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Australasia is a region with a high incidence of type 1 diabetes (T1D). There are approximately 140 000 individuals with T1D, and of these 10 000 are children. Although the region covers a huge geographical area, most children with T1D are managed by tertiary academic centers in the major capital cities. Local longitudinal data collection has been...
Context in source publication
Citations
... Demographic and clinical information was extracted from the Australasian Diabetes Data Network (ADDN), a prospective, longitudinal database with contributing centres comprising major diabetes clinics across Australia and New Zealand (28). Initiated in 2012, the registry has expanded to include data from 23 paediatric centres, 13 of which are in Australia. ...
Background
Technology use, including continuous glucose monitoring (CGM) and insulin pump therapy, is associated with improved outcomes in youth with type 1 diabetes (T1D). In 2017 CGM was universally funded for youth with T1D in Australia. In contrast, pump access is primarily accessed through private health insurance, self-funding or philanthropy. The study aim was to investigate the use of diabetes technology across different socioeconomic groups in Australian youth with T1D, in the setting of two contrasting funding models.
Methods
A cross-sectional evaluation of 4957 youth with T1D aged <18 years in the national registry was performed to determine technology use. The Index of Relative Socio-Economic Disadvantage (IRSD) derived from Australian census data is an area-based measure of socioeconomic status (SES). Lower quintiles represent greater disadvantage. IRSD based on most recent postcode of residence was used as a marker of SES. A multivariable generalised linear model adjusting for age, diabetes duration, sex, remoteness classification, and location within Australia was used to determine the association between SES and device use.
Results
CGM use was lower in IRSD quintile 1 in comparison to quintiles 2 to 5 (p<0.001) where uptake across the quintiles was similar. A higher percentage of pump use was observed in the least disadvantaged IRSD quintiles. Compared to the most disadvantaged quintile 1, pump use progressively increased by 16% (95% CI: 4% to 31%) in quintile 2, 19% (6% to 33%) in quintile 3, 35% (21% to 50%) in quintile 4 and 51% (36% to 67%) in the least disadvantaged quintile 5.
Conclusion
In this large national dataset, use of diabetes technologies was found to differ across socioeconomic groups. For nationally subsidised CGM, use was similar across socioeconomic groups with the exception of the most disadvantaged quintile, an important finding requiring further investigation into barriers to CGM use within a nationally subsidised model. User pays funding models for pump therapy result in lower use with socioeconomic disadvantage, highlighting inequities in this funding approach. For the full benefits of diabetes technology to be realised, equitable access to pump therapy needs to be a health policy priority.
... Using data from the Australasian Diabetes Data Network (ADDN), a prospective clinical diabetes registry established in 2012 [13,14], we examined BP in adolescents and young adults with T1D across Australia and New Zealand, and examined factors associated with BP in the hypertensive range in this population. ...
Aim:
Hypertension increases complication risk in type 1 diabetes (T1D). We examined blood pressure (BP) in adolescents and young adults with T1D from the Australasian Diabetes Data Network, a prospective clinical diabetes registry in Australia and New Zealand.
Methods:
This was a longitudinal study of prospectively collected registry data.
Inclusion criteria:
T1D (duration ≥ 1 year) and age 16-25 years at last visit (2011-2020). Hypertension was defined as (on ≥ 3 occasions) systolic BP and/or diastolic BP > 95th percentile for age < 18 years, and systolic BP > 130 and/or diastolic BP > 80 mmHg for age ≥ 18 years. Multivariable Generalised Estimating Equations were used to examine demographic and clinical factors associated with BP in the hypertensive range across all visits.
Results:
Data from 6338 young people (male 52.6%) attending 24 participating centres across 36,655 T1D healthcare visits were included; 2812 (44.4%) had BP recorded at last visit. Across all visits, 19.4% of youth aged < 18 years and 21.7% of those aged ≥ 18 years met criteria for hypertension. In both age groups, BP in the hypertensive range was associated with male sex, injection (vs. pump) therapy, higher HbA1c, and higher body mass index.
Conclusions:
There is a high proportion of adolescents and young adults reported with BP persistently in hypertensive ranges. Findings flag the additive contribution of hypertension to the well-established body of evidence indicating a need to review healthcare models for adolescents and young adults with T1D.
... Clapin et al. [16] describe Phase 1 (2012Phase 1 ( -2015 of the ADDN registry development. In this phase of ADDN, the focus was primarily on collecting and using data on paediatric patients coordinated with the Australasian Paediatric Endocrine Group (APEG [17]) and the Australian Diabetes Society (ADS [18]). ...
... Clapin et al. [16] describe Phase 1 (2012-2015) of the ADDN registry development. In this phase of ADDN, the focus was primarily on collecting and using data on paediatric patients coordinated with the Australasian Paediatric Endocrine Group (APEG [17]) and the Australian Diabetes Society (ADS [18]). ...
Australia has a high prevalence of diabetes, with approximately 1.2 million Australians diagnosed with the disease. In 2012, the Australasian Diabetes Data Network (ADDN) was established with funding from the Juvenile Diabetes Research Foundation (JDRF). ADDN is a national diabetes registry which captures longitudinal information about patients with type-1 diabetes (T1D). Currently, the ADDN data are directly contributed from 42 paediatric and 17 adult diabetes centres across Australia and New Zealand, i.e., where the data are pre-existing in hospital systems and not manually entered into ADDN. The historical data in ADDN have been de-identified, and patients are initially afforded the opportunity to opt-out of being involved in the registry; however, moving forward, there is an increased demand from the clinical research community to utilise fully identifying data. This raises additional demands on the registry in terms of security, privacy, and the nature of patient consent. General Data Protection Regulation (GDPR) is an increasingly important mechanism allowing individuals to have the right to know about their health data and what those data are being used for. This paper presents a mobile application being designed to support the ADDN data collection and usage processes and aligning them with GDPR. The app utilises Dynamic Consent—an informed specific consent model, which allows participants to view and modify their research-driven consent decisions through an interactive interface. It focuses specifically on supporting dynamic opt-in consent to both the registry and to associated sub-projects requesting access to and use of the patient data for research purposes.
... The software and database support and maintenance of ADDN are provided by Melbourne eResearch Group (MeG -www.eresearch.unimelb.edu.au) at the University of Melbourne. Clapin et al. [2] describes the initial phase of development up to 2016 where the focus was primarily paediatric data coordinated with the the Australasian Paediatric Endocrine Group (APEG 1 ). Through coordination with the Australian Diabetes Society (ADS 2 ) the platform has since evolved to include many adult sites both in Australia and New Zealand. ...
... These are assigned to each individual user. The ADDN data access model is described in [2]. ...
... For ADDN, (2) and (5) can be easily excluded. Pseudonymisation conducted for patients' data and the ethics committee responsible for governance are the safeguards of sensitive data processing. ...
Australia is a region with a high incidence of diabetes with approximately 1.2 million Australians diagnosed with this condition. In 2012, the Juvenile Diabetes Research Foundation (JDRF – www.jdrf.org.au) provided funding to establish the national registry - the Australasian Diabetes Data Network (ADDN – www.addn.org.au) populated with extensive longitudinal data on patients with Type-1 Diabetes (T1D). The ADDN registry has evolved over time and now includes data on over 20,000 patients from 22 paediatric centres and 11 adult centres across Australasia, i.e., where the data is uploaded from hospitals and not manually entered. This data has historically been de-identified at source, however moving forward there is increased demand from the clinical research community to link between data-sets using fully identifying data. In this context, this paper explores the challenges this poses with regards to the evolving processes that must be incorporated for data collection and use, e.g. e-Consent, and especially the impact of General Data Protection Regulation (GDPR) on the ADDN processes.
... The Australasian Diabetes Data Network (ADDN) is established as a national T1D registry and provides an opportunity to benchmark and evaluate these real-life outcomes on a larger sample across the nation, in both children and adults. 18 Centralised data collection of relevant CGM metrics will be required to inform these outcomes on an ongoing basis. ...
Aim:
To determine the clinical outcomes and evaluate the perspectives of children with Type 1 diabetes (T1D) and their parents managing their child on hybrid closed-loop (HCL) therapy.
Methods:
Children with T1D on HCL attending a tertiary diabetes centre between April 2019 and July 2021 were included. A retrospective analysis of glycaemic data was conducted to determine the clinical outcomes. Time spent in closed loop, time in target glucose range (TIR 3.9-10 mmol/L), hypoglycaemia and hyperglycaemia were collected at baseline, 4 weeks, 3 and 6 months post-HCL. User experience was assessed by questionnaires administered to parents of children with T1D.
Results:
Seventy-one children, mean (SD) age of 12.2 (3.2) years were commenced on HCL. Ten (14%) discontinued HCL use, with 60% discontinuing within the first 6 months. Glycaemic outcomes were analysed in 52 children. Time spent in closed loop was 78 (21) % at 4 weeks, declined to 69 (28) % at 3 months (P = 0.037) and 63 (34) % at 6 months (P = 0.001). The mean %TIR increased from 59.8 at baseline to 67.6 at 3 months and 65.6 at 6 months with a mean adjusted difference of 7.8% points [95% CI 3.6, 11.9] and 5.5% points [95% CI 1.4, 9.5], respectively. There was a reduction in time > 10 mmol/L and time < 3.9 mmol/L from baseline to 6 months. Although families faced challenges with technology, better glucose control with reduced glycaemic fluctuations were reported.
Conclusions:
HCL therapy is associated with improved glycaemia; however, adequate support and education are required for best outcomes.
... Every 6 months data are transferred from participating ADDN centers to a central ADDN registry. 11 ...
... Indigenous Australians accounted for 30% of participants from Australian centers. Median age at diagnosis was 13.7 years (range [6][7][8][9][10][11][12][13][14][15][16][17][18] and median duration since diabetes diagnosis was 0.2 years at the first clinic visit. ...
Objectives
To assess the clinical and demographic characteristics of children and adolescents across Australia and New Zealand with type 2 diabetes.
Methods
We performed a descriptive audit of data prospectively reported to the Australasian Diabetes Data Network (ADDN) registry. Data were collected from six tertiary paediatric diabetes centres across Australia (New South Wales, Queensland, South Australia, Western Australia and Victoria) and New Zealand (Auckland). Children and adolescents diagnosed with type 2 diabetes aged <18 years with data reported to ADDN between 2012‐2017 were included. Age, sex, ethnicity, HbA1c, blood pressure, BMI, waist circumference and lipid profile at first visit were assessed.
Results
There were 269 cases of type 2 diabetes in youth reported to ADDN between 2012 and 2017. The most common ethnicities were Indigenous Australian in 56/243 (23%) and New Zealand (NZ) Maori or Pacifica in 47 (19%). Median age at diagnosis was 13.7 years and 94% of participants were overweight or obese. Indigenous Australian and Maori/Pacifica children were younger at diagnosis compared with non‐Indigenous children: median 13.3 years (Indigenous Australian); 13.1 years (Maori/Pacifica); 14.1 years (non‐Indigenous), p=0.005. HbA1c was higher in Indigenous Australian (9.4%) and Maori/Pacifica youth (7.8%) compared with non‐Indigenous (6.7%) p<0.001. BMI‐SDS was higher in Maori/Pacifica youth (2.3) compared with Indigenous Australian (2.1) and non‐Indigenous (2.2) p=0.011.
Conclusions
Indigenous Australian and Maori/Pacifica youth in ADDN were younger and had worse glycaemic control at diagnosis of type 2 diabetes. Our findings underscore the need to consider targeted and earlier screening in these “high risk” populations.
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... DKA was defined according to the International Society for Pediatric and Adolescent Diabetes (ISPAD) criteria (venous pH <7.3 or serum bicarbonate <15 mmol/l) or documented information on DKA (yes/no) according to the physician providing medical care at diagnosis. with a completeness of coverage of at least 90% during the study period, as previously described [21,25,[27][28][29][30][31][32][33]. All data owners gave the permission for publication. ...
Aims/hypothesisThe aim of this work was to evaluate geographical variability and trends in the prevalence of diabetic ketoacidosis (DKA), between 2006 and 2016, at the diagnosis of childhood-onset type 1 diabetes in 13 countries over three continents.Methods
An international retrospective study on DKA at diagnosis of diabetes was conducted. Data on age, sex, date of diabetes diagnosis, ethnic minority status and presence of DKA at diabetes onset were obtained from Australia, Austria, Czechia, Denmark, Germany, Italy, Luxembourg, New Zealand, Norway, Slovenia, Sweden, USA and the UK (Wales). Mean prevalence was estimated for the entire period, both overall and by country, adjusted for sex and age group. Temporal trends in annual prevalence of DKA were estimated using logistic regression analysis for each country, before and after adjustment for sex, age group and ethnic minority status.ResultsDuring the study period, new-onset type 1 diabetes was diagnosed in 59,000 children (median age [interquartile range], 9.0 years [5.5–11.7]; male sex, 52.9%). The overall adjusted DKA prevalence was 29.9%, with the lowest prevalence in Sweden and Denmark and the highest in Luxembourg and Italy. The adjusted DKA prevalence significantly increased over time in Australia, Germany and the USA while it decreased in Italy. Preschool children, adolescents and children from ethnic minority groups were at highest risk of DKA at diabetes diagnosis in most countries. A significantly higher risk was also found for females in Denmark, Germany and Slovenia.Conclusions/interpretationDKA prevalence at type 1 diabetes diagnosis varied considerably across countries, albeit it was generally high and showed a slight increase between 2006 and 2016. Increased awareness of symptoms to prevent delay in diagnosis is warranted, especially in preschool children, adolescents and children from ethnic minority groups.
... 2 3 Australia has a high incidence of T1DM, with >10 000 children affected nationally. 4 As such, T1DM has been classified as a national health priority by the Australian government. 5 To reduce the long-term burden of illness, it is important to identify and minimize risk factors for complications of T1DM, and ensure that treatment targets are met. ...
... Information about the 251 children with one or more eligible assessments of adherence to CPG for T1DM is provided in table 1. Almost half the children were aged 12 years or older, with almost equal number of males and females. Each child had a median of two healthcare visits across the 2 years (range [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. ...
Introduction
To estimate adherence to clinical practice guidelines in selected settings at a population level for Australian children with type 1 diabetes mellitus.
Research design and methods
Medical records of children with type 1 diabetes mellitus aged 0–15 years in 2012–2013 were targeted for sampling across inpatient, emergency department and community visits with specialist pediatricians in regional and metropolitan areas and tertiary pediatric hospitals in three states where approximately 60% of Australian children reside. Clinical recommendations extracted from two clinical practice guidelines were used to audit adherence. Results were aggregated across types of care (diagnosis, routine care, emergency care).
Results
Surveyors conducted 6346 indicator assessments from an audit of 539 healthcare visits by 251 children. Average adherence across all indicators was estimated at 79.9% (95% CI 69.5 to 88.0). Children with type 1 diabetes mellitus have higher rates of behavioral and psychological disorders, but only a third of children (37.9%; 95% CI 11.7 to 70.7) with suboptimal glycemic control (eg, hemoglobin A1c >10% or 86 mmol/mol) were screened for psychological disorders using a validated tool; this was the only indicator with <50% estimated adherence. Adherence by care type was: 86.1% for diagnosis (95% CI 76.7 to 92.7); 78.8% for routine care (95% CI 65.4 to 88.9) and 83.9% for emergency care (95% CI 78.4 to 88.5).
Conclusions
Most indicators for care of children with type 1 diabetes mellitus were adhered to. However, there remains room to improve adherence to guidelines for optimization of practice consistency and minimization of future disease burden.
... The ADDN is a longitudinal centralized, standardized data collection system for patients with all diabetes types, which commenced enrollment in 2012 (15). Data are documented locally by the participating centers in an electronic database, and anonymized data are transferred twice yearly to the central database. ...
Objective:
Celiac disease (CD) has a recognized association with type 1 diabetes. We examined international differences in CD prevalence and clinical characteristics of youth with coexisting type 1 diabetes and CD versus type 1 diabetes only.
Research design and methods:
Data sources were as follows: the Prospective Diabetes Follow-up registry (Germany/Austria); the T1D Exchange Clinic Network (T1DX) (U.S.); the National Paediatric Diabetes Audit (U.K. [England/Wales]); and the Australasian Diabetes Data Network (ADDN) (Australia). The analysis included 52,721 youths <18 years of age with a clinic visit between April 2013 and March 2014. Multivariable linear and logistic regression models were constructed to analyze the relationship between outcomes (HbA1c, height-standard deviation score [SDS], overweight/obesity) and type 1 diabetes/CD versus type 1 diabetes, adjusting for sex, age, and diabetes duration.
Results:
Biopsy-confirmed CD was present in 1,835 youths (3.5%) and was diagnosed at a median age of 8.1 years (interquartile range 5.3-11.2 years). Diabetes duration at CD diagnosis was <1 year in 37% of youths, >1-2 years in 18% of youths, >3-5 years in 23% of youths, and >5 years in 17% of youths. CD prevalence ranged from 1.9% in the T1DX to 7.7% in the ADDN and was higher in girls than boys (4.3% vs. 2.7%, P < 0.001). Children with coexisting CD were younger at diabetes diagnosis compared with those with type 1 diabetes only (5.4 vs. 7.0 years of age, P < 0.001) and fewer were nonwhite (15 vs. 18%, P < 0.001). Height-SDS was lower in those with CD (0.36 vs. 0.48, adjusted P < 0.001) and fewer were overweight/obese (34 vs. 37%, adjusted P < 0.001), whereas mean HbA1c values were comparable: 8.3 ± 1.5% (67 ± 17 mmol/mol) versus 8.4 ± 1.6% (68 ± 17 mmol/mol).
Conclusions:
CD is a common comorbidity in youth with type 1 diabetes. Differences in CD prevalence may reflect international variation in screening and diagnostic practices, and/or CD risk. Although glycemic control was not different, the lower height-SDS supports close monitoring of growth and nutrition in this population.
... The development of the ADDN has been described in detail elsewhere. 12 In this article, we report on glycaemic control, anthropometry, and insulin regimens in a cross-section of Australian children and adolescents with type 1 diabetes. ...
Objectives:
To assess glycaemic control, anthropometry and insulin regimens in a national sample of Australian children and adolescents with type 1 diabetes.
Design:
Cross-sectional analysis of de-identified, prospectively collected data from the Australasian Diabetes Data Network (ADDN) registry.
Setting:
Five paediatric diabetes centres in New South Wales, Queensland, South Australia, Victoria and Western Australia.
Participants:
Children and adolescents (aged 18 years or under) with type 1 diabetes of at least 12 months' duration for whom data were added to the ADDN registry during 2015.
Main outcome measures:
Glycaemic control was assessed by measuring haemoglobin A1c (HbA1c) levels. Body mass index standard deviation scores (BMI-SDS) were calculated according to the CDC-2000 reference; overweight and obesity were defined by International Obesity Task Force guidelines. Insulin regimens were classified as twice-daily injections (BD), multiple daily injections (MDI; at least three injection times per day), or continuous subcutaneous insulin infusion (CSII).
Results:
The mean age of the 3279 participants was 12.8 years (SD, 3.7), mean diabetes duration was 5.7 years (SD, 3.7), and mean HbA1c level 67 mmol/mol (SD, 15); only 27% achieved the national HbA1c target of less than 58 mmol/mol. The mean HbA1c level was lower in children under 6 (63 mmol/mol) than in adolescents (14-18 years; 69 mmol/mol). Mean BMI-SDS for all participants was 0.6 (SD, 0.9); 33% of the participants were overweight or obese. 44% were treated with CSII, 38% with MDI, 18% with BD.
Conclusions:
Most Australian children and adolescents with type 1 diabetes are not meeting the recognised HbA1c target. The prevalence of overweight and obesity is high. There is an urgent need to identify barriers to achieving optimal glycaemic control in this population.
