There are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. We investigate the time from symptom onset to diagnosis and hospitalization or the length of stay (LoS) in the hospital, and whether there are differences in the population. Sciensano collected information on 14,618 hospitalized patients with COVID-19 admissions from 114 Belgian hospitals between 14 March and 12 June 2020. The distributions of different event times for different patient groups are estimated accounting for interval censoring and right truncation of the time intervals. The time between symptom onset and hospitalization or diagnosis are similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient (longest delay in age group 20–60 years) and whether or not the patient lives in a nursing home (additional 2 days for patients from nursing home). The median LoS in hospital varies between 3 and 10.4 days, with the LoS increasing with age. The hospital LoS for patients that recover is shorter for patients living in a nursing home, but the time to death is longer for these patients. Over the course of the first wave, the LoS has decreased.

Wasting (weight-for-height z score [WHZ] <−2 of the National Center for Health Statistics [NCHS] reference median) affects approximately 10% of the world's children younger than 5 years¹ and is an important contributor to the population-attributable risk of child mortality and overall burden of disease.² New outpatient and community-based models for the treatment of wasting have been shown effective in the rehabilitation of children with severe wasting.^3,4 These models are made possible largely with the use of ready-to-use therapeutic foods (RUTFs). These foods are energy-dense, micronutrient-enriched pastes with a nutritional profile similar to the traditional F-100 milk-based diet used in inpatient therapeutic feeding programs and are often made up of peanuts, oil, sugar, and milk powder.^5,6

Introduction: In recent years, HIV testing frequency has increased, resulting in more people being diagnosed during seroconversion with a temporarily low CD4 count. Using the current consensus definition of late HIV presentation ('presenting for care with a CD4 count < 350 cells/μL or an AIDS‐defining event, regardless of CD4 count') these individuals would be incorrectly assigned as being diagnosed late.Methods: In spring 2022, a European expert group convened to revise the current late HIV presentation consensus definition. A survey on data availability to apply this revised definition was sent to nominated European focal points responsible for HIV surveillance (n = 53).Results: Experts agreed that the updated definition should refer to late HIV diagnosis rather than presentation and include the following addition: People with evidence of recent infection should be reclassified as 'not late', with evidence of recent infection considered hierarchically. The individual must have: (i) laboratory evidence of recent infection; (ii) a last negative HIV test within 12 months of diagnosis; or (iii) clinical evidence of acute infection. People with evidence of being previously diagnosed abroad should be excluded. A total of 18 countries responded to the survey; 83% reported capturing CD4 count and/or AIDS at diagnosis through national surveillance, 67% captured last negative test and/or previous HIV diagnosis, 61% captured seroconversion illness at diagnosis and 28% captured incident antibody results.Conclusions: Accurate data on late diagnosis are important to describe the effects of testing programmes. Reclassification of individuals with recent infection will help to better identify populations most at risk of poor HIV outcomes and areas for intervention.

Background Cancer seems to have an independent adverse prognostic effect on COVID-19-related mortality, but uncertainty exists regarding its effect across different patient subgroups. We report a population-based analysis of patients hospitalised with COVID-19 with prior or current solid cancer versus those without cancer. Methods We analysed data of adult patients registered until 24 May 2020 in the Belgian nationwide database of Sciensano. The primary objective was in-hospital mortality within 30 days of COVID-19 diagnosis among patients with solid cancer versus patients without cancer. Severe event occurrence, a composite of intensive care unit admission, invasive ventilation and/or death, was a secondary objective. These endpoints were analysed across different patient subgroups. Multivariable logistic regression models were used to analyse the association between cancer and clinical characteristics (baseline analysis) and the effect of cancer on in-hospital mortality and on severe event occurrence, adjusting for clinical characteristics (in-hospital analysis). Results A total of 13 594 patients (of whom 1187 with solid cancer (8.7%)) were evaluable for the baseline analysis and 10 486 (892 with solid cancer (8.5%)) for the in-hospital analysis. Patients with cancer were older and presented with less symptoms/signs and lung imaging alterations. The 30-day in-hospital mortality was higher in patients with solid cancer compared with patients without cancer (31.7% vs 20.0%, respectively; adjusted OR (aOR) 1.34; 95% CI 1.13 to 1.58). The aOR was 3.84 (95% CI 1.94 to 7.59) among younger patients (<60 years) and 2.27 (95% CI 1.41 to 3.64) among patients without other comorbidities. Severe event occurrence was similar in both groups (36.7% vs 28.8%; aOR 1.10; 95% CI 0.95 to 1.29). Conclusions This population-based analysis demonstrates that solid cancer is an independent adverse prognostic factor for in-hospital mortality among patients with COVID-19. This adverse effect was more pronounced among younger patients and those without other comorbidities. Patients with solid cancer should be prioritised in vaccination campaigns and in tailored containment measurements.

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. 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). 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. 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. No funding source was available for this study.