A cost-effectiveness analysis of levodopa/carbidopa intestinal gel compared to standard care in late stage Parkinson's disease in the UK.

OBJECTIVE

Evaluation of cost-effectiveness of levodopa/carbidopa intestinal gel (LCIG), compared to standard care (SC) in patients with advanced Parkinson's disease (aPD) in the UK.

DESIGN

Markov model to quantify costs and outcomes associated with LCIG versus SC in aPD patients at Hoehn and Yahr (H&Y) stages 3, 4 or 5 experiencing >50% OFF time per day. Time horizon was lifetime, LCIG treatment was assumed to last maximal 5 years after which patients revert to SC. Model comprised 12 aPD health states according to H&Y status and daily time spent in OFF state. Cost analyses are reported from a UK NHS and Personal Social Services perspective. Uncertainties were assessed through one-way sensitivity analyses.

COMPARATORS

LCIG, providing patients with continuous dopaminergic stimulation to maximise functional ON time during the day and SC, defined as medically determined best available oral medication.

MAIN OUTCOME MEASURES

Cost-effectiveness, based on quality adjusted life years gained, presented as an incremental cost-effectiveness ratio.

RESULTS

Lifetime analysis yields an incremental cost per QALY of £36,024 for LCIG compared to SC (incremental cost £39,644, QALY gain 1.1). Results were sensitive to time on treatment, health state on treatment initiation, and estimates of long term benefit (OWSA results from £32,127 to £66,421 per QALY). Findings must be considered in the context of the study limitations which were mainly due to data availability constraints.

CONCLUSIONS

LCIG is an effective treatment, reducing OFF time and improving quality of life in advanced PD. It provides value for money in levodopa-responsive aPD patients with severe motor fluctuations when no other treatment options are effective or suitable. Given LCIG is an orphan drug, it is reasonable to suggest that it may be considered cost-effective in the UK setting. However, further research is needed to complete current data gaps and increase robustness of the model.