Résumé
Objectives: The COVID19 pandemic caused over six million deaths worldwide as of 2022 and made necessary the rapid development of vaccines. The objective of this Systematic Literature Review is to summarise the main evidence from economic evaluations of vaccines against COVID19. Method(s): Searches were conducted on PubMed on July 13th 2022. The selected papers considered COVID19 vaccination scenarios without population limits. The types of study design examined were cost-benefit and cost-effectiveness analyses. Result(s): Overall, 16 articles from an initial list of 1842 were included in this review. Out of the 16 models, there were five Markov cohort models (three of them were combined with a decision tree model), four dynamic transmission models, three microsimulation models, three epidemiological models (without further information on the model structure) and one decision tree model. Model characteristics were considerably consistent between high-, middle- or low-income countries. Five studies considered both the healthcare and societal perspective, while seven studies reported only the former, and one only the latter. Two studied did not specify the study perspective. Ten of the studies did not consider any level of herd immunity, and no study considered cross-protection. Although eight studies used "naive" comparisons between vaccines, none of the studies conducted thorough indirect treatment comparison. All the models suggest that vaccines are cost-effective as they prevent death and transmission, and reduce the severity of cases. Although the sources of effectiveness estimates were always stated, the details of those studies were rarely reported. Nevertheless, the outcome measures and the key parameters used in the models were generally clearly stated and justified. Conclusion(s): This SLR highlights several challenges for conducting Health Economic evaluations of COVID19 vaccines. The quality of the models and their estimates suffered from the very fast pace of COVID19 research. Therefore, economic evidence on vaccination programs requires additional rigorous research.Copyright © 2023
Résumé
Objectives: COVID-19 standard of care (SoC) has rapidly evolved, and many treatments are now in development which will require HTA following regulatory approval. Given the evolving COVID-19 treatment landscape, up-to-date evidence synthesis may challenge HTA. The aim of this study was to evaluate the methodological implications of the rapidly evolving evidence base required for HTA. Methods: A systematic literature review (SLR) in Embase and Medline of published clinical, economic and health-related quality of life (HRQoL) COVID-19 literature was conducted to generate evidence for HTA. Results: COVID-19 entries have increased exponentially compared to other diseases. A ‘COVID-19’ search in Embase generated 146,882 hits from October 2019-May 2021. Of these, 1,305 (estimated number per week (ENPW): 101) were published in 2019, 88,098 (ENPW: 1,694) in 2020 and 57,479 (ENPW: 2,699) in 2021. In comparison, there were 14,073 entries for multiple myeloma (MM), with 4,322 (ENPW: 333) in 2019, 7,224 (ENPW: 139) in 2020 and 2,527 (ENPW: 119) in 2021. An Embase and Medline search for COVID-19 clinical studies (October 2019 to May 2021), resulted in 7,279 entries, with only 63 (ENPW: 5) in 2019 (October to December), 3,746 (ENPW: 72) in 2020 and 3,470 (ENPW: 163) up to May 2021. In comparison, the MM SLR resulted in fewer clinical publications (1,964 entries, 614/47, 1,056/20 and 294/14 in 2019-2021). Similar trends were observed for economic and HRQoL studies. Overall, the COVID-19 SLR reported >70% clinical and >90% economic and HRQoL studies compared to a MM SLR intended for HTA. Conclusions: COVID-19 entries in databases are rapidly increasing weekly. Due to the evolving landscape and abundant evidence, challenges remain to synthesise evidence that is current and reflective of SoC. Based on this experience, a six-month time limit is recommended. COVID-19 evidence generation for HTA requires consensus around the requirements to ensure recommendations are based on up-to-date evidence.