key: cord-0976978-xis8ysyr
authors: Kianersi, Sina; Ludema, Christina; Macy, Jonathan T.; Chen, Chen; Rosenberg, Molly
title: 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
date: 2022-02-22
journal: Addiction
DOI: 10.1111/add.15835
sha: 60ec4cb98c81888125838d169efe5ac7d570c6f6
doc_id: 976978
cord_uid: xis8ysyr

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.

Coronavirus disease 2019 , the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a major public health burden on college campuses. As of 26 May 2021, more than 700 000 SARS-CoV-2 infections have been reported from colleges and universities in the United States, with the vast majority of the cases among students [1] . COVID-19 has a wide range of symptoms, such as fever, cough, fatigue and dyspnea [2] . In some cases, COVID-19 causes long-lasting symptoms (long COVID [3] ), such as loss of smell, impaired concentration and memory problems among young adults [4] . Acquiring COVID-19 and outbreaks of this disease on college campuses adversely impact students' mental health and school performance and results in an increase in missed school days through isolation or quarantine requirements [5, 6] . Finally, SARS-CoV-2 infection spread among college students can overflow into other segments of the community with higher risk for severe COVID-19 outcomes. Early increases in COVID-19 cases among college-aged (18-24 years) adults have been followed by increases in cases among older adults who are at higher risk of severe disease [7] [8] [9] . Identifying modifiable risk factors for SARS-CoV-2 transmission among college students is imperative to prevent and control COVID-19 outbreaks on college campuses as well as among more vulnerable subpopulations in the community.

Alcohol consumption is an underexplored yet plausible risk factor for SARS-CoV-2 infection and transmission. It is a prevalent modifiable risky behavior, particularly among college students [10] . In 2018, 51% of college-aged adults reported drinking alcohol in the past 30 days; 24% reported binge drinking and 6% reported heavy drinking [11] . Alcohol consumption might increase individuals' susceptibility to SARS-CoV-2 infection through two inter-related pathways: cognitive/ behavioral and pathophysiological.

Alcohol consumption causes cognitive distortion and brings about behavioral changes that could increase the risk of SARS-CoV-2 transmission and infection [12] [13] [14] . It weakens vigilance, information processing, spatial working memory and performance of complex tasks and increases impulsivity [15] [16] [17] . These cognitive changes plausibly disrupt compliance with COVID-19 protective behaviors, including mask-wearing and physical distancing [12, 13, 18] . Moreover, the alcohol use and cognitive distortion relationship might be cyclical.

Young adults tend to drink more alcohol in groups [19] , and more alcohol consumption exacerbates cognitive distortion which consequently can result in more non-compliance with COVID-19 protective measures [20] [21] [22] .

Moreover, alcohol consumption impairs innate and adaptive immune subsystems' responses to respiratory infections [23] [24] [25] [26] through disrupting various immunological functionalities, such as reducing T and B cell counts, impairing neutrophil production and damaging alveolar barrier function [23] [24] [25] . Similarly, we expect pathophysiological changes in the lungs due to alcohol consumption could contribute to SARS-CoV-2 infections [24] . Lastly, alcohol consumption has also been found to increase susceptibility to respiratory complications, such as pneumonia [27] and acute respiratory distress syndrome [28] . Hence, alcohol consumption might also worsen COVID-19 prognosis.

The association between alcohol consumption and COVID-19 is not well understood. In commentaries and a non-quantitative review, researchers have suggested that the associations between alcohol use and COVID-19 incidence as well as COVID-19 severity need to be evaluated [24, [29] [30] [31] . However, no quantitative study has been conducted to evaluate the associations between alcohol consumption and COVID-19 incidence and severity among college students, a population with prevalent excessive alcohol drinking and frequent COVID-19 outbreaks.

The primary objective was to longitudinally evaluate the association between high-risk alcohol consumption and SARS-CoV-2 seroconversion among college students. We hypothesized that students with high-risk alcohol consumption were more likely to experience SARS-CoV-2 seroconversion. Because seroconversion may have been imperfectly detected with our antibody tests, our secondary objective was to evaluate the association between high-risk alcohol consumption and self-reported positive SARS-CoV-2 polymerase chain reaction (PCR) with reverse transcription (RT-PCR) testing history.

We further assessed the association between high-risk alcohol consumption and symptomatic COVID-19 as another secondary outcome. The hypotheses were not pre-registered and consequently the results should be considered exploratory. [14] . 

The main exposure was high-risk alcohol consumption, measured with Alcohol Use Disorders Identification Test (AUDIT) (self-report version) [37] . Previous studies have established AUDIT as a valid measurement tool for use among young adults and college students [38, 39] . AUDIT has 10 questions. The first three regard frequency and quantity of alcohol consumption, questions four to six regard drinking behavior during the last year and the last four questions regard drinking problems during the last year. Each question can contribute a score from 0 to 4, and correspondingly a total AUDIT score can range from 0 to 40 [37] . In our main analysis, we used an AUDIT score of 8 or more (AUDIT ≥ 8 versus AUDIT < 8) as the cut-off score for high-risk drinking, as established in prior studies [37, 39] . AUDIT was measured once, in the on-line baseline survey.

To explore the sensitivity of our findings we used three sets of secondary exposures. We used AUDIT score as a continuous variable to avoid residual confounding and loss of power [40] . Further, AUDIT-C is an effective and brief three-question measurement tool for detecting high-risk alcohol consumption [41] , validated for use among college students [42] . We used a cut-off score of 7 for males and 5 for females when using AUDIT-C to identify at-risk drinkers [42] .

Lastly, in each of the four follow-up surveys, we collected weekly alcohol use data using a quantity-frequency measure. The questions used for collecting these data were similar to that from the behavioral risk factor surveillance system [43] , although slightly re-worded. Using throughput chemiluminescent immunoassay (CLIA) [45] was used as the reference standard test to evaluate the accuracy of the rapid assay kits that we used in the current study. Compared to the reference standard test, the rapid assay kits showed a 64% of sensitivity and a 100% of specificity in detecting SARS-CoV-2 seroconversion.

We chose two secondary outcomes: 

In the baseline survey, participants self-reported the following demographics: age (years), sex at birth (female versus male), race (Asian, black, multi-racial, other, white), year in school (1st-4th and 5th), residence (on-versus off-campus) and Greek membership 1 (yes versus no). For descriptive analysis, we dichotomized the age variable at the legal age of drinking in the United States (21 years).

We also dichotomized the race variable (white versus non-white).

Participants had the option to choose 'do not know' when responding to the baseline survey questions. 'Do not know' responses were set to missing in the analysis. Lastly, in the parent randomized controlled study (RCT), participants were randomized to receive their antibody testing results either ≤ 24 hours (group 1) or 4 weeks (group 2) after their antibody testing [33] . In our inferential analyses, we accounted for this variable.

We did not perform power analysis for the current cohort study as this study was leveraged from the RCT study.

To evaluate the representativeness of our study sample, we compared the sample characteristics with that of the IUB undergraduate population using official IUB enrollment reports [46] . We used multiple imputation (MI, fully conditional method) and created 20 imputed data sets to address missingness, specifically to account for the missing values of the seroconversion outcome (Supporting information, Box S2) [47, 48] . To account for the imperfect sensitivity of the antibody tests in detecting seroconversion, we used logistic regression with a maximum likelihood analysis approach [49, 50] In our sensitivity analysis, we used total AUDIT score (continuous variable), AUDIT-C and quantity and frequency of alcohol consumption (any drinking and heavy drinking) and re-estimated the RR for the associations between these exposures and the outcomes. The data analysis was conducted using SAS software, version 9.4 (Cary, NC, USA) (analysis SAS code available in Supporting information, Box S2).

We used Python version 3.7.6 for data visualization (Python Software Foundation, Beaverton, OR, USA).

Of the 7499 sampled IUB undergraduate students, 3430 did not meet one or more of the inclusion criteria and 2672 were non-responders The response rate for the current study was estimated to be 27% (Supporting information, Box S1). This response rate is above average compared to other, similar, studies [54] [55] [56] . Of the 1027 participants who tested negative at baseline, 808 returned for their end-line antibody test (retention rate = 79%). A total of 736 participants completed all follow-up surveys.

Age median was 20 (interquartile range = 2). Students were mostly female (64%), white (79%), senior undergraduate student (30%) and non-Greek affiliated (77%) ( Table 1) . Approximately 69% of participants reported living off-campus. The study sample seemed to be representative of the IUB undergraduate population for most demographics (Supporting information, Table S1 ). However, female students were over-represented in our sample relative to their representation in the overall IUB student body.

There were significant socio-demographic differences between participants with high-risk alcohol consumption (AUDIT score ≥ 8) and

low-risk alcohol consumption (AUDIT score < 8 AUDIT score data were available for 1009 (of the 1027) participants (n missing = 18) ( Table 1) . AUDIT score median was 5 with an interquartile range of 7 (Supporting information, Figure S1 ). Approximately 32% of participants were at high-risk alcohol consumption.

Of the 808 participants who tested negative at baseline and com- Figure 2 ). The proportion of missing values for primary outcome was similar among the low-and high-risk groups.

Overall 

In adjusted models and after MI ( 

We found similar results when we used AUDIT-C instead of AUDIT as the exposure variable ( 

We found that undergraduate students with high-risk alcohol consumption were at higher risk for SARS-CoV-2 seroconversion, compared to students with no such risk. We found similar results when we used an alternative outcome (self-reported new SARS-CoV-2 infection). In sensitivity analyses, we found similar results when we used other alcohol consumption exposures (continuous AUDIT score,

AUDIT-C and heavy drinking). This study highlights the important role that alcohol may play in the spread of COVID-19 on college campuses.

Few studies have evaluated similar associations between alcohol use and COVID-19. In our literature screening of more than 660 titles on PubMed, we identified eight relevant study reports [57] [58] [59] [60] [61] [62] [63] [64] . Two studies did not find any association between alcohol consumption and COVID-19 [57, 60] . Four studies identified excessive alcohol use as a risk factor for COVID-19 diagnosis [63] , poor prognosis [62] and severity [58, 64] . One study found excessive alcohol use as a risk factor for COVID-19 death in patients with obesity but not in those without obesity [52] . One study found low-dose alcohol intake (< 100 g alcohol per week) to be a protective factor for COVID-19

hospitalization [50] . Lastly, in a previous cross-sectional analysis report using baseline data of the RCT study, we found that drinking alcohol more than once a week increased the likelihood of SARS-CoV-2 seropositivity [14] .

These studies were heterogeneous in their methodology, target population and exposure and outcome measurements. Only one study was conducted in the United States (among hospital patients) [63] . questionnaires [57] [58] [59] and semi-structured interviews [62] . To our knowledge, no study used the validated AUDIT screening tool. AUDIT screens a longer period compared to other measurement tools and can identify harmful drinking patterns and chronic alcohol consumption, which are linked to adverse physical consequences [65] . Studies used different ways to measure COVID-19 outcome, such as the RT-PCR test [61] , electronic health records [63] or COVID-19 hospitalization [57, 59, 60] . No study used SARS-CoV-2 seroconversion.

Serological tests can detect previously infected individuals even if they were not tested for active infection using RT-PCR test.

We further observed a statistically significant association between continuous AUDIT score and SARS-CoV-2 seroconversion.

These findings suggest that there might be a dose-response relationship between alcohol consumption and SARS-CoV-2 seroconversion.

Another study found that low-dose alcohol use is associated with lower risk for COVD-19 hospitalization [59] . Previous studies have found similar protective associations for low to moderate alcohol use and other respiratory infections, such as the common cold [66] . Further studies are needed to fully understand the relationship.

Study design Some aspects of our study design influence the interpretation of our 

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, although the antibody testing kits in this study had a low sensitivity. However, RR estimates tend to be less biased when, as in our study, the outcome is not prevalent, and it is measured with perfect (100%) specificity but low sensitivity [50] . We counted for the low sensitivity of antibody tests in our statistical analysis. Further, we used a secondary outcome (self-reported new SARS-CoV-2 infection) that does not depend upon 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 collect self-reported data on alcohol use, AUDIT, AUDIT-C and quantity-frequency index. Because alcohol use data were selfreported, the data might suffer from recall and social desirability biases. However, all these measurement tools are validated. Collecting real-time alcohol use data using ecological momentary assessment tools might help to reduce these biases [67] .

We used random sampling to identify our potential study participants.

Because the demographics of IUB undergraduates are comparable to those of other large campuses, we might be able to generalize our findings to American college students. However, there were some differences between the random sample and IUB undergraduate population, specifically in the 'sex at birth' variable. The response rates of 27% might seem low; however, our response rate is considered greater than average when compared to other studies on college campuses [55, 56] .

Our findings suggest that high-risk alcohol consumption is associated with higher risk for SARS-CoV-2 infection/seroconversion. 

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Not applicable. Greek housing is a type of community housing for North American college students who are members of fraternity and sorority social organizations [68] .