Coinfections and comorbidities in African health systems: At the interface of infectious and noninfectious diseases.

There is a disease epidemiological transition occurring in Africa, with increasing incidence of noninfectious diseases, superimposed on a health system historically geared more toward the management of communicable diseases. The persistence and sometimes emergence of new pathogens allows for the occurrence of coinfections and comorbidities due to both infectious and noninfectious diseases. There is therefore a need to rethink and restructure African health systems to successfully address this transition. The historical focus of more health resources on infectious diseases requires revision. We hypothesise that the growing burden of noninfectious diseases may be linked directly and indirectly to or further exacerbated by the existence of neglected tropical diseases (NTDs) and other infectious diseases within the population. Herein, we discuss the health burden of coinfections and comorbidities and the challenges to implementing effective and sustainable healthcare in Africa. We also discuss how existing NTD and infectious disease intervention programs in Africa can be leveraged for noninfectious disease intervention. Furthermore, we explore the potential for new technologies-including artificial intelligence and multiplex approaches-for diagnosis and management of chronic diseases for improved health provision in Africa.

Universal Health Coverage in Africa: Coinfections and Comorbidities.

At the 67th session of the World Health Organization (WHO) Regional Committee meeting in August 2017, African health ministers adopted a range of transformational actions intended to strengthen health systems in countries, leading to Universal Health Coverage (UHC). A critical challenge for UHC is the existence of coinfections and noncommunicable diseases (NCDs), characterised by comorbidities.

A Qualitative Evaluation of a Decision Support System for District-Level Disease Surveillance in Sierra Leone.

A decision support system for district-level disease surveillance was piloted with the Port Loko District Health Management Team in Sierra Leone. Through a qualitative evaluation, the study explores the impact of the system on disease surveillance workflows. Results indicate that the system aided decision making for operational tasks, and reduced the time taken to analyze and report surveillance data. In addition, the study discusses the challenges of deploying a pilot system during the Ebola recovery in Sierra Leone, and proposes a high-level architecture for a modular, interoperable decision support system for disease surveillance for public health decision makers in low-resource health systems.

Design and evaluation of a web-based decision support tool for district-level disease surveillance in a low-resource setting.

During the 2014 West African Ebola Virus outbreak it became apparent that the initial response to the outbreak was hampered by limitations in the collection, aggregation, analysis and use of data for intervention planning. As part of the post-Ebola recovery phase, IBM Research Africa partnered with the Port Loko District Health Management Team (DHMT) in Sierra Leone and GOAL Global, to design, implement and deploy a web-based decision support tool for district-level disease surveillance. This paper discusses the design process and the functionality of the first version of the system. The paper presents evaluation results prior to a pilot deployment and identifies features for future iterations. A qualitative assessment of the tool prior to pilot deployment indicates that it improves the timeliness and ease of using data for making decisions at the DHMT level.

Multi-material 3-D viscoelastic model of a transtibial residuum from in-vivo indentation and MRI data.

Although the socket is critical in a prosthetic system for a person with limb amputation, the methods of its design are largely artisanal. A roadblock for a repeatable and quantitative socket design process is the lack of predictive and patient specific biomechanical models of the residuum. This study presents the evaluation of such a model using a combined experimental-numerical approach. The model geometry and tissue boundaries are derived from magnetic resonance imaging (MRI). The soft tissue non-linear elastic and viscoelastic mechanical behavior was evaluated using inverse finite element analysis (FEA) of in-vivo indentation experiments. A custom designed robotic in-vivo indentation system was used to provide a rich experimental data set of force versus time at 18 sites across a limb. During FEA, the tissues were represented by two layers, namely the skin-adipose layer and an underlying muscle-soft tissue complex. The non-linear elastic behavior was modeled using 2nd order Ogden hyperelastic formulations, and viscoelasticity was modeled using the quasi-linear theory of viscoelasticity. To determine the material parameters for each tissue, an inverse FEA based optimization routine was used that minimizes the combined mean of the squared force differences between the numerical and experimental force-time curves for indentations at 4 distinct anatomical regions on the residuum. The optimization provided the following material parameters for the skin-adipose layer: [c=5.22kPam=4.79γ=3.57MPaτ=0.32s] and for the muscle-soft tissue complex [c=5.20kPam=4.78γ=3.47MPaτ=0.34s]. These parameters were evaluated to predict the force-time curves for the remaining 14 anatomical locations. The mean percentage error (mean absolute error/ maximum experimental force) for these predictions was 7±3%. The mean percentage error at the 4 sites used for the optimization was 4%.