A meta-meta-analysis of co-infection, secondary infections, and antimicrobial resistance in COVID-19 patients

Abstract

The newly discovered coronavirus SARS-CoV-2 has sparked a worldwide pandemic of COVID-19, which has caused havoc on medical infrastructures, economies, and cultures around the world. Determining the whole scenario is essential since SARS-CoV-2 variants and sub-variants keep appearing after vaccinations and booster doses. The objective of this secondary meta-analysis is to analysis co-infection, secondary infections, and antimicrobial resistance (AMR) in COVID-19 patients. This study used five significant databases to conduct a systematic review and an overlap meta-analysis to evaluate the pooled estimates of co-infections and secondary infections. The summary of the meta-analysis showed an overall co-infection effect of 26.19% (95% confidence intervals CI: 21.39-31.01, I² =98.78, n = 14 meta-analysis) among patients with COVID-19. A coinfection effect of 11.13% (95% CI: 9.7-12.56, I² =99.14, n = 11 meta-analysis) for bacteria; 9.69% (95% CI: 1.21-7.90, I² =98.33) for fungal and 3.48% (95% CI: 2.15-4.81, I² =95.84) for viruses. A secondary infection effect of 19.03% (95% CI: 9.53-28.54, I² =85.65) was pooled from 2 meta-analyses (Ave: 82 primary studies). This is the first study that compiles the results of all the previous three years meta-analyses into a single source and offers strong proof of co-infections and secondary infections in COVID-19 patients. Early detection of co-infection and AMR is crucial for COVID-19 patients in order to effective treatment.

Keywords: Antimicrobial resistant (AMR); COVID-19; Co-infection; Pathogens; SARS-CoV-2; Secondary infection.