• Machine Learning-Based Predict...

*Result*: Machine Learning-Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study.

Title:
Machine Learning-Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study.
Authors:
Hueniken K; 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., Somé NH; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Institute for Clinical Evaluative Sciences, Toronto, ON, Canada.; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada., Abdelhack M; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada., Taylor G; School of Engineering, University of Guelph, Guelph, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada., Elton Marshall T; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.; Department of Health Sciences, Lakehead University, Thunder Bay, ON, Canada., Wickens CM; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada., Hamilton HA; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada., Wells S; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.; School of Psychology, Deakin University, Burwood, Australia., Felsky D; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.; Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
Source:
JMIR mental health [JMIR Ment Health] 2021 Nov 17; Vol. 8 (11), pp. e32876. Date of Electronic Publication: 2021 Nov 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101658926 Publication Model: Electronic Cited Medium: Print ISSN: 2368-7959 (Print) Linking ISSN: 23687959 NLM ISO Abbreviation: JMIR Ment Health Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Toronto : JMIR Publications Inc., [2014]-
References:
Arch Intern Med. 2006 May 22;166(10):1092-7. (PMID: 16717171)
Sci Rep. 2021 Jan 21;11(1):1980. (PMID: 33479383)
Dialogues Clin Neurosci. 2015 Sep;17(3):249-60. (PMID: 26487806)
Acta Psychiatr Scand. 2003 Sep;108(3):163-74. (PMID: 12890270)
J Occup Environ Med. 2020 Sep;62(9):686-691. (PMID: 32890205)
QJM. 2020 Jun 22;:. (PMID: 32569360)
J Gerontol B Psychol Sci Soc Sci. 2021 Jan 18;76(2):e24-e29. (PMID: 32470120)
Depress Anxiety. 2021 Mar;38(3):286-306. (PMID: 33225514)
J Affect Disord. 2020 Oct 1;275:119-124. (PMID: 32658813)
Can Public Policy. 2020 Jul 1;46(Suppl 1):S55-S65. (PMID: 38629977)
Lancet. 2020 Mar 14;395(10227):912-920. (PMID: 32112714)
N Engl J Med. 2020 Aug 6;383(6):510-512. (PMID: 32283003)
J Psychiatr Res. 2011 Aug;45(8):1027-35. (PMID: 21439576)
Psychometrika. 2006 Sep;71(3):425-440. (PMID: 19946599)
J Med Internet Res. 2020 Nov 6;22(11):e24018. (PMID: 33027032)
Psychiatry Clin Neurosci. 2020 Apr;74(4):281-282. (PMID: 32034840)
JAMA Psychiatry. 2021 Apr 1;78(4):372-379. (PMID: 33533876)
J Clin Psychol. 1986 Jan;42(1):28-33. (PMID: 3950011)
J Glob Health. 2020 Jun;10(1):010331. (PMID: 32355556)
Am J Public Health. 2016 Aug;106(8):1422-6. (PMID: 27196650)
Public Health. 2020 Aug;185:243-245. (PMID: 32688099)
Psychol Assess. 2004 Dec;16(4):360-72. (PMID: 15584795)
Soc Sci Med. 2020 Nov;265:113389. (PMID: 33039732)
Lancet Child Adolesc Health. 2021 May;5(5):316-318. (PMID: 33864741)
Contributed Indexing:
Keywords: COVID-19; anxiety; cross-sectional; depression; distress; emotion; emotional distress; machine learning; mental health; model; prediction; survey; symptom
Entry Date(s):
Date Created: 20211027 Latest Revision: 20240818
Update Code:
20260130
PubMed Central ID:
PMC8601369
DOI:
10.2196/32876
PMID:
34705663
Database:
MEDLINE

*Further Information*

*Background: 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.
Objective: 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.
Methods: 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).
Results: 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<sup>2</sup>=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.
Conclusions: 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.
(©Katrina Hueniken, Nibene Habib Somé, Mohamed Abdelhack, Graham Taylor, Tara Elton Marshall, Christine M Wickens, Hayley A Hamilton, Samantha Wells, Daniel Felsky. Originally published in JMIR Mental Health (https://mental.jmir.org), 17.11.2021.)*