Cost effectiveness of treatment for amblyopia: an analysis based on a probabilistic Markov model

Abstract

Aims: To estimate the long term cost effectiveness of treatment for amblyopia in 3 year old children.

Methods: A cost utility analysis was performed using decision analysis including a Markov state transition model. Incremental costs and effects during the children's remaining lifetime were estimated. The model took into account the costs and success rate of treatment as well as effects of unilateral and bilateral visual impairment caused by amblyopia and other eye diseases coming along later in life on quality of life (utility). Model parameter values were obtained from the literature, and from a survey of experts. For the utility of unilateral visual impairment a base value of 0.96 was assumed. Costs were estimated from a third party payer perspective for the year 2002 in Germany. Costs and effects were discounted at 3%. Uncertainty was assessed by univariate and probabilistic sensitivity analysis (Monte-Carlo simulation).

Results: The incremental cost effectiveness ratio (ICER) of treatment was euro2369 per quality adjusted life year (QALY). In univariate sensitivity analysis the ICER was most sensitive to uncertainty concerning the utility of unilateral visual impairment-for example, if this utility was 0.99, the ICER would be euro9148/QALY. Monte-Carlo simulation yielded a 95% uncertainty interval for the ICER of euro710/QALY to euro38 696/QALY; the probability of an ICER smaller than euro20 000/QALY was 95%.

Conclusion: Treatment for amblyopia is likely to be very cost effective. Much of the uncertainty in results comes from the uncertainty regarding the effect of amblyopia on quality of life. In order to reduce this uncertainty the impact of amblyopia on utility should be investigated.

Figures

Figure 1

Decision tree with Markov processes for comparison of strategy “treatment“ with strategy “no treatment.“ (□) decision node; (○) chance node. Below the branches of the decision tree, the labels of model parameters representing probabilities (proportions) are stated (see table 1); (#) 1 − probability of other branch; (+) yes; (−) no. Markov health states are represented by ovoids and possible transitions between those states are shown by arrows. Variable names adjacent to the arrows are the transition probabilities of the model (see table 1). Variable names refer to men; for women tp_mono_m, tp_vis_m, and tpDeathm are replaced by tp_mono_f, tp_vis_f, and tpDeathf.

Figure 2

Results of univariate sensitivity analysis.

Figure 3

Effect of utility of unilateral visual impairment on incremental cost effectiveness ratio (ICER) of the strategy “treatment“ (when base values used for all other model parameters).

Figure 4

Joint distribution of incremental costs and effects of the strategy “treatment“ plotted on the cost effectiveness plane. Results of 10 000 Monte-Carlo simulations; lines represent 2.5%, 50%, and 97.5% percentiles of incremental cost effectiveness ration (ICER).

Figure 5

Cost effectiveness acceptability curves of the strategy “treatment” for various discount rates.