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

Abstract

SARS-CoV-2 lineage B.1.1.7, a variant first detected in the United Kingdom in September 2020¹, has spread to multiple countries worldwide. Several studies have established that B.1.1.7 is more transmissible than preexisting variants, but have not identified whether it leads to any change in disease severity². We analyse a dataset linking 2,245,263 positive SARS-CoV-2 community tests and 17,452 COVID-19 deaths in England from 1 September 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 of mutations in this lineage preventing PCR amplification of the spike gene target (S gene target failure, SGTF¹). Based on 4,945 deaths with known SGTF status, we estimate that the hazard of death associated with SGTF is 55% (95% CI 39-72%) higher after adjustment for age, sex, ethnicity, deprivation, care home residence, local authority of residence and test date. This corresponds to the absolute risk of death for a 55-69-year-old male increasing from 0.6% to 0.9% (95% CI 0.8-1.0%) within 28 days after a positive test in the community. Correcting for misclassification of SGTF and missingness in SGTF status, we estimate a 61% (42-82%) higher hazard of death associated with B.1.1.7. Our analysis suggests that B.1.1.7 is not only more transmissible than preexisting SARS-CoV-2 variants, but may also cause more severe illness.

Extended Data Fig. 1.. Missingness in SGTF status and proximity to SGTF-capable Lighthouse lab.

The geographical location of the six Lighthouse Labs in the United Kingdom; missingness is higher in the lower-tier local authorities (shaded regions) which are closer to a Lighthouse lab that is not capable of producing an SGTF reading.

Extended Data Fig. 2.. Kaplan-Meier plots of survival within 60 days of positive test for SGTF versus non-SGTF.

Plots are stratified by sex, age group, place of residence, ethnicity, NHS England region, IMD decile (in 5 groups), and specimen date. Note that 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. Shaded areas show 95% CIs.

Extended Data Fig. 3.. Schoenfeld residuals for survival model by SGTF stratified by LTLA and specimen date.

Model uses linear terms for age and IMD and a 28-day followup using complete cases. Schoenfeld residual tests (two-sided) give (a) P = 0.001 for SGTF; (b) P = 0.039 for age; (c) P = 0.101 for sex; (d) P = 0.937 for IMD decile; (e–g) P = 0.969 for ethnicity; (h–i) P = 0.064 for residence type; and P = 0.027 globally. Trend line shows mean and 95% CIs of a loess regression.

Extended Data Fig. 4.. Comparison of missingness models.

Q-Q plot (left; mean and 95% CIs) and distribution of weights (right) under different missingness models assessed for IPW with (a) a cauchit link, (b) a robit link (Student’s t distribution with d.f. = 4), and (c) a logit link.

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 (i.e., non-B.1.1.7) specimens (orange, Modelled non-VOC SGTF) and a logistically growing proportion of VOC 202012/01 (i.e., 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, p_VOC = 0 for non-SGTF specimens and p_VOC = P(VOC|SGTF) for SGTF specimens. Lines show medians and shaded areas show 95% credible intervals.

Extended Data Fig. 6.. Ct values for SGTF versus non-SGTF.

The distribution of Ct values for (a) ORF1ab and (b) N gene targets among specimens collected between 1 January–14 February 2021.

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 (i.e., 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.. 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 positive test by specimen date included in the analysis. c Kaplan-Meier plot showing survival (95% confidence intervals) among individuals tested in the community in England with and without SGTF, in the subset with SGTF measured. Inset shows the full y–axis range. d–i Crude death rates (point estimates and 95% CIs) among SGTF versus non-SGTF cases (in the subset with SGTF measured, n = 1,146,534) for deaths within 28 days of positive test stratified by broad age groups and (d) sex, (e) place of residence, (f) ethnicity, (g) index of multiple deprivation, (h) NHS England region, and (i) specimen date. Horizontal bars show the overall crude death rates (95% CIs) by age group irrespective of SGTF status.

Fig. 2.. Survival analyses.

a–d Estimated hazard ratio of death (mean and 95% CIs) within 28 days of positive test for (a) SGTF, complete-cases analysis; (b) SGTF, IPW analysis; (c) p_VOC, complete-cases analysis; and (d) p_VOC, IPW analysis, in model stratified by LTLA and specimen date and adjusted for the other covariates. e Estimated hazard ratio of death (point estimates and 95% CIs) across each model investigated. Death types are coded as follows: dX, all deaths within X days of a positive test; c28, death-certificate-confirmed COVID-19 deaths within 28 days; e60, all deaths within 60 days plus all death-certificate-confirmed COVID-19 deaths 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 (see Methods).