Evaluating the Cost-Utility of Continuous Glucose Monitoring in Individuals with Type 1 Diabetes: A Systematic Review of the Methods and Quality of Studies Using Decision Models or Empirical Data

Abstract

Introduction: This review presents a critical appraisal of differences in the methodologies and quality of model-based and empirical data-based cost-utility studies on continuous glucose monitoring (CGM) in type 1 diabetes (T1D) populations. It identifies key limitations and challenges in health economic evaluations on CGM and opportunities for their improvement.

Methods: The review and its documentation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. Searches for articles published between January 2000 and January 2023 were conducted using the MEDLINE, Embase, Web of Science, Cochrane Library, and Econlit databases. Published studies using models and empirical data to evaluate the cost utility of all CGM devices used by T1D patients were included in the search. Two authors independently extracted data on interventions, populations, model settings (e.g., perspectives and time horizons), model types and structures, clinical outcomes used to populate the model, validation, and uncertainty analyses. They subsequently met to confirm consensus. Quality was assessed using the Philips checklist for model-based studies and the Consensus Health Economic Criteria (CHEC) checklist for empirical studies. Model validation was assessed using the Assessment of the Validation Status of Health-Economic decision models (AdViSHE) checklist. The extracted data were used to generate summary tables and figures. The study protocol is registered with PROSPERO (CRD42023391284).

Results: In total, 34 studies satisfied the selection criteria, two of which only used empirical data. The remaining 32 studies applied 10 different models, with a substantial majority adopting the CORE Diabetes Model. Model-based studies often lacked transparency, as their assumptions regarding the extrapolation of treatment effects beyond available evidence from clinical studies and the selection and processing of the input data were not explicitly stated. Initial scores for disagreements concerning checklists were relatively high, especially for the Philips checklist. Following their resolution, overall quality scores were moderate at 56%, whereas model validation scores were mixed. Strikingly, costing approaches differed widely across studies, resulting in little consistency in the elements included in intervention costs.

Discussion and conclusion: The overall quality of studies evaluating CGM was moderate. Potential areas of improvement include developing systematic approaches for data selection, improving uncertainty analyses, clearer reporting, and explaining choices for particular modeling approaches. Few studies provided the assurance that all relevant and feasible options had been compared, which is required by decision makers, especially for rapidly evolving technologies such as CGM and insulin administration. High scores for disagreements indicated that several checklists contained questions that were difficult to interpret consistently for quality assessment. Therefore, simpler but comprehensive quality checklists may be needed for model-based health economic evaluation studies.

Fig. 1

Process of selecting studies following the PRISMA 2020 guidelines. PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Fig. 2

Summary of the cost-effectiveness results of the included studies comparing to self-monitoring of blood glucose levels using finger pricks or other CGM devices. Note: Some of the studies reported multiple ICERs. The results for three studies [7, 28, 30] are not included because of missing incremental costs and/or QALY data. CGM continuous glucose monitoring, ICERs incremental cost-effectiveness ratios, QALYs quality-adjusted life-years, SMBG self-monitoring of blood glucose, WTP willingness to pay

Fig. 3

Overview of the comparative analyses covered in the studies. Some studies included multiple comparisons. CSII continuous subcutaneous insulin infusion, HCL hybrid closed loop, is-CGM intermittently-scanned continuous glucose monitoring, MDI multiple daily injections, NR not reported, rt-CGM real-time continuous glucose monitoring, SAP sensor-augmented pump, SMBG self-monitoring of blood glucose

Fig. 4

Summary of quality scores for the Philips checklist. Descriptions are short versions of the full-item texts, see Online Resource 3 for full-item descriptions and how these were operationalized by the authors. Note: Online Resource 11 provides an explanation of the items. NA not applicable, NR not reported, Y/N ‘yes’ or ‘no’ unclear

Fig. 5

Model quality in studies using empirical data and the CHEC-extended checklist [22, 23]. CHEC Consensus Health Economic Criteria, NA not applicable

Fig. 6

Scores for model validation tests performed and reported in the papers, supplemental information, or direct references for the 32 model-based economic evaluations. Notes: Papers applying the CORE Diabetes Model that referred to the study by McEwan et al. 2014 [64], scored ‘yes’ for cross-validation tests (2 and 10), comparing model outcomes with empirical data (12), testing with alternative input data (11), and other tests (13). This is because the model has undergone validation within the Mount Hood Diabetes Challenge Network. The same situation applies to the Sheffield model [56]. AdViSHE Assessment of the Validation Status of Health-Economic decision models, NA not applicable, NR not reported