An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients (preprint)

BackgroundWhilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. MethodsHere, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. ResultsOur analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61 - 0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. ConclusionsAlthough clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome.

Seroprevalence of COVID-19 among health workers in the Kathmandu valley, Nepal (SEVID-KaV): a longitudinal cohort study (preprint)

Coronavirus Disease 2019 (COVID-19) burden is often underestimated when relying on case-based incidence reports. Seroprevalence studies accurately estimate infectious disease burden by estimating the population that has developed antibodies following an infection. Sero-Epidemiology of COVID-19 in the Kathmandu valley (SEVID-KaV) is a longitudinal survey of hospital-based health workers in the Kathmandu valley. Between December 3-25, we sampled 800 health workers from 20 hospitals and administered a questionnaire eliciting COVID-19 related history and tested for COVID-19 IgG antibodies. We then used a probabilistic multilevel regression model with post-stratification to correct for test accuracy, the effect of hospital-based clustering, and to establish representativeness. 522 (65.2%) of the participants were female, 372 (46%) were between ages 18-29, and 7 (0.9%) were 60 or above. 287 (36%) of the participants were nurses. About 23% of the participants previously had a PCR positive infection. 321 (40.13%) individuals tested positive for COVID-19 antibodies. Adjusted for test accuracy and weighted by age, gender and occupation category, the seroprevalence was 38.17% (95% Credible Interval (CrI) 29.26%–47.82%). Posterior predictive hospital-wise seroprevalence ranged between 38.1% (95% CrI 30.7.0%– 44.1%) and 40.5% (95% CrI 34.7%–47.0%).