Allocation Of Scarce Medical Resources Research Articles

To what extent do lay people and healthcare providers differ in the allocation of scarce medical resources in the context of the COVID-19 pandemic?

Studying the most ethical way to allocate scarce medical resources has been of interest within the last year, due to shortages associated with the COVID-19 pandemic. This study aimed to establish differences between what healthcare providers (HCP) and laypeople consider to be the most ethical way to prioritise the distribution of scarce resources. Healthcare providers ( n = 100) and laypeople ( n = 102) were asked to rank ethical principles from most to least ethical for the allocation of ICU beds in a COVID-19 outbreak, COVID-19 vaccinations and organ transplantation, and the rankings from each scenario across the two groups were compared. Results indicated that HCPs preferred utilitarian principles informed by medical knowledge, such as ‘prognosis’, whereas laypeople preferred less situation-specific and prioritarian principles, such as ‘sickest first’, replicating pre-pandemic findings. Laypeople showed increased in-group agreement when ranking principles, potentially due to the collective experience of the pandemic. Demographic variables such as older age, identifying as Black, Asian or minority ethnic and greater experience with COVID-19, were also associated with a preference for ‘prognosis’.

Game Theory-Based Decision Making for the Allocation of Scarce Medical Resources in the COVID-19 Situation

Game Theory-Based Decision Making for the Allocation of Scarce Medical Resources in the COVID-19 Situation

Dissecting the mediating and moderating effects of depression on the associations between traits and coronary artery disease: A two-step Mendelian randomization and phenome-wide interaction study

BackgroundDepression is often present concurrently with coronary artery disease (CAD), a disease with which it shares many risk factors. However, the manner in which depression mediates and moderates the association between traits (including biomarkers, anthropometric indicators, lifestyle behaviors, etc.) and CAD is largely unknown. MethodsIn our causal mediation analyses using two-step Mendelian randomization (MR), univariable MR was first used to investigate the causal effects of 108 traits on liability to depression and CAD. The traits with significant causal effects on both depression and CAD, but not causally modulated by depression, were selected for the second-step analyses. Multivariable MR was used to estimate the direct effects (independent of liability to depression) of these traits on CAD, and the indirect effects (mediated via liability to depression) were calculated. To investigate the moderating effect of depression on the association between 364 traits and CAD, a cross-sectional phenome-wide interaction study (PheWIS) was conducted in a study population from UK Biobank (UKBB) (N=275,257). Additionally, if the relationship between traits and CAD was moderated by both phenotypic and genetically predicted depression at a suggestive level of significance (Pinteraction≤0.05) in the PheWIS, the results were further verified by a cohort study using Cox proportional hazards regression. ResultsUnivariable MR indicated that 10 of 108 traits under investigation were significantly associated with both depression and CAD, which showed a similar direct effect compared to the total effect for most traits. However, the traits “drive faster than speed limit” and “past tobacco smoking” were both exceptions, with the proportions mediated by depression at 24.6% and 7.2%, respectively. In the moderation analyses, suggestive evidence of several traits was found for moderating effects of phenotypic depression or susceptibility to depression, as estimated by polygenic risk score, including chest pain when hurrying, reason of smoking quitting and weight change. Consistent results were observed in survival analyses and Cox regression. ConclusionThe independent role of traits in CAD pathogenesis regardless of depression was highlighted in our mediation analyses, and the moderating effects of depression observed in our study may be helpful for CAD risk stratification and optimized allocation of scarce medical resources.

SEARCHING FOR A FAIR ALLOCATION OF SCARCE MEDICAL RESOURCES UNDER EU LAW: A NEED FOR BALANCE FOR THE ELDERLY?

Fair allocation of scarce medical resources during emergency situations such as the COVID-19 pandemic is a highly challenging task. The pandemic has revealed that age was invoked as a criterion in terms of granting access to medical treatment in some European Union countries such as Italy. This paper analyses whether or not the use of chronological age alone might be justified for any such differential treatment in the event of scarcity. The paper seeks to demonstrate that older persons should not be automatically excluded from receiving access to healthcare which might otherwise amount to direct discrimination under European Union law.

The Possibilities, Limits, and Complexities of Triage in COVID-19 Regime

The new and prevailing Corona virus (COVID-19) pandemic is an extremely contagious virus. Scientific research has gone far in the study and treatment of the virus. One of the things known about it at present is that its spread depends on social contact. In this paper, I consider the challenge that allocation of scarce medical resources poses in the fight against COVID-19. Millions have been infected, just as the number of diseased also runs in thousands. The allocation of scarce medical resources during the COVID-19 pandemic regime poses a challenge to healthcare providers. In attempting to save the lives of COVID-19 patients, how should we allocate ventilators or vaccines? Since ventilators, or as at present vaccines, are scarce compared to the number of patients that need it for survival, who should get one? To address this challenge, healthcare providers often resort to triage, especially in Emergency Departments (EDs) and intensive care units (ICUs). In this paper, I discuss the possibilities, limits, and complexities associated with the principle of triage in the distribution of scarce medical resources in the treatment and attempt to save the lives of COVID-19 patients. I contend that triage as a principle of distribution of scarce health resources fails in the distribution of scarce life-saving resources to COVID-19 patients. I aim to show that the triage protocol approach fails in terms of clinical and non-clinical evidence as well as regarding procedural issues associated with its application.

Reserve system design for allocation of scarce medical resources in a pandemic: some perspectives from the field

AbstractReserve systems are a tool to allocate scarce resources when stakeholders do not have a single objective. This paper introduces some basic concepts about reserve systems for pandemic medical resource allocation. At the onset of the Covid-19 pandemic, we proposed that reserve systems can help practitioners arrive at compromises between competing stakeholders. More than a dozen states and local jurisdictions adopted reserve systems in initial phases of vaccine distribution. We highlight several design issues arising in some of these implementations. We also offer suggestions about ways practitioners can take advantage of the flexibility offered by reserve systems.

Identifying And Exploring Bias In Public Opinion On Scarce Resource Allocation During The COVID-19 Pandemic.

The COVID-19 pandemic offers an opportunity to examine public opinion regarding the allocation of scarce medical resources. In this conjoint experiment on a nationally representative sample of US adults, we examined how a range of patient characteristics affect respondents' willingness to allocate a ventilator between two patients with equal likelihood of short-term survival and how this differs by respondents' attributes. Respondents were 5.5percentage points less likely to allocate a ventilator to a patient with a disability than to a nondisabled patient. Disability bias was correlated with older age cohorts and higher education levels of respondents. Liberal and moderate respondents were more likely to give a ventilator to Black and Asian patients than to White patients. Conservatives were much less likely to allocate a ventilator to transgender patients than to cisgender patients. These findings demonstrate the importance of bias mitigation and civil rights enforcement in health policy making, especially under conditions of scarcity.

Addressing Shortcomings in Contingency Standards of Care

Addressing Shortcomings in Contingency Standards of Care

A Comprehensive Review of Machine Learning Used to Combat COVID-19.

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

North American COVID-19 Myocardial Infarction (NACMI) Risk Score for Prediction of In-Hospital Mortality.

BackgroundIn-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is higher in those with COVID-19 than in those without COVID-19. The factors that predispose to this mortality rate and their relative contribution are poorly understood. This study developed a risk score inclusive of clinical variables to predict in-hospital mortality in patients with COVID-19 and STEMI.MethodsBaseline demographic, clinical, and procedural data from patients in the North American COVID-19 Myocardial Infarction registry were extracted. Univariable logistic regression was performed using candidate predictor variables, and multivariable logistic regression was performed using backward stepwise selection to identify independent predictors of in-hospital mortality. Independent predictors were assigned a weighted integer, with the sum of the integers yielding the total risk score for each patient.ResultsIn-hospital mortality occurred in 118 of 425 (28%) patients. Eight variables present at the time of STEMI diagnosis (respiratory rate of >35 breaths/min, cardiogenic shock, oxygen saturation of <93%, age of >55 ​years, infiltrates on chest x-ray, kidney disease, diabetes, and dyspnea) were assigned a weighted integer. In-hospital mortality increased exponentially with increasing integer risk score (Cochran-Armitage χ2, P ​< ​.001), and the model demonstrated good discriminative power (c-statistic ​= ​0.81) and calibration (Hosmer-Lemeshow, P ​= ​.40). The increasing risk score was strongly associated with in-hospital mortality (3.6%-60% mortality for low-risk and very high–risk score categories, respectively).ConclusionsThe risk of in-hospital mortality in patients with COVID-19 and STEMI can be accurately predicted and discriminated using readily available clinical information.

Polarized Citizen Preferences for the Ethical Allocation of Scarce Medical Resources in 20 Countries.

Objective. When medical resources are scarce, clinicians must make difficult triage decisions. When these decisions affect public trust and morale, as was the case during the COVID-19 pandemic, experts will benefit from knowing which triage metrics have citizen support. Design. We conducted an online survey in 20 countries, comparing support for 5 common metrics (prognosis, age, quality of life, past and future contribution as a health care worker) to a benchmark consisting of support for 2 no-triage mechanisms (first-come-first-served and random allocation). Results. We surveyed nationally representative samples of 1000 citizens in each of Brazil, France, Japan, and the United States and also self-selected samples from 20 countries (total N = 7599) obtained through a citizen science website (the Moral Machine). We computed the support for each metric by comparing its usability to the usability of the 2 no-triage mechanisms. We further analyzed the polarizing nature of each metric by considering its usability among participants who had a preference for no triage. In all countries, preferences were polarized, with the 2 largest groups preferring either no triage or extensive triage using all metrics. Prognosis was the least controversial metric. There was little support for giving priority to healthcare workers. Conclusions. It will be difficult to define triage guidelines that elicit public trust and approval. Given the importance of prognosis in triage protocols, it is reassuring that it is the least controversial metric. Experts will need to prepare strong arguments for other metrics if they wish to preserve public trust and morale during health crises.HighlightsWe collected citizen preferences regarding triage decisions about scarce medical resources from 20 countries.We find that citizen preferences are universally polarized.Citizens either prefer no triage (random allocation or first-come-first served) or extensive triage using all common triage metrics, with “prognosis” being the least controversial.Experts will need to prepare strong arguments to preserve or elicit public trust in triage decisions.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

COVID-19 Vaccine Refusal and Fair Allocation of Scarce Medical Resources

COVID-19 Vaccine Refusal and Fair Allocation of Scarce Medical Resources

Dynamic Allocation of Medical Resources During the Outbreak of Epidemics

During the outbreak of epidemics such as coronavirus disease (COVID-19), the local hospitals often withstand a sharp increase of patient influx, which renders the healthcare system on the verge of collapse. To alleviate the situation, the effective allocation of scarce medical resources during the pandemic plays a vital role. The essence of the healthcare system in time of emergency is to stay functional, and to be able to diagnose and hospitalize as many patients as possible. Fangcang shelter hospital, as a novel way to temporarily increase the capacity of the local healthcare system, is proven to be effective against the COVID-19 pandemic. To improve the performance of the healthcare system with Fangcang, many practical factors need to be taken into account, such as the patient deterioration during waiting to be admitted, the referral mechanism according to the severity of the patients, and the selective admission regulations. To address the high volatility and time-varying feature of the COVID-19, a multistage and multi-type medical service network model is established, and a dynamic allocation strategy of the medical resources at each stage is proposed based on a stochastic optimization problem, which is then solved via the fluid queueing approximation. Combined with the real data collected from Wuhan, it is revealed that the proposed algorithm could help with the allocation of medical resources during the outbreak of epidemics. Even with limited medical resources available, the method could still maintain a guaranteed service level while keeping the healthcare system operational. Furthermore, the simulation analysis validates that our method can effectively allocate medical resources at each stage, so as to stabilize the system performance and fulfill the medical demand for multiple types of patients. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To fend off the outbreak of epidemics, the lessons have to be learned from the past. The successful control of the spread of COVID-19 in Wuhan (China) is a classical example of applying modern medical practices and management tools. In the present article, the treating procedure of COVID-19 in Wuhan is modeled as a multistage decision problem, which includes the screening with nucleic acid testing, the further testing, the treatment of patients with mild/severe symptoms, or even critical patients. The introduction of Fangcang shelter hospital is crucial for winning the battle against COVID-19. The current study attempts to determine the timing of introducing the Fangcang shelter hospital during the outbreak of a major epidemic, and helps allocate the medical resources needed to contain the spread of the virus. It is discovered that the actual number of beds in the Fangcang shelter hospital is far more than what is necessary, and it would be better to have built the Fangcang some time in advance. In the meantime, the number of designated hospitals for COVID-19 is in line with the results obtained via the optimal staffing strategy proposed here, but it is also noticeable that these hospitals should be released of duty sooner to fight against not only COVID-19 but also other diseases in reality.

Ventilator Allocations: The Effect of Mere Identifiability

The COVID-19 crisis has raised a dire dilemma among medical professionals. Faced with a shortage of critical equipment and supplies, how do hospital administrators and physicians determine whether to divert resources from one patient to another? Most decision-makers will prioritize saving younger patients over older ones, because older patients generally have a much shorter life expectancy. But emotions, such as those elicited when a patient's name is known and the patient is thereby humanized, can interfere with rational decision-making. At the height of the pandemic, we conducted three studies in which participants were asked to imagine being hospital officials tasked with allocating ventilators under two conditions: when the affected patients were and were not identified by name. Participants were less likely to reassign a ventilator from an older patient to a younger one when the patients had been named than when they had not. These results suggest that decisionmakers are more likely to make the efficient choice—the one that should save more years of life—when the individuals affected by the choice remain anonymous. When patients are humanized by being named, less rational and more emotional considerations appear to govern how people choose to distribute lifesaving equipment. Our findings imply that keeping patients anonymous may help facilitate the efficient allocation of scarce medical resources.

The accuracy of nurse-led triage of adult patients in the emergency centre of urban private hospitals.

Background: Triage is applied in emergency centres (ECs) to assign degrees of urgency to illnesses or injuries to decide in which order to treat patients, especially when there are many patients or casualties, facilitating the allocation of scarce medical resources. A triage nurse determines triage priority by assessing patients using an established triage tool with specific criteria. The South African Triage Scale is widely used in South African ECs. Although the South African Triage Scale has been adopted and implemented in both private and public healthcare ECs in South Africa, few studies have assessed the accuracy of nurse-led triage in private ECs.Aim: To determine the accuracy of nurse-led triage in ECs in urban, private hospitals.Methods: A quantitative, descriptive, retrospective study was done. Three private hospitals with similar average patient volumes were purposively selected. We sampled the nursing notes as follows: 1) we stratified nursing notes by nurse qualification and then 2) for each category of nurse we stratified nursing notes according to triage priority level and 3) then systematically randomly selected the recommended number of notes from each triage priority level for each nurse category. We retrospectively audited 389 EC nursing notes to determine the accuracy of nurse-led triage. For each note, we independently applied the South African Triage Scale, and then determined agreement between our score and the score determined by the triage nurse.Results: We recorded 342 triage errors, consisting of triage early warning scores (TEWS) errors (n = 168), discriminator errors (n = 97) and additional investigation errors (n = 77). Overall agreement between the triage nurses and our scores was 71.7% (n = 279). Triage errors (n = 110) consisted of 3.9% (n = 15) over-triage errors and 24.4% (n = 95) under-triage errors. The highest level of agreement was between our scores and the scores of the emergency trained registered nurses (85%) and enrolled nursing assistants (78%).Conclusion: In South African ECs, the South African Triage Scale is not always correctly applied, which can lead to almost a quarter (24.4%) of cases being under-triaged and not receiving timeous care. Our results suggest that emergency trained registered nurses are well equipped to be triage nurses, and that this skill should be developed in South African nursing curricula.

Development of decision-making considerations to support equitable patient selection in paediatric haemophilia trials

Abstract Background Clinical trials for investigational haemophilia treatments such as gene therapy offer a potentially life-changing opportunity to those who are selected for enrolment. However, the number of enrolment slots available for these trials is often greatly exceeded by the number of eligible patients. Many of the strategies that are commonly used to select candidates for participation can be highly unsystematic, inequitable, and subjective. A more rigorous set of criteria is therefore needed to evaluate each candidate's suitability for trial participation in order to eliminate bias in selection and fulfill the ethical principle of justice. Aims To review current knowledge and issues in patient selection for paediatric haemophilia clinical trials with competitive availability, and to develop a more objective standard for decision-making that takes into account the needs of all involved parties. Methods A literature search on the ethics of trial participant selection and the practice of fairly distributing limited medical resources was conducted to identify previous literature and best practices in the area. A list of essential decision-making considerations was then designed to guide the selection of paediatric participants for haemophilia therapy trials through iterative group discussions between a diverse team of health professionals at McMaster Children's Hospital, Hamilton, ON, Canada. Results Current practices in resolving this ethical issue are highly heterogenous, although there are some common themes and recommendations. The three main criteria supported by the team and the literature search for inclusion in the considerations were: medical need, need for support, and potential safety considerations for the patient. Three measures for evaluating each criterion were developed and added for consideration during the decision-making process. The role of patient selection in meeting the scientific aims of the trial was also considered. Conclusion Attempting to create an equitable, systematic decision-making procedure for clinical trial participant selection involves a wide variety of competing values and ethical considerations, and discrepancies between recommendations are commonplace. The criteria presented here are intended to be used as a guideline to assist the equitable selection of paediatric patients for participation in haemophilia clinical trials with highly limited enrolment, although it may have some applicability to other areas of clinical research or therapeutic areas concerned with the allocation of scarce medical resources. Next steps should involve speaking with patients, community members and other stakeholders in order to include their perspectives. Assessment of medical need, potential support needs, and safety considerations form the basis of criteria for discussions around how to make enrolment in paediatric haemophilia clinical trials more equitable © Shutterstock

Commentary on "The Father-Daughter Dinner Dance: A Waltz With Ethics and COVID-19".

This story—“The Father-Daughter Dinner Dance: A Waltz With Ethics and COVID-19”—is based on conversations I have had with friends, family, and colleagues about the allocation of medical resources during the COVID-19 pandemic. The characters and conversation in this story are all fictional. Although there is a strong public health effort to flatten the curve of infected individuals with COVID-19 over a longer period of time so that the health care system does not become overwhelmed, it is very likely that this pandemic will cause medical shortages. We are already hearing about a lack of personal protective equipment, ICU beds, and lifesaving technologies such as ventilators.1 During medical shortages, decision makers turn to bioethicists for advice on how to prioritize difficult decisions in an ethical and systematic manner. It is well noted that utilitarian approaches to decision making during times of scarce resources in health care are deemed acceptable.2 The basic criterion of utilitarianism as formulated by Jeremy Bentham in 1789 is the greatest happiness for the greatest number.3 In health care, it is desirable to save the greatest number of lives or the most life-years by giving priority to individuals likely to survive the longest after a given treatment.4 One of the most influential medical journals recently published an article that offered 6 recommendations for the fair allocation of scarce medical resources using a utilitarian perspective.1 Utilitarian approaches do not take into account that some health care professionals discriminate against certain groups of people. Although most health professionals are not likely to intentionally discriminate against their patients, they may hold negative attitudes that could cause their patients to feel disrespected and misunderstood.5 Research has demonstrated how some negative attitudes toward certain stigmatized groups are more acceptable than others. For example, negative attitudes toward people with mental illness or obesity are more acceptable than explicit racist or sexist beliefs.6,7 Negative attitudes become harmful stereotypes that create systemic barriers to health equity and result in biased clinical decision making in diagnosis and treatment of marginalized groups. In light of the knowledge we have about the negative health effects of discrimination on marginalized groups, it is frightening to think how a utilitarian approach could create life-threatening disadvantages during discussions of the fair allocation of scarce resources. This story highlights how social positions and identities intersect to create an environment of bias and discrimination against certain members of society.8 The daughter is a young, educated woman. She does not seem to have a chronic illness. She has completed a philosophy degree, but there is some pressure on universities to eliminate the humanities in favor of degrees deemed more productive. We do not have information on the father’s educational attainment, but we know that he has a blue-collar job and that he has a mental illness. The father and daughter’s social identities interact with the concept of ageism, which usually occurs against older adults; in this case, though, ageism is shown to affect younger adults depending on their health status. The mother, who comes into the story briefly, brings issues of gender and body image as intersections that combine with mental health to highlight further injustice and inequity. The purpose of creating a story on such a timely and complicated topic is to encourage the reader to think about how injustice toward one group can end up perpetuating systems of inequities toward other groups of people, especially when utilitarian approaches to lifesaving resource allocation are left unquestioned.

Етичний компас медичних рішень під час пандемії COVID-19

The article actualizes and structures significant problems of the medical sphere that arise in the context of the COVID-19 pandemic, in terms of the relations vectors: global world – state – person, state – clinic – society, clinic – doctor (medical staff), clinic – patient, doctor (medical staff) – the patient. The authors presented the evolutionary context of the norming process of medical resources and emphasized the hierarchical scheme of the regulation process of scarce resources norming in the health care system under the pandemic crisis conditions. The paper substantiates approaches to making medical decisions on “sorting” and applying a number of its forms depending on various regional, national, religious, and local models of the ethical values system formation. Emphasis is placed on American and European models of bioethics. The authors consider the utilitarian approach to preparing medical solutions of “clinical sorting” to level ethical catastrophes in a pandemic based on Catholic ethics. The issues of regulated norming of scarce medical resources and the “clinical triage” of patients during the COVID-19 pandemic in Ukraine have been investigated. The main bioethical dilemma of the COVID-19 pandemic is outlined. The authors actualize criteria and models of ethical medical solutions for equitable allocation of scarce medical resources in the context of the COVID-19 pandemic. These criteria and models are defined in the Ethical Guidelines for Responding to COVID-19 of the Bioethics Committee at the Council of Europe, the US Department of Health and Human Services; in normative documents in the field of ethics of medical decisions during the COVID-19 pandemic of the National Medical Associations, chambers, centres of bioethics of Italy, Hungary, USA, Great Britain; in the guidelines of national, religious and local institutions for the preparation of medical decisions for the levelling ethical catastrophes during the pandemic and the studies of international bioethics experts. The paper identified the need to formalize the fair distribution of scarce resources during the COVID-19 pandemic in Ukraine. The authors suggested recommendations for the implementation of ethical values and priorities for their application in critical conditions of shortage of medical resources and personnel in the COVID-19 pandemic in the health care system of Ukraine.

Awareness of ethical dilemmas enhances public support for the principle of saving more lives in the United States: A survey experiment based on ethical allocation of scarce ventilators

Recommendations by health experts to deal with public health emergencies are primarily guided by the principle of “saving more lives”. It is unclear whether people perceive this principle as ethically more legitimate than some other principle such as “saving more life-years”. Understanding the answer to this question is particularly relevant to the allocation of scarce medical resources during public health emergencies. Different principles typically lead to different allocations, and consequently have dramatically different implications as to who survives and who dies. We fielded an online randomized controlled survey experiment in the context of scarce ventilator allocation with a demographically representative sample of US adults (n = 700) from October 22 to October 30, 2020. Participants faced hypothetical situations where they had to allocate few available ventilators among several needy patients. The experiment was designed such that the allocation decision made by a participant can be used to infer the principle in line with their personal ethical values. We interpret this inferred principle as the one that the participant perceives to be most legitimate. The treatment group, but not the control group, was provided balanced information that described the ethical dilemmas faced by experts in developing ventilator allocation guidelines. Nearly half of the participants in the control group perceive saving more lives the most legitimate principle. Despite the balanced nature of the information, the perceived legitimacy of saving more lives was 7·6 percentage points higher in the treatment group. The magnitude of this impact was particularly strong among republican-leaning participants, a subgroup that has less trust in experts according to previous research. Our findings suggest that enhancing public awareness of ethical dilemmas faced by health experts can increase the perceived legitimacy of their proposed guidelines even among those with lower trust in experts.