Investigation of factors associated with mental health during the early part of the COVID-19 pandemic in South Korea based on machine learning algorithms: A cohort study

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic is among the most critical public health problems worldwide in the last three years. We tried to investigate changes in factors between pre- and early stages of the COVID-19 pandemic. METHODS: The data of 457,309 participants from the 2019 and 2020 Community Health Survey were examined. Four mental health-related variables were selected for examination as a dependent variable (patient health questionnaire-9, depression, stress, and sleep time). Other variables without the aforementioned four variables were split into three groups based on the coefficient values of lasso and ridge regression models. The importance of each variable was calculated and compared using feature importance values obtained from three machine learning algorithms. RESULTS: Psychiatric and sociodemographic variables were identified, both during the pre- and early pandemic periods. In contrast, during the early pandemic period, average sleep time variables ranked the highest with the dependent variables regarding the experience of depression. The difference in sleep time before and after the pandemic was validated by the results of paired t-tests, which were statistically significant (p-value < 0.05). CONCLUSIONS: Changes in the importance of mental health factors in the early pandemic period in South Korea were identified. For each mental health-dependent variable, average sleep time, experience of depression, and experience of accidents or addictions were found to be the most important factors. House type and type of residence were also found in regions with larger populations and a higher number of confirmed cases.