accident and emergency ae attendance in england following infection with sars cov 2 CORD-Papers-2022-06-02 (Version 1)

Title: Accident and emergency (AE) attendance in England following infection with SARS-CoV-2 Omicron or Delta
Abstract: The SARS-CoV-2 Omicron variant is increasing in prevalence around the world. Accurate estimation of disease severity associated with Omicron is critical for pandemic planning. We found lower risk of accident and emergency (AE) attendance following SARS-CoV-2 infection with Omicron compared to Delta (HR: 0.39 (95% CI: 0.30 to 0.51; P
Published: 2022-05-03
DOI: 10.1101/2022.05.03.22274602
DOI_URL: http://doi.org/10.1101/2022.05.03.22274602
Author Name: Grint D J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/grint_d_j
Author Name: Wing K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wing_k
Author Name: Gibbs H P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gibbs_h_p
Author Name: Evans S J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/evans_s_j
Author Name: Williamson E J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/williamson_e_j
Author Name: Bhaskaran K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bhaskaran_k
Author Name: McDonald H I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mcdonald_h_i
Author Name: Walker A J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/walker_a_j
Author Name: Evans D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/evans_d
Author Name: Hickman G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hickman_g
Author Name: Mathur R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mathur_r
Author Name: Schultze A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/schultze_a
Author Name: Rentsch C T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rentsch_c_t
Author Name: Tazare J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tazare_j
Author Name: Douglas I J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/douglas_i_j
Author Name: Curtis H J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/curtis_h_j
Author Name: Morton C E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/morton_c_e
Author Name: Bacon S C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bacon_s_c
Author Name: Davy S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/davy_s
Author Name: MacKenna B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mackenna_b
Author Name: Inglesby P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/inglesby_p
Author Name: Croker R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/croker_r
Author Name: Parry J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/parry_j
Author Name: Hester F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hester_f
Author Name: Harper S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/harper_s
Author Name: DeVito N J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/devito_n_j
Author Name: Hulme W J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hulme_w_j
Author Name: Bates C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bates_c
Author Name: Cockburn J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cockburn_j
Author Name: Mehrkar A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mehrkar_a
Author Name: Goldacre B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/goldacre_b
Author Name: Eggo R M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_r_m
Author Name: Tomlinson L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tomlinson_l
sha: 9e03b62c54c150d4691e49b54b1a5b3087b0c1be
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2022.05.03.22274602v1?rss=1 https://doi.org/10.1101/2022.05.03.22274602
has_full_text: TRUE
Keywords Extracted from Text Content: SARS-CoV-2 SARS-CoV-2 Omicron medRxiv preprint Figure 3 people medRxiv pre-define UTLA line IMD OpenSAFELY UK.(1 patient https://github.com/opensafely/SGTF-Omi medRxiv preprint DAG AE https://opensafely.org/ NHS B.1.617.2 B.1.1.529 P=0.052 TaqPath lighthouse laboratories SARS-CoV-2 medRxiv preprint Figure 1 NHSX NHS
Extracted Text Content in Record: First 5000 Characters:The SARS-CoV-2 Omicron variant is increasing in prevalence around the world. Accurate estimation of disease severity associated with Omicron is critical for pandemic planning. We found lower risk of accident and emergency (AE) attendance following SARS-CoV-2 infection with Omicron compared to Delta (HR: 0.39 (95% CI: 0.30 -0.51; P<.0001). For AE attendances that lead to hospital admission, Omicron was associated with an 85% lower hazard compared with Delta (HR: 0.14 (95% CI: 0.09 -0.24; P<.0001)). The SARS-CoV-2 Omicron variant is increasing in prevalence around the world. Accurate estimation of disease severity associated with Omicron is critical for pandemic planning. We found lower risk of accident and emergency (AE) attendance following SARS-CoV-2 infection with Omicron compared to Delta (HR: 0.39 (95% CI: 0.30 -0.51; P<.0001). For AE attendances that lead to hospital admission, Omicron was associated with an 85% lower hazard compared with Delta (HR: 0.14 (95% CI: 0.09 -0.24; P<.0001)). The SARS-CoV-2 variant B.1.1.529 (Omicron) was first identified in South Africa in late 2021. Analysis has found that Omicron is more transmissible than the predominant B.1.617.2 variant (Delta) and it has since become the dominant strain throughout the UK.(1) Only a small proportion of Omicron cases are identified by whole-genome sequencing. In PCR assays for SARS-CoV-2 processed by TaqPath lighthouse laboratories, missingness in one spike protein gene target occurs with the Omicron variant, but not the Delta variant. Spike gene target failure (SGTF) is therefore a proxy for Omicron identification, and has been shown to have excellent sensitivity in England over the study period.(1) Working on behalf of NHS England, we estimate the risk of accident and emergency (AE) attendance following confirmation of SARS-CoV-2 infection in England, comparing infection with Omicron to Delta, after accounting for demographic factors and comorbidities (Supplement 3). All data were linked, stored and analysed securely within the OpenSAFELY platform https://opensafely.org/ (Supplement 1). The OpenSAFELY dataset is based on 24 million people currently registered with GP surgeries using TPP SystmOne software, covering 40% of England's population. Pseudonymized data include coded diagnoses, medications and physiological parameters. All code is shared openly for review and re-use under MIT open license (https://github.com/opensafely/SGTF-Omi). We used linked GP, SARS-CoV-2 testing, vaccination and emergency care data (Supplement 2) to define the study cohort of people first testing positive for SARS-CoV-2 between 5 th December 2021 and 1 st January 2022. The study was analysed according to the pre-define study protocol (https://github.com/opensafely/SGTF-Omi-research/tree/main/docs), in line with previous work. (2, 3) SGTF status was known for 330,380/755,432 (44%) people with a first confirmed SARS-CoV-2 infection between 5 th December 2021 and 1 st January 2022 (237,430 Omicron; 92,950 Delta). A total of 660 (341 Omicron; 319 Delta) AE attendances were recorded with SARS-CoV-2 recorded as the patient diagnosis prior to 21 st January 2022, when follow-up was administratively censored. The exposure groups were similar in terms of sex, ethnicity, and regional distribution (Table 1, Supplement 4). The median age of the Omicron group was higher (35 years (interquartile range (IQR) 24 -49)) vs. 32 (11 -44), with more comorbidities (2+ comorbidities: 2.4% vs. 1.4%). A lower proportion of Omicron cases were unvaccinated (17.1% vs. 43.0%) while a higher proportion had received a booster vaccination (23.2% vs. 5.1%) compared to Delta at the time of diagnosis. Delta diagnoses were more frequent in the first week of the study period, while Omicron diagnoses predominated thereafter. Consequently, median follow-up time was shorter among the Omicron group (26 days (IQR: 23 -31)) than the Delta group (39 days (34 -43)) ( Figure 1 ). . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 3, 2022. ; https://doi.org/10.1101/2022.05.03.22274602 doi: medRxiv preprint We estimated the relative hazard of AE attendance with Omicron compared to Delta using Cox proportional hazards regression models stratified by upper tier local authority area (UTLA).(4) Covariate adjustment was informed by a directed acyclic graph (DAG) (Supplement 5). Follow-up began at the date of positive SARS-CoV-2 test and was censored at the earliest of death, AE attendance with diagnosis coded as SARS-CoV-2, or 7-days prior to the emergency care data lock (28 th January 2022). Omicron was consistently associated with lower hazard of AE attendance compared to Delta. In fullyadjusted analysis accounting for demographics, vaccination status, and comorbidities, the hazard of AE attendance w
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