a Department of Epidemiology and Biostatistics, Finnish Institute of Occupational Health.
b Rehabilitation Foundation.
Simo V Virtanen, Department of Epidemiology and Biostatistics, Finnish Institute of Occupational Health, Topeliuksenkatu 41 a A, FIN-00250 Helsinki, Finland. E-mail: Simo.Virtanen{at}occuphealth.fi
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Abstract |
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Methods Data came from the Longitudinal Census file, which included censuses since 1970 (every 5 years). The cohort consisted of men who were in the same occupation in 1975 and 1980, and who were between 25 and 64 years old in 1980. Farm work, mining and military occupations were excluded. Cardiovascular mortality of this cohort was followed up 19811994 (5.4 million person-years). Information on marital status, education and income was updated in 1985 and 1990. Working conditions were evaluated at occupational level (job exposure matrix). Poisson regression analyses were conducted to estimate the impact of independent variables on mortality. Inequalities were assessed in relation to occupational class and occupational category.
Results According to the models, elimination of unfavourable working conditions would have reduced the number of all cardiovascular deaths by 8%, myocardial infarctions by 10%, and cerebrovascular deaths by 18%. The most influential job exposures appeared to be high workload, low control, noise, and shift work. Income had a strong effect on mortality.
Conclusions Working conditions explained a relatively small portion of socioeconomic inequalities in mortality. Inequalities associated with occupational category and class were more attributable to varying levels of education and income.
Keywords Cardiovascular diseases, mortality, socioeconomic factors, occupational exposure, Finland, men
Accepted 30 November 2001
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Introduction |
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Individual risk factors are one set of explanations for socioeconomic inequalities in health.17,18 Alcohol use, smoking, lack of exercise, and a diet high in fat and sodium have been linked to high cardiovascular mortality rates. Such factors can be referred to as lifestyle although they do not always reflect choices that individuals have made freely: low education or income often limit opportunities to maintain a balanced diet or exercise properly. In other words, factors that cannot be characterized simply as choices are also at work, and their influence turns mortality differences into inequalities. One of these factors is education; others include occupation and its by-products: income and social class. Health inequalities associated with any of them fall under the general heading of socioeconomic inequalities.
Since work is a major component of the socioeconomic factors, it may contribute to socioeconomic inequalities in a variety of ways.15,16 Different jobs pay differently and bestow different degrees of social standing. In addition, different jobs involve varying sets of chemical, biological, physical, and ergonomic hazards which may bring about ill health.1921 Finally, more and more interest has recently been focused on psycho-social influences on health, and these influences can also depend on the nature and organization of work.16,22 Evidence of large mortality differences between specific occupations suggests that working conditions might be an important source of health inequalities in Finland, assuming that occupations with poor working conditions are unequally distributed between various socioeconomic groups.23 The aim of this article is to try to estimate the degree to which unfavourable working conditions are responsible for socioeconomic inequalities in cardiovascular mortality. Our analyses focus on men because, among Finns of working age, they have substantially higher cardiovascular mortality rates than women, and the patterns of mortality vary considerably between the sexes.1
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Data and Methods |
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The cohort was a closed one, and there were two selection criteria: (1) age between 25 and 64 in 1980 census, and (2) same occupation in 1975 and 1980 censuses.23 The objective of these restrictions was to focus the analyses on the stable workforce and reduce the impact of occupational mobility. After all restrictions, the male cohort before exclusions consisted of about 507 000 men (50% of employed men aged 2564 in 1980). Data were then cross-classified by various background variables and person-time calculated for the follow-up period 19811994.
Census variables
Most of the census information came from registers: age (in 1980), marital status (unmarried, married, or separated/divorced/ widowed), education (highest level), and income. Information on professional status (wage-earner or self-employed) and occupation was based on questions on the census form. Although misclassification cannot be eliminated, information from Finnish censuses appeared to be of reasonably good quality.23,24
Follow-up was divided into three periods: 19811985, 1986 1990, and 19911994. Since the cohort was closed, the period effect was subject to the effects of aging as well as true period effects. Time was also built into the analyses in another way: information on marital status, education, and income was first recorded in 1980 and then updated according to 1985 and 1990 census information.
Exposure information
The information concerning working conditions came from a job exposure matrix (FINJEM) developed at the Finnish Institute of Occupational Health.25 FINJEM used the same occupational classification as the census data (311 detailed categories and 9 main categories). For most exposures, occupations were put into one of three categories: unexposed, low exposure (three lowest quartiles of the exposed), and high exposure (top quartile of the exposed). Classification was based on the product between the prevalence and the level of exposure. However, psycho-social exposures, where prevalence was assumed to be 100% for all occupations, were categorized based on the level only, and their distributions did not follow percentiles.
Socioeconomic indicators
Two indicators of socioeconomic position were used: occupational class (officially called socioeconomic status) and occupational category (Table 1). We chose these two indicators because both were based on the same longitudinal census classification of occupations as exposure data. Two other variables, education and income, have often been used as indicators of socioeconomic position. We used these variables only as confounders.
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Methods
The analytical method used was Poisson regression modelling of mortality rates. Rate ratio estimates were used as indicators of excess risk and, hence, inequality. We used a method similar to one used in other papers where sequential models had been fitted, and the impact of explanatory variables was measured by the degree to which unexplained inequality was reduced from one model to the next.15,17,26
The method where sequentially fitted models are examined requires that a comparison group be selected appropriately. It may not be sufficient to designate one occupational category, for example, as the comparison group because that category is not necessarily optimal on other relevant factors. Thus, such a category does not provide the maximum contrast against other categories. This is not a problem if the study has sufficient power to detect even small differences between socioeconomic groups. Since our preliminary analyses suggested that power might be a problem, we decided to restrict our comparison group to those who had optimal conditions on both socioeconomic indicators and the lowest job exposure. However, all those who had the best socioeconomic conditions, had some exposure to potentially harmful working conditions.
FINJEM included a large number of exposures that were not relevant factors for cardiovascular mortality risk. Literature and preliminary analyses limited our attention to a set of potential occupational risk factors: organic solvents (aliphatic, aromatic, and chlorinated hydrocarbons and other solvents), lead, diesel exhaust, cadmium, arsenic, cold, noise, sedentary work, heavy lifting, shift work, lack of control, workload, and social demands. The comparison group was unexposed to these factors with the exception of intermediate exposures to workload and social demands.
Deaths
Table 1 also presents vital statistics of the final cohort. Dates and causes of death were retrieved from the national register, and the Finnish translation of the International Classification of Diseases (Ninth Revision) was used. There were a total of 16 344 cardiovascular deaths (codes 390459), 8378 acute myocardial infarctions (410), and 2428 cerebrovascular deaths (430438). Cardiovascular deaths made up over 54% of all disease deaths (codes 001799) and 45% of all deaths in the cohort.
Logic of analysis
For each disease group, we estimated three sets of models: (1) background models with age, marital status, period, and each socioeconomic variable separately, (2) a partially adjusted model with age, marital status, period, professional status, education, income, and both socioeconomic variables, and (3) a fully adjusted model with confounders, both socioeconomic variables, and job exposure variables. These were estimated with PROC GENMOD of SAS 8.2. Job exposure variables were restricted to ones that showed excess mortality risk, regardless of size of that risk. In other words, we wanted to give these variables every benefit of the doubt in order to see what would be their maximum impact on socioeconomic mortality gradients.
The solution to use a common comparison group for both socioeconomic variables meant that linear dependencies prevented the direct estimation of inequalities. Instead, we estimated intermediate models where each socioeconomic variable had a reference category, and the comparison group was indicated by a separate variable. Inequality estimates were then computed as separate contrasts between the comparison group and each socioeconomic group.
The comparison group was not entirely unexposed. This was reflected in the fully adjusted model, but not in the partially adjusted one. However, we used the information from the full model to adjust the comparison group downward as if it were unexposed. The adjustment required an assumption that the impact of the relevant factor was uniform across all socioeconomic groups. Reductions in inequality between partially and fully adjusted models were calculated by (RRpartialRRfull)/ (RRpartial1) where RRpartial was the rate ratio in partially adjusted model and RRfull was the rate ratio in fully adjusted model.
Fully adjusted models also estimated rate ratios for exposure categories. However, rate ratios do not tell us much about the impacts of job exposures unless the proportion of those exposed is also known. Having had both of these figures we were able to calculate etiologic fractions (EF) for each exposure.27,28 We used the following multiple-category formula: where pci was the proportion of cases in ith exposure category (including unexposed) and RRi was the rate ratio for that category. Since exposure information was at the aggregate level (311 occupations), exposures were quite strongly related. Consequently, impacts of specific exposures should be treated with caution. To assess the total impact of all exposure variables we used the parameters from fully adjusted models to calculate the number of expected deaths under the assumption of all occupations having the lowest exposure.
Education and income were included in the models before job exposure variables. In partially adjusted models, the impacts of the former could have included some of the impact of the latter, producing inequality estimates that were too low. To investigate this possibility we examined the differences in estimates of education and income between partially and fully adjusted models.
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Results |
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The impacts of job exposure variables in fully adjusted models are shown in Table 2. The smallest rate ratios were near 1.0 whereas the largest exceeded 1.2. For all cardiovascular causes, workload appeared to have the most influence (indicated by EF), followed by shift work and diesel exhaust. The impact of workload on myocardial infarctions was of similar magnitude, but the impacts of sedentary work, noise, and lack of control appeared to become stronger when the focus shifted to a more specific cause of death. Finally, shift work was the most influential factor for cerebrovascular deaths, followed by lack of control and diesel exhaust.
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Finally, we examined whether the impacts of education and income in partially adjusted models could have absorbed some of the influence of job exposure variables. Table 6 demonstrates quite clearly that this was not the case, since parameter estimates of education and income were barely affected by the inclusion of job exposures. Of these two variables, income seemed to be a stronger predictor of cardiovascular mortality although the scales were quite different. Excess mortality estimates associated with income ranged from 15% to 105% in the six models.
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Discussion |
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Physical and physiological stress factors, noise and sedentary work, had weaker impacts than the psycho-social ones. Their effects supported the views of Olsen and Kristensen,19 although sedentary work was defined more narrowly in FINJEM. In the absence of appropriate data, we made no adjustment for physical exercise during leisure. Among chemical exposures, organic solvents (especially chlorinated hydrocarbons), lead, and diesel exhaust appeared to have some link with cardiovascular mortality, consistent with previous literature19,21,30 In part, the minor role of chemical factors in accounting for cardiovascular deaths reflected their low prevalence. Many chemicals that had been identified as possible cardiovascular risk factors were not included in the FINJEM at all since their prevalence was estimated to be lower than 5% during the evaluation period (19451997).25 Consequently, their contribution toward inequalities should have been very small, even if the associated risks had been sizable.
One multi-faceted factor, regularity of working hours, had a sizable effect on cerebrovascular mortality. Irregular working hours (shift, evening, and night work) were quite prevalent in this cohort. To some extent, this may have reflected the fact that the cohort was limited to men who remained in the same occupation for a 5-year period, and this group was dominated by industrial occupations. In part, predominance of irregular working hours may have been also an artifact of aggregate data since occupations, rather than individuals, were categorized according to their typical working time arrangements. Although this factor is listed as psycho-social in FINJEM, its impact operates through a variety of pathways: in a recent review Bøggild and Knutsson identified mismatch of circadian rhythms, lack of social support, stress, and behavioural changes as possible causal mechanisms.31
Working conditions were evaluated by a job exposure matrix. This matrix represented an extensive effort by numerous experts on Finnish work life. It incorporated a time dimension that enhanced its usefulness in a follow-up study like this. Nevertheless, exposure information was based on occupation and consequently it ignored intra-occupational variation. This may have been one reason for the relative weakness of exposure variables as predictors of mortality. In addition, use of aggregate-level exposure data made it more difficult to evaluate a large number of exposure variables simultaneously since the number of specific occupations was only 311. The risk of developing linear dependencies between predictors meant that preliminary steps were necessary to screen exposure variables for the final analyses.
In summary, the results presented above suggested that occupational exposures had a moderate impact on Finnish males' cardiovascular mortality rates, in comparison with the impacts of income and education. Working conditions had more impact on inequalities associated with occupational category than occupational class. This suggests that prestige and social standing are essential components of socioeconomic mortality differences, in addition to actual working conditions.
KEY MESSAGES
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Acknowledgments |
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