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Background: Type 1 Diabetes (T1D) is associated with increased risk of eating disorders. This study aimed to (1) assess adherence of Australasian paediatric T1D clinics to international guidelines on screening for disordered eating and (2) identify barriers and enablers to the use of screening tools for the identification of disordered eating., Methods: A 24-item survey covering five content domains: clinic characteristics, identification of disordered eating, screening tool use, training and competence, and pathways for referral, was sent to Australasian clinics caring for ≥150 children and adolescents with T1D., Results: Of 13 eligible clinics, 10 participated. Two reported rates of disordered eating of >20%, while eight reported rates < 5%. All clinics used the routine clinical interview as the primary method of screening for disordered eating. Only one used screening tools; these were not diabetes-specific or routinely used. Barriers to use of screening tools included shortage of time and lack of staff confidence around use ( n = 7, 70%). Enablers included staff training in disordered eating., Conclusions: Screening tools for disordered eating are not utilised by most Australasian paediatric T1D clinics. Overall, low reported rates of disordered eating suggest that it may be undetected, potentially missing an opportunity for early intervention.

Aims: To identify foods that cause problematic postprandial blood glucose levels (BGLs) in children and young people with type 1 diabetes, the strategies families use to manage these foods and the impact of continuous glucose monitoring (CGM) on nutritional management., Methods: This was a cross-sectional survey of 100 families attending a paediatric diabetes centre in Australia., Results: Participants (n = 100) had a mean age of 13.0 ± 3.6 years; diabetes duration 5.2 ± 4.0 years; HbA1c 53 ± 0.9 mmol/mol (7.0 ± 0.8%); 52% used multiple daily injections (MDI, ≥4 injections/day); 48% used insulin pump therapy; and overall, 60% used CGM. Ninety-one participants (91%) identified problematic foods, including pizza (60%), pasta (55%) and rice (31%). Of these, 96% used one or more strategies to manage BGLs, including correcting BGLs more often (51%), use of a combination bolus (39%) and increasing the meal insulin dose (32%). Participants who gave additional meal insulin (n = 28) increased the dose by 10% to 25%. All MDI users (n = 15) gave additional insulin pre-prandially. Of those using CGM, 88% (n = 53) reported an increased awareness of the glycaemic impact of foods, and 27% (n = 16) had subsequently made changes to their management including avoiding and/or restricting new foods (n = 7)., Conclusions: Families with type 1 diabetes reported foods such as pizza, pasta and rice as problematic and used strategies such as increasing the insulin dose to minimise their glycaemic impact. CGM contributed to the awareness of problematic foods. Clinicians should discuss these foods and, if challenging, provide targeted strategies including adjusting the insulin dose and delivery pattern to improve postprandial glycaemia., (© 2020 Dietitians Australia.)

Aim: To determine the insulin requirement for a high-fat, high-protein breakfast to optimise postprandial glycaemic excursions in children and young people with type 1 diabetes using insulin pumps., Methods: In all, 27 participants aged 10-23 years, BMI <95 th percentile (2-18 years) or BMI <30 kg/m 2 (19-25 years) and HbA 1c ≤64 mmol/mol (≤8.0%) consumed a high-fat, high-protein breakfast (carbohydrate: 30 g, fat: 40 g and protein: 50 g) for 4 days. In this cross-over trial, insulin was administered, based on the insulin-to-carbohydrate ratio (ICR) of 100% (control), 120%, 140% and 160%, in an order defined by a randomisation sequence and delivered in a combination bolus, 60% ¼ hr pre-meal and 40% over 3 hr. Postprandial sensor glucose was assessed for 6 hr., Results: Comparing 100% ICR, 140% ICR and 160% ICR resulted in significantly lower 6-hr areas under the glucose curves: mean (95%CI) (822 mmol/L.min [605,1039] and 567 [350,784] vs 1249 [1042,1457], p ≤ 0.001) and peak glucose excursions (4.0 mmol/L [3.0,4.9] and 2.7 [1.7,3.6] vs 6.0 [5.0,6.9],p < 0.001). Rates of hypoglycaemia for 100%-160% ICR were 7.7%, 7.7%, 12% and 19% respectively (p ≥ 0.139). With increasing insulin dose, a step-wise reduction in mean glucose excursion was observed from 1 to 6 hr (p = 0.008)., Conclusions: Incrementally increasing the insulin dose for a high-fat, high-protein breakfast resulted in a predictable, dose-dependent reduction in postprandial glycaemia: 140% ICR improved postprandial glycaemic excursions without a statistically significant increase in hypoglycaemia. These findings support a safe, practical method for insulin adjustment for high-fat, high-protein meals that can be readily implemented in practice to improve postprandial glycaemia., (© 2021 Diabetes UK.)

Aim: To determine the glycaemic impact of an increased insulin dose, split insulin dose and regular insulin for a high fat, high protein breakfast in people with type 1 diabetes using multiple daily injections (≥4/day)., Methods: In this cross-over trial, participants received the same high fat, high protein breakfast (carbohydrate:30 g, fat:40 g, protein:50 g) for 4 days. Four different insulin strategies were randomly allocated and tested; 100% of the insulin-to-carbohydrate ratio (ICR) given in a single dose using aspart insulin (100Asp), 125% ICR given in a single dose using aspart (125Asp) or regular insulin (125Reg) and 125% ICR given in a split dose using aspart insulin (100:25Asp). Insulin was given 0.25 hr pre-meal and for 100:25Asp, also 1 hr post-meal. Postprandial sensor glucose was measured for 5 hr., Results: In all, 24 children and adults were participated. The 5-hr incremental area under the curves for 100Asp, 125Asp, 125Reg and 100:25Asp were 620 mmol/L.min [95% CI: 451,788], 341 mmol/L.min [169,512], 675 mmol/L.min [504,847] and 434 mmol/L.min [259,608], respectively. The 5-hr incremental area under the curve for 125Asp was significantly lower than for 100Asp (p = 0.016) and for 125Reg (p = 0.002). There was one episode of hypoglycaemia in 125Reg., Conclusions: For a high fat, high protein breakfast, giving 125% ICR preprandially, using aspart insulin significantly improved postprandial glycaemia without hypoglycaemia. There was no additional glycaemic benefit from giving insulin in a split dose (100:25%) or replacing aspart with regular insulin., (© 2021 Diabetes UK.)

Objective: The integration of quality indicators into the accreditation process has been recognized as a promising strategy worldwide. This study was to explore the implementation patterns of hospital accreditation through the lens of a systems-theory based model, and determine an international accreditation implementation typology., Design: A qualitative comparative study of five established international hospital accreditation systems was undertaken based on a systems-theoretic holistic healthcare systems relationship model. A set of key attributes relevant to three systems-theoretic model relationships guided data collection, comparison and synthesis., Setting: Hospital accreditation systems in five countries: America, Canada, Australia, Taiwan and France., Results: An accreditation implementation typology was developed based on the data synthesis of the similarities and differences among the relationships. A typology including five implementation types of hospital accreditation systems (TYPE I-V) was induced. TYPE I is a basic stand-alone accreditation system. The higher types represent stronger relationships among accreditation system, healthcare organizations and quality measurement systems. The five settings have shifted their accreditation approaches from the basic type (TYPE I)., Conclusions: The implementation typology of hospital accreditation could serve as a roadmap for refining hospital accreditation systems toward an integrative approach for continuous quality improvement., (© The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations (HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20(th) centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on "poorer performing" HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20(th) centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO's size, and facilitates more realistic estimates of potential system gains.

Background: Root cause analysis (RCA) is often adopted to complement epidemiologic investigation for outbreaks and infection-related adverse events in hospitals; however, RCA has been argued to have limited effectiveness in preventing such events. We describe how an innovative systems analysis approach halted repeated scabies outbreaks, and highlight the importance of systems thinking for outbreaks analysis and sustaining effective infection prevention and control., Methods: Following RCA for a third successive outbreak of scabies over a 17-month period in a 60-bed respiratory care ward of a Taiwan hospital, a systems-oriented event analysis (SOEA) model was used to reanalyze the outbreak. Both approaches and the recommendations were compared., Results: No nosocomial scabies have been reported for more than 1975 days since implementation of the SOEA. Previous intervals between seeming eradication and repeat outbreaks following RCA were 270 days and 180 days. Achieving a sustainable positive resolution relied on applying systems thinking and the holistic analysis of the system, not merely looking for root causes of events., Conclusion: To improve the effectiveness of outbreaks analysis and infection control, an emphasis on systems thinking is critical, along with a practical approach to ensure its effective implementation. The SOEA model provides the necessary framework and is a viable complementary approach, or alternative, to RCA., (Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Objective: The aim of the study was to determine accreditation surveyors' and hospitals' use and perceived usefulness of clinical indicator reports and the potential to establish the control relationship between the accreditation and reporting systems. The control relationship refers to instructional directives, arising from appropriately designed methods and efforts towards using clinical indicators, which provide a directed moderating, balancing and best outcome for the connected systems., Design: Web-based questionnaire survey., Setting: Australian Council on Healthcare Standards' (ACHS) accreditation and clinical indicator programmes., Results: Seventy-three of 306 surveyors responded. Half used the reports always/most of the time. Five key messages were revealed: (i) report use was related to availability before on-site investigation; (ii) report use was associated with the use of non-ACHS reports; (iii) a clinical indicator set's perceived usefulness was associated with its reporting volume across hospitals; (iv) simpler measures and visual summaries in reports were rated the most useful; (v) reports were deemed to be suitable for the quality and safety objectives of the key groups of interested parties (hospitals' senior executive and management officers, clinicians, quality managers and surveyors)., Conclusions: Implementing the control relationship between the reporting and accreditation systems is a promising expectation. Redesigning processes to ensure reports are available in pre-survey packages and refined education of surveyors and hospitals on how to better utilize the reports will support the relationship. Additional studies on the systems' theory-based model of the accreditation and reporting system are warranted to establish the control relationship, building integrated system-wide relationships with sustainable and improved outcomes.

Background: Clinical indicators (CIs) are used to assess, compare and determine the potential to improve the care provided by hospitals and physicians. The results for Australian hospitals in 1998-2000 have been reported using a new methodology. The gamma-Poisson hierarchical model was used to correct for the effects of sampling variation by obtaining the empirical Bayesian shrunken estimates for the CI proportions for each hospital. Then, an estimate of the potential system gains that could be achieved if the mean proportion was shifted to the 20th centile is obtained for each of the 185 CIs. The results are sed to prioritize quality improvement activity., Objectives: To describe the 20th centile method of calculating potential system gains in the health care system; to determine the impact of using the beta-binomial model rather than the gamma-Poisson model to obtain shrunken estimates for the CI proportions; and to compare the computationally simpler Method of Moments (MoM) with the maximum likelihood (ML) method for parameter estimation., Methods: The formulae for the gamma-Poisson and beta-binomial shrinkage estimators were compared analytically. Each of the shrinkage estimators and the two methods of parameter estimation were applied to the Obstetric and Gynecological CIs, and the results compared. RESULTS The comparison of the formulae for the two shrinkage estimators showed that the gamma-Poisson model results in: greater shrinkage towards the overall mean. This was verified empirically using the clinical indicators. Additionally, the MoM was not a viable alternative to the ML method., Conclusions: The gamma-Poisson model provided smaller estimates of the potential system gains by up to 6.7% of the numerator for the clinical indicators. The difference in estimation increased with increasing mean proportions and between-hospital variation. We recommend that the beta-binomial model should be used on the basis of both theoretical and empirical grounds.