A methodology for estimating exposure-controlled crash risk using traffic Police crash data

Exposure control or case-control methodologies are common techniques for estimating crash risks, however they require either observational data on control cases or exogenous exposure data, such as vehicle-kilometres travelled. This study proposes an alternative methodology for estimating crash risk of road user groups, whilst controlling for exposure under a variety of roadway, traffic and environmental factors by using readily available police-reported crash data. In particular, the proposed method employs a combination of a log-linear model and quasi-induced exposure technique to identify significant interactions among a range of roadway, environmental and traffic conditions to estimate associated crash risks. The proposed methodology is illustrated using a set of police-reported crash data from January 2004 to June 2009 on roadways in Queensland, Australia. Exposure-controlled crash risks of motorcyclists—involved in multi-vehicle crashes at intersections—were estimated under various combinations of variables like posted speed limit, intersection control type, intersection configuration, and lighting condition. Results show that the crash risk of motorcycles at three-legged intersections is high if the posted speed limits along the approaches are greater than 60 km/h. The crash risk at three-legged intersections is also high when they are unsignalized. Dark lighting conditions appear to increase the crash risk of motorcycles at signalized intersections, but the problem of night time conspicuity of motorcyclists at intersections is lessened on approaches with lower speed limits. This study demonstrates that this combined methodology is a promising tool for gaining new insights into the crash risks of road user groups, and is transferrable to other road users.

Building indicators in media assistance; four ways evaluation reports present “change”

The international aid and development community has supported programs that aim to build the capacity of media professionals or contribute to an enabling environment throughout the past 20 years. However, two decades on from the first modern media assistance programs, the sector is still struggling to identify, measure and understand the changes effected by their programs. There are questions raised as to whether it is even feasible to identify impacts on society and governance. This paper draws on some preliminary findings from a comparative thematic analysis of 47 evaluation documents of media assistance programs. The aim of this analysis is to identify trends in impact evaluation practice in the media assistance field, as well as the strengths and weaknesses of different evaluation approaches. This paper presents four types of social change claims commonly presented in reports; hypothetical changes, introduction of new opportunities, concrete examples of immediate impacts, and analysis of ongoing social and political changes. Although these types may appear as a spectrum from weak to strong, the interactions are perhaps more accurately understood using metaphors such as building blocks. This paper explores these types in more detail and suggests that a robust set of impacts-types could be useful in developing more grounded theories of change and indicators.

How accurate is the identification of serious traffic injuries by Police? The concordance between Police and hospital reported traffic injuries

Police reported crash data are the primary source of crash information in most jurisdictions. However, the definition of serious injury within police-reported data is not consistent across jurisdictions and may not be accurate. With the Australian National Road Safety Strategy targeting the reduction of serious injuries, there is a greater need to assess the accuracy of the methods used to identify these injuries. A possible source of more accurate information relating to injury severity is hospital data. While other studies have compared police and hospital data to highlight the under-reporting in police-reported data, little attention has been given to the accuracy of the methods used by police to identify serious injuries. The current study aimed to assess how accurate the identification of serious injuries is in police-reported crash data, by comparing the profiles of transport-related injuries in the Queensland Road Crash Database with an aligned sample of data from the Queensland Hospital Admitted Patients Data Collection. Results showed that, while a similar number of traffic injuries were recorded in both data sets, the profile of these injuries was different based on gender, age, location, and road user. The results suggest that the ‘hospitalisation’ severity category used by police may not reflect true hospitalisations in all cases. Further, it highlights the wide variety of severity levels within hospitalised cases that are not captured by the current police-reported definitions. While a data linkage study is required to confirm these results, they highlight that a reliance on police-reported serious traffic injury data alone could result in inaccurate estimates of the impact and cost of crashes and lead to a misallocation of valuable resources.

Phytophotodermatitis : case reports of children presenting with blistering after preparing lime juice

Letter to the Editor We read with interest the case report entitled ‘‘Contact with fig tree sap: An unusual cause of burn injury’’ by Mandalia et al. [1] and would like to report our similar experience with phytophotodermatitis caused by lime juice. Phototoxic dermatitis is understandably easily confused with a burn, particularly when a patient presents with large blisters of unknown mechanism. At the Royal Children’s Hospital Burns Centre, this injury was treated in the same manner as a burn and is described here...

Impact of cognitive behaviour therapy via mail for cessation of benzodiazepine use : a series of case reports

Benzodiazepines are widely prescribed to manage sleep disorders, anxiety and muscular tension. While providing short-term relief, continued use induces tolerance and withdrawal, and in older users, increases the risk of falls. However, long-term prescription remains common, and effective interventions are not widely available. This study developed a self-managed cognitive behaviour therapy package for cessation of benzodiazepine use delivered to participants via mail (M-CBT) and trialled its effectiveness as an adjunct to a general practitioner (GP)-managed dose reduction schedule. In the pilot trial, participants were randomly assigned to GP management with immediate or delayed M-CBT. Significant recruitment and engagement problems were experienced, and only three participants were allocated to each condition. After immediate M-CBT, two participants ceased use, while none receiving delayed treatment reduced daily intake by more than 50%. Across the sample, doses at 12 months remained significantly lower than baseline, and qualitative feedback from participants was positive. While M-CBT may have promise, improved engagement of GPs and participants is needed for this approach to substantially impact on community-wide benzodiazepine use.

An evaluation of medication review reports across different settings

Background There is a growing body of evidence which supports that a pharmacist conducted medication review increases the health outcomes for patients. A pharmacist integrated into a primary care medical centre may offer many potential advantages in conducting medication reviews in this setting however research describing this is presently limited. Objective To compare medication review reports conducted by pharmacists practicing externally to a medical centre to those medication review reports conducted by an integrated practice pharmacist. The secondary objective was to compare medication review reports conducted by pharmacists in the patient’s home to those conducted in the medical centre. Setting A primary care medical centre, Brisbane, Australia Method A retrospective analysis of pharmacist conducted medication reviews prior to and after the integration of a pharmacist into a medical centre. Main outcome measures Types of drug related problems identified by the Pharma cists, recommended intervention for drug related problems made by the pharmacist, and the extent of implementation of pharmacist recommendations by the general practitioner. Results The primary drug related problem reported in the practice pharmacist phase was Additional therapy required as compared to Precautions in the external pharmacist phase. The practice pharmacist most frequently recommended to add drug with Additional monitoring recommended most often in the external pharmacists. During the practice pharmacist phase 71 % of recommendations were implemented and was significantly higher than the external pharmacist phase with 53 % of recommendations implemented (p\0.0001). Two of the 23 drug related problem domains differed significantly when comparing medication reviews conducted in the patient’s home to those conducted in the medical centre.

Breaking the code of silence : the Wood Royal Commission into New South Wales police : a brief overview

The three-volume Final Report of the Wood inquiry into NSW Police (Royal Commission Into the New South Wales Police Service, 'Final Report, Vol I: Corruption; Vol II: Reform; Vol III: Appendices', May 1997) was publicly released on 15 May 1997, to much media fanfare. The Sydney Morning Herald (SMH) devoted an 8-page special report on I May to the pending release of the Inquiry Report, headed The Police Purge. On the day of the public release of the Report, the SMH five-page 'Special Report' under the banner The Police Verdict was headlined Wood, Carr Split on Drugs. The Australian led with Call for Drug Law Revamp, Force Overhaul to Fight Corruption, Wood Attacks Culture of Greed, and the Daily Telegraph front page 'Final Verdict' was True Blue Strategy for an Honest Police Force...

Levels of social trust among men from refugee backgrounds after the 2011 Queensland floods

Purpose This paper examines the relationship between flood exposure and levels of social trust among a cohort of adult men from refugee backgrounds who were affected by the 2011 Queensland floods in Australia. Design/methodology/approach A quantitative questionnaire was administered to 141 men from refugee backgrounds almost two years after the 2011 Queensland floods. The survey was administered in person by trained peer in-terviewers, and included a number of standardised instruments assessing respondents’ so-cio-demographic characteristics, levels of social trust towards and from neighbours, the police, the wider Australian community, and the media, and exposure to and impact of the floods. Multiple logistic regression analyses were used to assess the relationship between flood exposure and social trust adjusting for pre-disaster levels of trust and other potentially confounding variables. Findings Participants with higher levels of flood exposure were significantly more likely to report greater levels of trust both towards and from their neighbours, the wider Australian community, and the media, and they were also more likely to believe that most people can be trusted. Research limitations/implications Although the study reports on data collected two years after the floods, the analysis has adjusted for pre-disaster measures of social trust and other socio-demographic variables. Originality/value Our paper has highlighted the important place of social trust and social capital for refugee communities in a post-disaster setting. Disaster responses that support social capital among marginalised populations are critical to increasing community resilience and supporting recovery.

The impact of OCR accuracy on automated cancer classification of pathology reports

Objective To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Method Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. Results The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. Conclusions The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

Classification of pathology reports for cancer registry notifications

Objective: To develop a system for the automatic classification of pathology reports for Cancer Registry notifications. Method: A two pass approach is proposed to classify whether pathology reports are cancer notifiable or not. The first pass queries pathology HL7 messages for known report types that are received by the Queensland Cancer Registry (QCR), while the second pass aims to analyse the free text reports and identify those that are cancer notifiable. Cancer Registry business rules, natural language processing and symbolic reasoning using the SNOMED CT ontology were adopted in the system. Results: The system was developed on a corpus of 500 histology and cytology reports (with 47% notifiable reports) and evaluated on an independent set of 479 reports (with 52% notifiable reports). Results show that the system can reliably classify cancer notifiable reports with a sensitivity, specificity, and positive predicted value (PPV) of 0.99, 0.95, and 0.95, respectively for the development set, and 0.98, 0.96, and 0.96 for the evaluation set. High sensitivity can be achieved at a slight expense in specificity and PPV. Conclusion: The system demonstrates how medical free-text processing enables the classification of cancer notifiable pathology reports with high reliability for potential use by Cancer Registries and pathology laboratories.

Clinician-driven automated classification of limb fractures from free-text radiology reports

The aim of this research is to report initial experimental results and evaluation of a clinician-driven automated method that can address the issue of misdiagnosis from unstructured radiology reports. Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to disperse information resources and vast amounts of manual processing of unstructured information, a point-of-care accurate diagnosis is often difficult. A rule-based method that considers the occurrence of clinician specified keywords related to radiological findings was developed to identify limb abnormalities, such as fractures. A dataset containing 99 narrative reports of radiological findings was sourced from a tertiary hospital. The rule-based method achieved an F-measure of 0.80 and an accuracy of 0.80. While our method achieves promising performance, a number of avenues for improvement were identified using advanced natural language processing (NLP) techniques.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

ShARe/CLEF eHealth Evaluation Lab 2013, Task 3: Information retrieval to address patients' questions when reading clinical reports

This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2013. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the effect of using additional information such as the discharge summaries and external resources such as medical ontologies on the IR effectiveness. The participants were allowed to submit up to seven runs, one mandatory run using no additional information or external resources, and three each using or not using discharge summaries.