Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut 06520-8034, USA.
Immunization Unit, Pan American Health Organization, Washington DC 20037, USA.
Vaccine. 2020 Jan 10;38(2):323-329. doi: 10.1016/j.vaccine.2019.10.010. Epub 2019 Oct 28.
Passive surveillance data are often the only available source of data that can be used to evaluate the population-level impact of vaccination, but such data often suffer from important limitations such as changes in surveillance efforts. This study provides an example of how to identify important signatures of rotavirus vaccine impact, including evaluating the overall effectiveness and changes in rotavirus seasonal dynamics.
We used data from a standardized sentinel rotavirus surveillance network in six Latin American countries (Bolivia, El Salvador, Guatemala, Honduras, Paraguay, and Venezuela) from 2004 to 2017. A random-effects model was used to evaluate changes in the proportion of rotavirus-associated hospitalizations following vaccine introduction. Harmonic regression models were used to estimate vaccine impact on the number of rotavirus hospitalizations, controlling for trends in rotavirus-negative cases. Changes to rotavirus seasonality were evaluated using center of gravity analysis, wavelet analysis, and harmonic regression.
All countries observed declines in the proportion of rotavirus-positive acute diarrhea samples with a mean reduction of 16% (95% confidence interval: 10-22%). We estimate that each 10% increase in vaccine coverage was associated with declines in the number of rotavirus-positive cases, ranging from 4.3% (1.3-7.2%) in Honduras to 21.4% (16.8-25.9%) in Venezuela. The strength of the seasonal peak in rotavirus incidence became smaller after vaccine introduction in Guatemala, Honduras, and Venezuela. Seasonal peaks also shifted later in the surveillance year, especially in higher-mortality countries.
The combination of methods we applied have different strengths that allow us to identify common signatures of rotavirus vaccine impact.
被动监测数据通常是唯一可用于评估疫苗接种对人群影响的可用数据来源，但此类数据通常存在重要的局限性，例如监测工作的变化。本研究提供了一个示例，说明了如何识别轮状病毒疫苗影响的重要特征，包括评估总体效力和轮状病毒季节性动态变化。
我们使用了来自六个拉丁美洲国家（玻利维亚、萨尔瓦多、危地马拉、洪都拉斯、巴拉圭和委内瑞拉） 2004 年至 2017 年标准化的哨点轮状病毒监测网络的数据。采用随机效应模型评估疫苗引入后与轮状病毒相关的住院比例的变化。采用谐波回归模型，在控制轮状病毒阴性病例趋势的情况下，估计疫苗对轮状病毒住院人数的影响。采用重心分析、小波分析和谐波回归评估轮状病毒季节性变化。
所有国家均观察到轮状病毒阳性急性腹泻样本比例下降，平均下降 16%（95%置信区间：10-22%）。我们估计，疫苗覆盖率每增加 10%，轮状病毒阳性病例数就会减少 4.3%（1.3-7.2%）至 21.4%（16.8-25.9%），范围为洪都拉斯至委内瑞拉。在引入疫苗后，危地马拉、洪都拉斯和委内瑞拉的轮状病毒发病率季节性高峰的强度减弱。季节性高峰也在监测年度后期推迟，尤其是在死亡率较高的国家。
我们应用的方法各有优势，可以识别轮状病毒疫苗影响的共同特征。
