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Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic

Zhuang Liu1, Rongxun Liu2,3, Yue Zhang1

  • 1School of Public health, China Medical University, Shenyang, Liaoning, China.

BMC Psychiatry
|October 13, 2021
PubMed

Insights

This study identified three mental health subgroups among medical students during COVID-19. Factors like demographics and COVID-19 knowledge influenced depression and anxiety, informing targeted mental health support.

Area of Science:

  • Medical Education
  • Psychiatry
  • Public Health

Background:

  • The COVID-19 pandemic created a global health emergency, significantly impacting medical students' mental well-being.
  • Understanding depression and anxiety patterns in this demographic is crucial for developing effective support strategies.

Purpose of the Study:

  • To identify distinct subgroups of medical students based on depression and anxiety symptoms during the COVID-19 epidemic in China.
  • To explore the factors influencing these mental health subgroups.

Main Methods:

  • Latent class analysis was used to categorize 29,663 medical students based on depression (PHQ-9) and anxiety (GAD-7) symptoms.
  • Multinomial logistic regression identified factors associated with different mental health classes.

Main Results:

  • Three subgroups emerged: low symptoms (16,017), mild mental health (9,321), and poor mental health (4,325).
  • Factors influencing poor and mild mental health included sex, education, drinking, psychiatric history, COVID-19 knowledge, infection fear, and mental health education participation.

Conclusions:

  • Latent class analysis effectively categorizes medical students by mental health status during health crises.
  • Demographics, disease history, COVID-19 specific factors, and lifestyle significantly influence mental health outcomes.
  • Targeted psychological interventions for distinct subgroups are recommended to enhance medical students' overall well-being.
Abstract

Keywords:
AnxietyCOVID-19DepressionLatent class analysisMedical students

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