Safeguarding Rural Australia : Introduction, Framework and Background to Secondary Data Analysis

This report provides an introduction to our analyses of secondary data with respect to violent acts and incidents relating to males living in rural settings in Australia. It clarifies important aspects of our overall approach primarily by concentrating on three elements that required early scoping and resolution. Firstly, a wide and inclusive view of violence which encompasses measures of violent acts and incidents and also data identifying risk taking behaviour and the consequences of violence is outlined and justified. Secondly, the classification used to make comparisons between the city and the bush together with associated caveats is outlined. The third element discussed is in relation to national injury data. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in five subsequent reports in this series.

Data Report No. 1 : Self-harm Including Suicide

This report focuses on our examination of extant data which have been sourced with respect to self-harm and suicide in Australia. Moreover, specific areas of concern regarding elevated rates of suicide for rural males and data anomalies which emerged during our examination of these data are discussed. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

Safeguarding Rural Australia : Data Report No. 2 - Intentional Violence

This report focuses on our examination of extant data which have been sourced with respect to intentional violence perpetrated or experienced by males in regional and remote Australia. The nature of intentional violent acts can be physical, sexual or psychological or involve deprivation or neglect. We have presented under the headings of: self-harm including suicide; homicide; assault, sexual assault and the threat of assault; child abuse; other family and intimate partner violence; harassment, stalking and bullying; alcohol related social violence; and animal abuse. State variations in interpersonal violence are also presented. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

Safeguarding Rural Australia: Data Report No. 3 - Unintentional Violence

This report focuses on our examination of extant data which have been sourced with respect to unintentional serious and violent injuries to males living in regional and remote Australia. Such injuries typically might be caused by, for example, transport accidents, occupational exposures and hazards, burns and so on. Thus unintentional violent incidents cause physical trauma the consequences of which can sometimes lead to chronic conditions including psychological harm or substance abuse. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

Safeguarding rural Australia : addressing masculinity and violence in rural settings. Data report No. 4 - Risky behaviour - misuse of alcohol, illicit drugs, firearms use and abuse, other risky behaviour

This report focuses on our examination of extant data which have been sourced with respect to personally and socially risky behaviour associated with males living in regional and remote Australia . The AIHW (2008: PHE 97:89) defines personally risky behaviour, on the one hand, as working, swimming, boating, driving or operating hazardous machinery while intoxicated with alcohol or an illicit drug. Socially risky behaviour, on the other hand, is defined as creating a public disturbance, damaging property, stealing or verbally or physically abusing someone while intoxicated with alcohol or an illicit drug. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

Safeguarding rural Australia : addressing masculinity and violence in rural settings. Data report no. 5. Consequences of violence - juvenile offenders, long-term health consequences, anxiety and repression, other chronic disabilities

This report considers extant data which have been sourced with respect to some of the consequences of violent acts and incidents and risky behaviour for males living in regional and remote Australia . This has been collated and presented under the headings: juvenile offenders; long-term health consequences; anxiety and repression; and other chronic disabilities. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

The X-Factor and its relationship to golfing performance

It is often postulated that an increased hip to shoulder differential angle (`X-Factor') during the early downswing better utilises the stretch-shorten cycle and improves golf performance. The current study aims to examine the potential relationship between the X-Factor and performance during the tee-shot. Seven golfers with handicaps between 0 and 10 strokes comprised the low-handicap group, whilst the high-handicap group consisted of eight golfers with handicaps between 11 and 20 strokes. The golfers performed 20 drives and three-dimensional kinematic data were used to quantify hip and shoulder rotation and the subsequent X-Factor. Compared with the low-handicap group, the high-handicap golfers tended to demonstrate greater hip rotation at the top of the backswing and recorded reduced maximum X-Factor values. The inconsistencies evident in the literature may suggest that a universal method of measuring rotational angles during the golf swing would be beneficial for future studies, particularly when considering potential injury.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Creating an asset registry for railway electrical traction equipment with open standards

An asset registry arguably forms the core system that needs to be in place before other systems can operate or interoperate. Most systems have rudimentary asset registry functionality that store assets, relationships, or characteristics, and this leads to different asset management systems storing similar sets of data in multiple locations in an organisation. As organisations have been slowly moving their information architecture toward a service-oriented architecture, they have also been consolidating their multiple data stores, to form a “single point of truth”. As part of a strategy to integrate several asset management systems in an Australian railway organisation, a case study for developing a consolidated asset registry was conducted. A decision was made to use the MIMOSA OSA-EAI CRIS data model as well as the OSA-EAI Reference Data in building the platform due to the standard’s relative maturity and completeness. A pilot study of electrical traction equipment was selected, and the data sources feeding into the asset registry were primarily diagrammatic based. This paper presents the pitfalls encountered, approaches taken, and lessons learned during the development of the asset registry.

Is the new age phenomenon connected to delusion-like experiences? Analysis of survey data from Australia

Recent studies have shown that delusion-like experiences (DLEs) are common among general populations. This study investigates whether the prevalence of these experiences are linked to the embracing of New Age thought. Logistic regression analyses were performed using data derived from a large community sample of young adults (N = 3777). Belief in a spiritual or higher power other than God was found to be significantly associated with endorsement of 16 of 19 items from Peters et al. (1999b) Delusional Inventory following adjustment for a range of potential confounders, while belief in God was associated with endorsement of four items. A New Age conception of the divine appears to be strongly associated with a wide range of DLEs. Further research is needed to determine a causal link between New Age philosophy and DLEs (e.g. thought disturbance, suspiciousness, and delusions of grandeur).

Eco-structure data forms : classification of data analysis using perceived design affordances for musical outcomes

This paper explores a method of comparative analysis and classification of data through perceived design affordances. Included is discussion about the musical potential of data forms that are derived through eco-structural analysis of musical features inherent in audio recordings of natural sounds. A system of classification of these forms is proposed based on their structural contours. The classifications include four primitive types; steady, iterative, unstable and impulse. The classification extends previous taxonomies used to describe the gestural morphology of sound. The methods presented are used to provide compositional support for eco-structuralism.

TraSe algorithm : automatic evolutionary morphing of electronic dance music

The TraSe (Transform-Select) algorithm has been developed to investigate the morphing of electronic music through automatically applying a series of deterministic compositional transformations to the source, guided towards a target by similarity metrics. This is in contrast to other morphing techniques such as interpolation or parameters or probabilistic variation. TraSe allows control over stylistic elements of the music through user-defined weighting of numerous compositional transformations. The formal evaluation of TraSe was mostly qualitative and occurred through nine participants completing an online questionnaire. The music generated by TraSe was generally felt to be less coherent than a human composed benchmark but in some cases judged as more creative.

The impact of health information technology on the quality of medical and health care : a systematic review

Objective: To systematically review the published evidence of the impact of health information technology (HIT) on the quality of medical and health care specifically clinicians’ adherence to evidence-based guidelines and the corresponding impact this had on patient clinical outcomes. In order to be as inclusive as possible the research examined literature discussing the use of health information technologies and systems in both medical care such as clinical and surgical, and other health care such as allied health and preventive services.----- Design: Systematic review----- Data Sources: Relevant literature was systematically searched on English language studies indexed in MEDLINE and CINAHL(1998 to 2008), Cochrane Library, PubMed, Database of Abstracts of Review of Effectiveness (DARE), Google scholar and other relevant electronic databases. A search for eligible studies (matching the inclusion criteria) was also performed by searching relevant conference proceedings available through internet and electronic databases, as well as using reference lists identified from cited papers.----- Selection criteria: Studies were included in the review if they examined the impact of Electronic Health Record (EHR), Computerised Provider Order-Entry (CPOE), or Decision Support System (DS); and if the primary outcomes of the studies were focused on the level of compliance with evidence-based guidelines among clinicians. Measures could be either changes in clinical processes resulting from a change of the providers’ behaviour or specific patient outcomes that demonstrated the effectiveness of a particular treatment given by providers. ----- Methods: Studies were reviewed and summarised in tabular and text form. Due to heterogeneity between studies, meta-analysis was not performed.----- Results: Out of 17 studies that assessed the impact of health information technology on health care practitioners’ performance, 14 studies revealed a positive improvement in relation to their compliance with evidence-based guidelines. The primary domain of improvement was evident from preventive care and drug ordering studies. Results from the studies that included an assessment for patient outcomes however, were insufficient to detect either clinically or statistically important improvements as only a small proportion of these studies found benefits. For instance, only 3 studies had shown positive improvement, while 5 studies revealed either no change or adverse outcomes.----- Conclusion: Although the number of included studies was relatively small for reaching a conclusive statement about the effectiveness of health information technologies and systems on clinical care, the results demonstrated consistency with other systematic reviews previously undertaken. Widescale use of HIT has been shown to increase clinician’s adherence to guidelines in this review. Therefore, it presents ongoing opportunities to maximise the uptake of research evidence into practice for health care organisations, policy makers and stakeholders.

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.