To determine if new vaccines are effective, researchers often closely monitor trends in disease rates for a city or community. However, these observations can be confounded by changes in the health or behavior of the population, so a better “control” comparison is needed. One social science comparison technique called “the synthetic control method” presents a unique way to address this problem: combine the information from several possible control communities to create a superior aggregated control built from many possible controls.

Using this method, authors of a recent paper in PNAS found that vaccines for pneumonia significantly reduce pneumonia disease incidence. This paper works on two levels: it provides compelling evidence that vaccinations against pneumonia are effective in specific populations, and it shows that this social science technique can be effectively used to study human health.

Previous studies that attempted to examine the effects of pneumonia vaccines started with a comparison between the pre-vaccination rates of pneumonia and another infection. These were compared to post-vaccination differences in the rates of the same two diseases—a technique called difference-in-difference. If the ratio of the infections changes in the appropriate direction, it’s a sign the vaccine is effective.