The economic impact of different blood glucose monitoring systems in the diabetic patient: Analysis of real-world data from an Italian local health authority

Background

This analysis compared the direct healthcare costs of two blood glucose self-monitoring systems, conventional strips vs. flash glucose monitoring (FGM) in a real-world clinical practice setting of an Italian Local Health Unit (LHU). Cost-effectiveness evaluation of FGM was also performed.

Methodology

A retrospective analysis was conducted using administrative databases of the LHU of Barletta-Andria-Trani province (BAT). During the inclusion period (January 2017–June 2019), patients with at least one prescription for multiple daily injection (MDI) insulin therapy were identified, and stratified by prescriptions for either FGM, or ≥ 1100 strips/year or < 1100 strips/year. The index-date was that of the first FGM/strip prescription. After balancing groups by propensity score matching (PSM), total direct healthcare costs at one-year follow-up were analysed.

Results/Discussion

Among 3560 strip users, 809 (22.7%) had ≥ 1100 strips/year, 2751 (77.3%) < 1,100 strips/year, while 101 used FGM. The average annual costs were €3614 in the patients with ≥ 1100 strips and €4064 for FGM (P = 0.288), with similar expenses for drugs (respectively €1981 vs. €2251, P = 0.336), and outpatient services (€382 vs. €520, P = 0.263). Hospitalization costs tended to be lower in FGM users than strips users (respectively €271 vs. €459, P = 0.393) and this difference compensated the higher device costs (€1,022 and €791, P < 0.001).

Conclusion/Perspectives

The present real-life analysis showed that above 70% diabetics receive lower strip prescriptions than recommended. The economic data revealed that, in front of a higher device cost, FGM resulted in reduced expenditures for hospitalizations, demonstrating an Incremental Cost-effectiveness ratio (ICER) = €362.90/quality-adjusted life years (QALY) for patients adherent to the treatment, with controlled diabetes. The limitations of the analysis lie in its observational design and the reduced availability of passive mobility data (related to the flows of the pharmaceutical database, that of hospitalizations and specialist services) as to 2019, which could have led to cost underestimation.