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Psychosomatic Medicine 67:740-747 (2005)
© 2005 American Psychosomatic Society

ORIGINAL ARTICLES

Job Strain and Early Atherosclerosis: The Cardiovascular Risk in Young Finns Study

Mirka Hintsanen, MA, Mika Kivimäki, PhD, Marko Elovainio, PhD, Laura Pulkki-Råback, PhD, Pertti Keskivaara, MA, Markus Juonala, MD, Olli T. Raitakari, MD, PhD and Liisa Keltikangas-Järvinen, PhD

From the Department of Psychology, University of Helsinki, Helsinki, Finland (M.H., M.K., M.E., L.P.-R., P.K., L.K.-J.); University of Helsinki and Finnish Institute of Occupational Health, Helsinki, Finland (M.K.). National Research and Development Centre for Welfare and Health, Helsinki, Finland (M.E.); Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (M.J.); Department of Clinical Physiology, University of Turku, Turku, Finland (O.T.R.).

Address correspondence and reprint requests to Liisa Keltikangas-Järvinen, PhD, Department of Psychology, P.O. Box 9 (Siltavuorenpenger 20 D), 00014 University of Helsinki, Helsinki, Finland. E-mail: Liisa.Keltikangas-jarvinen{at}helsinki.fi


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
Objective: The purpose of this study was to determine whether job strain and social support are associated with early atherosclerosis measured by carotid intima-media thickness (IMT) in young adults.

Methods: The subjects were 478 men and 542 women (mean age 32.3) who were participating in the ongoing prospective Cardiovascular Risk in Young Finns study. Job strain was defined as a joint effect of job demands and job control. Early atherosclerosis was determined with IMT ultrasound. The associations between job strain, social support, and IMT were evaluated using multiple linear regressions.

Results: In men, job strain was associated with increased IMT after adjustment for age. This association was not attenuated by additional adjustment for established risk factors of coronary heart disease. In women, job strain was not associated with IMT. No 3-way interaction of job demand, job control, and social support on IMT was found.

Conclusion: These findings suggest that job strain may be related to atherosclerosis already in its early nonsymptomatic stages in men.

Key Words: job strain • work stress • social support • intima-media thickness • IMT • atherosclerosis

Abbreviations: BMI = body mass index; CHD = coronary heart disease; CRYF = Cardiovascular Risk in Young Finns; HDL = high-density lipoprotein; IMT = intima-media thickness; LDL = low-density lipoprotein; OSQ = Occupational Stress Questionnaire; PAI = physical activity index; SES = socioeconomic status.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
The demand-control model (1) has achieved special attention in research on the health effects of work stress. This model specifies 2 independent inputs influencing job strain: job demands and control of the work situation. Job demands refer to psychological stress, such as time pressure and having too much work (2,3). Job control involves organization of work in terms of the possibility of using one’s skills (skill discretion) and workers’ authority to make decisions concerning their own activities (decision authority or job decision latitude). According to the model, job strain and related health problems occur in situations where high demands coincide with low control. There has been some disagreement whether additive effects are sufficient for supporting the model or should multiplicative interactive effects also be required (4). Job strain has been formulated in several ways, and a wide variety of methods for testing interactions has been employed in different studies (5).

The demand-control model was later extended by adding a third dimension, namely, social support (6,7). According to this extended model (demand-control-support model) job demands, job control, and social support interact so that strain is highest when demands are high, whereas control and social support are low.

An association between work stress, measured by the demand-control model, and severe forms of coronary heart disease (CHD), such as myocardial infarction, stroke, and cardiovascular mortality, has been repeatedly reported (8–13). However, direct evidence that work stress is a predictive factor of CHD is limited. CHD is a multifactorial disease that produces clinically significant changes relatively late. The fact that only a small number of studies on work stress and the early stages of CHD have been conducted so far may stem from the lack of techniques for assessing subclinical stages of CHD.

Recently, noninvasive techniques, such as ultrasound measure of intima-media thickness (IMT), have been developed to directly assess early stages of atherosclerotic process. Carotid IMT is highly correlated with cardiovascular risk factors (14) and is related to the extent and severity of CHD (15).

The few previous studies that have examined the association between job strain or its components and IMT have produced mixed findings. According to the results of Rosvall et al. (16), women who experienced high demands and low job control had thicker IMT compared with women with a combination of low demands and high job control (with a mean difference of 0.15 mm). In addition, women who had high demands and high control had thicker IMT compared with women with a combination of low demands and high control (mean difference 0.10 mm). No association between components of job strain and IMT was found for men. However, Nordstrom and coworkers (17) found demands and intrusion of work concerns into home life to be associated with thicker IMT in men but not in women. A study by Muntaner et al. (18) reported no association between job strain and IMT, but higher levels of skill discretion and decision authority were associated with lower IMT. In the only longitudinal study that has used IMT as an outcome measure, the Kuopio Ischemic Heart Disease Risk Factor Study of Finnish men, high job demands combined with low economic rewards (19) or high stress-induced reactivity (20) predicted thicker IMT.

As all of the reviewed studies employed subjects who were at least 40 years old, the results cannot be generalized to younger populations. Furthermore, most of the studies were unable to take into account other known risk factors of CHD, including physical inactivity (21) and a lack of social support (10). Most of the previous studies were also not able to allow for the effect of occupational status, despite the fact that indicators of socioeconomic position are important factors that may confound the association between job strain and CHD (22). To date, the demand-control model, exploring combined effects of job demands and job control, has only been employed in 2 (cross-sectional) studies, and no previous study has explored the interaction between job strain and social support.

In the present study, we examined the relationship between work stress, as defined by demand-control model, and the development of early atherosclerosis, as indicated by carotid IMT ultrasound. We used several different formulations of job strain and tested both additive and interactive effects of job demands and job control. The population-based sample included men and women at age 39 years or younger who lacked manifest symptoms of cardiovascular disease. We took into account a wide variety of known risk factors of CHD, including physical inactivity and level of social support, and tested the 3-way interaction effect of job demand, job control, and social support on IMT.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
Participants
The participants were derived from the ongoing prospective Cardiovascular Risk in Young Finns study (CRYF) beginning in 1980. The subjects for the original sample in 1980 (n = 3596) were selected randomly from 6 different age cohorts in the population register of the Social Insurance Institution, a database that covers the whole population of Finland. The design of the CRYF study and the selection of the sample have been described in detail by Åkerblom et al. (23). The CRYF cohort was enrolled into the study already in 1980, but all measurements of the present study were carried out in 2001 to 2002, when job strain, social support and carotid IMT were assessed from 2104 participants (58.5% of the 1980 cohort). Previous attrition analyses have shown that the participants lost in follow-up were more likely to be male and younger. Using age-adjusted analysis, no significant differences between dropouts and participants were found in low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and body mass index (BMI) (24).

In the present study, participants who were not currently employed in a full-time job (n = 574) or had missing information regarding employment status (n = 117) or missing values on some of the study variables (n = 389) were excluded. In addition, participants who had a diagnosis of diabetes mellitus or ischemic heart disease (n = 4) were not included in the analyses. Thus, complete data were received from 542 (53.1%) women and 478 (46.9%) men aged 24 to 39 years (mean age 32.3 years). Participants were from all 6 age cohorts of the CRYF study, 24-, 27-, 30-, 33-, 36-, and 39-year-olds, each age group forming 10.2%, 16.0%, 17.4%, 18.0%, 19.2%, and 19.2% of the current sample, respectively. Although the participants were fairly young, 30.5% of men and 28.4% of women were recorded working full time already in 1992, ie, 9 to 10 years before the measurement of IMT. Participants gave written informed consent, and the study was approved by local ethics committees.

Measurement of Job Strain and Social Support
Job demands were measured with a 3-item scale from the Occupational Stress Questionnaire (OSQ, {alpha} = 0.87) (25) developed at the Finnish Institute of Occupational Health. The OSQ has been widely used in Finland, and the validity of the OSQ items has been satisfactory in studies involving a total of over 25,000 employees in various occupations (26,27). The items used in the current study were "Do you have to hurry to get your work done?" "Does your work have phases that are too difficult?" and "Is your work mentally strenuous?" These items correspond quite closely to demands in Karasek’s (1) Job Content Questionnaire. Responses were obtained on a 5-point scale ranging from 1 (never) to 5 (all the time).

Job control was measured with the Job Content Questionnaire (28), which includes 9 items for job control ({alpha} = 0.87). Responses were given on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree).

We constructed 5 alternative formulations of job strain that have previously been used in studies of the demand-control model (5). The quadrant term was based on dichotomized demands and job control scores at the medians. Employees with high strain were those with job demand score above the median and job control score below the median. All other employees belonged to the no-strain group. The second alternative, linear term, was a continuous job strain variable obtained from the following equation: (0.5 x job demand score) – (0.5 x job control score). The third alternative, quotient term, was formed by dividing job demands by job control. The fourth alternative was a multiplicative interaction term (demand x control) calculated for each gender using centralized values for demand and control. In the fifth alternative, the distributions of demands and job control were divided into thirds (5,12,29). As in a previous study by Green and Johnson (29) the highest 2 tertiles in demands combined with lowest 2 tertiles in job control formed the high-strain category, and lowest 2 tertiles in demands combined with highest 2 tertiles in control formed the low-strain category. All other combinations were placed into the intermediate strain category. Job strain was coded as an ordinal variable ranging from 1 to 3, with higher values indicating higher strain. Figure 1 presents the composition of the tertile-based job strain variable.



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  		Figure 1. Distribution of different levels of job strain.

 

Social support was assessed with the Perceived Social Support Scale-Revised, consisting of 12 items ({alpha} = 0.94) measuring social support received from family and friends (30). Responses were given on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). In all scales, sums were calculated for only those participants who had responded to a minimum of 50% of the items of the scale.

Carotid Atherosclerosis
To assess carotid IMT, ultrasound studies were performed using Sequoia 512 ultrasound mainframes (Acuson, CA) with 13.0-MHz linear array transducers. The studies were performed between September 2001 and January 2002. The left carotid artery was scanned by ultrasound technicians following a standardized protocol (14). In brief, a magnified image was recorded of the angle showing the greatest distance between the lumen-intima interface and the media-adventitia interface. A moving scan (duration 5 seconds) that included the beginning of the carotid bifurcation and the common carotid artery was recorded and stored in digital format on optical discs for subsequent off-line analysis. The digitally stored scans were manually analyzed by a single reader who was blinded to subjects’ details. The analyses were performed using ultrasonic calipers. From the 5-second clip image, the best quality end-diastolic frame was selected (incident with the R-wave on a continuously recorded electrocardiogram). From this image, at least 4 measurements of the common carotid far wall were taken approximately 10 mm proximal to the bifurcation in order to derive mean carotid IMT. We have reported a 6.4% between-visit coefficient and a 5.2% between-observer coefficient of variation in the IMT measurements (14). According to the review of Kanters and others (31) the intraobserver variation coefficient has varied from 2.4% to 10.6%, and between-observer variation coefficient has varied from 3.1% to 18.3% in previous studies. Fallibility of IMT measurements was tested utilizing generalizability theory (32). Generalizability analyses revealed very high values for generalizability coefficient (0.82 and 0.96) and index of dependability (0.81 and 0.83) in both single-facet design and 2-facet design, respectively, indicating high reliability of IMT measurements. (Here single-facet design refers to between-visit comparisons, whereas 2-facet design refers to between-observer comparisons and between measurement comparisons—4 from both observers). However, it should be noted that the exceptionally high value of generalizability coefficient for 2-facet design (0.96) may, in part, stem from the high number of measurements (4 from both observers). Generalizability coefficient is analogous to reliability coefficient in classical test theory, whereas index of dependability reflects similarity in absolute values for different measurements (32,33).

CHD Risk Factors
The following CHD risk factors were measured: educational level, occupational group, smoking status, alcohol consumption, physical activity, BMI, HDL, and LDL.

Socioeconomic status (SES) was indicated by educational level and occupational group. We measured SES with educational level, along with occupational group, because educational level has some characteristics preferable to other indicators of SES. Occupational segregation causes gender bias to occupational status, which is absent from educational level (34). In addition, an important factor related to various effects of SES is access to information and that differs according to educational level (35). Educational level was classified as (1) low (comprehensive school), (2) intermediate (secondary education), or (3) high (academic; graduated from a polytechnic or studying at or graduated from a university). Classification into occupational groups was based on the criteria of the Central Statistical Office of Finland. Three groups were formed: (1) manual, (2) lower nonmanual, and (3) upper nonmanual. Entrepreneurs, who formed a very heterogeneous group of their own in the original measure, were placed to the aforementioned occupational groups according to educational level (low, intermediate, and high education corresponding to manual, lower nonmanual, and upper nonmanual occupational groups respectively). In the International Standard Classification of Occupations (36), entrepreneurs are classified according to the number of employees in their company. We were not able to follow this procedure, because the data lacked variation in the number of employees.

Health-related behaviors included smoking status (daily smoking), alcohol consumption (how often beer, wine, or spirits was used at least 6 portions or more at a time (1 portion equals to12 g): 1 = once a year or never, 2 = 2–6 times a year, 3 = once a month, 4 = 2–3 times a month, 5 = once a week, 6 = at least twice a week), and physical activity (an index formed of 5 variables describing intensity of physical activity, frequency of intensive physical activity, hours/week of intensive physical activity, average duration of physical activity, and participation in structured sports, eg, in a sports club). The physical activity index (PAI, {alpha} = 0.78) has been described in detail by Telama et al. (37). "One hour a week of intensive physical activity" was coded as 1, and sports club membership was coded as follows: no = 1, yes = 2, yes, once a week = 2, yes, many hours/times a week = 3. High scores on PAI indicate high physical activity.

Biological risk factors measured were BMI (kg/m2), HDL cholesterol, and LDL cholesterol. All measurements of lipid levels were performed in duplicate in the same laboratory. Standard enzymatic methods were used for measuring levels of HDL cholesterol. LDL cholesterol concentration was calculated using the Friedewald formula (38). The use of these methods has been described previously (39,40).

Statistical Analysis
Comparison of the associations between alternative job strain formulations and IMT showed the greatest effect size (ß = 0.102) for the tertile-based solution (Figure 1). Thus, the results are reported for this formulation, but the age-adjusted and fully adjusted models are also shown for the alternative formulations of job strain. All the analyses were performed for men and women separately as the interaction term for sex and job strain on IMT was close to significance (p = .096; age adjusted), and previous research has reported divergent findings for men and women in the associations between job strain or its components and IMT (16,17). The association between IMT and job strain was evaluated using multiple linear regression. Six different regression models were formed. The first model included an adjustment for age. The other models were, in addition to age, adjusted for: (2) SES (education, occupational status), (3) health-related behaviors (smoking, alcohol, physical activity), (4) social support, (5) biological risk factors (BMI, HDL and LDL cholesterol), and finally, (6) all aforementioned covariates, forming a fully adjusted model. SPSS software (versions 11.5 and 12.0.1) was used to perform all the analyses.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
Sample Attrition
Any differences between included and excluded participants at the 2001 follow-up were relatively small. Participants who were excluded from the current sample tended to be slightly younger (means 30.7 versus 32.3, t = 7.606, df = 2102, p < .001), and less educated (means 2.16 versus 2.23, t = 2.880, df = 2000, p = .004) than participants included in this study. The excluded participants were also more likely to be female (62.1% versus 53.1%, {chi}2 = 17.243, df = 1, p < .001) and they tended to have lower occupational status (means 1.84 versus 1.97, t = 3.604, df = 1720, p < .001) and thinner IMT (means 0.57 versus 0.59, t = 2.973, df = 1654.272, p = .003), use less alcohol (means 2.41 versus 2.62, t = 3.388, df = 1976, p = .001), be physically less active (means 9.44 versus 9.72, t = 2.662, df = 1888, p = .008), and were more likely to smoke more (at least once a day; 25.9% versus 20.2%, {chi}2 = 9.022, df = 1, p = .003) than participants included in the study.

Job Strain and IMT
Table 1 presents means and standard deviations for the study variables for men and women separately. Women reported higher job strain compared with men (t = 2.837, df = 1018, p = .005). Of women, 35.6%, and of men, 27.4% reported high job strain, whereas 26.6% of women and 32.2% of men reported low job strain. We found no difference in job demands between men and women (t = 1.649, df = 1018, p = .099), but there was a difference between men and women in job control (t = –3.074, df = 1018, p = .002) and in social support (t = 7.723, df = 950.537, p < .001). In women, strain, demand, and control were not correlated with IMT. In contrast, among men, strain and demand were significantly correlated with IMT (r = 0.100, p = .029 and r = 0.134, p = .003), although job control was not. Age, BMI, and LDL cholesterol, which are traditional risk factors for CHD, were positively correlated with IMT in men (r = 0.314, p < .001, r = 0.206, p < .001, r = 0.161, p < .001) and in women (r = 0.298, p < .001, r = 0.195, p < .001, r = 0.095, p = .027). Occupational status and social support were inversely correlated with job strain in both genders (r = –0.197, p < .001, r = –0.276, p < .001 in men, r = –0.098, p = .022, r = –0.141, p = .001 in women). Education in men (r = –0.127, p = .005) and physical activity in women (r = –0.113, p = .008) were also inversely correlated with strain. Age was not significantly correlated with job strain in either gender.


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  		TABLE 1. Participant Characteristics

 

The results of the multiple regression analyses for tertile-based job strain formulation are presented in Tables 2 and 3. In men, job strain was significantly associated with IMT after adjustment for age. This association was not attenuated by additional adjustments. The strongest associations for job strain were reached in the model adjusted for social support and in the fully adjusted model (Table 2). Job demand was also significantly associated with IMT in men after adjustment for age (p = 0.028). Again, the association was not attenuated by additional adjustments for CHD risk factors. When job strain and job demand were simultaneously entered to an age-controlled model, neither had a significant effect on IMT. This was not due to multicolinearity.


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  		TABLE 2. Multiple Regression Analyses of Job Strain and CHD Risk Factors Predicting Carotid IMT in Men

 


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  		TABLE 3. Multiple Regression Analyses of Job Strain and CHD Risk Factors Predicting Carotid IMT in Women

 
In women, job strain (Table 3) and job demand (p = .455, age adjusted) were not significantly associated with IMT in any of the models. Job control was not associated with IMT either in men or women (p = .396 and p = .383 for men and women, respectively, age adjusted).

The results of the regression models for all different job strain formulations in men are presented in Table 4. The associations between job strain and IMT were statistically significant for the linear term score and the quotient term score in addition to the tertile-based score. Nonsignificant association was observed for the quadrant term, which was the only dichotomous formulation of job strain. We tested a multiplicative interaction term (demand x control) for each gender using centralized values and adjusting for age and main effects of job demand and job control. The interaction term was not significantly associated with IMT (p = .109, and p = .924, for men and women, respectively).


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  		TABLE 4. Multiple Regression Analyses of Different Formulations of Job Strain Predicting Carotid IMT in Men

 

Social support was not associated with IMT either in men or in women in an age-adjusted model (p = .225, adjusted R2 = 0.098, R2 change for social support = 0.003, and p = .945, adjusted R2 = 0.085, R2 change for social support < 0.001 for men and women, respectively). Three-way interaction effects of social support, job demand, and job control on IMT were analyzed for each gender using centralized values. No 3-way interaction effects on IMT were found beyond the 2-way interactions and main effects of age, job demand, job control, and social support.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 NOTES
 REFERENCES
 
Our study showed a robust association between job strain, as indicated by a combination of low job control and high demands and increased thickness of the carotid intima-media (IMT) in men. This association was replicable across 3 different job-strain formulations used in previous studies. The association between job strain and IMT could not be accounted for by other known risk factors for atherosclerosis, such as age, social support, SES, smoking, alcohol consumption, physical activity, BMI, and serum cholesterol. High job demands, which were also associated with increased IMT, explained a larger proportion of the effect of job strain on IMT than low job control. In women, no significant relationship between job strain or its components and IMT was found.

A major strength of this study is that the development of atherosclerosis was measured noninvasively with IMT ultrasound, which made it possible to study the early, nonsymptomatic phase of atherosclerosis among adults without manifest symptoms of cardiovascular diseases. This kind of information is especially valuable because little is known of the role of job strain in the early stages of atherosclerosis. A further advantage arising from studying the nonsymptomatic phase of atherosclerosis is that no symptoms of CHD were present that could bias the perception of job strain and act as confounders of the associations between job strain and IMT.

In young populations, such as our sample of healthy men and women aged 24 to 39 years, noninvasive measurements of carotid IMT have been used as a surrogate marker of cardiovascular health (41). Prospective studies in older subjects have shown that even a 0.1-mm increase in carotid IMT may increase the subsequent risk of cardiovascular heart disease events by approximately 30% (42,43). We have previously observed that childhood risk factors predict increased IMT in adulthood in the present cohort (14).

In our sample, the mean difference in IMT between high- and low-strain groups was 0.03 mm in men. The effects of job strain are assumed to accumulate throughout working life. Given that the mean age of the participants was only 32.3 years and that the participants had relatively short working careers, the magnitude of the difference attained in the present study can be considered as fairly large. Measurement errors in IMT can attenuate the relationship between job strain and IMT. In this study, however, mean values from multiple IMT measurements were used, the intraobserver and between-observer variations for measurements corresponded to those reported in previous studies (31), and generalizability analyses indicated very high reliability for IMT measurements. The fact that the results were replicated with several job strain formulations underpins the robustness of the results. Average IMT was higher in men than in women of similar age, which has also been detected in previous studies (44).

Based on their review, Theorell and Karasek (45) concluded that the job-strain hypothesis is not as firmly supported in women as it is in men. Gender differences in work involvement may partly explain this distinction. It has been observed that in men, certain temperament profiles characterized by features easily leading to overcommitment are associated with biological risk factors of CHD (46). As men are reported to have higher work involvement (47), work-related strain may cause stronger health effects in men than in women.

The lack of any association between early atherosclerosis and job strain in women in the present study may also relate to other reasons. First, the subjects were fairly young, and atherosclerosis develops later in women than in men, as the female sex seems to be a protective factor until menopause (48). Second, it has been suggested that for women the strain arising from unpaid work done at home (eg, child care) should also be taken into account because women still carry a larger share of the responsibility for doing domestic duties (49). These additional sources of strain may confound the associations between job strain and IMT for women.

In line with our results on men, previous studies have found associations between components of job strain and IMT (17–20). However, negative findings between job strain and IMT have also been reported. Rosvall et al. (16) and Muntaner et al. (18) found no association between job strain and IMT in men who were substantially older than our study population. Their findings accord with research on CHD in which weaker associations between job strain and CHD have been found in studies using samples with participants above age 55 than in studies using samples with participants under that age (45). Selection effects may contribute to such differences. As only subjects working full time are usually included in studies examining associations between job strain and CHD, the results of these studies may show somewhat weaker associations than results from samples that include the participants that have lost their job. This is caused by "the healthy worker effect," that is, healthy people tend to remain in the work force, while those with the most health problems tend to select themselves out. This effect is likely to be emphasized in older populations (as CHD is more common in older people), leading to differing research findings for different age groups.

We found that higher job demands were associated with higher IMT. Moreover, this association largely explained the effect of job strain on IMT. A recent study by Kuper and Marmot (50) has implied that the role of job demands may be stronger than earlier research has suggested. Our results of IMT are in line with this suggestion. In job-strain research, a large variety of different scales has been employed to indicate the components of the demand-control model. We used the OSQ, a nonstandard measure of workload, to indicate demands as this measure has been widely used in Finland (26,27). Further research is needed to assess whether our findings are replicable to those obtained with more standard measures of job demands, such as Job Content Questionnaire.

Our results gave support for an additive interaction between job demand and job control in men. In job-strain literature, many methods for testing interactions between job demand and job control are widely used and accepted (5,11). We found support for interaction effects in analyses across 3 different job-strain formulations, including quotient term. The multiplicative interaction term was significant at level of p = .109, but for this test, our cohort may have been underpowered. This is because substantially larger sample size is needed to detect multiplicative interaction effects than main effects. Indeed, multiplicative interaction test has also resulted in nonsignificant findings in other studies with significant effects for direct job-strain formulations (5). It is possible that a larger sample size would have produced a significant effect in the present study.

The enlarged hypothesis of the demand-control-support model was not supported by the present results. Although this confirms the results of earlier studies (51), some methodological limitations may have contributed to our null findings. Although we had a fairly large sample size, it is possible that our analyses lacked sufficient statistical power to measure the 3-way interaction. Another reason for not finding this interaction may be that social support was not measured specifically as support at work but merely as a general measure of social support received from family and friends.

Methodological Considerations
It is noteworthy that excluded participants had several factors featuring higher CHD risk, but they did not have higher IMT than the participants included in the study. A likely explanation for this contradiction is the younger age of the excluded participants as age is strongly associated with IMT.

As our study was cross-sectional, no conclusions about cause-and-effect relationships can be drawn. A disadvantage in study design was that we did not control for strain experienced by the participants in their previous occupations. However, in the current study a large proportion (n = 284) of the participants had graduated during the 5 years preceding the study. Taking the previous work history into account would have excluded students and reduced the number of participants significantly. Thus, long exposure to severe work stress was not explored by our data, which might have led to underestimation of the association between job strain and IMT.

Although we used self-reports to measure job strain, the possibility of a bias caused by disease perceptions was unlikely as the participants were free from ischemic heart disease and diabetes. Unlike in some previous studies, extensive exclusions because of cardiovascular disease were avoided because of the age profile of our sample.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
NOTES
 REFERENCES
 
Our results show that job strain and high job demands may be related to atherosclerosis already in its early nonsymptomatic stages in men. These associations were observable in a relatively young population, they were robust across various alternative indicators of job strain, and they seemed not to be accounted for by other risk factors of CHD. Longitudinal research is needed to examine and confirm whether job strain contributes to the development of atherosclerosis already from its early nonsymptomatic stages to its later, clinically relevant phase.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
REFERENCES
 

This study was supported by the Academy of Finland (grants 105195 (M.K.), 209514 (L.K.J.), and 209518 (L.K.J.)), the Signe and Ane Gyllenberg Foundation (L.K.J.), The Finnish Cultural Foundation (L.P.R.), the Otto A. Malm’s donation fund (M.H.), and the Oskar Öflund’s Foundation (M.H.).

DOI:10.1097/01.psy.0000181271.04169.93


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 

  1. Karasek RA. Job demands, job decision latitude, and mental strain: implications for job redesign. Adm Sci Q 1979;24:285–308.[CrossRef]
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