Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context

Abstract

Background: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods: This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date.Results: The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%); and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June.Conclusions: The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2.

Designing Social Distancing Policies for the COVID-19 Pandemic: A probabilistic model predictive control approach

“…• δ H (d −1 ) is the average per-day "effective" recovery rate within the subset of hospitalized cases (H) that finally recover. As reported recently [16], the overall median length of stay (LoS) in hospitals, which for any practical means it coincides with the recovery period, decreased steadily from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June. Based on the above, we have set the recovery period for the hospitalized cases as δ H = 1 /24 for the period until March 8, δ H = 1 /18 for the period March 9-March 20, δ H = 1 /14 for the period March 21-April 10, and δ H = 1 /10 for the period April 11-May 4.…”

“…• γ(d −1 ) is the average per-day "effective" mortality rate within the subset of H(t) hospitalized cases that finally die. The median time from hospitalization to death for Lombardy during the early phase of the first wave of the pandemic (until April) has been reported to be seven days through the first wave, with an interquartile range (3-13) [31,16]. We should also note that the time from hospitalization to death subsequently increased as medical personnel gained experience.…”

“…Based on a study considering 38,715 hospitalized COVID-19 patients in Lombardy between 21 February and 21 April 2020, this ratio was relatively high [33,16]) mainly due to the saturation of ICU beds. In particular as reported in a recent published study [16] • ǫ(d −1 ) is the fraction of the exposed cases that enter compartment I S , i.e. those that will experience mild to severe symptoms and ask for medical care.…”

“…In this work, we address a probabilistic model predictive control (PMPC) approach constrained to a compartmental model extending beyond the conventional SEIR approach to account for hospitalization and quarantine and importantly for the uncertainty in the level of asymptomatic cases in the population. In particular, the development of our model is based on epidemiological data from the region of Lombardy that have been very recently published [16], thus extending the model proposed by [17]. In that work, [17] addressed a model that includes a compartment for the asymptomatic cases and proposed an optimization algorithm to assess the uncertainty that characterizes the actual number of infected cases in the total population, which is mainly due to the large percentage of asymptomatic or mildly symptomatic cases and the uncertainty regarding the DAY-ZERO of the outbreak.…”

Trends in outcomes following COVID-19 symptom onset in Milan: a cohort study

“…The majority of studies of mortality have either estimated the risk in hospital cohorts1–7 or in specific settings such as care homes8–10 and a cruise ship 11. There have been fewer studies of mortality among the general population.…”