Incorporating Information on Control Diseases Across Space and Time to Improve Estimation of the Population-level Impact of Vaccines.

Abstract

The synthetic control method evaluates the impact of vaccines while adjusting for a set of control time series representing diseases that are unaffected by the vaccine. However, noise in control time series, particularly in areas with small counts, can obscure the association with the outcome, preventing proper adjustments. To overcome this issue, we investigated the use of temporal and spatial aggregation methods to smooth the controls and allow for adjustment of underlying trends. We evaluated the impact of pneumococcal conjugate vaccine on all-cause pneumonia hospitalizations among adults ≥80 years of age in 25 states in Brazil from 2005 to 2015. Pneumonia hospitalizations in this group indicated a strong increasing secular trend over time that may influence estimation of the vaccine impact. First, we aggregated control time series separately by time or space before incorporation into the synthetic control model. Next, we developed distributed lags models (DLMs) to automatically determine what level of aggregation was most appropriate for each control. The aggregation of control time series enabled the synthetic control model to identify stronger associations between outcome and controls. As a result, the aggregation models and DLMs succeeded in adjusting for long-term trends even in smaller states with sparse data, leading to more reliable estimates of vaccine impact. When synthetic control struggles to identify important prevaccine associations due to noise in control time series, users can aggregate controls over time or space to generate more robust estimates of the vaccine impact. DLMs automate this process without requiring prespecification of the aggregation level.