Background: The coronavirus disease 2019 (COVID-19) pandemic continues to grow worldwide, and systematic reviews (SRs)/meta-analyses (MAs) on COVID-19 can efficiently guide evidence-based clinical practice. However, SRs/MAs with weaknesses can mislead clinical practice and pose harm to patients, and too many useless SRs/MAs could pose confusion and waste sources. A "living" overview of SRs/MAs aims to provide an open, accessible and frequently updated resource summarizing the highest-level evidence of COVID-19, that can help evidence-users to quickly identify trusted evidence to guide the practice. This study aims to systematically give an overview SRs/MAs of COVID-19, assess their quality, and identify the best synthesis of evidence. Methods: Databases including Medline, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM) and WanFang were systematically searched on May 1, 2020 using relevant terms for identify SRs/MAs related to COVID-19. The study selection, data extraction and quality assessment will be performed by independent reviewers, and results will be crosschecked. The authoritative tools (AMSTAR-2, PRISMA and its extensions) will be used to assess the methodological quality and reporting quality of included SRs/MAs, and potential influence factors will be explored. The consistency of conclusions will be compared among reviews and the best evidence will be summarized. In addition, we will conduct exploratory meta-analyses (MAs) of individual studies when applicable. Data will be reported as number with (or) percentage, risk ratio (RR) or odds ratio (OR), mean difference (MD) or standardized mean difference (SMD) with 95% confidence interval (CI) according to the specific results. R3.6.1 and Microsoft Excel 2016 will be used to analyze and manage data. Results: The results of this overview will be submitted to a peer-reviewed journal for publication. Conclusions: In this study, we will present for the first time, an overview of SRs/MAs, which provides a comprehensive, dynamic evidence landscape on prevalence, prevention, diagnosis, treatment, and prognosis of COVID-19.

To evaluate the association between shift work and risk of type 2 diabetes mellitus, we searched PubMed, EMBASE and Web of Science from their inception to June 8, 2019. Observational studies examining the relationship between shift work and type 2 diabetes were included. Subgroup analyses were conducted to explore whether specific characteristics would affect the relationship. A dose-response relationship was estimated by using generalized least squares trend regression. Finally, twelve cohort studies and nine cross-sectional studies were included (inter-rater agreement, k = 0.96). The result of meta-analysis indicated that shift work was associated with an increased risk of type 2 diabetes (relative risk = 1.10, 95% confidence interval = 1.05-1.14). Subgroup analyses demonstrated that female shift workers have increased risk of type 2 diabetes while male not observed, health care workers showed the highest risk compared with civil servants and manual workers, and night shift and rotating shift were associated with an increased risk of type 2 diabetes. Dose-response meta-analysis based on three cohorts among female workers indicated that there might be a positive association between duration of shift work and the risk of type 2 diabetes. In conclusion, shift work is positively associated with an increased risk of type 2 diabetes. Among female workers, with the years of exposure to shift work prolonged, the risk of type 2 diabetes might increase accordingly. In the future, more studies are needed to confirm the results of dose-response analysis.