Gaining Access to Data Sources in Statutory Social Work Agencies: The Long and Winding Road

This paper details a researcher's experience of gaining access to three statutory social work agencies in order to conduct a study examining how social workers respond to family support cases and how parents and carers experience the intervention of social workers in these cases. The stages in gaining access are outlined, the gate-keepers involved at each stage are identified and some of the difficulties encountered are highlighted and discussed. The paper concludes that researchers need to give greater priority to access considerations and that social work agencies need to give greater priority to co-operation with researchers.

Wildlife tracking data management: a new vision

To date, the processing of wildlife location data has relied on a diversity of software and file formats. Data management and the following spatial and statistical analyses were undertaken in multiple steps, involving many time-consuming importing/exporting phases. Recent technological advancements in tracking systems have made large, continuous, high-frequency datasets of wildlife behavioral data available, such as those derived from the global positioning system (GPS) and other animal-attached sensor devices. These data can be further complemented by a wide range of other information about the animals’ environment. Management of these large and diverse datasets for modelling animal behaviour and ecology can prove challenging, slowing down analysis and increasing the probability of mistakes in data handling. We address these issues by critically evaluating the requirements for good management of GPS data for wildlife biology. We highlight that dedicated data management tools and expertise are needed. We explore current research in wildlife data management. We suggest a general direction of development, based on a modular software architecture with a spatial database at its core, where interoperability, data model design and integration with remote-sensing data sources play an important role in successful GPS data handling.

Towards optimal clinical and epidemiological registration of haematological malignancies: Guidelines for recording progressions, transformations and multiple diagnoses

Haematological malignancies (HM) represent over 6% of the total cancer incidence in Europe and affect all ages, ranging between 45% of all cancers in children and 7% in the elderly. Thirty per cent of childhood cancer deaths are due to HM, 8% in the elderly. Their registration presents specific challenges, mainly because HM may transform or progress in the course of the disease into other types of HM. In the context of cancer registration decisions have to be made about classifying subsequent notifications on the same patient as the same tumour (progression), a transformation or a new tumour registration. Allocation of incidence date and method of diagnosis must also be standardised. We developed European Network of Cancer Registries (ENCR) recommendations providing specific advice for cancer registries to use haematology and molecular laboratories as data sources, conserve the original date of incidence in case of change of diagnosis, make provision for recording both the original as well as transformed tumour and to apply precise rules for recording and counting multiple diagnoses. A reference table advising on codes which reflect a potential transformation or a new tumour is included. This work will help to improve comparability of data produced by population-based cancer registries, which are indispensable for aetiological research, health care planning and clinical research, an increasing important area with the application of targeted therapies.

Exploiting evidence from unstructured data to enhance master data management

Master data management (MDM) integrates data from multiple
structured data sources and builds a consolidated 360-
degree view of business entities such as customers and products.
Today’s MDM systems are not prepared to integrate
information from unstructured data sources, such as news
reports, emails, call-center transcripts, and chat logs. However,
those unstructured data sources may contain valuable
information about the same entities known to MDM from
the structured data sources. Integrating information from
unstructured data into MDM is challenging as textual references
to existing MDM entities are often incomplete and
imprecise and the additional entity information extracted
from text should not impact the trustworthiness of MDM
data.
In this paper, we present an architecture for making MDM
text-aware and showcase its implementation as IBM InfoSphere
MDM Extension for Unstructured Text Correlation,
an add-on to IBM InfoSphere Master Data Management
Standard Edition. We highlight how MDM benefits from
additional evidence found in documents when doing entity
resolution and relationship discovery. We experimentally
demonstrate the feasibility of integrating information from
unstructured data sources into MDM.

Query cost estimation through remote system contention states analysis over the Internet

Query processing over the Internet involving autonomous data sources is a major task in data integration. It requires the estimated costs of possible queries in order to select the best one that has the minimum cost. In this context, the cost of a query is affected by three factors: network congestion, server contention state, and complexity of the query. In this paper, we study the effects of both the network congestion and server contention state on the cost of a query. We refer to these two factors together as system contention states. We present a new approach to determining the system contention states by clustering the costs of a sample query. For each system contention state, we construct two cost formulas for unary and join queries respectively using the multiple regression process. When a new query is submitted, its system contention state is estimated first using either the time slides method or the statistical method. The cost of the query is then calculated using the corresponding cost formulas. The estimated cost of the query is further adjusted to improve its accuracy. Our experiments show that our methods can produce quite accurate cost estimates of the submitted queries to remote data sources over the Internet.

A comparison of self-report and health care provider data to assess surveillance definitions of influenza-like illness in outpatients

Objective: Several surveillance definitions of influenza-like illness (ILI) have been proposed, based on the presence of symptoms. Symptom data can be obtained from patients, medical records, or both. Past research has found that agreements between health record data and self-report are variable depending on the specific symptom. Therefore, we aimed to explore the implications of using data on influenza symptoms extracted from medical records, similar data collected prospectively from outpatients, and the combined data from both sources as predictors of laboratory-confirmed influenza. Methods: Using data from the Hutterite Influenza Prevention Study, we calculated: 1) the sensitivity, specificity and predictive values of individual symptoms within surveillance definitions; 2) how frequently surveillance definitions correlated to laboratory-confirmed influenza; and 3) the predictive value of surveillance definitions. Results: Of the 176 participants with reports from participants and medical records, 142 (81%) were tested for influenza and 37 (26%) were PCR positive for influenza. Fever (alone) and fever combined with cough and/or sore throat were highly correlated with being PCR positive for influenza for all data sources. ILI surveillance definitions, based on symptom data from medical records only or from both medical records and self-report, were better predictors of laboratory-confirmed influenza with higher odds ratios and positive predictive values. Discussion: The choice of data source to determine ILI will depend on the patient population, outcome of interest, availability of data source, and use for clinical decision making, research, or surveillance. © Canadian Public Health Association, 2012.

Preliminary analysis of organic Rankine cycles to improve vehicle efficiency

This paper presents the background rationale and key findings for a model-based study of supercritical waste heat recovery organic Rankine cycles. The paper’s objective is to cover the necessary groundwork to facilitate the future operation of a thermodynamic organic Rankine cycle model under realistic thermodynamic boundary conditions for performance optimisation of organic Rankine cycles. This involves determining the type of power cycle for organic Rankine cycles, the circuit configuration and suitable boundary conditions. The study focuses on multiple heat sources from vehicles but the findings are generally applicable, with careful consideration, to any waste heat recovery system. This paper introduces waste heat recovery and discusses the general merits of organic fluids versus water and supercritical operation versus subcritical operation from a theoretical perspective and, where possible, from a practical perspective. The benefits of regeneration are investigated from an efficiency perspective for selected subcritical and supercritical conditions. A simulation model is described with an introduction to some general Rankine cycle boundary conditions. The paper describes the analysis of real hybrid vehicle data from several driving cycles and its manipulation to represent the thermal inertia for model heat input boundary conditions. Basic theory suggests that selecting the operating pressures and temperatures to maximise the Rankine cycle performance is relatively straightforward. However, it was found that this may not be the case for an organic Rankine cycle operating in a vehicle. When operating in a driving cycle, the available heat and its quality can vary with the power output and between heat sources. For example, the available coolant heat does not vary much with the load, whereas the quantity and quality of the exhaust heat varies considerably. The key objective for operation in the vehicle is optimum utilisation of the available heat by delivering the maximum work out. The fluid selection process and the presentation and analysis of the final results of the simulation work on organic Rankine cycles are the subjects of two future publications.

An Extended Event Reasoning Framework for Decision Support under Uncertainty

To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.

Positioning Clinical Nurse Specialists and Nurse Practitioners as Change Champions to Implement a Pain Protocol in Long-Term Care

Pain management for older adults in long-term care (LTC) has been recognized as a problem internationally. The purpose of this study was to explore the role of a clinical nurse specialist (CNS) and nurse practitioner (NP) as change champions during the implementation of an evidence-based pain protocol in LTC. In this exploratory, multiple-case design study, we collected data from two LTC homes in Ontario, Canada. Three data sources were used: participant observation of an NP and a CNS for 18 hours each over a 3-week period; CNS and NP diaries recording strategies, barriers, and facilitators to the implementation process; and interviews with members of the interdisciplinary team to explore perceptions about the NP and CNS role in implementing the pain protocol. Data were analyzed using thematic content analysis. The NP and CNS used a variety of effective strategies to promote pain management changes in practice including educational outreach with team members, reminders to nursing staff to highlight the pain protocol and educate about practice changes, chart audits and feedback to the nursing staff, interdisciplinary working group meetings, ad hoc meetings with nursing staff, and resident assessment using advanced skills. The CNS and NP are ideal champions to implement pain management protocols and likely other quality improvement initiatives.

Abdominal Aortic Aneurysm Genetic Associations: Mostly False? A Systematic Review and Meta-analysis

OBJECTIVE/BACKGROUND: Many associations between abdominal aortic aneurysm (AAA) and genetic polymorphisms have been reported. It is unclear which are genuine and which may be caused by type 1 errors, biases, and flexible study design. The objectives of the study were to identify associations supported by current evidence and to investigate the effect of study design on reporting associations.

METHODS: Data sources were MEDLINE, Embase, and Web of Science. Reports were dual-reviewed for relevance and inclusion against predefined criteria (studies of genetic polymorphisms and AAA risk). Study characteristics and data were extracted using an agreed tool and reports assessed for quality. Heterogeneity was assessed using I(2) and fixed- and random-effects meta-analyses were conducted for variants that were reported at least twice, if any had reported an association. Strength of evidence was assessed using a standard guideline.

RESULTS: Searches identified 467 unique articles, of which 97 were included. Of 97 studies, 63 reported at least one association. Of 92 studies that conducted multiple tests, only 27% corrected their analyses. In total, 263 genes were investigated, and associations were reported in polymorphisms in 87 genes. Associations in CDKN2BAS, SORT1, LRP1, IL6R, MMP3, AGTR1, ACE, and APOA1 were supported by meta-analyses.

CONCLUSION: Uncorrected multiple testing and flexible study design (particularly testing many inheritance models and subgroups, and failure to check for Hardy-Weinberg equilibrium) contributed to apparently false associations being reported. Heterogeneity, possibly due to the case mix, geographical, temporal, and environmental variation between different studies, was evident. Polymorphisms in nine genes had strong or moderate support on the basis of the literature at this time. Suggestions are made for improving AAA genetics study design and conduct.

Sepsis: the LightCycler SeptiFast Test MGRADE®, SepsiTest™ and IRIDICA BAC BSI assay for rapidly identifying bloodstream bacteria and fungi – a systematic review and economic evaluation

Background: Sepsis can lead to multiple organ failure and death. Timely and appropriate treatment can reduce in-hospital mortality and morbidity. Objectives: To determine the clinical effectiveness and cost-effectiveness of three tests [LightCycler SeptiFast Test MGRADE® (Roche Diagnostics, Risch-Rotkreuz, Switzerland); SepsiTest™ (Molzym Molecular Diagnostics, Bremen, Germany); and the IRIDICA BAC BSI assay (Abbott Diagnostics, Lake Forest, IL, USA)] for the rapid identification of bloodstream bacteria and fungi in patients with suspected sepsis compared with standard practice (blood culture with or without matrix-absorbed laser desorption/ionisation time-offlight mass spectrometry). Data sources: Thirteen electronic databases (including MEDLINE, EMBASE and The Cochrane Library) were searched from January 2006 to May 2015 and supplemented by hand-searching relevant articles. Review methods: A systematic review and meta-analysis of effectiveness studies were conducted. A review of published economic analyses was undertaken and a de novo health economic model was constructed. A decision tree was used to estimate the costs and quality-adjusted life-years (QALYs) associated with each test; all other parameters were estimated from published sources. The model was populated with evidence from the systematic review or individual studies, if this was considered more appropriate (base case 1). In a secondary analysis, estimates (based on experience and opinion) from seven clinicians regarding the benefits of earlier test results were sought (base case 2). A NHS and Personal Social Services perspective was taken, and costs and benefits were discounted at 3.5% per annum. Scenario analyses were used to assess uncertainty. Results: For the review of diagnostic test accuracy, 62 studies of varying methodological quality were included. A meta-analysis of 54 studies comparing SeptiFast with blood culture found that SeptiFast had an estimated summary specificity of 0.86 [95% credible interval (CrI) 0.84 to 0.89] and sensitivity of 0.65 (95% CrI 0.60 to 0.71). Four studies comparing SepsiTest with blood culture found that SepsiTest had an estimated summary specificity of 0.86 (95% CrI 0.78 to 0.92) and sensitivity of 0.48 (95% CrI 0.21 to 0.74), and four studies comparing IRIDICA with blood culture found that IRIDICA had an estimated summary specificity of 0.84 (95% CrI 0.71 to 0.92) and sensitivity of 0.81 (95% CrI 0.69 to 0.90). Owing to the deficiencies in study quality for all interventions, diagnostic accuracy data should be treated with caution. No randomised clinical trial evidence was identified that indicated that any of the tests significantly improved key patient outcomes, such as mortality or duration in an intensive care unit or hospital. Base case 1 estimated that none of the three tests provided a benefit to patients compared with standard practice and thus all tests were dominated. In contrast, in base case 2 it was estimated that all cost per QALY-gained values were below £20,000; the IRIDICA BAC BSI assay had the highest estimated incremental net benefit, but results from base case 2 should be treated with caution as these are not evidence based. Limitations: Robust data to accurately assess the clinical effectiveness and cost-effectiveness of the interventions are currently unavailable. Conclusions: The clinical effectiveness and cost-effectiveness of the interventions cannot be reliably determined with the current evidence base. Appropriate studies, which allow information from the tests to be implemented in clinical practice, are required.

A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.