Mebrahtu T.F.,University of Leeds |
Feltbower R.G.,University of Leeds |
Petherick E.S.,Bradford Institute of Health Research |
Parslow R.C.,University of Leeds
Journal of Epidemiology and Community Health | Year: 2015
Background: Childhood growth patterns have been proposed as a key predictor of health during childhood and adult life. In earlier studies, however, the statistical methodologies employed failed to uncover the more subtle patterns in growth trajectories. Methods: Study participants were 1364 singleton term children (602 white British and 762 Pakistani origin) drawn from the Born in Bradford (BiB) prospective cohort. Weights were measured at 0, 1, 3, 6, 12, 18, 24 and 36 months. Age-specific and sex-specific standardised weight scores were derived based on the World Health Organisation growth standards. Missing growth data were estimated using Full Information Maximum Likelihood (FIML) method. Growth Mixture Model was used to analyse growth patterns of children from birth until 36 months. Results: On average, Pakistani children were 190 g lighter than white British children at birth. Based on our Growth Mixture Model results, the study children had three distinct growth patterns: 'normal growers' (95.9%), 'fast growers' (2.5%) and 'slow growers' (1.6%). The Pakistani children were more likely to be in either the 'fast' (OR=2.90; 95% CI 0.91 to 9.25) or 'slow' (OR=15.63; 95% CI 1.06 to 230) grower class than the white British. Pakistani children showed faster growth than the white British between 3 and 36 months of age. Conclusions: In this growth study we have identified that the study children have three distinct growth patterns. These growth patterns may provide greater insight in predicting the risk of childhood or early adulthood diseases in life-course studies.
Kasbekar A.V.,Foundation University |
Davies F.,Foundation University |
Upile N.,Foundation University |
Ho M.W.,Bradford Institute of Health Research |
Roland N.J.,Foundation University
Annals of the Royal College of Surgeons of England | Year: 2016
INTRODUCTION The management of vacuum neck drains in head and neck surgery is varied. We aimed to improve early drain removal and therefore patient discharge in a safe and effective manner. METHODS The postoperative management of head and neck surgical patients with vacuum neck drains was reviewed retrospectively. A new policy was then implemented to measure drainage three times daily (midnight, 6am, midday). The decision for drain removal was based on the most recent drainage period (at <3ml per hour). A further patient cohort was subsequently assessed prospectively. The length of hospital stay was compared between the cohorts. RESULTS The retrospective audit included 51 patients while the prospective audit included 47. The latter saw 16 patients (33%) discharged at least one day earlier than they would have been under the previous policy. No adverse effects were noted from earlier drain removal. CONCLUSIONS Measuring drainage volumes three times daily allows for more accurate assessment of wound drainage, and this can lead to earlier removal of neck drains and safe discharge.
Agency: GTR | Branch: EPSRC | Program: | Phase: Research Grant | Award Amount: 977.83K | Year: 2016
This cross-disciplinary project aims to develop novel data mining and visualization tools and techniques, which will transform peoples ability to analyse quantitative and coded longitudinal data. Such data are common in many sectors. For example, health data is classified using a hierarchy of hundreds of thousands of Read Codes (a thesaurus of clinical terms), with analysts needing to provide business intelligence for clinical commissioning decisions, and researchers tacking challenges such modelling disease risk stratification. Retailers such as Sainsburys sell 50,000+ types of products, and want to combine data from purchasing, demographic and other sources to understand behavioural phenomena such as the convenience culture, to guide investment and reduce waste. To solve these needs, public and private sector organisations require an infrastructure that provides far more powerful analytical tools than are available today. Todays analysis tools are deficient because they (a) are crude for assessing data quality, (b) often involve analysis techniques are designed to operate on aggregated, rather than fine-grained, data, and (c) are often laborious to use, which inhibits users from discovering important patterns. The QuantiCode project will address these deficiencies by bringing together experts in statistics, modelling, visualization, user evaluation and ethics. The project will be based in the Leeds Institute for Data Analytics (LIDA), which houses the ESRC Consumer Data Research Centre (£5m ES/L011891/1) and the MRC Medical Bioinformatics Centre (£7m ES/L011891/1), and provides a development facilities complete with high-performance computing (HPC), visualization and safe rooms for sensitive data. Our project will deliver proof of concept visual analytic systems, which we will evaluate with a wide variety of users drawn from our partners and researchers/external users based in LIDA. At the outset of the project we will engage with our partners to identify alysis use cases and requirements that drive the details of our research, which is divided into four workpackages (WPs). WP1 (Data Fusion) will develop governance principles for the analysis of fine-grained data from multiple sources, implement tools to substantially reduce the effort of linking those sources, and develop new techniques to visualize completeness, concordance, plausibility, and other aspects of data quality. WP2 (Analytical Techniques) and WP3 (Abstraction Models) are the projects technical core. WP2 will deliver a new, robust approach for modelling data as they appear naturally in health and retail data (irregularly dispersed or sampled over time), scaling that approach with stochastic control to guide learning and resource usage, and developing a low-effort question-posing visual interface to drastically lower the human effort of investigating data and finding patterns. WP3 (Abstraction Models) focuses on data granularity, and will deliver a tool that implements a working version of the governance principles we develop in WP1, and new computational and interactive techniques for exploring abstraction spaces to create inputs suited to each aspect of analysis. WP4 will implement the above tools and techniques in three versions of our proof of concept system, evaluating each with our partners and LIDA researchers/users. This will ensure that our solutions are compatible with, and scale to, challenging real-world data analysis problems. Success criteria will be time saved, increased analysis scope, notable insights, and tackling previously unfeasible types of analysis - all compared against a baseline provided by users current analysis tools. We will encourage adoption via showcases, workshops and licensed installations at our partners sites. The projects legacy will include tools that are embedded as an integral part of the LIDA infrastructure, a plan for their on-going development, and a research roadmap.
Johnson J.,University of Leeds |
Johnson J.,Bradford Institute of Health Research |
Jones C.,University of Birmingham |
Lin A.,University of Western Australia |
And 4 more authors.
Psychiatry Research | Year: 2014
Shame is associated with a range of psychological disorders, and is a trans-diagnostic moderator of the association between stressors and symptoms of disorder. However, research has yet to investigate shame in relation to specific psychotic symptoms in clinical groups. In order to address this, the present study investigated shame in young adults with mental health problems, to test whether shame was i) directly associated with paranoia, a prevalent psychotic symptom, and ii) a moderator of the association between stress and paranoia. Sixty participants completed measures of stressful events, paranoia, shame, depression and anxiety. Results from a cross-sectional regression analysis suggested that shame was associated with paranoia after the stressful life event measure was entered into the model, and shame moderated the association between stress and paranoia. For individuals scoring high on shame, shame amplified the association between stress and paranoia, but for low-shame individuals, the association between stress and paranoia was non-significant. These findings suggest that high levels of shame could confer vulnerability for paranoia amongst clinical groups, and that resistance to experiencing shame could be a marker of resilience. © 2014 Elsevier Ireland Ltd.
Dowding D.W.,University of Leeds |
Currie L.M.,University of British Columbia |
Borycki E.,University of Victoria |
Clamp S.,University of Leeds |
And 13 more authors.
Studies in Health Technology and Informatics | Year: 2013
The Nursing Informatics International Research Network (NIIRN) is a group of experts who are collaborating on the development of internationally relevant research programs for nursing informatics. In this paper we outline key findings of a survey exploring international research priorities for nursing informatics. The survey was available online during May-August 2012. Respondents were asked to rate each of 20 listed research topics in terms of respondent's views of its priority for nursing informatics research. 468 completed surveys were received representing respondents from six World Health Organization regions. The two most highly ranked areas of importance for research were development of systems to provide real time feedback to nurses and assessment of the impact of HIT on nursing care and patient outcomes. The lowest ranked research topics were theory development and integrating genomic data into clinical information systems. The identification of these priorities provides a basis for future international collaborative research in the field of nursing informatics. © 2013 IMIA and IOS Press.