Twitter data: what do they represent?

Scholarly research into the uses of social media has become a major area of growth in recent years, as the adoption of social media for public communication itself has continued apace. While social media platforms provide ready avenues for data access through their Application Programming interfaces, it is increasingly important to think through exactly what these data represent, and what conclusions about the role of social media in society the research which is based on such data therefore enables. This article explores these issues especially for one of the currently leading social media platforms: Twitter.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Retrospective observational study of patients who present to Australian rural emergency departments with undifferentiated chest pain

Aim To identify the demographic and clinical characteristics of patients who present to Australian rural emergency departments with chest pain. Design Retrospective, observational study Setting Rural emergency departments (ED) in Queensland, Australia Participants 337 consecutive adult patients with undifferentiated chest pain that presented between 1st September 2013 and 30th November 2013. Main outcome measures Service indicators, discharge diagnoses and disposition Results Presentations for undifferentiated chest pain represented 3.5% of all patient presentations during the sampling period. The mean age of patients was 48 years and 54% were male. Overall, 92% of patients left the ED within the 4-hour NEAT target. The majority of presentations were related to cardiac concerns (39%), followed by non-cardiac chest pain (17%), musculoskeletal (15%) and respiratory (10%) conditions. More than half of these patients were discharged at the completion of the ED service (52.8%), 40.6% were admitted, 3.3% left at own risk, 2.4% did not wait and less than 1% of patients required transfer to another hospital directly from the ED. Conclusions This study has provided information on the characteristics and processes of care for patients presenting to Australian rural EDs with undifferentiated chest pain that will inform service planning and further research to evaluate the effectiveness of care for these patients.

Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer

Seeking new biomarkers for epithelial ovarian cancer, the fifth most common cause of death from all cancers in women and the leading cause of death from gynaecological malignancies, we performed a meta-analysis of three independent studies and compared the results in regard to clinicopathological parameters. This analysis revealed that GAS6 was highly expressed in ovarian cancer and therefore was selected as our candidate of choice. GAS6 encodes a secreted protein involved in physiological processes including cell proliferation, chemotaxis, and cell survival. We performed immunohistochemistry on various ovarian cancer tissues and found that GAS6 expression was elevated in tumour tissue samples compared to healthy control samples (P < 0.0001). In addition, GAS6 expression was also higher in tumours from patients with residual disease compared to those without. Our data propose GAS6 as an independent predictor of poor survival, suggesting GAS6, both on the mRNA and on the protein level, as a potential biomarker for ovarian cancer. In clinical practice, the staining of a tumour biopsy for GAS6 may be useful to assess cancer prognosis and/or to monitor disease progression.

Improving national hospice/palliative care service symptom outcomes systematically through point-of-care data collection, structured feedback and benchmarking

PURPOSE Every health care sector including hospice/palliative care needs to systematically improve services using patient-defined outcomes. Data from the national Australian Palliative Care Outcomes Collaboration aims to define whether hospice/palliative care patients' outcomes and the consistency of these outcomes have improved in the last 3 years. METHODS Data were analysed by clinical phase (stable, unstable, deteriorating, terminal). Patient-level data included the Symptom Assessment Scale and the Palliative Care Problem Severity Score. Nationally collected point-of-care data were anchored for the period July-December 2008 and subsequently compared to this baseline in six 6-month reporting cycles for all services that submitted data in every time period (n = 30) using individual longitudinal multi-level random coefficient models. RESULTS Data were analysed for 19,747 patients (46 % female; 85 % cancer; 27,928 episodes of care; 65,463 phases). There were significant improvements across all domains (symptom control, family care, psychological and spiritual care) except pain. Simultaneously, the interquartile ranges decreased, jointly indicating that better and more consistent patient outcomes were being achieved. CONCLUSION These are the first national hospice/palliative care symptom control performance data to demonstrate improvements in clinical outcomes at a service level as a result of routine data collection and systematic feedback.

Separating resource rents from intra-marginal rents in fisheries economics survey data

Economic surveys of fisheries are undertaken in several countries as a means of assessing the economic performance of their fisheries. The level of economic profits accruing in the fishery can be estimated from the average economic profits of the boats surveyed. Economic profits consist of two components—resource rent and intra-marginal rent. From a fisheries management perspective, the key indicator of performance is the level of resource rent being generated in the fishery. Consequently, these different components need to be separated out. In this paper, a means of separating out the rent components is identified for a heterogeneous fishery. This is applied to the multi-purpose fleet operating in the English Channel. The paper demonstrates that failing to separate out these two components may result in a misrepresentation of the economic performance of the fishery.

Simulation-based density estimation for time series using covariate data

This paper proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

What women want : qualitative analysis of consumer evaluations of maternity care in Queensland, Australia

Background Maternity care reform plans have been proposed at state and national levels in Australia, but the extent to which these respond to maternity care consumers’ expressed needs is unclear. This study examines open-text survey comments to identify women’s unmet needs and priorities for maternity care. It is then considered whether these needs and priorities are addressed in current reform plans. Methods Women who had a live single or multiple birth in Queensland, Australia, in 2010 (n 3,635) were invited to complete a retrospective self-report survey. In addition to questions about clinical and interpersonal maternity care experiences from pregnancy to postpartum, women were asked an open-ended question “Is there anything else you’d like to tell us about having your baby?” This paper describes a detailed thematic analysis of open-ended responses from a random selection of 150 women (10% of 1,510 who responded to the question). Results Four broad themes emerged relevant to improving women’s experiences of maternity care: quality of care (interpersonal and technical); access to choices and involvement in decision-making; unmet information needs; and dissatisfaction with the care environment. Some of these topics are reflected in current reform goals, while others provide evidence of the need for further reforms. Conclusions The findings reinforce the importance of some existing maternity reform objectives, and describe how these might best be met. Findings affirm the importance of information provision to enable informed choices; a goal of Queensland and national reform agendas. Improvement opportunities not currently specified in reform agendas were also identified, including the quality of interpersonal relationships between women and staff, particular unmet information needs (e.g., breastfeeding), and concerns regarding the care environment (e.g., crowding and long waiting times).

Large-scale analysis of soccer matches using spatiotemporal tracking data

Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (approx 400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.

Identifying team style in soccer using formations learned from spatiotemporal tracking data

To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.

Can mesenchymal stromal cells differentiate into corneal cells? A systematic review of published data

The majority of stem cell therapies for corneal repair are based upon the use of progenitor cells isolated from corneal tissue, but a growing body of literature suggests a role for mesenchymal stromal cells (MSC) isolated from non-corneal tissues. While the mechanism of MSC action seems likely to involve their immuno-modulatory properties, claims have emerged of MSC transdifferentiation into corneal cells. Substantial differences in methodology and experimental outcomes, however, have prompted us to perform a systematic review of the published data. Key questions used in our analysis included; the choice of markers used to assess corneal cell phenotype, the techniques employed to detect these markers, adequate reporting of controls, and tracking of MSC when studied in vivo. Our search of the literature revealed 28 papers published since 2006, with half appearing since 2012. MSC cultures established from bone marrow and adipose tissue have been best studied (22 papers). Critically, only 11 studies employed appropriate markers of corneal cell phenotype, along with necessary controls. Ten out of these 11 papers, however, contained positive evidence of corneal cell marker expression by MSC. The clearest evidence is observed with respect to expression of markers for corneal stromal cells by MSC. In comparison, the evidence for MSC conversion into either corneal epithelial cells or corneal endothelial cells is often inconsistent or inconclusive. Our analysis clarifies this emerging body of literature and provides guidance for future studies of MSC differentiation within the cornea as well as other tissues.