Children and adolescents with type 1 diabetes in Aotearoa New Zealand: An online survey of workforce and outcomes 2021.

AIMS: To survey the national workforce that manages children and adolescents with type 1 diabetes (T1D) in Aotearoa New Zealand and compare with glycaemic outcomes for 2021. METHODS: A representative from each tertiary and regional diabetes service in Aotearoa New Zealand was asked to participate in an online survey assessing health-care professional (HCP) workforce numbers operating for the 2021 calendar year. Regional full-time-equivalent (FTE), glycaemic outcomes and population demographics were compared to a previously reported workforce surveys (2015 and 2019). RESULTS: Seventeen sites responded - including all four large tertiary centres - serving >99% of children and adolescents with T1D in Aotearoa New Zealand. HCP resourcing varied across sites, with median (range) HCP/100 patient ratios of: doctors: 0.40 (0.16-1.11), nurses: 1.19 (0.29-5.56), dietitians: 0.25 (0-1.11) and psychologist/social workers: 0 (0-0.26). No site met all of the International Society of Paediatric and Adolescent Diabetes (ISPAD) recommendations of HCP/100 patient ratios. Measures of socio-economic deprivation predicted HbA1c, rather than the diabetes clinic attended. Overall, only 15.1% (240/1585) of patients had an HbA1c less than the recommended 53 mmol/mol. CONCLUSIONS: The Aotearoa New Zealand workforce for children and adolescents with T1D is under-resourced and no site meets the ISPAD recommendations. There has been no significant increase in HCP/100 patient ratios compared to previous workforce surveys over the last decade. Few children and adolescents with T1D meet the recommended HbA1c. Resourcing according to recommended clinical need is required if equity in outcomes for young people with T1D is to be addressed.

Interviews with Indigenous Maori with type 1 diabetes using open-source automated insulin delivery in the CREATE randomised trial.

Purpose: Open-source automated insulin delivery (AID) is used by thousands of people with type 1 diabetes (T1D), but has unknown generalisability to marginalised ethnic groups. This study explored experiences of Indigenous Maori participants in the CREATE trial with use of an open-source AID system to identify enablers/barriers to health equity. Methods: The CREATE randomised trial compared open-source AID (OpenAPS algorithm on an Android phone with a Bluetooth-connected pump) to sensor-augmented pump therapy. Kaupapa Maori Research methodology was used in this sub-study. Ten semi-structured interviews with Maori participants (5 children, 5 adults) and whanau (extended family) were completed. Interviews were recorded and transcribed, and data were analysed thematically. NVivo was used for descriptive and pattern coding. Results: Enablers/barriers to equity aligned with four themes: access (to diabetes technologies), training/support, operation (of open-source AID), and outcomes. Participants described a sense of empowerment, and improved quality of life, wellbeing, and glycaemia. Parents felt reassured by the system's ability to control glucose, and children were granted greater independence. Participants were able to use the open-source AID system with ease to suit whanau needs, and technical problems were manageable with healthcare professional support. All participants identified structures in the health system precluding equitable utilisation of diabetes technologies for Maori. Conclusion: Maori experienced open-source AID positively, and aspired to use this therapy; however, structural and socio-economic barriers to equity were identified. This research proposes strength-based solutions which should be considered in the redesign of diabetes services to improve health outcomes for Maori with T1D.Trial Registration: The CREATE trial, encompassing this qualitative sub-study, was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12620000034932p) on the 20th January 2020. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01215-3.

Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled Trial.

Aim: To assess long-term efficacy and safety of open-source automated insulin delivery (AID) in children and adults (7-70 years) with type 1 diabetes. Methods: Both arms of a 24-week randomized controlled trial comparing open-source AID (OpenAPS algorithm within a modified version of AndroidAPS, preproduction DANA-i™ insulin pump, Dexcom G6 continuous glucose monitor) with sensor-augmented pump therapy (SAPT), entered a 24-week continuation phase where the SAPT arm (termed SAPT-AID) crossed over to join the open-source AID arm (termed AID-AID). Most participants (69/94) used a preproduction YpsoPump® insulin pump during the continuation phase. Analyses incorporated all 52 weeks of data, and combined between-group and within-subject differences to calculate an overall "treatment effect" of AID versus SAPT. Results: Mean time in range (TIR; 3.9-10 mmol/L [70-180 mg/dL]) was 12.2% higher with AID than SAPT (95% confidence interval [CI] 10.4 to 14.1; P < 0.001). TIR was 56.9% (95% CI 54.2 to 59.6) with SAPT and 69.1% (95% CI 67.1 to 71.1) with AID. The treatment effect did not differ by age (P = 0.39) or insulin pump type (P = 0.37). HbA1c was 5.1 mmol/mol lower [0.5%] with AID (95% CI -6.6 to -3.6; P < 0.001). There were no episodes of diabetic ketoacidosis or severe hypoglycemia with either treatment over the 48 weeks. Six participants (all in SAPT-AID) withdrew: three with hardware issues, two preferred SAPT, and one with infusion-site skin irritation. Conclusion: Further evaluation of the community derived automated insulin delivery (CREATE) trial to 48 weeks confirms that open-source AID is efficacious and safe with different insulin pumps, and demonstrates sustained glycemic improvements without additional safety concerns.

Prevalence and incidence of type 1 diabetes in children aged 0-14 years old in New Zealand in 2021.

AIM: National prevalence and incidence data are important for understanding population trends and allocating health-care resources. We aimed to provide a current national snapshot of prevalence and annual incidence rates for children aged 0-14 with type 1 diabetes (T1D) in Aotearoa New Zealand and to identify differences associated with demographic variables. METHODS: Paediatric diabetes centres across Aotearoa were invited to record anonymised demographic and diabetes data on children under their services between 1 October 2020 and 30 September 2021. National prevalence and incidence were calculated using usually resident population counts from the 2018 census. The effect of ethnicity on prevalence and incidence was assessed using Poisson regression. RESULTS: There were 1209 children aged 0-14 with T1D in October 2021. The national prevalence was 131/100 000 (95% confidence interval (CI) 124-139). European children had twice the prevalence as those of Maori or Pacific ethnicity (P < 0.001). There was no effect by gender (P = 0.3) and prevalence predictably increased with age. The annualised incidence of T1D was 23/100 000 (95% CI 20-26). European children were 2.6 times as likely as Maori children to be diagnosed with T1D in that year (incidence rate ratio = 2.6, 95% CI 1.7-4.2). Regional differences in prevalence and incidence were noted, potentially due to the ethnicity differences across regions. Unadjusted prevalence and incidence decreased with lower socio-economic status, likely due to an over-representation of non-Europeans living in the most deprived areas. CONCLUSIONS: T1D affects an ethnically diverse population in Aotearoa and important regional differences exist that may impact workforce planning.

Inequity in access to continuous glucose monitoring and health outcomes in paediatric diabetes, a case for national continuous glucose monitoring funding: A cross-sectional population study of children with type 1 diabetes in New Zealand.

Background: Continuous glucose monitoring (CGM) improves glycaemia for people affected by type 1 diabetes (T1D), but is not funded in Aotearoa/New Zealand. This study explores the impact of non-funded CGM on equity of access and associated glycaemic outcomes. Methods: Cross-sectional population-based study collected socio-demographic (age, gender, prioritised ethnicity, socioeconomic status) and clinical data from all regional diabetes centres in New Zealand with children <15 years with T1D as of 1st October 2021. De-identified data were obtained from existing databases or chart review. Outcomes compared socio-demographic characteristics between those using all forms of CGM and self-monitoring of blood glucose (SMBG), and association with HbA1c. Findings: 1209 eligible children were evaluated: 70.2% European, 18.1% Maori, 7.1% Pacific, 4.6% Asian, with even distribution across socioeconomic quintiles. Median HbA1c was 64 mmol/mol (8.0%), 40.2% utilised intermittently scanned (is)CGM, and 27.2% real-time (rt)CGM. CGM utilisation was lowest with Pacific ethnicity (38% lower than Maori) and the most deprived socioeconomic quintiles (quintile 5 vs. 1 adjusted RR 0.69; 95% CI, 0.57 to 0.84). CGM use was associated with regional diabetes centre (P < 0.001). The impact of CGM use on HbA1c differed by ethnicity: Maori children had the greatest difference in HbA1c between SMBG and rtCGM (adjusted difference -15.3 mmol/mol; 95% CI, -21.5 to -9.1), with less pronounced differences seen with other ethnicities. Interpretation: Inequities in CGM use exist based on prioritised ethnicity and socioeconomic status. Importantly, CGM was independently associated with lower HbA1c, suggesting that lack of CGM funding contributes to health disparity in children with T1D. Funding: Australasian Paediatric Endocrine Group (APEG), Canterbury Medical Research Foundation, Starship Foundation.

Open-Source Automated Insulin Delivery in Type 1 Diabetes.

BACKGROUND: Open-source automated insulin delivery (AID) systems are used by many patients with type 1 diabetes. Data are needed on the efficacy and safety of an open-source AID system. METHODS: In this multicenter, open-label, randomized, controlled trial, we assigned patients with type 1 diabetes in a 1:1 ratio to use an open-source AID system or a sensor-augmented insulin pump (control). The patients included both children (defined as 7 to 15 years of age) and adults (defined as 16 to 70 years of age). The AID system was a modified version of AndroidAPS 2.8 (with a standard OpenAPS 0.7.0 algorithm) paired with a preproduction DANA-i insulin pump and Dexcom G6 CGM, which has an Android smartphone application as the user interface. The primary outcome was the percentage of time in the target glucose range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter) between days 155 and 168 (the final 2 weeks of the trial). RESULTS: A total of 97 patients (48 children and 49 adults) underwent randomization (44 to open-source AID and 53 to the control group). At 24 weeks, the mean (±SD) time in the target range increased from 61.2±12.3% to 71.2±12.1% in the AID group and decreased from 57.7±14.3% to 54.5±16.0% in the control group (adjusted difference, 14 percentage points; 95% confidence interval, 9.2 to 18.8; P<0.001), with no treatment effect according to age (P = 0.56). Patients in the AID group spent 3 hours 21 minutes more in the target range per day than those in the control group. No severe hypoglycemia or diabetic ketoacidosis occurred in either group. Two patients in the AID group withdrew from the trial owing to connectivity issues. CONCLUSIONS: In children and adults with type 1 diabetes, the use of an open-source AID system resulted in a significantly higher percentage of time in the target glucose range than the use of a sensor-augmented insulin pump at 24 weeks. (Supported by the Health Research Council of New Zealand; Australian New Zealand Clinical Trials Registry number, ACTRN12620000034932.).

Learning challenges of healthcare professionals supporting open-source automated insulin delivery.

BACKGROUND: Open-source automated insulin delivery (AID) is a user-driven treatment modality used by thousands globally. Healthcare professionals' (HCPs) ability to support users of this technology is limited by a lack of knowledge of these systems. AIMS: To describe the challenges experienced by HCPs supporting participants' use of open-source automated insulin delivery in the Community deRivEd AuTomatEd insulin delivery (CREATE) study. METHODS: Data were collected prospectively from the study team's fortnightly meetings and Slack Workspace (Slack Technologies, Ltd. 2018) during the first 4 months of the trial. Key topics were identified from minutes of meetings. Slack conversations were categorised by topic, with the number of posts per conversation, number of sites per conversation and involvement of experts in open-source AID being recorded. RESULTS: In the first 4 months of the trial, there were 254 conversations in Slack with a mean of 5.2 (±4.25) posts per conversation. The most frequent learning challenge was insulin pump and cannula problems relating to the DANA-iTM insulin pump, which totalled 24.0% of all conversations. Experts on open-source AID use were involved in 83.3% of conversations. CONCLUSIONS: A significant proportion of challenges related to specific devices, rather than AID. Challenges relating to the functioning of open-source AID were more likely to involve input from experts in open-source AID. This is the first report of challenges experienced by a multidisciplinary team in a supported open-source environment that may inform expectations in routine clinical care.

Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine.

Digital innovations have led to an explosion of data in healthcare, driving processes of democratization and foreshadowing the end of the paternalistic era of medicine and the inception of a new epoch characterized by patient-centered care. We illustrate that the "do it yourself" (DIY) automated insulin delivery (AID) innovation of diabetes is a leading example of democratization of medicine as evidenced by its application to the three pillars of democratization in healthcare (intelligent computing; sharing of information; and privacy, security, and safety) outlined by Stanford but also within a broader context of democratization. The heuristic algorithms integral to DIY AID have been developed and refined by human intelligence and demonstrate intelligent computing. We deliver examples of research in artificial pancreas technology which actively pursues the use of machine learning representative of artificial intelligence (AI) and also explore alternate approaches to AI within the DIY AID example. Sharing of information symbolizes the core philosophy behind the success of the DIY AID evolution. We examine data sharing for algorithm development and refinement, for sharing of the open-source algorithm codes online, for peer to peer support, and sharing with medical and scientific communities. Do it yourself AID systems have no regulatory approval raising safety concerns as well as medico-legal and ethical implications for healthcare professionals. Other privacy and security factors are also discussed. Democratization of healthcare promises better health access for all and we recognize the limitations of DIY AID as it exists presently, however, we believe it has great potential.