Prospective surveillance of multivariate spatial disease data

Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented

Foodborne illness among school children in Ga east, Accra

Background: A food borne illness was reported in Ga- East district of Greater Accra Region among school children in May, 2007 after eating food provided at school. The objective of the investigation was to determine the source, mode of contamination and the causative agent.Methods: A case-control study was conducted, cases were schoolchildren with abdominal symptoms and controls were children of the same sex and class without any symptom during the same period. The school children were selected by systematic sampling. Food handlers and the children were interviewed by a structured questionnaire. Food handlers were physically examined and their stools and blood examined. The kitchen for food preparation was inspected. Risks of food borne infection from the foods eaten were determined using attack rates .Results: The minimum, peak and maximum incubation periods were 2, 11 and 61 hours respectively. The source was rice and groundnut soup (with the highest attack rate difference). Stool and blood samples of food handlers were not infective. Storage facility for food items was poor. No food samples were available for organism isolation. A protocol to prevent such outbreaks was nonexistent.Conclusion: The short incubation period and symptoms presented suggest an infective origin. The storage of the meat may potentially have been the point of contamination. The study showed that the schoolchildren ate contaminated food although the investigation could not determine the causative agent. Protocols to prevent such outbreaks need to be developed for the schools.Keywords: Food borne, illness, contaminated food, school children, Accr

Enhancing Resource Management through Prediction-based Policies

Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly scheduling application tasks to cores. Additionally, these runtime systems offer policies to cope with application phases that lack in parallelism to fill all cores. However, these policies are usually static and favor either performance or energy efficiency. In this paper, we have extended a task-based runtime system with a lightweight monitoring and prediction infrastructure that dynamically predicts the optimal number of cores required for each application phase, thus improving both performance and energy efficiency. Through the execution of several benchmarks in multi-/many-core systems, we show that our prediction-based policies have competitive performance while improving energy efficiency when compared to state of the art policies.Comment: Postprint submitted and published at Euro-Par2020: International European Conference on Parallel and Distributed Computing (Springer) (https://link.springer.com/chapter/10.1007%2F978-3-030-57675-2_31

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

Background: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples. Methods: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants’ accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. Results: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively). Conclusions: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains

Does implementation matter if comprehension is lacking? A qualitative investigation into perceptions of advance care planning in people with cancer

Purpose: While advance care planning holds promise, uptake is variable and it is unclear how well people engage with or comprehend advance care planning. The objective of this study was to explore how people with cancer comprehended Advance Care Plans and examine how accurately advance care planning documentation represented patient wishes. Methods: This study used a qualitative descriptive design. Data collection comprised interviews and an examination of participants’ existing advance care planning documentation. Participants included those who had any diagnosis of cancer with an advance care plan recorded: Refusal of Treatment Certificate; Statement of Choices; and/or Enduring Power of Attorney (Medical Treatment) at one cancer treatment centre. Results: Fourteen participants were involved in the study. Twelve participants were female (86%). The mean age was 77 (range: 61-91) and participants had completed their advance care planning documentation between 8 and 72 weeks prior to the interview (mean 33 weeks). Three themes were evident from the data: Incomplete advance care planning understanding and confidence; Limited congruence for attitude and documentation; Advance care planning can enable peace of mind. Complete advance care planning understanding was unusual; most participants demonstrated partial comprehension of their own advance care plan, and some indicated very limited understanding. Participants’ attitudes and their written document congruence was limited, but advance care planning was seen as helpful. Conclusions: This study highlighted advance care planning was not a completely accurate representation of patient wishes. There is opportunity to improve how patients comprehend their own advance care planning documentation

Physical and mental health comorbidity is common in people with multiple sclerosis: nationally representative cross-sectional population database analysis

<b>Background</b> Comorbidity in Multiple Sclerosis (MS) is associated with worse health and higher mortality. This study aims to describe clinician recorded comorbidities in people with MS. <p></p> <b>Methods</b> 39 comorbidities in 3826 people with MS aged ≥25 years were compared against 1,268,859 controls. Results were analysed by age, gender, and socioeconomic status, with unadjusted and adjusted Odds Ratios (ORs) calculated using logistic regression. <p></p> <b>Results</b> People with MS were more likely to have one (OR 2.44; 95% CI 2.26-2.64), two (OR 1.49; 95% CI 1.38-1.62), three (OR 1.86; 95% CI 1.69-2.04), four or more (OR 1.61; 95% CI 1.47-1.77) non-MS chronic conditions than controls, and greater mental health comorbidity (OR 2.94; 95% CI 2.75-3.14), which increased as the number of physical comorbidities rose. Cardiovascular conditions, including atrial fibrillation (OR 0.49; 95% CI 0.36-0.67), chronic kidney disease (OR 0.51; 95% CI 0.40-0.65), heart failure (OR 0.62; 95% CI 0.45-0.85), coronary heart disease (OR 0.64; 95% CI 0.52-0.71), and hypertension (OR 0.65; 95% CI 0.59-0.72) were significantly less common in people with MS. <p></p> <b>Conclusion</b> People with MS have excess multiple chronic conditions, with associated increased mental health comorbidity. The low recorded cardiovascular comorbidity warrants further investigation

Is it harder to know or to reason? Analyzing two-tier science assessment items using the Rasch measurement model

Two-tier multiple-choice (TTMC) items are used to assess students’ knowledge of a scientific concept for tier 1 and their reasoning about this concept for tier 2. But are the knowledge and reasoning involved in these tiers really distinguishable? Are the tiers equally challenging for students? The answers to these questions influence how we use and interpret TTMC instruments. We apply the Rasch measurement model on TTMC items to see if the items are distinguishable according to different traits (represented by the tier), or according to different content sub-topics within the instrument, or to both content and tier. Two TTMC data sets are analyzed: data from Singapore and Korea on the Light Propagation Diagnostic Instrument (LPDI), data from the United States on the Classroom Test of Scientific Reasoning (CTSR). Findings for LPDI show that tier-2 reasoning items are more difficult than tier-1 knowledge items, across content sub-topics. Findings for CTSR do not show a consistent pattern by tier or by content sub-topic. We conclude that TTMC items cannot be assumed to have a consistent pattern of difficulty by tier—and that assessment developers and users need to consider how the tiers operate when administering TTMC items and interpreting results. Researchers must check the tiers’ difficulties empirically during validation and use. Though findings from data in Asian contexts were more consistent, further study is needed to rule out differences between the LPDI and CTSR instruments