Predictors of Anxiety in Middle-Aged and Older European Adults: A Machine Learning Comparative Study

Stephen R. Aichele ()
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Stephen R. Aichele: Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO 80523, USA

Social Sciences, 2024, vol. 13, issue 11, 1-14

Abstract: Anxiety in older adults is a prevalent yet under-recognized condition associated with significant societal and individual burdens. This study used a machine learning approach to compare the relative importance of 57 risk and protective factors for anxiety symptoms in a population-representative sample of middle-aged and older European adults (N = 65,684; ages 45–103 years; 55.7% women; 15 countries represented). The results revealed loneliness and self-rated poor health as primary risk factors (Nagelkerke R 2 = 0.272), with additional predictive contributions from country of residence, functional limitations, financial distress, and family care burden. Notably, follow-up analysis showed that none of the 16 social network variables were associated with loneliness; rather, cohabitating with a partner/spouse was most strongly associated with reduced loneliness. Further research is needed to elucidate directional associations between loneliness and anxiety (both general and sub-types). These findings underscore the imperative of addressing loneliness for mitigating anxiety and related mental health conditions among aging populations.

Keywords: generalized anxiety; loneliness; social isolation; social network; cognition; population; aging (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2024
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