The Cost of Control: Cost-effectiveness Analysis of Hybrid Closed-Loop Therapy in Youth

Type 1 diabetes is challenging to optimally manage. Hypoglycemia poses an acute risk to health and may seriously impact quality of life and productivity, while prolonged exposure to hyperglycemia increases the risk of microvascular and macrovascular complications (1–6). Advancements in insulin pump therapy, continuous glucose monitoring, and educational strategies for self-management have the potential to improve glycemia and reduce complications of diabetes. However, only a minority of young people internationally are meeting glycemic targets despite the progress in technology (7,8). Furthermore, type 1 diabetes is still associated with increased mortality, with the highest risk among those with a younger age of onset (9–12).

Hybrid closed-loop (HCL) therapy is currently the most advanced management strategy available for type 1 diabetes. Current-generation HCL therapy integrates insulin pumps and continuous glucose monitoring devices with decision algorithms to automatically increase or decrease basal insulin to help correct hyperglycemia and mitigate hypoglycemia. While the automation of HCL therapy does not eliminate the need for fastidious user oversight, HCL systems do appear to provide the best potential for optimal glycemic outcomes among pediatric and adult populations (13–18).

The high acquisition cost of HCL therapy is a barrier to equitable access for people with type 1 diabetes (18–21). The cost of technology must also be weighed against the significant costs of diabetes and its complications for people with type 1 diabetes, their carers, health care systems, and society in general (22–25). At present, published economic data regarding HCL therapy are scarce (26). Two economic evaluations reported that HCL therapy was cost-effective compared with insulin pump therapy primarily among adults in Sweden and the U.K. (27,28). Our recent cohort-based cost-effectiveness analysis also reported that HCL therapy was likely cost-effective compared with insulin injections and capillary glucose testing among adults with type 1 diabetes in Australia (29). However, we are unaware of cost-effectiveness analyses regarding HCL therapy among young people or that consider a comparator group using insulin pumps or injections with any glucose monitoring system.

We thus aimed to evaluate the cost-effectiveness of HCL therapy compared with current care among young people with type 1 diabetes in Australia by using a patient-level simulation model. Facilitating and informing key aspects of our analyses were the results of an Australian, multicenter, parallel-group, randomized controlled trial (RCT) for the Australian JDRF Closed Loop Research Group (30).

Development and reporting of our model followed the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, the Diabetes Modeling Input Checklist, as well as the Assessment of the Validation Status of Health-Economic decision models (AdViSHE) checklist (Supplementary “Reporting and validation,” Supplementary Tables 4–6, and Supplementary Figs. 1 and 2) (31–33). A patient-level Markov model with annual cycles was constructed to evaluate the cost-effectiveness of HCL therapy compared with current care among young people aged 12 years, with follow-up until 25 years of age. This age range was chosen to mirror the Australian pediatric RCT that informed this economic analysis (30). A secondary analysis was performed to evaluate the cost-effectiveness of HCL therapy compared with current care from 12 years of age until death. Microsimulation was used to allow transition probabilities to vary based on different characteristics. The perspective was that of the Australian health care system. Future costs and outcomes were discounted by 5% annually based on guidelines by the Australian Government Department of Health (34). The primary outcome of interest was the incremental cost-effectiveness ratio (ICER) in terms of net cost per quality-adjusted life year (QALY) gained.

Health states considered by the model comprised type 1 diabetes with no complications or else any combination of diabetic eye disease, diabetic nephropathy, diabetic foot disease, acute myocardial infarction (AMI), stroke, congestive heart failure (CHF), unstable angina, and death. Diabetic eye disease was modeled with stages including “diabetic retinopathy” (level 20–35 according to the Early Treatment Diabetic Retinopathy Study [ETDRS] grading scale), “sight-threatening diabetic retinopathy” (level ≥43 on the ETDRS scale), “macular edema” (extrafoveal and/or clinically significant macular edema according to ETDRS classification), or blindness (both eyes) (35). Diabetic nephropathy was modeled with stages including moderately increased albuminuria, severely increased albuminuria, and end-stage renal disease. Regression of eye disease or renal disease was not modeled directly. Diabetic foot disease was modeled with health states including uncomplicated diabetic foot ulcer, complicated/infected diabetic foot ulcer, healed diabetic foot ulcer, minor amputation (at or below the ankle), infected amputation, major amputation (above the ankle), and healed amputation (36). The interrelated nature of complications was captured by transition probabilities for health states using the shared risk factor of HbA_1c as well as incorporating renal health states as part of annual cardiovascular risk prediction. Tracker variables were implemented to facilitate the independent modeling of different health states. Mortality was modeled to be a result of cardiovascular disease, renal causes, or “other” causes. Simulated individuals continued to cycle through the model until 25 years of age in the primary analysis and up to death with a theoretical limit of 100 years of age in the secondary analysis. Supplementary Fig. 2 graphically presents the model structure.

Transition probabilities were derived from published sources to simulate disease progression (Supplementary Table 1). Contemporary published data were sought for each modeled health state, and age-specific all-cause mortality was drawn from published Australian data for people with type 1 diabetes (37).

The treatment effect of HCL therapy on HbA_1c determined the difference in transition probabilities between treatment groups for all vascular complications. Rates of severe and nonsevere hypoglycemia were modeled to reflect the possibility of having multiple hypoglycemic events in a year rather than annual transition probabilities per event. All hypoglycemic events were assumed to be nonfatal. To predict the annual probability of the first macrovascular event, the Steno T1 Risk Engine model was used and involved the variables of age, sex, diabetes duration, systolic blood pressure, LDL cholesterol, HbA_1c, presence or absence of moderately or severely increased albuminuria, estimated glomerular filtration rate (eGFR) categorized as >40 or <40 mL/min/1.73 m², smoking status, and participation in regular exercise (38). Owing to the population-level data source for renal health states reporting on albuminuria or end-stage renal disease rather than eGFR levels, it was pragmatically assumed that eGFR was >40 mL/min/1.73 m² when using the Steno T1 Risk Engine model unless patients transitioned to end-stage renal disease. Recurrent cardiovascular events had to be preceded by a cardiovascular event at least 1 year earlier, and renal death could only occur in the presence of renal pathology arising at least 1 year prior. To predict the probability of recurrent cardiovascular events, the results of “A new model for 5-year risk of CVD in Type 1 diabetes” were annualized (39). This cardiovascular risk prediction model includes variables of age, diabetes duration, HbA_1c, systolic blood pressure, total cholesterol-to-HDL cholesterol ratio, smoking status, presence or absence of severe albuminuria, and the presence of a previous event (39). Random numbers were generated to determine patient sex, smoking status, and participation in regular exercise according to cohort-level probabilities as inputs for the cardiovascular risk prediction models. Results from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study were used to define the proportion of events comprising AMI, stroke, or CHF (40). Case fatality data were used to derive mortality rates for AMI, stroke, and CHF. Unstable angina and lower-extremity amputations were assumed to be nonfatal. Because projected cardiovascular and renal mortality rates were modeled separately in relation to glycemic control, previously reported estimates for these causes of death were subtracted from the published aggregate of Australian all-cause mortality data to avoid double counting.

All costs were reported in 2021 Australian dollars (AUD), with adjustment for inflation using the health consumer price index (41). Multiple sources were used to estimate costs (Supplementary Table 2). For type 1 diabetes without any complications, costs were sourced from the largest Australian cost-of-illness study comprising 2,200 people with type 1 diabetes selected from an Australian diabetes register (National Diabetes Services Scheme [NDSS]) (22). For diabetic retinopathy among those with type 1 diabetes in Australia, costs were estimated based on Medicare Benefits Schedule item numbers, the Pharmaceutical Benefits Scheme for medications, and the intensity of monitoring and intervention was based on clinical guidelines, landmark clinical trials, and the expert opinion of ophthalmologists. For diabetic nephropathy, costs were derived from Australian economic projections published by Kidney Health Australia reflecting the cost of kidney disease for the general population (42,43). For diabetic foot disease, costs were derived from an Australian cost-effectiveness analysis regarding the optimal care for diabetic foot ulcers among those with type 1 or type 2 diabetes (36). Costs for the inpatient and subsequent outpatient management of stroke, regardless of diabetes status, were derived from Australian cost-effectiveness analyses (44,45), and the costs for other cardiovascular diseases among the general population were derived from a report by the Australian Heart Foundation (46). A conservative 50% reduction was applied to costs for fatal cardiovascular events to account for only 50% of deaths occurring in hospitals throughout Australia, regardless of diabetes status (47,48). For severe hypoglycemia, an Australian economic evaluation was used that comprised costs for ambulance callout, hospital admission, accident and emergency visit, glucagon injection, and follow-up visits (49). Nonsevere hypoglycemic events were assumed to have no associated costs, but an annual disutility was applied.

Regarding intervention costs, the analysis included incremental costs of an HCL system over current care, which was defined by the baseline utilization rates of insulin pump therapy or multiple daily injections of insulin with either capillary glucose testing or continuous glucose monitoring in the Australian pediatric RCT (30). The only commercially available HCL systems in Australia at the time of writing comprised the MiniMed 670G/770G (Medtronic), infusion sets, reservoirs, batteries, Guardian Sensor 3 (Medtronic), transmitter, and transmitter charger. Twice-daily capillary glucose testing was assumed for calibration when using the HCL system or continuous glucose monitoring compared with eight tests per day for capillary glucose testing. Among those in the comparator arm using continuous glucose monitoring apart from HCL therapy, it was assumed that available and subsidized brands (AMSL Diabetes and Medtronic Diabetes Australia) were used in equal proportions. Cohort-level utilization rates of insulin pumps, injections, capillary glucose testing, and continuous glucose monitoring of those ≤25 and >25 years of age derived an overall weighted cost for management in the comparator arm. The previously described Australian cost of illness study presented aggregated costs of type 1 diabetes that captured the cost of multiple daily injections and capillary glucose testing. Consistent with current government subsidies, the cost of insulin pump consumables, but not insulin pumps, was added to the aggregated cost of type 1 diabetes among those using insulin pump therapy in the comparator arm.

Utilities for the health state of type 1 diabetes without complications were drawn from the same Australian cost of illness study described above (22). In the absence of Australian data sources and sparsity of detail internationally, utilities for the different stages of diabetic eye disease were drawn from Japanese and Canadian patient preference-based time trade-off analyses that informed prior cost-effectiveness analyses (50–52). To facilitate the inclusion of separate utilities for albuminuria as well as hemodialysis and peritoneal dialysis, European analyses of people with type 2 diabetes or on chronic dialysis were implemented and were consistent with the available more general data regarding people with type 1 diabetes (53–55). To facilitate the inclusion of separate utilities for diabetic foot ulceration, infection, as well as minor or major amputations, with or without infection, European time trade-off analyses were chosen that had been used previously in Australian cost-effectiveness analyses (36,56). Disutilities for AMI, stroke, CHF, and angina were taken from a population-based time trade-off analysis among people with type 2 diabetes in the U.K. (57). Annual disutilities of −0.0475 and −0.0041 were used for severe and nonsevere hypoglycemia, respectively. Disutility for hypoglycemia was derived as the average of daytime and nocturnal events from a large Canadian time trade-off survey among people with type 1 diabetes (58). Disutilities for multihealth conditions were summed. See Supplementary Table 3 for a list of utilities/disutilities used in the analysis.

The model was populated by convention with 10,000 individual patient-level simulations profiled largely from a 6-month Australian pediatric RCT of HCL therapy compared with current care (30). Individuals entered the model at 12 years of age, with diabetes duration of 7 years, and with no vascular complications. Rates of severe and nonsevere hypoglycemia among the pediatric population and those modeled to continue through adulthood were drawn from large registries, the DCCT/EDIC study, cohort studies as well as the 6-month Australian RCT (2,30,59–67). Treatment effects for HCL therapy on HbA_1c and nonsevere hypoglycemia reduction were drawn from the same Australian pediatric RCT and were assumed to be constant over follow-up for primary and secondary analyses (30). HCL therapy was also conservatively assumed to reduce severe hypoglycemia by 50% in the base-case. Baseline utilization of technology types among participants in the Australian pediatric RCT defined the comparator arm for our economic evaluation, were assumed to remain constant until 25 years of age, and comprised insulin pump therapy (81%), multiple daily injections of insulin (19%), continuous glucose monitoring (59%), and capillary glucose testing (41%) (30). For the secondary analyses that followed up individuals until death, clinical profiles and utilization rates of technology types after 25 years of age were based on data from the Australian National Diabetes Audit, which has reported that 27% and 23% of adults with type 1 diabetes used insulin pumps and continuous glucose monitoring, respectively, and were also assumed to remain constant over time (20). Cohort-level mean (±SD) values for biomarkers required for cardiovascular risk prediction in adulthood were also derived from the Australian National Diabetes Audit and were assumed to be equivalent in the treatment and comparator groups (68). Broadly in keeping with current funding initiatives in Australia, those using continuous glucose monitoring were assumed to receive no government subsidy after 25 years of age, unless part of the modeled intervention of HCL therapy (20). Those using insulin pumps apart from HCL therapy were also assumed to receive the current government subsidy that is limited to consumables (reservoirs and cannulas) and insulin regardless of age. A detailed profile of the simulated individuals and treatment effects is provided in Table 1.

Probabilistic sensitivity analyses with 10,000 iterations were performed to account for uncertainty across the input parameters. Univariate scenario analyses were also considered to explore model assumptions that comprised extending the horizon from 13 years to a lifetime; reducing discount rates for future costs and outcomes to 3.5%, 1.5%, and 0%; increasing and decreasing the cost of HCL therapy by 10%; reducing the treatment effect of HCL therapy on nonsevere hypoglycemia by 25% and 50%; increasing the baseline rate of nonsevere hypoglycemia to 104 events per person/year; reducing the treatment effect of HCL therapy on severe hypoglycemia by 50% and 100%; assuming no treatment effect on HbA_1c; assuming the baseline HbA_1c was 7.0% (53 mmol/mol) or 9.0% (75 mmol/mol); assuming the baseline incidence of severe hypoglycemia was 16.6 or 6.7 per 100 person-years based on local population studies (64,69); and assuming the treatment effect on HbA_1c increased from 0.3% (3.3 mmol/mol) to 1.0% (10.9 mmol/mol) over a lifetime horizon with the baseline HbA_1c unchanged or else increased to 9.0% (75 mmol/mol). All univariate scenario analyses were replicated over a lifetime horizon, except for assuming reduced base rates of severe hypoglycemia based on results reported among a pediatric population. In addition, the treatment effect of HCL therapy on HbA_1c was only increased from 0.3% (3.3 mmol/mol) to 1.0% (10.9 mmol/mol) among adults over a lifetime horizon based on results of meta-analyses among adults (13,14). Microsoft Excel (Microsoft Corp., Redmond, WA) and TreeAge Pro Healthcare 2021 R1.1 (TreeAge Software LLC, Williamstown, MA) were used for the economic analyses.

Compared with current care, the use of HCL therapy was associated with gains of 1.15 (discounted) QALYs per person until 25 years of age, at a net cost (discounted) of AUD $37,827, resulting in an ICER of AUD $32,789 per QALY gained. At the commonly accepted willingness-to-pay (WTP) threshold of AUD $50,000 per QALY gained in Australia (70–72), 93.3% of simulations predicted the HCL system to be cost-effective.

Sensitivity analyses found that results were most sensitive to the base rate and treatment effect on nonsevere hypoglycemia, the cost of HCL systems, the time horizon, and the treatment effect of HCL therapy on HbA_1c. Assuming the base rate of nonsevere hypoglycemia was increased to 104 events per person/year in keeping with the rates reported by the International Society for Pediatric and Adolescent Diabetes (ISPAD) Clinical Practice Consensus Guidelines for hypoglycemia (66), led to an ICER of AUD $18,436 per QALY gained while reducing the treatment effect of HCL therapy on nonsevere hypoglycemia by 50% increased the ICER to AUD $54,668 per QALY gained. In contrast, assuming no treatment effect on severe hypoglycemia for people aged 12 to 25 years increased the ICER only slightly to AUD $37,452 per QALY gained. Reducing or increasing the cost of HCL therapy by 10% led to ICERs of AUD $24,234 or AUD $40,864 per QALY gained, respectively.

Extending the time horizon to a lifetime increased the ICER to AUD $48,882 per QALY gained. In the context of a lifetime horizon, increasing the relative treatment effect of HCL therapy on HbA_1c from −0.3% (3.3 mmol/mol) to −1.0% (10.9 mmol/mol) reduced the ICER to AUD $42,271 per QALY gained, or AUD $43,136 per QALY gained if the baseline HbA_1c was also increased to 9.0% (75 mmol/mol). Results over a lifetime were also particularly sensitive to the cost of HCL therapy as well as the base rate of nonsevere hypoglycemia and the treatment effect of HCL therapy on nonsevere hypoglycemic events. Reducing the recommended 5% discount rate for future costs and health outcomes increased the ICER. Results of the base-case and scenario analyses are presented in Tables 2 and 3, respectively.

The cost-effectiveness plane and scatter plot (Fig. 1) presents the variation in modeled outcomes, with HCL therapy tending to offer greater effectiveness in QALYs but at a greater cost than current care. The ellipse in Fig. 1 represents the 95% confidence for modeled results. The cost-effectiveness acceptability curves for the primary and secondary analyses (Supplementary Fig. 3 and 4, respectively) present the proportion of 10,000 iterations that were cost-effective for HCL therapy and current care at various WTP thresholds. Tornado diagrams of sensitivity analyses of HCL therapy compared with current care are presented in Supplementary Figs. 5 and 6 and provide further support for our choice of variables and scenarios for deterministic sensitivity analyses presented in Table 3.

Our modeled economic evaluation produced an ICER of AUD $32,789 per QALY gained for HCL therapy relative to current care. HCL therapy is thus likely to be cost-effective compared with current care among young people with type 1 diabetes in Australia based on the commonly accepted WTP threshold of AUD $50,000 per QALY gained (70–72). Sensitivity analyses demonstrated the results were robust.

Our analyses regarding HCL therapy had a unique focus on young people as well as the comparison with a range of preexisting management devices. However, results were consistent with the few published economic evaluations, primarily among adults, that similarly reported HCL therapy to be cost-effective despite different health care systems, WTP thresholds, modeling perspectives, models, and different clinical comparators (27–29). HCL therapy has been consistently modeled to increase QALYs over a lifetime, with the increment ranging from 1.73 and 1.90 compared with insulin pump therapy (27,28), through to 3.72 compared with insulin injections (29) in the U.K., Sweden, and Australia, respectively. This is in keeping with our sensitivity analyses over a lifetime horizon that modeled an increment of 2.15 QALYs compared with current care. However, prior modeled cohorts have been much older, with analyses starting at 18 years of age in Australia (29), or a mean age of 37.8 (SD 16.5) years in the U.K. and Sweden (27,28). Previous analyses have also limited their comparison of HCL systems to insulin pump therapy or insulin injections. Therefore, our focus on the cost-effectiveness of HCL therapy among young people compared with current care adds important breadth to the growing landscape of economic evidence.

In addition to the primary analysis among young people, a secondary analysis using a lifetime horizon was included due to the lifelong nature of type 1 diabetes. Consistent between the primary and secondary analyses was that nonsevere hypoglycemia and the cost of technology remained key drivers for cost-effectiveness. The important role of utility gains from reduced hypoglycemia or “fear” of hypoglycemia as well as technology costs have also been reported by the few other economic evaluations of HCL therapy to date (27–29). Furthermore, because most vascular complications develop in adulthood, the relative impact of HbA_1c reductions from HCL therapy on ICER values was also much greater over the lifetime horizon compared with the primary analysis over 13 years. Our conservative analyses over a lifetime horizon produced an ICER of AUD $48,882 per QALY gained. Combined with our primary analysis, the results suggest that HCL therapy may be cost-effective in young people and across the life span compared with current care in Australia. Any reductions in technology costs over time would also be predicted to decrease the ICER further. However, base rates of nonsevere hypoglycemia and treatment effects of HCL therapy on HbA_1c and nonsevere hypoglycemia over a lifetime may impact these conclusions.

The strengths of our study included patient-level simulations providing more granular detail than cohort-based models and a diverse multidisciplinary team developing the model and ensuring face validity (Supplementary File; Reporting and validation). Treatment effects of HCL therapy for HbA_1c and nonsevere hypoglycemia were derived from an Australian RCT (30). The comparator arm used for our analyses may also be more generalizable than other studies because participants and their clinicians were free to use any management technology apart from HCL therapy throughout the trial, reflecting real-world clinical practice (30). In addition, conservative estimates for baseline glycemia were implemented, and extensive sensitivity analyses for key drivers of cost-effectiveness supported our base-case results. Costs and utilities for the baseline health state of type 1 diabetes without complications were sourced from Australia’s largest cost of illness study (22), and all other cost data were taken from Australian sources.

We also acknowledge that there were limitations of the available data sources and model assumptions. Outcome projections over a number of years were extrapolated from short-term clinical data in the absence of long-term studies for modern technologies. Like most economic evaluations, generalizability of results may be limited by the implementation of RCT data with recruited and often highly motivated participants. Treatment effects and base rates of severe hypoglycemia were also unclear in the literature. However, data from the landmark DCCT/EDIC study were implemented for base rates of severe hypoglycemia as they were considered conservative compared with population studies and meta-regression of RCTs (1,2,64–67,73,74). Sensitivity analyses also considered lower baseline rates of severe hypoglycemia as reported by other Australian studies (64,69). Regarding the treatment-effect of HCL therapy on severe hypoglycemia, the majority of recent trials have been of short duration and reported zero events among young people, although similarly low rates were present in comparator arms as well. In contrast to other economic evaluations that assumed HCL therapy completely prevented severe hypoglycemia over a lifetime in the base-case (27,28), we were more conservative and considered a treatment effect of 50% with sensitivity analyses ranging from 0 to 95%. As with other published economic models, utility values derived from populations with type 2 diabetes and/or from different countries were implemented when comparable detail was not available for those with type 1 diabetes in Australia. Experienced ophthalmologists also assisted with the development of model assumptions regarding the investigation and management of diabetic eye disease in the absence of adequate publicly available data. Owing to limited available data sources, a number of assumptions were made during model development. The costs of end-stage renal disease and of CVD were based on populations irrespective of comorbid diabetes status, and the cost of diabetic foot disease was based on a population with any diabetes type. Fatal cardiovascular event costs were conservatively reduced by 50% to account for only 50% of deaths occurring in hospitals throughout Australia (47,48); however, assuming no adjustment for death costs or assuming zero costs for death had minimal impact on the primary or secondary analysis (data not shown). Results were limited to the health care system perspective, although any societal benefits from HCL therapy would likely reduce the ICER further. Similar to other validated models, historical results from the DCCT/EDIC study were used to apportion modeled cardiovascular events as AMI, unstable angina, stroke, or CHF. In addition, eGFR was assumed to be <40 mL/min/1.73 m² only for those modeled to have end-stage renal disease. Deterministic and probabilistic sensitivity analyses addressed uncertainty in the model, but it was not feasible to address all clinically relevant scenarios.

In addition to cost-effectiveness analyses, further research is needed into the indirect costs and budget impact of HCL therapy as well as real-world and patient-relevant outcomes. Clinical trials and existing diabetes registries could be leveraged to collect standardized economic data to strengthen models, address economic uncertainty, and appraise implementation strategies. The increasing focus on time-in-range over HbA_1c as a metric of glycemic control also requires longer-term data regarding the complications of diabetes and quality of life to facilitate inclusion in future economic modeling. Furthermore, while most economic evaluations focus on the perspective of the health care system for funding decisions, the burden of health care costs on people with type 1 diabetes and their carers should also drive further research into health equity.

Overall, HCL therapy is likely cost-effective compared with current care by reducing HbA_1c and the incidence of hypoglycemia among those with type 1 diabetes in Australia. This first positive finding among young people adds to the weight of growing international economic evidence among adults that HCL therapy appears to represent good value for money in a relatively short time frame and across the life span. Future research into diabetes management technologies should also focus on budget impact and incorporating health equity into funding decisions.

Acknowledgments. The authors thank the participating sites for the randomized controlled trial, which comprised Perth Children’s Hospital, Nedlands, Western Australia; The Children’s Hospital at Westmead, Westmead, New South Wales; Women and Children’s Hospital, North Adelaide, South Australia; Royal Children’s Hospital, Parkville, Victoria; and John Hunter Children’s Hospital, New Lambton Heights, New South Wales. The authors also thank Prof. Peter van Wijngaarden (Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia) and Dr. Peter Heydon (Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia; Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia; Liverpool Hospital, Liverpool, New South Wales, Australia) for their expert advice regarding clinical assumptions in the model for diabetic eye disease. This economic evaluation was developed through collaborations as part of the JDRF Global Centre of Excellence in Diabetes Research.

Funding. A.P. was supported by the Royal Australasian College of Physicians/Diabetes Australia Research Establishment Fellowship. Funding and support for the randomized controlled trial, “Effect of a hybrid closed-loop system on glycemic and psychosocial outcomes in children and adolescents with type 1 diabetes: a randomized clinical trial” comprised: the JDRF Australia Type 1 Diabetes Clinical Research Network grant 4-SRA-2016-350-M-B (T.W.J.), Special Research Initiative of the Australian National Health and Medical Research Council grant APP1078190 (T.W.J.), and PCH Foundation grant 9729 (T.W.J.). The National Health and Medical Research Council Clinical Trials Centre was supported by its National Health and Medical Research Council Program grant. In-kind support was provided by Medtronic through the provision of insulin pumps, sensors, and transmitters for the study. Roche Diabetes Care provided the glucometers for the study. For the randomized controlled trial, M.B.A. was supported by the Department of Health/Raine Clinical Research Fellowship from Western Australia.

JDRF Australia provided input into the study design of the randomized controlled trial. The funders of the trial had no role in collection, analysis, and interpretation of the data and in manuscript preparation.

Duality of Interest. E.Z. has received grants from Amgen, AstraZeneca, Pfizer, and Shire outside the submitted work. M.B.A. reports receiving speaker honoraria fees from Medtronic outside the submitted work. E.A.D. reports participation in educational meetings on behalf of the Telethon Kids Institute for Eli Lilly (payment to institution). D.L. has received grants from AstraZeneca, Pfizer, AbbVie, and Bristol Myer Squibb, and has received personal fees from AstraZeneca, Astellas, and Bayer outside the submitted work. S.Z. reports participation in advisory boards, expert committees, or educational meetings outside the submitted work on behalf of Monash University for Boehringer-Ingelheim, Eli Lilly, Sanofi, AstraZeneca, Novo Nordisk, and MSD Australia (payment to institution). T.W.J. reports receiving grants from the JDRF for study costs and grants from Perth Children's Hospital Foundation for study costs during the conduct of the study. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. A.P. performed analysis and drafted the manuscript. E.C., E.Z., M.B.A., E.A.D., T.W.J., D.L., and S.Z. assisted manuscript drafting and review. E.C., E.Z., and D.L. also provided advice regarding analysis. All authors read and approved the manuscript. S.Z. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented as an abstract at The Australasian Diabetes Congress 2021, virtual event, 11–13 August 2021, and at the First Annual International Hypoglycaemia Study Group Advances in Hypoglycaemia Scientific Conference (https://www.xcdsystem.com/adc/program/vPZbWzx/index.cfm?pgid=2327&print=1&printmode=1), 17–18 November 2021. An abstract was published after submission to The Australasian Diabetes Congress 2021. Pease A, Callander E, Zomer E, et al. Cost-effectiveness analysis of hybrid closed-loop therapy versus current care among young people with type 1 diabetes: a patient-level simulation.