Balancing Mental Health: Predictive Modeling for Healthcare Workers During Public Health Crises

Background During public health emergencies such as SARS, Ebola, and COVID-19, healthcare workers (HCWs) are often required to confront these crises, potentially leading to adverse mental health outcomes. Consequently, they are at a heightened risk of experiencing symptoms of depression and anxiety. It is widely recognized that psychological disorders can lead to severe consequences. Despite this, there remains a scarcity of research focused on developing predictive models to forecast the depression and anxiety levels of healthcare workers under these challenging conditions. Methods A total of 349 HCWs were selected from a Class-A tertiary hospital in the city of Shenyang, Liaoning Province in China. Depression and anxiety were assessed using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) scale, respectively. This study employed a random forest classifier(RFC) to predict the depression and anxiety levels of HCWs from three perspectives: individual, interpersonal, and institutional. Moreover, we employed The Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of imabalanced data distribution. Results The prevalence of depression and anxiety among HCWs were 28.37% and 33.52%, respectively. The prediction model was developed using a training dataset (70%) and a test dataset (30%). The area under the curve (AUC) for depression and anxiety were 0.88 and 0.72, respectively. Additionally, the mean values of the 10-fold cross-validation results were 0.77 for the depression prediction model and 0.79 for the anxiety prediction model. For the depression prediction model, the top ten most significant predictive factors were: burnout, resilience, emotional labor, adaptability, working experience( < = 1year), physician, social support, average work time last week(9–11 hours), age(28–30 years), age(31–35 years old). For the anxiety prediction model, the top ten most significant predictive factors were: burnout, adaptability, emotional labor, age(31–35), average work time last week(9–11 hours), resilience, physician, social support, working experience( < = 1 year), female. Conclusions It is essential to develop multiple interventions that provide support both before and after a public health emergency, aiming at mitigating symptoms of depression and anxiety. SMOTE is a practical method for addressing imbalances in datasets. Mitigating burnout among HCWs, bolstering their resilience and adaptability, and ensuring reasonable work hours are crucial steps to prevent adverse mental health problems.