Using routinely collected hospital data for child maltreatment surveillance : issues, methods and patterns

Background: International data on child maltreatment are largely derived from child protection agencies, and predominantly report only substantiated cases of child maltreatment. This approach underestimates the incidence of maltreatment and makes inter-jurisdictional comparisons difficult. There has been a growing recognition of the importance of health professionals in identifying, documenting and reporting suspected child maltreatment. This study aimed to describe the issues around case identification using coded morbidity data, outline methods for selecting and grouping relevant codes, and illustrate patterns of maltreatment identified. Methods: A comprehensive review of the ICD-10-AM classification system was undertaken, including review of index terms, a free text search of tabular volumes, and a review of coding standards pertaining to child maltreatment coding. Identified codes were further categorised into maltreatment types including physical abuse, sexual abuse, emotional or psychological abuse, and neglect. Using these code groupings, one year of Australian hospitalisation data for children under 18 years of age was examined to quantify the proportion of patients identified and to explore the characteristics of cases assigned maltreatment-related codes. Results: Less than 0.5% of children hospitalised in Australia between 2005 and 2006 had a maltreatment code assigned, almost 4% of children with a principal diagnosis of a mental and behavioural disorder and over 1% of children with an injury or poisoning as the principal diagnosis had a maltreatment code assigned. The patterns of children assigned with definitive T74 codes varied by sex and age group. For males selected as having a maltreatment-related presentation, physical abuse was most commonly coded (62.6% of maltreatment cases) while for females selected as having a maltreatment-related presentation, sexual abuse was the most commonly assigned form of maltreatment (52.9% of maltreatment cases). Conclusion: This study has demonstrated that hospital data could provide valuable information for routine monitoring and surveillance of child maltreatment, even in the absence of population-based linked data sources. With national and international calls for a public health response to child maltreatment, better understanding of, investment in and utilisation of our core national routinely collected data sources will enhance the evidence-base needed to support an appropriate response to children at risk.

Reliability of routinely collected hospital data for child maltreatment surveillance

Background: Internationally, research on child maltreatment-related injuries has been hampered by a lack of available routinely collected health data to identify cases, examine causes, identify risk factors and explore health outcomes. Routinely collected hospital separation data coded using the International Classification of Diseases and Related Health Problems (ICD) system provide an internationally standardised data source for classifying and aggregating diseases, injuries, causes of injuries and related health conditions for statistical purposes. However, there has been limited research to examine the reliability of these data for child maltreatment surveillance purposes. This study examined the reliability of coding of child maltreatment in Queensland, Australia. Methods: A retrospective medical record review and recoding methodology was used to assess the reliability of coding of child maltreatment. A stratified sample of hospitals across Queensland was selected for this study, and a stratified random sample of cases was selected from within those hospitals. Results: In 3.6% of cases the coders disagreed on whether any maltreatment code could be assigned (definite or possible) versus no maltreatment being assigned (unintentional injury), giving a sensitivity of 0.982 and specificity of 0.948. The review of these cases where discrepancies existed revealed that all cases had some indications of risk documented in the records. 15.5% of cases originally assigned a definite or possible maltreatment code, were recoded to a more or less definite strata. In terms of the number and type of maltreatment codes assigned, the auditor assigned a greater number of maltreatment types based on the medical documentation than the original coder assigned (22% of the auditor coded cases had more than one maltreatment type assigned compared to only 6% of the original coded data). The maltreatment types which were the most ‘under-coded’ by the original coder were psychological abuse and neglect. Cases coded with a sexual abuse code showed the highest level of reliability. Conclusion: Given the increasing international attention being given to improving the uniformity of reporting of child-maltreatment related injuries and the emphasis on the better utilisation of routinely collected health data, this study provides an estimate of the reliability of maltreatment-specific ICD-10-AM codes assigned in an inpatient setting.

Getting research data ‘out there’ : collaborative solutions to identifying, describing and making research data more visible

At QUT research data refers to information that is generated or collected to be used as primary sources in the production of original research results, and which would be required to validate or replicate research findings (Callan, De Vine, & Baker, 2010). Making publicly funded research data discoverable by the broader research community and the public is a key aim of the Australian National Data Service (ANDS). Queensland University of Technology (QUT) has been innovating in this space by undertaking mutually dependant technical and content (metadata) focused projects funded by ANDS. Research Data Librarians identified and described datasets generated from Category 1 funded research at QUT, by interviewing researchers, collecting metadata and fashioning metadata records for upload to the Australian Research Data commons (ARDC) and exposure through the Research Data Australia interface. In parallel to this project, a Research Data Management Service and Metadata hub project were being undertaken by QUT High Performance Computing & Research Support specialists. These projects will collectively store and aggregate QUT’s metadata and research data from multiple repositories and administration systems and contribute metadata directly by OAI-PMH compliant feed to RDA. The pioneering nature of the work has resulted in a collaborative project dynamic where good data management practices and the discoverability and sharing of research data were the shared drivers for all activity. Each project’s development and progress was dependent on feedback from the other. The metadata structure evolved in tandem with the development of the repository and the development of the repository interface responded to meet the needs of the data interview process. The project environment was one of bottom-up collaborative approaches to process and system development which matched top-down strategic alliances crossing organisational boundaries in order to provide the deliverables required by ANDS. This paper showcases the work undertaken at QUT, focusing on the Seeding the Commons project as a case study, and illustrates how the data management projects are interconnected. It describes the processes and systems being established to make QUT research data more visible and the nature of the collaborations between organisational areas required to achieve this. The paper concludes with the Seeding the Commons project outcomes and the contribution this project made to getting more research data ‘out there’.

The variant concept of transportation disadvantaged : evidence from Aydin, Turkey and Yamaga, Japan

Transportation disadvantaged groups, in the previous studies, are defined as those who are low income earners, family dependent, limited access to private motor vehicles and public transport services, and also those who oblige to spend relatively more time and money on their trips. Additionally those disable, young and elderly are considered among the natural groups of transportation disadvantaged. Although in general terms this definition seems correct, it is not specific enough to become a common universal definition that could apply to all urban contexts. This paper investigates whether travel difficulty perceptions vary and so does the definition of transportation disadvantaged in socio-culturally different urban contexts. For this investigation the paper undertakes a series of statistical analysis in the case study of Yamaga, Japan, and compares the findings with a previous case study, where the same methodology, hypothesis, and assumptions were utilized in a culturally and demographically different settlement of Aydin, Turkey. After comparing the findings observed in Aydin with the statistical analysis results of Yamaga, this paper reveals that there can be no explicitly detailed universal definition of transportation disadvantaged. The paper concludes by stating characteristics of transportation disadvantage is not globally identical, and policies and solutions that work in a locality may not show the same results in another socio-cultural context.

A criminological analysis : using real-time monitoring to gather data on online predators

The Internet presents a constantly evolving frontier for criminology and policing, especially in relation to online predators – paedophiles operating within the Internet for safer access to children, child pornography and networking opportunities with other online predators. The goals of this qualitative study are to undertake behavioural research – identify personality types and archetypes of online predators and compare and contrast them with behavioural profiles and other psychological research on offline paedophiles and sex offenders. It is also an endeavour to gather intelligence on the technological utilisation of online predators and conduct observational research on the social structures of online predator communities. These goals were achieved through the covert monitoring and logging of public activity within four Internet Relay Chat(rooms) (IRC) themed around child sexual abuse and which were located on the Undernet network. Five days of monitoring was conducted on these four chatrooms between Wednesday 1 to Sunday 5 April 2009; this raw data was collated and analysed. The analysis identified four personality types – the gentleman predator, the sadist, the businessman and the pretender – and eight archetypes consisting of the groomers, dealers, negotiators, roleplayers, networkers, chat requestors, posters and travellers. The characteristics and traits of these personality types and archetypes, which were extracted from the literature dealing with offline paedophiles and sex offenders, are detailed and contrasted against the online sexual predators identified within the chatrooms, revealing many similarities and interesting differences particularly with the businessman and pretender personality types. These personality types and archetypes were illustrated by selecting users who displayed the appropriate characteristics and tracking them through the four chatrooms, revealing intelligence data on the use of proxies servers – especially via the Tor software – and other security strategies such as Undernet’s host masking service. Name and age changes, which is used as a potential sexual grooming tactic was also revealed through the use of Analyst’s Notebook software and information on ISP information revealed the likelihood that many online predators were not using any safety mechanism and relying on the anonymity of the Internet. The activities of these online predators were analysed, especially in regards to child sexual grooming and the ‘posting’ of child pornography, which revealed a few of the methods in which online predators utilised new Internet technologies to sexually groom and abuse children – using technologies such as instant messengers, webcams and microphones – as well as store and disseminate illegal materials on image sharing websites and peer-to-peer software such as Gigatribe. Analysis of the social structures of the chatrooms was also carried out and the community functions and characteristics of each chatroom explored. The findings of this research have indicated several opportunities for further research. As a result of this research, recommendations are given on policy, prevention and response strategies with regards to online predators.

Decentralised Data Fusion with Parzen Density Estimates

Decentralised sensor networks typically consist of multiple processing nodes supporting one or more sensors. These nodes are interconnected via wireless communication. Practical applications of Decentralised Data Fusion have generally been restricted to using Gaussian based approaches such as the Kalman or Information Filter This paper proposes the use of Parzen window estimates as an alternate representation to perform Decentralised Data Fusion. It is required that the common information between two nodes be removed from any received estimates before local data fusion may occur Otherwise, estimates may become overconfident due to data incest. A closed form approximation to the division of two estimates is described to enable conservative assimilation of incoming information to a node in a decentralised data fusion network. A simple example of tracking a moving particle with Parzen density estimates is shown to demonstrate how this algorithm allows conservative assimilation of network information.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Predicting single-vehicle fatal crashes for two-lane rural highways in Southeastern United States

The rural two-lane highway in the southeastern United States is frequently associated with a disproportionate number of serious and fatal crashes and as such remains a focus of considerable safety research. The Georgia Department of Transportation spearheaded a regional fatal crash analysis to identify various safety performances of two-lane rural highways and to offer guidance for identifying suitable countermeasures with which to mitigate fatal crashes. The fatal crash data used in this study were compiled from Alabama, Georgia, Mississippi, and South Carolina. The database, developed for an earlier study, included 557 randomly selected fatal crashes from 1997 or 1998 or both (this varied by state). Each participating state identified the candidate crashes and performed physical or video site visits to construct crash databases with enhance site-specific information. Motivated by the hypothesis that single- and multiple-vehicle crashes arise from fundamentally different circumstances, the research team applied binary logit models to predict the probability that a fatal crash is a single-vehicle run-off-road fatal crash given roadway design characteristics, roadside environment features, and traffic conditions proximal to the crash site. A wide variety of factors appears to influence or be associated with single-vehicle fatal crashes. In a model transferability assessment, the authors determined that lane width, horizontal curvature, and ambient lighting are the only three significant variables that are consistent for single-vehicle run-off-road crashes for all study locations.

Developing research data management services at QUT

QUT Library and the High Performance Computing and Research Support (HPC) Team have been collaborating on developing and delivering a range of research support services, including those designed to assist researchers to manage their data. QUT’s Management of Research Data policy has been available since 2010 and is complemented by the Data Management Guidelines and Checklist. QUT has partnered with the Australian Research Data Service (ANDS) on a number of projects including Seeding the Commons, Metadata Hub (with Griffith University) and the Data Capture program. The HPC Team has also been developing the QUT Research Data Repository based on the Architecta Mediaflux system and have run several pilots with faculties. Library and HPC staff have been trained in the principles of research data management and are providing a range of research data management seminars and workshops for researchers and HDR students.

An analysis of the variability in Queensland WiM data

The Queensland Department of Main Roads uses Weigh-in-Motion (WiM) devices to covertly monitor (at highway speed) axle mass, axle configurations and speed of heavy vehicles on the road network. Such data is critical for the planning and design of the road network. Some of the data appears excessively variable. The current work considers the nature, magnitude and possible causes of WiM data variability. Over fifty possible causes of variation in WiM data have been identified in the literature. Data exploration has highlighted five basic types of variability specifically: ----- • cycling, both diurnal and annual;----- • consistent but unreasonable data;----- • data jumps;----- • variations between data from opposite sides of the one road; and ----- • non-systematic variations.----- This work is part of wider research into procedures to eliminate or mitigate the influence of WiM data variability.

Novice riders and the predictors of riding without motorcycle protective clothing

Background While helmet usage is often mandated, few motorcycle and scooter riders make full use of protection for the rest of the body. Little is known about the factors associated with riders’ usage or non-usage of protective clothing. Methods Novice riders were surveyed prior to their provisional licence test in NSW, Australia. Questions related to usage and beliefs about protective clothing, riding experience and exposure, risk taking and demographic details. Multivariable Poisson regression models were used to identify factors associated with two measures of usage, comparing those who sometimes vs rarely/never rode unprotected and who usually wore non-motorcycle pants vs motorcycle pants. Results Ninety-four percent of eligible riders participated and usable data was obtained from 66% (n = 776). Factors significantly associated with riding unprotected were: youth (17–25 years) (RR = 2.00, 95% CI: 1.50–2.65), not seeking protective clothing information (RR = 1.29, 95% CI = 1.07–1.56), non-usage in hot weather (RR = 3.01, 95% CI: 2.38–3.82), awareness of social pressure to wear more protection (RR = 1.48, 95% CI: 1.12–1.95), scepticism about protective benefits (RR = 2.00, 95% CI: 1.22–3.28) and riding a scooter vs any type of motorcycle. A similar cluster of factors including youth (RR = 1.17, 95% CI: 1.04–1.32), social pressure (RR = 1.32, 95% CI: 1.16–1.50), hot weather (RR = 1.30, 95% CI: 1.19–1.41) and scooter vs motorcycles were also associated with wearing non-motorcycle pants. There was no evidence of an association between use of protective clothing and other indicators of risk taking behaviour. Conclusions Factors strongly associated with non-use of protective clothing include not having sought information about protective clothing and not believing in its injury reduction value. Interventions to increase use may therefore need to focus on development of credible information sources about crash risk and the benefits of protective clothing. Further work is required to develop motorcycle protective clothing suitable for hot climates.

Risky driving or risky drivers? Exploring driving and crash histories of illegal street racing offenders

Illegal street racing has received increased attention in recent years from road safety professionals and the media as jurisdictions in Australia, Canada, and the United States have implemented laws to address the problem, which primarily involves young male drivers. Although some evidence suggests that the prevalence of illegal street racing is increasing, obtaining accurate estimates of the crash risk of this behavior is difficult because of limitations in official data sources. Although crash risk can be explored by examining the proportion of incidents of street racing that result in crashes, or the proportion of all crashes that involve street racing, this paper reports on the findings of a study that explored the riskiness of involved drivers. The driving histories of 183 male drivers with an illegal street racing conviction in Queensland, Australia, were compared with a random sample of 183 male Queensland drivers with the same age distribution. The offender group was found to have significantly more traffic infringements, license sanctions, and crashes than the comparison group. Drivers in the offender group were more likely than the comparison group to have committed infringements related to street racing, such as speeding, "hooning," and offenses related to vehicle defects or illegal modifications. Insufficient statistical capacity prevented full exploration of group differences in the type and nature of earlier crashes. It was concluded, however, that street racing offenders generally can be considered risky drivers who warrant attention and whose risky behavior cannot be explained by their youth alone.

The influence of underreported crashes on hotspot identification

Hot spot identification (HSID) plays a significant role in improving the safety of transportation networks. Numerous HSID methods have been proposed, developed, and evaluated in the literature. The vast majority of HSID methods reported and evaluated in the literature assume that crash data are complete, reliable, and accurate. Crash under-reporting, however, has long been recognized as a threat to the accuracy and completeness of historical traffic crash records. As a natural continuation of prior studies, the paper evaluates the influence that under-reported crashes exert on HSID methods. To conduct the evaluation, five groups of data gathered from Arizona Department of Transportation (ADOT) over the course of three years are adjusted to account for fifteen different assumed levels of under-reporting. Three identification methods are evaluated: simple ranking (SR), empirical Bayes (EB) and full Bayes (FB). Various threshold levels for establishing hotspots are explored. Finally, two evaluation criteria are compared across HSID methods. The results illustrate that the identification bias—the ability to correctly identify at risk sites--under-reporting is influenced by the degree of under-reporting. Comparatively speaking, crash under-reporting has the largest influence on the FB method and the least influence on the SR method. Additionally, the impact is positively related to the percentage of the under-reported PDO crashes and inversely related to the percentage of the under-reported injury crashes. This finding is significant because it reveals that despite PDO crashes being least severe and costly, they have the most significant influence on the accuracy of HSID.