Hueniken Katrina, Somé Nibene Habib, Abdelhack Mohamed, Taylor Graham, Elton Marshall Tara, Wickens Christine M, Hamilton Hayley A, Wells Samantha, Felsky Daniel
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
JMIR Ment Health. 2021 Nov 17;8(11):e32876. doi: 10.2196/32876.
The COVID-19 global pandemic has increased the burden of mental illness on Canadian adults. However, the complex combination of demographic, economic, and lifestyle factors and perceived health risks contributing to patterns of anxiety and depression has not been explored.
The aim of this study is to harness flexible machine learning methods to identify constellations of factors related to symptoms of mental illness and to understand their changes over time during the COVID-19 pandemic.
Cross-sectional samples of Canadian adults (aged ≥18 years) completed web-based surveys in 6 waves from May to December 2020 (N=6021), and quota sampling strategies were used to match the English-speaking Canadian population in age, gender, and region. The surveys measured anxiety and depression symptoms, sociodemographic characteristics, substance use, and perceived COVID-19 risks and worries. First, principal component analysis was used to condense highly comorbid anxiety and depression symptoms into a single data-driven measure of emotional distress. Second, eXtreme Gradient Boosting (XGBoost), a machine learning algorithm that can model nonlinear and interactive relationships, was used to regress this measure on all included explanatory variables. Variable importance and effects across time were explored using SHapley Additive exPlanations (SHAP).
Principal component analysis of responses to 9 anxiety and depression questions on an ordinal scale revealed a primary latent factor, termed "emotional distress," that explained 76% of the variation in all 9 measures. Our XGBoost model explained a substantial proportion of variance in emotional distress (r=0.39). The 3 most important items predicting elevated emotional distress were increased worries about finances (SHAP=0.17), worries about getting COVID-19 (SHAP=0.17), and younger age (SHAP=0.13). Hopefulness was associated with emotional distress and moderated the impacts of several other factors. Predicted emotional distress exhibited a nonlinear pattern over time, with the highest predicted symptoms in May and November and the lowest in June.
Our results highlight factors that may exacerbate emotional distress during the current pandemic and possible future pandemics, including a role of hopefulness in moderating distressing effects of other factors. The pandemic disproportionately affected emotional distress among younger adults and those economically impacted.
新冠疫情全球大流行增加了加拿大成年人的精神疾病负担。然而，人口统计学、经济和生活方式因素以及感知到的健康风险等复杂组合对焦虑和抑郁模式的影响尚未得到探讨。
本研究旨在利用灵活的机器学习方法来识别与精神疾病症状相关的因素组合，并了解在新冠疫情期间这些因素随时间的变化情况。
2020年5月至12月期间，加拿大成年人（年龄≥18岁）的横断面样本分6波完成了基于网络的调查（N = 6021），并采用配额抽样策略，以使讲英语的加拿大人口在年龄、性别和地区方面达到匹配。调查测量了焦虑和抑郁症状、社会人口学特征、物质使用情况以及对新冠疫情的感知风险和担忧。首先，主成分分析用于将高度共病的焦虑和抑郁症状浓缩为一个基于数据驱动的情绪困扰指标。其次，极端梯度提升（XGBoost），一种能够对非线性和交互关系进行建模的机器学习算法，用于将该指标对所有纳入的解释变量进行回归分析。使用SHapley加法解释（SHAP）来探索变量重要性和随时间的影响。
对9个焦虑和抑郁问题的有序量表回答进行主成分分析，揭示了一个主要潜在因素，称为“情绪困扰”，它解释了所有9个指标中76%的变异。我们的XGBoost模型解释了情绪困扰中很大一部分方差（r = 0.39）。预测情绪困扰升高的3个最重要因素是对财务状况的担忧增加（SHAP = 0.17）、对感染新冠的担忧（SHAP = 0.17）以及较年轻的年龄（SHAP = 0.13）。希望感与情绪困扰相关，并调节了其他几个因素的影响。预测的情绪困扰随时间呈现非线性模式，5月和11月预测症状最高，6月最低。
我们的研究结果突出了在当前大流行及未来可能的大流行期间可能加剧情绪困扰的因素，包括希望感在调节其他因素的困扰作用方面的作用。大流行对较年轻成年人以及那些受到经济影响的人的情绪困扰影响尤为严重。
