112 lines
3.8 KiB
YAML
112 lines
3.8 KiB
YAML
abstract: 'Background and purpose: The COVID-19 pandemic has posed considerable
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challenges to people''s mental health, and the prevalence of anxiety and
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depression increased substantially during the pandemic. Early detection
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of potential depression is crucial for timely preventive interventions;
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therefore, there is a need for depression prediction. Data and methods:
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This study was based on survey data collected from 5001 Norwegians (3001
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in 2020 and 2000 in 2021). Machine learning models were used to predict
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depression risk and to select models with the best performance for each
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pandemic phase. Probability thresholds were chosen based on
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cost-sensitive analysis, and measures such as accuracy (ACC) and the
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area under the receiver operating curve (AUC) were used to evaluate the
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models'' performance. Results: The study found that decision tree models
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and regularised regressions had the best performance in both 2020 and
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2021. For the 2020 predictions, the highest accuracies were obtained
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using gradient boosting machines (ACC = 0.72, AUC = 0.74) and random
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forest algorithm (ACC = 0.71, AUC = 0.75). For the 2021 predictions, the
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random forest (ACC = 0.76, AUC = 0.78) and elastic net regularisation
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(ACC = 0.76, AUC = 0.78) exhibited the best performances. Highly ranked
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predictors of depression that remained stable over time were
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self-perceived exposure risks, income, compliance with nonpharmaceutical
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interventions, frequency of being outdoors, contact with family and
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friends and work-life conflict. While epidemiological factors (having
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COVID symptoms or having close contact with the infected) influenced the
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level of psychological distress to a larger extent in the relatively
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early stage of pandemic, the importance of socioeconomic factors
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(gender, age, household type and employment status) increased
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substantially in the later stage. Conclusion: Machine learning models
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consisting of demographic, socioeconomic, behavioural and
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epidemiological features can be used for fast `first-hand'' screening to
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diagnose mental health problems. The models may be helpful for
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stakeholders and healthcare providers to provide early diagnosis and
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intervention, as well as to provide insight into forecasting which
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social groups are more vulnerable to mental illness in which social
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settings.'
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affiliation: 'Bakkeli, NZ (Corresponding Author), Oslo Metropolitan Univ, Ctr Res
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Pandem \& Soc, Consumpt Studies Norway, POB 4,St Olavs Plass, N-0130 Oslo, Norway.
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Bakkeli, Nan Zou, Oslo Metropolitan Univ, Ctr Res Pandem \& Soc, Consumpt Studies
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Norway, POB 4,St Olavs Plass, N-0130 Oslo, Norway.'
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article-number: 08944393211069622
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author: Bakkeli, Nan Zou
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author-email: Nan.Bakkeli@OsloMet.no
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author_list:
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- family: Bakkeli
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given: Nan Zou
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da: '2023-09-28'
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doi: 10.1177/08944393211069622
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earlyaccessdate: FEB 2022
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eissn: 1552-8286
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files: []
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issn: 0894-4393
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journal: SOCIAL SCIENCE COMPUTER REVIEW
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keywords: 'mental health; depression; COVID-19; social determinants of health;
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inequality; machine learning'
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keywords-plus: 'MENTAL-HEALTH; SOCIAL DETERMINANTS; PRIMARY-CARE; DEPRESSION; ANXIETY;
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POPULATION; DISORDERS; WORKERS; IMPACT; WUHAN'
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language: English
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month: AUG
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number: '4'
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number-of-cited-references: '70'
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orcid-numbers: Bakkeli, Nan/0000-0002-4089-020X
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pages: 1227-1251
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papis_id: 6a9f4be6ca6f5635ae9697191a17ed7f
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ref: Bakkeli2023predictingpsychologi
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times-cited: '1'
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title: 'Predicting Psychological Distress During the COVID-19 Pandemic: Do Socioeconomic
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Factors Matter?'
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type: article
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unique-id: WOS:000769618400001
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usage-count-last-180-days: '1'
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usage-count-since-2013: '6'
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volume: '41'
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web-of-science-categories: 'Computer Science, Interdisciplinary Applications; Information
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Science \&
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Library Science; Social Sciences, Interdisciplinary'
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year: '2023'
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