Patterns of the physical, cognitive, and mental health status of older individuals in a real-life primary care setting and differences in coping styles - Wittlinger, Thomas; Bekić, Sanja; Guljaš, Silva; Periša, Vlatka; Volarić, Mile; Trtica Majnarić, Ljiljana (Frontiers in Medicine, 2022)
Background The types of older patients with multimoribity (coexisting diseases) are highly heterogeneous and complex, which hampers delivering of individualized and patient-centered care to these patients. Purpose The aim of this study was to show how physical frailty, mental disorders, and cognitive impairment cluster together and how these clusters are associated with comorbidities, stressful events, and coping styles. Methods Participants were older individuals (≥60), attenders of PC, who were mobile and not suffering from dementia. For screening participants on physical frailty, cognitive impairment, and mental disorders, we used Fried`s phenotype model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS) and the Geriatric Depression Scale (GDS). For testing participants on coping styles, we used the 14-scale questionnaire Brief COPE. To identify clusters, we used the algorithm fuzzy k-means. To further describe the clusters, we examined differences in age, gender, number of chronic diseases and medications prescribed, some diagnoses of chronic diseases, life events, body mass index, renal function, expressed as the glomerular filtration rate, and coping styles. Results The most appropriate cluster solution was the one with three clusters, that were termed as: functional (FUN) (N=139), dysfunctional (DFUN) (N=81), and cognitively impaired (COG-IMP) (N=43). The cluster FUN was associated with positive reframing coping style. Religion and self-blame were coping mechanisms specifically associated only with cluster DFUN; self-distraction only with cluster COG-IMP; and these two latter clusters shared the mechanisms of behavioral disengagement and denial. Conclusions The research approach presented in this study could provide a new framework for decoding patient complexity. Gaining insights into this complexity is expected to improve personalized prevention and treatment strategies for older individuals with multimorbidity.