COVID-19 Hospitalization Trends in Rural Versus Urban Areas in the United States.

Since the summer of 2020, the rate of coronavirus cases in the United States has been higher in rural areas than in urban areas, raising concerns that patients with coronavirus disease 2019 (COVID-19) will overwhelm under-resourced rural hospitals. Using data from the University of Minnesota COVID-19 Hospitalization Tracking Project and the U.S. Department of Health and Human Services, we document disparities in COVID-19 hospitalization rates between rural and urban areas. We show that rural-urban differences in COVID-19 admission rates were minimal in the summer of 2020 but began to diverge in fall 2020. Rural areas had statistically higher hospitalization rates from September 2020 through early 2021, after which rural-urban admission rates re-converged. The insights in this article are relevant to policymakers as they consider the adequacy of hospital resources across rural and urban areas during the COVID-19 pandemic.

Effectiveness of a telephone-based nursing intervention to reduce hospital utilization by COVID-19 patients.

OBJECTIVE: Determine the effectiveness of a COVID-19 remote monitoring and management program in reducing preventable hospital utilization. DESIGN: A retrospective cohort study utilizing data from electronic health records. SAMPLE: Two hundred ninety-three patients who tested positive for COVID-19 at a drive-through testing site in Michigan. [Correction added on 11 April 2022, after first online publication: In the preceding sentence, "Two hundred and ninety-third" has been corrected to "Two hundred ninety-three" in this version.] The intervention group, consisting of 139 patients, was compared to a control group of 154 patients. MEASUREMENTS: The primary outcome was the 30-day probability of hospital utilization. The covariates included in the analysis were age, gender, tobacco use, body mass index (BMI), race, and ethnicity. INTERVENTION: A nurse-led, telephone-based active management protocol for COVID-19 patients who were isolating at home. RESULTS: The intervention group had a non-statistically significant 42% reduction in risk of hospital utilization within 30 days of a positive COVID-19 test when compared to the control group (HR = 0.578, p-value .111, HR 95% CI [0.29, 1.13]). CONCLUSIONS: A nurse-led remote monitoring and management program for COVID-19 reduced the probability of 30-day hospital utilization. Although the findings were not statistically significant, the program yielded practical significance by reducing hospital utilization, in-person interaction, and the risk of infection for healthcare workers.

Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic

Hospitals and health-care institutions need to plan the resources required for handling the increased load, i.e., beds and ventilators during the COVID-19 pandemic. BaBSim.Hospital, an open-source tool for capacity planning based on discrete event simulation, was developed over the last year to support doctors, administrations, health authorities, and crisis teams in Germany. To obtain reliable results, 29 simulation parameters such as durations and probabilities must be specified. While reasonable default values were obtained in detailed discussions with medical professionals, the parameters have to be regularly and automatically optimized based on current data. We investigate how a set of parameters that is tailored to the German health system can be transferred to other regions. Therefore, we use data from the UK. Our study demonstrates the flexibility of the discrete event simulation approach. However, transferring the optimal German parameter settings to the UK situation does not work-parameter ranges must be modified. The adaptation has been shown to reduce simulation error by nearly 70%. The simulation-via-optimization approach is not restricted to health-care institutions, it is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods. © 2021 European Union

Hospital preparedness during epidemics using simulation: the case of COVID-19.

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

Planning for the assistance of critically ill patients in a Pandemic Situation: The experience of Vall d'Hebron University Hospital.

INTRODUCTION: In the context of community transmission of the virus, the impact of the pandemic on health-care systems, mainly on intensive care units (ICU), was expected to be devastating. Vall d'Hebron University Hospital (HUVH) implemented an unprecedented critical patient-care planning and management of resources. METHODS: We describe a cohort of critically ill patients during the first two months of the pandemic (from March 3, 2020, to May 2, 2020) in HUVH, Barcelona. In this manuscript, we report our previsions, strategies implemented, and the outcomes obtained. RESULTS: Three-thousand and thirty-three patients were admitted to the HUVH Critical Care Units. Throughout the study period, the proportion of patients on IMV or IMV and ECMO remained above 78%. Most patients were men (65%); the most common age group was 60-70 years. Twenty-three patients received ECMO, and eighteen were cannulated at another center and transferred to HUVH. At the end of the study, fourteen patients were successfully decannulated, three patients died, and the rest of the patients were still on ECMO. Eight pregnant women have been treated in the ICU, with a survival rate of 100%. The ICU mortality of patients younger than 60 years was 3.2%. The mean ICU stay of both survivors and nonsurvivors was 14 days. CONCLUSION: The adequate preparation for resource expansion for critically ill patients care, main challenges, and overall positive results can serve as a precedent for similar future scenarios.

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.

OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Impact of COVID-19 on healthcare organisation and cancer outcomes.

BACKGROUND: Changes in the management of patients with cancer and delays in treatment delivery during the COVID-19 pandemic may impact the use of hospital resources and cancer mortality. PATIENTS AND METHODS: Patient flows, patient pathways and use of hospital resources during the pandemic were simulated using a discrete event simulation model and patient-level data from a large French comprehensive cancer centre's discharge database, considering two scenarios of delays: massive return of patients from November 2020 (early-return) or March 2021 (late-return). Expected additional cancer deaths at 5 years and mortality rate were estimated using individual hazard ratios based on literature. RESULTS: The number of patients requiring hospital care during the simulation period was 13,000. In both scenarios, 6-8% of patients were estimated to present a delay of >2 months. The overall additional cancer deaths at 5 years were estimated at 88 in early-return and 145 in late-return scenario, with increased additional deaths estimated for sarcomas, gynaecological, liver, head and neck, breast cancer and acute leukaemia. This represents a relative additional cancer mortality rate at 5 years of 4.4 and 6.8% for patients expected in year 2020, 0.5 and 1.3% in 2021 and 0.5 and 0.5% in 2022 for each scenario, respectively. CONCLUSIONS: Pandemic-related diagnostic and treatment delays in patients with cancer are expected to impact patient survival. In the perspective of recurrent pandemics or alternative events requiring an intensive use of limited hospital resources, patients should be informed not to postpone care, and medical resources for patients with cancer should be sanctuarised.

Early-stage predictors of the acute phase duration in uncomplicated COVID-19 pneumonia.

OBJECTIVE: In this study, we aimed to highlight the common early-stage clinical and laboratory variables independently related to the acute phase duration in patients with uncomplicated coronavirus disease (COVID-19) pneumonia. METHODS: In hospitalized patients, the acute phase disease duration was followed using the Brescia-COVID respiratory severity scale. Noninvasive ventilation was administered based on clinical judgment. Patients requiring oropharyngeal intubation were excluded from the study. For parameters to be measured at the hospital entrance, age, clinical history, National Early Warning Score 2 (a multiparametric score system), partial pressure of oxygen in arterial blood/fraction of inspired oxygen (P/F ratio), C-reactive protein, and blood cell count were selected. RESULTS: In 64 patients, age (direct relationship), P/F, and platelet number (inverse relationship) independently accounted for 43% of the acute phase duration of the disease (P < .001). CONCLUSIONS: For the first time, the present results revealed that the acute phase duration of noncomplicated pneumonia, resulting from severe acute respiratory syndrome coronavirus 2, is independently predicted from a patient's age, as well as based on the hospital entrance values of P/F ratio and peripheral blood platelet count.

A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic.

BACKGROUND: As the COVID-19 pandemic continues, there is a question of whether hospitals have adequate resources to manage patients. We aim to investigate global hospital bed (HB), acute care bed (ACB), and intensive care unit (ICU) bed capacity and determine any correlation between these hospital resources and COVID-19 mortality. METHOD: Cross-sectional study utilizing data from the World Health Organization (WHO) and other official organizations regarding global HB, ACB, ICU bed capacity, and confirmed COVID-19 cases/mortality. Descriptive statistics and linear regression were performed. RESULTS: A total of 183 countries were included with a mean of 307.1 HBs, 413.9 ACBs, and 8.73 ICU beds/100,000 population. High-income regions had the highest mean number of ICU beds (12.79) and HBs (402.32) per 100,000 population whereas upper middle-income regions had the highest mean number of ACBs (424.75) per 100,000. A weakly positive significant association was discovered between the number of ICU beds/100,000 population and COVID-19 mortality. No significant associations exist between the number of HBs or ACBs per 100,000 population and COVID-19 mortality. CONCLUSIONS: Global COVID-19 mortality rates are likely affected by multiple factors, including hospital resources, personnel, and bed capacity. Higher income regions of the world have greater ICU, acute care, and hospital bed capacities. Mandatory reporting of ICU, acute care, and hospital bed capacity/occupancy and information relating to coronavirus should be implemented. Adopting a tiered critical care approach and targeting the expansion of space, staff, and supplies may serve to maximize the quality of care during resurgences and future disasters.

COVID-19 treatment resource disparities and social disadvantage in New York City.

Black and Hispanic communities in the U.S. have endured a disproportionate burden of COVID-19-related morbidity and mortality. Racial and ethnic health disparities such as these are frequently aggravated by inequitable access to healthcare resources in disadvantaged communities. Yet, no known studies have investigated disadvantaged communities' access to COVID-19-related healthcare resources. The current study accordingly examined racial and ethnic differences in (1) April 2020 COVID-19 total and positive viral test rates across 177 New York City (NYC) ZIP Code Tabulation Areas (ZCTA); and (2) November 2019-April 2020 licensed and intensive care unit (ICU) hospital bed access across 194 NYC ZCTAs. Pairwise analyses indicated higher COVID-19 total and positive test rates per 1000 persons in majority Black and Hispanic vs. majority White ZCTAs (CI [0.117, 4.55]; CI [2.53, 5.14]). Multiple linear regression analyses indicated that higher percentage of Black and Hispanic residents predicted more total COVID-19 tests per 1000 persons (p < 0.05). In contrast, majority Black and Hispanic ZCTAs had fewer licensed and ICU beds (CI [6.50, 124.25]; CI [0.69, 7.16]), with social disadvantage predicting lower licensed and ICU bed access per 1000 persons (p < 0.01). While news reports of inequitable access to COVID-19-related healthcare resources in ethnocultural minority communities have emerged, this is the first study to reveal that social disadvantage may be a major driver of hospital resource inequities in Black and Hispanic communities. Thus, it will be imperative to enact policies that ensure equitable allocation of healthcare resources to socially disadvantaged communities to address current and future public health crises.

Planning for the assistance of critically ill patients in a Pandemic Situation: The experience of Vall d'Hebron University Hospital. / Organización de la atención a pacientes críticos en situación de pandemia: Experiencia del Hospital Vall d'Hebron durante el brote de neumonía por SARS-CoV-2.

INTRODUCTION: In the context of community transmission of the virus, the impact of the pandemic on health-care systems, mainly on intensive care units (ICU), was expected to be devastating. Vall d́Hebron University Hospital (HUVH) implemented an unprecedented critical patient-care planning and management of resources. METHODS: We describe a cohort of critically ill patients during the first two months of the pandemic (from March 3, 2020, to May 2, 2020) in HUVH, Barcelona. In this manuscript, we report our previsions, strategies implemented, and the outcomes obtained. RESULTS: Three-thousand and thirty-three patients were admitted to the HUVH Critical Care Units. Throughout the study period, the proportion of patients on IMV or IMV and ECMO remained above 78%. Most patients were men (65%); the most common age group was 60-70 years. Twenty-three patients received ECMO, and eighteen were cannulated at another center and transferred to HUVH. At the end of the study, fourteen patients were successfully decannulated, three patients died, and the rest of the patients were still on ECMO. Eight pregnant women have been treated in the ICU, with a survival rate of 100%. The ICU mortality of patients younger than 60 years was 3.2%. The mean ICU stay of both survivors and nonsurvivors was 14 days. CONCLUSION: The adequate preparation for resource expansion for critically ill patients care, main challenges, and overall positive results can serve as a precedent for similar future scenarios.

Evaluation and treatment of thoracic outlet syndrome during the global pandemic due to SARS-CoV-2 and COVID-19.

The global SARS-CoV-2/COVID-19 pandemic has required a reduction in nonemergency treatment for a variety of disorders. This report summarizes conclusions of an international multidisciplinary consensus group assembled to address evaluation and treatment of patients with thoracic outlet syndrome (TOS), a group of conditions characterized by extrinsic compression of the neurovascular structures serving the upper extremity. The following recommendations were developed in relation to the three defined types of TOS (neurogenic, venous, and arterial) and three phases of pandemic response (preparatory, urgent with limited resources, and emergency with complete diversion of resources). • In-person evaluation and treatment for neurogenic TOS (interventional or surgical) are generally postponed during all pandemic phases, with telephone/telemedicine visits and at-home physical therapy exercises recommended when feasible. • Venous TOS presenting with acute upper extremity deep venous thrombosis (Paget-Schroetter syndrome) is managed primarily with anticoagulation, with percutaneous interventions for venous TOS (thrombolysis) considered in early phases (I and II) and surgical treatment delayed until pandemic conditions resolve. Catheter-based interventions may also be considered for selected patients with central subclavian vein obstruction and threatened hemodialysis access in all pandemic phases, with definitive surgical treatment postponed. • Evaluation and surgical treatment for arterial TOS should be reserved for limb-threatening situations, such as acute upper extremity ischemia or acute digital embolization, in all phases of pandemic response. In late pandemic phases, surgery should be restricted to thrombolysis or brachial artery thromboembolectomy, with more definitive treatment delayed until pandemic conditions resolve.

Effect of delay in diagnosis on transmission of COVID-19.

The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities of China is a growing global concern. Delay in diagnosis and limited hospital resources lead to a rapid spread of COVID-19. In this study, we investigate the effect of delay in diagnosis on the disease transmission with a new formulated dynamic model. Sensitivity analyses and numerical simulations reveal that, improving the proportion of timely diagnosis and shortening the waiting time for diagnosis can not eliminate COVID-19 but can effectively decrease the basic reproduction number, significantly reduce the transmission risk, and effectively prevent the endemic of COVID-19, e.g., shorten the peak time and reduce the peak value of new confirmed cases and new infection, decrease the cumulative number of confirmed cases and total infection. More rigorous prevention measures and better treatment of patients are needed to control its further spread, e.g., increasing available hospital beds, shortening the period from symptom onset to isolation of patients, quarantining and isolating the suspected cases as well as all confirmed patients.