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American Journal of Epidemiology Advance Access originally published online on June 20, 2006
American Journal of Epidemiology 2006 164(4):349-357; doi:10.1093/aje/kwj212
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Original Contribution

Psychosocial Determinants of Coronary Heart Disease in Middle-Aged Women: A Prospective Study in Sweden

Hannah Kuper1, Hans-Olov Adami2,3, Töres Theorell4 and Elisabete Weiderpass2,5

1 Clinical Research Unit, London School of Hygiene and Tropical Medicine, London, United Kingdom
2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
3 Department of Epidemiology, Harvard University, Boston, MA
4 Division of Psychosocial Factors and Health, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
5 The Cancer Registry of Norway, Oslo, Norway

Correspondence to Dr. Hannah Kuper, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom (e-mail: hannah.kuper{at}lshtm.ac.uk).

Received for publication July 22, 2005. Accepted for publication February 21, 2006.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
A social gradient in coronary heart disease (CHD) has been documented in a variety of settings, predominantly among men. This study aimed to establish whether a social gradient in CHD existed in a group of Swedish women and whether it could be explained by established coronary risk factors or psychosocial factors. The Women's Lifestyle and Health Cohort Study includes 49,259 women from Sweden aged 30–50 years at baseline (1991–1992), when an extensive questionnaire was completed. There was complete follow-up through linkages to national registries until the end of 2002, during which time 210 cases of incident fatal CHD or nonfatal myocardial infarction occurred. Risk of CHD was significantly inversely related to years of education, the socioeconomic status proxy (hazard ratio comparing the lowest with the highest education group = 3.3, 95% confidence interval: 2.2, 4.7). This association was reduced after adjustment for established coronary risk factors (smoking, body mass index, alcohol consumption, diabetes, hypertension, exercise; hazard ratio = 1.9, 95% confidence interval: 1.3, 2.8). Job strain and social support were weakly related to CHD and did not explain the gradient by years of education. Self-rated health was strongly related to CHD, mediated by established coronary risk factors. Results show a strong gradient in CHD by years of education explained by established coronary risk factors but not by job strain or social support.

coronary disease; social support; socioeconomic factors; workplace

Abbreviations: CHD, coronary heart disease; CI, confidence interval; HR, hazard ratio


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Coronary heart disease (CHD) remains the leading cause of mortality in the world for both men and women, and it is becoming increasingly widespread in low-income countries (1Go). In Sweden, cardiovascular disease is the leading cause of mortality, with ischemic heart disease causing about half of all cardiovascular disease deaths (2Go). Low socioeconomic status is a strong predictor of CHD risk, whether socioeconomic status is measured through education, income and wealth, or employment grade (reviewed by Kaplan and Keil (3Go) and Gonzalez et al. (4Go)), although few studies have been carried out among women (5Go–13Go). A social gradient in CHD may exist because people in lower social strata have unhealthier lifestyles and so are predisposed to developing CHD. Alternatively, people in lower social strata may have less access to medical care. There is also a growing literature showing a link between job stress and other psychosocial factors and CHD (reviewed by Kuper et al. (14Go) and Rosengren et al. (15Go)), which may contribute to the social gradient in disease (9Go, 16Go).

The association between psychosocial factors and risk of CHD has been little investigated for women. Psychosocial factors include job stress and social support. The dominant model describing the role of job stress in the etiology of CHD is the Karasek-Theorell "job strain model," which argues that "job strain" arises when the worker faces simultaneously high demands and low control (17Go, 18Go). A large number of studies have demonstrated a relation between job stress and risk of CHD, although this association has been investigated infrequently for women (reviewed by Schnall et al. (19Go) and Kuper et al. (14Go)). Social support at work may buffer some of the negative effects of job strain on the development of CHD (20Go). Social support in general also appears to protect against CHD. Adverse psychosocial exposures may be associated with unhealthy behaviors or could have a direct biologic effect on health (21Go). Self-rated health is a good measure of general health status, which predicts future morbidity and mortality and current health behaviors (22Go).

This paper presents results from a large prospective cohort study of women carried out in Sweden (23Go). The aim was to examine whether a social gradient in CHD incidence exists for middle-aged women. Using comprehensive assessment of CHD risk factors and validated CHD outcome measures, we investigated whether the social gradient in CHD can be explained by established coronary risk factors and/or psychosocial factors.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Study population
Participants in the Women's Lifestyle and Health Cohort Study were enrolled during 1991 and 1992, when 49,259 women aged 30–50 years residing in the Uppsala Health Care Region returned a completed mailed questionnaire (23Go). Each woman is identified by a unique 10-digit national registration number, which encodes information on date of birth and gender (24Go). For the current study, we excluded 36 women because of a lack of vital status information in the available register files or because they emigrated or died before the start of follow-up, 68 women who had experienced a myocardial infarction before the start of follow-up, and 1,089 women who did not provide information about their years of education (the primary exposure variable). The final study population included 48,066 women.

Exposure classification
At baseline, the women completed a detailed self-administered questionnaire. Self-reported total years of school attendance was used as the indicator of socioeconomic status (25Go). Years of school attendance was divided into four categories: 7–9, 10–12, 13–15, and 16 or more. In Sweden, compulsory school attendance increased from 7 to 9 years in 1959. Therefore, 7–9 years of education equates to primary school with at most 2 years of additional professional education. Women with 10–12 years of education may have completed secondary school or up to 5 years of professional training. Education lasting 13–15 years corresponds to a university bachelor's degree or several professional training sessions at a lower level. The highest category includes women with more than 16 years of education, which mainly corresponds to a university master's level.

Work characteristics were measured by using established questionnaires for the central components of the job strain model, that is, job demands (five questions), decision latitude (six questions), and social support at work (six questions) (17Go, 18Go). Six questions measured the structure and function of social support. Scores for each scale were calculated as the sum of the item scores. The few subjects for whom one or two items on a scale were missing were assigned an average score based on the items they did answer; those for whom more than two items on a scale were missing were excluded from the analyses (job demands, n = 421; job control, n = 288; social support at work, n = 675; and social support, n = 353).

The study population was divided into tertiles for each of the psychosocial factors based on the responses across all of the women. Four quadrants of job strain ("active work"—high demand and high control, "high strain"—high demand and low control, "low strain"—low demand and high control, and "passive work"—low demand and low control) were constructed by cross-tabulating job demands and job control, both divided into two groups at the median. Women were also asked to give a personal assessment of their health (very good, good, poor, very poor).

Participants reported on established coronary risk factors, including cigarette smoking, exercise, alcohol consumption, weight, height, diabetes, and high blood pressure. The health-seeking behavior of the women was measured by asking them about the frequency of breast self-examination, mammography screening, and gynecologic check-ups.

Follow-up and CHD endpoints
Follow-up of the cohort was achieved through linkages with existing nationwide health registers, using the unique national registration number of the women, to ensure virtually complete follow-up with respect to death, emigration, and CHD. Information on death and emigration was collected through linkage to Statistics Sweden. Information on myocardial infarction was gathered through linkage to the National Hospital Discharge Register (International Classification of Diseases, Ninth Revision, code for acute myocardial infarction (410)) and the National Causes of Death Register (cases coded as CHD deaths). The start of follow-up was defined as the date that the returned questionnaire was received. Person-years were calculated from the start of follow-up to the primary diagnosis of fatal CHD or nonfatal myocardial infarction, date of emigration or death, or the end of follow up (December 31, 2002), whichever occurred first. The average length of follow-up was 135 months. In total, there were 210 events (189 nonfatal myocardial infarction and 21 fatal CHD).

Statistical analysis
We compared the baseline distribution of coronary risk factors for women in the four education strata by using analysis of variance for the continuous variables and cross-tabulation and the {chi}2 statistic for the categorical variables. We calculated hazard ratios by using the Cox proportional hazards model (26Go) to assess whether years of education predicted the age-adjusted incidence of CHD events. We interpreted hazards ratios as estimates of relative risk, and these ratios were reported with 95 percent confidence intervals. We tested for trends across categories of variables by assigning equally spaced values (e.g., 1, 2, 3, or 4) to the categories and treating the variables as continuous in the Cox proportional hazards model. All analyses were adjusted for age at baseline, categorized by 5-year intervals. The models were successively adjusted for established coronary risk factors: body mass index (<18.5, 18.5–<25, 25–<30, ≥30 kg/m2), alcohol consumption (0, <1.7, 1.7–4.4, ≥4.4 g/day), cigarette smoking (never smoker; <5, 5–<10, ≥10 pack-years), self-reported diabetes (yes/no), self-reported high blood pressure (yes/no), and exercise (rating of overall physical activity level: very low, low, normal, high, very high).

We calculated hazard ratios by using the Cox proportional hazards model to assess whether job strain variables predicted the age-adjusted incidence of CHD events, separating data for full- and part-time workers. A Cox proportional hazards model was also created to assess whether general social support, self-rated health, and health-seeking behavior, in turn, predicted the age-adjusted incidence of CHD events. These associations were adjusted for established CHD risk factors. The association between education and CHD incidence was adjusted sequentially for psychosocial variables, restricted to the full- and part-time workers. Women for whom data on prevalent CHD (n = 3,596), diabetes (n = 3,404), or hypertension (n = 2,343) were missing were assumed not to have prevalent disease.

Ethics
The study was approved by the Data Inspection Board in Sweden and by the regional ethical committee. Consent was assumed by the return of the postal questionnaire.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Of 48,066 women included in the study, 19.7 percent had completed 9 or fewer years of education, 39.1 percent had completed 10–12 years, 32.9 percent had completed 13–16 years, and 8.3 percent had completed at least 16 years (table 1). At baseline, the average age of the women was 40.3 years (standard deviation, 5.8). This population, on average, was relatively healthy; 44.3 percent rated their health as very good, 51.5 percent as good, and only 4.2 percent as poor or very poor. Moreover, mean body mass index was 23.4 kg/m2 (standard deviation, 3.7), mean alcohol consumption was 3.5 g/day (standard deviation, 4.5), and the prevalence of high blood pressure (9.3 percent) and diabetes (1.3 percent) was low. A quarter of the women (25.3 percent) rated their physical activity level as "high" or "very high," and 59.7 percent rated it as normal. A total of 40.7 percent of the women had never been cigarette smokers; among ever smokers, 15.3 percent had smoked for less than 5 pack-years, 14.8 percent had smoked for 5–10 pack-years, and 29.2 percent had smoked for 10 pack-years or longer.


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  		TABLE 1. Relation between years of education and coronary risk factors for participants in the Women's Lifestyle and Health Cohort Study, Sweden, 1991–2002

 
A longer period of education was associated with a lower prevalence of risk factors for CHD such as smoking, high body mass index, low level of physical activity, diabetes mellitus, and high blood pressure. Women with higher educational levels were also significantly more likely to have high self-rated health, high job demands, high job control, and high social support. However, they had somewhat less favorable health-seeking behavior profiles because they were less likely to perform breast self-examinations and to attend regular mammography screening.

We found a significant inverse relation between years of education and age-adjusted incidence of fatal CHD/nonfatal myocardial infarction (p for trend < 0.0001) (table 2). For these analyses, we combined group 3 (13–15 years of education) and group 4 (≥16 years of education) since only nine events occurred in the highest education group. Women in the lowest education group were about three times more likely than women in the highest education group to have an event during follow-up (hazard ratio (HR) = 3.3, 95 percent confidence interval (CI): 2.2, 4.7). This association was attenuated after adjustment for pack-years of smoking (HR = 2.5, 95 percent CI: 1.7, 3.7) and body mass index (HR = 2.5, 95 percent CI:1.7, 3.8), but adjustment for other established risk factors had less of an effect. Most of the relation between education and the incidence of CHD was explained after controlling for the full set of established risk factors (HR comparing the lowest with the highest education group = 1.9, 95 percent CI: 1.3, 2.8), although this association remained statistically significant; the inverse association between level of education and risk of CHD remained highly statistically significant (p = 0.003).


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  		TABLE 2. Proportional-hazards-derived adjusted hazard ratios (and 95% confidence intervals) for the association between years of education and incidence of fatal coronary heart disease/nonfatal myocardial infarction, serially adjusted for traditional coronary risk factors, Women's Lifestyle and Health Cohort Study, Sweden, 1991–2002

 
There were 19,859 full-time workers and 16,182 part-time workers in this study population, whereas the remaining women worked at home or were unemployed. We found no clear relation for full- or part-time workers between job control, job demands, or job strain and the incidence of fatal CHD/nonfatal myocardial infarction (table 3). Among women with low social support at work, the risk of CHD was elevated for part-time workers (HR = 2.2, 95 percent CI: 1.1, 4.4) but not for full-time workers. The association between adverse psychosocial work conditions and risk of CHD was consistently stronger in younger women (aged 30–39 years at baseline) than in older women (aged 40–49 years at baseline), although the small number of events in the young age group hampered statistical tests for effect modification (data not shown). When we compared decision authority and skill discretion, the two components of job control, the association was stronger between skill discretion and risk of CHD, although it remained nonsignificant. Combining the analyses for full- and part-time workers did not change these patterns (data not shown).


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  		TABLE 3. Proportional-hazards-derived adjusted hazard ratios (and 95% confidence intervals) for the association of baseline job control, job demands, and job strain with incidence of fatal coronary heart disease/nonfatal myocardial infarction in full- and part-time workers, Women's Lifestyle and Health Cohort Study, Sweden, 1991–2002

 
We found a strong relation between poor self-rated health and incidence of fatal CHD/nonfatal myocardial infarction (HR comparing good self-rated health with poor self-rated health = 3.9, 95 percent CI: 2.4, 6.3) (table 4). This hazard ratio for poor versus good self-rated health was attenuated, but still significant, after adjustment for established coronary risk factors (HR = 1.7, 95 percent CI: 1.0, 2.9). Health-seeking behaviors were unrelated to the incidence of CHD and the education gradient in CHD (data not shown).


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  		TABLE 4. Proportional-hazards-derived adjusted hazard ratios (and 95% confidence intervals) for the association of self-rated health and social support with incidence of fatal coronary heart disease/nonfatal myocardial infarction, Women's Lifestyle and Health Cohort Study, Sweden, 1991–2002

 
The gradient in risk of fatal CHD/nonfatal myocardial infarction between women with low and high levels of education was slightly weaker when the analysis was restricted to women employed either full- or part-time, although it remained strong (HR comparing the lowest with the highest education group = 2.8, 95 percent CI: 1.8, 4.3) (table 5). The education gradient was essentially unchanged after adjustment for job demands, social support at work, social support, or self-rated health, but it was strengthened slightly after adjustment for job control (HR = 3.2, 95 percent CI: 2.0, 5.1). The education gradient was slightly strengthened after we controlled for the full set of psychosocial risk factors (HR = 3.0, 95 percent CI: 1.9, 4.8).


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  		TABLE 5. Proportional-hazards-derived adjusted hazard ratios (and 95% confidence intervals) for the association between years of education and incidence of fatal coronary heart disease/nonfatal myocardial infarction in full- or part-time workers, serially adjusted for psychosocial risk factors, Women's Lifestyle and Health Cohort Study, Sweden, 1991–2002

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
We found a strong gradient in risk of fatal CHD/nonfatal myocardial infarction by educational level in this group of middle-aged Swedish women. Most of this relation was explained by established coronary risk factors but not by psychosocial work characteristics, social support, self-rated health, or health-seeking behaviors. Indeed, the association of psychosocial work characteristics and social support with risk of CHD was weak. Some association was apparent between poor self-rated health and risk of an event during follow-up, but it was explained by established coronary risk factors.

Many studies have demonstrated a relation between low socioeconomic status and CHD risk in men (reviewed by Kaplan and Keil (3Go) and Gonzalez et al. (4Go)), but few have looked at this relation in women. Those investigations of women that do exist confirm our finding of a strong social gradient in the development of CHD (5Go–13Go). We propose three pathways to explain this gradient. First, people in lower social strata have unhealthier lifestyles and so are predisposed to developing CHD. Second, access to medical care is related to social class. Third, people in lower social classes experience adverse psychosocial conditions, which predisposes them to CHD (9Go, 16Go) whether directly (21Go) or through established coronary risk factors and health-seeking behaviors. Our results support the first pathway; that is, women in lower social groups have adverse coronary risk factors and are thereby predisposed to develop CHD. Only a few studies of women have shown that most of the variation in CHD incidence across the social gradient is explained by established risk factors (12Go, 13Go). More frequently, studies of women have shown that established coronary risk factors fail to explain entirely the social variation in risk of CHD among women (5Go, 9Go, 11Go, 27Go–29Go), potentially because residual confounding existed because risk factors were measured at only one point in time and may have been incompletely captured (30Go).

Recent prospective studies that have reported results for women have not demonstrated a relation between job stress and CHD (31Go–33Go) or CHD risk factors (34Go), although earlier reports from cohort studies of women showed a positive association (35Go, 36Go). Job stress may be relatively less important in women than in men because the additional work women face in their homes (especially women with many children) could conceal the deleterious health effects of job strain. Women are also more likely to work part-time, and, to our knowledge, the effect of job strain on health has not been previously investigated in detail for part-time workers. Women and men may have different types of jobs, so the job-strain models developed for men may not be applicable to women. In our study, the average length of follow-up was 11 years; consequently, many of the women would have changed job-strain exposure during follow-up and some may have retired (potentially in particular those experiencing high job strain), thereby undermining the association between baseline job strain and incidence of CHD. In an extensive review of 27 studies, self-rated health predicted mortality in all but four studies, despite careful control for physical health status at baseline (22Go). Self-rated health captures subtle symptoms of disease and undiagnosed disease; hence, the association between self-rated health and risk of CHD observed in our study that persisted after adjustment for coronary risk factors could be the result of incomplete control for baseline health status.

We found little relation between social support and incidence of CHD, which was in line with some reports from cohort studies of women (37Go, 38Go) but not others (39Go, 40Go). No relation was detected between health-seeking behavior and risk of CHD, although health-seeking behavior was measured relatively crudely. However, a lack of association between health-seeking behavior and risk of CHD was expected because Sweden has a national health care system.

Study limitations
The exposure variables were measured at baseline only. This factor would not have had an important effect on the association between education, our main exposure variable, and CHD because education is unlikely to change for women in this age group. For the other psychosocial variables, any misclassification is expected to be nondifferential with respect to outcome, which could have led to an underestimation of effects (41Go). Although the confounders were measured at baseline only, residual confounding would be expected to be low and in the direction of confounders. Some potentially important psychosocial factors, including depression and anxiety (14Go) and control at home (42Go) as well as other measures of stress in the home environment, were not included in the baseline questionnaire. We did not adjust for diet and hyperlipidemia, recognized risk factors for CHD in women (43Go), and hypertension was measured through self-report only. Lack of adjustment for these variables may have resulted in some uncontrolled confounding of the models, although the most important potential confounders (i.e., smoking, body mass index, exercise) were included. Socioeconomic disadvantage in childhood is a potential risk factor for CHD that was not included in the present study, although it may be relatively less important than disadvantage in adulthood (44Go).

Study strengths
This prospective study is one of the few measuring the social gradient in CHD in women, and one of the few that simultaneously assessed the role of established coronary risk factors and psychosocial risk factors in explaining the social gradient in CHD. It is also one of the few studies investigating the effect of job strain on CHD in women, which was measured by using standardized questionnaires, and we analyzed women in full- and part-time employment separately. Unlike in studies of specific professional groups, the women in this cohort were involved in a range of employment activities, and a validation exercise from the Norwegian part of the cohort confirms that the cohort participants were representative of the general population (45Go). We used validated outcomes to measure incident CHD in women who were disease free at baseline, limiting the effect of recall bias. Disease endpoints were obtained through the In-Patient Register and Mortality Register, which allowed complete follow-up of the cohort. Furthermore, this study was large and involved extended follow-up.

Public health implications
Globally, CHD remains the leading cause of mortality in both men and women, and it is also becoming increasingly important in low-income countries (1Go). Social gradients in CHD have been repeatedly noted in men, which may offer insight into the prevention of CHD so that the incidence in the lowest social strata approaches the incidence in the highest social strata. It appears from the results of this study that a social gradient in CHD also exists in women and that established risk factors, particularly smoking, explain most of this social gradient. Therefore, a health promotion campaign aimed at reducing the prevalence of established coronary risk factors such as smoking in lower educational groups may reduce the population incidence of CHD, although other correlates of low education need further investigation.

Conclusion
In this cohort of middle-aged Swedish women followed for on average more than 11 years, we found a strong gradient in risk of fatal CHD/nonfatal myocardial infarction by educational level. Most of this gradient was explained by established coronary risk factors but not by job characteristics, other psychosocial variables, or health-seeking behavior.


   ACKNOWLEDGMENTS
 
In Sweden, the survey was supported by the Swedish Council for Planning and Co-ordination of Research, Swedish Cancer Society, STINT (The Swedish Foundation for International Cooperation in Research and Higher Education) Organon, Pharmacia, Medical Products Agency, and Schering-Plough. The authors' work was independent of the funders. The travel costs for Drs. Hannah Kuper and Elisabete Weiderpass were supported by a joint program grant from the Royal Society.

The authors thank Dr. Fredrik Granath, who participated in the early stages of the study; Dr. Weimin Ye for helping with linkages with the Causes of Death registry and inpatient registry; and Sven Sandin and Juhua Luo for data management.

Conflict of interest: none declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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