Using LASSO regression to estimate the population-level impact of pneumococcal conjugate vaccines

Abstract

The pneumococcal conjugate vaccines (PCVs) protect against diseases caused by Streptococcus pneumoniae, such as meningitis, bacteremia, and pneumonia. It is challenging to estimate their population-level impact due to the lack of a perfect control population and the subtleness of signals when the endpoint – like all-cause pneumonia – is non-specific. Here we present a new approach to estimate PCVs’ impact – using LASSO regression to predict the counterfactual outcome for vaccine impact inference. We first used a simulation study to test the performance of LASSO regression and established methods including the synthetic control (SC) approach. We found that LASSO achieved accurate and precise estimation, even in complex simulation scenarios where the association between outcome and all control variables was non-causal. We then applied LASSO to real-world data and found that it yielded estimates of vaccine impact similar to SC. The LASSO method is accurate, easily implementable, and can be applied to study the impact of PCVs and of other vaccines.