- Email: [email protected]
Unfairness and the social gradient of metabolic syndrome in the Whitehall II Study
Journal of Psychosomatic Research 63 (2007) 413 – 419
Unfairness and the social gradient of metabolic syndrome in the Whitehall II StudyB Roberto De Vogli4, Eric Brunner, Michael G. Marmot International Institute for Society and Health, Department of Epidemiology and Public Health, University College London, London, United Kingdom Received 22 September 2006; received in revised form 3 April 2007; accepted 10 April 2007
Abstract Objectives: Little work has investigated the relationship between unfairness and risk factors for heart disease. We examine the role of unfairness in predicting the metabolic syndrome and explaining the social gradient of the metabolic syndrome. Methods: The design is a prospective study with an average follow-up of 5.8 years. Participants were 4128 males and 1715 females of 20 civil service departments in London (Whitehall II study). Sociodemographics, unfairness, employment grade, behavioral risk factors, and other psychosocial factors were measured at baseline (Phase 3, 1991–1993). Waist circumference, triglycerides, high-density lipoprotein (HDL) cholesterol, fasting glucose, and hypertension were used to define metabolic syndrome at follow-up (Phase 5, 1997–2000), according to the National Cholesterol Education Program/Adult Treatment Panel III guidelines. Results: Unfairness is positively associated with
waist circumference, hypertension, triglycerides, and fasting glucose and negatively associated with serum HDL cholesterol. High levels of unfairness are also associated with the metabolic syndrome [odds ratio (OR)=1.72, 95% CI=1.31–2.25], after adjustment for age and gender. After additional adjustment for employment grade, behavioral risk factors, and other psychosocial factors, the relationship between high unfairness and metabolic syndrome weakened but remained significant (OR=1.37, 95% CI=1.00–1.93). When adjusting for unfairness, the social gradient of metabolic syndrome was reduced by approximately 10%. Conclusion: Unfairness may be a risk factor for the metabolic syndrome and its components. Future research is needed to study the biological mechanisms linking unfairness and the metabolic syndrome. D 2007 Elsevier Inc. All rights reserved.
Keywords: Metabolic syndrome; Unfairness; Justice; Social class; Psychosocial factors
Introduction Fairness, defined as the quality of treating people equally or in a way that is right or reasonable [1], is widely B The Whitehall II study has been supported by grants from the Medical Research Council; British Heart Foundation; Health and Safety Executive; Department of Health; National Heart Lung and Blood Institute (HL36310), US National Institutes of Health (NIH): National Institute on Aging (AG13196), US NIH; Agency for Health Care Policy Research (HS06516); and the John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health. R.D. is supported by the National Institute on Aging. M.M. is supported by an MRC Research Professorship. 4 Corresponding author. International Institute for Society and Health, Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, WC1E 6BT London, United Kingdom. E-mail address: [email protected] (R. De Vogli).
0022-3999/07/$ – see front matter D 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2007.04.006
recognized as a key principle of human behavior, social relations, and the organization of society [2–5]. Acts of relational and societal unfairness can be conceptualized as violations of people’s dignity or self-respect [6,7] that may produce negative stress-related reactions that increase the risk of poor mental and physical health [8,9]. In a recent study, participants reporting higher levels of unfairness were more likely to experience an incident coronary event (excluding self-reported cases), compared to those with low or medium unfairness, after adjustment for age, gender, employment grade, traditional coronary risk factors, and other psychosocial work characteristics [10]. Unfairness was also an independent predictor of poor mental and physical health functioning [10]. Mediating mechanisms through which unfairness affects heart disease have not been investigated yet, and we are not aware of studies that
414
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
examined the role of unfairness in predicting the metabolic syndrome, a cluster of risk factors [11] (dyslipidaemia, abdominal obesity, hypertension, and glucose intolerance) for cardiovascular disease [12] and diabetes [13]. Research shows that the risk factors composing the metabolic syndrome are not equally distributed across social groups: people of lower socioeconomic status are more likely to develop metabolic risk factors or the metabolic syndrome as demonstrated by studies using different indicators such as education [14], social position [15], and household income [16]. Although the evidence of a social gradient of the metabolic syndrome is relatively robust, the mechanisms involved in this association have not been completely elucidated. Recent research proposed psychosocial characteristics such as chronic stress [17], work stress [18], job control [19], and social isolation [20] as plausible explanatory factors for one’s social position and/or the metabolic syndrome. When considering separate components of the metabolic syndrome, psychological distress is associated with greater fasting glucose [21], blood pressure [22], lipids [23], insulin levels [24], central obesity [25], and triglycerides [26]. Psychosocial factors also influence behavioral risk factors such as smoking and physical inactivity [27] that have been found to be important determinants of metabolic disorders [28,29]. In this study, we first analyze the distribution of each component of the metabolic syndrome across different levels of unfairness. Then, we investigate the relationship between unfairness and the metabolic syndrome, adjusting for age, gender, employment grade, behavioral risk factors, and other psychosocial factors. We finally test the hypothesis that unfairness contributes to the explanation of social inequality in metabolic syndrome.
Material and methods Participants The target population of the Whitehall II study, a prospective cohort study, is represented by all nonindustrial civil servants aged 35–55 years, who worked in the London offices of 20 civil service departments at baseline (1985– 88). Full details of the methodology of the final cohort which consisted of 10 308 (3414 women) are reported elsewhere [30]. Of the 8298 participants with available information on unfairness at Phase 3 (1991–1993), 70.4% (5843 participants, 1715 females) had data on the metabolic syndrome at Phase 5 (1997–2000). Sociodemographic factors, employment grade, unfairness, and behavioral risk factors were measured at Phase 3 (1991–1993) through a standardized questionnaire. Participants were followed up for an average of 5.8 years (S.D.=0.44; range=4.2–7.3). Participants lost to follow-up were significantly older ( Pb .001), more likely to be in the lowest employment grade, and in the highest category of unfairness.
Metabolic syndrome At the screening examination, waist circumference, serum triglycerides, serum high-density lipoprotein (HDL) cholesterol, fasting serum glucose, and blood pressure were measured to define metabolic syndrome according to the National Cholesterol Education Program (NCEP)/Adult Treatment Panel III (ATPIII) guidelines [31]. Cases of metabolic syndrome were defined by the presence of three or more of the following abnormalities: waist circumference N102 cm in men and 88 cm in women, triglycerides z1.60 mmol/l or treatment with lipid lowering medications, HDL cholesterol b1.03 mmol/l in men and b1.29 mmol/l in women or treatment with lipid lowering medications, blood pressure N130/85 mmHg or treatment with blood pressurelowering medications, and fasting glucose z6.11 mmol/l or treatment for diabetes. Waist circumference was taken as the smallest circumference at or below the costal margin. HDL cholesterol concentration was determined from fasting blood samples. The oral glucose tolerance test was administered following an overnight fast or in the afternoon after no more than a light fat-free breakfast eaten before 8.00 a.m. A second blood sample was taken 2 h later and analyzed using the electrochemical glucose oxidase method. Blood pressure was measured two times after 5-min rest with the Hawksley random-zero sphygmomanometer, and the mean value was used (mmHg). Unfairness Unfairness was assessed by the following single-item question: bI often have the feeling that I am being treated unfairly.Q Participants rated their response on a 6-point scale (1=strongly disagree, 2=moderately disagree, 3=slightly disagree, 4=slightly agree, 5=moderately agree, 6=strongly agree). The variable was categorized into four levels of unfairness: Responses 1 and 2 were combined into a new category, bno unfairness;Q Responses 3 and 4 have become categories blowQ and bmoderate,Q respectively; Responses 5 and 6 were collapsed into a new category, bhigh.Q Employment grade Employment grade was derived from a questionnaire asking details about job title and job characteristics. As in earlier studies using the Whitehall II cohort, the hierarchy of employment grades consisted of three levels (administrative, professional/executive, and clerical) based on salary, work role, and occupational seniority. Behavioral risk factors Behavioral risk factors at baseline included cigarette smoking, physical exercise (vigorous, moderate, and light), alcohol intake (abstainer, moderate drinker, and heavy
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
drinker), and fruit and vegetable consumption (daily vs. not daily). Vigorous physical activity refers to subjects who reported N1.5 h of vigorous activity per week and moderate to participants who reported N1.5 h/week of moderate activity but b1.5 h/week of vigorous activity; mild exercise refers to subjects who reported b1.5 h/week of vigorous or moderate activity. Moderate drinkers were defined as those participants drinking between 1 and 21 U of alcohol per week for men and between 1 and 14 U of drinks for women; heavy drinkers were those who consumed more than 21 U of alcohol per week for men and 14 U of drinks for women. Other psychosocial factors Additional psychosocial risk factors at Phase 3 included organizational justice, job strain, and effort–reward imbalance. Organizational justice was measured from the selfreported justice scale, as used in an earlier study using the Whitehall cohort [32] (five items, Cronbach’s alpha=.658). However, we used information from Phase 2 (1989–1990) for the two items that were unavailable at Phase 3. As measured in previous research, job strain was derived from the difference between the job demands (4 items, alpha=.675) and job control (15 items, alpha=.637) [33], while effort– reward imbalance was measured as a ratio of effort (five items, alpha=.698) to reward (six items, alpha=.718) [34]. Participants were divided into three groups (high, medium, and low) in each of the three psychosocial risk factors, based on the distributions of scores. Statistical analysis All statistical analyses were performed using the software package SPSS 11.0. Multiple logistic regression was used to estimate the age- and gender-adjusted odds ratios (ORs) for the metabolic syndrome and each of its components at follow-up (Phase 5, 1997–2000), using the unfairness categories (no, low, medium, and high) at baseline (Phase 3, 1991–1993) as independent variables (separate analyses for each). Tests for linear trend across unfairness categories were calculated. There was no significant interaction by gender in the relation between unfairness and each component of the metabolic syndrome. Therefore, analyses for men and women were combined. Logistic regression was also used to determine whether baseline unfairness predicted metabolic syndrome at followup after adjustment for age, gender, and employment grade. We additionally adjusted for behavioral and psychosocial risk factors. In additional analyses, we tested the causal direction from unfairness and metabolic syndrome by excluding the participants who were obese (body mass index N30) or with the metabolic syndrome at baseline. In order to estimate the relative contribution of unfairness in explaining the social gradient of the metabolic syndrome, a number of multiple models was developed. We fitted a first model with metabolic syndrome as the dependent
415
variable and age, gender, and employment grade (high, medium, and low) as independent variables. We then developed three separate models analyzing the contribution of unfairness, health behaviors, and other psychosocial characteristics in explaining inequalities in the metabolic syndrome (Models 2, 3, and 4, respectively). In another model assessing the aggregated effect of unfairness and health behaviors on the social gradient of metabolic syndrome, adjustments were made for age, gender, unfairness, smoking, physical activity, and alcohol consumption. A subsequent model included both employment grade and health behaviors. In the final model, we added other psychosocial factors (organizational justice, job strain, and effort–reward imbalance). We estimated the log odds of having the metabolic syndrome for low vs. high employment grade, where the percentage reduction in the employment grade coefficient on adjustment for unfairness, behaviors, and other psychosocial characteristics assessed the plausibility of these factors as mediators of the social gradient in the syndrome.
Results Of 5843 participants with available information on both unfairness from Phase 3 (1991–1993) and metabolic syndrome at Phase 5 (1997–2000), a total of 449 men (10.9%) and 190 women (11.1%) satisfied NCEP/ATPIII criteria for the metabolic syndrome. Table 1 presents the age- and gender-adjusted ORs for the metabolic syndrome and the five components of the metabolic syndrome by levels of unfairness. Overall, subjects who strongly or moderately agreed that they were often treated unfairly had a 72% increased risk of developing the metabolic syndrome compared with those reporting no levels of unfairness. The risk increased by 30% among those who either slightly agreed or disagreed being often treated unfairly. Increasing levels of unfairness were consistently associated with high waist circumference, hypertension, triglycerides, fasting glucose, and decreased serum HDL cholesterol. Table 2 presents different multivariate models using the metabolic syndrome at follow-up as dependent variable and unfairness at baseline as the main independent variable, with the inclusion of age, gender, employment grade, health behaviors, and psychosocial factors as covariates. After adjustment for age, gender, and employment grade, individuals in the highest unfairness group still had a 60% increased risk of developing the metabolic syndrome compared to those with no unfairness. When additionally adjusting for health behaviors (smoking, physical activity, alcohol intake, and fruit and vegetable consumption), a further small reduction of the relationship was observed. In the fully adjusted model, which included age, gender, employment grade, unfairness, health behaviors, and other psychosocial factors, the relationship between high unfairness and
1.00 1.29 (1.02–1.63) 1.30 (1.05–1.59) 1.72 (1.31–2.25) 299/3130, 109/923, 153/1284, 78/506, .001 176/3116, 1.00 57/921, 1.14 (0.84–1.55) 80/1283, 1.15 (0.87–1.51) 52/504, 2.00 (1.44–2.77) .001 363/3130, 1.00 115/923, 1.12 (0.89–1.41) 161/1284, 1.11 (0.91–1.36) 84/506, 1.46 (1.12–1.90) .042 381/2522, 145/769, 223/1091, 95/431, .001 485/2876, 1.00 150/823, 1.11 (0.90–1.35) 237/1150, 1.29 (1.08–1.53) 102/458, 1.41 (1.10–1.79) .005
1.00 1.16 (0.98–1.38) 1.20 (1.03–1.41) 1.47 (1.18–1.82) 699/3127, 224/923, 316/1280, 141/506, .001
1.00 1.27 (1.03–1.57) 1.38 (1.14–1.66) 1.43 (1.10–1.85)
n, OR (95% CI) n, OR (95% CI) n, OR (95% CI) n, OR (95% CI) n, OR (95% CI)
Unfairness No Low Medium High P for linear effect
n, OR (95% CI)
Waist circumference (N102 cm in men or N88cm in women) Serum HDL cholesterol (b1.03 mmol/l in men or b1.29 mmol/l in women or lipid-lowering medications)
Serum triglycerides (z1.69 mmol/l or lipid-lowering medications)
Hypertension (z130/85 mmHg or antihypertensive medication)
Fasting blood glucose (z6.11 mmol/l or antidiabetic medication)
Metabolic syndrome (z3 of the ATPIII criteria)
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419 Table 1 Multivariate age- and gender-adjusted odds ratios for the prevalence of metabolic syndrome and its components at follow-up (Phase 5, 1997–2000) by increasing levels of unfairness at baseline (Phase 3, 1991–1993)
416
metabolic syndrome remained statistically significant ( Pb .001). In the analyses excluding participants with the metabolic syndrome or obesity at baseline, the linear effect of the association between unfairness and the metabolic syndrome was significant ( Pb .015). When considering the high unfairness category, although the smaller number of cases resulted in a nonsignificant association, the effect size was robust to adjustment. We conducted additional analyses (data not shown) regarding the potential effect of negative emotions such as hostility and depression in explaining the relationship between unfairness and the metabolic syndrome. When adjusting for hostility, the relationship between high unfairness and metabolic syndrome was reduced from hazard ratio (HR)=1.62, 95% CI=1.13–2.31 to HR=1.52, 95% CI=1.06–2.18, which corresponds to an attenuation of 12.6%. When adjusting for depression, the effect of high unfairness on the metabolic syndrome remained relatively constant (from HR=1.72, 95% CI=1.31–2.26 to HR=1.69, 95% CI=1.28–2.24). Table 3 shows the employment grade differences in the metabolic syndrome and the effect of adjusting for unfairness, health behaviors, and other psychosocial factors. The age- and gender-adjusted relationship between social position and metabolic syndrome was strongly significant (Model 1). The social gradient of the metabolic syndrome was reduced by 10.4% when introducing unfairness into the analysis (Model 2). Health behaviors and other psychosocial factors contributed to a reduction of 12.1% (Model 3) and 13.8% (Model 4) of the association between employment grade and the metabolic syndrome. Unfairness and behavioral risk factors, together, explained more than 20% of the relationship between social position and the metabolic syndrome (Model 5). When adjusting for all factors, individuals in the low employment grade had a 79% increased risk of developing the metabolic syndrome, compared to those in high employment grade. However, the difference in the log odds between the highest and lowest employment grades was reduced by 29.2% (Model 6).
Discussion This study shows that the extent to which people are treated unfairly independently predicts the metabolic syndrome and its components. Although the effect of unfairness on the metabolic syndrome was reduced adjusting for employment grade and health behaviors (smoking, exercise, alcohol intake, and fruit and vegetable consumption), such factors did not reduce the association to nonsignificance. Unfairness exerts an effect on the metabolic syndrome that is partly independent of other psychosocial characteristics, including organizational justice, job strain, and effort– reward imbalance. The study also showed that unfairness partially explains the association between social position
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
417
Table 2 Logistic regression-derived ORs of the associations between unfairness at baseline (Phase 3, 1991–1993) with the metabolic syndrome (at follow-up (Phase 5, 1997–2000), adjusting for age, gender, employment grade, health behaviors, and other psychosocial factors Metabolic syndrome (z3 of the ATPIII criteria) Excluding participants with obesity or the metabolic syndrome at baseline
Including participants with obesity or the metabolic syndrome at baseline
Unfairness No Low Medium High P for linear effect a b
Age+gender+ health behaviorsa
Age+gender+ employment grade+ health behaviorsa
Age+gender+ employment grade+ health behaviorsa+ other psychosocial factorsb
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
Cases/n
OR (95% CI)
1.00 1.35 (1.05–1.75) 1.32 (1.05–1.66) 1.60 (1.17–2.19) .001
1.00 1.33 (1.03–1.73) 1.30 (1.02–1.64) 1.51 (1.10–2.08) .001
1.00 1.29 (0.99–1.67) 1.24 (0.98–1.56) 1.38 (1.01–1.90) .001
1.00 1.30 (0.99–1.68) 1.23 (0.96–1.56) 1.36 (1.00–1.93) .001
99/2144 39/636 66/890 24/314
1.00 1.21 (0.81–1.81) 1.54 (1.09–2.16) 1.46 (0.89–2.40) .015
Age+gender+ employment grade n 235/2515 93/762 127/1057 57/396
Adjusted for age+gender+ employment grade+ health behaviorsa+ other psychosocial factorsb
Health behaviors included smoking, physical activity, alcohol intake, and fruit and vegetable consumption. Other psychosocial factors included job strain, effort–reward imbalance, and organizational justice.
and the metabolic syndrome, although a larger proportion of the gradient is explained by health behaviors and other psychosocial factors. There are a number of possible mechanisms elucidating why unfairness may be a mediator of the relationship between social position and the metabolic syndrome. Although low social position does not account for the entire effect of unfairness, the frequent experience of being treated unfairly and disrespected is associated with poorer socioeconomic circumstances. First, people in subordinate or low social positions are often structurally dependent on others for resources and rewards, especially on people in positions of authority and power [35]. As a result, they are more exposed to acts of injustice in the distribution of such resources and rewards. Second, they are more likely to be treated as inferiors or ignored, given their own social status value [9]. Third, they may be more likely to ruminate over minor acts of unfair treatment compared to people in higher social positions
[35], especially because they are more likely to face uncertain or unpredictable events. Frequent experiences of unfair treatment can be conceptualized as negative aspects of social relations producing psychological distress that, in the long term, may influence health through emotional, behavioral, and biological reactions. Emotional or affective responses to unfair treatment may include inward-focused and/or outward-focused negative emotions depending on attributions of blame relative to acts of injustice. Inward-focused negative emotions occur when individuals, who are treated unfairly, evaluate themselves negatively or make internal attributions of responsibility. Outward-focused negative emotions occur when individuals evaluate others and externalize blame for the acts of injustice [36]. Inward-focused negative responses to acts of unfairness may include feelings of being devalued or insecurity about personal worth, which are precursors of depression and anxiety. These mental health conditions have
Table 3 Association between employment grade (Phase 3, 1991–1993) and the metabolic syndrome at follow-up (Phase 5, 1997–2000) and the effect of adjustment for unfairness, behavioral factors, and other psychosocial factors OR for metabolic syndrome (z3 of the ATPIII criteria) Low employment grade Adjustments
ORa (95% CI)
Attenuation (%)b
P for linear effect
Model Model Model Model Model Model
2.29 2.10 2.04 2.08 1.92 1.79
10.4% 13.8% 11.6% 21.2% 29.2%
.001 .001 .001 .001 .001
a b c d
1: 2: 3: 4: 5: 6:
Age+gender Age+gender+unfairness Age+gender+health behaviorsc Age+gender+other psychosocial Factorsd Age+gender+unfairness+health behaviorsc Age+gender+unfairness+health behaviorsc+other psychosocial factorsd
(1.75–2.99) (1.60–2.76) (1.54–2.69) (1.53–2.84) (1.45–2.54) (1.29–2.49)
Reference: high employment grade. Percentage reduction in log odds from Model 1. Health behaviors included smoking, physical activity, alcohol intake, and fruit and vegetable consumption. Other psychosocial factors included job strain, effort–reward imbalance, and organizational justice.
418
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
been associated with increased risk of metabolic syndrome [37]. Alternatively, unfair treatment can produce emotional or behavioral reactions such as anger and hostility that individuals adopt to defend their own dignity or personal identity. Such emotional responses as well have been found to be associated with the metabolic syndrome [38]. Our findings indicate that the relationship between unfairness and the metabolic syndrome is more likely to be mediated by outward-focused negative emotions such as hostility, rather than inward-focused negative emotions such as depression. It is plausible that repeated experiences of injustice involves two neuroendocrine bstressQ pathways: the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system [39]. Unfairness may also influence the metabolic syndrome through anger and hostility that are associated to alterations of the HPA axis and the sympathetic nervous system. High-hostility individuals experience neuroendocrine changes including elevated circulating levels of catecholamines [40] and augmented secretion of epinephrine in the sympathetic nervous system [41]. Hostility has also been found to be associated with increased incidence of hypertension [42], elevated fasting glucose [43], waist-to-hip ratios, serum triglycerides, and reduced HDL concentration [38]. This study presents some limitations, especially when considering our exposure measure. Self-reported unfairness may not reflect an objective evaluation of unfair treatment or may be influenced by personality characteristics of respondents. Previous work on unfairness at work showed that self-reported unfairness adequately reflects reality, since there is a high degree of congruence between subordinate’s perceptions of their supervisors across multiple measurement points and between the perceptions of supervisors by their peers [44] and superiors [45]. The reliance on a single item of unfairness could lead to potential biases due to problems of validity and reliability. A single item measuring the concept of interest cannot be tested for internal consistency and may present potential problems of random measurement error. The lack of multidimensionality of our unfairness measure makes it impossible to disentangle the specific effects of relational unfairness (e.g., acts of unfair treatment received from close persons) from those of societal unfairness (e.g., injustices received from people in positions of authorities). Both types of unfairness can be influenced by social position and may produce stressful reactions such as negative emotions and biological responses leading to the metabolic syndrome. Another limitation of this study regards the potential biases relative to the exclusion of participants with incomplete data on the measured variables. Participants who have been excluded from the study were more likely to be in the low social position and exposed to high levels of unfairness, suggesting that biases could have resulted in an underestimation of the effect of unfairness on the metabolic syndrome. Finally, generalization of results may be questioned especially because British civil servants may not adequately
represent the general population (blue-collar workers are not included). However, the majority of the working population is now employed in white-collar jobs, and the exclusion of the upper and lower tails of the social hierarchy is likely having produced a further underestimation of the health effects of unfairness. In summary, this prospective study shows that the frequency with which people are treated unfairly has significant effects on the metabolic syndrome and its components. These findings also show that unfairness partially explains why people in lower socioeconomic positions are more exposed to the clustering of risk factors defining the metabolic syndrome. Future research needs to study the role of alterations of the HPA and the sympathetic nervous system in explaining the link between unfairness and single components of the metabolic syndrome.
Acknowledgments We thank all participating Civil Service departments and their welfare, personnel, and establishment officers; the Occupational Health and Safety Agency; the Council of Civil Service Unions; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team.
References [1] Oxford advanced learner’s dictionary. New York7 Oxford University Press, New York. [2] Miller D. Disrespect and the experience of injustice. Annu Rev Psychol 2001;52:527 – 53. [3] Brosnan J, Waal FD. Monkeys reject unequal pay. Nature 2003;425: 297 – 9. [4] Johnson D, Stopka P, Knights S. The puzzle of human cooperation. Nature 2003;421:911 – 2. [5] Kahneman D, Knetsch J, Thaler R. Fairness and the assumptions of economics. J Bus 1986;59:285 – 300. [6] Horton R. Rediscovering human dignity. Lancet 2004;364:1081 – 5. [7] Marmot M. Dignity and inequality. Lancet 2004;364:1019 – 21. [8] Marmot M. Status syndrome: how your social standing directly affects your health and life expectancy. London7 Bloomsbury, 2004. [9] Wilkinson R. The impact of inequality: how to make sick societies healthier. New York7 New Press, 2005. [10] De Vogli R, Ferrie J, Chandola T, Kivimaki M, Marmot M. Unfairness and health: evidence from the Whitehall II Study. J Epidemiol Community Health 2007;61:513–8. [11] Reaven G. Syndrome X: is one enough? Am Heart J 1994;127: 1439 – 42. [12] Lakka H, Laaksonen D, Lakka T, Niskanen L, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 2002;288: 2709 – 16. [13] Hanson R, Imperatore G. Components of the metabolic syndrome and incidence of type 2 diabetes. Diabetes 2002;51:3120 – 7. [14] Silventoinen K, Pankow J, Jousilathi P, Hu G, Tuomiletho J. Educational inequalities in the metabolic syndrome and coronary heart disease among middle-aged men and women. Int J Epidemiol 2005;34:327 – 34.
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419 [15] Brunner E, Marmot M, Nanchahal K, Shipley M, Stansfeld S, Juneja M, et al. Social inequality in coronary risk: central obesity and the metabolic syndrome. Diabetologia 1997;40:1341 – 9. [16] Dallongeville J, Cottel D, Ferrieres J, Arveiler D, Bingham A, Ruidavets J. Household income is associated with the risk of metabolic syndrome in a sex-specific manner. Diabetes Care 2005;28:409 – 15. [17] Vitaliano P, Scanlan J, Zhang J, Savage M, Hirsch I, Siegler I. A path model of chronic stress, the metabolic syndrome, and coronary heart disease. Psychosom Med 2002;64:418 – 35. [18] Chandola T, Brunner E, Marmot M. Chronic stress at work and the metabolic syndrome: prospective study. BMJ 2006;332:521 – 5. [19] Hemingway H, Shipley M, Brunner E, Britton A, Malik M, Marmot M. Does autonomic function link social position to coronary risk? The Whitehall II study. Circulation 2005;111:3071 – 7. [20] Mittleman H, Wamala S, Schenck-Gustafsson K, Orth-Gomer K. Social relations and the metabolic syndrome in middle-age Swedish women. J Cardiovasc Risk 1999;6:391 – 7. [21] Hall J. Plasma glucose and lactic alterations in response to a stressful exam. Biol Psychol 1979;8:179 – 88. [22] Kawakami N, Haratani T, Araki S. Job strain and arterial blood pressure, serum cholesterol, and smoking as risk factors for coronary heart disease in Japan. Int Arch Occup Environ Health 1988;71:429 – 32. [23] McCann B, Magee M, Broyles F, Vaughan M, Albers J, Knopp R. Acute psychological stress and epinephrine infusion in normolipidemic and hyperlipidemic men: effects on plasma lipid and apoprotein concentrations. Psychosom Med 1995;57:165 – 76. [24] Alvarez M, Portilla L, Gonzalez R, Ezcurra E. Insulin response to a short stress period. Psychoneuroendocrinology 1989;14:241 – 4. [25] Brunner E, Chandola T, Marmot M. Prospective effect of job strain on general and central obesity in the Whitehall II Study. Am J Epidemiol 2007;165:828 – 37. [26] Glueck C, Tieger M, Kunkel R, Tracy T, Speirs J, Streicher P, et al. Improvement in symptoms of depression and in an index of life stressors accompany treatment of severe hypertriglyceridemia. Biol Psychiatry 1993;34:240 – 52. [27] De Vogli R, Gnesotto R, Goldstein M, Andersen R, Cornia G. The lack of social gradient of health behaviors and psychosocial factors in Northern Italy. Soz Praventivmed 2005;50:197 – 205. [28] Rennie K, McCarthy N, Yazdgerdi S, Marmot M, Brunner E. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003;32:600 – 6. [29] Dzien C, Dzien-Bischinger C, Hoppichler F, Lechleitner M. The metabolic syndrome as a link between smoking and cardiovascular disease. Diabetes Obes Metab 2004;6:127 – 32.
419
[30] Marmot M, Davey-Smith G, Stansfeld S. Health inequalities among civil servants: the Whitehall II study. Lancet 1991;8:1387 – 93. [31] NCEP. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2489 – 97. [32] Kivim7ki M. Justice at work Decreases risk of coronary heart disease among employees: the Whitehall II Study. Arch Intern Med 2005;165: 2245 – 51. [33] Kuper H, Marmot M. Job strain, job demands, decision latitude, and risk of coronary heart disease within the Whitehall II study. J Epidemiol Community Health 2003;57:147 – 53. [34] Kuper H, Singh-Manoux A, Siegrist J, Marmot M. When reciprocity fails: effort–reward imbalance in relation to coronary heart disease and health functioning within the Whitehall II study. Occup Environ Med 2002;59:777 – 84. [35] Kramer R. Divergent realities and convergent disappointments in the hierarchic relation: trust and the intuitive auditor at work. In: Kramer R, Tyler T, editors. Trust in organizations: frontiers of theory and research. London7 Sage Publications, 1996. pp. 216 – 45. [36] Barclay L, Skarlicki D, Pugh S. Exploring the role of emotions in injustice perceptions and retaliation. J Appl Psychol 2005;90: 629 – 43. [37] Todaro J, Shen B, Niaura R, Spiro A, Ward K. Prospective study of negative emotions and CHD incidence: the normative aging study. Am J Cardiol 2003;92:901 – 6. [38] Niaura R, Banks S, Ward K, Stoney C, Spiro A, Alwin C. Hostility and the metabolic syndrome in older males: the normative aging study. Psychosom Med 2000;62:7 – 16. [39] Brunner E. Socioeconomic determinants of health: stress and the biology of inequality. BMJ 1997;314:1472. [40] Clutter E. Regulations of glucose metabolism by sympathochromaffin catecholamines. Diabetes Metab Rev 1988:4 – 15. [41] Williams R, Barefoot J, Shekelle R. The health consequences of hostility. In: Chesney M, Roseman R, editors. Anger and hostility in cardiovascular and behavioral disorders. New York7 McGraw Hill Inc, 1985. pp. 173 – 85. [42] Irvine J, Garner D, Craig H, Logan A. Prevalence of Type A behaviour in untreated hypertensive individuals. Hypertension 1991;18:72 – 8. [43] Vitaliano P, Scanlan J, Krenz C. Insulin and glucose: relationships with hassles, anger, and hostility in non diabetic older adults. Psychosom Med 1996;58:489 – 99. [44] Maurer T, Raiu N, Collins W. Peer and subordinate performance appraisal measurement equivalence. J Appl Psychol 1998;83:693 – 702. [45] Riggio R, Cole E. Agreement between subordinate and superior ratings of supervisory performance and effects on self and job satisfaction. J Occup Organ Psychol 1992;65:151 – 8.
Unfairness and the social gradient of metabolic syndrome in the Whitehall II StudyB Roberto De Vogli4, Eric Brunner, Michael G. Marmot International Institute for Society and Health, Department of Epidemiology and Public Health, University College London, London, United Kingdom Received 22 September 2006; received in revised form 3 April 2007; accepted 10 April 2007
Abstract Objectives: Little work has investigated the relationship between unfairness and risk factors for heart disease. We examine the role of unfairness in predicting the metabolic syndrome and explaining the social gradient of the metabolic syndrome. Methods: The design is a prospective study with an average follow-up of 5.8 years. Participants were 4128 males and 1715 females of 20 civil service departments in London (Whitehall II study). Sociodemographics, unfairness, employment grade, behavioral risk factors, and other psychosocial factors were measured at baseline (Phase 3, 1991–1993). Waist circumference, triglycerides, high-density lipoprotein (HDL) cholesterol, fasting glucose, and hypertension were used to define metabolic syndrome at follow-up (Phase 5, 1997–2000), according to the National Cholesterol Education Program/Adult Treatment Panel III guidelines. Results: Unfairness is positively associated with
waist circumference, hypertension, triglycerides, and fasting glucose and negatively associated with serum HDL cholesterol. High levels of unfairness are also associated with the metabolic syndrome [odds ratio (OR)=1.72, 95% CI=1.31–2.25], after adjustment for age and gender. After additional adjustment for employment grade, behavioral risk factors, and other psychosocial factors, the relationship between high unfairness and metabolic syndrome weakened but remained significant (OR=1.37, 95% CI=1.00–1.93). When adjusting for unfairness, the social gradient of metabolic syndrome was reduced by approximately 10%. Conclusion: Unfairness may be a risk factor for the metabolic syndrome and its components. Future research is needed to study the biological mechanisms linking unfairness and the metabolic syndrome. D 2007 Elsevier Inc. All rights reserved.
Keywords: Metabolic syndrome; Unfairness; Justice; Social class; Psychosocial factors
Introduction Fairness, defined as the quality of treating people equally or in a way that is right or reasonable [1], is widely B The Whitehall II study has been supported by grants from the Medical Research Council; British Heart Foundation; Health and Safety Executive; Department of Health; National Heart Lung and Blood Institute (HL36310), US National Institutes of Health (NIH): National Institute on Aging (AG13196), US NIH; Agency for Health Care Policy Research (HS06516); and the John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health. R.D. is supported by the National Institute on Aging. M.M. is supported by an MRC Research Professorship. 4 Corresponding author. International Institute for Society and Health, Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, WC1E 6BT London, United Kingdom. E-mail address: [email protected] (R. De Vogli).
0022-3999/07/$ – see front matter D 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2007.04.006
recognized as a key principle of human behavior, social relations, and the organization of society [2–5]. Acts of relational and societal unfairness can be conceptualized as violations of people’s dignity or self-respect [6,7] that may produce negative stress-related reactions that increase the risk of poor mental and physical health [8,9]. In a recent study, participants reporting higher levels of unfairness were more likely to experience an incident coronary event (excluding self-reported cases), compared to those with low or medium unfairness, after adjustment for age, gender, employment grade, traditional coronary risk factors, and other psychosocial work characteristics [10]. Unfairness was also an independent predictor of poor mental and physical health functioning [10]. Mediating mechanisms through which unfairness affects heart disease have not been investigated yet, and we are not aware of studies that
414
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
examined the role of unfairness in predicting the metabolic syndrome, a cluster of risk factors [11] (dyslipidaemia, abdominal obesity, hypertension, and glucose intolerance) for cardiovascular disease [12] and diabetes [13]. Research shows that the risk factors composing the metabolic syndrome are not equally distributed across social groups: people of lower socioeconomic status are more likely to develop metabolic risk factors or the metabolic syndrome as demonstrated by studies using different indicators such as education [14], social position [15], and household income [16]. Although the evidence of a social gradient of the metabolic syndrome is relatively robust, the mechanisms involved in this association have not been completely elucidated. Recent research proposed psychosocial characteristics such as chronic stress [17], work stress [18], job control [19], and social isolation [20] as plausible explanatory factors for one’s social position and/or the metabolic syndrome. When considering separate components of the metabolic syndrome, psychological distress is associated with greater fasting glucose [21], blood pressure [22], lipids [23], insulin levels [24], central obesity [25], and triglycerides [26]. Psychosocial factors also influence behavioral risk factors such as smoking and physical inactivity [27] that have been found to be important determinants of metabolic disorders [28,29]. In this study, we first analyze the distribution of each component of the metabolic syndrome across different levels of unfairness. Then, we investigate the relationship between unfairness and the metabolic syndrome, adjusting for age, gender, employment grade, behavioral risk factors, and other psychosocial factors. We finally test the hypothesis that unfairness contributes to the explanation of social inequality in metabolic syndrome.
Material and methods Participants The target population of the Whitehall II study, a prospective cohort study, is represented by all nonindustrial civil servants aged 35–55 years, who worked in the London offices of 20 civil service departments at baseline (1985– 88). Full details of the methodology of the final cohort which consisted of 10 308 (3414 women) are reported elsewhere [30]. Of the 8298 participants with available information on unfairness at Phase 3 (1991–1993), 70.4% (5843 participants, 1715 females) had data on the metabolic syndrome at Phase 5 (1997–2000). Sociodemographic factors, employment grade, unfairness, and behavioral risk factors were measured at Phase 3 (1991–1993) through a standardized questionnaire. Participants were followed up for an average of 5.8 years (S.D.=0.44; range=4.2–7.3). Participants lost to follow-up were significantly older ( Pb .001), more likely to be in the lowest employment grade, and in the highest category of unfairness.
Metabolic syndrome At the screening examination, waist circumference, serum triglycerides, serum high-density lipoprotein (HDL) cholesterol, fasting serum glucose, and blood pressure were measured to define metabolic syndrome according to the National Cholesterol Education Program (NCEP)/Adult Treatment Panel III (ATPIII) guidelines [31]. Cases of metabolic syndrome were defined by the presence of three or more of the following abnormalities: waist circumference N102 cm in men and 88 cm in women, triglycerides z1.60 mmol/l or treatment with lipid lowering medications, HDL cholesterol b1.03 mmol/l in men and b1.29 mmol/l in women or treatment with lipid lowering medications, blood pressure N130/85 mmHg or treatment with blood pressurelowering medications, and fasting glucose z6.11 mmol/l or treatment for diabetes. Waist circumference was taken as the smallest circumference at or below the costal margin. HDL cholesterol concentration was determined from fasting blood samples. The oral glucose tolerance test was administered following an overnight fast or in the afternoon after no more than a light fat-free breakfast eaten before 8.00 a.m. A second blood sample was taken 2 h later and analyzed using the electrochemical glucose oxidase method. Blood pressure was measured two times after 5-min rest with the Hawksley random-zero sphygmomanometer, and the mean value was used (mmHg). Unfairness Unfairness was assessed by the following single-item question: bI often have the feeling that I am being treated unfairly.Q Participants rated their response on a 6-point scale (1=strongly disagree, 2=moderately disagree, 3=slightly disagree, 4=slightly agree, 5=moderately agree, 6=strongly agree). The variable was categorized into four levels of unfairness: Responses 1 and 2 were combined into a new category, bno unfairness;Q Responses 3 and 4 have become categories blowQ and bmoderate,Q respectively; Responses 5 and 6 were collapsed into a new category, bhigh.Q Employment grade Employment grade was derived from a questionnaire asking details about job title and job characteristics. As in earlier studies using the Whitehall II cohort, the hierarchy of employment grades consisted of three levels (administrative, professional/executive, and clerical) based on salary, work role, and occupational seniority. Behavioral risk factors Behavioral risk factors at baseline included cigarette smoking, physical exercise (vigorous, moderate, and light), alcohol intake (abstainer, moderate drinker, and heavy
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
drinker), and fruit and vegetable consumption (daily vs. not daily). Vigorous physical activity refers to subjects who reported N1.5 h of vigorous activity per week and moderate to participants who reported N1.5 h/week of moderate activity but b1.5 h/week of vigorous activity; mild exercise refers to subjects who reported b1.5 h/week of vigorous or moderate activity. Moderate drinkers were defined as those participants drinking between 1 and 21 U of alcohol per week for men and between 1 and 14 U of drinks for women; heavy drinkers were those who consumed more than 21 U of alcohol per week for men and 14 U of drinks for women. Other psychosocial factors Additional psychosocial risk factors at Phase 3 included organizational justice, job strain, and effort–reward imbalance. Organizational justice was measured from the selfreported justice scale, as used in an earlier study using the Whitehall cohort [32] (five items, Cronbach’s alpha=.658). However, we used information from Phase 2 (1989–1990) for the two items that were unavailable at Phase 3. As measured in previous research, job strain was derived from the difference between the job demands (4 items, alpha=.675) and job control (15 items, alpha=.637) [33], while effort– reward imbalance was measured as a ratio of effort (five items, alpha=.698) to reward (six items, alpha=.718) [34]. Participants were divided into three groups (high, medium, and low) in each of the three psychosocial risk factors, based on the distributions of scores. Statistical analysis All statistical analyses were performed using the software package SPSS 11.0. Multiple logistic regression was used to estimate the age- and gender-adjusted odds ratios (ORs) for the metabolic syndrome and each of its components at follow-up (Phase 5, 1997–2000), using the unfairness categories (no, low, medium, and high) at baseline (Phase 3, 1991–1993) as independent variables (separate analyses for each). Tests for linear trend across unfairness categories were calculated. There was no significant interaction by gender in the relation between unfairness and each component of the metabolic syndrome. Therefore, analyses for men and women were combined. Logistic regression was also used to determine whether baseline unfairness predicted metabolic syndrome at followup after adjustment for age, gender, and employment grade. We additionally adjusted for behavioral and psychosocial risk factors. In additional analyses, we tested the causal direction from unfairness and metabolic syndrome by excluding the participants who were obese (body mass index N30) or with the metabolic syndrome at baseline. In order to estimate the relative contribution of unfairness in explaining the social gradient of the metabolic syndrome, a number of multiple models was developed. We fitted a first model with metabolic syndrome as the dependent
415
variable and age, gender, and employment grade (high, medium, and low) as independent variables. We then developed three separate models analyzing the contribution of unfairness, health behaviors, and other psychosocial characteristics in explaining inequalities in the metabolic syndrome (Models 2, 3, and 4, respectively). In another model assessing the aggregated effect of unfairness and health behaviors on the social gradient of metabolic syndrome, adjustments were made for age, gender, unfairness, smoking, physical activity, and alcohol consumption. A subsequent model included both employment grade and health behaviors. In the final model, we added other psychosocial factors (organizational justice, job strain, and effort–reward imbalance). We estimated the log odds of having the metabolic syndrome for low vs. high employment grade, where the percentage reduction in the employment grade coefficient on adjustment for unfairness, behaviors, and other psychosocial characteristics assessed the plausibility of these factors as mediators of the social gradient in the syndrome.
Results Of 5843 participants with available information on both unfairness from Phase 3 (1991–1993) and metabolic syndrome at Phase 5 (1997–2000), a total of 449 men (10.9%) and 190 women (11.1%) satisfied NCEP/ATPIII criteria for the metabolic syndrome. Table 1 presents the age- and gender-adjusted ORs for the metabolic syndrome and the five components of the metabolic syndrome by levels of unfairness. Overall, subjects who strongly or moderately agreed that they were often treated unfairly had a 72% increased risk of developing the metabolic syndrome compared with those reporting no levels of unfairness. The risk increased by 30% among those who either slightly agreed or disagreed being often treated unfairly. Increasing levels of unfairness were consistently associated with high waist circumference, hypertension, triglycerides, fasting glucose, and decreased serum HDL cholesterol. Table 2 presents different multivariate models using the metabolic syndrome at follow-up as dependent variable and unfairness at baseline as the main independent variable, with the inclusion of age, gender, employment grade, health behaviors, and psychosocial factors as covariates. After adjustment for age, gender, and employment grade, individuals in the highest unfairness group still had a 60% increased risk of developing the metabolic syndrome compared to those with no unfairness. When additionally adjusting for health behaviors (smoking, physical activity, alcohol intake, and fruit and vegetable consumption), a further small reduction of the relationship was observed. In the fully adjusted model, which included age, gender, employment grade, unfairness, health behaviors, and other psychosocial factors, the relationship between high unfairness and
1.00 1.29 (1.02–1.63) 1.30 (1.05–1.59) 1.72 (1.31–2.25) 299/3130, 109/923, 153/1284, 78/506, .001 176/3116, 1.00 57/921, 1.14 (0.84–1.55) 80/1283, 1.15 (0.87–1.51) 52/504, 2.00 (1.44–2.77) .001 363/3130, 1.00 115/923, 1.12 (0.89–1.41) 161/1284, 1.11 (0.91–1.36) 84/506, 1.46 (1.12–1.90) .042 381/2522, 145/769, 223/1091, 95/431, .001 485/2876, 1.00 150/823, 1.11 (0.90–1.35) 237/1150, 1.29 (1.08–1.53) 102/458, 1.41 (1.10–1.79) .005
1.00 1.16 (0.98–1.38) 1.20 (1.03–1.41) 1.47 (1.18–1.82) 699/3127, 224/923, 316/1280, 141/506, .001
1.00 1.27 (1.03–1.57) 1.38 (1.14–1.66) 1.43 (1.10–1.85)
n, OR (95% CI) n, OR (95% CI) n, OR (95% CI) n, OR (95% CI) n, OR (95% CI)
Unfairness No Low Medium High P for linear effect
n, OR (95% CI)
Waist circumference (N102 cm in men or N88cm in women) Serum HDL cholesterol (b1.03 mmol/l in men or b1.29 mmol/l in women or lipid-lowering medications)
Serum triglycerides (z1.69 mmol/l or lipid-lowering medications)
Hypertension (z130/85 mmHg or antihypertensive medication)
Fasting blood glucose (z6.11 mmol/l or antidiabetic medication)
Metabolic syndrome (z3 of the ATPIII criteria)
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419 Table 1 Multivariate age- and gender-adjusted odds ratios for the prevalence of metabolic syndrome and its components at follow-up (Phase 5, 1997–2000) by increasing levels of unfairness at baseline (Phase 3, 1991–1993)
416
metabolic syndrome remained statistically significant ( Pb .001). In the analyses excluding participants with the metabolic syndrome or obesity at baseline, the linear effect of the association between unfairness and the metabolic syndrome was significant ( Pb .015). When considering the high unfairness category, although the smaller number of cases resulted in a nonsignificant association, the effect size was robust to adjustment. We conducted additional analyses (data not shown) regarding the potential effect of negative emotions such as hostility and depression in explaining the relationship between unfairness and the metabolic syndrome. When adjusting for hostility, the relationship between high unfairness and metabolic syndrome was reduced from hazard ratio (HR)=1.62, 95% CI=1.13–2.31 to HR=1.52, 95% CI=1.06–2.18, which corresponds to an attenuation of 12.6%. When adjusting for depression, the effect of high unfairness on the metabolic syndrome remained relatively constant (from HR=1.72, 95% CI=1.31–2.26 to HR=1.69, 95% CI=1.28–2.24). Table 3 shows the employment grade differences in the metabolic syndrome and the effect of adjusting for unfairness, health behaviors, and other psychosocial factors. The age- and gender-adjusted relationship between social position and metabolic syndrome was strongly significant (Model 1). The social gradient of the metabolic syndrome was reduced by 10.4% when introducing unfairness into the analysis (Model 2). Health behaviors and other psychosocial factors contributed to a reduction of 12.1% (Model 3) and 13.8% (Model 4) of the association between employment grade and the metabolic syndrome. Unfairness and behavioral risk factors, together, explained more than 20% of the relationship between social position and the metabolic syndrome (Model 5). When adjusting for all factors, individuals in the low employment grade had a 79% increased risk of developing the metabolic syndrome, compared to those in high employment grade. However, the difference in the log odds between the highest and lowest employment grades was reduced by 29.2% (Model 6).
Discussion This study shows that the extent to which people are treated unfairly independently predicts the metabolic syndrome and its components. Although the effect of unfairness on the metabolic syndrome was reduced adjusting for employment grade and health behaviors (smoking, exercise, alcohol intake, and fruit and vegetable consumption), such factors did not reduce the association to nonsignificance. Unfairness exerts an effect on the metabolic syndrome that is partly independent of other psychosocial characteristics, including organizational justice, job strain, and effort– reward imbalance. The study also showed that unfairness partially explains the association between social position
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
417
Table 2 Logistic regression-derived ORs of the associations between unfairness at baseline (Phase 3, 1991–1993) with the metabolic syndrome (at follow-up (Phase 5, 1997–2000), adjusting for age, gender, employment grade, health behaviors, and other psychosocial factors Metabolic syndrome (z3 of the ATPIII criteria) Excluding participants with obesity or the metabolic syndrome at baseline
Including participants with obesity or the metabolic syndrome at baseline
Unfairness No Low Medium High P for linear effect a b
Age+gender+ health behaviorsa
Age+gender+ employment grade+ health behaviorsa
Age+gender+ employment grade+ health behaviorsa+ other psychosocial factorsb
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
Cases/n
OR (95% CI)
1.00 1.35 (1.05–1.75) 1.32 (1.05–1.66) 1.60 (1.17–2.19) .001
1.00 1.33 (1.03–1.73) 1.30 (1.02–1.64) 1.51 (1.10–2.08) .001
1.00 1.29 (0.99–1.67) 1.24 (0.98–1.56) 1.38 (1.01–1.90) .001
1.00 1.30 (0.99–1.68) 1.23 (0.96–1.56) 1.36 (1.00–1.93) .001
99/2144 39/636 66/890 24/314
1.00 1.21 (0.81–1.81) 1.54 (1.09–2.16) 1.46 (0.89–2.40) .015
Age+gender+ employment grade n 235/2515 93/762 127/1057 57/396
Adjusted for age+gender+ employment grade+ health behaviorsa+ other psychosocial factorsb
Health behaviors included smoking, physical activity, alcohol intake, and fruit and vegetable consumption. Other psychosocial factors included job strain, effort–reward imbalance, and organizational justice.
and the metabolic syndrome, although a larger proportion of the gradient is explained by health behaviors and other psychosocial factors. There are a number of possible mechanisms elucidating why unfairness may be a mediator of the relationship between social position and the metabolic syndrome. Although low social position does not account for the entire effect of unfairness, the frequent experience of being treated unfairly and disrespected is associated with poorer socioeconomic circumstances. First, people in subordinate or low social positions are often structurally dependent on others for resources and rewards, especially on people in positions of authority and power [35]. As a result, they are more exposed to acts of injustice in the distribution of such resources and rewards. Second, they are more likely to be treated as inferiors or ignored, given their own social status value [9]. Third, they may be more likely to ruminate over minor acts of unfair treatment compared to people in higher social positions
[35], especially because they are more likely to face uncertain or unpredictable events. Frequent experiences of unfair treatment can be conceptualized as negative aspects of social relations producing psychological distress that, in the long term, may influence health through emotional, behavioral, and biological reactions. Emotional or affective responses to unfair treatment may include inward-focused and/or outward-focused negative emotions depending on attributions of blame relative to acts of injustice. Inward-focused negative emotions occur when individuals, who are treated unfairly, evaluate themselves negatively or make internal attributions of responsibility. Outward-focused negative emotions occur when individuals evaluate others and externalize blame for the acts of injustice [36]. Inward-focused negative responses to acts of unfairness may include feelings of being devalued or insecurity about personal worth, which are precursors of depression and anxiety. These mental health conditions have
Table 3 Association between employment grade (Phase 3, 1991–1993) and the metabolic syndrome at follow-up (Phase 5, 1997–2000) and the effect of adjustment for unfairness, behavioral factors, and other psychosocial factors OR for metabolic syndrome (z3 of the ATPIII criteria) Low employment grade Adjustments
ORa (95% CI)
Attenuation (%)b
P for linear effect
Model Model Model Model Model Model
2.29 2.10 2.04 2.08 1.92 1.79
10.4% 13.8% 11.6% 21.2% 29.2%
.001 .001 .001 .001 .001
a b c d
1: 2: 3: 4: 5: 6:
Age+gender Age+gender+unfairness Age+gender+health behaviorsc Age+gender+other psychosocial Factorsd Age+gender+unfairness+health behaviorsc Age+gender+unfairness+health behaviorsc+other psychosocial factorsd
(1.75–2.99) (1.60–2.76) (1.54–2.69) (1.53–2.84) (1.45–2.54) (1.29–2.49)
Reference: high employment grade. Percentage reduction in log odds from Model 1. Health behaviors included smoking, physical activity, alcohol intake, and fruit and vegetable consumption. Other psychosocial factors included job strain, effort–reward imbalance, and organizational justice.
418
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419
been associated with increased risk of metabolic syndrome [37]. Alternatively, unfair treatment can produce emotional or behavioral reactions such as anger and hostility that individuals adopt to defend their own dignity or personal identity. Such emotional responses as well have been found to be associated with the metabolic syndrome [38]. Our findings indicate that the relationship between unfairness and the metabolic syndrome is more likely to be mediated by outward-focused negative emotions such as hostility, rather than inward-focused negative emotions such as depression. It is plausible that repeated experiences of injustice involves two neuroendocrine bstressQ pathways: the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system [39]. Unfairness may also influence the metabolic syndrome through anger and hostility that are associated to alterations of the HPA axis and the sympathetic nervous system. High-hostility individuals experience neuroendocrine changes including elevated circulating levels of catecholamines [40] and augmented secretion of epinephrine in the sympathetic nervous system [41]. Hostility has also been found to be associated with increased incidence of hypertension [42], elevated fasting glucose [43], waist-to-hip ratios, serum triglycerides, and reduced HDL concentration [38]. This study presents some limitations, especially when considering our exposure measure. Self-reported unfairness may not reflect an objective evaluation of unfair treatment or may be influenced by personality characteristics of respondents. Previous work on unfairness at work showed that self-reported unfairness adequately reflects reality, since there is a high degree of congruence between subordinate’s perceptions of their supervisors across multiple measurement points and between the perceptions of supervisors by their peers [44] and superiors [45]. The reliance on a single item of unfairness could lead to potential biases due to problems of validity and reliability. A single item measuring the concept of interest cannot be tested for internal consistency and may present potential problems of random measurement error. The lack of multidimensionality of our unfairness measure makes it impossible to disentangle the specific effects of relational unfairness (e.g., acts of unfair treatment received from close persons) from those of societal unfairness (e.g., injustices received from people in positions of authorities). Both types of unfairness can be influenced by social position and may produce stressful reactions such as negative emotions and biological responses leading to the metabolic syndrome. Another limitation of this study regards the potential biases relative to the exclusion of participants with incomplete data on the measured variables. Participants who have been excluded from the study were more likely to be in the low social position and exposed to high levels of unfairness, suggesting that biases could have resulted in an underestimation of the effect of unfairness on the metabolic syndrome. Finally, generalization of results may be questioned especially because British civil servants may not adequately
represent the general population (blue-collar workers are not included). However, the majority of the working population is now employed in white-collar jobs, and the exclusion of the upper and lower tails of the social hierarchy is likely having produced a further underestimation of the health effects of unfairness. In summary, this prospective study shows that the frequency with which people are treated unfairly has significant effects on the metabolic syndrome and its components. These findings also show that unfairness partially explains why people in lower socioeconomic positions are more exposed to the clustering of risk factors defining the metabolic syndrome. Future research needs to study the role of alterations of the HPA and the sympathetic nervous system in explaining the link between unfairness and single components of the metabolic syndrome.
Acknowledgments We thank all participating Civil Service departments and their welfare, personnel, and establishment officers; the Occupational Health and Safety Agency; the Council of Civil Service Unions; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team.
References [1] Oxford advanced learner’s dictionary. New York7 Oxford University Press, New York. [2] Miller D. Disrespect and the experience of injustice. Annu Rev Psychol 2001;52:527 – 53. [3] Brosnan J, Waal FD. Monkeys reject unequal pay. Nature 2003;425: 297 – 9. [4] Johnson D, Stopka P, Knights S. The puzzle of human cooperation. Nature 2003;421:911 – 2. [5] Kahneman D, Knetsch J, Thaler R. Fairness and the assumptions of economics. J Bus 1986;59:285 – 300. [6] Horton R. Rediscovering human dignity. Lancet 2004;364:1081 – 5. [7] Marmot M. Dignity and inequality. Lancet 2004;364:1019 – 21. [8] Marmot M. Status syndrome: how your social standing directly affects your health and life expectancy. London7 Bloomsbury, 2004. [9] Wilkinson R. The impact of inequality: how to make sick societies healthier. New York7 New Press, 2005. [10] De Vogli R, Ferrie J, Chandola T, Kivimaki M, Marmot M. Unfairness and health: evidence from the Whitehall II Study. J Epidemiol Community Health 2007;61:513–8. [11] Reaven G. Syndrome X: is one enough? Am Heart J 1994;127: 1439 – 42. [12] Lakka H, Laaksonen D, Lakka T, Niskanen L, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 2002;288: 2709 – 16. [13] Hanson R, Imperatore G. Components of the metabolic syndrome and incidence of type 2 diabetes. Diabetes 2002;51:3120 – 7. [14] Silventoinen K, Pankow J, Jousilathi P, Hu G, Tuomiletho J. Educational inequalities in the metabolic syndrome and coronary heart disease among middle-aged men and women. Int J Epidemiol 2005;34:327 – 34.
R. De Vogli et al. / Journal of Psychosomatic Research 63 (2007) 413 – 419 [15] Brunner E, Marmot M, Nanchahal K, Shipley M, Stansfeld S, Juneja M, et al. Social inequality in coronary risk: central obesity and the metabolic syndrome. Diabetologia 1997;40:1341 – 9. [16] Dallongeville J, Cottel D, Ferrieres J, Arveiler D, Bingham A, Ruidavets J. Household income is associated with the risk of metabolic syndrome in a sex-specific manner. Diabetes Care 2005;28:409 – 15. [17] Vitaliano P, Scanlan J, Zhang J, Savage M, Hirsch I, Siegler I. A path model of chronic stress, the metabolic syndrome, and coronary heart disease. Psychosom Med 2002;64:418 – 35. [18] Chandola T, Brunner E, Marmot M. Chronic stress at work and the metabolic syndrome: prospective study. BMJ 2006;332:521 – 5. [19] Hemingway H, Shipley M, Brunner E, Britton A, Malik M, Marmot M. Does autonomic function link social position to coronary risk? The Whitehall II study. Circulation 2005;111:3071 – 7. [20] Mittleman H, Wamala S, Schenck-Gustafsson K, Orth-Gomer K. Social relations and the metabolic syndrome in middle-age Swedish women. J Cardiovasc Risk 1999;6:391 – 7. [21] Hall J. Plasma glucose and lactic alterations in response to a stressful exam. Biol Psychol 1979;8:179 – 88. [22] Kawakami N, Haratani T, Araki S. Job strain and arterial blood pressure, serum cholesterol, and smoking as risk factors for coronary heart disease in Japan. Int Arch Occup Environ Health 1988;71:429 – 32. [23] McCann B, Magee M, Broyles F, Vaughan M, Albers J, Knopp R. Acute psychological stress and epinephrine infusion in normolipidemic and hyperlipidemic men: effects on plasma lipid and apoprotein concentrations. Psychosom Med 1995;57:165 – 76. [24] Alvarez M, Portilla L, Gonzalez R, Ezcurra E. Insulin response to a short stress period. Psychoneuroendocrinology 1989;14:241 – 4. [25] Brunner E, Chandola T, Marmot M. Prospective effect of job strain on general and central obesity in the Whitehall II Study. Am J Epidemiol 2007;165:828 – 37. [26] Glueck C, Tieger M, Kunkel R, Tracy T, Speirs J, Streicher P, et al. Improvement in symptoms of depression and in an index of life stressors accompany treatment of severe hypertriglyceridemia. Biol Psychiatry 1993;34:240 – 52. [27] De Vogli R, Gnesotto R, Goldstein M, Andersen R, Cornia G. The lack of social gradient of health behaviors and psychosocial factors in Northern Italy. Soz Praventivmed 2005;50:197 – 205. [28] Rennie K, McCarthy N, Yazdgerdi S, Marmot M, Brunner E. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003;32:600 – 6. [29] Dzien C, Dzien-Bischinger C, Hoppichler F, Lechleitner M. The metabolic syndrome as a link between smoking and cardiovascular disease. Diabetes Obes Metab 2004;6:127 – 32.
419
[30] Marmot M, Davey-Smith G, Stansfeld S. Health inequalities among civil servants: the Whitehall II study. Lancet 1991;8:1387 – 93. [31] NCEP. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2489 – 97. [32] Kivim7ki M. Justice at work Decreases risk of coronary heart disease among employees: the Whitehall II Study. Arch Intern Med 2005;165: 2245 – 51. [33] Kuper H, Marmot M. Job strain, job demands, decision latitude, and risk of coronary heart disease within the Whitehall II study. J Epidemiol Community Health 2003;57:147 – 53. [34] Kuper H, Singh-Manoux A, Siegrist J, Marmot M. When reciprocity fails: effort–reward imbalance in relation to coronary heart disease and health functioning within the Whitehall II study. Occup Environ Med 2002;59:777 – 84. [35] Kramer R. Divergent realities and convergent disappointments in the hierarchic relation: trust and the intuitive auditor at work. In: Kramer R, Tyler T, editors. Trust in organizations: frontiers of theory and research. London7 Sage Publications, 1996. pp. 216 – 45. [36] Barclay L, Skarlicki D, Pugh S. Exploring the role of emotions in injustice perceptions and retaliation. J Appl Psychol 2005;90: 629 – 43. [37] Todaro J, Shen B, Niaura R, Spiro A, Ward K. Prospective study of negative emotions and CHD incidence: the normative aging study. Am J Cardiol 2003;92:901 – 6. [38] Niaura R, Banks S, Ward K, Stoney C, Spiro A, Alwin C. Hostility and the metabolic syndrome in older males: the normative aging study. Psychosom Med 2000;62:7 – 16. [39] Brunner E. Socioeconomic determinants of health: stress and the biology of inequality. BMJ 1997;314:1472. [40] Clutter E. Regulations of glucose metabolism by sympathochromaffin catecholamines. Diabetes Metab Rev 1988:4 – 15. [41] Williams R, Barefoot J, Shekelle R. The health consequences of hostility. In: Chesney M, Roseman R, editors. Anger and hostility in cardiovascular and behavioral disorders. New York7 McGraw Hill Inc, 1985. pp. 173 – 85. [42] Irvine J, Garner D, Craig H, Logan A. Prevalence of Type A behaviour in untreated hypertensive individuals. Hypertension 1991;18:72 – 8. [43] Vitaliano P, Scanlan J, Krenz C. Insulin and glucose: relationships with hassles, anger, and hostility in non diabetic older adults. Psychosom Med 1996;58:489 – 99. [44] Maurer T, Raiu N, Collins W. Peer and subordinate performance appraisal measurement equivalence. J Appl Psychol 1998;83:693 – 702. [45] Riggio R, Cole E. Agreement between subordinate and superior ratings of supervisory performance and effects on self and job satisfaction. J Occup Organ Psychol 1992;65:151 – 8.