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Sex-specific Impact of Lifestyle Factors on Sick Leave in Serbian Working Population: Findings from the National Health Survey

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10 September 2024

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11 September 2024

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Abstract
Sick leave is influenced by various modifiable lifestyle factors and sex differences. This study examines the impact of body mass index, fruit and vegetable consumption, physical activity, smoking, and alcohol consumption on sick leave among Serbia's working population, focusing on sex differences. Data from the 2019 National Health Survey of Serbia were analyzed, focusing on a representative sample of 4,652 individuals aged 18-65. Chi-square tests and logistic regression models assessed the relationships between lifestyle factors and sick leave, adjusting for sociodemographic, work-related, and health-related variables. Among respondents, 15.8% reported sick leave in the past 12 months, with higher rates among women in both, short-term (13.9% vs. 10.6%) and long-term sick leave (3.4% vs. 2.2%). For men, obesity (OR = 2.6), poor dietary habits (fruit OR = 2.1; vegetables OR = 2.8), smoking (OR = 1.9), and risky alcohol consumption (OR = 4.1) significantly increase the likelihood of sick leave. In women, smoking (OR = 1.8) and risky alcohol consumption (OR = 3.1) are major predictors, while BMI and physical activity show less impact. Fruit and vegetable consumption and physical activity have inconsistent effects on sick leave, indicating the need for further research. This study identifies obesity, poor dietary habits, smoking, and alcohol consumption as key predictors of sick leave in men. In women, smoking and risky alcohol consumption are major predictors. Smoking is a risk factor for both sexes, while risky alcohol consumption emerges as a prominent predictor and is a significant predictor overall. These findings highlight the need for targeted public health interventions to address these lifestyle factors and reduce sick leave rates. Further research is needed to clarify the roles of dietary habits and physical activity in influencing sick leave.
Keywords: 
Subject: Public Health and Healthcare  -   Public, Environmental and Occupational Health

1. Introduction

Sick leave poses a significant challenge in occupational and public health, with various modifiable lifestyle factors playing a significant role. Lifestyle behaviors such as smoking, alcohol consumption, obesity, diet habits, and low physical activity contribute to lost work years due to disability and premature mortality [1]. Unhealthy behaviors account for 15–30% of all sick leave [2,3,4], and 20% of long-term sick leave cases [5]. Expo-sure to multiple health-related lifestyle factors is associated with an increased risk of both short-term and long-term sick leave [5]. Despite well-established associations between modifiable lifestyle factors and sick leave, there is a significant gap in comprehensive re-search examining their combined effects across various populations and occupational settings. This gap highlights the urgent need for more in-depth studies to identify at-risk groups and create targeted interventions to reduce sick leave and enhance workforce health outcomes. Additionally, the sex gap in sick leave, as noted by Østby et al. [6], re-mains an important and largely unexplained issue that warrants further investigation.
High body mass index (BMI) is strongly linked to increased sick leave [5,6,7,8]. Both overweight and obese are at significant risk for sick leave in both sexes, with those having a BMI of 25 kg/m² or higher showing higher rates of sick leave compared to non-obese individuals [7]. Central obesity, characterized by excess abdominal fat, further exacerbates sick leave due to associated health problems [9]. In Europe, higher BMI is particularly as-sociated with increased sick leave in southern and western countries, like Malta, Italy, Spain, and France, while the lowest impact was observed in Romania [1]. Changes in weight, including both weight gain and loss, are significant predictors of sick leave [10].
While obesity plays a significant role in sick leave, the influence of dietary factors alone is less pronounced [5]. High-quality diets, rich in fruits and vegetables, may reduce sick leave, but their effect is minimal without additional lifestyle changes [5,6]. While some research suggests that such diets can significantly lower sick leave [9], the overall evidence is limited, and dietary habits generally have a minor impact on sick leave [4,5]. Despite the Mediterranean diet's known benefits for disease prevention, a Spanish study found no significant link between this diet and reduced sick leave [11]. Poor dietary habits may in-crease sick leave, particularly when combined with low physical activity [5].
Low physical activity is strongly linked to increased sick leave [11,12,13,14]. The relation-ship between physical activity and sick leave is complex, showing both positive and negative associations [9,12,15]. For example, a Spanish study found that high levels of lei-sure-time physical activity reduced sick leave, especially among women [16]. However, findings vary due to differences in outcome measures, job types, and working conditions [10,17]. Intensive physical activity is associated with significantly shorter sick leave com-pared to those who are sedentary or lightly active [11].
Tobacco use is a significant lifestyle factor linked to increased sick leave [14,17]. Daily smokers face a 30% higher risk of frequent and prolonged sick leave compared to non-smokers [17]. This strong association persists across various demographics and study conditions, although some studies report inconsistent results [12,15,18]. The impact is especially pronounced in men, likely due to higher smoking rates [17]. Despite some variability, daily smoking consistently correlates with higher rates of sick leave [19].
Alcohol consumption is a significant lifestyle factor influencing sick leave [14,17]. The relationship between alcohol use and sick leave varies by outcome and sex. High alcohol consumption is sometimes linked to fewer sick leave days compared to non-drinkers [17], but this effect is more pronounced for long-term sick leave among men [20]. However, findings are inconsistent, with some studies showing no significant link between alcohol use and sick leave [11,17]. Overall, the impact of alcohol consumption on sick leave is complex and varies, particularly affecting long-term sick leave in men [20].
Unhealthy lifestyle behaviors vary across occupational and socioeconomic groups, with skilled manual workers, self-employed individuals, and those working long hours at higher risk [21,22]. These groups are more likely to smoke, drink heavily, and be physically inactive, contributing to increased sick leave rates [20]. While office workers tend to be more inactive and drink heavily, they smoke less [21]. Agricultural workers generally have lower risks for these behaviors, whereas shift workers are more prone to smoking [21]. Some research finds no significant differences in lifestyle behaviors across occupational groups, highlighting the need for more nuanced studies [21].
Sex differences in sick leave are notable, with women generally exhibiting higher absenteeism rates than men [1,23]. Lifestyle factors like smoking, obesity, and alcohol use impact sick leave differently by sex. Overweight and obese women tend to have higher sick leave rates compared to men [7,25,26]. However, some studies suggest that sex differences in the effects of these lifestyle factors on sick leave may be less pronounced [5,24]. Alcohol consumption particularly influences long-term sick leave in men, contributing significantly to the disparities in sick leave rates between manual and non-manual workers [20].
Exposure to multiple health-related risk factors like low physical activity, poor diet, obesity, alcohol consumption, and smoking is strongly linked to higher sick leave rates [4,5,25]. Improving these lifestyle factors according to health recommendations could en-hance workability and reduce sick leave-associated costs [3,4,26]. Research indicates that workplace interventions targeting nutrition and physical activity can reduce sick leave, and improve work capacity [27]. However, evidence on how these factors interact with sick leave remains inconsistent. Some studies have found no association between obesity and sick leave across both sexes [15] or specifically among men [28]. Additionally, some research shows no significant link between physical activity and sick leave [15]. De Bortoli et al. [5] recently found no association between a healthy diet and reduced sick leave. Similarly, the impact of alcohol consumption on sick leave is uncertain, with some studies reporting no increased risk [15,17], while others found no significant effect of drinking attitudes on sick leave [29]. These varying results may be due to differences in job types [1,5,15,28]. Ultimately, sick leave is a multifactorial issue influenced not only by lifestyle factors but also by job characteristics [1,30,31], and the overall health status of the working population [3].
In Serbia, 16.4% of residents reported being on sick leave in 2019 [32]. Also, over half of the population was either overweight, including 36.0% who were pre-obese and 20.0% who were obese [33]. Daily fruit consumption was at 40.0%, while 50.0% consumed vegetables daily, with higher rates in Belgrade and among those with higher education and better socioeconomic status [32]. Only 11% of adults met the recommended 150 minutes of weekly aerobic physical activity [32]. Tobacco use is a significant concern, with 32.0% of individuals aged 15 and older smoking, more common among men [32]. Alcohol consumption is also notable, with around 50% of the population drinking alcohol, and 3.0% consuming it daily, primarily among men [32].
Despite the recognized link between lifestyle factors and sick leave, comprehensive research examining the interaction of these risk factors within the Serbian working population is notably lacking. This gap highlights the need for further investigation to identify at-risk groups and develop targeted interventions [3,5], aimed at reducing sick leave and improving workforce health in Serbia. Additionally, as highlighted by Østby et al. [6], the observed sex differences in sick leave remain largely unexplained, emphasizing the importance of exploring these disparities in greater detail. To the best of our knowledge, there is no specific research on the impact of modifiable lifestyle factors on sick leave in Serbia, despite the widespread prevalence of these risk factors within the Serbian population. The significant burden of obesity, smoking, physical inactivity, and unhealthy dietary habits among the Serbian working population has yet to be thoroughly examined concerning their impact on sick leave.
This study aims to investigate the associations between body mass index, dietary habits (fruit and vegetable intake), physical activity, smoking, alcohol consumption, and sick leave in Serbia, addressing this critical gap. Additionally, it will explore whether these associations differ between men and women, contributing to the development of targeted interventions to reduce sick leave and improve workforce health outcomes in Serbia.

2. Materials and Methods

2.1. Study Design

This study involves a secondary analysis of data from the Serbian National Health Survey of the Republic of Serbia, conducted between October and December 2019. The survey was conducted by the Republic Bureau of Statistics, the Institute of Public Health of Serbia "Dr. Milan Jovanovic Batut," and the Ministry of Health of the Republic of Serbia. The European Health Survey-third wave (EHIS-wave 3) methodology and resources were used in the study [34]. Due to the COVID-19 pandemic, the release of the findings was delayed, with results becoming publicly available in 2021.
The study employed a cross-sectional design with a representative sample of Serbian residents, excluding those in Kosovo due to unavailable data. A stratified two-stage sampling method was employed, to ensure national and regional representation. The sample was stratified by geographical regions, including Belgrade Region, Vojvodina Region, Šumadija and Western Serbia, Southern Serbia, and Eastern Serbia. The study aimed to include 6,000 households, projected to cover around 15,000 individuals aged 15 and older. In total, 5,114 households were selected, with 13,178 responses from individuals aged 15 and over, resulting in a response rate of 97.0%. Of those who agreed to participate, 11,790 completed the self-completion form.
For this study, the final sample consisted of 4,652 working population, defined as those aged 18 to 63 for women and 18 to 65 for men. We focused on individuals residing in private homes, excluding those in collective homes or institutions. Individuals outside these age ranges, as well as those with incomplete surveys, were excluded. This sampling approach ensured that the analysis was representative of the working population within these specified age limits.

2.2. Ethical and Legal Aspects

Ethical approval was not necessary for this study since it used secondary data. Informed consent was obtained from all participants before data collection. The study complied with international ethical standards, including the Declaration of Helsinki, and adhered to Serbian legislation. Participants were provided with written information about the study’s purpose, their rights, and contact details for any inquiries or concerns. Written consent was secured from each participant. To ensure the privacy and confidentiality of research participants, all necessary measures were implemented in compliance with the General Data Protection Regulation, and data were anonymized, securely stored, and reported in aggregate form, according to the methodology of the European Health Interview Survey - Wave 3 (EHIS - Wave 3) [34]. Permission for the use of secondary data was granted by the Institute of Public Health of Serbia "Dr. Milan Jovanović Batut," and the database was transferred to the University of Kragujevac, Medical Faculty, for further research.

2.3. Survey Instrument

The survey instruments for this study were based on the EHIS Wave 3 questionnaires [34], which adhere to internationally recognized standards and were adapted for the Serbian context. Data collection employed three types of questionnaires: a household questionnaire, an adult questionnaire for individuals aged 15 and older, and self-completion questionnaires for the same age group. The household questionnaire collected socio-economic information about all members of the household. The adult questionnaire, administered in person, gathered detailed information from individuals aged 15 and older. Additionally, self-completion questionnaires allowed respondents to provide personal data independently.
For this study, demographic variables (sex, age, region, marital status), socioeconomic factors (education level, wealth index), and lifestyle factors (body mass index, fruit and vegetable consumption, aerobic physical activity, smoking status, alcohol consumption). Participants' sex was self-reported, and the data were analyzed for sex-based differences in sick leave patterns. Age was categorized into five groups: 18-25, 26-35, 36-45, 46-55, and 56-65. Education levels were classified as college/university, secondary, and primary school. Regions of Serbia were divided into Belgrade (Capital), Northern, Central and Western, and Southern and Eastern Serbia. Marital status included categories such as married, single, divorced, and widowed. The Wealth Index categorizes households into five quintiles based on their economic status: poorest (Q1), poorer (Q2), middle (Q3), richer (Q4), and richest (Q5). These quintiles, each representing an equal portion of the population, are used to analyze socio-economic disparities among study participants. Occupations were classified according to ISCO08 [35] and EHIS Wave 3 questionnaire recommendation for data dissemination [34] into four groups: managers, professionals, technicians, and associate professionals; clerical support workers, service, and sales workers; skilled manual workers; and elementary occupations. Job physical effort was categorized into heavy (heavy labor or physically demanding work), moderate (tasks involving moderate effort, like walking), and light (mostly sitting or standing). Self-rated health was assessed as very good/good, fair, or bad/very bad.
Participants were categorized according to their body mass index (BMI) using the following classifications: underweight (BMI < 18.5 kg/m²), normal weight (BMI between 18.5 and 24.9 kg/m²), pre-obesity (BMI between 25 and 29.9 kg/m²), and obesity (BMI ≥ 30 kg/m²). These categories are based on the World Health Organization's guidelines [36]. Fruit and vegetable consumption was categorized as daily, 4 to 6 times per week, 1 to 3 times per week, or never/occasionally. Compliance with WHO recommendations for health-enhancing physical activity (HEPA) [37] was assessed by summing the weekly minutes spent cycling and engaging in leisure time sports, fitness, or recreational activities (aerobic physical activity). Based on guidelines recommending 150 minutes of moderate-intensity activity per week, participants were categorized as high (≥150 min/week), moderate (11-149 min/week), or low (0-10 min/week) in their aerobic physical activity levels. Smoking status was assessed with the question: “Do you smoke tobacco products?” Participants could choose from: “Yes, daily,” “Yes, occasionally,” “Not anymore,” or “I have never smoked.” Responses were classified into never smoker, former smoker, and current smoker. The AUDIT-C evaluated alcohol consumption by calculating daily intake based on participants' reported consumption [34]. According to Serbian guidelines, low-risk drinking was defined as ≤ 13 grams of pure alcohol per day for women and ≤ 26 grams per day for men [38]. The weekly intake was then calculated by averaging the total volume of alcohol consumed over the past 12 months, measured in grams of pure alcohol per week. Based on this, alcohol consumption was categorized into four groups: lifetime abstainers, former drinkers, non-weekly drinkers, and low-risk drinkers (women: 0–91 grams per week, men: 0–182 grams per week). Consumption above these thresholds was classified as risky drinking (women: >91 grams per week, men: >182 grams per week).
The dependent variable is sick leave, assessed based on participants' reports of missing work due to health reasons in the previous 12 months. Responses were categorized into three groups: no sick leave, short-term sick leave (up to 30 days), and long-term sick leave (over 30 days), reflecting Serbian norms, where the National Health Insurance Fund provides financial assistance to employers following 30 consecutive days of sick leave [39].

2.4. Statistical Methods

Chi-square tests (χ²) assessed associations between categorical variables, with significance set at p<0.05. Logistic regression models evaluated the impact of independent variables on sick leave, with results presented as odds ratios (ORs) and 95% confidence intervals (CIs). Multivariate models were selected based on theoretical relevance and practical importance, with multicollinearity addressed using variance inflation factors (VIF), with all values below the critical threshold of 5. Model fit was confirmed by the Hosmer-Lemeshow test (p > 0.05) and residual analyses showed no significant outliers. Model fit was assessed using Nagelkerke R², and accuracy was confirmed by ROC curve analysis. Cross-validation demonstrated model stability across different data subsets. Outliers and influential data points were checked using Cook's distance and other metrics, with adjustments made where necessary to maintain model validity. All analyses were conducted with IBM SPSS Statistics, Version 20.0 (IBM Corp., Armonk, NY).

3. Results

The research included 4,652 respondents, with an average age of 42.68 years, categorized by sex. Chi-square tests reveal statistically significant differences across several variables. Women were the most represented in the 36-45 age group (30.8%) (χ² = 17.604, p = 0.001). In terms of education, the highest proportion of women had completed college or university (34.8%) compared to men (24.6%), while the majority of men had completed middle school (68.0% vs. 58.1% for women) (χ² = 59.007, p < 0.001). Regionally, both sexes were most concentrated in Central and Western Serbia, with men having a higher prevalence (32.9%) compared to women (29.4%) (χ² = 11.964, p = 0.008). Marital status data indicate that a higher percentage of women were married (73.1%) compared to men (68.0%) (χ² = 145.876, p < 0.001). Regarding the wealth index, women were more represented in the richer class (31.2%) compared to men (27.9%) (χ² = 14.735, p = 0.005). Occupational data indicate that men were predominantly engaged in skilled manual work (42.1%), while women were the most represented in managerial or professional roles (38.4%) (χ² = 241.120, p < 0.001). In terms of job physical effort, the highest percentage of women were in jobs requiring light or no physical effort (49.6%), whereas men were the most involved in jobs requiring moderate physical effort (43.0%) (χ² = 246.003, p < 0.001). Finally, self-rated health data show that a higher percentage of men reported very good or good health (84.5%) compared to women (80.2%) (χ² = 14.611, p = 0.001) (Table 1).
In total, in the twelve months before the study, 15.8% of respondents had sick leave due to personal health issues, with a higher rate among women (18.2%) compared to men (13.9%). Women also had higher rates of both, short-term (13.9%) and long-term sick leave (3.4%) compared to men (10.6% and 2.2%, respectively). Age is significantly associated with sick leave, with men aged 56-65 years having the highest proportion of long-term sick leave (46.4%) (χ² = 44.649, p < 0.001). Education levels also show a significant relationship with sick leave, with men holding a middle school education exhibiting the highest prevalence of both short-term (68.0%) and long-term sick leave (64.3%) (χ² = 15.872, p = 0.003). Regional disparities were evident, with men from Vojvodina showing the highest prevalence of short-term sick leave (32.4%), while those from Central and Western Serbia exhibited the highest prevalence of long-term sick leave (35.7%) (χ² = 13.322, p = 0.038). Marital status and wealth class did not show statistically significant associations with sick leave, although married men from the richest class had a slightly higher prevalence of long-term sick leave (35.7%). Occupational categories did not show significant differences, however, skilled manual workers had the highest prevalence of long-term sick leave (44.6%). Job physical effort was marginally non-significant (p = 0.057). Finally, self-rated health was strongly associated with sick leave (χ² = 167.279, p < 0.001), with men reporting bad or very bad health showing a 17.9% prevalence of long-term sick leave. Men with very good or good self-rated health had significantly higher both short-term (68.3%) and long-term sick leave (48.2%) (Table 2).
Among women, age is significantly associated with sick leave, with women aged 46-55 years showing the highest proportion of short-term sick leave (35.8%) and those aged 36-45 years having the highest proportion of long-term sick leave (31.9%) (χ² = 25.890, p = 0.001). Education levels did not show a statistically significant relationship with sick leave, though women with higher education (24.6%) had a slightly higher prevalence of long-term sick leave compared to those with primary school education (11.6%) (χ² = 5.126, p = 0.275). Regional disparities were evident, with women from Central and Western Serbia showing the highest prevalence of short-term sick leave (35.5%), while women living in Belgrade (the capital) had the highest prevalence of long-term sick leave (34.8%) (χ² = 24.143, p = 0.000). Marital status also showed a significant association with sick leave, with married women having the highest prevalence of both short (71.6%) and long-term sick leave (84.1%) (χ² = 18.935, p = 0.004). The wealth index demonstrated a significant relationship, with women from the richest class having the highest prevalence of both short (37.9%) and long-term sick leave (34.8%) (χ² = 17.746, p = 0.023). Occupational categories did not show significant differences. Job physical effort was marginally significant, with women reporting light physical effort having the highest prevalence of short-term sick leave (49.6%) and those with moderate physical effort showing the highest prevalence of long-term sick leave (44.9%) (χ² = 10.319, p = 0.035). Finally, self-rated health was strongly associated with sick leave (χ² = 141.699, p < 0.001). Women reporting bad or very bad health had a 20.3% prevalence of long-term sick leave. Women reporting very good or good self-rated health had a higher prevalence of both, short-term (67.0%), and long-term sick leave (44.9%) (Table 3).
Table 4 presents the prevalence of sick leave among men based on lifestyle factors. Among BMI categories, the highest prevalence of both, short-term (46.6%), and long-term sick leave (43.8%) was found in those classified as pre-obese (χ² = 1.375, p = 0.967), though these differences were not statistically significant. For fruit consumption, the highest prevalence of short-term sick leave was observed among men consuming fruit 4 to 6 times a week (38.3%), while the highest prevalence of long-term sick leave was seen in those consuming fruit at least once a day (37.7%) (χ² = 9.432, p = 0.151), although these differences were not statistically significant. In contrast, men who consumed vegetables at least once a day exhibited the highest prevalence of both short-term (45.2%) and long-term sick leave (58.9%) (χ² = 13.805, p = 0.032). Leisure-time aerobic physical activity levels did not show significant differences in sick leave prevalence (χ² = 6.501, p = 0.165). Smoking status revealed that never-smokers had the highest prevalence of short-term sick leave (43.8%), while current smokers had the highest prevalence of long-term sick leave (38.1%) (χ² = 12.797, p = 0.012). Finally, alcohol consumption patterns indicated that non-weekly drinkers had the highest prevalence of both short-term (35.9%) and long-term sick leave (40.5%) (χ² = 22.491, p = 0.004).
Table 5 presents the prevalence of sick leave among women based on lifestyle factors. Among BMI categories, the highest prevalence of both short-term (55.3%) and long-term sick leave (38.7%) was observed in women classified as normal weight (χ² = 17.300, p = 0.008). For fruit consumption, the highest prevalence of both short-term (46.5%) and long-term sick leave (56.5%) was found among women consuming fruit at least once a day (χ² = 14.154, p = 0.028). Regarding vegetable consumption, women who consumed vegetables at least once a day had the highest prevalence of both short-term (60.6%) and long-term sick leave (76.8%) (χ² = 18.851, p = 0.004). Leisure-time aerobic physical activity levels showed that women with low levels of physical activity had the highest prevalence of both short-term (86.4%) and long-term sick leave (85.5%) (χ² = 10.452, p = 0.033). Smoking status revealed that never-smokers had the highest prevalence of short-term sick leave (46.2%), while current smokers had the highest prevalence of long-term sick leave (55.4%) (χ² = 18.746, p = 0.001). Finally, alcohol consumption patterns indicated that non-weekly female drinkers had the highest prevalence of short-term sick leave (43.9%), while lifetime abstainers had the highest prevalence of long-term sick leave (41.3%). However, the overall differences in alcohol consumption patterns were not statistically significant (χ² = 11.451, p = 0.177).
When analyzing men, the body mass index significantly impacts sick leave across all models. Individuals with obesity exhibit more than three times the odds of sick leave compared to those classified as underweight, with an odds ratio (OR) of 3.007 in Model 1a (CI: 1.607-6.119). Although this effect diminishes in Model 4d (OR = 2.613), it remains statistically significant, indicating that obesity continues to be a substantial predictor of sick leave even after adjusting for socio-demographic, occupational, and health factors. Overweight individuals also show a significant impact, with OR values ranging from 2.607 to 2.802, suggesting increased odds of sick leave compared to underweight individuals. Normal weight (BMI = 18.5-24.9) has a similar, though slightly lesser, impact compared to overweight and obesity, with OR values between 2.333 and 2.452, underscoring the significant role of body weight in determining sick leave risk.
Fruit and vegetable consumption has distinct effects on sick leave. Consuming fruit never/occasionally is associated with a significant impact sick leave likelihood of sick leave among men (OR = 1.713 in Model 1a, OR = 2.188 in Model 4d), suggesting that regular fruit consumption may act as a protective factor. Similar findings are shown in vegetable consumption. Men who rarely or never consume vegetables exhibit higher odds of sick leave, with OR values ranging from 1.501 to 2.828.
Low or moderate leisure-time aerobic physical activity is associated with increased chances of sick leave compared to individuals with high physical activity. Odds ratios (OR) for low physical activity range from 1.801 to 1.651, suggesting that insufficient physical activity may contribute to a higher risk of health problems leading to sick leave among men.
Current male smokers have significantly higher odds of experiencing sick leave compared to non-smokers, with an odds ratio (OR) of 2.016 (CI: 1.207-3.404) in Model 1a, decreasing slightly to 1.915 (CI: 1.161-3.376) in Model 4d. This indicates that smoking is a significant risk factor for sick leave among men across all models, even after accounting for additional sociodemographic, work-related, and health-related factors. Former male smokers also show increased odds of sick leave, with OR values ranging from 1.713 to 1.909.
Risky alcohol consumption has the strongest impact on sick leave among men, with odds ratios (OR) ranging from 4.004 in Model 2b to 4.111 in Model 4d. These values indicate that men engaging in risky drinking have significantly higher odds of sick leave compared to abstainers. Low-risky drinking among men also shows a substantial effect, with OR values between 3.510 and 3.770, suggesting that moderate alcohol consumption can also increase the risk of health issues leading to sick leave. Non-weekly drinking exhibits elevated risk, with OR values from 2.512 in Model 1a to 2.818 in Model 4d, suggesting that even non-weekly drinking can increase sick leave. Former alcohol consumers also exhibit elevated risk, with OR values from 3.117 in Model 1a to 3.327 in Model 4d, reflecting the lasting health consequences of previous excessive alcohol consumption.
Table 6. Unadjusted and adjusted odds ratio (OR) with 95% confidence interval (CI) for the association of lifestyle factors with sick leave - male.
Table 6. Unadjusted and adjusted odds ratio (OR) with 95% confidence interval (CI) for the association of lifestyle factors with sick leave - male.
Variables Model 1
OR (95% CI)		 Model 2
OR (95% CI)		 Model 3
OR (95% CI)		 Model 4
OR (95% CI)
Body mass index Underweight 1 1 1 1
Normal weight 2.501 (1.201-5.111)* 2.333 (1.115-4.754)* 2.411 (1.158-4.800)* 2.452 (1.202-5.103)*
Pre-obese 2.802 (1.405-5.640)* 2.607 (1.318-5.211)* 2.730 (1.351-5.425* 2.662 (1.363-5.287)*
Obese 3.007 (1.607-6.119)* 2.819 (1.568-5.659)* 2.850 (1.504-5.704)* 2.613 (1.414-5.200)*
Fruit consumption At least once a day 1 1 1
4 to 6 times a week 1.506 (0.912-2.705) 1.562 (0.901-2.805) 1.607 (0.951-2.904) 1.711 (1.001-3.119)
1 to 3 times a week 1.819 (1.119-3.052) 1.852 (1.153-3.060) 1.850 (1.107-3.088) 1.808 (1.115-3.010)
Never/occasionally 1.713 (1.139-2.576)* 2.050 (1.357-3.272)* 2.103 (1.488-3.334)* 2.188 (1.367-3.339)*
Vegetable consumption At least once a day 1 1 1 1
4 to 6 times a week 0.870 (0.570-1.328) 0.910 (0.592-1.399) 0.910 (0.591-1.401) 0.921 (0.593-1.431)
1 to 3 times a week 0.949 (0.570-1.580) 0.947 (0.563-1.591) 0.983 (0.583-1.657) 0.986 (0.579-1.679)
Never/occasionally 1.501 (0.901-2.702)* 2.744 (1.081-6.965)* 2.749 (1.079-7.000)* 2.828 (1.086-7.367)*
Leisure-time aerobic physical activity High 1 1 1 1
Moderate 1.514 (0.910-2.702) 1.631 (1.095-2.801) 1.552 (0.902-2.700) 1.601 (1.007-2.814)
Low 1.801 (1.011-3.201) 1.751 (0.977-3.179) 1.704 (0.901-3.052) 1.651 (0.844-2.944)
Smoking status Never smoker 1 1 1 1
Former smoker 1.909 (1.169-3.406) 1.861 (1.037-3.308) 1.809 (1.007-3.313) 1.713 (1.116-3.165)
Current smoker 2.016 (1.207-3.404)* 1.910 (1.520-3.304)* 1.858 (1.109-3.305)* 1.915 (1.161-3.376)*
Alcohol consumption Lifetime abstainers 1 1 1 1
Former drinking 3.117 (1.837-5.620)* 3.129 (1.834-5.811)* 3.241 (1.915-6.005)* 3.327 (1.910-6.201)*
Non-weekly drinking 2.512 (1.602-4.211) 2.613 (1.650-4.370) 2.659 (1.652-4.488) 2.818 (1.712-4.608)
Low-risk drinking 3.510 (2.101-6.108)* 3.593 (2.271-6.271)* 3.613 (2.249-6.332)* 3.770 (2.262-6.412)*
Risky drinking 4.153 (2.301-6.410)** 4.004 (2.958-6.405)** 4.001 (2.942-6.273)** 4.111 (2.829-6.884)**
Statistical significance: ∗ - p < 0.05; ∗∗ - p < 0.01. Model 1 unadjusted odds ratio for lifestyle-related factors with sick leave. Model 2 adjusted the odds ratio for Model 1 with the addition of sociodemographic factors with sick leave. Model 3 adjusted the odds ratio for Model 2 with the addition of work-related factors with sick leave. Model 4 adjusted the odds ratio for Model 3 with the addition of health-related factors with sick leave.
In women, obesity has a positive but statistically non-significant effect on sick leave across all models. In Model 1a, the odds ratio (OR) is 2.215 (CI: 0.989-4.967), decreasing to 1.575 in Model 4d (CI: 0.687-3.611) as additional factors are included. Although these values are above 1, the broad confidence intervals indicate that the results are not statistically significant, suggesting that while obesity may increase sick leave risk, further research is needed to clarify this association. Overweight shows a similar pattern with OR values between 1.097 and 1.323, but these are also not statistically significant. This implies that women with overweight have a slightly higher risk of sick leave compared to underweight individuals, although this risk is not substantial. Normal weight demonstrates the least impact on sick leave, with OR values ranging from 1.084 to 1.215, indicating that normal weight does not significantly alter the risk of sick leave compared to underweight women.
In women, the consumption of fruit 4 to 6 times a week shows an unexpected effect. In Model 1a, the odds ratio (OR) is 0.915 (CI: 0.639-2.292), and in Models 2b, 3c, and 4d, the values remain below 1, suggesting that regular fruit consumption may reduce the risk of sick leave. However, the wide confidence intervals indicate that these results are not statistically significant. Conversely, occasional or rare fruit consumption (1 to 3 times a week or never/rarely) has OR values above 1, but the confidence intervals are too broad to be statistically significant. These findings suggest that further research is needed to better understand how fruit consumption affects health and sick leave.
In this analysis, vegetable consumption does not show a significant impact on sick leave among women. The odds ratios (OR) for women consuming vegetables less than once a day are below 1 across all models, but the wide confidence intervals suggest that the effect might not be strong or is difficult to detect with the available data. This indicates that vegetable consumption may not have a substantial effect on sick leave or that the effect is not easily observable in this dataset.
Women with low or moderate physical activity levels have increased odds of sick leave compared to those with high levels of physical activity. The odds ratios (OR) for low physical activity range from 1.551 in Model 1a to 1.361 in Model 4d, with similar OR values for moderate physical activity. Although these values are greater than 1, the wide confidence intervals suggest that the results are not statistically significant.
Current female smokers exhibit significantly higher odds of sick leave compared to non-smokers, with odds ratios (OR) ranging from 1.811 in Model 1a to 1.809 in Model 4d, indicating a consistently high risk. These results are statistically significant, highlighting the need for smoking cessation interventions to reduce sick leave. Former smokers also show increased odds of sick leave, with OR values between 1.803 and 1.967, suggesting that smoking has long-term negative effects on health that persist even after quitting.
Risky alcohol consumption has the strongest impact on sick leave among women, with odds ratios (OR) ranging from 2.771 in Model 1a to 3.116 in Model 4d. These results suggest that women who engage in risky drinking have a significantly increased risk of sick leave compared to those who abstain from alcohol. However, the wide confidence intervals indicate the need for further research to confirm these findings. Low-risk alcohol consumption and former drinkers also show a slightly elevated risk of sick leave compared to non-drinkers, with OR values just above 1. Although these findings suggest a potential increased risk, the lack of statistical significance highlights the necessity for additional studies.
Table 7. Unadjusted and adjusted odds ratio (OR) with a 95% confidence interval (CI) for the association of lifestyle factors with sick leave - female.
Table 7. Unadjusted and adjusted odds ratio (OR) with a 95% confidence interval (CI) for the association of lifestyle factors with sick leave - female.
Variables Model 1
OR (95% CI)		 Model 2
OR (95% CI)		 Model 3
OR (95% CI)		 Model 4
OR (95% CI)
Body mass index Underweight 1 1 1 1
Normal weight 1.215 (0.556-2.487) 1.112 (0.446-2.324) 1.084 (0.451-2.287) 1.125 (0.489-2.414)
Pre-obese 1.323 (0.609-2.873) 1.119 (0.493-2.538) 1.097 (0.479-2.518) 1.154 (0.505-2.642)
Obese 2.215 (0.989-4.967) 1.759 (0.767-3.732) 1.701 (0.748-3.738) 1.575 (0.687-3.611)
Fruit consumption At least once a day 1 1 1
4 to 6 times a week 0.915 (0.639-2.292) 0.972 (0.664-1.472) 0.998 (0.679-1.436) 0.968 (0.644-1.346)
1 to 3 times a week 1.245 (0.590-2.593) 1.456 (0.678-3.218) 1.523 (0.694-3.331) 1.258 (0.567-2.763)
Never/occasionally 2.315 (0.468-11.465) 2.594 (0.517-12.915) 2.738 (0.558-13.527) 2.703 (0.535-14.872)
Vegetable consumption At least once a day 1 1 1 1
4 to 6 times a week 0.840 (0.534-1.321) 0.867 (0.545-1.380) 0.822 (0.513-1.316) 0.920 (0.566-1.493)
1 to 3 times a week 0.639 (0.344-1.185) 0.668 (0.354-1.262) 0.655 (0.345-1.244) 0.716 (0.371-1.381)
Never/occasionally 1.055 (0.303-3.677) 1.369 (0.380-4.929) 1.472 80.397-5.456) 1.280 (0.323-5.076)
Leisure-time aerobic physical activity High 1 1 1 1
Moderate 1.512 (0.872-2.621) 1.479 (0.856-2.578) 1.461 (0.847-2.583) 1.187 (0.702-2.011)
Low 1.551 (0.891-2.701) 1.482 (0.859-2.612) 1.432 (0.820-2.511) 1.361 (0.775-2.387)
Smoking status Never smoker 1 1 1 1
Former smoker 1.950 (1.151-3.310)* 1.803 (1.045-3.019)* 1.917 (1.106-3.302)* 1.967 (1.167-3.384)*
Current smoker 1.811 (1.261-2.601)* 1.879 (1.341-2.635)* 1.812 (1.302-2.602)* 1.809 (1.221-2.651)*
Alcohol consumption Lifetime abstainers 1 1 1 1
Former drinking 1.611 (0.721-3.521) 1.591 (0.707-3.501) 1.581 (0.702-3.481) 1.681 (0.747-3.781)
Non-weekly drinking 1.512 (0.872-2.621) 1.523 (0.879-2.652) 1.552 (0.878-2.651) 1.575 (0.912-2.741)
Low-risk drinking 1.462 (0.689-3.101) 1.459 (0.688-3.095) 1.491 (0.703-3.211) 1.561 (0.735-3.411)
Risky drinking 2.771 (0.721-15.482)* 2.931 (0.815-16.431)* 3.051 (0.851-17.501)* 3.116 (0.872-18.112)*
Statistical significance: ∗ - p < 0.05; ∗∗ - p < 0.01. Model 1 unadjusted odds ratio for lifestyle-related factors with sick leave. Model 2 adjusted the odds ratio for Model 1 with the addition of sociodemographic factors with sick leave. Model 3 adjusted the odds ratio for Model 2 with the addition of work-related factors with sick leave. Model 4 adjusted the odds ratio for Model 3 with the addition of health-related factors with sick leave.

4. Discussion

This research aimed to analyze the modifiable lifestyle factors associated with sick leave among Serbia's working population, with a particular focus on sex differences and their impact on sick leave. The study included 4,652 respondents, with an average age of 42.68 years, categorized by sex. The results of the first study show significant differences in sociodemographic variables. Women were more represented in the 36-45 age group and had higher educational attainment, with 34.8% completing college or university compared to 24.6% of men. Men were predominantly engaged in skilled manual work, while women were more often in managerial roles. Regional concentration was highest in Central and Western Serbia for both sexes. Additionally, a higher percentage of men reported very good or good health (84.5% vs. 80.2% for women).
Our study reveals that 15.8% of respondents reported sick leave due to personal health issues in the past year, with a higher prevalence among women compared to men. Serbia's rates are between those of Spain (8.8% for men and 11.9% for women) and the United Kingdom (7.9% for men and 12.3% for women) [1]. Germany reports the highest rates (20.4% for men and 22.9% for women), while Turkey and Romania have much lower rates (Turkey: 2.1% for men and 0.9% for women; Romania: 0.7% for men and 0.4% for women) [1]. The findings reveal significant associations between age, education level, and self-rated health with sick leave. For men, the highest rates of long-term sick leave were observed in those aged 56-65, with significant regional disparities, especially in Vojvodina and Central and Western Serbia. Men with middle school education experienced the highest overall sick leave rates. Poor self-rated health was strongly linked to long-term sick leave, indicating a need for targeted interventions for older men, those with lower education levels, and individuals with poor health. For women, significant factors associated with sick leave included age, marital status, wealth index, and self-rated health. Women aged 46-55 had the highest rates of short-term sick leave, while those aged 36-45 had the highest rates of long-term sick leave. Regional disparities were also evident, with Central and Western Serbia showing the highest short-term sick leave rates and Belgrade having the highest long-term sick leave prevalence. Additionally, married women and those from higher wealth classes had more frequent sick leave, and light physical job effort was linked to higher short-term sick leave, whereas moderate effort was associated with long-term sick leave. Women with poor self-rated health had the highest rates of long-term sick leave. Our findings align with previous studies [1,23,40] that emphasize the impact of socio-demographic factors and self-rated health on sick leave. For instance, Lidwall [41] noted sex segregation in occupational sick leave patterns. Contrary to past research suggesting that women in lower wealth classes have higher sick leave rates [31], our results show that women from higher wealth classes reported more frequent sick leave. This discrepancy calls for further investigation into how wealth index classes affect sick leave. Additionally, while Mensah et al. [30] found that men reported poorer self-rated health compared to women across 30 European countries, our study found that females more frequently rated their health as less than very good. These inconsistencies suggest potential variations in health perception and reporting. Our results highlight the need for sex-specific health interventions in Serbia, including better access to healthcare, flexible workplace policies, and enhanced social support for women. Targeted strategies are essential for addressing the needs of older male workers, those with lower education levels, and individuals with poor health. A comprehensive approach that considers both sex-specific and broader demographic factors is crucial for developing effective interventions to reduce absenteeism and improve health outcomes.
The analysis of lifestyle factors revealed interesting patterns in the prevalence of sick leave, though many of these differences were not statistically significant. The regression analysis reveals notable associations between BMI and sick leave for both sexes, though patterns differ. For men, obesity significantly affects sick leave, with those classified as obese having over three times the odds of taking sick leave in an unadjusted model compared to underweight individuals. This effect remains statistically significant even after adjusting for sociodemographic, occupational, and health factors. Pre-obese male participants also have increased odds of sick leave. For women, the association between obesity and sick leave is positive but not statistically significant. This indicates that while there is an increased risk of sick leave for obese women, the evidence is not strong enough to be conclusive. Pre-obese women also show a slightly increased risk of sick leave compared to underweight, but this finding is not substantial. Overall, while obesity is a clear predictor of sick leave among men, the relationship for women is less pronounced and requires further investigation to clarify underlying factors. These findings align with many previous research indicating a strong link between obesity and sick leave [7,8,42,43], although some studies suggest varying results due to differences in job types [15,28]. Such discrepancies may arise from differences in the occupations of the study populations.
The results of the study reveal distinct patterns regarding fruit and vegetable consumption and their impact on sick leave, with notable differences between sexes. The results of the study reveal distinct patterns regarding fruit and vegetable consumption and their impact on sick leave, with notable differences between male and female participants. Among men, consuming fruit never or occasionally is associated with increased odds of sick leave, suggesting that regular fruit consumption may serve as a protective factor. Similar trends are observed with vegetable consumption, highlighting the importance of a balanced diet in maintaining health and reducing the risk of sick leave. These findings align with previous research by Canerva et al. [12], which emphasizes the role of fruit and vegetable intake in promoting overall health. For women, the relationship between fruit and vegetable consumption and sick leave appears complex. Women who consume fruit and vegetables daily report higher prevalence rates of both short-term and long-term sick leave, suggesting that those with healthier dietary habits may be more proactive in managing underlying health conditions that require time off work. Despite this, the data does not show a strong protective effect of daily fruit and vegetable consumption on reducing sick leave. Notably, while regular fruit consumption (4 to 6 times a week) seems to indicate a potential reduction in sick leave risk in regression analysis, this finding is not statistically significant. Similarly, vegetable consumption does not show a significant impact on sick leave, with results indicating that any effect might be weak or difficult to detect. These findings highlight the need for further research to fully understand the influence of fruit and vegetable consumption on sick leave among women. Overall, while regular fruit and vegetable consumption seems to help reduce sick leave among men, the relationship for women is more complex and may be influenced by other factors that need further exploration. This variation in findings is not surprising, as some studies, like those by Fitzgerald et al. [9], have demonstrated a positive impact of a healthy diet on reducing sick leave, while others, including De Bortoli et al. [5], found no significant association.
The results of the study show that, among men, low or moderate levels of leisure-time aerobic physical activity are associated with higher chances of sick leave compared to high physical activity levels. Specifically, men with low physical activity levels face an increased risk of health problems that may lead to more frequent sick leave. Similarly, for women, those with low and moderate levels of leisure-time aerobic physical activity also show a higher prevalence of sick leave. This finding is consistent with several studies indicating that insufficient physical activity is linked to increased sick leave [11,16,42]. Although our results indicate that women with low or moderate physical activity levels have higher odds of sick leave compared to those with high physical activity levels, these results were not statistically significant. This suggests that further research with more precise measurements or larger sample sizes is needed to clarify these associations. Overall, these findings underscore the importance of regular physical activity in potentially reducing sick leave. They highlight the role of an active lifestyle in maintaining overall health and suggest that promoting higher levels of physical activity could help decrease sick leave rates.
Our study reveals that smoking status is a significant determinant of sick leave, with notable differences between current smokers, former smokers, and never-smokers. Current smokers exhibit a significantly higher prevalence of long-term sick leave, consistent with the established health risks associated with smoking. This suggests that smoking cessation programs could be particularly effective in reducing long-term sick leave among both men and women. For men, current smokers have notably higher odds of sick leave compared to never-smokers, reinforcing smoking as a major risk factor for sick leave even when adjusted for sociodemographic, work-related, and health-related factors. Former smokers also show increased odds of sick leave, indicating that the negative health effects of smoking may persist over time, though their risk is lower compared to current smokers. Similarly, among women, current smokers have significantly higher odds of sick leave, with the data highlighting smoking as a major risk factor for long-term sick leave. Former female smokers demonstrate even more elevated odds of sick leave, suggesting that smoking’s adverse health effects can continue to impact health even after quitting. These findings underscore the importance of targeted smoking cessation interventions to mitigate sick leave. Our results align with previous research showing a strong association between smoking and increased sick leave [3,42], emphasizing the need for effective smoking cessation strategies to improve overall health outcomes and reduce absenteeism.
For men, results from regression analysis revealed that risky drinking is particularly impactful, with men engaging in risky alcohol use showing the highest odds of sick leave. Low-risk drinking among men also shows a substantial effect on sick leave, indicating that any level of weekly alcohol consumption can raise health risks leading to sick leave. Former drinkers also display an elevated risk, highlighting the lasting health effects of previous alcohol consumption. For women, the pattern is different. Risky drinking is associated with an increased risk of sick leave, but the results are less conclusive, with broad confidence intervals suggesting a need for further study. Low-risk drinking and former alcohol use show only a slight elevation in sick leave risk compared to non-drinkers, and these findings are not statistically significant, indicating that alcohol's impact on sick leave among women may be less pronounced or more complex. Some authors [15,17] have found that alcohol consumption does not elevate the risk of sick leave; the other no drinking attitudes differences in the influence on sick leave [29], however, many more studies [20,40,44] confirm the association between alcohol consumption and increased sick leave risk in both men and women.
This analysis underscores the significant impact of lifestyle factors such as BMI, dietary habits, smoking, and alcohol consumption on the likelihood of sick leave among men in Serbia. The findings highlight the importance of targeted interventions to reduce sick leave rates and improve health outcomes in male workers. In contrast, the analysis of sick leave among women presents a more complex picture, with mixed associations between lifestyle factors and sick leave. While factors like smoking and risky alcohol consumption are associated with increased sick leave, others, such as BMI, fruit and vegetable consumption, and physical activity, show less consistent patterns. This suggests that the effects of these lifestyle factors on sick leave in women may be more nuanced and require further investigation to fully understand. The lack of a significant association between alcohol consumption and sick leave among women is particularly surprising and indicates that alcohol's impact may be more relevant to short-term rather than long-term sick leave. Overall, understanding these modifiable lifestyle factors is crucial for developing effective strategies to reduce sick leave and enhance productivity in both men and women.
Understanding modifiable lifestyle factors is essential for developing effective interventions to reduce sick leave and improve productivity. To address the factors influencing sick leave, several targeted strategies could be employed. Firstly, implementing comprehensive workplace wellness programs that focus on weight management can be beneficial. These programs could include offering nutritional guidance and promoting physical activity. Secondly, establishing smoking cessation initiatives can support employees who wish to quit smoking, thereby reducing related health issues. Additionally, improving access to healthier food options and providing nutrition education within the workplace could enhance fruit and vegetable consumption. For alcohol-related concerns, increasing awareness about its health impacts and providing resources for those struggling with alcohol use could be effective. Regular health screenings can further assist in early detection and prevention of health problems. These approaches aim to improve overall employee health, reduce the incidence of sick leave, and boost productivity.

Strengths and Limitations

This study has several limitations, including the potential bias from self-reported health behaviors and the use of specific cut-points for health behaviors and sick leave that may not align with other studies. The 30-day sick leave threshold complicates comparisons with studies using different cut-points, and missing data and non-participation may affect the findings. Despite this, the data were deemed representative. Additionally, the higher prevalence of self-completion questionnaires due to the sensitivity of questions on smoking and alcohol use could influence results. Participants were also more likely to be married, have a middle school education, and belong to higher occupational classes, which may lead to conservative findings.
On the other hand, the study's strengths include its representative sample of Serbia's working population, achieved through a rigorous stratified two-stage sampling method. The study follows the European Health Interview Survey methodology, ensuring international comparability. Using a national census framework supports the reliability of the findings, and including reserve, households helps address non-response bias.

5. Conclusions

This research highlights the significant impact of lifestyle factors on sick leave within the Serbian working population, with a particular emphasis on sex differences. For male respondents, key factors such as obesity, dietary habits, smoking, and alcohol consumption consistently predict the likelihood of sick leave. Effective interventions targeting weight management, diet habits improvement, smoking cessation, and reducing risky alcohol consumption are likely to lower sick leave rates. Such measures not only promise to enhance individual health but also offer benefits for healthcare systems and employers by alleviating associated burdens. Further research with a diverse population and extended follow-up is essential to elucidate the mechanisms through which these factors affect health and to refine public health strategies accordingly.
In contrast, the findings for women reveal that smoking and risky alcohol consumption are the most substantial predictors of sick leave. These results underline the urgent need for public health initiatives aimed at mitigating these risks. Although BMI and physical activity are associated with sick leave, their impact in this study is not statistically significant, suggesting the possibility of other more influential factors or the need for further investigation. The role of fruit and vegetable consumption remains ambiguous, with wide confidence intervals indicating that more research is needed to clarify its impact on health and sick leave. Overall, the study emphasizes the necessity of tailored interventions and ongoing research to better understand and address the diverse factors affecting sick leave across different populations.

Author Contributions

Conceptualization, S.K. and D.S.; methodology, T.G., N.J., and T.M.; software, D.V.; validation, S.K., N.Dj. and D.V.; formal analysis, T.G.; investigation, S.K.; resources, D.S.; data curation, S.K., N.Dj., and D.M..; writing-original draft preparation, S.K., D.V., and S.Dj; writing-review and editing, T.G. and S.K.; visualization, D.V., D.M. and T.M.; supervision, D.S. and S.K.; project administration, S.K.; funding acquisition, N.Dj. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study adhered to the Declaration of Helsinki and received the necessary permissions for scientific research. The Institute for Public Health of the Republic of Serbia “Milan Jovanović Batut” officially transferred the 2019 National Health Survey database to the University of Kragujevac for this research.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Due to privacy and ethical restrictions, the data used in this study are not available for public access. The rights to the data are held by the Institute of Public Health of Serbia, "Milan Jovanović Batut." The database was officially transferred to the University of Kragujevac to conduct further research.

Acknowledgments

This research was supported by the Ministry of Education of the Republic of Serbia (Contract number: 451-03-65/2024-03/200111), and by the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia (Contract Number 451-03-66/2024-03/200172).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Antczak, E.; Miszczyńska, K.M. Causes of Sickness Absenteeism in Europe-Analysis from an Intercountry and Gender Perspective. Int J Environ Res Public Health 2021, 18, 11823. [Google Scholar] [CrossRef] [PubMed]
  2. Salmela, J.; Lahti, J.; Kanerva, N.; Rahkonen, O.; Kouvonen, A.; Lallukka, T. Latent classes of unhealthy behaviours and their associations with subsequent sickness absence: a prospective register-linkage study among Finnish young and early midlife employees. BMJ Open 2023, 13, e070883. [Google Scholar] [CrossRef] [PubMed]
  3. Virtanen, M.; Ervasti, J.; Head, J.; Oksanen, T.; Salo, P.; Pentti, J.; Kouvonen, A.; Väänänen, A.; Suominen, S.; Koskenvuo, M.; et al. Lifestyle factors and risk of sickness absence from work: a multicohort study. Lancet Public Health 2018, 3, e545–54. [Google Scholar] [CrossRef] [PubMed]
  4. Oellingrath, I.M.; De Bortoli, M.M.; Svendsen, M.V.; Fell, A.K.M. Lifestyle and work ability in a general working population in Norway: a cross-sectional study. BMJ Open 2019, 9, e026215. [Google Scholar] [CrossRef]
  5. De Bortoli, M.M.; Oellingrath, I.M.; Fell, A.K.M.; Burdorf, A.; Robroek, S.J.W. Influence of lifestyle risk factors on work ability and sick leave in a general working population in Norway: a 5-year longitudinal study. BMJ Open 2021, 11, e045678. [Google Scholar] [CrossRef]
  6. Østby, K.A.; Mykletun, A.; Nilsen, W. Explaining the gender gap in sickness absence. Occup Med Oxf Engl 2018, 68, 320–326. [Google Scholar] [CrossRef]
  7. Amiri, S.; Behnezhad, S. Body mass index and risk of sick leave: A systematic review and meta-analysis. Clin Obes 2019, 9, e12334. [Google Scholar] [CrossRef]
  8. Linaker, C.H.; D’Angelo, S.; Syddall, H.E.; Harris, E.C.; Cooper, C.; Walker-Bone, K. Body Mass Index (BMI) and Work Ability in Older Workers: Results from the Health and Employment after Fifty (HEAF) Prospective Cohort Study. Int J Environ Res Public Health 2020, 17, 1647. [Google Scholar] [CrossRef]
  9. Fitzgerald, S.; Kirby, A.; Murphy, A.; Geaney, F. Obesity, diet quality and absenteeism in a working population. Public Health Nutr 2016, 19, 3287–3295. [Google Scholar] [CrossRef]
  10. Svärd, A.; Lahti, J.; Mänty, M.; Roos, E.; Rahkonen, O.; Lahelma, E.; Lallukka, T. Weight change among normal weight, overweight and obese employees and subsequent diagnosis-specific sickness absence: A register-linked follow-up study. Scand J Public Health 2020, 48, 155–163. [Google Scholar] [CrossRef]
  11. Jiménez-Mérida, R.; Romero-Saldaña, M.; de-Pedro-Jiménez, D.; Alcaide-Leyva, JM.; Cantón-Habas, V.; Álvarez-Fernández, C.; Álvarez-Fernández, C. Lifestyle, Type of Work, and Temporary Disability: An Incidence Study of the Working Population. Int J Environ Res Public Health 2022, 19, 14932. [Google Scholar] [CrossRef] [PubMed]
  12. Kanerva, N.; Pietiläinen, O.; Lallukka, T.; Rahkonen, O.; Lahti, J. Unhealthy lifestyle and sleep problems as risk factors for increased direct employers’ cost of short-term sickness absence. Scand J Work Environ Health 2018, 44, 192–201. [Google Scholar] [CrossRef]
  13. Kerner, I.; Rakovac, M.; Lazinica, B. Leisure-time physical activity and absenteeism. Arh Hig Rada Toksikol 2017, 68, 159–170. [Google Scholar] [CrossRef]
  14. Shiri, R.; Hiilamo, A.; Rahkonen, O.; Robroek, S.J.W.; Pietiläinen, O.; Lallukka, T. Predictors of working days lost due to sickness absence and disability pension. Int Arch Occup Environ Health 2021, 94, 843–854. [Google Scholar] [CrossRef] [PubMed]
  15. Rabacow, F.M.; Levy, R.B.; Menezes, P.R.; do Carmo Luiz, O.; Malik, A.M.; Burdorf, A. The influence of lifestyle and gender on sickness absence in Brazilian workers. BMC Public Health 2014, 14, 317. [Google Scholar] [CrossRef] [PubMed]
  16. López-Bueno, R.; Sundstrup, E.; Vinstrup, J.; Casajús, JA.; Andersen, L.L. High leisure-time physical activity reduces the risk of long-term sickness absence. Scand J Med Sci Sports 2020, 30, 939–946. [Google Scholar] [CrossRef] [PubMed]
  17. Lund, I.; Moan, IS.; Edvardsen, H.M.E. The relative impact of smoking, alcohol use and drug use on general sickness absence among Norwegian employees. BMC Public Health 2019, 19, 500. [Google Scholar] [CrossRef]
  18. Nielsen, N.B.S.; Gunnarsen, L.; Iburg, K.M. The impact of stress and lifestyle factors on short-term sickness absence in a large Danish industrial company. Scand J Public Health 2023, 51, 204–214. [Google Scholar] [CrossRef]
  19. Hori, A.; Inoue, Y.; Kuwahara, K.; Kunugita, N.; Akter, S.; Nishiura, C.; Kinugawa, C.; Endo, M.; Ogasawara, T.; Nagahama, S.; et al. Smoking and Long-Term Sick Leave in a Japanese Working Population: Findings of the Japan Epidemiology Collaboration on Occupational Health Study. Nicotine Tob Res 2021, 23, 135–142. [Google Scholar] [CrossRef]
  20. Landberg, J.; Hemmingsson, T.; Sydén, L.; Ramstedt, M. The Contribution of Alcohol Use, Other Lifestyle Factors and Working Conditions to Socioeconomic Differences in Sickness Absence. Eur Addict Res 2020, 26, 40–51. [Google Scholar] [CrossRef]
  21. Lee, S.Y.; Jung, S.; Lee, W. Status of Workers’ Health Behavior and the Association between Occupational Characteristics and Health Behavior. Int J Environ Res Public Health 2022, 19, 13021. [Google Scholar] [CrossRef] [PubMed]
  22. Knežević, S.; Gajić, T.; Sekulić, D.; Djoković, F.; Vukolić, D.; Zrnić, M. Work-life balance: a historical review and insights for the hospitality industry. Tur Posl 2023, 91–99. [Google Scholar] [CrossRef]
  23. Mastekaasa, A.; Dale-Olsen, H.; Hellevik, T.; Løset, GK.; Østbakken, K.M. Gender difference in sickness absence: Do managers evaluate men and women differently with regard to the appropriateness of sickness absence? Scand J Public Health 2021, 49, 125–131. [Google Scholar] [CrossRef]
  24. Troelstra, S.A.; Coenen, P.; Boot, C.R.; Harting, J.; Kunst, A.E.; van der Beek, A.J. Smoking and sickness absence: a systematic review and meta-analysis. Scand J Work Environ Health 2020, 46, 5–18. [Google Scholar] [CrossRef] [PubMed]
  25. Salmela, J.; Lahti, J.; Mauramo, E.; Pietiläinen, O.; Rahkonen, O.; Kanerva, N. Associations of changes in diet and leisure-time physical activity with employer’s direct cost of short-term sickness absence. Eur J Sport Sci 2020, 20, 240–248. [Google Scholar] [CrossRef]
  26. Knežević, S.; Gajić, T.; Vukolić, D.; Zrnić, M.; Đorđević, S. Lifestyle medicine: Empowering health through behavior modifications. In Proceedings of the International scientific and Professional Conference POLITEHNIKA 2023, Academy of Applied Technical Studies, Belgrade, Serbia, 15 December 2023. [Google Scholar]
  27. Grimani, A.; Aboagye, E.; Kwak, L. The effectiveness of workplace nutrition and physical activity interventions in improving productivity, work performance and workability: a systematic review. BMC Public Health 2019, 19, 1676. [Google Scholar] [CrossRef] [PubMed]
  28. Keramat, S.A.; Alam, K.; Gow, J.; Biddle, S.J.H. Gender differences in the longitudinal association between obesity, and disability with workplace absenteeism in the Australian working population. PloS One 2020, 15, e0233512. [Google Scholar] [CrossRef] [PubMed]
  29. Hashemi, N.S.; Dalen, I.; Skogen, J.C.; Sagvaag, H.; Gimeno, Ruiz de Porras, D. ; Aas, R.W. Do Differences in Drinking Attitudes and Alcohol-Related Problems Explain Differences in Sick Leave? A Multilevel Analysis of 95 Work Units Within 14 Companies From the WIRUS Study. Front Public Health 2022, 10, 817726. [Google Scholar] [CrossRef]
  30. Mensah, A.; Toivanen, S.; Diewald, M. Working Hours, Sleep Disturbance and Self-Assessed Health in Men and Women: A Multilevel Analysis of 30 Countries in Europe. Front Public Health 2022, 10, 818359. [Google Scholar] [CrossRef] [PubMed]
  31. Nwaru, C.A.; Berglund, T.; Hensing, G. Occupational prestige and sickness absence inequality in employed women and men in Sweden: a registry-based study. BMJ Open 2021, 11, e050191. [Google Scholar] [CrossRef]
  32. Health statistical yearbook of Republic of Serbia 2022, Institute of Public Health of Serbia “Dr Milan Jovanović Batut”. 2022. Available online: https://www.batut.org.rs/index.php?category_id=109 (accessed on 29 July 2024).
  33. The 2019 Serbian National Health Survey, Statistical Office of the Republic of Serbia. 2020. Available online: https://www.stat.gov.rs/en-us/vesti/20210429-istrazivanje-zdravlja-stan/ (accessed on 21 July 2024).
  34. European Health Interview Survey (EHIS Wave 3) - Methodological Manual, Eurostat. 2018. Available online: https://ec.europa. eu/eurostat/documents/3859598/8762193/KS-02-18-240-EN-N.pdf/5fa53ed4-4367-41c4-b3f5-260ced9ff2f6 (accessed on 20 July 2024).
  35. International Standard Classification of Occupations (ISCO), ILOSTAT. 2008. Available online: https://ilostat.ilo.org/methods/concepts-and-definitions/classification-occupation/ (accessed on 20 July 2024).
  36. A healthy lifestyle - WHO recommendations, WHO. 2010. Available online: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations (accessed on 27 July 2024).
  37. Physical activity, WHO. 2020. Available online: https://www.who.int/news-room/fact-sheets/detail/physical-activity (accessed on 20 July 2024).
  38. The National Clinical Practice Guidelines for the Diagnosis and Treatment of Alcoholism. Ministry of health. 2013. Available online: https://www.drlazetic.com/nacionalni-vodic-dobre-klinicke-prakse-za-dijagnostikovanje-i-lecenje-alkoholizma/ (accessed on 20 July 2024).
  39. Health Insurance Act. The Government of the Republic of Serbia 2023. Available online: https://www.paragraf.rs/propisi/zakon_o_zdravstvenom_osiguranju.html (accessed on 30 July 2024).
  40. Landberg, J.; Ramstedt, M. Distribution of sickness absence risk across different levels and patterns of drinking: findings from the Stockholm Public Health Cohort. Nordisk Alkohol Nark 2021, 38, 305–318. [Google Scholar] [CrossRef]
  41. Lidwall, U. Gender composition in occupations and branches and medically certified sick leave: a prospective population study. Int Arch Occup Environ Health 2021, 94, 1659–1670. [Google Scholar] [CrossRef] [PubMed]
  42. Oellingrath, I.M.; Bortoli, M.M.D.; Svendsen, M.V.; Fell, A.K.M. Lifestyle and work ability in a general working population in Norway: a cross-sectional study. BMJ Open 2019, 9, e026215. [Google Scholar] [CrossRef] [PubMed]
  43. Ferrie, J.E.; Head, J, Shipley. ; M.J, Vahtera, J.; Marmot, M.G.; Kivimäki, M. BMI, obesity, and sickness absence in the Whitehall II study. Obesity (Silver Spring) 2007, 15, 1554–1564. [Google Scholar] [CrossRef] [PubMed]
  44. Ervasti, J.; Kivimäki, M.; Head, J.; Goldberg, M.; Airagnes, G.; Pentti, J.; Tuula Oksanen, T.; Paula Salo, P.; Sakari Suominen, S.; Jokela, M.; et al. Sociodemographic Differences Between Alcohol Use and Sickness Absence: Pooled Analysis of Four Cohort Studies. Alcohol Alcohol 2018, 53, 95–103. [Google Scholar] [CrossRef]
Table 1. Sociodemographic description of participants.
Table 1. Sociodemographic description of participants.
Variables Sex Pearson Chi-Square/df/p *
Male Female
N % N %
Age
18-25 206 7.9 109 5.3 17.604/4/0.001
26-35 584 22.5 450 21.9
36-45 721 27.7 633 30.8
46-55 668 25.7 556 27.1
56-65 420 16.2 305 14.9
Education
College/University 638 24.6 714 34.8 59.007/2/0.000
Middle school 1767 68.0 1193 58.1
Primary school 192 7.4 146 7.1
Region of Serbia
Belgrade (Capital) 645 24.8 589 28.7 11.964/3/0.008
Vojvodina 625 24.0 508 24.7
Central and Western Serbia 855 32.9 603 29.4
Southern and Eastern Serbia 474 18.2 353 17.2
Marital status
Married 1762 68.0 1495 73.1 145.876/3/0.000
Single 696 26.9 316 15.5
Divorced 121 4.7 167 8.2
Widowed 13 0.5 67 3.3
Wealth index (class)
Richest 726 27.9 640 31.2 14.735/4/0.005
Richer 640 24.6 531 25.9
Middle 540 20.8 418 20.4
Poor 413 15.9 297 14.5
Poorest 280 10.8 167 8.1
Occupations
Managers, professionals, and technicians 710 27.6 779 38.4 241.120/3/0.000
Clerical support, service, and sales workers 621 24.1 623 30.7
Skilled manual workers 1086 42.1 421 20.7
Elementary occupations 160 6.2 207 10.2
Job physical effort
Heavy 517 21.2 104 5.3 246.003/2/0.000
Moderate 1050 43.0 889 45.1
None/light 877 35.9 979 49.6
Self-rated health
Very good/good 2067 84.5 1583 80.2 14.611/2/0.001
Fair 322 13.2 328 16.6
Bad/very bad 56 2.3 63 3.2
* Statistical significance was set at p < 0.05.
Table 2. Prevalence of sick leave by sociodemographic characteristics, male.
Table 2. Prevalence of sick leave by sociodemographic characteristics, male.
Variables Total Sick leave
N % No Short-term Long-term Pearson Chi-Square/df/p *
N % N % N %
Age
18-25 203 7.9 186 8.3 15 5.5 2 3.6 44.649/8/0.000
26-35 576 22.4 505 22.6 66 24.3 5 8.9
36-45 712 27.7 629 28.1 73 26.8 10 17.9
46-55 660 25.7 578 25.8 69 25.4 13 23.2
56-65 415 16.2 340 15.2 49 18.0 26 46.4
Education
College/University 635 24.8 568 25.4 53 19.5 14 25.0 15.872/4/0.003
Middle school 1741 67.9 1520 68.0 185 68.0 36 64.3
Primary school 188 7.3 148 6.6 34 12.5 6 10.7
Region of Serbia
Belgrade (Capital) 636 24.8 554 24.8 67 24.6 15 26.8 13.322/6/0.038
Vojvodina 619 24.1 521 23.3 88 32.4 10 17.9
Central and Western Serbia 847 33.0 752 33.6 75 27.6 20 35.7
Southern and Eastern Serbia 464 18.1 411 18.4 42 15.4 11 19.6
Marital status
Married 1735 67.8 1498 67.1 196 72.3 41 73.2 7.975/6/0.240
Single 690 27.0 618 27.7 62 22.9 10 17.9
Divorced 121 4.7 105 4.7 11 4.1 5 8.9
Widowed 13 0.5 11 0.5 2 0.7 0 0.0
Wealth index (class)
Richest 718 28.0 623 27.8 75 27.6 20 35.7 12.648/8/0.125
Richer 636 24.8 561 25.1 61 22.4 14 25.0
Middle 529 20.6 448 20.0 70 25.7 11 19.6
Poor 411 16.0 374 16.7 31 11.4 6 10.7
Poorest 272 10.6 232 10.4 35 12.9 5 8.9
Occupations
Managers, professionals, and technicians 707 27.8 626 28.2 66 24.4 15 26.8 5.579/6/0.472
Clerical support, service, and sales workers 613 24.1 538 24.2 63 23.2 12 21.4
Skilled manual workers 1069 42.0 926 41.7 118 43.5 25 44.6
Elementary occupations 157 6.2 129 5.8 24 8.9 4 7.1
Job physical effort
Heavy 512 21.1 432 20.6 72 26.5 8 14.3 9.171/4/0.057
Moderate 1044 43.0 906 43.1 116 42.6 22 39.3
None/light 873 35.9 763 36.3 84 30.9 26 46.4
Self-rated health
Very good/good 2060 84.8 1848 87.9 185 68.3 27 48.2 167.279/4/0.000
Fair 316 13.0 226 10.8 71 26.2 19 33.9
Bad/very bad 53 2.2 28 1.3 15 5.5 10 17.9
* Statistical significance was set at p < 0.05.
Table 3. Prevalence of sick leave by sociodemographic characteristics, female.
Table 3. Prevalence of sick leave by sociodemographic characteristics, female.
Variables Total Sick leave
N % No Short-term Long-term Pearson Chi-Square/df/p *
N % N % N %
Age
18-25 108 5.3 99 5.9 9 3.2 0 0.0 25.890/8/0.001
26-35 440 21.7 382 22.8 40 14.2 18 26.1
36-45 626 30.9 511 30.5 93 33.0 22 31.9
46-55 551 27.2 431 25.7 101 35.8 19 27.5
56-65 303 14.9 254 15.1 39 13.8 10 14.5
Education
College/University 702 34.6 581 34.6 104 36.9 17 24.6 5.126/4/0.275
Middle school 1183 58.3 981 58.5 158 56.0 44 63.8
Primary school 143 7.1 115 6.9 20 7.1 8 11.6
Region of Serbia
Belgrade (Capital) 579 28.6 456 27.2 99 35.1 24 34.8 24.143/6/0.000
Vojvodina 502 24.8 430 25.6 54 19.1 18 26.1
Central and Western Serbia 600 29.6 483 28.8 100 35.5 17 24.6
Southern and Eastern Serbia 347 17.1 308 18.4 29 10.3 10 14.5
Marital status
Married 1474 73.0 1214 72.7 202 71.6 58 84.1 18.935/6/0.004
Single 314 15.5 278 16.7 33 11.7 3 4.3
Divorced 166 8.2 125 7.5 35 12.4 6 8.7
Widowed 66 3.3 52 3.1 12 4.3 2 2.9
Wealth index (class)
Richest 632 31.2 501 29.9 107 37.9 24 34.8 17.746/8/0.023
Richer 526 25.9 444 26.5 67 23.8 15 21.7
Middle 411 20.3 342 20.4 60 21.3 9 13.0
Poor 292 14.4 251 15.0 31 11.0 10 14.5
Poorest 167 8.2 139 8.3 17 6.0 11 15.9
Occupations
Managers, professionals, and technicians 766 38.2 624 37.7 112 39.9 30 43.5 7.314/6/0.293
Clerical support, service, and sales workers 617 30.8 521 31.5 76 27.0 20 29.0
Skilled manual workers 418 20.8 352 21.3 55 19.6 11 15.9
Elementary occupations 205 10.2 159 9.6 38 13.5 8 11.6
Job physical effort
Heavy 103 5.3 76 4.7 18 6.4 9 13.0 10.319/4/0.035
Moderate 884 45.3 729 45.5 124 44.0 31 44.9
None/light 965 49.4 796 49.7 140 49.6 29 42.0
Self-rated health
Very good/good 1568 80.2 1348 84.1 189 67.0 31 44.9 141.699/4/0.000
Fair 325 16.6 227 14.2 74 26.2 24 34.8
Bad/very bad 61 3.1 28 1.7 19 6.7 14 20.3
* Statistical significance was set at p < 0.05.
Table 4. Prevalence of sick leave according to lifestyle factors, male.
Table 4. Prevalence of sick leave according to lifestyle factors, male.
Variables Total Sick leave
N % No Short-term Long-term Pearson Chi-Square/df/p *
N % N % N %
Body mass index
Underweight 17 0.8 16 0.9 1 0.4 0 0.0 1.375/6/0.967
Normal weight 579 28.0 496 27.8 68 28.8 15 31.3
Pre-obese 972 47.1 841 47.2 110 46.6 21 43.8
Obese 497 24.1 428 24.0 57 24.2 12 25.0
Fruit consumption
At least once a day 867 37.2 766 37.6 81 33.3 20 37.7 9.432/6/0.151
4 to 6 times a week 178 30.1 158 29.3 15 38.3 13 24.5
1 to 3 times a week 703 25.1 597 25.3 93 22.2 15 28.3
Never/occasionally 585 7.6 516 7.8 54 6.2 5 9.4
Vegetable consumption
At least once a day 1155 47.6 999 47.5 123 45.2 33 58.9 13.805/6/0.032
4 to 6 times a week 736 30.3 649 30.9 71 26.1 16 28.6
1 to 3 times a week 462 19.0 393 18.7 64 23.5 5 8.9
Never/occasionally 76 3.1 60 2.9 14 5.1 2 3.6
Leisure-time aerobic physical activity
High 216 8.9 191 9.1 23 8.8 2 3.8 6.501/4/0.165
Moderate 428 17.7 374 17.8 50 19.2 4 7.7
Low 1776 73.4 1542 73.2 188 72.0 46 88.5
Smoking status
Never smoker 893 48.2 788 49.2 91 43.8 14 33.3 12.797/4/0.012
Former smoker 236 12.7 194 12.1 30 14.4 12 28.6
Current smoker 723 39.0 620 38.7 87 41.8 16 38.1
Alcohol consumption
Lifetime abstainers 401 24.0 366 25.2 29 15.8 6 16.2 22.491/8/0.004
Former drinking 153 9.1 131 9.0 19 10.3 3 8.1
Non-weekly drinking 680 40.6 599 41.3 66 35.9 15 40.5
Low-risk drinking 367 21.9 295 20.3 60 32.6 12 32.4
Risky drinking 72 4.3 61 4.2 10 5.4 1 2.7
* Statistical significance was set at p < 0.05.
Table 5. Prevalence of sick leave according to lifestyle factors, female.
Table 5. Prevalence of sick leave according to lifestyle factors, female.
Variables Total Sick leave
N % No Short-term Long-term Pearson Chi-Square/df/p *
N % N % N %
Body mass index
Underweight 45 2.7 39 2.9 5 2.1 1 1.6 17.300/6/0.008
Normal weight 938 56.5 784 57.6 130 55.3 24 38.7
Pre-obese 464 28.0 381 28.0 62 26.4 21 33.9
Obese 212 12.8 158 11.6 38 16.2 16 25.8
Fruit consumption
At least once a day 859 44.0 689 43.0 131 46.5 39 56.5 14.154/6/0.028
4 to 6 times a week 987 50.6 832 52.0 130 46.1 25 36.2
1 to 3 times a week 90 4.6 69 4.3 18 6.4 3 4.3
Never/occasionally 16 0.8 11 0.7 3 1.1 2 2.9
Vegetable consumption
At least once a day 1093 56.0 869 54.3 171 60.6 53 76.8 18.851/6/0.004
4 to 6 times a week 579 29.7 496 31.0 71 25.2 12 17.4
1 to 3 times a week 240 12.3 204 12.8 32 11.3 4 5.8
Never/occasionally 39 2.0 31 1.9 8 2.8 0 0.0
Leisure-time aerobic physical activity
High 166 8.6 144 9.0 19 7.2 3 4.3 10.452/4/0.033
Moderate 225 11.1 191 11.9 17 6.4 7 10.1
Low 1553 80.3 1265 79.1 229 86.4 59 85.5
Smoking status
Never smoker 812 53.9 689 56.0 102 46.2 21 37.5 18.746/4/0.001
Former smoker 152 10.1 116 9.4 32 14.5 4 7.1
Current smoker 543 36.0 425 34.6 87 39.4 31 55.4
Alcohol consumption
Lifetime abstainers 598 46.0 500 46.8 79 42.2 19 41.3 11.451/8/0.177
Former drinking 113 8.7 93 8.7 12 6.4 8 17.4
Non-weekly drinking 509 39.1 412 38.6 82 43.9 15 32.6
Low-risk drinking 75 5.8 59 5.5 13 7.0 3 6.5
Risky drinking 6 0.5 4 0.4 1 0.5 1 2.2
* Statistical significance was set at p < 0.05.
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