Valutazione economica della tossina botulinica per la profilassi dell'emicrania cronica

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Abstract

Objective

To evaluate the cost-effectiveness of onabotulinumtoxinA versus placebo in patients with chronic migraine from the Italian National Health Service and a societal perspective.

Design

The economic analysis is based on a Markov model developed to evaluate costs and effects for a cohort of patients treated with onabotulinumtoxinA or placebo over a 2-year timeframe. The model is based on six health states (headache present on 0–3; 4–9; 10–14; 15–19; 20–23; 24–28 days/month) and combines data from published clinical trials (PREEMPT pooled data) with direct and indirect costs. Costs and effects were both discounted at 3% per year. Deterministic and probabilistic sensitivity analyses were carried out to test the robustness of the results.

Results

In the base-case, onabotulinumtoxinA was associated with an incremental number of QALYs vs. placebo of 0.04 per patient; the incremental cost per patient was €208; the incremental cost per QALY gained was therefore €4,899. From the perspective of society, the strategy of chronic migraine treatment with onabotulinumtoxinA is dominant compared with placebo. Results are slightly sensitive to the utilities used for placebo and onabotulinumtoxinA groups. Multivariate analysis showed that there is a 69% chance that onabotulinumtoxinA is cost-effective if society is willing to pay €30,000/QALY and a 68% chance at a willingness to pay threshold of €20,000/QALY.

Conclusions

OnabotulinumtoxinA appears to improve clinical outcomes at a reasonable cost, and may actually be cost-saving when compared to placebo if the societal perspective is adopted. Its additional costs seem to be offset by savings associated with a decrease in resource use and an increase in productivity. The uncertainty around some of the parameters used in the model suggests that the results should be interpreted with caution. The availability of utilities and resource use data specific to the Italian context would further reduce the level of uncertainty in the results.

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