Influenza Vaccination and COVID-19 Mortality in the USA: An Ecological Study

Abstract

The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.

Keywords: COVID-19; influenza; influenza vaccine; mortality; vaccination coverage.

Figure 1

Steps of data collection and processing. Initial dataset comprised 3244 counties. COVID-19 data up to 14 December 2020 were imported from Johns Hopkins University repository. Since this repository does not report Rhode Island COVID-19-related deaths at the county level, that information was obtained from the New York Times repository instead. Counties with missing information regarding the variables of interest or confounders and those with zero cases were excluded from analysis. The final dataset used for the main analysis comprised 2482 counties.

Figure 2

Steps used in this analysis, including data collection, preprocessing, and different propensity-score adjustment methods. COVID-19 data with other population and health characteristics were collected from six different sources and pooled into the COVID-19 census package. Counties meeting inclusion criteria were used in further analysis.

Figure 3

Effect of influenza vaccination coverage in the elderly on COVID-19 mortality after adjusting for the potential confounding variables. (A) Absolute correlation between potential confounders and influenza vaccination before (grey circles) and after (white squares) propensity-score (PS) stratification. (B) Mortality-rate ratio (MRR) of COVID-19 associated with a 10% increase in influenza vaccination coverage using different PS regression methods to adjust for confounding variables. Each symbol represents different PS regression methods: (a) strata: data divided into three strata on the basis of the estimated PS. In each stratum, we fit a quasi-Poisson regression model using the number of COVID-19 deaths as the outcome and influenza vaccination coverage in the elderly as the exposure variable, adjusting for the PS and using the total county population as offset; (b) tertiles: we divided the PS into tertiles and included it as a factor in the main model; (c) quintiles: same with quintiles; and (d) continuous: we used the PS as a continuous term in the outcome model. In all models, we also adjusted for US states. For stratified analyses, the MRR shown is the inverse-variance weighted MRR across the three strata.