COVID-19 mortality in Italy varies by patient age, sex and pandemic wave

BackgroundSARS-CoV-2 has caused a worldwide epidemic of enormous proportions, which resulted in different mortality rates in different countries for unknown reasons. AimWe aimed to evaluate which independent parameters are associated with risk of mortality from COVID-19 in a series that includes all Italian cases, ie, more than 4 million individuals infected with the SARS-CoV-2 coronavirus. MethodsWe analyzed factors associated with mortality using data from the Italian national database of SARS-CoV-2-positive cases, including more than 4 million cases, >415 thousand hospitalized for coronavirus disease-19 (COVID-19) and >127 thousand deceased. For patients for whom age, sex and date of infection detection were available, we determined the impact of these variables on mortality 30 days after the date of diagnosis or hospitalization. ResultsMultivariable Cox analysis showed that each of the analyzed variables independently affected COVID-19 mortality. Specifically, in the overall series, age was the main risk factor for mortality, with HR >100 in the age groups older than 65 years compared with a reference group of 15-44 years. Male sex presented an excess risk of death (HR = 2.1; 95% CI, 2.0-2.1). Patients infected in the first pandemic wave (before 30 June 2020) had a greater risk of death than those infected later (HR = 2.7; 95% CI, 2.7-2.8). ConclusionsIn a series of all confirmed SARS-CoV-2-infected cases in an entire European nation, elderly age was by far the most significant risk factor for COVID-19 mortality, confirming that protecting the elderly should be a priority in pandemic management. Male sex and being infected during the first wave were additional risk factors associated with COVID-19 mortality.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole exome sequencing data of about 4,000 SARS-CoV-2-positive individuals were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthly, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Lung expression of genes encoding SARS-CoV-2 cell entry molecules and antiviral restriction factors: interindividual differences are associated with age and germline variants

Germline variants in genes involved in SARS-CoV-2 cell entry (i.e. ACE2 and TMPRSS2) may influence the susceptibility to infection, as may polymorphisms in genes involved in the innate host response to viruses (e.g. APOBEC3 family). We searched for polymorphisms acting, in lung tissue, as expression quantitative trait loci (eQTLs) for 15 candidate COVID-19 susceptibility genes, selected for their roles in virus cell entry and host antiviral responses. No significant eQTLs were identified for ACE2 and TMPRSS2 genes, whose expression levels did not associate with either sex or age of the 408 patients whose non-diseased lung tissue was analyzed. Instead, we identified seven cis-eQTLs (FDR<0.05) for APOBEC3D and APOBEC3G (rs139296, rs9611092, rs139331, rs8177832, rs17537581, rs61362448, and rs738469). The genetic control of the expression of APOBEC3 genes, which encode enzymes that interfere with virus replication, may explain interindividual differences in risk or severity of viral infections. Future studies should investigate the role of host genetics in COVID-19 patients using a genome-wide approach, to identify other genes whose expression levels are associated with susceptibility to SARS-CoV-2 infection or COVID-19 severity. Author summaryIdentification of expression quantitative trait loci (eQTLs) has become commonplace in functional studies on the role of individual genetic variants in susceptibility to diseases. In COVID-19, it has been proposed that individual variants in SARS-CoV-2 cell entry and innate host response genes may influence the susceptibility to infection. We searched for polymorphisms acting, in non-diseased lung tissue of 408 patients, as eQTLs for 15 candidate COVID-19 susceptibility genes, selected for their roles in virus cell entry and host antiviral responses. Seven cis-eQTLs were detected for APOBEC3D and APOBEC3G genes, which encode enzymes that interfere with virus replication. No significant eQTLs were identified for ACE2 and TMPRSS2 genes. Therefore, the identified eQTLs may represent candidate loci modulating interindividual differences in risk or severity of SARS-CoV-2 virus infection.

The effectiveness of non-pharmaceutical interventions in containing epidemics: a rapid review of the literature and quantitative assessment

The number of confirmed COVID-19 cases has rapidly increased since discovery of the disease in December 2019. In the absence of medical countermeasures to stop the spread of the disease (i.e. vaccines), countries have responded by implementing a suite of non-pharmaceutical interventions (NPIs) to contain and mitigate COVID-19. Individual NPIs range in intensity (e.g. from lockdown to public health campaigns on personal hygiene), as does their impact on reducing disease transmission. This study uses a rapid review approach and investigates evidence from previous epidemic outbreaks to provide a quantitative assessment of the effectiveness of key NPIs used by countries to combat the COVID-19 pandemic. Results from the study are designed to help countries enhance their policy response as well as inform transition strategies by identifying which policies should be relaxed and which should not.