Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics

BACKGROUND

In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue.

OBJECTIVE

We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff.

METHODS

Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel.

RESULTS

COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04).

CONCLUSIONS

Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Impact of an ICU bed capacity optimisation method on the average length of stay and average cost of hospitalisation following implementation of China's open policy with respect to COVID-19: a difference-in-differences analysis based on information management system data from a tertiary hospital in southwest China

OBJECTIVES

Following the implementation of China's open policy with respect to COVID-19 on 7 December 2022, the influx of patients with infectious diseases has surged rapidly, necessitating hospitals to adopt temporary requisition and modification of ward beds to optimise hospital bed capacity and alleviate the burden of overcrowded patients. This study aims to investigate the effect of an intensive care unit (ICU) bed capacity optimisation method on the average length of stay (ALS) and average cost of hospitalisation (ACH) after the open policy of COVID-19 in China.

DESIGN AND SETTING

A difference-in-differences (DID) approach is employed to analyse and compare the ALS and ACH of patients in four modified ICUs and eight non-modified ICUs within a tertiary hospital located in southwest China. The analysis spans 2 months before and after the open policy, specifically from 5 October 2022 to 6 December 2022, and 7 December 2022 to 6 February 2023.

PARTICIPANTS

We used the daily data extracted from the hospital's information management system for a total of 5944 patients admitted by the outpatient and emergency access during the 2-month periods before and after the release of the open policy in China.

RESULTS

The findings indicate that the ICU bed optimisation method implemented by the tertiary hospital led to a significant reduction in ALS (HR -0.6764, 95% CI -1.0328 to -0.3201, p=0.000) and ACH (HR -0.2336, 95% CI -0.4741 to -0.0068, p=0.057) among ICU patients after implementation of the open policy. These results were robust across various sensitivity analyses. However, the effect of the optimisation method exhibits heterogeneity among patients admitted through the outpatient and emergency channels.

CONCLUSIONS

This study corroborates a significant positive impact of ICU bed optimisation in mitigating the shortage of medical resources following an epidemic outbreak. The findings hold theoretical and practical implications for identifying effective emergency coordination strategies in managing hospital bed resources during sudden public health emergency events. These insights contribute to the advancement of resource management practices and the promotion of experiences in dealing with public health emergencies.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.

The estimated disease burden of COVID-19 in Japan from 2020 to 2021

BACKGROUND

To date, it is not fully understood to what extent COVID-19 has burdened society in Japan. This study aimed to estimate the total disease burden due to COVID-19 in Japan during 2020-2021.

METHODS

We stratify disease burden estimates by age group and present it as absolute Quality Adjusted Life Years (QALYs) lost and QALYs lost per 100,000 persons. The total estimated value of QALYs lost consists of (1) QALYs lost brought by deaths due to COVID-19, (2) QALYs lost brought by inpatient cases, (3) QALYs lost brought by outpatient cases, and (4) QALYs lost brought by long-COVID.

RESULTS

The total QALYs lost due to COVID-19 was estimated as 286,782 for two years, 114.0 QALYs per 100,000 population per year. 71.3% of them were explained by the burden derived from deaths. Probabilistic sensitivity analysis showed that the burden of outpatient cases was the most sensitive factor.

CONCLUSIONS

The large part of disease burden due to COVID-19 in Japan from the beginning of 2020 to the end of 2021 was derived from Wave 3, 4, and 5 and the proportion of QALYs lost due to morbidity in the total burden increased gradually. The estimated disease burden was smaller than that in other high-income countries. It will be our future challenge to take other indirect factors into consideration.

Technical Innovation in Critical Care in a World of Constraints: Lessons from the COVID-19 Pandemic

The COVID-19 crisis was characterized by a massive need for respiratory support, which has unfortunately not been met globally. This situation mimicked those which gave rise to critical care in the past. Since the polio epidemic in the 50's, the technological evolution of respiratory support has enabled health professionals to save the lives of critically-ill patients worldwide every year. However, much of the current innovation work has turned around developing sophisticated, complex, and high-cost standards and approaches whose resilience is still questionable upon facing constrained environments or contexts, as seen in resuscitation work outside intensive care units, during pandemics, or in low-income countries. Ventilatory support is an essential life-saving tool for patients with respiratory distress. It requires an oxygen source combined to a ventilatory assistance device, an adequate monitoring system, and properly trained caregivers to operate it. Each of these elements can be subject to critical constraints, which we can no longer ignore. The innovation process should incorporate them as a prima materia, whilst focusing on the core need of the field using the concept of frugal innovation. Having a universal access to oxygen and respiratory support, irrespective of the context and constraints, necessitates: i) developing cost-effective, energy-efficient, and maintenance-free oxygen generation devices; ii) improving the design of non-invasive respiratory devices (for example, with oxygen saving properties); iii) conceiving fully frugal ventilators and universal monitoring systems; iv) broadening ventilation expertise by developing end-user training programs in ventilator assistance. The frugal innovation approach may give rise to a more resilient and inclusive critical care system. This paradigm shift is essential for the current and future challenges.

Validation framework for epidemiological models with application to COVID-19 models

Mathematical models have been an important tool during the COVID-19 pandemic, for example to predict demand of critical resources such as medical devices, personal protective equipment and diagnostic tests. Many COVID-19 models have been developed. However, there is relatively little information available regarding reliability of model predictions. Here we present a general model validation framework for epidemiological models focused around predictive capability for questions relevant to decision-making end-users. COVID-19 models are typically comprised of multiple releases, and provide predictions for multiple localities, and these characteristics are systematically accounted for in the framework, which is based around a set of validation scores or metrics that quantify model accuracy of specific quantities of interest including: date of peak, magnitude of peak, rate of recovery, and monthly cumulative counts. We applied the framework to retrospectively assess accuracy of death predictions for four COVID-19 models, and accuracy of hospitalization predictions for one COVID-19 model (models for which sufficient data was publicly available). When predicting date of peak deaths, the most accurate model had errors of approximately 15 days or less, for releases 3-6 weeks in advance of the peak. Death peak magnitude relative errors were generally in the 50% range 3-6 weeks before peak. Hospitalization predictions were less accurate than death predictions. All models were highly variable in predictive accuracy across regions. Overall, our framework provides a wealth of information on the predictive accuracy of epidemiological models and could be used in future epidemics to evaluate new models or support existing modeling methodologies, and thereby aid in informed model-based public health decision making. The code for the validation framework is available at https://doi.org/10.5281/zenodo.7102854.

Application of big data and artificial intelligence in epidemic surveillance and containment

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Impact of healthcare strain on access to mechanical ventilation and mortality of hospitalized COVID-19 patients: a retrospective cohort study

OBJECTIVE

Healthcare saturation has been a prominent worry during the COVID-19 pandemic. Increase of hospital beds with mechanical ventilators has been central in Mexico's approach, but it is not known whether this actually improves access to care and the resulting quality of it. This study aimed to determine the impact of healthcare strain and other pre-specified variables on dying from coronavirus disease 2019 (COVID-19) without receiving invasive mechanical ventilation (IMV).

METHODS

A retrospective cohort study was conducted using open data from Mexico City between 8 May 2020 and 5 January 2021. We performed Cox proportional hazards models to identify the strength of the association between proposed variables and the outcomes.

RESULTS

Of 33 797 hospitalized patients with suspected or confirmed COVID-19, 19 820 (58.6%) did not require IMV and survived, 5414 (16.1%) required IMV and were intubated and 8563 (25.3%) required IMV but died without receiving it. A greater occupation of IMV-capable beds increased the hazard of death without receiving IMV (hazard ratio [HR] 1.56, comparing 90% with 50% occupation). Private healthcare was the most protective factor for death without IMV (HR 0.14).

CONCLUSIONS

Higher hospital bed saturation increased the hazard of dying without being intubated and worsened the outcomes among mechanically ventilated patients. Older age also increased the hazard of the outcomes, while private healthcare dramatically decreased them.

The Rescue of the Romanian Health System by the Emergency Departments during the Fourth Wave of COVID-19 Pandemic

The COVID-19 pandemic has led to the confrontation of the health system with the need to identify solutions for providing medical care to a very large number of patients. The main objective of our study was to describe the measures taken to provide optimal medical care to patients who presented themselves in one of the large emergency hospitals of Romania in the fourth wave of the COVID-19 pandemic. Material and Methods: We conducted a retrospective, observational study on a group of 1417 patients. The statistical analysis was performed using R. Results: The average length of stay of patients in the emergency departments was approximately 2.6 h, increasing to up to 15 days in some more severe cases. For rapid antigen tests, the highest positivity rate for SARS-CoV-2 was identified in patients aged >75 years (53%). Among the identified risk factors associated with the need for mechanical ventilation were advanced age (α < 0.001) and lack of vaccination against SARS-CoV-2 (α < 0.001). Discussion and conclusions: A method of saving the Romanian health system in full hospital bed occupancy conditions in the wards proved to be the provision of medical care in emergency departments.

Health System- and Patient-Related Factors Associated with COVID-19 Mortality among Hospitalized Patients in Limpopo Province of South Africa's Public Hospitals

South Africa has recorded the highest COVID-19 morbidity and mortality compared to other African regions. Several authors have linked the least amount of death in African countries with under-reporting due to poor health systems and patients' health-seeking behaviors, making the use of clinical audits more relevant for establishing the root causes of health problems, and improving quality patient care outcomes. Clinical audits, such as mortality audits, have a significant role in improving quality health care services, but very little is documented about the outcomes of the audits. Therefore, the study sought to determine the health care system and patient-related factors associated with COVID-19 mortality by reviewing the COVID-19 inpatient mortality audit narration reports. This was a retrospective qualitative research approach of all hospitalized COVID-19 patients, resulting in death between the first and second COVID-19 pandemic waves. Thematic analysis employed inductive coding to identify themes from mortality audits from all 41 public hospitals in Limpopo Province, South Africa. Four themes with seventeen sub-themes emerged: sub-standard emergency medical care provided, referral system inefficiencies contributed to delays in access to health care services, the advanced age of patients with known and unknown comorbidities, and poor management of medical supplies and equipment, as a health system and patient-related factors that contributed to the high mortality of COVID-19 patients. There is a need to routinely conduct clinical audits to identify clinical challenges and make recommendations for health promotion, risk communication, and community engagement. We recommend reviewing and expanding the scope of practice for health-care providers during epidemics and pandemics that include aspects such as task-shifting.

Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review

The SARS-CoV-2 pandemic has put unprecedented pressure on the hospital sector around the world. It has shown the importance of preparing and planning in the future for an outbreak that overwhelms every aspect of a hospital on a rapidly expanding scale. We conducted a scoping review to identify, map, and systemize existing knowledge about the relationships between COVID-19 and hospital infrastructure adaptation and capacity planning worldwide. We searched the Web of Science, Scopus, and PubMed and hand-searched gray papers published in English between December 2019 and December 2021. A total of 106 papers were included: 102 empirical studies and four technical reports. Empirical studies entailed five reviews, 40 studies focusing on hospital infrastructure adaptation and planning during the pandemics, and 57 studies on modeling the hospital capacity needed, measured mostly by the number of beds. The majority of studies were conducted in high-income countries and published within the first year of the pandemic. The strategies adopted by hospitals can be classified into short-term (repurposing medical and non-medical buildings, remote adjustments, and establishment of de novo structures) and long-term (architectural and engineering modifications, hospital networks, and digital approaches). More research is needed, focusing on specific strategies and the quality assessment of the evidence.

A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data

The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries.

Modelling the Effect of COVID-19 Mass Vaccination on Acute Hospital Admissions

BACKGROUND

Managing high levels of acute COVID-19 bed occupancy can affect the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible impact on future bed pressures remained subject to considerable uncertainty. The aim of this study was to model the effect of vaccination on projections of acute and intensive care bed demand within a one million resident healthcare system located in South West England.

METHODS

An age-structured epidemiological model of the Susceptible-Exposed-Infectious-Recovered (SEIR) type was fitted to local data up to the time of the study, in early March 2021. Model parameters and vaccination scenarios were calibrated through a system-wide multi-disciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists, and academics. Scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021.

RESULTS

Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert a third wave in autumn 2021 but would produce a median peak bed requirement approximately 6% (IQR: 1% to 24%) of that experienced during the second wave (January 2021). A two-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11% to 146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns) then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19% respectively, an amount which would seriously pressure hospital capacity.

CONCLUSION

Modelling influenced decision making among senior managers in setting COVID-19 bed capacity levels, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.

Modeling the evolution of SARS-CoV-2 under non-pharmaceutical interventions and testing

Background and objectives

Social and behavioral non-pharmaceutical interventions (NPIs), such as mask-wearing, social distancing and travel restrictions, as well as diagnostic tests, have been broadly implemented in response to the COVID-19 pandemic. Epidemiological models and data analysis affirm that wide adoption of NPIs helps to control the pandemic. However, SARS-CoV-2 has extensively demonstrated its ability to evolve. Therefore, it is crucial to examine how NPIs may affect the evolution of the virus. Such evolution could have important effects on the spread and impact of the pandemic.

Methodology

We used evo-epidemiological models to examine the effect of NPIs and testing on two evolutionary trajectories for SARS-CoV-2: attenuation and test evasion.

Results

Our results show that when stronger measures are taken, selection may act to reduce disease severity. Additionally, the timely application of NPIs could significantly affect the competition between viral strains, favoring the milder strain. Furthermore, a higher testing rate can select for a test-evasive viral strain, even if that strain is less infectious than the detectable competing strain. Importantly, if a less detectable strain evolves, epidemiological metrics such as confirmed daily cases may distort our assessment of the pandemic.

Conclusions and implications

Our results highlight the important implications NPIs can have on the evolution of SARS-CoV-2.

Lay Summary

We used evo-epidemiological models to examine the effect of non-pharmaceutical interventions and testing on two evolutionary trajectories for SARS-CoV-2: attenuation and test evasion. Our results show that when stronger measures are taken, selection may act to reduce disease severity.

ICU-Associated Costs during the Fourth Wave of the COVID-19 Pandemic in a Tertiary Hospital in a Low-Vaccinated Eastern European Country

The COVID-19 pandemic has been associated with a tremendous financial and social impact. The pressure on healthcare systems worldwide has increased with each pandemic wave. The present study assesses the impact of the COVID-19 pandemic on healthcare-derived costs of critically ill patients during the fourth wave of the COVID-19 pandemic in a tertiary hospital in Romania. We prospectively included patients admitted to a single-centre intensive care unit (ICU) during the fourth wave of the COVID-19 pandemic. Median daily costs were calculated from financial records and divided in three groups: administrative costs, treatment costs and investigation costs. These were then compared to two retrospective cohorts of non-COVID-19 patients admitted to the same ICU during the same time interval in 2020 and 2019. Demographic data and the management of SARS-CoV-2 infection and of associated organ dysfunctions were recorded to identify risk factors for higher costs. Our results show that the COVID-19 pandemic has been associated with a 70.8% increase in total costs compared to previous years. This increase was mainly determined by an increase in medication and medical-device-related costs. We identified the following as risk factors for increased costs: higher degrees of lung involvement, severity of respiratory dysfunction, need for renal replacement therapy and the use of antiviral or immunomodulatory therapy. Costs were higher in patients who had a shorter duration of hospitalization. In conclusion, the COVID-19 pandemic is associated with increased costs for patients, and rapid measures need to be taken to ensure adequate financial support during future pandemic waves, especially in developing countries.

Impact of regional heterogeneity on the severity of COVID-19

The main objective of the study is to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. We included 27,865 cases registered between January 2020 and February 2021 in the COVID-19 Registry of Japan, to examine the relationship between the National Early Warning Score (NEWS) of COVID-19 patients on the day of admission and the prefecture where the patients live. A hierarchical Bayesian model was used to examine the random effect of each prefecture in addition to the patients' backgrounds. Additionally, we compared the results of two models; one model included the number of beds secured for COVID-19 patients in each prefecture as one of the fixed effects, and the other model did not. The results indicated that the prefecture had a substantial impact on the severity of COVID-19 on admission, even when considering the effect of the number of beds separately. Our analysis revealed a possible association between regional heterogeneity and increased/decreased risk of severe COVID-19 infection on admission. This heterogeneity was derived not only from the number of beds secured in each prefecture but also from other factors.

Communicating uncertainty in epidemic models

While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.

Epidemiological models and COVID-19: a comparative view

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials-some real examples are invoked-we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.