Development of a Choice-framework for Covid vaccines in India using a multi-criteria decision analysis approach.

BACKGROUND: Vaccines were crucial in controlling the Covid-19 pandemic. As more vaccines receive regulatory approval, stakeholders will be faced with several options and must make an appropriate choice for themselves. We proposed a multi-criteria decision analysis (MCDA) framework to guide decision-makers in comparing vaccines for the Indian context. METHODS: We adhered to the ISPOR guidance for the MCDA process. Seven vaccine options were compared under ten criteria. Through three virtual workshops, we obtained opinions and weights from citizens, private-sector hospitals, and public health organisations. Available evidence was rescaled and incorporated into the performance matrix. The final score for each vaccine was calculated for the different groups. We performed different sensitivity analyses to assess the consistency of the rank list. RESULTS: The cost, efficacy and operational score of the vaccines had the highest weights among the stakeholders. From the six scenario groups, Janssen had the highest score in four. This was driven by the advantage of having a single dose of vaccination. In the probabilistic sensitivity analysis for the overall group, Covaxin, Janssen, and Sputnik were the first three options. The participants expressed that availability, WHO approvals and safety, among others, would be crucial when considering vaccines. CONCLUSIONS: The MCDA process has not been capitalised on in healthcare decision-making in India and LMICs. Considering the available data and stakeholder preference at the time of the study, Covaxin, Janssen, and Sputnik were preferred options. The choice framework with the dynamic performance matrix is a valuable tool that could be adapted to different population groups and extended based on increasing vaccine options and emerging evidence. *ISPOR - The Professional Society for Health Economics and Outcomes Research.

Using MCDA to assist an Intermunicipal community develop a resilience strategy in face of the pandemic caused by the SARS-CoV-2.

The topic of regional economic resilience has been the subject of intense debate in the academic and political fields over the past decade and gained a new sense of urgency because of the pandemic caused by the SARS-CoV-2 virus as territories faced relevant impacts on their economies and social structures. The economic downturn, the increase in unemployment, and the deterioration of social conditions lead policy makers to search for solutions to make their territories more resilient to this type of event. The current article discusses how multicriteria decision analysis (MCDA) was used to help a Portuguese Intermunicipal Community, formed by 16 councils, develop a strategy to make its territory more cohesive, competitive, sustainable, and resilient. In addition to discussing an innovative application of a MCDA technique, this article illustrates how, through a MCDA approach, it was possible to reach a consensus among several policymakers, despite each of them having their own political agendas.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies.

BACKGROUND: COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS: We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS: Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS: This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Multi-Criteria Decision Analysis In Healthcare: Scientometric And Bibliometric Analysis

Introduction. Multi-criteria decision analysis (MCDA) is a useful tool in complex decision-making situations and has been used in medical fields to evaluate treatment options and drug selection. We aimed to provide valuable insights on the use ofMCDAin health care through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain using a scientometric and bibliometric analysis. Methods. Publications related to MCDA in health care were identified by searching the Web of Science Core Collection on 14 July 2021. Three bibliometric software programs (VOSviewer, Bibliometrix, and CiteSpace) were used to conduct the analysis. Results. A total of 410 publications were identified from 196 academic journals (average yearly growth rate of 32% from 1999 to 2021), with 23,637 co-cited references by 871 institutions from 70 countries or regions. The USA was the most productive country (n=80), while the Universiti Pendidikan Sultan Idris (n=16), Universite de Montreal (n= 13), and Syreon Research Institute (n=12) were the most productive institutions. The biggest nodes in every cluster of author networks were Aos Alaa Zaidan, Mireille Goetghebeur, and Zoltan Kalo. The top journals in terms of number of articles (n=17) and citations (n=1,673) were Value in Health and the Journal of Medical Systems, respectively. The research hotspots mainly included the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. In the recent literature there was more emphasis on coronavirus disease 2019 (COVID-19) and fuzzy Technique for Order Preference by Similarities to Ideal Solution (TOPSIS). Big data, telemedicine, TOPSIS, and the fuzzy AHP, which are well-developed and important themes, may be the trends in future research. Conclusions. This study provides a holistic picture of the MCDArelated literature published in health care. MCDA has a broad application in different topic areas and would be helpful for practitioners, researchers, and decision makers working in health care when faced with complex decisions. It can be argued that the door is still open for improving the role ofMCDAin health care, both in its technologies and its application.

Priority setting in cardiovascular research in Iran using standard indigenous methods.

Background: Determining cardiovascular disease (CVD) research priorities is essential given the high burden of these diseases, limited financial resources, and competing priorities. This study aimed to determine the research priorities in CVD field in Iran using standard indigenous methods. Materials and Methods: An extensive search was done in relevant international and national studies. Then, an indigenous standard multistage approach based on multicriteria decision analysis steps was adapted to local situation and implemented. This process included forming a working group of experts in priority setting methodology, identifying the context and prioritization framework, discussing the methodology with the National Network of CVD Research (NCVDR) members who ultimately determined the priority research topics, weighted topics criteria, ranked topics, and reviewed all determined research priorities for final report. Results: Thirteen cardiovascular research priorities were determined by the NCVDR members. The first five priorities based on their scores include studies in hypertension, prevention and control of ischemic heart disease (IHD) and its risk factors, burden of IHD, Registration of CVDs, and COVID-19 and CVDs. Conclusion: Cardiovascular research priorities were determined using a standard indigenous approach by national experts who are the NCVDR members. These priorities can be used by researchers and health decision makers.

A Proposal to Assess the Benefits of Urban Ecosystem Services

The Millennium Ecosystem Assessment in 2005 defined and categorized the concept of Ecosystem Services and the strategic role of natural capital. The need to rethink our cities and public spaces is even more pressing in the COVID-19 era. In this context, green strategies could be the answer to the new demands raised by citizens about the built and natural environment. Green roofs, along with the other green spaces, form the city’s green network, contribute to improving the quality of life and wellbeing of citizens. The present contribution aims to evaluate green roofs from an ecosystem perspective, by considering the evidence of their benefits on inhabitants’ wellbeing, their ability to mitigate climate change and preserve biodiversity. A proposal for an integrated evaluation model is presented to take into account the different dimensions of value in the study of Ecosystem Services (ESs) and to support decision makers (DMs) in the definition of actions able to increase the quality of life in cities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A Bibliometric Analysis.

Objective: Multicriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain. Methods: A bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords. Results: A total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research. Conclusion: This study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).

Assessing the impact of COVID-19 on the efficiency of Portuguese state-owned enterprise hospitals.

This paper uses Value-Based Data Envelopment Analysis (VBDEA), to assess the impact of the COVID-19 pandemic on the efficiency of 37 state-owned enterprises (SOE) hospitals by employing data publicly available from the Portuguese Health Service database between January and November 2019 and 2020, respectively. Furthermore, a productivity index (specifically adjusted to the VBDEA approach) is also used that allows identifying which factors are behind the relative efficiency changes of these hospitals. The factors considered to perform the efficiency assessment of the Portuguese SOE hospitals include labour, capacity, and activity-related indicators. Out of the 37 SOE hospitals, 21 and 17 were efficient in 2019 and 2020, respectively. Irrespective of the value functions considered, the hospitals more often viewed as a reference for best practices were Santa Maria Maior, Tâmega e Sousa and Entre Douro e Vouga. Santa Maria Maior and Algarve were the only hospitals found to be robustly efficient for both years. Overall, the majority of SOE hospitals showed negative productivity (except for Évora and Santa Maria Maior) and all of them presented negative technological change, thus highlighting the massive impact that the COVID-19 outbreak has had on the performance of these hospitals. An additional conclusion is that inefficient hospitals substantially increased all their resources in 2020 as compared to inefficient hospitals in 2019, suggesting that the inefficiency of these hospitals was not due to the lack of resources. Finally, irrespective of the model employed, the hospitals located in the Portuguese northern region were more resilient to the COVID-19 crisis. All in all, to become more resilient (even for future COVID-19 outbreaks), hospitals should undertake changes that are advantageous irrespective of the obstacles they face and that are even beneficial during normal times. A culture of cooperation within and across hospitals should also be cultivated, which allows exchanging resources where they can be used more efficiently.

Analyzing the impact of COVID-19 on the financial performance of the hospitality and tourism industries: an ensemble MCDM approach in the Indian context

Purpose The Indian hospitality and tourism industries, major economic growth drivers and employment generators, have been greatly affected by the outbreak of the COVID-19 pandemic. In FY 2020, the Indian tourism sector created 39 million jobs and contributed nearly US$194.3bn, or 6.8%, to India's gross domestic product. The purpose of this study is to focus on ranking 22 listed hotels and 9 listed travel agencies in India based on their performance across 14 selected financial parameters in both the pre-COVID-19 year ending in March 2019 and the post-COVID-19 year ending in March 2021 to understand how the pandemic affected their businesses. Design/methodology/approach This research proposes to analyze the impact of the COVID-19 pandemic on the financial performance of 22 listed Indian hotels and 9 listed travel agencies evaluated over 14 financial parameters using a pipeline of two recently developed multicriteria decision-making techniques, method based on the removal effects of criteria (MEREC) and grey-based combined compromised solution (CoCoSo). First, the criteria weights are objectively determined using MEREC, and then the financial performances of the selected companies in both the hospitality and tourism industries are separately assessed using CoCoSo to get their overall performance score, based on which the companies are ranked in order of preference. Findings It was observed that Westlife Development, Lemon Tree Hotels, Indian Tourism Development Corporation, Royal Orchid and Country Club performed significantly poorer than their peers in the aftermath of the pandemic, whereas EIH, Advani Hotels and Resorts and TGB Banquets performed relatively better. Travel agencies Easy Trip and International Travel House performed particularly poorly because of the pandemic, but VMV Holidays performed relatively better in FY 2021. Practical implications The findings of the analysis will aid portfolio construction, corporate investment decisions, competition research, government policymaking and industrial analysis. Originality/value The proposed model is novel because it fills the research gap in the application of the integrated MEREC-CoCoSo method to study the impact of COVID-19 on the hospitality and tourism sectors in India.

Daily food planning for families under Covid-19: combining analytic hierarchy processes and linear optimisation.

In many households, preparation of food in normal times proves to be problematic, particularly when parents endeavour to keep their children on a balanced diet. The COVID-19 pandemic has further exacerbated this problem imposing the requirement of social distancing, which led to disruptions in the food supply chain and multiplication of responsibilities faced by families with children. The present study revisits the standard "Diet Problem" to address these challenges and to develop a participatory approach to provide a diversified weekly meal plan that is easy and fun but simultaneously complies with the unique requirements of each participant. This is done by providing a novel framework, which combines linear optimisation with the Parsimonious Analytic Hierarchy Process, a method for individual choices. This novel approach to participatory modelling is tested within two young family settings in Brazil. The model produced through this contemporary framework provides a weekly menu that best meets expectations of the members of a young family in the context of the COVID-19 pandemic.

Sustainable Mobility Engagement and Co-planning;a Multicriteria Analysis Based Transferability Guide

Engagement in sustainable mobility planning seems to act as a starting point to unlock a new era of responsible and sustainable behaviors. After almost a two-years experience of a global crisis (COVID-19) revealing that the only way out is through jointly walking on the way into sustainability and resilience, engaging people in shifting to sustainable mobility options has become an imperative need. The current paper exploits Multi-Criteria Decision Analysis (MCDA) in building a methodological 5-step framework for evaluating the transferability potentials of good practices (GPs) in citizens’ sensibilization and engagement in sustainable mobility. 10 good practices were selected in order to cover the whole cycle of sustainable mobility planning (SUMP cycle) while representatives from different EU Regions were involved in the assessment procedure resulting in this way in a general transferability guide. The guide, tailored to each case, can be a very useful tool in the hands of single authorities while making their mobility engagement plan. © 2022, Springer Nature Switzerland AG.

Continuous manufacturing technologies in upstream pharmaceutical supply chains: Combining engineering and managerial criteria

The COVID-19 pandemic exposed vulnerabilities in upstream pharmaceutical supply chains (PSC). One is that the global supply of active pharmaceutical ingredients (APIs) is overly dependent on few locations and large-scale batch manufacturing. Regulators hope to enable more dependable location decisions and improved processing quality with the adoption of advanced technologies such as process intensification through continuous manufacturing (CM). Conceptual work suggests that the benefits of shifting from batch to CM accrue end-to-end across the PSC. Yet detailed quantitative information about CM is limited at an early stage of evaluation, and too specialised to inform managerial decisions about PSC reconfiguration. Supply chain and engineering criteria are rarely combined in the early-stage evaluation of alternative CM technologies. Extant CM research typically overlooks implications for supply chain managers. To address the current gap, this article evaluates, at an early stage of adoption, alternative CM reactor technologies for the synthesis of APIs in selected therapeutic areas. With evidence from secondary data, relevant technologies and criteria are identified, and their relative importance is evaluated in a semi-quantitative fashion following analytical hierarchy process (AHP) principles, ensuring that findings are intelligible to both engineers and managers. The proposed empirical work enriches previous conceptual frameworks predicated on volume-variety considerations. Specifically, findings suggest that, all things considered, microreactor technologies outperform alternatives. However, PSC managerial considerations introduce nuances in specific therapeutic areas, for example, antivirals where a tension between complex chemistry and the need for flexibility in unit operations may favour batch manufacturing. For analgesics the need to exploit the existing manufacturing base whilst addressing inventory reduction favours technologies that incorporate elements of batch and CM. The proposed analysis is in line with real-world decisions that global medicines manufacturers are increasingly facing, as governments seek to develop local health countermeasures to the COVID-19 pandemic in the absence of detailed information.

Prioritization of livestock transboundary diseases in Belgium using a multicriteria decision analysis tool based on drivers of emergence.

During the past decade, livestock diseases have (re-)emerged in areas where they had been previously eradicated or never been recorded before. Drivers (i.e. factors of (re-)emergence) have been identified. Livestock diseases spread irrespective of borders, and therefore, reliable methods are required to help decision-makers to identify potential threats and try stopping their (re-)emergence. Ranking methods and multicriteria approaches are cost-effective tools for such purpose and were applied to prioritize a list of selected diseases (N = 29 including 6 zoonoses) based on the opinion of 62 experts in accordance with 50 drivers-related criteria. Diseases appearing in the upper ranking were porcine epidemic diarrhoea, foot-and-mouth disease, low pathogenic avian influenza, African horse sickness and highly pathogenic avian influenza. The tool proposed uses a multicriteria decision analysis approach to prioritize pathogens according to drivers and can be applied to other countries or diseases.