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Objectives: To explore the prevalence of frailty, association between frailty and mortality, and transitions between frailty states in urban- and regional-living First Nations Australians., Study Design: Secondary analysis of longitudinal data from the Koori Growing Old Well Study. First Nations Australians aged 60 years or more from five non-remote communities were recruited in 2010-2012 and followed up six years later (2016-2018). Data collected at both visits were used to derive a 38-item Frailty Index (FI). The FI (range 0-1.0) was classified as robust (<0.1), pre-frail (0.1- < 0.2), mildly (0.2- < 0.3), moderately (0.3- < 0.4) or severely frail (≥0.4)., Main Outcome Measures: Association between frailty and mortality, examined using logistic regression and transitions in frailty (the percentage of participants who changed frailty category) during follow-up., Results: At baseline, 313 of 336 participants (93 %) had sufficient data to calculate a FI. Median FI score was 0.26 (interquartile range 0.21-0.39); 4.79 % were robust, 20.1 % pre-frail, 31.6 % mildly frail, 23.0 % moderately frail and 20.5 % severely frail. Higher baseline frailty was associated with mortality among severely frail participants (adjusted odds ratio 7.11, 95 % confidence interval 2.51-20.09) but not moderately or mildly frail participants. Of the 153 participants with a FI at both baseline and follow-up, their median FI score increased from 0.26 to 0.28., Conclusions: Levels of frailty in this First Nations cohort are substantially higher than in similar-aged non-Indigenous populations. Screening for frailty before the age of 70 years may be warranted in First Nations Australians. Further research is urgently needed to determine the factors that are driving such high levels of frailty and propose solutions to prevent or manage frailty in this population., Competing Interests: Declaration of competing interest The authors declare that they have no competing interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

It is important to assess the impact a vaccination programme has on the burden of disease after it is implemented. For example, this may reveal herd immunity effects or vaccine-induced shifts in the incidence of disease or in circulating strains or serotypes of the pathogen. In this article we summarise the key features of infectious diseases that need to be considered when trying to detect any changes in the burden of diseases at a population level as a result of vaccination efforts. We outline the challenges of using routine surveillance databases to monitor infectious diseases, such as the identification of diseased cases and the availability of vaccination status for cases. We highlight the complexities in modelling the underlying patterns in infectious disease rates (e.g. presence of autocorrelation) and discuss the main statistical methods that can be used to control for periodicity (e.g. seasonality) and autocorrelation when assessing the impact of vaccination programmes on burden of disease (e.g. cosinor terms, generalised additive models, autoregressive processes and moving averages). For some analyses, there may be multiple methods that can be used, but it is important for authors to justify the method chosen and discuss any limitations. We present a case study review of the statistical methods used in the literature to assess the rotavirus vaccination programme impact in Australia. The methods used varied and included generalised linear models and descriptive statistics. Not all studies accounted for autocorrelation and seasonality, which can have a major influence on results. We recommend that future analyses consider the strength and weakness of alternative statistical methods and justify their choice., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: Recently, two simple clinical scores were published to predict survival in trauma patients. Both scores may successfully guide major trauma triage, but neither has been independently validated in a hospital setting., Methods: This is a cohort study with 30-day mortality as the primary outcome to validate two new trauma scores-Mechanism, Glasgow Coma Scale (GCS), Age, and Pressure (MGAP) score and GCS, Age and Pressure (GAP) score-using data from the UK Trauma Audit and Research Network. First, an assessment of discrimination, using the area under the receiver operating characteristic (ROC) curve, and calibration, comparing mortality rates with those originally published, were performed. Second, we calculated sensitivity, specificity, predictive values, and likelihood ratios for prognostic score performance. Third, we propose new cutoffs for the risk categories., Results: A total of 79,807 adult (≥16 years) major trauma patients (2000-2010) were included; 5,474 (6.9%) died. Mean (SD) age was 51.5 (22.4) years, median GCS score was 15 (interquartile range, 15-15), and median Injury Severity Score (ISS) was 9 (interquartile range, 9-16). More than 50% of the patients had a low-risk GAP or MGAP score (1% mortality). With regard to discrimination, areas under the ROC curve were 87.2% for GAP score (95% confidence interval, 86.7-87.7) and 86.8% for MGAP score (95% confidence interval, 86.2-87.3). With regard to calibration, 2,390 (3.3%), 1,900 (28.5%), and 1,184 (72.2%) patients died in the low, medium, and high GAP risk categories, respectively. In the low- and medium-risk groups, these were almost double the previously published rates. For MGAP, 1,861 (2.8%), 1,455 (15.2%), and 2,158 (58.6%) patients died in the low-, medium-, and high-risk categories, consonant with results originally published. Reclassifying score point cutoffs improved likelihood ratios, sensitivity and specificity, as well as areas under the ROC curve., Conclusion: We found both scores to be valid triage tools to stratify emergency department patients, according to their risk of death. MGAP calibrated better, but GAP slightly improved discrimination. The newly proposed cutoffs better differentiate risk classification and may therefore facilitate hospital resource allocation., Level of Evidence: Prognostic study, level II.

Introduction: Low systolic blood pressure (SBP) is an important secondary insult following traumatic brain injury (TBI), but its exact relationship with outcome is not well characterised. Although a SBP of <90 mmHg represents the threshold for hypotension in consensus TBI treatment guidelines, recent studies suggest redefining hypotension at higher levels. This study therefore aimed to fully characterise the association between admission SBP and mortality to further inform resuscitation endpoints., Methods: We conducted a multicentre cohort study using data from the largest European trauma registry. Consecutive adult patients with AIS head scores >2 admitted directly to specialist neuroscience centres between 2005 and July 2012 were studied. Multilevel logistic regression models were developed to examine the association between admission SBP and 30 day inpatient mortality. Models were adjusted for confounders including age, severity of injury, and to account for differential quality of hospital care., Results: 5057 patients were included in complete case analyses. Admission SBP demonstrated a smooth u-shaped association with outcome in a bivariate analysis, with increasing mortality at both lower and higher values, and no evidence of any threshold effect. Adjusting for confounding slightly attenuated the association between mortality and SBP at levels <120 mmHg, and abolished the relationship for higher SBP values. Case-mix adjusted odds of death were 1.5 times greater at <120 mmHg, doubled at <100 mmHg, tripled at <90 mmHg, and six times greater at SBP<70 mmHg, p<0.01., Conclusions: These findings indicate that TBI studies should model SBP as a continuous variable and may suggest that current TBI treatment guidelines, using a cut-off for hypotension at SBP<90 mmHg, should be reconsidered., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The planning and delivery of care systems require knowledge on the ways in which individuals access available services that are funded by a range of health and community services. The aims of this study were to identify distinct groups of Home and Community Care (HACC) clients in New South Wales, Australia, based on patterns of actual service use, and to understand the health and social needs and resources of client groups that access different mixes of services. Multiple data sets linked at the individual level - including the 45 and Up Study community survey, the HACC Minimum Data Set and the Admitted Patient Data Collection for hospitals - provide an innovative basis to investigate the complexity of access to service use. Data were collected between 2006 and 2008. A cluster analysis based on clients' type and volume of community service use was conducted on the 4890 HACC clients in the linked dataset and nine distinct clusters of clients were identified. Three of these clusters were considered 'complex', in terms of the range of community and hospital assistance received, while the others comprised mainly of one or two dominant service types. The analytical approach and findings developed here provide a client-centred approach to monitor and evaluate access to local service systems that are being reformed to better integrate the delivery of health and community services currently funded and managed separately by national and state governments., (© 2011 Blackwell Publishing Ltd.)