The coronavirus disease 2019 (COVID-19) pandemic has exposed humans to the highest physical and mental risks. Thus, it is becoming a priority to probe the mental health problems experienced during the pandemic in different populations. We performed a meta-analysis to clarify the prevalence of postpandemic mental health problems. Seventy-one published papers (n = 146,139) from China, the United States, Japan, India, and Turkey were eligible to be included in the data pool. These papers reported results for Chinese, Japanese, Italian, American, Turkish, Indian, Spanish, Greek, and Singaporean populations. The results demonstrated a total prevalence of anxiety symptoms of 32.60% (95% confidence interval (CI): 29.10–36.30) during the COVID-19 pandemic. For depression, a prevalence of 27.60% (95% CI: 24.00–31.60) was found. Further, insomnia was found to have a prevalence of 30.30% (95% CI: 24.60–36.60). Of the total study population, 16.70% (95% CI: 8.90–29.20) experienced post-traumatic stress disorder (PTSD) symptoms during the COVID-19 pandemic. Subgroup analysis revealed the highest prevalence of anxiety (63.90%) and depression (55.40%) in confirmed and suspected patients compared with other cohorts. Notably, the prevalence of each symptom in other countries was higher than that in China. Finally, the prevalence of each mental problem differed depending on the measurement tools used. In conclusion, this study revealed the prevalence of mental problems during the COVID-19 pandemic by using a fairly large-scale sample and further clarified that the heterogeneous results for these mental health problems may be due to the nonstandardized use of psychometric tools.

Given the lack of biomarkers to guide psychiatric diagnoses, machine learning has been increasingly applied to develop brain-based hallmarks. Thus, neuroimaging-based artificial intelligence (AI) models have proliferated rapidly during the past 3 decades. Rather than enabling a clinician-based diagnosis of discrete symptoms, neuroimaging-based AI models enable an objective, symptom-centered, individualized and neurobiologically explicable estimate of psychiatric conditions.^1,2 In this vein, these models show considerable potential to be translated for use in early diagnosis, treatment development, and clinical decision-making.^3-7 However, lack of an evidence-based systematic appraisal of AI models impedes stakeholder enthusiasm for applying them in clinical practice.^8,9

Background: Non-suicidal self-injury (NSSI) behavior among college students is a focus of attention in current society. In the information era, the Internet serves as a public health concern and as an effective pathway for prevention. In order to reduce NSSI behavior, we explore its influence factors, especially the relations between neuroticism, emotion regulation (ER), depression, and NSSI behavior. Methods: A total of 450 college students were surveyed with the Big Five Inventory-2, Emotion Regulation Questionnaire, Self-Rating Depression Scale, and Adolescent Non-Suicidal Self-Injury Assessment Questionnaire. Results: Regression analysis showed that neuroticism significantly negatively predicted emotion regulation, while it positively predicted depression and NSSI. Multiple mediation modeling demonstrated that neuroticism and emotion regulation had no significant direct effects on NSSI. However, neuroticism could indirectly affect NSSI through four pathways of multiple mediating effects, including depression, cognitive reappraisal-depression, expressive suppression-depression, and cognitive reappraisal-expressive suppression-depression. Conclusions: Neuroticism positively predicts depression and NSSI behavior, and affects NSSI through the mediating effect of ER and depression. Therefore, amelioration of neuroticism from the perspectives of emotion regulation and depression is recommended, so as to reduce NSSI behavior among college students with highly neurotic personalities.