OBJECTIVES: Nirmatrelvir+ritonavir is indicated for the treatment of patients with mild to moderate coronavirus disease 2019 (COVID-19) who are at increased risk of progressing to severe disease. This economic systematic literature review (eSLR) aimed to identify the key methods and results of economic analyses of this treatment globally.
METHODS: The eSLR protocol was registered in PROSPERO (CRD42024594675). Embase, PubMed, Cochrane, and EconLit were searched on 26 September 2024 to identify articles published from 2022 through 2024. Conference and health technology assessment agency websites also were searched.
RESULTS: Overall, 22 economic analyses were identified after screening 320 titles and abstracts and 43 full texts. Most economic analyses were conducted in the United States (N=8), comprised cost-utility analyses (N=10), or used a short-term decision tree with a long-term Markov model (N=5). Economic analyses varied in terms of eligible population characteristics, COVID-19 variants, clinical setting, currency, and cost-year. In most cost-utility analyses (N=7), nirmatrelvir+ritonavir was cost-effective compared with standard of care or best supporting care. Cost-effectiveness analyses results varied depending on eligible population characteristics and context. In 2 budget impact models, nirmatrelvir+ritonavir resulted in fewer hospitalizations and modest budget impacts of $1,302,304 and $2,733,745; including post-COVID-19 conditions in these models showed that the treatment resulted in cost savings. A cost-consequence analysis showed that nirmatrelvir+ritonavir produced per-patient cost savings of $625 and reduced hospital days by 0.74 days. Key limitations of included economic analyses were discussed.
CONCLUSIONS: The economic evidence supports nirmatrelvir+ritonavir for the treatment of patients with mild to moderate COVID-19 at high risk of severe disease. Factors influencing the cost-effectiveness of this treatment include age, vaccination status, risk factors for severe disease, and post-COVID-19 conditions. A key limitation of these models is the difficulty of modeling the dynamic nature of COVID-19.
