Evaluation of the 2018–2019 vaccine effectiveness against medically attended influenza-like illness using medical records and claims data

Abstract

BackgroundHealthcare administrative databases are a rich source of information that could be leveraged to estimate real-world influenza vaccine effectiveness (VE). We aimed to evaluate the VE of standard egg-based influenza vaccines and determine if administrative healthcare data provide accurate VE estimates compared to the US CDC data. MethodsThis retrospective cohort study was conducted during the 2018–2019 influenza season. Individuals who had at least one relevant record per year between 2015 and 2019 in their electronic medical record were included. Individuals were considered protected 14 days after receiving an influenza vaccine. The outcome was the occurrence of medically attended influenza-like illness (MA-ILI) defined by clinical diagnostic codes. Adjusted odds ratios (aORs) were derived from multivariate logistic regression and adjusted VE (aVEs) were calculated using 100 × (1-aORs). ResultsA total of 5,066,980 individuals were included in the analysis with 1,307,702 (25.8%) considered vaccinated. Overall, the median age was 54 (IQR, 32–66) and 58.1% were female. Vaccine protection against MA-ILI was moderate in children and low in adults. All estimates were lower than VEs reported by the CDC for the 2018–2019 influenza season. Our results were robust to potential loss to follow up, but misclassification bias and residual confounding led to underestimation of the 2018–2019 aVE. When stratified by the number of primary care visits, aVE estimates and vaccination coverage increased with the number of primary care visits, reaching estimates similar to those obtained by the US CDC and US national vaccination coverage among those with ≥ 6 primary care visits, resulting in significant positive vaccine protection in frequent healthcare users. ConclusionsModerate and low aVEs were observed during the 2018–2019 season using administrative healthcare data, which was likely due to detection and misclassification biases, correlated with healthcare seeking behaviour, leading to an underestimation of the 2018–2019 influenza VE.