Selected article for: "binary logistic regression and gender age"
Author: Wickens, Christine M.; Hamilton, Hayley A.; Elton-Marshall, Tara; Nigatu, Yeshambel T.; Jankowicz, Damian; Wells, Samantha
Title: Household- and employment-related risk factors for depressive symptoms during the COVID-19 pandemic
  • Cord-id: by2ms8q4
  • Document date: 2021_3_15
    • ID: by2ms8q4
    Snippet: OBJECTIVES: The COVID-19 pandemic has generated multiple psychological stressors, which may increase the prevalence of depressive symptoms. Utilizing Canadian survey data, this study assessed household- and employment-related risk factors for depressive symptoms during the pandemic. METHODS: A sample of 1005 English-speaking Canadian adults aged 18+ years completed a web-based survey after physical distancing measures were implemented across Canada. Hierarchical binary logistic regression analys
    Document: OBJECTIVES: The COVID-19 pandemic has generated multiple psychological stressors, which may increase the prevalence of depressive symptoms. Utilizing Canadian survey data, this study assessed household- and employment-related risk factors for depressive symptoms during the pandemic. METHODS: A sample of 1005 English-speaking Canadian adults aged 18+ years completed a web-based survey after physical distancing measures were implemented across Canada. Hierarchical binary logistic regression analyses were conducted to examine the associations of depressive symptoms with household- (household size, presence of children, residence locale) and employment-related (job with high risk of COVID-19 exposure, working from home, laid off/not working, financial worry) risk factors, controlling for demographic factors (gender, age, education, income). RESULTS: About 20.4% of the sample reported depressive symptoms at least 3 days per week. The odds of experiencing depressive symptoms 3+ days in the past week were higher among women (AOR = 1.67, p = 0.002) and younger adults (18–29 years AOR = 2.62, p < 0.001). After adjusting for demographic variables, the odds of experiencing depressive symptoms were higher in households with 4+ persons (AOR = 1.88, p = 0.01), in households with children aged 6 to 12 years (AOR = 1.98, p = 0.02), among those with a job at high risk for exposure to COVID-19 (AOR = 1.82, p = 0.01), and those experiencing financial worry due to COVID-19 (‘very worried’ AOR = 8.00, p < 0.001). CONCLUSION: Pandemic responses must include resources for mental health interventions. Additionally, further research is needed to track mental health trajectories and inform the development, targeting, and implementation of appropriate mental health prevention and treatment interventions.

    Search related documents:
    Co phrase search for related documents
    • account variance and acute respiratory syndrome: 1
    • account variance and logistic regression: 1, 2, 3
    • acute respiratory syndrome and adjusted model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute respiratory syndrome and adjusted odd: 1, 2, 3, 4, 5
    • acute respiratory syndrome and adjusted odd ratio: 1, 2, 3, 4, 5
    • acute respiratory syndrome and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute respiratory syndrome and logistic regression analysis: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute respiratory syndrome and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute respiratory syndrome and logistic regression model obtain: 1
    • acute respiratory syndrome and low category: 1, 2, 3, 4
    • acute respiratory syndrome and low educational attainment: 1, 2
    • adjusted model and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • adjusted model and logistic regression analysis: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • adjusted model and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • adjusted model and logistic regression model obtain: 1, 2
    • adjusted odd and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • adjusted odd and logistic regression analysis: 1, 2, 3
    • adjusted odd and logistic regression model: 1, 2
    • adjusted odd ratio and logistic regression model: 1, 2