Most common causes of death worldwide by age group, 2002 (adapted from WHO prehospital trauma care systems) [2].
\r\n\t
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Despite apparent differences in understanding and use of the concept in different settings, international health security, in its most general sense, prioritizes cross-border threats to the modern and future achievements of nations such as the emergence of disease epidemics, bioterrorism, and climate change [1]. The gravity of these threats and their potential impact on people the world over has fostered greater health diplomacy between nations. There is greater cooperation and health information sharing now than in any other time in history, allowing for an evaluation of the impact contemporary systems of care may have on international health security. While global public health achievements in large part stem from greater ability to prevent and control the spread of infectious disease, this is not the sole determinate of global health and life expectancy. In the modern era, the leading causes of mortality worldwide are not infectious (Table 1). Cardiovascular disease is, in fact, the number one cause of mortality worldwide, inclusive of all ages and demographics. Cerebrovascular disease is the second most common cause of death. Out-of-hospital cardiac arrest, traumatic injury, and peripartum complications leading to neonatal death continue to have a significant impact on global mortality. Emergency medical services and programs such as ATLS, Advanced Cardiac Life Support (ACLS), and the Neonatal Resuscitation Program (NRP) were created to help deliver essential knowledge and skills to communities with low resources and increased disease burden. In this chapter, we will review the impact of these programs and initiatives aimed at improving health outcomes globally.
\nRank | \n0–4 years | \n5–14 years | \n15–29 years | \n30–44 years | \n45–59 years | \n≥60 years | \nAll ages | \n
---|---|---|---|---|---|---|---|
1 | \nLower respiratory infections 1,890,008 | \nChildhood cluster diseases 219,434 | \nHIV/AIDS 707,277 | \nHIV/AIDS 1,178,856 | \nIschemic heart disease 1,043,978 | \nIschemic heart disease 5,812,863 | \nIschemic heart disease7,153,056 | \n
2 | \nDiarrheal diseases 1,577,891 | \nRoad traffic injuries 130,835 | \nRoad traffic injuries 302,208 | \nTuberculosis 390,004 | \nCerebrovascular diseases 623,099 | \nCerebrovascular diseases 4,685,722 | \nCerebrovascular diseases5,489,591 | \n
3 | \nLow birth weight 1,149,168 | \nLower respiratory infections 127,782 | \nSelf-inflicted injuries 251,806 | \nRoad traffic injuries 285,457 | \nTuberculosis 400,708 | \nCOPD 2,396,739 | \nLower respiratory infections 3,764,415 | \n
4 | \nMalaria 1,098,446 | \nHIV/AIDS 108,090 | \nTuberculosis 245,818 | \nIschemic heart disease 231,340 | \nHIV/AIDS 390,267 | \nLower respiratory infections 1,395,611 | \nHIV/AIDS 2,818,762 | \n
5 | \nChildhood cluster diseases 1,046,177 | \nDrowning 86,327 | \nInterpersonal violence 216,169 | \nSelf-inflicted injuries 230,490 | \nCOPD 309,726 | \nCancers of respiratory system 927,889 | \nCOPD 2,743,509 | \n
6 | \nBirth asphyxia and birth trauma 729,066 | \nTropical cluster diseases 35,454 | \nLower respiratory infections 92,522 | \nInterpersonal violence 165,796 | \nCancers of respiratory system 261,860 | \nDiabetes 749,977 | \nDiarrheal diseases 1,766,447 | \n
7 | \nHIV/AIDS 370,706 | \nFires 33,046 | \nFires 90,845 | \nCerebrovascular diseases 124,417 | \nCirrhosis of the liver 250,208 | \nHypertensive heart disease 732,262 | \nTuberculosis 1,605,063 | \n
8 | \nCongenital heart disease 223,569 | \nTuberculosis 32,762 | \nDrowning 87,499 | \nCirrhosis of the liver 100,101 | \nRoad traffic injuries 221,776 | \nStomach cancer 605,395 | \nChildhood cluster diseases 1,359,548 | \n
9 | \nProtein energy malnutrition 138,197 | \nProtein energy malnutrition 30,763 | \nWar 71,680 | \nLower respiratory infections 98,232 | \nSelf-inflicted injuries 189,215 | \nTuberculosis 495,199 | \nCancers of respiratory system 1,238,417 | \n
10 | \nSTDs (except HIV) 76,871 | \nMeningitis 30,694 | \nHypertensive heart disease 61,711 | \nPoisoning 81,930 | \nStomach cancer 185,188 | \nColon or rectal cancer 476,902 | \nMalaria 1,221,432 | \n
Most common causes of death worldwide by age group, 2002 (adapted from WHO prehospital trauma care systems) [2].
An organized procedure was followed to ensure a high quality review of the literature regarding the subject of interest. First, a comprehensive search of peer-reviewed journals was completed based on a wide range of key terms including, but not limited to, “global health,” “health security,” and “health systems.” Databases searched included PubMed, Ovid, and Google Scholar. Next, a search of websites such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and the National Institutes of Health (NIH) was conducted for policy and review statements on major threats to health security and leading cause of mortality worldwide. Based on these findings, further literature review was conducted using key terms such as “Trauma,” “Cardiovascular Disease,” “Stroke,” “Maternal Health,” “ATLS,” “ACLS,” “EMS,” and “NRP.” Literature review continued with articles identified as having potential for further review from the references sections of articles previously collected. The literature search ultimately generated 109 articles referenced in this review, which were published between 1980 and 2019. The collective information gained from this literature review was synthesized to identify the impact of programs and initiatives aimed at improving outcomes from the greatest threats to health security. These were organized into sections and are presented as examples of the extent to which these systems of care impact health security internationally.
\nTraumatic injury is a disease without boundaries; it is one of the leading causes of morbidity and mortality worldwide and places a particularly heavy burden upon countries with limited resources. Road injuries alone killed 1.4 million people in 2016, about three-quarters (74%) of whom were men and boys [3]. Despite greater knowledge of injury causes and prevention, the growing global population, traffic, and urbanization cause morbidity and mortality secondary to trauma to remain a major health concern worldwide. Ensuring timely access to advanced trauma care as an international health security measure requires an organized network of prehospital emergency care and a standardized system of trauma care that can be replicated and delivered to patients in rural community hospitals and major academic tertiary care centers alike.
\nOne of the largest initiatives in improving trauma care to-date has been the Advanced Trauma Life Support (ATLS) course. This training program was developed in 1978 by the American College of Surgeons following the tragic event of an orthopedic surgeon piloting his plane, who crashed into a Nebraska cornfield with his family, killing his wife and severely injuring his three children [4]. Insufficiency in the system of emergency medical care was recounted by this surgeon, who called for a system change to improve the care for trauma victims everywhere. ATLS focuses on the initial stabilization and resuscitation of the trauma patient, referencing the “Golden Hour” as the most important, as 30% of all trauma deaths occur within 60 minutes of injury [5]. Despite a paucity of data on the effect ATLS has on trauma mortality, existing evidence supports its practice as a means of decreasing mortality and improving systems of care globally [6, 7].
\nOver 90% of deaths related to injury occur in low-income countries where the availability of prevention programs, emergency services, and centers capable of prompt, advanced resuscitation is limited. The majority of these deaths are caused by road traffic injuries [8]. Establishing early advanced trauma care is essential to decreasing global morbidity and mortality due to trauma and is, in part, accomplished with the dissemination of trauma education programs [9]. Studies have shown that as the number of ATSL-trained professional increases, the rates of preventable and potentially preventable deaths decreases (Figure 1) [10].
\nChange in mortality over time with increasing number of ATLS-trained providers (adapted from Navarro et al. [
One study of trauma-related deaths before and after the implementation of focused trauma education courses in the capital of Rwanda, including ATLS, found the mortality of severely injured patients decreased significantly in the 6 months following their initiation [7]. Another study reported improved management of trauma patients by practitioners from countries throughout East, Central, and Southern Africa after institution of a primary trauma care course [11]. In the Netherlands, the introduction of ATLS resulted in a significantly improved trauma outcome in the first hour after admission [12]. A study on the impact of mandatory ATLS training on processes of care in rural America found improvement after categorization of trauma centers [6]. Improvement in trauma patient outcomes has also been reported after ATLS training in Trinidad and Tobago [13].
\nSince its inception, ATLS has gone through several iterations. Its principles have become standard of care in over 50 countries worldwide, with over 1 million physicians trained since the mid-1990s [14, 15, 16]. ATLS has developed into a global resuscitation program, with confirmed results in terms of improved patient outcomes, processes of care, and teaching.
\nCardiovascular disease (CVD) is by far the leading cause of death worldwide. An estimated 17.9 million people died from CVD in 2016, representing 31% of all global deaths, 85% of which are due to heart attack and stroke [3, 17]. Over three-quarters of CVD deaths take place in low-income countries, illustrating a disparity in care and the need for further resource allocation and education. Despite the global burden of CVD, there have been remarkable advances in treatment and prevention. The field of resuscitation has been evolving for more than two centuries with the American Heart Association (AHA) formally endorsing cardiopulmonary resuscitation (CPR) in 1963 [18].
\nBasic life support (BLS) and advanced cardiac life support (ACLS) guidelines have evolved over the past several decades based on a combination of scientific evidence and expert consensus. The AHA and European Resuscitation Council developed the most recent ACLS Guidelines in 2010 using a comprehensive review of resuscitation literature performed by the International Liaison Committee on Resuscitation (ILCOR). These were updated in 2015 and 2018 [19, 20, 21, 22, 23, 24, 25, 26]. The efficacy of these guidelines is well borne out in the literature with clear reductions in in-hospital and out-of-hospital mortality when the most critical interventions (e.g., defibrillation, CPR, and rapid transport to an advanced care) are initiated early (Figure 2) [24, 27, 28, 29, 30, 31].
\nExposure to prehospital bystander interventions among patients who achieved neurologically intact survival. EMS = emergency medical services; apatients received both bystander and EMS defibrillation (adapted from Nakahara et al. [
Numerous large-scale randomized clinical trials have demonstrated the benefit of timely interventions as well, including antiplatelet therapy, thrombolysis, and cardiac catheterization [17, 32]. Results from these trials have been incorporated into guidelines for inpatient and outpatient cardiac care internationally [33, 34].
\nDespite these well-established guidelines for the management of ACS, there are still strong differences with regard to the epidemiology, diagnosis, and treatment of patients with ACS, leading to diverging morbidity and mortality rates throughout the globe [17]. Reasons for such differences among different global populations are multifactorial and include differences in population genetics, access to care, diet, socioeconomic status, and treatment modalities employed regionally (i.e., invasive vs. non-invasive strategies) [35, 36]. To address these disparities, much work has been done to universalize treatment protocols by bringing systems of care to areas most in need.
\nStent for Life (SFL), a European Association of Percutaneous Cardiovascular Interventions (EAPCI) and Congress of the European Association of Percutaneous Cardiovascular Interventions (EuroPCR) coalition, was established in 2008 as a non-profit international network of national cardiac societies and partnering organizations. The mission of SFL was to address inequalities in ST-elevation acute myocardial infarction (STEMI) patients’ access to a life-saving revascularization treatment throughout Europe.
\nEffective from 2008 to 2016 in 23 countries, mainly in Europe, this initiative significantly improved the delivery of guideline-compliant therapy and patient access to primary percutaneous coronary intervention (p-PCI), thereby reducing mortality and morbidity in patients suffering from acute myocardial infarction (AMI) [37, 38]. Since 2017, the Stent—Save a Life Initiative (SSLI) was founded as the global extension and continuation of Stent for Life. The SSLI works to identify regions and countries with an unmet medical need in the optimal treatment of ACS and implement an action program to increase patient access to primary PCI where indicated.
\nThe advancement of international health security is seen in the work of these and similar organizations which work to identify opportunities and challenges in building systems of care in emerging countries, such as India, China, South Africa, and Mexico, where CAD mortality is increasing and becoming a significant healthcare problem [39, 40, 41, 42]. Geographic mapping and situational analyses have shown that adherence to STEMI guidelines is influenced by many factors and varies from country to country, from region to region, and no one model fits all communities. Greater understanding of system-level barriers and unique challenges in the regional context will facilitate the development of more effective strategies for improving the treatment and preventing CVD globally.
\nCerebrovascular disease and acute stroke syndromes are a leading cause of mortality and disability worldwide. According to the Global Burden of Disease Study published in December, 2018 the estimated lifetime risk of stroke for a 25 year old during their remaining lifespan is 25% [43]. Stroke is the third leading cause of death and first leading cause of major disability in North America. Over the last several decades developed countries have experienced reductions in stroke-related morbidity and mortality [44]. Mortality from stroke has decreased by 60% in the United States alone, but remains the fifth leading cause of death [45, 46, 47]. For most developed countries, this experience has been similar. Over the last 20 years, high-income countries have experienced an age-standardized decrease in incidence, mortality and disease burden (as measured by disability-adjusted life year loss rates) of 13, 37, and 21%, respectively [48, 49]. Nonetheless, Stroke accounts for almost 5% of all disability-adjusted life-years and 10% of all deaths worldwide [50].
\nWhile progress has been made in stroke care in developed countries, the global experience of cerebrovascular disease is less encouraging. In 2013, 6.4 million deaths (11.8% of all global deaths) were a result of stroke [51]. Stroke remains the third leading cause of years-of-potential-life lost worldwide [52]. Between 1990 and 2010, the incidence of ischemic stroke increased by 37% and that of hemorrhagic stroke increased by 47%; the total number of deaths attributable to ischemic and hemorrhagic stroke increased by 20% over that same period [48]. By 2030 there could be as many as 12 million stroke deaths, 70 million stroke survivors, and >200 million disability-adjusted life years lost from stroke each year [44]. Not surprisingly, the majority of the burden of disease is borne by low- and middle income countries at the center of the global stroke epidemic [53]. While high-income, developed countries have been experiencing significant declines in stroke incidence in recent years, undeveloped, low-income countries have experienced increases in incidence by as much as 100% [54]. The substantial regional and country-level variation in stroke disease, with hotspots of particularly high-stroke incidence, mortality, and morbidity in Eastern Europe, East and Southeast Asia, Central Africa, and Oceania has become known as the “Global Stroke Belt” [55]. The most affected countries in the global stroke belt have a >10-fold higher age-standardized stroke mortality rate than the least affected countries [56].
\nThere have been significant advances in stroke care in recent years. Timely revascularization has been by far the most powerful predictor of improved outcome in patients with acute ischemic stroke [57]. Given the time-sensitive nature of therapeutic interventions and the specialized care required by those affected, regional systems of care have evolved in different forms to provide patients the best functional outcomes possible. The emergence of advanced imaging modalities and endovascular interventions have had a significant impact on the organization of acute stroke care, as communities strive to deliver the most up-to-date, evidence-based treatments effectively.
\nEvidence shows that organized care within specialized stroke units is associated with better quality of care and reduced rates of death and disability [58]. There is significant variation, however, within and between countries in access to stroke care and the organizational models of such care [59]. In several countries, acute stroke services are being centralized into “hub and spoke” systems in which hospital providing different levels of care work together to create a centralized system in which all patients with acute ischemic stroke are taken to specialized centers, rather than the nearest hospital [60]. Research suggests that in countries where such models exist (e.g., the United States, Canada, the Netherlands, Denmark, and Australia), there is greater provision of evidence-based therapies by increasing access to specialist care and thrombolysis [61, 62, 63]. Other countries, such as the United Kingdom, have found decreases in mortality and hospital length-of-stay where hyperacute stroke services were centralized to a small number of highly specialized, high-volume centers [64]. In other countries, such as Greece, a centrally administered rotation system for 24-hour on-call services exists for specialized stroke care [65].
\nIn the United States (U.S.), regionalization of care around specialized centers played a large role in stroke dropping from the third to the fifth most common cause of death [55]. In the year 2000, the Brain Attack Coalition recommended the establishment of primary stroke centers (PSCs). Primary Stroke Center certification recognizes hospitals that meet standards to support better outcomes for stroke care. Studies from the American Heart Association/American Stroke Association Get With The Guidelines-Stroke Program have demonstrated that PSC certification improves many key process measures of stroke care [66].
\nStudies have shown that not only do hospitals with PSC certification experience lower mortality rates but also the mortality benefit appears to be independent of hospital size or time since certification (Figure 3) [67, 68]. This suggests that the process of obtaining certification in advanced stroke care alone may improve outcomes, regardless of hospital size. Since 2012, hospitals may become certified as comprehensive stroke centers (CSCs), another designation based on the Brain Attack Foundation’s recommendations for establishing systems of stroke care which requires an ability to provide more-complex services, including 24/7 availability of endovascular procedures. According to a large national study of stroke centers in the U.S., CSCs were significantly better at providing prompt acute treatment (i.e., thrombolysis and endovascular clot retrieval) for patients with acute ischemic stroke, but in-hospital mortality was similar between hospital settings [69]. The fact that less technically sophisticated centers provided non-inferior care to larger, more advanced centers has significant implications. For communities with evolving systems of care and in nations where resources are limited, a focus on providing established medical therapies to the most number of people in the most timely manner possible may be more beneficial than investing in expensive advanced technologies.
\nMortality at designated stroke centers and nondesignated hospitals. CI = confidence interval; anegative values indicate lower mortality rates at designated vs. nondesignated hospitals (adapted from Xian et al. [
The health of women and children is vital to creating a healthy world. Ensuring access to appropriate perinatal care is vital to international health security. Despite great progress, there are still too many mothers and children dying—mostly from causes that could have been prevented. Every day, there are approximately 800 deaths from preventable causes related to pregnancy and childbirth, 99% of which occur in developing countries. Despite decreasing rates of childhood mortality worldwide, neonatal deaths account for nearly half of all deaths in children less than 5 years old [70]. Intrapartum-related events such as birth asphyxia contribute to approximately one-quarter of neonatal deaths, many of which can be decreased by simple resuscitative and newborn care maneuvers. Neonatal deaths now comprise ~45% of all childhood deaths in children less than 5 years old, resulting in 2.7 million lives lost each year [71].
\nThe challenge of delivering neonatal resuscitative interventions is complicated by the fact that childbirth often occurs outside of healthcare facilities; up to 60% in some parts of the world [72]. Implementation of properly performed neonatal resuscitation remains low in countries with the highest neonatal mortality rates [72]. Adequate basic neonatal resuscitation can prevent many intrapartum deaths. Studies suggest that an additional 20–40% of lives would be saved with the institution of basic neonatal resuscitation where it is needed most [73, 74].
\nUnderstanding the impact that neonatal resuscitation delivered by trained healthcare workers can have on mortality is essential to improving international health security. Implementation of neonatal resuscitation programs has been shown to decrease intrapartum stillbirth rates and early neonatal mortality. Skilled birth attendance and newborn resuscitation are evidence-based interventions directed at the moment when the lifetime risk for mortality is highest [73, 74].
\nThe American Academy of Pediatrics released the Neonatal Resuscitation Program (NRP) in 1997, the first standardized training program for neonatal resuscitation of its kind. The NRP is an educational program that translates the science of resuscitation into practice. The initial goals of the NRP were to promote evidence-based care for newborns and to ensure the presence of at least one professional trained in neonatal resuscitation at every delivery in the United States (US) [75]. Hands-on learning with mannequins and a simple, transportable program structure led to widespread adoption outside the US. The use of NRP has spread globally with countries adapting the program to function within the context of their own healthcare environments [76]. NRP continues to be a driving force for the development of initiatives to reduce newborn mortality by promoting an action-oriented approach that trains a variety of providers, aids in the acquisition of resuscitation equipment, and promotes the importance of newborn health to proper authorities [77]. Implementation of NRP in various countries has resulted in a reduction in birth asphyxia-related mortality, in addition to increased use of bag-mask ventilation in newborn resuscitation [78, 79].
\nDrawbacks of the NRP, however, exist, and are primarily related to its complexity, which necessitates training in well-resourced settings. This has also been shown to result in inadequate skills retention after training when the learner does not practice neonatal resuscitation regularly [80]. These and other challenges to the adoption of NRP in low-resource settings may lead to the assumption that resuscitation cannot be accomplished without advanced equipment and facilities [81]. Research over the last two decades, however, has shown that over 98% of babies respond with spontaneous breathing after basic resuscitation, including drying, warmth, stimulation, and bag-mask ventilation [72, 82].
\nThus, NRP courses taught in resource-limited settings have been modified to focus on the initial steps of resuscitation while omitting discussion of more complex interventions such as intubation, medications, and umbilical line placement. Adaptations of NRP have improved both educational outcomes and skills when used to teach providers in low-resource settings [79, 83, 84]. They also have led to decreases in perinatal mortality. In a multinational randomized trial of 62,366 births in rural communities, utilization of a modified NRP and newborn care package resulted in a 30% reduction in still birth rates [85]. Such experiences demonstrate that low-resource settings require a curriculum specifically designed and targeted for their needs.
\nWith the goal of equipping caretakers in all practice settings with the basic knowledge and skills necessary to perform adequate neonatal resuscitation, a task force organized by the American Academy of Pediatrics set out to develop a simplified, standardized curriculum based on NRP. The result was the Helping Babies Breathe (HBB) program, which became available in 2010. The HBB curriculum is portable, low cost, and teaches a simpler algorithm than NRP, focusing on stimulation, drying, clearing the airway, and bag-mask ventilation. Instead of spending time evaluating a baby’s condition, interventions begin immediately with sequential evaluation of crying, breathing, and heart rate. Elements of essential newborn care are incorporated into the curriculum, including encouragement of breastfeeding, cleanliness, and warmth at delivery. The World Health Organization (WHO) released guidelines on basic newborn resuscitation in 2012, which were largely consistent with the recommendations and action plan of HBB [86].
\nEducational evaluations of HBB in developing countries have shown the course to be well received since their dissemination workshops in HBB have been shown to improve knowledge and skills in basic neonatal resuscitation immediately after training [87, 88, 89, 90, 91]. Most encouraging for the further spread of the program have been studies evaluating differences in performance based on the type of provider. While physicians perform better in pre-workshop assessments of both knowledge and skills of basic resuscitation, after HBB training, nurses perform as well in simulation as physicians [89, 91]. This and similar evidence support the training of doctors, nurses, midwives, and all others involved in newborn care. This is vital as, globally, midwives and other non-physicians care for the majority of deliveries.
\nSince the introduction of HBB, over 300,000 providers have been trained in 77 countries [92]. Fifty-two countries have established nationally led programs. Analysis of 80,000 births after HBB training demonstrated a 47% reduction in early neonatal deaths and a 24% reduction in fresh stillbirth rates (Figure 4) [94].
\nStillbirth and intrapartum mortality rates overtime during implementation of HBB in a Nepalese tertiary care center. QIC = quality improvement cycle; QIT = quality improvement team (adapted from Kc et al. [
Studies in developing countries where HBB was adopted have shown a decrease in perinatal mortality and in stillbirth rates [93, 95]. A systematic review evaluating whether the implementation of a standardized formal neonatal resuscitation training program in low- and middle-income countries improved neonatal outcomes found that early neonatal mortality (first week of life) decreased by 15% and reduction of 28-day mortality by 45% [96].
\nOn a global scale, many successes have been achieved with the implementation of neonatal resuscitation: decreased neonatal mortality, decreased still birth rates, and increased use of alternative providers. Despite the successes of implementation of neonatal resuscitation education, training providers in and of itself is insufficient to close the gaps in quality of newborn care. An estimated two-thirds of the world’s 2.7 million newborn deaths may be prevented with basic pre- and postnatal care [97]. While there may be successes to celebrate, there is still much work to be done to improve newborn care and reduce neonatal mortality globally.
\nAn emergency medical service can be described as a comprehensive system, which provides the arrangements of personnel, facilities, and equipment for the effective, coordinated, and timely delivery of health and safety services to victims of sudden illness or injury [98]. The goal of EMS is to provide timely health care for out-of-hospital medical emergencies in order to prevent unnecessary mortality or long-term morbidity [2].
\nTimely access to healthcare in an emergency situation is a fundamental component of health security. Prompt provision of prehospital emergency care coupled with rapid movement of the ill and injured to a health-care facility can save lives, reduce the incidence of short-term disability, and markedly improve long-term outcomes. The World Health Organization regards EMS system as an integral part of any effective and functional health care system [99]. In developed nations, emergency medical service (EMS) has evolved into a key link in the chain of survival for those suffering out-of-hospital illness or injury, and contributes significantly to the overall function of a healthcare system and health of a society. The birth and evolution of emergency medical services has, however, been a very slow process and has occurred on different timelines around the world.
\nAlthough modern EMS initially developed during Napoleon’s time to aid injured soldiers, few major changes occurred in EMS until the 1960s. The adaptation of prehospital care and transport principles from the military to the civilian arena accelerated after the Vietnam War, as returning veterans helped raise awareness of the disparities in care. By the end of the conflict, gunshot victims had better chances of survival in the jungles of Vietnam than they had in the streets of major cities across America. Between 1960 and 1970, a number of medical, historical, and social forces converged, leading to the development of a more structured EMS system in the United States and abroad [100].
\nSince the 1970s, emergency health care delivery has evolved from two different models with different philosophies and distinct features, referred to as the Anglo-American and Franco-German models of care [2]. Although the categorical distinctions between these two models were more obvious leading up to the twentieth century, modern EMS systems around the world have evolved along one of these two frameworks while adopting various components of the other.
\nThe Franco-German model is based on a “stay and stabilize” philosophy where advanced medical care is brought to the patient. Emergency services are run by physicians who respond to patient’s homes or the scene of an accident where they provide advanced care. Patients are either stabilized and provided follow-up directions or are transported to the hospital for admission. This results in fewer EMS transports and fewer patients being seen and treated in emergency departments. This approach to prehospital care, where EMS is an extension of the hospital, is widely implemented in continental Europe (e.g., France, Germany, Greece, Malta, and Austria) where emergency medicine is a young specialty.
\nBy contrast, the Anglo-American model of EMS care is based on a “scoop and run” philosophy [101]. The objective in this model is to rapidly bring patients to the hospital with less time spent on prehospital treatment and interventions. Here, EMS services are allied with public safety services such as fire or police departments rather than public health services or hospitals [102]. Specialized emergency medical technicians (EMTs) and paramedics provide direct patient care with remote physician oversight. In countries where this model prevails, emergency medicine is well developed and recognized as a separate medical specialty [103]. Patients are transported to emergency departments (EDs) where the majority of evaluation and treatment is begun. Patients are then either discharged or admitted to the wards for further treatment. Examples of countries utilizing this model include the United States, Canada, New Zealand, and Australia.
\nWhile both models of EMS share a similar focus for the severely ill and injured (i.e., stabilization and transport), it is the delivery of non-life-threatening care where the greatest difference is found. Whereas the Franco-German model places more emphasis on treating patients in their homes and avoiding transport when possible, the Anglo-American model transports the majority of patients for evaluation in an emergency department [104]. Given the significant differences in these systems, much comparative research has been done on patient outcome and cost-effectiveness between the two models. Outcomes, however, are difficult to interpret because of the disparate nature of each model. Each operates in a different context with different goals. The lack of unified standards makes direct comparison difficult and there is no evidence that one model is better than the other [105, 106, 107].
\nUnfortunately, to this day, the capacity to provide the most basic level of prehospital emergency care is lacking in many countries around the world. While EMS providers have developed an extended role in dealing with medical emergencies and have access to advanced clinical technologies in some countries, in others their education and training is much more limited due to a lack of funding, resources, and organizational guidance. Regardless of how simple or sophisticated a prehospital care system may be, the essential elements of an effective model are not outside the capabilities of developing nations. These elements, shown to decrease morbidity and mortality, include prompt communication and activation of the prehospital emergency response system, immediate system response, and simultaneous treatment and transport to formal medical care [99]. It is important to note that there is little evidence that advanced prehospital interventions benefit more than a small subset of the most critically ill or injured. Studies have shown that the majority of cases require treatment within the skill set of responders trained only in basic life support [102]. This has major implications for nations with limited resources, where advanced life support materials and programs may harm a system by diverting precious resources from less glamorous, but more effective measures that benefit a larger number of people. Ultimately, the model chosen for creating and administering an EMS system best suited to a particular demographic will be influenced by regional resources, culture and values, but should always be the result of local and national governments working together toward a common goal of greater health security [108].
\nIn an increasingly interconnected world, the potential for threats to international health security such as pandemics, bioterrorism, and radionuclear exposure are of increasing concern. The research and design of medical countermeasures in the form of vaccines, antimicrobials, therapeutics, and diagnostics that address the public health and medical consequences of chemical, biological, radiological, and nuclear events is an area of active research and development. In 2014, the United States in partnership with international organizations and nearly 30 partner countries launched the Global Health Security Agenda (GHSA) to accelerate progress to improve prevention, detection, and response capabilities for public health emergencies [109]. The GHSA calls for improved global access to medical countermeasures and establishes as a target the development of national policy frameworks for sending and receiving medical countermeasures from and to international partners during public health emergencies. International health countermeasures such as vaccines, antidotes, and decontamination supplies are now stockpiled by several countries to protect their own populations and by international organizations such as the WHO for the benefit of the international community (typically those with limited resources). Much work remains, however. During the H1N1 influenza pandemic in 2009, legal, regulatory, logistical, and funding barriers slowed the spread of vaccine and revealed how implementing health security measures on a global scale remains a significant challenge [109]. While imperfect in its implementation, the network established by the GHSA continues to evolve and help combat future threats to international health security. Greater efforts are needed to develop a framework to deploy medical countermeasures internationally, thus increasing global capacity to respond to public health emergencies.
\nThe concept of health security means different things to different people. In developed nations, the concept is that of addressing threats to public health such as the spread of disease and bioterrorism. In developing nations, threats to health also include access to care and modern therapies. In order to address health security on a global scale, government authorities and public health institutions must incorporate access to modern systems of care addressing the major determinants of health and primary causes of mortality into the focus of international health security. In this chapter, we have discussed how organized systems of care stand to improve the health of communities on a global scale. With greater emphasis on establishing these and other systems in developing countries, greater health security can be brought to communities that need it most.
\nACLS | advanced cardiac life support |
ACS | acute coronary syndrome |
AHA | American Heart Association |
AMI | acute myocardial infarction |
ATLS | Advanced Trauma Life Support |
BLS | basic life support |
CDC | Centers for Disease Control and Prevention |
CPR | cardiopulmonary resuscitation |
CSC | comprehensive stroke center |
CVD | cardiovascular disease |
EAPCI | European Association of Percutaneous Cardiovascular Interventions |
ED | emergency department |
EMS | emergency medical services |
EMT | emergency medical technician |
EuroPCR | Congress of the European Association of Percutaneous Cardiovascular Interventions |
GHSA | Global Health Security Agenda |
HBB | Helping Babies Breath |
IHS | international health security |
ILCOR | International Liaison Committee on Resuscitation |
NIH | National Institutes for Health |
NRP | Neonatal Resuscitation Program |
PSC | Primary Stroke Center |
p-PCI | primary percutaneous coronary intervention |
SFL | Stent for Life |
STEMI | ST-elevation acute myocardial infarction |
SSLI | Stent-Save a Life Initiative |
WHO | World Health Organization |
Humans have been using alloys since ancient times. Arsenical Bronze, an alloy of copper and arsenic, was used as far back as the 5th millennium BCE. Sumerians first started alloying copper and tin to form Bronze in the third millennium BC. Bronze was much more challenging than its parent elements. Hence, societies wielding bronze weapons skillfully subjugated their neighboring communities. Meteoric iron, a naturally occurring alloy of iron and nickel, was in use from 3200 BCE. Persians started using carbon steel in the 16th century BCE. With this began the Iron Age. Our modern society also relies heavily on steel.
Typically, alloys have one or two primary components and other components with small amounts. The primary ingredients are chosen for the alloy’s leading properties, and the smaller components are selected for specific additional properties. High entropy alloys’ development is essential in resolving the limitations of conventional alloys, especially in extreme temperature and loading conditions. High entropy alloys compared to traditional alloys do not have any dominating elements; all the elements present in these alloys either have an equal or near-equal element. As a result, these types of alloy features, as their name suggests, high entropy, i.e., reducing the Gibbs free energy of solid solution and their high configuration entropy mixing is stable at higher temperatures. These characteristics make HEAs a suitable option for high – performance applications such as gas turbines and other aerospace structures, tools, gears, and bearings.
High entropy alloys (HEA) or multi-component alloys are created from equal or similar proportions of multiple elements. There are generally two agreed-upon definitions of HEAs. It should have more than five elements with concentrations ranging from 5–35% of each component, or the entropy of mixing should be greater than 1.5R, where R is the gas constant. Due to each constituent element being a principal constituent, these alloys are characterized by larger configuration entropy. For a 2-component system, the entropy of mixing is given by Eq. (1).
From Figure 1, the entropy increase is maximum when both the components are of equal proportions. It is easy to follow that for multi-component systems; configurational entropy increases with the increasing number of components. It is maximum for any given number of components when all the constituents’ proportions are the same. High entropy alloy can be produced by processing in the gas phase, by methods like sputtering or molecular beam epitaxy, in the liquid phase by methods like arc melting, induction melting, and Bridgman solidification, or in the solid phase by methods like mechanical alloying.
The illustrated concept of random mixing of elements.
Figure 2 shows tetrahedral compositional space for a 4-element alloy [1]. The corner points represent pure elements. The alloys typically studied lie near the corner points or the edges. One or two primary elements have significant concentration and other elements of minimal concentrations. HEAs are a novel class of materials that lie near the centroid of the compositional space. Research accelerated in this field after the independent publication of Jien-Wei Yeh and Brian Cantor’s papers in 2004. HEAs show great strength to weight ratios, fracture resistance, tensile strength, corrosion, and oxidation resistance. HEAs alloy properties are affected by the high-entropy effect, lattice distortion effect, sluggish diffusion effect, and cocktail effect. The crystal structure of HEAs consists of either face-centered cubic (fcc), body-centered cubic (bcc) crystal structure, and hexagonal close-packed (hcp) structure or a mix of any two. VNbMoTaW has excellent refractory properties, CoCrFeMnNi has excellent low-temperature mechanical properties, TaNbHfZrTi shows superconductivity. The reasons for such exceptional properties of HEAs are their microstructures. The most common structures are the Multiphase, single-phase FCC, and single-phase BCC. Generally, when a single-phase HEA is formed, it tends to create a BCC structure, as BCC lattices can accommodate a more extensive range of atomic sizes.
Compositional space for 4-element alloy [
Many techniques are used to explain and guess the different phases of the HEAs based on their parameters, like Valence Electron Concentration (VEC), thermodynamic effects of enthalpy of mixing, and atomic sizes. Phases can be predicted using CALPHAD, molecular dynamics simulations, and kinetic mechanisms. However, using the traditional methods to predict phase formation is very time-consuming and computationally expensive. Density Functional Theory (DFT) is traditionally use to indicate phases by correlating parameters and fitting to existing data. Nevertheless, this method is inadequate given that not much is known about the compositional spaces’ central regions. Traditional experimentation with HEAs is done by exploring the compositional and thermodynamic space based on trial-and-error strategies. Recently, Artificial Intelligence (AI) has been used to predict the formation of HEAs. AI use self-learning mechanisms to find patterns in given data. AI is not bound to follow fixed rules in order to obtain the solutions of a specified problem; instead, it solves each unique situation in its way. It is this unique feature of AI, which can be used to tackle the vagueness of a given system. Phase formation in HEAs is also such a vague system. AI and its subsets, Machine Learning, and Deep Learning can significantly reduce the time component involved in designing HEAs with desired properties by quickly traversing the compositional space.
Adaptive Neuro-Fuzzy Interface System (ANFIS) is an AI algorithm, which is used for phase prediction in FCC, BCC, and multi-phase HEAs. ANFIS is constructed using Artificial Neural Network (ANN) and fuzzy logic. ANN mimics the working of a human brain. Fuzzy logic is a mathematical model that works on degrees of truths, not just absolute truth and false. ANFIS method is suitable to predict the phases in HEAs [2]. Generally two approaches as depicted in Figures 3 and 4 are used in practice to model the AI problem. The first approach used the constituent elements as the inputs, while the second used a set of six crucial parameters in the formation of HEAs. In the first approach, the input elements chosen are Fe, Ni, Ti, Mn, Pd, V, Mo, Nb, Si, Ta, Hf, Zr, and W. In the second approach, atomic size parameter (δ), enthalpy of mixing (ΔΘmix), configurational entropy (Sc), single dimensionless thermodynamic parameter (Φ), intrinsic strain (εrms) and valence electronic concentration (VEC) are the six input parameters. Φ is the thermodynamic parameter; δ and VEC are the parameters of atomic scales; ΔΘmix and Sc are the parameters for nuclear interactions. The first model’s accuracy was 84.21%, while it was 80% for the second.
A framework of ANFIS: Composition-based modeling [
A framework of ANFIS: Parameter-based modeling [
Machine Learning (ML) is a subset of Artificial intelligence. ML offers flexibility; as new data becomes available; it is more rapidly able to construct relations between input and output data. Kevin Kaufmann and Kenneth S. Vecchio used an “ML-HEA” to predict the solid solution-forming ability of the HEA using thermodynamic data from ThermoCalc and chemical features with a random forest machine-learning model [3]. They also compared the ML-HEA model with other traditional models like CALPHAD and LTVC, as mention in Table 1.
Material systems | Known solid solution (Neither, FCC, or BCC) | CALPHAD | LVTC |
---|---|---|---|
Binaries | 117 of 117 (100%) | 110 of 117 (94%) | 102 of 117 (87.2%) |
Ternaries | N/A | 362 of 441 (82.1%) | 279 of 441 (63.3%) |
Quaternaries | 8 of 8 (100%) | N/A | 690 of 1110 (62.2%) |
Quinaries | 9 of 9 (100%) | N/A | 94 of 130 (72.3%) |
Comparison of ML-HEA with CALPHAD and LVTC [3].
The ML-HEA model correctly predicted the phase for 134 alloy systems with a known phase. This model is in 94% agreement with the CALPHAD model, and 82.1% agreement with the DFT based LTVC model. However, until the central region of the compositional space is further explored, it will be impossible to truly know which of these three models (ML-HEA, CALPHAD, and LTVC) is more accurate in predicting the phase of the HEAs.
Chang et al. used ML with ANN to predict the composition of non-equimolar AlCoCrFeMnNi to get the highest hardness HEAs [4]. In their limited data set of 91 alloys, they also included the alloys of Cu and Mn. The input for the network is the eight elements present in the alloy, and the target output is the hardness of the HEAs. Figure 5 shows good agreement between the experimental hardness and predicted hardness. Pearson’s correlation coefficient = 0.97, R-squared correlation = 0.94, and mean absolute error = 36 Vickers Hardness, and Table 2 shows good agreement between the predicted hardness and experimental hardness from ML3 to ML5. Here ML3, ML4 and ML5 are high entropy alloy.
Predicted versus proper hardness for HEAs, where different colors indicate the variation of the standard deviation [
Alloy | Al | Co | Cr | Fe | Mn | Ni | Experimental hardness | Predicted hardness (HV) |
---|---|---|---|---|---|---|---|---|
Al03 | 5.66 | 18.87 | 18.87 | 18.87 | 18.87 | 18.87 | 125 ± 3 | 242 ± 98 |
ML1 | 11 | 18 | 22 | 22 | 5 | 22 | 198 ± 6 | 303 ± 38 |
ML2 | 30.5 | 16 | 18.5 | 16.5 | 5 | 13.5 | 522 ± 8 | 505 ± 35 |
ML3 | 30 | 6 | 35 | 6 | 18 | 5 | 605 ± 14 | 670 ± 94 |
ML4 | 25.5 | 9 | 35 | 10 | 15.5 | 5 | 628 ± 13 | 670 ± 111 |
ML5 | 24 | 18 | 35 | 10 | 7.5 | 5.5 | 650 ± 12 | 670 ± 98 |
Nominal composition (%) of Al03 and ML alloys and their hardness values [4].
Cheng Wen et al. [5] used ML to predict hardness in an AlCoCrCuFeNi HEA system. They used two approaches using the compositions and other descriptive factors such as atomic radii, the difference in electronegativity, VEC, mixing enthalpy, and configurational entropy. They used hardness data of 155 HEAs, with the highest hardness of 735 HV. Pei et al. [6] used machine learning to identify the three most important physical properties of the elements (besides the Hume-Rothery rules) that describe the HEAs’ formation. They developed a new method based on these additional features to predict the FCC, BCC or HCP structure of the HEAs. They used a large dataset of 1252 HEAs to train a model to predict the formation of phases with an accuracy of 93%. For each constituent element of each HEA, they considered 85 elemental properties. From these 85 properties, they can identify the three most essential quantities: molar volume, bulk modulus, and melting temperature. They used these quantities to develop a model based on the physical nature of the elements. This new model was 73% accurate in predicting the phase of the HEAs. Figure 6 is a hardness map plotted with the variation of the element Al, Co, and Mn contents. The availability of data machine learning technique for alloy design can be a valuable tool to predict HEAs’ properties and constituent elements.
Hardness plot with a variation of Al, Co, and Mn contents and fixed Co content (black dashed line is the boundary of<30% risk) [
Similarly, Figure 7 used ML and the Design of experiment approach for the accelerated design of HEAs. The first machine-learning model is trained with an existing dataset consisting of hardness and composition of constituent elements. The model is applied to a search in the materials space for unknown value hardness. Based on hardness predictions, a function to select the next set of experiments is performed—this helps to choose an alloy for experimentation.
A schematic of the ML and DOE based iterative design loop for accelerated design of HEAs.
Figure 8 described the proposed methodology for the implementation of AI to develop a new high entropy alloy. AI algorithm create an AI model by training on the experimental dataset for training, and then this trained AI model is fed with the desired properties of the new model. The output of the AI model is a set of experiments for the creation of a new alloy. The set of experiments suggested by AI model is a subset of an extensive set that will result from all possible combinations of input parameters. Presently trial and error method is used to narrow down this search space, but the use of AI model can lead to selecting a more appropriate subset from this search space. Further, to evolve the AI model with time output of each suggested experiment should be added to the experimental dataset for training and a new AI model is created after each update of the dataset.
Methodology for the implementation of AI for the discovery of new alloy.
The strategy to design and develop of high entropy alloy for achieving the desired strength in the material using the artificial intelligence approach is described below with a detailed explanation:
Dataset creation: Data of experiments conducted to create new HEA can be obtained from available literature to create a dataset. This database need to be divided into three subsets (60:20:20): training, validation, and test dataset.
Selection of machine learning (ML) algorithm: For ML algorithm, various options like linear regression model, support vector machine, artificial neural network, and deep neural networks should be explored. Starting with the most straightforward algorithm viz. linear regression, each algorithm’s performance after training should be evaluated on training and validation dataset (via a loss function like RMSE for expected and actual value of hardness) using k-fold cross-validation. Based on its performance and complexity the ML algorithm can be selected.
Training of AI model: AI model created based on selecting the ML algorithm is to be fed with known composition data from the training dataset. The output of this model is the estimated value for the hardness of HEA. Several epochs of training is to be needed to arrive at an acceptable trained AI model.
Apart from composition data several physical properties of constituent elements like the difference in atomic radii between composition elements, the difference in electronegativity between components, the valence electron concentration, the mixing enthalpy, the configurational entropy, the U parameter (which is related to the entropy, enthalpy, and the melting point), the L parameter (which is associated with an atom’s configuration on a lattice and its radius) and the g parameter (the solid angles of atomic packing for the elements with the most significant and most minor atomic sizes) are related to the intrinsic properties and affect the final hardness. So further AI model should also be trained by accepting both composition data and physical properties as inputs.
Training and test datasets with bootstrapping should be used for training the AI model. A bootstrap training set can be generated by resampling the original training data and testing data with replacement. On each made training set, an AI model is created. Each of these AI models can then run on the original training dataset to give different hardness values for each training sample. This give the output as estimated hardness and standard deviation for the estimated hardness.
Use of AI model in the design of experiments: By assuming an available range of values for each constituent element, a virtual search space can be created for experiments to conducted of creation of new HEA. Using this virtual space as input to generated AI models, an estimate for hardness and corresponding confidence (standard deviation) can be computed. Candidates in virtual space can then be selected for desired hardness and sorted standard deviation value for the design of experiments.
Artificial Intelligence is a valuable tool to model, discover, and optimize new alloys that enable predicting individual material properties as a function of composition. Artificial Intelligence (AI) allows the prediction of a set of experiments to be conducted to discover new alloy having desired properties. Thus, AI can be used as an effective tool to optimize the development of new alloys. The use of AI for predicting phase, hardness, and other HEAs properties is a gateway to many possibilities. It is the flexibility of AI to adapt to new data that enables further improvement of the models’ accuracy, which will lead to a better understanding of phases of HEAs.
Synthesizing HEAs is a complicated and expensive process, so using traditional modeling to explore compositional space and synthesizing each alloy is impractical. AI can be used so that it is not necessary to explore the entire composition space. AI models can handle voluminous experiment data and are bound to outperform human counterparts. Therefore, AI can be effectively employed to competently and promptly recognize particular High Entropy alloys with desired mechanical properties.
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\n\nAll translated Chapters have to be properly attributed in accordance with the requirements included in IntechOpen's Attribution Policy. Besides proper attribution translated sections of Works must include the following sentence: "This is an unofficial translation of a work published by IntechOpen. The publisher has not endorsed this translation".
\n\nAll rights to Books and other compilations are reserved by IntechOpen. The copyright to Books and other compilations is subject to a Copyright separate from any that exists in the included Works.
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Drawing from a telephone questionnaire survey involving a sample of 300 (150 male and 150 female) owners of Italian micro-enterprises, located in Marche Region, findings suggest that entrepreneurs mostly dealt with the crisis through a defensive approach (restructuring and resizing strategies). Compared to men, female entrepreneurs had a lower propensity towards offensive strategies (innovation, development and growth). Differences in the approach towards the crisis were also identified with respect to company’s age, industry and impact of the crisis. These findings contribute to the debate on gender-based differences in behaviors, attitudes and preferences. Moreover, they can help to identify economic policy measures useful to help men and women entrepreneurs to address the crisis. Policy-makers who want to support female entrepreneurship should consider these aspects, in order to identify tools and policies that can help female firms to resist the crisis more effectively in the short-term and to seize new market opportunities in the recovery phase. The sample is restricted to sole proprietors and to a limited geographical context. 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This development has seriously challenged the environmental considerations and escalated fish health measures to combat existing and evolving problems. As an answer to these challenges and because of relatively high profit margins, aquaculture of harvested Atlantic salmon has also had a speedy innovation path. This chapter will give a theoretical background and an empirical analysis based on data collection at three companies, two in Norway and one in Chile. The focus is on how innovations take place in different stages of the production process, and how these are built into the production and managerial system. The results show, as expected, links between company operations and the actual innovations, but these links do not have the same structure in Norway and Chile. Factors like human and financial resources, technology, and company organization seem to explain most of the differences between how innovations take place in the companies.",book:{id:"9550",slug:"entrepreneurship-contemporary-issues",title:"Entrepreneurship",fullTitle:"Entrepreneurship - Contemporary Issues"},signatures:"Knut Ingar Westeren",authors:[{id:"322340",title:"Prof.",name:"Knut Ingar",middleName:null,surname:"Westeren",slug:"knut-ingar-westeren",fullName:"Knut Ingar Westeren"}]},{id:"73449",title:"Collaborative Entrepreneurship for Continuous Innovation: A Strategic Alliance Perspective",slug:"collaborative-entrepreneurship-for-continuous-innovation-a-strategic-alliance-perspective",totalDownloads:642,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Strategic alliances act as a platform to implement collaborative entrepreneurship while exposing a range of challenges. By capitalizing on entrepreneurial opportunities for continuous innovation, alliance partners can promote the productive utilization of resource-pooling systems and facilitate innovation processes for value co-creation. Simultaneously, the heterogeneity of partners in terms of different motivations and interests interferes with the advancement of collaborative entrepreneurship for resource exchange and orchestration. The objective of this paper is thus to explore how to deal with the potential coordination issues that can make an alliance vulnerable and its returns diminished through a preliminary integrative approach to the interface between collaborative entrepreneurship and strategic alliances. From this approach, three elements that can contribute to leverage values of collaborative entrepreneurship for continuous innovation are identified: social capital, entrepreneurial orientation, and interorganizational learning. Based on the discussion about the functions of each element in the context of alliance partners’ dynamic interactions, a model of analysis on collaborative entrepreneurship for continuous innovation is proposed. 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