Protocol for a realist synthesis of health systems responsiveness in low-income and middle-income countries

Introduction Health systems responsiveness is a key objective of any health system, yet it is the least studied of all objectives particularly in low-income and middle-income countries. Research on health systems responsiveness highlights its multiple elements, for example, dignity and confidentiality. Little is known, however, about underlying theories of health systems responsiveness, and the mechanisms through which responsiveness works. This realist synthesis contributes to bridging these two knowledge gaps. Methods and analysis In this realist synthesis, we will use a four-step process, comprising: mapping of theoretical bases, formulation of programme theories, theory refinement and testing of programme theories using literature and empirical data from Ghana and Vietnam. We will include theoretical and conceptual pieces, reviews, empirical studies and grey literature, alongside the primary data. We will explore responsiveness as entailing external and internal interactions within health systems. The search strategy will be purposive and iterative, with continuous screening and refinement of theories. Data extraction will be combined with quality appraisal, using appropriate tools. Each fragment of evidence will be appraised as it is being extracted, for its relevance to the emerging programme theories and methodological rigour. The extracted data pertaining to contexts, mechanisms and outcomes will be synthesised to identify patterns and contradictions. Results will be reported using narrative explanations, following established guidance on realist syntheses. Ethics and dissemination Ethics approvals for the wider RESPONSE (Improving health systems responsiveness to neglected health needs of vulnerable groups in Ghana and Vietnam) study, of which this review is one part, were obtained from the ethics committees of the following institutions: London School of Hygiene and Tropical Medicine (ref: 22981), University of Leeds, School of Medicine (ref: MREC19-051), Ghana Health Service (ref: GHS-ERC 012/03/20) and Hanoi University of Public Health (ref: 020-149/DD-YTCC). We will disseminate results through academic papers, conference presentations and stakeholder workshops in Ghana and Vietnam. PROSPERO registration number CRD42020200353. Full record: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020200353

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease

Global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017

Background Understanding the patterns of HIV/AIDS epidemics is crucial to tracking and monitoring the progress of prevention and control efforts in countries. We provide a comprehensive assessment of the levels and trends of HIV/AIDS incidence, prevalence, mortality, and coverage of antiretroviral therapy (ART) for 1980–2017 and forecast these estimates to 2030 for 195 countries and territories. Methods We determined a modelling strategy for each country on the basis of the availability and quality of data. For countries and territories with data from population-based seroprevalence surveys or antenatal care clinics, we estimated prevalence and incidence using an open-source version of the Estimation and Projection Package—a natural history model originally developed by the UNAIDS Reference Group on Estimates, Modelling, and Projections. For countries with cause-specific vital registration data, we corrected data for garbage coding (ie, deaths coded to an intermediate, immediate, or poorly defined cause) and HIV misclassification. We developed a process of cohort incidence bias adjustment to use information on survival and deaths recorded in vital registration to back-calculate HIV incidence. For countries without any representative data on HIV, we produced incidence estimates by pulling information from observed bias in the geographical region. We used a re-coded version of the Spectrum model (a cohort component model that uses rates of disease progression and HIV mortality on and off ART) to produce age-sex-specific incidence, prevalence, and mortality, and treatment coverage results for all countries, and forecast these measures to 2030 using Spectrum with inputs that were extended on the basis of past trends in treatment scale-up and new infections. Findings Global HIV mortality peaked in 2006 with 1·95 million deaths (95% uncertainty interval 1·87–2·04) and has since decreased to 0·95 million deaths (0·91–1·01) in 2017. New cases of HIV globally peaked in 1999 (3·16 million, 2·79–3·67) and since then have gradually decreased to 1·94 million (1·63–2·29) in 2017. These trends, along with ART scale-up, have globally resulted in increased prevalence, with 36·8 million (34·8–39·2) people living with HIV in 2017. Prevalence of HIV was highest in southern sub-Saharan Africa in 2017, and countries in the region had ART coverage ranging from 65·7% in Lesotho to 85·7% in eSwatini. Our forecasts showed that 54 countries will meet the UNAIDS target of 81% ART coverage by 2020 and 12 countries are on track to meet 90% ART coverage by 2030. Forecasted results estimate that few countries will meet the UNAIDS 2020 and 2030 mortality and incidence targets. Interpretation Despite progress in reducing HIV-related mortality over the past decade, slow decreases in incidence, combined with the current context of stagnated funding for related interventions, mean that many countries are not on track to reach the 2020 and 2030 global targets for reduction in incidence and mortality. With a growing population of people living with HIV, it will continue to be a major threat to public health for years to come. The pace of progress needs to be hastened by continuing to expand access to ART and increasing investments in proven HIV prevention initiatives that can be scaled up to have population-level impact

Learner proficiency and EFL learning through task rehearsal and performance

© The Author(s) 2019. This study examined the impact of learner proficiency on the occurrence and resolution of language-related episodes (LREs) in rehearsal of interactive speaking tasks and on the subsequent use of language items from LREs during performance of the same tasks in front of the class. Forty-eight learners from six intact English as a foreign language (EFL) classes at a Vietnamese high school were divided into three proficiency groups, each consisting of eight dyads. Group 1 was higher proficiency (HH) dyads; Group 2 consisted of mixed proficiency dyads (HL) and Group 3 was lower proficiency (LL) dyads. All the pairs rehearsed and then consecutively performed a problem-solving task and a debate task in two separate classroom lessons. The total data included 48 rehearsals and 48 corresponding performances collected during normal classroom hours. The results show that, overall, LL dyads encountered more language problems (more LREs) in rehearsal than HH dyads and they were less likely to resolve them successfully. However, they were able to use a majority of the correct resolutions in the performance as well as their higher proficiency counterparts. The lower proficiency learners were also found to employ memorizing and local rehearsing strategies to retain ideas and language items as they rehearsed for the upcoming performance. These findings have pedagogical implications for teaching and learning through tasks in EFL contexts and beyond

Predicting Alzheimer's disease progression using deep recurrent neural networks

Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the minimalRNN with "model filling" compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020

Using the WHO Self-Reporting Questionnaire-20 (SRQ-20) to Detect Symptoms of Common Mental Disorders among Pregnant Women in Vietnam: a Validation Study

Trang Thi Hanh Do,1 Quyen Thi Tu Bui,2 Bui Thi Thu Ha,3 Thi Minh Le,3 Vui Thi Le,3 Quynh-Chi Thai Nguyen,3 Kimberly Joyce Lakin,4 Tung Thanh Dang,5 Loi Van Bui,5 Thien Cong Le,5 An Thi Ha Tran,5 Hien Thi Thu Pham,5 Tuan Van Nguyen5 1Faculty of Environmental and Occupational Health, Hanoi University of Public Health, Hanoi, Vietnam; 2Faculty and Fundamental Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 3Faculty of Social and Behavioral Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 4Nossal Institute for Global Health, Melbourne School of Population and Global Health, Melbourne, Victoria, Australia; 5The National Institute of Mental Health, Bach Mai Hospital, Hanoi, VietnamCorrespondence: Quyen Thi Tu Bui, Department of Biostatistics, Faculty of Fundamental Sciences, Hanoi University of Public Health, 1A Duc Thang Street, Bac Tu Liem District, Hanoi, Vietnam, Tel +84 912 225 245, Fax +84 24 6266 2385, Email [email protected]: Detection of antenatal common mental disorders in low-resource settings like Vietnam is important and requires a reliable, valid and practical screening tool. Currently, there is no such tool validated for use among pregnant women in Vietnam. This study aims to assess the validity of the Vietnamese version of the 20-item Self Reporting Questionnaire (SRQ-20) by evaluating its reliability, factorial structure, and performance in detecting common mental disorder (CMD) symptoms, thereby identifying the optimum cut-off score for CMD screening among pregnant women in Vietnam.Participants and Methods: A total of 210 pregnant women from four rural communes participated in a face-to-face interview using the Vietnamese version of the SRQ-20, followed by a clinical diagnostic interview based on ICD-10 diagnostic criteria of CMDs. The reliability of the SRQ-20 was assessed by calculating the scale’s Cronbach’s alpha to measure internal consistency. Factor analyses were undertaken to examine the factor structure of the instrument. The Receiver Operating Characteristic (ROC) curve analysis was performed to assess the performance of the SRQ-20 against the clinical diagnosis and to identify the optimum cut-off score.Results: Internal consistency was good, with a Cronbach’s alpha of 0.87. Factor analyses resulted in a 4-factor solution. The area under the ROC curve (AUC) for detection of CMDs was 0.90. The optimum cut-off score of the SRQ-20 for detection of CMD symptoms among Vietnamese pregnant women was 5/6.Conclusion: The Vietnamese version of the SRQ-20 has the capacity to detect CMDs among pregnant women effectively and is recommended for use as a screening tool for CMDs in antenatal care settings in Vietnam.Keywords: SRQ-20, screening, common mental disorders, pregnant women, Vietna

Molecular watchmaking: ab initio Emulsion polymerization by RAFT-controlled self-assembly

Controlled radical polymerization using RAFT has the potential to make polymers with virtually any desired molecular architecture. For this to be implemented on an industrial scale, it must be performed by polymerization in disperse media. However, simply adding a RAFT agent to a conventional emulsion polymerization recipe leads to a loss of molecular weight control and formation of coagulum, probably because of nucleation in droplets, which is normally an unlikely phenomenon in emulsion polymerizations. Recently, a method has been devised for implementing RAFT in ab initio emulsion polymerization that avoids droplets in the particle formation stage. The molecular weight distribution of the polymer thus formed shows that molecular weight control is maintained throughout the polymerization. A model is developed to predict the particle size formed in this new type of emulsion polymerization. The new methodology enables synthesis of novel dispersions where molecular architecture can be precisely controlled, such as structured core-shell particles

Influence of Fibre-Reinforced Load Transfer Platform Supported Embankment on Floating Columns Improved Soft Soils

© Springer Nature Singapore Pte Ltd. 2020. Fibre reinforcement has been proved to be effective in improving geotechnical characteristics of both untreated and cemented soils, such as shear and compressive strength, bearing capacity, ductility and load-settlement behaviour. The application of fibre-reinforced soils could be beneficial to construction of embankments over soft soils because it can maintain its proper strength and bearing capacity when suffering from large total and differential settlements. In this study, fibre-reinforced cemented soil foundation is proposed to be used as a fibre-reinforced load transfer platform (FRLTP) combined with columns supported (CS) embankment constructed on multilayers of soft soils. To investigate the effect of addition of FRLTP into the CS embankment system, a numerical investigation based on the finite element analysis (FEA) using PLAXIS 2D was conducted. Moreover, a parametric analysis was carried out to evaluate the influence of the FRLTP thickness on the performance of the CS embankment when considering the vertical and differential settlements during the embankment construction and post-construction stages. The predicted results indicate that the vertical settlement and the lateral deformation considerably reduce with the insertion of FRLTP into the CS embankment system. Meanwhile, the outcomes of the parametric study reveal that the FRLTP thickness has a significant influence on the enhancement in the time-dependent differential settlement. Although the vertical settlement significantly decreases with increasing the FRLTP thickness, the post-construction vertical settlement was predicted to be most likely independent of the FRLTP thickness. The findings of this study could enable geotechnical engineers and designers to design a time-dependent performance-based FRLTP for a CS embankment over soft soils and aim to enhance the related design codes