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Understanding risk factors of recurrent anxiety symptomatology in an older population with mild to severe depressive symptoms: A Bayesian approach
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Abstract
Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important. Gaining insights into these factors through an explainable, probabilistic approach can enhance improved management. A Bayesian network (BN) is well-suited for this purpose. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A BN model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The BN model was also compared to four machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. The model demonstrated generalisation abilities while being less complex and more explainable than other machine learning models. Key factors associated with recurrence of anxiety symptomatology were: “Not being able to stop or control worrying”; “Becoming easily annoyed or irritable”; “Trouble relaxing”; and “depressive symptomatology severity”. These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology.
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Publisher
MDPI
Citation
Applied Sciences 14(16),
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Funding Information
Sao Paulo Research Foundation (process number 2017/50094 2) and the Joint Global Health Trials initiative, jointly funded by the Department of Health and Social Care(DHSC), the Foreign, Commonwealth & Development Office (FCDO), . FAPESP supported CAN (2018/19343-9)and (2022/05107-7), MM (2021/03849-3). M.S. is supported by CNPq-Brazil (307579/2019-0)
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Article
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https://creativecommons.org/licenses/by-nc-sa/4.0/