Aims: To determine if the frequency of severe diabetic ketoacidosis at presentation of new‐onset type 1 diabetes to an Australian tertiary centre increased during the initial period of restrictions resulting from the COVID‐19 pandemic (March to May 2020).Methods: Data were collected on presentations of newly diagnosed type 1 diabetes as well as on all paediatric presentations to the emergency department of a tertiary centre between 2015 and 2020. Data from the period of initial COVID restrictions in Australia (March to May 2020) were compared to the period March to May of the previous 5 years (pre‐pandemic periods).Results: The number of new diagnoses of type 1 diabetes was comparable in the pandemic period and pre‐pandemic periods (11 in 2020 vs range 6–10 in 2015–2019). The frequency of severe diabetic ketoacidosis was significantly higher in the pandemic period compared to the pre‐pandemic periods (45% vs 5%; P <0.003), odds ratio 16.7 (95% CI 2.0, 194.7). The overall frequency of diabetic ketoacidosis was also significantly higher during the pandemic period (73% vs 26%; P <0.007), odds ratio 7.5 (95% CI 1.7, 33.5). None of the individuals tested positive for COVID‐19. Presentations of people aged <18 years to the emergency department decreased by 27% in the pandemic period compared to the average of the pre‐pandemic periods (4799 vs 6550; range 6268 to 7131).Conclusions: A significant increase in the frequency of severe diabetic ketoacidosis at presentation of type 1 diabetes was observed during the initial period of COVID‐19 restrictions. We hypothesize that concern about presenting to hospital during a pandemic led to a delay in diagnosis. These data have important implications for advocacy of seeking healthcare for non‐pandemic‐related conditions during a global pandemic.

Introduction Young children with type 1 diabetes (T1D) consume more saturated fat and less fruit and vegetables than recommended. A common challenge in this age group is unpredictable appetite potentially impacting the way parents manage diabetes cares at mealtimes. This small study aimed to assess nutritional intake and mealtime routines of young children with T1D in a clinic where the majority of children were achieving glycemic targets. A secondary aim was to explore association of eating pattern with HbA1c. Methods A retrospective, cross-sectional review of children aged less than 7.0 years with T1D attending a pediatric diabetes service in Australia was performed (n=24). Baseline characteristics, glycated hemoglobin (HbA1c), a 3-day weighed food diary and a mealtime management survey were collected. Results Twenty-two children (55% male) were included aged 4.9±1.3 years (mean±SD), HbA1c 47±10 mmol/mol (6.4%±0.9%), body mass index Z-score 0.8±0.9 and diabetes duration 1.7±1.1 years. Preprandial insulin use was reported in 95% of children. Macronutrient distribution (% energy intake) was carbohydrate (48%±4%), protein (16%±2%) and fat (33%±5%) with saturated fat (15%±3%). The majority of children did not meet vegetable and lean meat/protein intake recommendations (0% and 28%, respectively). HbA1c was not correlated with daily total carbohydrate, protein or fat intake (p>0.05). HbA1c was significantly higher in children offered food in a grazing pattern compared with those offered regular meals (mean 61 mmol/mol vs 43 mmol/mol (7.7% vs 6.1%), p=0.01). Conclusions Dietary quality is a concern in young children with T1D with excessive saturated fat and inadequate vegetable intake. Our results suggest that young children meeting glycemic targets give insulin before meals and follow a routine eating pattern.

Purpose. Although clinical indicators allow individual providers to monitor and improve their own performance and quality of care, another important role for the indicators is to provide comparative information across all providers. We show that the ‘league table’ approach is ineffective, and provide an alternative method that uses the comparative rates to quantify the potential for improvement at both the provider and the national level. Data sources. The methods are applied to English and Australian hospital clinical indicators. Methods. The key is to regard clinical indicators as screening tools that measure performance in one or more dimensions. All screening processes require explicit tests to determine whether the result should be classified as either positive (requires further investigation) or negative (requires continued monitoring). A clinical indicator will be defined as positive if any of the three following criteria are met: (1) large variation between all areas or hospitals, as defined by the 20th centile gains: requires improvement in the health care system; (2) large variation between strata (rural/urban, teaching/non-teaching, public/private, State): requires action in the relevant stratum; (3) outlier hospitals: requires quality improvement in the individual hospitals. Two techniques are used to determine whether any of the three criteria are positive: (1) empirical Bayesian estimation to calculate ‘shrunken’ rates; and (2) use of the 20th centile to quantify the potential gains or improvement. Results. For 185 Australian indicators, 55 clinical indicators had system gains involving better outcomes for at least 1000 patients per indicator. Using a set of criteria and subjective judgement, we identified some key areas for quality improvement in Australia. Conclusion. Ranking of hospitals does not quantify the potential gains that could be achieved. Indicators that measure health care processes should be reported by quantifying the potential gains, thus encouraging action. Estimating the gains across many indicators allows priorities to be established, such as identifying the areas with the greatest potential for improvement. The main tasks are to then provide the tools and resources to tackle those areas with the most gains.