Investigations of Influenza Vaccine Effectiveness: Assessing Repeat Vaccination and Illness Duration among Breakthrough Cases

Abstract

Background: Influenza causes significant morbidity and mortality every year. Our best line of defensive against influenza is vaccination; however, despite decades of study, questions remain about the full impact of the vaccine. Influenza viruses undergo antigenic drift, requiring annual updates to the vaccine composition. Consequently, the effectiveness of the influenza vaccine differs year to year. One knowledge gap is understanding the impact of prior vaccination on current season vaccine effectiveness. For the first aim, we considered the antigenic distance hypothesis, which postulates negative interference on a given season's vaccine effectiveness when prior and current season vaccine viruses are similar and positive interference when the prior season vaccine virus and current season circulating viruses are similar, as a framework to describe how prior vaccination can negatively or positively impact the current season's A(H3N2) vaccine effectiveness. Another gap in knowledge is how vaccines might impact length of illness among breakthrough cases. For the second aim, we examined vaccine effectiveness (of all virus types) on illness duration among individuals who tested positive for influenza. We focused on individuals who sought ambulatory care and explored the potential for routinely collected data to serve as a proxy for illness duration.Methods: We used data from the United States Influenza Vaccine Effectiveness Network (US Flu VE Network), a collaborative of hospitals and universities across the United States and the Centers for Disease Control and Prevention that estimated influenza vaccine effectiveness every year since the 2004–2005 season. Annual vaccine effectiveness was estimated using a test-negative design. Each year, consenting individuals presenting to ambulatory care were enrolled into the study and tested for influenza. For the first aim, we focused on seasons 2016–2017, when the vaccine A(H3N2) virus was updated from the prior season, and 2017–2018, when the vaccine A(H3N2) virus remained the same as the prior season. For influenza-positive individuals we determined whether the genetic clade of infecting viruses matched the prior season's vaccine virus and categorized individuals into match/mismatch groups. Multiple imputation was used to estimate clade designations for individuals without genetic characterization data. We estimated adjusted odds ratios (aOR) and vaccine effectiveness (VE) by season and match/mismatch status of infecting viruses and the prior season's vaccine virus. For the second aim, we calculated illness duration based on survey responses to questions about returning to normal activities. We estimated vaccine effectiveness against illness 7 days (versus recovering in <7 days) among influenza-positive individuals from seasons 2013–2014 to 2018–2019. We conducted random forest and area under the receiver operating characteristic (ROC) curve (AUC) analyses to assess whether data from medical records (e.g., International Classification of Diseases codes) could be used to accurately classify illness duration. Results: In the first aim, we did not observe negative interference in 2016–2017, when the vaccine viruses were not the same, as expected per the antigenic distance hypothesis. In 2017–2018, negative interference was not observed for cases with infecting viruses that did not match the prior season's vaccine virus, but was observed for cases with matching clades (between repeat vaccinees and current season only vaccinees, aOR=1.27; 95% CI: 1.01, 1.61), which was inconsistent with the antigenic distance hypothesis. In the second aim, we did not demonstrate a substantial difference in recovery time between vaccinated and unvaccinated influenza-positive individuals (VE=10%; 95% CI, 0%, 10%). In subgroup analyses, significant vaccine effectiveness estimates were observed in seasons 2015–2016 and 2018–2019, as well as for B/Victoria viruses across all seasons. The top variables for predicting illness duration 7 days was site, age, virus type, antiviral prescription, and chest x-ray order. However, the ROC/AUC (AUC=0.65; 95% CI: 0.63, 0.67) results showed poor performance of the random forest model to accurately classify illness duration. Conclusion: Our results in the first aim were consistent with the antigenic distance hypothesis in one of the two seasons studied. In other studies, the 2017–2018 season was characterized by subclade heterogeneity, which may explain the differential vaccine effectiveness estimates. Furthermore, in the second aim our results did not demonstrate significant differences in illness duration by vaccination status among the medically attended influenza-positive ambulatory care population, though there may be differences in some subgroups. A limitation of both these studies was missing data. We used multiple imputation and sensitivity analyses to address this challenge; however, we acknowledge the limits to any inferences made from these results.