INTRODUCTION
The pandemic is not only a biological threat, but also a socioeconomic phenomenon for people's lives. Moreover, only infection control methods are insufficient to fight the pandemic . As a result of social isolation, restrictions, and quarantine measures during the pandemic , it is known that the need for labor in many sectors has decreased, many people are in danger of losing their jobs, or have experienced a decrease in their monthly income.[1 2 3 4 5 pandemic were also affected by social, physical, and mental aspects.[6
On the other hand, it is known that economic activities are one of the causes of social mobility that causes the spread of infectious diseases. All around the world about 3.3 billion people (about 40% of the world population) are in active employment.[7 Covid-19 pandemic , initial cases were seen in workers employed in the seafood and wild animal markets.[8 occupational exposure . These occupational groups consist of tourism, sales, accommodation, transportation, and security workers.[9 10 11
The aim of this study is to investigate the occupational distribution of the patients diagnosed with Covid-19 , in two dimensions that work-related Covid-19 transmission (WRCT) and pandemic -related economic worsening (PREW).
SUBJECTS AND METHODS
In this cross-sectional study, it was planned to reach all workers in active employment who were diagnosed with Covid-19 at Dokuz Eylül University Hospital between March 19, 2020 and September 4, 2020. The phone numbers of the cases were obtained from the surveillance records of the Department of Public Health. Ethical approval was obtained from the local ethics committee of Dokuz Eylul University (No: 2021/21-2). There were 1505 cases in our hospital who were diagnosed with Covid-19 . Cases other than the age range of 15 to 65 years and cases registered as students or retired were excluded. The remaining 873 cases were called by phone and 644 cases were reached. Three hundred seventy-three cases were excluded because they were not in employment when they were diagnosed with Covid-19 . Thirty-eight workers refused to participate in the study. Two hundred and thirty-three workers were included in the study. The response rate was 86%. Cases that were called a maximum of 3 times and could not be reached were categorized as not reached [Figure 1 ].
Figure 1: Flow chart of the study population
The data were collected by 4 occupational physicians via telephone interview in a 3-month period (between March and June 2021). The structured questionnaire consisting of 23 questions were prepared with the contribution of all researchers. A pretest was applied in a group of 5 participants. No changes were required in the questions according to the pilot study. Therefore, these participants were not excluded in order to reach the all study population. Interview standards were determined at the meetings before the data collection process. Each interviewer collected data in line with these standards. Audio and video recordings of the conversations were not taken.
Variables of the study
The outcomes of the study were as follows:
WRCT: The answers given by the workers to the related questions were classified by occupational medicine physicians as WRCT or non-WRCT [Figure 2 ]. According to this evaluation, participants who stated that they did not go to work during the 14 days before the diagnosis of Covid-19 were categorized as non-WRCT. Participants who stated that they lived in the same house with someone diagnosed with covid-19 during 14 days before the illness were also categorized as non-WRCT. In other participiants, self-reports of them were taken as basis. The question was “what is your opinion on how Covid-19 infected you?” and those who thought “from workplace or commuting to work” were categorized as WRCT. Those who thought “from nonwork environment (family-friend meetings, restaurant, cafeteria, airport, sports facility, etc.)” were categorized as non-WRCT. And the fourth question was “during 14 days before the illness was there anyone diagnosed with covid-19 who had worked in the same workplace with you?” If the answer to this question was “yes,” it was accepted as evidence that strengthens the possibility of WRCT.
The third question may seem subjective. However, nonoccupational exposures of the participants (history of going abroad, covid-positive family member, using of public transport, social meetings, etc) were asked in detail in the questionnaire and cross-validated with the answers to the third question. No inconsistent responses were obtained.
PREW: Workers who reported quitting their job and/or a decrease in monthly income during the pandemic process were included in this group.
Figure 2: “WRCT” or “non-WRCT” classification criteria. † Work-related Covid-19 transmission, †† Non work-related Covid-19 transmission
The independent variables of the study were as follows: Age, gender, occupational class, social class , instutition, sector, working from home, covid-19 transmission period, transportation to work, and closed-crowded working conditions.
Closed-crowded working conditions: The cases were asked to score their work environment according to the closed-crowded conditions in terms of the risk of covid-19 transmission. The participants were informed in advance that they should take into account the intensity of personal close contact at the workplace. Likert-type measurement was made as 1 point for the lowest risk and 10 points for the highest risk. Then, the median value was taken as the cutoff point and the “low-risk categories” and “high-risk categories” were determined.
Covid-19 transmission period12
Occupational classification : International Standard Classification of Occupations (ISCO-08) was used.[13 Table 1 ]. Then, in the comparative analysis, the occupational classes were combined as follows and the difference between the 3 groups was investigated.
Table 1: Occupational classification modification scheme
Managers, professionals, and clerical support workers (modified groups 1 + 2 + 4 )
Health care workers (modified group 3 )
Other workers (service and sales workers, manuel workers, drivers, cleaners, protective services workers, and armed forces) (modified groups 5 + 6 + 7 + 8 + 9 )
Social classification : Erikson Goldthorpe and Portocarero social class chart was used[14 pandemic .
Statistical analysis
Descriptive statistics is presented with number, percentage, mean-standard deviation, and median–minumum and maximum values. Chi square test was used in comparison of proportions. Median values were used as the cutoff point in converting numeric variables to categorical variables. Significance level was accepted as P <.05.
RESULTS
Of the 233 workers who accepted to participate to the study, 52% were male (n = 120). The mean age was 37.7 years (±9.2). Only 7 workers (3%) had a history of going abroad or contacting illness from someone who had been abroad. Considering the sectorial distribution of the cases, 196 (84%) were working in the services. There was no case working in the agricultural sector. More than a quarter of the workers (27%) who diagnosed with covid-19 were health care workers. Demographic characteristics of the participants in the study are presented in Table 2 .
Table 2: Demographic characteristics of the participants in the study
Twenty-six (11%) of the participants had started to work from home during the pandemic . Of the cases who could work from home, 12 (46%) were professionals, 7 (27%) managers, 4 (15%) service and sales workers, and 3 (12%) were clerical support workers. All these cases were in the “white collar” social class . The distribution according to gender was equal and the mean age was 39.2 years (±9.9). Most of those working from home were in the private sector (n = 18, 69%). Considering the distribution of workers who work from home according to the source of contamination; transmission from household was detected in 9 cases (35%), transmission from outside (restaurant, cafeteria, airport, sports facility, etc) in 10 cases (38%), and transmission from workplace in 7 cases (27%). Seven workers who defined transmission from the workplace, switched to working from home after being diagnosed with covid-19 . Thus, while WRCT was detected in only 7 (26%) cases working from home, it was found in 110 (53%) cases who did not work from home (P =0.012). In terms of transmission from household, there was no significant difference between those who work from home and those who do not work from home (P =0.14).
When health care workers and nonhealth care workers were compared, WRCT was found to be 3.6 times higher in health care workers (95% confidence interval [CI] = 1.92-6.87; see [Tables 4 and 5 , Supplemental Digital Content]). In health care workers, 87% (n = 7) of the health care assistants, 83% (n = 15) of the doctors, 70% (n = 12) of the nurses, 60% (n = 3) of the cleaning staff, and 57% (n = 8) of the health technicians were determined as WRCT.
Table 3: Factors affecting on WRCT and PREW in the working population
Table 4: Comparison of WRCT frequencies according to occupational classes
Table 5: Comparison of PREW frequencies according to occupational classes
Excluding 47 cases who did not go to work in the 14 days before contracting the disease, no significant difference was observed in terms of WRCT according to transportation to work (P =0.57). There was no significant differences between early transmission period and late transmission period in terms of WRCT (P =0.058).
PREW was observed in 53 workers (28%). Twenty workers had quit their jobs (9%).
PREW was higher in private sector (odds ratio [OR] = 6.69, 95% CI = 3.1-14.5). PREW was found significantly high in petty-bourgeois (self-employed and small business owners) compared with other social classes (P =0.001). Factors affecting on WRCT and PREW in the working population were presented in Table 3 .
The frequencies of WRCT and PREW were evaluated in two dimensions according to the occupational classes of the cases. PREW was found at the highest rate in shopkeepers (58%, n = 7), while WRCT was detected the least (8%, n = 1). In health care workers, WRCT was found at the highest rate (73%, n = 45), while the PREW rate was low (10%, n = 6). In the professionals class, both PREW (11%, n = 2) and WRCT (21%, n = 4) were lower. The occupations that were affected in both dimensions were drivers and service-sales workers [Figure 3 ].
Figure 3: WRCT and PREW frequencies (%) of workers. (Note: The size of the circles is proportional to the number of covid-19 patient.)
DISCUSSION
In this study, the relationship between the covid-19 pandemic and occupational classes was examined in two dimensions. WRCT was most prevalent in health care workers, PREW was most prevalent in shopkeepers and drivers. Professionals, most of whom could work from home, were considered the most fortunate occupational group in both dimensions.
Health care workers, who were at the frontline of the pandemic , were 3.6 times more at risk for WRCT (OR = 3.6, 95% CI = 1.92-6.87) in this study. WRCT in health care workers has been widely discussed in the literature in terms of transmission among colleagues due to the nature of work conditions such as close teamwork and shift work, as well as the risk of close contact with covid-19 patients.[15 16 covid-19 . This rate has been reported lower (between 16.7% and 55%) in previous studies. This is because the health care workers in the WRCT group were likely misclassified into “unidentified” or “unknown” groups in previous studies.[15 17 18 19 n = 53, 46%), but also protective services workers (n = 11, 10%) and sales and service workers (n = 10, 9%). In the literature, it has been mentioned that WRCT is common in these occupational groups with high social contact.[20–22]
Due to social isolation, restrictions and quarantine measures during the pandemic , there has been an economic crisis all over the world. The negative impacts of this crisis have been expected mostly in those with less job security, workers with lower income and those in low-skilled jobs.[23 24 Covid-19 pandemic .[25 26 P =0.001). According to occupational classes, PREW was found to be higher in other workers group (service and sales workers, manual workers, drivers, cleaners and helpers, protective services workers, and armed forces). Similar to this results in a study in Iran, it is mentioned that private sector employees, self-employed, those with low education level are vulnerable groups.[27 pandemic due to state policy in Turkey, PREW may have been found to be lower than expected in manual workers in our study. However, with the removal of the legal protection umbrella, it is expected that manual workers will be more affected by the economic crisis.[28
In our study, WRCT was significantly lower in the class of professionals, 63% of whom had switched to the work from home model (P =0.008). In fact, working from home is not a new concept, but it has become quite common during the pandemic process. Although there are still ongoing discussions on issues such as work-life balance, working hours, performance evaluation, and occupational health, thanks to this working model, the virus risk has been reduced, while ensuring business continuity.[29–31] In a previous cohort study, it was reported that the risk of covid-19 in airport workers is 4.5 times less even for whose partners work from home.[32
When the effects of the pandemic were evaluated in two dimensions; WRCT was found to be significantly higher in public sector workers, while PREW was higher in private sector workers (P <.001 for both). This may be an important proof that in the public sector in Turkey, there is high job security, while the occupational health organization is still not fully managed. But we know that the basic principle and main target in occupational health concept is to cover the health of all workers, including the public.[33
Strengths and limitations
The strength of the study is that the population of the study was determined in the light of the strong surveillance records in our center and that the majority of the population (86%) was included in the study. The possibility of misclassification is minimized by the fact that the phone calls were made by the occupational medicine physicians with the standard diagnosis algorithm.
This study has some limitations. Historical data were collected from the participants by telephone interview. Therefore, there may be a recall bias in terms of the predisease working status of the workers. This situation may be in the direction of ignoring occupational risks, especially for self-employed workers. Due to the single-center nature of the research, the number of participants in some occupational groups is low. However, there was a difference between the groups. It suggests that the difference may actually be higher (bias toward null).
In this study, only the cases in active employment were examined. However, there is a need for more studies that include fragile groups such as uninsured workers and immigrants, whose economic and biological impact of the pandemic is expected to be more destructive.[34
CONCLUSIONS
As professional activities have an impact on the spread of the pandemic , the Covid-19 pandemic process has also had an impact on working life. Along with the biological risks of Covid-19 , economic and social consequences are also important. Administrative measures should be developed at the policy level, especially for those self-employed workers and private sector workers.
Ethical considerations and disclosure
Ethical approval was obtained from the local ethics committee of Dokuz Eylul University (No: 2021/21-2).
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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