How representative is the Victorian Emergency Minimum Dataset (VEMD) for population-based injury surveillance in Victoria? A retrospective observational study of administrative healthcare data.

OBJECTIVE: The Victorian Emergency Minimum Dataset (VEMD) is a key data resource for injury surveillance. The VEMD collects emergency department data from 39 public hospitals across Victoria; however, rural emergency care services are not well captured. The aim of this study is to determine the representativeness of the VEMD for injury surveillance. DESIGN: A retrospective observational study of administrative healthcare data. SETTING AND PARTICIPANTS: Injury admissions in 2014/2015-2018/2019 were extracted from the Victorian Admitted Episodes Dataset (VAED) which captures all Victorian hospital admissions; only cases that arrived through a hospital's emergency department (ED) were included. Each admission was categorised as taking place in a VEMD-contributing versus a non-VEMD hospital. RESULTS: There were 535 477 incident injury admissions in the study period, of which 517 207 (96.6%) were admitted to a VEMD contributing hospital. Male gender (OR 1.13 (95% CI 1.10 to 1.17)) and young age (age 0-14 vs 45-54 years, OR 4.68 (95% CI 3.52 to 6.21)) were associated with VEMD participating (vs non-VEMD-participating) hospitals. Residing in regional/rural areas was negatively associated with VEMD participating (vs non-VEMD participating) hospitals (OR=0.11 (95% CI 0.10 to 0.11)). Intentional injury (assault and self-harm) was also associated with VEMD participation. CONCLUSIONS: VEMD representativeness is largely consistent across the whole of Victoria, but varies vastly by region, with substantial under-representation of some areas of Victoria. By comparison, for injury surveillance, regional rates are more reliable when based on the VAED. For local ED-presentation rates, the bias analysis results can be used to create weights, as a temporary solution until rural emergency services injury data is systematically collected and included in state-wide injury surveillance databases.

Suicide following hospital admission for mental health conditions, physical illness, injury and intentional self-harm in Victoria, Australia.

OBJECTIVE: The majority of suicide decedents have had contact with health services close to their death. Some of these contacts include admissions to hospitals for physical and mental health conditions, injury and intentional self-harm. This study aims to establish and quantify the risks of suicide following hospital admission for a range of mental and physical illnesses. METHODS: A retrospective analysis was carried out on existing morbidity and mortality data in Victoria. Data was extracted from the Victorian Admitted Episodes Dataset and the Victorian Suicide Register. Unplanned hospital admissions among adult patients (> = 15 years of age), discharged between 01 January 2011 and 31 December 2016 (2,430,154 admissions), were selected. Standardised Mortality Ratios were calculated for conditions with at least five linked suicides within one year of discharge from hospital. RESULTS: Forty-three conditions defined at the three-digit level of the International Statistical Classification of Diseases and Related Health Problems 10th Revision, were associated with at least five subsequent suicides (within one year of hospital discharge); 14 physical illnesses, 5 symptoms, signs and abnormal clinical and laboratory findings, 12 mental health conditions, and 12 types of injury and poisonings. The highest Standardised Mortality Ratios were for poisonings (range; 27.8 to 140.0) and intentional self-harm (78.8), followed by mental health conditions (range; 15.5 to 72.9), symptoms, signs and abnormal clinical and laboratory findings (range; 1.4 to 43.2) and physical illnesses (range; 0.7 to 4.9). CONCLUSIONS: Hospital admissions related to mental health conditions and injury and poisonings including self-harm were associated with a greater risk of suicide than physical conditions. Mental health conditions such as depressive episodes, personality disorders and psychotic episodes, injuries caused by intentional-self-harm and poisonings by certain types of drugs, carbon monoxide and hormones such as insulin can be prioritised for targeting suicide prevention initiatives for persons discharged from hospitals.

Trends in Victorian burn injuries 2008-2017.

OBJECTIVES: To describe incidence and characteristics of hospital presentations and deaths due to burn injury in the Australian state of Victoria from 2008 to 2017 and identify trends in incidence and patterns. METHODS: Three population-based datasets were used to ascertain burn-related hospital admissions, emergency department presentations, and deaths. These were the Victorian Admitted Episodes Dataset (VAED), Victorian Emergency Minimum Dataset (VEMD), and the Cause of Death-Unit Record File (COD-URF), respectively. Descriptive statistics on demographics (age and gender), burn injury characteristics (intent, cause, burn size and body region) and hospital burden (length of stay (LOS) and costs) were used to present the profile of patients. Incidence rates by age, gender and intent were calculated. Trend analysis on incidence was carried out using forced Poisson Regression models with the natural logarithm of the annual populations as an offset. Incident rate ratios were used to interpret the models. Risk ratios were used to compare the risk differences between population sub-groups. A negative binomial model was used to test the association between LOS and age and the total body surface area (TBSA) of the burn. RESULTS: Overall males had higher rates of death, admission and ED presentation. For adults, the elderly had the highest rates of deaths and admissions while for children, the very young had highest rates for admissions and presentations. Exposure to smoke, fire and flames was the most common cause of deaths, and contact with heat and hot substances was most common among ED presentations. The elderly and those with Total Body Surface Area (TBSA) burn ≥20% had a higher risk of longer hospital stay. Rates of severe burns and deaths from burns remained stable during the study period in the setting of an annual 2% increase in population. Paediatric hospital admission rates decreased over time. CONCLUSION: The risk of sustaining burn injury, the types of burn and outcomes, varied by age and gender. We found evidence of a limited decrease in burn injury rates in some sub-groups: appropriate and effective targeted prevention strategies for burns are needed to avoid the significant short and long-term suffering experienced.

Australian Injury Comorbidity Indices (AICIs) to predict burden and readmission among hospital-admitted injury patients.

BACKGROUND: Existing comorbidity measures predict mortality among general patient populations. Due to the lack of outcome specific and patient-group specific measures, the existing indices are also applied to non-mortality outcomes in injury epidemiology. This study derived indices to capture the association between comorbidity, and burden and readmission outcomes for injury populations. METHODS: Injury-related hospital admissions data from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia were analyzed. Various multivariable regression models were run and results used to derive both binary and weighted indices that quantify the association between comorbidities and length of stay (LOS), hospital costs and readmissions. The new and existing indices were validated internally among patient subgroups, and externally using data from the states of New South Wales and Western Australia. RESULTS: Twenty-four comorbidities were significantly associated with overnight stay, twenty-seven with LOS, twenty-eight with costs, ten with all-cause and eleven with non-planned 30-day readmissions. The number of and types of comorbidities, and their relative impact were different to the associations established with the existing Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Measure (ECM). The new indices performed equally well to the long-listed ECM and in certain instances outperformed the CCI. CONCLUSIONS: The more parsimonious, up to date, outcome and patient-specific indices presented in this study are better suited for use in present injury epidemiology. Their use can be trialed by hospital administrations in resource allocation models and patient classification models in clinical settings.

The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study.

BACKGROUND: Hospital-admitted patients are at risk of experiencing certain adverse outcomes during their hospital-stay. Patients may need to be admitted to the intensive care unit or be placed on the ventilator while there is also a possibility for complications to develop. Pre-existing comorbidity could increase the risk of these outcomes. The Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Measure (ECM), originally derived for mortality outcomes among general medical populations, are widely used for assessing these in-hospital complications even among specific injury populations. This study derived indices to specifically capture the effect of comorbidity on intensive care unit and ventilator use as well as hospital-acquired complications for injury patients. METHODS: Retrospective data on injury hospital-admissions from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia was analysed. Results from multivariable regression analysis were used to derive the Australian Injury Comorbidity Indices (AICIs) for intensive care unit and ventilator hours and hospital-acquired complications. The AICIs, CCI and ECM were validated on data from Victoria and two other Australian states. RESULTS: Five comorbidities were significantly associated with intensive care unit hours, two with ventilator hours and fifteen with hospital-acquired complications for hospitalised injury patients. Not all diseases listed in the CCI or ECM were found to be associated with these outcomes. The AICIs performed equally well in terms of predictive ability to the long-listed ECM and in most instances outperformed the CCI. CONCLUSIONS: Associations between outcomes and comorbidities vary based on the type of outcome measure. The new comorbidity indices developed in this study provide a relevant, parsimonious and up-to-date method to capture the effect of comorbidity on in-hospital complications among admitted injury patients and is better suited for use in that context compared to the CCI and ECM.

Complications, burden and in-hospital death among hospital treated injury patients in Victoria, Australia: a data linkage study.

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.