Using LASSO Regression to Estimate the Population-Level Impact of Pneumococcal Conjugate Vaccines

• Published in: American journal of epidemiology Vol. 192; no. 7; pp. 1166 - 1180
• Main Authors: Wong, Anabelle, Kramer, Sarah C, Piccininni, Marco, Rohmann, Jessica L, Kurth, Tobias, Escolano, Sylvie, Grittner, Ulrike, Domenech de Cellès, Matthieu
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 07.07.2023; Oxford Publishing Limited (England); Oxford University Press (OUP)
• Subjects: Bacteremia; Bayes Theorem; Bayesian analysis; Conjugates; Disease control; Estimation; Humans; Infant; Life Sciences; Meningitis; Pneumococcal Infections - epidemiology; Pneumococcal Infections - prevention & control; Pneumococcal Vaccines; Pneumonia; Pneumonia - epidemiology; Practice of Epidemiology; Probability theory; Santé publique et épidémiologie; Streptococcus infections; Streptococcus pneumoniae; United States; Vaccines; Vaccines, Conjugate; United States; Mexico; LASSO regression; pneumococcal conjugate vaccines; epidemiologic methods; vaccine impact; least absolute shrinkage and selection operator; counterfactual prediction; Streptococcus pneumoniae
• ISSN: 0002-9262; 1476-6256
• DOI: 10.1093/aje/kwad061

Abstract 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—such as all-cause pneumonia—is nonspecific. Here we present a new approach for estimating the impact of PCVs: using least absolute shrinkage and selection operator (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 the outcome and all control variables was noncausal. We then applied LASSO to real-world hospitalization data from Chile (2001–2012), Ecuador (2001–2012), Mexico (2000–2013), and the United States (1996–2005), and found that it yielded estimates of vaccine impact similar to SC. The LASSO method is accurate and easily implementable and can be applied to study the impact of PCVs and other vaccines.