quality of life. Therefore, the study of depression and the factors that determine it is of particular interest and attracts researchers from many disciplines, not only from the medical field but also from the field of psychology, social sciences, etc. This doctoral thesis addresses the phenomenon of depression using mathematical tools and information derived from the SHARE study (Survey of Health, Ageing and Retirement in Europe) which refers to respondents over 50 years old, residing in European countries. Depression is measured using the standard EURO-D scale, consisting of 12 symptoms, and a binary variable (EURODCAT) which separates those suffering from 0 to 3 symptoms (non-sufferers) from those suffering from 4 and above symptoms (sufferers). Initially, the contribution of childhood factors as well as early, middle and later adulthood to depression in later life is assessed. These factors cover health and socioeconomic variables, adverse experiences and demographic characteristics. The direct and indirect effects of these factors on depression, as well as on its two sub-scales, affective suffering and motivational symptoms are examined. The analysis focuses on mechanisms and mediators explaining the indirect impact of childhood factors. Next, based on cumulative disadvantage theory, the cumulative effect of health, socioeconomic status, and adverse experiences on depression are examined in a model that includes their main effects as well as their interactions. Finally, factors contributing to changes in depression levels following the financial crisis of 2008 are being investigated. The study of the phenomenon concludes with an analysis of factors from concurrent life, including depression, that determine the structural components of the scale of loneliness as defined in the SHARE study. The results of the above analyses show that women suffer from depression at twice the rate of men. In addition, the predictive power of childhood factors is highlighted, which remains strong even when factors from across the adult life of respondents are added to the models. Moreover, it is found that factors affecting the subscales of affective suffering and motivational symptoms differentiate. Concerning cumulative disadvantages, health appears to have the greatest relative impact for both men and women, as well as for European populations. In addition, improved health as well as a sense of satisfaction with life contribute positively to decreasing depression levels, while deteriorating health and cognitive function as well as lower levels of life satisfaction increase depression levels. Finally, in terms of the components of the scale of loneliness, chronic illnesses contribute to a lack of companionship and to loneliness. Better financial status is associated with lower levels of isolation while having children is associated with lower levels of both isolation and loneliness. Life satisfaction is associated with lower levels of a lack of companionship and loneliness.

Department

Σχολή Χρηματοοικονομικής και Στατιστικής. Τμήμα Στατιστικής και Ασφαλιστικής Επιστήμης

Number of pages

302

Language

Greek

URI

https://dione.lib.unipi.gr/xmlui/handle/unipi/12745
http://dx.doi.org/10.26267/unipi_dione/168

Collections

Τμήμα Στατιστικής και Ασφαλιστικής Επιστήμης

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