Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras, Portugal.
Sci Rep. 2017 Jun 12;7(1):3049. doi: 10.1038/s41598-017-02955-y.
Although mean efficacy of multivalent pneumococcus vaccines has been intensively studied, variance in vaccine efficacy (VE) has been overlooked. Different net individual protection across settings can be driven by environmental conditions, local serotype and clonal composition, as well as by socio-demographic and genetic host factors. Understanding efficacy variation has implications for population-level effectiveness and other eco-evolutionary feedbacks. Here I show that realized VE can vary across epidemiological settings, by applying a multi-site-one-model approach to data post-vaccination. I analyse serotype prevalence dynamics following PCV7, in asymptomatic carriage in children attending day care in Portugal, Norway, France, Greece, Hungary and Hong-Kong. Model fitting to each dataset provides site-specific estimates for vaccine efficacy against acquisition, and pneumococcal transmission parameters. According to this model, variable serotype replacement across sites can be explained through variable PCV7 efficacy, ranging from 40% in Norway to 10% in Hong-Kong. While the details of how this effect is achieved remain to be determined, here I report three factors negatively associated with the VE readout, including initial prevalence of serotype 19F, daily mean temperature, and the Gini index. The study warrants more attention on local modulators of vaccine performance and calls for predictive frameworks within and across populations.
虽然多价肺炎球菌疫苗的平均疗效已得到深入研究,但疫苗疗效(VE)的差异却被忽视了。不同环境条件、当地血清型和克隆组成,以及社会人口和遗传宿主因素,都会导致不同的个体净保护效果。了解疗效的差异对于人群水平的效果和其他生态进化反馈都有影响。在这里,我通过在接种疫苗后应用多地点-一模型方法来分析数据,展示了在不同的流行病学环境中,实际 VE 可以有所不同。我分析了在葡萄牙、挪威、法国、希腊、匈牙利和中国香港,儿童日托中心无症状携带肺炎球菌的情况下,7 价肺炎球菌结合疫苗(PCV7)接种后血清型流行动态。对每个数据集的模型拟合提供了针对疫苗接种后获得和肺炎球菌传播参数的特定地点的疫苗疗效估计值。根据该模型,不同地点的血清型替换的变化可以通过 PCV7 疗效的变化来解释,其范围从挪威的 40%到中国香港的 10%。虽然这种效应如何实现的细节仍有待确定,但我报告了三个与 VE 读数呈负相关的因素,包括血清型 19F 的初始流行率、日平均温度和基尼指数。该研究值得更多关注疫苗性能的局部调节剂,并呼吁在人群内部和之间建立预测框架。