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Review 1: "Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study"

This preprint claims that, although vaccination may reduce the risk, infection with VOC results in a higher hospitalization risk. Both reviewers found it to be reliable but suggested an adjustment for calendar time in the primary analysis would have produced a better output.

Published onOct 22, 2021
Review 1: "Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study"
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key-enterThis Pub is a Review of
Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study

AbstractBackgroundThe COVID-19 pandemic is now dominated by variant lineages; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the risk of hospitalization following infection with nine variants of concern or interest (VOC/VOI).MethodsOur study includes individuals with positive SARS-CoV-2 RT-PCR in the Washington Disease Reporting System and with available viral genome data, from December 1, 2020 to July 30, 2021. The main analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for the risk of hospitalization following infection with a VOC/VOI, adjusting for age, sex, and vaccination status.FindingsOf the 27,814 cases, 23,170 (83.3%) were sequenced through sentinel surveillance, of which 726 (3.1%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.17, 95% CI 2.15-4.67), Beta (HR: 2.97, 95% CI 1.65–5.35), Delta (HR: 2.30, 95% CI 1.69-3.15), and Alpha (HR 1.59, 95% CI 1.26–1.99) compared to infections with an ancestral lineage. Following VOC infection, unvaccinated patients show a similar higher hospitalization risk, while vaccinated patients show no significant difference in risk, both when compared to unvaccinated, ancestral lineage cases.InterpretationInfection with a VOC results in a higher hospitalization risk, with an active vaccination attenuating that risk. Our findings support promoting hospital preparedness, vaccination, and robust genomic surveillance.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.



Paredes et al. have analyzed the impact of SARS-CoV-2 variants of concern (VOC) on the risk of hospitalization, using a dataset of viral sequences linked to medical records in over 20,000 cases in Washington State. A significant strength of this study is that the main analysis used specimens randomly selected for sequencing through a sentinel surveillance program rather than those specifically chosen for sequencing for clinical reasons or for targeted outbreak investigations. This means that the results of the analysis are more likely to reflect the impact of the VOCs on hospitalisation risk in the general population.

The estimates in this article are wholly consistent with previous published values for the impact of the Alpha and Delta VOCs on hospitalization risk in unvaccinated individuals. The article also provides estimates of the elevated risk of hospitalization for other less common VOCs, for which there exists less high-quality evidence in the literature. The protective effect of vaccination is also estimated separately for each VOC, although this is limited due to a dichotomous comparison based on initial vaccine doses rather than giving a full breakdown by vaccine types and dose number and/or intervals for 2-dose regimens.

Establishing causal effects of factors of interest from retrospective observational data is always challenging, but the methodology used for statistical analysis is appropriate and broadly in line with previous peer-reviewed papers on the impact of VOCs. The authors have adjusted their analyses for age and sex, which are important predictors of clinical outcomes following SARS-CoV-2 infection. Ideally, the adjustment would also include pre-existing co-morbidities for each person, in case these were associated with the risk of infection with a VOC. Most similar analyses have presented results with adjustment for calendar time, to account for the potential impact of seasonal variations and healthcare pressures on outcomes. The authors make the argument that they prefer not to adjust for calendar time – which I think would be challenged by some other research groups – but they do also present such analyses, without a substantial impact on their findings. The discussion section appropriately acknowledges limitations of the dataset and the analysis such as the potential impact of vaccination on testing behaviour.

I am a statistician. After reading the current draft of the article, I am left with some technical queries that I think would ideally be addressed before final publication of the work. These include clarifying the exact structure used for the mixed effects models in analyzing the definitions of zero timepoints and any potential censoring conditions for time-to-event outcomes. However, any such clarifications are unlikely to have a major impact on the text or the conclusions of the article.

Claims are reliable by the data and methods used.


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