Rapid establishment of a national surveillance of COVID-19 hospitalizations in Belgium

Goethem, Nina Van; Vilain, Aline; Wyndham-Thomas, Chloé; Deblonde, Jessika; Bossuyt, Nathalie; Lernout, Tinne; Gonzalez, Javiera Rebolledo; Quoilin, Sophie; Melis, Vincent; Beckhoven, Dominique Van

doi:10.1186/s13690-020-00505-z

Cited by 42 publications

(51 citation statements)

References 2 publications

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“…This study was based on the national clinical surveillance of hospitalized patients with COVID-19 implemented by Sciensano, the Belgian Institute of Health, and supported by the Federal Health Ministry, in order to obtain a minimal health-related dataset for patients diagnosed with COVID-19 admitted to Belgian hospitals. Details on the surveillance program have been published [10] . The clinical surveillance program was applied to all Belgian hospitals/ICUs; nearly 72% of them contributed to the data collection [10] .…”

Section: Methodsmentioning

confidence: 99%

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The role of organizational characteristics on the outcome of COVID-19 patients admitted to the ICU in Belgium

Taccone

Goethem

Pauw

et al. 2021

The Lancet Regional Health - Europe

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Background Several studies have investigated the predictors of in-hospital mortality for COVID-19 patients who need to be admitted to the Intensive Care Unit (ICU). However, no data on the role of organizational issues on patients’ outcome are available in this setting. The aim of this study was therefore to assess the role of surge capacity organisation on the outcome of critically ill COVID-19 patients admitted to ICUs in Belgium. Methods We conducted a retrospective analysis of in-hospital mortality in Belgian ICU COVID-19 patients via the national surveillance database. Non-survivors at hospital discharge were compared to survivors using multivariable mixed effects logistic regression analysis. Specific analyses including only patients with invasive ventilation were performed. To assess surge capacity, data were merged with administrative information on the type of hospital, the baseline number of recognized ICU beds, the number of supplementary beds specifically created for COVID-19 ICU care and the “ICU overflow” (i.e. a time-varying ratio between the number of occupied ICU beds by confirmed and suspected COVID-19 patients divided by the number of recognized ICU beds reserved for COVID-19 patients; ICU overflow present when this ratio is ≥ 1.0). Findings Over a total of 13,612 hospitalised COVID-19 patients with admission and discharge forms registered in the surveillance period (March, 1 to August, 9 2020), 1903 (14.0%) required ICU admission, of whom 1747 had available outcome data. Non-survivors (n=632, 36.1%) were older and had more frequently various comorbid diseases than survivors. In the multivariable analysis, ICU overflow, together with older age, presence of comorbidities, shorter delay between symptom onset and hospital admission, absence of hydroxychloroquine therapy and use of invasive mechanical ventilation and of ECMO, was independently associated with an increased in-hospital mortality. Similar results were found among the subgroup of invasively ventilated patients. In addition, the proportion of supplementary beds specifically created for COVID-19 ICU care to the previously existing total number of ICU beds was associated with increased an in-hospital mortality among invasively ventilated patients. The model also indicated a significant between-hospital difference in in-hospital mortality, not explained by the available patients and hospital characteristics. Interpretation Surge capacity organisation as reflected by ICU overflow or the creation of COVID-19 specific supplementary ICU beds were found to negatively impact ICU patient outcomes. Funding No funding source was available for this study.

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Section: Methodsmentioning

confidence: 99%

“…Details on the surveillance program have been published [10] . The clinical surveillance program was applied to all Belgian hospitals/ICUs; nearly 72% of them contributed to the data collection [10] . Available ICU beds in Belgium before pandemics were 2000 (i.e.…”

Section: Methodsmentioning

confidence: 99%

The role of organizational characteristics on the outcome of COVID-19 patients admitted to the ICU in Belgium

Taccone

Goethem

Pauw

et al. 2021

The Lancet Regional Health - Europe

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“…Belgian hospitals are obliged to report the daily number of new hospitalizations to the Scientific Institute of Public Health, Belgium (Sciensano), which are made publicly available through an online platform [13]. Age-specific hospitalization data were collected through the clinical surveillance database of COVID-19 hospitalized patients [14]. This database is an ongoing multicenter registry collecting information on hospital admission related to COVID-19 infection.…”

Section: Methodsmentioning

confidence: 99%

“…Patient-specific characteristics are collected through two online questionnaires: one related to admission and one related to discharge. As the reporting is strongly recommended by the Belgian Risk Management Group, the reporting coverage is high including more than 70% of all hospitalized COVID-19 cases during the first wave [14]. Based on this information, the weekly age distribution of hospitalized cases (see Figure D1 in Appendix D) is derived such that the total daily incidence of hospitalizations is transformed to be age-specific.…”

Section: Methodsmentioning

confidence: 99%

Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

Abrams

Wambua

Santermans

et al. 2020

Preprint

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Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.

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“…The Belgian national public health research institute (Sciensano) collected and aggregated COVID-19 hospitalisation data according to an established hospital surveillance protocol [ 13 ]. Although some data are available at the municipality level, other important data sets were only collected per hospital ( n = 103).…”

Section: Methodsmentioning

confidence: 99%

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case

et al. 2021

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Background The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. Methods To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio–temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. Results Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. Conclusion Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.

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Rapid establishment of a national surveillance of COVID-19 hospitalizations in Belgium

Cited by 42 publications

References 2 publications

The role of organizational characteristics on the outcome of COVID-19 patients admitted to the ICU in Belgium

The role of organizational characteristics on the outcome of COVID-19 patients admitted to the ICU in Belgium

Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case

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