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Objective: Non‐fatal injuries sustained from animal–vehicle collisions are a globally under‐recognised road safety issue, with limited data on these crash types. The present study aimed to quantify the number and causes of major trauma events resulting from animal–vehicle collisions. Methods: The study was a retrospective analysis of major trauma cases occurring in Victoria, Australia, between 2007 and 2016, using data from the population‐based Victorian State Trauma Registry. To identify animal–vehicle collisions, Victorian State Trauma Registry injury codes were combined with text‐mining of the text description of the injury event. Results: Over the 10 year period, there were 152 major trauma patients who were admitted to Victorian trauma‐receiving hospitals due to vehicle collisions with animals. The crude population‐based incidence rate for animal–vehicle collisions increased by 6.7% per year (incidence rate ratio 1.07; 95% confidence interval 1.01–1.13; P = 0.02). Conclusion: Development of systematic recording methods of animal–vehicle collisions will improve reporting of these crash types to assist future studies in implementing effective countermeasures. [ABSTRACT FROM AUTHOR]

Background: This study examines spatial relationships between alcohol outlet density and the incidence of alcohol-related crashes. The few prior studies conducted in this area used relatively large spatial units; here we use highly resolved units from Melbourne, Australia (Statistical Area level 1 [SA1] units: mean land area=0.5 km(2); SD=2.2 km(2)), in order to assess different micro-scale spatial relationships for on- and off-premise outlets.Methods: Bayesian conditional autoregressive Poisson models were used to assess cross-sectional relationships of three-year counts of alcohol-related crashes (2010-2012) attended by Ambulance Victoria paramedics to densities of bars, restaurants, and off-premise outlets controlling for other land use, demographic and roadway characteristics.Results: Alcohol-related crashes were not related to bar density within local SA1 units, but were positively related to bar density in adjacent SA1 units. Alcohol-related crashes were negatively related to off-premise outlet density in local SA1 units.Conclusions: Examined in one metropolitan area using small spatial units, bar density is related to greater crash risk in surrounding areas. Observed negative relationships for off-premise outlets may be because the origins and destinations of alcohol-affected journeys are in distal locations relative to outlets. [ABSTRACT FROM AUTHOR]

Road trauma remains a significant public health problem. We aimed to identify sub-groups of motor vehicle collisions in Victoria, Australia, and the association between collision characteristics and outcomes up to 24 months post-injury. Data were extracted from the Victorian State Trauma Registry for injured drivers aged ≥16 years, from 2010 to 2016, with a compensation claim who survived ≥12 months post-injury. People with intentional or severe head injury were excluded, resulting in 2735 cases. Latent class analysis was used to identify collision classes for driver fault and blood alcohol concentration (BAC), day and time of collision, weather conditions, single vs. multi-vehicle and regional vs. metropolitan injury location. Five classes were identified: (1) daytime multi-vehicle collisions, no other at fault; (2) daytime single-vehicle predominantly weekday collisions; (3) evening single-vehicle collisions, no other at fault, 36% with BAC ≥ 0.05; (4) sunrise or sunset weekday collisions; and (5) dusk and evening multi-vehicle in metropolitan areas with BAC < 0.05. Mixed linear and logistic regression analyses examined associations between collision class and return to work, health (EQ-5D-3L summary score) and independent function Glasgow Outcome Scale - Extended at 6, 12 and 24 months. After adjusting for demographic, health and injury characteristics, collision class was not associated with outcomes. Rather, risk of poor outcomes was associated with age, sex and socioeconomic disadvantage, education, pre-injury health and injury severity. People at risk of poor recovery may be identified from factors available during the hospital admission and may benefit from clinical assessment and targeted referrals and treatments.