Abstract
BACKGROUND: Pandemics can lead to mental health problems such as depression. This meta-analysis of meta-analyses aimed to estimate the precise prevalence of depression during the COVID-19 pandemic. METHODS: Web of Science, PubMed, Scopus, and Embase were searched for published meta-analyses using relevant keywords, such as depression, prevalence, COVID-19, and meta-analysis up to March 18, 2024 according to the PRISMA guidelines. Relevant journals as well as the search engine Google Scholar were manually searched to discover more articles. The AMSTAR tool was used for quality assessment. A random-effects model was used for the analysis. All analyses were conducted using the STATA 17 software. RESULTS: Of 535 records, 82 meta-analyses were included. The results showed that the overall prevalence of depression was 30% [95% CI: 29-32] with a high heterogeneity (I(2): 90.98%). The highest prevalence of depression according to population group was found in medical students (40% [30-49]), specific groups (40% [3-78]), and patients (36% [27-45]). The results of meta-regression based on the different times between the start of COVID-19 and the last date of search in the articles (week) and the prevalence of depression, showed that each past week of Covid-19 increases the prevalence of depression by almost 0.00021% [95% CI: -0.00025, 0.00068], P-value: 0.36, but "time" is not a significant predictor of an increase in depression. CONCLUSIONS: The results showed that the prevalence of depression was high during the COVID-19 pandemic, particularly among medical students. Policy makers should pay more attention to these groups and those who are at greater risk. Primary mental health interventions and policies are necessary to support the mental health of these individuals during the pandemic.