PNAS – Proceedings of the National Academy of Sciences of the United States
of America
http://www.pnas.org/content/early/

Biological Sciences – Applied Biological Sciences – Social Sciences – Economic Sciences: Estimating the population-level impact of vaccines using synthetic controls
Christian A. W. Bruhn, Stephen Hetterich, Cynthia Schuck-Paim, Esra Kürüm, Robert J. Taylor,
Roger Lustig, Eugene D. Shapiro, Joshua L. Warren, Lone Simonsen, and Daniel M. Weinberger
PNAS 2017 ; published ahead of print February 1, 2017, doi:10.1073/pnas.1612833114

Significance
Pneumococcus, a bacterial pathogen, is among the most important causes of pneumonia globally. Quantifying the impact of pneumococcal conjugate vaccines (PCVs) on pneumonia is challenging due to time trends unrelated to the vaccine. We use a method developed for website analytics and economics called “synthetic controls” to disentangle changes in pneumonia rates caused by the vaccine from changes caused by unrelated factors. We found that PCVs significantly reduce all-cause pneumonia hospitalizations in young children, and reduce hospitalizations for invasive pneumococcal disease and pneumococcal pneumonia in children and adults. In contrast to previous studies, we did not detect a decline in all-cause pneumonia hospitalizations in older adults in any of the five countries following the introduction of the vaccine in children.

Abstract
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.