Budget impact of treating commercially insured type 1 and type 2 diabetes patients in the United States with insulin degludec compared to insulin glargine.

OBJECTIVE

To quantify the annual budget impact if all US commercially insured type 1 diabetes mellitus patients on basal-bolus therapy (T1DMBBT), type 2 diabetes mellitus patients on basal-oral therapy (T2DMBOT), and type 2 diabetes mellitus patients on basal-bolus therapy (T2DMBBT) switched from insulin glargine (IGlar) to insulin degludec (IDeg).

METHODS

A short-term (1 year) budget impact model was developed to evaluate the costs of IDeg vs. IGlar in three treatment groups (T1DMBBT, insulin-naïve T2DMBOT, and T2DMBBT) through a simulation for a potential US health plan population of 35 million. The analysis captured direct medical costs associated with insulin treatment (insulin, needles, and self-monitored glucose testing) and costs related to managing hypoglycemic episodes. There were a total of 59,780 T1DMBBT patients, 383,145 T2DMBOT patients, and 171,325 T2DMBBT patients expected to be using long-acting insulin. A sensitivity analysis on the entire US population was also conducted.

RESULTS

Among T1DMBBT patients, IDeg was associated with an annual cost savings of -$357.13 per patient per year (PPPY), driven primarily by reduced insulin utilization. IDeg was also found to be cost saving among T2DMBOT patients (-$1206.61 PPPY), driven primarily by reductions in the cost of treating severe hypoglycemic episodes. Among T2DMBBT patients, IDeg was associated with an additional cost to the plan of $1420.04 PPPY; however, this result was driven by a higher insulin dose for IDeg compared to IGlar. Overall, IDeg demonstrated cost savings of $240 million per year, which accounted for total cost savings of 3.5% vs. IGlar.

CONCLUSIONS

The results of this analysis suggest that the reduced insulin utilization and fewer hypoglycemic episodes associated with IDeg may translate into reduced costs for payers. The model is limited by simplification of a complex disease state and assumptions surrounding disease state, treatment patterns, and costs. Therefore, results may not accurately reflect actual health plans or real-world practice patterns.