Prevalence of depressive symptoms and associated factors during the COVID-19 pandemic: A national-based study

• Published in: Journal of affective disorders Vol. 333; pp. 1 - 9
• Main Authors: Dong, Xing-Xuan, Li, Dan-Lin, Miao, Yi-Fan, Zhang, Tianyang, Wu, Yibo, Pan, Chen-Wei
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.07.2023; Published by Elsevier B.V
• Subjects: Adult; BP neural network; China - epidemiology; COVID-19 - epidemiology; Depression - epidemiology; Depression - psychology; Depressive symptoms; Female; Humans; Internet Use - statistics & numerical data; Logistic Models; Loneliness; Male; Middle Aged; Neural Networks, Computer; Pandemics; Prevalence; Research Paper; Risk Factors; Sleep Quality; Stress, Psychological - epidemiology; Young Adult; China; Depressive symptoms; BP neural network; Prevalence; Risk factors
• ISSN: 0165-0327; 1573-2517
• DOI: 10.1016/j.jad.2023.04.034

Previous studies have reported that the prevalence of depression and depressive symptoms was significantly higher than that before the COVID-19 pandemic. This study aimed to explore the prevalence of depressive symptoms and evaluate the importance of influencing factors through Back Propagation Neural Network (BPNN). Data were sourced from the psychology and behavior investigation of Chinese residents (PBICR). A total of 21,916 individuals in China were included in the current study. Multiple logistic regression was applied to preliminarily identify potential risk factors for depressive symptoms. BPNN was used to explore the order of contributing factors of depressive symptoms. The prevalence of depressive symptoms among the general population during the COVID-19 pandemic was 57.57 %. The top five important variables were determined based on the BPNN rank of importance: subjective sleep quality (100.00 %), loneliness (77.30 %), subjective well-being (67.90 %), stress (65.00 %), problematic internet use (51.20 %). The prevalence of depressive symptoms in the general population was high during the COVID-19 pandemic. The BPNN model established has significant preventive and clinical meaning to identify depressive symptoms lay theoretical foundation for individualized and targeted psychological intervention in the future. •This study established multi-layer perception neural network model to predict depressive symptoms among Chinese population.•Subjective sleep quality, loneliness, subjective well-being, stress and problematic internet use were stronger contributors.•This study aims to provide a solid theoretical basis for later individualized and targeted psychological intervention.