Cost-effectiveness of Freestyle Libre Systems for People with Type 2 Diabetes Mellitus on Basal Insulin Therapy in the Netherlands: An Economic Evaluation from a Societal Perspective Within a Publicly Funded Healthcare System

Abstract

Introduction

Healthcare expenditure for the treatment of type 2 diabetes mellitus (T2DM) in the Netherlands is high, mainly due to the cost of treating diabetes-related complications. Guidelines recommend sensor-based glucose monitoring systems for people living with T2DM and using insulin, but these are not reimbursed in the Netherlands for those using basal insulin only. The objective of this study was to assess the cost-effectiveness of glucose monitoring with FreeStyle Libre systems (FSL), compared with capillary-based self-monitoring of blood glucose (SMBG), for people living with T2DM on basal insulin, from the perspective of the Dutch publicly funded healthcare system.

Methods

The patient-level microsimulation model DEDUCE (DEtermination of Diabetes Utilities, Costs, and Effects) was used to estimate the incidence of complications and acute diabetes events (ADEs; hypoglycemia and diabetic ketoacidosis). The effect of FSL was modeled as a 0.5% reduction in glycated hemoglobin level, which DEDUCE translates to a lower rate of complications, and as reductions in ADEs and absenteeism. Costs (in 2024 euros) and utilities were discounted at 3% and 1.5%, respectively. Outcomes were assessed as quality-adjusted life years (QALYs).

Results

FSL was associated with 0.53 more QALYs than SMBG (12.77 vs. 12.24), at an additional cost of €8021. The resulting incremental cost-effectiveness ratio (ICER) for FSL versus SMBG was €15,181/QALY. The increased acquisition cost of FSL (€19,738) was partially offset by reductions in costs associated with complications, ADEs, and absenteeism. Probabilistic sensitivity analysis showed that FSL was 52% likely to be cost-effective at a willingness-to-pay threshold of €20,000/QALY, and > 99% likely at thresholds ≥ €40,000/QALY. FSL had an ICER of below €50,000/QALY in all scenarios investigated.

Conclusion

From a Dutch publicly funded healthcare system perspective, FSL can be considered to be cost-effective compared with SMBG for people living with T2DM on basal insulin therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13300-025-01821-9.

Plain Language Summary

Effective glucose monitoring is important for people living with type 2 diabetes mellitus, reducing the risk of experiencing high or low blood sugar levels and of developing long-term complications. Glucose monitoring can be done using finger sticks and test strips or sensor-based devices such as the FreeStyle Libre systems (FSL). In this study, we modeled the cost-effectiveness of FSL in people with type 2 diabetes mellitus on basal insulin in the Netherlands. FSL use was considered to reduce the risk of acute events related to high or low blood sugar and of diabetes complications, both based on published studies. The modeled costs included the costs of glucose monitoring, of treating complications, and of time off work due to diabetes. FSL use was predicted to lead to better outcomes for people with type 2 diabetes mellitus, measured as quality-adjusted life years (a measure of health which combines life expectancy with quality of life), while reducing the costs of treating acute events and complications. Overall, FSL is likely to be considered to be a cost-effective use of Dutch healthcare system resources.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13300-025-01821-9.

Key Summary Points

Why carry out this study?

Healthcare expenditure for the treatment of type 2 diabetes mellitus (T2DM) in the Netherlands is high, mainly due to diabetes-related complications

Previous studies have shown FreeStyle Libre systems (FSL) to be cost-effective in populations of people with T2DM using basal insulin only, but no previous cost-effectiveness analysis has been conducted specifically for the Dutch population and healthcare system

The objective of this study was to assess the cost-effectiveness of FSL, compared with capillary-based self-monitoring of blood glucose (SMBG), for people living with T2DM on basal insulin, from the perspective of the Dutch publicly funded healthcare system

What was learned from this study?

FSL was cost-effective versus SMBG, providing more quality-adjusted life years (QALYs) at a cost of €15,181/QALY

This study shows that, from a Dutch publicly funded healthcare system perspective, FSL can be considered to be cost-effective compared with SMBG for people living with T2DM on basal insulin therapy

Introduction

In the Netherlands, the prevalence of type 2 diabetes mellitus (T2DM) has more than doubled since 2004, with a prevalence of approximately 6.0%, corresponding to around 1.1 million people in total in 2022 [1, 2]. In 2018, healthcare expenditure for the treatment of people with T2DM exceeded €8 billion [3], presenting a challenge to the Dutch publicly funded healthcare system.

Healthcare resource use due to T2DM mainly reflects care of diabetes-related complications, rather than of the condition itself [4, 5]. In a 2019–2020 observational study of 193,840 adults treated for diabetes (including type 1 diabetes mellitus, T1DM) in Dutch hospitals, mean annual costs were €6978, of which only €1109 was for diabetes care [4]. The total economic burden of diabetes also includes the costs of productivity losses due to the condition and its complications. It has been estimated that in 2016, productivity losses made up 9.8% (€584 million of a total €5.9 billion) of the total costs of T2DM in the Netherlands [6].

The progressive nature of T2DM means that many patients will eventually require insulin, typically implemented initially as basal insulin therapy [7, 8]. Optimal glycemic control, incorporating effective glucose monitoring, is essential to people living with T2DM and using insulin [8, 9]. Recent consensus and standards documents in the USA and Europe now advise consideration of sensor-based glucose monitoring systems for people with diabetes on basal insulin therapy [8, 10].

FreeStyle Libre systems (FSL) are factory-calibrated sensor-based glucose monitoring systems designed to provide reliable, comprehensive monitoring of glucose variation and changes in response to diabetes medications including insulin, while reducing the burden of glucose monitoring for patients [11]. The American Diabetes Association (ADA) recommends continuous glucose monitoring devices such as FSL for people with diabetes on any type of insulin, including basal insulin [10]. For people with T2DM using basal insulin, real-world studies have shown that acquisition of FSL is associated with improvements in glycemic parameters, health-related quality of life (HRQoL), and treatment satisfaction, as well as with reductions in diabetes-related hospitalization and work absenteeism [12–15].

In the Netherlands, FSL has been available since 2014, and is reimbursed for people with T2DM who are on intensive insulin (i.e., basal–bolus) therapy. However, FSL is not reimbursed for those using basal insulin only [16]. While several studies have examined the cost-effectiveness of FSL in populations of people with T2DM using basal insulin only [17, 18], none of these studies are specific to the Dutch population and healthcare system.

The objective of this study was to assess the cost-effectiveness of FSL versus capillary-based self-monitoring of blood glucose (SMBG) for people living with T2DM on basal insulin, from the perspective of the Dutch publicly funded healthcare system.

Methods

Microsimulation Model

This analysis used the patient-level microsimulation model DEDUCE (DEtermination of Diabetes Utilities, Costs, and Effects) [19]. The model estimates the incidence of ADEs (hypoglycemia or diabetic ketoacidosis, DKA), complications, and death in each 1-year cycle, assigning costs and utilities accordingly. The complications of T2DM are modeled in DEDUCE using the RECODe risk equations, which were developed using individual-patient data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial [19–21]. The risk of experiencing each complication is predicted as a function of patients’ age, sex, ethnicity, smoking status, cardiovascular history, systolic blood pressure, medication use, glycated hemoglobin (HbA1c) level, total cholesterol level, high-density lipoprotein cholesterol level, serum creatinine level, and urine albumin/creatinine ratio [19]. In each cycle, simulated patients could experience all-cause mortality, based on the RECODe risk equations [19], or could die as a result of DKA, as described below.

Time Horizon, Perspective, and Discounting

The perspective taken was that of the Dutch publicly funded healthcare system, including societal costs due to productivity losses. A 50-year time horizon was used to capture all costs and benefits in patients’ lifetimes. Costs were discounted at 3%, and utilities were discounted at 1.5%, as recommended in the 2024 Dutch guidelines for economic evaluations in healthcare [22].

Clinical Inputs

Baseline characteristics for the modeled population (Table 1) were derived from real-world studies and clinical trials [13, 20, 23]. In the absence of suitable population-level Dutch data, mean age at model entry was taken from a recent Swedish registry study [13]. Mean baseline HbA1c level was based on the Canadian IMpact of flash glucose Monitoring in pEople with type 2 Diabetes Inadequately controlled with non-insulin Antihyperglycaemic ThErapy (IMMEDIATE) trial [23]. Sex and additional baseline laboratory parameters, risk factors, and medication use were taken from the ACCORD trial [20].

Table 1.

Patient characteristics

Variable	Value (SD)	Source
Population characteristics
Demographics
Age at model entry, years	62.9 (12.4)	Nathanson et al. 2025 [13]
Sex, % female	38.3%	Basu et al. 2017 (ACCORD) [20]
Ethnicity
White	100%	Assumption
Black	0%
Hispanic	0%
Predictive factors used in model
Baseline laboratory parameters
HbA1c level, %	8.2 (1.5)	Aronson et al. 2023 (IMMEDIATE) [23]
SBP, mmHg	136.5 (17.1)	Basu et al. 2017 (ACCORD) [20]
Total cholesterol, mg/dL	183.20 (41.7)
HDL cholesterol, mg/dL	41.8 (11.6)
Serum creatinine, mg/dL	0.9 (0.2)
Urine albumin/creatinine ratio	99.2 (359.4)
Baseline risk factor status
% current smokers	12.0%	Basu et al. 2017 (ACCORD) [20]
% with CVD	35.7%
Baseline medication use
Blood pressure	84.2%	Basu et al. 2017 (ACCORD) [20]
Statins	64.0%
Oral antidiabetics	83.0%
Anticoagulants	3.0%

Data are mean (SD) or percentage of patients

ACCORD Action to Control Cardiovascular Risk in Diabetes, CVD cardiovascular disease, HbA1c glycated hemoglobin, HDL high-density lipoprotein, IMMEDIATE IMpact of flash glucose Monitoring in pEople with type 2 Diabetes Inadequately controlled with non-insulin Antihyperglycaemic ThErapy, SBP systolic blood pressure, SD standard deviation

The impact of FSL use on patients’ HbA1c level was modeled as an immediate absolute 0.5% reduction (Table 2) [12, 13, 24–36]. This reduction was agreed by Dutch experts on the basis of the literature, in which published reductions in HbA1c associated with FSL vary between 0.29% and 1.1%; a 0.5% reduction was also observed in a 12-month real-world study conducted in the USA [24]. The reduction was assumed to be persistent over the model time horizon.

Table 2.

Model inputs

	FSL	SMBG	Source
Clinical inputs
HbA1c benefit
One-time absolute reduction in HbA1c	0.50%	0.00%	Expert opinion and Miller et al. 2020 [24]
Hypoglycemic events
SHE (annual rate)	0.18%	0.25%	Nathanson et al. 2025 [13]
NSHE (events per year)	16.50	23.31	Bergenstal et al. 2021 [25]; Edridge et al. 2015 [26]
DKA events
DKA (annual rate)	0.34%	1.37%	Guerci et al. 2023 [12]
Mortality DKA (probability per event)	4.7%		Sagy et al. 2021 [27]
Costs
Glucose monitoring costs
Test strip and lancet cost	–	€0.37	Abbott data on file
Sensor cost	€56.51	–
Annual cost	€1474.31	€148.10	Calculateda
ADE costs
SHE, per event	€1036.00		de Groot et al. 2018 [28]
DKA, per event	€9477.75
Costs for complications, year 1 (subsequent years)
Myocardial infarction	€11,834.94 (€2092.07)		van Schoonhoven et al. 2019 [29]
Congestive heart failure	€13,727.14 (€3498.83)
Blindness	€3590.29 (€0)
Renal failure	€93,824.84 (€72,602.31)
Stroke	€36,022.32 (€8652.32)
Indirect costs from absenteeism
Mean daily wage	€102.47		Netherlands Central Bureau of Statistics [30]
Days lost to absenteeism	1.93	10.43	FLARE-NL4 [15]
Annual indirect costs	€197.76	€1068.72	Calculated
Utilities
General utilities
Baseline health utility	0.785		Takahara et al. 2019 [31]
Fingerstick disutility	0.03b		Matza et al. 2017 [32]
ADE disutilities
SHE, per event	0.0183		Bilir et al. 2018 [33]
NSHE, per event	0.00163		Bilir et al. 2018 [33]
DKA, per event	0.0091		Peasgood et al. 2016 [34]; Jorissen et al. [38]
Disutilities for complications, year 1 (subsequent years)
Myocardial infarction	0.0409 (0.012)		CADTH 2017 [35]
Congestive heart failure	0.0635 (0.018)
Blindness	0.0498 (0.0498)
Renal failure	0.263 (0.263)
Stroke	0.0524 (0.04)		Shao et al. 2019 [36]

ADE acute diabetic event, CADTH Canadian Agency for Drugs and Technologies in Health, DKA diabetic ketoacidosis, FLARE-NL FLAsh monitor Registry in the Netherlands, FSL FreeStyle Libre systems, HbA1c glycated hemoglobin, NSHE non-severe hypoglycemic event, SHE severe hypoglycemic event, SMBG self-monitoring of blood glucose

^aBased on 26.09 flash CGM sensors or 400 SMBG test strips and lancets per year

^bThe model applies a fingerstick disutility only to one cycle

Rates of severe hypoglycemic events (SHE) with FSL and SMBG were based on Swedish registry data from a recent study comparing 2292 people with T2DM on basal insulin who were using FSL with 43,424 who were not [13]. The rate of non-severe hypoglycemic events (NSHEs) for SMBG was taken from a meta-analysis of observational studies [26]. Applying the 29% reduction in hypoglycemia observed in a US real-world study to the SMBG rate gave the NSHE rate expected with FSL (Table 2) [25].

The incidence of DKA was based on the French reimbursement claims database study RELIEF (Table 2), which found a 75% reduction in hospitalization for DKA after acquisition of FSL [12]. In the absence of Dutch data, mortality due to DKA was based on existing real-world data and was assumed not to differ between the comparators [27].

Direct Costs

Costs were inflated to 2024 euro values. The cost of FSL was calculated on the basis of the 14-day sensor lifespan, equating to 26.09 sensors annually. For SMBG, the cost was calculated on the basis of the Dutch reimbursement policy of 400 test strips and lancets per year (Table 2).

Costs of ADEs were taken from an analysis of Dutch patients in the Global Hypoglycaemia Assessment Tool (HAT) study [28, 37]. NSHEs were assumed not to have any associated costs.

Costs associated with diabetes-related complications were taken from a systematic review of clinical costs in the Netherlands [29].

Indirect Costs

The cost of absenteeism was included in the model. A mean daily wage of €102.47 was assumed for all patients, based on Dutch statistical data [30]. The number of days lost to absenteeism per year was calculated by multiplying the percentage of patients with absenteeism in 6 months in the Flash Monitor Register in the Netherlands (FLARE-NL4) study by the estimated number of days of work missed among those with absenteeism [15]. The retirement age applied was 68 years.

Utilities

A baseline health utility of 0.785 was used [31].

Disutility values for ADEs and diabetes-related complications were taken from published studies [33, 35, 36, 38] and were applied per event for ADEs and per year for complications (Table 2). Patients using FSL were assumed to have a one-time utility improvement of 0.03 versus SMBG, corresponding to the benefit of avoiding fingerstick testing, based on the results of a UK time trade-off study [32].

Analysis Approach

The reference case was a deterministic analysis. The DEDUCE model was run using Microsoft Excel for 10,000 patients. A fixed cycle length of 1 year was used, and half-cycle correction was applied. Health outcomes were assessed as quality-adjusted life years (QALYs).

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Probabilistic Sensitivity Analysis

Probabilistic sensitivity analysis was conducted varying discount rates, treatment-effects, complications, utilities, and costs. A total of 1000 model iterations were run. The variability reported in the relevant data sources was used to define parameter uncertainty; where this was not available, the standard error was assumed to be 10% of the mean.

Scenario Analyses

Scenario analyses were conducted to investigate the effect of varying the model time horizon (5, 10, or 20 years), discounting (0%, 3%, or 5% for both costs and utilities), SMBG test frequency (0.5, 1.0, 1.66, or 3.0 per day), SMBG strip and lancet cost, alternative HbA1c reductions with FSL (based on the minimum [0.29%] and maximum [1.1%] HbA1c reductions observed in the literature [13, 39]), and the exclusion of indirect costs.

Results

Base-Case Cost-effectiveness Analysis

Use of FSL was associated with an additional €19,738 in acquisition costs, compared with SMBG. This was partially offset by reductions in costs associated with complications and ADEs, leading to total costs that were €8021 higher with FSL than with SMBG (€168,211 vs. €160,190; Supplementary Table 1). The reduced incidence of potentially fatal DKA and the lowered HbA1c associated with FSL led to 0.38 more life years than with SMBG (17.58 vs. 17.21). In addition, FSL provided 0.53 more QALYs than SMBG (12.77 vs. 12.24; Supplementary Table 2).

The resulting incremental cost-effectiveness ratio (ICER) for FSL versus SMBG was €15,181/QALY (Table 3).

Table 3.

Base-case cost-effectiveness results

	FSL	SMBG	Incremental
Costs	€168,211	€160,190	€8021
Lys	17.58	17.21	0.38
QALYs	12.77	12.24	0.53
ICER (Cost/QALY)			€15,181

FSL FreeStyle Libre systems, ICER incremental cost-effectiveness ratio, LY life year, SMBG self-monitoring of blood glucose, QALY quality-adjusted life year

Probabilistic Sensitivity Analysis

The probabilistic reference case ICER (€19,653/QALY) was similar to that of the base-case analysis (Supplementary Table 3). FSL was 52% likely to be cost-effective at a willingness-to-pay (WTP) threshold of €20,000/QALY, and > 99% likely at thresholds ≥ €40,000/QALY (Fig. 1).

Fig. 1.

Probabilistic scatterplot and cost-effectiveness acceptability curve. Each scatterplot datapoint represents one probabilistic sensitivity analysis iteration out of 1000 total iterations, with each iteration run with 10,000 people. QALY quality-adjusted life year, SMBG self-monitoring of blood glucose

Scenario Analyses

Scenario analyses showed that the ICER was insensitive to variations in time horizon but was increased slightly when the same discount rate was used for costs and effects (Table 4). Increasing SMBG frequency or cost reduced the ICER, with the scenario assuming three SMBG tests per day giving an ICER of €8084/QALY.

Table 4.

Deterministic scenario analysis results

Parameter	Reference case input	Scenario analysis input	Δ Costs (€)	Δ QALYs	ICER (€/QALY)
Base case			8021	0.53	15,181
Time horizon	50 years	5 years	1925	0.10	18,381
		10 years	3027	0.20	15,459
		20 years	5686	0.36	15,803
Discounting	Cost discount: 3.0% Utility discount: 1.5%	Cost discount: 0% Utility discount: 0%	13,486	0.68	19,784
		Cost discount: 3.0% Utility discount: 3.0%	8021	0.42	18,977
		Cost discount: 5.0% Utility discount: 5.0%	6090	0.33	18,615
Number of strips/lancets per day	1.1	0.5	9195	0.53	17,401
		1.0	8210	0.53	15,538
		1.66	6910	0.53	13,078
		3.0	4272	0.53	8084
Testing strip/lancet price	€0.37	€0.10	9596	0.53	18,161
		€0.72	6271	0.53	11,869
One-time absolute HbA1c reduction	FSL: 0.5% SMBG: 0.0% (Expert opinion and Miller et al. 2020 [24])	FSL: 0.29% SMBG: 0.0% (Nathanson et al. 2025 [13])	11,911	0.43	27,513
		FSL: 1.1% SMBG: 0.0% (Carlson et al. 2022 [39])	2288	0.77	2954
Indirect costs	Included	Excluded	11,749	0.53	22,236

FSL FreeStyle Libre systems, HbA1c hemoglobin A1c, ICER incremental cost-effectiveness ratio, QALY quality-adjusted life year, SMBG self-monitoring of blood glucose

Varying the HbA1c reduction associated with FSL had a substantial impact on the ICER. When a 0.29% reduction was used the ICER increased to €27,513/QALY. By contrast, use of a 1.1% reduction lowered the ICER to €2954/QALY.

Exclusion of costs due to productivity losses increased the ICER, to €22,236/QALY.

Discussion

This economic evaluation demonstrated that for Dutch people with T2DM on basal insulin monitoring their glucose levels with FSL is a cost-effective option compared with SMBG. In the Netherlands, WTP thresholds of €20,000, €50,000, and €80,000 per QALY are used, depending on the severity of illness [22, 40]. The base-case ICER for FSL versus SMBG was below €20,000/QALY, probabilistic sensitivity analysis found a 100% likelihood of FSL being cost-effective at a WTP threshold of €50,000, and all scenarios tested also gave ICERs below €50,000/QALY.

The scenario analysis results showed that the ICER was sensitive to the reduction in HbA1c level associated with FSL. This is expected given the design of the model, in which HbA1c determines the risk of patients experiencing complications in each year. It is notable that in the scenario testing a smaller HbA1c improvement than the base case (0.29% vs. 0.5%), FSL would still be likely to be considered cost-effective. The ICER was substantially reduced when SMBG testing at a frequency of three times per day was considered. However, it is notable that reimbursement of test strips and lancets is limited to approximately one test per day in the Netherlands, and that the incidence of the increased costs associated with more frequent testing would fall on people with T2DM rather than on the healthcare system.

The cost-effectiveness of FSL, compared with SMBG, has previously been assessed in populations of people with T2DM on basal insulin in several countries, demonstrating cost-effectiveness across diverse healthcare settings. From an Italian healthcare system perspective, Del Prato et al. found FSL to have an ICER of €10,556/QALY, with a 100% likelihood of being cost-effective at a WTP threshold of €20,000/QALY [17]. In a US study, FSL was found to be dominant to SMBG (i.e., to provide more QALYs at a lower cost) for people with T2DM on basal insulin covered by Medicaid [18]. Similarly, a Canadian study that included costs due to work absenteeism found that from a private payer perspective FSL was dominant to SMBG for people with T2DM on intensive insulin, basal insulin, or non-insulin therapies [41].

These results are supported by the findings of several real-world studies demonstrating FSL to be associated with clinical benefits among people with T2DM on basal insulin. A prospective study in the Netherlands has demonstrated positive effects on glycemic outcomes, HRQoL, and disease burden [15]. A retrospective study using the French Système National des Données de Santé reimbursement claims database found initiation of FSL to be associated with a statistically significant reduction in hospitalization due to ADEs, with 75% and 44% fewer admissions for DKA and hypoglycemia, respectively, observed after FSL acquisition, compared with the previous 12 months [12]. Recently, a Swedish comparative study using the National Diabetes Register found a statistically significant reduction in all-cause hospitalization among people with T2DM on basal insulin who were using FSL, compared with a matched control group on SMBG [13]. In addition, real-world data from Canada have shown statistically significant reductions in HbA1c after acquisition of FSL among people with T2DM using basal insulin [14].

ADA treatment guidelines for T2DM recommend the use of sensor-based glucose monitoring technology for people with T2DM who are using insulin [10]. However, currently FSL is only reimbursed in the Netherlands for people with T2DM if they are using intensive insulin [16]. This economic analysis demonstrates that FSL could improve treatment outcomes for people with T2DM on basal insulin in a cost-effective manner.

This study has some limitations. In the absence of Dutch-specific data for all model inputs, use of clinical data from multiple sources was required, as no individual study could provide all the necessary inputs. However, data from European populations comparable to Dutch T2DM cohorts were used where possible [13, 42–44], all costs were taken from Dutch sources, and scenario analysis showed that FSL was cost-effective across a range of estimates, based on the literature, for the associated HbA1c benefit [13, 39]. A limitation of the risk equations used in the model is that some micro- and macrovascular complications (e.g., neuropathy, foot ulcer, amputation, and pulmonary arterial disease) could not be considered. Because the risk of developing these complications is expected to correlate with HbA1c level, their omission is likely to be conservative with respect to the cost-effectiveness of FSL. The modeling approach also assumes that patients will remain on their glucose monitoring method indefinitely without switching methods or increasing the number of daily tests conducted. This is also likely to be conservative, given that, unlike SMBG, the costs of glucose monitoring with FSL would not increase if glucose levels were checked more regularly because of insulin intensification.

Taken together, these findings are in line with evidence from other countries and support reconsideration of reimbursement of FSL for people with T2DM on basal insulin in the Netherlands.

Conclusion

From a Dutch publicly funded healthcare system perspective, FSL can be considered to be cost-effective compared with SMBG for people living with T2DM on basal insulin therapy.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contribution

Peter van Dijk, Chris Chesters, Jack Timmons, Kirk Szafranski, Julia Bakker, and Fleur Levrat-Guillen confirm that they meet the International Committee of Medical Journal Editors (ICMJE) uniform requirements for authorship, and that they were involved in the design of the study, and in the interpretation of data; were involved in the drafting and critical revision of the work for important intellectual content; and approved the final version to be published.

Funding

This study, the preparation of the manuscript and publication fees were funded by Abbott.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Declarations

Conflict of Interest

Peter van Dijk received study support from Abbott, Dexcom, and Menarini. All this support has gone to his employer. Kirk Szafranski is an employee of EVERSANA, which has received project funding from Abbott. Julia Bakker reports no relevant disclosures. Chris Chesters and Fleur Levrat-Guillen are employees of Abbott. Jack Timmons was an employee of Abbott at the time this study was conducted.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.