Cost-effectiveness of hybrid closed-loop artificial pancreas systems in patients with type 1 diabetes

Table of Contents

List of Tables

List of Figures

List of Abbreviations

1 Introduction
1.1 Background and Critique of Literature
1.2 Research Gap and Rationale for Study
1.3 Research Questions, Aims, and Objectives of Study

2 Methodology
2.1 Systematic Literature Search
2.2 Grey Literature Search
2.3 Eligibility Criteria
2.4 Selection Process
2.5 Assessment of Methodological Quality
2.6 Data Extraction
2.7 Data Synthesis
2.8 Risk of Bias Assessment

3 Results
3.1 Study Selection
3.2 Characteristics of Included Studies
3.3 Critical Appraisal of Included Studies
3.4 Findings of the Review
3.5 Research Pipeline for Cost-Effectiveness of HCL

4 Discussion
4.1 Interpretation of Results in the Context of Existing Evidence
4.2 Limitations of the Evidence Included in the Review
4.3 Strengths and Limitations of the Review Process
4.4 Implications of Results for Practice and Policy
4.5 Implications of Results for the Future Research Agenda
4.6 Conclusion

5 Final Notes
5.2 Registration and Protocol
5.3 Support and Competing Interests
5.4 Availability of Material

References

Appendices

Appendix A — Populated PRISMA 2021 Checklist

Appendix B — Populated PRISMA Abstract Checklist

Appendix C — Search Strategy

Appendix D — Initial Quality Checklist for Potential Studies

Appendix E — Approximation of Standardised ICERs (via PPP and Inflation)

Appendix F — JBI Data Extraction Form

Appendix G — Characteristics of Included Studies (JBI-Based)

Appendix I — CHEERS Critical Appraisal Checklist

Methods

A systematic search was conducted following the PRISMA 2020 guidelines in MEDLINE, Embase, and CENTRAL via Ovid in October 2021. A review of trial registries and grey literature, as well as reference list and forward citation search, complemented the search. Predefined eligibility criteria were used. Retrieved studies underwent CHEERS-based quality assessment. Data were subsequentiy extracted via standardised data extraction form. Results were presented through narrative synthesis, dominance ranking framework, and display of standardised ICERs (in 2021 GBP) in a cost-effectiveness plane.

Results

Of the 213 records retrieved via systematic review and 331 identified through other search methods, 8 were included in the review and 11 in the research pipeline. Overall, included studies demonstrated good quality. Standardised ICERs ranged from 5,688 to 30,293 GBP per QALY gained. Limitations of included evidence were discussed in detail and contain lack of long-term effectiveness data and inter-system comparisons, as well as possible conflicts of interest.

Conclusion

Despite limited generalisability due to heterogeneity in methodology and settings, overall results suggest cost-effectiveness for HCL systems compared to current treatment standards such as CSII, CGM, and SAP. Additional high-quality, large scale, and long­term economic research is required to assess health economic outcomes for HCL technology in clinical practice.

Funding and Registration

This study did not receive any funding. A PROSPERO registration form was drafted (Record ID 287191).

Keywords

Systematic review, diabetes technology, closed-loop systems, economic evaluation, cost­effectiveness, quality assessment

List of Tables

Table 1: Available and Emerging HCL Artificial Pancreas Systems in 2021

Table 2: Eligibility Criteria for Inclusion and Exclusion of Studies

Table 3: Key Characteristics and Results of Included Studies

Table 4: CHEERS-based Assessment of Methodological Quality of Included Studies

Table 5: CHEERS-based Overall Methodological Quality of Included Studies

Table 6: Outcome-Based Dominance Classification of Included Studies

Table 7: Cost-Effectiveness of Included Studies, sorted by Standardised ICER

Table 8: Overview of Economic Evaluation Studies within the Research Pipeline

List of Figures

Figure 1: Artificial Pancreas Classification, Characteristics, and Key Components

Figure 2: Cost-Effectiveness Plane for Economic Evaluation Outcomes

Figure 3: PubMed Trend for APS and Health Economic Records (2000 — 2021)

Figure 4: PRISMA Flowchart Describing the Process of Study Selection

Figure 5: Cost-Effectiveness Plane of Included Studies

List of Abbreviations

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1 Introduction

1.1 Background and Critique of Literature

Diabetes mellitus affects 463 million people worldwide, while the global prevalence is estimated to reach 700 million by 2045 (Saeedi et al., 2019). With 10% of total global health expenditure spent on diabetes, the ‘pandemic disease’ exerts a considerable economic burden on health care systems worldwide (IDF, 2019, Toniolo et al., 2019).

Type 1 diabetes (T1D) patients can only achieve normoglycaemia^[I] through external insulin substitution and adherence to a strict insulin regimen (Kovatchev, 2018, ADA, 2020) . Rigorous monitoring of blood glucose levels and insulin dosing is crucial to maintain glycaemic control and avoid acute hypoglycaemia, medical complications, and long-term comorbidities (Evans Kreider et al., 2017). Besides the lifelong burden of a chronic degressive disease patients also face time-consuming manual monitoring obligations (Hameed and Kleinberg, 2020).

By offering convenience through automation, subcutaneous continuous glucose monitoring (CGM) devices have largely replaced the practice of manual self-monitoring of blood glucose (SMBG) via finger prick testing (Benjamin, 2002, Welsh and Thomas, 2019). On the other hand, continuous subcutaneous insulin infusion (CSII) pumps — which provide a steady flow of basal insulin and partiy additional meal-related bolus insulin (Pickup, 2002) — have not yet superseded manual insulin dosing practice via multiple daily injections (MDI) (ADA, 2020). Thus, potential benefits from using advanced CGM devices stay limited to human capacities. On the other hand, increased adoption of CSIIs or automated insulin delivery (AID) systems would allow for more effective diabetes management, leading to fewer complications, substantial time savings, and improved quality of life (Kowalski, 2009, Bernand, 2017, Boughton and Hovorka, 2021).

Besides patients’ increasing acceptance for artificial pancreas systems over the past years (Oukes et al., 2019), the field of diabetes technology has experienced substantial progress in bioelectronic sensor technology, insulin delivery automation, and algorithmic control design (Weinstock, 2011, Kovatchev, 2018, JDRF, 2020). Given this vast rate of innovation in an ‘already complex field’ (Trevitt et al., 2016) which frequendy lacks clear delineation in existing literature, Figure 1 provides an overview of key technologies and components on the developmental path towards a ‘truly artificial pancreas’ (Boughton and Hovorka, 2021).

Figure 1: Artificial Pancreas Classification, Characteristics, and Key Components

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Source: Author’s development and illustration based on Kowalski (2015), Trevitt et al. (2016), Bemand (2017),

Boughton and Hovorka (2021), and Keelarathna et al. (2021).

In their broader sense, artificial pancreas systems (APS) subsume three generations of diabetes technology devices. All three generations consist of three key components: (I) A continuous glucose monitoring (CGM) sensor, (II) a wearable continuous subcutaneous insulin infusion (CSII) device, and (III) an algorithm-based control unit interrelating the first two components via a compatible smart device (Kovatchev, 2018).

Sensor-augmented pumps (SAP) as first-generation APS are based on relatively simple algorithms to trigger out-of-range alerts and suspend insulin flow whenever critical thresholds are reached or predicted. They include (1) low-glucose suspend (LGS), (2) predictive low-glucose suspend (pLGS), and (3) hypoglycaemia-hyperglycaemia-minimiser (HHM) devices. Automated insulin delivery (AID) systems as second-generation devices contain more sophisticated algorithmic control systems and comprise (4) hybrid closed- loop (HCL), (5) closed-loop (CL) devices. Third-generation devices include (6) fully automated multi-hormone closed-loop APS (MH-CL). In order to reach full bionic pancreas functionality, third generation devices contain additional glucagon or amylin administration pumps. MH-CLs are expected to reach FDA approval in 2022 (Boughton and Hovorka, 2021).

The most advanced commercially available APS are second-generation HCL devices that automatically adjust basal insulin delivery rates. They still lack meal-related bolus insulin delivery options to reach a fully automated closed-loop status (Leelarathna et al., 2021). Following the initial FDA approval of the MiniMed 670G by Medtronic in September 2016 (Boughton and Hovorka, 2019), the landscape of available systems includes Medtronic’s MiniMed 670G/770G/780G, the Tandem t:slim X2 Control-IQ, and the CamAPS FX. The iLet bionic pancreas and the Omnipod 5 system are currently awaiting approval, while several non-regulated DIY systems (i.a. AndroidAPS, OpenAPS, and Loop) for home-build, open-source algorithm control are already available (Gawrecki et al., 2021). Table 1 provides an exhaustive overview of existing HCL technologies with a particular focus on their constituents and their regulatory approval.

Table 1: Available and Emerging HCL Artificial Pancreas Systems in 2021

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Source: Based on Cai (2021), Ceelarathna et al (2021), and additional device-specific sources as stated above.

Overall, successfully introducing new technologies such as HCL into clinical practice strongly depends on treatment cost reimbursement decisions made by policymakers and budget holders (Seidel et al., 2019). This evidence-based decision-making process is mainly guided by cost-effectiveness considerations and requires health economic evaluations and corresponding systematic reviews (Anderson, 2010, Thielen et al., 2016).

Economic evaluation studies commonly contain incremental cost-effectiveness ratios (ICERs), for which incremental costs and incremental health outcomes (i.e., quality- adjusted life years, QALY) of an intervention are compared to an established treatment standard (Drummond et al., 2015).

As shown in Figure 2, ICERs may be displayed in a cost-effectiveness plane (Briggs and Tambour, 1998), including willingness-to-pay ICER thresholds, which express the maximum acceptable cost per QALY gained and facilitate evidence-based resource allocation in somewhat ambiguous cases (Marseille et al., 2014, Woods et al., 2016).

Figure 2: Cost-Effectiveness Plane for Economic Evaluation Outcomes

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Source: Author’s illustration based on Briggs and Tambour (1998), Drummond et al. (2015).

A wide range of multicentre, randomised trials has demonstrated incremental health- related outcomes for HCL systems compared to manual diabetes treatment pathways (i.a., SMBG, MDI) and predecessor technology (i.a., SAP, CSII, CGM) (i.a., Garg et al., 2017, Tauschmann et al., 2018, Brown et al., 2019, Breton et al., 2020, Bergenstal et al., 2021, Haidar et al., 2021). These clinical outcomes specifically cover improved glucose control, longer time-in-range, a reduction of hypoglycaemia risk, and optimised glycated haemoglobin (HbAlC) levels. The latest research suggests an even more significant clinical advantage of fully closed-loop systems and multi-hormonal systems over currently marketed HCL systems (Blauw et al., 2021, Garcia-Tirado et al., 2021).

In terms of HCL-related cost differences, a recent systematic review on the cost­effectiveness of diabetes care technologies (Pease et al., 2020b) has only identified a single study in which the cost-effectiveness of HCL systems has been compared to CSII with SMBG (Jendle et al., 2019). While early modelling studies have indicated cost-saving potentials for healthcare systems through APS reimbursement (O'Grady et al., 2012), studies on long-term technology costs and clinical outcomes in the relatively novel field are urgently needed to derive the technology’s cost-effectiveness and address existing reimbursement concerns (Bekiari et al., 2018, Houlden et al., 2021).

Overall, the general field of APS research has experienced a strong uptake of publications compared to its relatively small proportion of cost-effectiveness studies (Figure 3). Further health economic research on available systems might promote equitable access and a more widespread clinical adoption of HCL technology (Boughton and Hovorka, 2019, Fuchs and Hovorka, 2020, Addala et al., 2021), contributing to ‘closing the loop’ (Kowalski, 2009) with a fully-automated, viable, safe, and equitable digital-age treatment of diabetes (Kovatchev, 2018, Contreras et al., 2020).

Figure 3: PubMed Trend for APS and Health Economic Records (2000 — 2021)

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Source: Author’s illustration based on a PubMed search for APS [Search query: "artificial pancreas" OR "automated insulin"] and their cost-effectiveness [Search query: ("artificial pancreas" OR "automated insulin") AND (“cost” OR "cost-effective*" OR “economic”)], last updated on 15th October2021.

1.2 Research Gap and Rationale for Study

In recent years, the latest APS generation has attracted considerable attention from the public and the clinical research community for its promise of more effective and efficient diabetes treatment. Despite a plethora of trials on improved clinical outcomes and initial studies on cost-effectiveness, exhaustive economic evaluations of the technology or systematic review of cost-effectiveness analyses are non-existent at this stage. Thus, ambiguity about the long-term cost-effectiveness of HCL APS prevails, limiting the widespread adoption into clinical practice. Through the considerate search for records, evaluation of studies, and development of suggestions for future research, this study may help to bridge the identified research gap and support the adoption of the technology as a new standard of care. Replication of the detailed systematic search approach may also facilitate future research.

1.3 Research Questions. Aims, and Objectives of Study

This study aims to systematically review the extent, nature, and quality of available evidence of HCL APS cost-effectiveness. The declared aim is to aggregate and evaluate current evidence, identify upcoming evidence and systematically structure further required research within the field.

Research question 1: Based on the current evidence base, are hybrid closed-loop artificial pancreas systems a cost-effective treatment alternative for T1D patients in clinical practice?

- To identify what evidence exists in the current health economic literature
- To critically assess the quality and compare evidence of existing economic evaluations

Research question 2: Which ongoing studies and future research approaches might support policymakers in their reimbursement consideration of hybrid closed-loop artificial pancreas systems?

- To identify upcoming cost-effectiveness studies in the research pipeline
- To review remaining issues for cost-effectiveness evaluation of HCL APS and specify directives for future research

2 Methodology

This systematic review on the cost-effectiveness of hybrid closed-loop APS adheres to the JBI Guidance for Systematic Reviews of Economic Evaluations (Gomersall et al., 2014, Gomersall et al., 2015). The report follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines, using the flow diagram template, the checklist for systematic reviews, and the abstract checklist (Page et al., 2021a, Page et al., 2021b). Recommendations from ISPOR’s good practice report for systematic reviews with cost-effectiveness outcomes are closely considered (Mandrik et al., 2021). A study protocol has been drafted for registration at the International Prospective Register of Systematic Reviews (PROSPERO, Record ID 287191, Appendix D).

2.1 Systematic Literature Search

A systematic literature search was conducted in MEDLINE via Ovid, PubMed, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) via Ovid in October 2021. The search strategy was developed for initial use in MEDLINE and then adapted for additional databases. The search strategy tool PICO (Population, Intervention, Comparison, Outcome) was used to identify key concepts from the research question (Richardson et al., 1995).

Preliminary Google Scholar searches were performed to derive relevant keywords in the form of free-text search terms and acronyms. Controlled vocabulary in the form of Medical Subject Headings (MeSH) was derived from initially found studies (Baumann, 2016). Search terms were then adapted through advanced search techniques — namely truncation, wildcards, and proximity operators. Identified subject headings were exploded whenever appropriate. A peer-reviewed economic search hedge (CRD, 2013) and brand names of HCL devices were included to improve the sensitivity of the search. All identified terms were combined via Boolean operators as included in Appendix C (Lasserson et al., 2019).

Overall, adherence to the Cochrane handbook for systematic reviews of interventions (v6.2) was ensured to aid the transparency, rigorousness and replicability of the systematic search (Higgins et al., 2019b, Lasserson et al., 2019). During the refinement and validation stages of building the systematic search strategy, a subject liaison librarian from UCL Library Services was involved to verify the approach and reduce potential bias. In addition, the Peer Review of Electronic Literature Search Strategies (PRESS) 2015 guideline evidence-based checklist was used to self-revise the final search strategy (McGowan et al., 2016).

Finally, hand-searching reference lists of relevant studies (snowballing), forward and backward citation searches in Web of Science, keyword searches in leading diabetes- focussed journals, and extensive grey literature search complemented the systematic search (Higgins et al., 2019a).

2.2 Grey Literature Search

Additional grey literature was hand searched following PHE’s index of grey literature (Public Health England, 2021). The author used search engines for grey literature (i.a., BASE, CORE), websites of governmental health care institutions (i.a., FDA, CADTH, NIHR, SHTG), registers for theses and dissertations (i.a., EThOS, NDLTD, OATD), as well as general search engines (i.a., Google Scholar). In addition, the identification of studies within the research pipeline was handled separately: Clinical trial and study protocol registries (i.a., ClinicalTrials.gov, PROSPERO, WHO’s ICTRP) and diabetes- focussed conference listings (i.a., ATTD) were searched to identify ongoing or upcoming research. The complete search protocol for all databases, websites, and registers, including search terms, filters, search dates, and results can be found in Appendix C.

2.3 Eligibility Criteria

This systematic review generally includes articles for which tide, abstract, or full-text indicate cost-effectiveness outcomes from original HCL APS-related research studies irrespective of their methodological approaches (i.a., RCT-based, clinical, or modelling study). Research on non-insulin-dependent diabetes patients was excluded. Studies were included if published between the year of commercialisation of the technology (1st January 2016) and the end of the research period (31st October 2021), limited to English, German, French, Spanish, and Portuguese. Since the HCL-APS market and related health economics research are still at an early stage, full-text articles and (conference) abstracts were considered eligible. Table 2 displays the list of inclusion and exclusion criteria used during literature selection to decide whether a study was eligible.

Table 2: Eligibility Criteria for Inclusion and Exclusion of Studies

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Source: Author’s development and illustration.

2.4 Selection Process

Following the initial identification of studies from MEDLINE (via Ovid), EMBASE (via Ovid), PubMed, CENTRAL and grey literature sources, EndNote (Version X9.3.3) was utilised for automated de-duplication and organisation of remaining search results (Bramer et al., 2016). The author subsequently screened all electronic citations and available abstracts of the identified records to select literature eligible for full-text review. Full texts were then reviewed to determine eligibility for inclusion into the study based on the above criteria.

2.5 Assessment of Methodological Quality

During the screening process, identified studies were initially assessed against items on an individually developed checklist based on the Joanna Briggs Institute (JBI) critical appraisal checklist for economic evaluations as shown in Appendix E (Gomersall et al., 2015), which itself originates from Drummond’s checklist for critical quality assessment of economic evaluations in healthcare (Drummond et al., 2015).

The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist (Appendix I) was adopted as an elaborate appraisal form for the final scoring model to assess methodological quality (Husereau et al., 2013a, Husereau et al., 2013b).

2.6 Data Extraction

An abbreviated version of the predetermined JBI data extraction template for economic evaluations was used to standardise extraction and summarise study results (Gomersall et al., 2015). Alongside the results for cost-effectiveness measures (costs, QALYs, and ICER), the extracted data covered descriptive data on study type, intervention, comparator, study population, country and currency, as well as type, perspective, and horizon of the performed economic evaluation.

2.7 Data Synthesis

Due to the methodological variability of included studies regarding research design and chosen comparators, pooling cost-effectiveness estimates or meta-analysis was not deemed appropriate. Instead, descriptive and economic outcome data were tabularly displayed. Results from included studies were described, interpreted, and evaluated through a narrative synthesis of findings. Key outcomes and results were reported and discussed following the PRISMA 2020 guidelines (Page et al., 2021a).

To interpret findings, a hierarchical decision matrix tool with an incorporated dominance ranking score was utilised (Gomersall et al., 2015, Lo et al., 2020). Cost-effectiveness estimates retrieved from the studies were transferred to 2021 GBP/QALY gained by applying OECD’s purchasing power parities (OECD, 2021) and adjusting for inflation via average annual UK consumer price indices (Clark, 2021), as shown in Appendix F (Turner et al., 2019). The resulting standardised ICERs were displayed in a joint cost­effectiveness plane. Finally, central findings from this systematic review were translated into implications for policy and future research.

2.8 Risk of Bias Assessment

In order to validate this review’s methodology and to increase the generalisability of results, search methods, sources, and search terms were cross-checked against comparable, peer-reviewed study protocols of systematic reviews in diabetes technology while incorporating established approaches such as the economic search hedge. A subject liaison librarian from UCL Library Services was involved in refining and validating the search approach to increase credibility and robustness and minimise evidence selection and publication biases. The extensive search for grey literature and inclusion of the retrieved results where appropriate further aided the reduction of publication bias risks (McDonagh et al., 2013).

In the light of the single author, potential selection biases were addressed through feedback from peers and supervisors. Eligibility criteria were described in detail to grant consistent application in study selection (McDonagh et al., 2013). A fellow student reviewed the eligibility determination of identified papers. Where needed, consensus was formed through discussion.

To address the potential risk of bias within the included studies, the author utilised an abbreviated JBI critical appraisal checklist (Gomersall et al., 2015) for initial study eligibility as well as the CHEERS checklist (Husereau et al., 2013a), Husereau et al. (2013b) for profound quality assessment of included studies (Appendix E and I). Decisions taken within the critical appraisal process were discussed with peers.

Result extraction and synthesis were led by standardised approaches to reduce the bias of selective outcome reporting. Ensured adherence to the formal PRISMA protocol as a peer-reviewed standard methodology (Appendix A and B) should significandy reduce the overall potential for bias (Drucker et al., 2016).

3 Results

3.1 Study Selection

The systematic database search strategy identified 163 records; 50 additional records were identified through register searches. After automated and manual removal of duplicates and initial screening, 42 remaining records underwent a full-text review and revealed 12 studies that meet the eligibility criteria for inclusion into the review (n=7) or the research pipeline (n=5). A total of 331 records were identified through other search methods. After screening and manual removal of duplicates (i.e., previously obtained results from the systematic review), an additional 7 records were included in the review (n=l) and the research pipeline (n=6).

Figure 4 depicts the search and selection process as per PRISMA 2020 flow diagram for newly conducted systematic reviews (Page et al., 2021a). The following chapter provides the key characteristics and central findings of the 8 studies included in the review. Chapter 3.5 provides a separate overview of the 11 retrieved reports included in the research pipeline.

Figure 4: PRISMA Flowchart Describing the Process of Study Selection

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Source: Author’s illustration based on Page et al. (2021a).

3.2 Characteristics of Included Studies

Following the JBI data extraction approach (Appendices G and H), central study characteristics and results can be found in Table 3.

Four out of the eight studies are journal articles, three of them are (conference) abstracts, and one is a master’s thesis. Countries of study origin include Austria (n=l), Australia (n=l), Netherlands (n=l), Norway (n=l), Sweden (n=2), the UK (n=l), and the US (n=l) with according currencies and differing willingness-to-pay thresholds.

The types of HCL intervention include Medtronic’s MiniMed 670G (n=5) and MiniMed 780G (n=2) as well as an unspecified hybrid closed-loop APS (n=l). Comparators range from fully manual approaches (SMBG with MDI) (Pease et al., 2020a) to manual glucose monitoring with continuous automated insulin secretion (SMBG with CSII) (Jendle et al., 2019, Roze et al., 2021) and interstitial continuous glucose monitoring (isCGM) with CSII or MDI (Jendle et al., 2021). Two conference abstracts use isCGM with MDI as comparator (Cohen et al., 2021, Serne et al., 2021) and the third compares HCL to an unspecified SAP (Rai et al., 2018). Schildbach (2019) assesses HCL against both SAP and SMBG with CSII.

Half the studies take a societal perspective, while the other half choose a healthcare payer perspective for their economic evaluation. All four journal articles and the master’s thesis state a lifetime time horizon, whereas one abstract takes on a one-year perspective, and the remaining two do not state any time horizon.

Discounting rates range from 3% to 5% and are applied similarly to cost and outcomes wherever stated. Apart from a range of clinical outcome measures, reported results include incremental costs, incremental outcomes (in AQALY), and ICERs as aggregate measures in seven out of eight included studies. As Rai et al. (2018) do not provide an ICER, their stated differences in costs and QALYs were used for an approximation.

In terms of uncertainty measures, five authors provide deterministic sensitivity analysis results with additional probabilistic analyses in two studies (Pease et al., 2020a, Schildbach, 2019). The three (conference) abstracts either state to have performed sensitivity analysis without specifying the type (Cohen et al., 2021) or do not explicidy mention to account for uncertainty via sensitivity analyses.

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Source: Author’s development and illustration based on the JBI data extraction form in Appendix G with a more aggregatedpresentation of results; 1 Transferred to 2021 GBP via OECD ’r Purchasing Power Panties and annual average UK Consumer Price Indices.

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¹ Normoglycemic target ranges include a pre-prandial capillary plasma glucose target of 80-130 mg/dL and a peak post-prandial capillary plasma glucose of <180mg/dL (American Diabetes Association, 2020).