OBJECTIVE

A long-term health economic analysis was performed to establish the cost-effectiveness of real-time continuous glucose monitoring (RT-CGM) (Dexcom G6) versus self-monitoring of blood glucose (SMBG) alone in U.K.-based patients with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

The analysis used the IQVIA CORE Diabetes Model. Clinical input data were sourced from the DIAMOND trial in adults with T1D. Simulations were performed separately in the overall population of patients with baseline HbA1c ≥7.5% (58 mmol/mol), and a secondary analysis was performed in patients with baseline HbA1c ≥8.5% (69 mmol/mol). The analysis was performed from the National Health Service health care payer perspective over a lifetime time horizon.

RESULTS

In the overall population, G6 RT-CGM was associated with a mean incremental gain in quality-adjusted life expectancy of 1.49 quality-adjusted life years (QALYs) versus SMBG (mean [SD] 11.47 [2.04] QALYs versus 9.99 [1.84] QALYs). Total mean (SD) lifetime costs were also pounds sterling (GBP) 14,234 higher with RT-CGM (GBP 102,468 [35,681] versus GBP 88,234 [39,027]) resulting in an incremental cost-effectiveness ratio of GBP 9,558 per QALY gained. Sensitivity analyses revealed that the findings were sensitive to changes in the quality-of-life benefit associated with reduced fear of hypoglycemia and avoidance of fingerstick testing as well as the HbA1c benefit associated with RT-CGM use.

CONCLUSIONS

For U.K.-based T1D patients, the G6 RT-CGM device is associated with significant improvements in clinical outcomes and, over patient lifetimes, is a cost-effective disease management option relative to SMBG on the basis of a willingness-to-pay threshold of GBP 20,000 per QALY gained.

Real-time continuous glucose monitoring (RT-CGM) devices are small, discrete devices equipped with a subcutaneous sensor that measures glucose levels in the interstitial fluid every 5 min. RT-CGM enables a detailed analysis of intraday and interday fluctuations in glucose levels, as well as the magnitude and duration of fluctuations and the proportion of time spent in euglycemia, hypoglycemia, and hyperglycemia.

Clinical trials in adults with type 1 diabetes (T1D) treated with multiple daily injections of insulin have shown that, relative to self-monitoring of blood glucose (SMBG), RT-CGM is associated with a statistically significant reduction in HbA1c (1), a significant reduction in the amount of time spent in hypoglycemia, and significant changes in patient-reported outcomes including diabetes distress, hypoglycemic confidence (the degree to which patients feel secure in their ability to avoid hypoglycemic events), and fear of hypoglycemia (FoH) (2). FoH refers to unpleasant symptoms and situations that may be frightening to the individual or caregiver (3), have a detrimental effect on quality of life (QoL), and negatively influence disease management (4,5). Additionally, in the HypoDE study in patients with T1D with impaired hypoglycemia awareness or history of severe hypoglycemia, RT-CGM (using the Dexcom G5 RT-CGM system) resulted in a significant decrease in the incidence of hypoglycemic events (6). However, the G5 system has now been superseded by the more sophisticated and factory calibrated Dexcom G6 RT-CGM system, which means that it can be used without SMBG. However, patients have the option to calibrate. The technology underpinning RT-CGM is continually evolving, and the G6 system offers additional benefits relative to the earlier systems including a predictive low-glucose alert, which warns patients if a glucose level of ≤55 mg/dL is predicted to occur within the next 20 min, allowing patients to take action to prevent hypoglycemia.

In the U.K., the use of RT-CGM is advocated in specific groups of people with T1D. For adults, this includes those with more than one episode of severe hypoglycemia annually, unawareness of hypoglycemia, frequent asymptomatic hypoglycemia leading to difficulties in daily activities, extreme FoH or HbA1c >9.0% (75 mmol/mol) despite frequent SMBG (7). On the basis of the hypoglycemia criteria alone, it is estimated that up to 25% of people with T1D may be eligible for RT-CGM (8). However, despite the guidance issued by the National Institute for Health and Care Excellence (NICE), funding for RT-CGM is limited, meaning that inequality exists around the availability of RT-CGM in the U.K. Indeed, in one recent study in patients and caregivers, 26% were wholly self-funded (9). Evidence from long-term cost-effectiveness analyses that balance the acquisition cost of RT-CGM against its clinical and economic benefits are needed to assist U.K.-based policy makers and payers in making informed reimbursement decisions. Consequently, the aim of the current analysis was to assess the long-term health economic outcomes associated with the use of the Dexcom G6 RT-CGM device versus SMBG alone in patients with T1D in the U.K.

Model Structure

The analysis was performed using the IQVIA CORE Diabetes Model (CDM). The CDM is a validated computer simulation model that can be used to project long-term outcomes in T1D or type 2 diabetes. The CDM consists of a total of 17 separate but interdependent submodels that simulate long-term disease progression. Long-term cardiovascular, ophthalmic, and renal complications are included in the CDM as well as peripheral neuropathy, foot ulcer, and amputation and acute events including severe and nonsevere hypoglycemic events (NSHEs).

Patient Population and Treatment Effects

Cost-effectiveness analyses were performed separately in two different simulated patient cohorts to establish the cost-effectiveness of RT-CGM versus SMBG alone, based on the findings of the DIAMOND trial in patients with T1D. The primary analysis was performed in a cohort based on the overall T1D population in the DIAMOND trial (NCT02282397) (1), which was a prospective randomized trial consisting of two separate patient cohorts (T1D and type 2 diabetes). For enrollment in the T1D cohort, patients were required to be ≥25 years of age, treated for ≥1 year with multiple daily injections of insulin, with HbA1c 7.5–10.0% and no use of personal CGM in the 3 months prior to enrollment. The T1D cohort comprised a total of 158 patients randomly allocated to either CGM or usual care (SMBG) for 24 weeks. The primary end point was change in HbA1c from baseline to week 24 and prespecified secondary outcomes included proportion of patients with HbA1c <7.0%, time-in-range, duration of hypoglycemia and hyperglycemia, glucose variability, change in hypoglycemia unawareness and change in frequency of blood glucose monitoring. A full description of the DIAMOND trial methodology and results has been published by Beck et al. (1).

At baseline, the mean age and SD was 48 (13) years, mean (SD) duration of diabetes was 20 (14) years, and mean (SD) HbA1c was 8.6% (0.6%) (70 [6.0] mmol/mol). In this cohort, the treatment effect in terms of change in HbA1c from baseline was assumed to be −1.0% (11 mmol/mol) in the RT-CGM arm and −0.4% (4 mmol/mol) in the SMBG arm, based on 24-week data from the DIAMOND trial. Hypoglycemic event rates were also sourced from the DIAMOND trial. The rate of severe hypoglycemic events (SHEs; defined as an event requiring medical assistance) was assumed to be 4.2 per 100 patient-years for the RT-CGM arm and 12.2 per 100 person-years in the SMBG arm (1). NSHE rates were assumed to be 5,840 and 10,950 per 100 patient-years for the RT-CGM and SMBG groups, respectively (10).

A secondary analysis was performed in which the simulated patient cohort was limited to patients with baseline HbA1c ≥8.5% (69 mmol/mol) and baseline demographics and cohort characteristics were based on a subgroup of patients with baseline HbA1c ≥8.5% (69 mmol/mol) in the DIAMOND trial. Here, mean (SD) HbA1c was 9.1% (0.4%) (76 [4] mmol/mol), mean (SD) age was 46 (13) years and mean (SD) duration of diabetes was 20 (14) years. In this group, the treatment effect in terms of change in HbA1c from baseline was assumed to be −1.3% (14 mmol/mol) and −0.5% (5 mmol/mol) in the RT-CGM and SMBG arms, respectively (11). SHE rates were also sourced from DIAMOND trial data and were 3.8 per 100 patient-years for patients in the RT-CGM arm and 0 per 100 patient-years for the SMBG arm. Rates of NSHEs were 9,880 and 13,520 per 100 patient-years for the RT-CGM and SMBG groups, respectively (Dexcom, data on file).

Health State Utilities

In the RT-CGM arm of both cohorts a QoL benefit associated with reduced FoH was also included. In the DIAMOND trial, FoH was measured using the worry subscale of the Hypoglycemia Fear Survey (HFS-II) (adjusted for baseline value) (2). For the CGM arm, mean FoH decreased from 15.8 at baseline to 13.5 at week 24, and the corresponding values in the SMBG arm were 17.3 and 17.7, respectively, resulting in a between-group difference of 3.17 units (adjusted for baseline values). This difference was converted to a utility gain in the RT-CGM arm of 0.02536, based on a published mapping of the HFS (using only the eight-question worry subscale) to the EuroQol 5-dimension questionnaire (EQ-5D) (4), wherein a 1-unit change in HFS score corresponded to a 0.008-unit change in EQ-5Dindex score. Patients in the RT-CGM arm were also assumed to have an additional utility benefit of 0.03 owing to avoiding fingerstick SMBG testing multiple times per day, based on published data by Matza et al. (12). Matza et al. used time trade-off methodology to compare utility values between patients using flash glucose monitoring with those using SMBG (based on a usage of three SMBG tests per day), and it was assumed that the utility value of patients using flash glucose monitoring would be equal to that of patients using RT-CGM, as both reduce or eliminate the need for SMBG. Therefore, the total utility benefit assumed for patients using RT-CGM was 0.05536 (0.0236 for reduced FoH + 0.03 for avoidance of daily fingerstick testing).

Disutilities associated with diabetes-related complications were sourced from a literature review (13). For patients with no complications, a utility value of 0.90 was assumed on the basis of baseline QoL findings from the DIAMOND trial (2). The disutility values associated with hypoglycemic events were sourced from Evans et al. (14). The disutility associated with NSHEs was adjusted based on the number of events experienced per year on the basis of an algorithm published by Lauridsen et al. (15).

Costs

In terms of treatment costs, for patients on SMBG (in both cohorts), a mean SMBG usage of 4.6 tests per day was assumed on the basis of findings from the DIAMOND trial (1), and costs were derived from published sources (7,16). Patients using RT-CGM (in both cohorts) were conservatively assumed to use 1.5 SMBG tests per 10-day period for occasional calibration and confirmatory testing (Dexcom, data on file). Annual costs for the Dexcom G6 RT-CGM system were based on current U.K. prices and assumed an annual use of 36 sensors and four transmitters (Dexcom, data on file). Total annual treatment costs were GBP 1,850 in the RT-CGM arm (including costs associated with occasional fingerstick testing) and GBP 486 in the SMBG arm (pharmacy costs associated with insulin were not accounted in the analysis).

Direct costs associated with treatment and management of complications were taken from the published literature (1727) and, where necessary, inflated to 2018 GBP using the consumer price index health component.

Sensitivity Analyses

A series of one-way sensitivity analyses were performed to determine key drivers of cost-effectiveness. These included sensitivity analysis around treatment effect (HbA1c change and incidence of hypoglycemic events), utility benefit associated with reduced FoH, frequency of SMBG in the SMBG arm, baseline utility value, time horizon, and cost of complications.

Time Horizon, Perspective, and Discount Rate

The analysis was performed over a lifetime time horizon, as T1D is a chronic disease, and from the perspective of the U.K. health care payer (National Health Service and personal social services). Future costs and clinical outcomes were discounted at a rate of 3.5% per annum in line with U.K.-specific guidance (28).

In the base case analysis for the overall T1D population, the use of RT-CGM relative to SMBG was associated with an incremental gain in quality-adjusted life expectancy of 1.49 quality-adjusted life years (QALYs) (mean [SD] 11.47 [2.04] QALYs versus 9.99 [1.84] QALYs) (Table 1). Over the lifetime time horizon, total mean (SD) costs were GBP 14,234 higher with RT-CGM (GBP 102,468 [35,681] versus GBP 88,234 [39,027]), resulting in an incremental cost-effectiveness ratio (ICER) of GBP 9,558 per QALY gained for RT-CGM versus SMBG. The higher overall costs with RT-CGM were driven primarily by the higher costs associated with the RT-CGM system itself. However, the improvement in long-term clinical outcomes with RT-CGM resulted in savings in terms of the mean per-patient lifetime costs associated with hypoglycemic events and cardiovascular, renal, and ophthalmic complications driven by a lower cumulative incidence of long-term complications (see Supplementary Material). A cost-effectiveness acceptability curve was plotted (Fig. 1), which showed that, at a willingness-to-pay (WTP) threshold of GBP 20,000 per QALY gained, the likelihood of RT-CGM being considered cost-effective versus SMBG was ∼99%.

Table 1

Summary findings of base case analyses

RT-CGMSMBGDifference
Overall T1D cohort    
 Total costs, GBP 102,468 (35,681) 88,234 (39,027) 14,234 
 Quality-adjusted life expectancy, QALYs 11.47 (2.04) 9.99 (1.84) 1.49 
 ICER, GBP per QALY gained  9,558  
 Probability of RT-CGM being cost-effective at WTP threshold of GBP 20,000 per QALY gained, %  99.2  
Baseline HbA1c ≥8.5% (69 mmol/mol) cohort    
 Total costs, GBP 107,659 (37,202) 94,483 (42,071) 13,176 
 Quality-adjusted life expectancy, QALYs 11.50 (1.96) 10.11 (1.77) 1.39 
 ICER, GBP per QALY gained  9,478  
 Probability of RT-CGM being cost-effective at WTP threshold of GBP 20,000 per QALY gained, %  97.7  
RT-CGMSMBGDifference
Overall T1D cohort    
 Total costs, GBP 102,468 (35,681) 88,234 (39,027) 14,234 
 Quality-adjusted life expectancy, QALYs 11.47 (2.04) 9.99 (1.84) 1.49 
 ICER, GBP per QALY gained  9,558  
 Probability of RT-CGM being cost-effective at WTP threshold of GBP 20,000 per QALY gained, %  99.2  
Baseline HbA1c ≥8.5% (69 mmol/mol) cohort    
 Total costs, GBP 107,659 (37,202) 94,483 (42,071) 13,176 
 Quality-adjusted life expectancy, QALYs 11.50 (1.96) 10.11 (1.77) 1.39 
 ICER, GBP per QALY gained  9,478  
 Probability of RT-CGM being cost-effective at WTP threshold of GBP 20,000 per QALY gained, %  97.7  

Values are mean (SD) unless otherwise stated.

Figure 1

Cost-effectiveness acceptability curve in the overall T1D population (based on DIAMOND trial patient population). Acceptability curve based on second-order Monte Carlo simulation based on 1,000 iterations each based on a cohort of 1,000 simulated patients.

Figure 1

Cost-effectiveness acceptability curve in the overall T1D population (based on DIAMOND trial patient population). Acceptability curve based on second-order Monte Carlo simulation based on 1,000 iterations each based on a cohort of 1,000 simulated patients.

Close modal

The findings of the secondary analysis in patients with baseline HbA1c ≥8.5% (69 mmol/mol) were similar to those reported for the overall population. In this patient group, mean baseline HbA1c was 9.1% (76 mmol/mol). For patients with elevated baseline HbA1c the use of RT-CGM was associated with an incremental gain of 1.39 QALYs versus SMBG (Table 1). Total mean lifetime costs were GBP 13,176 higher with RT-CGM, resulting in an ICER of GBP 9,478 per QALY gained. Analysis of the cost-effectiveness acceptability curve showed that at a WTP threshold of GBP 20,000 per QALY gained, the probability of RT-CGM being considered cost-effective versus SMBG was 98%.

Sensitivity analyses in both patient cohorts revealed that the results were most sensitive to changes in assumptions relating to the QoL benefit due to reduced FoH and fingerstick testing, the change in HbA1c with RT-CGM and SMBG use (Tables 2 and 3), although it was notable that changes in HbA1c treatment effect had a greater influence on cost-effectiveness in patients with elevated baseline HbA1c than in the overall patient cohort (Table 3).

Table 2

Summary findings of sensitivity analyses in the overall T1D cohort

AnalysisQALYsCosts, GBPICER, GBP per QALY gained
RT-CGMSMBGRT-CGMSMBG
Base case 11.47 9.99 102,468 88,234 9,558 
FoH utility 0.01968 11.37 9.99 102,468 88,234 10,253 
RT-CGM utility benefit = 0* 10.49 9.99 102,468 88,234 28,225 
RT-CGM utility benefit −50%* 10.98 9.99 102,468 88,234 14,280 
RT-CGM utility benefit +50%* 11.97 9.99 102,468 88,234 7,183 
HbA1c change −50% 11.37 9.99 106,568 88,234 13,230 
HbA1c change +50% 11.56 9.99 99,561 88,234 7,171 
SHE rate RT-CGM arm −50% 11.49 9.99 102,364 88,234 9,396 
SHE rate RT-CGM arm +50% 11.46 9.99 103,226 88,234 10,178 
NSHE rate RT-CGM arm −50% 11.68 9.99 102,468 88,234 8,375 
NSHE rate RT-CGM arm +50% 11.41 9.99 102,468 88,234 9,986 
SMBG = 4 per day 11.47 9.99 102,468 87,115 10,309 
SMBG = 5.2 per day 11.47 9.99 102,468 89,352 8,807 
SMBG = 10 per day 11.47 9.99 102,468 98,301 2,798 
Utility T1D no complications = 0.672 10.50 9.08 102,468 88,234 9,980 
Time horizon 10 years 5.24 4.63 32,843 23,467 15,324 
Time horizon 25 years 9.54 8.38 72,241 58,307 11,975 
Complication costs −20% 11.47 9.99 88,556 72,301 10,915 
Complication costs +20% 11.47 9.99 116,382 104,169 8,200 
AnalysisQALYsCosts, GBPICER, GBP per QALY gained
RT-CGMSMBGRT-CGMSMBG
Base case 11.47 9.99 102,468 88,234 9,558 
FoH utility 0.01968 11.37 9.99 102,468 88,234 10,253 
RT-CGM utility benefit = 0* 10.49 9.99 102,468 88,234 28,225 
RT-CGM utility benefit −50%* 10.98 9.99 102,468 88,234 14,280 
RT-CGM utility benefit +50%* 11.97 9.99 102,468 88,234 7,183 
HbA1c change −50% 11.37 9.99 106,568 88,234 13,230 
HbA1c change +50% 11.56 9.99 99,561 88,234 7,171 
SHE rate RT-CGM arm −50% 11.49 9.99 102,364 88,234 9,396 
SHE rate RT-CGM arm +50% 11.46 9.99 103,226 88,234 10,178 
NSHE rate RT-CGM arm −50% 11.68 9.99 102,468 88,234 8,375 
NSHE rate RT-CGM arm +50% 11.41 9.99 102,468 88,234 9,986 
SMBG = 4 per day 11.47 9.99 102,468 87,115 10,309 
SMBG = 5.2 per day 11.47 9.99 102,468 89,352 8,807 
SMBG = 10 per day 11.47 9.99 102,468 98,301 2,798 
Utility T1D no complications = 0.672 10.50 9.08 102,468 88,234 9,980 
Time horizon 10 years 5.24 4.63 32,843 23,467 15,324 
Time horizon 25 years 9.54 8.38 72,241 58,307 11,975 
Complication costs −20% 11.47 9.99 88,556 72,301 10,915 
Complication costs +20% 11.47 9.99 116,382 104,169 8,200 
*

Combined utility benefit associated with reduced FoH and avoidance of fingerstick testing.

Difference in HbA1c between treatment arms increased/decreased by ∼50% compared with base case.

On the basis of the Sheffield Type 1 Diabetes Policy Model (available at https://www.sheffield.ac.uk/polopoly_fs/1.268462!/file/13.05.pdf).

Table 3

Summary findings of sensitivity analyses in T1D patients with elevated HbA1c (≥8.5%) at baseline

AnalysisQALYsCosts, GBPICER, GBP per QALY gained
RT-CGMSMBGRT-CGMSMBG
Base case 11.50 10.11 107,659 94,483 9,478 
FoH utility 0.01968 11.40 10.11 107,659 94,483 10,238 
RT-CGM utility benefit = 0* 10.49 10.11 107,659 94,483 34,287 
RT-CGM utility benefit −50%* 11.00 10.11 107,659 94,483 14,852 
RT-CGM utility benefit +50%* 12.00 10.11 107,659 94,483 6,960 
HbA1c change −49% 11.36 10.11 113,385 94,483 15,128 
HbA1c change +49% 11.62 10.11 103,042 94,483 5,650 
SHE rate RT-CGM arm −50% 11.52 10.11 107,234 94,483 9,025 
SHE rate RT-CGM arm +50% 11.48 10.11 108,233 94,483 9,996 
NSHE rate RT-CGM arm −50% 11.75 10.11 107,659 94,483 8,004 
NSHE rate RT-CGM arm +50% 11.42 10.11 107,659 94,483 10,041 
SMBG = 4 per day 11.50 10.11 107,659 93,344 10,297 
SMBG = 5.2 per day 11.50 10.11 107,659 95,621 8,659 
SMBG = 10 per day 11.50 10.11 107,659 104,731 2,107 
Utility T1D no complications = 0.672 10.53 9.23 107,659 94,483 10,137 
Time horizon 10 years 5.17 4.64 32,997 22,549 19,539 
Time horizon 25 years 9.49 8.43 73,996 59,316 13,888 
Complication costs −20% 11.50 10.11 92,849 77,331 11,162 
Complication costs +20% 11.50 10.11 122,472 111,638 7,793 
AnalysisQALYsCosts, GBPICER, GBP per QALY gained
RT-CGMSMBGRT-CGMSMBG
Base case 11.50 10.11 107,659 94,483 9,478 
FoH utility 0.01968 11.40 10.11 107,659 94,483 10,238 
RT-CGM utility benefit = 0* 10.49 10.11 107,659 94,483 34,287 
RT-CGM utility benefit −50%* 11.00 10.11 107,659 94,483 14,852 
RT-CGM utility benefit +50%* 12.00 10.11 107,659 94,483 6,960 
HbA1c change −49% 11.36 10.11 113,385 94,483 15,128 
HbA1c change +49% 11.62 10.11 103,042 94,483 5,650 
SHE rate RT-CGM arm −50% 11.52 10.11 107,234 94,483 9,025 
SHE rate RT-CGM arm +50% 11.48 10.11 108,233 94,483 9,996 
NSHE rate RT-CGM arm −50% 11.75 10.11 107,659 94,483 8,004 
NSHE rate RT-CGM arm +50% 11.42 10.11 107,659 94,483 10,041 
SMBG = 4 per day 11.50 10.11 107,659 93,344 10,297 
SMBG = 5.2 per day 11.50 10.11 107,659 95,621 8,659 
SMBG = 10 per day 11.50 10.11 107,659 104,731 2,107 
Utility T1D no complications = 0.672 10.53 9.23 107,659 94,483 10,137 
Time horizon 10 years 5.17 4.64 32,997 22,549 19,539 
Time horizon 25 years 9.49 8.43 73,996 59,316 13,888 
Complication costs −20% 11.50 10.11 92,849 77,331 11,162 
Complication costs +20% 11.50 10.11 122,472 111,638 7,793 
*

Combined utility benefit associated with reduced FoH and avoidance of fingerstick testing.

Difference in HbA1c between treatment arms increased/decreased by ∼49% compared with base case.

On the basis of the Sheffield Type 1 Diabetes Policy Model (available at https://www.sheffield.ac.uk/polopoly_fs/1.268462!/file/13.05.pdf).

In the overall patient cohort, in the sensitivity analysis in which no QoL benefit with RT-CGM was assumed, the gain in quality-adjusted life expectancy was reduced, leading to the ICER increasing to GBP 28,225 per QALY gained (Table 2). When limited to patients with baseline HbA1c ≥8.5% (69 mmol/mol), the corresponding ICER was GBP 34,287 per QALY gained, which is above the commonly cited upper limit of GBP 30,000 per QALY gained for the WTP threshold in the U.K. In an analysis where SMBG use was assumed to be 10 strips per day, the ICER was reduced to GBP 2,798 per QALY gained. In the U.K., NICE guidance recommends RT-CGM for people with T1D who continue to have HbA1c >9.0% (75 mmol/mol) despite SMBG testing ≥10 times per day (Table 2); the present analysis shows that RT-CGM is likely to be highly cost-effective in this patient population.

Sensitivity analysis in the cohort with baseline HbA1c ≥8.5% (69 mmol/mol) produced results analogous to those reported in the overall cohort (Table 3). However, in this cohort the ICER was more sensitive to changes in HbA1c treatment effect, driven by the larger difference in absolute HbA1c values.

The findings presented here suggest that in the U.K., for patients with T1D, with similar characteristics to those enrolled in the DIAMOND trial (e.g., mean age of 48 years, mean duration of disease of 20 years), over a lifetime time horizon, the use of G6 RT-CGM is cost-effective versus SMBG alone. The clinical benefit in terms of HbA1c reduction is greatest for patients with elevated HbA1c levels at baseline (11). However, in the base cases for both the overall population of T1D patients and the secondary analysis in patients with elevated baseline HbA1c (≥8.5% [69 mmol/mol]), the ICER for G6 RT-CGM versus SMBG was below GBP 20,000 per QALY gained, which is the commonly cited U.K. WTP threshold.

It should be noted that the findings presented here may be conservative. The clinical input data were sourced from the DIAMOND trial, in which the Dexcom G4 Platinum RT-CGM system was used. This has now been superseded by the G6 RT-CGM system, which incorporates the additional “Urgent Low Soon” alert feature, allowing patients to take action to prevent hypoglycemia. This feature may contribute to the significant reduction in hypoglycemic events reported with the G6 relative to previous generation devices (29,30). Additionally, the G6 is the first RT-CGM system to receive U.S. Food and Drug Administration designation for safety and accuracy as part of an integrated system (31). The accuracy of the G6 system, assessed in terms of the mean absolute relative difference between RT-CGM and reference values is below 10% (29), making it one of the most accurate RT-CGM devices currently available. It is therefore plausible that the treatment effect in this analysis in terms of hypoglycemic event rate, and potentially also FoH, in patients using RT-CGM may have been underestimated. Indeed, the Urgent Low Soon alert feature may be particularly valuable in terms of alerting patients to an impending risk of nocturnal hypoglycemia, especially since FoH has been shown to be more pronounced at night (32). Additionally, the present analysis was performed using the CDM, in which HbA1c is the single most important determinant of long-term clinical outcomes. In the DIAMOND trial, RT-CGM was shown to significantly reduce glycemic variability and increase the proportion of time spent in the euglycemic range relative to SMBG (1). However, glycemic variability was not considered in the current analysis as, at present, the long-term clinical implications of changes in glycemic variability remain largely unknown.

Sensitivity analyses revealed that RT-CGM was most cost-effective in patient groups meeting the NICE eligibility criteria for RT-CGM, in particular, those with elevated HbA1c at baseline, FoH, frequent SHEs, and high levels of SMBG use. In particular, cost-effectiveness of RT-CGM was strongly influenced by the QoL benefit associated with RT-CGM owing to reduced FoH and avoidance of fingerstick testing. FoH is common in patients with T1D, although U.K.-specific data are lacking. In the U.K., NICE guidelines recommend the use of RT-CGM for adult patients with extreme FoH as well as for those with impaired awareness of hypoglycemia or a history of frequent SHEs, who collectively represent a substantial proportion of the overall T1D population. The detrimental effect of FoH on QoL is well documented (4,31), but there is an interplay between FoH, impaired awareness of hypoglycemia, history of hypoglycemia, and QoL. Moreover, the effects of FoH are not limited to QoL, and FoH can negatively influence disease management behaviors (4). Patients with FoH may adopt behaviors such as deliberately reducing insulin dose, increasing carbohydrate consumption, or avoiding physical activity to reduce the risk of hypoglycemia. Consequently, interventions that can reduce patients’ FoH by alerting them prior to entering a hypoglycemic range (so that this can be prevented) are likely to be of considerable value in terms of overall disease management.

RT-CGM is associated with other benefits from the patient perspective that cannot readily be captured within a health economic analysis. In the DIAMOND trial, RT-CGM was associated with a significant change in hypoglycemic confidence. Patients felt secure and more able to avoid hypoglycemia (2). In qualitative interviews with U.K.-based patients and parents of children with T1D, some interviewees reported that RT-CGM imbued them with a greater insight and understanding of disease processes and how insulin dose, food intake, and physical activity influenced glucose levels (33).

Despite the documented clinical benefits with RT-CGM, high levels of patient satisfaction, and the 2015 guidelines issued by NICE, RT-CGM remains underutilized in the U.K., even in patients meeting the NICE eligibility criteria (8,9) with a lack of funding representing a key barrier to uptake. In 2018, only 21% of Clinical Commissioning Groups routinely funded RT-CGM in patients meeting the NICE eligibility criteria, with a further 60% of Clinical Commissioning Groups making decisions on an individual case-by-case basis (34). This inequality in funding suggests that the initial acquisition cost of RT-CGM may be a barrier to widespread uptake in the U.K. The findings presented here suggest that RT-CGM improves long-term outcomes relative to SMBG and that initial acquisition costs are at least partially offset by savings owing to a reduction in the incidence of long-term complications. Indeed, in both a population of people with T1D with HbA1c ≥7.5% (58 mmol/mol) and a cohort limited to those with HbA1c ≥8.5% (69 mmol/mol), RT-CGM was projected to be cost-effective relative to SMBG.

This analysis is the first to assess the cost-effectiveness of the most recently introduced and sophisticated RT-CGM devices in the U.K. The findings presented here broadly align with those of a previous cost-effectiveness analysis of the Dexcom G5 RT-CGM system versus SMBG in T1D in Canada (35), which also used clinical data from the DIAMOND trial and found that the ICER for RT-CGM versus SMBG was 33,789 Canadian dollars per QALY gained (GBP 20,496; September 2019 exchange rates).

The analysis is associated with a number of limitations. In particular, a limitation inherent in any long-term analysis is the use of short-term (<1 year) trial data to project outcomes over a lifetime time horizon. Recent findings from the COMISAIR study showed that the improvements in glycemic control and time in range with RT-CGM were maintained over 3 years (36). Here, at 3 years, mean HbA1c levels were 0.7–1.1% lower for RT-CGM groups compared with SMBG groups (35), which is similar to the magnitude of treatment effect seen in the DIAMOND study. However, longitudinal data over time frames of ≥10 years are not yet available owing to the recency of the latest generation of RT-CGM devices, and therefore the projection of HbA1c trajectories over longer time periods is reliant on assumptions and the extrapolation of available data. However, in the absence of long-term data, computer simulation modeling is recognized as a viable alternative. Additionally, in qualitative studies, some patients have expressed a sense of alarm fatigue when using RT-CGM (32,37), and no QoL decrement to account for this was included. Allied to this, the utility benefit attached to lack of SMBG fingerstick testing was not sourced directly from the DIAMOND trial and was also based on an SMBG frequency of three tests per day, which is lower than the mean of 4.6 tests per day observed in the SMBG arm of the DIAMOND trial. It is possible that the disutility associated with SMBG, particularly in patients with long duration of disease, may be overestimated and that some patients may consider SMBG to be part of their daily routine and may even find it reassuring.

Conclusion

The findings presented here provide U.K.-based payers and policymakers with valuable information relating to the cost-effectiveness of RT-CGM in T1D. Long-term health economic analyses suggest that RT-CGM improves glycemic control and reduces the risk for long-term diabetes-related complications. For U.K. patients with T1D, the G6 RT-CGM device is associated with significant improvements in clinical outcomes and, over patient lifetimes, is a cost-effective disease management option relative to SMBG, based on a WTP threshold of GBP 20,000 per QALY gained.

This article contains supplementary material online at https://doi.org/10.2337/figshare.12515492.

Duality of Interest. This study was supported by Dexcom. S.R. is a current employee of HEVA HEOR, which has received consulting fees from Dexcom relating to the preparation of the analysis. J.I. and P.L. are current employees of Dexcom, which manufactures the G6 RT-CGM device. J.S.-P. is a current employee of Ossian Health Economics and Communications, which has received consulting fees relating to the preparation of the manuscript. M.J. is a current employee of Device Access UK Ltd., which has received consulting fees from Dexcom.

Author Contributions. S.R. performed collation of the model input data and the CORE modeling analysis. J.I. and P.L. were responsible for project inception and design and provided input on the modeling analysis. J.S.-P. prepared the first draft of the manuscript. M.J. provided critical input and expertise relating to U.K.-based analyses. All authors contributed to critical revision of the first draft and all subsequent drafts of the manuscript. S.R. 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.

1.
Beck
RW
,
Riddlesworth
T
,
Ruedy
K
, et al.;
DIAMOND Study Group
.
Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial
.
JAMA
2017
;
317
:
371
378
2.
Polonsky
WH
,
Hessler
D
,
Ruedy
KJ
,
Beck
RW
;
DIAMOND Study Group
.
The impact of continuous glucose monitoring on markers of quality of life in adults with type 1 diabetes: further findings from the DIAMOND randomized clinical trial
.
Diabetes Care
2017
;
40
:
736
741
3.
Haugstvedt
A
,
Wentzel-Larsen
T
,
Aarflot
M
,
Rokne
B
,
Graue
M
.
Assessing fear of hypoglycemia in a population-based study among parents of children with type 1 diabetes - psychometric properties of the hypoglycemia fear survey - parent version
.
BMC Endocr Disord
2015
;
15
:
2
4.
Currie
CJ
,
Morgan
CL
,
Poole
CD
,
Sharplin
P
,
Lammert
M
,
McEwan
P
.
Multivariate models of health-related utility and the fear of hypoglycaemia in people with diabetes
.
Curr Med Res Opin
2006
;
22
:
1523
1534
5.
Martyn-Nemeth
P
,
Quinn
L
,
Penckofer
S
,
Park
C
,
Hofer
V
,
Burke
L
.
Fear of hypoglycemia: influence on glycemic variability and self-management behavior in young adults with type 1 diabetes
.
J Diabetes Complications
2017
;
31
:
735
741
6.
Heinemann
L
,
Freckmann
G
,
Ehrmann
D
, et al
.
Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): a multicentre, randomised controlled trial
.
Lancet
2018
;
391
:
1367
1377
7.
National Institute for Health and Care Excellence
.
NICE guideline NG17: Type 1 diabetes in adults: diagnosis and management [Internet]
.
8.
Oliver
N
.
Continuous glucose monitoring adoption in the United Kingdom - an economic and policy perspective
.
Eur Endocrinol
2017
;
13
:
73
75
9.
Parkin
CG
,
Holloway
M
,
Truesdell
J
,
Walker
TC
.
Is continuous glucose monitoring underappreciated in the UK
?
Eur Endocrinol
2017
;
13
:
76
80
10.
Riddlesworth
T
,
Price
D
,
Cohen
N
,
Beck
RW
.
Hypoglycemic event frequency and the effect of continuous glucose monitoring in adults with type 1 diabetes using multiple daily insulin injections
.
Diabetes Ther
2017
;
8
:
947
951
11.
Billings
LK
,
Parkin
CG
,
Price
D
.
Baseline glycated hemoglobin values predict the magnitude of glycemic improvement in patients with type 1 and type 2 diabetes: subgroup analyses from the DIAMOND study program
.
Diabetes Technol Ther
2018
;
20
:
561
565
12.
Matza
LS
,
Stewart
KD
,
Davies
EW
,
Hellmund
R
,
Polonsky
WH
,
Kerr
D
.
Health state utilities associated with glucose monitoring devices
.
Value Health
2017
;
20
:
507
511
13.
Beaudet
A
,
Clegg
J
,
Thuresson
PO
,
Lloyd
A
,
McEwan
P
.
Review of utility values for economic modeling in type 2 diabetes
.
Value Health
2014
;
17
:
462
470
14.
Evans
M
,
Khunti
K
,
Mamdani
M
, et al
.
Health-related quality of life associated with daytime and nocturnal hypoglycaemic events: a time trade-off survey in five countries
.
Health Qual Life Outcomes
2013
;
11
:
90
15.
Lauridsen
JT
,
Lønborg
J
,
Gundgaard
J
,
Jensen
HH
.
Diminishing marginal disutility of hypoglycaemic events: results from a time trade-off survey in five countries
.
Qual Life Res
2014
;
23
:
2645
2650
16.
The East of England Priorities Advisory Committee
.
Guidance Statement: FreeStyle Libre Glucose Monitoring System 1.0
,
PrescQIPP
,
2017
. Available from http://www.southstaffordshirejointformulary.nhs.uk/docs/CDD/PrescQiPP%20freestyle%20libre%20Sept%202017.pdf. Accessed 2 July 2020
17.
Walker
S
,
Asaria
M
,
Manca
A
, et al
.
Long-term healthcare use and costs in patients with stable coronary artery disease: a population-based cohort using linked health records (CALIBER)
.
Eur Heart J Qual Care Clin Outcomes
2016
;
2
:
125
140
18.
Beaudet
A
,
Palmer
JL
,
Timlin
L
, et al
.
Cost-utility of exenatide once weekly compared with insulin glargine in patients with type 2 diabetes in the UK
.
J Med Econ
2011
;
14
:
357
366
19.
Alva
ML
,
Gray
A
,
Mihaylova
B
,
Leal
J
,
Holman
RR
.
The impact of diabetes-related complications on healthcare costs: new results from the UKPDS (UKPDS 84)
.
Diabet Med
2015
;
32
:
459
466
20.
Xu
XM
,
Vestesson
E
,
Paley
L
, et al
.
The economic burden of stroke care in England, Wales and Northern Ireland: using a national stroke register to estimate and report patient-level health economic outcomes in stroke
.
Eur Stroke J
2018
;
3
:
82
91
21.
UK Government. UK Government National tariff payment system: BZ23Z: Vitreous Retinal Procedures - category 1; Outpatient Procedure tariff [Internet]
,
2015
.
22.
Meads
C
,
Hyde
C
.
What is the cost of blindness
?
Br J Ophthalmol
2003
;
87
:
1201
1204
23.
MIMS Online
.
Duloxetine 60 mg daily (first-line treatment of Neuropathy in Diabetes [NICE CG87]) [Internet]
,
2016
.
Available from https://www.mims.co.uk/drugs/pain/pain-fever/duloxetine. Accessed 13 June 2019
24.
Kerr
M
,
Rayman
G
,
Jeffcoate
WJ
.
Cost of diabetic foot disease to the National Health Service in England
.
Diabet Med
2014
;
31
:
1498
1504
25.
Zimny
S
,
Voigt
A
,
Schatz
H
,
Pfohl
M
.
Prediction of wound radius reductions and healing times in neuropathic diabetic foot ulcers
.
Diabetes Care
2003
;
26
:
959
960
26.
Ghatnekar
O
,
Willis
M
,
Persson
U
.
Cost-effectiveness of treating deep diabetic foot ulcers with Promogran in four European countries
.
J Wound Care
2002
;
11
:
70
74
27.
Evans
M
,
Wolden
M
,
Gundgaard
J
,
Chubb
B
,
Christensen
T
.
Cost-effectiveness of insulin degludec compared with insulin glargine for patients with type 2 diabetes treated with basal insulin - from the UK health care cost perspective
.
Diabetes Obes Metab
2014
;
16
:
366
375
28.
National Institute for Health and Care Excellence
.
Developing NICR guidelines: the manual [Internet]
,
2018
.
29.
Welsh
JB
,
Gao
P
,
Derdzinski
M
, et al
.
Accuracy, utilization, and effectiveness comparisons of different continuous glucose monitoring systems
.
Diabetes Technol Ther
2019
;
21
:
128
132
30.
Puhr
S
,
Derdzinski
M
,
Welsh
JB
,
Parker
AS
,
Walker
T
,
Price
DA
.
Real-world hypoglycemia avoidance with a continuous glucose monitoring system’s predictive low glucose alert
.
Diabetes Technol Ther
2019
;
21
:
155
158
31.
United States Food and Drug Administration
.
FDA authorizes first fully interoperable continuous glucose monitoring system, streamlines review pathway for similar devices [Internet]
,
2018
.
32.
Martyn-Nemeth
P
,
Schwarz Farabi
S
,
Mihailescu
D
,
Nemeth
J
,
Quinn
L
.
Fear of hypoglycemia in adults with type 1 diabetes: impact of therapeutic advances and strategies for prevention - a review
.
J Diabetes Complications
2016
;
30
:
167
177
33.
Lawton
J
,
Blackburn
M
,
Allen
J
, et al
.
Patients’ and caregivers’ experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study
.
BMC Endocr Disord
2018
;
18
:
12
34.
Perera
R
,
Oliver
N
,
Wilmot
E
,
Marriott
C
.
Variations in access to and reimbursement for continuous glucose monitoring systems for people living with type 1 diabetes across England
.
Diabet Med
2018
;
35
:
1617
1618
35.
Chaugule
S
,
Graham
C
.
Cost-effectiveness of G5 Mobile continuous glucose monitoring device compared to self-monitoring of blood glucose alone for people with type 1 diabetes from the Canadian societal perspective
.
J Med Econ
2017
;
20
:
1128
1135
36.
Šoupal
J
,
Petruželková
L
,
Grunberger
G
, et al
.
Glycemic outcomes in adults with T1D are impacted more by continuous glucose monitoring than by insulin delivery method: 3 years of follow-up from the COMISAIR study
.
Diabetes Care
2020
;
43
:
37
43
37.
Pickup
JC
,
Ford Holloway
M
,
Samsi
K
.
Real-time continuous glucose monitoring in type 1 diabetes: a qualitative framework analysis of patient narratives
.
Diabetes Care
2015
;
38
:
544
550
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.