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2.
Euro Surveill ; 27(11)2022 03.
Article in English | MEDLINE | ID: covidwho-1753318

ABSTRACT

When SARS-CoV-2 Omicron emerged in 2021, S gene target failure enabled differentiation between Omicron and the dominant Delta variant. In England, where S gene target surveillance (SGTS) was already established, this led to rapid identification (within ca 3 days of sample collection) of possible Omicron cases, alongside real-time surveillance and modelling of Omicron growth. SGTS was key to public health action (including case identification and incident management), and we share applied insights on how and when to use SGTS.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Humans , Membrane Glycoproteins/genetics , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Viral Envelope Proteins/genetics
3.
N Engl J Med ; 386(16): 1532-1546, 2022 04 21.
Article in English | MEDLINE | ID: covidwho-1730372

ABSTRACT

BACKGROUND: A rapid increase in coronavirus disease 2019 (Covid-19) cases due to the omicron (B.1.1.529) variant of severe acute respiratory syndrome coronavirus 2 in highly vaccinated populations has aroused concerns about the effectiveness of current vaccines. METHODS: We used a test-negative case-control design to estimate vaccine effectiveness against symptomatic disease caused by the omicron and delta (B.1.617.2) variants in England. Vaccine effectiveness was calculated after primary immunization with two doses of BNT162b2 (Pfizer-BioNTech), ChAdOx1 nCoV-19 (AstraZeneca), or mRNA-1273 (Moderna) vaccine and after a booster dose of BNT162b2, ChAdOx1 nCoV-19, or mRNA-1273. RESULTS: Between November 27, 2021, and January 12, 2022, a total of 886,774 eligible persons infected with the omicron variant, 204,154 eligible persons infected with the delta variant, and 1,572,621 eligible test-negative controls were identified. At all time points investigated and for all combinations of primary course and booster vaccines, vaccine effectiveness against symptomatic disease was higher for the delta variant than for the omicron variant. No effect against the omicron variant was noted from 20 weeks after two ChAdOx1 nCoV-19 doses, whereas vaccine effectiveness after two BNT162b2 doses was 65.5% (95% confidence interval [CI], 63.9 to 67.0) at 2 to 4 weeks, dropping to 8.8% (95% CI, 7.0 to 10.5) at 25 or more weeks. Among ChAdOx1 nCoV-19 primary course recipients, vaccine effectiveness increased to 62.4% (95% CI, 61.8 to 63.0) at 2 to 4 weeks after a BNT162b2 booster before decreasing to 39.6% (95% CI, 38.0 to 41.1) at 10 or more weeks. Among BNT162b2 primary course recipients, vaccine effectiveness increased to 67.2% (95% CI, 66.5 to 67.8) at 2 to 4 weeks after a BNT162b2 booster before declining to 45.7% (95% CI, 44.7 to 46.7) at 10 or more weeks. Vaccine effectiveness after a ChAdOx1 nCoV-19 primary course increased to 70.1% (95% CI, 69.5 to 70.7) at 2 to 4 weeks after an mRNA-1273 booster and decreased to 60.9% (95% CI, 59.7 to 62.1) at 5 to 9 weeks. After a BNT162b2 primary course, the mRNA-1273 booster increased vaccine effectiveness to 73.9% (95% CI, 73.1 to 74.6) at 2 to 4 weeks; vaccine effectiveness fell to 64.4% (95% CI, 62.6 to 66.1) at 5 to 9 weeks. CONCLUSIONS: Primary immunization with two doses of ChAdOx1 nCoV-19 or BNT162b2 vaccine provided limited protection against symptomatic disease caused by the omicron variant. A BNT162b2 or mRNA-1273 booster after either the ChAdOx1 nCoV-19 or BNT162b2 primary course substantially increased protection, but that protection waned over time. (Funded by the U.K. Health Security Agency.).


Subject(s)
COVID-19 Vaccines , COVID-19 , /therapeutic use , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , Case-Control Studies , Humans , Immunization, Secondary/adverse effects , SARS-CoV-2/genetics
4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327707

ABSTRACT

Background: The SARS-CoV-2 Omicron variant (B.1.1.529) has rapidly replaced the Delta variant (B.1.617.2) to become dominant in England. This epidemiological study assessed differences in transmissibility between the Omicron and Delta using two methods and data sources. Methods Omicron and Delta cases were identified through genomic sequencing, genotyping and S-gene target failure in England from 5-11 December 2021. Secondary attack rates for Omicron and Delta using named contacts and household clustering were calculated using national surveillance and contact tracing data. Logistic regression was used to control for factors associated with transmission. Findings Analysis of contact tracing data identified elevated secondary attack rates for Omicron vs Delta in household (15.0% vs 10.8%) and non-household (8.2% vs 3.7%) settings. The proportion of index cases resulting in residential clustering was twice as high for Omicron (16.1%) compared to Delta (7.3%). Transmission was significantly less likely from cases, or in named contacts, in receipt of three compared to two vaccine doses in household settings, but less pronounced for Omicron (aRR 0.78 and 0.88) compared to Delta (aRR 0.62 and 0.68). In non-household settings, a similar reduction was observed for Delta cases and contacts (aRR 0.84 and 0.51) but only for Omicron contacts (aRR 0.76, 95% CI: 0.58-0.93) and not cases in receipt of three vs two doses (aRR 0.95, 0.77-1.16). Interpretation Our study identified increased risk of onward transmission of Omicron, consistent with its successful global displacement of Delta. We identified a reduced effectiveness of vaccination in lowering risk of transmission, a likely contributor for the rapid propagation of Omicron.

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296519

ABSTRACT

Abstract Background A rapid increase in cases due to the SARS-CoV-2 Omicron (B.1.1.529) variant in highly vaccinated populations has raised concerns about the effectiveness of current vaccines. Methods We used a test-negative case-control design to estimate vaccine effectiveness (VE) against symptomatic disease caused by the Omicron and Delta variants in England. VE was calculated after primary immunisation with two BNT162b2 or ChAdOx1 doses, and at 2+ weeks following a BNT162b2 booster. Results Between 27 November and 06 December 2021, 581 and 56,439 eligible Omicron and Delta cases respectively were identified. There were 130,867 eligible test-negative controls. There was no effect against Omicron from 15 weeks after two ChAdOx1 doses, while VE after two BNT162b2 doses was 88.0% (95%CI: 65.9 to 95.8%) 2-9 weeks after dose 2, dropping to between 34 and 37% from 15 weeks post dose 2.From two weeks after a BNT162b2 booster, VE increased to 71.4% (95%CI: 41.8 to 86.0%) for ChAdOx1 primary course recipients and 75.5% (95%CI: 56.1 to 86.3%) for BNT162b2 primary course recipients. For cases with Delta, VE was 41.8% (95%CI: 39.4-44.1%) at 25+ weeks after two ChAdOx1 doses, increasing to 93.8% (95%CI: 93.2-94.3%) after a BNT162b2 booster. With a BNT162b2 primary course, VE was 63.5% (95%CI: 61.4 to 65.5%) 25+ weeks after dose 2, increasing to 92.6% (95%CI: 92.0-93.1%) two weeks after the booster. Conclusions Primary immunisation with two BNT162b2 or ChAdOx1 doses provided no or limited protection against symptomatic disease with the Omicron variant. Boosting with BNT162b2 following either primary course significantly increased protection.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-292702

ABSTRACT

Background:  It is unclear whether smoking increases the risk of COVID-19 hospitalisation. We first examined the association of smoking status with hospitalisation for COVID-19 compared with hospitalisation for other respiratory viral infections a year previous. Second, we examined the concordance between smoking status recorded on the electronic health record (EHR) and the contemporaneous medical notes.Methods:: This case-control study enrolled adult patients (446 cases and 211 controls) at a single National Health Service trust in London, UK. The outcome variable was type of hospitalisation (COVID-19 vs. another respiratory virus a year previous). The exposure variable was smoking status (never/former/current smoker). Logistic regression analyses adjusted for age, sex, socioeconomic position and comorbidities were performed. The study protocol and analyses were pre-registered in April 2020 on the <ns3:ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri" ns4:href="https://doi.org/10.17605/OSF.IO/URFHN">Open Science Framework.Results:: Current smokers had lower odds of being hospitalised with COVID-19 compared with other respiratory viruses a year previous (OR=0.55, 95% CI=0.31-0.96, p=.04). There was no significant association among former smokers (OR=1.08, 95% CI=0.72-1.65, p=.70). Smoking status recorded on the EHR (compared with the contemporaneous medical notes) was incorrectly recorded for 168 (79.6%) controls (χ2(3)=256.5, p=<0.001) and 60 cases (13.5%) (χ2(3)=34.2, p=<0.001).Conclusions:: In a single UK hospital trust, current smokers had reduced odds of being hospitalised with COVID-19 compared with other respiratory viruses a year previous, although it is unclear whether this association is causal. Targeted post-discharge recording of smoking status may account for the greater EHR-medical notes concordance observed in cases compared with controls.

7.
Addiction ; 116(6): 1319-1368, 2021 06.
Article in English | MEDLINE | ID: covidwho-1231070

ABSTRACT

AIMS: To estimate the association of smoking status with rates of (i) infection, (ii) hospitalization, (iii) disease severity and (iv) mortality from SARS-CoV-2/COVID-19 disease. DESIGN: Living rapid review of observational and experimental studies with random-effects hierarchical Bayesian meta-analyses. Published articles and pre-prints were identified via MEDLINE and medRxiv. SETTING: Community or hospital, no restrictions on location. PARTICIPANTS: Adults who received a SARS-CoV-2 test or a COVID-19 diagnosis. MEASUREMENTS: Outcomes were SARS-CoV-2 infection, hospitalization, disease severity and mortality stratified by smoking status. Study quality was assessed (i.e. 'good', 'fair' and 'poor'). FINDINGS: Version 7 (searches up to 25 August 2020) included 233 studies with 32 'good' and 'fair' quality studies included in meta-analyses. Fifty-seven studies (24.5%) reported current, former and never smoking status. Recorded smoking prevalence among people with COVID-19 was generally lower than national prevalence. Current compared with never smokers were at reduced risk of SARS-CoV-2 infection [relative risk (RR) = 0.74, 95% credible interval (CrI) = 0.58-0.93, τ = 0.41]. Data for former smokers were inconclusive (RR = 1.05, 95% CrI = 0.95-1.17, τ = 0.17), but favoured there being no important association (21% probability of RR ≥ 1.1). Former compared with never smokers were at somewhat increased risk of hospitalization (RR = 1.20, CrI = 1.03-1.44, τ = 0.17), greater disease severity (RR = 1.52, CrI = 1.13-2.07, τ = 0.29) and mortality (RR = 1.39, 95% CrI = 1.09-1.87, τ = 0.27). Data for current smokers were inconclusive (RR = 1.06, CrI = 0.82-1.35, τ = 0.27; RR = 1.25, CrI = 0.85-1.93, τ = 0.34; RR = 1.22, 95% CrI = 0.78-1.94, τ = 0.49, respectively), but favoured there being no important associations with hospitalization and mortality (35% and 70% probability of RR ≥ 1.1, respectively) and a small but important association with disease severity (79% probability of RR ≥ 1.1). CONCLUSIONS: Compared with never smokers, current smokers appear to be at reduced risk of SARS-CoV-2 infection, while former smokers appear to be at increased risk of hospitalization, increased disease severity and mortality from COVID-19. However, it is uncertain whether these associations are causal.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , SARS-CoV-2 , Smoking/epidemiology , Smoking/mortality , Bayes Theorem , Hospitalization , Humans , Mortality , Prevalence , Risk , Severity of Illness Index
8.
Addiction ; : No Pagination Specified, 2020.
Article in English | APA PsycInfo | ID: covidwho-1208492

ABSTRACT

Aims To estimate the association of smoking status with rates of (i) infection, (ii) hospitalization, (iii) disease severity and (iv) mortality from SARS-CoV-2/COVID-19 disease. Design Living rapid review of observational and experimental studies with random-effects hierarchical Bayesian meta-analyses. Published articles and pre-prints were identified via MEDLINE and medRxiv. Setting Community or hospital, no restrictions on location. Participants Adults who received a SARS-CoV-2 test or a COVID-19 diagnosis. Measurements Outcomes were SARS-CoV-2 infection, hospitalization, disease severity and mortality stratified by smoking status. Study quality was assessed (i.e. 'good', 'fair' and 'poor'). Findings Version 7 (searches up to 25 August 2020) included 233 studies with 32 'good' and 'fair' quality studies included in meta-analyses. Fifty-seven studies (24.5%) reported current, former and never smoking status. Recorded smoking prevalence among people with COVID-19 was generally lower than national prevalence. Current compared with never smokers were at reduced risk of SARS-CoV-2 infection [relative risk (RR) = 0.74, 95% credible interval (CrI) = 0.58-0.93, tau = 0.41]. Data for former smokers were inconclusive (RR = 1.05, 95% CrI = 0.95-1.17, tau = 0.17), but favoured there being no important association (21% probability of RR >= 1.1). Former compared with never smokers were at somewhat increased risk of hospitalization (RR = 1.20, CrI = 1.03-1.44, tau = 0.17), greater disease severity (RR = 1.52, CrI = 1.13-2.07, tau = 0.29) and mortality (RR = 1.39, 95% CrI = 1.09-1.87, tau = 0.27). Data for current smokers were inconclusive (RR = 1.06, CrI = 0.82-1.35, tau = 0.27;RR = 1.25, CrI = 0.85-1.93, tau = 0.34;RR = 1.22, 95% CrI = 0.78-1.94, tau = 0.49, respectively), but favoured there being no important associations with hospitalization and mortality (35% and 70% probability of RR >= 1.1, respectively) and a small but important association with disease severity (79% probability of RR >= 1.1). Conclusions Compared with never smokers, current smokers appear to be at reduced risk of SARS-CoV-2 infection, while former smokers appear to be at increased risk of hospitalization, increased disease severity and mortality from COVID-19. However, it is uncertain whether these associations are causal. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

11.
Epidemiol Infect ; 148: e41, 2020 02 26.
Article in English | MEDLINE | ID: covidwho-2270

ABSTRACT

Novel Coronavirus (2019-nCoV [SARS-COV-2]) was detected in humans during the last week of December 2019 at Wuhan city in China, and caused 24 554 cases in 27 countries and territories as of 5 February 2020. The objective of this study was to estimate the risk of transmission of 2019-nCoV through human passenger air flight from four major cities of China (Wuhan, Beijing, Shanghai and Guangzhou) to the passengers' destination countries. We extracted the weekly simulated passengers' end destination data for the period of 1-31 January 2020 from FLIRT, an online air travel dataset that uses information from 800 airlines to show the direct flight and passengers' end destination. We estimated a risk index of 2019-nCoV transmission based on the number of travellers to destination countries, weighted by the number of confirmed cases of the departed city reported by the World Health Organization (WHO). We ranked each country based on the risk index in four quantiles (4th quantile being the highest risk and 1st quantile being the lowest risk). During the period, 388 287 passengers were destined for 1297 airports in 168 countries or territories across the world. The risk index of 2019-nCoV among the countries had a very high correlation with the WHO-reported confirmed cases (0.97). According to our risk score classification, of the countries that reported at least one Coronavirus-infected pneumonia (COVID-19) case as of 5 February 2020, 24 countries were in the 4th quantile of the risk index, two in the 3rd quantile, one in the 2nd quantile and none in the 1st quantile. Outside China, countries with a higher risk of 2019-nCoV transmission are Thailand, Cambodia, Malaysia, Canada and the USA, all of which reported at least one case. In pan-Europe, UK, France, Russia, Germany and Italy; in North America, USA and Canada; in Oceania, Australia had high risk, all of them reported at least one case. In Africa and South America, the risk of transmission is very low with Ethiopia, South Africa, Egypt, Mauritius and Brazil showing a similar risk of transmission compared to the risk of any of the countries where at least one case is detected. The risk of transmission on 31 January 2020 was very high in neighbouring Asian countries, followed by Europe (UK, France, Russia and Germany), Oceania (Australia) and North America (USA and Canada). Increased public health response including early case recognition, isolation of identified case, contract tracing and targeted airport screening, public awareness and vigilance of health workers will help mitigate the force of further spread to naïve countries.


Subject(s)
Air Travel , Coronavirus Infections/transmission , Disease Outbreaks , Pneumonia, Viral/transmission , Risk Assessment , Africa/epidemiology , Airports , Betacoronavirus , COVID-19 , China/epidemiology , Communicable Diseases, Imported , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Humans , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Population Surveillance , SARS-CoV-2 , South America/epidemiology , Travel Medicine
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