Global Diabetes Prevalence in COVID-19 Patients and Contribution to COVID-19- Related Severity and Mortality: A Systematic Review and Meta-analysis.

BACKGROUND: COVID-19 and diabetes both contribute to large global disease burdens. PURPOSE: To quantify the prevalence of diabetes in various COVID-19 disease stages and calculate the population attributable fraction (PAF) of diabetes to COVID-19-related severity and mortality. DATA SOURCES: Systematic review identified 729 studies with 29,874,938 COVID-19 patients. STUDY SELECTION: Studies detailed the prevalence of diabetes in subjects with known COVID-19 diagnosis and severity. DATA EXTRACTION: Study information, COVID-19 disease stages, and diabetes prevalence were extracted. DATA SYNTHESIS: The pooled prevalence of diabetes in stratified COVID-19 groups was 14.7% (95% CI 12.5-16.9) among confirmed cases, 10.4% (7.6-13.6) among nonhospitalized cases, 21.4% (20.4-22.5) among hospitalized cases, 11.9% (10.2-13.7) among nonsevere cases, 28.9% (27.0-30.8) among severe cases, and 34.6% (32.8-36.5) among deceased individuals, respectively. Multivariate metaregression analysis explained 53-83% heterogeneity of the pooled prevalence. Based on a modified version of the comparative risk assessment model, we estimated that the overall PAF of diabetes was 9.5% (7.3-11.7) for the presence of severe disease in COVID-19-infected individuals and 16.8% (14.8-18.8) for COVID-19-related deaths. Subgroup analyses demonstrated that countries with high income levels, high health care access and quality index, and low diabetes disease burden had lower PAF of diabetes contributing to COVID-19 severity and death. LIMITATIONS: Most studies had a high risk of bias. CONCLUSIONS: The prevalence of diabetes increases with COVID-19 severity, and diabetes accounts for 9.5% of severe COVID-19 cases and 16.8% of deaths, with disparities according to country income, health care access and quality index, and diabetes disease burden.

Cancers and COVID-19 Risk: A Mendelian Randomization Study.

Observational studies have shown increased COVID-19 risk among cancer patients, but the causality has not been proven yet. Mendelian randomization analysis can use the genetic variants, independently of confounders, to obtain causal estimates which are considerably less confounded. We aimed to investigate the causal associations of cancers with COVID-19 outcomes using the MR analysis. The inverse-variance weighted (IVW) method was employed as the primary analysis. Sensitivity analyses and multivariable MR analyses were conducted. Notably, IVW analysis of univariable MR revealed that overall cancer and twelve site-specific cancers had no causal association with COVID-19 severity, hospitalization or susceptibility. The corresponding p-values for the casual associations were all statistically insignificant: overall cancer (p = 0.34; p = 0.42; p = 0.69), lung cancer (p = 0.60; p = 0.37; p = 0.96), breast cancer (p = 0.43; p = 0.74; p = 0.43), endometrial cancer (p = 0.79; p = 0.24; p = 0.83), prostate cancer (p = 0.54; p = 0.17; p = 0.58), thyroid cancer (p = 0.70; p = 0.80; p = 0.28), ovarian cancer (p = 0.62; p = 0.96; p = 0.93), melanoma (p = 0.79; p = 0.45; p = 0.82), small bowel cancer (p = 0.09; p = 0.08; p = 0.19), colorectal cancer (p = 0.85; p = 0.79; p = 0.30), oropharyngeal cancer (p = 0.31; not applicable, NA; p = 0.80), lymphoma (p = 0.51; NA; p = 0.37) and cervical cancer (p = 0.25; p = 0.32; p = 0.68). Sensitivity analyses and multivariable MR analyses yielded similar results. In conclusion, cancers might have no causal effect on increasing COVID-19 risk. Further large-scale population studies are needed to validate our findings.

Critical timing and extent of public health interventions to control outbreaks dominated by SARS-CoV-2 variants in Australia: a mathematical modelling study.

OBJECTIVES: The exact characteristics of a coronavirus disease 2019 (COVID-19) outbreak that trigger public health interventions are poorly defined. The aim of this study was to assess the critical timing and extent of public health interventions to contain COVID-19 outbreaks in Australia. METHODS: A practical model was developed using existing epidemic data in Australia. The effective combinations of public health interventions and the critical number of daily cases for intervention commencement under various scenarios of changes in transmissibility of new variants and vaccination coverage were quantified. RESULTS: In the past COVID-19 outbreaks in four Australian states, the number of reported cases on the day that interventions commenced strongly predicted the size and duration of the outbreaks. In the early phase of an outbreak, containing a wildtype-dominant epidemic to a low level (≤10 cases/day) would require effective combinations of social distancing and face mask use interventions to be commenced before the number of daily reported cases reaches six. Containing an Alpha-dominant epidemic would require more stringent interventions that commence earlier. For the Delta variant, public health interventions alone would not contain the epidemic unless the vaccination coverage was ≥70%. CONCLUSIONS: This study highlights the importance of early and decisive action in the initial phase of an outbreak. Vaccination is essential for containing variants.