Clustering Countries in the Context of the Pandemic and Underlying Conditions

The COVID-19 pandemic has revealed the state of underlying conditions of countries in terms of health system, sanitary infrastructure, governance, among others. This study aims to classify countries using COVID-19-related variables such as the lethality rate, the contagion growth rate, the stringency index, and underlying conditions of countries directly related to COVID-19 such as access to clean water, hospital beds per 10000 inhabitants, government effectiveness index, population older than 65 years old and economic growth rate. To determine the clusters of a set of countries from all continents (29 from Africa, 35 from Asia, 35 from Europe, 11 from North America, 2 from Oceania and 8 from South America), the k-means partitioning method is used. This approach consists in constructing partitions and evaluate their intra-class and inter-class similarity. Based on the results, three clusters are identified: i. Severely affected countries with high stringency and moderate capacity, ii. Moderately affected countries with moderate stringency and high capacity and iii. Severely affected countries with low stringency but low capacity.

Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

Potential Risk Factors for Length of Hospitalization in COVID-19 Patients: A Cross-sectional Study

Background: Identifying the potential risk factors of the length of stay in hospital (LOSH) in COVID-19 patients could help the health system meet future demand for hospital beds. Objectives: This study aimed to determine the factors affecting the length of stay in hospital in COVID-19 patients in Hamadan, the west of Iran. Methods: This cross-sectional study recruited 512 hospitalized COVID-19 patients in Hamadan city. Demographic, clinical, and medical laboratory characteristics of the patients and their survival status were assessed by a checklist. Univariate and multiple negative binomial regressions were used by Stata 12. Results: The median hospitalization length for COVID-19 patients was five days (range: 0 to 47). In the discharged patients, the adjusted incidence rate ratios (95% CI) of LOSH for females, rural residents, patients with a history of diabetes and cardiovascular disease, SPO2 less than 88%, prothrombin time higher than 13 s, platelet count lower than 130 × 1000 µL, blood sugar higher than 105 mg/dL, and intensive care unit experience were 1.16 (1.03, 1.44), 1.22 (1.03, 1.44), 1.43 (1.07, 1.92), 1.41 (1.23, 1.61), 0.82 (0.71, 0.93), 1.32 (1.11, 1.56), 1.18 (1.03, 1.36), and 1.85 (1.59, 2.17) compared to their references, respectively. Conclusions: Our study added new insight into LOSH determining factors that could be used for future planning in combating the need for hospital beds. The present study revealed that some demographic, social, and clinical variables could increase the IRR of a more extended hospital stay.

Dermatovenerological service of Ukraine in the context of ongoing reform

The problems of preserving the dermatovenerological health of the population are extremely relevant for Ukraine, where diseases of the skin and subcutaneous tissue occupy the 5th place in the structure of morbidity. Objective — study of the structure of the dermatovenerological service in the context of reforming the healthcare system at this stage (end of 2021) and its characteristics. Materials and methods. The results of the analysis of the state of resources of medical institutions of communal ownership of dermatovenerological profile in Ukraine are presented. Results and discussion. Today, in Ukraine, the number of hospital beds for 24-hour hospitals for adults is 188 in the regions (Zaporizhzhia, Rivne, Kharkiv, Cherkasy) and 220 beds in Kyiv city, which is extremely insufficient. There are 24 beds for the pediatric patients in the regions (Luhansk, Rivne, Cherkasy) and in the city of Kyiv — 60. The number of day hospital beds for adults has significantly decreased — to 248 in the regions (Dnipropetrovs’k, Zaporizhzhia, Kyiv, Lviv, Poltava, Kherson, Cherkasy, Chernivtsi and Kramatorsk city), for pediatric patients — 61 (Dnipropetrovs’k, Kropyvnytskyi, Mykolaiv, Poltava regions). In 2021, 11 dermatovenerologic dispensaries were liquidated. Instead of them, dermatovenero­logic centers and subdivisions subordinate to regional hospitals were formed. Thus, there is an unfavorable restructuring of the bed fund of institutions providing dermatovenerological care. Conclusions. A decrease in the number of state and municipal institutions providing free dermatovenerological care will lead to the worsening of the epidemic situation, the spread of sexually transmitted infections and reduction of the availability of specialized dermatovenerological care for the population in a difficult economic and social situation.

SIR Modeling the Dual Disaster Impacts of Omicron B.1.1.529 and Natural Disaster Events on Simulated 6 Months (December 2021 – May 2022) Healthcare System Resiliences in Fragile SE Asia Ring of Fire Ecosystems

For some countries that have experienced numerous natural disasters, including massive earthquakes and tsunamis, managing the COVID-19 pandemic can be very challenging. This situation arises considering that the disaster can directly and indirectly affect the healthcare system’ s capacity to serve the COVID-19 cases. With severely damaged healthcare facilities due to the disaster, there will be severely ill COVID-19 cases unmanaged. The coupling and interplay between these two phenomena can indeed be catastrophic. One of the regions where this issue becomes concerned is in Southeast Asia, where most of the Asian countries lie in the fragile ring of fire ecosystem, contributing to the high tsunami and earthquake disasters in the world. At the same time, Asia is one of the regions that have been severely impacted due to the current COVID-19 Delta Variant. Recently, a more contagious Omicron Variant has emerged and put a more massive burden on the healthcare facilities that are impacted by disasters. Then, in this situation, this paper aims to assess healthcare resilience in managing the Omicron pandemic amid disaster impacts. SIR simulation was used to determine whether severely ill Omicron cases were below or above healthcare and ICU capacity under different vaccination coverage. Our result confirms that vaccination coverage was the imminent factor in reducing the severely ill cases in every healthcare facility, whether the facilities were damaged or not. Increasing vaccination coverage from 30% to 60% will significantly reduce the number of severely ill cases that fall below the capacity of healthcare. Based on the current SIR model on the Omicron epidemic variables and Ro, it is estimated that the Omicron will reach its peak after 180 days in February 2022 and will totally disappear in May 2022 in this modeled area. When healthcare system facilities were fully operational and no disaster happened, combined with 60% vaccination rates, all Omicron case numbers were below and under the available hospital beds and even available ICU beds. While the situation is changed when a disaster occurs and causes 30% damage or reduction to healthcare facilities. In this situation, there are portions of Omicron cases that cannot be managed by the healthcare system since the cases have exceeded the available beds. The situations become more apparent where the healthcare facilities are severely damaged and lose 60% of their functionality. In this situation, all modeled Omicron cases and even the severe cases have exceeded the ICU capacity.

Spatiotemporal distribution of human brucellosis in Inner Mongolia, China, in 2010–2015, and influencing factors

Human brucellosis is caused by Brucella species and remains a major burden in both human and domesticated animal populations, especially in Inner Mongolia, China. The aims of this study were to analyze the spatiotemporal trends in human brucellosis in Inner Mongolia during 2010 to 2015, to explore the factors affecting the incidence of brucellosis. The results showed that the annual incidence was 29.68–77.67 per 100,000, and peaked from March to June. The majority of human brucellosis was male farmers and herdsmen, aged 40–59 years. The high-risk areas were mainly Xilin Gol League and Hulunbeier City. The incidence of human brucellosis in Inner Mongolia decreased during 2010 to 2015, although the middle and eastern regions were still high-risk areas. The regions with larger number of sheep and cattle, lower GDP per capita, less number of hospital beds, higher wind speed, lower mean temperature more likely to become high-risk areas of human brucellosis.

MAKESHIFT HOSPITAL DURING COVID-19 NEED OF THE HOUR

As declared by WHO COVID-19 is a pandemic. Till date more than 34 million people are infected and more than 4.5 lakh died in India only. Such types of viral pandemics place us at a sustained demand of healthcare infrastructure for providing better health services to patients. Such demands lead to the development of situation where we need to be innovative and ready to work in limited resources setting for long duty hours. The purpose of makeshift COVID hospitals is to solve the severe shortage of hospital beds in India and providing good health care services to patients.

Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital

An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negative log likelihood loss function using a heuristic nonlinear weighting method based on sample percentage. Theoretical and practical results reveal that, when compared to existing algorithms, the XGBoost method based on nonlinear weighting may achieve higher classification accuracy and better prediction performance, which is beneficial in treating more patients with fewer hospital beds.

Development of a Motorized Hospital Bed with Swerve Drive Modules for Holonomic Mobility

In hospitals; transferring patients using hospital beds is time consuming and inefficient. Additionally; the task of frequently pushing and pulling beds poses physical injury risks to nurses and caregivers. Motorized hospital beds with holonomic mobility have been previously proposed. However; most such beds come with complex drivetrain which makes them costly and hinders larger-scale adoption in hospitals. In this study; a motorized hospital bed that utilizes a swerve drive mechanism is proposed. The design takes into account simplicity which would allow for minimum modification of the existing beds. Two DC motors for steering and propulsion are used for a single swerve drive module. The control of the propulsion motor is achieved by a combination of trajectory planning based on quintic polynomials and PID control. Further; the control performance of the proposed bed was evaluated; and the holonomic mobility of its prototype was successfully demonstrated. An average error of less than 3% was obtained for motion with a constant velocity; however; larger values in the range of 15% were observed for other conditions, such as accelerating and decelerating.

Making complex decisions in uncertain times: experiences of Dutch GPs as gatekeepers regarding hospital referrals during COVID-19—a qualitative study

Background General practitioners often act as gatekeeper, authorizing patients’ access to hospital care. This gatekeeping role became even more important during the current COVID-19 crisis as uncertainties regarding COVID-19 made estimating the desirability of hospital referrals (for outpatient or inpatient hospitalization) complex, both for COVID and non-COVID suspected patients. This study explored Dutch general practitioners’ experiences and ethical dilemmas faced in decision making about hospital referrals in times of the COVID-19 pandemic. Methods Semi-structured interviews with Dutch general practitioners working in the Netherlands were conducted. Participants were recruited via purposive sampling. Thematic analysis was conducted using content coding. Results Fifteen interviews were conducted, identifying four themes: one overarching regarding (1) COVID-19 uncertainties, and three themes about experienced ethical dilemmas: (2) the patients’ self-determination vs. the general practitioners’ paternalism, (3) the general practitioners’ duty of care vs. the general practitioners’ autonomy rights, (4) the general practitioners’ duty of care vs. adequate care provision. Conclusions Lack of knowledge about COVID-19, risks to infect loved ones, scarcity of hospital beds and loneliness of patients during hospital admission were central in dilemmas experienced. When developing guidelines for future crises, this should be taken into account.