The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: A systematic review and meta-analysis

Abstract

The COVID-19 pandemic and its accompanying infection control measures introduced significant disruptions to the routines of many higher education students around the world. It also deprived them of in-person counselling services and social support. These changes have put students at a greater risk of developing mental illness. The objective of this review is to assess the prevalence of depressive symptoms, anxiety symptoms and sleep disturbances in higher education students during the pandemic. A systematic search of English and Chinese databases was conducted current to January 1^st, 2021. The quality of included studies was evaluated using a modified Newcastle-Ottawa scale. Prevalence of depressive symptoms, anxiety symptoms and sleep disturbances were pooled using random-effects meta-analysis. Eighty-nine studies (n=1,441,828) were included. The pooled prevalence of depressive symptoms, anxiety symptoms, and sleep disturbances was 34%, 32% and 33%, respectively. The prevalence values differ based on geographical regions, diagnostic criteria, education level, undergraduate year of study, financial situation, living arrangements and gender. Overall, the prevalence of depressive symptoms and anxiety symptoms synthesized in this study was higher compared to pre-pandemic prevalence in similar populations. Evidently, mental health screening and intervention should be a top priority for universities and colleges during the pandemic.

Keywords: Anxiety; COVID-19; Depression; Pandemic; Sleep Disturbance; Student; University.

Fig. 1

PRISMA flowchart for the identification and selection of observational studies. Abbreviations: CINAHL Cumulative Index to Nursing and Allied Health Literature; CNKI Chinese National Knowledge Infrastructure; CQVIP Chongqing VIP Information.

Fig. 2

Forest plot for the pooling of depression prevalence. Studies were separated into subgroups based on the screening tool and cutoff values used for evaluating depression. The differences between subgroups were statistically significant (P<0.01). Abbreviations: CI Confidence Interval; PHQ-9 Patient Health Questionnaire-9; DASS-21D Depression Anxiety Stress Scales, depression subscale; SCL-90 Symptom Checklist-90; SDS Zung Self-Rating Depression Scale; CES-D Center for Epidemiologic Studies Depression Scale; FCV-19S Fear of COVID-19 Scale; PQEEPH Psychological Questionnaires for Emergent Events of Public Health; PROMIS-8D Patient-Reported Outcomes Measurement Information System Depression-8, BID-13 13-item Beck Depression Inventory, KADS Kutcher Adolescent Depression Scale, PHQ-8 Patient Health Questionnaire-8

Fig. 3

Forest plot for the pooling of anxiety prevalence. Studies were separated into subgroups based on the screening tool and cutoff values used for evaluating anxiety. The differences between subgroups were statistically significant (P<0.01). Abbreviations: CI Confidence Interval; GAD-7 General Anxiety Disorder 7-item scale; SAS Zung Self-Rating Anxiety Scale; SCL-90 Symptom Checklist-90; DASS-21A Depression Anxiety Stress Scales, anxiety subscale; EAS Existence of Anxiety Scale; HAM-A Hamilton Anxiety Rating Scale; PQEEPH Psychological Questionnaires for Emergent Events of Public Health, STAI State-Trait Anxiety Inventory.

Fig. 4

Forest plot for the pooling of sleep disturbances prevalence. Studies were separated into subgroups based on the screening tool and cutoff values used for evaluating sleep disturbances. The differences between subgroups were statistically significant (P<0.01). Abbreviations: CI Confidence Interval; PSQI Pittsburgh Sleep Quality Index; ISI Insomnia Severity Index, SRSS Self-Rating Scale of Sleep.