High-risk alcohol consumption may increase the risk of SARS-CoV-2 seroconversion: a prospective seroepidemiologic cohort study among American college students

Abstract

Aims: To estimate the associations between high-risk alcohol consumption and (1) SARS-CoV-2 seroconversion, (2) self-reported new SARS-CoV-2 infection, and (3) symptomatic COVID-19. Design: Prospective cohort Setting: Indiana University Bloomington (IUB), a public university of 34,660 students in southern Indiana. Participants: At the beginning of the fall 2020 semester, we randomly sampled N=1,267 IU undergraduate students, aged 18 years or older and residing in Monroe County, IN. Measurements: Primary exposure was high-risk alcohol consumption measured with the AUDIT questionnaire. We used an AUDIT score of 8 or more as the cut-off score when detecting high-risk alcohol consumption. Primary outcome was SARS-CoV-2 seroconversion, assessed with two SARS-CoV-2 antibody tests, at baseline and endline. Secondary outcomes were a) self-reported new SARS-CoV-2 infection at the study endline, and b) self-reported symptomatic COVID-19 at baseline. Findings: Prevalence of high-risk alcohol consumption was 34%. We found that students with high-risk alcohol consumption status had 2.34 [95% CI: (1.29, 4.24)] times the risk of SARS-CoV-2 seroconversion and 1.89 [95% CI: (1.08, 3.32)] times the risk of self-reporting a positive SARS-CoV-2 infection, compared to students with no such risk. Moreover, students with high-risk alcohol consumption were 18% more likely to develop symptomatic COVID-19, though this association was not statistically significant. Similar results were found after adjusting for sex at birth, race, and year in school. Findings from sensitivity analyses corroborated these results and suggested potential for a dose-response relationship. Conclusions: In this sample of American college students, high-risk alcohol consumption was associated with higher risk for SARS-CoV-2 seroconversion/infection. These findings could have implications for colleges' reopening planning in fall 2021.

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

“…The finest model was RF with a ntree of 200 and a mtry of 3, and the subsequent attributes obtained by Xgboost: BMI, glu, cocr, HDL, quislt, trig, age, slps3, LDL, crea, SBP, phyactmet, EtOH_q, and weight. These relevant features are firmly related to COVID-19 infections, such as the consumption of alcoholic drinks ( 94 – 96 ), sleep disorders ( 97 ), BMI ( 98 – 100 ), age ( 101 , 102 ), and physical activity ( 103 ).…”