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 select variables in a synthetic control model to predict the counterfactual outcome for vaccine impact inference. We first used a simulation study based on hospitalization data from Mexico (2000-2013) to test the performance of LASSO and established methods including the synthetic control model with Bayesian variable selection (SC). 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 hospitalization data from Chile (2001-2012), Ecuador (2001-2012), Mexico (2000-2013) and the US (1996-2005), 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.
© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.