key: cord-0915947-mm833v2p
authors: Umasabor-Bubu, Ogie Q.; Bubu, Omonigho M.; Mbah, Alfred K.; Nakeshbandi, Mohamed; Taylor, Tonya N.
title: Association between Influenza Vaccination and severe COVID-19 outcomes at a designated COVID-only hospital in Brooklyn.
date: 2021-04-20
journal: Am J Infect Control
DOI: 10.1016/j.ajic.2021.04.006
sha: 614d0e09306939612284786946411b0f745ef6c3
doc_id: 915947
cord_uid: mm833v2p

Maintaining influenza vaccination at high coverage has the potential to prevent a proportion of COVID-19 morbidity and mortality. We examined whether flu-vaccination is associated with severe COVID-19 disease, as measured by ICU-admission, ventilator-use, and mortality. Other outcome measures included hospital length of stay and total ICU days. Our findings showed that flu-vaccination was associated with a significantly reduced likelihood of an ICU admission especially among aged <65 and non-obese patients. Public health promotion of flu-vaccination may help mitigate the overwhelming demand for critical COVID-19 care pending the large-scale availability of COVID-19 vaccines.

In the United States, the Coronavirus disease-2019 (COVID-19) pandemic has resulted in 31.3 million confirmed cases and over 562,000 deaths as of April 13, 2021. 1 Experts had warned of a possible second wave in late fall and winter, corresponding with the influenza (flu) season.

Influenza co-infection with COVID-19 brings with it challenges of clinically distinguishing both infectious agents, test cross-reactivity and accuracy; and possibly enhancing the risk for severe COVID-19. This can significantly affect downstream public health efforts to properly identify COVID-19 cases and contain the outbreak particularly in resource-limited settings. Fluvaccination reduces Influenza disease severity and hospitalizations among at-risk populations, such as children, older adults (aged 65>), and pregnant women. Mathematical models suggest that maintaining high influenza-vaccination coverage has the potential to significantly reduce the proportion of COVID-19 morbidity and mortality, and the risk of cross-infection. 2, 3 Here, we examined whether prior flu-vaccination reduces the likelihood of COVID-19 disease severity, as measured by hospital length of stay, ICU-admission, ICU length of stay, ventilator-use, and inhospital mortality. Continuous outcome measures included hospital length of stay defined as total hospital stay days from admission to discharge or death, and ICU length of stay , defined as total ICU stay days from admission to ICU to discharge from ICU or death. COVID-19 diagnosis was confirmed using quantitative reverse transcription-polymerase chain reaction (RT-PCR) assay of nasopharyngeal swabs.

Descriptive statistics were calculated for demographic and clinical data for the sample.

Characteristics of the study groups (self-reported flu vaccination Yes vs. No) were compared using Pearson's chi-square test, or Fisher's exact test where appropriate, for categorical variables (e.g., sex) and t-test for continuous variables (e.g., age). Clinical data comparison between fluvaccinated groups included use of the Charlson's co-morbidity index scores, 4 to account for multiple comorbidities that could be potentially associated with COVID19 infection. Adjusted multivariate logistic regression analyses quantified the effect of flu-vaccination on the rates of ICU-admission, ventilator-use and mortality (see Table ) . Adjusted multivariate linear regression analyses quantified the effect of flu-vaccination on hospital and ICU length of stay. Since individuals who are older than 65 years and those who are obese are more likely to develop severe COVID-19 disease, 5 we also conducted stratified analyses by age (<65 and ≥65 years) and BMI (<30 and ≥ 30 kg/m 2 ) were also conducted. Covariates/potential confounders were selected a priori and were chosen based on the literature and their clinical relevance to COVID-19 disease; and included length of hospital stay (when not used as the outcome); demographic variables, co-morbid conditions and presenting symptoms/signs (see Table 2 legend). Statistical analyses were performed using SAS Institute Inc. version 9.4.

patients.Of the 588 RCT-PCR confirmed COVID-19 positives, 35% self-reported being fluvaccinated, 47.1% were women, 88.3% were black, 18.7% required ICU-admission, 13.9% required ventilator-use, and 39.5% died during in-hospital stay. The mean (SD) age was 68.4 (14.5) years, BMI was 30.3 (9.5) kg/m 2 , and length of stay was 7.9 (9.1) days. Hypertension (79.4%), diabetes (52.9%) and hyperlipidemia (34.9%) were also the most common comorbidities. Fever (85.9%), cough (76.5% ), and dyspnea (68.0%) were the three most common COVID-19 symptoms. Rates for both self-reported flu-vaccinated statuses ( Table 2) .

In this sample of COVID-19 hospitalized patients in Brooklyn, during the first wave of the COVID-19 pandemic, mortality and ventilation rates were actually higher in non-flu-vaccinated patients compared to flu-vaccinated patients but the differences were not statistically significant.

However, non-flu-vaccinated hospitalized patients were two times more likely to have required an ICU admission, relative to flu-vaccinated hospitalized patients after adjusting for pertinent confounders. Stratified analysis of this association showed effects significantly stronger and present for ages <65 and non-obese patients. Non-flu-vaccinated patients aged <65 and nonobese patients were four and approximately three times more likely to have required an ICU admission, relative to their flu-vaccinated hospitalized counterparts, respectively. Our findings suggest that the risk of non-obese and ages <65 patients requiring an ICU-admission due to COVID-19 may be reduced if they were flu-vaccinated. A recent study found that higher regional rates of flu-vaccinated adults > 65 years old was associated with lower regional rates of COVID-19 deaths. 6 We did not find any association between flu-vaccination and hospital length of stay, total ICU days, mechanical ventilator use, and mortality perhaps due to the low prevalence of flu vaccination in this Brooklyn patient sample and in the US overall (36% vs 48%). 7 Possible explanations of this finding could be the well-documented protective effects of flu-vaccination for co-morbid conditions. 8-10 Flu vaccine behavior is a marker for patients' healthy behaviors and this could possibly explain the findings. It also could be because of an already primed innate immunity from flu-vaccination, especially in <65 year olds and non-obese, that results in an effective rapid immunogenic response. 11 However, it seems that once a certain threshold is reached, possibly due to an overwhelming and immunosuppressive inflammatory/cytokine storm, 12 that necessitates an ICU-admission, the protective effect is no longer seen. Importantly, these findings add significant rationale supporting the public health promotion of flu-vaccination suggesting that this could mitigate the enormous demand for critical care that can overwhelm healthcare systems especially with possible escalating COVID-19 infections during the flu season and second wave.

The key limitation of this study is the self-report assessment of flu-vaccination. Future studies should use objective measures of flu-vaccination and evaluate possible mechanisms explaining this association.

Our findings suggest that public health promotion of flu-vaccination may help mitigate the overwhelming demand for critical COVID-19 care that devastated underserved communities and under sourced healthcare systems, especially as we patiently wait for the large-scale availability of COVID-19 vaccines.

Deidentified patient data collected for the study, the statistical analysis plan and a data dictionary defining each field in the set, will be made available upon request. Data will be shared with investigator support after approval of the IRB and a signed data access agreement. 

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Abbreviations: BMI: body mass index; COVID-19: corona virus disease 2019; ICU: intensive care unit; RCT-PCR: reverse transcriptionpolymerase chain reaction

* P-value significant at ≤0.05

-value significant at ≤0.0125 level. ǂ P-value = 0.054 rounded to 3 decimal places

Adjusted for the following covariates/potential confounders selected a priori: length of hospital stay; age, sex, race and body mass index (BMI)

hypertension, diabetes, coronary artery disease, stroke, chronic kidney disease, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), end stage renal disease (ESRD), asthma, malignant tumor; fever, cough, dyspnea, chest pain, respiratory illness, pneumonia, anemia, myalgia, and diarrhea. Statistical analyses were performed using SAS (version 9