High Death Toll Research Articles

The Influence of The Socialization of "Ojek Sigap: Ojek Siap Gerak Cepat (Quick Response Motorcycle Taxi: Ready To Move Fast) on Knowledge of First Aid For Accidents of Online Ojek Drivers in Jambi City

Road traffic accidents are the leading cause of death worldwide, with the current global death toll reaching 1.24 million per year. This figure is expected to increase to 3.6 million per year by 2030. Indonesia ranks third highest in Asia, after China and India, with 38,279 deaths due to road traffic accidents in 2015. Jambi City, despite having a lower accident rate than other provinces, still faces significant challenges with 392 deaths in 2018. Lack of knowledge about first aid (P3K) is one of the factors contributing to the high death toll. Given the widespread use of online motorcycle taxis on the road, their drivers have the potential to become witnesses or victims of accidents. Therefore, it is necessary to improve knowledge and skills in first aid among online motorcycle taxi drivers to improve road safety. The study used the Pre - Experimental research method with a Prepost Test Design design with 28 respondents. The provision of material using educational videos and mnemonic cards related to initial handling of accidents. Statistical analysis using the Wilcoxon test showed very significant results with a value of P = 0.0000 and Z = -4.658. A very low P value (less than 0.05) indicates that there is a statistically significant difference between the level of knowledge of respondents before and after the socialization. This negative Z value indicates that there is a significant increase in the knowledge of respondents after they have participated in the socialization program. The results of the study indicate that there is an Influence of the Socialization of "OJEK SIGAP” on Knowledge of First Aid for Accidents for Online Ojek Drivers in Jambi City.

Reckoning with COVID-19 denial: Brazil's exemplary model for global response.

In the aftermath of the coronavirus disease 2019 (COVID-19) pandemic in Brazil, accountability is crucial for those who denied the severity of the virus, spreading false information and causing harm. Some individuals have already faced legal proceedings against them, revealing economic motivations behind their actions. It is equally important to hold doctors accountable for prescribing ineffective treatments, putting the population at risk. The leaders of the denial movement and the federal government, who mishandled the pandemic, should be held accountable for the high death toll. Seeking justice from the legislative and executive branches is necessary, along with exemplary measures for those who spread misinformation about COVID-19.

Can we develop a more targeted approach to mitigating seismic risk?

The recent high death tolls caused by large earthquakes are a further indication that earthquakes remain one of the most destructive natural hazards in the world and can seriously threaten the achievement of disaster reduction goals. To effectively reduce the existing seismic risk, the limited available mitigation resources should be allocated to areas with the most severe potential risk. However, identifying localized concentrations of risk requires detailed studies. Here, we propose a strategy to delineate regional high seismic risk zone at a fine resolution and with high confidence. We demonstrate this strategy by using the seismic hazard and loss estimation results for earthquake scenarios with a magnitude of Mw 7.5 for the Jiaocheng fault of the Shanxi Rift System, China. Our analyses reveal that the delineated zone accounts for only ~7% of the regional land area but for ~85% of the total financial loss. We recommend prioritizing seismic risk mitigation measures in such high-risk zones, especially for densely populated cities in seismically active areas, to better meet the disaster risk reduction targets in the Sendai Framework.

3-chymotrypsin-like protease in SARS-CoV-2.

Coronaviruses constitute a significant threat to the human population. Severe acute respiratory syndrome coronavirus-2, SARS-CoV-2, is a highly pathogenic human coronavirus that has caused the coronavirus disease 2019 (COVID-19) pandemic. It has led to a global viral outbreak with an exceptional spread and a high death toll, highlighting the need for effective antiviral strategies. 3-Chymotrypsin-like protease (3CLpro), the main protease in SARS-CoV-2, plays an indispensable role in the SARS-CoV-2 viral life cycle by cleaving the viral polyprotein to produce 11 individual non-structural proteins necessary for viral replication. 3CLpro is one of two proteases that function to produce new viral particles. It is a highly conserved cysteine protease with identical structural folds in all known human coronaviruses. Inhibitors binding with high affinity to 3CLpro will prevent the cleavage of viral polyproteins, thus impeding viral replication. Multiple strategies have been implemented to screen for inhibitors against 3CLpro, including peptide-like and small molecule inhibitors that covalently and non-covalently bind the active site, respectively. In addition, allosteric sites of 3CLpro have been identified to screen for small molecules that could make non-competitive inhibitors of 3CLpro. In essence, this review serves as a comprehensive guide to understanding the structural intricacies and functional dynamics of 3CLpro, emphasizing key findings that elucidate its role as the main protease of SARS-CoV-2. Notably, the review is a critical resource in recognizing the advancements in identifying and developing 3CLpro inhibitors as effective antiviral strategies against COVID-19, some of which are already approved for clinical use in COVID-19 patients.

Epidemiology of penile cancer in Poland compared to other European countries.

To examine the epidemiology of penile cancer in Poland compared to other European countries. Incidence and mortality data were acquired from the national cancer registries in Europe and WHO Mortality Database, respectively. The data are presented as age-standardised morbidity and mortality rates, calculated according to the standard population of the world. We utilised Joinpoint analysis to assess the trends in morbidity and mortality and calculated the average rate of increase or decrease (Annual Percentage Change, Average Annual Percentage Change). Additionally, we estimate the proxy survival rates for each country. Our study is the first to cover the incidence of penile cancer in many European countries and estimates an approximate survival rate for large populations, which is rarely cited in the literature. The 40+ age group presented graphically in the article covered more than 90% of penile cancer cases and deaths. In the countries examined, there was an excess of deaths over incidence in the oldest age groups (75 years or older). Poland had intermediate incidence and mortality rates. Unlike many European countries, Poland is witnessing an increasing trend of penile cancer mortality. The higher death toll among those aged 75 years or older may suggest a lack of recognition of cancer symptoms and inadequate attention to elderly patients by the healthcare system. There is also evidence of underreporting penile cancer cases. Establishing centralised healthcare systems for rare cancers is a commendable development that should be emulated by other European countries, including Poland.

Turkish earthquake death tolls: lessons from downward counterfactual analysis and informal construction

Earthquake death tolls are a basic statistical measure of the capability of a country to manage seismic risk. The extremely high Turkish death toll of 50,000 from the Kahramanmaraş earthquake doublet of 6 February 2023 is the product of a cascade of detrimental factors. These need to be explained if lessons from this disaster are to be learned. This is the purpose and objective of this paper, which is a contribution to the interdisciplinary Frontiers research topic on integrated perspectives on the 2023 Turkey and Syria earthquakes: advancing understanding and preparedness across earth sciences, engineering and public health. This paper covers these three disciplines by focusing on casualties, and identifying crucial aspects of earth sciences and engineering which contributed to the high death toll. First, there was a surprising combination of multiple fault segment ruptures, and a high level of ground motion relative to the risk-based Turkish code, indicative of the under-representation of the M7.5+ earthquake doublet event in the national probabilistic seismic hazard model. This combination of fault segment ruptures was missing from all seismic source models. Furthermore, the capability of buildings to cope with strong ground motion was much reduced by informal construction methods, which eroded the margin of safety needed to avoid building collapse. The extent of building code non-compliance was widely underestimated in seismic risk models. Non-compliance is often hard to identify, but construction amnesties make non-compliance more transparent and trackable. The disastrous outcome of the Kahramanmaraş earthquake doublet of 6 February 2023 has drawn global attention to systemic building code non-compliance, and the open official acceptance of informal housing. To demonstrate that this key systemic risk is far from being just a Turkish problem, notably in Istanbul, the challenge of Italian informal housing is highlighted within the context of international building code non-compliance.

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Confinement tonicity on epidemic spreading

Emerging and re-emerging pathogens are latent threats in our society with the risk of killing millions of people worldwide, without forgetting the severe economic and educational backlogs. From COVID-19, we learned that self isolation and quarantine restrictions (confinement) were the main way of protection till availability of vaccines. However, abrupt lifting of social confinement would result in new waves of new infection cases and high death tolls. Here, inspired by how an extracellular solution can make water move into or out of a cell through osmosis, we define confinement tonicity. This can serve as a standalone measurement for the net direction and magnitude of flows between the confined and deconfined susceptible compartments. Numerical results offer insights on the effects of easing quarantine restrictions.

Examining the Factors Influencing Tsunami Evacuation Action Selection in Thailand: A Comprehensive Study Involving Local Residents, Non-Local Workers, and Travelers

Tsunamis are a substantial natural threat in Thailand, as evidenced by the 2004 Indian Ocean tsunami. Effective evacuation is vital to reduce casualties and property damage. However, despite improved warning systems, high death tolls still occur, indicating complex evacuation behavior influenced by various factors. This study examines these factors among diverse groups in Phuket and Phang Nga, Thailand. A survey of 1000 locals, non-local workers, and travelers assesses threat and coping appraisals, past tsunami experiences, gender, age, and tsunami evacuation intention and action selection. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the data based on the hypotheses related to the Protection Motivation Theory (PMT). The results of the analyses show that threat and coping appraisals significantly predict tsunami evacuation intention, and gender influences threat perception related to evacuation. Variations among respondent types emphasize the need for tailored disaster preparedness and response strategies. This study offers crucial insights for policymakers, emergency responders, and disaster management stakeholders, underlining the significance of further research into the intricate interplay of individual and contextual factors shaping tsunami evacuation behavior.

Corruption in Turkey and earthquakes: Policies for tackling corruption in the construction sector

AbstractThe earthquake in Turkey has resulted in thousands of fatalities and injuries and is considered one of the worst natural disasters in recent history. The disaster has also brought to light another major issue in Turkey, namely, corruption. Failure to comply with building regulations and successive government amnesties for unlicensed construction contributed to the high death toll. This paper aims to draw attention to the ongoing issue of corruption and the potential life‐saving benefits of ethical practices in business and government. Finally, relevant useful policies targeting corruption in real estate have been proposed.

Privacy Considerations of Location Tracking in Social Welfare Applications

COVID-19 rapidly spread worldwide starting in December 2019, reaching its peak during the first quarter of 2021. As of October 28, 2021, COVID-19 deaths have surpassed 5,000,000 globally, with the highest death tolls in the United States, Brazil, and India. Governments scrambled to combat the pandemic using various techniques, including social welfare and pandemic tracking applications. This scramble accelerated the progress of the digital revolution through the proliferation of numerous social welfare applications worldwide. These applications are used for tracking vaccination status, contact tracing, social distancing, symptoms, and positive cases, as well as for enforcing quarantine and lockdown policies and detecting violations. Artificial intelligence and other technology advances raise concerns about security, privacy, and equity since many of these applications work with personal information from one’s health records, employment information, and location data. This paper examines such applications in the Kingdom of Saudi Arabia, the Kingdom of Bahrain, and the United States. Develop best practices that can be followed to enhance the security of and equitable access to social applications.

A Novel Framework for Detection of Harmful Snakes Using YOLO Algorithm

The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. The idea is to construct a machine learning model to identify the snakes, which will be trained on a vast set of images of snakes. These images will be carefully selected to include the snake patterns, skin color of different species of snakes (extraction of the unique features). This is done to train the model properly to identify different types of snakes that the farmer encounters in the fields. Most famers are unaware and have a need for a way to detect these snakes so that they can either avoid them or get rid of them. Hence, this system can be implemented to prevent thousands of deaths and can help famers alleviate the repercussions of snake bites. Mistakes in recognizing potentially harmful animal species based only on visual cues are major contributors to the high death toll from venomous animal attacks. Since they spend so much time in the fields, where rice and wheat are cultivated, farmers are at a higher risk of being bitten by a snake than the general population. Because of their ignorance, illiterate farmers are more inclined to believe in superstitions, which can lead to their untimely deaths from snakebites despite medical intervention. Animals that would normally pose no threat to humans are responsible for the deaths of thousands of people every year because of environmental factors. However, because it is hard for humans to recognize these dangers, a new design paradigm has been developed to make it simpler. Researchers in the field of animal biology can use it to search for endangered species. Predators may enter gardens and green areas like tea and coffee plantations. There is not yet a plan in place to implement automated sorting for discovering distinctions. By applying the suggested framework to photographs of potentially dangerous animals, several factors useful for studying animal organization may be easily identified. For the classifier to run in real-time, it is suggested that YOLO be utilized for double processing within the framework. Furthermore, the YOLO technique enables quick animal recognition in a similar fashion. First, the YOLO method will be used to determine whether a snake is present in the proposed work. Here, 87 percent precision is also reached in the detection of snakes.

XCovNet: A wide deep learning model for CXR-based COVID-19 detection

The high death toll and economic impact of the COVID-19 pandemic emphasize the need for effective population screening technologies. The high cost, limited availability, and slow nature of CT scans and PCR-based tests renders them impractical for frequent use among the general public. As chest X-ray (CXR) imaging is fast and economical, a high accuracy CXR-based test would be well-suited for such screenings. Deep learning algorithms are widely used to aid medical image diagnoses. We use a collection of state-of-the-art pre-trained deep neural network models with additional layers to detect the COVID-19 cases from a sample of healthy, COVID-19, and pneumonia patients. We observed models trained with concatenated features of multiple pre-trained deep learning architectures outperform the individual and ensemble models. Our final model obtained a recall of greater than 98% and a precision of greater than 95% on two separate datasets. The wide architecture of xCovNet may contribute to its robust behavior, providing a systematic approach to construct a reliable deep learning model for emerging datasets.

Prehospital management of earthquake crush injuries: A collective review.

Earthquakes are natural disasters which can destroy the rural and urban infrastructure causing a high toll of injuries and death without advanced notice. We aim to review the prehospital medical management of earthquake crush injuries in the field. PubMed was searched using general terms including rhabdomyolysis, crush injury, and earthquake in English language without time restriction. Selected articles were critically evaluated by three experts in disaster medicine, emergency medicine, and critical care. The medical response to earthquakes includes: (1) search and rescue; (2) triage and initial stabilization; (3) definitive care; and (4) evacuation. Long-term, continuous pressure on muscles causes crush injury. Ischemia-reperfusion injury following the relieving of muscle compression may cause metabolic changes and rhabdomyolysis depending on the time of extrication. Sodium and water enter the cell causing cell swelling and hypovolemia, while potassium and myoglobin are released into the circulation. This may cause sudden cardiac arrest, acute extremity compartment syndrome, and acute kidney injury. Recognizing these conditions and treating them timely and properly in the field will save many patients. Majority of emergency physicians who have worked in the field of the recent Kahramanmaraş 2023, Turkey, earthquakes, have acknowledged their lack of knowledge and experience in managing earthquake crush injuries. We hope that this collective review will cover the essential knowledge needed for properly managing seriously crushed injured patients in the earthquake field.

COVID-19 vaccine hesitancy among older adult Thai Muslim people: A case-control study

The highest death toll during the COVID-19 pandemic in Thailand was among unvaccinated older adults. Deaths among older adults in Narathiwat Province were related to a lower COVID-19 vaccination rate. This study explored the factors influencing COVID-19 vaccination hesitancy in a Muslim community with low vaccination rates. A case-control study was conducted with participants assigned to the unvaccinated “case” group and the vaccinated “control” group. The sample was Thai older adults who practiced Islam and lived in Narathiwat Province. Purposive sampling was conducted by selecting a case house and finding the control around the case house in a 1:2 ratio. This study included 141 participants with a median age of 67 years (interquartile range [IQR], 8). More than 80% of both groups were retired and lived with their extended relatives. More participants in the vaccinated group believed that vaccines could prevent death from COVID-19 than in the unvaccinated group. In the multivariable analysis, factors related to the low perceived importance of the COVID-19 vaccination (odds ratio [OR] = 2.63) and a lack of vaccine recommendation from religious leaders (OR = 4.67) significantly influenced COVID-19 vaccination refusal. The COVID-19 guidance from religious leaders and the perceived importance of COVID-19 vaccination influence vaccination uptake among Muslim older adults in Thailand.

Impact of COVID-19 on Outpatient Malnutrition Centers in Urban and Rural Burkina Faso.

Although the numbers of SARS-CoV-2 infections and related deaths are relatively low in sub-Saharan Africa, the pandemic might lead to a high indirect death toll there. We determined the impact of the COVID-19 pandemic on the management of malnourished children in urban and rural areas. We analyzed data from two Centers for Rehabilitation, Education & Nutrition (CRENs), one in the capital and one in a rural center, both run by the Camillian Fathers. We compared data from the year before the pandemic (2019) with the first 2 years during the pandemic (2020/2021). In the urban CREN, there was a sharp reduction in new patients enrolled, from 340 in the pre-pandemic year to 189 during the first pandemic year and 202 in the second year. The follow-up was significantly shorter during the first pandemic year, with a rebound in the second year (pre: 57 days versus 42 and 63 days for the first and second years, respectively). In the rural CREN, the situation was different: The numbers of patients did not show any significant variation between the pre-pandemic year (191) and the first and second pandemic years (223 and 179, respectively). Different perceptions of the pandemic in urban (high, more testing, more COVID) and rural (low, less information and testing) areas may partly explain this difference. The discrepancy between the decreasing numbers of malnourished children in specialized care during the pandemic-especially in the urban area-is contrary to the lockdown-induced increase in food insecurity and warrants attention to avoid an increase in the silent epidemic of malnourished children in Africa.

Fitting Birth and Death Queuing Models using Maximum Likelihood Estimation with Application to COVID-19 Pandemic in Sub-Saharan Africa

The COVID-19 pandemic has revealed facts about deficiencies in health resource planning of some countries having relatively high case count and death toll. The virus has undergone an observed increase of cases that led to a global pandemic. Many authors have developed different models for predicting or observing the current trend of COVID-19 pandemic.

Risk Assessment and Adaptation to Climate Change Impacts in Mexico for Indigenous and Women

The present article<strong> </strong>studies the policy, numbers, and costs of disaster risk management (DRM) in Mexico, a country highly exposed to climate change, due to two oceans warming up. The PEISOR methodology facilitates interrelating complex interactions and pressures between the natural and the societal system (P), where dangerous effects (E) occur in extreme events, such as floods, landslides, and drought. The impacts (I) of global warming, the pressure of historical poverty, and vulnerable regions were affected by the COVID-19 pandemic. These societal outcomes (SO) are aggravated by gender and ethnic discrimination. The governmental response (R) has built up an alternative health system with access to medical attention. About DRM against climate catastrophes, loss and damage (L&D) policies prioritized cash transfers to affected people. This policy increased the dependency of poor people but produced electoral benefits for the leading party. Nevertheless, this DRM limits adaptation and resilience-building among social groups living in exposed regions mainly in the South, where indigenous groups suffer from low human development index and extreme poverty. The article also compares the quantitative costs of disasters in Mexico during the last five decades. Growing L&D invoices for the government and affected people occurred predominantly during the last decade. Worsening climate conditions, combined with the COVID-19 pandemic, public insecurity, and extreme poverty, represent survival threats for exposed people, where only a local bottom-up resilience-building may create an integrated DRM. In conclusion, the reactive policy of L&D has raised the electoral support of needed people but limited adaption to deal with extremer climate impacts. The official DRM policy impacts allocated 96% of the disaster budget for reconstruction and emergency management and only 4% for prevention. Especially affected are women, girls, and indigenous people with the highest death toll. Empowering these vulnerable groups would create greater resilience, where training in care economy, and environmental restoration could reduce the risks. The lack of adaptation also created a dependency on foreign countries for climate advice, hurricane tracking, early warnings, and disaster recovery, where affected people are trapped in poverty and often forced to migrate.

Railway Accidents Prevention – A Systematic Analysis

Rail transport is one of the safest means of transport. However, there have been in history some accidents with high death tolls, which could have been prevented had specific train protection systems been in place or had specific courses of action been correctly carried out. This article aims to present several railway accidents in history and the systems or actions which could have prevented them, respectively that were or should have been introduced after the inquiries that have taken place after these accidents.

The Intergenerational Transmission of World War I on Female Labour

AbstractDemographic shocks tied to World War I’s high death toll induced many women to enter the labour force in the immediate post-war period. I document a positive impact of these newly employed women on the labour force participation of subsequent generations of women until today. I also find that the war permanently altered attitudes toward the role of women in the labour force. I decompose this impact into three channels of intergenerational transmission: transmission from mothers to daughters, transmission from mothers-in-law to daughters-in-law via their sons and transmission through local social interactions.