Abstract
Work is a major social determinant of health. We conducted a cross-sectional study to explore the association between coronary heart disease (CHD), its risk factors, and the working environment among Japanese male workers. We collected data from 10,572 workers (mean age 49.9 yr) who underwent annual medical check-ups in Toyama, Japan, in 2016. This study included data from health check-ups and questionnaires on medical history of CHD, hypertension, and diabetes, and the use of medication. The working environment included company size and industry categories. Company size was classified into 4 categories according to the number of full-time workers (1–20, 21–100, 101–300, 301–). The industry category was classified into 10 categories. Logistic regression analysis was performed to explore the association. In total, 1.5% of patients had a history of CHD and 31.5% and 11.0% of participants were suffering from hypertension and diabetes, respectively. Compared to workers in a large company, those in a smaller company were more likely to have CHD. Moreover, there was a significant association between CHD’s risk factors and working in the transportation industry. Health providers, including medical doctors, should consider employee working environment as a potential risk factor for CHD.
Keywords: Cardiovascular, Coronary heart disease, Hypertension, Diabetes, Company size, Industry category
Introduction
Coronary heart disease (CHD), which includes myocardial infarction and angina pectoris, is the leading cause of morbidity and mortality in developed countries1). Heart disease, including CHD and heart failure, results in serious illness, disability, and decreased quality of life. In Japan, heart disease is the second leading cause of death, following cancer, accounting for 15.3% of deaths in 20182). Because most cases of heart failure are caused by CHD, disease prevention in the younger generation is crucial. In addition to the traditional CHD risk factors such as hypertension, diabetes, obesity, and unhealthy lifestyles, it is important to identify other risk factors such as psychological and socioeconomic factors.
Occupation provides individuals with the means for living and influences socioeconomic status and health behaviors such as smoking, diet, physical activity, and psychological distress3). To date, inequalities in health attributable to work-related factors have been reported in the public health sector worldwide, particularly in Western countries4,5,6,7). Work-related factors that have been associated with CHD are occupational grade and stressful workplace5, 8). Lower occupational grade, including blue-collar and unskilled manual workers, and workplaces with high demand or low control were associated with the prevalence, incidence, and mortality of CHD3, 5).
However, other work-related factors such as company size and industry categories have not been fully surveyed. Because workers within the same industry are likely to have similar lifestyles and health behaviors9), clarifying the association between these work-related factors and CHD can aid in understanding the other background factors affecting CHD.
The prevalence of CHD and its traditional risk factors such as hypertension and diabetes differ depending on the working environment, including company size and industry categories, because working time and work-related stress may differ according to business stability and social needs in this rapidly changing society with a stagnant economy in Japan. Our previous study showed the age-adjusted mortality rates of Japanese male workers between 1965 and 1995, and found that the mortality rate due to ischemic heart disease was higher in transportation and service workers than in other occupational groups10). We hypothesized that the health differences by working environment or poor health in the transportation industry would persist because of a surge in electronic commerce, such as online shopping and shipping, since 2010. Therefore, we aimed to explore the association between CHD, its risk factors, and the working environment among the general population of male workers in Japan.
Methods
Study design and participants
This was a large-scale cross-sectional study using health check-up data. Under the Industrial Safety and Health Law of Japan, all employers are required to perform medical check-ups of all employees at least once a year. The participants in this study were male workers who underwent an annual health check-up at a hospital in Toyama, Japan, in 2016. The prevalence of CHD in female is low and the actual number of female workers with CHD in this study was only 17 out of 6,000 participants (=0.3%). Due to the small statistical power, we analyzed only male workers. We collected data from 10,572 male workers aged 30–75 yr to assess the prevalence of CHD and its risk factors. Workers younger than 30 yr were also excluded from our study due to the low prevalence of CHD.
This study was approved for academic purposes by the Ethics Committee of University of Toyama (R2019107). The data we received had already been de-identified. Consent was obtained via the opt-out approach.
Measurements
Anthropometric, blood pressure (BP), and blood sampling
Height and weight were measured, and body mass index (BMI) was calculated as weight (kg) divided by the square of height (m). Right brachial BP was measured by well-trained nurses while the participants were seated, after at least 5 min of rest, using an automated sphygmomanometer. All blood samples were collected in a fasting state. Serum total cholesterol, triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL) cholesterol, glucose, and hemoglobin A1c (HbA1c) levels were measured. Fasting conditions were confirmed by inquiries (>10 h without a meal). Hypertension was defined as a systolic pressure ≥140 mmHg, diastolic pressure ≥90 mmHg, or the use of antihypertensive drugs, according to the guidelines in Japan11). Diabetes was defined as a fasting glucose level ≥126 mg/dl, HbA1c (NGSP) ≥6.5%, or the use of antihyperglycemic drugs. Dyslipidemia was defined as TG ≥140 mg/dl, LDL cholesterol level ≥140 mg/dl, HDL cholesterol level <40 mg/dl, or the use of antihyperlipidemic drugs.
Questionnaire on lifestyle and current and past medical history
Smoking habits, diet, physical exercise, and sleep habits were assessed using a self-administered questionnaire, which is the standard questionnaire used in specific health check-ups in Japan12). Dietary questionnaires included, “Do you have an evening meal within 2 h before bedtime 3 d or more per week?” and “Do you eat snacks after the evening meal 3 d or more per week?” Physical exercise was assessed using two inquiries: “Have you been exercising at least 2 d per week, at least 30 min each at an intensity that causes a slight sweat, for at least 1 yr?” and “Do you walk for at least 1 h every day or have equivalent physical activities in your daily life?” The questionnaire for sleep habits included: “Do you feel refreshed after a night’s sleep?” Except for smoking habits (current smoker, past smoker, or never smoker), all the responses were answered in the “yes” or “no” format. Regarding physical exercise, answering “yes” in either inquiry was defined as having an exercise habit. We also inquired about the current and past medical history of CHD (angina pectoris and myocardial infarction), hypertension, diabetes, and dyslipidemia. The answers to the medical history questions were dichotomized as “yes” or “no”.
Working environment
The company size was based on the number of full-time workers. Referring to previous studies and the governmental categorization of small- and medium-sized companies13,14,15), we divided the workers into four categories (1–20, 21–100, 101–300, and 301–).
Company industry was categorized according to the Japan Standard Industry Classification16), which is compatible with the North American Industry Classification System17), based on 20 categories (from A: Agriculture and Forestry, to T: unclassified). We only used 10 categories: manufacturing; agriculture, forestry, and fishing; construction; utilities; transportation; wholesale trade; professional services; health care; other services; and public administration. Other business categories such as mining, information, and finance were excluded because the number of workers was small (<90), due to the low prevalence of CHD in our study.
Statistical analysis
Participant characteristics were collected and the proportion of categorical data and the mean and standard deviation (SD) of BMI and age were calculated (Table 1). We then assessed the distribution of unhealthy lifestyles by work environment (Table 2). The association between a medical history of CHD and working environment was assessed using multivariable logistic regression analysis (Table 3). Adjusted odds ratios (aOR) and 95% confidence interval (95% CI) were calculated. We employed two models: company size and industry category models. Age, BMI, lifestyle factors, and lifestyle-related diseases, such as hypertension, were included as confounders in both models. Hosmer-Lemeshow goodness-of-fit test showed p=0.222 in the company size model and p=0.430 in the industrial category model, meaning good fit in these analyses. After learning that hypertension and diabetes were significantly associated with CHD, we explored the association between these two diseases and the working environment (Tables 4 and 5), with a p-value <0.05 considered to be significant. Statistical analyses were performed using IBM SPSS Statistics Ver. 25 for Windows (Chicago, IL, USA).
Table 1. Basic characteristics of the participants.
| Number n=10,572 |
% without missing |
% no missing |
||
|---|---|---|---|---|
| Company size (number of employees) | ||||
| 1–20 | 1,196 | 11.3 | 15.9 | |
| 21–100 | 3,251 | 30.8 | 43.2 | |
| 101–300 | 1,681 | 15.9 | 22.4 | |
| 301– | 1,393 | 13.2 | 18.5 | |
| Missing | 3,051 | 28.9 | ||
| Industry category | ||||
| Manufacturing | 4,348 | 41.1 | 43.8 | |
| Agriculture, Forestry, and Fishing | 327 | 3.1 | 3.3 | |
| Construction | 2,027 | 19.2 | 20.4 | |
| Utilities | 183 | 1.7 | 1.8 | |
| Transportation | 1,153 | 10.9 | 11.6 | |
| Wholesale trade | 745 | 7.0 | 7.5 | |
| Professional services | 112 | 1.1 | 1.1 | |
| Health care | 505 | 4.8 | 5.1 | |
| Other services (Waste treatment and disposal, electronic and precision equipment repair and maintenance) | 382 | 3.6 | 3.8 | |
| Public administration | 156 | 1.5 | 1.6 | |
| Missing | 634 | 6.0 | ||
| BMI | ||||
| Continuous variable, mean (SD) | 23.8 (3.7) | - | - | |
| Age (30–75 yr) | ||||
| Continuous variable mean (SD) | 49.9 (11.3) | - | - | |
| Smoking habits | ||||
| Current smoker | 4,379 | 41.4 | 43.1 | |
| Past smoker | 3,320 | 31.4 | 32.7 | |
| Never | 2,458 | 23.3 | 24.2 | |
| Missing | 415 | 3.9 | ||
| Eat dinner within 2 h before bedtime | ||||
| 3 times or more / week | 4,010 | 37.9 | 39.5 | |
| Not | 6,141 | 58.1 | 60.5 | |
| Missing | 421 | 4.0 | ||
| Eat snacks after dinner | ||||
| 3 times or more / week | 2,494 | 23.6 | 24.6 | |
| Not | 7,660 | 72.5 | 75.4 | |
| Missing | 418 | 4.0 | ||
| Exercise habits | ||||
| Yes | 4,633 | 43.8 | 45.6 | |
| Not | 5,522 | 52.2 | 54.4 | |
| Missing | 417 | 3.9 | ||
| Sleep habits | ||||
| Enough (feeling refreshed after a night’s sleep) | 6,299 | 59.6 | 62.1 | |
| Not | 3,846 | 36.4 | 37.9 | |
| Missing | 427 | 4.0 | ||
| Hypertension | ||||
| No | 7,011 | 66.3 | 68.5 | |
| Yes | 3,219 | 30.4 | 31.5 | |
| Missing | 342 | 3.2 | ||
| Diabetes | ||||
| No | 8,511 | 80.5 | 89.0 | |
| Yes | 1,053 | 10.0 | 11.0 | |
| Missing | 1,008 | 9.5 | ||
| Dyslipidemia | ||||
| No | 7,001 | 66.2 | 68.0 | |
| Yes | 3,291 | 31.1 | 32.0 | |
| Missing | 280 | 2.6 | ||
| History of coronary heart disease | ||||
| No | 10,005 | 94.6 | 98.5 | |
| Yes | 149 | 1.4 | 1.5 | |
| Missing | 418 | 4.0 | ||
BMI: body mass index; SD: standard deviation.
Table 2. Distribution of unhealthy lifestyles by company size and industry category.
| Smoking | Eat dinner within 2 h of bedtime | Eat snacks after dinner | Exercise habits | Sleep habits | ||
|---|---|---|---|---|---|---|
| Current or past smoker | Yes | Yes | Not | Not enough | ||
| Number of employees | % | % | % | % | % | |
| 1–20 | 81.2 | 36.7 | 22.9 | 55.2 | 33.0 | |
| 21–100 | 76.5 | 40.1 | 25.1 | 55.1 | 38.6 | |
| 101–300 | 72.3 | 45.6 | 27.8 | 56.6 | 48.1 | |
| 301– | 72.1 | 40.2 | 25.3 | 53.8 | 36.0 | |
| χ2 test | p<0.001 | p<0.001 | p<0.05 | p=0.499 | p<0.001 | |
| Industry category | % | % | % | % | % | |
| Manufacturing | 70.8 | 38.9 | 27.6 | 54.5 | 40.1 | |
| Agriculture, forestry, and fishing | 81.8 | 43.8 | 15.3 | 42.5 | 35.5 | |
| Construction | 83.5 | 39.8 | 22.9 | 52.6 | 32.4 | |
| Utilities | 78.1 | 33.0 | 24.2 | 52.2 | 40.1 | |
| Transportation | 82.8 | 43.1 | 20.5 | 63.8 | 39.5 | |
| Wholesale trade | 76.0 | 43.9 | 23.4 | 51.1 | 39.0 | |
| Professional services | 69.7 | 36.6 | 17.0 | 71.4 | 37.3 | |
| Health care | 65.7 | 34.8 | 29.0 | 48.1 | 36.4 | |
| Other services (Waste treatment and disposal, electronic and precision equipment repair and maintenance) | 79.5 | 37.9 | 24.7 | 51.9 | 40.4 | |
| Public administration | 70.1 | 30.8 | 16.4 | 52.2 | 32.8 | |
| χ2 test | p<0.001 | p=0.001 | p<0.001 | p<0.001 | p<0.001 | |
Table 3. Prevalence and factors associated with coronary heart disease.
| Company size model n=6,814 | Industry category model n=8,913 | ||||||
|---|---|---|---|---|---|---|---|
| Number | Prevalence % |
Multivariable aOR (95% CI) |
Number | Prevalence % |
Multivariable aOR (95% CI) |
||
| Number of employees | |||||||
| 1–20 | 1,107 | 1.6 | 1.53 (0.69–3.40) | ||||
| 21–100 | 2,946 | 1.8 | 2.02 (1.01–4.02) | ||||
| 101–300 | 1,511 | 1.5 | 1.74 (0.81–3.76) | ||||
| 301– | 1,250 | 0.8 | 1 | ||||
| Industry category | |||||||
| Manufacturing | 3,854 | 1.2 | 1 | ||||
| Agriculture, forestry, and fishing | 298 | 0.7 | 0.42 (0.10–1.79) | ||||
| Construction | 1,860 | 2.0 | 1.19 (0.76–1.86) | ||||
| Utilities | 178 | 1.1 | 0.73 (0.17–3.11) | ||||
| Transportation | 1,060 | 1.9 | 1.11 (0.65–1.92) | ||||
| Wholesale trade | 675 | 1.9 | 1.24 (0.66–2.35) | ||||
| Professional services | 94 | 3.2 | 1.83 (0.54–6.21) | ||||
| Health care | 434 | 0.9 | 0.64 (0.22–1.80) | ||||
| Other services (Waste treatment and disposal, electronic and precision equipment repair and maintenance) | 335 | 2.7 | 1.14 (0.54–2.40) | ||||
| Public administration | 125 | 2.4 | 0.89 (0.26–2.96) | ||||
| BMI | |||||||
| Continuous variable | 1.08 (1.03–1.15) | 1.09 (1.04–1.14) | |||||
| Age (30–75 yr) | |||||||
| Continuous variable | 1.10 (1.08–1.13) | 1.09 (1.07–1.12) | |||||
| Smoking habits | |||||||
| Current smoker | 2,996 | 0.8 | 1.13 (0.55–2.34) | 3,859 | 0.8 | 1.04 (0.55–1.96) | |
| Past smoker | 2,235 | 3.0 | 2.60 (1.35–4.99) | 3,002 | 3.2 | 2.72 (1.56–4.76) | |
| Never | 1,583 | 0.7 | 1 | 2,052 | 0.7 | 1 | |
| Eat dinner within 2 h before bedtime | |||||||
| 3 times or more / week | 2,745 | 1.3 | 0.91 (0.60–1.39) | 3,480 | 1.3 | 0.84 (0.58–1.22) | |
| Not | 4,069 | 1.6 | 1 | 5,433 | 1.7 | 1 | |
| Eat snacks after dinner | |||||||
| 3 times or more / week | 1,704 | 1.5 | 1.15 (0.72–1.85) | 2,176 | 1.6 | 1.02 (0.67–1.55) | |
| Not | 5,110 | 1.5 | 1 | 6,737 | 1.4 | 1 | |
| Exercise habits | |||||||
| Yes | 3,014 | 1.1 | 1 | 4,033 | 1.4 | 1 | |
| Not | 3,800 | 1.9 | 1.67 (1.09–2.58) | 4,880 | 1.7 | 1.18 (0.83–1.68) | |
| Sleep habits | |||||||
| Enough | 4,133 | 1.3 | 1 | 5,533 | 1.3 | 1 | |
| Not | 2,681 | 1.9 | 1.72 (1.16–2.56) | 3,380 | 2.0 | 1.96 (1.38–2.77) | |
| Hypertension | |||||||
| No | 4,649 | 0.9 | 1 | 5,992 | 0.9 | 1 | |
| Yes | 2,165 | 2.9 | 1.41 (0.92–2.17) | 2,921 | 3.0 | 1.50 (1.03–2.17) | |
| Diabetes | |||||||
| No | 6,112 | 1.1 | 1 | 7,934 | 1.2 | 1 | |
| Yes | 702 | 5.0 | 1.96 (1.25–3.08) | 979 | 5.0 | 2.01 (1.37–2.95) | |
| Dyslipidemia | |||||||
| No | 4,525 | 1.6 | 1 | 5,915 | 1.6 | 1 | |
| Yes | 2,289 | 1.4 | 0.72 (0.47–2.17) | 2,998 | 1.6 | 0.84 (0.58–1.20) | |
OR: odds ratio; aOR: adjusted odds ratio; CI: confidence interval; BMI: body mass index.
All variables in the table were simultaneously included in multivariable analysis. Bold indicates the statistical significance, p<0.05.
Table 4. Prevalence and factors associated with hypertension.
| Company size model n=7,270 | Industry category model n=9,550 | ||||||
|---|---|---|---|---|---|---|---|
| Number | Prevalence % |
Multivariable aOR (95% CI) |
Number | Prevalence % |
Multivariable aOR (95% CI) |
||
| Number of employees | |||||||
| 1–20 | 1,152 | 32.7 | 1.03 (0.85–1.25) | ||||
| 21–100 | 3,128 | 30.8 | 1.03 (0.89–1.21) | ||||
| 101–300 | 1,624 | 28.9 | 1.09 (0.91–1.31) | ||||
| 301– | 1,366 | 28.5 | 1 | ||||
| Industry category | |||||||
| Manufacturing | 4,205 | 27.5 | 1 | ||||
| Agriculture, forestry, and fishing | 313 | 34.8 | 1.09 (0.83–1.44) | ||||
| Construction | 1,936 | 34.6 | 1.02 (0.89–1.16) | ||||
| Utilities | 182 | 30.8 | 0.94 (0.66–1.35) | ||||
| Transportation | 1,108 | 37.3 | 1.20 (1.03–1.41) | ||||
| Wholesale trade | 710 | 31.4 | 0.92 (0.76–1.11) | ||||
| Professional services | 110 | 31.8 | 0.91 (0.58–1.44) | ||||
| Health care | 489 | 27.4 | 0.87 (0.69–1.11) | ||||
| Other services (Waste treatment and disposal, electronic and precision equipment repair and maintenance) | 364 | 40.1 | 1.01 (0.79–1.30) | ||||
| Public administration | 133 | 36.1 | 0.62 (0.42–0.92) | ||||
| BMI | |||||||
| Continuous variable | 1.19 (1.17–1.21) | 1.19 (1.17–1.20) | |||||
| Age (30–75 yr) | |||||||
| Continuous variable | 1.09 (1.08–1.09) | 1.09 (1.08–1.09) | |||||
| Smoking habits | |||||||
| Current smoker | 3,188 | 26.6 | 0.98 (0.85–1.14) | 4,139 | 27.5 | 0.95 (0.83–1.08) | |
| Past smoker | 2,299 | 39.4 | 1.18 (1.01–1.38) | 3,089 | 39.9 | 1.10 (0.96–1.26) | |
| Never | 1,783 | 25.0 | 1 | 2,322 | 26.5 | 1 | |
| Eat dinner within 2 h before bedtime | |||||||
| 3 times or more / week | 2,972 | 29.7 | 1.17 (1.04–1.31) | 3,782 | 30.7 | 1.17 (1.05–1.29) | |
| Not | 4,298 | 30.6 | 1 | 5,768 | 31.7 | 1 | |
| Eat snacks after dinner | |||||||
| 3 times or more / week | 1,847 | 25.3 | 0.78 (0.68–0.89) | 2,360 | 25.8 | 0.76 (0.68–0.86) | |
| Not | 5,423 | 31.9 | 1 | 7,190 | 33.1 | 1 | |
| Exercise habits | |||||||
| Yes | 3,259 | 28.0 | 1 | 4,369 | 29.7 | 1 | |
| Not | 4,011 | 32.0 | 1.09 (0.97–1.22) | 5,181 | 32.6 | 1.06 (0.96–1.17) | |
| Sleep habits | |||||||
| Enough | 4,411 | 31.0 | 1 | 5,928 | 32.1 | 1 | |
| Not | 2,859 | 29.0 | 0.94 (0.84–1.06) | 3,622 | 30.0 | 0.95 (0.86–1.06) | |
OR: odds ratio; aOR: adjusted odds ratio; CI: confidence interval.
All variables in the table were simultaneously included in multivariable analysis. Bold indicates the statistical significance, p<0.05.
Table 5. Prevalence and factors associated with diabetes.
| Company size model n=6,818 | Industry category model n=8,920 | ||||||
|---|---|---|---|---|---|---|---|
| Number | Prevalence % |
Multivariable aOR (95% CI) |
Number | Prevalence % |
Multivariable aOR (95% CI) |
||
| Number of employees | |||||||
| 1–20 | 1,109 | 11.3 | 0.95 (0.72–1.26) | ||||
| 21–100 | 2,948 | 10.2 | 0.94 (0.74–1.19) | ||||
| 101–300 | 1,511 | 10.0 | 1.06 (0.81–1.38) | ||||
| 301– | 1,250 | 10.1 | 1 | ||||
| Industry category | |||||||
| Manufacturing | 3,857 | 9.1 | 1 | ||||
| Agriculture, forestry, and fishing | 298 | 11.1 | 0.94 (0.63–1.41) | ||||
| Construction | 1,862 | 11.8 | 0.96 (0.79–1.16) | ||||
| Utilities | 178 | 9.6 | 0.83 (0.48–1.44) | ||||
| Transportation | 1,060 | 14.4 | 1.32 (1.07–1.65) | ||||
| Wholesale trade | 675 | 12.0 | 1.10 (0.84–1.44) | ||||
| Professional services | 94 | 13.8 | 1.17 (0.62–2.22) | ||||
| Health care | 436 | 11.7 | 1.15 (0.82–1.61) | ||||
| Other services (Waste treatment and disposal, electronic and precision equipment repair and maintenance) | 335 | 13.4 | 0.84 (0.59–1.19) | ||||
| Public administration | 125 | 15.2 | 0.86 (0.51–1.46) | ||||
| BMI | |||||||
| Continuous variable | 1.21 (1.18–1.24) | 1.20 (1.18–1.22) | |||||
| Age (30–75) | |||||||
| Continuous variable | 1.09 (1.08–1.10) | 1.09 (1.08–1.10) | |||||
| Smoking habits | |||||||
| Current smoker | 2,996 | 9.3 | 1.17 (0.92–1.47) | 3,860 | 9.9 | 1.22 (1.00–1.49) | |
| Past smoker | 2,236 | 13.2 | 1.09 (0.86–1.37) | 3,004 | 13.9 | 1.11 (0.91–1.36) | |
| Never | 1,586 | 8.2 | 1 | 2,056 | 8.7 | 1 | |
| Eat dinner within 2 h before bedtime | |||||||
| 3 times or more / week | 2,746 | 10.3 | 1.15 (0.97–1.36) | 3,482 | 10.7 | 1.07 (0.92–1.24) | |
| Not | 4,072 | 10.3 | 1 | 5,438 | 11.2 | 1 | |
| Eat snacks after dinner | |||||||
| 3 times or more / week | 1,705 | 9.7 | 1.01 (0.83–1.23) | 2,177 | 10.8 | 1.12 (0.95–1.33) | |
| Not | 5,113 | 10.5 | 1 | 6,743 | 11.1 | 1 | |
| Exercise habits | |||||||
| Yes | 3,015 | 9.0 | 1 | 4,036 | 10.1 | 1 | |
| Not | 3,803 | 11.4 | 1.22 (1.03–1.44) | 4,884 | 11.7 | 1.12 (0.97–1.29) | |
| Sleep habits | |||||||
| Enough | 4,134 | 10.7 | 1 | 5,536 | 11.3 | 1 | |
| Not | 2,684 | 9.8 | 0.92 (0.77–1.10) | 3,384 | 10.6 | 0.97 (0.84–1.13) | |
OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; BMI: body mass index.
All variables in the table were simultaneously included in multivariable analysis. Bold indicates the statistical significance, p<0.05.
Results
Characteristics of overall participants
Table 1 shows the characteristics of participants with and without missing data. The mean (SD) age of the participants was 49.9 (SD 11.3) yr. In total, 149 (1.5%) patients had a history of CHD and 3,219 (31.5%) and 1,053 (11.0%) were suffering from hypertension and diabetes, respectively. Regarding company size, more than half of the companies were categorized as having 1–20 (15.9%) and 21–100 (43.2%) employees. Regarding the industry category, most participants worked in the manufacturing industry (43.8%), followed by construction (20.4%) and transportation (11.6%).
We demonstrate the distribution of unhealthy lifestyles by company size and industry category in Table 2. Current or past smokers were more prevalent among workers of smaller companies (81.2% in “1–20 employees” and 76.5% in “21–100 employees”). There were no distinctive traits in the other lifestyle habits. Regarding the industry category, transportation workers were more likely to smoke (82.8%), eat dinner within 2 h of bedtime (43.1%), and be inactive (63.8%).
Table 3 shows the prevalence of and factors associated with CHD. A higher prevalence of CHD was observed in smaller companies, compared to those with “301–”. In the multivariable analysis (all variables in the table were simultaneously included), company size had a significant or marginal association with CHD (aOR=1.53, 95% confidence interval: 95% CI, 0.69–3.40 for company size “1–20”; aOR=2.02, 95% CI, 1.01–4.02 for “21–100”, and aOR=1.74, 95% CI, 0.81–3.76 for “101–300”). The prevalence varied in the industry category model (from 0.7% in agriculture, forestry, and fishing to 3.2% in professional services); however, there was no significant association among the categories in the multivariable analysis. Lifestyle factors such as smoking habits (past smoker) and sleep habits were significantly associated with CHD in both models. Similarly, hypertension and diabetes were significantly or marginally associated with CHD in both models.
Then, we assessed the association of hypertension and diabetes. Table 4 shows the association between hypertension and the working environment. In the industry category model, transportation was associated with hypertension (aOR=1.20, 95% CI, 1.03–1.41). In the company size model, a higher prevalence of hypertension was observed in smaller companies, although this was not significant in the multivariable analysis. In Table 5, diabetes and working environments are analyzed. Although there was no trend in company size, transportation in the industry category model had a significant association with diabetes (aOR=1.32, 95% CI, 1.07–1.65).
Discussion
The current study revealed a high prevalence of CHD among workers in smaller companies, which was statistically significant in companies with 21–100 employees, and a significant association between hypertension, diabetes, and the transportation industry. To our knowledge, few studies comprehensively assessing CHD, its risk factors, and the working environment have been conducted in Japan. Therefore, our findings provide valuable information that encourages health providers, including medical doctors, to consider working environments as CHD risk factors.
Company size can be used as a proxy for working class. This is because working conditions, including salary, quality industrial health, and safety activities, for employees in large companies have traditionally been better and more stable than those in small companies18, 19). To date, many studies have demonstrated the association between working class (e.g., manager, professional, service, and blue-collar) and CHD20,21,22). However, the association between CHD and company size has rarely been explored. In Japan, only one study has explored the association between company size and cardiovascular disease (CVD) mortality23). This study showed that employees of small companies (1–29 employees) had a significantly higher hazard ratio in total CVD mortality from a 20 yr follow-up study. Despite the differences in study design, our cross-sectional study showed similar results: workers belonging to smaller companies were more likely to have a higher CHD prevalence. As our study was conducted among a working-age population, health inequality in CHD may have occurred at an earlier stage of life than expected.
Moreover, our study adjusted for traditional risk factors, such as hypertension, diabetes, and unhealthy lifestyles. Therefore, we demonstrated not only the importance of controlling traditional risk factors but also identified other potential mechanisms leading to the association between small companies and CHD in this study. In addition to the finding that lifestyle-related diseases and unhealthy lifestyles are known to be more prevalent in workers of smaller companies than in those in large companies13, 24), we suggest other factors affecting the development of CHD among workers in small companies. One plausible explanation is the difficulty in accessing health check-ups14). Workplaces with 50 employees or more are required to appoint health supervisors and occupational physicians according to the Industrial Safety and Health Act in Japan19, 25). Therefore, workers in large companies have easier access to medical staff than those in smaller companies. Psychological distress also seems to be greater for workers in smaller companies during a stagnant economy. In addition to the traditional risk factors for CHD, these working factors should also be considered by health providers.
CHD prevalence among industry categories in our study was relatively high in the construction (2.0%), transportation (1.9%), wholesale trade (1.9%), professional services (3.2%), and public administration (2.4%) industries (Table 3). However, the total number of workers with a history of CHD (outcome number) was small in our study, and no significant association was observed between CHD and industry category after adjusting for traditional factors such as hypertension, diabetes, and lifestyle. Several studies in the U.S. and Japan have shown a higher mortality rate among transport and service workers due to ischemic heart disease10, 26, 27), using the national census or big data. However, these studies did not adjust for traditional risk factors because information on lifestyle and lifestyle-related diseases was not available. In the future, studies with a larger number of workers with CHD and traditional risk factors are needed to clarify the association between industry category and CHD.
We demonstrated that workers in the transportation industry were more likely to smoke, eat dinner within two hours of bedtime, and be inactive (Table 2). Our results are consistent with those of a previous Japanese study. Hozawa et al. showed that male workers in the transportation industry were more likely to have unhealthy lifestyles such as smoking, walking less, eating before bedtime, and skipping breakfast28). Furthermore, multivariable analysis in our study revealed a significant association between hypertension and diabetes with working in the transportation industry (Tables 4 and 5). Even after adjusting for lifestyle, transportation had higher aORs for both hypertension and diabetes. Our findings are consistent with those of previous studies29,30,31). These studies also explored the association between industry categories and lifestyle-related diseases such as hypertension, diabetes, and metabolic syndrome in multivariable analyses and found that transportation was significantly associated with a higher prevalence of lifestyle-related diseases. As lifestyle factors were adjusted as confounders, it is conceivable that other factors may play a role in the development of hypertension and diabetes among transportation workers. There are two plausible explanations for the high prevalence of these diseases in the transportation industry. First is the long working duration. According to a governmental survey in Japan, the average working time in the transportation industry (excluding part-time workers) ranked the highest for all industries at 174.7 h per month32). Second, the irregularity of working patterns that the transportation business entails. The workload of transportation can vary depending on customer needs, the broader economic situation, and changes in the industry, such as the surge in online shopping and shipping since around 2010, posing challenges to the work schedule of employees in the transportation industry. Moreover, long working hours and irregular work schedule can prevent workers from leading healthy lifestyles, affecting vegetable intake and resting time, which were not adjusted for in our study. As a result, transportation workers may find it more difficult to attend medical appointments within standard working hours than workers in other industries. Physicians or medical practitioners should take these difficulties into consideration and offer transportation workers flexible appointments or longer duration of prescriptions to enable them to manage health-related issues. Further studies on industry categories and health should examine factors such as working time, control over working schedule, and accessibility to medical settings as potential risk factors for lifestyle-related diseases.
This study has several limitations. First, the cross-sectional design did not allow for causal inferences. A prospective study may be more preferable to clarify the influence of working environments on health. Second, although the industry category based on the name of the company is clear, the individual’s precise role within the industry is unclear. For example, within a transportation company, there are not only drivers but also office clerks or managers. However, the unclassified roles of individual workers might lessen our findings between industry categories and health. The real associations between transportation, hypertension, and diabetes may be stronger than those we showed. Third, although the company size and industry category were included in our analyses, other work-related factors such as working hours and work-related stresses were not assessed because the health check-up data did not contain information about these factors. In the future, comprehensive information of all work-related factors should be included in the analysis. Fourth, there might be a selection bias because only workers attending health check-ups, reflecting high health consciousness, were included in this study. Furthermore, it is possible that workers with CHD, hypertension, or diabetes already attending hospitals regularly might not have undertaken annual health check-ups. This suggests that the actual prevalence of CHD, hypertension, and diabetes would be higher than that recorded here. Despite these limitations, our findings are still important and suggest that the working environment should also be considered as a potential risk factor for CHD.
Conclusions
Prevalence of CHD was high in small companies, and a significant association was observed between hypertension, diabetes, and the transportation industry. Taking into account working environments can be useful in the prevention of CHD, and health providers, including medical doctors, should consider the working environment as a risk factor for CHD.
Availability of Data and Material
The datasets in the current study are not publicly available because the institution which provided the data did not agree to share the data with the third party. On reasonable request, some of data used in our study are available from the corresponding author.
Conflict of Interest
We declare no competing interest in this article.
Author Contributions
MY and MS collected all data used in this research. MY performed the analyses, interpreted the results, and wrote the manuscript. TT and MS gave technical support, conceptual advice, and critical review. All authors read and approved the final manuscript.
Funding
This work was supported by the JSPS KAKENHI Grant Number JP18K07465.
Acknowledgments
We wish to express our gratitude to all of the participants in a health check-ups and staffs in JCHO Takaoka Fushiki Hospital.
References
- 1.Barquera S, Pedroza-Tobías A, Medina C, Hernández-Barrera L, Bibbins-Domingo K, Lozano R, Moran AE. (2015) Global overview of the epidemiology of atherosclerotic cardiovascular disease. Arch Med Res 46, 328–38. [DOI] [PubMed] [Google Scholar]
- 2.Ministry of Health, Labour and Welfare Health Policy Bureau. Current status on cardiovascular diseases in Japan. https://www.mhlw.go.jp/content/10905000/000585305.pdf (in Japanese). Accessed July 24, 2022.
- 3.Marmot MG, Smith GD, Stansfeld S, Patel C, North F, Head J, White I, Brunner E, Feeney A. (1991) Health inequalities among British civil servants: the Whitehall II study. Lancet 337, 1387–93. [DOI] [PubMed] [Google Scholar]
- 4.Collins SM, Karasek RA, Costas K. (2005) Job strain and autonomic indices of cardiovascular disease risk. Am J Ind Med 48, 182–93. [DOI] [PubMed] [Google Scholar]
- 5.Wilkinson R.G., Marmot M. (2003) World Health Organization. Social determinants of health: the solid facts.
- 6.Kenneth D, Rosenman KD .(2007) Occupational heart disease. In: Environmental and Occupational Medicine, Rom WN (Eds.), 4th Ed., 681–93, Lippincott Williams & Wilkins, Philadelphia. [Google Scholar]
- 7.Sekine M, Chandola T, Martikainen P, Marmot M, Kagamimori S. (2009) Socioeconomic inequalities in physical and mental functioning of British, Finnish, and Japanese civil servants: role of job demand, control, and work hours. Soc Sci Med 69, 1417–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Karasek R, Baker D, Marxer F, Ahlbom A, Theorell T. (1981) Job decision latitude, job demands, and cardiovascular disease: a prospective study of Swedish men. Am J Public Health 71, 694–705. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kelly IR, Dave DM, Sindelar JL, Gallo WT. (2014) The impact of early occupational choice on health behaviors. Rev Econ Househ 12, 737–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kagamimori S, Kitagawa T, Nasermoaddeli A, Wang H, Kanayama H, Sekine M, Dilixat Y. (2004) Differences in mortality rates due to major specific causes between Japanese male occupational groups over a recent 30-year period. Ind Health 42, 328–35. [DOI] [PubMed] [Google Scholar]
- 11.The Japanese Society of Cardiovascular Disease Prevention. Hand book for prevention of cardiovascular disease 2014. 7th Ed. Hokendojinsya, Tokyo (in Japanese). [Google Scholar]
- 12.Ministry of Health, Labour and Welfare in Japan. Specific Health Checkups and Specific Health Guidance. https://www.mhlw.go.jp/english/wp/wp-hw3/dl/2-007.pdf. Accessed July 29, 2022.
- 13.Kubo N, Usami T, Haruyama Y, Muto T, Kimura K, Yukawa S, Kimura T, Yamane N. (2006) Characteristics of lifestyle and health status of workers in small-scale enterprises in Japan. Ind Health 44, 161–5. [DOI] [PubMed] [Google Scholar]
- 14.Seko R, Kawado M, Saito S, Shibuya T, Miyamoto M, Yamada H, Taniwaki H, Hashimoto S. (2019) Associations of occupation, employment type and company size with actions related to health examinations among Japanese employees. Ind Health 57, 537–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.The Small and Medium Enterprise Agency. Small and Medium-sized Enterprise Basic Act. https://www.chusho.meti.go.jp/sme_english/outline/08/01_01.html. Accessed September 24, 2022.
- 16.Ministry of Internal Affairs and Communications. Japan Standard Industrial Classification. https://www.soumu.go.jp/toukei_toukatsu/index/seido/sangyo/02toukatsu01_03000023.html. Accessed July 24, 2022.
- 17.United States Census Bureau. North American Industry Classification System. https://www.census.gov/naics/. Accessed July 29, 2022.
- 18.National Tax Agency in Japan. Statistical survey of actual status for salary in the private sector; 2020. https://www.nta.go.jp/publication/statistics/kokuzeicho/minkan2020/minkan.htm (in Japanese). Accessed July 29, 2022.
- 19.Kayashima K. (2013) [Current status of occupational health activities and the way that occupational health services should be offered to small- and medium-scale enterprises]. J UOEH 35Suppl, 53–8 (in Japanese). [DOI] [PubMed] [Google Scholar]
- 20.Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, Ricceri F, d’Errico A, Barros H, Bochud M, Chadeau-Hyam M, Clavel-Chapelon F, Costa G, Delpierre C, Fraga S, Goldberg M, Giles GG, Krogh V, Kelly-Irving M, Layte R, Lasserre AM, Marmot MG, Preisig M, Shipley MJ, Vollenweider P, Zins M, Kawachi I, Steptoe A, Mackenbach JP, Vineis P, Kivimäki M, LIFEPATH consortium (2017) Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women. Lancet 389, 1229–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Woodward M, Peters SAE, Batty GD, Ueshima H, Woo J, Giles GG, Barzi F, Ho SC, Huxley RR, Arima H, Fang X, Dobson A, Lam TH, Vathesatogkit P, Asia Pacific Cohort Studies Collaboration (2015) Socioeconomic status in relation to cardiovascular disease and cause-specific mortality: a comparison of Asian and Australasian populations in a pooled analysis. BMJ Open 5, e006408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zaitsu M, Kato S, Kim Y, Takeuchi T, Sato Y, Kobayashi Y, Kawachi I. (2019) Occupational class and risk of cardiovascular disease incidence in Japan: nationwide, multicenter, hospital-based case-control study. J Am Heart Assoc 8, e011350 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Okuda N, Kadota A, Nishi N, Miura K, Ohkubo T, Miyagawa N, Satoh A, Kita Y, Hayakawa T, Takashima N, Fujiyoshi A, Okayama A, Okamura T, Ueshima H, NIPPON DATA90 Research Group (2019) Association of work situation with cardiovascular disease mortality risk among working-age Japanese men—a 20-year follow-up of NIPPON DATA90. Circ J 83, 1506–13. [DOI] [PubMed] [Google Scholar]
- 24.Hoshuyama T, Hino Y, Kayashima K, Morita T, Goto H, Minami M, Sakuragi S, Tanaka C, Takahashi K. (2007) Inequality in the health status of workers in small-scale enterprises. Occup Med (Lond) 57, 126–30. [DOI] [PubMed] [Google Scholar]
- 25.Ministry of Health, Labour and Welfare Health Policy Bureau. Occupational physician. https://www.mhlw.go.jp/new-info/kobetu/roudou/gyousei/anzen/dl/080123-1a.pdf (in Japanese). Accessed July 24, 2022.
- 26.Robinson CF, Burnett CA. (2005) Truck drivers and heart disease in the United States, 1979–1990. Am J Ind Med 47, 113–9. [DOI] [PubMed] [Google Scholar]
- 27.Yoshino C, Sugimori H, Tanaka T, Yoshida K. (2005) Analysis of secular changes of standardized mortality rations in Japanese companies by size and type of business 1987–1999. St. Marrianna Med J 33, 43–52(in Japanese). [Google Scholar]
- 28.Hozawa H, Takeuchi A, Oguma Y. (2019) Prevalence of metabolic syndrome and lifestyle characteristics by business type among Japanese workers in small- and medium-sized enterprises. Keio J Med 68, 54–67. [DOI] [PubMed] [Google Scholar]
- 29.Santana AIC, Merces MCD, Magalhães LBNC, Costa ALB, D’Oliveira A. (2020) Association between metabolic syndrome and work: an integrative review of the literature. Rev Bras Med Trab 18, 185–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Davila EP, Florez H, Fleming LE, Lee DJ, Goodman E, LeBlanc WG, Caban-Martinez AJ, Arheart KL, McCollister KE, Christ SL, Clark JC, 3rd, Clarke T. (2010) Prevalence of the metabolic syndrome among U.S. workers. Diabetes Care 33, 2390–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Hidaka T, Hayakawa T, Kakamu T, Kumagai T, Hiruta Y, Hata J, Tsuji M, Fukushima T. (2016) Prevalence of metabolic syndrome and its components among Japanese workers by clustered business category. PLoS One 11, e0153368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ministry of Health, Labour and Welfare. Monthly Labour Survey 2020. https://www.mhlw.go.jp/toukei/itiran/roudou/monthly/r02/20cr/dl/pdf20cr.pdf (in Japanese). Accessed July 29, 2022.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets in the current study are not publicly available because the institution which provided the data did not agree to share the data with the third party. On reasonable request, some of data used in our study are available from the corresponding author.