Projecting hospital utilization during the COVID-19 outbreaks in the United States

How has the COVID-19 pandemic changed treatment preferences of patients with proximal ureteral stones?

Background

The impact of the coronavirus disease 2019 (COVID-19) pandemic on patient decision making remains uncertain. This study aimed to investigate the effect of the pandemic on treatment preferences of patients with proximal ureteral stones.

Materials and methods

Retrospective data regarding treatment preferences of patients diagnosed with symptomatic proximal ureteral stones between July 2018 and November 2021 at a single center were analyzed. Data from 493 patients were analyzed according to 2 groups, including patients diagnosed during the COVID-19 pandemic and those diagnosed during an equivalent period of time before the pandemic.

Results

Preference for conservative treatment increased during the COVID-19 pandemic (p = 0.009). In patients who had previously undergone shock wave lithotripsy (SWL), the preference for SWL decreased and the preference for conservative treatment increased during the COVID-19 pandemic (p = 0.042). Multiple logistic regression analysis revealed a significant correlation between a preference for conservative treatment during the pandemic and no prior spontaneous stone passage (p = 0.003; odds ratio [OR], 2.48; 95% confidence interval [CI], 1.45-4.23), no hydronephrosis (p = 0.035; OR, 3.57; 95% CI, 1.34-9.49), and a visual analog scale score of 4 or less (p = 0.018; OR, 1.97; 95% CI, 1.15-3.38).

Conclusions

A significant increase in the preference for conservative treatment was observed among patients diagnosed during the pandemic, and patients with a history of SWL demonstrated a preference shift from SWL to conservative treatment.

The impact of a positive COVID-19 test on timeliness of radiation in patients receiving brachytherapy

BACKGROUND

Delays in initiating and completing brachytherapy may have adverse oncologic outcomes for patients with cervical, uterine, and prostate cancer. The impact of the COVID-19 pandemic on brachytherapy in the United States has not been well-characterized.

OBJECTIVES

We aim to evaluate how a positive COVID-19 test affected timeliness of treatment for patients undergoing brachytherapy for cervical, uterine, and prostate cancer.

METHODS

We queried the National Cancer Database to identify patients diagnosed with cervical, uterine, and prostate cancer in 2019 and 2020 who received brachytherapy in their treatment. Patients who tested positive for COVID-19 between cancer diagnosis and start of radiation were compared to those who did not test positive for COVID-19. Time in days from cancer diagnosis to initiation of radiation was compared using two-sample t-tests with p < 0.05 signifying significant differences.

RESULTS

We identified 38,341 patients with cervical (n = 6,925), uterine (n = 18,587), and prostate cancer (n = 12,829). Rates of COVID-19 positivity were cervical cancer (n = 135; 2%), uterine cancer (n = 236; 1.3%), and prostate cancer (n = 141; 1%). Of those, 35% of cervical, 49% of uterine, and 43% of prostate cancer patients tested positive between their cancer diagnosis and initiation of radiation. Median days to radiation was significantly longer in these patients: 78 versus 51 for cervical cancer (p < 0.01), 150 versus 104 for uterine cancer (p < 0.01), and 154 versus 124 for prostate cancer (p < 0.01).

CONCLUSIONS

For patients with cervical, uterine, and prostate cancer diagnosed between 2019-2020, testing positive for COVID-19 after their cancer diagnosis was associated with a delay to initiation of radiation by 4-7 weeks.

Implementation of community health care services to counter the SARS-CoV2 pandemic

BACKGROUND

The COVID-19 pandemic has ravaged many countries worldwide since December 2019. The high infection rates, and the need for health care assistance for individuals with comorbidities, strained the national health care systems around the world. Outbreak peaks increased the burden on hospitals that where perceived as high-risk places by people, who often decided to cancel or defer hospital visits. Thus, Italian Local Health Authorities had to develop new organizational models to meet the increased health care needs of the population. The aim of this study is to assess the impact of strengthened community health services on the hospital burden.

METHODS

We analysed the number of Emergency Department access at the Hospital De Lellis covered by the Local Health Authority in Rieti, from March 2020 to November 2021. We then assessed the effects of community health services: the Special District Continuing Care Units (SDCUs) and the the COVID hub, on the COVID-19-related ED access, admission and mortality rates. A Chi-squared test for trend and three multivariable logistic regression models were used to investigate the trends and the possible predictors of COVID ED access, COVID hospital admissions, and deaths.

RESULTS

Being male (OR = 1.41, CI95% 1.05-1.90; p = 0.022) and older age (OR = 1.03, CI95% 1.02-1.04; p < 0.0001) increase the likelihood of hospitalisation for Sars-CoV-2. The implementation of the nursing and medical SDCUs contributed to reducing COVID-19-related deaths (OR = 0.09, CI95% 0.03-0.29; p < 0.0001). The simultaneous implementation of the COVID hub and of the nursing SDCUs had a synergistic effect in reducing the likelihood of hospitalisation (OR = 0.24, CI95% 0.09-0.65; p = 0.005). The subsequent implementation of the medical SDCUS has further contributed to lowering the admission rates. These protective effects persisted also after potential cofounders, such as age, sex, clinical condition on admission, and the immunisation status, were adjusted.

CONCLUSIONS

These measures have helped in the management of patients in a complex context such as that of a pandemic by reducing the hospital load and playing an important role in the management of the pandemic. Further studies could assess the transferability of this model in a non-pandemic context.

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters.

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19

The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden-Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84-0.94 for the raw data and ρ = 0.91-0.98 for smoothed 7-day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real-time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

Covid19Vaxplorer: a free, online, user-friendly COVID-19 Vaccine Allocation Comparison Tool

Background

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison.

Methods

We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison.

Results

We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters. Covid19Vaxplorer allows users in 183 regions in the world to compare several vaccination strategies simultaneously, adjusting parameters to their local epidemics, infrastructure and logistics. Covid19Vaxplorer is an online, free, user-friendly tool that facilitates evidence-based decision making for vaccine distribution.

A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.

Use of Community SARS-CoV-2 Case Counts and Instantaneous Reproductive Number to Predict Short-Term COVID-19 Hospital Admission Volumes

The severe acute respiratory syndrome (SARS-CoV-2) pandemic and high hospitalization rates placed a tremendous strain on hospital resources, necessitating the use of models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV-2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV-2 in New York, New York (October 2020-April 2021) for its accuracy in predicting numbers of coronavirus disease 2019 (COVID-19) admissions 3, 5, 7, and 10 days into the future, comparing predicted admissions with actual admissions for each day at a large integrated health-care delivery network. The mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for 5-day predictions, 12.4%-13.2% for 7-day predictions, and 17.1%-17.8% for 10-day predictions).

Impact of the COVID-19 pandemic on non-COVID-19 community-acquired pneumonia: a retrospective cohort study

BACKGROUND

The COVID-19 pandemic could impact frequency and mortality of non-COVID-19 community-acquired pneumonia (CAP). Changes in frequency, patient mix, treatment and organ dysfunction could cascade together to increase mortality of CAP during compared with pre-COVID-19.

METHODS

Hospitalised CAP patients at St. Paul's Hospital, Vancouver, Canada pre-COVID-19 (fiscal years 2018/2019 and 2019/2020) and during COVID-19 pandemic (2020/2021 and 2021/2022) were evaluated.

RESULTS

In 5219 CAP patients, there was no significant difference prepandemic versus during pandemic in mean age, gender and Charlson Comorbidity Score. However, hospital mortality increased significantly from pre-COVID-19 versus during COVID-19 (7.5% vs 12.1% respectively, (95% CI for difference: 3.0% to 6.3%), p<0.001), a 61% relative increase, coincident with increases in ICU admission (18.3% vs 25.5%, respectively, (95% CI for difference: 5.0% to 9.5%) p<0.001, 39% relative increase) and ventilation (12.7% vs 17.5%, respectively, (95% CI for difference: 2.8% to 6.7%) p<0.001, 38% relative increase). Results remained the same after regression adjustment for age, sex and Charlson score. CAP hospital admissions decreased 27% from pre-COVID-19 (n=1349 and 1433, 2018/2019 and 2019/2020, respectively) versus the first COVID-19 pandemic year (n=1047 in 2020/2021) then rose to prepandemic number (n=1390 in 2021/2022). During prepandemic years, CAP admissions peaked in winter; during COVID-19, the CAP admissions peaked every 6 months.

CONCLUSIONS AND RELEVANCE

This is the first study to show that the COVID-19 pandemic was associated with increases in hospital mortality, ICU admission and invasive mechanical ventilation rates of non-COVID-19 CAP and a transient, 1-year frequency decrease. There was no winter seasonality of CAP during the COVID-19 pandemic era. These novel findings could be used to guide future pandemic planning for CAP hospital care.

Excess mortality during the Coronavirus disease pandemic in Korea

BACKGROUND

Although the ongoing epidemics of Coronavirus disease 2019 (COVID-19) may have affected the mortality trend of the nation, the national level assessment of excess mortality (changes in overall mortality in the entire population) is still scarce in Korea. Therefore, this study evaluated the excess mortality during the COVID-19 pandemic in Korea using the certified mortality data.

METHODS

Monthly mortality and population data from January 2013 to June 2022 was obtained from the National Health Insurance Service database and Statistics Korea. A quasi-Poisson interrupted time-series model adjusted for age structure, population, seasonality, and long-term trends was used to estimate the counterfactual projections (expected) of mortality during the COVID-19 pandemic (March 2020 to June 2022). The absolute difference (observed-expected) and ratio (observed / expected) of mortality were calculated. Stratified analysis based on pandemic years (years 2020, 2021, and 2022), sex, and age groups (aged 0-4, 5-19, 20-64, and ≥ 65 years) were conducted.

RESULTS

An 8.7% increase in mortality was observed during the COVID-19 pandemic [absolute difference: 61,277 persons; ratio (95% confidence interval (CI)): 1.087 (1.066, 1.107)]. The gap between observed and estimated mortality became wider with continuation of the pandemic [ratio (95% CI), year 2020: 1.021 (1.003, 1.040); year 2021: 1.060 (1.039, 1.080), year 2022: 1.244 (1.219, 1.270)]. Although excess mortality across sex was similar, the adult [aged 20-64, ratio (95% CI): 1.059 (1.043, 1.076)] and elderly [aged 65-, ratio (95% CI): 1.098 (1.062, 1.135)] population showed increased excess mortality during the pandemic.

CONCLUSIONS

Despite Korea's successful quarantine policy response, the continued epidemic has led to an excess mortality. The estimated mortality exceeded the number of deaths from COVID-19 infection. Excess mortality should be monitored to estimate the overall impact of the pandemic on a nation.

A Multisite Investigation of Areas for Improvement in COVID-19 Surge Capacity Management

The congressionally authorized National Disaster Medical System Pilot Program was created in December 2019 to strengthen the medical surge capability, capacity, and interoperability of affiliated healthcare facilities in 5 regions across the United States. The COVID-19 pandemic provided an unprecedented opportunity to learn how participating healthcare facilities handled medical surge events during an active public health emergency. We applied a modified version of the Barbisch and Koenig 4-S framework (staff, stuff, space, systems) to analyze COVID-19 surge management practices implemented by healthcare stakeholders at 5 pilot sites. In total, 32 notable practices were identified to increase surge capacity during the COVID-19 pandemic that have potential applications for other healthcare facilities. We found that systems was the most prevalent domain of surge capacity among the identified practices. Systems and staff were discussed across all 5 pilot sites and were the 2 domains co-occurring most often within each surge management practice. These results can inform strategies for scaling up and optimizing medical surge capability, capacity, and interoperability of healthcare facilities nationwide. This study also specifies areas of surge capacity worthy of strategic focus in the pilot's planning and implementation efforts while more broadly informing the US healthcare system's response to future large-scale, medical surge events.

Quality of Life in Healthcare Workers during COVID-19-A Longitudinal Study

The COVID-19 pandemic occurred in 2020, and affected people's daily life worldwide at work and at home. Healthcare workers are a professional group with heavy workloads, and during the COVID-19 pandemic, their burden increased. The literature from earlier outbreaks describes risks for affected mental health in frontline workers, and the main aim of this study is to examine healthcare workers' quality of life during the COVID-19 pandemic. In addition, we sought to assess if there was any difference in working at a pandemic ward compared to anon-pandemic ward. In this longitudinal and descriptive study, a total of 147 healthcare workers assessed their perceived health every third month over one year using the RAND-36 health survey. RAND-36 is a general instrument that consists of 36 questions and is widely used for assessing quality of life. The healthcare workers in this study showed reductions in perceived quality of life during the first six months of the COVID-19 pandemic. Healthcare workers on a pandemic ward reported a lower score in RAND-36 compared to healthcare workers on a non-pandemic ward. Registered nurses and licensed practical nurses seemed more negatively affected in their quality of life than physicians. Compared to data from the general Swedish population, healthcare workers in this study had less energy during this period.

Impact of the COVID-19 pandemic on the education and procedural volume of fellows in critical care medicine - a cross-sectional survey

BACKGROUND

The COVID-19 pandemic has changed the way medical education is delivered. The purpose of this study was to assess the impact of the COVID-19 pandemic on the education and procedural volume of critical care and pulmonary critical care fellows.

METHODS

We conducted a cross-sectional, internet-based, voluntary, anonymous, national survey of adult critical care fellows and academic attending physicians in critical care and pulmonary critical care fellowship programs in the United States between December 2020 and February 2021. Survey questions covered both didactic and non-didactic aspects of education and procedural volumes. Answers were ranked on a 5-point Likert scale. Survey responses were summarized by frequency with percentage. Differences between the responses of fellows and attendings were assessed with the Fisher's exact or Chi-Square test, using Stata 16 software (StataCorp LLC, College Station, TX).

RESULTS

Seventy four individuals responded to the survey; the majority (70.3%) were male; less than one-third (28.4%) female. Respondents were evenly split among fellows (52.7%) and attendings (47.3%). 41.9% of survey respondents were from the authors' home institution, with a response rate of 32.6%. Almost two-thirds (62.2%) reported that fellows spend more time in the ICU since the onset of the pandemic. The majority noted that fellows insert more central venous catheters (52.7%) and arterial lines (58.1%), but perform fewer bronchoscopies (59.5%). The impact on endotracheal intubations was mixed: almost half of respondents (45.9%) reported fewer intubations, about one-third (35.1%) more intubations. Almost all respondents (93.0%) described fewer workshops; and one-third (36.1%) fewer didactic lectures. The majority (71.2%) noted less time available for research and quality improvement projects; half (50.7%) noted less bedside teaching by faculty and more than one-third (37.0%) less fellow interaction with faculty. Almost one-half of respondents (45.2%) reported an increase in fellows' weekly work hours.

CONCLUSION

The pandemic has caused a decrease in scholarly and didactic activities of critical care and pulmonary critical care fellows. Fellows spend more time in ICU rotations, insert more central and arterial lines, but perform fewer intubations and bronchoscopies. This survey provides insights into changes that have occurred in the training of critical care and pulmonary critical care fellows since the onset of the COVID-19 pandemic.

Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study

BACKGROUND

Decision-makers impose COVID-19 mitigations based on public health indicators such as reported cases, which are sensitive to fluctuations in supply and demand for diagnostic testing, and hospital admissions, which lag infections by up to two weeks. Imposing mitigations too early has unnecessary economic costs while imposing too late leads to uncontrolled epidemics with unnecessary cases and deaths. Sentinel surveillance of recently-symptomatic individuals in outpatient testing sites may overcome biases and lags in conventional indicators, but the minimal outpatient sentinel surveillance system needed for reliable trend estimation remains unknown.

METHODS

We used a stochastic, compartmental transmission model to evaluate the performance of various surveillance indicators at reliably triggering an alarm in response to, but not before, a step increase in transmission of SARS-CoV-2. The surveillance indicators included hospital admissions, hospital occupancy, and sentinel cases with varying levels of sampling effort capturing 5, 10, 20, 50, or 100% of incident mild cases. We tested 3 levels of transmission increase, 3 population sizes, and conditions of either simultaneous transmission increase or lagged increase in the older population. We compared the indicators' performance at triggering alarm soon after, but not prior, to the transmission increase.

RESULTS

Compared to surveillance based on hospital admissions, outpatient sentinel surveillance that captured at least 20% of incident mild cases could trigger an alarm 2 to 5 days earlier for a mild increase in transmission and 6 days earlier for a moderate or strong increase. Sentinel surveillance triggered fewer false alarms and averted more deaths per day spent in mitigation. When transmission increase in older populations lagged the increase in younger populations by 14 days, sentinel surveillance extended its lead time over hospital admissions by an additional 2 days.

CONCLUSIONS

Sentinel surveillance of mild symptomatic cases can provide more timely and reliable information on changes in transmission to inform decision-makers in an epidemic like COVID-19.

Health Care Logistics Network Design and Analysis on Pandemic Outbreaks: Insights From COVID-19

Health care systems throughout the world are under pressure as a result of COVID-19. It is over two years since the first case was announced in China and health care providers are continuing to struggle with this fatal infectious disease in intensive care units and inpatient wards. Meanwhile, the burden of postponed routine medical procedures has become greater as the pandemic has progressed. We believe that establishing separate health care institutions for infected and non-infected patients would provide safer and better quality health care services. The aim of this study is to find the appropriate number and location of dedicated health care institutions which would only treat individuals infected by a pandemic during an outbreak. For this purpose, a decision-making framework including two multi-objective mixed-integer programming models is developed. At the strategic level, the locations of designated pandemic hospitals are optimized. At the tactical level, we determine the locations and operation durations of temporary isolation centers which treat mildly and moderately symptomatic patients. The developed framework provides assessments of the distance that infected patients travel, the routine medical services expected to be disrupted, two-way distances between new facilities (designated pandemic hospitals and isolation centers), and the infection risk in the population. To demonstrate the applicability of the suggested models, we perform a case study for the European side of Istanbul. In the base case, seven designated pandemic hospitals and four isolation centers are established. In sensitivity analyses, 23 cases are analyzed and compared to provide support to decision makers.

COVID-19 dynamics in an Ohio prison

Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic preparedness and response. Here, we use mathematical and statistical models to analyze publicly available data on the spread of SARS-CoV-2 reported by the Ohio Department of Rehabilitation and Corrections (ODRC). Results from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among Marion Correctional Institution (MCI) inmates. Extremely rapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within 3 weeks of the first reported inmate case. The dynamical survival analysis (DSA) framework that we use allows the derivation of explicit likelihoods based on mathematical models of transmission. We find that these data are consistent with three non-exclusive possibilities: (i) a basic reproduction number &gt;14 with a single initially infected inmate, (ii) an initial superspreading event resulting in several hundred initially infected inmates with a reproduction number of approximately three, or (iii) earlier undetected circulation of virus among inmates prior to April. All three scenarios attest to the vulnerabilities of prisoners to COVID-19, and the inability to distinguish among these possibilities highlights the need for improved infection surveillance and reporting in prisons.

Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis

INTRODUCTION

Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis.

METHODS

We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1).

RESULTS

The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts.

CONCLUSIONS

The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.

Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models

The variety of diseases is increasing day by day, and the demand for hospitals, especially for emergency and radiology units, is also increasing. As in other units, it is necessary to prepare the radiology unit for the future, to take into account the needs and to plan for the future. Due to the radiation emitted by the devices in the radiology unit, minimizing the time spent by the patients for the radiological image is of vital importance both for the unit staff and the patient. In order to solve the aforementioned problem, in this study, it is desired to estimate the monthly number of images in the radiology unit by using deep learning models and statistical-based models, and thus to be prepared for the future in a more planned way. For prediction processes, both deep learning models such as LSTM, MLP, NNAR and ELM, as well as statistical based prediction models such as ARIMA, SES, TBATS, HOLT and THETAF were used. In order to evaluate the performance of the models, the symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) metrics, which have been in demand recently, were preferred. The results showed that the LSTM model outperformed the deep learning group in estimating the monthly number of radiological case images, while the AUTO.ARIMA model performed better in the statistical-based group. It is believed that the findings obtained will speed up the procedures of the patients who come to the hospital and are referred to the radiology unit, and will facilitate the hospital managers in managing the patient flow more efficiently, increasing both the service quality and patient satisfaction, and making important contributions to the future planning of the hospital.

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.

Simultaneous response to multiple disasters: Integrated planning for pandemics and large-scale earthquakes

Since the beginning of COVID-19, individuals who have SARS-CoV-2 infectious have brought a heavy burden on the healthcare system. Unavoidably, along with pandemics, large-scale disasters, which are possibly emerging, may double the current health crisis. For a powerful disaster response plan, the health services should be prepared for the overwhelming number of disaster victims and infected individuals The proposed framework determines the appropriate number and location of temporary healthcare facilities for large-scale disasters while considering the burden of ongoing pandemic diseases. In this study, first, a multi-period, mix-integer mathematical model is developed to find the location and number of disaster emergency units and disaster medical facilities. Second, we develop an epidemic compartmental model to stimulate the negative effects of the disaster on disease spread and a mixed-integer mathematical model to find optimal number and the location of pandemic hospitals and isolation centers. To validate the mathematical models, a case study is conducted for a district of Istanbul, Turkey.

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

With the global spread of various human-to-human epidemics, public health issues have become a focus of attention. Therefore, it is of great importance to improve the quantitative risk assessment of the construction of resilient cities in terms of epidemic disasters. Starting with the dimensions of social activities and material space, this paper took Qingdao, China, with a population of 5 million, as an example, and took its seven municipal districts as the research scope. In this paper, five risk factors, including the Population density index, Night light index, Closeness index of roads, Betweenness index of roads and Functional mixed nuclear density index were selected for weighted superposition analysis. We conducted a quantitative assessment of the spatial risk of epidemic disaster so as to obtain the classification and spatial structure of the epidemic disaster risk intensity. The results show that: ① The roads with a large traffic flow are most likely to lead to the risk of urban spatial agglomeration, and the areas with a large population density and large mixture of infrastructure functions are also important factors causing the risk of epidemic agglomeration. ② The analysis results regarding the population, commerce, public services, transportation, residence, industry, green space and other functional places can reflect the high-risk areas for epidemic diseases with different natures of transmission. ③ The risk intensity of epidemic disasters is divided into five risk grade areas. Among them, the spatial structure of epidemic disasters, composed of the first-level risk areas, is characterized by "one main area, four secondary areas, one belt and multiple points" and has the characteristics of spatial diffusion. ④ Catering, shopping, life services, hospitals, schools and transportation functional places are more likely to cause crowd gathering. The management of these places should be focused on prevention and control. At the same time, medical facilities should be established at fixed points in all high-risk areas to ensure the full coverage of services. In general, the quantitative assessment of the spatial risk of major epidemic disasters improves the disaster risk assessment system in the construction of resilient cities. It also focuses on risk assessment for public health events. It is helpful to accurately locate the agglomeration risk areas and epidemic transmission paths that are prone to outbreak or cause epidemic transmission in cities so as to assist the relevant practitioners in containing the epidemic from the initial stage of transmission in a timely manner and prevent the further spread of the epidemic.

Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference

BACKGROUND

One of the main challenges of the COVID-19 pandemic is to make sense of available, but often heterogeneous and noisy data. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain from summer 2020 to summer 2021.

METHODS

We use data on new daily cases and hospitalizations reported by the Spanish Ministry of Health to implement a Bayesian inference method that allows making short-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country.

RESULTS

We show how to use the temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0.090 [0.086-0.094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3.5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities.

CONCLUSIONS

We observe important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status, and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.

Generating simple classification rules to predict local surges in COVID-19 hospitalizations

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Cultivating patient-centered care competence through a telemedicine-based course: An explorative study of undergraduate medical students' self-reflective writing

Background

The COVID-19 pandemic has encouraged adaptations of learning methods in clinical clerkship. There have been limited reports on the merits of involving medical students in telemedicine. This study, therefore, aims to investigate students' reflection on what they learned and identify the challenges and benefits of doctor-patient interaction through their experience in a telemedicine-based course.

Methods

A 4 week telemedicine-based course for medical students to participate in telemonitoring of COVID-19 patients undergoing self-isolation was conducted. This is a qualitative study using an interpretive phenomenology design to investigate students' self-reflection on their experiences in monitoring COVID-19 patients. Students were asked to reflect on their experience upon completion of the course through 750-1,000 words essays. A thematic analysis which considers units of meaning based on students' experiences was completed.

Results

Our study identified four main themes gathered from students' experiences related to the telemedicine-based course: communication and education, professionalism and professional identity formation, system-based practice, and patient-centered care.

Conclusion

The course was part of an integrative effort involving multiple parties to tackle the burden on the nation's healthcare system during the pandemic. Telemedicine is part of future medical practice which supports the medical curriculum adaptability along with attempts to develop future-proof medical doctors through various clinical learning experiences.

Clinical Risk Factors of Need for Intensive Care Unit Admission of COVID-19 Patients; a Cross-sectional Study

Introduction

It could be beneficial to accelerate the hospitalization of patients with the identified clinical risk factors of intensive care unit (ICU) admission, in order to control and reduce COVID-19-related mortality. This study aimed to determine the clinical risk factors associated with ICU hospitalization of COVID-19 patients.

Methods

The current research was a cross-sectional study. The study recruited 7182 patients who had positive PCR tests between February 23, 2020, and September 7, 2021 and were admitted to Afzalipour Hospital in Kerman, Iran, for at least 24 hours. Their demographic characteristics, underlying diseases, and clinical parameters were collected. In order to analyze the relationship between the studied variables and ICU admission, multiple logistic regression model, classification tree, and support vector machine were used.

Results

It was found that 14.7 percent (1056 patients) of the study participants were admitted to ICU. The patients' average age was 51.25±21 years, and 52.8% of them were male. In the study, some factors such as decreasing oxygen saturation level (OR=0.954, 95%CI: 0.944-0.964), age (OR=1.007, 95%CI: 1.004-1.011), respiratory distress (OR=1.658, 95%CI: 1.410-1.951), reduced level of consciousness (OR=2.487, 95%CI: 1.721-3.596), hypertension (OR=1.249, 95%CI: 1.042-1.496), chronic pulmonary disease (OR=1.250, 95%CI: 1.006-1.554), heart diseases (OR=1.250, 95%CI: 1.009-1.548), chronic kidney disease (OR=1.515, 95%CI: 1.111-2.066), cancer (OR=1.682, 95%CI: 1.130-2.505), seizures (OR=3.428, 95%CI: 1.615-7.274), and gender (OR=1.179, 95%CI: 1.028-1.352) were found to significantly affect ICU admissions.

Conclusions

As evidenced by the obtained results, blood oxygen saturation level, the patient's age, and their level of consciousness are crucial for ICU admission.

"Predictors of in-hospital mortality in adult cancer patients with COVID-19 infection presenting to the emergency department: A retrospective study"

BACKGROUND

Adult cancer patients are at higher risk of morbidity and mortality following COVID-19 infection. Being on the front lines, it is crucial for emergency physicians to identify those who are at higher risk of mortality. The aim of our study was to determine the predictors of in-hospital mortality in COVID-19 positive cancer patients who present to the emergency department.

METHODS

This is a retrospective cohort study conducted on adult cancer patients who presented to the ED of the American university of Beirut medical center from February 21, 2020, till February 21, 2021, and were found to have COVID-19 infection. Relevant data was extracted and analyzed. The association between different variables and in-hospital mortality was tested using Student's t test and Fisher's exact test or Pearson's Chi-square where appropriate. Logistic regression was applied to factors with p <0.2 in the univariate models.

RESULTS

The study included 89 distinct patients with an average age of 66 years (± 13.6). More than half of them were smokers (52.8%) and had received chemotherapy within 1 month of presentation (52.8%). About one third of the patients died (n = 31, 34.8%). Mortality was significantly higher in patients who had recently received chemotherapy (67.7% vs 44.8%, p = .039), a history of congestive heart failure (CHF)(p = .04), higher levels of CRP (p = 0.048) and/or PCT(p<0.04) or were tachypneic in the ED (P = 0.016).

CONCLUSIONS

Adult cancer patients with COVID-19 infection are at higher risks of mortality if they presented with tachypnea, had a recent chemotherapy, history of CHF, high CRP, and high procalcitonin levels at presentation.

Implications of the pandemic for the construction of nurses' identity based on the journalistic media

OBJECTIVES

to analyze the work of nurses portrayed in the journalistic media and its impact on the construction of professional nursing identity.

METHODS

this is qualitative, retrospective, descriptive and documentary research, with 51 reports from Folha de São Paulo. Time frame from March to December 2020. Thematic Content Analysis carried out from Claude Dubar's theoretical perspective. Organization and coding of data performed with the help of ATLAS.ti®.

RESULTS

three categories emerged: Working conditions in the pandemic - a problem that worsened; Impacts of the pandemic on daily work; Feelings generated by the pandemic.

CONCLUSIONS

despite adversities, such as the precariousness of health institutions, inadequate working conditions for nurses, lack of basic items of individual protection, negative feelings and hopelessness, these professionals used their knowledge, skills and innovations in the act of caring, which contributed to reconstructing their professional identity.

Effect of Comorbidities on the Infection Rate and Severity of COVID-19: Nationwide Cohort Study With Propensity Score Matching

BACKGROUND

A vaccine against COVID-19 has been developed; however, COVID-19 transmission continues. Although there have been many studies of comorbidities that have important roles in COVID-19, some studies have reported contradictory results.

OBJECTIVE

This study was conducted using real-world data from COVID-19 patients in South Korea and aimed to investigate the impact of patient demographics and comorbidities on the infection rate and severity of COVID-19.

METHODS

Data were derived from a nationwide South Korean COVID-19 cohort study with propensity score (PS) matching. We included infected individuals who were COVID-19-positive between January 1, 2020, and May 30, 2020, and PS-matched uninfected controls. PS matching was performed to balance the baseline characteristics of each comorbidity and to adjust for potential confounders, such as age, sex, Charlson Comorbidity Index, medication, and other comorbidities, that were matched with binary variables. The outcomes were the confirmed comorbidities affecting the infection rate and severity of COVID-19. The endpoints were COVID-19 positivity and severe clinical outcomes of COVID-19 (such as tracheostomy, continuous renal replacement therapy, intensive care unit admission, ventilator use, cardiopulmonary resuscitation, and death).

RESULTS

The COVID-19 cohort with PS matching included 8070 individuals with positive COVID-19 test results and 8070 matched controls. The proportions of patients in the severe group were higher for individuals 60 years or older (severe clinical outcomes for those 60 years or older, 16.52%; severe clinical outcomes for those of other ages, 2.12%), those insured with Medicaid (Medicaid, 10.81%; other insurance, 5.61%), and those with disabilities (with disabilities, 18.26%; without disabilities, 5.07%). The COVID-19 infection rate was high for patients with pulmonary disease (odds ratio [OR] 1.88; 95% CI 1.70-2.03), dementia (OR 1.75; 95% CI 1.40-2.20), gastrointestinal disease (OR 1.74; 95% CI 1.62-1.88), stroke (OR 1.67; 95% CI 1.23-2.27), hepatobiliary disease (OR 1.31; 95% CI 1.19-1.44), diabetes mellitus (OR 1.28; 95% CI 1.16-1.43), and cardiovascular disease (OR 1.20; 95% CI 1.07-1.35). In contrast, it was lower for individuals with hyperlipidemia (OR 0.73; 95% CI 0.67-0.80), autoimmune disease (OR 0.73; 95% CI 0.60-0.89), and cancer (OR 0.73; 95% CI 0.62-0.86). The severity of COVID-19 was high for individuals with kidney disease (OR 5.59; 95% CI 2.48-12.63), hypertension (OR 2.92; 95% CI 1.91-4.47), dementia (OR 2.92; 95% CI 1.91-4.47), cancer (OR 1.84; 95% CI 1.15-2.94), pulmonary disease (OR 1.72; 95% CI 1.35-2.19), cardiovascular disease (OR 1.54; 95% CI 1.17-2.04), diabetes mellitus (OR 1.43; 95% CI 1.09-1.87), and psychotic disorders (OR 1.29; 95% CI 1.01-6.52). However, it was low for those with hyperlipidemia (OR 0.78; 95% CI 0.60-1.00).

CONCLUSIONS

Upon PS matching considering the use of statins, it was concluded that people with hyperlipidemia could have lower infection rates and disease severity of COVID-19.

Injury mortality and morbidity changes due to the COVID-19 pandemic in the United States

Introduction

The COVID-19 pandemic significantly changed society. We aimed to examine the systematic impact of the COVID-19 on injury burden in the United States.

Methods

We extracted mortality and morbidity data from CDC WONDER and WISQARS. We estimated age-standardized injury mortality rate ratio and morbidity rate ratio (MtRR and MbRR) with 95% confidence interval (95% CI) for all injuries, all unintentional injuries, homicide/assault by all methods, suicide/self-harm by all methods, as well as other 11 specific unintentional or intentional injury categories. Injury rate ratios were compared for 2020 vs. 2019 to those of 2019 vs. 2018 to demonstrate the influence of the COVID-19 pandemic on fatal and nonfatal injury burden. The ratio of MtRRs (RMtRR) and the ratio of MbRRs (RMbRR) with 95% CI between 2020 vs. 2019 and 2019 vs. 2018 were calculated separately.

Results

The COVID-19 pandemic was associated with an increase in injury mortality (RMtRR = 1.12, 95% CI: 1.11, 1.13) but injury morbidity decreased (RMbRR = 0.88, 95% CI: 0.88, 0.89) when the changes of these rates from 2019 to 2020 were compared to those from 2018 to 2019. Mortality disparities between the two time periods were primarily driven by greater mortality during the COVID-influenced 2020 vs. 2019 from road traffic crashes (particularly motorcyclist mortality), drug poisoning, and homicide by firearm. Similar patterns were not present from 2019 vs. 2018. There were morbidity reductions from road traffic crashes (particularly occupant and pedestrian morbidity from motor vehicle crashes), unintentional falls, and self-harm by suffocation from 2019 to 2020 compared to the previous period. Change patterns in sexes and age groups were generally similar, but exceptions were observed for some injury types.

Conclusions

The COVID-19 pandemic significantly changed specific injury burden in the United States. Some discrepancies also existed across sex and age groups, meriting attention of injury researchers and policymakers to tailor injury prevention strategies to particular populations and the environmental contexts citizens face.

Leveraging network structure to improve pooled testing efficiency

Screening is a powerful tool for infection control, allowing for infectious individuals, whether they be symptomatic or asymptomatic, to be identified and isolated. The resource burden of regular and comprehensive screening can often be prohibitive, however. One such measure to address this is pooled testing, whereby groups of individuals are each given a composite test; should a group receive a positive diagnostic test result, those comprising the group are then tested individually. Infectious disease is spread through a transmission network, and this paper shows how assigning individuals to pools based on this underlying network can improve the efficiency of the pooled testing strategy, thereby reducing the resource burden. We designed a simulated annealing algorithm to improve the pooled testing efficiency as measured by the ratio of the expected number of correct classifications to the expected number of tests performed. We then evaluated our approach using an agent-based model designed to simulate the spread of SARS-CoV-2 in a school setting. Our results suggest that our approach can decrease the number of tests required to regularly screen the student body, and that these reductions are quite robust to assigning pools based on partially observed or noisy versions of the network.

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator

BACKGROUND

The Severe Acute Respiratory Syndrome (SARS)-Coronavirus 2 (CoV-2) pandemic pressure on healthcare systems can exhaust ventilator resources, especially where resources are restricted. Our objective was a rapid preclinical evaluation of a newly developed turbine-based ventilator, named the ACUTE-19, for invasive ventilation.

METHODS

Validation consisted of (a) testing tidal volume (V_T) delivery in 11 simulated models, with various resistances and compliances; (b) comparison with a commercial ventilator (VIVO-50) adapting the United Kingdom Medicines and Healthcare products Regulatory Agency-recommendations for rapidly manufactured ventilators; and (c) in vivo testing in a sheep before and after inducing acute respiratory distress syndrome (ARDS) by saline lavage.

RESULTS

Differences in V_T in the simulated models were marginally different (largest difference 33ml [95%-confidence interval (CI) 31-36]; P<.001ml). Plateau pressure (P_plat) was not different (-0.3cmH₂O [95%-CI -0.9 to 0.3]; P=.409), and positive end-expiratory pressure (PEEP) was marginally different (0.3 cmH₂O [95%-CI 0.2 to 0.3]; P<.001) between the ACUTE-19 and the commercial ventilator. Bland-Altman analyses showed good agreement (mean bias, -0.29, [limits of agreement, 0.82 to -1.42], and mean bias 0.56 [limits of agreement, 1.94 to -0.81], at a P_plat of 15 and 30cmH₂O, respectively). The ACUTE-19 achieved optimal oxygenation and ventilation before and after ARDS induction.

CONCLUSIONS

The ACUTE-19 performed accurately in simulated and animal models yielding a comparable performance with a VIVO-50 commercial device. The acute 19 can provide the basis for the development of a future affordable commercial ventilator.

The Effect of COVID-19 on United States Pediatric Emergency Departments and Its Impact on Trainees

Introduction: The purpose of this study was to quantify the effects of the coronavirus disease 2019 (COVID-19) pandemic on pediatric emergency departments (PED) across the United States (US), specifically its impact on trainee clinical education as well as patient volume, admission rates, and staffing models. Methods: We conducted a cross-sectional study of US PEDs, targeting PED clinical leaders via a web-based questionnaire. The survey was sent via three national pediatric emergency medicine distribution lists, with several follow-up reminders. Results: There were 46 questionnaires included, completed by PED directors from 25 states. Forty-two sites provided PED volume and admission data for the early pandemic (March-July 2020) and a pre-pandemic comparison period (March-July 2019). Mean PED volume decreased >32% for each studied month, with a maximum mean reduction of 63.6% (April 2020). Mean percentage of pediatric admissions over baseline also peaked in April 2020 at 38.5% and remained 16.4% above baseline by July 2020. During the study period, 33 (71.1%) sites had decreased clinician staffing at some point. Only three sites (6.7%) reported decreased faculty protected time. All PEDs reported staffing changes, including decreased mid-level use, increased on-call staff, movement of staff between the PED and other units, and added tele-visit shifts. Twenty-six sites (56.5%) raised their patient age cutoff; median was 25 years (interquartile ratio 25-28). Of 44 sites hosting medical trainees, 37 (84.1%) reported a decrease in number of trainees or elimination altogether. Thirty (68.2%) sites had restrictions on patient care provision by trainees: 28 (63.6%) affected medical students, 12 (27.3%) affected residents, and two (4.5%) impacted fellows. Fifteen sites (34.1%) had restrictions on procedures performed by medical students (29.5%), residents (20.5%), or fellows (4.5%). Conclusion: This study highlights the marked impact of the COVID-19 pandemic on US PEDs, noting decreased patient volumes, increased admission rates, and alterations in staffing models. During the early pandemic, educational restrictions for trainees in the PED setting disproportionately affected medical students over residents, with fellows’ experience largely preserved. Our findings quantify the magnitude of these impacts on trainee pediatric clinical exposure during this period.

An opinion on Wastewater-Based Epidemiological Monitoring (WBEM) with Clinical Diagnostic Test (CDT) for detecting high-prevalence areas of community COVID-19 Infections

Wastewater-Based Epidemiological Monitoring (WBEM) is an efficient surveillance tool during the COVID-19 pandemic as it meets all requirements of a complete monitoring system including early warning, tracking the current trend, prevalence of the disease, detection of genetic diversity as well asthe up-surging SARS-CoV-2 new variants with mutations from the wastewater samples. Subsequently, Clinical Diagnostic Test is widely acknowledged as the global gold standard method for disease monitoring, despite several drawbacks such as high diagnosis cost, reporting bias, and the difficulty of tracking asymptomatic patients (silent spreaders of the COVID-19 infection who manifest nosymptoms of the disease). In this current reviewand opinion-based study, we first propose a combined approach) for detecting COVID-19 infection in communities using wastewater and clinical sample testing, which may be feasible and effective as an emerging public health tool for the long-term nationwide surveillance system. The viral concentrations in wastewater samples can be used as indicatorsto monitor ongoing SARS-CoV-2 trends, predict asymptomatic carriers, and detect COVID-19 hotspot areas, while clinical sampleshelp in detecting mostlysymptomaticindividuals for isolating positive cases in communities and validate WBEM protocol for mass vaccination including booster doses for COVID-19.

Priming close social contact protective behaviors enhances protective social norms perceptions, protection views, and self-protective behaviors during disasters

Many people do not make choices that minimize risk in the face of health and environmental threats. Using pre-registered analyses, we tested whether a risk communication that primed perceptions about health-protective preparation and behavior of close social contacts promoted protection views and protective behaviors. From December 10-24, 2020, we fielded a 2 (threat vignette: wildfire or COVID-19) x 3 (social contact prime: control, inaction, or action) experiment to a representative sample of 1,108 California residents facing increased COVID-19 cases/deaths, who had recently experienced the most destructive wildfire season in California history. Outcome variables were protection views and protective behavior (i.e., information seeking). Across threat conditions, stronger social norms, efficacy, and worry predicted greater protection views and some protective behaviors. Priming social-contact action resulted in greater COVID-19 information-seeking compared to the control. In the wildfire smoke condition, priming social contact action and inaction increased perceived protective behavior social norms compared to the control; social norms partially mediated the relationships of priming with protection views and protective behaviors; and having existing mask supplies enhanced the relationship between priming inaction and greater protection views compared to priming action or the control. Findings highlight the importance of social influence for health protection views and protective behaviors. Communications enhancing social norms that are sensitive to resource contexts may help promote protective behaviors.

Length of hospital stay and survival of hospitalized COVID-19 patients during the second wave of the pandemic: A single centre retrospective study from Slovenia

Background

As of writing, there are no publications pertaining to the prediction of COVID-19-related outcomes and length of stay in patients from Slovene hospitals.

Objectives

To evaluate the length of regular ward and ICU stays and assess the survival of COVID-19 patients to develop better prediction models to forecast hospital capacity and staffing demands in possible further pandemic peaks.

Methods

In this retrospective, single-site study we analysed the length of stay and survival of all patients, hospitalized due to the novel coronavirus (COVID-19) at the peak of the second wave, between November 18th 2020 and January 27th 2021 at the University Clinic Golnik, Slovenia.

Results

Out of 407 included patients, 59% were male. The median length of stay on regular wards was 7.5 (IQR 5–13) days, and the median ICU length of stay was 6 (IQR 4–11) days. Age, male sex, and ICU stay were significantly associated with a higher risk of death. The probability of dying in 21 days at the regular ward was 14.4% (95% CI [10.9–18%]) and at the ICU it was 43.6% (95% CI [19.3-51.8%]).

Conclusion

The survival of COVID-19 is strongly affected by age, sex, and the fact that a patient had to be admitted to ICU, while the length of hospital bed occupancy is very similar across different demographic groups. Knowing the length of stay and admission rate to ICU is important for proper planning of resources during an epidemic.

State-controlled epidemic in a game against a novel pathogen

The pandemic reminded us that the pathogen evolution still has a serious effect on human societies. States, however, can prepare themselves for the emergence of a novel pathogen with unknown characteristics by analysing potential scenarios. Game theory offers such an appropriate tool. In our game-theoretical framework, the state is playing against a pathogen by introducing non-pharmaceutical interventions to fulfil its socio-political goals, such as guaranteeing hospital care to all needed patients, keeping the country functioning, while the applied social restrictions should be as soft as possible. With the inclusion of activity and economic sector dependent transmission rate, optimal control of lockdowns and health care capacity management is calculated. We identify the presence and length of a pre-symptomatic infectious stage of the disease to have the greatest effect on the probability to cause a pandemic. Here we show that contrary to intuition, the state should not strive for the great expansion of its health care capacities even if its goal is to provide care for all requiring it and minimize the cost of lockdowns.

The 1968 Influenza Pandemic and COVID-19 Outcomes

Past pandemic experience can affect health outcomes in future pandemics. This paper focuses on the last major influenza pandemic in 1968 (H3N2), which killed up to 100,000 people in the US. We find that places with high influenza mortality in 1968 experienced 1-4% lower COVID-19 death rates. Our identification strategy isolates variation in COVID-19 rates across people born before and after 1968. In places with high 1968 influenza incidence, older cohorts experience lower COVID-19 death rates relative to younger ones. The relationship holds using county and patient-level data, as well as in hospital and nursing home settings. Results do not appear to be driven by systemic or policy-related factors, instead suggesting an individual-level response to prior influenza pandemic exposure. The findings merit investigation into potential biological and immunological mechanisms that account for these differences-and their implications for future pandemic preparedness.

Urban Spatial Accessibility of Primary Care and Hypertension Control and Awareness on Chicago's South Side: A Study From the COMPASS Cohort

BACKGROUND

Understanding the relationship between hypertension and spatial accessibility of primary care can inform interventions to improve hypertension control and awareness, especially among disadvantaged populations. This study aims to investigate the association between spatial accessibility of primary care and hypertension control and awareness.

METHODS

Participant data from the COMPASS (Chicago Multiethnic Prevention and Surveillance Study) between 2013 and 2019 were analyzed. All participants were geocoded. Locations of primary care providers in Chicago were obtained from MAPSCorps. A score was generated for spatial accessibility of primary care using an enhanced 2-step floating catchment area method. A higher score indicates greater accessibility. Measured hypertension was defined as systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥80 mm Hg. Logistic regression was used to estimate odds ratio and 95% CI for hypertension status in relation to accessibility score quartiles.

RESULTS

Five thousand ninety-six participants (mean age, 53.4±10.8) were included. The study population was predominantly non-Hispanic black (84.0%), over 53% reported an annual household income <$15 000, and 37.3% were obese. Measured hypertension prevalence was 78.7% in this population, among which 37.7% were uncontrolled and 41.0% were unaware. A higher accessibility score was associated with lower measured hypertension prevalence. In fully adjusted models, compared with the first (lowest) quartile of accessibility score, the odds ratio strengthened from 0.82 (95% CI, 0.67-1.01) for the second quartile to 0.75 (95% CI, 0.62-0.91) for the third quartile, and further to 0.73 (95% CI, 0.60-0.89) for the fourth (highest) quartile. The increasing trend had a P<0.01. Similar associations were observed for both uncontrolled and unaware hypertensions. When stratified by neighborhood socioeconomic status, a higher accessibility score was associated with lower rates of unaware hypertension in both disadvantaged and nondisadvantaged neighborhoods.

CONCLUSIONS

Better spatial accessibility of primary care is associated with improved hypertension awareness and control.

COVID-19 differentiated measures for unvaccinated individuals: The need for clear goals and strong justifications

Numerous countries and jurisdictions have implemented differential COVID-19 public health restrictions based on individual vaccination status to mitigate the public health risks posed by unvaccinated individuals. Although it is scientifically and ethically justifiable to introduce such vaccination-based differentiated measures as a risk-based approach to resume high-risk activities in an ongoing pandemic, their justification is weakened by lack of clarity on their intended goals and the specific risks or potential harms they intend to mitigate. Furthermore, the criteria for the removal of differentiated measures may not be clear, which raises the possibility of shifting goalposts without clear justification and with potential for unfairly discriminatory consequences. This paper seeks to clarify the ethical justification of COVID-19 vaccination-based differentiated measures based on a public health risk-based approach, with focus on their deployment in domestic settings. We argue that such measures should be consistent with the principal goal of COVID-19 vaccination programmes, which is to reduce the incidence of severely ill patients and associated healthcare burdens so as to protect a health system. We provide some considerations for the removal of vaccination-based differentiated measures based on this goal.

Major Shifts in Outpatient Cirrhosis Care Delivery Attributable to the COVID-19 Pandemic: A National Cohort Study

The coronavirus disease 2019 (COVID-19) pandemic has disrupted health care delivery in the United States, with increased reliance on telemedicine visits as opposed to in-person outpatient appointments. We used national data to evaluate shifts in modes of hepatology outpatient care for patients with cirrhosis during the pandemic. This was a retrospective cohort study among U.S. veterans with cirrhosis. We used linear regression to evaluate absolute and percentage changes from baseline in hepatology in-person visits and telemedicine visits from January 1, 2020, to August 11, 2020. The proportion of in-person and telemedicine visits were plotted geographically to demonstrate state-level shifts in care delivery over time. Patient-level characteristics in the pre-COVID and during-COVID periods were also compared. We identified 5,618 in-person and 6,210 telemedicine hepatology visits among patients with cirrhosis. In-person visits significantly declined (-16.0% per week; 95% confidence interval [CI] -20.7, -11.2; P < 0.001), while telemedicine visits significantly increased (61.3% per week; 95% CI 45.1, 77.5; P < 0.001) in the early during-COVID period. At the U.S. state level, we found that nearly all states experienced a significant shift toward telemedicine over the course of several weeks. Patients over the age of 70 years and Black patients were less likely to receive telemedicine visits in the pre-COVID period (each P < 0.05), although these differences were eliminated in the during-COVID periods. Conclusion: Among patients with cirrhosis, hepatology outpatient care delivery has shifted heavily toward telemedicine due to COVID-19. This occurred across the United States, and changes have been sustained through August 2020. Expanded telemedicine visits among older patients and Black patients may reflect dedicated efforts to increased access to care among these groups.

A customized early warning score enhanced emergency department patient flow process and clinical outcomes in a COVID‐19 pandemic

Objective

Methods

Results

Conclusion

Evaluation of Perioperative Complications and Mortality in Covid-19 Patients Who Had Emergency Surgery

Objective: The risk for adverse outcomes in COVID-19 patients necessitates further scrutiny in Covid 19 patients in providing appropriate surgical indications and perioperative surgical safety precautions. In this study, we aimed to contribute to elective surgery resumption about infection with early and late postoperative complications and mortality in patients with RT-PCR (+) and clinically suspicious COVID-19 who underwent emergency surgery in our hospital. Methods: A total of 86 patients who have been operated on in our institution for emergency surgery over the age of 18 who were diagnosed with SARS-CoV-2 infection seven days before or 30 days after surgery were enrolled in the study. In this retrospective study, the primary outcome has been established as mortality factors and survival within postoperative 30 days. Results: Regarding the primary outcome as 30-day survival, every 1-year increase in age increased the risk of death by two folds. Patients with one or more comorbidities have an increased risk of death 13 times and those with two or more have an increased risk of death 23 times. Patients in intensive care units increase the risk of death by 8.5 times compared to those who are not hospitalized. On the contrary, an increase in hemoglobin level was shown to reduce the risk of death by 0.8 times. Conclusion: The need for intensive care and mortality is high, especially after emergency surgery, in patients with COVID19 symptoms and more than one comorbidity. Surgical indications of such patients should be well investigated.

SARS-CoV-2 testing strategies for outbreak mitigation in vaccinated populations

Although COVID-19 vaccines are globally available, waning immunity and emerging vaccine-evasive variants of concern have hindered the international response and transition to a post-pandemic era. Testing to identify and isolate infectious individuals remains the most proactive strategy for containing an ongoing COVID-19 outbreak. We developed a stochastic, compartmentalized model to simulate the impact of using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) assays, rapid antigen tests, and vaccinations on SARS-CoV-2 spread. We compare testing strategies across an example high-income country (the United States) and low- and middle-income country (India). We detail the optimal testing frequency and coverage in the US and India to mitigate an emerging outbreak even in a vaccinated population: overall, maximizing testing frequency is most important, but having high testing coverage remains necessary when there is sustained transmission. A resource-limited vaccination strategy still requires high-frequency testing to minimize subsequent outbreaks and is 16.50% more effective in reducing cases in India than the United States. Tailoring testing strategies to transmission settings can help effectively reduce disease burden more than if a uniform approach were employed without regard to epidemiological variability across locations.

Network-Informed Constrained Divisive Pooled Testing Assignments

Frequent universal testing in a finite population is an effective approach to preventing large infectious disease outbreaks. Yet when the target group has many constituents, this strategy can be cost prohibitive. One approach to alleviate the resource burden is to group multiple individual tests into one unit in order to determine if further tests at the individual level are necessary. This approach, referred to as a group testing or pooled testing, has received much attention in finding the minimum cost pooling strategy. Existing approaches, however, assume either independence or very simple dependence structures between individuals. This assumption ignores the fact that in the context of infectious diseases there is an underlying transmission network that connects individuals. We develop a constrained divisive hierarchical clustering algorithm that assigns individuals to pools based on the contact patterns between individuals. In a simulation study based on real networks, we show the benefits of using our proposed approach compared to random assignments even when the network is imperfectly measured and there is a high degree of missingness in the data.

Patient care in rapid-expansion intensive care units during the COVID-19 pandemic crisis

Background

The coronavirus-2019 (COVID-19) pandemic highlighted the unfortunate reality that many hospitals have insufficient intensive care unit (ICU) capacity to meet massive, unanticipated increases in demand. To drastically increase ICU capacity, NewYork-Presbyterian/Weill Cornell Medical Center modified its existing operating rooms and post-anaesthesia care units during the initial expansion phase to accommodate the surge of critically ill patients.

Methods

This retrospective chart review examined patient care in non-standard Expansion ICUs as compared to standard ICUs. We compared clinical data between the two settings to determine whether the expeditious development and deployment of critical care resources during an evolving medical crisis could provide appropriate care.

Results

Sixty-six patients were admitted to Expansion ICUs from March 1^st to April 30^th, 2020 and 343 were admitted to standard ICUs. Most patients were male (70%), White (30%), 45–64 years old (35%), non-smokers (73%), had hypertension (58%), and were hospitalized for a median of 40 days. For patients that died, there was no difference in treatment management, but the Expansion cohort had a higher median ICU length of stay (q = 0.037) and ventilatory length (q = 0.015). The cohorts had similar rates of discharge to home, but the Expansion ICU cohort had higher rates of discharge to a rehabilitation facility and overall lower mortality.

Conclusions

We found no significantly worse outcomes for the Expansion ICU cohort compared to the standard ICU cohort at our institution during the COVID-19 pandemic, which demonstrates the feasibility of providing safe and effective care for patients in an Expansion ICU.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background

The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans.

Goal

This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result.

Methods

With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model.

Findings

We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases).

Discussion

With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area

Accurately predicting the number of severe and critical COVID-19 patients is critical for the treatment and control of the epidemic. Social media data have gained great popularity and widespread application in various research domains. The viral-related infodemic outbreaks have occurred alongside the COVID-19 outbreak. This paper aims to discover trustworthy sources of social media data to improve the prediction performance of severe and critical COVID-19 patients. The innovation of this paper lies in three aspects. First, it builds an improved prediction model based on machine learning. This model helps predict the number of severe and critical COVID-19 patients on a specific urban or regional scale. The effectiveness of the prediction model, shown as accuracy and satisfactory robustness, is verified by a case study of the lockdown in Hubei Province. Second, it finds the transition path of the impact of social media data for predicting the number of severe and critical COVID-19 patients. Third, this paper provides a promising and powerful model for COVID-19 prevention and control. The prediction model can help medical organizations to realize a prediction of COVID-19 severe and critical patients in multi-stage with lead time in specific areas. This model can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data. The model can also facilitate optimal scheduling of medical resources as well as prevention and control policy formulation.