Evolution of outcomes for patients hospitalised during the first 9 months of the SARS-CoV-2 pandemic in France: A retrospective national surveillance data analysis

Time warping between main epidemic time series in epidemiological surveillance

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package.

COVID-19 deaths on weekends

BACKGROUND

Mortality statistics about daily deaths might change on weekends due to delays in reporting, uneven staffing, a different mix of personnel, or decreased efficiency. We hypothesized that reported deaths for COVID-19 might increase on weekends compared to weekdays.

METHODS

We collected data from the World Health Organization COVID-19 database. All deaths from March 7, 2020 to March 7, 2022 were included (two years). The primary analysis evaluated mean daily deaths on weekends compared to the preceding five workdays. Analyses were replicated in ten individual countries: United States, United Kingdom, France, Germany, Italy, Spain, Russia, India, Brazil, and Canada.

RESULTS

The mean COVID-19 daily deaths was higher on weekends compared to weekdays (8,532 vs. 8,083 p < 0.001), equal to a 6% relative increase (95% confidence interval 3% to 8%). The highest absolute increase was in the United States (1,483 vs. 1,220 deaths, p < 0.001). The second highest absolute increase was in Brazil (1,061 vs. 823 deaths, p < 0.001). The increase in deaths on weekends remained significant during the earlier and later months of the pandemic, as well as during the greater and lesser weeks of the pandemic.

CONCLUSIONS

The apparent increased COVID-19 deaths reported on weekends might potentially reflect patient care, confound community trends, and affect the public perception of risk.

Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state

The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.

Health system, surveillance and the COVID-19 pandemic response in France

France was the first European country to confirm cases of COVID-19, being one of the most affected by the pandemic in the first wave. This case study analyzed the measures adopted by the country in the fight against COVID-19 in 2020 and 2021, correlating it to the characteristics of its health and surveillance system. As a welfare state, it relied on compensatory policies and protection of the economy, as well as increased investments in health. There were weaknesses in the preparation and delay in the implementation of the coping plan. The response was coordinated by the national executive power, adopting strict lockdowns in the first two waves, mitigating restrictive measures in the other waves, after the increase in vaccination coverage and in the face of population resistance. The country faced problems with testing, case and contact surveillance and patient care, especially in the first wave. It was necessary to modify the health insurance rules to expand coverage, access and better articulation of surveillance actions. It indicates lessons learned about the limits of its social security system, but also the potential of a government with a strong response capacity in the financing of public policies and regulation of other sectors to face the crisis.

Do pathogens always evolve to be less virulent? The virulence–transmission trade-off in light of the COVID-19 pandemic

The direction the evolution of virulence takes in connection with any pathogen is a long-standing question. Formerly, it was theorized that pathogens should always evolve to be less virulent. As observations were not in line with this theoretical outcome, new theories emerged, chief among them the transmission–virulence trade-off hypotheses, which predicts an intermediate level of virulence as the endpoint of evolution. At the moment, we are very much interested in the future evolution of COVID-19’s virulence. Here, we show that the disease does not fulfill all the assumptions of the hypothesis. In the case of COVID-19, a higher viral load does not mean a higher risk of death; immunity is not long-lasting; other hosts can act as reservoirs for the virus; and death as a consequence of viral infection does not shorten the infectious period. Consequently, we cannot predict the short- or long-term evolution of the virulence of COVID-19.

Mental disorders, COVID-19-related life-saving measures and mortality in France: A nationwide cohort study

BACKGROUND

Meta-analyses have shown that preexisting mental disorders may increase serious Coronavirus Disease 2019 (COVID-19) outcomes, especially mortality. However, most studies were conducted during the first months of the pandemic, were inconclusive for several categories of mental disorders, and not fully controlled for potential confounders. Our study objectives were to assess independent associations between various categories of mental disorders and COVID-19-related mortality in a nationwide sample of COVID-19 inpatients discharged over 18 months and the potential role of salvage therapy triage to explain these associations.

METHODS AND FINDINGS

We analysed a nationwide retrospective cohort of all adult inpatients discharged with symptomatic COVID-19 between February 24, 2020 and August 28, 2021 in mainland France. The primary exposure was preexisting mental disorders assessed from all discharge information recorded over the last 9 years (dementia, depression, anxiety disorders, schizophrenia, alcohol use disorders, opioid use disorders, Down syndrome, other learning disabilities, and other disorder requiring psychiatric ward admission). The main outcomes were all-cause mortality and access to salvage therapy (intensive-care unit admission or life-saving respiratory support) assessed at 120 days after recorded COVID-19 diagnosis at hospital. Independent associations were analysed in multivariate logistic models. Of 465,750 inpatients with symptomatic COVID-19, 153,870 (33.0%) were recorded with a history of mental disorders. Almost all categories of mental disorders were independently associated with higher mortality risks (except opioid use disorders) and lower salvage therapy rates (except opioid use disorders and Down syndrome). After taking into account the mortality risk predicted at baseline from patient vulnerability (including older age and severe somatic comorbidities), excess mortality risks due to caseload surges in hospitals were +5.0% (95% confidence interval (CI), 4.7 to 5.2) in patients without mental disorders (for a predicted risk of 13.3% [95% CI, 13.2 to 13.4] at baseline) and significantly higher in patients with mental disorders (+9.3% [95% CI, 8.9 to 9.8] for a predicted risk of 21.2% [95% CI, 21.0 to 21.4] at baseline). In contrast, salvage therapy rates were significantly higher than expected in patients without mental disorders (+4.2% [95% CI, 3.8 to 4.5]) and lower in patients with mental disorders (-4.1% [95% CI, -4.4; -3.7]). The main limitations of our study point to the assessment of COVID-19-related mortality at 120 days and potential coding bias of medical information recorded in hospital claims data, although the main study findings were consistently reproduced in multiple sensitivity analyses.

CONCLUSIONS

COVID-19 patients with mental disorders had lower odds of accessing salvage therapy, suggesting that life-saving measures at French hospitals were disproportionately denied to patients with mental disorders in this exceptional context.

Impact of combination preventative interventions on hospitalization and death under the pandemic of SARS-CoV-2 Omicron variant in China

With a large population most susceptible to Omicron and emerging SARS-CoV-2 variants, China faces uncertain scenarios if reopening its border. Thus, we aimed to predict the impact of combination preventative interventions on hospitalization and death. An age-stratified susceptible-infectious-quarantined-hospitalized-removed-susceptible (SIQHRS) model based on the new guidelines of COVID-19 diagnosis and treatment (the ninth edition) was constructed to simulate the transmission dynamics of Omicron within 365 days. At baseline, we assumed no interventions other than 60% booster vaccination in individuals aged ≤60 years and 80% in individuals aged >60 years, quarantine and hospitalization. Oral antiviral medications for COVID-19 and nonpharmaceutical interventions (NPIs) such as social distancing and antigen self-testing were considered in subsequent scenarios. Sensitivity analyses were conducted to reflect different levels of interventions. A total of 0.73 billion cumulative quarantines (95% CI 0.53-0.83), 33.59 million hospitalizations (22.41-39.31), and 0.62 million deaths (0.40-0.75) are expected in 365 days. The case fatality rate with pneumonia symptoms (moderate, severe and critical illness) is expected to be 1.83% (1.68-1.99%) and the infected fatality rate is 0.38‰ (0.33-0.4‰). The highest existing hospitalization and ICU occupations are 3.11 (0.30-3.85) and 20.33 (2.01-25.20) times of capacity, respectively. Sensitivity analysis showed that interventions can be adjusted to meet certain conditions to reduce the total number of infections and deaths. In conclusion, after sufficient respiratory and ICU beds are prepared and the relaxed NPIs are in place, the SARS-CoV-2 Omicron variant would not seriously impact the health system.

What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?

The Omicron transmission has infected nearly 600,000 people in Shanghai from March 26 to May 31, 2022. Combined with different control measures taken by the government in different periods, a dynamic model was constructed to investigate the impact of medical resources, shelter hospitals and aerosol transmission generated by clustered nucleic acid testing on the spread of Omicron. The parameters of the model were estimated by least square method and MCMC method, and the accuracy of the model was verified by the cumulative number of asymptomatic infected persons and confirmed cases in Shanghai from March 26 to May 31, 2022. The result of numerical simulation demonstrated that the aerosol transmission figured prominently in the transmission of Omicron in Shanghai from March 28 to April 30. Without aerosol transmission, the number of asymptomatic subjects and symptomatic cases would be reduced to 130,000 and 11,730 by May 31, respectively. Without the expansion of shelter hospitals in the second phase, the final size of asymptomatic subjects and symptomatic cases might reach 23.2 million and 4.88 million by May 31, respectively. Our results also revealed that expanded vaccination played a vital role in controlling the spread of Omicron. However, even if the vaccination rate were 100%, the transmission of Omicron should not be completely blocked. Therefore, other control measures should be taken to curb the spread of Omicron, such as widespread antiviral therapies, enhanced testing and strict tracking quarantine measures. This perspective could be utilized as a reference for the transmission and prevention of Omicron in other large cities with a population of 10 million like Shanghai.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

Changing trends of patient characteristics and treatment pathways during the COVID-19 pandemic: A cross-sectional analysis of 72,459 inpatient cases from the German Helios database

Background

This study compared patient profiles and clinical courses of SARS-CoV-2 infected inpatients over different pandemic periods.

Methods

In a retrospective cross-sectional analysis, we examined administrative data of German Helios hospitals using ICD-10-codes at discharge. Inpatient cases with SARS-CoV-2 infection admitted between 03/04/2020 and 07/19/2022 were included irrespective of the reason for hospitalization. All endpoints were timely assigned to admission date for trend analysis. The first pandemic wave was defined by change points in time-series of incident daily infections and compared with different later pandemic phases according to virus type predominance.

Results

We included 72,459 inpatient cases. Patients hospitalized during the first pandemic wave (03/04/2020-05/05/2020; n = 1,803) were older (68.5 ± 17.2 vs. 64.4 ± 22.6 years, p < 0.01) and severe acute respiratory infections were more prevalent (85.2 vs. 53.3%, p < 0.01). No differences were observed with respect to distribution of sex, but comorbidity burden was higher in the first pandemic wave. The risk of receiving intensive care therapy was reduced in all later pandemic phases as was in-hospital mortality when compared to the first pandemic wave. Trend analysis revealed declines of mean age and Elixhauser comorbidity index over time as well as a decline of the utilization of intensive care therapy, mechanical ventilation and in-hospital mortality.

Conclusion

Characteristics and outcomes of inpatients with SARS-CoV-2 infection changed throughout the observational period. An ongoing evaluation of trends and care pathways will allow for the assessment of future demands.

Transition matrices model as a way to better understand and predict intra-hospital pathways of covid-19 patients

Since January 2020, the SARS-CoV-2 pandemic has severely affected hospital systems worldwide. In Europe, the first 3 epidemic waves (periods) have been the most severe in terms of number of infected and hospitalized patients. There are several descriptions of the demographic and clinical profiles of patients with COVID-19, but few studies of their hospital pathways. We used transition matrices, constructed from Markov chains, to illustrate the transition probabilities between different hospital wards for 90,834 patients between March 2020 and July 2021 managed in Paris area. We identified 3 epidemic periods (waves) during which the number of hospitalized patients was significantly high. Between the 3 periods, the main differences observed were: direct admission to ICU, from 14 to 18%, mortality from ICU, from 28 to 24%, length of stay (alive patients), from 9 to 7 days from CH and from 18 to 10 days from ICU. The proportion of patients transferred from CH to ICU remained stable. Understanding hospital pathways of patients is crucial to better monitor and anticipate the impact of SARS-CoV-2 pandemic on health system.

From individual-based epidemic models to McKendrick-von Foerster PDEs: a guide to modeling and inferring COVID-19 dynamics

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e.g., infective stage, clinical state, risk factor class). We show that recording each infected individual’s infection age, i.e., the time elapsed since infection, has three benefits. First, regardless of the number of types, the age distribution of the population can be described by means of a first-order, one-dimensional partial differential equation (PDE) known as the McKendrick-von Foerster equation. The frequency of type i is simply obtained by integrating the probability of being in state i at a given age against the age distribution. This representation induces a simple methodology based on the additional assumption of Poisson sampling to infer and forecast the epidemic. We illustrate this technique using French data from the COVID-19 epidemic. Second, our approach generalizes and simplifies standard compartmental models using high-dimensional systems of ordinary differential equations (ODEs) to account for disease complexity. We show that such models can always be rewritten in our framework, thus, providing a low-dimensional yet equivalent representation of these complex models. Third, beyond the simplicity of the approach, we show that our population model naturally appears as a universal scaling limit of a large class of fully stochastic individual-based epidemic models, where the initial condition of the PDE emerges as the limiting age structure of an exponentially growing population starting from a single individual.

Absence of Mortality Differences Between the First and Second COVID-19 Waves in Kidney Transplant Recipients

Introduction

SARS-CoV-2 pandemic evolved in 2 consecutive waves during 2020. Improvements in the management of COVID-19 led to a reduction in mortality rates among hospitalized patients during the second wave. Whether this progress benefited kidney transplant recipients (KTRs), a population particularly vulnerable to severe COVID-19, remained unclear.

Methods

In France, 957 KTRs were hospitalized for COVID-19 in 2020 and their data were prospectively collected into the French Solid Organ Transplant (SOT) COVID registry. The presentation, management, and outcomes of the 359 KTRs diagnosed during the first wave were compared to those of the 598 of the second wave.

Results

Baseline comorbidities were similar between KTRs of the 2 waves. Maintenance immunosuppression was reduced in most patients but withdrawal of antimetabolite (73.7% vs. 58.4%, P < 0.001) or calcineurin inhibitor (32.1% vs. 16.6%, P < 0.001) was less frequent during the second wave. Hydroxychloroquine and azithromycin that were commonly used during the first wave (21.7% and 30.9%, respectively) but were almost abandoned during the second wave. In contrast, the use of high dose corticosteroids doubled (19.5% vs. 41.6%, P < 0.001). Despite these changing trends in COVID-19 management, 60-day mortality was not statistically different between the 2 waves (25.3% vs. 23.9%; Log Rank, P = 0.48) and COVID-19 hospitalization period was not associated with death due to COVID-19 in multivariate analysis (Hazard ratio 0.89, 95% confidence interval 0.67–1.17, P = 0.4).

Conclusion

We conclude that changing of therapeutic trends during 2020 did not reduce COVID-19 related mortality among KTRs. Our data indirectly support the importance of vaccination and neutralizing monoclonal anti-SARS-CoV-2 antibodies to protect KTRS from severe COVID-19.

ICU strain and outcome in COVID-19 patients—A multicenter retrospective observational study

Purpose

To compare the characteristics, management, and prognosis of patients admitted to intensive care units (ICU) for coronavirus disease (COVID)-19 during the first two waves of the outbreak and to evaluate the relationship between ICU strain (ICU demand due to COVID-19 admissions) and mortality.

Methods

In a multicentre retrospective study, 1166 COVID-19 patients admitted to five ICUs in France between 20 February and 31 December 2020 were included. Data were collected at each ICU from medical records. A Cox proportional-hazards model identified factors associated with 28-day mortality.

Results

640 patients (55%) were admitted during the first wave (February to June 2020) and 526 (45%) during the second wave (July to December 2020). ICU strain was lower during the second wave (-0.81 [-1.04 –-0.31] vs. 1.18 [-0.34–1.29] SD when compared to mean COVID-19 admission in each center during study period, P<0.001). Patients admitted during the second wave were older, had more profound hypoxemia and lower SOFA. High flow nasal cannula was more frequently used during the second wave (68% vs. 39%, P<0.001) and intubation was less frequent (46% vs. 69%, P<0.001). Neither 28-day mortality (30% vs. 26%, P = 0.12) nor hospital mortality (37% vs. 31%, P = 0.27) differed between first and second wave. Overweight and obesity were associated with lower 28-day mortality while older age, underlying chronic kidney disease, severity at ICU admission as assessed by SOFA score and ICU strain were associated with higher 28-day mortality. ICU strain was not associated with hospital mortality.

Conclusion

The characteristics and the management of patients varied between the first and the second wave of the pandemic. Rather than the wave, ICU strain was independently associated with 28-day mortality, but not with hospital mortality.

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.

COVID-19 in patients with hematologic malignancy

The coronavirus infectious disease (COVID-19) shows a remarkable symptomatic heterogeneity. Several risk factors including advanced age, previous illnesses and a compromised immune system contribute to an unfavorable outcome. In patients with hematologic malignancy, the immune response to severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is significantly reduced explaining why the mortality rate of hematologic patients hospitalized for a SARS-CoV-2 infection is about 34%. Active immunization is an essential pillar to prevent SARS-CoV-2 infections in patients with hematologic malignancy. However, the immune response to SARS-CoV-2 vaccines may be significantly impaired, as only half of patients with hematologic malignancy develop a measurable anti-viral antibody response. The subtype of hematologic malignancy and B-cell depleting treatment predict a poor immune response to vaccination. Recently, antiviral drugs and monoclonal antibodies for pre-exposure or post-exposure prophylaxis and for early treatment of COVID-19 have become available. These therapies should be offered to patients at high risk for severe COVID-19 and vaccine non-responder. Importantly, as the virus evolves, some therapies may lose their clinical efficacy against new variants. Therefore, the ongoing pandemic will remain a major challenge for patients with hematologic malignancy and their caregivers who need to constantly monitor the scientific progress in this area.

Evaluating COVID-19 Booster Vaccination Strategies in a Partially Vaccinated Population: A Modeling Study

BACKGROUND

Several countries are implementing COVID-19 booster vaccination campaigns. The objective of this study was to model the impact of different primary and booster vaccination strategies.

METHODS

We used a compartmental model fitted to hospital admission data in France to analyze the impact of primary and booster vaccination strategies on morbidity and mortality, assuming waning of immunity and various levels of virus transmissibility during winter.

RESULTS

Strategies prioritizing primary vaccinations were systematically more effective than strategies prioritizing boosters. Regarding booster strategies targeting different age groups, their effectiveness varied with immunity and virus transmissibility levels. If the waning of immunity affects all adults, people aged 30 to 49 years should be boosted in priority, even for low transmissibility levels.

CONCLUSIONS

Increasing the primary vaccination coverage should remain a priority. If a plateau has been reached, boosting the immunity of younger adults could be the most effective strategy, especially if SARS-CoV-2 transmissibility is high.

One Year Prevalence of Venous Thromboembolism in Hospitalized COVID-19 Patients in France: Patients' Characteristics, Time Trends, and Outcomes

BACKGROUND

 Patients hospitalized with coronavirus disease-2019 (COVID-19) are at high risk of deep venous thrombosis (DVT) and pulmonary embolism (PE).

OBJECTIVES

 The aims were to provide time trends in the 2020 nation-wide prevalence of venous thromboembolism (VTE) in patients hospitalized with a COVID-19 diagnosis in France, and to describe in-hospital and up to 30-day postdischarge death.

METHODS

 All patients hospitalized in France with a COVID-19 diagnosis in 2020 were selected. Crude and age-adjusted prevalence of VTE and PE was computed by 4-week intervals and for the overall study period using Poisson regression. Time trends in in-hospital and 30-day postdischarge case-fatality rates were evaluated by comparing each 4-week intervals to weeks 10 to 14 corresponding to the first part of the first lockdown using logistic regression models.

RESULTS

 Among the 287,638 patients hospitalized with a COVID-19 diagnosis in 2020 in France, 14,985 (5.2%) had a concomitant VTE, with 10,453 (3.6%) having PE and 4,532 (1.6%) having DVT. In patients admitted to intensive care units, the crude prevalence of VTE and PE reached 16.1 and 11.0% respectively during the first lockdown. After adjustment, the prevalence of VTE and PE decreased during the year 2020 but a rebound was observed during the second lockdown. In-hospital case-fatality rates among hospitalized COVID-19 patients with PE globally decreased between the first and the second epidemic waves.

CONCLUSION

 Our study showed a decrease in the incidence of symptomatic VTE and PE in hospitalized COVID-19 patients, and a decreased time trend of outcomes during the second wave compared with the first one.

First and second waves among hospitalised patients with COVID-19 with severe pneumonia: a comparison of 28-day mortality over the 1-year pandemic in a tertiary university hospital in Italy

OBJECTIVE

The first COVID-19-19 epidemic wave was over the period of February-May 2020. Since 1 October 2020, Italy, as many other European countries, faced a second wave. The aim of this analysis was to compare the 28-day mortality between the two waves among COVID-19 hospitalised patients.

DESIGN

Observational cohort study. Standard survival analysis was performed to compare all-cause mortality within 28 days after hospital admission in the two waves. Kaplan-Meier curves as well as Cox regression model analysis were used. The effect of wave on risk of death was shown by means of HRs with 95% CIs. A sensitivity analysis around the impact of the circulating variant as a potential unmeasured confounder was performed.

SETTING

University Hospital of Modena, Italy. Patients admitted to the hospital for severe COVID-19 pneumonia during the first (22 February-31 May 2020) and second (1 October-31 December 2020) waves were included.

RESULTS

During the two study periods, a total of 1472 patients with severe COVID-19 pneumonia were admitted to our hospital, 449 during the first wave and 1023 during the second. Median age was 70 years (IQR 56-80), 37% women, 49% with PaO2/FiO2 <250 mm Hg, 82% with ≥1 comorbidity, median duration of symptoms was 6 days. 28-day mortality rate was 20.0% (95% CI 16.3 to 23.7) during the first wave vs 14.2% (95% CI 12.0 to 16.3) in the second (log-rank test p value=0.03). After including key predictors of death in the multivariable Cox regression model, the data still strongly suggested a lower 28-day mortality rate in the second wave (aHR=0.64, 95% CI 0.45 to 0.90, p value=0.01).

CONCLUSIONS

In our hospitalised patients with COVID-19 with severe pneumonia, the 28-day mortality appeared to be reduced by 36% during the second as compared with the first wave. Further studies are needed to identify factors that may have contributed to this improved survival.

Pressure on the Health-Care System and Intensive Care Utilization During the COVID-19 Outbreak in the Lombardy Region of Italy: A Retrospective Observational Study in 43,538 Hospitalized Patients

During the spring of 2020, the COVID-19 epidemic caused an unprecedented demand for intensive care resources in Lombardy, Italy. Using data on 43,538 hospitalized patients admitted between February 21 and July 12, 2020, we evaluated variations in intensive care unit (ICU) admissions and mortality over three periods: the early phase (February 20-March 13), the period of highest pressure on healthcare (March 14-April 25, when COVID-19 patients exceeded the ICU pre-pandemic bed capacity), and the declining phase (April 26-July 12).

Compared to the early phase, patients above 70 years of age were admitted less often to an ICU during highest pressure on healthcare (odds ratio OR 0.47, 95%CI: 0.41-0.54) with longer delays (incidence rate ratio IRR 1.82, 95%CI: 1.52-2.18), and lower chances of death in ICU (OR 0.47, 95%CI: 0.34-0.64). Patients under 56 years of age reported more limited changes in the probability (OR 0.65, 95%CI: 0.56-0.76) and delay to ICU admission (IRR 1.16, 95%CI: 0.95-1.42) and an increased mortality (OR 1.43, 95%CI: 1.00-2.07). In the declining phase, all quantities decreased for all age groups.

These patterns may suggest that limited healthcare resources during the peak epidemic phase in Lombardy forced a shift in ICU admission criteria to prioritize patients with higher chances of survival.

Responses against infectious disease pandemics: a narrative review on COVID-19

Currently, the world is facing the coronavirus disease 2019 (COVID-19) pandemic. With this, an emerging infectious disease pandemic in the absence of effective antiviral agents and vaccines for a novel virus is no different from the 1918 influenza pandemic, which became a great disaster for humankind. We also experienced a global lockdown with a stringent implementation of social distancing, which is a first for mankind living in the present day, and has led to enormous economic damage and restrictions on individual freedom. The microorganism that will cause the next pandemic may be a highly fatal avian influenza virus, another coronavirus, or a completely different microorganism. This COVID-19 pandemic is an enormous lesson for humankind and is tantamount to a vaccine in preparation for the next pandemic. Important and urgent undertakings were given to each country in terms of complementing laws and regulations for a stronger and more resilient healthcare system, such as investment in research and development for new rapid diagnostic technologies, vaccines, new therapeutic agents, among others.

Evaluation of medication changes following severe COVID-19 infection: a multicentre evaluation

Background

Critically ill patients often experience several transitions of care following critical illness. Research has explored the challenges which patients have with medication management across these transitions. It is unclear whether patients admitted to critical care due to COVID-19 will have similar challenges. The aim of this study was to explore medication management in critical care survivors following severe COVID-19.

Methods

Between 3 and 7 months post hospital discharge, patients who had been admitted to critical care due to severe COVID-19 were invited to an established recovery service. During the clinic consultation a medication review was performed by a pharmacist. This included medicines reconciliation, assessing the appropriateness of each of the prescribed medications and identification of medication changes. We also assessed changes to pain management in the discharge period.

Results

In total, 78 patients had a full medication review available. Over 70% of patients were taking an increased dose of medicine or a new medicine at clinic. There was a significant overall increase in new medication during the clinic consultation, across different British National Formulary classifications (OR: 1.73 (95% CI: 1.28 to 2.34), p<0.001). Compared with pre critical care admission, there was a significant increase in the number of patients taking regular analgesia following severe COVID-19 infection (23 (29.5%) vs 39 (50%), p<0.001).

Conclusion

Following severe COVID-19, patients may require new or increasing doses of medicines. Ongoing review of these patients is crucial to ensure optimal outcomes.

Spatio-temporal characteristics of the novel coronavirus attention network and its influencing factors in China

The outbreak of a novel coronavirus pneumonia (COVID-19), wherein more than 200 million people have been infected and millions have died, poses a great threat to achieving the United Nations 2030 sustainable development goal (SDGs). Based on the Baidu index of 'novel coronavirus', this paper analyses the spatial and temporal characteristics of and factors that influenced the attention network for COVID-19 from January 9, 2020, to April 15, 2020. The study found that (1) Temporally, the attention in the new coronavirus network showed an upward trend from January 9 to January 29, with the largest increase from January 23 to January 29 and a peak on January 29, and then a slow downward trend. The level of attention in the new coronavirus network was basically flat when comparing January 22 and March 4. (2) Spatially, first, from the perspective of regional differences, the network attention in the eastern and central regions decreased in turn. The network users in the eastern region exhibited the highest attention to the new coronavirus, especially in Guangdong, Shandong, Jiangsu and other provinces and cities. The network attention in Tibet, Xinjiang, Qinghai and Ningxia in the western region was the lowest in terms of the national network attention. Second, from the perspective of interprovincial differences, the attention in the new coronavirus network was highly consistent with the Hu Huanyong line of China's population boundary. The east of the Hu Huanyong line is densely populated, and the network showed high concern, mostly ranking at the third to fifth levels. (3) The number of Internet users in the information technology field, the population, and the culture and age characteristics of individuals are important factors that influence the novel coronavirus attention network.

Burden and characteristics of COVID-19 in the United States during 2020

The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 2020^1-3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported^4-8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3-15.9%) in March to 24.5% (18.6-32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6-75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6-1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year's end.