Journal List > Ann Occup Environ Med > v.26(1) > 1124843

Lee, Park, Min, Min, Hwang, Leem, Kim, Jeon, Heo, and Moon: The relationship between working condition factors and well-being

Abstract

Objectives

Working conditions can exert influence on the physical, mental, and even social health of workers. Well-being is an appropriate index for the evaluation of a person's overall health. This paper investigated the association between various working conditions and worker's well-being.

Methods

Data from 10,019 interviews were collected from the second wave of the Korean Working Conditions Survey (2010) conducted by the Korea Occupational Safety and Health Agency between June and October 2010. The data from 5,995 employed workers were examined in this study. Well-being was measured through the WHO Five Well-Being Index (1998 version). Sociodemographic and working conditions were analyzed. Adjusted odds ratios for well-being were calculated with adjusted sociodemographic factors, working condition factors, or both.

Results

Workers' well-being was significantly higher when they were satisfied with their working conditions (OR = 1.656, 95% CI = 1.454-1.885), when their actual working hours were the same as their anticipated working hours (OR = 1.366, 95% CI: 1.120-1.666) or exceeding less than 10 hours (OR = 1.245, 95% CI: 1.004-1.543), and when their employment was stable (OR = 1.269, 95% CI: 1.098-1.467).

Conclusions

This study supports the association between working condition factors and well-being in workers.

Introduction

Working conditions have significant influence on a worker's quality of life. Workers spend more than a half of their waking hours on working [1]. Therefore, naturally, working conditions may exert influence on other areas of life, including eating, sleeping, housing, and developing interpersonal relationships, directly or indirectly. Moreover, Working conditions affect workers' physical and mental health [2]. Due to industrial development, working conditions have become more diverse and complicated. Therefore, it is essential to closely measure and evaluate the influence of each working condition on workers' health as society continues to evolve and change.
As industry has developed and the problems presented by poverty and illness have been resolved, society's priority has transitioned from “survival” to “better life”. “The measurement for well-being (such as WHO well-being index)” is an appropriate measurement of a subject's physical, mental, and social health because it encompasses the absence of both illness and negative emotions [3]. Well-being so closely related to mental health that subjects with a low WHO Five well-being index score are often recommended for depression screening tests [4]. Well-being has been linked with physical health by several studies that demonstrate that satisfied individuals have stronger immune systems and enjoy better physical health [5]-[7]. Additionally, there seems to be a positive feedback loop between a worker's well-being and productivity. Individuals with high well-being are more productive in occupational activities than are individuals with low well-being, and productive individuals are happier than non-productive individuals [8].
Many previous studies on the influence of working conditions on health have focused on the occurrence of specific diseases like musculoskeletal illness [9],[10], occupational cancers [11], and sleep disorders [12],[13] in risky working conditions, or the influence of job stresses, job satisfaction on life habits like smoking and drinking [14],[15]. Although a few studies examined the influence of psychological occupational factors on well-being in specific occupations like teaching [16], there is a lack of research on the influence of overall working conditions on well-being. The present study examines how the working conditions are related to employed workers' well-being. Data were gathered from the second wave of the Korean Working Conditions Survey.

Materials and methods

Working conditions survey

This study used a sample from the second wave of the Korean Working Conditions Survey (2010) conducted by the Korea Occupational Safety and Health Agency. The methodology and survey questionnaire used for the second Korean Working Conditions Survey is similar to those used in the European Working Conditions Survey, and the second survey built on investigations begun in the first survey (2006).
Briefly, the goal of the survey was to gather comprehensive information on Korean working conditions to shed light the nature and types of changes affecting the workforce and the quality of work-life for employees. The specific objective of the survey was to develop social and occupational health indicators for the working environment. We used multi-stage, random sampling from the Enumeration Districts in the 2005 Population and Housing Census. The survey was carried out at a number of different sampling areas, determined using probability proportional to population size and to population density. The survey data were collected from a nationally representative sample of the economically active population 15 years and older, excluding retirees, unemployed, homemakers, and students [17].
In this study, we defined the subjects as only ‘employed workers', so we exclude ‘a self-employed worker' or ‘an unpaid worker for familial business. With these exclusion criteria, among 10,019 survey interviews conducted at workers' homes between June and October 2010, the data from only 5,995 employed workers were used in this study.
The Institutional Review Board of Inha University Hospital approved the study protocol.

WHO five well-being index

Well-being was evaluated through the WHO Five Well-Being Index (the 1998 version). Although the index was originally designed to measure well-being in diabetic patients [18], its effectiveness has been supported in diagnostic depression screening [4] and evaluation of emotional well-being in patients with chronic diseases including cardiovascular diseases [18] and Parkinson's disease [19], and in young children [20], and elderly adults [21].
The index consists of five positively worded items, each of which reflects the presence or absence of well-being. Subjects respond to questions about their positive feelings within the last two weeks on 6-point scale (0-5). A raw score lower than 13 out of 30 or an individual item score of 0 or 1 on any of the five items implies poor well-being [22], and a raw point score considerably below 13 may necessitate screening for depression with the Major Depression Inventory (under ICD-10) [4]. This study has evaluated the states of well-being of the subjects by classifying subjects with a raw score below 13 or a score of 0 or 1 on one of more items into the “poor well-being” group. Meanwhile, those who responded to all the items with a score of 2 or higher and had a total score higher than 13 were assigned to the “fair well-being” group [22].

Definition of independent variables

The survey collected information on several sociodemographic factors. Age, educational levels and monthly income were classified. Based on survey responses, the degrees of difficulty balancing income and expenses with total monthly earnings were divided into “difficult,” “somewhat difficult,” “somewhat easy,” and “easy.” Overall subjective health status, smoking status and alcohol assumption scale were also remarked. Subjects were grouped into “non-drinkers,” “moderate drinkers,” and “excessive drinkers” (i.e., individuals who consumed more than 2 glasses of alcohol a day).
Information on working condition factors was also collected and organized. Occupations were divided into 9 categories based on the Korean employment classification of occupation. Length of employment was divided into “<1 year,” “1-5 years,” “5-10 years,” “10+ years.” Employment types were divided into “contingent” and “regular” employees, and employment stability (“stable” or “unstable”) was determined by researchers based on the subjects' responses. The presence and the absence of shifts were noted, and total weekly work hours were categorized into “<40 hours,” “40 hours,” ”40-60 hours,” and “60+ hours.” Based on the calculated difference between the actual and desired working time, we had categories of individuals who “lacked work time,” worked “10+ hours longer” than desired, worked “<10 hours longer” than desired or experienced “no difference” between actual and desired work time. Monthly work on the weekends was divided into “4+ days a month,” “1-4 days,” and “none.” Work condition satisfaction was divided into “satisfied” and “unsatisfied.” Degree of work stress was divided into the categories of “mild,” “moderate,” and “severe.”

Statistical analysis

All data were analyzed with the SPSS (version 14.0) after encoding was completed. A descriptive analysis was carried out on sociodemographic and working condition factors. Pearson's chi-squared analysis was carried out between each factor and well-being. Logistic regressions yielded crude and adjusted odds ratios of working condition factors and the well-being. For logistic regression analysis, three models were used for adjusting effect of other factors. The model 1 was adjusted for sociodemographic factors (gender, age, education, monthly income, balancing income and expenses, health status, smoking, and drinking); the model 2 was adjusted for working conditional factors (type of occupation, number of years employed, employment type, employment stability, weekly working time, difference between actual and desired working time, monthly weekend work, satisfaction with working conditions, and work stress); and the model 3 was adjusted for both sociodemographic and working conditional factors. Pearson correlation analysis was used to test for multicollinearity among individual factors. The significance threshold was 0.05.

Results

Sociodemographic factors and well-being

The average raw score of the WHO Five Well-being Index in the 5,995 participants was 13.29 (SD: 5.53); 2,415 (40.3%) were in the poor well-being group, while 3,580 (59.7%) were in the fair well-being group.
Well-being did not significantly differ between genders. Subjects who were under 30 or 40-49 years old had higher well-being scores. Subjects with higher education levels had significantly higher scores of well-being. Subjects with income greater than 3 million KRW had significantly higher well-being than those with lower incomes. Subjects who experienced a balance between their income and expenses (income-expense balance) had significantly higher well-being than those who faced an imbalance between income and expenses. Workers who reported better health status had significantly higher scores of well-being. Smoking and drinking were both related to well-being (Table 1).
Table 1
Sociodemographic factors and well-being
aoem-26-34-i001
      Well-being  
    Total Poor Fair  
    N N % N % p-value*
Total   5,995 2,415 40.3 3,580 59.7  
Gender Male 3,304 1,341 40.6 1,963 59.4 0.595
Female 2,691 1,074 39.9 1,617 60.1
Age <29 901 328 36.4 573 63.6 <0.001
30-39 1,833 790 43.1 1,043 56.9
40-49 1,662 602 36.2 1,060 63.8
50-59 1,018 426 41.8 592 58.2
>60 581 269 46.3 312 53.7
Education Middle school or lower 917 455 49.6 462 50.4 <0.001
High school 2,578 1,057 41.0 1,521 59.0
Junior college 755 325 43.0 430 57.0
College or higher 1,745 578 33.1 1,167 66.9
Monthly income (KRW) <1 million 1,326 572 43.1 754 56.9 <0.001
1≤, <2 million 2,251 939 41.7 1,312 58.3
2≤, <3 million 1,382 547 39.6 835 60.4
≥3 million 1,036 357 34.5 679 65.5
Balancing income and expenses Difficult 1,149 669 58.2 480 41.8 <0.001
Somewhat difficult 2,122 986 46.5 1,136 53.5
Somewhat easy 1,740 525 30.2 1,215 69.8
Easy 984 235 23.9 749 76.1
Health status Bad 255 171 67.1 84 32.9 <0.001
Normal 1,396 831 59.5 565 40.5
Good 3,572 1,253 35.1 2,319 64.9
Very good 772 160 20.7 612 79.3
Smoking Non smoker 3,420 1,328 38.8 2,092 61.2 0.025
Current smoker 1,999 850 42.5 1,149 57.5
Ex-smoker 576 237 41.1 339 58.9
Drinking Non-drinker 1,715 688 40.1 1,027 59.9 <0.001
Moderate drinker 2,574 976 37.9 1,598 62.1
Excessive drinker 1,706 751 44.0 955 56.0
*p-value: Pearson's chi-squared test.

Working condition factors and well-being

A univariate analysis revealed that of the 9 types of jobs, clerks had higher well-being, whereas subjects working in agriculture, forestry and fishing had lower well-being. Workers with 5-10 years' experience showed somewhat higher well-being than workers with other lengths of work experience in univariate analysis, all the other working condition factors, employment types, employment stability, weekly working time, difference between the actual and desired working time, monthly weekend work, working condition satisfaction, work stress, had statistically significant relationship with workers' subjective well being except shift working (Table 2).
Table 2
Working condition factors and well-being
aoem-26-34-i002
      Well-being  
    Total Poor Fair  
    N N % N % p-value*
Type of occupation Unskilled 1,143 531 46.5 612 53.5 <0.001
Semi-skilled 445 195 43.8 250 56.2
Skilled 685 300 43.8 385 56.2
Expertise 492 175 35.6 317 64.4
Sales 727 278 38.2 449 61.8
Service 922 402 43.6 520 56.4
Clerk 1,494 492 32.9 1,002 67.1
Senior management 53 18 34.0 35 66.0
Agriculture, forestry and fishing 34 24 70.6 10 29.4
Years employed <1 yr 1,166 486 41.7 680 58.3 0.023
1≤, <5 yrs 2,847 1,179 41.4 1,668 58.6
5≤, ≤10 yrs 967 350 36.2 617 63.8
>10 yrs 1,015 400 39.4 615 60.6
Employment type Contingent 1,288 601 46.7 687 53.3 <0.001
Regular 4,707 1,814 38.5 2,893 61.5
Employment stability Unstable 1,871 830 44.4 1,041 55.6 <0.001
Stable 4,124 1,585 38.4 2,539 61.6
Shift working Present 605 243 40.2 362 59.8 0.950
Absent 5,390 2,172 40.3 3,218 59.7
Weekly working time <40 hrs 929 388 41.8 541 58.2 <0.001
40 hrs 1,893 648 34.2 1,245 65.8
40<, ≤60 hrs 2,687 1,142 42.5 1,545 57.5
>60 hrs 486 237 48.8 249 51.2
Difference between the actual and desired working time ≥10 hrs longer 774 395 51.0 379 49.0 <0.001
<10 hrs longer 1,128 461 40.9 667 59.1
No difference 3,390 1,240 36.6 2,150 63.4
Lack of working time 703 319 45.4 384 54.6
Monthly weekend work More than 4 days 835 368 44.1 467 55.9 <0.001
1 ~ 4 days 2,611 1,166 44.7 1,445 55.3
none 2,549 881 34.6 1,668 65.4
Working condition satisfaction Dissatisfied 1,795 1,004 55.9 791 44.1 <0.001
Satisfied 4,200 1,411 33.6 2,789 66.4
Work stress Severe 1,855 789 42.5 1,066 57.5 0.018
Moderate 2,472 995 40.3 1,477 59.7
Mild 1,668 631 37.8 1,037 62.2
*p-value: Pearson's chi-squared test.
In the analysis of the 9 types of jobs, only the model 2 showed a significant difference with an OR of 1.251 in office workers (95% CI: 1.042-1.502), and an OR of 0.433 in farmers and fishermen (95% CI: 0.200-0.937), but the difference was not significant in model 3. No relationship was observed between employment types and well-being in model 1, 2 and 3. A significant relationship between employment stability and well-being was present. Well-being was significantly higher when employment was stable, and this pattern remained constant in all the 3 models (model 1: OR = 1.204, 95% CI: 1.063-1.365; model 2: OR = 1.222, 95% CI: 1.065-1.402; model 3: OR = 1.269, 95% CI: 1.098-1.467).
A relationship was found between well-being and the difference between workers' actual and desired working time. Subjects who reported that their actual working hours were as desired or exceeding less than 10 hours showed significantly higher well-being than subjects who worked ≥10 hours longer than desired. The OR was 1.533 (95% CI: 1.294-1.816), 1.297 (95% CI: 1.065-1.580) in model 1, 1.401 (95% CI: 1.159-1.693), 1.305 (95% CI: 1.064-1.602) in model 2, and 1.366 (95% CI: 1.120-1.666), 1.245 (95% CI: 1.004-1.543) in model 3. Additionally, subjects who did not work on weekends showed higher well-being than those who worked 4+ weekend days a month. These results were constant only in model 1.
Satisfaction or non-satisfaction with working conditions was related to well-being. In all the three models, subjects who were satisfied with their working conditions demonstrated significantly higher well-being. (Model 1: OR = 1.723, 95% CI: 1.519-1.955; Model 2: OR = 2.209, 95% CI: 1.957-2.492; Model 3: OR = 1.656, 95% CI: 1.454-1.885).
After adjusted for sociodemographic or working conditional factor, unremarkable relationship was found between degree of job stress and well-being (Table 3).
Table 3
Working condition factors and well being (Logistic regression analysis)
aoem-26-34-i003
    Crude OR Model 1 Model 2 Model 3
    OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Type of occupation Unskilled 1.000 - 1.000 - 1.000 - 1.000 -
Semi-skilled 1.112 0.892-1.387 1.183 0.924-1.515 1.194 0.946 -1.506 1.198 0.932 -1.540
Skilled 1.113 0.920-1.347 1.032 0.817-1.303 1.046 0.851 -1.286 0.996 0.785 -1.264
Expertise 1.572 1.264-1.955 0.974 0.731-1.298 1.192 0.942 -1.507 0.927 0.691 -1.243
Sales 1.401 1.160-1.694 1.008 0.804-1.262 1.206 0.982 -1.481 0.980 0.777 -1.236
Service 1.122 0.943-1.336 0.910 0.744-1.114 1.067 0.885 -1.286 0.933 0.758 -1.148
Clerk 1.767 1.508-2.071 1.203 0.963-1.503 1.251 1.042 -1.502 1.043 0.828 -1.314
Senior management 1.687 0.944-3.014 1.086 0.571-2.068 1.167 0.640 -2.128 0.956 0.499 -1.834
Agriculture, forestry and fishing 0.362 0.171-0.763 0.430 0.195-0.947 0.433 0.200 -0.937 0.468 0.209 -1.048
Years employed <1 yr 1.000 - 1.000 - 1.000 - 1.000 -
1≤, <5 yrs 1.011 0.881-1.161 0.991 0.849-1.158 0.931 0.802 -1.081 0.970 0.827 -1.138
5≤, ≤10 yrs 1.260 1.057-1.501 1.271 1.038-1.557 1.079 0.892 -1.307 1.180 0.957 -1.454
>10 yrs 1.099 0.926-1.304 1.083 0.871-1.347 0.862 0.711 -1.046 0.972 0.775 -1.220
Employment type Contingent 1.000 - 1.000 - 1.000 - 1.000 -
Regular 1.395 1.232-1.580 1.098 0.949-1.271 1.112 0.950 -1.303 0.982 0.828 -1.164
Employment stability Unstable 1.000 - 1.000 - 1.000 - 1.000 -
Stable 1.277 1.143-1.427 1.204 1.063-1.365 1.222 1.065 -1.402 1.269 1.098 -1.467
Weekly working time <40 hrs 1.000 - 1.000 - 1.000 - 1.000 -
40 hrs 1.378 1.173-1.619 1.067 0.871-1.306 0.975 0.795 -1.195 0.971 0.773 -1.219
40<, ≤60 hrs 0.970 0.834-1.129 0.861 0.709-1.046 0.922 0.749 -1.134 0.962 0.760 -1.218
>60 hrs 0.754 0.604-0.939 0.773 0.596-1.002 0.962 0.723 -1.279 1.040 0.758 -1.427
Difference between the actual and desired working time ≥10 hrs longer 1.000 - 1.000 - 1.000 - 1.000 -
<10 hrs longer 1.508 1.254-1.813 1.297 1.065-1.580 1.305 1.064 -1.602 1.245 1.004 -1.543
No difference 1.807 1.544-2.115 1.533 1.294-1.816 1.401 1.159 -1.693 1.366 1.120 -1.666
Lack of working time 1.255 1.022-1.540 1.444 1.148-1.817 1.142 0.879 -1.483 1.283 0.975 -1.688
Monthly weekend work 1-4 days 1.000 - 1.000 - 1.000 - 1.000 -
None 0.977 0.835-1.142 0.953 0.804-1.130 0.858 0.726 -1.013 0.887 0.744 -1.058
>4 days 1.492 1.272-1.749 1.328 1.115-1.583 1.118 0.928 -1.347 1.135 0.932 -1.383
Satisfaction with working conditions Dissatisfied 1.000 - 1.000 - 1.000 - 1.000 -
Satisfied 2.509 2.241-2.809 1.723 1.519-1.955 2.209 1.957 -2.492 1.656 1.454 -1.885
Work stress Severe 1.000 - 1.000 - 1.000 - 1.000 -
Moderate 1.099 0.972-1.242 1.051 0.921-1.199 1.005 0.885 -1.142 0.990 0.865 -1.133
Mild 1.216 1.063-1.392 1.144 0.987-1.327 1.112 0.963 -1.284 1.031 0.885 -1.201
Model 1: Adjusted for sociodemographic factors.
Model 2: Adjusted for working conditional factors.
Model 3: Adjusted for both sociodemographic and working conditional factors.

Discussion

We conducted this study to evaluate the association between general working condition factors and employed workers' well-being in a nationwide representative sample of Korea. After we adjusted sociodemographic and working condition factors, workers' well-being was significantly higher when they were satisfied with their working conditions (OR = 1.656, 95% CI: 1.454-1.885), when their actual working hours were the same as their desired working hours (OR = 1.366, 95% CI: 1.120-1.666) or exceeding less than 10 hours (OR = 1.245, 95% CI: 1.004-1.543), and when their employment was stable (OR = 1.269, 95% CI: 1.098-1.467).
This current study is the first one that studied the relationship between general working conditional factors and workers' well-being. Therefore, few comparable outcomes exist. Previous findings [23] merely compared a few working conditions such as employees to the unemployed, and professionals to inexperienced workers. Previous studies have not addressed the relationship between other working conditional factors and well-being.
The sociodemographic factors were drawn from a nationwide survey on working conditions and included gender, age, educational level, monthly income, income-expense balance, health status, smoking, and drinking. The workers' well-being has no differences between the genders. This finding is consistent with a previous study that gender accounts little for the variance in individual differences in well-being [24]. Older subjects tended to have lower well-being. This result contradicts the finding that older individuals are happier with their lives than younger individuals [25]. However, another study [26] proposed that older individuals tended to have lower well-being than younger individuals because the former experience more health problems and less satisfaction with the future than the latter. That study and the current one both found a positive correlation between education levels and well-being. Another research [27] has suggested a correlation between income and well-being at lower income levels but not at higher income levels. In the current study, the subjects with a monthly income of 3 million or more KRW had significantly higher well-being than subjects with a lower income. However, income-expense balance had a stronger correlation with well-being than absolute monthly income did. Subjects who reported it was either somewhat difficult or easy to balance their income and expenses experienced higher well-being than those who reported it was difficult.
Subjects with normal, good, or very good self-reported health status had higher well-being than those with poor self-reported health status. This finding accords with the results of a previous study [23]. Interestingly, only 264 subjects (4.2%) had bad self-reported health status, whereas 4,571 (72.6%) reported their health status was “good” and “very good.” We can hypothesize that there is a “healthy worker effect” that may occur in surveys that are distributed only to workers who are currently employed.
Current smokers had somewhat lower scores of well-being than non-smokers, respectively. Moderate drinkers showed higher well-being status than non-drinkers. Excessive drinkers showed the lowest well-being status. This result is consistent with previous findings [28]-[30] that suggest that smoking and excessive drinking are detrimental to physical as well as mental health, as measured by life satisfaction and well-being.
Fifth EWCS (2010) evaluated mental well-being of the workers by using WHO-Five well-being index. According to the fifth EWCS Overview report (2012), the result of the index regarded as a good predictor of mortality and functional ability. 20% (male: 18%, female: 22%) of the workers of the EU 27 countries were classified into 'mental health at risk'. Percentage of the elder workers (above 50 year-old) was 7 percent point more than the younger's (below 35 year-old). In this study, the percentage of low mental well-being group in Korean employed workers was 40.3%. Comparing between this study and fifth EWCS Overview report, well-being of the Korean employed workers were lower than average of the 27 EU counties. The relations between ages and well-being or job and well-being were similar. There are little information about the relation between well-being and working conditional factors in EWCS report [31].
This study, however, has several limitations. First, although it demonstrates a relationship between working condition factors and well-being, we cannot make conclusions regarding causality. Second, we did not take into account the “healthy worker effect” during our analysis, which could influence our results. Third, we did not examine variations in individual personality traits that could influence workers' well-being. Although an existing study explored the hypothesis that individuals' positive personalities are closely related to their well-being [32], we were not able to investigate personality traits because the working conditions survey did not contain the necessary items. Fourth, we didn't assessed the relationship between well-being and physically or chemically hazardous working conditional factors which could be exist on specific job such as noise, heavy metals, organic chemicals. However, some of these factors could be included in type of occupation.

Conclusions

This is the first study that analyzed association between general working conditions and workers' well-being. We expect further research to figure out causal relationships between general or specific working conditions and workers' well-being.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

BJ: The first author of this article. He designed the study, interpreted the data, and drafted the manuscript. SG: The corresponding author of this article. He suggested the study design, interpreted the data, and revised the manuscript. KB: He consulted the study method and revised the manuscript. JY: she revised the manuscript. SHH: she translated the manuscript. JH: He suggested the study design and revised the manuscript. HC: He suggested the study design and revised the manuscript. SHJ: He interpreted the data and revised the manuscript. YS: He interpreted the data and revised the manuscript. SHM: She drafted the manuscript. All authors have approved the final version of the manuscript.

Acknowledgements

This work was supported by Korea occupational safety and health agency.

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