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. 2021 May;593(7858):270-274.
doi: 10.1038/s41586-021-03426-1. Epub 2021 Mar 15.

Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7

Nicholas G Davies  1 , Christopher I Jarvis  2 ; CMMID COVID-19 Working Group; W John Edmunds  2 , Nicholas P Jewell  3   4 , Karla Diaz-Ordaz #  3   4 , Ruth H Keogh #  3   4
Collaborators, Affiliations

Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7

Nicholas G Davies et al. Nature. 2021 May.

Abstract

SARS-CoV-2 lineage B.1.1.7, a variant that was first detected in the UK in September 20201, has spread to multiple countries worldwide. Several studies have established that B.1.1.7 is more transmissible than pre-existing variants, but have not identified whether it leads to any change in disease severity2. Here we analyse a dataset that links 2,245,263 positive SARS-CoV-2 community tests and 17,452 deaths associated with COVID-19 in England from 1 November 2020 to 14 February 2021. For 1,146,534 (51%) of these tests, the presence or absence of B.1.1.7 can be identified because mutations in this lineage prevent PCR amplification of the spike (S) gene target (known as S gene target failure (SGTF)1). On the basis of 4,945 deaths with known SGTF status, we estimate that the hazard of death associated with SGTF is 55% (95% confidence interval, 39-72%) higher than in cases without SGTF after adjustment for age, sex, ethnicity, deprivation, residence in a care home, the local authority of residence and test date. This corresponds to the absolute risk of death for a 55-69-year-old man increasing from 0.6% to 0.9% (95% confidence interval, 0.8-1.0%) within 28 days of a positive test in the community. Correcting for misclassification of SGTF and missingness in SGTF status, we estimate that the hazard of death associated with B.1.1.7 is 61% (42-82%) higher than with pre-existing variants. Our analysis suggests that B.1.1.7 is not only more transmissible than pre-existing SARS-CoV-2 variants, but may also cause more severe illness.

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Figures

Extended Data Fig. 1 ∣
Extended Data Fig. 1 ∣. Missingness in SGTF status and proximity to SGTF-capable Lighthouse laboratories.
The geographical location of the six Lighthouse laboratories in the UK; missingness is higher in the lower-tier local authorities (shaded regions) that are closer to a Lighthouse laboratory that is not capable of producing an SGTF reading. Map source: Office for National Statistics.
Extended Data Fig. 2 ∣
Extended Data Fig. 2 ∣. Kaplan–Meier plots of survival within 60 days of a positive test for SGTF versus non-SGTF samples.
Plots are stratified by sex, age group, place of residence, ethnicity, NHS England region, IMD decile (in five groups) and specimen date. Note that the y-axis ranges differ among panels. These curves show the crude survival within each group (unadjusted for other covariates), and so do not necessarily signify differences in the effect of SGTF on survival for any specific group due to possible confounding factors. Shaded areas show 95% confidence intervals.
Extended Data Fig. 3 ∣
Extended Data Fig. 3 ∣. Schoenfeld residuals for survival model by SGTF stratified by LTLA and specimen date.
The model uses linear terms for age and IMD and a 28-day follow-up using complete cases. a–i, Residuals for SGTF (a), age (b), sex (c), IMD (d), ethnicity (e–g), and residence type (h, i). Two-sided Schoenfeld residual tests were performed. P=0.001 for SGTF (a); P = 0.039 for age (b); P = 0.101 for sex (c); P = 0.937 for IMD decile (d); P = 0.969 for ethnicity (e–g); P = 0.064 for residence type (h–i); and P = 0.027 globally. The trend line shows the mean and 95% confidence intervals of a loess regression.
Extended Data Fig. 4 ∣
Extended Data Fig. 4 ∣. Comparison of missingness models.
a–c, Q−Q plot (left; mean and 95% confidence intervals) and distribution of weights (right) under different missingness models assessed for IPW with a cauchit link (a), a robit link (Student’s t-distribution with d.f. = 4) (b) and a logit link (c).
Extended Data Fig. 5 ∣
Extended Data Fig. 5 ∣. Misclassification model.
For each NHS England region, we fit a beta-binomial model (purple, modelled SGTF) to the observed SGTF frequencies among Pillar 2 tests (black, observed SGTF), which estimates a constant proportion of ‘false-positive’ SGTF samples among non-VOC 202012/01 (that is, non-B.1.1.7) specimens (orange, modelled non-VOC SGTF) and a logistically growing proportion of VOC 202012/01 (that is, B.1.1.7) specimens over time (blue, modelled VOC). This allows us to model the conditional probability that a specimen with SGTF represents VOC 202012/01 (teal, P(VOC∣SGTF)). For our misclassification survival analysis, pVOC = 0 for non-SGTF specimens and pVOC = P(VOC∣SGTF) for SGTF specimens. Lines show medians and shaded areas show 95% credible intervals. Dashed vertical lines show the date on which P(VOC∣SGTF) first exceeds 0.5.
Extended Data Fig. 6 ∣
Extended Data Fig. 6 ∣. Ct values for SGTF versus non-SGTF.
a, b, The distribution of Ct values for orflab (a) and N (b) gene targets among specimens collected between 1 January and 14 February 2021.
Extended Data Fig. 7 ∣
Extended Data Fig. 7 ∣. S-gene dropout in community tests relative to a random sample of SARS-CoV-2 infections in the community.
Comparison of the proportion of samples with S-gene dropout in the Pillar 2 (that is, community testing) sample used in this analysis compared to Office for National Statistics (ONS) random sampling of the community. This comparison suggests that S-gene dropout samples are not overrepresented in testing data relative to the prevalence of S-gene dropout in the community, suggesting that the increased hazard of death among positive community tests estimated in this study is not the result of a decrease in the average propensity for test-seeking among individuals infected with B.1.1.7. Point and ranges for ONS data show mean and 95% credible intervals.
Fig. 1 ∣
Fig. 1 ∣. Descriptive analyses.
a, The number of samples with and without SGTF by day from 1 November 2020 to 14 February 2021, the period covered by our main analysis. b, Number of deaths within 28 days of a positive test by specimen date for all data included in the analysis. c, Kaplan–Meier plot showing survival (point estimates and 95% confidence intervals) among individuals tested in the community in England with and without SGTF, in the subset for whom SGTF was measured. The inset shows the full y-axis range. d–i, Crude death rates (point estimates and 95% confidence intervals) among SGTF versus non-SGTF cases (in the subset for whom SGTF was measured; n = 1,146,534) for deaths within 28 days of a positive test stratified by broad age groups and sex (d), residence type (e), ethnicity (f), IMD decile (g), region of NHS England (h) and specimen date (i). Horizontal bars show the overall crude death rates (point estimates and 95% confidence intervals) by age group irrespective of SGTF status.
Fig. 2 ∣
Fig. 2 ∣. Survival analyses.
a–d, Estimated hazard ratio of death (point estimate and 95% confidence intervals) within 28 days of a positive test for the SGTF analysis for complete cases (a), SGTF analysis with IPW (b), pVOC analysis for complete cases (c) and pVOC, analysis with IPW (d) in a model stratified by LTLA and specimen date and adjusted for the other covariates. e, Estimated hazard ratio of death (point estimates and 95% confidence intervals) across each model investigated. Death types are coded as follows: dX, all deaths within X days of a positive test; dNA, all deaths with no restriction on follow-up time; c28, death-certificate-confirmed deaths associated with COVID-19 within 28 days; e60, all deaths within 60 days plus all death-certificate-confirmed deaths associated with COVID-19 within any time period. S, spline term (for age or IMD); L, linear term (for age or IMD); NHSE, NHS England region (n = 7); UTLA, upper-tier local authority (n = 150); LTLA, lower-tier local authority (n = 316). LTLA start date signifies a start date chosen separately for each LTLA; Y:tstop signifies an interaction term between covariate Y and time since positive test (eth: ethnicity, res: residence type); pVOC2 signifies sequence-based misclassification adjustment (see Methods).
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