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The dual epidemic of non-communicable diseases (NCDs) and human immuno-deficiency virus (HIV) in Sub-Saharan Africa has increased substantially in recent years, with cardiovascular disease representing a significant contributor to the regional burden of disease. Very little is known about the cardiovascular health of people deprived of their liberty in the region. The purpose of this study was to collate extant literature on the topic.

A scoping review mapped and described what is known about cardiovascular disease in prison populations in Sub-Saharan Africa. A systematic search of empirical literature with no date limitation was conducted in English. Sixteen studies representing six Sub-Saharan African countries (Cameroon, Nigeria, Guinea, Burkina Faso, Ghana and Ethiopia) were charted, categorised and thematically analysed.

Seven key themes were identified: custodial deaths and autopsy; cardiorespiratory fitness and exercise; cardiovascular disease and elderly people in prison; cardiovascular disease and women in prison; dietary deficiencies; influence of sleep patterns on cardiovascular disease; and other associated risk factors. Most natural deaths at autopsy of custodial deaths were due to cardiovascular disease. Cardiorespiratory fitness was low in prisons, and poor sleep patterns and dietary deficiencies are likely contributors to the burden of cardiovascular disease in prisons. The needs of elderly and female prison populations are ill-considered.

To the best of the authors’ knowledge, this is the first known attempt to scope extant literature on cardiovascular disease in Sub-Saharan African prisons. A strategic focus on the cardiovascular health of people in prison is warranted. Routine monitoring and expansion of existing prison health-care services and integration of NCD services with infectious disease (HIV and tuberculosis) programmes in prisons are required.

This study aims to investigate the effects of the historical prevalence of infectious diseases on contemporary entrepreneurship. Previous studies reveal numerous proximate causes of entrepreneurship, but little is known about the fundamental determinants of this widespread economic concern.

The central hypothesis is that historical pathogens exert persistent impacts on present-day entrepreneurship. The authors provide support for the underlying hypothesis using ordinary least squares and two-stage least squares with cross-sectional data from 125 countries consisting of the averages between 2006 and 2018.

Past diseases reduce entrepreneurship both directly and indirectly. The strongest indirect effects occur through GDP per capita, property rights, innovation, entrepreneurial attitudes, entrepreneurial abilities, entrepreneurial aspirations and skills. This result is robust to many sensitivity tests. Policymakers may take these findings into account and incorporate disease pathogens into the design of entrepreneurship.

The novelty of this paper lies in the adoption of a historical approach that sheds light on the deep historical roots of cross-country differences in entrepreneurship.

Front-of-pack nutritional labels have been extensively studied to support consumers in making healthier and more informed food choices. However, existing research has gathered conflicting evidence about which category of label, nutrient-specific or summary labels, is more effective. As a result, the European Union has postponed its decision on selecting a unified label to collect additional information. This study specifically focusses on individuals with noncommunicable diseases, an overlooked yet relevant segment of consumers who can significantly benefit from the proper use of nutritional labels in their self-care.

In a sequence of three studies grounded in the front-of-pack acceptance model and focussing on customers with specific noncommunicable diseases, the authors examined the different effects of the NutrInform Battery and Nutri-Score on food acceptance and portion selection. This research involved the use of structural equation modelling and ANOVA and was conducted with a cumulative sample of 2,942 EU adults, residing in countries with or without previous exposure to nutritional labels.

The results suggest that among individuals with noncommunicable diseases, nutrient-specific labels are perceived as more useful and easier to use. They also generate a better attitude towards the usage of nutritional labels and are more effective in nudging those consumers towards a proper selection of portions.

The results provide valuable insights into how front-of-pack nutritional labels can impact the food choices of individuals with noncommunicable diseases and have implications for public health policies.

Investigation of the effects of NutrInform Battery and Nutri-Score on consumers with noncommunicable diseases, an area currently under-researched.

Given the popularity of online health communities (OHCs) and medical question-and-answer (Q&A) services, it is increasingly important to understand what constitutes useful answers and user-adopted standards in healthcare domain. However, few studies provide insights into how health information characteristics, provider characteristics and recipient characteristics jointly influence user information adoption decisions. To fill this research gap, this study examines the combined effects of physicians' certainty tone as information characteristics, seniority as provider characteristics and disease severity as recipient characteristics on patients' health information adoption.

Drawing on dual-process theory and information adoption model, an extended information adoption model is established in this study to examine the effect of attitude certainty on patients' health information adoption, and the moderating effects of online seniority and offline seniority, as well as patient motivation level—disease severity. Utilizing logit regression models, the authors empirically tested the hypotheses based on 4,224 Q&A records from a popular Chinese OHC.

The results show that (1) attitude certainty has a significant positive impact on patients' health information adoption, (2) the relationship between attitude certainty and information adoption is negatively moderated by physicians' online seniority, but is positively moderated by offline seniority; (3) there is a negative three-way interaction effect of attitude certainty, online seniority and disease severity on patients' health information adoption.

This study extends the information adoption model to examine the two-way interaction between argument quality and source reliability, as well as the three-way interaction with user motivation level, especially for health information adoption in the healthcare field. These findings also provide direct practical applications for knowledge contributors and OHCs.

This study aims to examine the effects of the historical prevalence of infectious diseases on contemporary gender equality. Previous studies reveal the persistence of the effects of historical diseases on innovation, through the channel of culture.

Drawing on the parasite stress theory, the authors propose a framework which argues that historical prevalence of infectious disease reduces contemporary gender equality. The study uses ordinary least squares and two-stage least squares in a cross-section with data from 122 countries between 2000 and 2021.

This study provide support for the underlying hypothesis. Past diseases reduce gender equality both directly and indirectly. The strongest indirect effects occur through innovation output. Gender equality analysis may take these findings into account and incorporate disease pathogens into the design of international social policy.

This study complements the extant literature by assessing the nexus between historical prevalence of infectious diseases and gender equality.

Geopolitical risks (GPR) and increase in equity market volatility due to health pandemics have great implications on assets prices around the world. Many empirical studies have focused on the effects of these risks on different financial assets. The purpose of this paper is to contribute to this related literature by examining the dynamic effects of GPRs and infectious diseases–induced equity market volatility on regional and global house price indexes.

This paper explores the asymmetric effects of infectious diseases and GPRs on house prices across different market conditions using the quantile regression approach. This technique enables us to examine the nonlinear asymmetric effects of GPRs and infectious diseases on both global and regional house price indexes using daily data from January 1, 2011, to June 3, 2022. It focuses on both the effects of a composite measure of GPR as well as the disaggregated effects of threats and acts (war) on the real estate markets under different market conditions.

The main findings of this study demonstrates that the effects of geopolitical and infectious diseases–related risks vary differently across regional real estate markets and the nature of the GPR. In particular, the effects of geopolitical threats are stronger than those of geopolitical acts, especially for the European, Asia-Pacific and North American regions during bullish market periods. Except for the effects of geopolitical threats during real estate market downturns, the African real estate market appears to be insulated from the effects of GPRs across all market conditions. Also, the authors show that infectious diseases increase losses in real estate investments when the market condition is bearish for all markets and could extend toward the normal market period for the North American, Asia-Pacific and European markets. However, across all the market conditions, the effects of the composite index of GPRs are not significant for the Asia-Pacific and European regional markets. Results are mixed for the remaining markets, especially for the global market. Whereas during bearish market periods, the effect is positive, it becomes negative when the market condition become normal and insignificant when it becomes bullish. For the North American and African regional markets, the effect is positive under the bearish market state.

Increase in equity market volatility due to infectious diseases as well as conflicts and tensions among major powers, including potential risks of financial instability, all lead to significant increase in shocks to financial markets. To the best of the authors’ knowledge, this is the first study to analyze the asymmetric and comparative effects of GPRs and infectious diseases–related equity market volatility on real estate investments across different regions and market conditions. Because of the complexity of these risks and policy shifts, and the characteristics and heterogeneity of different regional financial markets, the impacts of shock from these risks are intuitively diverse, with practical implications for portfolio management.

This study aims to synthesize the existing serious games designed to promote mental health in adolescents with chronic illnesses.

This study conducted a review following the guidelines of Joanna Briggs Institute and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Searches were conducted in databases PubMed, Scopus, Web of Science, Cochrane Library, cumulative index to nursing and allied health literature, PsycINFO, China national knowledge infrastructure Wanfang, VIP Database for Chinese Technical Periodicals and SinoMed from inception to February 12, 2023.

A total of 14 studies (describing 14 serious games) for improving the mental health of adolescents with chronic diseases were included. Of all the included games, 12 were not described as adopting any theoretical framework or model. The main diseases applicable to serious games are cancer, type 1 diabetes and autism spectrum disorder. For interventional studies, more than half of the study types were feasibility or pilot trials. Furthermore, the dosage of serious games also differs in each experiment. For the game elements, most game elements were in the category “reward and punishment features” (n = 50) and last was “social features” (n = 4).

Adolescence is a critical period in a person’s physical and mental development throughout life. Diagnosed with chronic diseases during this period will cause great trauma to the adolescents and their families. Serious game interventions have been developed and applied to promote the psychological health field of healthy adolescents. To the best of the authors’ knowledge, this study is the first to scope review the serious game of promoting mental health in the population of adolescents with chronically ill. At the same time, the current study also extracted and qualitatively analyzed the elements of the serious game.

The world is shifting towards the fourth industrial revolution (Industry 4.0), symbolising the move to digital, fully automated habitats and cyber-physical systems. Industry 4.0 consists of innovative ideas and techniques in almost all sectors, including Smart health care, which recommends technologies and mechanisms for early prediction of life-threatening diseases. Cardiovascular disease (CVD), which includes stroke, is one of the world’s leading causes of sickness and deaths. As per the American Heart Association, CVDs are a leading cause of death globally, and it is believed that COVID-19 also influenced the health of cardiovascular and the number of patients increases as a result. Early detection of such diseases is one of the solutions for a lower mortality rate. In this work, early prediction models for CVDs are developed with the help of machine learning (ML), a form of artificial intelligence that allows computers to learn and improve on their own without requiring to be explicitly programmed.

The proposed CVD prediction models are implemented with the help of ML techniques, namely, decision tree, random forest, k-nearest neighbours, support vector machine, logistic regression, AdaBoost and gradient boosting. To mitigate the effect of over-fitting and under-fitting problems, hyperparameter optimisation techniques are used to develop efficient disease prediction models. Furthermore, the ensemble technique using soft voting is also used to gain more insight into the data set and accurate prediction models.

The models were developed to help the health-care providers with the early diagnosis and prediction of heart disease patients, reducing the risk of developing severe diseases. The created heart disease risk evaluation model is built on the Jupyter Notebook Web application, and its performance is calculated using unbiased indicators such as true positive rate, true negative rate, accuracy, precision, misclassification rate, area under the ROC curve and cross-validation approach. The results revealed that the ensemble heart disease model outperforms the other proposed and implemented models.

The proposed and developed CVD prediction models aims at predicting CVDs at an early stage, thereby taking prevention and precautionary measures at a very early stage of the disease to abate the predictive maintenance as recommended in Industry 4.0. Prediction models are developed on algorithms’ default values, hyperparameter optimisations and ensemble techniques.

According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease.

A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases.

The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6.

The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(n^k+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.