COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample ...size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19.
The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable.
Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81-3.90), male sex (OR: 2.05, 95% CI: 1.39-3.04) and severe obesity (OR: 2.57, 95% CI: 1.31-5.05). Active cancer (OR: 1.46, 95% CI: 1.04-2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles.
Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.
As countries strive toward universal health coverage, mobile wireless technologies—mHealth tools—in support of enumeration, registration, unique identification, and maintenance of health records will ...facilitate improved health system performance. Electronic forms and registry systems will enable routine monitoring of the coverage of essential interventions for individuals within relevant target populations. A cascading model is presented for prioritizing and operationalizing the role of integrated mHealth strategies.
Objectives
Given the large‐scale adoption and deployment of mobile phones by health services and frontline health workers (FHW), we aimed to review and synthesise the evidence on the feasibility and ...effectiveness of mobile‐based services for healthcare delivery.
Methods
Five databases – MEDLINE, EMBASE, Global Health, Google Scholar and Scopus – were systematically searched for relevant peer‐reviewed articles published between 2000 and 2013. Data were extracted and synthesised across three themes as follows: feasibility of use of mobile tools by FHWs, training required for adoption of mobile tools and effectiveness of such interventions.
Results
Forty‐two studies were included in this review. With adequate training, FHWs were able to use mobile phones to enhance various aspects of their work activities. Training of FHWs to use mobile phones for healthcare delivery ranged from a few hours to about 1 week. Five key thematic areas for the use of mobile phones by FHWs were identified as follows: data collection and reporting, training and decision support, emergency referrals, work planning through alerts and reminders, and improved supervision of and communication between healthcare workers. Findings suggest that mobile based data collection improves promptness of data collection, reduces error rates and improves data completeness. Two methodologically robust studies suggest that regular access to health information via SMS or mobile‐based decision‐support systems may improve the adherence of the FHWs to treatment algorithms. The evidence on the effectiveness of the other approaches was largely descriptive and inconclusive.
Conclusions
Use of mHealth strategies by FHWs might offer some promising approaches to improving healthcare delivery; however, the evidence on the effectiveness of such strategies on healthcare outcomes is insufficient.
Objectifs
Compte tenu de l'adoption et du déploiement à grande échelle des téléphones portables par les services de santé et les agents de la santé de première ligne (ASPL), nous avons cherché à analyser et synthétiser les données sur la faisabilité et l'efficacité des services basés sur les outils mobiles sur la prestation des soins de santé.
Méthodes
Cinq bases de données ‐ Medline, Embase, la santé mondiale, Google Scholar et Scopus‐ ont été systématiquement recherchées pour des articles scientifiques pertinents publiés entre 2000 et 2013. Les données ont été extraites et synthétisées selon trois thèmes: la faisabilité de l'utilisation d'outils mobiles par les ASPL, la formation requise pour l'adoption d'outils mobiles et l'efficacité de ces interventions.
Résultats
42 études ont été incluses dans cette revue. Avec une formation adéquate, les ASPL étaient capables d'utiliser les téléphones portables pour améliorer divers aspects de leurs activités professionnelles. La formation des ASPL à l’’utilisation des téléphones portables pour la délivrance de soins de santé varie de quelques heures à environ une semaine. Cinq domaines thématiques clés pour l'utilisation des téléphones portables par les ASPL ont été identifiés: la collecte et l'enregistrement des données, la formation et l'aide à la décision, les transferts d'urgence, la planification du travail grâce à des alertes et des rappels et l'amélioration de la supervision et de la communication entre les agents de la santé. La collecte des données par les téléphones portables semble améliorer la rapidité de la collecte et la réduction des taux d'erreur, et améliorer l'exhaustivité des données. Deux études méthodologiquement robustes suggèrent que l'accès régulier à des informations de santé par SMS ou par des systèmes d'aide aux décisions basés sur les téléphones portables peut améliorer l'adhérence des ASPL aux algorithmes de traitement. Les données sur l'efficacité des autres approches étaient largement descriptives et peu concluantes.
Conclusions
L'utilisation des stratégies mHealth par les ASPL pourrait offrir des approches prometteuses pour améliorer la prestation des soins de santé. Toutefois, les preuves de l'efficacité de ces stratégies sur les résultats des soins de santé sont insuffisantes.
Objetivos
Dada la adopción a gran escala y el despliegue de teléfonos móviles en los servicios sanitarios y entre los trabajadores sanitarios de primera línea (TSPL), nuestro objetivo era revisar y sintetizar la evidencia sobre la viabilidad y efectividad de los servicios móviles en la atención sanitaria.
Métodos
Se realizó una búsqueda sistemática en cinco bases de datos ‐ Medline, Embase, Global Health, Google Scholar y Scopus – de artículos con revisión por pares publicados entre el 2000 y 2013. Se extrajeron los datos y se sintetizaron en tres temáticas: viabilidad del uso de herramientas móviles por TSPL, entrenamiento requerido para la adopción de herramientas móviles y efectividad de tales intervenciones.
Resultados
Se incluyeron 42 estudios en la revisión. Con el entrenamiento adecuado, los TSPL fueron capaces de utilizar los teléfonos móviles para mejorar varios aspectos de sus actividades laborales. El entrenamiento de los TSPL necesario para la utilización de los teléfonos móviles en la atención sanitaria estaba entre unas pocas horas y hasta casi una semana. Se identificaron cinco áreas temáticas para el uso de teléfonos móviles por TSPL: Recolección y reporte de datos, entrenamiento y apoyo a las decisiones, traslados por emergencias, planificación del trabajo mediante alertas y recordatorios, y una supervisión mejorada de la comunicación entre los trabajadores sanitarios. La recogida de datos en el móvil parecía mejorar la rapidez de la recolección de datos, reducir las tasas de error y mejorar la integridad de los datos. Dos estudios metodológicamente robustos sugieren que el acceso regular a información sanitaria vía SMS o sistemas de soporte a las decisiones en dispositivos móviles podría mejorar la adherencia de los TSPL a algoritmos de tratamiento. La evidencia sobre la efectividad de otras opciones era en general descriptiva e inconclusa.
Conclusiones
El uso de estrategias de salud móvil por TSPL podría ofrecer algunas opciones prometedoras para mejorar la atención sanitaria; sin embargo, la evidencia sobre la efectividad de dichas estrategias en los resultados de la atención sanitaria es insuficiente.
Healthcare challenges in low and middle income countries (LMICs) have been the focus of many digital initiatives that have aimed to improve both access to healthcare and the quality of healthcare ...delivery. Moving beyond the initial phase of piloting and experimentation, these initiatives are now more clearly focused on the need for effective scaling and integration to provide sustainable benefit to healthcare systems.Based on real-life case studies of scaling digital health in LMICs, five key focus areas have been identified as being critical for success. Firstly, the intrinsic characteristics of the programme or initiative must offer tangible benefits to address an unmet need, with end-user input from the outset. Secondly, all stakeholders must be engaged, trained and motivated to implement a new initiative, and thirdly, the technical profile of the initiative should be driven by simplicity, interoperability and adaptability. The fourth focus area is the policy environment in which the digital healthcare initiative is intended to function, where alignment with broader healthcare policy is essential, as is sustainable funding that will support long-term growth, including private sector funding where appropriate. Finally, the extrinsic ecosystem should be considered, including the presence of the appropriate infrastructure to support the use of digital initiatives at scale.At the global level, collaborative efforts towards a less-siloed approach to scaling and integrating digital health may provide the necessary leadership to enable innovative solutions to reach healthcare workers and patients in LMICs. This review provides insights into best practice for scaling digital health initiatives in LMICs derived from practical experience in real-life case studies, discussing how these may influence the development and implementation of health programmes in the future.
The rapid diffusion and growing number of applications of artificial intelligence large language models has generated excitement and public discourse around their potential to improve human health. ...However, this enthusiasm has been accompanied by concerns that such content-generative systems may be biased, produce misleading or inaccurate information, and could relinquish data privacy and ownership controls to technology firms looking to commercialize large language models and commodify data.2 Some have questioned whether commercial pressures have led to public releases of these technologies without adequate ascertainment of their safety and performance.3 Large language models generate responses that can appear authoritative and plausible to an end-user; however, without adequate controls in place, the veracity and accuracy of responses may be extremely poor.4 These models may be trained on data for which explicit consent may not have been provided, and they may not protect sensitive data (including health data) that users voluntarily feed into the artificial intelligence-based tool. Large language models, usually trained on large amounts of raw data, may encode biases in the data that can undermine inclusiveness, equality and equity.5 Furthermore, building such large data models has an environmental (mostly in carbon dioxide emissions) and financial impact that is often overlooked in costing analyses.6 Artificial intelligence tools are increasingly being applied to public health priorities,7 and have the potential to assist with pattern recognition and classification problems in medicine - for example, early detection of disease, diagnosis and medical decision-making.8'9 The increase in sophistication of artificial intelligence systems is now marked in days and weeks, as opposed to months and years. This speed outpaces the regulatory and review capacity of most agencies charged with protecting public health and providing oversight of technologies applied to health and well-being.
Hepatitis E is an acute, viral hepatitis primarily transmitted through the fecal-oral route. The first major epidemic of hepatitis E virus (HEV) was reported in 1955 in Delhi, India. Since that time, ...numerous epidemics have been reported across the low- and middle-income countries in Asia and Africa. Even in the absence of large-scale outbreaks, hepatitis E is an important cause of clinical hepatitis. Serologic studies across Asia and Africa show a high prevalence of anti-HEV antibodies. Interest in hepatitis E has increased over the last two decades. However, there are many unanswered questions about the epidemiology of hepatitis E, including a low clinical illness rate in children and the high case fatality rate in pregnant women. Widespread usage of a hepatitis E vaccine may serve to relieve the burden of HEV disease in low- and middle-income countries in Africa and Asia.
To improve the completeness of reporting of mobile health (mHealth) interventions, the WHO mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) ...checklist. The development process for mERA consisted of convening an expert group to recommend an appropriate approach, convening a global expert review panel for checklist development, and pilot testing the checklist. The guiding principle for the development of these criteria was to identify a minimum set of information needed to define what the mHealth intervention is (content), where it is being implemented (context), and how it was implemented (technical features), to support replication of the intervention. This paper presents the resulting 16 item checklist and a detailed explanation and elaboration for each item, with illustrative reporting examples. Through widespread adoption, we expect that the use of these guidelines will standardise the quality of mHealth evidence reporting, and indirectly improve the quality of mHealth evidence.
Hepatitis E Virus (HEV) infection is a newly recognized serious threat to global public health and Africa is suspected to be among the most severely affected regions in the world. Understanding HEV ...epidemiology in Africa will expedite the implementation of evidence-based control policies aimed at preventing the spread of HEV including policies for the use of available resources such as HEV vaccines.
Here we present a comprehensive review of HEV epidemiology in Africa based on published data. We searched for articles on HEV epidemiology in Africa from online databases such as PubMed, Scopus, and ISI Web of Science and critically reviewed appropriate publications to extract consistent findings, identify knowledge gaps, and suggest future studies.
Taking a particularly high toll in pregnant women and their fetuses, HEV has infected human populations in 28 of 56 African countries. Since 1979, 17 HEV outbreaks have been reported about once every other year from Africa causing a reported 35,300 cases with 650 deaths.
In Africa, HEV infection is not new, is widespread, and the number of reported outbreaks are likely a significant underestimate. The authors suggest that this is a continent-wide public health problem that deserves the attention of local, regional and international agencies to implement control policies that can save numerous lives, especially those of pregnant women and their fetuses.
Digital health: a path to validation Mathews, Simon C; McShea, Michael J; Hanley, Casey L ...
NPJ digital medicine,
12/2019, Volume:
2, Issue:
1
Journal Article
Peer reviewed
Open access
Digital health solutions continue to grow in both number and capabilities. Despite these advances, the confidence of the various stakeholders - from patients and clinicians to payers, industry and ...regulators - in medicine remains quite low. As a result, there is a need for objective, transparent, and standards-based evaluation of digital health products that can bring greater clarity to the digital health marketplace. We believe an approach that is guided by end-user requirements and formal assessment across technical, clinical, usability, and cost domains is one possible solution. For digital health solutions to have greater impact, quality and value must be easier to distinguish. To that end, we review the existing landscape and gaps, highlight the evolving responses and approaches, and detail one pragmatic framework that addresses the current limitations in the marketplace with a path toward implementation.
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