Changing patterns of SARS-CoV-2 infection through Delta and Omicron waves by vaccination status, previous infection and neighbourhood deprivation: a cohort analysis of 2.7 M people
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Background
Our study examines if SARS-CoV-2 infections varied by vaccination status, if an individual had previously tested positive and by neighbourhood socioeconomic deprivation across the Delta and Omicron epidemic waves of SARS-CoV-2.
Methods
Population cohort study using electronic health records for 2.7 M residents in Cheshire and Merseyside, England (3rd June 2021 to 1st March 2022). Our outcome variable was registered positive test for SARS-CoV-2. Explanatory variables were vaccination status, previous registered positive test and neighbourhood socioeconomic deprivation. Cox regression models were used to analyse associations.
Results
Originally higher SARS-CoV-2 rates in the most socioeconomically deprived neighbourhoods changed to being higher in the least deprived neighbourhoods from the 1st September 2021, and were inconsistent during the Omicron wave. Individuals who were fully vaccinated (two doses) were associated with fewer registered positive tests (e.g., individuals engaged in testing between 1st September and 27th November 2021—Hazards Ratio (HR) = 0.48, 95% Confidence Intervals (CIs) = 0.47–0.50. Individuals with a previous registered positive test were also less likely to have a registered positive test (e.g., individuals engaged in testing between 1st September and 27th November 2021—HR = 0.16, 95% CIs = 0.15–0.18. However, the Omicron period saw smaller effect sizes for both vaccination status and previous registered positive test.
Conclusions
Changing patterns of SARS-CoV-2 infections during the Delta and Omicron waves reveals a dynamic pandemic that continues to affect diverse communities in sometimes unexpected ways.
Article activity feed
-
-
SciScore for 10.1101/2022.04.05.22273169: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Broad ethnic groups were used: White, Asian, Black, Mixed and Other. Whitesuggested: RRID:MMRRC_037613-MU)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: SARS-CoV-2 infections were identified based on a registered positive test. There was limited community testing availability during the first wave of infections and access to lateral flow tests were not available until late 2020 (6th November in Liverpool only, 3rd December rest of region). These …
SciScore for 10.1101/2022.04.05.22273169: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Broad ethnic groups were used: White, Asian, Black, Mixed and Other. Whitesuggested: RRID:MMRRC_037613-MU)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: SARS-CoV-2 infections were identified based on a registered positive test. There was limited community testing availability during the first wave of infections and access to lateral flow tests were not available until late 2020 (6th November in Liverpool only, 3rd December rest of region). These issues may lead to missed infections that would not be reported in our data resulting in under-counts for previous positive tests. Not all individuals may get tested, nor register their test, leading to undercounts of infections in our measures. We attempted to account for some of these issues by restricting analyses to individuals who had registered a negative test in the month before due to established inequalities in testing uptake (29). The impact of this can be seen by comparing the models to analyses for all residents (e.g., Table 2 and Appendix Table C). We also report significant inequalities in who reported negative tests across our exposure variables (Appendix Table A) which may bias underlying associations. The range of bias we are unable to observe shows how difficult it is to investigate these phenomena using routine data, so our results should not be over-interpreted. Our analyses are descriptive and exploratory. We could not investigate the mechanisms that may underlie the associations we report (e.g., the processes that explain why social inequalities changed over time). We also are unable to account for all potential confounders or explanatory factors (e....
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
