Network analysis of Diagnostic Medical Device Development for Infectious Diseases Prevalent in South Africa

Infectious diseases are a major health concern in South Africa and many other developing countries. The local development of medical devices for infectious diseases in such settings, utilizing the local knowledge base, has the potential to improve the accuracy of and access to diagnoses and to lead to the devices being more context-appropriate and affordable. The aim of this project was to examine the landscape of diagnostic medical device development targeting infectious diseases prevalent in South Africa for the period 2000-2016, particularly with regard to collaboration between institutions in different sectors and the contributions of different collaborators. Such knowledge would be beneficial to future technological and policy developments aimed at improving access to diagnostic services and treatment in the South African context. Collaboration across four sectors was considered: university, hospital, industry and science councils and facilities. A bibliometric analysis was performed, and publications documenting medical device development for diagnosis of infectious diseases were extracted. Co-authorship of the journal and conference articles was used as a proxy for collaboration across organisations. Affiliation data extracted from the publications were used to generate a collaboration network. Netdraw, a network visualisation tool, was used to visualize the network, and network metrics such as degree centrality, betweenness centrality and closeness centrality, as well as group density measures, were produced using UCINET software. The collaboration network and the network metrics were used to determine which organisations have collaborated and which collaborators have played the most active and influential roles in diagnostic device development. The university sector was found to make the largest contribution to the development of diagnostic medical devices in South Africa, and also played a key role in transmitting information throughout the network due to its high frequency of connections to other organisations. The most prevalent type of inter-sectoral collaboration was between universities and science councils and facilities, while universities were found to collaborate most amongst themselves with regard to intrasectoral collaboration. Foreign organisations played a prominent role in diagnostic device development between 2012 and 2016. Tuberculosis was the most prevalent infectious disease in diagnostic device development research, and computer-aided detection was a common feature of research on diagnostic device development

A-Novel in this Technique Analysis Tuberculosis Patient Using Hybrid CNN and SVM Model

Tuberculosis (TB) remains a global health challenge with a significant impact on public health worldwide. This study addresses the prevalence and detection of T.B., focusing on India, a country experiencing rapid economic growth and notable transitions in various sectors. Despite advancements in healthcare delivery, communicable and non-communicable diseases, including TB, continue to pose substantial threats to health security. The proposed method for T.B. analysis integrates key modules, encompassing data collection, preprocessing, feature extraction, image splitting, classification, and performance estimation. Utilizing machine learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN), the study aims to enhance accuracy and efficiency in T.B. patient detection. The dataset includes chest X-rays from diverse sources, aiming to provide a comprehensive understanding of T.B. patterns. The study emphasizes the interconnected challenges of malnutrition and T.B., highlighting the importance of nutritional status assessment in public health. The linkage between undernutrition and infectious diseases, including TB, underscores the need for early detection and intervention. The proposed method offers a systematic approach to evaluating and addressing nutritional deficiencies in patients with chronic illnesses, contributing to improved clinical outcomes

A qualitative study on the experiences and perspectives of public sector patients in Cape Town in managing the workload of demands of HIV and type 2 diabetes co-morbidity

Health systems' strengthening is essential in South Africa in an era of the convergence of communicable and non-communicable diseases. Whilst TB is ranked first in all-cause mortality, non-communicable diseases which include cerebrovascular disease and diabetes mellitus follow; with HIV/AIDS in fourth place. In the Western Cape, diabetes mellitus and HIV are the top two causes of death accounting for 6.8% and 5.8% respectively (StatsSA, 2015b). As the burden of non-communicable disease continues to increase significantly due to more South Africans presenting these co-morbid conditions, the complexity of managing these chronic conditions has increased. The reorganisation of primary health services to better cater for patients with multiple chronic conditions has become an imperative in South Africa but still in its infancy. However, how chronic patients with multi-morbidities experience the current services and what their perceived needs are in order to enhance the management of their conditions both at point of healthcare and in their daily lives is not widely understood. Below, is an outline of the three parts presented in this dissertation. Part A is the study protocol, which gives a background of the intersection of communicable and noncommunicable diseases in South Africa, focusing on HIV and type two diabetes (hereafter HIV/T2D) co-morbidity. A qualitative design was employed. In-depth interviews were conducted with ten patients living with HIV/T2D co-morbidity and six health workers who interacted with these patients. Ethical considerations such as potential risks and benefits; confidentiality, autonomy and informed consent are also highlighted in the protocol. Part B is the structured literature review on chronic care in low and middle-income countries (LMICs). Two sub-sections are presented with the first focusing on LMICs excluding South Africa; and the second for South Africa only. Theoretical frameworks, which were applied to managing chronic conditions and empirical studies on HIV/T2D in these LMICs, are reviewed. Reference to the Cumulative Complexity Model (CCM), will also provide an indepth understanding of the prospects of strengthening the primary healthcare system in South Africa to address chronic conditions more effectively. Part C is the journal-ready manuscript of the data collected in the qualitative study. It consists of the background, methods, results, discussion and conclusions. Findings describe patients' experiences of the primary healthcare services and the daily challenges of living with and managing HIV and T2D among a sample of ten patients attending a clinic in Cape Town. Health worker perspectives on managing HIV/T2D co-morbidity are also presented. Both patients and healthworkers also shared strategies on how health interventions could be more responsive to HIV/T2D co-morbidity. Hence, further contributions are made in the knowledge base of strengthening chronic conditions. However, further research with different subsets of patients living with not only HIV/T2D but also other co-morbid or multi-morbid conditions is important for improvements in health policy-making in South Africa

Smart Face Masks for Covid-19 Pandemic Management: A Concise Review of Emerging Architectures, Challenges and Future Research Directions

Smart sensing technology has been playing tremendous roles in digital healthcare management over time with great impacts. Lately, smart sensing has awoken the world by the advent of Smart Face Masks (SFM) in the global fight against the deadly Coronavirus (Covid-19) pandemic. In turn, a number of research studies on innovative SFM architectures and designs are emerging. However, there is currently no study that has systematically been conducted to identify and comparatively analyze the emerging architectures and designs of SFMs, their contributions, socio-technological implications, and current challenges. In this paper, we investigate the emerging SFMs in response to Covid-19 pandemic and provide a concise review of their key features and characteristics, design, smart technologies, and architectures. We also highlight and discuss the socio-technological opportunities posed by the use of SFMs and finally present directions for future research. Our findings reveal four key features that can be used to evaluate SFMs to include reusability, self-power generation ability, energy awareness and aerosol filtration efficiency. We discover that SFM has potential for effective use in human tracking, contact tracing, disease detection and diagnosis or in monitoring asymptotic populations in future pandemics. Some SFMs have also been carefully designed to provide comfort and safety when used by patients with other respiratory diseases or comorbidities. However, some identified challenges include standards and quality control, ethical, security and privacy concerns

Examining HIV and Tuberculosis Using a Decision Support Systems Computer Simulation Model: The Case of the Russian Federation

The aim of this paper is to describe the development and use of a computer simulation model that can be used as a Decision Support System (DSS) to tackle the critical public health issues of the chronic diseases, HIV and HIV related Tuberculosis in the Russian Federation. The model was developed to enable health officials and decision makers to determine the impact of policies to control the chronic diseases spread in an area of Russia. This area, like many others in Russia and elsewhere, have recently witnessed an explosion of HIV infections and a worrying spread of the Multi Drug Resistant form of Tuberculosis (MDRTB). The conclusions drawn is that a high population coverage with Highly Active Anti Retroviral Treatment (HAART) (75% or higher), allied with high MDRTB cure rates, reduces cumulative deaths by 60%, with limited impact below this level. The contributions that this research offers are the development of a simulation model that can be applied as a DSS by public health officials and managers in order to inform policy making. By doing so, ways of best controlling the spread of HIV and MDRTB and reduce the mortality rate from these serious public health threats is provided

The Internet of Things in Healthcare. An Overview

La prestación de servicios de salud está experimentando enormes cambios alrededor del mundo. El envejecimiento de la población, la creciente incidencia de enfermedades crónicas, y la escasez de recursos se están convirtiendo en una carga pesada para los actuales sistemas de salud y podrían comprometer la prestación de servicios de salud en las próximas décadas. Por otro lado, la creciente popularidad de dispositivos para el cuidado de la salud y el bienestar, junto con avances en comunicaciones inalámbricas y en sensores abren la puerta a nuevos modelos para la prestación de servicios de salud respaldados por el Internet de las cosas (IoT). Este artículo presenta una revisión general de las tendencias que están impulsando el desarrollo de aplicaciones para el cuidado de la salud basadas en IoT, y las describe brevemente a nivel de sistema.The provision of healthcare is experimenting enormous changes worldwide. Population ageing, rising incidence of chronic diseases, and shortages of resources are placing a heavy burden in current healthcare systems and have the potential to risk the delivery of healthcare in the next few decades. On the other hand, the growing popularity of smart devices for healthcare and wellness, along with advances in wireless communications and sensors are opening the door to novel models of health care delivery supported by the Internet of things (IoT). This paper presents a review of the trends that are driving the development of IoT-based applications for healthcare and briefly describe them at a system level