key: cord-1018479-vdak7m5u authors: Mioch, Dymphie; Kuiper, Sandra; van den Bijllaardt, Wouter; van Jaarsveld, Cornelia H.M.; Kluytmans, Jan; Lodder, Esther; Wissing, Michel D. title: SARS-CoV-2 antibodies in employees working in non-medical contact-intensive professions in the Netherlands: baseline data from the prospective COco-study date: 2021-10-08 journal: Prev Med Rep DOI: 10.1016/j.pmedr.2021.101594 sha: fabd0d163232371eb27806b8c82974ce0fcaf20a doc_id: 1018479 cord_uid: vdak7m5u COVID-19 has made a global impact since early 2020, requiring characterization of the SARS-CoV-2 virus, including transmission risk. The COco-study aims to evaluate the risk for COVID-19 infections in two non-medical contact-intensive professions. COco is a prospective cohort study evaluating SARS-CoV-2 antibodies in hairdressers and hospitality personnel in the province of North-Brabant in the Netherlands, using a total antibody enzyme-linked immunosorbent assay. Baseline data from June/July 2020 were analyzed. Participants filled out a questionnaire, providing information on demographics, health, work situation, and risk factors for COVID-19. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using logistic regression. In June/July 2020, 497 participants were enrolled: 236 hairdressers, 259 hospitality employees, and two participants worked in both industries. Hospitality staff was more frequently seropositive than hairdressers (14.2% versus 8.0%, respectively; OR 1.9, 95% CI 1.1-3.4). Furthermore, a high education level (OR 3.0, 95% CI: 1.7-5.6) and increased alcohol use (OR, 7 glasses per week increment: 1.3, 95% CI: 1.1-1.5) were associated with seropositivity. Of the 56 seropositive participants, 18 (32%) had not experienced any COVID-19 symptoms. The symptoms anosmia/ageusia differed most evidently between seropositive and seronegative participants (53.6% versus 5.7%, respectively; P<0.001 (chi-squared test)). In conclusion, four months after the first identified COVID-19 patient in the Netherlands, employees in the hospitality industry had significantly more frequently detectable SARS-CoV-2 antibodies than hairdressers. On February 27 th , 2020, the first Dutch citizen was diagnosed with COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [1] . The first wave hit the Netherlands in March 2020. To estimate the percentage of a population that has been infected with the SARS-CoV-2 virus, serology studies are conducted to measure antibodies against SARS-CoV-2 [2, 3] . In April 2020, a seroprevalence of 2.7-2.8% was reported in the Dutch population [3, 4] , which increased to 4. 5-5.5% in May-July [5, 6] . A partial lockdown was implemented in the Netherlands in mid-March, including measures such as physical distancing and closure of some businesses [7] . Subsequently, transmission decreased in the Netherlands, as in most other high-income countries. However, little is known about the efficacy of individual components of the strategies used, and/or the contribution of seasonal changes [8] [9] [10] . One component was the closure of non-medical contact-intensive professions, such as the hospitality industry and hairdressers. While some studies have suggested that these businesses may have contributed significantly to COVID-19 outbreaks , the extent of their contribution remains unknown, while closure of such businesses had large impacts on society. Furthermore, it is unknown whether employees in such professions are at increased risk for COVID-19. Considering that employees risk being in close contact with customers or colleagues infected with SARS-CoV-2, one would expect that they have an increased risk, and subsequently form a risk for transmission to their colleagues and customers, particularly when being a-/ presymptomatic but contagious . For abovementioned reasons, a prospective cohort study evaluating antibody levels against SARS-CoV-2 in employees working in two non-medical, contact-intensive professions, namely hairdressers and the hospitality industry (e.g. bars, restaurants, casinos), was initiated in June 2020 (the COco-study). We evaluate the percentage of employees infected with SARS-CoV-2 by measuring antibodies, while collecting various data on transmission risk via questionnaires. The COco-study is a prospective cohort study. Its primary objective is to evaluate whether hairdressers and/or hospitality personnel have a significantly higher chance for SARS-CoV-2. For this purpose, baseline seroprevalence was measured in June-July 2020. The study is being conducted in the western part of the province of North-Brabant in the Netherlands. This province had the highest COVID-19 incidence during the first wave in the Netherlands [6, 11] . Participants were eligible when working as hospitality staff or hairdressers in this region (Breda, Roosendaal and surrounding municipalities) for ≥100 hours during the 3 months before enrolment. People were excluded if their age was <18 years, if they were reluctant to venepuncture, incapacitated or unwilling to give informed consent, or a blood or plasma donor. The latter exclusion criteria was included since we are planning to compare seroprevalence in our cohort to seroprevalence in a matched cohort of blood and plasma donors in the region [12] . Recruitment started on June 1, 2020, and was completed on July 14. Hospitality personnel was primarily recruited via the national organization representing hospitality businesses (Koninklijke Horeca Nederland, KHN). This organization contacted all hospitality businesses in the region to inform them about our study protocol. Participants, both hospitality staff and hairdressers, were also recruited via social media, the website of the regional public health service (Gemeentelijke Gezondheidsdienst, GGD) of West-Brabant, and word by mouth advertising. Additionally, hairdressers were recruited by distribution of flyers. For hairdressers, we ensured inclusion of hairdressers in large companies, in 'mon-and-pop' stores, and hairdressers who do not own a store but cut hair from their home or their customer's home. As more people volunteered than our required sample size, we applied 'first come, first serve'. We aimed to recruit 238 hairdressers and 260 hospitality personnel, based on our power calculation (supplementary data). The number of participants working in the hospitality industry was higher, because we expected a higher dropout rate in this industry due to job changes. The COco-study has been approved by the Medical research Ethics Committees United (MEC-U) at Nieuwegein (project number A20.247/R20.041). It follows laws and guidelines on research with human subjects, including international standards such as the Declaration of Helsinki. Participation was voluntary after providing written informed consent. Data were stored pseudonymously using a study code; individual participants could not be identified by this code and only researchers involved with the COco-study had access to these data. Wantai SARS-CoV-2 antibody (Ab) ELISA is a qualitative double-antigen sandwich immunoassay that detects all SARS-CoV-2 immunoglobin isotypes (IgA, IgM, or IgG) against the receptor binding domain of the spike protein. It has an estimated sensitivity of 96% and specificity of 99% [13] . Samples were considered positive when the signal to cut-off ratio was >1.0. The highest signal to cut-off ratio detectable was 13.2: stronger signals were registered as 13.2. Questionnaires were sent at baseline (see supplementary data). We analyzed data from the baseline questionnaire, using the variables: work setting (hairdresser, hospitality industry), job position (various categories), age (in years), sex (male/female), born in the Netherlands (yes/no), household size (1-15 persons), education level (low, middle, high), financial difficulties (yes/no), workplace location (by municipality), working hours (per week), chronic disease (yes/no), body-mass index (BMI), smoking status in 2020 (yes/no), smoking quantity (number of cigarettes per day), alcohol use in 2020 (yes/no), alcohol quantity (number of glasses per week), and reported symptoms related to COVID-19 (yes/no, per symptom). A participant was considered to have a chronic disease if he/she mentioned a chronic disease or listed medication which is used for chronic diseases only. BMI was calculated by weight in kilograms divided by height in meters squared; a participant had a normal weight, overweight, or obesity if the BMI was <24.9 kg/m 2 , 25.0-29.9 kg/m 2 , >30.0 kg/m 2 , respectively. Education level was divided into three categories (low, middle, high): participants were considered to have received low education when the level of completed education was primary or pre-vocational secondary education, middle education when participants completed vocational secondary education or post-secondary education not at a university level (such as a hairdressing school), and high education when the participant had a college degree or higher. Some participants did not provide a single number to describe their average number of working hours per week. If they provided a range, its mean was registered; if they provided a minimum number of hours, this minimum was registered. One participant provided 'normal week' as an answer, which we labelled as unknown. Descriptive statistics and frequencies were used to analyze baseline characteristics. Baseline characteristics were compared using chi-square tests for dichotomous categorical variables and Wilcoxon rank-sum tests for numerical variables, due to the non-normal distribution of numerical variables. The ordinal categorical variable education (low, middle and high) was analyzed using a Wilcoxon rank-sum test. Chi-squared tests were used to compare COVID-19 related symptoms between seropositive and seronegative participants. Uni-and bivariable logistic regression models were used to calculate odds ratios and their respective 95% confidence intervals for variables associated with seroprevalence of SARS-CoV-2 antibodies. All assumptions for binary logistic regression were met. Due to limited statistical power, multivariable analyses were limited to one covariate: when conducting multivariable analyses with multiple covariates, odds ratios remained similar, but confidence intervals widened significantly (data not shown). Hence, all covariates were analyzed in bivariable analyses. Numerical variables were analyzed as continuous variables to attain highest statistical power; categorical variables were entered as dichotomous variables. Participants with missing data were excluded from analyses; this applied to only one participant who had an unknown number of working hours. P-values below 0.05 were considered statistically significant in all analyses. All analyses were conducted using SPSS Statistics 24.0. In total, 502 individuals were recruited for the COco-study ( In total, 11.3% (56/497) of participants tested positive for SARS-CoV-2 antibodies. We first studied which symptoms, experienced in 2020 until the baseline measurement, were associated with seropositivity (table 2) . 202 patients mentioned symptoms that could have been related to COVID-19. Of those, 19% (n=38) were seropositive. Eighteen of the 56 seropositive participants (32.1%) did not report a symptom, suggesting asymptomatic infections or recall bias. When evaluating specific symptoms, anosmia/ageusia (loss of smell/taste) differed most evidently between seropositive and seronegative participants (53.6% versus 5.7%, respectively; P<0.001). Furthermore, seropositive individuals reported more frequently recorded fever (37.5% versus 22 Next, we compared the percentage of seropositive patients in subgroups (tables 3 and 4). Hospitality staff tested seropositive more frequently compared to hairdressers (14.2% versus 8.0%, respectively), the odds ratio (OR) being 1.9 (95% confidence interval (CI): 1.1-3.4). Seropositive individuals were found in all job functions with >2 participants. Within other subgroups, notably high seropositivity rates were observed in participants who consumed >21 alcohol units per week (28.9%), participants with a high education (22.7%) and participants working 8-20 hours per week as a hairdresser or hospitality staff (17.6%), while seropositivity rates were low for obese participants (3.1%) and smokers (6.7%; table 3). In univariable analyses, a high education level (OR 3.0, 95% CI: 1.7-5.6) and increased alcohol use (OR, 7 glasses per week increment: 1.4, 95% CI: 1.1-1.5) were significantly associated with seropositivity, while increased smoking was associated with seronegativity (OR, 5 cigarettes per day increment: 0.6, 95% CI: 0.4 -0.9; table 4). In bivariable analyses (table 4), we evaluated the association between participants characteristics and seropositivity, adjusting for work setting (hairdressers versus hospitality staff; middle column), as well as the association between work setting and seropositivity, adjusting for one other covariable per analysis (right column). The association between work setting and seropositivity remained significant While it is acknowledged that employees in contact professions are at increased risk of being exposed to SARS-CoV-2 , most studies evaluating SARS-CoV-2 transmission in contact professions focus on healthcare workers [14] . However, healthcare workers receive training and have experience in using protective equipment , while employees working in non-medical contact-intensive professions do not. Due to this difference, it is important to study non-medical contact-intensive professions separately. As far as we know, the COco-study is the largest cohort study worldwide evaluating SARS-CoV-2 transmission in workers in non-medical contact-intensive professions. We opted to include hairdressers and hospitality personnel. The hospitality industry is often mentioned as a major source of SARS-CoV-2 transmission [15, 16] . Considering that hospitality personnel often sees many customers per day in crowded environments, it is conceivable that transmission occurs frequently. Indeed, Fisher et al. concluded that patients who tested positive for SARS-CoV-2 reported to have visited a bar or restaurant more frequently than negative tested patients [17] . A Japanese study reported that restaurants and bars were the second most frequent source of clusters (16%), after healthcare facilities (30%) [16] . On the other hand, close contact with customers is frequently brief. Furthermore, clusters are easier identified in bars and restaurant as friends and family often meet in groups, facilitating tracing the source of someone's SARS-CoV-2 infection. Therefore, we wanted to study whether hospitality personnel was actually at increased risk for SARS-CoV-2 infections. Hairdressers were selected as a second large non-medical, contact-intensive profession. Hairdressers will generally see fewer customers per day than hospitality personnel, but for a longer period of time, being unable to keep physical distancing. Some studies have suggested that hairdressers are at increased risk for COVID-19 [15] . However, hairdressers are often behind the customer, reducing the chance that a hairdresser will be in contact with droplets of saliva compared to direct face-to-face contact. Furthermore, another study reported no transmission to customers when two hairdressers with COVID-19 used face masks, but was limited in its size [18] . We observed that employees in the hospitality industry had significantly more frequently SARS-CoV-2 antibodies than hairdressers (14.2% vs. 8.0%). Additionally, a high education level and increased alcohol use were associated with seropositivity. During the first wave, the province of North-Brabant had an estimated seroprevalence of 8.4% in June 2020 [3] . Vos et al. concluded that the seroprevalence in the area of the COco-study was slightly lower than in the rest of the province [3] . In a second study, seroprevalence was estimated at 6-8% in the study area in May 2020 [5] . We need to be cautious interpreting these findings, as seropositivity rates varied widely between counties in the province, and were amongst others dependent on sex, age, education level and socioeconomic status. However, comparing these percentages to our data, it suggests that the seroprevalence in hairdressers is similar or only slightly higher compared to the general population. Previously, Dutch researchers reported that hairdressers tested more frequently positive for COVID-19 compared to other populations [15] . However, that study measured the positive percentages in hairdressers who were getting tested, not the positive percentage in a general population of hairdressers. Additionally, the data of that study was gathered when polymerase chain reaction (PCR) tests were limitless available, while limited in the time period we studied. The higher seroprevalence rate in hospitality personnel could indicate that they are at increased risk of SARS-CoV-2 at work compared to the general population, similar to other contact professions such as medical staff who do not work with COVID-19 patients [19] . However, alternative explanations for our study group exist. Two events are thought to have facilitated viral spread in the Netherlands in late Smoking was inversely associated with seropositivity, while patients with existing lung conditions have an increased risk for severe COVID-19 [21] . Various explanations for this discrepancy exist. Smoking generally reduces inflammatory responses and may therefore reduce antibody formation after COVID-19 [22] . Second, smokers may have been warned as additional risks for smokers were communicated early, resulting in more careful behavior. Third, our population was relatively young, smokers may not have developed lung disease yet. Only a small fraction (19%) of participants who experienced symptoms associated with COVID-19 was seropositive. Since participants only had mild symptoms, it is possible that not everybody who had been infected, developed detectable antibodies [13, 23, 24] . However, symptoms were recorded during a time in which other viruses were circulating, which we consider the primary explanation for the relatively low seropositivity rate. Interestingly, 32% of seropositive participants did not report any symptom in 2020, suggesting asymptomatic infections [25, 26] . However, this may be an over-or underestimation: we cannot rule out recall bias, but we did not confirm whether participants actually had COVID-19 at the time they experienced symptoms due to the limited availability of PCR tests. Analyses of the longitudinal data of the COco-study will provide more insight into the antibody response duration. Anosmia and/or ageusia were the most important indicators for seropositivity. Previous studies also reported that these two symptoms are characteristic for COVID-19 disease . The COco-study may assist policymakers making decisions concerning measures to control viral spread. The current study found that hairdressers are not at increased risk for SARS-CoV-2, despite minimal additional hygienic measures. Hence, this industry is likely to contribute minimally to SARS-CoV-2 transmission. Our data suggest that SARS-CoV-2 transmission may be higher in the hospitality industry. Analysis of longitudinal data is needed to evaluate whether measures, such as limited seating and face masks, may eliminate this increased transmission risk in the hospitality industry. We cannot conclude from our current data whether participants were infected during or before/after work. Nevertheless, with these data available, we think that hospitality businesses should be encouraged to take additional measures to prevent SARS-CoV-2 transmission to staff and customers. This study has several limitations. Due to the sample size, we were limited in conducting multivariable analyses. However, in all bivariable analyses we conducted, the odds ratio remained above 1 for hospitality staff compared to hairdressers, suggesting that hospitality staff had a higher risk for COVID-19 compared to hairdressers regardless of the covariate used. Our study lacks a control group in the general population. Instead, we compared our data to representative measurements in the general population done by other groups. However, creating a control group without confounding will be challenging considering the many factors determining SARS-CoV-2 infection risk. Our study may have been subject to selection bias, but seems to adequately represent an average population of hairdressers and hospitality staff (supplementary table 1). For example, 90% of our hairdressers were female, versus 91% of all hairdressers in the Netherlands. The largest discrepancy was in the education level of hospitality workers: more hospitality staff was highly educated in our study population compared to the average population. Most likely this is an error in the way participants answered the questionnaire: many students in our study population considered themselves highly educated while still in college, which would be registered at a lower level in the average population. Finally, due to limited availability of PCR tests in the period before starting the study, we do not have information to objectively confirm whether they had been infected with SARS-CoV-2: only 4 participants had been tested positive by PCR. In the longitudinal analyses, we will be able to compare seroconversion rates to positive PCR test rates, as PCR tests became widely available after the baseline measurement. In conclusion, we observed that employees in the hospitality industry had significantly more frequently SARS-CoV-2 antibodies than hairdressers. The seroprevalence amongst hairdressers was similar to the seroprevalence as recorded in the general population. Future analyses of longitudinal data in our cohort will allow further evaluation of the role of the hospitality industry and hairdressers in SARS-CoV-2, and which measures successfully restrict transmission in these industries. The authors wish to thank all hairdressers and employees in the hospitality industry who voluntarily participate in this study. We would further like to thank the Dutch National Institute for Public Health and the Environment (RIVM) for providing the ELISA kits used to measure seroprevalence in our study population, and Eric Vos at the RIVM for providing data from the PIENTER study. We would like to thank the employees of Microvida for conducting the ELISA tests, and Anita Blerck, Desiree Bosker and Karin de Vries at the regional public health service of West-Brabant (GGD) for contacting participants to ensure participation in questionnaires and schedule appointments. We would also like to thank Royal Horeca the Netherlands (KHN) and the municipality of Breda for their help with the recruitment of participants. Finally, we would like to thank the management team of the regional public health service of West-Brabant, in particular Mark van Beers, and regional physician consultants of the RIVM for their support for and input in this study. Conflict of interest: The authors do not have a conflict of interest to declare. The COco-study receives financial support from the regional public health service (GGD) of West-Brabant and the Dutch National Institute for Public Health and the Environment (RIVM). These are government-funded, non-commercial organizations, and they did not influence any aspect of the content of the study. Baseline characteristics of the two study groups, hospitality personnel and the hairdressers, were compared using Wilcoxon Rank Sum tests (numerical variables) or chi-squared tests (categorical variables). P-values below 0.05 were considered statistically significant and marked bold. All values are n (%) unless specified otherwise. a Patients were considered to have a chronic disease if they reported a chronic illness and/or were chronic medication users. b For workplace location, we compared those working in Breda/Roosendaal to those working in other cities or villages. Percentages were calculated by dividing the number of seropositive participants by the total number of participants in that specific subgroup (n/N). a Participants who answered that they drank 1 alcohol unit for less than 1 day per week, were considered to drink 0.5 alcohol units per week. 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In the bivariable logistic regression results (columns 3 and 4), column 3 reports the odds ratio for the variable listed in the first column, after adjusting for work setting (hospitality staff/hairdressers). The last column reports the odds ratio for seropositivity for hospitality personnel compared to hairdressers when adjusting for the covariable listed in the first column. E.g., the odds ratio for hospitality personnel to be seropositive was 2.2 (95% confidence interval 1.2-4.1) when adjusting for sex.The two participants working both as a hairdresser and in the hospitality industry were excluded from these analyses. *, p<0.05; **, p<0.01; ***, p<0.001 ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Highlights COco-study -COco is a prospective cohort study conducted in the Netherlands, 2020-2021.-COco evaluates COVID-19 transmission risk in non-medical contact professions.-Hospitality staff had more frequently SARS-CoV-2 antibodies than hairdressers.-Anosmia and ageusia were the most important symptoms associated with seropositivity.-A high education level and alcohol use were also associated with seropositivity.