Literature DB >> 33857407

Lack of detail in population-level data impedes analysis of SARS-CoV-2 variants of concern and clinical outcomes.

Sean Wei Xiang Ong1, Barnaby Edward Young2, David Chien Lye3.   

Abstract

Entities:  

Year:  2021        PMID: 33857407      PMCID: PMC8041357          DOI: 10.1016/S1473-3099(21)00201-2

Source DB:  PubMed          Journal:  Lancet Infect Dis        ISSN: 1473-3099            Impact factor:   25.071


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The SARS-CoV-2 lineage B.1.1.7 is characterised by a suite of defining mutations in the immunodominant spike protein, including a signature Asp501Tyr substitution in the receptor-binding domain. First reported in December 2020, in the UK, the variant's discovery coincided with a substantial surge in case numbers and fatalities in the UK, raising concerns that this variant was both more infectious and virulent than previous variants. Epidemiological and modelling studies have yielded good evidence that B.1.1.7 is more transmissible than other variants.1, 2 However, conclusions as to the effects of B.1.1.7 on disease severity are less certain. Confounding factors including health-care resource use, demographic changes, and socio-behavioural trends affect clinical outcomes, including mortality, and are difficult to adjust for without detailed, robust, patient-level data. In The Lancet Infectious Diseases, Dan Frampton and colleagues report their findings from such a study. Analysing a cohort of 341 patients, including 198 (58%) with B.1.1.7 infections, the authors correlated outcomes with granular clinical data. Their observation that B.1.1.7 infections were associated with increased viral loads corroborates findings from two other studies4, 5 and provides a mechanistic hypothesis that increased transmissibility is via increased respiratory shedding. Yet, disease severity and clinical outcomes between patients with B.1.1.7 and non-B.1.1.7 infections were similar after adjusting for differences in age, sex, ethnicity, and comorbidities. Importantly, this study was done from Nov 9, to Dec 20, 2020, before the late-December peak in UK COVID-19 infections, avoiding any confounding effect of the availability of health-care resources on mortality. This finding is in contrast with three studies that reported increased mortality associated with lineage B.1.1.7 (table ).6, 7, 8 Several factors might explain this discordance. Two of these studies were based on a community-based testing dataset, whereas Frampton and colleagues studied a cohort of patients admitted to hospital, which included substantially more older adults than the other studies did. Although the proportion of patients with severe illness was not reported by the other studies, this proportion was probably much lower than that in Frampton and colleagues' study. Hence, although these large community studies found a significant difference in mortality at a population level, the absolute risk increase affecting individual patients is probably minimal.
Table

Comparison of studies assessing the effect of lineage B.1.1.7 on disease severity and clinical outcomes

Frampton et al3Challen et al6Davies et al7Grint et al8
Patient recruitmentHospitalised patients with confirmed COVID-19Public health data from community-based testing datasetPublic health data from community-based testing datasetPublic health data from both community and hospital-based testing dataset
Study datesNov 9, to Dec 20, 2020Oct 1, 2020, to Jan 28, 2021Nov 1, 2020, to Jan 23, 2021Nov 16, 2020, to Jan 11, 2021
Number of participants341 (69%) included of 496 available patients screened109 812 (11·6%) included of 941 518 available patients screened1 146 534 (51·1%) included of 2 245 263 available patients screened184 786 (41·9%) included of 441 161 available patients screened
Age of participants, yearsMedian 60 (IQR 47–75)Mean 46·3 (SD 11·0)1–34 (513 726/1 14 6534 [44·8%]); 35–54 (403 313/1 14 6534 [35·2%]); 55–69 (175 983/1 14 6534 [15·3%]); 70–84 (440 46/1 14 6534 [3·8%]); ≥85 (9446/1 14 6534 [0·8%])Median 38·0 (IQR 24·0–52·0); mean 38·2 (SD 18·1)
Detection of lineage B.1.1.7Whole-genome sequencing and matching to COG-UK Mutation Explorer databaseSurrogate measure using S-gene negativity on Thermo TaqPath COVID-19 multiplex PCR assaySurrogate measure using S-gene negativity on Thermo TaqPath COVID-19 multiplex PCR assaySurrogate measure using S-gene negativity on Thermo TaqPath COVID-19 multiplex PCR assay
ControlsNon-B.1.1.7 infectionsS-gene positive patientsS-gene positive patientsS-gene positive patients
Primary outcomeClinical severity as defined by WHO ordinal scale ≥6; mortality at 28 daysMortality at 28 daysMortality at 28 daysMortality at 28 days
Overall rate of severe disease36·9%Data not availableData not availableData not available
Overall mortality rate16·2%0·3%0·9%0·5%
Effect on mortalityNo significant differenceHR 1·64 (95% CI 1·32–2·04)HR 1·55 (95% CI 1·39–1·72)HR 1·67 (95% CI 1·34–2·09)

HR=hazard ratio.

Comparison of studies assessing the effect of lineage B.1.1.7 on disease severity and clinical outcomes HR=hazard ratio. Furthermore, instead of whole-genome sequencing as used by Frampton and colleagues, these studies used S-gene target failure (SGTF) on PCR assay as a surrogate measure for detection of lineage B.1.1.7. The B.1.1.7 variant is associated with nucleotide deletions that prevent S gene target amplification by several commercial tests. This method gave rise to notable selection bias because S-gene data were unavailable in certain areas because of assay availability, with half of patients in both studies having unknown S gene status. Crucially, missing SGTF status was associated with older age and place of residence, with most patients in care homes having absent S-gene data, where mortality rates from COVID-19 are highest. Thus, although limited by a much smaller dataset, the study by Frampton and colleagues has important advantages over the three community studies. These advantages include the use of whole-genome sequencing, recruitment of hospitalised patients, and a population reflective of the spectrum of severity in whom increased virulence will have the greatest effect on outcomes. The finding that lineage B.1.1.7 infection did not confer increased risk of severe disease and mortality in this high-risk cohort is reassuring but requires further confirmation in larger studies. These differences between B.1.1.7 and non-B.1.1.7 lineages mirror those of other virological sub-groups of SARS-CoV-2. Similarly conflicting data was initially reported when variants carrying the Asp614Gly substitution emerged and became the dominant variants worldwide over the first few months of the pandemic in 2020. Early population-level data suggested that this substitution was associated with increased COVID-19 mortality but later cohort studies found no effect on disease severity.9, 10 In a study we did in Singapore comparing different SARS-CoV-2 clades, Asp614Gly was associated with increased viral loads without changes in severity or transmission. Genetic drift and selection pressures (in particular with passive antibody treatments and vaccination) will continue to engender changes in SARS-CoV-2 and might result in the emergence of variants of high consequence—variants that are more virulent, escape from host immunity, or are resistant to treatment. Active, timely, and broad-based genomic surveillance is crucial for their early detection. But careful epidemiologic and clinical assessment, coupled with a healthy scepticism, is important when assessing claims of the effect of these variants. BEY reports personal fees from Roche and Sanofi, outside the submitted work. All other authors declare no competing interests.
  13 in total

1.  Impact of second wave of COVID-19 pandemic on the hesitancy and refusal of COVID-19 vaccination in Puducherry, India: a longitudinal study.

Authors:  Jeyanthi Anandraj; Yuvaraj Krishnamoorthy; Parthibane Sivanantham; Jilisha Gnanadas; Sitanshu Sekhar Kar
Journal:  Hum Vaccin Immunother       Date:  2021-11-30       Impact factor: 3.452

2.  A tentative assessment of the changes in transmissibility and fatality risk associated with Beta SARS-CoV-2 variants in South Africa: an ecological study.

Authors:  Shi Zhao; Zhihang Peng; Maggie H Wang
Journal:  Pathog Glob Health       Date:  2021-12-20       Impact factor: 3.735

3.  Post-Vaccination Coronavirus Disease 2019: A Case-Control Study and Genomic Analysis of 119 Breakthrough Infections in Partially Vaccinated Individuals.

Authors:  Ioannis Baltas; Florencia A T Boshier; Charlotte A Williams; Nadua Bayzid; Marius Cotic; José Afonso Guerra-Assunção; Dianne Irish-Tavares; Tanzina Haque; Jennifer Hart; Sunando Roy; Rachel Williams; Judith Breuer; Tabitha W Mahungu
Journal:  Clin Infect Dis       Date:  2022-08-25       Impact factor: 20.999

4.  Clinical and Virological Features of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants of Concern: A Retrospective Cohort Study Comparing B.1.1.7 (Alpha), B.1.351 (Beta), and B.1.617.2 (Delta).

Authors:  Sean Wei Xiang Ong; Calvin J Chiew; Li Wei Ang; Tze Minn Mak; Lin Cui; Matthias Paul H S Toh; Yi Ding Lim; Pei Hua Lee; Tau Hong Lee; Po Ying Chia; Sebastian Maurer-Stroh; Raymond T P Lin; Yee Sin Leo; Vernon J Lee; David Chien Lye; Barnaby Edward Young
Journal:  Clin Infect Dis       Date:  2022-08-24       Impact factor: 20.999

Review 5.  The ins and outs of SARS-CoV-2 variants of concern (VOCs).

Authors:  Mostafa Salehi-Vaziri; Mehdi Fazlalipour; Seyed Mahmood Seyed Khorrami; Kayhan Azadmanesh; Mohammad Hassan Pouriayevali; Tahmineh Jalali; Zabihollah Shoja; Ali Maleki
Journal:  Arch Virol       Date:  2022-01-28       Impact factor: 2.685

6.  Socio-Demographic Characteristics of COVID-19 Vaccine Recipients in Kwara State, North Central Nigeria.

Authors:  Ahmad Ibrahim Al-Mustapha; Musa Imam Abubakar; Muftau Oyewo; Rita Enyam Esighetti; Oluwaseun Adeolu Ogundijo; Lukman Dele Bolanle; Oluwatosin Enoch Fakayode; Abdullateef Saliman Olugbon; Michael Oguntoye; Nusirat Elelu
Journal:  Front Public Health       Date:  2022-01-05

7.  Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2.

Authors:  Shi Zhao; Jingzhi Lou; Lirong Cao; Hong Zheng; Marc K C Chong; Zigui Chen; Renee W Y Chan; Benny C Y Zee; Paul K S Chan; Maggie H Wang
Journal:  BMC Infect Dis       Date:  2021-10-07       Impact factor: 3.090

8.  The Disease Severity and Clinical Outcomes of the SARS-CoV-2 Variants of Concern.

Authors:  Lixin Lin; Ying Liu; Xiujuan Tang; Daihai He
Journal:  Front Public Health       Date:  2021-11-30

9.  Increase in Viral Load in Patients With SARS-CoV-2 Delta Variant Infection in the Republic of Korea.

Authors:  Jeong-Min Kim; Jee Eun Rhee; Myeongsu Yoo; Heui Man Kim; Nam-Joo Lee; Sang Hee Woo; Hye-Jun Jo; Donghyok Kwon; Sangwon Lee; Cheon Kwon Yoo; Eun-Jin Kim
Journal:  Front Microbiol       Date:  2022-03-03       Impact factor: 5.640

10.  Identifying SARS-CoV-2 Variants of Concern through saliva-based RT-qPCR by targeting recurrent mutation sites.

Authors:  Rachel E Ham; Austin R Smothers; Rui Che; Keegan J Sell; Congyue Annie Peng; Delphine Dean
Journal:  medRxiv       Date:  2022-03-04
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