ObjectiveTo evaluate the diagnostic accuracy of teleretinal screening compared with face-to-face examination for detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD).Methods and analysisThis study adhered to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA). A comprehensive search of OVID MEDLINE, EMBASE and Cochrane CENTRAL was performed from January 2010 to July 2021. QUADAS-2 tool was used to assess methodological quality and applicability of the studies. A bivariate random effects model was used to perform the meta-analysis. Referrable DR was defined as any disease severity equal to or worse than moderate non-proliferative DR or diabetic macular oedema (DMO).Results28 articles were included. Teleretinal screening achieved a sensitivity of 0.91 (95% CI: 0.82 to 0.96) and specificity of 0.88 (0.74 to 0.95) for any DR (13 studies, n=7207, Grading of Recommendations, Assessment, Development and Evaluation (GRADE) low). Accuracy for referrable DR (10 studies, n=6373, GRADE moderate) was lower with a sensitivity of 0.88 (0.81 to 0.93) and specificity of 0.86 (0.79 to 0.90). After exclusion of ungradable images, the specificity for referrable DR increased to 0.95 (0.90 to 0.98), while the sensitivity remained nearly unchanged at 0.85 (0.76 to 0.91). Teleretinal screening achieved a sensitivity of 0.71 (0.49 to 0.86) and specificity of 0.88 (0.85 to 0.90) for detection of AMD (three studies, n=697, GRADE low).ConclusionTeleretinal screening is highly accurate for detecting any DR and DR warranting referral. Data for AMD screening is promising but warrants further investigation.PROSPERO registration numberCRD42020191994.
Introduction continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria. Methods we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon. Results in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources. Conclusion substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further.
Introduction: Evidence on authorship trends of health research conducted about or in Africa shows that there is a lack of local researchers in the first and last authorship positions, with high income country collaborations taking up these positions. The differences in authorship calls into question power imbalances in global health research and who benefits from the production of new discoveries and innovations. Health studies may further go on to inform policy and clinical practice within the region having an impact on public health. This paper aims to compare the differences in authorship between COVID-19 and relevant infectious diseases in Africa.Materials and Methods: We will conduct a bibliometric analysis comparing authorship for COVID-19 research during a public health emergency with authorship for four other infectious diseases of relevance to Africa namely: Ebola, Zika Virus (ZIKV), Tuberculosis (TB) and Influenza. Our scoping review will follow the framework developed by Arksey and O'Malley and reviewed by Levac et al. We will search MEDLINE (Ovid), African Index Medicus (AIM), Eastern Mediterranean Region (IMEMR) Index Medicus, Embase (Ovid), and Web of Science (Clarivate). We will compare the different trends of disease research between the selected diseases. This study is registered with OSF registries and is licensed with the Academic Free License version 3.0. The open science registration number is 10.17605/OSF.IO/5ZPGN.
Background: Long-term sequelae associated with pneumococcal sepsis (PS) in pediatric patients in existing literature is currently unclear. Aim: To review the evidence on sequelae and prognostic factors associated with PS among pediatric patients. Method: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. We screened six databases from their inception to January 15, 2021. Study population were neonates, infants, children and adolescents less than 18 years old with suspected or confirmed PS disease. Outcomes included sequelae types, prognostic factors, pooled death estimate and length of hospital stay (LOS) for survivors and deceased patients. Quality of studies was assessed using Joanna Briggs Institute appraisal checklists. Results: We screened 981 abstracts, and 24 full-text articles for final review. Septic shock was the most prevalent physical sequelae reported (13%, n=1492 patients). No functional, cognitive or neurological sequelae were reported in included studies. Meta-analysis of pooled mortality estimate was 14.6% (95%CI: 9.9 -19.4%). Prognostic factors associated with increased risk of PS sequelae and death included pediatric risk of mortality score ≥ 10 and co-infection with meningitis. LOS for survivors and non-survivors ranged between 5-30 days and 1-30 days. Nine included studies met at least 50% of the quality assessment criteria. Conclusion: Physical sequelae and death are the PS sequelae types currently identified in existing literature. Lack of information about other possible sequelae types suggests the long-term consequences of PS disease maybe underreported, especially in resource-limited settings. Future studies should consider exploring reasons for the existing of this knowledge gap.
Background With the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders. Methods This is a systematic review and meta-analysis. A literature search will be conducted on Ovid MEDLINE, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000, to December 20, 2021. Studies will be selected via screening the titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included; articles that do not will be excluded. The systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After the full-text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. The study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in the R statistical software. Discussion This study will provide novel insights into the diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist in article screening, which may serve as a reference for improving the efficiency and accuracy of future large systematic reviews. Trial registration PROSPERO, CRD42021274441
Background: Predicting potential healthcare resource use under different scenarios will help to prepare the healthcare system for a surge in COVID-19 patients. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria. Method: We adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included expected cumulative number of cases, days until depletion resources, and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon. Results: In our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, the current number of ventilator resources in Nigeria (conservative resources scenario), were expended within five days, and 901 patients may die while waiting for hospital resources in conservative resource scenario. In our expanded resource scenarios, ventilated ICU beds were depleted in all three epidemic trajectories within 60 days. Acute care resources were only sufficient in the best-case and intermediate epidemic scenarios, combined with a substantial increase in healthcare resources. Conclusion: Current hospital resources are inadequate to manage the COVID-19 pandemic in Nigeria. Given Nigeria's limited resources, it is imperative to increase healthcare resources and maintain aggressive public health measures to reduce COVID-19 transmission.
IntroductionThe emergence of a regional or global scale infectious disease outbreak often requires the implementation of economic relief programmes in affected jurisdictions to sustain societal welfare and, presumably, population health. While economic relief programmes are considered essential during a regional or global health crisis, there is no clear consensus in the literature about their health and non-health benefits and their impact on promoting equity. Thus, our objective is to map the current state of the literature with respect to the types of individual-level economic relief programmes implemented during infectious disease outbreaks and the impact of these programmes on the effectiveness of public health measures, individual and population health, non-health benefits and equity.Methods and analysisOur scoping review is guided by the updated Arksey and O’Malley scoping review framework. Eligible studies will be identified in eight electronic databases and grey literature using text words and subject headings of the different pandemic and epidemic infectious diseases that have occurred, and economic relief programmes. Title and abstract screening and full-text screening will be conducted independently by two trained study reviewers. Data will be extracted using a pretested data extraction form. The charting of the key findings will follow a thematic narrative approach. Our review findings will provide in-depth knowledge on whether and how benefits associated with pandemic/epidemic individual-level economic relief programmes differ across social determinants of health factors.This information is critical for decision-makers as they seek to understand the role of pandemic/epidemic economic mitigation strategies to mitigate the health impact and reduce inequity gap.Ethics and disseminationSince the scoping review methodology aims to synthesise evidence from literature, this review does not require ethical approval. Findings of our review will be disseminated to health stakeholders at policy meetings and conferences; published in a peer-review scientific journal; and disseminated on various social media platforms.
BackgroundWith the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders.MethodsThis is a systematic review and meta-analysis. A literature search will be conducted on Ovid Medline, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000 to December 20, 2021. Studies will be selected via screening titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included. Systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After full text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. Study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in R statistical software.DiscussionThis study will provide novel insights on diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist article screening, which may serve as a reference for improving efficiency and accuracy of future large systematic reviews.Systematic Review Registration: This study is registered on PROSPERO (CRD42021274441).
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