Bruhn Christian A W, Hetterich Stephen, Schuck-Paim Cynthia, Kürüm Esra, Taylor Robert J, Lustig Roger, Shapiro Eugene D, Warren Joshua L, Simonsen Lone, Weinberger Daniel M
Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT 06520.
Sage Analytica, Portland, ME 04101.
Proc Natl Acad Sci U S A. 2017 Feb 14;114(7):1524-1529. doi: 10.1073/pnas.1612833114. Epub 2017 Feb 1.
When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The "synthetic controls" (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.
当引入一种新疫苗时，监测疾病发病率趋势以确保疫苗有效并量化其影响至关重要。然而，观察性研究的估计可能会受到医疗保健利用率的无关变化、人群潜在健康状况的变化或报告变化的混淆。其他疾病通常用于检测和调整这些变化，但事先选择合适的对照疾病是一项重大挑战。最初为网站分析和社会科学开发的“合成对照”（因果影响）方法提供了一个有吸引力的解决方案。通过这种方法，潜在的比较时间序列被组合成一个综合序列，并用于生成一个反事实估计值，该估计值可以与干预后的感兴趣时间序列进行比较。我们试图估计美洲五个国家引入肺炎球菌结合疫苗（PCV）后全因肺炎住院率的变化。使用合成对照，我们发现所有五个国家婴儿的全因肺炎住院率大幅下降（平均下降20%），而年轻人和中年人的估计因国家而异，并且可能受到2009年流感大流行的影响。与之前的报告不同，我们在任何国家都未检测到老年人全因肺炎发病率下降。与替代方法相比，合成对照有望提高疫苗影响研究的准确性，并提高不同人群之间结果的可比性。
