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Objectives: To examine individual, medication, system, and healthcare related predictors of hospitalization and emergency department (ED) presentation within 90 days of entering the aged care sector, and to create risk‐profiles associated with these outcomes. Design and setting: Retrospective population‐based cohort study using data from the Registry of Senior Australians. Participants: Older people (aged 65 and older) with an aged care eligibility assessment in South Australia between January 1, 2013 and May 31, 2016 (N = 22,130). Measurements Primary outcomes were unplanned hospitalization and ED presentation within 90 days of assessment. Individual, medication, system, and healthcare related predictors of the outcomes at the time of assessment, within 90 days or 1‐year prior. Fine–Gray models were used to calculate subdistribution hazard ratios (sHR) and 95% confidence intervals (CI). Harrell's C‐index assessed predictive ability. Results: Four thousand nine‐hundred and six (22.2%) individuals were hospitalized and 5028 (22.7%) had an ED presentation within 90 days. Predictors of hospitalization included: being a man (hospitalization sHR = 1.33, 95% CI 1.26–1.42), ≥3 urgent after‐hours attendances (hospitalization sHR = 1.21, 95% CI 1.06–1.39), increasing frailty index score (hospitalization sHR = 1.19, 95% CI 1.11–1.28), individuals using glucocorticoids (hospitalization sHR = 1.11, 95% CI 1.02–1.20), sulfonamides (hospitalization sHR = 1.18, 95% CI 1.10–1.27), trimethoprim antibiotics (hospitalization sHR = 1.15, 95% CI 1.03–1.29), unplanned hospitalizations 30 days prior (hospitalization sHR = 1.13, 95% CI 1.04–1.23), and ED presentations 1 year prior (hospitalization sHR = 1.07, 95% CI 1.04–1.10). Similar predictors and hazard estimates were also observed for ED presentations. The hospitalization models out‐of‐sample predictive ability (C‐index = 0.653, 95% CI 0.635–0.670) and ED presentations (C‐index = 0.647, 95% CI 0.630–0.663) were moderate. Conclusions: One in five individuals with aged care eligibility assessments had unplanned hospitalizations and/or ED presentation within 90 days with several predictors identified at the time of aged care eligibility assessment. This is an actionable period for targeting at‐risk individuals to reduce hospitalizations. [ABSTRACT FROM AUTHOR]

Aim: To estimate the non-medical out-of-pocket costs for families with a child in hospital.Methods: This study was a survey of 225 parents of paediatric inpatients on nine wards of an Australian public paediatric teaching hospital on two separate days. Our primary outcomes were the costs associated with: (i) time taken off work to care for the child in hospital; (ii) time off work or contributed by family and friends to care for other dependents; and (iii) travel, meals, accommodation and incidental expenses during the child's stay. Demographic data included postcode (to assess distance, socio-economic status and remoteness), child's age, ward and whether this was their child's first admission.Results: Mean patient age was 6.5 years (standard deviation 5.2). On an average per patient day basis, parents took 1.12 days off work and spent 0.61 (standard deviation 0.53) nights away from home, with 83.8% of nights away at the child's bedside. Parents spent Australian dollars (AUD)89 per day on travel and AUD36 on meals and accommodation. Total costs (including productivity costs) were AUD589 per patient day. Higher costs per patient day were correlated with living in a more remote area (0.48) and a greater travel distance to the hospital (0.41). A higher number of days off work was correlated (0.69) with number of school days missed.Conclusion: These results demonstrate the considerable time and financial resources expended by families caring for a child in hospital and are important inputs in evaluating health-care interventions that affect risk of hospitalisation and length of stay in paediatric care. [ABSTRACT FROM AUTHOR]

Objective: To examine frailty prevalence in Australian older adults. Methods: Frailty was measured using a modified Fried Frailty Phenotype (FFP) in a combined cohort of 8804 Australian adults aged ≥65 years (female 86%, median age 80 (79-82) years) from the Dynamic Analyses to Optimise Ageing Project and the North West Adelaide Health Study. Results: Using the FFP, 21% of participants were frail while a further 48% were prefrail. Chi-squared testing of frailty among four age groups (65-69, 70-74, 75-79 and 80-84 years) for sex, and marital status revealed that frailty was significantly higher for women (approximately double that of men), increased significantly with advancing age for both sexes, and was significantly higher for women who were widowed, divorced or never married. Conclusion: If frailty could be prevented or reversed, it would have an impact on a larger number of older people. [ABSTRACT FROM AUTHOR]