Characteristics and outcomes of COVID-19 patients during B.1.1.529 (Omicron) dominance compared to B.1.617.2 (Delta) in 89 German hospitals

Abstract

Background The SARS-CoV-2 variant B.1.1.529 (Omicron) was first described in November 2021 and became the dominant variant worldwide. Existing data suggests a reduced disease severity with Omicron infections in comparison to B.1.617.2 (Delta). Differences in characteristics and in-hospital outcomes of COVID-19 patients in Germany during the Omicron period compared to Delta are not thoroughly studied. ICD-10-code-based severe acute respiratory infections (SARI) surveillance represents an integral part of infectious disease control in Germany. Methods Administrative data from 89 German Helios hospitals was retrospectively analysed. Laboratory-confirmed SARS-CoV-2 infections were identified by ICD-10-code U07.1 and SARI cases by ICD-10-codes J09-J22. COVID-19 cases were stratified by concomitant SARI. A nine-week observational period between December 6, 2021 and February 6, 2022 was defined and divided into three phases with respect to the dominating virus variant (Delta, Delta to Omicron transition, Omicron). Regression analyses adjusted for age, gender and Elixhauser comorbidities were applied to assess in-hospital patient outcomes. Results A total cohort of 4,494 inpatients was analysed. Patients in the Omicron dominance period were younger (mean age 47.8 vs. 61.6; p < 0.01), more likely to be female (54.7% vs. 47.5%; p < 0.01) and characterized by a lower comorbidity burden (mean Elixhauser comorbidity index 5.4 vs. 8.2; p < 0.01). Comparing Delta and Omicron periods, patients were at significantly lower risk for intensive care treatment (adjusted odds ratio 0.72 [0.57–0.91]; p = 0.005), mechanical ventilation (adjusted odds ratio 0.42 [0.31–0.57]; p < 0.001), and in-hospital mortality (adjusted odds ratio 0.42 [0.32–0.56]; p < 0.001). This also applied mostly to the separate COVID-SARI group. During the Delta to Omicron transition, case numbers of COVID-19 without SARI exceeded COVID-SARI for the first time in the pandemic’s course. Conclusion Patient characteristics and outcomes differ during the Omicron dominance period as compared to Delta suggesting a reduced disease severity with Omicron infections. SARI surveillance might play a crucial role in assessing disease severity of future SARS-CoV-2 variants.

Non-COVID-19 mortality in France, April 2020 - June 2022: reduction compared to pre-pandemic mortality patterns, relative increase during the Omicron period, and the importance of detecting SARS-CoV-2 infections

“…This suggests that evaluating patterns of non-COVID-19 mortality during the pandemic period in France in relation to patterns in all-cause mortality prior to the pandemic should be useful for estimating the reduction in non-COVID-19 mortality compared to pre-pandemic mortality, and for examining the degree to which SARS-CoV-2 infection was detected and characterized as a cause of death during different periods of the pandemic in France. We used a previously developed model [11] to estimate the baseline and trend for the rates of non-influenza mortality between 2015-2019, as well as the rates of influenza-associated mortality between 2015-2019 -- see more on influenza-associated mortality in France, including its relation to vaccination and antiviral use in [23]. Those estimates were then used to estimate expected mortality during the pandemic period and compare it to the recorded non-COVID-19 mortality.…”

“…Similarly, we split the B/Yamagata incidence indicator into two, corresponding to the periods before and starting the 2017-2018 B/Yamagata epidemic. Finally, we note that it takes 1-2 weeks between influenza illness and influenza-associated mortality [11].…”

“…Here, Baseline represents weekly rates of mortality not associated with influenza circulation, and Baseline ( t ) is modelled to have annual periodicity in week t . We use periodic cubic splines to model the baseline mortality rates whose shape is unknown [11]. The trend Trend ( t ) is modelled as a linear polynomial in week t (see Discussion).…”

“…The trend Trend ( t ) is modelled as a linear polynomial in week t (see Discussion). Finally, to account for the autocorrelation in the noise, we use a bootstrap procedure (resampling the noise on different weeks) to estimate the confidence bounds for various quantities evaluated in the model [11].…”

“…The relative risk for complications, including hospital admission and death in adults is lower for the Omicron variant compared to the Delta variant, though those relative risks vary with age, with the relative risk for severe outcomes, including death for Omicron vs. Delta being greatest for the oldest adults [8,9]. This is related to differences in disease manifestation for Omicron infections vs. Delta infections for both ED admissions [10] and hospitalizations [11], which is also related to the above changes in coding for hospitalized patients with a detected Omicron infection in France [7]. Better understanding is needed regarding how differences in disease manifestation for complications stemming from Omicron infections compared to earlier SARS-CoV-2 variants are related to differences in treatment of Omicron infections, and to patterns in non-COVID-19 mortality (some of which is Omicron-associated, though not recognized as COVID-19 mortality).…”