COVID-19 vaccine effectiveness against symptomatic infection and hospitalisation in Belgium, July 2021 to May 2022.

BackgroundThe Belgian COVID-19 vaccination campaign aimed to reduce disease spread and severity.AimWe estimated SARS-CoV-2 variant-specific vaccine effectiveness against symptomatic infection (VEi) and hospitalisation (VEh), given time since vaccination and prior infection.MethodsNationwide healthcare records from July 2021 to May 2022 on testing and vaccination were combined with a clinical hospital survey. We used a test-negative design and proportional hazard regression to estimate VEi and VEh, controlling for prior infection, time since vaccination, age, sex, residence and calendar week of sampling.ResultsWe included 1,932,546 symptomatic individuals, of whom 734,115 tested positive. VEi against Delta waned from an initial estimate of 80% (95% confidence interval (CI): 80-81) to 55% (95% CI: 54-55) 100-150 days after the primary vaccination course. Booster vaccination increased initial VEi to 85% (95% CI: 84-85). Against Omicron, an initial VEi of 33% (95% CI: 30-36) waned to 17% (95% CI: 15-18), while booster vaccination increased VEi to 50% (95% CI: 49-50), which waned to 20% (95% CI: 19-21) 100-150 days after vaccination. Initial VEh for booster vaccination decreased from 96% (95% CI: 95-96) against Delta to 87% (95% CI: 86-89) against Omicron. VEh against Omicron waned to 73% (95% CI: 71-75) 100-150 days after booster vaccination. While recent prior infections conferred higher protection, infections occurring before 2021 remained associated with significant risk reduction against symptomatic infection. Vaccination and prior infection outperformed vaccination or prior infection only.ConclusionWe report waning and a significant decrease in VEi and VEh from Delta to Omicron-dominant periods. Booster vaccination and prior infection attenuated these effects.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model.

Objectives: To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium. Materials and methods: Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay. Results: Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities. Conclusion: As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.

Severity of infection with the SARS-CoV-2 B.1.1.7 lineage among hospitalized COVID-19 patients in Belgium.

INTRODUCTION: The pathogenesis of COVID-19 depends on the interplay between host characteristics, viral characteristics and contextual factors. Here, we compare COVID-19 disease severity between hospitalized patients in Belgium infected with the SARS-CoV-2 variant B.1.1.7 and those infected with previously circulating strains. METHODS: The study is conducted within a causal framework to study the severity of SARS-CoV-2 variants by merging surveillance registries in Belgium. Infection with SARS-CoV-2 B.1.1.7 ('exposed') was compared to infection with previously circulating strains ('unexposed') in terms of the manifestation of severe COVID-19, intensive care unit (ICU) admission, or in-hospital mortality. The exposed and unexposed group were matched based on the hospital and the mean ICU occupancy rate during the patient's hospital stay. Other variables identified as confounders in a Directed Acyclic Graph (DAG) were adjusted for using regression analysis. Sensitivity analyses were performed to assess the influence of selection bias, vaccination rollout, and unmeasured confounding. RESULTS: We observed no difference between the exposed and unexposed group in severe COVID-19 disease or in-hospital mortality (RR = 1.15, 95% CI [0.93-1.38] and RR = 0.92, 95% CI [0.62-1.23], respectively). The estimated standardized risk to be admitted in ICU was significantly higher (RR = 1.36, 95% CI [1.03-1.68]) when infected with the B.1.1.7 variant. An age-stratified analysis showed that among the younger age group (≤65 years), the SARS-CoV-2 variant B.1.1.7 was significantly associated with both severe COVID-19 progression and ICU admission. CONCLUSION: This matched observational cohort study did not find an overall increased risk of severe COVID-19 or death associated with B.1.1.7 infection among patients already hospitalized. There was a significant increased risk to be transferred to ICU when infected with the B.1.1.7 variant, especially among the younger age group. However, potential selection biases advocate for more systematic sequencing of samples from hospitalized COVID-19 patients.

Clinical Severity of SARS-CoV-2 Omicron Variant Compared with Delta among Hospitalized COVID-19 Patients in Belgium during Autumn and Winter Season 2021-2022.

This retrospective multi-center matched cohort study assessed the risk for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality in hospitalized patients when infected with the Omicron variant compared to when infected with the Delta variant. The study is based on a causal framework using individually-linked data from national COVID-19 registries. The study population consisted of 954 COVID-19 patients (of which, 445 were infected with Omicron) above 18 years old admitted to a Belgian hospital during the autumn and winter season 2021-2022, and with available viral genomic data. Patients were matched based on the hospital, whereas other possible confounders (demographics, comorbidities, vaccination status, socio-economic status, and ICU occupancy) were adjusted for by using a multivariable logistic regression analysis. The estimated standardized risk for severe COVID-19 and ICU admission in hospitalized patients was significantly lower (RR = 0.63; 95% CI (0.30; 0.97) and RR = 0.56; 95% CI (0.14; 0.99), respectively) when infected with the Omicron variant, whereas in-hospital mortality was not significantly different according to the SARS-CoV-2 variant (RR = 0.78, 95% CI (0.28-1.29)). This study demonstrates the added value of integrated genomic and clinical surveillance to recognize the multifactorial nature of COVID-19 pathogenesis.

Investigating COVID-19 Vaccine Impact on the Risk of Hospitalisation through the Analysis of National Surveillance Data Collected in Belgium.

The national vaccination campaign against SARS-CoV-2 started in January 2021 in Belgium. In the present study, we aimed to use national hospitalisation surveillance data to investigate the recent evolution of vaccine impact on the risk of COVID-19 hospitalisation. We analysed aggregated data from 27,608 COVID-19 patients hospitalised between October 2021 and February 2022, stratified by age category and vaccination status. For each period, vaccination status, and age group, we estimated risk ratios (RR) corresponding to the ratio between the probability of being hospitalised following SARS-CoV-2 infection if belonging to the vaccinated population and the same probability if belonging to the unvaccinated population. In October 2021, a relatively high RR was estimated for vaccinated people > 75 years old, possibly reflecting waning immunity within this group, which was vaccinated early in 2021 and invited to receive the booster vaccination at that time. In January 2022, a RR increase was observed in all age categories coinciding with the dominance of the Omicron variant. Despite the absence of control for factors like comorbidities, previous infections, or time since the last administered vaccine, we showed that such real-time aggregated data make it possible to approximate trends in vaccine impact over time.

Association between COVID-19 Primary Vaccination and Severe Disease Caused by SARS-CoV-2 Delta Variant among Hospitalized Patients: A Belgian Retrospective Cohort Study.

We aimed to investigate vaccine effectiveness against progression to severe COVID-19 (acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission and/or death) and in-hospital death in a cohort of hospitalized COVID-19 patients. Mixed effects logistic regression analyses were performed to estimate the association between receiving a primary COVID-19 vaccination schedule and severe outcomes after adjusting for patient, hospital, and vaccination characteristics. Additionally, the effects of the vaccine brands including mRNA vaccines mRNA-1273 and BNT162b2, and adenovirus-vector vaccines ChAdOx1 (AZ) and Ad26.COV2.S (J&J) were compared to each other. This retrospective, multicenter cohort study included 2493 COVID-19 patients hospitalized across 73 acute care hospitals in Belgium during the time period 15 August 2021-14 November 2021 when the Delta variant (B1.617.2) was predominant. Hospitalized COVID-19 patients that received a primary vaccination schedule had lower odds of progressing to severe disease (OR (95% CI); 0.48 (0.38; 0.60)) and in-hospital death (OR (95% CI); 0.49 (0.36; 0.65)) than unvaccinated patients. Among the vaccinated patients older than 75 years, mRNA vaccines and AZ seemed to confer similar protection, while one dose of J&J showed lower protection in this age category. In conclusion, a primary vaccination schedule protects against worsening of COVID-19 to severe outcomes among hospitalized patients.

Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients.

BACKGROUND: SARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. METHODS: A causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants. DISCUSSION: A well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries. TRIAL REGISTRATION: Each individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29 ). OSF project created on 18 May 2021.

Prey colonization in freshwater landscapes can be stimulated or inhibited by the proximity of remote predators.

Recent findings suggest that the colonization of habitat patches may be affected by the quality of surrounding patches. For instance, patches that lack predators may be avoided when located near others with predators, a pattern known as risk contagion. Alternatively, predator avoidance might also redirect dispersal towards nearby predator-free patches resulting in so-called habitat compression. However, it is largely unknown how predators continue to influence these habitat selection behaviours at increasing distances from outside of their own habitat patch. In addition, current information is derived from artificial mesocosm experiments, while support from natural ecosystems is lacking. This study used bromeliad landscapes as a natural model system to study how oviposition habitat selection of Diptera responds to the cues of a distant predator, the carnivorous elephant mosquito larva. We established landscapes containing predator-free bromeliad habitat patches placed at increasing distances from a predator-containing patch, along with replicate control landscapes. These patches were then left to be colonized by ovipositing bromeliad insects. We found that distance to predators modulates habitat selection decisions. Moreover, different dipteran families had different responses suggesting different habitat selection strategies. In some families, predator-free patches at certain distances from the predator patch were avoided, confirming risk contagion. In other families, these patches received higher numbers of colonists providing evidence of predator-induced habitat compression. We confirm that effects of predators in a natural ecosystem can extend beyond the patch in which the predator is present and that the presence or absence of remote predator effects on habitat selection depends on the distance to predators. The notion that perceived habitat quality can depend on conditions in neighbouring patches forces habitat selection studies to adopt a landscape perspective and account for the effects of both present and remote predators when explaining community assembly in metacommunities.