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A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

The purpose of this study is to look at the structure of the interactions between the board of directors’ chairman qualities such as chairman independence, tenure, ethnicity, age- and impression management (IM).

The research population consists of non-financial Malaysian companies listed on Bursa Malaysia’s Main Market, using data gathered via annual reports and DataStream. The study relies on the ordinary least square regression to test the direct relationships between the directors’ chairman characteristics and IM. Moreover, robustness and sensitivity tests were used to examine the effectiveness of chairman characteristics with IM. Furthermore, the results rely on the FGLS regression as an additional test. The study found that chairman independence, chairman ethnicity and chairman age have a significant impact on IM.

The results reveal that chairman independence has a negative association with qualitative IM (IMSC1). Moreover, chairman ethnicity has a positively significant relationship with qualitative IM (IMSC1) and quantitative IM (IMSC2). Also, the effectiveness of chairman characteristics has a negative and significant association with IMSC1.

The primary goal of this paper is to fill a gap in the literature and to open up opportunities for more in-depth research on the subject. So far, there has been no research into the impact of the board chairman’s (BC) personality on IM. This study serves as a warning to policymakers, businesses and their stakeholders, as well as researchers, about the importance of BC characteristics, which may impede the effectiveness of corporate governance mechanisms. The paper provides a framework for investigating these characteristics in the context of IM.

Although supporting and assessing the non-academic “impact” of research are not entirely new developments in higher education, academics and research institutions are under increasing pressure to produce work that has a measurable influence outside the academy. With a view to supporting the solution of complex societal issues with evidence and expertise, and against the background of increased emphasis on impact in the United Kingdom's 2021 Research Excellence Framework (REF2021) and a proliferation of impact guides and tools, this article offers a simple, easy to remember framework for designing impactful research. We call this framework “The 7Cs of Impact” – Context, Communities, Constituencies, Challenge, Channels, Communication and Capture.

Drawing on core elements of the Policy Institute at King's College London's Impact by Design training course and the authors' practical experience in supporting and delivering impact, this paper outlines how this framework can help address key aspects across the lifecycle of a research project and plan, from identifying the intended impact of research and writing it into grants and proposals, to engaging project stakeholders and assessing whether the project has had the desired impact.

While preparations for current and future REF submissions may benefit from using this framework, this paper sets out the “7Cs” with a more holistic view of impact in mind, seeking to aid researchers in identifying, capturing, and communicating how research projects can and do contribute to the improvement in society.