METHODS

Kaunas is the second largest city of Lithuania, a former socialist country in a transition market economy, with an area of 132 km² and a population of about 400 000. The study base population comprised all 25–64 year old men, residing in 12 districts of the city, participating in the MONICA project. All surviving patients with a first time myocardial infarction that occurred from 1 October 2000 to 30 September 2002 were eligible for the study. The risk of myocardial infarction among different occupational categories was studied using a case-control study design.

Identification of cases and controls

All able bodied surviving patients with a first time myocardial infarction treated in Kaunas hospitals were considered eligible for the case-control study. The criteria for inclusion were that the participants had to be wage earners currently in employment and under 64 years of age at the time of the inclusion. A case was a person with a clinical diagnosis coded I21 of the 10th revision of International Classification of Diseases (ICD-10) in the hospital registry. In total 257 male first time myocardial infarction cases were registered, 210 (81.7%) of these were interviewed at the hospital during their first hospitalisation week. Random controls were selected from the study base, stratified by gender and age, drawn from the population registers of the 12 districts. Controls were eligible if a clinical diagnosis of ischaemic heart disease or angina pectoris had not been recorded in their medical documents and they did not report chest pain or revealed other evidence of ischaemic heart disease during the interview. A total of 439 controls were selected, 108 (24.6%) of them refused to participate, 15 (3.4%) had chest pain complaints and history of ischaemic heart disease. In total 316 controls were included into the study (response rate 71.9%). After we excluded those with missing data on occupation and unemployed (7 cases and 29 controls), 203 cases (78.9% of all registered) and 287 controls (65.4%) with complete data remained (ratio 1:1.4).

Data on socioeconomic status and risk factors

Cases and controls were interviewed by trained doctors in their local hospitals, using identical standardised questionnaires, which included information on occupation, demographic, socioeconomic conditions, psychosocial factors at work, and health behavioural factors. Data on history of raised arterial blood pressure, diagnosed by a doctor, were taken from records, and the subjects underwent anthropometric measurements, such as height and weight. Education was categorised into eight or less years of schooling, secondary, and university. The International Standard Classification of Occupations as previously described¹⁵ was used to classify occupations into 10 occupational categories. The last occupation in full time employment for at least one year has been coded. Adjustment for socioeconomic status has been performed when comparing the 1st occupational category with the 2nd, 3rd, 4th, 6th and, respectively, the 2nd and the 3rd occupational categories with the rest of the white collar occupations, and comparing the 8th occupational category with the 0th, 5th, 6th, 7th, and 9th and, respectively, the 7th occupational category with the rest of the blue collar occupations.

Psychosocial work environment

Data regarding psychosocial job demands and control were obtained from the Swedish version of the Karasek demand-control questionnaire. Five items investigated demands and six items focused on job control. Each question had four frequency response categories ranging from “never” to “always”. The internal reliability was studied using Cronbach’s α (0.84 for job demands and 0.85 for job control). The external reliability was studied using κ (0.90 for job demands and 0.93 for job control).

In the analysis we used a dichotomisation at the median scores to categorise persons into score above the median for work demands and control as “high” and below the median as “low” (median value for job demands was 11, for job control 14). Job strain was derived from the ratio between psychosocial job demands and control (job strain ratio).

Statistical methods

We used SPSS 10.0 for Windows in the statistical analysis. The mean and standard error of mean of self reported job control and job demands scores were calculated for each occupational category for cases and controls. Analysis of variance was used to test for significant differences in means. To adjust for potential confounding effects of the selected risk factors multiple logistic regression analysis was performed. Potential cofounders included in the models were factors age, socioeconomic status, arterial hypertension, smoking, and obesity. The adjusted OR along with their 95% CI for each risk factor relative to the reference category were assessed and the effect of the risk factors on the risk of myocardial infarction was evaluated.

RESULTS

Table 1 presents the distribution of occupational categories, age, and job characteristics among cases and controls. Cases and controls were similar according to their age. The mean values for job demands were lower in the cases’ group. As the 7th occupational category (craft and related trades workers) was quite large and distributed quite equally among cases and controls, it was chosen as referent category for further comparisons among occupational categories. The highest myocardial infarction risk groups were legislators, senior officials and managers (1st occupational category) and plant and machine operators and assemblers (8th occupational category) as compared with craft and related trades workers (7th occupational category). The age adjusted odds ratio estimates did not change substantially after adjustment for standard risk factors, for example, smoking, arterial hypertension, and obesity.

The mean values of job demands and job strain were similar in the cases and controls among the occupational categories (table 2). Therefore, in future calculations only results for job control are presented. The age adjusted odds ratio for high job demands was 0.65 (95% CI 0.45 to 0.94), for job strain 1.08 (95% CI 0.75 to 1.55).

Low job control was associated with an increased myocardial infarction risk in the population of 25–64 year old Kaunas men (age adjusted odds ratio was 1.99). Adjusting for socieconomic status increased the risk to 2.68, and for cardiovascular risk factors decreased the magnitude of the association to 2.31 (table 3).

Table 4 presents the effect of low job control for all occupational categories. The 0th, 4th, 5th, 6th, and 9th occupational categories were excluded from the analysis because of the small number of subjects. The odds ratio estimates for low job control were significantly increased in the high risk occupational categories (1st, 8th) as well as in non-increased myocardial infarction risk (2nd, 3rd, 7th), for example, the odds ratios were similar. The adjustment for socioeconomic status increased the effect of low job control for all the occupational categories, further adjustment for standard risk factors attenuated odds ratio estimates to insignificant for the 2nd and 3rd occupational categories.

DISCUSSION

We conducted a case-control study among able bodied men in Kaunas using the short Swedish version of the demand-control questionnaire to show the effect of psychosocial factors at work on myocardial infarction risk for different occupational categories. The main finding of our study was that low job control independently had a significant effect on myocardial infarction risk in all the occupational categories of Kaunas men, Lithuania, a country in a transition market economy.

Some possible limitations of the study, which include myocardial infarction diagnostic bias, selection, coding, and recall bias could occur in this study. The method of finding cases and case ascertainment in this study was comparable to that of myocardial infarction registers set up in accordance with principles adopted in the WHO MONICA programme. Nevertheless, there were several sources of errors that may have caused a misclassification of the clinical diagnosis. These errors include diagnostic errors in silent cases, coding errors or other clerical errors in data recording. However, this type of misclassification is probably unrelated to occupation.

Recall bias could occur in the self reported information on the psychosocial work environment as well. One potential source of recall bias is the fact that the subjects in the case group were interviewed after the myocardial infarction. Such bias has been labelled search for meaning. A large Swedish case-control study showed, however,⁶ that search for meaning is not a problem for job control in this type of study of first myocardial infarction. Accordingly in our study such recall bias is unlikely to have had any effect on the findings for job control. If there would be any effect at all it would be in the direction of inflated findings in the direction of high demands—increased risk. But the findings were in the opposite direction in this study.

The cardinal effect of low job control on increased risk of coronary heart disease and myocardial infarction has been revealed in the epidemiological studies in the countries of welfare^4–81214 as well in the country of the previous Soviet Bloc.¹⁰ Although the recent results from Whitehall II study on British civil servants indicated that job strain, high job demands, and, to some extent, low decision latitude are associated with an increased risk of coronary heart disease.¹⁷ The 25 year follow up of the Chicago Western Electric study also found both job control and job demand negatively associated with coronary heart disease mortality.¹⁸ Our results indicated that low job control independently had a significant effect on myocardial infarction risk in the 25–64 year old male population even after adjustment for socioeconomic status and standard risk factors as smoking, arterial hypertension, obesity. Job strain remains a risk factor in the more “Western” countries, whereas job control is a more important risk factor in the transition countries.

In Western countries men in lower social positions are at increased myocardial infarction risk.¹² The results from a case-control study in Kaunas, Lithuanian, a country in a transition market economy, did not support this point.¹⁵ Although a large cross-sectional study among managers in Germany has concluded that managers showed somewhat lower cardiovascular risk levels than did other professional groups¹⁹ and there is some evidence that managerial stress could be moderated by a high level of job control,²⁰ our study revealed that 1st occupational category (legislators, senior officials, and managers) were at increased myocardial infarction risk even after adjustment for standard risk factors. The possible explanation could be that the transition causes enormous problems for the administrators. The necessity to learn something about information technology and new ways of management on the one hand and the difficulties to do it on the other hand might be a cause of profound feeling of lack of control.

The second occupational category of the increased myocardial infarction risk was the 8th (plant and machine operators and assemblers). It is well known that repetitive work holds low control of work pace and that level of epinephrine in assembly line workers is increased.²¹

The main objective of our article was to evaluate the effect of low job control on myocardial infarction risk for the occupational categories in Kaunas men. We carried out an epidemiological case-control study among the able bodied male population in Kaunas and showed that low job control had significant effect for the occupational categories of increased myocardial infarction risk as well as for the non-increased risk, for example, there was no effect modification from employment status.

In Western societies there is evidence that the risk of myocardial infarction is higher in lower social positions and that a substantial contribution to the socioeconomic gradient was from low job control.¹³ This point of view was confirmed in the Stockholm heart epidemiology program (SHEEP) in Sweden indicating that low job control could explain 30% of the increased myocardial infarction risk among manual workers.¹² Job control was positively related to education and social group in the cross-sectional study in a random population sample in Gothenborg, Sweden.²² The British Whitehall II study (prospective cohort) on civil servants indicated that adjusting for employment grade somewhat reduced the strength of the association between job control and coronary heart disease.¹⁷ Wide ranges of clerical occupations do not represent all the occupational categories, especially blue collar. The Danish case-control study among men in all occupations, white collar as well as blue collar, indicated that the association between socioeconomic status and myocardial infarction risk could not be explained by the psychosocial factors at work.¹⁴

The comparative cross sectional Lithuanian-Swedish study showed that in both countries the unfavourable psychosocial work characteristics have been more prevalent in low social classes.¹⁶ According to our results, in two occupational categories of the increased myocardial infarction risk low job control plays the essential part, for example, for the “highest”—1st occupational category (legislators, senior, officials, and managers) and “not the lowest”—8th category (plant and machine operators and assemblers). Though low job control is relevant for the rest of the occupational categories as 2nd (professionals), 3rd (technicians and associate professionals), and 7th (craft and related trades workers). Thus, we can conclude that low job control is not more distinctive for low social classes in Kaunas, the second largest city of Lithuania, it seems an important first time myocardial infarction risk factor for all the occupational categories. As the intervention studies showed that increase in job control over three years was associated with decrease in cigarette smoking²³ and decreased the risk of coronary heart disease,⁷ this suggests that policy implications should be directed towards improving the working climate by means of increasing job control.