Bicycle injuries carry a considerable burden to the ED and the incidence of presentations appears to be rising. The current triage data, designed to provide a rapid assessment for medical urgency, are limited to describing broad demographics, trends and causes.
A challenge in utilising health sector injury data for Product Safety purposes is that clinically coded data have limited ability to inform regulators about product involvement in injury events, given data entry is bound by a predefined set of codes. Text narratives collected in emergency departments can potentially address this limitation by providing relevant product information with additional accompanying context. This study aims to identify and quantify consumer product involvement in paediatric injuries recorded in emergency department-based injury surveillance data. A total of 7743 paediatric injuries were randomly selected from Queensland Injury Surveillance Unit database and associated text narratives were manually reviewed to determine product involvement in the injury event. A Product Involvement Factor classification system was used to categorise these injury cases. Overall, 44% of all reviewed cases were associated with consumer products, with proximity factor (25%) being identified as the most common involvement of a product in an injury event. Only 6% were established as being directly due to the product. The study highlights the importance of utilising injury data to inform product safety initiatives where text narratives can be used to identify the type and involvement of products in injury cases.
Background Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data. Objective This paper describes the evaluation of a machine learning-based Decision Support Tool (DST) to assist injury surveillance departments in the validation, coding and use of their data, comparing outcomes in coding time and accuracy pre- and post-implementation. Methods Manually coded injury surveillance data has been used to develop, train and iteratively refine a machine learning-based classifier to enable semi-automated coding of injury narrative data. This paper describes a trial implementation of the machine learning-based DST in the Queensland Injury Surveillance Unit (QISU) workflow using a major pediatric hospital's emergency department data comparing outcomes in coding time and accuracy pre- and post-implementation. Results The study found a 10% reduction in manual coding time after the DST was introduced. The Kappa statistics analysis in both DST-assisted and unassisted data shows increases in accuracy across three data fields; injury intent (85.4% unassisted vs. 94.5% assisted), external cause (88.8% unassisted vs. 91.8% assisted) and injury factor (89.3% unassisted vs. 92.9% assisted). The classifier was also used to produce a timely report monitoring injury patterns during the COVID-19 pandemic. Hence, it has the potential for near real-time surveillance of emerging hazards to inform public health responses. Conclusions The integration of the DST into the injury surveillance workflow shows benefits as it facilitates timely reporting and acts as a DST in the manual coding process.
Mandatory standard regulation is used within Australia to ensure the safety of consumer products, preventing product-related injury. Standard regulation is particularly important for products designed for use by children, who are highly vulnerable to sustaining product-related injuries due to their small size and inability to identify product hazards. This project aims to investigate how effectively information regarding product-related injuries is able to be captured within Australian health and coronial data. Further, it aims to investigate the extent to which child injury occurs for products for which mandatory safety standards exist through the review of available data. This study highlights significant limitations in injury surveillance data for identification and monitoring of child product-related injuries. This in turn limits the evidence base to assess the efficacy of existing regulations. Available data show baby walkers, cots, prams, nightwear, and bunk beds to be associated with a considerable number of child hospital presentations, admissions, and deaths. A significant scope for improvement in current product injury recording practices in the health sector exists.
Objective: To provide an epidemiological understanding of the types of injuries treated in ED in Australian children, describe the impact of these injuries in volume and severity, and assess the patterns by demographic and temporal factors. Methods: ED data from six major paediatric hospitals in four Australian states over the period 2011-2017 were analysed to identify childhood injury patterns by nature of injury and body region, as well as sex, age group and temporal factors. Results: A total of 486 762 ED presentations for injury in children aged 0-14 years were analysed. The most common injuries for all age groups were fractures of the upper extremities. Leading injury diagnosis groups varied by age groups and sex. Overall, children aged 1-2 years had the highest number of ED presentations for injury, and from birth more males than females presented to ED with injuries with the highest absolute sex difference observed for 10to 14-year-olds. Seventeen percent of children who presented to ED were admitted to hospital with the leading type of hospitalised injury being fractures. Little monthly variation in ED presentations was observed, except for higher presentations for drowning in summer months, and for most injury types, ED presentations were higher during weekends and daytime. Conclusions: This is the first largescale quantification of paediatric injury-related ED presentation patterns in Australia since the conclusion of the National Injury Surveillance and Prevention Program about 30 years ago. It provides valuable information to inform paediatric ED resourcing decisions as well as important evidence for injury prevention practitioners.
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