Social factors related to depression during COVID-19

Background: Depression can impact both the administration and efficacy of vaccines. Identifying social factors that contribute to depression, especially during a pandemic, is important for both current and future public health issues. Publicly available data can help identify key social factors contributing to depression. Method: For each US state, information regarding their change in depression as measured by the Patient Health Questionnaire 2, predominant political affiliation, coronavirus disease 19 cases/100k, and lockdown severity were gathered. Structural equation modeling using latent change scores was conducted to assess the longitudinal relationships among depression, cases/100k, and state social restrictions. Results: Higher initial levels of lockdown severity and depression predicted rank-order decreases in themselves over time. Correlations among the latent change variables reveal that changes in lockdown severity are negatively related to changes in cases/100k and changes in lockdown severity are positively related to changes in depression after controlling for the other variables. Conclusion: Significant rank-order decreases in depression from T1 to T2 in blue states (who tend to vote for Democrats) vs red states (who tend to vote for Republicans) suggest that decreases in depression may be impacted by the population density and/or political views of that state. Rank-order increases in lockdown measures were negatively associated with rank-order increases in COVID-19 infections, demonstrating strong evidence that lockdown measures do help decrease the spread of COVID-19. Political affiliation and/or population density should be measured and assessed to help facilitate future public health efforts.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Social factors related to depression during COVID-19 ; volume:4 ; number:1 ; year:2023 ; extent:7 ; Open health ; 4, Heft 1 (2023) (gesamt 7)

Beteiligte, Orts- und Zeitangaben

• Urheber: Aumer, Katherine ; Erickson, Michael A. ; Tsukayama, Eli

Weitere Informationen

• DOI: 10.1515/ohe-2022-0030
• URN: urn:nbn:de:101:1-2023022213045799676477
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 11:01 MESZ