Back

COVID-19 and Influenza: Vaccination Before and During the Pandemic among the Lebanese Adult Population

Anthony Elia; Nedal Taha; Sima Tokajian

2021-02-12 infectious diseases
10.1101/2021.02.10.21251392 medRxiv
Show abstract

Influenza is a common respiratory tract disease that has been around for years. Vaccination remains the most cost-effective measure to avoid infection. Influenza vaccination rates in Lebanon, like elsewhere in the world, are known to be suboptimal. The emergence and spread of SARS-CoV-2 led to a global health crisis. This study aimed at assessing the impact of the COVID-19 pandemic on the tendency of the general adult population residing in Lebanon to vaccinate against influenza. A quantitative cross-sectional study was conducted between November and December 2020 using a structured questionnaire passed on 1055 individuals to determine factors influencing vaccination habits prior to and during the COVID-19 pandemic. The factors were analyzed using univariate, bivariate, and multivariate analyses. The majority (69.7%) of the study participants never received the influenza vaccine within the last 5 years, 20% vaccinated occasionally, and only 10.3% were yearly vaccinated. Among individuals who never got the influenza vaccine within the last 5 years, 20.7% reported their willingness to vaccinate this winter significantly increasing the vaccination percentage. Participants concerned about the COVID-19 pandemic showed an enhanced willingness to vaccinate against both. Influenza and COVID-19 vaccination rates, nevertheless, are still considerably lower than the recommended coverage. The COVID-19 pandemic caused a significant increase in the tendency to vaccinate against influenza. Yet, urgent vaccination strategies should be implemented to boost vaccine uptake across all demographics to consequently diminish the burden conflicted by influenza, COVID-19, and ultimately other infectious diseases.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.