Relationship between high‐risk alcohol consumption and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) seroconversion: a prospective sero‐epidemiological cohort study among American college students

  1. Sina Kianersi
  2. Christina Ludema
  3. Jonathan T. Macy
  4. Chen Chen
  5. Molly Rosenberg

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Abstract

Aims

To estimate the associations between high‐risk alcohol consumption and (1) severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) seroconversion, (2) self‐reported new SARS‐CoV‐2 infection and (3) symptomatic COVID‐19.

Design

Prospective cohort study.

Setting

Indiana University Bloomington (IUB), IN, USA.

Participants

A total of 1027 IUB undergraduate students (64% female), aged 18 years or older, residing in Monroe County, Indiana, seronegative for SARS‐CoV‐2 at study baseline.

Measurements

Primary exposure was high‐risk alcohol consumption measured with an Alcohol Use Disorders Identification Test (AUDIT) questionnaire score of 8 or more. Primary outcome was SARS‐CoV‐2 seroconversion since baseline, assessed with two SARS‐CoV‐2 antibody tests, at baseline (September 2020) and end‐line (November 2020). Secondary outcomes were (a) self‐reported new SARS‐CoV‐2 infection at the study end‐line and (b) self‐reported symptomatic COVID‐19 at baseline.

Findings

Prevalence of high‐risk alcohol consumption was 32 %. In models adjusted for demographics, students with high‐risk alcohol consumption status had 2.44 [95% confidence interval (CI) = 1.35, 4.25] times the risk of SARS‐CoV‐2 seroconversion and 1.84 (95% CI = 1.04, 3.28) times the risk of self‐reporting a positive SARS‐CoV‐2 infection, compared with students with no such risk. We did not identify any association between high‐risk alcohol consumption and symptomatic COVID‐19 (prevalence ratio = 1.17, 95% CI = 0.93, 1.47). Findings from sensitivity analyses corroborated these results and suggested potential for a dose–response relationship.

Conclusions

Among American college students, high‐risk alcohol consumption appears to be associated with higher risk for severe acute respiratory syndrome coronavirus 2 seroconversion/infection.

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  1. Version published to 10.1111/add.15835
  2. ScreenIT

    SciScore for 10.1101/2021.08.03.21261444: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: The IU Human Subjects and Institutional Review boards approved the study protocol (Protocol #2008293852).
    Consent: Participants provided informed consent through an online eConsent framework.
    Sex as a biological variableWe used a cut-off score of 7 for males and 5 for females when using AUDIT-C to identify at-risk drinkers (41).
    RandomizationWe acquired a random sample of IUB undergraduate students (n=7,499).
    Blindingnot detected.
    Power AnalysisStudy size: We did not perform power analysis for the current cohort study as this study was leveraged from the RCT study.
    Cell Line AuthenticationAuthentication: AUDIT-C is an effective and brief three-question measurement tool for detecting high-risk alcohol consumption (40), validated for use among college students (41).

    Table 2: Resources

    Antibodies
    SentencesResources
    The parent study was a randomized controlled trial evaluating the effect of receiving SARS-CoV-2 antibody test results on participants’ compliance with protective behavior against COVID-19 (33).
    COVID-19
    suggested: None
    Participants needed to be seronegative for SARS-CoV-2 antibodies at baseline (n=1027).
    SARS-CoV-2
    suggested: None
    We used SARS-CoV-2 IgM/IgG rapid assay kit (Colloidal Gold method) to test participants for SARS-CoV-2 IgM and IgG antibodies.
    SARS-CoV-2 IgM
    suggested: (Bethyl Cat# E88-302, RRID:AB_2892019)
    IgG antibodies
    suggested: None
    The antibody test result was interpreted as positive if one or both of IgG and IgM antibody types were detected in the blood sample.
    IgM
    suggested: None
    Software and Algorithms
    SentencesResources
    Antibody testing results were entered into the REDCap (Research Electronic Data Capture (35, 36)) data capturing system (14).
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    The data analysis was conducted using SAS software, Version 9.4 (Cary, NC, USA).
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)
    We used Python for data visualization (version 3.7.6, Python Software Foundation, Beaverton, OR, US).
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: Study design: Some aspects of our study design influence the interpretation of our findings. Our study design was observational and therefore inferring a causal relationship between alcohol use and SARS-CoV-2 seroconversion is difficult because of the potential for unmeasured confounding. Moreover, the associations between high-risk alcohol consumption and secondary outcome of symptomatic COVID-19 were evaluated cross-sectionally and with a small sample size. Thus, our ability to evaluate the temporal ordering between high-risk alcohol consumption and symptomatic COVID-19 outcome is limited. Nonetheless, we used a robuster study design (prospective cohort) with larger sample sizes when evaluating the associations between high-risk alcohol consumption and seroconversion and self-reported SARS-CoV-2 new infection outcomes. Measures: We chose different measurement tools for assessing the exposure and outcome, each of which have some strengths and limitations. We used biological antibody testing to measure seroconversion outcome. Antibody testing kits can capture undetected previous SARS-CoV-2 infections. Yet, the antibody testing kits in this study had a low sensitivity. However, we used a secondary outcome (self-reported new SARS-CoV-2 infection) that does not depend on antibody positivity. Previously, we found a strong association between self-reported SARS-CoV-2 infection and antibody testing variables (14). Similarly, we used different measures to ...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04620798Active, not recruitingLongitudinal COVID-19 Antibody Testing in Indiana University…

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.08.03.21261444v1 on medRxiv