abstract: 'Background and purpose: The COVID-19 pandemic has posed considerable

  challenges to people''s mental health, and the prevalence of anxiety and

  depression increased substantially during the pandemic. Early detection

  of potential depression is crucial for timely preventive interventions;

  therefore, there is a need for depression prediction. Data and methods:

  This study was based on survey data collected from 5001 Norwegians (3001

  in 2020 and 2000 in 2021). Machine learning models were used to predict

  depression risk and to select models with the best performance for each

  pandemic phase. Probability thresholds were chosen based on

  cost-sensitive analysis, and measures such as accuracy (ACC) and the

  area under the receiver operating curve (AUC) were used to evaluate the

  models'' performance. Results: The study found that decision tree models

  and regularised regressions had the best performance in both 2020 and

  2021. For the 2020 predictions, the highest accuracies were obtained

  using gradient boosting machines (ACC = 0.72, AUC = 0.74) and random

  forest algorithm (ACC = 0.71, AUC = 0.75). For the 2021 predictions, the

  random forest (ACC = 0.76, AUC = 0.78) and elastic net regularisation

  (ACC = 0.76, AUC = 0.78) exhibited the best performances. Highly ranked

  predictors of depression that remained stable over time were

  self-perceived exposure risks, income, compliance with nonpharmaceutical

  interventions, frequency of being outdoors, contact with family and

  friends and work-life conflict. While epidemiological factors (having

  COVID symptoms or having close contact with the infected) influenced the

  level of psychological distress to a larger extent in the relatively

  early stage of pandemic, the importance of socioeconomic factors

  (gender, age, household type and employment status) increased

  substantially in the later stage. Conclusion: Machine learning models

  consisting of demographic, socioeconomic, behavioural and

  epidemiological features can be used for fast `first-hand'' screening to

  diagnose mental health problems. The models may be helpful for

  stakeholders and healthcare providers to provide early diagnosis and

  intervention, as well as to provide insight into forecasting which

  social groups are more vulnerable to mental illness in which social

  settings.'
affiliation: 'Bakkeli, NZ (Corresponding Author), Oslo Metropolitan Univ, Ctr Res
  Pandem \& Soc, Consumpt Studies Norway, POB 4,St Olavs Plass, N-0130 Oslo, Norway.

  Bakkeli, Nan Zou, Oslo Metropolitan Univ, Ctr Res Pandem \& Soc, Consumpt Studies
  Norway, POB 4,St Olavs Plass, N-0130 Oslo, Norway.'
article-number: 08944393211069622
author: Bakkeli, Nan Zou
author-email: Nan.Bakkeli@OsloMet.no
author_list:
- family: Bakkeli
  given: Nan Zou
da: '2023-09-28'
doi: 10.1177/08944393211069622
earlyaccessdate: FEB 2022
eissn: 1552-8286
files: []
issn: 0894-4393
journal: SOCIAL SCIENCE COMPUTER REVIEW
keywords: 'mental health; depression; COVID-19; social determinants of health;

  inequality; machine learning'
keywords-plus: 'MENTAL-HEALTH; SOCIAL DETERMINANTS; PRIMARY-CARE; DEPRESSION; ANXIETY;

  POPULATION; DISORDERS; WORKERS; IMPACT; WUHAN'
language: English
month: AUG
number: '4'
number-of-cited-references: '70'
orcid-numbers: Bakkeli, Nan/0000-0002-4089-020X
pages: 1227-1251
papis_id: 6a9f4be6ca6f5635ae9697191a17ed7f
ref: Bakkeli2023predictingpsychologi
times-cited: '1'
title: 'Predicting Psychological Distress During the COVID-19 Pandemic: Do Socioeconomic
  Factors Matter?'
type: Article
unique-id: WOS:000769618400001
usage-count-last-180-days: '1'
usage-count-since-2013: '6'
volume: '41'
web-of-science-categories: 'Computer Science, Interdisciplinary Applications; Information
  Science \&

  Library Science; Social Sciences, Interdisciplinary'
year: '2023'