Vibration exposure and myocardial infarction incidence: the VHEEP case–control study (2024)

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Abstract

Introduction

Risk factors for cardiovascular disease (CVD) have frequently been described and previously have predominantly been associated with lifestyle factors, hypertension, cholesterol level and gender [1]. Lately, studies have been published that deal with additional risk factors associated with the work environment [2–6]. Physical factors such as noise have been reported to be associated with an increased risk of CVD [7]. Noise exposure is often accompanied by exposure to vibration, which can influence the individual by being transmitted either through the hands (hand–arm vibration, HAV) or through the whole body (whole-body vibration, WBV) [8]. HAV exposure is associated with disorders in blood vessels, nerves, muscles and bones, while WBV exposure can lead to lower back pain [8–11]. Experimental studies have shown that exposure to vibration leads to elevated blood pressure (bp), changes in heart-rate variability and changes in peripheral vascular contraction [12–16]. Epidemiological studies of risk factors for acute myocardial infarction (MI) have shown a higher incidence of contracting hypertension among vibration-exposed individuals compared to unexposed controls [17,18]. Tamaian and Cocarla [19] have shown a higher prevalence of ischemic heart disease in vibration-exposed individuals than in unexposed controls. Studies specifically addressing the relationship between vibration exposure and MI have to our knowledge not yet been published in English-speaking journals.

Aim

The general aim of this study was to assess the risk of contracting acute MI in relation to various vibration-exposure characteristics. More specifically, the aims were to

• (i) assess the risk of contracting MI in relation to vibration exposure and

• (ii) assess the relation between level of exposure and response.

Methods

The Västernorrland heart epidemiology programme [VHEEP, a part of the Stockholm heart epidemiology programme (SHEEP) study] was the source of data. VHEEP is a population-based case–control study of risk factors for acute MI. The study base comprised all Swedish citizens living in the county of Västernorrland, who were 45–65 years of age and free from earlier clinically diagnosed MI. Cases were defined as all non-fatal and fatal first events of acute MI, first episode. Male and female cases and controls were identified during the period March 1993 to March 1995. The VHEEP study design is identical to that of the SHEEP study (previously described in detail [20–22]), differing only regarding regional catchment area and an additional set of questions covering exposure, among other things, to vibration.

Cases were identified from three sources and included at the time the disease occurred. The sources were (i) the coronary and intensive care units at the internal medicine departments of all the hospitals providing emergency treatment within the county of Västernorrland, (ii) the hospital discharge register for the same county and (iii) death certificates from the National Register of Causes of Death at Statistics Sweden. The criteria for determining MI were those accepted by the Swedish Association of Cardiologists in 1991.

One control per case was randomly selected from the study base after stratification for sex, age and hospital catchment area. The controls were selected within 2 days of case incidence from the computerized registers of the Västernorrland county population. Each control candidate was also checked for a history of MI since 1985 in the hospital discharge register for Västernorrland counties (as for cases). A waiting list of five control candidates per case were sampled simultaneously, so that a potentially non-responding control could be replaced by another, who was part of the study base at the time of case incidence. This substitution was made to maintain the power of the study and does not affect the non-participation rate. Before finally including a control in the study, case history information on previous MI was also requested. All controls were alive, regardless of whether or not the cases survived the MI. The routines and criteria for identification of cases and controls are described in detail elsewhere [5,20].

The exposure categorization was previously described in detail [20] and is thus only summarized here. Information on potential risk factors was collected by self-administered questionnaire. For deceased cases, exposure information was collected by a self-administered questionnaire completed by a close relative. This was administrated 6 months after the death of the individual case. In addition, hospital cases and their controls were offered a brief health examination including bp, blood samples and body measurements. This health examination was carried out no less than 3 months after inclusion in the study. Unless the subject explicitly declined to participate, up to four reminders were issued. All questionnaires were returned by mail. On receipt, they were checked and missing information was supplied during a telephone interview. Subjects, who stated in the questionnaire that they had suffered earlier MI, were contacted and interviewed regarding symptoms and treatment. If the information obtained suggested the occurrence of a previous clinically diagnosed MI, the subject was excluded from the study.

As described previously [20], data from both the questionnaire and the health examination were combined when the subjects were classified into exposure categories.

A vibration exposure assessment was conducted on an individual basis for both work and leisure time separately and together. The assessment was based on self-stated information on different occupations during lifetime and number of years spent in each occupation. This was combined with expert made estimations on daily exposure time and exposure magnitude in each occupation. The assessment for leisure time was based on self-reported type of vehicles/machines, number of exposed hours/day and number of exposed years.

The assessment of exposure magnitudes (frequency weighted acceleration) was based on type of vibration source reported (type of vehicle or vibrating tool) and expert estimations based on previously conducted measurements (in accordance with standard [9,10]).

The accumulated vibrations for work and leisure time were added together to give a total exposure for HAV and WBV, respectively.

To examine the possibility of an exposure–response relationship, each exposure group was divided into tertiles. For the group with combined HAV and WBV exposure, this division was made with indexation of tertiles for HAV and WBV tertiles, respectively. Subjects with low or medium exposure to both HAV and WBV were categorized as low exposed. Those with medium or high for either HAV or WBV were categorized as medium exposed, while those with high exposure for both HAV and WBV were categorized as high exposed.

The exposure categorization regarding the risk factors considered potential confounders in the present analyses [obesity: body mass index (BMI) ≥ 30; smoking: current, ex, no and high bp: 160/90] has been described previously [20].

The VHEEP study comprised only few women exposed to vibration. Thus, only men were included in the statistical analysis. Only cases and controls with complete information from the self-administered questionnaire were included in the analysis. Since diabetes is a highly contributory factor to vascular disease, people who stated in the questionnaire that they had diabetes and were treated with insulin (presumably diabetes Type I; nine cases and four controls) were not included in the statistical analysis. Still, we wanted to control for other possible forms of diabetes (e.g. diabetes Type II) and therefore included diabetes in the regression model. The diabetes variable included in the regression model defined diabetes Type II as those stating diabetes with drug treatment or diet control at the time of inclusion in the study.

Computer software SPSS® was used for the statistical analysis. Estimates of relative risks were based on odds ratios (ORs) from binary logistic regression. In what will be referred to as ‘Model 1’, only design variables were controlled for: age (in 5-year periods) and hospital catchment area. Furthermore, in ‘Model 2’, potential confounders as well as design variables were adjusted for, i.e. overweight, hypertension, smoking and diabetes. Stratified analyses were made for the two groups: (i) subjects exposed to HAV only and (ii) subjects exposed to both HAV and WBV. The number of subjects exposed to WBV only (e.g. sailors, engine drivers, stewardess) were four cases and 15 controls. This group was too small to form a single stratum in the analyses and did not suit the stratum with combined exposure to HAV and WBV. Therefore, this group was also excluded from analysis. To estimate the overall risk of contracting MI when vibration exposed, analysis was made for all those in groups (i) and (ii). Since the vibration exposure was skewed, the vibration dose is given as the median exposure rather than the mean.

Results

The participation rate was 85% among the cases and 82% among the referents. Further, information on lifetime occupational history (i.e. the basis for determining vibration exposure) was incomplete for 17 cases and 20 controls. Thus, the statistical analyses comprised 475 subjects, 218 cases (157 exposed, 61 non-exposed) and 257 controls (155 exposed, 102 non-exposed) (Table 1). Descriptive data on subjects as well as on potential confounders in the different exposure groups are given in Table 1. Table 2 shows the distribution of working time (years) in occupational groups for cases and controls, respectively. The most frequent vibration-exposed occupation was metal workers among both cases and controls. The non-vibration-exposed occupation most common among cases and controls was engineers. As shown in Table 3, the number of subjects exposed during occupation and/or leisure time is similar for cases and controls. The distribution between low, medium and high exposed are also similar for cases and controls. Table 4 shows descriptive information on accumulated lifetime vibration exposure for the two exposure groups only HAV and both HAV and WBV. Combining both exposure groups (only HAV and both HAV and WBV) yielded an overall estimated relative risk of 1.6 (95% CI: 1.1–2.4) of contracting MI if exposed to vibration during lifetime (not given in table).

Tables 5 and 6 show ORs for contracting MI in relation to the accumulated vibration dose during lifetime for the two exposure groups HAV only (Table 5) and combined exposure to HAV and WBV (Table 6). Based on accumulated lifetime dose, exposure to HAV increases the adjusted OR by ∼40% (Table 5) while exposure to both HAV and WBV increases the risk of MI by >80% (Table 6).

The main effect of adjustment for potential confounders was a reduced risk estimate in the subgroup ‘high exposed’ among those exposed to both HAV and WBV (Tables 5 and 6, comparing Model 1 and Model 2 analyses).

Discussion

The results show that work-entailing exposure to vibration is associated with an increased risk of acute MI. The risk increment is statistically significant for the group exposed to both HAV and WBV (1.84; CI: 1.16–2.90), but not for the group exposed to HAV only (1.38; CI: 0.83–2.28). Analyses were also made on exposure closer in time to infarction (12 months preceding inclusion). The overall risks were similar for accumulated lifetime exposure and exposure closer in time to infarction. This might be due to that the subjects exposed at the time of inclusion also had been exposed before, making it difficult to determine if more resent or past exposure contributes most to the risk.

When examining the tertile analysis for the combined exposure group, no exposure–response relation is found (Table 6) and for the group exposed to HAV only, the tertile analysis suggests an inverse exposure–response relationship (Table 5). Considering overall risk, regardless of exposure level, the group exposed to both HAV and WBV has a higher accumulated vibration exposure than those exposed to HAV only (Table 4). The combined exposure group had a slightly higher OR (1.84, Table 6) than the group exposed to HAV only (1.38, Table 5), which itself may indicate an exposure–response relation.

The vibration-exposed occupation groups most frequently represented in this study were metal workers, professional drivers, building construction and forestry workers. Several occupations within these groups are exposed to high workload, often in combination of little ability to control the work situation, and are mainly blue-collar workers from lower social class. High workload as well as having a blue-collar occupation has been studied and found to be risk factors for MI [25]. However, the individual risk factors (e.g. obesity, smoking) that are more common among lower social classes are adjusted for in regression model 2. Although we had information on both workload and socio-economic status, it could not be used in the present regression analyses. One reason was that workload exposure information was restricted to the last 5 years of occupational activity before inclusion, while the vibration exposure was accumulated from start of occupational career.

The reason for not including socio-economic status was that cases as well as controls could have changed between blue- and white-collar work during their occupational careers.

As seen in Table 3, 25% of the cases and 19% of the controls have either only leisure time exposure or combined leisure time and occupational exposure. Due to the rather low frequency and the similarity in frequency between cases and controls, the leisure time exposure is not likely to contribute to the risk increment for the cases compared to the controls.

We found an OR of 1.8 among the combined exposure group compared to an OR of 1.4 among the group exposed to only HAV. This higher risk could be explained either by the higher accumulated exposure level or by possible risks associated with certain areas of employment included. Previous studies show an increased risk of MI among bus drivers [26], who, among several other risk factors, are also exposed to WBV. Since our combined exposure group included bus and/or truck drivers, this might explain the higher risk in this group compared to the group exposed to a single form of vibration. Although, when subjects working as drivers at the time of inclusion were excluded in the regression model in a subanalysis, the OR increased from 1.45 to 1.76 (95% CI: 1.13–2.74). Thus in this study, it does not seem that drivers are at higher risk of contracting MI than those engaged in other vibration-exposed occupations.

Occupations associated with vibration exposure often imply exposure also to other environmental hazards, such as noise and exhaust fumes and particles from engines. Noise has repeatedly been reported as a comprising risk for MI [7]. There is a high level of association between exposure to noise and exposure to vibration, thus these exposures are difficult to separate in exposure assessment. The two exposures were not separated in this study; thus, the results might be interpreted as an effect due to noise and/or vibration exposure. There are studies showing that vibration exposure affects the autonomous nervous system [27,28] and can thereby give rise to changes in bp and vascular contraction. The association between vibration and noise exposure and disturbances to the autonomous nervous system is supported by Palmer et al. [29], who show an increased risk of severe hearing loss among those suffering from Raynaud's phenomena.

Engine exhaust is associated with MI [30] and the risk might be mediated by the effect of particles [31]. Several of the jobs in this study involve exposure to noise, exhaust and/or particles, but this has not been controlled for in the regression model.

About 10% of the cases were deceased, thus information on exposure for these individuals was collected from a close relative. This proxy information may be somewhat less precise than data collected from the exposed persons themselves. However, overstatement of vibration exposure among the cases is less likely since exposure to vibration is not a well-known risk factor of MI. Thus, this potentially less accurate exposure assessed group of individuals is not likely to contribute to an overestimation of the risk of MI. Pain in the upper extremities is a common symptom of MI. Since upper extremity pain also may occur in connection with drilling and other heavy work tasks associated with vibration exposure, vibration-induced incidences of MI may, in theory, remain undetected. However, with the case inclusion criteria applied, and when including also deceased cases, we do not think that such a selection bias exists in the present study.

The participation rate must be considered satisfactory and is almost equal for cases and controls. Information on occupational history was missing for a low proportion of the participating subjects, equal for cases and controls. Further, we have no reason to believe that non-participation was related to vibration exposure in a systematic way. Altogether, we do not think that the information loss affects the risk estimates to any larger extent.

The used method for estimating the vibration exposure may be influenced by recall bias both for number of years in each occupation, exposure time and type of vehicle/vibrating tool used. Furthermore, there are variations in vibration magnitude within an occupation, which may have affected the total accumulated lifetime vibration exposure. Although not based on specific measurements, epidemiological studies have shown that the reliability in this type of data is acceptable [32].

Conflicts of interest

None declared.

The Swedish Council for Working Life and Social Research, Sweden, provided financial support for this study.

References

© The Author 2006. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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