1 INSERM U687, National Hospital of Saint-Maurice (HNSM), Saint-Maurice, France
2 Department of Epidemiology and Public Health, University College London, London, United Kingdom
3 Centre for Social Epidemiology and Population Health, School of Public Health, University of Michigan, Ann Arbor, MI
Correspondence to Dr. Archana Singh-Manoux, INSERM U687, HNSM, 14 Rue de Val d'Osne, 94415 Saint-Maurice Cédex, France (e-mail: Archana.Singh-Manoux{at}st-maurice.inserm.fr).
Received for publication September 3, 2004. Accepted for publication December 21, 2004.
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ABSTRACT |
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cognition; cohort studies; intelligence; socioeconomic factors
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INTRODUCTION |
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The terms "cognitive ability" and "intelligence" are mostly used interchangeably, although the former appears to be more frequently used in the epidemiologic literature and the latter in the psychological literature. Cognitive psychologists have disagreed on the exact nature of intelligence for over a hundred years, with some favoring one general construct underlying all intelligence and others believing in multiple factors (11). Research into the links between cognitive ability and health has focused on the role played by general intelligence (12
15
). A group of 52 experts defined it as "... a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience" (16
, p. 13).
Interest in the relation between cognitive ability and health is driven by two different strands of research. The first comes from research into aging, examining the links between cognitive ability and survival. Many studies have found poor cognitive ability in old age to be a strong predictor of mortality (1724
). The causal pathways linking cognition to mortality remain unclear, although it has been suggested that an accelerated decline in cognitive function is an indicator of disease or accelerated biologic aging and, thus, related to mortality (17
, 24
, 25
). As socioeconomic circumstances are known to shape cognitive development (26
), it is also possible that cognitive ability in old age is a marker of accumulated socioeconomic advantage or disadvantage.
The paper by O'Toole and Stankov (27) in 1992 showing an intelligence test taken at army recruitment to predict midlife mortality in the Australian Veterans Health Study is the second source of interest in the link between intelligence and health. More recently, studies have linked cognitive ability in childhood to adult morbidity and mortality (15
, 28
30
). On the basis of these results, Gottfredson (13
) has proposed that intelligence is the "fundamental cause" of social inequalities in health. Cognitive ability or more precisely the general intelligence factor is seen to lie behind both socioeconomic achievement and health (13
). Gottfredson (13
) and Gottfredson and Deary (14
) have argued that technologic advances in modern societies make cognitive competence increasingly important for health. They propose that inadequate health self-care is the principal mechanism by which intelligence is related to social inequalities in health.
In this paper, we examine the links among cognitive ability, different measures of socioeconomic position, and health in midlife with a view to addressing the following questions:
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MATERIALS AND METHODS |
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Risk factors, including socioeconomic position (four temporally distinct measures), and cognitive ability
Childhood socioeconomic position, earliest of the socioeconomic position measures, was assessed with a latent variable made up of two measures: father's social class and socioeconomic circumstances in childhood. Father's social class was assessed using the Registrar General's Social Class classification. In order to assess socioeconomic circumstances in childhood, respondents were asked to recall family conditions before they were 16 years of age. A four-item scale was used: father/mother unemployed when they wanted to be working, family had continuing financial problems, family did not have an inside toilet, and family did not have a car. Participants responded either "yes" or "no," and the "yes" responses were summed so that a high score indicated poor socioeconomic circumstances in childhood. Principal component analysis on the two measures of childhood socioeconomic position revealed one factor explaining 68.2 percent of the variance.
Education was measured as the highest level of education achieved, with the respondent choosing one of 11 categories. This was regrouped into five standard hierarchical levels: 1) no formal education, 2) lower secondary education, 3) higher secondary education, 4) degree, and 5) higher degree.
Occupational position was the British civil service grade of employment at phase 1. Employment grade ranges from 1 to 6, with grade 1 representing the highest level and grade 6 the lowest. People in different grades differ with respect to salary, social status, and level of responsibility.
Household wealth at phase 5 (19971999), the most recent measure of socioeconomic position, was measured using a question where respondents were asked to assess their total assets ("amount of money the respondent would have if she/he cashed in all household assetshouse, car, caravan, boat, jewelryand paid off all the debts"). The six categories measuring household wealth ranged from "less than £4,999" to "more than £500,000."
Cognitive ability (intelligence) was assessed in the analysis by a measure of fluid intelligence (Alice Heim 4, part I (AH 4-I)), seen to be isomorphic with general intelligence (32). The AH 4-I is composed of a series of 65 items: 32 verbal and 33 mathematical reasoning items of increasing difficulty (33
). This is a test of inductive reasoning that measures the ability to identify patterns and to infer principles and rules. Cognitive testing was introduced to the Whitehall study midway through phase 3, and consequently cognitive data are available on 40 percent of the available sample at phase 3 and on the entire sample at phase 5. In the analyses reported here, cognitive data are drawn from phase 5. As general intelligence is seen to be a trait that is stable from infancy into middle age (32
, 34
), our use of this measure from phase 5 is not problematic. The correlation between phases 3 and 5 of the AH 4-I measure, on the smaller sample on which it is available (n = 2,556), is 0.85 (p < 0.0001), further demonstrating the stability at this age of this construct.
Health outcomes assessed at phase 5 (19971999)
Coronary heart disease consisted of fatal coronary heart disease or incidence of nonfatal myocardial infarction or angina between phases 1 and 5. A total of 10,300 (99.9 percent) participants were "flagged" at the National Health Service Central Registry, providing information on date and cause of death. Coronary death was indicated by International Classification of Diseases, Ninth Revision, codes 410414 (35). Potential nonfatal myocardial infarction and angina events were ascertained by questionnaire items on chest pain (the World Health Organization Rose Questionnaire) (36
), treatment (nitrates or revascularization), recall of a doctor's diagnosis on a questionnaire item at phases 15, and investigation (exercise electrocardiography, stress imaging, or angiography). The latter were verified against clinical records. Twelve-lead resting electrocardiograms (digital electrocardiograph; Siemens Mingorec, Erlington, Germany) were performed at study phases 1, 3, and 5 and classified according to the Minnesota code (36
, 37
). Two independent trained coders carried out the classification of myocardial infarction and angina, with adjudication by a third coder in the (rare) event of disagreement.
Health Functioning was assessed using the Short Form 36 General Health Survey scales (38). The Short Form 36 is a 36-item questionnaire that covers issues relating to physical, psychological, and social functioning. It is coded into eight scales: physical functioning, social functioning, role limitations due to physical problems, role limitations due to emotional problems, vitality, bodily pain, general health perception, and general mental health. These eight scales of the Short Form 36 can be summarized into physical and mental components scores using factor analysis (39
, 40
). Poor health functioning was indicated by being in the worst quintile for physical and mental components scores.
Minor psychiatric morbidity was assessed at phase 5 using the General Health Questionnaire. The General Health Questionnaire is a 30-item screening questionnaire for minor psychiatric disorders and is suitable for use in general population samples (41). A threshold of 4/5 on the General Health Questionnaire was chosen; all those scoring 04 were considered noncases and those scoring
5 were considered cases.
Self-rated health was assessed via the following question: "In general, would you say your health is excellent/very good/good/fair/poor?" For the purposes of this study, participants reporting the two poorest levels of health were categorized as having "poor" perceived general health.
Statistical methods
The relative index of inequality (RII) was used to examine the relation between socioeconomic position and health outcomes (42). The RII is a regression-based summary measure widely used in social inequalities research because it takes into account the size of all the social groups in a socioeconomic hierarchy (43
, 44
). This index is calculated by ranking the socioeconomic categories on a scale from the lowest, which is 0, to the highest, which is 1. Each category covers a range on the scale proportional to its population size and is given a value on the scale corresponding to the midpoint of its range. The morbidity rate of the socioeconomic position groups is then regressed on this measure of their relative position. The RII resembles relative risk in that it compares the health of the extremes of the social distribution, but it is estimated using the data on all social groups and is weighted to account for the size of social groups. The RII is interpreted as the ratio of the morbidity of the most disadvantaged to the most advantaged. Thus, if the index is 1.5, then the morbidity rate of the most disadvantaged is 1.5 times as high as that of the most advantaged; an RII of 1.00 would indicate equal morbidity across the socioeconomic hierarchy.
We used the RII to first assess the magnitude of the association between cognitive ability and the health outcomes. The second set of analyses examined the relation between different indicators of socioeconomic position and health and then examined the extent to which these relations could be explained by cognition. The first step here was to calculate RIIs showing the age-adjusted relation between each measure of socioeconomic position and ill health. The next step introduced cognitive ability to the model with age and socioeconomic position, its contribution being expressed by the percentage reduction in RII (percent reduction = (RIIsocioeconomic position, controlling for age RIIsocioeconomic position, controlling for age and cognitive ability)/(RIIsocioeconomic position, controlling for age 1) x 100). The final set of analyses consisted of simultaneously entering age, socioeconomic position, and cognitive ability as predictors. All analyses were carried out separately for men and women.
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RESULTS |
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DISCUSSION |
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Cognitive ability does not, in any of our analyses, fully explain the relation between socioeconomic position and health. The mean reduction in RII for childhood socioeconomic position was 12 percent in men and 18 percent in women; for education, 46 percent in men; for employment grade, 29 percent in men and 44 percent in women; and for household wealth, 13 percent in men and women. Several of the relations between socioeconomic position and health survive adjustment for cognitive ability. All significant relations between household wealth and health outcomes remain significant after the inclusion of cognitive ability in the model. The same is true for employment grade in men; among women, two of three significant associations with employment grade remain after adjustment for cognitive ability. Childhood socioeconomic position in men continues to be significantly associated with four of five of the health outcomes after adjustment for cognitive ability. These results suggest that different measures of socioeconomic position are not interchangeable, and they are not proxies for mental resources.
The final set of analyses assesses the effects of the predictors mutually adjusted for each other. The only significant effect of cognitive ability was on physical functioning in women. None of the other associations was independent of socioeconomic position. On the other hand, household wealth was significantly associated with all health outcomes, perhaps because it is a cumulative measure reflecting aspects of socioeconomic circumstances across the life course. The significant effects associated with education are counterintuitive, as they suggest that high educational achievement is predictive of poorer health. This has been previously examined in the Whitehall II study data. It is plausible that better-educated individuals have poorer health than those less educated given that they have achieved the same wealth and occupational status (45). In white collar professions such as the British civil service, education is strongly linked to occupational attainment and mobility, and discrepancies between the two are associated with poor health.
It has been hypothesized that income, occupation, and education would have successively stronger associations with health because of their increasing correlation with general intelligence (13, 14
). Our results do not support this proposition. Education was not significantly associated with any of the health outcomes in women, and it was not associated with mental functioning (mental component score) and minor psychiatric disorder (General Health Questionnaire) among men. Furthermore, among the different measures of socioeconomic position, it has the weakest relation with all the health outcomes examined. Results in tables 3 and 4 clearly show that the extent of social inequalities in health varies by the measure of social position used. Household wealth has the strongest relation with health outcomes for both sexes, except for physical functioning in women, where employment grade shows a stronger association. It is likely that different measures of socioeconomic position represent different facets of social position and are differently related to health outcomes (46
, 47
).
The interrelation among socioeconomic position, cognitive ability (intelligence), and health is an important question and increasingly manifest in the social inequalities literature. The view, proposed by Gottfredson (13), that intelligence is the fundamental cause of social inequalities sees cognitive ability to be the driving force behind both socioeconomic attainment and health. The alternate view is that, although socioeconomic position and cognitive ability are related to each other, social inequalities in health cannot be explained by group differences in intelligence. Our results show the constructs of both health and socioeconomic position to be multifaceted; not all health outcomes show the same social patterning, and not all measures of socioeconomic position are similarly related to health.
Recent work on cognitive ability (intelligence) highlights its importance to health. Gottfredson (13) and Gottfredson and Deary (14
) have argued that effective self-care is the primary mechanism through which intelligence influences health. Intelligence is seen to promote faster and more complete learning, resulting in better preventive self-care and better compliance with medication instructions. Whalley and Deary (15
) have proposed four mechanisms to explain the association between childhood intelligence and mortality. These are childhood intelligence as a record of bodily insults, as an indictor of system integrity, as related to healthy behaviors, and as a predictor of entry into safer environments. We found intelligence to have some independent association with health. Given the association between socioeconomic position and intelligence, it is likely that some of the pathways to health are shared. Further research is required to clarify the shared and independent pathways. This should also help to explain why cognitive ability has a differential impact on different health outcomes, strongest here with self-rated health and weakest with minor psychiatric disorder.
A number of limitations should be noted. First, data here are from white collar civil service employees and cannot be assumed to represent general populations. However, participants cover a wide range of the socioeconomic spectrum, with annual full-time salaries in 1995 ranging from £4,995 to £150,000. Second, cognitive ability in this study was assessed in midlife, and the variable used is a proxy for intelligence. However, this is unlikely to affect our results as it has been argued that general intelligence is a stable, individual trait and isomorphic with the measure used in these analyses (32). Finally, results are affected by higher rates of missing data among the older and lower-grade participants, leading to underestimation of the effects of socioeconomic position.
In conclusion, our results show cognitive ability to be important for health outcomes but not to be driving social inequalities in health. The pathways linking education and intelligence to health appear to be similar, although education is not a particularly strong correlate of health in these data. Social inequalities are multifaceted; inequalities linked to educational disadvantage represent only one aspect of inequality. As education is closely linked with cognitive ability, it is not surprising that a large proportion of the relation between education and health, when it exists, is explained by cognitive ability. However, other measures of socioeconomic position, employment grade and household wealth in particular, are associated with health independently of cognitive ability. A major challenge for future research is to identify the mechanism(s) through which intelligence influences health.
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ACKNOWLEDGMENTS |
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The authors 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; and all members of the Whitehall II study team.
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References |
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