Assessing sports injury rates corrected for participation rates and evaluating the relative severity of injuries is important for monitoring safety. Our findings can assist decisions about which sports should be the focus of injury prevention efforts.

Background A wide range of outcome measures can be calculated for hospital-treated injury patients. These include mortality, use of critical care services, complications, length of stay, treatment costs, readmission and nursing care after discharge. Each address different aspects and phases of injury recovery and can yield vastly different results. This study aims to: (1) measure and report this range of outcomes in hospital-treated injury patients in a defined population; and (2) describe the associations between injury characteristics, socio-demographics and comorbidities and the various outcomes. Methods A retrospective analysis was conducted of injury-related hospital admissions from July 2012 to June 2014 (152,835 patients) in Victoria, Australia. The admission records were linked within the dataset, enabling follow-up, to assess the outcomes of in-hospital death, burden, complications and 30-day readmissions. Associations between factors and outcomes were determined using univariate regression analysis. Results The proportion of patients who died in hospital was 0.9%, while 26.8% needed post-discharge care. On average patients had 2.4 complications (confidence interval (CI) 2.4–2.5) related to their initial injury, the mean cost of treating a patient was Australian dollars 7013 (CI 6929–7096) and the median length of stay was one day (inter quartile range 1–3). Intensive-care-unit-stay was recorded in 3% of the patients. All-cause 30-day readmissions occurred in 12.3%, non-planned 30-day readmissions in 7.9%, while potentially avoidable 30-day readmissions were observed in 3.2% of the patients. Increasing age was associated with all outcomes. The need for care post-discharge from hospital was highest among children and the oldest age group (85 years and over). Injury severity was associated with all adverse outcomes. Increasing number of comorbidities increased the likelihood of all outcomes. Overall, outcomes are shown to differ by age, gender, comorbidities, body region injured, injury type and injury severity, and to a lesser extent by socio-economic areas. Conclusions Outcomes and risk factors differ depending on the outcome measured, and the method used for measuring the outcome. Similar outcomes measured in different ways produces varying results. Data linkage has provided a valuable platform for a comprehensive overview of outcomes, which can help design and target secondary and tertiary preventive measures. Electronic supplementary material The online version of this article (10.1186/s12889-019-7080-y) contains supplementary material, which is available to authorized users.

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Study objective: Existing comorbidity indices such as the Charlson comorbidity index are dated yet still widely used. This study derives and validates up-to-date comorbidity indices for hospital-admitted injury patients, specific to mortality outcomes. Methods: Injury-related hospital admissions data for 2 cohorts of patients in the Australian state of Victoria were linked to mortality data: July 2012 to June 2014 (161,334 patients) and July 2006 to June 2015 (614,762 patients). Logistic regression models were fitted, and results were used to derive binary and weighted comorbidity indices to predict mortality outcomes. The indices were validated with data from New South Wales (Australia). Results: There were 11 comorbidity groups identified as associated with inhospital death (cohort 1), 13 with 30-day mortality, and 19 with 1-year mortality (cohort 2). The newly derived weights for comorbidities were very different from the Charlson comorbidity index weights for some conditions. The area under the curve statistics for inhospital death, 30-day mortality, and 1-year mortality were similar for the newly derived binary comorbidity indices (0.920, 0.923, and 0.910, respectively), the Charlson comorbidity index (0.915, 0.919, and 0.906, respectively), and the Elixhauser comorbidity measure (0.924, 0.923, and 0.908, respectively). The false-negative rates for the new binary indices (15.8%, 15.8%, and 16.3%, respectively) were statistically equal to those of the Charlson comorbidity index (17.4%, 16.3%, and 16.5%, respectively) and the Elixhauser comorbidity measure (15.2%, 14.8%, and 16.3%, respectively). Conclusion: The newly derived Australian Injury Comorbidity Indices, which are a binary representation of individual conditions associated with the outcome of interest, are useful in quantifying the effect of comorbidity among injury patients. They include a shorter list of conditions than existing indices such as the Charlson comorbidity index and Elixhauser comorbidity measure, are up to date, and consider the individual association of each condition over a summed score such as the Charlson comorbidity index. Indices that quantify the effect of comorbidities should consider the population, disease prevalence, and outcome of interest and require periodic updating. [