Introduction The recovery of other pathogens in patients with SARS-CoV-2 infection has been reported, either at the time of a SARS-CoV-2 infection diagnosis (co-infection) or subsequently (superinfection). However, data on the prevalence, microbiology, and outcomes of co-infection and superinfection are limited. The purpose of this study was to examine the occurrence of co-infections and superinfections and their outcomes among patients with SARS-CoV-2 infection. Patients and methods We searched literature databases for studies published from October 1, 2019, through February 8, 2021. We included studies that reported clinical features and outcomes of co-infection or superinfection of SARS-CoV-2 and other pathogens in hospitalized and non-hospitalized patients. We followed PRISMA guidelines, and we registered the protocol with PROSPERO as: CRD42020189763. Results Of 6639 articles screened, 118 were included in the random effects meta-analysis. The pooled prevalence of co-infection was 19% (95% confidence interval [CI]: 14%-25%, I2 = 98%) and that of superinfection was 24% (95% CI: 19%-30%). Pooled prevalence of pathogen type stratified by co- or superinfection were: viral co-infections, 10% (95% CI: 6%-14%); viral superinfections, 4% (95% CI: 0%-10%); bacterial co-infections, 8% (95% CI: 5%-11%); bacterial superinfections, 20% (95% CI: 13%-28%); fungal co-infections, 4% (95% CI: 2%-7%); and fungal superinfections, 8% (95% CI: 4%-13%). Patients with a co-infection or superinfection had higher odds of dying than those who only had SARS-CoV-2 infection (odds ratio = 3.31, 95% CI: 1.82–5.99). Compared to those with co-infections, patients with superinfections had a higher prevalence of mechanical ventilation (45% [95% CI: 33%-58%] vs. 10% [95% CI: 5%-16%]), but patients with co-infections had a greater average length of hospital stay than those with superinfections (mean = 29.0 days, standard deviation [SD] = 6.7 vs. mean = 16 days, SD = 6.2, respectively). Conclusions Our study showed that as many as 19% of patients with COVID-19 have co-infections and 24% have superinfections. The presence of either co-infection or superinfection was associated with poor outcomes, including increased mortality. Our findings support the need for diagnostic testing to identify and treat co-occurring respiratory infections among patients with SARS-CoV-2 infection.

Introduction: The recovery of other respiratory viruses in patients with SARS-CoV-2 infection has been reported, either at the time of a SARS-CoV-2 infection diagnosis (co-infection) or subsequently (superinfection). However, data on the prevalence, microbiology and outcomes of co-infection and super infection are limited. The purpose of this study was to examine occurrence of respiratory co-infections and superinfections and their outcomes among patients with SARS-CoV-2 infection. Patients and Methods: We searched literature databases for studies published from October 1, 2019, through June 11, 2020. We included studies that reported clinical features and outcomes of co-infection or super-infection of SARS-CoV-2 and other pathogens in hospitalized and non-hospitalized patients. We followed PRISMA guidelines and we registered the protocol with PROSPERO as: CRD42020189763. Results: Of 1310 articles screened, 48 were included in the random effects meta-analysis. The pooled prevalence of co-infection was 12% (95% confidence interval (CI): 6%-18%, n=29, I2=98%) and that of super-infection was 14% (95% CI: 9%-21%, n=18, I2=97%). Pooled prevalence of pathogen type stratified by co- or super-infection: viral co-infections, 4% (95% CI: 2%-7%); viral super-infections, 2% (95% CI: 0%-7%); bacterial co-infections, 4% (95% CI: 1%-8%); bacterial super-infections, 6% (95% CI: 2%-11%); fungal co-infections, 4% (95% CI: 1%-8%); and fungal super-infections, 4% (95% CI: 0%-11%). Compared to those with co-infections, patients with super-infections had a higher prevalence of mechanical ventilation [21% (95% CI: 13%-31%) vs. 7% (95% CI: 2%-15%)] and greater average length of hospital stay [mean=12.5 days, standard deviation (SD) =5.3 vs. mean=10.2 days, SD= 6.7]. Conclusions: Our study showed that as many as 14% of patients with COVID-19 have super-infections and 12% have co-infections. Poor outcomes were associated with super-infections. Our findings have implications for diagnostic testing and therapeutics, particularly in the upcoming respiratory virus season in the Northern Hemisphere.

BackgroundUpper respiratory tract infections (URTIs) are among the most frequent reasons for physician office visits in paediatrics. Despite their predominant viral aetiology, URTIs continue to be treated with antimicrobials. We explored general practitioners' (GPs) prescribing behaviour for antimicrobials in children (≤ 16 years) with URTIs in Trinidad, using the guidelines from the Centers for Disease Control and Prevention (CDC) as a reference.MethodsA cross-sectional study was conducted on 92 consenting GPs from the 109 contacted in Central and East Trinidad, between January to June 2003. Using a pilot-tested questionnaire, GPs identified the 5 most frequent URTIs they see in office and reported on their antimicrobial prescribing practices for these URTIs to trained research students.ResultsThe 5 most frequent URTIs presenting in children in general practice, are the common cold, pharyngitis, tonsillitis, sinusitis and acute otitis media (AOM) in rank order. GPs prescribe at least 25 different antibiotics for these URTIs with significant associations for amoxicillin, co-amoxiclav, cefaclor, cefuroxime, erythromycin, clarithromycin and azithromycin (p < 0.001). Amoxicillin alone or with clavulanate was the most frequently prescribed antibiotic for all URTIs. Prescribing variations from the CDC recommendations were observed for all URTIs except for AOM (50%), the most common condition for antibiotics. Doctors practicing for >30 years were more likely to prescribe antibiotics for the common cold (p = 0.014). Severity (95.7%) and duration of illness (82.5%) influenced doctors' prescribing and over prescribing in general practice was attributed to parent demands (75%) and concern for secondary bacterial infections (70%). Physicians do not request laboratory investigations primarily because they are unnecessary (86%) and the waiting time for results is too long (51%).ConclusionsAntibiotics are over prescribed for paediatric URTIs in Trinidad and amoxicillin with co-amoxiclav were preferentially prescribed. Except for AOM, GPs' prescribing varied from the CDC guidelines for drug and duration. Physicians recognise antibiotics are overused and consider parents expecting antibiotics and a concern for secondary bacterial infections are prescribing pressures. Guidelines to manage URTIs, ongoing surveillance programs for antibiotic resistance, public health education on non-antibiotic strategies, and postgraduate education for rational pharmacotherapy in general practice would decrease inappropriate antibiotic use in URTIs.

Highlights Physical distancing is necessary to mitigate SARS-CoV-2 transmission Difficult to implement distancing in complex healthcare settings Systems engineering approaches highlight work system elements and interactions Work system analyses identify individual challenges and solutions for distancing

IntroductionClostridioides difficile infection (CDI) is one of the most common healthcare-associated infections in the USA, having high incidence in intensive care units (ICU). Antibiotic use increases risk of CDI, with fluoroquinolones (FQs) particularly implicated. In healthcare settings, antibiotic stewardship (AS) and infection control interventions are effective in CDI control, but there is little evidence regarding the most effective AS interventions. Preprescription authorisation (PPA) restricting FQs is a potentially promising AS intervention to reduce CDI. The FQ Restriction for the Prevention of CDI (FIRST) trial will evaluate the effectiveness of an FQ PPA intervention in reducing CDI rates in adult ICUs compared with preintervention care, and evaluate implementation effectiveness using a human-factors and systems engineering model.Methods and analysisThis is a multisite, stepped-wedge, cluster, effectiveness-implementation clinical trial. The trial will take place in 12 adult medical-surgical ICUs with ≥10 beds, using Epic as electronic health record (EHR) and pre-existing AS programmes. Sites will receive facilitated implementation support over the 15-month trial period, succeeded by 9 months of follow-up. The intervention comprises a clinical decision support system for FQ PPA, integrated into the site EHRs. Each ICU will be considered a single site and all ICU admissions included in the analysis. Clinical data will be extracted from EHRs throughout the trial and compared with the corresponding pretrial period, which will constitute the baseline for statistical analysis. Outcomes will include ICU-onset CDI rates, FQ days of therapy (DOT), alternative antibiotic DOT, average length of stay and hospital mortality. The study team will also collect implementation data to assess implementation effectiveness using the Systems Engineering Initiative for Patient Safety model.Ethics and disseminationThe trial was approved by the Institutional Review Board at the University of Wisconsin-Madison (2018-0852-CP015). Results will be made available to participating sites, funders, infectious disease societies, critical care societies and other researchers.Trial registration numberNCT03848689.