Eurosurveillance
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BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak. ⋯ ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.
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IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease (COVID-19). People infected with SARS-CoV-2 may exhibit no or mild non-specific symptoms; thus, they may contribute to silent circulation of the virus among humans. Since SARS-CoV-2 RNA can be detected in stool samples, monitoring SARS-CoV-2 RNA in waste water (WW) has been proposed as a complementary tool to investigate virus circulation in human populations. ⋯ Of note, the viral genome could be detected before the epidemic grew massively (around 8 March). Equally importantly, a marked decrease in the quantities of genome units was observed concomitantly with the reduction in the number of new COVID-19 cases, 29 days following the lockdown. ConclusionThis work suggests that a quantitative monitoring of SARS-CoV-2 genomes in WW could generate important additional information for improved monitoring of SARS-CoV-2 circulation at local or regional levels and emphasises the role of WW-based epidemiology.
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BackgroundThe first wave of the coronavirus disease (COVID-19) pandemic spread rapidly in Spain, one of Europe's most affected countries. A national lockdown was implemented on 15 March 2020. AimTo describe reported cases and the impact of national lockdown, and to identify disease severity risk factors. ⋯ Patients with cardiovascular or renal conditions were at higher risk for severe outcomes. A high proportion of cases were HCWs. Enhanced surveillance and control measures in these subgroups are crucial during future COVID-19 waves.
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BackgroundEvidence for face-mask wearing in the community to protect against respiratory disease is unclear. AimTo assess effectiveness of wearing face masks in the community to prevent respiratory disease, and recommend improvements to this evidence base. MethodsWe systematically searched Scopus, Embase and MEDLINE for studies evaluating respiratory disease incidence after face-mask wearing (or not). ⋯ ConclusionWearing face masks may reduce primary respiratory infection risk, probably by 6-15%. It is important to balance evidence from RCTs and observational studies when their conclusions widely differ and both are at risk of significant bias. COVID-19-specific studies are required.
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Comparative Study
Clinical characteristics and risk factors associated with severe COVID-19: prospective analysis of 1,045 hospitalised cases in North-Eastern France, March 2020.
BackgroundIn March 2020, the COVID-19 outbreak was declared a pandemic by the World Health Organization. AimOur objective was to identify risk factors predictive of severe disease and death in France. MethodsIn this prospective cohort study, we included patients ≥ 18 years old with confirmed COVID-19, hospitalised in Strasbourg and Mulhouse hospitals (France), in March 2020. ⋯ Risk factors associated with death were advanced age (OR: 2.7 per 10-year increase; 95% CrI: 2.1-3.4), male sex (OR: 1.7; 95% CrI: 1.1-2.7), immunosuppression (OR: 3.8; 95% CrI: 1.6-7.7), diabetes (OR: 1.7; 95% CrI: 1.0-2.7), chronic kidney disease (OR: 2.3; 95% CrI: 1.3-3.9), dyspnoea (OR: 2.1; 95% CrI: 1.2-3.4) and inflammatory parameters. ConclusionsOverweightedness, obesity, advanced age, male sex, comorbidities, dyspnoea and inflammation are risk factors for severe COVID-19 or death in hospitalised patients. Identifying these features among patients in routine clinical practice might improve COVID-19 management.