Automated Insulin Delivery Versus Standard of Care in the Management of People Living with Type 1 Diabetes and HbA1c <8%: A Cost

Abstract

Introduction:

Automated insulin delivery (AID) systems improve glycemic control in people living with type 1 diabetes (PwT1D). AID is cost-effective versus other management approaches in a range of country settings and populations. This cost–utility analysis adds an evaluation of the MiniMed^TM 780G system versus standard of care (SoC) in PwT1D and baseline glycated hemoglobin (HbA1c) level <8% not reaching glycemic targets, conducted from a societal perspective in The Netherlands.

Methods:

The analysis was run using the IQVIA CORE Diabetes Model, over 50 years. Costs were discounted at 3% per year, effects at 1.5% per year. Baseline cohort characteristics and treatment effects were sourced from the MiniMed 780G arm of a prospective multicenter study. Costs and utility estimates were taken from Dutch databases and published sources. Sensitivity analyses were conducted to address uncertainty.

Results:

AID improved life expectancy by 0.52 years and quality-adjusted life expectancy by 0.99 quality-adjusted life-years (QALYs) versus SoC. AID was associated with an incremental combined cost of EUR 28,635 due to higher acquisition costs, which were partially offset by reduced direct treatment costs for diabetes-related complications and reduced indirect costs due to less time off work. Based on combined costs, the MiniMed 780G system was associated with an incremental cost–utility ratio of EUR 29,836 per QALY gained.

Conclusions:

For PwT1D in The Netherlands, who had a baseline HbA1c <8% and do not reach glycemic targets, AID system initiation was projected to improve long-term clinical outcomes and reduce both direct costs for the treatment of diabetes-related complications and productivity losses. From a societal perspective, the MiniMed 780G likely represents good value for money in The Netherlands.

Introduction

Type 1 diabetes (T1D) is a chronic autoimmune condition, in which insulin deficiency after the loss of pancreatic islet beta-cells leads to elevated blood glucose levels.¹ In people living with T1D (PwT1D), high levels of glycated hemoglobin (HbA1c) increase the risk of microvascular complications such as diabetic retinopathy and nephropathy, macrovascular complications such as coronary heart and cerebrovascular disease, and mortality.^1

–5 Higher HbA1c levels and more frequent severe hypoglycemic events (SHEs) have also been linked to a notable risk of cognitive decline in older PwT1D.⁶

Treatment for T1D aims to reduce blood glucose levels, ideally to <7% (<53 mmol/mol) in adults, using intensive insulin therapy.^1,7 While effective, insulin therapy comes with a risk of hypoglycemia, which reduces quality of life (QoL). Actual and anticipated hypoglycemic episodes impair the private, social, and professional life of PwT1D, and frequent worrying about hypoglycemia increases the disease-associated burden.^8
–10

Guidelines recommend continuous glucose monitoring (CGM) for individuals at high hypoglycemia risk.⁷ CGM reduces blood glucose levels and hypoglycemia and improves time in range (TIR) and QoL.^11,12 CGM devices are increasingly coupled with insulin pumps that continuously deliver insulin. In their most advanced form to date, these technologies are combined into automated insulin delivery (AID), or advanced hybrid closed-loop, systems. These systems include CGM and insulin pumps with algorithms that calculate insulin doses required to keep glucose levels in target ranges. They are quickly becoming the standard of care (SoC) in T1D.^11,13,14 Closed-loop systems are highly effective, increasing TIR more than alternative management strategies and ranking best for the percentage of time spent in range.^15,16 AID systems are also associated with shorter, less severe rebound hyperglycemia after a hypoglycemic event, outperforming, for example, sensor-augmented pumps.¹⁷

Both clinical and real-world data have shown the considerable benefits of one closed-loop system, the MiniMed™ 780G system. The ADAPT randomized controlled trial (RCT) compared this system with multiple daily injections of insulin (MDI) plus intermittently scanned CGM (isCGM) in people with HbA1c of ≥8%, reporting an HbA1c difference of 1.42% in favor of the MiniMed 780G system.¹⁸ A network meta-analysis of RCTs found the MiniMed 780G system to achieve the highest TIR of all considered hybrid closed-loop systems.¹⁹ Real-world data from >100,000 users confirmed consistent benefits in achieving glycemic targets and TIR, including a TIR of >72% and a time in tight glucose range of >48%.^20,21 Twelve-month Dutch data in PwT1D indicated an improvement in HbA1c of 0.5%, from a baseline of 7.6%, subsequent to initiating use of the MiniMed 780G system.²² Notably, this improvement was consistent across age groups and observed regardless of prior treatment.

AID benefits have translated into favorable value-for-money assessments in several settings. The MiniMed 780G system was cost-effective from a societal perspective versus isCGM plus MDI or continuous subcutaneous insulin infusion in Sweden, from a societal perspective versus isCGM plus MDI in Greece, and from a health care payer perspective versus isCGM plus MDI in Singapore.^23
–25 An analysis covering six European countries showed the system as cost-effective from a health care payer perspective versus isCGM plus MDI in people not achieving glycemic treatment goals, with benefits driven by the reduced incidence of long-term complications.²⁶

The present study adds a cost–utility analysis of AID, with the MiniMed 780G system, versus SoC in PwT1D and baseline HbA1c <8% (64 mmol/mol) not reaching glycemic targets in The Netherlands. The setting was chosen due to the room for improvement in Dutch diabetes care noted by Varkevisser et al., who observed that less than 30% of PwT1D achieved HbA1c targets overall.²⁷ Specifically, Dutch Pediatric and Adult Registry of Diabetes (DPARD) data indicated a median HbA1c of 7.6% across people living with diabetes, but 36% of those with an HbA1c <8% did not reach internationally approved glycemic targets.²⁸ The added value and cost-effectiveness of AID were of particular interest as health economic evidence on AID in this group is sparse. Given the projected value of improved glycemic control in The Netherlands,²⁹ increased AID use in PwT1D and a baseline HbA1c <8% (64 mmol/mol) not reaching glycemic targets were anticipated to translate into meaningful benefits from a societal perspective in The Netherlands.

Materials and Methods

Model structure

The IQVIA CORE Diabetes Model (CDM) version 9.5 was used to conduct the analysis. The CDM is a nonproduct-specific computer simulation model that projects long-term clinical outcomes and costs associated with interventions for type 1 and 2 diabetes mellitus. A full model description and validation exercises have been published previously.^30
–32 Model outcomes relevant for the present analysis included life expectancy, quality-adjusted life expectancy (QALE), expressed in quality-adjusted life-years (QALYs), and cumulative complication incidence, as well as direct, indirect, and total lifetime costs. The main outcome was the incremental cost–utility ratio (ICUR), expressed as the total cost per QALY gained.

Analysis settings

The base case analysis was performed over a time horizon of 50 years, from a societal perspective as recommended by Dutch guidance.³³ Future costs were discounted at 3% per year, future benefits at 1.5%. Costs were reported in 2021 Euros. Cost-effectiveness was assessed at the three commonly used willingness-to-pay thresholds in The Netherlands (€20,000/50,000/80,000 per QALY gained) and also at €36,000 per QALY gained, which corresponds to the upper end of the range, converted to Euros, used by the National Institute for Health and Care Excellence in the United Kingdom.^34
–36

Baseline cohort characteristics and clinical treatment effects

Baseline cohort characteristics and treatment effects were taken from the AID arm of a prospective multicenter study in adolescents and adults with type 1 diabetes in Spain.³⁷ The AID arm included 75 participants with a mean (standard deviation) age of 40 years (11.8) and a baseline HbA1c of 7.43% (1.07) (Table 1). The incremental treatment effect of AID versus SoC was a reduction in HbA1c of 0.55%, to 6.88% (0.60), at 3 months.

Table 1.

Baseline Cohort Characteristics

	Baseline mean (standard deviation)
Age, years	40 (11.8)
Women, %	61
Diabetes duration, years	22 (13.5)
HbA1c, %	7.43 (1.07)
HbA1c, mmol/mol	58 (12)

Source: 75 participants treated with MiniMed™ 780G from Beato-Víbora et al.³⁷

HbA1c, glycated hemoglobin.

In the prospective study, no SHEs, defined as events that required medical assistance, were observed with AID. Conservatively, it was assumed that no SHEs occurred with SoC.

In line with previous analyses, full device adherence was assumed and users in the simulated cohort remained on the assigned device during the entire simulation.²⁶ Physiological parameter progression, including for HbA1c after treatment initiation and in the SoC arm, and mortality were modeled based on CDM defaults based on published sources, again in line with previous assessments.^23,26

Costs

Treatment costs in the SoC arm were the weighted mean of annual costs for isCGM, real-time CGM (rtCGM), and insulin pumps. The weights were derived from a sample of PwT1D treated at the University of Amsterdam, of whom 11% used rtCGM, 29% used an insulin pump, and the remainder used isCGM (Medtronic, data on file). The annual cost of isCGM management was based on 26 sensors per year, whereas the cost of rtCGM and insulin pump treatment was the mean cost across the CGM systems and insulin pumps, respectively, that are most used in The Netherlands. The cost of AID was based on the annualized pump cost, assuming that the pump needed replacing every 4 years. The costs further included the annual cost of one Simplera sensor per week. Notably, the annual cost of AID did not include the diabetes management fee because fewer than five visits per year are necessary with AID.

The direct costs considered in the analysis included management and screening costs, as well as the costs of cardiovascular, renal, ophthalmological, and neuropathy/foot ulcer costs, in addition to acute event costs for SHE and diabetic ketoacidosis. Estimates for these costs were sourced from a cost–utility analysis²⁹ of immediate relative to delayed glycemic control in The Netherlands, which reported 2021 costs obtained from Dutch cost databases and guidelines,^{38

–45} as well as the published literature.^{46

–62} Indirect costs were calculated in line with Moes et al., using an age at first income of 25 years and a retirement age of 67 years.²⁹ Mean salaries for women and men and the number of days worked per year were sourced from Dutch costing manuals and labor statistics, respectively.^18,53 The number of days off work due to diabetes complications was based on published estimates for chronic complications and hypoglycemia, with no days off specified for eye disease due to a lack of data.^50,63 Indirect costs were then calculated as the share of total working days missed, applied to the annual salary. The friction cost method was used to assess productivity loss.³³

Utilities

Health state utility values and event-associated utility decrements were sourced from the literature for long-term complications, diabetic ketoacidosis, and SHE (Table 2).^{26,64

–72} The diminishing approach by Lauridsen et al. was used to model the impact of nonsevere hypoglycemia on utility.⁷³ In addition, a disutility of −0.004 was applied per unit increase in body mass index above 25 kg/m², based on the polynomial approach implemented in the CDM.⁶⁴

Table 2.

Health State Utilities/Event Disutilities

State/event	Mean (SD)	Source
Type 1 diabetes baseline, no complications	0.9000 (0.0048)	Solli et al.⁶⁴
Myocardial infarction event	–0.1810 (0.0050)	Solli et al.⁶⁴
Postmyocardial infarction	0.7190 (0.0066)	Solli et al.⁶⁴
Angina	0.7190 (0.0066)	Solli et al.⁶⁴
Congestive heart failure	0.7190 (0.0066)	Solli et al.⁶⁴
Stroke event	–0.2910 (0.0080)	Solli et al.⁶⁴
Poststroke	0.6090 (0.0090)	Solli et al.⁶⁴
Peripheral vascular disease	0.8200 (0.0080)	Tabaei et al.⁶⁵
Microalbuminuria	0.8640 (0.0070)	Ahola et al.⁶⁶
Gross proteinuria	0.8640 (0.0080)	Ahola et al.⁶⁶
Hemodialysis	0.7000 (0.2600)	Knoll et al.⁶⁷
Peritoneal dialysis	0.6300 (0.2600)	Knoll et al.,⁶⁷ assumed to be 90% of the value for hemodialysis
Renal transplant	0.8000 (0.1700)	Knoll et al.⁶⁷
Background diabetic retinopathy	0.8930 (0.0210)	Ahola et al.⁶⁶
Background diabetic retinopathy, wrongly treated	0.8930 (0.0220)	Assumed to be the same as background diabetic retinopathy
Proliferative diabetic retinopathy, laser treated	0.8520 (0.0220)	Hart et al.⁶⁸
Proliferative diabetic retinopathy, no laser	0.8520 (0.0220)	Hart et al.⁶⁸
Macular edema	0.8930 (0.0210)	Assumed to be the same as background diabetic retinopathy
Severe vision loss	0.8370 (0.0220)	Solli et al.⁶⁴
Cataract	0.8930 (0.0210)	Assumed to be the same as background diabetic retinopathy
Neuropathy	0.5420 (0.0080)	Solli et al.⁶⁴
Active ulcer	–0.0830 (0.0189)	Solli et al.⁶⁴
Amputation event	–0.1700 (0.0110)	Lee et al.⁶⁹
Postamputation	0.7300 (0.3000)	Lee et al.⁶⁹
Nonsevere hypoglycemic event, daytime	–0.0040 (0.0010)	Evans et al.⁷⁰
Nonsevere hypoglycemic event, nocturnal	–0.0080 (0.0010)	Evans et al.⁷⁰
Severe hypoglycemic event, daytime	–0.0470 (0.0010)	Evans et al.⁷⁰
Severe hypoglycemic event, nocturnal	–0.0510 (0.0010)	Evans et al.⁷⁰
Per 1-unit increase in body mass index >25 kg/m²	–0.0040	Solli et al.⁶⁴

Note: Standard deviations (SDs) were IQVIA CORE Diabetes Model defaults.

Sensitivity analyses

Several deterministic sensitivity analyses (DSAs) were conducted to identify drivers of cost–utility outcomes. A first set of analyses was run using 10 and 20 years as the time horizon. Several DSAs were run for SHE incidence in SoC, with rates per 100 person-years set to 1, 2, and 5 events, respectively. In addition, three analyses were conducted in which the rates of SHE requiring medical assistance were set to 10.7, 21.3, and 23.7 per 100 person-years,^37,74 respectively, whereas the rate for SHE not requiring medical assistance was set to nine events per 100 person-years. Additional analyses were performed around the HbA1c benefits of AID, which were assumed to be −0.6% and −0.4%, respectively, whereas an additional analysis combined a benefit of −0.4% with a SHE rate of five events per 100 person-years in the SoC arm. Another analysis investigated a 0.4% HbA1c reduction with MiniMed 780G, starting from a baseline HbA1c of 7.6% (the mean HbA1c value reported in DPARD).²⁸

Probabilistic sensitivity analysis (PSA) was also performed. Results from the PSA were plotted on the cost-effectiveness plane and as a cost-effectiveness acceptability curve.

Ethics

The present analysis uses previously collected data and studies. It does not contain any studies with human participants or animals performed by any of the authors.

Results

Base case analysis

In the base case, AID was projected to be associated with mean life expectancy gains of 0.52 years and mean QALE gains of 0.99 QALYs relative to SoC (Fig. 1).

FIG. 1.

Base case results. AID, automated insulin delivery; QALY, quality-adjusted life-year; SoC, standard of care.

These gains with AID resulted from the reduced incidence of diabetes complications due to lower HbA1c levels, which implied a later onset of complications (Fig. 2). Relative to people in SoC, those with AID lived almost 3 years longer without any complication.

FIG. 2.

Projected mean time alive without complications.

Total direct costs were projected to be EUR 31,722 higher with AID (EUR 121,962) than with SoC (EUR 90,240). The difference was driven by acquisition costs, which were partially offset by reduced direct costs for the treatment of complications, including cardiovascular, kidney, and eye disease, as well as neuropathy (Fig. 3).

FIG. 3.

Breakdown of mean direct treatment costs. CVD, cardiovascular disease.

Total indirect costs were lower with AID (EUR 12,974) than with SoC (EUR 16,061) as lower complication incidence with AID reduced time off work and productivity losses. The projected total combined costs were EUR 134,936 for AID versus EUR 106,301 for SoC, equivalent to an incremental total combined cost for AID of EUR 28,635.

Combining the projected QALE gains and incremental costs with AID versus SoC yielded an ICUR of EUR 28,936 per QALY gained from a Dutch societal perspective.

Sensitivity analyses

Results were robust across DSAs (Table 3). Shorter time horizons increased ICURs relative to the base case, as fewer benefits from AID treatment were captured than in the lifetime analysis. Sensitivity to SHE rates in the SoC arm and to HbA1c reductions with AID was limited although, as would be expected, higher SHE rates with SoC reduced while smaller HbA1c benefits with AID increased ICURs.

Table 3.

Deterministic Sensitivity Analyses

Analysis	QALE (QALYs)			Combined costs (EUR)			Combined costs (EUR) per QALY gained
Analysis	AID	SoC	Δ	AID	SoC	Δ	Combined costs (EUR) per QALY gained
10-year time horizon	7.96	7.88	0.08	46,872	33,178	13,694	164,795
20-year time horizon	13.94	13.64	0.31	106,642	87,127	19,514	63,629
1 SHE per 100 person-years in SoC	21.30	20.30	1.00	134,936	106,458	28,478	28,418
2 SHE per 100 person-years in SoC		20.29	1.02		106,615	28,321	27,914
5 SHE per 100 person-years in SoC		20.25	1.05		107,087	27,849	26,470
10.7 SHE requiring and 9 SHE not requiring medical assistance per 100 person-years in SoC	21.30	20.11	1.20	134,936	107,979	26,956	22,522
21.3 SHE requiring and 9 SHE not requiring medical assistance per 100 person-years in SoC		19.97	1.33		109,643	25,292	19,027
23.7 SHE requiring and 9 SHE not requiring medical assistance per 100 person-years in SoC		19.94	1.36		110,020	24,915	18,330
HbA1c reduction of 0.6% with AID	21.37	20.31	1.06	134,251	106,301	27,949	26,393
HbA1c reduction of 0.4% with AID	20.71	19.99	0.72	136,832	106,301	30,351	43,899
HbA1c reduction of 0.4% with AID and 5 SHE per 100 person-years in SoC	21.01	20.25	0.76	136,832	107,087	29,745	39,242
HbA1c reduction of 0.4% with AID with a baseline HbA1c of 7.6%	20.71	19.99	0.72	139,030	109,535	29,494	41,039

Note: Unless specified differently, SHE was assumed to require medical assistance.

AID, automated insulin delivery; QALE/Y, quality-adjusted life expectancy/year; QALYs, quality-adjusted life-years; SHE, severe hypoglycemic event; SoC, standard of care.

In PSA, all pairs of incremental QALE and cost were in the northeast quadrant, indicating gains in QALE at higher costs (Fig. 4). At willingness-to-pay thresholds of EUR 20,000, 36,000, 50,000, and 80,000 per QALY gained, there was a 7.7%, 76.4%, 93.1%, and 98.8% probability, respectively, that AID was cost-effective versus SoC (Fig. 5). Overall, the probability that AID was cost-effective versus SoC was >50% from a willingness-to-pay threshold of EUR 29,000 per QALY gained.

FIG. 4.

Cost-effectiveness scatter plot. Each point represents one incremental QALY/incremental cost pair from the probabilistic sensitivity analysis. QALE/Y, quality-adjusted life expectancy/year; WTP, willingness to pay.

FIG. 5.

Cost-effectiveness acceptability curve. The willingness-to-pay threshold in The Netherlands is EUR 36,000 per QALY gained.

Discussion

From a societal perspective, in The Netherlands, the MiniMed 780G system was found to be good value for money relative to the current SoC, which consists of rtCGM, isCGM, and insulin pumps, in PwT1D and a baseline HbA1c <8% (64 mmol/mol) who did not achieve glycemic targets. AID system use increased QALE by approximately one QALY because of reduced complication incidence and delayed complication onset attributable to lower HbA1c levels. These findings were robust across scenarios and assumptions.

The direct cost was projected to be higher with AID systems than SoC due to the higher mean lifetime treatment costs associated with the MiniMed 780G system. This finding is aligned with previous analysis for this and similar devices and has been noted as a potential barrier to more widespread uptake.¹³ It is noteworthy, however, that higher acquisition costs were partly offset by reduced costs for managing diabetes-related complications, including cardiovascular and kidney disease.

AID was also associated with lower indirect costs versus SoC, as fewer and delayed complications due to improved HbA1c implied less time off work, thereby reducing productivity losses. These benefits are important to The Netherlands, where 2016 productivity losses and indirect cost of complications due to T1D were estimated at EUR 64.8 million and EUR 38.4 million, respectively.⁷⁵ The Netherlands are also experiencing a labor shortage, which has been recommended to be addressed, in part, by preventing the premature or temporary exit of staff from the workforce.^76,77 Technologies such as AID that reduce time off work in PwT1D can contribute to avoid such early or temporary exits.

The present analysis has some limitations. First, 3-month clinical effects were projected over 50 years, which is necessarily associated with uncertainty, as is typical for health economic analyses projecting long-term outcomes from short-term clinical and real-world benefits. The use of an established and validated model for projection, however, was considered to mitigate some of the uncertainty, as was the available evidence that the HbA1c level achieved with AID after 3–4 months is indicative of long-term levels and sustained over time.^78
–80 Second, Dutch data could not be identified for all inputs, including treatment effects, utility values, and days off work, with data sourced from outside The Netherlands used instead. Future analyses should consider replicating the present analyses when input estimates for a Dutch setting become available. Third, as noted by Moes et al., the IQVIA CDM is unable to capture the long-term effects of treatment on early retirement,²⁹ which likely implied an underestimation of the long-term indirect benefits associated with AID. The benefits of the MiniMed 780G system on TIR, which have been observed in the target and across several other populations,^{19
–21,37,81} could also not be considered in the present analysis. Again, this likely underestimated the benefits associated with AID. Complementary analyses investigating the clinical and economic effects of TIR improvements, including for novel treatment approaches, drugs, and devices, would be welcome as guideline recommendations put increasing emphasis on TIR targets for the treatment of T1D.^82,83

Conclusions

The present cost–utility analysis indicated that the MiniMed 780G system was likely cost-effective versus SoC, which did not include AID, in PwT1D and baseline HbA1c <8% (64 mmol/mol) who did not reach glycemic targets, from a societal perspective in The Netherlands. The higher acquisition costs of the MiniMed 780G system were partly offset by reduced direct costs for the management of diabetes-related complications and by reduced indirect costs due to less time spent off work. AID increased QALE by almost a full QALY relative to SoC. Based on conventional willingness-to-pay thresholds, the MiniMed 780G system can be considered good value for money in The Netherlands.

Footnotes

Authors’ Contributions

E.H.S.: Investigation (equal) and writing—reviewing and editing (equal); M.I.B.: Conceptualization (equal), data curation (lead), methodology (equal), formal analysis (lead), and writing—reviewing and editing (equal); S.d.P.: Conceptualization (equal), methodology (equal), and writing—reviewing and editing (equal); J.S.-P.: Writing—reviewing and editing (equal); J.P.: Data curation (equal), writing—original draft (lead), and visualization (lead); and O.C.: Conceptualization (equal), methodology (equal), and writing—reviewing and editing (equal).

Disclosure Statement

E.H.S. is an employee of Amsterdam UMC. M.I.B., S.d.P., and O.C. are current employees and shareholders of Medtronic. J.S.-P. and J.P. are current employees of Covalence Research Ltd., which has received consulting fees from Medtronic related to the preparation of this article.

Funding Information

Funding related to the preparation of this article was provided by Medtronic International Trading SARL, Tolochenaz, Switzerland.

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Automated Insulin Delivery Versus Standard of Care in the Management of People Living with Type 1 Diabetes and HbA1c <8%: A Cost–Utility Analysis in The Netherlands

Abstract

Introduction:

Methods:

Results:

Conclusions:

Introduction

Materials and Methods

Model structure

Analysis settings

Baseline cohort characteristics and clinical treatment effects

Costs

Utilities

Sensitivity analyses

Ethics

Results

Base case analysis

Sensitivity analyses

Discussion

Conclusions

Footnotes

Authors’ Contributions

Disclosure Statement

Funding Information

References