Assessment of the Barriers and Enablers of the Use of mHealth Systems in Sub-Saharan Africa According to the Perceptions of Patients, Physicians, and Health Care Executives in Ethiopia: Qualitative Study.

BACKGROUND: Digital technologies are increasingly being used to deliver health care services and promote public health. Mobile wireless technologies or mobile health (mHealth) technologies are particularly relevant owing to their ease of use, broad reach, and wide acceptance. Unlike developed countries, Sub-Saharan Africa experiences more challenges and obstacles when it comes to deploying, using, and expanding mHealth systems. In addition to barriers, there are enabling factors that could be exploited for the design, implementation, and scaling up of mHealth systems. Sub-Saharan Africa may require tailored solutions that address the specific challenges facing the region. OBJECTIVE: The overall aim of this study was to identify the barriers and enablers for using mHealth systems in Sub-Saharan Africa from the perspectives of patients, physicians, and health care executives. METHODS: Multi-level and multi-actor in-depth semistructured interviews were employed to qualitatively explore the barriers and enablers of the use of mHealth systems. Data were collected from patients, physicians, and health care executives. The interviews were audio recorded, transcribed verbatim, translated, and coded. Thematic analysis methodology was adopted, and NVivo software was used for the data analysis. RESULTS: Through this rigorous study, a total of 137 determinants were identified. Of these determinants, 68 were identified as barriers and 69 were identified as enablers. Perceived barriers in patients included lack of awareness about mHealth systems and language barriers. Perceived enablers in patients included need for automated tools for health monitoring and an increasing literacy level of the society. According to physicians, barriers included lack of available digital health systems in the local context and concern about patients' mHealth capabilities, while enablers included the perceived usefulness in reducing workload and improving health care service quality, as well as the availability of mobile devices and the internet. As perceived by health care executives, barriers included competing priorities alongside digitalization in the health sector and lack of interoperability and complete digitalization of implemented digital health systems, while enablers included the perceived usefulness of digitalization for the survival of the highly overloaded health care system and the abundance of educated manpower specializing in technology. CONCLUSIONS: mHealth systems in Sub-Saharan Africa are hindered and facilitated by various factors. Common barriers and enablers were identified by patients, physicians, and health care executives. To promote uptake, all relevant stakeholders must actively mitigate the barriers. This study identified a promising outlook for mHealth in Sub-Saharan Africa, despite the present barriers. Opportunities exist for successful integration into health care systems, and a user-centered design is crucial for maximum uptake.

Spatiotemporal distribution of climate-sensitive disease incidences in ethiopia: a longitudinal retrospective analysis of Malaria, Meningitis, Cholera, Dysentery, Leishmaniasis and Dengue fever between 2010 and 2022/2023.

BACKGROUND: Understanding the temporal and geographic distribution of disease incidences is crucial for effective public health planning and intervention strategies. This study presents a comprehensive analysis of the spatiotemporal distribution of disease incidences in Ethiopia, focusing on six major diseases: Malaria, Meningitis, Cholera and Dysentery, over the period from 2010 to 2022, whereas Dengue Fever and Leishmaniasis from 2018 to 2023. METHODS: Using data from Ethiopian public health institute: public health emergency management (PHEM), and Ministry of Health, we examined the occurrence and spread of each disease across different regions of Ethiopia. Spatial mapping and time series analysis were employed to identify hotspots, trends, and seasonal variations in disease incidence. RESULTS: The findings reveal distinct patterns for each disease, with varying cases and temporal dynamics. Monthly wise, Malaria exhibits a cyclical pattern with a peak during the rainy and humid season, while Dysentery, Meningitis and Cholera displays intermittent incidences. Dysentery cases show a consistent presence throughout the years, while Meningitis remains relatively low in frequency but poses a potential threat due to its severity. Dengue fever predominantly occurs in the eastern parts of Ethiopia. A significant surge in reported incident cases occurred during the years 2010 to 2013, primarily concentrated in the Amhara, Sidama, Oromia, Dire Dawa, and Benishangul-Gumuz regions. CONCLUSIONS: This study helps to a better understanding of disease epidemiology in Ethiopia and can serve as a foundation for evidence-based decision-making in disease prevention and control. By recognizing the patterns and seasonal changes associated with each disease, health authorities can implement proactive measures to mitigate the impact of outbreaks and safeguard public health in the region.

Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings.

Objectives: Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis. Methods: The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose. Results: The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use. Conclusions: With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.

Development of birth weight estimation model for Ethiopian population from sonographic evaluation.

BACKGROUND: Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation. METHODS: In this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement. RESULTS: The model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model. CONCLUSION: The findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.

mHealth in Sub-Saharan Africa and Europe: Context of current health, healthcare status, and demographic structure.

Introduction: The advent of digital systems and global mobile phone availability presents an opportunity for better healthcare access and equity. However, the disparity in the usage and availability of mHealth systems between Europe and Sub-Saharan Africa (SSA) has not been explored in relation to current health, healthcare status, and demographics. Objective: This study aimed to compare mHealth system availability and use in SSA and Europe in the above-mentioned context. Methods: The study analyzed health, healthcare status, and demographics in both regions. It assessed mortality, disease burden, and universal health coverage. A systematic narrative review was conducted to thoroughly assess available data on mHealth availability and use, guiding future research in the field. Results: SSA is on the verge of stages 2 and 3 in the demographic transition with a youthful population and high birth rate. Communicable, maternal, neonatal, and nutritional diseases contribute to high mortality and disease burden, including child mortality. Europe is on the verge of stages 4 and 5 in the demographic transition with low birth and death rates. Europe's population is old, and non-communicable diseases (NCDs) pose major health challenges. The mHealth literature adequately covers cardiovascular disease/heart failure, and cancer. However, it lacks approaches for respiratory/enteric infections, malaria, and NCDs. Conclusions: mHealth systems in SSA are underutilized than in Europe, despite alignment with the region's demographics and major health issues. Most initiatives in SSA lack implementation depth, with only pilot tests or small-scale implementations. Europe's reported cases highlight actual implementation and acceptability, indicating a strong implementation depth of mHealth systems.

mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe.

Background: mHealth can help with healthcare service delivery for various health issues, but there's a significant gap in the availability and use of mHealth systems between sub-Saharan Africa and Europe, despite the ongoing digitalization of the global healthcare system. Objective: This work aims to compare and investigate the use and availability of mHealth systems in sub-Saharan Africa and Europe, and identify gaps in current mHealth development and implementation in both regions. Methods: The study adhered to the PRISMA 2020 guidelines for article search and selection to ensure an unbiased comparison between sub-Saharan Africa and Europe. Four databases (Scopus, Web of Science, IEEE Xplore, and PubMed) were used, and articles were evaluated based on predetermined criteria. Details on the mHealth system type, goal, patient type, health concern, and development stage were collected and recorded in a Microsoft Excel worksheet. Results: The search query produced 1020 articles for sub-Saharan Africa and 2477 articles for Europe. After screening for eligibility, 86 articles for sub-Saharan Africa and 297 articles for Europe were included. To minimize bias, two reviewers conducted the article screening and data retrieval. Sub-Saharan Africa used SMS and call-based mHealth methods for consultation and diagnosis, mainly for young patients such as children and mothers, and for issues such as HIV, pregnancy, childbirth, and child care. Europe relied more on apps, sensors, and wearables for monitoring, with the elderly as the most common patient group, and the most common health issues being cardiovascular disease and heart failure. Conclusion: Wearable technology and external sensors are heavily used in Europe, whereas they are seldom used in sub-Saharan Africa. More efforts should be made to use the mHealth system to improve health outcomes in both regions, incorporating more cutting-edge technologies like wearables internal and external sensors. Undertaking context-based studies, identifying determinants of mHealth systems use, and considering these determinants during mHealth system design could enhance mHealth availability and utilization.

The impact of khat chewing on heart activity and rehabilitation therapy from khat addiction in healthy khat chewers.

Khat is a flowering plant whose leaves and stems are chewed for excitement purposes in most of east African and Arabian countries. Khat can cause mood changes, increased alertness, hyperactivity, anxiety, elevated blood pressure, and heart diseases. However, the effect of khat on the heart has not been studied exclusively. The purpose of this study was to investigate the impact of khat chewing on heart activity and rehabilitation therapy from khat addiction in healthy khat chewers. ECG signals were recorded from 50 subjects (25 chewers and 25 controls) before and after chewing session to investigate the effect of khat on heart activity. In addition, ECG signals from 5 subjects were recorded on the first and eightieth day of rehabilitation therapy for investigating the effect of rehabilitation from khat addiction. All the collected signals were annotated, denoised and features were extracted and analysed. After chewing khat, the average heart rate of the chewers was increased by 5.85%, with 3 subjects out of 25 were prone to tachycardia. 1.66% QRS duration and 23.56% R-peak amplitude reduction were observed after chewing session. Moreover, heart rate variability was reduced by 19.74% indicating the effect of khat on suppressing sympathetic and parasympathetic nerve actions. After rehabilitation therapy, the average heart rate was reduced by 11.66%, while heart rate variability (HRV), QRS duration, and RR interval were increased by 25%, 3.49%, and 12.53%, respectively. Statistical analysis results also confirmed that there is a significance change (p < 0.05) in ECG feature among pre- and post-chewing session. Our findings demonstrate that, khat chewing raises heart rate, lowers heart rate variability, or puts the heart under stress by lowering R-peak amplitude and QRS duration, which in turn increases the risk of premature ventricular contraction and arrhythmia. The results also show that rehabilitation therapy from khat addiction has a major impact on restoring cardiac activity to normal levels.

Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques.

Purpose: Cervical cancer is the 4th most common cancer among women, worldwide. Incidence and mortality rates are consistently increasing, especially in developing countries, due to the shortage of screening facilities, limited skilled professionals, and lack of awareness. Cervical cancer is screened using visual inspection after application of acetic acid (VIA), papanicolaou (Pap) test, human papillomavirus (HPV) test and histopathology test. Inter- and intra-observer variability may occur during the manual diagnosis procedure, resulting in misdiagnosis. The purpose of this study was to develop an integrated and robust system for automatic cervix type and cervical cancer classification using deep learning techniques. Methods: 4005 colposcopy images and 915 histopathology images were collected from different local health facilities and online public datasets. Different pre-trained models were trained and compared for cervix type classification. Prior to classification, the region of interest (ROI) was extracted from cervix images by training and validating a lightweight MobileNetv2-YOLOv3 model to detect the transformation region. The extracted cervix images were then fed to the EffecientNetb0 model for cervix type classification. For cervical cancer classification, an EffecientNetB0 pre-trained model was trained and validated using histogram matched histopathological images. Results: Mean average precision (mAP) of 99.88% for the region of interest (ROI) extraction, and test accuracies of 96.84% and 94.5% were achieved for the cervix type and cervical cancer classification, respectively. Conclusion: The experimental results demonstrate that the proposed system can be used as a decision support tool in the diagnosis of cervical cancer, especially in low resources settings, where the expertise and the means are limited.

Planning and Budgeting of Medical Devices Among Ethiopian Public Hospitals.

Background: Planning and budgeting of medical devices allow a healthcare institution to properly use funds, acquire quality and efficient medical devices, and improve healthcare service delivery. The lack of proper policy in the procurement and management of medical devices causes inappropriate usage of funds and impedes the quality of a product. This study aimed to identify the current practices and gaps in the planning and budgeting of medical devices in Ethiopian public hospitals. In this study, an assessment was conducted in all regional public hospitals to assess the current status of medical device management, identify the gaps, and provide suggestions for areas of improvement. Methods: A descriptive cross-sectional design was used for the study assessment where a structured data collection tool was utilized to collect data. A multi-stage stratified random sampling proportionate to size technique was employed for the sampling of public hospitals in all regions of Ethiopia. The collected data was analyzed using SPSS version 26 software. Results: The availability of medical equipment development plans, budgeting, and spare parts procurement plans were found to be below 50% in public hospitals. It was also noted that 40.3% of hospitals do not prepare medical device technical specifications during procurement orders. Moreover, the engagement of biomedical engineers/technicians in the planning and procurement of medical devices was found to be below 50%. Conclusion: This assessment showed that there is a need for improvement in the development of procurement plans and preparation of technical specifications for medical devices in Ethiopian public hospitals. Developing policies and strategies for the proper use of funds in the procurement of medical devices, involving biomedical engineering professionals in the planning, procurement and use of medical devices could help to improve the quality, optimized utilization and efficiency of medical devices and ultimately enhance healthcare service delivery.

Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO2 signals.

Sleep apnoea is a potentially serious sleep disorder that is characterised by repetitive episodes of breathing interruptions. Traditionally, sleep apnoea is commonly diagnosed in an attended sleep laboratory setting using polysomnography (PSG). The manual diagnosis of sleep apnoea using PSG is, however complex, and time-consuming, as many physiological variables are usually measured overnight using numerous sensors attached to patients. In PSG sleep laboratories, an expert human observer is required to work overnight, and the diagnosis accuracy is dependent on the physician's experience. A quantitative and objective method is required to improve the diagnosis efficacy, decrease the complexity and diagnosis time and to ensure a more accurate diagnosis. The purpose of this study was then to develop an automatic sleep apnoea and severity classification using a simultaneously recorded electrocardiograph (ECG) and saturation of oxygen (SpO2) signals based on a machine learning algorithm. Different ECG and SpO2 time domain and frequency domain features were extracted for training different machine learning algorithms. For sleep apnoea classification, an accuracy of 99.1%, specificity of 98.1% and sensitivity of 100% were achieved using a support vector machine (SVM) based on combined ECG and SpO2 features. Similarly, for severity classification, an 88.9% accuracy, 90.9% specificity and 85.7% sensitivity have been obtained. For both apnoea and severity classification, using the combined features was found to be more accurate, and this is typically important when either channel is poor quality, the system can make an analysis based on the other channel and achieve good accuracy.

Treatment device for neonatal birth asphyxia related Hypoxic Ischemic Encephalopathy.

BACKGROUND: Birth asphyxia is a leading cause of neonatal brain injury, morbidity, and mortality globally. It leads to a multi-organ dysfunction in the neonate and to a neurological dysfunction called Hypoxic Ischemic Encephalopathy (HIE). Cooling therapy is commonly used to slow or stop the damaging effects of birth asphyxia. However, most of the cooling devices used in the healthcare facility do not have a rewarming functionality after cooling therapy. A separate rewarming device, usually a radiant warmer or incubator is used to rewarm the infant after therapy, causing additional burden to the healthcare system and infant families. The objective of this project was, therefore, to design and develop a cost-effective and efficient total body cooling and rewarming device. METHODS: Our design includes two water reservoirs that operate by pumping cold and warm sterile water to a mattress. After decreasing the infant's core body temperature to 33.5 °C, the system is designed to maintain it for 72 h. Feedback for temperature regulation is provided by the rectal and mattress temperature sensors. Once the cooling therapy is completed, the system again rewarms the water inside the mattress and gradually increases the neonate temperature to 36.5-37 °C. The water temperature sensors' effectiveness was evaluated by adding 1000 ml of water to the reservoir and cooling and warming to the required level of temperature using Peltier. Then a digital thermometer was used as a gold standard to compare with the sensor's readings. This was performed for five iterations. RESULTS: The prototype was built and gone through different tests and iterations. The proposed device was tested for accuracy, cost-effectiveness and easy to use. Ninety-three point two percent accuracy has been achieved for temperature sensor measurement, and the prototype was built only with a component cost of less than 200 USD. This is excluding design, manufacturing, and other costs. CONCLUSION: A device that can monitor and regulate the neonate core body temperature at the neuroprotective range is designed and developed. This is achieved by continuous monitoring and regulation of the water reservoirs, mattress, and rectal temperatures. The device also allows continuous monitoring of the infant's body temperature, mattress temperature, reservoir temperature, and pulse rate. The proposed device has the potential to play a significant role in reducing neonatal brain injury and death due to HIE, especially in low resource settings, where the expertise and the means are scarce.

Quantitative analysis of blood cells from microscopic images using convolutional neural network.

Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the presence of infection, inflammation, edema, bleeding, and other blood-related issues. Current microscopic methods used for blood cell counting are very tedious and are highly prone to different sources of errors. Besides, these techniques do not provide full information related to blood cells like shape and size, which play important roles in the clinical investigation of serious blood-related diseases. In this paper, deep learning-based automatic classification and quantitative analysis of blood cells are proposed using the YOLOv2 model. The model was trained on 1560 images and 2703-labeled blood cells with different hyper-parameters. It was tested on 26 images containing 1454 red blood cells, 159 platelets, 3 basophils, 12 eosinophils, 24 lymphocytes, 13 monocytes, and 28 neutrophils. The network achieved detection and segmentation of blood cells with an average accuracy of 80.6% and a precision of 88.4%. Quantitative analysis of cells was done following classification, and mean accuracy of 92.96%, 91.96%, 88.736%, and 92.7% has been achieved in the measurement of area, aspect ratio, diameter, and counting of cells respectively.Graphical abstract Graphical abstract where the first picture shows the input image of blood cells seen under a compound light microscope. The second image shows the tools used like OpenCV to pre-process the image. The third image shows the convolutional neural network used to train and perform object detection. The 4th image shows the output of the network in the detection of blood cells. The last images indicate post-processing applied on the output image such as counting of each blood cells using the class label of each detection and quantification of morphological parameters like area, aspect ratio, and diameter of blood cells so that the final result provides the number of each blood cell types (seven) and morphological information providing valuable clinical information.

Repeatability and reproducibility of prospective motion- and shim corrected 2D glycoCEST MRI.

BACKGROUND: Repeated glycoCEST MRI measurements on the same subject should produce similar results under the same environmental and experimental conditions. However, fluctuations in the static B0 field, which may occur between and within measurements due to heating of the shim iron or subject motion, may alter results and affect reproducibility. Here we investigate the repeatability and reproducibility of glycoCEST measurements and examine the effectiveness of a real-time shim- and motion navigated chemical exchange saturation transfer (CEST) sequence to improve reproducibility. METHODS: In nine subjects, double volumetric navigated (DvNav)-CEST acquisitions in the calf muscle were repeated five times in each of two sessions-the first without correction, and the second with real-time shim- and motion correction applied. In both sessions a dynamically changing field was introduced by running a 5-minute gradient intensive diffusion sequence. We evaluated the effect of the introduced B0 inhomogeneity on the reproducibility of glycoCEST, where the small chemical shift difference between the hydroxyl and bulk water protons at 3 T makes CEST quantification extremely sensitive to magnetic field inhomogeneities. RESULTS: With real-time shim- and motion correction, glycoCEST results were relatively consistent with mean coefficient of variation (CoV) 2.7%±1.4% across all subjects, whereas without correction the results were less consistent with CoV 84%±71%. CONCLUSIONS: Our results demonstrate that real-time shim- and motion correction can mitigate effects of B0 field fluctuations and improve reproducibility of glycoCEST data. This is important when conducting longitudinal studies or when using glycoCEST MRI to assess treatment or physiological responses over time.