Strengthening rural health placements for medical students: Lessons for South Africa from international experience

Background. This article derives lessons from international experience of innovative rural health placements for medical students. It provides pointers for strengthening South African undergraduate rural health programmes in support of the government’s rural health, primary healthcare and National Health Insurance strategies.Methods. The article draws on a review of the literature on 39 training programmes around the world, and the experiential knowledge of 28 local and international experts consulted through a structured workshop.Results. There is a range of models for rural health placements: some offer only limited exposure to rural settings, while others offer immersion experiences to students. Factors facilitating successful rural health placements include faculty champions who drive rural programmes and persuade faculties to embrace a rural mission, preferential selection of students with a rural background, positioning rural placements within a broader rural curriculum, creating rural training centres, the active nurturing of rural service staff, assigning students to mentors, the involvement of communities, and adapting rural programmes to the local context. Common obstacles include difficulties with student selection, negative social attitudes towards rural health, shortages of teaching staff, a sense of isolation experienced by rural students and staff, and difficulties with programme evaluation.Conclusions. Faculties seeking to expand rural placements should locate their vision within new health system developments, start off small and create voluntary rural tracks, apply preferential admission for rural students, set up a rural training centre, find practical ways of working with communities, and evaluate the educational and clinical achievements of rural health placements

Optimisation and Landscape Analysis of Computational Biology Models: A Case Study

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean delay equations (BDEs) provide a signi ficant reduction in parameter numbers, whilst still providing dynamical complexity comparable to more biochemically detailed models, such as those based on differential equations. Here, we explore several approaches to optimising BDEs to timeseries data, using a simple circadian clock model as a case study. We compare the ffectiveness of two optimisers on our problem: a genetic algorithmf(GA) and an elite accumulative sampling (EAS) algorithm that provides robustness to data discretisation. Our results show that both methods are able to distinguish effectively between alternative architectures, yielding excellent ts to data. We also perform a landscape analysis, providing insights into the properties that determine optimiser performance (e.g. number of local optima and basin sizes). Our results provide a promising platform for the analysis of more complex GRNs, and suggest the possibility of leveraging cost landscapes to devise more effi cient optimisation schemes.This work was financially supported by the Engineering and Physical Sciences Research Council [grant numbers EP/N017846/1, EP/N014391/1], and made use of the Zeus and Isca supercomputing facilities provided by the University of Exeter HPC Strategy

On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution’s uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust fitness approximations across the entire search history rather than a fixed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Voronoi-Based Archive Sampling for Robust Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordWe propose a framework for estimating the quality of solutions in a robust optimisation setting by utilising samples from the search history and using MC sampling to approximate a Voronoi tessellation. This is used to determine a new point in the disturbance neighbourhood of a given solution such that – along with the relevant archived points – they form a well-spread distribution, and is also used to weight the archive points to mitigate any selection bias in the neighbourhood history. Our method performs comparably well with existing frameworks when implemented inside a CMA-ES on 9 test problems collected from the literature in 2 and 10 dimensions.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Robust Optimisation using Voronoi-Based Archive Sampling

Engineering and Physical Sciences Research Council (EPSRC

Robust Multi-Modal Optimisation

Robust and multi-modal optimisation are two important topics that have received significant attention from the evolutionary computation community over the past few years. However, the two topics have usually been investigated independently and there is a lack of work that explores the important intersection between them. This is because there are real-world problems where both formulations are appropriate in combination. For instance, multiple ‘good’ solutions may be sought which are distinct in design space for an engineering problem – where error between the computational model queried during optimisation and the real engineering environment is believed to exist (a common justification for multi-modal optimisation) – but also engineering tolerances may mean a realised design might not exactly match the inputted specification (a robust optimisation problem). This paper conducts a preliminary examination of such intersections and identifies issues that need to be addressed for further advancement in this new area. The paper presents initial benchmark problems and examines the performance of combined state-of-the-art methods from both fields on these problems.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social Informatic

Do Quality Improvement Initiatives Improve Outcomes for Patients in Antiretroviral Programs in Low- and Middle-Income Countries? A Systematic Review.

BACKGROUND: There have been a range of quality improvement (QI) and quality assurance initiatives in low- and middle-income countries to improve antiretroviral therapy (ART) treatment outcomes for people living with HIV. To date, these initiatives have not been systematically assessed and little is known about how effective, cost-effective, or sustainable these strategies are in improving clinical outcomes. METHODS: We conducted a systematic review adhering to PRISMA guidelines (PROSPERO ID: CRD42017071848), searching PubMed, MEDLINE, Embase, Web of Science, and the Cochrane database of controlled trials for articles reporting on the effectiveness of QI and quality assurance initiatives in HIV programs in low- and middle-income countries in relation to ART uptake, retention in care, adherence, viral load suppression, mortality, and other outcomes including cost-effectiveness and long-term sustainability. RESULTS: One thousand eight hundred sixty articles were found, of which 29 were included. QI approaches were categorized as follows: (1) health system approaches using QI methods; (2) QI learning networks including collaboratives; (3) standard-based methods that use QI tools to improve performance gaps; and (4) campaigns using QI methods. The greatest improvements were seen in ART uptake [median increase of 14.0%; interquartile range (IQR) -9.0 to 29.3], adherence [median increase of 22.0% (IQR -7.0 to 25.0)], and viral load suppression [median increase 26.0% (IQR -8.0 to 26.0)]. CONCLUSIONS: QI interventions can be effective in improving clinical outcomes; however, there was significant variability, making it challenging to identify which aspects of interventions lead to clinical improvements. Standardizing reporting and assessment of QI initiatives is needed, supported by national quality policies and directorates, and robust research

The muscle protein dysferlin accumulates in the Alzheimer brain

Dysferlin is a transmembrane protein that is highly expressed in muscle. Dysferlin mutations cause limb-girdle dystrophy type 2B, Miyoshi myopathy and distal anterior compartment myopathy. Dysferlin has also been described in neural tissue. We studied dysferlin distribution in the brains of patients with Alzheimer disease (AD) and controls. Twelve brains, staged using the Clinical Dementia Rating were examined: 9 AD cases (mean age: 85.9 years and mean disease duration: 8.9 years), and 3 age-matched controls (mean age: 87.5 years). Dysferlin is a cytoplasmic protein in the pyramidal neurons of normal and AD brains. In addition, there were dysferlin-positive dystrophic neurites within Aβ plaques in the AD brain, distinct from tau-positive neurites. Western blots of total brain protein (RIPA) and sequential extraction buffers (high salt, high salt/Triton X-100, SDS and formic acid) of increasing protein extraction strength were performed to examine solubility state. In RIPA fractions, dysferlin was seen as 230–272 kDa bands in normal and AD brains. In serial extractions, there was a shift of dysferlin from soluble phase in high salt/Triton X-100 to the more insoluble SDS fraction in AD. Dysferlin is a new protein described in the AD brain that accumulates in association with neuritic plaques. In muscle, dysferlin plays a role in the repair of muscle membrane damage. The accumulation of dysferlin in the AD brain may be related to the inability of neurons to repair damage due to Aβ deposits accumulating in the AD brain

Responsiveness of SF-36 Health Survey and Patient Generated Index in people with chronic knee pain commenced on oral analgesia: analysis of data from a randomised controlled clinical trial

Purpose. (1) to assess the responsiveness of the Short Form 36 Health Survey (SF-36) and Patient Generated Index (PGI) in people with knee pain who were given oral analgesics; and (2) to perform content analysis of the SF-36 and PGI aiming to identify differences between the instruments and causes of different responsiveness. Methods. An observational study nested within a randomised controlled trial comparing oral paracetamol, ibuprofen or a combination of the two in 884 community-derived people with chronic knee pain. Each participant was given the SF-36 and PGI questionnaires to fill out at baseline, day 10, week 7 and week 13 after commencement on analgesia. Responsiveness was measured as a standardised response mean from baseline and contents of the instruments were analysed. Results. The PGI showed the greater responsiveness to analgesics than the SF-36 throughout the study period. Only the Bodily Pain Score of the SF-36 showed comparable responsiveness to the PGI. The standardised response mean of the PGI at 13 weeks was 0.61 (95% confidence interval 0.51 to 0.72), and that of the Bodily Pain Score of the SF-36 was 0.49 (95% confidence interval 0.39 to 0.58). Content analysis of the PGI identified multiple areas which are not represented in the SF-36 which may help explain its performance. Conclusions. Overall the PGI is more responsive than the SF-36 to commonly used oral analgesics taken for knee pain. The PGI is able to elicit areas of individualised health related quality of life which are not captured by the SF-36