Emergency Station Research Articles

Locating Emergency Stations Using Multi-Criteria Decision-Making (MCDM) Methods: Application of Ankara Province

An earthquake ranks first among natural disasters in Turkey, with a rate of 70%. Minimizing the effects of a disaster after it occurs is possible by establishing effective disaster management. Especially the earthquakes that occurred on February 6, 2023, showed how important it is to deliver aid to the disaster area. In this study, the problem of determining the location of emergency stations to be established in order to deliver aid to the disaster area in case of a disaster was examined. There are studies in the literature to determine the locations of emergency stations to be established in provincial centers. In this study, the locations of emergency stations to be established in the non-central districts of Ankara were tried to be determined using multi-criteria decision-making (MCDM) methods. In the study, firstly, the criteria that would be effective in the location selection of emergency stations were determined, and the weights of these criteria were calculated with the Pythagorean Fuzzy Analytical Hierarchy Process (PFAHP) method. In the second stage of the study, 16 districts outside the center of Ankara were compared with the Pythagorean Fuzzy Technique for Order Preference by Similarity To An Ideal Solution (PFTOPSIS) method, and the districts where emergency stations should be opened first were evaluated.

A study of prehospital EMS response time and influencing factors in the main urban area of Chongqing, China.

Shortening the prehospital emergency medical service (EMS) response time is crucial for saving lives and lowering mortality and disability rates in patients with sudden illnesses. Descriptive analyses of prehospital EMS response time and each component were conducted separately using ambulance trip data from the 120 Dispatch Command Centre in the main urban area of Chongqing in 2021, and then, logistic regression analyses were used to explore the influencing factors. The median prehospital EMS response time in the main urban area of Chongqing was 14.52 min and the mean was 16.14 min. A 44.89% of prehospital EMS response time exceeded 15 min. Response time was more likely to surpass this threshold during peak hours and in high population density areas. Conversely, lower probabilities exceeding 15 min were observed during the night shift, summer, and autumn seasons, and areas with a high density of emergency station. 33.28% of preparation time was >3 min, with the night shift and high population density areas more likely to be >3 min, while for the summer and autumn seasons, high Gross National Product (GDP) per capita areas had a lower likelihood of having preparation time >3 min. 45.52% of travel time was >11 min, with peak hours, summer and autumn, and high GDP per capita areas more likely to have had a travel time >11 min, while night shift and high emergency station density areas had a lower likelihood of travel time >11 min. The primary factors influencing prehospital EMS response time were shifts, traffic scenarios, seasons, GDP per capita, emergency station density, and population density. Relevant departments can devise effective interventions to reduce response time through resource allocation and department coordination, staff training and work arrangement optimisation, as well as public participation and education, thereby enhancing the efficiency of prehospital emergency medical services.

Perceived stress and coping strategies after unsuccessful cardiopulmonary resuscitation among pre-hospital emergency technicians: A multicenter cross-sectional study

BackgroundEffective stress management after unsuccessful cardiopulmonary resuscitation (CPR) requires the determination of perceived stress and identification and reinforcement of effective coping strategies. Therefore, the present study was conducted to determine perceived stress and coping strategies after unsuccessful CPR for the first time among Iranian pre-hospital emergency technicians. MethodsA cross-sectional, descriptive, correlational, multicenter study was conducted in 2022. The research population included all pre-hospital emergency technicians working in 44 urban, road, and aerial pre-hospital emergency stations in 22 counties of Ilam Province, west of Iran. A total of 374 technicians worked in these stations, which were included in the study by census method according to the inclusion criteria. The data collection tools were the Questionnaire of perceived stress after unsuccessful CPR and Brief Coping Orientation to Problems Experienced (COPE) Inventory. The collected data were analyzed with SPSS software version 27 using descriptive and analytical statistics (Pearson correlation test, multiple linear regression analysis, independent sample T-test, and ANOVA) at a significance level of p ≤ 0.05. ResultsThe perceived stress was at a moderate level with a mean score of 65.56 ± 14.21. Among six coping strategies, problem-focused coping had the highest (3.01 ± 0.59) and denial had the lowest mean value (1.94 ± 0.79). Perceived stress had a significant direct relationship with 5 coping strategies, including problem-focused coping, support-focused coping, emotion-focused coping, self-distraction, and religion (p < 0.05). The strongest relationship was found between perceived stress and self-distraction followed by emotion-focused coping. According to the results of multiple regression analysis, emotion-focused coping and self-distraction had predictive roles. ConclusionThe participants used different coping strategies to cope with the stress of unsuccessful CPR. The most common coping mechanisms used by the participants were problem-focused, emotion-focused, and support-focused coping. Pre-hospital emergency officials can assist pre-hospital emergency technicians in coping with occupational stress, including the stress of unsuccessful CPR, through organizing educational courses, teaching effective coping strategies, and offering support programs.

Диспетчерское сопровождение оказания первой помощи очевидцами события при внегоспитальной остановке кровообращения у пострадавшего: реалии и перспективы

Summary. Purpose of the study. The purpose of this work is to analyze the practical experience of remote support of first aid in out-of-hospital circulatory arrest using the example of the State Budgetary Institution of Health “Emergency Station of the Gelendzhik Resort City” of the Ministry of Health of the Krasnodar Territory. Material and methods. The empirical basis of the scientific research was the analytical material on dispatching support for the provision of first aid in case of out-of-hospital circulatory arrest, contained in the works of Russian and foreign scientists. A study was made of the regulations governing the organization of first aid, including dispatching support for its provision. Research results and their analysis. The experience of the GBUZ “Emergency Station of the Resort City of Gelendzhik” of the Ministry of Health of the Krasnodar Territory showed that the effectiveness of cardiopulmonary resuscitation in cases of circulatory arrest before the arrival of an ambulance, when eyewitnesses provided first aid during dispatching support (the proportion of successful resuscitations – 8.6%), was similar to that in cases of circulatory arrest in the presence of emergency medical professionals (7.4%). In this regard, one of the options for increasing the frequency and quality of CPR by eyewitnesses of the incident may be to accompany the CPR by a telephone emergency dispatcher.

„Polska” i mesjanizm w pismach s. Heleny Rafaeli Majewskiej CSA, wileńskiej apostołki Bożego miłosierdzia

Helena Rafaela Majewska CSA (1902–1967) infiltrated the habitless congregation of the Sisters of Angels. Mystic; propagator of God’s mercy; penitent and collaborator of Fr. Michał Sopoćko – Vilnius confessor of Sister Faustyna Kowalska; continuator of her message. Majewska left a spiritual Diary from the years 1941–1943, in which a patriotic and messianic theme appears, the author will appear as an element of the message of mercy and the perspective of the fate of Poland revealed to her by God. The texts constitute their important sources, from which come the records of the Way of the Cross of the Polish Nation, where the mystic assigned the events of the subsequent World War II to the emergency station. The analysis of Majewska’s spiritual records is valuable both from the point of view of theological and historical sciences. In the first case, they reveal the obligation of patriotism in the formation and experience of religion in the early 20th century and during the Second Mandatory War. In the second one – revealing the elements of Polish messianism and allowing the use of interpretation of historical events at the time they occurred.

Evaluating the use of role-play simulations in teaching management of medical emergencies in the dental clinic

BackgroundPractical hands-on training is necessary for clinical competence in managing medical emergencies in the dental setting. Therefore, the King Abdulaziz University Faculty of Dentistry developed a role-play simulation-based clinical module for training clinical years (Years 4, 5, and 6) dental students in medical emergency management between 2016 and 2020. This study aimed to assess the knowledge and skills of years 4, 5, and 6 dental students before and after the completion of the role-play simulation-based medical emergency training module.MethodsA mandatory four-hour annual training module was designed consisting of a 45-minute lecture on the medical emergency basic action plan and overview, four hands-on stations, and six role-play simulation-based medical emergency stations. A 16-item multiple choice test was used to assess the knowledge of the students before and after the training module every academic year. An objective structured clinical exam (OSCE) on a medical emergency scenario was administered to the students graduating each year. The difference between the pre- and post-module test scores and the one-time OSCE pass/fail rate were analyzed statistically using appropriate tests.ResultsA total of 846 students participated in the study between 2017 and 2020: 275 once, 483 twice, 87 thrice, and one participated four times; in total, 1,506 assessments were conducted. Overall, the pre-test and post-test mean scores were 9.4 ± 3.0 and 13.5 ± 1.6, respectively. All students showed significant improvements in the post-test scores compared to the pre-test scores. Year 4 students showed the highest improvement, followed by Year 5 and Year 6. There was a statistically significant association between the pass/fail rate of the OSCE station and the number of times the module was taken.ConclusionsThe significant improvement in the scores of clinical year students in this study supports an annual mandate for all dental students to undergo simulation-based competency training in their clinical years. Teaching institutions are recommended to utilize simulation and hands-on instruction to teach medical emergency management.

Object detection and activity recognition in video surveillance using neural networks

PurposeThis paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.Design/methodology/approachThe goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.FindingsThe results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.Originality/valueThis paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

Violence against ambulance workers: comparative study of the stations of Yerevan and Gyumri

Introduction. According to the World Health Organization (WHO), violence is widespread in the world, about 1 million people dying from various forms of violence each year. Currently, violence in the workplace is increasing, especially in emergency department against medical personal. Purpose of the research: to study the perception of violence by medical workers in ambulance stations in the cities of Yerevan and Gyumri, to identify the types, causes and qualitative characteristics of the prevalence of violence against medical ambulance workers. Give a comparative description of the violence situation in Yerevan and Gyumri stations.&#x0D; Material and methods. Qualitative research was carried out among the medical staff of emergency stations of Yerevan and Gyumri cities using the in-depth interview method in 2021. The tool was the guide, the total number of participants was 61.&#x0D; Results. The survey found that violence against emergency workers was common: 42 out of the 61 participants reported about lifetime experience of any type of violent behavior of patients or their relatives. Among the types of violence, physical and psychological were mentioned most often. &#x0D; Conclusions. Violence is a common, frequent occurrence in the emergency department. Emergency medical personnel primarily perceive violence in its psychological and physical manifestations. Among the reasons are, in particular, the apparent delays of the emergency personnel, the nervous and mental overstrain of the abusers, and the use of alcohol.

Urban-sub-urban-rural variation in the supply and demand of emergency medical services.

Emergency medical services (EMSs) are a critical component of health systems, often serving as the first point of contact for patients. Understanding EMS supply and demand is necessary to meet growing demand and improve service quality. Nevertheless, it remains unclear whether the EMS supply matches the demand after the 2016 healthcare reform in China. Our objective was to comprehensively investigate EMS supply-demand matching, particularly among urban vs. sub-urban vs. rural areas. Data were extracted from the Tianjin Medical Priority Dispatch System (2017-2021). From supply and demand perspectives, EMS resources and patient characteristics were analyzed. First, we performed a descriptive analysis of characteristics, used Moran's I to explore the spatial layout, and used the Gini coefficient to evaluate the equity of EMS supply and demand. Second, we analyzed urban-sub-urban-rural variation in the characteristics of EMS supply and demand by using the chi-square test. Finally, we examined the association between the EMS health resource density index and the number of patients by using the Spearman correlation and divided supply-demand matching types into four types. In 2021, the numbers of medical emergency stations and ambulances were 1.602 and 3.270 per 100,000 population in Tianjin, respectively. There were gradients in the health resource density index of the number of emergency stations (0.260 vs. 0.059 vs. 0.036; P = 0.000) in urban, sub-urban, and rural areas. There was no spatial autocorrelation among medical emergency stations, of which the G values by population, geographical distribution, and the health resource density index were 0.132, 0.649, and 0.473, respectively. EMS demand was the highest in urban areas, followed by sub-urban and rural areas (24.671 vs. 15.081 vs. 3.210 per 1,000 population and per year; P = 0.000). The EMS supply met the demand in most districts (r = 0.701, P = 0.003). The high supply-high demand types with stationary demand trends were distributed in urban areas; the low supply-high demand types with significant demand growth trends were distributed in sub-urban areas; and the low supply-low demand types with the highest speed of demand growth were distributed in rural areas. EMS supply quantity and quality were promoted, and the supply met the demand after the 2016 healthcare reform in Tianjin. There was urban-sub-urban-rural variation in EMS supply and demand patterns.

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch.

Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.

Locating the Emergency Centers on High-risk Roads Leading to Bojnurd using Analytical Hierarchy Process-Fuzzy Method (FAHP)

Introduction: According to the report of the World Health Organization represent-ative in Iran and expressing regret over the increasing and cautionary trend of injuries and deaths due to traffic accidents, road casualties in this country have been increasing since 2017, and they are among the five most important causes of death in Iran. Therefore, the present study aimed to locate the emergency stations on the roads leading to Bojnurd, North Khorasan Province, Iran, in order to reduce injuries and deaths and provide timely services to the injured and accident victims. Method: This applied research used a positivism philosophy, library documents, specialized references, previous studies, field research, observations, and interviews. Based on experts' opinions, 10 locating factors were selected for weighting using multi-criteria decision-making methods (FAHP). Afterward, a fine-grained zoning map was provided in GIS utilizing FUZZY logic. Results: Physical, demographic, social, and geographic data were used in this study to locate the emergency stations on the roads of this province. The results revealed four desirable locations for the construction of emergency statins on the roads leading to Bojnurd. Conclusion: The selection of the correct place to construct emergency stations plays a significant role in reducing the time for the emergency vehicle to reach the patient's bedside. Therefore, the location and placement of emergency centers and the arrangement of these services on the road, as well as the optimal distribution of these centers have a significant impact on the type of performance and quality of service

DETERMINATION OF DEPRESSION, ANXIETY, AND STRESS LEVELS OF PARAMEDICS AND EMERGENCY MEDICAL TECHNICIANS IN THE COVID-19 PANDEMIC

Amaç: Bu çalışma, COVID-19 Pandemisi sürecinde 112 acil yardım istasyonu ve acil servislerde çalışan paramediklerin ve acil tıp teknisyenlerinin (ATT) depresyon, anksiyete ve stres düzeylerinin belirlenmesi amacıyla yapılmıştır.Gereç ve Yöntem: Tanımlayıcı, kesitsel nitelikteki bu araştırma Ekim 2020-Mayıs 2021 tarihleri arasında, 112 acil yardım istasyonlarında ve acil servislerde çalışan 232 paramedik ve acil tıp teknisyeni ile yapılmıştır. Araştırmanın verileri, kişisel bilgi toplama formu ve Depresyon Anksiyete ve Stres Ölçeği-21 (DASS-21) kullanılarak Google Forms aracılığı ile online olarak toplanmıştır. Veriler SPSS 21 programında analiz edilmiştir.Bulgular: Çalışmaya 232 kişi katılmıştır. Bunların %52,6’sı erkek, 67,7’si paramedik, %77,6’sı 112 istasyonunda çalışmaktadır. Katılımcıların, Depresyon, Anksiyete ve Stres Ölçeği (DASS-21) toplam puan ortalaması 26,65±16,86 olup, alt ölçek puanlarına göre %34,9’unun çok ileri düzeyde depresyon; %30,2’sinin çok ileri düzeyde anksiyete ve %21,1’inin ise çok ileri düzeyde stres yaşadığı saptanmıştır. Çalışma sonucuna göre katılımcıların yaşı arttıkça anksiyete puan ortalamaları azalırken, bekar olan katılımcıların stres puan ortalamalarının yüksek olduğu belirlenmiştir.Sonuç: Bu çalışma COVID-19 Pandemisinde paramedik ve ATT’ lerin depresyon, anksiyete ve stres düzeylerinin yüksek olduğunu göstermiştir. Bu durumun COVID-19 Pandemisine bağlı olarak, çalışma koşullarının olumsuz hale gelmesi nedeniyle ortaya çıkmış olabileceği gibi, COVID-19 Pandemisinden bağımsız bir durum da olabileceği düşünülebilir. Bu durumun belirlenmesi için daha kapsamlı çalışmalara ihtiyaç vardır.

A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief

A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief

The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances.

In the present study, the optimization of medical services considering the role of intelligent traffic management is of concern. In this regard, a two-objective mathematical model of a medical emergency system is assessed in order to determine the location of emergency stations and determine the required number of ambulances to be allocated to the station. The objective functions are the maximization of covering the emergency demands and minimization of total costs. Moreover, the use of an intelligent traffic management system to speed up the ambulance is addressed. In this regard, the proposed two-objective mathematical model has been formulated, and a robust counterpart formulation under uncertainty is applied. In the proposed method, the values of the objective function increase as the problem becomes wider and, with a slight difference in large dimensions, converge in terms of the solution. The numerical results indicate that, as the problem's complexity increases, the robust optimization method is still effective because, with the increasing complexity of the problem, it can still solve large-scale problems in a reasonable time. Moreover, the difference between the value of the objective function in the proposed method and the presence of uncertainty parameters is very small and, in large dimensions, is quite logical and negligible. The sensitivity analysis shows that, with increasing demand, both the number of ambulances required and the amount of objective function have increased significantly.

Location Optimization of Emergency Station for Dangerous Goods Accidents Considering Risk

Emergency station is very important for emergency rescue work in hazardous chemical accidents. In order to ensure the efficiency of emergency rescue work, the setting characteristics and objectives of the emergency station should be comprehensively considered. A bi-level programming model of emergency station location for hazardous chemical accidents is established in this paper. The optimization objectives of the model include the minimum risk of emergency station location, the minimum construction and operation cost, the minimum weighted path distance, and the highest coverage level. Based on the NSGA-II algorithm, the model solving technology is designed and applied to the case analysis. The obtained results showed that the efficiency of emergency rescue is continuously improving with the increase in the number of emergency stations.

Identifying the vulnerable regions of emergency medical services based on the three-stage of accessibility: a case study in Xi\u2019an, China

BackgroundEmergency department crowding is an obstacle in the process of obtaining emergency care services, which will lead to the increase of time cost. Most studies focused on the direct access to emergency medical resources, and few studies took the crowding of hospital emergency department as an evaluation index to reflect the convenience of obtaining emergency medical resources. It is a significance for the identification of areas with insufficient access to emergency service resources with this method.MethodsThis paper utilizes the improved potential model and the inverted Two-Step Floating Catchment Area method, combined with network map API service data to evaluate response time, delivery time and waiting time (for emergency department crowding) spent in different residential areas of Xi’an City in the process of emergency. Meanwhile, the vulnerable regions of gaining emergency medical resources are identified through the comprehensive analysis of the three stages of emergency.ResultsThe studies show that the residents in built-up area are more convenient to get ambulance service and arrive at care hospitals than those in suburban areas, but they may face greater hospital crowdedness. Although suburban residents are faced with low hospital crowdedness, they spend more time on getting ambulances and going to care hospitals. The accessibility of emergency medical resources varies greatly among residents in different regions, with 5.38% of the residents were identified in the high-risk area distributing in suburban residential areas in the south of the city center, 21.92% in the medium risk area in the southern mountainous areas and the periphery of the core suburban areas of the city, and 46.11% in the low-risk area which are mainly distributed in built-up areas in gaining emergency medical services.ConclusionsObviously, getting an ambulance and arriving at the nearest hospital quickly shows that it is conducive to access to emergency resources. However, the impact of hospital emergency crowding can not be ignored, especially in the area surrounded by high-grade hospitals in the central area of the city. In considering the spatial layout of emergency stations and emergency hospitals, the dislocation distribution of hospitals at different levels should be reasonably adjusted to balance the equity of residents in obtaining emergency medical resources.

Features of the population immunity to SARS-CoV-2 development in staff members of medical organizations during the spread of COVID-19

Background. The ongoing COVID-19 pandemic generates the urgent need of monitoring the herd immunity among healthcare workers as a high-risk group for infection.&#x0D; Aim. Determining the level and structure of population immunity to SARS-CoV-2 in the medical workers during the second peak of COVID-19 from August to November 2020.&#x0D; Material and methods. The study involved 15,158 medical workers from 91 medical organizations of the Republic of Tatarstan. The age of the examined stuff members varied from 18 to 74 years, the average age was 45.480.1016 years, most of them were women (13 627, 89.870.258%). Testing for the presence of total antibodies (immunoglobulin classes G, A and M) to SARS- CoV-2 was performed once by the enhanced chemiluminescence method. Statistical processing was carried out by methods of variation statistics and correlation analysis according to the Pearson method using MS Excel and WinPepi.&#x0D; Results. Seroprevalence to SARS-CoV-2 in medical workers was 35.230.388%. An increase in seropositive individuals level from 27.440.265% in August to 57.910.499% in November and an increase in the average geometric mean titer of total antibodies (immunoglobulin classes A, M, G) from 84.43 in August to 101.09 in November 2020 were detected. The level of individuals with antibodies increased with age from 18 to 59 years and decreased in group of 6070 years. The largest proportion of seropositive to SARS-CoV-2 individuals was found among junior (42.011.384%) and middle grade (37.50.527%) medical staff of hospitals for patients with COVID-19, ambulance and emergency stations. Among the seropositive to the SARS-CoV-2 workers the proportion of asymptomatic forms of infection was 84.970.498%. According to the level of seroprevalence, municipal districts of the Republic of Tatarstan were divided into three groups: moderate 8.939.0; average 42.949.6, high level 53.164.4.&#x0D; Сonclusion. An increase in seroprevalence to SARS-CoV-2 was revealed among medical workers of the Republic of Tatarstan from August to November 2020 during the second peak of COVID-19.

Financial leasing in providing emergency stations with special vehicles

The article deals with the problem of providing emergency stations with special vehicles (ambulances) and possible ways to solve it: purchase, outsourcing and financial leasing of ambulances within the framework of the current legislative framework and the existing economic space. The main advantages and problem issues of each of these methods are highlighted. The expediency of more active use of financial leasing to solve this problem is substantiated.

Quantity and Quality of Healthcare Professionals, Transfer Delay and In-hospital Mortality Among ST-Segment Elevation Myocardial Infarction: A Mixed-Method Cross-Sectional Study of 89 Emergency Medical Stations in China.

BackgroundTransfer delay provokes prolongation of prehospital time, which contributes to treatment delay that endangers patients with ST-segment elevation myocardial infarction (STEMI). A key constraint in reducing transfer delay is the shortage of emergency healthcare workers. This study was to explore the influence of the quality and quantity of healthcare professionals at emergency medical stations on transfer delay and in-hospital mortality among STEMI patients.MethodsA cross-sectional study using mixed methods was conducted at 89 emergency stations in 9 districts in China's Shenzhen province. Based on a sample of 31 hospitals, 1,255 healthcare professionals, and 3,131 patients with STEMI, a generalized linear model was used to explore the associations between the quality and quantity of healthcare professionals and transfer delay and in-hospital mortality among STEMI patients. Qualitative data were collected and analyzed to explore the reasons for the lack of qualified healthcare professionals at emergency medical stations.ResultsThe analysis of the quantity of healthcare professionals showed that an increase of one physician per 100,000 individuals was associated with decreased transfer delay for patients with STEMI by 5.087 min (95% CI −6.722, −3.452; P < 0.001). An increase of one nurse per 100,000 individuals was associated with decreased transfer delay by 1.471 min (95% CI −2.943, 0.002; P=0.050). Analysis of the quality of healthcare professionals showed that an increase of one physician with an undergraduate degree per 100,000 individuals was associated with decreased transfer delay for patients with STEMI by 8.508 min (95% CI −10.457, −6.558; P < 0.001). An increase of one nurse with an undergraduate degree per 100,000 individuals was associated with decreased transfer delay by 6.645 min (95% CI −8.218, −5.072; P < 0.001). Qualitative analysis illustrated that the main reasons for low satisfaction of healthcare professionals at emergency medical stations included low income, limited promotion opportunities, and poor working environment.ConclusionsThe quantity and quality of emergency healthcare professionals are key factors influencing transfer delay in STEMI patients. The government should increase the quantity of healthcare professionals at emergency medical stations, strengthen the training, and improve their performance by linking with clinical pathways to enhance job enthusiasm among emergency healthcare professionals.

Optimal Location Prediction for Emergency Stations using Machine Learning

Optimal Location Prediction for Emergency Stations using Machine Learning