COVID-19 affected the world threatening medical, social, economic and community dimensions. Along with the other countries of the world, Saudi Arabia also witnessed critical emergencies leading to serious disaster due mainly to the overcrowding at certain urban residential localities. Nevertheless, the situation handled meticulously not only with measures and combating strategies but also with documentations and researches to build sustainable confrontation systems and lateral programs.This attempt is a consolidation of the academic efforts on COVID-19 in the country aiming to contribute to the knowledge base aiding to future planning on preparedness. An online survey of published academic research from peer reviewed journals were carried out during August-December 2022, using COVID-19 in Saudi Arabia as search criteria. Many areas of concern are assessed in the context which are classified into spread and burden, patient statistics, symptoms and other clinical dimensions, vaccinations and vaccine acceptance/hesitance, psychosocial impact, impact on education, impact on health staff, impact on migration, impact on nutrition, and control measures adopted.A consolidation of these research findings explains the scientific academic community alertness in raising up to the emergency pandemic situation, which facilitated strategy and policy formulations by the Government ministries and other governing bodies. These researches are linked to stagewise programmatic efforts to curtail the epidemic as a mode of accomplishments. The epidemic of COVID-19 gave a diverted epidemiological research, worldwide, to focus on emergence of new diseases of infection intensity beyond the traditional data trend and pattern analyses and determinants to reveal interconnections. Most of these analyses were carried out at national levels, although there are few international comparisons. One of the countries that witnessed serious spread, in the early days which controlled to the maximum with futuristic sustainable programs is Saudi Arabia. Both the programmatic health measures and the community level programs aided to create awareness paving way for support systems started with the Riyadh declaration on digital health during August, 2020 as a landmark innovation for resilient global emergency public health care systems , by various professionals, partially accurate, turned social media to spread myths and rumors , virus infect to humans by direct contact with animals and later through human to human transfer, have serious impact on genetic diversity, mutation, and recombination —males, non-natives, and governmental sector employees have comparatively higher acceptance than others (Al-Mohaithef and Padhi A large majority of people are willing to accept vaccine as they have confidence and trust in government decisions and healthcare system perceiving the risk of COVID-19, which depends largely on interventions of the health authorities, medical practitioners, and other entities (Alqahtani 5.Psychosocial impactNew methods of learning and training lead to stresses among health staff and patients creating anxiety and depression, thus demanding psychological support programs (Balhareth et al. In comparison, foreign nurses have greater awareness, positive attitudes, optimal prevention, and positive perceptions and thus the native staff need empowerment through prioritized training programs to handle cases precisely (Al-Dossary et al. 6.Impact on educationEducational days lost has been compensated with e-learning methods, which faced serious constraints due to its initiation Hoq . This edA study of dental interns reveals the impact of virtual leaning on preparedness towards clinical rotations and decreasing confidence affecting their study and work at hospital apart from patient management, cost of precautions, and psychological burden (Mohsin et al. An analysis explained negative and positive academic perceptions highlighting balancing of workload and improving technical support to reduce perceptions affecting satisfaction during the pandemic (Hassan et al. 7.Impact on health staffIncreased adverse psychological impact revealed among healthcare workers during COVID-19 include anxieties and stress due to risk of infection, high workload, stigma, and lack of necessary equipment (Alamri et al. 8.Impact on migrationOne of the researches on migrant health during COVID-19 explains the challenges of healthcare system facilitating residents including vulnerable migrants at high risk of acquiring and spreading COVID-19 infection (Ali et al. 9.Impact on nutritionThere are many studies on nutrition intake during COVID-19. One of them giving hope for future is a positive nutrition practice explaining a change in dietary patterns of Riyadh residents: home cooked meals with quality and quantity as per healthy food choices and nutritional balance (Alhusseini and Alqahtani 10.Control measures adoptedVirtual clinic initiatives, established at outpatient clinics, facilitated e-prescriptions (Alabdulkarim et al. Strategic actions, step by step, implemented by Ministry of Health, in Saudi Arabia enabled control of the epidemic (Yezli and Khan Pharmaceutical sector also went through amendments to offer medicines at doorstep and online counselling as part of the reorganization of global health care (Ahmad et al. In short, Saudi Arabia’s fight against COVID-19 can be traced through literature starting from the first infection at Eastern Region location to its spread all over the country causing burden of human lives of around 10.000 and morbidity of around one million cases. COVID-19 in Saudi Arabia has been through several phases as shown in Fig. Many researches were carried out in Saudi Arabia in the context of COVID-19 to explain dimensions of significance. Majority of the efforts focus of medical, clinical and psychosocial aspects. There are continuous efforts and scientific processes in the country’s academia, which shall boost the disaster preparedness and epidemic control and awareness building in such emergency situations. No doubt, researchers and academic attempts are encouraged through funding and infrastructure networking.


Osteoarthritis (OA) prevalence has increased 113% since 1990, and currently more than half a billion people worldwide are living with this slowly progressing, degenerative joint disease . OA poseOA is thought to have a multifactorial aetiology, with factors such as age, obesity, mechanical loading, inflammation, joint injury, and genetic predisposition playing an important role in its pathogenesis . HoweverObesity is one of the most significant modifiable risks of OA, not only because of the altered biomechanics of the joint but also due to the release of key inflammatory factors by adipose tissue, resulting in low-grade chronic inflammation . StudiesAnother target for treating the OA of obesity is the gut microbiome. The increased systemic inflammation which drives the impact of obesity on OA is now understood to be caused by shifts in the gut microbiome . Schott Lacticaseibacillus paracasei 8700:2, Lactiplantibacillus plantarum HEAL9, and L. plantarum HEAL19) following faecal microbiota transplantation (FMT) in mice, where the microbiome has been depleted, prevents DMM-induced cartilage damage and has a positive impact on the structure of subchondral bone, particularly at the femoral condyle [Gut microbiome manipulation also has the potential to exert a disease-modifying effect in OA associated with the destabilisation of medial meniscus (DMM). Using the DMM mouse model, we recently showed that treatment with a cocktail of probiotic strains  and a series of menaquinones (vitamin K2), that are naturally present primarily in green leafy vegetables (vitamin K1), and to a lesser extent in various animal-based and fermented foods (vitamin K2) . MenaquiAntioxidant supplements, such as vitamin C, vitamin E, and curcumin, have also been proposed to reduce OA symptoms. Antioxidants counteract oxidative stress by scavenging and neutralising free radicals that are generated by normal biological processes . Most anThe Impact of trace elements on OA has been recently reviewed by Li and colleagues . Here, aCountless studies in the wider scientific literature reporting on the relationship between nutrition and OA have been critical in furthering clinicians’ and scientists’ knowledge alike to help combat the initiation and progression of OA. This Special Issue welcomes studies summarizing the current evidence on nutrition and OA interaction. Strengthening the scientific knowledge regarding the mechanisms by which nutritional supplementation protects against OA is crucial for studying new intervention strategies. Such findings will not only cast new light on nutrition and its relationship with OA pathogenesis, but may also help pave the way for new forms of therapy based on dietary manipulation.


Some scholarscall this “healthcare gender bias”, where women’s symptoms are dismissed or neglected,leading to delays in diagnosis The consequences of the gender health data gap are not limited to clinical settings.Gender bias can impact the quality and representativeness of cause of death statisticsin civil registration and vital statistics (CRVS), due to misdiagnosis made byphysicians and misreport of certain conditions ,,,,,,,,,Some efforts have already been set in motion to start addressing these problems, like thepolicy of female mice inclusion and sex as a biological variable in clinical trials andscientific studies in 2016 by the U.S. National Institutes of Health (NIH) The gender health data gap is not trivial and has most likely prevented importantadvances in knowledge, treatment, and diagnosis of several health conditions.Disciplines, funding agencies, stakeholders, academic institutions, health care systemsand the pharmaceutical industry must undertake a concerted effort to identify and bridgethe gender health data gap.


Paralleling the obesity pandemic, the prevalence and socioeconomic burden of type 2 diabetes are growing worldwide, requiring immediate attention and novel cost-effective preventive and therapeutic strategies. Type 2 diabetes is a multifaceted chronic disorder characterized by a complex interplay of genetic and environmental factors leading to impaired insulin secretion and reduced insulin sensitivity, ultimately resulting in elevated blood glucose levels. Chronic hyperglycemia, in turn, has established its role as a robust predictor of major cardiovascular outcomes, which can severely impair patients’ expectancy and quality of life .Amidst this backdrop, nutrition therapy emerges as a crucial component in both the prevention and management of type 2 diabetes and its associated micro- and macrovascular complications ,4,5), asNutrients, entitled “Nutrients and Cardiometabolic Health in Type 2 Diabetes”, was to gather contributions exploring the impact of nutrients and dietary patterns on glucose regulation and overall cardiometabolic health in individuals with or at risk for type 2 diabetes. This Special Issue features four original articles focusing on the effects of different dietary patterns on multiple cardiometabolic risk factors [The purpose of this Special Issue of  factors ,8 and on factors ,10. It a factors , and one factors .In recent years, there has been a gradual transition in research focus from examining the role of individual foods or nutrients on cardiometabolic health to evaluating dietary patterns . DietaryA Mediterranean-style dietary pattern with balanced macronutrient composition is recommended for the management of obesity and associated cardiometabolic risk factors. Nevertheless, low-carbohydrate, high-protein diets have been proposed as an alternative to Mediterranean diets for their greater ability, at least in the short term, to induce body weight loss and improve glucose homeostasis by exploiting the beneficial actions of proteins ,15 whileBoosting participants’ adherence and motivation to weight loss interventions, while increasing health benefits and economic sustainability, is extremely challenging. In their clinical trial, Driscoll and colleagues  integratThe use of functional foods as an adjunct to lifestyle interventions represents a feasible and cost-effective strategy to improve the management of chronic diseases. Although an array of functional foods are available to offer nutritional support to individuals with diabetes, the glucose-lowering efficacy of these products is often modest. In their translational research project, Chauhan et al.  employedAlong with food quantity and quality, the daily distribution of energy and carbohydrate intake has a key role in the regulation of body weight and glucose metabolism . In theiFinally, Borg and colleagues  reviewedThe studies featured in this Special Issue are critical to advancing our understanding of how dietary factors can provide support for the clinical management of chronic diseases, warranting exploration of the complex interplay between nutrient quantity, quality, and timing on cardiometabolic outcomes. Deciphering the mechanisms by which nutrition impacts glucose homeostasis and the cardiovascular system may reveal innovative approaches for personalized dietary interventions that promote optimal glycemic control and improved clinical outcomes in individuals with or at risk for type 2 diabetes.


Aotus nancymaae in biomedical research, few studies have examined these values and with a limited number of animals only. A. nancymaae is widely used for research and often used to study the principal types of human malaria . These primates have also been used to study leishmaniasis, schistosomiasis, tuberculosis and various types of enteric infections. Furthermore, in recent years oral challenge models have been developed in A. nancymaae for Campylobacter jejuni, Shigella flexneri, and Enterotoxigenic Escherichia coli (ETEC). However, there is no updated data on biochemical and hematological parameters for A. nancymaae. Therefore, the aim of this study was to provide complete and accurate reference intervals of biochemical and hematological values to assess the health status indices of A. nancymaae housed in a vivarium prior to their use.Non-human primates (NHP), including New World monkeys (NWM), are used in varied fields of biomedical research encompassing infectious disease, behavioral sciences and drug and vaccine development. In these studies, normal reference values for biochemical and hematological parameters are often needed for comparison with experimental results. These blood parameters are useful guides in reaching a diagnosis, estimating the prognosis of the disease and monitoring the progress of treatment. Although there are various reports of biochemical and hematologic parameters in Old World monkeys, the range of reference values often differs between NHP species. Despite the importance of NWMs such as To characterize these parameters, a total of 1250 blood samples (1003 from juvenile and 247 from adult monkeys) were analyzed between 2007 to 2022. A total of 14 biochemical and 20 hematology parameters were measured.+), glucose (GLU), blood urea nitrogen (BUN), creatinine (CRE), hemoglobin (HGB) and lymphocyte (LYM) in relation to sex and age.A significant change was observed in the values of alkaline phosphatase (ALP), potassium (KA. nancymaae based on a large cohort which can be used to inform and guide future studies utilizing this animal model.In conclusion, this work establishes a comprehensive and systematic reference of serum biochemical and hematological parameters for All Authors: No reported disclosures


Smartphone and social media use is prevalent during adolescence, with high levels of use associated with lower levels of mental well-being. Secondary schools in the UK have introduced policies that restrict daytime use of smartphones and social media, but there is no evaluation on the impact of these policies on adolescent mental well-being. The SMART Schools Study aims to determine the impact of daytime restrictions of smartphone and social media use on indicators of adolescent mental well-being, anxiety, depression, physical activity, sleep, classroom behaviour, attainment and addictive social media use.This is a natural experimental observational study using mixed methods. Secondary schools within a 100 mile radius of the recruiting centre in the West Midlands (UK) have been categorised into two groups: Schools that restrict (intervention) and permit (comparator) daytime use of smartphones. We aim to recruit 30 schools  and 1170 pupils aged 12–13 and 14–15 years. We will collect data on mental well-being, anxiety and depressive symptoms, phone and social media use, sleep and physical activity from pupil surveys, and accelerometers. Policy implementation measures and data on individual pupil factors will be collected through school staff surveys, and website/policy analysis. Six case study schools will explore individual, school and family/home factors that influence relationships between school smartphone policies, smartphone/social media use, and mental well-being. Economic evaluation will be completed through a cost–consequence analysis from an education sector perspective.Ethical approval was obtained from the University of Birmingham’s Research Ethics Committee (ERN_22-0723). Parents/carers of pupil participants can complete a form to opt their child out of the study. Pupil, school staff and parent/carer participants are asked to complete online/written consent (or assent). Findings will be disseminated through policy briefings, resources for schools, social media, reports, and open access publications.ISRCTN77948572. This is a natural experimental observational study exploring the impact of school phone policies that restrict daytime use of smartphones and social media on adolescent mental well-being and associated behaviours, and the cost implications of these policies from an education sector perspective.Pupils will self-report data on time spent on their smartphones and social media by using data from their iOS or Android apps.Pupil outcomes will be collected from an online survey that includes validated measures for mental well-being, anxiety and depressive symptoms, addictive social media use, motives for social media use, health-related quality of life and demographic data; and from accelerometers measuring physical activity and sleep.Qualitative research will explore how individual, school and family/home factors influence relationships between school phone policies, phone/media use and mental well-being.This is a cross-sectional study and is limited to data collected in England.Globally, mental disorders  are the leading cause of disability in adolescents (age range: 10–19).6Smartphone and social media use is prevalent during adolescence, and accounts for the majority of their overall screen time.18In moderation, smartphone and social media use  can be advantageous for mental well-being and mental health,There is considerable evidence that school-based interventions can have beneficial effects on adolescent mental well-being and associated behavioural outcomes .School phone policies that restrict daytime phone/media use are an example of a current whole-school environment intervention. In the UK, Australia, Sweden, Czech Republic and elsewhere, many schools have introduced school policies that restrict daytime use of smartphones in order to reduce classroom disruptive behaviour and cyberbullying incidents and improve attainment.48The SMART Schools Study aims to determine the impact of school daytime restrictions of smartphone and social media use on adolescent mental well-being (primary outcome), anxiety, depression, physical activity, sleep, classroom behaviour, attainment and addictive social media use. We will do this by comparing impacts in two different secondary school phone policy contexts: (1) schools that do not permit smartphone use during recreational time in the school day (intervention); and (2) schools that permit smartphone use during recreational time (breaks/lunchtimes) (comparator). We will also explore how variation in school-based, individual and family/home factors influences the relationship between school phone policies, smartphone and social media use and mental well-being. We will conduct an economic evaluation in the form of a cost–consequence analysis from an education sector perspective.This is a natural experimental observational study using mixed methods, taking place between April 2022 and July 2024. Quantitative and qualitative data will be collected from all schools in the sample (n=30) to compare outcomes between restrictive (intervention) and permissive (comparator) school policies and to complete an economic evaluation. Qualitative data will be collected from six case study schools to understand the contextual factors that could influence relationships between school policies, smartphone and social media use and mental well-being.The intervention and data capture are directed by our logic model  which inInformed by our patient and public involvement (PPI) activities and school smartphone policy analysis, the components of the intervention  are presented in The sampling frame comprises UK secondary schools (ages 11–19) located within a 100 mile radius of the recruiting centre in the West Midlands. Sixty-four local authorities are included in the sampling frame from the West Midlands, East Midlands, East, South East, South West and North West. The schools are situated in regions of high and low levels of deprivation and in areas that have highTo improve the comparability of the two school groups, stratified sampling based on propensity scores was employed.In participating schools, two classes of mixed ability year 8 (age 12–13) and year 10 age 14–15) pupils are recruited. We are focusing on adolescents in this age range because of the age of onset and prevalence of mental illness, pupils aTo account for the imbalance of schools in our sample that have permissive (n=125) and restrictive policies (n=1220), we are recruiting schools using a 2:1 (restrictive: permissive) ratio. The primary outcome of mental well-being will be measured using the Warwick-Edinburgh Mental Well-Being Scale .In each participating class, we aim to recruit a minimum of 19–20 pupils (67% if estimated class size n=30). In studies with multiple layers of clustering (here classes within schools), it is conservative to treat clusters within clusters as one larger cluster, which is the approach used here.63School recruitment commenced in September 2022 and will continue until December 2023. A study advertisement has been emailed to all schools in the sampling frame. Following our propensity sampling approach, schools are then invited by post and email with a telephone follow-up. In participating schools, a School Liaison Member (SLM) of staff is identified and a school–university contract outlining expected commitments signed. Subsequently, a member of SLT responsible for the smartphone policy, and pupils and teachers from the year 8 and year 10 classes are recruited. £600 compensation is allocated to each school and a £5 voucher per pupil participant.Parents/carers are provided with written detailed information about the study, what their child’s participation will involve and how their child’s data will be processed. Schools are asked to assist in the distribution of this information to parents/carers in different formats .Data collection methods include self-administered surveys for pupils, teachers and a member of the SLT; accelerometer measured physical activity and sleep for pupils; and document analysis. All online surveys are completed using university-approved online survey software (REDCap). Pupils complete an online survey at one time point (in school time) that includes validated measures for mental well-being, anxiety and depressive symptoms, addictive social media use, motives for social media use, health-related quality of life and demographic variables  . Within A second online survey (in school time) to measure mental well-being  is complData are collected from the form teacher  for each participating pupil on: pupil attainment; classroom behaviour; and whether pupils are eligible for free school meals, have a special educational need or have English as an additional language . Within The SLT member responsible for the school smartphone policy is asked to complete survey questions on the SLT member’s role; the school timetable and school policies; features of the school smartphone policy; perceived time spent by school staff developing and implementing the school smartphone policy; knowledge and understanding of the school smartphone policy; and compliance with the school smartphone policy. The SLT member is sent the online survey by email.Pupils are asked to wear a wrist worn GENEActiv accelerometer watch for 24 hours a day for the subsequent 7 days after completing the survey.School policy documents and handbooks related to smartphones, social media, pupil behaviour, mental health/well-being, e-safety/IT policy and the wider school aims and ethos are collected. Documents related to the school timetable are also collected so that time spent in physical education, and time allocated for breaks and lunch can be quantified for each school.Six case study schools are being purposively sampled from the 30 participating schools in relation to two school characteristics: (1) smartphone policy type and duration to ensure a balance of restrictive (n=3) versus permissive policies (n=3), and a range of length of policy implementation in the six case studies; and (2) schools from low, medium and high area of deprivation, measured by the IDACI. An additional £300 compensation is allocated to case study schools.Across the case study schools, up to 36 focus groups (FGs) will be completed with adolescents (n=12), school staff (n=12) and parents/carers (n=12). Data collection is taking place following the school level data collection  and following obtaining written consent (or assent). In each case study school four to six FGs are completed: adolescents (n=2), school staff (n=1–2) and parents/carers (n=1–2). Each FG comprises four to six members and aims to balance gender and ethnicity (where possible). For adolescents, one FG is completed with year 8 (age 12–13) and one with year 10 (age 14–15) pupils. School staff FGs include SLT, school governors, teachers and support staff . Parents/carers of pupils from within the school  are recruited.FGs are led by research staff employing established elicitation and semistructured interview techniques.The primary analysis will examine the association between pupil mental well-being and school policy type (restrictive/permissive). Multilevel linear models will be developed, accounting for repeated measures, clustering of classes and schools and adjusting for the school-level variables included in the propensity score estimations alongside relevant individual-level sociodemographic variables. Secondary analyses will use the same modelling approach investigating differences in the secondary  and intermediary  outcomes between school policy groups. Differential association between school policy and the primary, secondary and intermediary outcomes will also be explored across: (1) socioeconomic position; (2) gender; and (3) ethnicity by including relevant interaction terms in the developed models.School policies, documents  and relevant website content will be analysed using document analysis.In view of the multiple outcomes of interest, complex nature of school budgets and emergent nature of economic evaluation of school-based interventions, a cost–consequence analysis will be conducted through the relevant data collected . This apGiven the purpose of the case study method to understand complexity and situatedness,71During research plan development, a teacher member of the investigator team advised on the study. We also consulted with adolescents and teachers through an online survey (teachers n=40) and five FG consultations . In addition, we undertook analysis of school websites to determine the nature of school smartphone policies. Information gathered through these activities helped to inform and refine the study design, including categorisation of school policy types, research questions, data collection methods, primary and secondary outcomes and the logic model .The approach to PPI during the SMART Schools Study has been constructed based on National Institute for Health Research guidelines.Full ethical approval was obtained from the University of Birmingham’s Science Technology, Engineering and Mathematics Research Ethics Committee on 8 July 2022 (ERN_22-0723).For participation in the study, parents/carers of pupil participants are not asked for active consent but are given the opportunity to complete and return a form to opt their child out of taking part in the study. Pupil, teacher, SLT and parent/carer participants are asked to provide online or written consent (or assent).We have also developed a safeguarding protocol for conducting research on mental health with adolescents. This safeguarding protocol has been developed in the context of our measure for depression (PHQ-9) that detects whether adolescents in our sample may have had self-harm or suicidal thoughts in the past 2 weeks. The protocol outlines key procedures during recruitment, data collection and debriefing periods to safeguard pupils, and include steps related to confidentiality and establishing efficient, secure and effective communication pathways between the research team and school leaders.Study sponsorship is provided by the University of Birmingham, with provision of research related costs supported by the National Institute for Health Research (NIHR). Data management and storage is compliant with the University of Birmingham’s policies and procedures. Participant data from online surveys and other sources will be pseudo-anonymised, stored on a University of Birmingham secure server and retained for 10 years through the University’s Research Data Archive. Data from this study will be owned by the University of Birmingham.Study oversight will be guided by an independently chaired Study Steering Committee (SSC) and a Data Management and Ethics Committee (DMEC). A study monitoring plan has been developed and agreed on by the SSC and DMEC. The current protocol has been reviewed and agreed on by all members of the SSC and DMEC.Dissemination activities will be co-produced with our PPI participants. We have planned the following dissemination outputs: policy briefings and research summaries for agencies on the impact of school smartphone policies on adolescent mental well-being; guidelines and resources for schools on the characteristics of school smartphone and social media policy implementation that positively influence mental well-being; blogs, podcasts, videos and infographics to raise awareness and understanding of relationships between smartphones and social media and mental well-being; an NIHR public report that summarises the main project findings; and peer review and open access publications focused on the main study findings.


The optimum economic outcome of financial system development depends on its level of efficiency. The purpose of this study is to investigate the effect of institutional quality on financial system efficiency. For empirical analysis, we have used a panel dataset of 108 countries from 1996–2020 and employed fixed effect regression and two stages least squares (2SLS) regression methods. The empirical results show that institutional quality has a significant positive effect on financial system efficiency. Particularly all the constituting elements—voice and accountability, political stability and absence of violence, regulatory quality, government effectiveness, rule of law, and control of corruption—of institutional quality are found to have a significant positive impact on financial system efficiency. Moreover, we found that the effect of institutional quality is more pronounced in countries with low-income levels and strong institutional quality. These findings are robust across several robustness tests conducted using additional controls, alternative methodologies, an alternative measure of institutional quality, and financial system efficiency. The results of the study suggest that policy makers should prioritize both enhancing and sustaining institutional quality to promote the efficiency of the financial system, which is crucial for sustainable growth and development. A well-developed financial sector is widely regarded as the foundation of economic growth in contemporary economic literature .Thus, the foregoing literature may not represent the overall efficiency of the financial system and do not provide holistic understanding of the financial efficiency, partly due to the lack of the required data for a comprehensive assessment of financial system efficiency. Recently IMF Svirydzenka  has compIn contrast to previous research on this topic that has predominantly focused on individual components of financial system and specific countries or regions, our study adopts a comprehensive approach. We examine the impact of institutional quality on the overall efficiency of the financial system, utilizing a comprehensive measure of financial system efficiency and broad international sample. This approach allows us to present a relatively inclusive view of the relationship between institutional quality and financial efficiency for comprehensive policy implications.Financial efficiency is one of the most important dimensions of financial development. “Financial efficiency represents the extent to which the financial system fulfills its functions” , p.271. The given research gap and significance of financial efficiency motivated us to explore how institutional quality could improve financial systems’ efficiency. Governments and international development organizations may find this important for improving the efficiency of the financial system for better economic results. Theoretically, the institutional quality may enhance financial system efficiency through increased competition in the financial sector –38; amelThis study makes several contributions to the existing institutional quality and financial system efficiency literature: Firstly, unlike extant literature, this study investigates the effect of institutional quality on overall financial system efficiency―measured with a comprehensive financial system efficiency index (IMF). By doing so, this study provides more comprehensive evidence regarding the effect of institutional quality on financial system efficiency than has been done in the previous literature. Furthermore, this study explores how institutional quality, and its constituent components affect the efficiency of the financial system and identified the aspects of institutions which are more important for financial system efficiency.Additionally, we have employed an international sample consisting of 108 countries which ensures broader applicability and generalizability of our findings. This sample size was chosen based on the maximum availability of the data for the variables under study. To deepen our understanding of how institutional quality affects financial system efficiency, we have categorized our sample by income levels: low income (LIC), middle income (MIC), and high income (HIC) countries and segmented the sample into quartiles of institutional quality, denoted as Q1 for high quality, Q2 for moderate, Q3 for average, and Q4 for low and explored institutional quality and financial efficiency nexuses in each category. This meticulous approach allows us to unravel the complexities and variations within our sample, enhancing the reliability and applicability of our analysis.Afterward, the study is arranged as follows: section 2 includes a review of the relevant theoretical and empirical literature, section 3 presents the details of the data, model, and methods, section 4 presents and discusses the empirical results, and section 5 contains the conclusion and policy implications.Financial efficiency―efficient allocation of financial resources with the lowest possible cost―is an important aspect of the financial sector development which plays a vital role in optimum economic growth and development , 43, 44.Legal, regulatory, and governance structures are widely acknowledged as crucial to the efficient and smooth functioning of financial institutions and markets and the optimal allocation of resources. For market forces to function and enhance economic performance, there needs to be in place a well-functioning institutional environment―sound protection of property rights, contract enforcement, and rule of law –47. A weNumerous studies have been conducted to investigate the impact of institutional quality on the financial efficiency of various components within the financial system. These components include commercial banks, foreign banks, stock markets, and microfinance institutions. Abaidoo and Agyapong (2023) documented a significant positive relationship between institutional quality and financial institutions efficiency in sub-Saharan African (SSA) countries. Specifically, factors such as control of corruption, government effectiveness regulatory quality, and rule of law were identified as important drivers of financial institutions efficiency within in SSA region. Strong institutional framework improves the efficiency levels of foreign banks by reducing informational agency and enforcement costs . Likewise, Chan, Aktan, Burton, and Koh (2021) recently demonstrated that institutional quality has a significant positive effect on the resource allocation efficiency of the foreign banks. Institutional quality particularly control of corruption significantly improves banks cost efficiency of Italian Cooperative Banks . While Lensink and Meesters (2014) using stochastic frontier analysis confirms the importance of institutional quality for commercial banks efficiency. Chan, Koh, Zainir, and Yong (2015) highlight two important indicator of institutional quality regulatory quality and political stability significantly improving bank’s efficiency in ASEAN-5. The efficiency of microfinance institutions in South Asian countries is significantly impacted by the quality of institutions . Iqbal Hussain, Kot, Kamarudin, and Huang Yee (2021) found that regulatory quality and government size promotes the efficiency of Micro finance institutions.It is indicated by Hasan, Wang, and Zhou (2009) that institutional environment plays vital role in improving the efficiency of banking sector in China and banks are found to operate more efficiently in regions with greater presence of better institutions and more property rights awareness. Country level quality of institutions significantly enhances the performance of stock markets . Bhatt (1991) highlighted the importance of institutional and policy frameworks for improving effectiveness and efficiency of Indian financial institutions. In this vein, Asongu (2011) also demonstrated that financial intermediary institutions have higher level of allocation efficiency in French civil-law countries. Institutional quality augments the effect of financial liberation on stock market efficiency in emerging markets . Egbendewe and Oloufade (2020) studied the effect of institutional quality on the banking sector competitiveness in developing countries using semi-parametric regression model and demonstrated that control on corruption government effectiveness and the rule of law has significant effect on the competitiveness and efficiency of banking sector.While the given literature has studied the impact of institutional quality on the efficiency of specific components of the financial system such as commercial banks, foreign banks, microfinance institutions, and stock markets, limited attention has been given to the overall efficiency of the financial system. The financial system is multifaceted, and it encompasses diverse financial institutions  and financial markets (stock and bond markets). The efficiency of a financial system is measured by “ability of these institutions to provide financial services at low cost and with sustainable revenues, and the level of activity of capital markets” . Thus, the foregoing literature may not represent the overall efficiency of the financial system and do not provide holistic understanding of the financial efficiency.Since, existing research on this subject has primarily concentrated on individual components of the financial system within specific countries or regions. To gain a more holistic understanding of the relationship between institutional quality and overall financial system efficiency, it is crucial to adopt an approach that encompasses a comprehensive measure of financial system efficiency and utilizes a broad international sample. By doing so, we can provide a comprehensive perspective on the impact of institutional quality on financial efficiency, thereby offering valuable insights for policymakers.The transaction cost theory  of new iInstitutional quality plays a vital role in preventing opportunistic behavior, reducing information asymmetry, and minimizing transaction costs associated with information ; FailureAdditionally, research demonstrates that strong institutions boost competition within the domestic financial sector, drawing in foreign players and potentially increasing the efficiency of the financial system , 36. In “the institutional quality may enhance the financial system’s efficiency.”Moreover, a strong institutional framework can also increase the efficiency of the financial system by reducing systemic risk and promoting the safety and stability of the financial sector. Financial stability results in greater efficiency as resources are saved from being used to manage systemic risks. Azmi, Anwer  found thTo analyze the effect of institutional quality on financial system efficiency, this study used a global sample of 108 countries between 1996 to 2020 , leaving a gap in understanding the broader relationship. Therefore, it is necessary to investigate the broader relationship between institutional quality and financial system efficiency using an international sample. The selection of 108 countries was driven by the availability of data on our study variables, ensuring the reliability and validity of our analysis. By incorporating a diverse range of countries from different regions and income levels, we aim to provide a comprehensive representation of the global financial landscape and generate insights applicable in various contexts. Moreover, to deepen our understanding, we conducted subgroup analyses based on income levels and quartiles of institutional quality. This approach allows us to explore variations and nuances in the relationship within different economic and institutional contexts, enhancing the robustness and applicability of our findings.In this study, the institutional quality is assessed as the independent variable, utilizing a composite index that has been calculated by taking the average of six Worldwide Governance Indicators (WGI)—Voice and Accountability, Political Stability and Absence of Violence, Regulatory Quality, Government Effectiveness, Rule of Law, and Control of Corruption . Each inFinancial system efficiency is the dependent variable in this study. Several measures of financial efficiency are being used in the existing literature. We used the financial system efficiency index provided by the International monetary fund [IMF 28] to measure financial system efficiency. The IMF developed the financial efficiency index separately for the financial institutions’ efficiency (FIE) and finance markets’ efficiency (FME). Both indices range between zero (0) and one (1). Consistent with existing literature we combined both FIE and FME indexes by averaging them and rescaling them with 100, into a financial system efficiency index to measure overall financial system efficiency in the current study. A similar procedure is adopted in the existing comparable studies.The choice of this proxy is based on several considerations. Firstly, the IMF’s financial system efficiency index is widely recognized and utilized in the literature, ensuring comparability and consistency with previous studies. Secondly, this dataset offers a comprehensive measure of financial system efficiency and covers a broader range of countries compared to the other data source. By using these proxies, we aimed to capture the multifaceted nature of financial system efficiency, encompassing both the efficiency of financial institutions and financial markets. This comprehensive approach enables a more robust evaluation of the link between institutional quality and finance. We believe that the selected proxies provide a reliable and meaningful assessment of financial system efficiency, allowing us to investigate the relationship with institutional quality factors in a rigorous manner.To mitigate potential omitted variables bias, this study has included several controls― economic growth, trade openness, foreign direct investment, and education― for financial system efficiency in the model. Economic growth is measured as GDP per capita (constant 2015USD); trade openness is the total of all imports and exports as a percentage of GDP, foreign direct investment is measured as a ratio of foreign direct investment to GDP, and education is proxied with a ratio of total gross primary schools’ enrollment. Moreover, we transformed economic growth, trade openness, foreign direct investment, and education into a logarithm form and used it in all estimations across this paper, following the comparable literature  for normWorld Development Indicators, Worldwide Governance Indicators, and IMF databases were used to gather data for these variables. Current study utilizes an econometric model to examine the relationship between institutional quality and financial system efficiency. Specifically, we employ a panel data regression model to analyze data from sample countries and test the hypothesis of the study. Panel data regression models are well-suited for our research as they allow us to account for both cross-sectional and time-series variations in the data. This approach enables us to capture the heterogeneity across countries and control for potential time-specific effects. So, drawing on the comparable literature following panel data econometric model is specific, to empirically analyze the impact of institutional quality on financial system efficiency.Where, Fin. Efficiency represents the financial system efficiency variable for country i in year/time t. Inst. quality denotes the quality of the institutions variable which is an explanatory variable in this study. X_it is a vector of control variables―economic growth, trade openness, foreign direct investment, and education. β, and γ signifies the parameters of the empirical model. Furthermore μ_iand δ_t represents country and year fixed effects and ε_it is the random error term.We have utilized Ordinary Least Squares (OLS) regression with country and year fixed effects for baseline estimation. This approach allows to control unobserved heterogeneity across countries and time-specific effects that may influence the relationship between institutional quality and financial system efficiency. By including these fixed effects, we can mitigate potential biases and obtain more accurate estimates of the impact of institutional quality on financial system efficiency. Furthermore, to address the potential endogeneity issue and establish a causal relationship between institutional quality and financial system efficiency, we employ the Two-Stage Least Squares (2SLS) estimator for additional analysis, which is designed to estimate the endogenous models robustly. The 2SLS method is an effective and widely used econometric technique that helps mitigate endogeneity issues by utilizing instrumental variables , 60 and We have utilized Ethnic fractionalization 2001 (ETH_F) from Alesina, Devleeschauwer , sourcedThis study aims to investigate the effects of institutional quality on financial system efficiency in an international sample of 108 countries during 1996–2020. To do so, the institutional quality is regressed on financial system efficiency, and the empirical outcomes are reported in Overall, the empirical findings show a significant and positive effect of institutional quality and its constituent factors, including voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption, on financial system efficiency. These findings align with existing literature which has consistently demonstrated a positive relationship between institutional quality and the efficiency of various components of the financial system. For instance, , 27, 55,Moreover, our findings support The transaction cost theory  of new iFurthermore, a strong institutional framework can also increase the efficiency of the financial system by reducing systemic risk and promoting the safety and stability of the financial sector. Financial stability results in greater efficiency as resources are saved from being used to manage systemic risks , Azmi, AThere may be an endogeneity issue in our model due to the omitted variables or potential reverse causality from financial system efficiency to institutional quality. To address potential endogeneity and omitted variable bias in our model, we used two-stage least squares (2SLS) with instrumental variables (IV). This approach has been utilized in previous comparable literature e.g., , 55 for The results show the instrumented institutional quality, and its constituent factors have a significant positive impact on the financial system efficiency and are consistent with the baseline results. These findings also complement the growing body of literature on institutional quality and various dimensions of financial development for example, , 62, 63 Our sample is heterogenous in terms of income levels and by grouping heterogeneous countries, the underlying relationship between financial sector efficiency and institutional quality may be obscured, and comparisons between countries cannot be made. Hence to address the heterogeneity bias and to understand the relationship between institutional quality and financial efficiency at various income levels, the estimation is carried out by splitting sample countries into three groups by income level―low income(LIC), middle income(MIC), and high income(HIC). The IMF’s income groups classification is utilized for income group analysis.The results  show thaFurther, to see how financial system efficiency responds to the institutional quality across various levels of institutional quality and identify any potential heterogeneity between the relationship. We carried out our estimation by splitting our sample into four quartiles of institutional quality. We divided our sample into four quartiles  of institutional quality and estimated the regression in each quartile. The sample is divided based on WGI institutional quality ranking―overall institutional quality ranking for each country is calculate by averaging individual ranking of six governance indicators reported by WGI. Doing so we can also identify the level of institutional quality that is associated with the highest level of financial efficiency.The results  suggest In the following sections, the robustness of our findings is tested using additional controls, alternative instruments, and using alternative measures of institutional quality and financial system efficiency.In this section, we have carried out our estimation by incorporating additional controls for financial system efficiency and using an alternative instrument for institutional quality to check the robustness of our baseline findings. Particularly we included legal origin and religion dummies in our model, drawing on the legal origin theory , 51 and Legal origin is the legal tradition of the company law or commercial code that each country inherits. Legal origin is equal to 1 if it is English Common Law, 2 if it is French Commercial Code, 3 if it is socialist/communist law, 4 if it is German Commercial Code, and 5 if it is Scandinavian Commercial Code. The data on legal origin is obtained from  and CIA Religion consists of Catholic, Protestant, Muslims, and other religions: These variables represent the percentage of the population of each country that belonged to Catholic, Protestant, Muslim or the percentage of the population that did not belong to “Catholic, Protestant, or Muslim” in 1980, respectively. The data source is La Porta, Lopez-de-Silanes  retrieveIn this section, we carried out our analysis using an alternative measure of institutional quality and financial system efficiency. We utilized here the worldwide governance indicators (WGI) ranking as an alternative to estimates, as a measure of institutional quality. WGI reports ranking of countries in terms of six dimensions of governance—Voice, and Accountability, Political Stability and Absence of Violence, Regulatory Quality, Government Effectiveness, Rule of Law, and Control of Corruption along with estimates . A higheThe results  regression methods. Our findings show that institutional quality and its various dimensions―voice and accountability, political stability and absence of violence, regulatory quality, government effectiveness, rule of Law, and control of corruption― have a significant positive impact on financial system efficiency. These results are consistent across both OLS and 2SLS.Furthermore, we observe heterogeneity in the effect of institutional quality based on income level and the level of institutional quality itself. Specifically, we find that institutional quality plays a crucial role in deriving financial system efficiency in low-income countries and higher quartiles of institutional quality. This highlights the importance of strengthening institutional quality across different contexts to enhance financial system efficiency. Our findings reinforce the notion that institutional quality is a vital component for improving the development and efficiency of the financial sector. These results remain consistent even after conducting several robustness tests, including additional controls, alternative instruments, alternative measures of governance quality and financial system efficiency, and split sample analysis.Based on our conclusions, we recommend policymakers to prioritize policies aimed at enhancing institutional quality, particularly in countries with poor institutional quality. By doing so, we can foster an environment that promotes the efficiency of the financial system, which is essential for achieving sustainable economic growth and development. Additionally, policymakers in countries with high institutional quality should continue to prioritize maintaining and further improving institutional quality to sustain and further enhance financial system efficiency. Moreover, policymakers need to target improvements in institutional quality, particularly in the higher quartiles as this is where institutional quality is observed to derive financial system efficiency significantly in the current study. Principally, the governments and regulators should focus on specific components of institutional quality which are particularly important for financial system efficiencies such as Voice and Accountability, Political Stability and Absence of Violence, Regulatory Quality, Government Effectiveness, Rule of Law, and Control of Corruption.Beside institutional reforms alone, policy makers should also consider the role of technological advancement in improving financial system efficiency. Embracing digitalization, innovative payment systems and fintech solutions can enhance the speed, convenience and security of financial transactions. Policy makers can support the adoption of these technologies through appropriate regulations, infrastructure development and promoting collaboration between financial institutions and technology providers. By addressing these key areas, policymakers can create an enabling environment that can foster a more efficient financial system.S1 Appendix(DOCX)Click here for additional data file.


Can a data-driven, quantitative approach help identify and prioritize opportunities in biomedical product innovation for medical disorders with high public health burden and high health care costs?In this cross-sectional pilot study of 13 conditions, a quantitative multicriteria decision-making model that evaluated the extent of biomedical product innovation relative to comprehensive measures of public health burden and health care costs identified diabetes, osteoarthritis, and drug-use disorders as having the highest overall gap score reflecting high public health burden, or high health care costs relative to low biomedical innovation in these medical disorders. Chronic kidney disease, chronic obstructive pulmonary disease, and cirrhosis and other liver diseases had the least amount of biomedical product innovation despite similar public health burden and health care cost scores.By analyzing the relative alignment between quantitative measures of biomedical product innovation, public health burden, and health care costs, this data-driven, quantitative model can help identify and prioritize investments that can have the greatest public health benefit. This proof-of-concept study examines whether a data-driven, quantitative approach can work to help identify and prioritize opportunities in biomedical product innovation for medical disorders with high public health burden and high health care costs. Prioritization and funding for health initiatives, including biomedical innovation, may not consistently target unmet public health needs.To (1) develop a quantitative, databased framework to identify and prioritize opportunities for biomedical product innovation investments based on a multicriteria decision-making model (MCDM) that includes comprehensive measures of public health burden and health care costs, and (2) pilot test the model.The Department of Health and Human Services (HHS) convened public and private experts to develop a model, select measures, and complete a longitudinal pilot study to identify and prioritize opportunities for investment in biomedical product innovations that have the greatest public health benefit. Cross-sectional and longitudinal data (2012-2019) for 13 pilot medical disorders were obtained from the Institute for Health Metrics Global Burden of Disease database (IHME GBD) and the National Center for Health Statistics (NCHS).The main outcome measure was an overall gap score reflecting high public health burden , or high health care costs  relative to low biomedical innovation. Sixteen innovation metrics were selected to reflect the pipeline of biomedical products from research and development to market approval. A higher score indicates a greater gap. Normalized composite scores were calculated for public health burden, cost, and innovation investment using the MCDM Technique for Order of Preference by Similarity to Ideal Solution method.Among the 13 conditions tested in the pilot study, diabetes (0.61), osteoarthritis (0.46), and drug-use disorders (0.39) had the highest overall gap score reflecting high public health burden, or high health care costs relative to low biomedical innovation in these medical disorders. Chronic kidney disease (0.05), chronic obstructive pulmonary disease (0.09), and cirrhosis and other liver diseases (0.10) had the least amount of biomedical product innovation despite similar public health burden and health care cost scores.In this cross-sectional pilot study, we developed and implemented a data-driven, proof-of-concept model that can help identify, quantify, and prioritize opportunities for biomedical product innovation. Quantifying the relative alignment between biomedical product innovation, public health burden, and health care cost may help identify and prioritize investments that can have the greatest public health benefit. Public health leaders and industry funders who rely on select metrics like mortality to inform strategic priorities and funding decisions can potentially overlook important considerations, including the effects of disability, cost of treatment, and health disparities. Furthermore, medical research priorities are frequently determined by expert consensus or stakeholder ranking9 Rees et al6 evaluated mortality, disability-adjusted life-years (DALY), and health care use for pediatric medical conditions and reported a positive correlation between National Institutes of Health (NIH) funding for pediatric research and only some, but not all, metrics of pediatric disease burden. Research evaluating funding allocation by cancer type found that funding was moderately proportional to the incidence of the cancer type and was poorly correlated with deaths and potential years of life lost (PYLL).10 A Healthcare, Technology, and Innovation for Economic Success  consortium in Europe developed indicators and indexes to measure and benchmark the extent of innovation in medical and life sciences across academia, industry, and government.7 Additional models such as the Navigating the Ecosystem of Translation Sciences (NETS)11 and the Drug Discovery, Development, and Deployment Map (4DM)12 accounted for the complex interactions in the innovation ecosystem and included metrics that measured the pipeline of biomedical products from research and development to market approval.13 To date, studies have not successfully integrated and simultaneously analyzed metrics from complementary domains, including public health burden, cost, and biomedical product innovation, to identify and prioritize opportunities for investment in biomedical product innovation.Few studies have analyzed comprehensive health data from diverse domains to identify gaps in focused areas such as research and innovation.In the backdrop of a resource-intensive COVID-19 pandemic, the Office of the Assistant Secretary for Health, Department of Health and Human Services (HHS) convened experts from the federal government, academia, industry, and the nonprofit sector to (1) develop a quantitative, data-driven framework to identify, track, and prioritize opportunities for biomedical product innovation for medical disorders with the highest public health burden and health care cost and (2) pilot test the model in select medical disorders.For this longitudinal study, a multidisciplinary team of epidemiologists, physicians, economists, engineers, businesspersons, and public health professionals developed a framework to define biomedical product innovation opportunities, select measures, identify data sources, and design an analytic strategy for the pilot study. Biomedical product innovation opportunities were defined as medical conditions in which the extent of innovation activity was relatively low compared with the high public health burden and higher health care costs. Three domain-specific working groups were tasked to develop definitions and identify US-specific metrics and data sources to assess and quantify public health burden, health care costs, and the extent of biomedical product innovation, respectively. All 3 working groups met periodically as a larger work group to collaborate, receive feedback, and integrate data from all 3 domains to inform the final framework and pilot study. This study was deemed exempt from written informed consent because all data used were publicly available.14 prevalence data sourced from the National Center for Health Statistics (NCHS),15 and measures of health disparity calculated by using the Healthy People 2020 disparity summary rate ratio with sex, age, and racial and ethnic distribution for the mortality data provided by NCHS.16 Health disparities are defined as preventable differences in the burden of disease or opportunities to achieve optimal health experienced by socially disadvantaged populations. Health disparities were calculated by taking the maximum ratio of the most favorable or least adverse group rate and the average rate of all other groups.17 In addition to evaluating cross-sectional mortality, prevalence, and YLD in 2019 for all age groups, longitudinal trends in public health data between 2015 to 2019 were included in the model.Public health burden was assessed by analyzing 4 related measures including mortality and years lived with disability (YLD) sourced from the Institute for Health Metrics Global Burden of Disease database (IHME GBD),18 The IHME’s methods for calculating health care cost measures are described in further detail elsewhere.19 Total public spending data reflected the sum of Medicare and Medicaid costs as well as spending on public health system infrastructure. Out-of-pocket health spending encompassed payments made by individuals at or after the time of health care delivery, including copayments or deductibles. Health insurance premiums were not included in the OOP spending metric. Total spending was the sum of public spending, private health spending, and OOP health spending and did not include indirect health spending, such as lost wages due to illness or transportation costs. All health care cost measures were sourced from IHME. In addition to the cross-sectional health care cost metrics for 2016, changes in costs for total spending, total public spending, and OOP spending were calculated from 2012 to 2016. This allowed for an examination of the longitudinal changes in health care costs over time for the medical disorders evaluated in the pilot study.Health care cost measures selected were total public spending, out-of-pocket (OOP) health expenses, and total spending and were extracted from IHME. It identifies government domestic, corporation, and nonprofit institution resources allocated for health and social insurance contributions using the World Health Organization’s (WHO) Global Health Expenditure Database (GHED).20; federal investment dollars and projects (NIH RePORTER)21; private investment dollars and deals by early or late stage (Pitchbook)22; clinical trials started, ended, and those with results posted 23; and US Food and Drug Administration (FDA) number of approvals for drugs, devices, and expedited pathways; private investment9; and venture capital funding in dollars and deals by early or late stage (Pitchbook). Each of these 16 metrics was quantified annually from 2015 to 2019 to analyze cross-sectional and longitudinal changes in innovation activity.For the purposes of this model, the extent of biomedical product innovation activity was defined as the volume and rate of advancement, development, and deployment of novel technologies, devices, diagnostics, and pharmacotherapies that have the potential to influence critical health-related and economic outcomes including disease prevalence, severity, quality of life, disparities in access, and the economic burden of disease. Through a review of the literature and expert input, the group generated more than 2 dozen measures to quantify the extent of innovation for a medical condition. These innovation measures were mapped to accessible data sources, and measures that represent parsimony among a singular concept  were identified. The data were assessed qualitatively based on each possible measure’s accessibility, face validity, usability, and usefulness. Overall, 6 innovation measure categories (with data sources) comprising 16 metrics were selected: patent applications and patents granted (Google Patents with USPTO)24 was employed to determine relative opportunities for investment in biomedical product innovation by integrating measures of public health burden, cost, and current innovation activity for each pilot medical conditions. The TOPSIS method normalizes the measures and inputs each measure as a weighted variable to compute the relative goodness of a solution by estimating the distance between alternative solutions in a decision matrix. Domains were weighted equally with a value of 1. Quantitative scores for each of the 3 domains of public health burden, health care cost, and biomedical product innovation were calculated. Public health burden and health care cost scores were positive values, ie, higher scores contribute to a higher overall gap score, whereas biomedical product innovation was considered a negative value, with lower innovation scores contributing to a higher overall gap score. The ideal solution was defined as medical conditions with low public health burden, or low cost and high biomedical innovation. An overall gap score for each medical condition was calculated using TOPSIS and reflects the relative ranking compared with 1 extreme alternative  and the other extreme possible alternative . Low scores indicate an imbalance with relatively high investment in innovation relative to public health burden and cost, whereas high scores indicate an imbalance with relatively low investment in innovation relative to public health burden and cost (STROBE) reporting guidelines.25We piloted the method on a small set of medical conditions that were likely to have high public health burden. We first compiled 3 lists of the top 10 medical conditions with the highest mortality, prevalence, and YLD metrics, respectively, based on 2019 IHME and NCHS data. Out of the 30 medical conditions, the top 13 were selected after removing duplicates that appeared in more than 1 list. A multicriteria decision-making (MCDM) Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodand cost . MedicalInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are listed in eTable 1 in The 13 medical disorders selected for the pilot study and the associated The conditions with the greatest public health burden score in 2019 were ischemic heart disease (0.48), drug use disorders (0.48), and osteoarthritis (0.47). Although ischemic heart disease had the highest national mortality rate (170.03 per 100 000), drug use disorders had the highest YLD (911.11/100 000), and osteoarthritis had the highest prevalence (15 813.80/100 000). An analysis of longitudinal data between 2016 and 2019 confirmed that ischemic heart disease had the highest increase in mortality, whereas drug use disorders had the greatest increase in YLD, and osteoarthritis had the greatest increase in prevalence. The greatest disparities in mortality rates were observed in drug use disorders, with the non-Hispanic American Indian/Alaska Native population having the highest mortality rate (eTable 2 in Diabetes (0.77), osteoarthritis (0.41), and Alzheimer disease (0.38) had the highest health care cost scores in 2015. Patients with Alzheimer disease and other dementias had the highest OOP spending ($59.97 billion). Diabetes had the highest total public spending ($152.10 billion) and total spending ($171.51 billion). The greatest decrease in OOP costs between 2012 and 2015 was observed for ischemic heart disease, whereas the total public spending and total spending increased for osteoarthritis and diabetes, respectively (eTable 3 in The least amount of innovation was observed in chronic kidney disease (0.95), chronic obstructive pulmonary disease (COPD) (0.91), and cirrhosis and other liver diseases (0.90). eTable 4 in Diabetes, osteoarthritis, and drug use disorders were the top 3 medical conditions with the highest overall gap score. The specific reason for the overall gap score was unique to each condition and reflects opportunities for action. For example, the high health care costs contributed to the overall gap score in diabetes; whereas the high public health burden score, due to the high prevalence, combined with high health care cost contributed to the overall gap score observed in osteoarthritis. The elevated overall gap score in drug use disorders was due to the high rates of disability and disparities, which increased the public health burden score.13 our framework may inform resource allocation decisions to address these gaps.We designed and pilot-tested a data-driven, quantitative model to identify, rank order, and prioritize opportunities for action by evaluating relative alignment between biomedical product innovation scores and measures of public health burden and health care cost for 13 medical conditions. This proof-of-concept study leverages a replicable framework to identify high-level gaps and opportunities that may be prioritized for investment by public health and industry leaders and stakeholders who fund research. Together with a granular analysis of potential reasons for the high-level gaps identified in specific medical disorders,7 by analyzing measures both of public health burden and of health care costs. This pilot study used a quantitative process, leveraged input from a broad set of stakeholders, and elicited input from a variety of sources including an advisory board of experts. This proof-of-concept study demonstrates the feasibility of a data-driven model to identify relative gaps in innovation. Follow-up work may include creating a public process that emphasizes the involvement of a broad set of well-represented stakeholders while simultaneously continuing to ensure that the process of identifying gaps remains transparent. Further iterations of the model should consider using a Delphi process and incorporating weights into the TOPSIS model.Our model addressed the limitations of prior studies31 This condition illustrates a paradox where high health care cost is a marker of public health need and burden, but increasing costs from innovation can create the appearance of an even higher need for innovation. Additional research must extend the gap scores generated by our model to target investments. Innovations in other aspects of health care, including optimizing health systems delivery, and implementation science may be required to improve outcomes in disorders with high public health burden and cost despite high levels of biomedical product innovation.Although the overall gap score provided a high-level indicator, an in-depth analysis of the relative contribution of domain scores is a crucial step to guide targeted actions to address areas of highest need. For example, when potential reasons for why diabetes emerged as having the highest overall gap score were examined, health care costs, specifically public spending, was a primary driver. Because diabetes had the highest biomedical product innovation scores, allocating additional resources to this domain exclusively may not be effective in addressing the individual and health system burdens of this disorder. Although the current study did not examine the root causes of the high costs associated with diabetes, it is possible that an increase in the number of prescriptions of newer and innovative antidiabetic medications contributed to the increasing costs related to diabetes.32 Although colorectal cancer and lung cancer had very similar cost and public health burden scores, the NIH research funding allocation for colorectal cancer (approximately $346 million) was less than half the funding for lung cancer (approximately $850 million). This difference in NIH research funding may be related to the large differences seen in other innovation measures for these 2 cancers, including the number of patents (3200 vs 4513), late-stage venture capital funding ($33.9 million vs $179 million), and private deals ($551 million vs $4196 million). Chronic kidney disease, cirrhosis and other liver disease, and COPD had lower innovation investment relative to other conditions with similar or lower public health burden and health care cost and represent a significant opportunity. Although the federal government recently incentivized innovation in chronic kidney disease treatment,33 there is a need for improvements in the innovation pipeline for COPD as well as cirrhosis and other chronic liver diseases. The number of patents granted, early venture capital funding, NIH funding, and number of expedited approvals were significantly lower for COPD. Similarly, there were significant gaps in the number of early and late venture capital investments, private deals clinical trials, and number of devices and expedited approvals for cirrhosis.Our model may also identify focused opportunities to spur biomedical product innovation for specific medical conditions . A frame34 Several regulatory options may help spur innovation in underinvested areas.36 By identifying gaps in specific phases of the biomedical innovation cycle, our findings may inform decisions to target resources, to include policies, to address the specific gap. Federal regulatory agencies can also enhance intellectual property (IP) protections  for low-innovation, high-cost, high–public health burden medical disorders identified using our framework. Private industry can promote open innovation initiatives, where input for research and development is gathered from the public or through collaboration with other industries, in areas with the highest relative gap scores. Academic organizations could use the gap scores to inform faculty recruiting priorities and technology development priorities.Quantifying innovation and relative gaps in the biomedical ecosystem is a complex problem given the numerous interactions, functional areas, and long cycles in biomedical discovery and health care. The need to identify innovation gaps and opportunities concerning public health burden and health care cost is especially important given the long lead times typically required to bring discovery to market. The development of a new therapy, from target identification through marketing approval, on average takes over 12 years and frequently longer.ICD-10 codes). Notably, the IHME mortality data did not record deaths related to osteoarthritis and depression because they were considered risk factors, but not direct causes of death . Future work may incorporate downstream effects of risk factors on mortality, although reliable data across conditions may not exist. Incomplete data among some conditions within racial and ethnic distributions prevented a comprehensive assessment of health disparities. Although the data reflect the different stages of the biomedical product innovation pipeline, they do not provide detailed understanding of why specific phases are lagging. Furthermore, the narrow definition of biomedical product innovation used in the study did not include investments in innovations in health care delivery. Because replication of this model in other contexts requires access to robust health systems and data collection infrastructure, gaps identified in this study may mainly reflect priorities in the US and other high-income countries with requisite capabilities. Finally, it is important to note that IHME captured costs exclude spending on major investments such as hospital construction, health worker education and training, as well as research and development.Although our framework considered public health burden, cost, and biomedical innovation, it did not include measures of barriers to the implementation of effective treatments such as workforce issues, health care system fragmentation that impedes access and continuity of care, and the extent of adoption and acceptance of evidence-based practices in health care. This proof-of-concept study was limited to a small subset of medical conditions. Had we included a larger set of conditions we may have identified other conditions with higher (or lower) gap scores. Data were obtained from multiple sources because all the data required for the study were not available in a single data repository. The IHME and NCHS differed in the methodology and timelines for data collection to include grouping of medical disorders (ie, Clinical Classification Software [CCS] codes, In this cross-sectional pilot study, we developed and implemented a data-driven, proof-of-concept model that can help identify, quantify, and prioritize opportunities for biomedical product innovation. By simultaneously analyzing complementary measures across the health ecosystem, we identified medical conditions with the largest biomedical product innovation opportunities relative to public health burden and health care costs. With appropriate infrastructure, recent data, and automated data analytic techniques, this methodology can be refined and scaled to generate timely information to inform investments to improve public health across the nation.


This review discusses the role of extracellular vesicles in pulmonary hypertension a serious and progressive lung disease characterized by high blood pressure and increased vascular resistance in pulmonary arteries. Extracellular vesicles are released by cells upon different stimuli, contain various molecules like proteins and genetic material derived from the parental cell and are involved in intercellular communication, both for homeostatic processes and in pathologic conditions. They seem to play a harmful role in pulmonary hypertension by carrying and transferring molecules that contribute to the progression of the disease. This review highlights how understanding the mechanisms involving extracellular vesicles in pulmonary hypertension could provide insights into the comprehension of the pathogenesis and in developing new therapies to manage this disease.The term pulmonary hypertension (PH) refers to different conditions, all characterized by increased pressure and resistance in the pulmonary arterial bed. PH has a wide range of causes ; however, idiopathic  PH exists. This chronic, progressive, and sometimes devastating disease can finally lead to right heart failure and eventually death, through pulmonary vascular remodeling and dysfunction. The exact nature of PH pathophysiology is sometimes still unclear. Extracellular vesicles (EVs), previously known as apoptotic bodies, microvesicles, and exosomes, are small membrane-bound vesicles that are generated by almost all cell types and can be detected in a variety of physiological fluids. EVs are involved in intercellular communication, thus influencing immunological response, inflammation, embryogenesis, aging, and regenerative processes. Indeed, they transport chemokines, cytokines, lipids, RNA and miRNA, and other biologically active molecules. Although the precise functions of EVs are still not fully known, there is mounting evidence that they can play a significant role in the pathophysiology of PH. In this review, after briefly recapping the key stages of PH pathogenesis, we discuss the current evidence on the functions of EVs both as PH biomarkers and potential participants in the distinct pathways of disease progression. Pulmonary hypertension (PH) is a heterogeneous condition defined by the increase in mean pulmonary arterial pressure, as assessed at right heart catheterization, to values >20 mmHg at rest . Even thExtracellular vesicles (EVs) are cell-derived membranous structures involved in multiple physiological and pathological processes ,3. They PH is a broad term, encompassing different conditions, all resulting in increased pulmonary arterial pressure and pulmonary vascular resistance.A pre-capillary and post-capillary form of PH can be distinguished based on the value of the pulmonary artery wedge pressure (PAWP); values of PAWP ≥ 15 mmHg define the post-capillary form [GROUP 1 Pulmonary arterial hypertension (PAH);GROUP 2 PH associated with left heart disease;GROUP 3 PH associated with lung diseases and/or hypoxia;GROUP 4 PH associated with pulmonary artery obstructions (including CTEPH);GROUP 5 PH with unclear and/or multifactorial mechanisms.In clinical practice, PH is classified based on the pathogenic mechanisms and the associated or causal conditions and divided in five groups:As previously mentioned, PAH group 1) is further divided into different subtypes, depending on the possible association with other pathological conditions. The most important entities belonging to group 1 are: idiopathic PAH (IPAH), heritable PAH, PAH associated with connective tissue disease (CTD-PAH) or HIV infection, and PAH associated with drugs and toxins [ is furthPH affects approximately 1% of the general population. The prevalence increases in the elderly; indeed, the incidence rises to 10% in individuals over 65 years. In most cases, PH is secondary to left heart disease, while chronic obstructive pulmonary disease is the second leading cause of PH. . When loThe age at diagnosis of PAH has been rising during the last decades, mostly because of a higher proportion of older patients diagnosed with IPAH, which was once viewed as a disease of the younger age . CurrentThe typical symptoms of PH are dyspnea , reduced exercise capacity, chest pain, and sometimes syncope. Irrespective of the initial cause, PH almost invariably results in chronic right heart failure, which is a devastating condition associated with a high burden of symptoms and elevate healthcare and social costs, and is mainly responsible for mortality in these patients ,13.The advances in the management of PH and the availability of new treatments have considerably improved PH prognosis over the last decades. However, PAH still shows a 3-year survival rate of approximately 70%. In contrast, the prognosis of CTEPH is influenced by the availability of pulmonary thromboendarterectomy: the 3-year survival rate can be as high as 90% after a successful surgery .As already said, most cases of PH are due to the advanced stages of common diseases, including chronic heart and respiratory failure (groups 2 and 3) . PAH repAnimal models and pathologic data in humans support a strong role for perivascular inflammation in the initiation and/or progression of PAH and pulmonary vascular remodeling . Along w−Exosomes originate from the endo-lysosomal pathway and are identified by the expression of tetraspanins . They have a dimension between 30–200 nm;−Microvesicles  are released by direct outward budding of the plasma membrane of activated cells. They have a dimension between 100–1000 nm and share the same membrane components with the parental cells;−Apoptotic bodies are released through blebbing of apoptotic cell membranes by cells undergoing apoptosis. Apoptotic bodies have a dimension between 1000–4000 nm.EVs are a family of spherical particles enclosed in a phospholipid bilayer and released by the cell under physiological, such as in brain cell–cell interaction  and pathIn practice, the differentiation among the different types of vesicles can prove very difficult; accordingly, the International Society for Extracellular Vesicles suggests the comprehensive term EVs . In thisThe existence and possible functions of EVs were first argued in 1946 by Chargaff while studying platelets and thromboplastin (currently known as tissue factor). He described EVs as cytoplasmic debris . In 1967+ and VE-cadherin+ EVs positively correlate with the mean pulmonary artery pressure (mPAP) and brain natriuretic peptide (BNP) of PH patients, underlying the potential of EEVs as biomarkers of hemodynamic severity of the disease. Indeed, high levels of E-selectin EEVs correlate with poor outcomes or death [EVs have been studied as potential biomarkers for several aspects of both PAH and CTEPH. For example, circulating EVs derived from different cell types are increased in PAH patients ,37,38,39or death . A recenor death . Along wor death ,48,49. DPulmonary arterial endothelial cells (PAECs) present an activated phenotype promoting angiogenesis and recruitment, proliferation, and differentiation of pulmonary arterial smooth muscle cells (PASMCs) through the secretion of growth factors such as transforming growth factor β (TGF-β). Although much progress has been made in the understanding of PAH, much remains to be investigated concerning the molecular mechanisms underlying PAH, particularly the role of EVs in its pathogenesis and their possible therapeutic applications.Several studies investigated the characteristics and the role of miRNAs in PAH pathogenesis see . miRNAs Pulmonary EVs derive from various cell types, including lung epithelial cells, macrophages, and pulmonary endothelial cells . SpecifiVarious studies have demonstrated that miRNAs are potential therapeutic targets for several diseases, including PAH. One of the most studied miRNAs is miR-143/145 cluster, which is expressed by vascular smooth muscle cells (VSMCs), and is upregulated in pulmonary artery VSMCs and lung tissue of patients with idiopathic and hereditary PAH . HoweverThe role of EVs in PH is an area of ongoing research and holds significant promise for understanding the pathogenesis and progression of the disease. EVs released by various cell types involved in PH, such as endothelial cells, smooth muscle cells, and immune cells, have been implicated in vascular dysfunction, inflammation, and remodeling. These EVs can transport bioactive molecules, including microRNAs, proteins, and lipids, which can impact recipient cells in the pulmonary vasculature, influencing vasoconstriction, smooth muscle cell proliferation, and endothelial dysfunction. While our knowledge about EVs in PH is still limited, emerging evidence suggests their involvement in the complex interplay between different cell types and the pathophysiology of PH. Further research is required to uncover the specific cargo and signaling pathways of EVs in different stages of the disease and to determine their potential as diagnostic biomarkers or therapeutic targets. Studying EVs in PH offers a promising avenue for advancing our understanding of this debilitating condition. Continued investigations into the role of EVs in PH may ultimately lead to the development of innovative diagnostic tools and targeted therapeutic interventions that can improve patient outcomes and potentially stop or reverse the progression of pulmonary hypertension.