Unhealthy changes in eating behavior among people experiencing trauma have been observed. There is, however, a lack of in-depth knowledge regarding the impact of the after effects of traumatic life experiences on eating behavior. Because eating behavior represents important components for promotion and maintenance of good health throughout life, this study aimed to explore inpatients’ lived experiences of the impact of traumatic stress on eating behavior.Thirteen female and two male inpatients (age range 28–62 years), recruited from a psychiatric clinic in Norway, participated in this qualitative explorative focus group study. The data analysis was performed using systematic text condensation.The results in the present study describe the participants’ experiences about the impact of traumatic stress on their eating behavior. Their discussions and descriptions disclosed problems that could be summarized into four main themes: “experiencing eating behaviors as coping strategies”; “experiencing being addicted to food and sweets”; “experiencing eating behaviors controlled by stress and emotions”; and “experiencing lack of appetite and reduced capacity to plan and prepare meals”.Traumatic stress can impact eating behavior in several complex ways that over time may cause adverse health consequences. The results add to an important understanding of changes in eating behavior that might appear in people struggling to cope with the after effects of traumatic life experiences to the existing literature. To better understand the complexity of how traumatic experiences may impact eating behavior, this knowledge is important and useful for health professionals offering support to those who experience struggling with eating behavior after traumatic experiences. This study aimed to explore trauma-exposed inpatients experiences about the impact of traumatic stress on eating behavior. Thirteen female and two male inpatients with a history of trauma, recruited from a psychiatric clinic in Norway, participated in this qualitative explorative focus group study. The results in the present study describe the participants’ experiences about the impact of after effects of traumatic experiences on eating behaviors. The findings are summarized into four main themes: “experiencing eating behaviors as coping strategies”; “experiencing being addicted to food and sweets”; “experiencing eating behaviors controlled by stress and emotions”; and “experiencing lack of appetite and reduced capacity to plan and prepare meals”. The results contribute to the existing literature and provide an important understanding of changes in eating behavior that might appear in people struggling with traumatic stress after traumatic experiences. This knowledge is important and useful for health professionals offering help to those struggling with their eating behavior after traumatic experiences. Traumatic stress, which refers to physical and psychological reactions occurring in varying degrees after traumatic experiences, characterized by symptoms such flashbacks; nightmares; avoidance; a sense of “numbness” and emotional blunting; anhedonia and acute burst of fear or panic , can cauTraumatic experiences might be a risk factor for developing disordered eating behavior. Childhood trauma are found to be a risk factor for developing EDs –9, and aEmotional regulation difficulties and emotional eating is suggested as mediators in the association between binge-eating and post-traumatic stress symptoms , and emoIn recent years the differentiation between, and potential overlap with, food addiction and EDs such as BN and BED have been discussed in the literature . Result Most previous research addressing changes in eating behaviors after traumatic experiences have been quantitative. There is still a lack of in-depth knowledge about the impact of lived experience with traumatic stress on eating behavior. Therefore, the aim of this qualitative study was to explore the experiences of inpatients with severe trauma-related mental health disorders and the impact this had on their eating behaviors.The present study is the first part of a research project exploring the occurrence of, and possible risk factors for, metabolic syndrome and type 2 diabetes mellitus after traumatic experiences. To explore and describe the subjective experiences of how traumatic stress impacts on eating behavior, we applied a explorative qualitative focus group design with an inductive approach . Using fParticipants were recruited from a psychiatric clinic in Norway which offers inpatient treatment to adults (> 18 years) with severe mental disorders such as PTSD, complicated with anxiety and depression. The clinic has a total of 25 beds where the patients live and receive intensive psychological treatment for a period of three months. The first contact with the informants was made by nurses and psychologists at the clinic who provided information about the study and obtained written consent. Thereafter, the first author contacted the recruited participants, gave information about time and place for the focus group interviews, number of participants in the focus group, and repeated the purpose of the study.Thirteen female and two male participants (age ranged 28–62 years) from the south-eastern part of Norway who received treatment at the clinic participated in the study. The mean age of the participants was 44 years (SD = 9.6). All participants in the study had a history of traumatic experiences and had received treatment for severe trauma-related disorders in the specialist health service for many years. According to the criteria for receiving treatment at the clinic, the participants had no current severe symptoms of psychosis, severe ED, acute suicidality, substance abuse, self-harm or violent behaviors.information power  I am scared of what will come, so I use food to suppress it. It is so uncomfortable. I do not know what it represents or… Or I am starting to realize, but what it represents is in the past and it is so disturbing for me. I cannot deal with it at the moment, so I just have to eat. […] The quantity and what I actually eat has varied throughout life, but I eat, and mainly sweets.The participants discussed and described eating behavior, e.g. binge eating, increased intake of fast food, sweets and salty snacks, restrictive and controlled eating motivated by fear and a strong urge to regulate or avoid discomfort related to stress and emotions, especially in relation to the previous trauma. As one female participant stated: “It is so easy to just eat”. The participants described a fear of emotions and traumatic memories that might arise if they did not eat. One participant explained:Some foods I can eat. Other foods just trigger my anxiety and restlessness and fear and chaos. [Hesitates] […] And that makes it very difficult today, if someone serves you some of this food […]. I will have to drown the food in something I like, like sour cream or gravy, to camouflage the taste, camouflage the memories. Instead one ends up eating loads of “bad” food, because this is food you manage to eat. That, in turn, resulting in you gaining weight.For a few participants, certain types of food, or the eating situation itself, triggered traumatic memories, gave feelings of bad conscience or generated full anxiety. The previous trauma made it difficult to sustain a varied diet and led to increased consumptions of fast food, salty snacks and sweets. Their eating behavior became, in a way, a form of strategy to avoid anxiety, panic or bad conscience. As one participant explained, “[…] then [after the trauma] I start throwing up and struggle a lot with food. In periods I binge-eat, which gives me bad conscience and I stop eating”. Some also hid their consumption of sweets and salty snacks, because of the fear they carried from the experienced trauma. One participant explained:Another theme discussed and described by the participants was the function of food, sweets or salty snacks for self-comforting. They would seek out food that comforted them or calmed emotions; eating a bag of potato chips instead of asking for a hug, or in tough times, eating pizza, salty snacks or sweets because they felt they deserved it. As one said, “I feel drawn to food items that I think will give me a sense of comfort”.or purge behavior e.g., by only eating unprocessed food; avoid eating potatoes, rice and pasta, or unhealthy overeating. As one explained, “I’m scared of what will happen emotionally if I eat more than my normal amount”. Other participants explained that eating too much or unhealthy foods affected their body-image negatively, or made them feel hypo-arousal which then triggered traumatic memories. In contrast, another participant experienced her body-image triggered consumption of unhealthy food because she forced herself into eating unhealthy to become as ugly as she felt, while another participant could use food as a form of self-punishment, explaining the following:[…] Since I was quite young, in addition to other types of self-harm, I have used… I could eat chocolate really fast and then purge it immediately after. I remember it as a way of keeping control. I also felt more alert and present afterwards. So food goes in, but it has to come out as well. This I could control (laughter), I couldn't control the other things.Other participants described restrictive and controlled eating, The urge to suppress emotions trumps the urge to eat normal food. I call it my medication- binging on the wrong kind of food. But of course, it is not a viable solution.Both binge eating and not eating at all was explained, by two participants, as a form of self-medication that made the “chaos” inside their head a bit calmer. “You can take a pill or you can stop eating, it will be a bit of the same”, one explained. Another participant described:It has just been a way to survive […]. It worked as a survival mechanism when I was young, but then it has just persisted because the emotions are sort of stuck in time. I still cannot relate to what happened. Do not want to know. So, I just have to go on, with the coping mechanisms. It keeps the emotions and discomfort at a distance. […]. It turns out it has had a much larger and deeper emotional function than I have realized. [Sighs] […]In the absence of other coping strategies, binge-eating or restrictive eating developed in childhood or adolescence as a coping strategy to “survive” painful emotions or forget severe traumatic experiences, was described by some participants. “If an adult had asked me as a child why I started eating a lot of food, perhaps things could have been discovered, and other things prevented”, one explained. Another participant explained:Food, in a way, is harmless, because it is acceptable as opposed to intoxication, alcohol or drugs…. Even though it has the same function, it is sort of okay because it’s legal and isn’t considered to be a serious problem. Being underweight or obese are seen as problems, but the problem is more complex and multifaceted, and is something I have trivialized my whole life (sighs). But in fact, there is so much more to it.In all three focus groups, some participants discussed and compared food, sweets and salty snacks with addiction and abuse of alcohol and drugs, and the complexity of why people might get addicted. For example, one participant explained:These participants experienced that it was especially difficult being addicted to food, compared to alcohol, because they still had to eat and relate to it several times a day. As one explained, “I might be able to hold my pantry clean for junk food, and avoid buying it if I’m steadfast enough, but I still have to deal with it in my everyday life you know”. In the context of addiction, a few participants also discussed how they experience being met by others if saying “no thanks” to sweets. As one explained, “Contrary to the positive feedback I would have got if I said no thanks to wine as a former alcoholics, I am met with questions and reluctance if I say no thanks to cake since because I am a former sugarholic”. About the experience of sugar addiction, another explained, “I don’t eat chocolate because I get so addicted. […], and I think I’m predisposed to be addicted”.In addition, the participants discussed how stressful periods with general hardship, or months of trauma therapy, made them crave especially for sugar, salt and fat. Several kilos of sweets could be consumed, and some described compensatory eating behaviors, such as purge behavior or restrictive eating to compensate for the intake of sugar. As one explained, “And then I restrict, only eating a minimal amount, really forcing my body back to my starting weight so I can go back to eating sweets again”. Some participants could lose their appetite, but still crave for sweets and salty snacks. As one explained, “It’s a large craving for the sweet and salty snacks, like Smash and such tasty snacks. It’s impossible to eat just one”. Another said that she could even dream about sweet buns, and just had to buy buns, because not buying them felt wrong.The bad conscience that comes after binging makes you isolate yourself and not leave the house. It’s a consequence from binging, a ripple effect.Some of the participants also shared that they were hiding drinking soda or their intake of sweets and salty snacks. Nobody ever saw them eating or drinking it, and that eating so much sweets led to isolation because of bad conscience, shame and guilt. One participant explained:Despite feeling their body becoming unwell consuming amounts of fast food, and knowing about the unhealthy consequences, some participants reported continuing to consume as they felt the consequences were insignificant in comparison to the strong need to suppress emotions and stress. As one stated, “It just has to be done”.[…] When I’m stressed and desperate I make really bad choices (crying) […] I can’t think clearly, but rationally I know it's not a good choice, but I end up making it anyway. Later on, when I have the time and the state of mind to reflect upon it, I don't understand why I'm choosing the way I do.In all three focus groups, participants discussed the experience that stress and emotions led to impulsive and uncontrolled food choices which resulted in binge-eating or increased intake of fast food, sweets and salty snacks. “It’s not something I feel that I control. I’m controlled by stress and emotions”, one explained. Another participant shared feelings of desperation in stressful times and difficulties in thinking clearly:It comes to a point when I’ve been sitting eating sweets, and suddenly it dawns upon me. You just feel bloated and stuff. There are more left, but I’ve come to my senses and realize “this is no good”. [Laughter]. So, I throw it in the trash. I have come to realize I have thrown away loads of money.For many participants, it became clearer for them, in the focus group discussion, how stress actually affected their eating behavior unconsciously. As one expressed, “[…] it just became clear now, because I’m talking about it, but otherwise just happens by itself”. Another told that she experienced eating sweets without knowing it, or that she could eat sweets, and then, suddenly become aware of it, thinking that it was not healthy. She explained the following:better periods’ in their lives, the participants explained that they made more conscious, thoughtful and healthy food choices.For a few participants, even if they made a list before grocery shopping, or aimed not to buy sweets, they got so stressed by all the people, that they ended up buying chocolate anyway. “I can feel my body just moving towards sweet buns and soda, like the body goes on autopilot”, one explained, while another described that she felt the whole world was raging when entering the checkout and seeing all the sweets. Afterwards, they could feel even more stressed because of bad conscience, and some expressed that they did not understand why they made such food choices in stressful situations. To compensate for consuming fast food and sweets, a few participants would punish themselves afterwards with fasting or purging. However, in what they called ‘Participants discussed their experiences of losing their appetite and feeling of hunger in difficult and stressful periods, and not having time or energy to plan and prepare meals because of stress, anxiety or depression. “I have no appetite these days. It’s like nothing tastes good at all, except from sweets or Cheese Doodles”, one explained, and another that she felt her body became allergic to everything except water or coffee when feeling stressed or anxious. Some described irregular diets and unstructured eating habits. As one explained, “At home, I don't have any routines, at least not regarding meals, you know. I get up late, and have breakfast at 6 pm, right”.It's all about energy. When I'm feeling stressed and don't have the energy or time to cook, I have a lot of cappuccino with hot milk and chocolate buns or take-away meals. Then it's what's simple, and nearby, like Burger King or other take-away shops. There are no healthy take-away options where I live, so it is about accessibility.The participants explained that they felt too exhausted thinking about what they should eat, and that it was easier to just resort to easy food solutions, such as junk food, hot-dog, noodles, kebab, pizza, sweet buns, cappuccino, Cheese Doodles, potato chips or sweets. Preparing food at all, could for some also be difficult. As one participant expressed, “When I am extremely exhausted and frail, and life feels terrible, just turning on the oven to heat up a pizza is unbearable”. So, the food had to be easy to prepare, and not time consuming:When you are in a better place, it’s physically possible to have a regular meal and keep it inside your body without it hurting or something.On the other side, in days of wellbeing and feeling calm, they felt more appetite, had the time and capacity to both plan and prepare healthy meals, and felt it was easier to actually eat food:why traumatic stress impacted their eating behaviors.The main finding from the present study is that the participants eating behaviors were severely affected in various complex ways as a result of the experience of stress, resulting in four main themes: experiencing eating behaviors as coping strategies”; “experiencing being addicted to food and sweets”; “experiencing eating behaviors controlled by stress and emotions”; and “experiencing lack of appetite and reduced capacity to plan and prepare meals”. In addition to the participants’ experiences of how traumatic stress impacted eating behaviors, they explained their experiences of survive illustrates the severity of the suffering and the forces that underlie behavior, which also appears in the participants' use of expressions like “a strong urge” and “scared of what will come”. Results from recent findings suggest that unhealthy eating behavior and diet quality in people who have experienced trauma can be understood by emotional eating, emotional regulation difficulties and emotional dysregulation [Our results show several ways of how traumatic stress in acute stressful situations, or connected to negative emotions, traumatic memories and restlessness can impact eating behavior. Using words like gulation , 36, 37.gulation . Similargulation , and pregulation , 41. Furgulation , might bgulation –44.Previous studies have focused on disordered eating and emotional regulation in people who have experienced trauma , 36, 37.Further, our results suggest that for some participants, eating behavior such as binge-eating or restrictive eating is experienced as a form of self-medication. Food is easy to access, and considering the function it could serve in regulation of mood and stress –23, is imotivates the behavior, i.e. the driving force behind individuals’ behavior. In the light of Hedonistic Motivational Theory [In order for healthcare professionals to understand patients’ eating behaviors as a strategy for regulating and controlling emotions and traumatic stress, it is important to be person-centered and to understand what l Theory , the behl Theory , howeverConsequences of maladaptive eating behavior, both consuming high amounts of sweets, salty snack and ultra-processed food, or restrictive eating, might have serious consequences for the individuals’ health. Long-term effects include a number of severe health consequences –48, inclIn the participants’ discussion about food, alcohol and drugs, they compared food, sugar, and their eating behavior to drugs and alcohol addiction. The concept of food addiction has been critically discussed in the literature , but theChronic stress has been found to indicate increased food addiction scores in mice being fed palatable food . If thisIn line with earlier findings showing that depression and stress-like mood increases cravings for chocolate , our resIt appears from our results that some participants experienced their eating behavior to be controlled by stress and emotions or experienced themselves as losing control and awareness over food choices, eating when feeling stressed and overwhelmed by emotions. There is no doubt that the participants experienced being controlled by stress and emotions, which clearly emerges from statements like “it’s not something that I feel that I control”. Apparently, they struggled with high levels of stress in some situations, using words like “desperate”. Previous research supports these observations , 19, 65.Traumatic stress can evoke two emotional extremes, either feeling too much (overwhelmed) or too little (numb) emotions . ExperieAdditionally, as in the second theme, participants talked about compensatory eating behaviors  since thSome participants experienced losing appetite and hunger when stressed. The fact that stress affects both hunger and appetite is a known phenomenon. Yau and Potenza  writes tThis fourth theme may differ from the above-stated three main themes; the participants described experiences of perceived stress in their daily life, and how this impacted on eating behaviors, without directly relate to previous trauma or traumatic stress. Despite this, the fourth theme was considered of importance and included. The participants experience about this theme was reported in all three focus groups, and it is conceivable that their experiences is indirectly connected with previous trauma, leading to lower current tolerance for and ability to cope with daily stress accompanied by changes in eating behaviors. For example, their experiences of exhaustion can be understood as posttraumatic stress symptoms, i.e., fatigue .Further, if people with a history of trauma experience that perceived stress in everyday life impact eating behaviors by causing reduced capacity, including time and energy, to plan and prepare meals, leading to intake of ultra-processed food, sweet and salty snacks. This provides important knowledge for further understanding and research in the area. Despite that perceived stress in everyday life is far less dramatic than major traumatic life experiences, daily problems that accumulate, persist over time and affect special vulnerable areas of the individual , may havThe present study using a qualitative approach, gives only an insight into a relatively small group of peoples’ experiences. Firstly, the small sample size and low heterogeneity of this group might not reflect all of the perspectives and nuanced information in this specific topic, and exploration should be conducted with care. Because of the relatively comprehensive research topic of our study, and no established theory used, final conclusions would require a larger sample size .Secondly, since trauma history, referred to as the individuals’ subjective experience of trauma, was an inclusion criterion in the study, we did not systematically measure PTSD-symptoms. However, the aim of the present study was to explore how inpatients experience the impact of traumatic stress on eating behavior, regardless of specific types of traumatic events. Thirdly, none of the informants took advantage of the opportunity to read the transcriptions and have, therefore, not been given the opportunity to make any corrections or comments to what they have said, which thus may affect the validity of the results . IncludiFourthly, translation involves the risk of losing elements of meaning regarding the participants’ accounts. Additionally, using focus groups to obtain data about a sensitive subject like this might affect the depth of the stories of the participants. However, the aim of the study was to explore the field, and using focus group interviews enabled collection of much data in a relatively short time, in addition to the fact that the discussions facilitated important topics that the researchers had not foreseen.Consequences of traumatic experiences can be serious, affecting individuals in psychological and physiological, as well as behavioral ways. Evidently, the participants’ experiences in this present study show that after effects of traumatic life experience impacts eating behavior in several complex ways . These results contribute to the literature in providing an understanding of changes in eating behavior that might appear in people struggling with traumatic stress after traumatic experiences. This knowledge is important and useful for health professionals who offer help to people struggling with eating behavior after traumatic experiences since it emphasizes the need to better understand the complexity of eating behavior that over time may have a negative impact on health. However, these present observations call for further research, understanding how traumatic experiences can impact eating behavior, both in terms of being able to offer evidence-based help, but also to understand the connection between trauma and physical health conditions. Further research is needed with a focus on possible associations between trauma, eating behavior that can cause adverse health consequences, stress and disordered eating. Mapping trauma history and PTSD-symptoms and the occurrence or symptoms of EDs, whilst investigating the impact of traumatic stress on eating behaviors, might be a further line of enquiry.


Open access funding enabled and organized by Projekt DEAL.The article “Supporting independence at home for people living with dementia: a qualitative ethnographic study of homecare”, written Monica Leverton, Alexandra Burton, Jules Beresford Dent, Penny Rapaport, Jill Manthorpe, Ignacia Azocar, Clarissa Giebel, Kathryn Lord, Claudia Cooper was originally published electronically on the publisher’s internet portal on 24 April 2021 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 9 September 2021 to © The Author(s) 2021 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The original article was corrected.


A theoretical framework predicts that using polygenic screening to select embryos against traits that depend on many genes has few benefits. Related research article Lencz T, Backenroth D, Granot-Hershkovitz E, Green A, Gettler K, Cho J, Weissbrod O, Zuk O, Carmi S. 2021. Utility of polygenic embryo screening for disease depends on the selection strategy. eLife10:e64716. doi: 10.7554/eLife.64716Using genetic tests to select ‘designer babies’ has been a subject of science fiction for generations, but it is now getting closer to becoming a reality. Like in many other areas of science, techniques are progressing much faster than ethical and policy discussions . Indeed,However, the plummeting cost of genetic sequencing and the sophistication of the tools used to predict characteristics based on the genetics of an individual will soon make it possible to screen for ‘complex traits’ – this is, traits that depend on many genes. This type of analysis is called polygenic screening. In short, it assesses the probability of an embryo exhibiting a trait  based on the collection of genetic variants it carries that are known to influence that trait. Polygenic screening could allow couples to produce several embryos through IVF, check how likely each one is to manifest one or several complex traits, and, based on that information, decide which embryo to implant. Unlike screening for conditions that depend on a single gene, however, polygenic screening cannot always guarantee that an embryo will manifest a condition. The traits that can be tested for using polygenic screening include many health outcomes, but also characteristics that are more controversial to target, such as the IQ of a future baby .Now, in eLife, Todd Lencz , Shai Carmi  and colleagues – including Daniel Backenroth (Hebrew University) as joint first author with Lencz – report on the usefulness of polygenic screening when testing for complex health traits, such as schizophrenia and Crohn’s disease . BrieflyLencz et al. largely take predictive accuracy as a given  illuminates an important asymmetry in the utility of selection. Take, for example, the case of a couple having to select one of five embryos for implantation. The benefits of ruling out the one or two embryos with the highest risk scores, and then selecting from the remaining three or four embryos by chance are small. This is because although a couple is choosing at random between three embryos that do not have the highest risk of the disease, each of them could still be at a moderate risk of having the disease. Instead, a better strategy is to pick the embryo with the lowest risk score.However, this result provides the first reason why parents, if they are well-informed, are unlikely to perform polygenic screening. Assuming that the negative attitudes most Americans have against enhancing traits through gene editing extend to genetic screening, parents are likely to want to screen embryos to avoid disease, but are probably against choosing the ‘best’ embryo .Even if parents (counter to intuition) did want to choose the ‘best’ embryo, deciding which is the ‘best’ soon becomes an impossible task. Lencz et al. only consider the situation where parents are choosing between embryos more or less likely to exhibit one or two complex traits; but what happens when several traits, each dependent on many genes, are of interest? A decrease in the risk of one health condition, for example, could lead to an increase in the risk of another. Trading off risks between schizophrenia and Crohn’s disease would be difficult, but when other characteristics such as genetic risks for height, IQ, and eye color are thrown into the mix, the decision becomes impossible. This ‘paradox of choice’ is a second reason that parents will not choose polygenic screening if they are properly informed about it .For both of these reasons, the analysis by Lencz et al. places critical focus on the lack of utility of genetic screening for complex traits, and the findings are an incredibly important contribution for science and for public and policy discussions. Nevertheless, a number of questions about the value of polygenic screening still remain. Lencz et al. answer the (constrained) question of which embryo to choose if you must choose one. Indeed, if there are five embryos with similar risks to pick from, Lencz et al. assume that parents will select one randomly. A related question is whether these parents should implant one of the five embryos they already have, or wait to use others. This may be answered using a method that predicts whether a couple are likely to produce a lower-risk embryo than the ones they already have, based on the parents’ genetic information .An obvious next step will be to study actual couples  because couples in the real world are likely to be more genetically similar than couples chosen at random . Like th


The pandemic caused by the spread of the SARS-CoV-2 virus forced governments around the world to impose lockdowns, which mostly involved restricting non-essential activities. Once the rate of infection is manageable, governments must implement strategies that reverse the negative effects of the lockdowns. A decision support system based on fuzzy theory and multi-criteria decision analysis principles is proposed to investigate the importance of a set of key indicators for post-COVID-19 reopening strategies. This system yields more reliable results because it considers the hesitation and experience of decision makers. By including 16 indicators that are utilized by international organizations for comparing, ranking, or investigating countries, our results suggest that governments and policy makers should focus their efforts on reducing violence, crime and unemployment. The provided methodology illustrates the suitability of decision science tools for tackling complex and unstructured problems, such as the COVID-19 pandemic. Governments, policy makers and stakeholders might find in this work scientific-based guidelines that facilitate complex decision-making processes. Virology scientists believe that there are approximately 1.7 million undiscovered types of viruses that are hosted only by mammals and birds. These scientists have no precise information about their nature, how they are transmitted to humans and spread, or their accompanying symptoms  environment. Since the emergence of multi-criteria GDM (MCGDM) methods in the early 1970s, they have been applied in different disciplines and contexts. MCGDM methods have been demonstrated to be suitable for finding solutions to complicated problems with conflicting criteria  methodology based on MCGDM approaches to identify the most important indicators that should be considered when a reopening strategy is implemented. A total of 16 criteria, which are grouped into four “dimensions”, are evaluated with respect to their impact on a given reopening strategy (see Table The reason for selecting these indicators and no others is based on a literature review carried out for these purposes. According to worldwide organizations such as the United Nations (UN), Organization for Economic Cooperation and Development (OECD), Bank for International Settlements (BIS) and Food and Agriculture Organization of the United Nations (FAO), among others, the cited indicators are widely utilized for ranking, comparing or investigating countries FAO ; UN 2021This study identifies 16 indicators in four categories, the economy, environmental protection, societal well-being and individual health and ranks their importance for reopening strategies after COVID-19 lockdowns have been lifted.This study employs a novel IVIFS-DSS methodology based on MCGDM approaches (entropy weighting and AHP) to help policy makers prioritize and select indicators that must be considered when a reopening strategy is carried out.This study provides practical guidance that can help policymakers and planners make better decisions during the ongoing pandemic and future pandemics.Our work aims to assist DMs and policy makers by identifying the best mix of indicators that should be considered when a reopening strategy is implemented. The novel contributions of this research are as follows:This section gathers the theoretical foundations behind the proposed methodology. It is composed by three subsections. Formal definitions for IFS and IVIFS are firstly presented. They are followed by detailed explanations of linguistic variables and theirs respective IVIFN equivalences. At the end, the IVIF DSS methodology, which comprises eight steps, is presented. For sake of simplicity, note that section four follows the same eight-step sequence that is here presented at subsection three.Z, then it can be expressed in the following way.After ordinary fuzzy set theory was proposed by Zadeh  to addrementclass2pt{minimmentclass2pt{minimA be an IVIFS, where In some cases, it is not easy to exactly define the membership degrees for certain elements of the IFSs. Thus, Atanassov and Gargov  proposedk et al.  and can For convenience, an IVIFN is denoted by α. Its formal definition was proposed by Xu and Yager  as \docuk et al. , Büyüközk et al. , Garg ank et al. One advantage of IVIFS with respect to other techniques is the possibility of elaborating reasoning assessments, which are given in terms of linguistic variables   Experience refers to the number of years that each DM has been in the professional field since the date of his/her first job. (2) Knowledge refers to the area in which the DM is specialized, and (3) responsibilities indicates whether the DM supervises other people or staff.k = 1,…,K represents the number of DMs participating in the study. Next, a fuzzy entropy measure is applied for each IVIFN to estimate the DM weight vector  Ye . Hence, Each DM is asked to provide his/her judgement regarding each dimension and the related indicators through individual pairwise comparison evaluation matrices. Linguistic terms, which are shown in Table k = 1,…,K. The IIFWA operator inputs the vector of the weights of the DMs, as illustrated in formula  is applied to aggregate the evaluation matrix  formula .\documenkth DM, the IVIFN is given by i-th dimension are computed in similar form. Finally, the IVIF values, which are based on the DMs’ evaluations, are aggregated into a unique main dimension by utilizing the IVIFWA operator, as shown in the following expression , for which the respective values are provided in Table to Saaty , a CR isThe weights of the dimensions and their indicators are objectively determined by using the IVIF values as inputs \documenti-th dimension is calculated 12), the 3) is the most important dimension, followed by “Economic growth” (C1), “Individual well-being” (C4) and “Environmental protection” (C2).The ranking of the dimensions, based on their weights, is carried out. “Societal well-being” (C32), “Reduction in unemployment” (C32), “Retail sales” (C13), “Freshwater source preservation” (C23) and “Nutrition” (C23). Note that “Societal well-being” and “Environmental protection” are the two dimensions with four indicators. The complete ranking of all indicators with respect to the global weights is provided in Table In relation to the global weights, the five most important indicators are “Reduction in crime and violence” , International Monetary Found (IMF) and Food and Agriculture Organization (FAO), stakeholders can find here a scientific-based approach for conducting further research related with the pandemic evolution.In this work, we do not attempt to set the foundations for designing post-COVID lockdown strategies. Our goal is much more modest. Considering that the COVID pandemic is a phenomenon with multiple variables, we contribute to the discussion of how this complex problem should be approached and highlight additional elements for analyses. Given that the DMs participating in this study are not directly involved in public policy making, our results are more illustrative than prescriptive. Considering this framework is based on an approach that takes generic indicators proposed by international organizations, country level implementations should be carefully studied, since each country has peculiarities and not all proposed indicators might be suitable for all countries. Thus, we attempt to put at the hand of governments, policymakers and stakeholders, consistent and evidence-based tools to evaluate difficult choices and avoid further deterioration in the economy, employment and wellbeing for societies and individuals. Additionally, the present work illustrates the suitability of decision science tools for tackling complex and unstructured problems, such as the COVID pandemic, and consequently make an original contribution in that direction. Additional research involving expert public policy makers in the DSS process is needed to produce helpful results for society. Further research related to the present pandemic or other pandemics that might arise in the future can find in this work a set of general guidelines through which decision-making processes can be conducted within a clear and structured framework.


The promise of personalised medicine (PM) to transform healthcare has sparked great enthusiasm in the last years. Yet, its lack of consensus around the nature and scope of the concept has ended in terminological confusion amongst the users in primary care. We aimed to investigate the perceptions of doctors and their patients in response to this evolving concept. This present article focuses on the general understanding of personalised medicine, underlining the confusion over the concept.Semi-structured comprehensive interviews were conducted with 10 general practitioners (GPs) and 10 of their patients. The purposive sampling took into account the doctor’s age, sex, and place of practice ; each doctor recruited one patient of the same age and sex. Each interview began with the same open-ended question about the participant’s knowledge of the topic, after which a working definition was provided to continue the discussion. Using the grounded theory method, the analysis consisted of open coding, axial coding and selective coding.From our present analysis focusing on the general understanding of PM, three main themes representing the concept emerged. The first two representations being “centred on the person as a whole” and “focused on alternative and complementary methods”, in which the therapeutic relationship was stated as key. The third theme “medicine open to innovation” involved the few participants who had a good understanding of the concept and could associate personalised medicine with genomics. For those who value therapeutic relationship, the risks of accepting innovation could result in “fast-food” medicine and interpersonal barriers.PM is predominantly unfamiliar in family medicine. It is misinterpreted as a holistic or integrative type of medicine. This semantic confusion probably lies in the choice of the label “personalised” or from the lack of a uniform definition for the term. The concept of personalised medicine has generated great enthusiasm in the last years, promising to revolutionise healthcare with optimised diagnostic, therapeutic and preventive strategies based on an individual’s genomic, epigenomic and proteomic profile . Today, . It has also been demonstrated that training, education and the development of best practices and guidelines would be key before adopting PM in everyday clinical practice ” (M9)“I hope to have always the time to discuss, to be able to give one hour to a patient who has existential or life questions. [ … ] Doctors are always needed to provide support” (M2)Their patients expressed a similar understanding of PM, where the person is the focal point. They emphasised the doctor-patient relationship, where GPs would take the time to listen and involve them in all aspects of care:“Nurses and doctors are now increasingly involved in patient care, and show concern for the patient. We now find an approach focused on the person in patient care and management, care that is centred on his needs, on his characteristics [ …]. ” (P7).Through this approach, they want to have a global vision of each patient’s situation:“I see a naturopathic practitioner, and I think she does that. Because she enquires about my emotions, my symptoms, and yes, she takes a global approach to my being. It’s about more than just telling me « you have a pimple, let’s see … does it itch? Etc etc [ … ]»” (P3)This interpretation of personalised medicine also derives from the caring perspective. It shares similarities with that of “the person as a whole”, in the way that one keeps the overall vision in mind, take the time for patients and provide them with the support required. According to the participants, these are prerequisites before introducing any medical innovations.Some patients referred to non-conventional approaches of medicine, where alternative and complementary methods meet traditional evidence-based medicine. When asked to give examples of personalised medicine, a patient brought up their naturopathic practitioner, who regards the person on both the physical and psychological levels:“So for me, what first comes to mind … is mainly research on a genetic level, to study either diseases, or specific genes which could be used to develop treatment for a patient [ … ]” (M10)“A type of medicine that is customised … truly individualised for each patient … based on their genetic characteristics. As you see, my body, my heredity is - umm - different from another person. So, I probably have (genetic) mutations … An effective treatment for me, is not necessarily so for someone else … ” (P7)These interviewees had knowledge of personalised medicine either from personal experience (in the area of oncology) or from the media. PM had indeed stimulated much public interest and awareness in 2013, from the widely broadcasted “Angelina effect”, in connection to heredity breast cancer.“[ … ] I read in the newspaper that Angelina Jolie had a double mastectomy because she did a genetic test, which showed that she had a high probability to develop breast cancer.” (P10)Despite the perceived benefits on a therapeutic level, GPs insisted that the emphasis of patient care should remain on clinical assessment.“I would still tend to say: “let’s do a physical exam (e.g. a cardiac exam) to have a baseline”. Then, I would advise my patients to have a follow-up a couple of years later in case. Even if the genetic risk profile is negative.” (M3)As a consequence of PM, a few doctors believe they would be relying more on standardised algorithms in patient management, thereby assuming a reductionist approach that could potentially alter interpersonal relationships. One patient expressed similar opinions and compared genomics to “fast food” medicine.“Because we rely on many standards and values such as, I don’t know, laboratory values, imaging tests, or other. And patients are also very … I want to say a little too dependent on these lab results, scan report. And when we are too absorbed in something, we lose the overall picture. So, I believe that it will somehow put a screen between doctors and the patient.” (M1)“I think the relationship between doctor and patient is multi-layered, so to say. There’s also social communication, which is as important, if not more [important], than medications. So, if you remove this layer and respond with “fast food” that is based on algorithms, this social role disappears. And from my experience on life and human relationships, without this, medicine would lose much of its usefulness.” (P5)Many dread the advent of interpersonal barriers from the implementation of PM in clinical practice.“I think that our society will go a little adrift with all this. In my opinion, we lose our true bearings and what’s important in life, relationships between people, empathy, fondness, affection and happiness. … I find that people are already quite self-absorbed and all about keeping focus on oneself, and I find this quite deleterious.” (M4)With the right tools at hand, some GPs will be willing to implement PM in family medicine.“It’s inevitable. Because techniques move forward, so we cannot go against the change. I think we should just go with the flow.” (M2)Last but not least, our interlocutors have highlighted uncertainties and challenges around the integration of genomic medicine in primary care. Of those, the need for research and further education to adapt to the change and manage possibly high demands, has emerged:“It would be necessary for doctors to be trained to know how to communicate the information efficiently and appropriately, and also to manage the stress that it (genetic information) can generate for the patient [ …]. ” (M1)Furthermore, they expressed the necessity of regulatory measures facing innovation, even if this does not seem easy.“So, I am for PM to be state regulated [ … ] I support freedom of choice, but while keeping solidarity and community spirit in mind. There are many issues with the present society, which is one with selfish people, one that is increasingly focused on personal gain.” (M8)“The problem is that there are two components of the law, there’s justice and justness. And in between, there’s all the issues concerning humans, lobbies, influences and ignorance.” (P5)This representation encompasses genomics and other new technological advancement of medicine. A few participants provided a relatively accurate definition of PM:Our study highlights that personalised medicine is a well-established concept in medical literature but it bears an entirely different meaning in primary care. From our results, it outlines a more integrative or holistic approach. In other terms, participants have largely confused PM with person-centred medicine or even integrative medicine.In primary care, the key point is to devote support and time to the patients for the shared decision-making process. For the few participants who understood that we were talking about medicine open to innovation, it was also important not to forget the doctor-patient relationship by promoting a reductionist vision. Innovation could only be introduced through research, training and regulation.The therapeutic relationship between carer and patient has evolved over the years, from the paternalistic model towards patient autonomy. One of the fundamental characteristics of family medicine, as defined by the WONCA, the academic association for general practice, is to develop a person-centred approach . The ideA primary cause for misinterpretation probably lies in the choice of the label “personalised” to define a technology that is derived from genomics. It could be speculated that renaming this technology with a more precise term could be less ambiguous. It should be noted that there is a non-exhaustive list of terms that have been used interchangeably in literature over the years, including precision medicine, genomic medicine, stratified medicine and individualized healthcare . BesidesSchleidgen et al. proposed a more precise definition of personalised medicine in their paper, in an attempt to help clarify the conceptual differences between stakeholders concerned with PM . AccordiThe main strength of this study is the inclusion of the perspective of both patients and GPs, giving a global representation of family medicine in both rural and urban regions of the French-speaking part of Switzerland. Furthermore, the team involved in this study is multi-disciplinary, with GPs, a public health doctor, a geneticist and an anthropologist. This ensured that the topics covered in the interviews involved the issues considered by key stakeholders.Collaborating with an anthropologist also led us to adopt an emic (interviewee’s point of view) rather than an etic (researcher’s point of view) approach.The purposive sampling of this study could be disputed as participating GPs chose the patients, based solely on criteria of the same age and sex. None of the researchers were involved in the recruitment of patients. Participating GPs have often admitted that patient recruitment for such a topic was no easy task. So, it is possible that the selection of patients was conditioned by some prior knowledge or interest. It is indeed difficult in such research to start from knowledge devoid of any background information. Another limitation is the sample size, although small, was large enough so that no new salient themes emerged after coding all twenty interviews. Therefore, it could be argued that the data are not necessarily generalizable to all GPs and users in family medicine. We must remember that this qualitative study was the preliminary part of a bigger one.All participants were interviewed on a voluntary basis and were all given an information sheet, briefly explaining the study. Both groups were informed that personalised medicine involved genetic risk profiling. However, details such as the aim and methodology of the study were additionally given to GPs. Although GPs had a more in-depth overview of the study, interviews were conducted with the same open-ended question and were led as an open discussion.The present study emphasises the need to reach consensus on the definition of personalised medicine, while taking in consideration all key stakeholders’ objectives and interest. A prerequisite to the implementation of genetic medicine in family practice is to understand the different points of view and to take into account the different languages: molecular biology and socio-cultural ones .In order to avoid any terminological ambiguity, we believe that “Genomic medicine” would be an appropriate term, considering the necessity of using genetic material to exploit this technology. However, according to Jaccard et al., “precision medicine” would be more fitting to limit the widespread opinions amongst GPs that medical care is already “personalised” . PrecisiIn this study, GPs would clearly have the pivoting role in interpreting genetic information for the patients in a shared-decision process. Hence, doctors stressed the need for further research, training and education, including guidance in the interpretation of genetic risk profiles, so as to limit the risks of over-medicalisation .


Funding for early-stage biomedical innovation has become more difficult to secure at the same time that medical breakthroughs seem to be occurring at ever increasing rates. One explanation for this counterintuitive trend is that increasing scientific knowledge can actually lead to greater economic risk for investors in the life sciences. While the Human Genome Project, high-throughput screening, genetic biomarkers, immunotherapies, and gene therapies have made a tremendously positive impact on biomedical research and, consequently, patient lives, they have also increased the cost and complexity of the drug development process, causing many investors to shift their assets to more attractive investment opportunities. This suggests that new business models and financing strategies can be used to reduce the risk and increase the attractiveness of biomedical innovation so as to bring new and better therapies to patients faster. It’s a pleasure and an honor for me to serve as President of the International Atlantic Economic Society. I want to begin by thanking Katherine Virgo and the IAES leadership for giving me this opportunity to address today’s conference attendees.I have to begin with a disclaimer. I am no biomedical expert by any means. In fact, I’m not even a healthcare economist. My stock and trade are in applying mathematical and statistical models to investing, risk management, financial stability, and other issues in financial economics. I became interested in healthcare for purely personal reasons—friends and family members dealing with various kinds of cancer. It was through that process that I came to understand that financial economics plays a tremendously important role in healthcare, sometimes too big a role. This theme, and what we can do to change it, is what I hope to share with you today.Let me start with the simple observation that biomedicine is at an inflection point. Here’s an example. In August 2015, James Earl Carter, former president of the United States, issued a press release announcing that he was suffering from stage 4 melanoma, a form of skin cancer that had metastasized to his liver and his brain. Stage 4 cancer is the most advanced form; there is no stage 5. Stage 5 is the morgue.Upon reading this sad announcement, I assumed that fairly shortly thereafter, we would be witnessing a funeral at Arlington National Cemetery to bury our 39th president, but that’s not what happened. What did happen a few months later was nothing short of a miracle. In December, President Carter announced that he was free of cancer. All his tumors were gone. In fact, by March 2016, he announced that he was no longer seeing his oncologist. As far as his doctors were concerned, he was “cured” of one of the deadliest forms of cancer, and at the very last stage of its course.For those of you who have ever dealt with this terrible affliction, you know that oncologists rarely ever use the word “cure” because cancer can mutate and return with a vengeance, but they’re now starting to use this word more often. While this is a remarkable story, I can assure you that President Carter is not unique in this respect. Many people today are actually being cured of cancer. This is what I’m referring to when I say that biomedicine is at an inflection point.Convergence: The Future of Health  published a report in 2016 that describes this inflection point. The title of their report is omics, the study of the business models and financing strategies needed to pay for all of these amazing therapeutics.This convergence has often been characterized as the so-called “omics” revolution: genomics (the study of the specific sequences of our deoxyribonucleic acid (DNA)); epigenomics (the study of the on/off switches that cause certain genes to be expressed and others to be suppressed); transcriptomics (the study of how these DNA sequences are translated into proteins that make life possible); proteomics ; metabolomics ; and, most recently, microbiomics . All of these “omics” have experienced dramatic advances over the last decade, with the exception of one. The “omics” that has lagged behind is econThis isn’t to say that financial economics has experienced no progress. On the contrary, many financial innovations have fueled the growth of a number of sectors. However, in the biopharma sector, the business models and funding strategies have not kept pace with the rapid pace of innovation, creating funding shortages on occasion, and typically at the early stages of preclinical drug discovery. Better ways of funding all of these amazing therapeutics is what I want to focus on today.Scientists and other stakeholders in this field often refer to something they call the “Valley of Death,” the stage of research and development (R&D) between fundamental scientific research and human clinical trials where funding is particularly scarce. At the later stages of drug development, there’s considerably more capital available. By the time a drug candidate has successfully gone through multiple rounds of clinical testing , it’s said to have been “de-risked,” at which point billions of dollars pour in to complete the clinical testing process and bring the drug to market. However, to take an idea from its inception in the laboratory all the way into the clinic  is very difficult. There isn’t nearly as much money for translational medicine as there is for late-stage drug development, hence the term “Valley of Death.”The question I asked when I first learned about this phenomenon is “why?” Why is it the case that, in the midst of all these amazing breakthroughs, it’s so difficult to get funding for early-stage drug discovery? It turns out that the answer, at least the one I’ve come to, is economics and more specifically, the impact of increasing risk and uncertainty on drug development.To unpack this answer, we must first briefly review how drugs are developed, a process that I’ve come to appreciate because of its underlying economic logic. The process starts with basic science in the laboratory. Once scientists understand a particular disease mechanism and wish to develop a drug to disrupt that mechanism, they need to test out their proposed method of disruption in human subjects. For obvious ethical reasons, before human trials are allowed, a drug candidate’s various properties must first be studied in Petri dishes, and then in animals like rats, dogs, or primates. If all goes well with these animal studies, then at some point scientists will feel confident enough to ask the U.S. Food and Drug Administration (FDA) for permission to administer the drug candidate to human subjects.This is known as a phase 1 clinical trial, which involves recruiting a small number of volunteers to help assess the safety and maximum tolerable dose of the drug candidate . If this phase goes well and the side effects, if any, are tolerable, a phase 2 trial is initiated in which a larger group of volunteers suffering from the disease targeted by the candidate are recruited to study the candidate’s efficacy. If the candidate is shown to be effective, then a much larger phase 3 trial is initiated to determine whether the efficacy still holds in larger and more heterogeneous populations. If safety and efficacy are established in this larger population, then the candidate becomes an approved drug. Such a process typically takes 5 to 10 years or longer, thousands to tens of thousands of volunteers, and several hundred million to billions of dollars.Part of the reason for this lengthy and expensive process is to ensure that human subjects are never exposed to unnecessary risks. However, the result is the fact that drug development is characterized by three properties: (1) it’s very costly; (2) it takes a long time; and (3) the historical probability of success is generally very low . Together, these three features make it extra difficult to develop a drug. From an economic and financial perspective, they make it extra difficult for investors to fund this process from beginning to end and it’s getting worse.Biomedical experts have coined a term for this troubling trend: Eroom’s Law  cocktail treatment, five anti-retroviral therapies that aren’t particularly effective individually, but when taken together, turn a deadly disease into a manageable chronic condition. Once scientists discovered that combinations can work where individual therapies don’t, it led to a number of combination therapies, including the therapy that Jimmy Carter received.There are currently approximately 2,800 approved drugs on the market today. How hard would it be to search through these drugs to find a pair that might be effective in treating a given disease? In fact, a number of biomedical experts believe that we already have all the drugs we need to treat any disease and just need to find the right combinations. However, with 2,800 drugs to choose from, there are 3,918,600 unique pairs. Each requires a separate and independent series of clinical trials to evaluate safety and efficacy, which will take many years and many millions of dollars, and with generally very low probabilities of success. If we consider triplets, quadruplets, or, as in the case of the AIDS cocktail, quintuplets, as well as other variables such as dosage regimens and biomarkers, it’s clearly impossible to do any kind of systematic search across all combinations, despite the fact that, from a scientific and ethical perspective, it makes sense to examine these possibilities.The fact that we’ve become smarter has greatly increased the complexity of drug discovery which, in turn, has implications for financial risk. For example, the number of ways that young upstart biotech companies can now render existing franchises of big pharma completely obsolete by developing more effective combination therapies has greatly increased. If there’s one thing financial economics has taught us, it’s that investors don’t like risk and they vote with their dollars.Just so we understand what we’re up against when dealing with investor risk preferences, let me offer an illustration that I use with first-year master of business administration (MBA) students in my introductory finance class. I ask them to choose one of four different financial investments displayed in Fig. Each of these investments has a different risk–reward tradeoff. The solid black line turns a dollar into two dollars over this unspecified investment period; not very interesting, but not particularly risky. The solid gray line turns a dollar into five dollars, more rewarding, but more volatile. The dotted black line is the most rewarding of all, turning a dollar into about $7.50, but with far more volatility. Finally, the dotted gray line is somewhere in the middle, turning a dollar into about $6.50, not nearly as rewarding as the dotted black line, but not nearly as risky. Please take a minute now to make a choice before reading on.It turns out that, among the vast majority of audiences to which I’ve presented this series of investments, the most popular choice by far is the dotted gray line because it seems to have the best tradeoff between risk and reward. I suspect this is what you chose as well.Let me now reveal what the four investments are Fig. . First oThe solid gray line is the Standard and Poor’s (S&P) 500; most of us already invest in this asset through our retirement funds, yet each year I run this poll, only one or two of my MBA students choose it. Had you invested in the U.S. stock market in 2008, congratulations, you would have done just fine since then.The dotted black line is the pharmaceutical company Pfizer; much more volatile but had you invested in 2008, you would have outperformed the S&P 500 by a significant margin. Only a handful of students  choose this option.What about the most popular choice, the dotted gray line? This is the performance of the Fairfield Sentry Fund, a private investment vehicle that served as a feeder fund for the Bernie Madoff Ponzi scheme. Investors in this fund lost everything in 2008 when the scheme collapsed, which is why I had to end my example in 2008.This is a classic illustration of human nature. We’re all drawn to high-yielding, low-risk assets. In financial economics, we have a concept for describing this tendency: the Sharpe ratio, which is defined as the excess expected return above the risk-free asset divided by the risk (as measured by the standard deviation). During the sample period, the Sharpe ratios of Pfizer and the S&P 500 were 0.43 and 0.54, respectively, whereas the Sharpe ratio of the Madoff Ponzi scheme  was 2.98, an order of magnitude larger than the other two. This is how Madoff was able to extract $50 billion of wealth from unsuspecting investors, who were mesmerized by Madoff’s high Sharpe ratio.As the “omics” revolution has increased the complexity and risk of biomedical assets, the Sharpe ratios of those assets have declined, other things equal, implying that capital will shift to lower-hanging fruit . This trend will be especially pronounced in the riskiest parts of the industry .Now consider another investment opportunity, this one involving a single asset that requires a $200 million upfront investment, will take 10 years before any return is generated, and has a probability of a positive cashflow of 5% . Would you be willing to invest in this opportunity?When I present this investment to my students, I usually get no takers. Every so often, a particularly intrepid student will raise their hand and ask, “in the 5% outcomes, can you tell us what the payoffs are?” Most investors don’t even bother asking. When they see numbers like $200 million upfront, a 10-year holding period, and a 95% failure rate, they have no interest.These are the back-of-the-envelope numbers for what it takes to develop and test a typical anti-cancer compound: a present value of $200 million in out-of-pocket costs, 10 years of clinical trials from phase 1 to 3, then FDA review, and an overall success rate of 5%.Let me restate this investment opportunity: an upfront payment of $200 million, a 10-year investment horizon, and then a 5% probability of a $12.3 billion payoff in year 10 or a 95% probability of nothing. Would you invest? These parameters imply an expected annualized rate of return of 11.9% during the investment horizon and an annualized standard deviation of 423.5%, a Sharpe ratio of 0.02. This is why so few investors are interested. It’s simply too risky a proposition.This is the nature of the challenge in the biopharma industry, and it’s also where financial economics may be able to play a useful role. I learned this in a very personal way years ago, when my mother was battling non-small cell lung cancer. She had exhausted the standard of care, nothing seemed to be working, so we started looking into various clinical trials. An MIT colleague introduced me to a very successful biotech company developing a number of anti-cancer therapies, including one for non-small cell lung cancer. I was privileged to meet with the chief scientific officer, who brought along his chief financial officer (CFO). I asked the two of them what I thought was a relatively straightforward question: “Does your source of financing have any influence on your scientific agenda?” I was quite interested in that agenda, because one of the projects on their list might have been able to help my mother. The chief scientific officer shook his head ironically, looked at his CFO, looked back at me, and said, “Influence?? Our funding drives our scientific agenda.”I was absolutely shocked by that response. As an economist, I get it (you have to pay for stuff), but as the son of a dying patient, I was absolutely outraged. What does stock market volatility, Fed policy, or interest rates have to do with whether you should treat cancer by angiogenesis inhibitors or immunotherapy? Nothing. Yet these financial considerations often drive even a successful company’s scientific agenda.As I said at the outset, I’m not a biomedical expert, but shouldn’t the science be driving the financing instead of the other way around? I think we have it backwards. That’s what started me down this path of trying to understand whether we can do better for patients by using different types of financing. I think we can. Financial engineering can play a significant role in turning this around, allowing the science to drive the financing.Let me give you an example. Suppose that instead of focusing on one anti-cancer program, we invest in 150 of them, all at the same time. Now, I know it’s going to take 150 × $200 million or $30 billion of capital to do this. Where are we going to get $30 billion? Economists have a very simple answer to this question: assume we have $30 billion.I realize this may be less than satisfying to non-economists, but I’ll come back to this shortly. If we did manage to raise $30 billion, then consider the investment profile of a fund investing in 150 mutually independent projects, each with the risk–reward profile of the project described earlier (we’ll return to the independence assumption later as well). It turns out that the economics of this business proposition are entirely different.As a consequence of diversification, the expected return is the same 11.9%, but the standard deviation becomes 34.6%, yielding a Sharpe ratio of 0.34. Would you be willing to invest in this opportunity? Many more investors would and that’s where the $30 billion will come from; from investors who are attracted to the much-improved performance prospects of a highly diversified portfolio of mutually uncorrelated anti-cancer programs.Now, a number of questions are prompted by this overly simplistic analysis of the biopharma industry, but perhaps the most immediate is, “Can we really raise $30 billion from investors?” The answer is, “It depends.” It depends on the underlying risk–reward profile of these 150 assets. If the assumption of independently and identically distributed (IID) projects with the risk–reward profile of Fig. 4How much debt can we issue? Again, it depends on the portfolio. With 150 IID projects, each with a 5% probability of success, the probability of three successes out of 150 “shots on goal” (to use a hockey or soccer term) is 98.18% according to the binomial distribution. Therefore, such a portfolio should be worth at least 3 × $12.3 billion = $36.9 billion with 98.18% probability in year 10. If we finance the cost of this portfolio by issuing 10-year pure discount bonds with a face value of $36.9 billion, the probability of default is simply 1–0.9818 = 0.0182, or 1.82%. This corresponds to a historical default rate of single-A-rated debt.Using other financial engineering tools such as securitization,At this point, many readers may be questioning the wisdom of this approach. After all, securitization and the associated alphabet-soup of financial securities figured prominently in the 2008 financial crisis. Why would we want to employ the same techniques here?In fact, it was studying the 2008 crisis that motivated me to follow this line of thinking. The financial crisis occurred not because these techniques didn’t work, but because these techniques worked far too well. Securitization is an extraordinarily powerful tool for raising large amounts of capital over a short period of time to focus on very specific purposes. Imagine if some of those purposes included curing cancer, Alzheimer’s, and the many other diseases that afflict patients today. Imagine if those purposes included some of the biggest challenges facing humankind, such as climate change, the looming energy crisis, space colonization, or decaying infrastructure.Of course, we do have reason to be wary. For example, one of the key lessons of 2008 is the critical role that correlation played in creating unanticipated losses in the subprime mortgage market. The same applies in the biomedical context. If our assumption of independence is correct, then the probability of at least three successes out of 150 programs is indeed 98.18% as the black curve in Fig. Therefore, the key to unlocking the tremendous pools of capital in corporate debt markets is to manage the correlation of the portfolio, something financial economists have understood since Markowitz’s  path-breFrom his perspective, the pharma industry is more like third-grade soccer. They tend to crowd around the same ideas rather than diversifying. Of course, there are compelling scientific reasons for pursuing similar therapeutic modalities. The current focus in oncology on immunotherapies is a case in point. Scientific breakthroughs in recruiting the human body’s own immune system to fight tumor cells has led to a number of first-in-class therapies, including pembrolizumab, or Keytruda, the drug that cured President Carter’s stage 4 melanoma.From a biomedical perspective, pursuing the most exciting therapeutic modalities that have been scientifically validated by recent research is sensible, especially given the high fixed costs associated with developing expertise in a given modality. However, from a financial perspective, the clustering of investments in the same therapeutic class does little to reduce risk. If one anti-PD1 therapy fails its clinical trial, that’s generally not positive news for similar anti-PD1 therapies in the same biopharma company’s pipeline. As a result, the Sharpe ratio of a highly correlated portfolio will attract less capital than that of a well-diversified portfolio.In fact, diversification is one of the most direct routes to a higher Sharpe ratio and lower cost of capital, and this aspect underscores a critical and puzzling feature of biomedical portfolios. Recall from the Sharpe-Lintner capital asset pricing model (CAPM) that the cost of capital of a given investment, where What do you think a typical drug company’s beta is? More specifically, how do the betas of early-stage drug discovery companies in the biotechnology sector compare to those of mature pharmaceutical companies?Common intuition would suggest that the betas of early-stage biotech companies should be close to zero. After all, what do stock market fluctuations have to do with whether a cancer drug program will succeed or fail? In the case of large pharmaceutical companies, this same logic suggests that their betas should be higher, due to the fact that they have more complex business relationships, products, and services, and are, therefore, more exposed to changes in general economic conditions.This intuition fails dramatically, as shown in Fig. The answer to the puzzle of big biotech betas has to do with the fact that biotech companies actually face two types of risk: scientific and financial. We’ve already discussed scientific risk, and almost by definition, such risk is indeed uncorrelated with aggregate economic fluctuations. However, if a biotech company runs out of cash, its progress comes to a full stop. Moreover, because the nature of biomedical innovation is so heavily dependent on skilled labor, the departure of key personnel due to financial disruptions can destroy significant shareholder value, often permanently. Therefore, a biotech company’s financial risk can dwarf its scientific risk ; hence the outsized betas of companies in this sector.Pharma companies are less sensitive to financial disruptions simply because they’re generating cash and usually profitable, hence they can weather the storms of recessions without interrupting their development programs. Also, given their size and franchise value, the departure of key personnel is unlikely to have the same impact as in the case of smaller biotech companies.Another way of thinking about big biotech betas is in terms of rollover risk. Imagine a financial services company that requires cash to run its business, and finances that cash using short-term debt contracts that constantly have to be renewed. As long as the economy is doing well, rolling over this debt should not be a problem. However, as soon as an economic downturn or credit crunch occurs, the company may not be able to refinance, at which point it’ll be forced to seek bankruptcy protection from its creditors. During such a process, it’s a safe bet that most of the customers of this company will take their business elsewhere, hastening the company’s demise.Even though a typical biotech company uses no debt financing, the principle is the same. If a company can’t finance its current operations , it will enter a state of financial distress, at which point key employees may depart, likewise hastening this company’s demise.An interesting piece of anecdotal evidence that supports the hypothesis of financing risk can be found in the biotech betas shown in Table The answer lies in financing risk. Between Moderna’s launch in 2010 and just prior to its IPO in November 2018, the company raised $2.6 billion in equity financing. As of 30 September 2018, the company had cash, cash equivalents, and investments of $1.2 billion. Its IPO on 6 December 2018 was the largest in the history of the biotech industry at the time, raising another $620 million and valuing the company at $7.5 billion. All of this occurred well before the COVID-19 pandemic and Moderna’s program to develop a vaccine for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) target.By raising so much cash, Moderna eliminated much of its financing risk for the next several years, giving it plenty of time and resources to develop several of its programs to completion, irrespective of general business conditions. Once the financing risk was addressed, the only remaining risk was scientific. This was already addressed at the time of the IPO by Moderna’s diversified portfolio of 21 mRNA-based drugs for a variety of diseases, of which 10 were in clinical trials. With both risks reduced, Moderna’s cost of capital should be much lower than less well-financed biotech peers, implying a much higher valuation based on standard discounted cashflow models and making it much more attractive to investors. On 18 December 2020, this financing strategy paid off handsomely: Moderna achieved its very first FDA approval for mRNA-1273.I realize that my highly stylized examples raise more questions than they answer. For example, do we really need $30 billion? What exactly is the market failure, if any, in the biopharma industry? Can you really find 150 uncorrelated biomedical assets? Is it organizationally feasible to manage 150 programs at the same time? Aren’t big pharma companies already doing this? Shouldn’t the government be doing this? My short answer to these questions is, “I have no clue. I am just a financial economist!”However, I have a much longer answer, one that I’ve been working on for the past decade. That answer is a series of research articles I’ve written in collaboration with a number of experts in the life sciences and the biopharma industry. In these articles, we simulate several new business models for funding therapeutic development programs in various types of cancer , and the associated development costs. This is, of course, a gross oversimplification of a much more complicated reality in which drug sales must be forecasted. Those forecasts depend on many factors, including competition, regulatory processes, drug pricing strategies, and health insurance reimbursement policies. However, Eq.  does capOf the three terms in Eq. , economiThe complexity created by this revolution means that no one person will know all that’s needed to take an idea for therapeutic intervention from conception to an approved drug. Therefore, financial and biomedical experts have to collaborate to develop the appropriate financing structures and business models for each unique context. To facilitate that collaboration, also posted on the Project ALPHA website are our various publications in healthcare finance and the corresponding software components for running the business simulations in those publications. All the software is distributed with open-source licenses, allowing anyone to download, run, and modify the code to suit their own assumptions and use cases.Most of these publications don’t appear in finance or economics journals, for the simple reason that there’s virtually nothing novel about the finance or economics involved, so I haven’t bothered submitting them to those journals. Ideas such as portfolio theory, securitization, and risk management have been well-known to financial economists for decades. The only novel aspect is the application of these techniques and models to the life sciences, which is why collaboration between finance and biomedicine is so important.A personal illustration of this process of collaboration involves my research on rare or orphan disease drug portfolios. According to the Orphan Drug Act of 1983, an orphan disease is one that affects fewer than 200,000 patients in the United States. These are diseases like hemophilia, cystic fibrosis, amyotrophic lateral sclerosis (ALS or Lou Gehrig’s Disease), Gaucher disease, Rett syndrome, and Duchenne muscular dystrophy. Even though any given orphan disease may affect only a small number of patients, there are over 7,000 different such diseases that collectively affect about 30 million Americans, more than the total number of cancer patients in the U.S.For a variety of reasons, including small patient populations, government incentives, and a better scientific understanding of a number of these conditions, as a class these diseases have a much higher PoS than many other categories. Therefore, we don’t need $30 billion to make progress here, nor do we need 150 programs to achieve sufficient diversification. According to our simulations . I want to be Harvey because of what happened in 2002. In that year, Harvey’s daughter was pregnant with her second child, a boy she named Andrew, who was diagnosed in utero with Gaucher disease.What are the chances of this happening? When Andrew turned 10 in 2012, he did develop the full-blown version of the disease, but he’s doing just fine, living a perfectly normal life thanks to the drug that his grandpa helped to develop over a decade before he was born.When I talked with Harvey about it, it was quite an emotional conversation. I asked him whether he had any idea that Gaucher disease ran in his family. He said he had no idea whatsoever, and observed that 1983 was a full two decades before the human genome was sequenced, so it would have been quite a bit more difficult to test for this condition back then than it is today. He said never in a million years could he have dreamed that what he was working on at Genzyme would someday save his grandson’s life. He did it because of scientific curiosity and the possibility that his expertise could help some patients in desperate need.invest in the drugs that will save the lives of our as-yet-unborn children, grandchildren, and great-grandchildren. Finance doesn’t have to be a zero-sum game if we don’t let it. If we have the right kind of financing at the right scale, we can actually do well by doing good.This is why I want to be Harvey Lodish. I don’t have a Ph.D. in molecular biology. I can’t help develop the drugs that will one day save the life of my as-yet-unborn grandchildren. However, I realized a few years ago that all of us can be Harvey Lodish, if we This is why I’m so grateful for the opportunity to deliver this presidential address. I hope to have stimulated your interest in the emerging field of healthcare finance, and would like to pass the torch to the next generation of economists who will no doubt develop even more sophisticated financing strategies for the biomedical ecosystem of the future. That ecosystem needs more financial economists, so I hope you’ll join me in this effort.I want to thank Katherine Virgo once more for this remarkable organization that she’s built and nurtured over decades. It’s been a pleasure and an honor to serve as president of the International Atlantic Economic Society. I congratulate Philippe Martin for being elected President-Elect, and my friend and former MIT colleague, Dimitri Vayanos, for being elected Vice President-Elect. Thank you.


N = 2,452) using a model recently developed by Pascual-Ezama et al. It is a variation of the classic die-under-the-cup task. It enabled us to identify individual dishonesty profiles and look for gender differences between them. The results show that the men were more prone to behave dishonestly than women with small rewards, who seem satisfied without maximizing the potential reward. However, the differences vanished when there was no reward. The men also showed more radical dishonest behavior than the women. The results also suggest that gender differences might be shaped by factors other than gender.Dishonesty has an enormous impact on all aspects of our society. It causes huge financial losses annually, so efforts to understand dishonest behavior have increased. However, one of the main questions yet to be answered is whether dishonesty varies according to gender. Do men behave more dishonestly than women? Although the literature points to a yes, there is still no consensus on the matter. We examined gender differences in dishonesty in a large sample ( Whether we like it or not, dishonesty seems to be inherent in the human condition. Unfortunately, dishonest behavior is a daily occurrence at every level of life: at work, at home, at school, and in various social settings. It is so common that 93% of the 2,624 participants in an extensive poll in 2004 reported different types of daily dishonest behaviors . HoweverSeveral pre-1990s studies e.g.,  showed tOthers  showed tperceived competence seems to be related to the proneness of their dishonest behavior. According to white lie  but were less likely than men to engage in a Pareto white lie . Finally, planning seems to be a factor involved in gender-dishonesty interactions. Some factors have already been presented. For instance, as mentioned above, per se. Most previous studies analyzed aggregated data, but they did not determine which, and under what conditions, individual subjects were cheating or lying. We took a novel approach in our study. We looked at dishonesty at a personal level to determine the nature of the particular dishonest behavior to compare reported versus real outcomes and thus establish direct comparisons between men and women. Based on the die-under-the-cup task, which was first proposed by However, a factor that has not been tested yet is based on the type of dishonest behavior elicited 1. 29 participants  report less than they obtain. We consider these participants as “incoherent”; the rest did not use the suggested website, so we could not get sufficient information from them. Respect the “incoherent” participants, it could be a mistake when they report, it is possible they do not understand the instructions correctly, or we can suppose any logical reason. Perhaps they have extreme social image concerns, and they believe that someone reporting a five is seen as “most likely dishonest.” Then they might consider reporting a 4. In that way, they only give up a small amount of money but gain a lot in the social image dimension. In any case, as we do not have information about the real reason, the number of excluded participants is minimal, and even if they had lied, they would not have done it with dishonest intentions but for self-image. Therefore, we decided to eliminate them. Concerning the rest of excluded participants, we cannot classify them as we do not have enough information. However, we have analyzed the distribution of reported outcomes to rule out a possible selection effect (see section “Results”). The final sample comprised 2,002 participants, of whom 1,004 were women and 998 were men; the average age was 34 (SD = 13) and 36 (SD = 11), respectively.To guarantee sufficient analytical power, we decided to run the experiment with a significantly sized sample of more than 2,000 participants . The 2,42. The experiment consisted of an adaptation of the die-under-the-cup task proposed by www.rollandflip.com or a similar website using their cell phone. They can use our suggested website to roll the die3 or any other website to roll a die, but importantly they use their cellphone, so the perception of no-supervision is high. They would receive no bonus if they rolled a 6, following https://www.rollandflip.com/ (or another similar site), select the “roll the die” option, and roll the die once.” The critical manipulation here was to link the real outcome and the reported one for a given person. We had access to the rollandflip.com database to match the rolls individually, controlling the exact moment every participant performed the task. Although we could consider that deception occurs place since participants maintain the perception of impunity while the researchers are monitoring their behavior, this procedure used by rollandflip.com website, allowing us to connect their real and reported outcomes to study honest and dishonest behavior in detail. The website www.rollandflip.com was created by researchers to record real outcomes from rolling a die or flipping a coin. We were able to record the real results, IP address, timestamp, the reported results, and the time the participants took to complete the task. Therefore, we were able to link data from https://rollandflip.com with https://behavioralexperiments.com to classify participants’ real behavior.The experiment was conducted using the MTurk platform, and the participants were paid after submitting their reportThe most relevant results are presented in the following three subsections. First, we show the typical population-level analysis as aggregated data from the reported results as if we did not have the real outcomes to make direct comparisons with previous studies in the field. Then, we show the individual-level analyses comparing reported and real outcomes. Finally, we group the subcategories of the (dis)honest classification into higher categories ; in each case, the men are compared with the women.p < 0.001), which indicated that both the men and women did not report the real outcome at the first die-roll; that is, they lied or cheated. Then, we tested for each die outcome to see whether the proportions differed from what would be expected by chance. As we can see in p = 0.08), which indicated that they cheated almost similarly. The main difference appears when analyzing results for maximizing “5” outcomes, that is, the maximum reward, although both are significantly above the chance level, men maximized the reward more than women .We examined whether the reported outcome distribution for the males and females differed from a uniform distribution, as is the case in classical inferred tasks aggregated analyses. A Kolmogorov–Smirnov (KS) test for one sample showed that both sample distributions differed significantly from the expected uniform distribution , the aggregated results did not show any gender differences when the general cheating behaviors were compared. Individual-level analyses made it possible to provide a more fine-grained picture of different forms of dishonesty, and this helped us to detect the potential gender differences that had been glimpsed in the population-level results.2 = 19.442, p < 0.01). In reality, they were eliminated conservatively. Beyond having exceeded a certain time and not using the recommended website, we did not know how they behaved. However, there was a high probability that a large proportion were radicals who took longer than the time limit we considered appropriate to classify them as such. This result was therefore logical and supported the finding that the men were more radical than the women.First, when we surveyed the participants who were eliminated because they did not follow the rules, we observed a greater number of men; of the 324 excluded participants, 212 were men and 112 were women; χSecond, the real distribution did not differ from the uniform expected distribution, and nor did the proportions see . This is2 = 0.05, p = 0.83). There were also no differences between the men and women when there was no reward . The results showed that 52.2% of the men and 41.0% of the women were dishonest. The statistical analysis of relative risk (RR) revealed that the men were more dishonest than the women; in particular, the men were 1.25 times more likely to be dishonest .Once we had checked the statistical assumptions that allowed us to compare men and women, we linked each participant’s real outcomes with the reported outcomes following We calculated the percentages again, but for each profile, as described in 2 = 17.12, p < 0.001] and maximizers . That is, regardless of maximizing or not, the men were more “radically dishonest” , the women seemed to be sufficiently satisfied to behave honestly. We hypothesized that the men needed a higher reward to act honestly. To test this, we analyzed the frequency of outcomes  for the “lucky honest” people according to gender . This result could be because as there are more male radicals, more women are throwing the die, and therefore, we could find a more significant number of lucky honest women. However, in this case, we should also find a higher number of women in the other groups, and this is not the case. It could also be the case that the proportion of female rolls is higher in the outcomes with prizes, and therefore more women will accept smaller outcomes. However, as we can see in As est” see . Second,o gender . Althoug2 = 5.37, p = 0.02). Although the differences in outcome “3” were not significant , the tendency was again more apparent among the men, who were more inclined to be satisfied with a “4” outcome. The women were more frequently satisfied with lower outcomes.If this was indeed the case, there should also have been differences between the men and women in the sub-maximizing cheater category. In particular, there should have been a greater proportion of women cheating with outcomes “2” or “3,” with men tending to wait for “4” outcomes to cheat more frequently. As maximizers; and those who decided to report a different outcome from “1” to “4” (which probably fitted the minimum outcome they considered before claiming a reward), namely, the sub-maximizers. In We merged the dis(honest) labels into three categories, again based on 2 = 36.2, p < 0.001]. No gender differences were apparent in the proportion of cheaters non-liars or liars. Finally, there were no differences between men and women regarding the gradient of dishonesty, whether the rewards were maximized or not. While these results were not significant, there were differences in outcomes in the earlier analysis.Analyzing differences by profile, we can see that, first, the difference between men and women in terms of the percentage of individuals exhibiting honest or dishonest behavior was significant: the women were more honest. Second, depending on the nature of the dishonest behavior, there were again differences between the men and women (see the third chi-square test in Although various studies have pointed toward a difference in dishonest behavior between men and women (often showing men as more prone to behave dishonestly), the nature of this difference has not been studied in detail. Certain factors, such as “perceived competence,” “individual” versus “grouped” dishonesty, competition, or planning dishonest behavior have revealed a more diverse picture showing—in some cases—no gender disparities and even situations in which women are more dishonest than men. The present study aimed to explore those potential gender differences in dishonesty in more detail by using the In particular, we observed that the women were more likely to be honest for lower rewards, while the men needed higher rewards to maintain honest behavior. The women seemed to be satisfied enough at lower rewards, which led them to decide not to cheat for higher ones see , 3. The What is also significant is that we replicated The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Comité de Ética de la Investigación UAM. The patients/participants provided their written informed consent to participate in this study.AM and DP-E developed the study concept and performed the testing and data collection. AM drafted the manuscript. BG-G provided critical revisions. All authors contributed to the data analysis, interpretation, and study design, and approved the final version of the manuscript for submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.