Background In Queensland alone, three children per week are involved in low speed vehicle run- overs. Accurately identifying LSVROs is time-consuming and resource intensive due to lack of consistent definition and inconsistent coding of events. Even if accurate case ascertainment can be achieved, detailed information required to inform prevention measures (i.e., risk factors) is not consistently recorded in current forensic & health record systems.

Aims The purpose of this collaboration is to address the shortcomings of the current systems by developing a reliable and sustainable prospective system of surveillance of LSVROs to improve case and risk factor identification, and inform prevention strategies.

Methods The collaborative comprises all of the key stakeholders including multiple primary service delivery organisations, data custodians, government organisations/policy advisory groups, and academic institutions. Representatives on the collaborative are responsible for identifying and effecting changes in their organisations/agencies through mechanisms such as modified data collection proformas, data collection practices and data elements.

Results/Outcomes Analyses of data collected from the prospective system will inform the development of a range of injury prevention interventions to reduce LSVRO incidents. The capacity and utility of the prospective data collection system will be evaluated by comparing data quality and capture with existing, routinely collected data. An ongoing operational review of the implementation of the prospective data collection system will also occur within the collaboration.

Significance/Contribution to the field: This is the first time that such a large Queensland based collaborative group has formally combined to improve and evaluate prospective injury data collection on a specific injury topic. This dataset will be the most comprehensive dataset to date on LSVROs, and will directly influence and guide policy and decision makers to implement strategies to reduce LSVROs in Queensland. This model can be implemented in similar data systems nationally, and may be applied to additional injury mechanisms.