Intensive Care Units Demand Research Articles

Inadequate intensive care physician supply in France: a point-prevalence prospective study

BackgroundThe COVID-19 pandemic has highlighted the importance of intensive care units (ICUs) and their organization in healthcare systems. However, ICU capacity and availability are ongoing concerns beyond the pandemic, particularly due to an aging population and increasing complexity of care. This study aimed to assess the current and future shortage of ICU physicians in France, ten years after a previous evaluation. A national e-survey was conducted among French ICUs in January 2022 to collect data on ICU characteristics, medical staffing, individual physician characteristics, and education and training capacities.ResultsAmong 290 ICUs contacted, 242 responded (response rate: 83%), representing 4943 ICU beds. The survey revealed an overall of 300 full time equivalent (FTE) ICU physician vacancies in the country. Nearly two-thirds of the participating ICUs reported at least one physician vacancy and 35% relied on traveling physicians to cover shifts. The ICUs most affected by physician vacancies were the ICUs of non-university affiliated public hospitals. The retirements expected in the next five years represented around 10% of the workforce. The median number of physicians per ICU was 7.0, corresponding to a ratio of 0.36 physician (FTE) per ICU bed. In addition, 27% of ICUs were at risk of critical dysfunction or closure due to vacancies and impending retirements.ConclusionThe findings highlight the urgent need to address the shortage of ICU physicians in France. Compared to a similar study conducted in 2012, the inadequacy between ICU physician supply and demand has increased, resulting in a higher number of vacancies. Our study suggests that, among others, increasing the number of ICM residents trained each year could be a crucial step in addressing this issue. Failure to take appropriate measures may lead to further closures of ICUs and increased risks to patients in this healthcare system.

Exploring the Synergistic Potential of Radiomics and Laboratory Biomarkers for Enhanced Identification of Vulnerable COVID-19 Patients.

Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT. Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting. The adapted integrated model classifying patients into "ICU/no ICU demand" comprises six radiomics and seven laboratory biomarkers. The models' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91. The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19

BackgroundDuring the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts.MethodsIn this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number (R_0), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios.ResultsWe found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number R_0. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters.ConclusionFor the scenarios where the value of information was high enough to justify monitoring, if CS and R_0 are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

Projecting Future Climate Impact on National Australian Respiratory-Related Intensive Care Unit Demand.

A robust climate-health projection model has the potential to improve health care resource allocation. We aim to explore the relationship between Australian intensive care unit (ICU) demand and various measures of the long-lived large-scale climate and to develop a future nationwide climate-health projection model. We investigated patients admitted to ICUs in Australia between January 2003 and December 2019 who were exposed to long-lived large-scale combined climatic measures of temperature and humidity. We analysed the projected demand for respiratory-related ICU average length of stay (in days) per capita (ICUD/C) with four historical and one future projection dataset. These datasets included: i) Australian and New Zealand Intensive Care Society adult patient database, ii) Socioeconomic Data and Applications Center gridded global historical population, iii) Australian Bureau of Statistics national historical population, iv) Japanese 55-year Reanalysis historical climate (JRA55), and v) the fifth Coupled Model Inter-comparison Project future climate projections. 148,638 patients with respiratory issues required intensive care between 2003 and 2019. The annual growth in the population density-weighted wet-bulb-globe temperature-a combined measure of temperature and humidity-is strongly correlated with the annual per capita growth ICUD/C for respiratory-related conditions (r=0.771; p<0.001). This relationship was applied to develop a model projecting future respiratory-related ICU demand with three possible future Representative Concentration Pathways (RCP). RCP2.6 (lowest carbon emission climate scenario) showed only a 33.4% increase in Australian ICUD/C demand by 2090, while the RCP8.5 (highest carbon emission climate scenario) demonstrated almost two-fold higher demand (66.1%) than RCP2.6 by2090. The annual growth in population density-weighted wet-bulb-globe temperature correlates with the annual growth in Australian ICUD/C for respiratory-related conditions. A model based on possible future climate scenarios can be developed to predict changes in ICU demand in response to CO2 changes over the coming decades.

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

PurposeTo 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.MethodsIn 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.Results640 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.ConclusionThe 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.

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

Recent literature has revealed a growing interest in methods for anticipating the demand for medical items and personnel at hospital, especially during turbulent scenarios such as the COVID-19 pandemic. In times like those, new variables appear and affect the once known demand behavior. This paper investigates the hypothesis that the combined Prophet-LSTM method results in more accurate forecastings for COVID-19 hospital Intensive Care Units (ICUs) demand than both standalone models, Prophet and LSTM (Long Short-Term Memory Neural Network). We also compare the model to well-established demand forecasting benchmarks. The model is tested to a representative Brazilian municipality that serves as a medical reference to other cities within its region. In addition to traditional time series components, such as trend and seasonality, other variables such as the current number of daily COVID-19 cases, vaccination rates, non-pharmaceutical interventions, social isolation index, and regional hospital beds occupation are also used to explain the variations in COVID-19 hospital ICU demand. Results indicate that the proposed method produced Mean Average Errors (MAE) from 13% to 45% lower than well established statistical and machine learning forecasting models, including the standalone models.

Optimal Social Distancing Policy for COVID-19 Control in Korea: A Model-Based Analysis.

BackgroundSince March 2020, when coronavirus disease 2019 (COVID-19) was declared a pandemic, many countries have applied unprecedented restrictive measures to contain the spread of the virus. This study aimed to explore the optimal social distancing policy for COVID-19 control in South Korea to safely reopen the society.MethodsWe developed an age-specific, deterministic compartment epidemic model to examine the COVID-19 control decision-making process, including the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 July 2021 and 30 December 2022. The model consists of the natural history of COVID-19, testing performance, vaccinations, and social distancing enforcement measures to detect and control SARS-CoV-2. We modelled potential intervention scenarios with three distinct components: 1) social distancing duration and level; 2) testing intensity; and 3) vaccination uptake rate. The primary and secondary outcomes were COVID-19 incidence and prevalence of severe patients requiring intensive care unit (ICU) care.ResultsFour (or more) months of social distancing (that can reduce 40–60% transmission) may mitigate epidemic resurgence and ICU demand in the future and keep the cases below the capacity limit if the testing intensity and vaccination rate remain constant or increase by 20% (with respect to the current level). In contrast, two months of strict social distancing enforcement may also successfully mitigate future epidemic surge and ICU demand as long as testing intensity and vaccination rates are increased by 20%.ConclusionIn South Korea, given the relatively high vaccination coverage and low incidence, four or more months of social distancing enforcement can effectively mitigate epidemic resurgence after lifting the social distancing measures. In addition, increasing the testing intensity and vaccination rate may help reduce necessary social distancing levels and duration to prevent a future epidemic resurgence and mitigate social and economic damage.

Impact of lockdowns on critical care service demand in a metropolitan hospital in Melbourne, Australia.

There is a growing recognition of the impact of lockdowns on non-COVID-19 demand for critical care services. While a reduction in demand has been postulated, there remains a paucity of quantitative data on the extent and nature of this reduction. The present study aims to quantify the impact of lockdown on critical care services, namely ED, intensive care unit (ICU), medical emergency team (MET) and emergency theatre (ET) demand, during the lockdown in Victoria, Australia. This is a single-centred, retrospective observational study on critical service demand, comparing activity levels during the lockdown (31 March to 27 October 2020) with the matched time period from 1year prior. There was a reduction in presentations to ED (27.2%), MET calls (27.4%), ICU patient episodes (14.5%) and ET bookings (5.8%). There was an unexpected increase in ICU admissions for metabolic diagnoses, comprising drug overdoses and diabetic ketoacidosis, and a reduction in respiratory ICU admissions. There was a reduction across all ED triage categories, which included triage 1 and 2 patients, indicating a reduction even in life-threatening and emergency presentations. Lockdowns lead to a significant reduction in ICU, MET call and ED demand, and to a lesser extent ET demand. This pattern should be considered in surge capacity and workforce redeployment planning. There are also impacts on public health epidemiology, with potential adverse consequences on mental health and chronic disease management. Further research on the impact of lockdowns on long-term disease outcomes is needed.

Model Input and Optimization: Improving the Speed and Accuracy of Our COVID-19 Hospitalization Forecasts

Background: During the COVID-19 pandemic, public-health decision makers have increasingly relied on hospitalization forecasts that are routinely provided, accurate, and based on timely input data to inform pandemic planning. In North Carolina, we adapted an existing agent-based model (ABM) to produce 30-day hospitalization forecasts of COVID-19 and non–COVID-19 hospitalizations for use by public-health decision makers. We sought to continually improve model speed and accuracy during forecasting. Methods: The geospatially explicit ABM included movement of agents (ie, patients) among 104 short-term acute-care hospitals, 10 long-term acute-care hospitals, 421 licensed nursing homes, and the community in North Carolina. Agents were based on a synthetic population of North Carolina residents (ie, &gt;10.4 million agents). We assigned SARS-CoV-2 infections to agents according to county-level susceptible, exposed, infectious, recovered (SEIR) models informed by reported COVID-19 cases by county. Agents’ COVID-19 severity and probability of hospitalization were determined using agent-specific characteristics (eg, age, comorbidities). During May 2020–December 2020, we produced weekly 30-day forecasts of intensive care unit (ICU) and non-ICU bed occupancy for COVID-19 agents and non–COVID-19 agents statewide and by region under a range of SARS-CoV-2 effective reproduction numbers. During the reporting period, we identified optimizations for faster results turnaround. We evaluated the incorporation of real-time hospital-level occupancy data at model initialization on forecast accuracy using mean absolute percent error (MAPE). Results: During May 2020–December 2020, we provided 31 weekly reports of 30-day hospitalization forecasts with a 1-day turnaround time. Reports included (1) raw and smoothed 7-day average values for 42 model output variables; (2) static visuals of ICU and non-ICU bed demand and capacity; and (3) an interactive Tableau workbook of hospital demand variables. Identifying code efficiencies reduced a single model runtime from ~100 seconds to 28 seconds. The use of cloud computing reduced simulation runtime from ~20 hours to 15 minutes. Across forecasts, the average MAPEs were 21.6% and 7.1% for ICU and non-ICU bed demand, respectively. By incorporating hospital-level occupancy data, we reduced the average MAPE to 6.5% for ICU bed demand and 3.9% for non-ICU bed demand, indicating improved accuracy. Conclusions: We adapted an ABM and continually improved it during COVID-19 forecasting by optimizing code and computing resources and including real-time hospital-level occupancy data. Planned SEIR model updates for enhanced forecasts include the addition of compartments for undocumented infections and recoveries as well as permission of reinfection from recovered compartments.Funding: NoDisclosures: None

Abstract P34: Health care disparities and outcomes of cancer patients with COVID-19: A pooled analysis of two Brazilian tertiary centers

Abstract Background: Patients with cancer, both active and previously treated, are at a higher risk of developing severe outcomes from COVID-19. During the pandemic, health care systems (HCS) have adapted the delivery of care, and disparities between private and public systems became even more striking. In Brazil, where 70% of the population depends on the public system, ICU demands largely exceed the capacity in most public centers, whereas in private centers the situation is less challenging. Herein we compare outcomes of patients with cancer and COVID-19 treated in the public and private HCS in Brazil. Methods: We used data from adult patients with solid malignancies who tested positive for COVID-19 and were admitted to two tertiary centers in the state of São Paulo. Patients who tested positive for SARS-CoV2 RNA real-time polymerase chain reaction (RT-PCR) were included. We collected data on baseline clinical conditions, cancer and treatment. Patients were classified by HCS: public system (public) versus (vs) private insurance coverage (private). The co-primary endpoints were all-cause mortality and a composite endpoint consisting of intensive-care-unit (ICU) admission, mechanical ventilation or death (ICU-MV-D). Chi-square, Fisher´s exact test and Mann-Whitney U test were used when appropriate. We assessed the association between outcomes and HCS using logistic regression analyses, adjusting for age, sex, current anticancer treatment and comorbidities. Results: From March 16 to October 20 2020, 124 patients were identified. Of those, 90 (72%) were from the public and 34 (28%) from the private HCS. There were no statistical differences in sex, smoking, primary tumor siteand staging between patients from both HCS. Conversely, patients treated in the private system were older [66 (SD 13.8) vs 74 (SD 15.1), p=0.004], had more comorbidities (64.7% vs 37.8% p=0.009), and were on anticancer treatment more frequently (64.7% vs 34.4% p=0.004) compared to public patients. There were no differences in all-cause mortality (public 40% vs private 44.1% p=0.69) between patients treated at the different HCS. Nevertheless, in the composite outcome, private system was significantly associated with increased risk of ICU-MV-D compared to the public system (79.4% and 57.8% p=0.030, respectively). The median time from COVID-19 diagnosis to ICU-MV-D was 13 vs 8 days (p=0.031) and to death was 25 vs 24 days (p=0.24), respectively for public and private HCS patients. In the multivariable logistic regression, HCS was not associated with death [adjusted odds ratio (aOR)=1.16 p=0.75] or ICU-MV-D (aOR=0.55, p=0.27). Conclusion: While patients in the private system were older and had more comorbidities, there were no differences in inpatients all-cause mortality between private and public systems. However, private patients were associated with increased ICU-MV-D. We hypothesize that these findings may reflect disparities in ICU availability, known to be higher in the private system. Further studies investigating this hypothesis are warranted. EDR and DVA co-senior authors. Citation Format: Marina Topanotti, Larissa L. Furlan, Catarina Marchon, Pedro Amaral, Barbara C. Beneton, Luiza A. Fadul, Maria Lucia Salomao, Suzana M. Lobo, Aline F. Fares, Daniel V. Araújo, Eliza D. Ricardo. Health care disparities and outcomes of cancer patients with COVID-19: A pooled analysis of two Brazilian tertiary centers [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr P34.

Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic

Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 (COVID-19) critical care capacity and mortality. To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain. This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020. Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient's hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient's stay divided by the maximum number of patients with COVID-19 in the ICU. All-cause mortality was recorded through 30 days after discharge from the hospital. Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361 (12.8%) patients dying in August (P < .001). Patients with COVID-19 who were treated in the ICU during periods of increased COVID-19 ICU demand had increased risk of mortality compared with patients treated during periods of low COVID-19 ICU demand (ie, demand of ≤25%); the adjusted hazard ratio for all-cause mortality was 0.99 (95% CI, 0.81-1.22; P = .93) for patients treated when COVID-19 ICU demand was more than 25% to 50%, 1.19 (95% CI, 0.95-1.48; P = .13) when COVID-19 ICU demand was more than 50% to 75%, and 1.94 (95% CI, 1.46-2.59; P < .001) when COVID-19 ICU demand was more than 75% to 100%. No association between COVID-19 ICU demand and mortality was observed for patients with COVID-19 not in the ICU. The association between COVID-19 ICU load and mortality was not consistent over time (ie, early vs late in the pandemic). This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.

The SARS-CoV-2 epidemic, a step towards recognizing the speciality of critical care nursing in France.

The SARS-CoV-2 epidemic, a step towards recognizing the speciality of critical care nursing in France.

Discrepancy between the Demand and Supply of Intensive Care Unit Beds in South Korea from 2011 to 2019: A Cross-Sectional Analysis

PurposeIntensive care unit (ICU) bed availability is key to critical patient care. In many countries, older patients generally account for a significant proportion of hospitalizations and ICU admissions. Therefore, considering the rapidly increasing aging population in South Korea, it is important to establish whether the demand for critical care is currently met by available ICU beds.Materials and MethodsWe evaluated a 9-year trend in ICU bed supply and ICU length of stay in South Korea between 2011 and 2019 in a population-based cross-sectional analysis, using data from the Korean Health Insurance Review & Assessment Service and Statistics database. We described the changes in ICU bed rates in adult (≥20 years) and older adult (≥65 years) populations. ICU length of stay was categorized similarly and was used to predict future ICU bed demands.ResultsThe ICU bed rate per 100000 adults increased from 18.5 in 2011 to 19.5 in 2019. In contrast, the ICU bed rate per 100000 older adults decreased from 127.6 in 2011 to 104.0 in 2019. ICU length of stay increased by 43.8% for adults and 55.6% for older adults. In 2019, the regional differences in the ICU bed rate nearly doubled, and the ICU length of stay increased six-fold. The ICU bed occupancy rate in South Korea is expected to rise to 102.7% in 2030.ConclusionThe discrepancy between the demand and supply of ICU beds in South Korea requires urgent action to anticipate future ICU demands.

A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic.

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.

Intensive Care Unit Capacity, Cancellation of Elective Surgery, and the US Pandemic Response.

Intensive Care Unit Capacity, Cancellation of Elective Surgery, and the US Pandemic Response.

Critical Care Demand and Intensive Care Supply for Patients in Japan with COVID-19 at the Time of the State of Emergency Declaration in April 2020: A Descriptive Analysis.

Background and objectives: The coronavirus disease 2019 (COVID-19) pandemic is overwhelming Japan’s intensive care capacity. This study aimed to determine the number of patients with COVID-19 who required intensive care and to compare the numbers with Japan’s intensive care capacity. Materials and Methods: Publicly available datasets were used to obtain the number of confirmed patients with COVID-19 undergoing mechanical ventilation and extracorporeal membrane oxygenation (ECMO) between 15 February and 19 July 2020 to determine and compare intensive care unit (ICU) and attending bed needs for patients with COVID-19, and to estimate peak ICU demands in Japan. Results: During the epidemic peak in late April, 11,443 patients (1.03/10,000 adults) had been infected, 373 patients (0.034/10,000 adults) were in ICU, 312 patients (0.028/10,000 adults) were receiving mechanical ventilation, and 62 patients (0.0056/10,000 adults) were under ECMO per day. At the peak of the epidemic, the number of infected patients was 651% of designated beds, and the number of patients requiring intensive care was 6.0% of ICU beds, 19.1% of board-certified intensivists, and 106% of designated medical institutions in Japan. Conclusions: The number of critically ill patients with COVID-19 continued to rise during the pandemic, exceeding the number of designated beds but not exceeding ICU capacity.

COVID-19 healthcare demand and mortality in Sweden in response to non-pharmaceutical mitigation and suppression scenarios

BackgroundWhile the COVID-19 outbreak in China now appears suppressed, Europe and the USA have become the epicentres, both reporting many more deaths than China. Responding to the pandemic, Sweden has taken a different approach aiming to mitigate, not suppress, community transmission, by using physical distancing without lockdowns. Here we contrast the consequences of different responses to COVID-19 within Sweden, the resulting demand for care, intensive care, the death tolls and the associated direct healthcare related costs.MethodsWe used an age-stratified health-care demand extended SEIR (susceptible, exposed, infectious, recovered) compartmental model for all municipalities in Sweden, and a radiation model for describing inter-municipality mobility. The model was calibrated against data from municipalities in the Stockholm healthcare region.ResultsOur scenario with moderate to strong physical distancing describes well the observed health demand and deaths in Sweden up to the end of May 2020. In this scenario, the intensive care unit (ICU) demand reaches the pre-pandemic maximum capacity just above 500 beds. In the counterfactual scenario, the ICU demand is estimated to reach ∼20 times higher than the pre-pandemic ICU capacity. The different scenarios show quite different death tolls up to 1 September, ranging from 5000 to 41 000, excluding deaths potentially caused by ICU shortage. Additionally, our statistical analysis of all causes excess mortality indicates that the number of deaths attributable to COVID-19 could be increased by 40% (95% confidence interval: 0.24, 0.57).ConclusionThe results of this study highlight the impact of different combinations of non-pharmaceutical interventions, especially moderate physical distancing in combination with more effective isolation of infectious individuals, on reducing deaths, health demands and lowering healthcare costs. In less effective mitigation scenarios, the demand on ICU beds would rapidly exceed capacity, showing the tight interconnection between the healthcare demand and physical distancing in the society. These findings have relevance for Swedish policy and response to the COVID-19 pandemic and illustrate the importance of maintaining the level of physical distancing for a longer period beyond the study period to suppress or mitigate the impacts from the pandemic.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

BackgroundSchool closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.MethodsWe provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.ResultsAt the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2–10.9%) of healthcare worker households and 5.2% (IQR 4.1–6.5%) and 6.8% (IQR 4.8–8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.ConclusionsSchool closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

Predicted Effects of Stopping COVID-19 Lockdown on Italian Hospital Demand.

Italy has been one of the first countries to implement mitigation measures to curb the coronavirus disease 2019 (COVID-19) pandemic. There is currently a debate on when and how such measures should be loosened. To forecast the demand for hospital intensive care unit (ICU) and non-ICU beds for COVID-19 patients from May to September, we developed 2 models, assuming a gradual easing of restrictions or an intermittent lockdown. We used a compartmental model to evaluate 2 scenarios: (A) an intermittent lockdown; (B) a gradual relaxation of the lockdown. Predicted ICU and non-ICU demand was compared with the peak in hospital bed use observed in April 2020. Under scenario A, while ICU demand will remain below the peak, the number of non-ICU will substantially rise and will exceed it (133%; 95% confidence interval [CI]: 94-171). Under scenario B, a rise in ICU and non-ICU demand will start in July and will progressively increase over the summer 2020, reaching 95% (95% CI: 71-121) and 237% (95% CI: 191-282) of the April peak. Italian hospital demand is likely to remain high in the next months. If restrictions are reduced, planning for the next several months should consider an increase in health-care resources to maintain surge capacity across the country.

Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries

To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs—increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal “break” when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R0) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a “schedule” of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally.