Cavazza Marianna, Sartirana Marco, Wang Yuxi, Falk Markus
Cergas (Centre for Research on Health and Social Care Management) - SDA Bocconi School of Management, Bocconi University, Milano, Italy.
Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milano, Italy.
Eur J Public Health. 2023 Oct 10;33(5):937-943. doi: 10.1093/eurpub/ckad125.
This study aimed to compare the cost-effectiveness of coronavirus disease 2019 (COVID-19) mass testing, carried out in November 2020 in the Italian Bolzano/Südtirol province, to scenarios without mass testing in terms of hospitalizations averted and quality-adjusted life-year (QALYs) saved.
We applied branching processes to estimate the effective reproduction number (Rt) and model scenarios with and without mass testing, assuming Rt = 0.9 and Rt = 0.95. We applied a bottom-up approach to estimate the costs of mass testing, with a mixture of bottom-up and top-down methodologies to estimate hospitalizations averted and incremental costs in case of non-intervention. Lastly, we estimated the incremental cost-effectiveness ratio (ICER), denoted by screening and related social costs, and hospitalization costs averted per outcome derived, hospitalizations averted and QALYs saved.
The ICERs per QALY were €24 249 under Rt = 0.9 and €4604 under Rt = 0.95, considering the official and estimated data on disease spread. The cost-effectiveness acceptability curves show that for the Rt = 0.9 scenario, at the maximum threshold willingness to pay the value of €40 000, mass testing has an 80% probability of being cost-effective compared to no mass testing. Under the worst scenario (Rt = 0.95), at the willingness to pay threshold, mass testing has an almost 100% probability of being cost-effective.
We provide evidence on the cost-effectiveness and potential impact of mass COVID-19 testing on a local healthcare system and community. Although the intervention is shown to be cost-effective, we believe the initiative should be carried out when there is initial rapid local disease transmission with a high Rt, as shown in our model.
本研究旨在比较2020年11月在意大利博尔扎诺/南蒂罗尔省进行的2019冠状病毒病（COVID-19）大规模检测与未进行大规模检测情况下，在避免住院和挽救质量调整生命年（QALY）方面的成本效益。
我们应用分支过程来估计有效再生数（Rt），并对有和没有大规模检测的情况进行建模，假设Rt = 0.9和Rt = 0.95。我们采用自下而上的方法来估计大规模检测的成本，采用自下而上和自上而下相结合的方法来估计避免的住院人数以及非干预情况下的增量成本。最后，我们估计了增量成本效益比（ICER），用筛查及相关社会成本、每个得出的结果避免的住院成本、避免的住院人数和挽救的QALY来表示。
考虑到疾病传播的官方数据和估计数据，在Rt = 0.9时，每QALY的ICER为24249欧元，在Rt = 0.95时为4604欧元。成本效益可接受性曲线表明，对于Rt = 0.9的情况，在最高支付意愿阈值为40000欧元时，与不进行大规模检测相比，大规模检测具有80%的成本效益概率。在最坏的情况（Rt = 0.95）下，在支付意愿阈值时，大规模检测几乎有100%的成本效益概率。
我们提供了关于COVID-19大规模检测对当地医疗系统和社区的成本效益及潜在影响的证据。尽管该干预措施显示具有成本效益，但我们认为，如我们模型所示，当出现初始快速的本地疾病传播且Rt较高时，应开展该举措。
