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  • Research article
  • Open Access

Cardiovascular disease risk differences between bus company employees and general workers according to the Korean National Health Insurance Data

Annals of Occupational and Environmental Medicine201830:32

https://doi.org/10.1186/s40557-018-0242-z

  • Received: 20 February 2018
  • Accepted: 23 April 2018
  • Published:

Abstract

Background

Bus drivers are known to be highly at risk of cardiovascular diseases. In this study, we assessed the cardiovascular disease prevalence of bus company employees in Seoul, South Korea, and compared the results to those of general workers.

Methods

We analyzed the 2014 Korean National Health Insurance (NHI) data and defined hypertension, diabetes, dyslipidemia, ischemic heart disease, and cerebrovascular disease based on the KCD-6 medical diagnoses. We used bus company employees as surrogate participants of bus drivers due to the characteristics of Korean NHI data. We identified bus company employees in Seoul based on one’s workplace which the insurance is registered. The prevalence of five diseases was compared between the bus company employees and general workers. We also calculated the odds ratios (OR) of five diseases between the bus company employees and general workers. To compensate the vast demographical differences between the two groups, we performed propensity score matching.

Results

Bus company employees have higher OR for having hypertension (OR 1.33, 95% CI: 1.28–1.39), diabetes mellitus (1.14, 95% CI: 1.08–1.22), and dyslipidemia (1.23, 95% CI: 1.17–1.29) than the general workers or propensity score matched controls. However, the OR of having ischemic heart disease were not significant. The OR of cerebrovascular disease were lower in bus company employees than in the general workers after adjusting the covariates, but similar in the propensity score matched model.

Conclusion

This study showed that the ORs of cardiovascular disease risk factors are high in bus company employees when compared to the general working population. Further studies with the longitudinal design should be conducted to confirm the causal association.

Keywords

  • Bus company employees
  • Cardiovascular disease
  • Commercial driver
  • National health insurance

Background

Cardiovascular diseases and related complications are the leading causes of death worldwide and are projected to gradually increase in the near future [1]. Occupation is one of the established risk factors of cardiovascular diseases and death [2]. Confirmed work-related cardiovascular disease cases may be associated with worker’s compensation [3]. In 2014, 355 workers were compensated for a work-related disease due to cardiovascular and neurovascular diseases in Korea [4].

Commercial drivers are known to carry diverse health problems, such as cardiovascular diseases. Their health problems may be more prevalent and severe than those in other types of occupation [5]. Long working hours, shift work, cabin ergonomic factors, loud noise, carbon monoxide, chemical materials, social isolation, and lack of decision-making by the authority are occupational health risk factors of commercial driving [5, 6]. Work-related diseases of commercial drivers have been studied in various aspects. The incidence of various diseases like bladder cancer, musculoskeletal diseases, depressive disorder, post-traumatic stress disorders, and cardiovascular diseases is higher in commercial drivers compared to other occupations [710]. Important risk factors of cardiovascular events, such as hypertension, diabetes mellitus, and obesity, are also more prevalent in commercial drivers [1115].

Drivers who carry passengers are highly at risk of stroke than drivers carrying goods [16]. Bus drivers’ cardiovascular health is closely related to public safety and is of high concern because the bus carries relatively large numbers of passenger at once. Being an exception of the labor standard act that prevents workers from working over 12 h a day in Korea, investigations on bus drivers in South Korea showed long working hours, which is a considerable risk factor of cardiovascular diseases [17].

However, studies on the actual health status of a relatively large group of commercial bus drivers were limited. We planned to determine cardiovascular disease prevalence and their risks on bus drivers and compared the results to that of general workers using Korean National Health Insurance data. We analyzed bus company employees’ data as surrogate participants of bus drivers because the Korean National Health Insurance data contain that of worker’s company only and we cannot determine one’s actual job characteristics in the company. This procedure can be admitted as a majority (over 90%) of bus company employees is bus drivers [18]. If we can find significant differences in the disease prevalence between bus company employees and general workers, this study may help prevent bus drivers from suffering work-related cardiovascular disease.

Methods

Data source

We used Korean National Health Insurance (NHI) data for this study. The National Health Insurance is mandatory for all residents in Korea and covers 100% of the Korean population [19, 20]. All members are categorized into three groups: employees or employees’ dependent family group, self-employed and his or her dependent family group, and medical aid beneficiary group [19]. The National Health Insurance System now offers four main databases: qualification and contribution, health insurance claim, health check-up, and long-term care insurance data [20, 21]. We used the 2014 data that was the latest one provided at the time of the study. We merged and analyzed the first three database using the data of the employee group only.

Study participants

We defined that any person who registered the National Health Insurance under the city bus companies in Seoul, Korea, since 2014 is a bus company employee. The total number of target city bus companies in Seoul was 65. We defined the general working group as everybody who were registered in the NHI under any company at the same time. We excluded those who did not receive biennial general health check-up in 2014. A total of 15,719 bus company employees and 8,033,907 general workers were enrolled.

Disease definition

We regarded that one has specific disease if the NHI claim outpatient records and hospital admissions contain any of the specified KCD-6 codes in its main and secondary diagnosis field one or more times in 2014. We listed the codes of hypertension, diabetes mellitus, dyslipidemia, ischemic heart disease, cerebrovascular disease in the Table 1.
Table 1

KCD-6 codes used in disease definition

Disease

Diagnosis

KCD-6 Code

Hypertension

primary (essential) hypertension

I10

hypertensive heart disease

I11

hypertensive renal disease

I12

hypertensive heart and renal disease

I13

Diabetes mellitus

non-insulin-dependent diabetes mellitus

E11

Dyslipidemia

disorders of lipoprotein metabolism and other lipidemias

E78

Ischemic heart disease

angina pectoris

I20

acute myocardial infarction

I21

subsequent myocardial infarction

I22

certain current complications following acute myocardial infarction

I23

other acute ischemic heart diseases

I24

chronic ischemic heart disease

I25

Cerebrovascular disease

subarachnoid hemorrhage

I60

intracerebral hemorrhage

I61

other non-traumatic intracranial hemorrhage

I62

cerebral infarction

I63

stroke, not specified as hemorrhage or infarction

I64

occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction

I65

occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction

I66

other cerebrovascular diseases

I67

cerebrovascular disorders in diseases classified elsewhere

I68

sequelae of cerebrovascular disease

I69

Cardiovascular risk factor definition

We extracted data from the general health check-up database to define cardiovascular risk factors, such as obesity, lack of exercise, smoking, and heavy drinking. Obesity is defined as having a body mass index (BMI, weight (kg)/height (m)2) of > 25. Lack of exercise is defined as performing moderate to high-intensity exercises in < 3 times a week. We regarded a participant as a smoker if he or she answered smoking status question as a current smoker. Heavy drinking is defined as a person who reportedly drink > 7 glasses of alcoholic beverage in a week.

Propensity score matching

We used propensity score matching to define a comparison group. The comparison group was from the general working group. The propensity score is defined as the inverse subject probability of receiving a treatment or being in a certain condition. The propensity score is estimated by using a multinomial logistic model on confounding factors between treatment and outcome [22, 23]. In this case, the condition is currently working in a bus company. Then, the propensity score (matched, adjusted, or weighted) was considered to estimate the distribution of effects in treated and untreated subjects [2224].

We performed a logistic regression to estimate the propensity score. We included seven variables, i.e., sex, age group, income level, obesity, smoking, heavy drinking, and lack of exercise in the score model. For every person in the bus company employee group, three persons with the most similar propensity score were selected from the general working group. We used greedy matching algorithm with eight digits. We chose one to three matching because one to four matching yielded statistically significant different characteristics in some variables between two groups while one to three matching showed no significantly different characteristic.

Data analysis

We calculated the prevalence of each disease in the bus company employee’s and general working group. The odds ratios (ORs) of five diseases were calculated for both the bus company employee and general working group. The logistic regression analysis was performed using the following two models: a crude model and an adjusted model which used age groups, sex, income level quartile, and cardiovascular risk factors such as obesity, lack of exercise, smoking status, and heavy drinking as covariates. We calculated the ORs of five diseases for bus company employee group and propensity score matched group using chi-squared tests. SAS 9.4 and Microsoft Excel 2016 were used for all statistical analyses. The significance level was set to p-value of < 0.05.

Results

Table 2 shows the general characteristics of bus company employees and general workers. About 96.3 and 64% of the bus company employee and general working groups were males. The mean age of bus company employees and general workers was 50.3 and 42.1 with the standard deviation of 7.30 and 11.50, respectively. About 83.2% of the bus company employees were in the third quartile based on the income level. Obesity were more prevalent in the general working group than in the bus company employee group, with 66.3 and 40.8% obese individuals in each group, respectively. About 23.9 and 53.3% of the bus company employee and general working groups were active smokers; 19.8 and 17.0% of bus company employees and general workers were heavy drinkers, respectively; and 37.6 and 61.3% of the bus company employee and general working groups lack adequate exercise, respectively. All seven characteristics were different between two groups with the p-value below 0.0001.
Table 2

General characteristics of bus company employees and general workers

 

Bus company employees

General workers

 

n

%

n

%

p-value

Total

 

15,719

100.0

8,033,907

100.0

 

Sex

Male

15,130

96.3

5,139,814

64.0

< 0.0001

Female

589

3.8

2,894,093

36.0

 

Age

15–29

52

0.3

1,242,608

15.5

< 0.0001

30–39

1088

6.9

2,338,147

29.1

 

40–49

5656

36.0

2,227,686

27.7

 

50–59

7638

48.6

1,668,020

20.8

 

> 60

1285

8.2

557,446

6.9

 

Income level

25%P

181

1.2

1,805,809

22.5

< 0.0001

50%P

640

4.1

1,852,773

23.1

 

75%P

13,072

83.2

2,135,139

26.6

 

100%P

1826

11.6

2,240,186

27.9

 

Obesitya

yes

6410

40.8

2,708,099

33.7

< 0.0001

no

9309

59.2

5,325,808

66.3

 

Smoking

yes

3756

23.9

4,282,941

53.3

< 0.0001

no

11,963

76.1

3,750,966

46.7

 

Heavy drinkingb

yes

3104

19.8

1,362,534

17.0

< 0.0001

no

12,615

80.3

6,671,373

83.0

 

Lack of exercisec

yes

5904

37.6

4,923,386

61.3

< 0.0001

no

9815

62.4

3,110,521

38.7

 

aObesity is defined as having a body mass index of above 25 kg/m2

bHeavy drinking is defined as a person who reportedly drink > 7 glasses of alcoholic beverage in a week

cLack of exercise is defined as performing moderate to high-intensity exercises in < 3 times a week

Table 3 shows the general characteristics of bus company employees and propensity score matched controls. A total of 47,250 controls were selected from the general working groups. The characteristics between the two groups were similar, as their characteristics were matched as close as possible.
Table 3

General characteristics of bus company employees and propensity score matched controls

 

Bus company employees

Matched Controls(1:3)

 

n

%

n

%

p-value

Total

 

15,719

100.0

47,257

100.0

 

Sex

Male

15,130

96.3

45,402

96.3

0.8841

Female

589

3.8

1755

3.7

 

Age

15–29

52

0.3

152

0.3

0.9999

30–39

1088

6.9

3268

6.9

 

40–49

5656

36.0

16,968

36.0

 

50–59

7638

48.6

22,914

48.6

 

> 60

1285

8.2

3855

8.2

 

Income level

25%P

181

1.2

573

1.2

0.9099

50%P

640

4.1

1888

4.0

 

75%P

13,072

83.2

39,230

83.2

 

100%P

1826

11.6

5466

11.6

 

Obesitya

yes

6410

40.8

19,254

40.8

0.9179

no

9309

59.2

27,903

59.2

 

Smoking

yes

3756

23.9

11,234

23.8

0.8542

no

11,963

76.1

35,923

76.2

 

Heavy drinkingb

yes

3104

19.8

9346

19.8

0.8533

no

12,615

80.3

37,811

80.2

 

Lack of exercisec

yes

5904

37.6

17,742

37.6

0.8941

no

9815

62.4

29,415

62.4

 

aObesity is defined as having a body mass index of > 25 kg/m2

bHeavy drinking is defined as a person who reportedly drink > 7 glasses of alcoholic beverage in a week

cLack of exercise is defined as performing moderate to high-intensity exercises in < 3 times a week

Table 4 presents five cardiovascular diseases’ prevalence between the bus company employees and general workers and propensity score matched controls. The prevalence of all five diseases was higher in the bus company employee group than that in the general working group. The prevalence of hypertension, diabetes, and dyslipidemia was higher in the bus company employee group than in the matched control group. However, the prevalence of cerebrovascular disease was lower in the bus company employee group than that in the matched control group. The prevalence of ischemic heart disease was insignificantly different between the two groups (p-value 0.7529).
Table 4

Cardiovascular disease prevalence of bus company employees and that of general workers and matched controls

 

Bus company employees

General workers

Matched Controls

(n = 15,719)

(n = 8,033,907)

(n = 47,257)

 

n

%

n

%

p-value

n

%

p-value

Hypertension

4340

27.6

999,718

12.4

< 0.0001

10,490

22.2

< 0.0001

Diabetes

1548

9.8

370,171

4.6

< 0.0001

4109

8.7

< 0.0001

Dyslipidemia

2747

17.5

790,913

9.8

< 0.0001

6927

14.7

< 0.0001

Ischemic Heart Disease

502

3.2

134,811

1.7

< 0.0001

1482

3.1

0.75

Cerebrovascular Disease

229

1.5

87,613

1.1

< 0.0001

808

1.7

0.03

We presented ORs for cardiovascular diseases of bus company employees compared to those of general workers in Table 5. In the crude model, ORs for five diseases were significantly higher in the bus company employee group. When we made the adjustment in the model with age groups, sex, income level quartiles, lack of exercise, smoking status, heavy drinking and obesity, ORs of hypertension (1.34, 95% CI: 1.29–1.40), diabetes (1.16, 95% CI: 1.10–1.22), dyslipidemia (1.18, 95% CI: 1.13–1.23) remained significantly higher. The OR of ischemic heart disease (1.00, 95% CI: 0.91–1.09) became non-significant, and the OR of cerebrovascular disease (0.81, 95% CI: 0.71–0.93) changed to be significantly lower. Finally, ORs for cardiovascular diseases of bus company employees compared to those of the propensity score matched controls were calculated and presented in Table 6. Bus company employees show significantly higher ORs for hypertension (1.33, 95% CI: 1.28–1.39), diabetes (1.14, 95% CI: 1.08–1.22), dyslipidemia (1.23, 95% CI: 1.17–1.29) compared to propensity score matched control group. The ORs for ischemic heart disease and cerebrovascular disease in the propensity score matched model were not significantly higher.
Table 5

Odds ratios for cardiovascular diseases of bus company employees compared to these of general workers

 

Odds ratio (95%CI)

Crude model

Adjusted modela

Hypertension

2.68

(2.59–2.78)

1.34

(1.29–1.40)

Diabetes

2.26

(2.15–2.39)

1.16

(1.10–1.22)

Dyslipidemia

1.94

(1.86–2.02)

1.18

(1.13–1.23)

Ischemic Heart Disease

1.93

(1.77–2.11)

1.00

(0.91–1.09)

Cerebrovascular Disease

1.34

(1.18–1.53)

0.81

(0.71–0.93)

aAdjusted for age groups, sex, income level quartiles, lack of exercise, smoking status, heavy drinking and obesity

Table 6

Odds ratios for cardiovascular diseases of bus company employees compared to those of propensity score matched controls

 

Odds ratio (95%CI)

Hypertension

1.33

(1.28–1.39)

Diabetes

1.14

(1.08–1.22)

Dyslipidemia

1.23

(1.17–1.29)

Ischemic Heart Disease

1.02

(0.92–1.13)

Cerebrovascular Disease

0.85

(0.73–0.98)

Discussion

We found that bus company employees have higher odds of having hypertension, diabetes mellitus, and dyslipidemia when compared to general workers or propensity score matched controls. These results were consistent with the previous studies as bus drivers, who are the majority of bus company employees, have occupational risk factors of cardiovascular diseases [6, 9, 10]. The odds of having ischemic heart disease showed no statistically significant difference between two groups in the adjusted model and the propensity score matched model. The odds of bus company employees having cerebrovascular disease were lower than the general workers after adjusting the covariates, which was consistent with our results when we used the propensity score matched controls.

The mechanisms of these associations could be explained as follows. Stress in commercial bus driving may cause negative neurophysiological effects as driving can be conceptualized as a kind of threat avoidance task [25, 26]. Driving can actually promote cardiovascular risk markers. One’s blood pressure and pulse rate were elevated during driving [25]. We might assume that bus drivers may execute worse health behaviors. This assumption, however, was not supported in this study population as self-reported health behaviors like smoking, heavy drinking, and lack of exercise were more prevalent in the general population than bus drivers. The other reason we can consider is the fact that the average age was significantly higher in the bus driver group than that in the general population. Therefore, we used statistical adjustment of age groups and propensity score matching to compensate the vast effects of age on the target diseases’ prevalence.

In Korea, the working hours are legally limited to same or below 40 h per week, which can be extended up to 68 h when there is an agreement between the employee and the employer. In the certain industries, including bus transportation, exceptions exist at this regulation. The working hours can be extended up to any level with an agreement. There is a report that average daily working hours of bus drivers in the Korea are from 11 h to 18 h [17]. It has been studied that long working hours are associated with increased risks of cardiovascular diseases [2730]. We cannot conclude this association in this study because there is no information of working hours. However, it is possible that long working hours in bus drivers could be additional burden to hypertension, diabetes, hyperlipidemia.

The insignificant relationship of ischemic heart diseases and the negative relationship of cerebrovascular diseases are partially due to the relatively severe consequences of the diseases. Those with history of these diseases have more likely worse health conditions and not capable of driving, especially special vehicles like bus. This may cause a kind of healthy worker effect, diminishing the current prevalence of the two disease groups [31]. Previous bus drivers who experienced ischemic heart disease and/or cerebrovascular disease might have not been able to return to work [32].

This study has several limitations. First, the Korean National Health Insurance data only contain the company information which a worker is registered. We could not infer a worker’s actual job characteristics based on the company’s information as workers may have various job characteristics in one company. Although most employees at the bus companies (> 90%) are bus drivers, we could not ensure the absence of bias caused by non-driving employees. Second, we could only use employees of bus companies in Seoul. As the number of employees in bus companies in Korea is estimated to be > 140,000 in 2015, 15,719 employees in Seoul may not sufficiently represent the whole industry [33]. Third, this is a cross-sectional designed study. Therefore, causal relationships between occupational risk factors among bus company employees and cardiovascular risks such as hypertension, diabetes, and dyslipidemia could not be inferred. We cannot conclude in the same context that the retirement of the bus company employees with previous ischemic heart disease and cerebrovascular disease caused the discordance of the results among the five diseases. A longitudinal study will be needed to overcome these limitations and to conclude causal relationships.

Despite these limitations, the present study has a few strong points. First, we could use the data of the whole working population of Korea. Although we inevitably excluded workers without general health check-up data, the number of the study participants were enough to represent the actual population. This fact enabled us to directly compare the actual disease prevalence between the two groups. Second, we utilized the propensity score matching technique to compensate the vast difference in base characteristics between bus company employee and general workers. The adjusted logistic regression model and the propensity score matched logistic regression model showed almost the same results. These two-way approaches support the reliability of results.

This study revealed that more intensive cardiovascular disease prevention measures for bus drivers and other bus company employees should be implemented to reduce the future risk of cardiovascular diseases. Some of the modifiable work-related risk factors, such as long working hours and cabin ergonomics, could be modulated also. Regular cardiovascular risk factor check-up for every bus company employee may be another effective measure.

To strengthen the evidence of this study’s findings, we suggest that further studies with longitudinal design should be conducted. Moreover, in the actual bus driver cohort, the dynamic occupational cohort that consisted of consecutive annual data of the Korean National Health Insurance can be established and analyzed to evaluate the causal relationship [31, 34].

Conclusions

We evaluated the cardiovascular disease prevalence of bus drivers and compared the results to that of the general working population using the Korean National Health Insurance data. We found that the odds for having hypertension, diabetes, and dyslipidemia were significantly higher in the bus driver group. To determine the clear causal relationship, further studies with longitudinal design are needed.

Abbreviations

BMI: 

Body Mass Index

KCD: 

Korean Classification of Diseases

NHI: 

National Health Insurance

OR: 

Odds ratio

Declarations

Acknowledgements

We thank all members of the Department of Preventive Medicine, Seoul National University. We also thank the Big Data Management Team members of Korean National Health Insurance Service and, of course, all bus drivers in Seoul.

Funding

This study is funded by the Korean Automobile & Transport Worker’s Federation by sponsoring the expenses to retrieve data for analysis from the Korean National Health Insurance database.

Availability of data and materials

The data that support the findings of this study are available in the Korean National Health Insurance System; however, these data were restricted to be used only under the license for this study and so are not publicly available. However, data are available upon reasonable request from the authors and with the permission from the Korean National Health Insurance System.

Authors’ contributions

J-HY, D-WL, and Y-CH designed the study. D-WL acquired the data. J-HY and D-WL analyzed the data. J-HY and D-WL wrote the manuscript. Y-CH, D-WL, M-SK critically revised the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. C-1608-066-784). Korean National Health Insurance Corporation also reviewed our data request before offering its database. The database was encrypted so that personal information was not discernible.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
(2)
Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea

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