Background Increasing evidence suggests the potential of Epstein-Barr virus (EBV) vaccination in preventing multiple sclerosis (MS). We aimed to explore the cost-effectiveness of a hypothetical EBV vaccination to prevent MS in an Australian setting.
Methods A five-state Markov model was developed to simulate the incidence and subsequent progression of MS in a general Australian population. The model inputs were derived from published Australian sources. Hypothetical vaccination costs, efficacy and strategies were derived from literature. Total lifetime costs, quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) were estimated for two hypothetical prevention strategies versus no prevention from the societal and health system payer perspectives. Costs and QALYs were discounted at 5% annually. One-way, two-way and probabilistic sensitivity analyses were performed.
Results From societal perspective, EBV vaccination targeted at aged 0 and aged 12 both dominated no prevention (ie, cost saving and increasing QALYs). However, vaccinating at age 12 was more cost-effective (total lifetime costs reduced by $A452/person, QALYs gained=0.007, ICER=−$A64 571/QALY gained) than vaccinating at age 0 (total lifetime costs reduced by $A40/person, QALYs gained=0.003, ICER=−$A13 333/QALY gained). The probabilities of being cost-effective under $A50 000/QALY gained threshold for vaccinating at ages 0 and 12 were 66% and 90%, respectively. From health system payer perspective, the EBV vaccination was cost-effective at age 12 only. Sensitivity analyses demonstrated the cost-effectiveness of EBV vaccination to prevent MS under a wide range of plausible scenarios.
Conclusions MS prevention using future EBV vaccinations, particularly targeted at adolescence population, is highly likely to be cost-effective.
- multiple sclerosis
- health economics
- health policy & practice
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information.
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Contributors AJP: is responsible for the overall content as guarantor, conceived the idea, designed and programmed the TreeAge model, identified and retrieved input data, drafted the manuscript. TZ: identified and retrieved input data, ran the final model simulations and drafted the manuscript. BVT: contributed to the writing of the final version of the manuscript. IvdM: contributed to the writing of the final version of the manuscript. JAC: retrieved input data and contributed to the writing of the final version of the manuscript.
Funding AJP, TZ, BVT, and IvdM receive salary support from the Medical Research Future Fund (EPCD0000008). JAC is funded by MS Research Australia Postdoctoral Research Fellowship Grant (number 19-0702). Bruce Taylor is funded by an NHMRC leadership Fellowship (GNT2009389).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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