Argante Lorenzo, Tizzoni Michele, Medini Duccio
Department of Physics and INFN, University of Turin, via Giuria 1, Turin, 10125, Italy.
ISI Foundation, via Alassio 11/C, Turin, 10126, Italy.
BMC Med. 2016 Jun 30;14:98. doi: 10.1186/s12916-016-0642-2.
Estimating the effectiveness of meningococcal vaccines with high accuracy and precision can be challenging due to the low incidence of the invasive disease, which ranges between 0.5 and 1 cases per 100,000 in Europe and North America. Vaccine effectiveness (VE) is usually estimated with a screening method that combines in one formula the proportion of meningococcal disease cases that have been vaccinated and the proportion of vaccinated in the overall population. Due to the small number of cases, initial point estimates are affected by large uncertainties and several years may be required to estimate VE with a small confidence interval.
We used a Monte Carlo maximum likelihood (MCML) approach to estimate the effectiveness of meningococcal vaccines, based on stochastic simulations of a dynamic model for meningococcal transmission and vaccination. We calibrated the model to describe two immunization campaigns: the campaign against MenC in England and the Bexsero campaign that started in the UK in September 2015. First, the MCML method provided estimates for both the direct and indirect effects of the MenC vaccine that were validated against results published in the literature. Then, we assessed the performance of the MCML method in terms of time gain with respect to the screening method under different assumptions of VE for Bexsero.
MCML estimates of VE for the MenC immunization campaign are in good agreement with results based on the screening method and carriage studies, yet characterized by smaller confidence intervals and obtained using only incidence data collected within 2 years of scheduled vaccination. Also, we show that the MCML method could provide a fast and accurate estimate of the effectiveness of Bexsero, with a time gain, with respect to the screening method, that could range from 2 to 15 years, depending on the value of VE measured from field data.
Results indicate that inference methods based on dynamic computational models can be successfully used to quantify in near real time the effectiveness of immunization campaigns against Neisseria meningitidis. Such an approach could represent an important tool to complement and support traditional observational studies, in the initial phase of a campaign.
由于侵袭性疾病的发病率较低，在欧洲和北美每10万人中发病率在0.5至1例之间，因此高精度和高精准度地评估脑膜炎球菌疫苗的有效性具有挑战性。疫苗有效性（VE）通常采用一种筛查方法进行评估，该方法在一个公式中结合了已接种疫苗的脑膜炎球菌病病例比例和总体人群中的接种比例。由于病例数量较少，初始点估计受到较大不确定性的影响，可能需要数年时间才能在较小的置信区间内估计VE。
我们使用蒙特卡罗最大似然法（MCML）来估计脑膜炎球菌疫苗的有效性，该方法基于脑膜炎球菌传播和疫苗接种动态模型的随机模拟。我们对模型进行校准以描述两次免疫活动：英国针对C群脑膜炎球菌（MenC）的活动以及2015年9月在英国启动的Bexsero活动。首先，MCML方法提供了MenC疫苗直接和间接效果的估计值，并与文献中发表的结果进行了验证。然后，我们在Bexsero不同VE假设下，根据相对于筛查方法的时间增益评估了MCML方法的性能。
MenC免疫活动的VE的MCML估计值与基于筛查方法和带菌研究的结果高度一致，但置信区间更小，并且仅使用计划接种疫苗后2年内收集的发病率数据即可获得。此外，我们表明MCML方法可以快速准确地估计Bexsero的有效性，相对于筛查方法，时间增益可能在2至15年之间，具体取决于从现场数据测量的VE值。
结果表明，基于动态计算模型的推断方法可以成功地用于近乎实时地量化针对脑膜炎奈瑟菌的免疫活动的有效性。这种方法可能是在活动初始阶段补充和支持传统观察性研究的重要工具。
