Increased risk of death in covid-19 hospital admissions during the second wave as compared to the first epidemic wave. A prospective dynamic cohort study in South London, UK
Bibliographic record
Abstract
ABSTRACT Objective To assess whether mortality of patients admitted for covid-19 treatment was different in the second UK epidemic wave of covid-19 compared to the first wave accounting for improvements in the standard of care available and differences in the distribution of risk factors between the two waves. Design Single-centre, analytical, dynamic cohort study. Participants 2,701 adults (≥18 years) with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and/or clinico-radiological diagnosis of covid-19, who required hospital admission to covid-19 specific wards, between January 2020 and March 2021. There were 884 covid-19 admissions during the first wave (before 30 Jun 2020) and 1,817 during the second wave. Outcome measures in-hospital covid-19 associated mortality, ascertained from clinical records and Medical Certificate Cause of Death. Results The crude mortality rate was 25% lower during the second wave (2.23 and 1.66 deaths per 100 person-days in first and second wave respectively). However, after accounting for age, sex, dexamethasone, oxygen requirements, symptoms at admission and Charlson Comorbidity Index, mortality hazard ratio associated with covid-19 hospital admissions was 1.62 (95% confidence interval 1.26, 2.08) times higher in the second wave compared to the first. Conclusions Analysis of covid-19 admissions recorded in St. Georges Hospital, shows a larger second epidemic wave, with a lower crude mortality in hospital admissions. Nevertheless, after accounting for other factors underlying risk of death for covid-19 admissions was higher in the second wave. These findings are temporally and ecologically correlated with an increased circulation of SARS-CoV-2 variant of concern 202012/1 (alpha).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".