Clustering of risk factors and social class in childhood and adulthood in British women's heart and health study: cross sectional analysis

Participants

The British women's heart and health study comprises women aged 60-79 years randomly selected from general practitioners' lists in 23 towns across England, Scotland, and Wales. Selection of towns, general practitioners, and participants was based on the methods used for the British regional heart study of men.13 A total of 4286 women (60% of those invited) participated, and baseline data were collected between April 1999 and March 2001. Participants completed a questionnaire and attended a local health centre, where they were interviewed by a research nurse, were physically examined, and gave a blood sample. General practitioners' medical records were also reviewed for each participant, and details of diagnoses of cardiovascular disease, diabetes and cancer extracted. Full methodological details have been published previously.14 We excluded participants with previous evidence of cardiovascular disease (doctor's diagnoses of coronary heart disease, stroke, peripheral vascular disease, angina) from the main analyses presented here.

Social class and risk factor measurements

We derived adult social class from the longest held occupation of the participant's husband for married women and her own for single women and childhood social class from the longest held occupation of the participant's father. Social class in childhood and adulthood was categorised into non-manual (social classes I to III non-manual) and manual (III manual to V) according to the registrar general's occupational classification.15

We considered risk factors included in the Framingham risk equations (see box).1 Blood samples were taken after women had fasted for six hours. We considered women to have hypertension if they had systolic blood pressure ≥ 160 mm Hg or diastolic blood pressure ≥ 95 mm Hg or were taking blood pressure medication.

Statistical analysis

We classified women into four socioeconomic groups: childhood non-manual/adult non-manual, childhood manual/adult non-manual, childhood non-manual/adult manual, childhood manual/adult manual. The prevalence of each risk factor (95% confidence intervals) was tabulated for each of the four groups with adjustment for age and town effects. We produced an age adjusted Pearson's partial correlation matrix for each of the continually distributed risk factors. We derived expected frequencies of co-occurrence of risk factors from none through to six risk factors by combining probabilities, assuming a binomial distribution and independence between them. Observed to expected ratios were estimated for all participants and separately for each of the four socioeconomic groups; in these analyses the expected frequencies were those predicted given the prevalences of risk factor within each socioeconomic group and indicate clustering when observed:expected ratios are high for no risk factors, low for a single risk factor, and high for three or more factors. We repeated analyses in women with existing coronary heart disease. To test the significance of the overall distribution of expected and observed counts within each social class group, we calculated %chi;² statistics with 3 degrees of freedom. In analyses we used robust standard errors, taking into account possible non-independence between women from the same towns, to estimate confidence intervals.

Clustering of risk factors

Correlation between risk factors does not mean that they are clustered. The appropriate analysis is to compare the expected with the observed distribution of risk factors, assuming that the risk factors are statistically independent of each other. Our analysis has simplified the underlying nature of the data, which include both normally and binomially distributed risk factors, by dichotomising the continuous variables and then modeling all risk factors as binary. The threshold used to define high risk may influence the degree of clustering found, as shown in an earlier study in which higher thresholds (90th centile) were associated with greater clustering.6 We used thresholds previously determined by their clinical utility in risk scoring, and, despite these being considerably lower than the 90th centile, clustering was still evident. Among women with diagnosed cardiovascular disease we found a similar pattern of clustering of risk factors.

Clustering and occurrence of cardiovascular disease

The importance of clustering is that the associations with cardiovascular disease tend to be greater than expected.6 Recent findings from the large study on atherosclerosis risk in communities have shown that of the 57 combinations of six components of insulin resistance syndrome, those with all six components have the largest excess carotid artery intimal-medial thickness, and these associations are synergistic.16

Socioeconomic position, risk factors, and coronary heart disease

Co-occurrence in childhood of risk factors for coronary heart disease tends to continue into adult life,17 18 and the associations between them and childhood and adult social class have been examined in several studies.9 Behavioural risk factors such as exercise and smoking correlate with adult social class,19 whereas obesity seems to be more consistently associated with childhood social class.20 Childhood social class also seems to be linked with other risk factors involved in insulin resistance syndrome.21 Evidence linking childhood socioeconomic position to coronary heart disease in adult life independently of adult socioeconomic position suggests that such associations are not necessarily mediated through lifelong disadvantage.22^–26 However, adjustment for adult socioeconomic position may result in attenuation of any childhood effect27 and might be interpreted as indicating that current rather than lifetime disadvantage is of greater relevance. It is more plausible to consider that the accumulation of socioeconomic disadvantage begins in childhood28 and is moulded by the prevailing social and economic context through which individuals live.29 Our failure to find greater clustering in disadvantaged women probably reflects the complex relations between risk factors and socioeconomic position and the risk factors selected for examination.

Study limitations

Our response rate (60%) was moderate but consistent with other large epidemiological surveys, including the health survey for England, in which participants were visited in their own homes.30 Distributions of cardiovascular risk factors in women in our study are similar to those for older women in the health survey for England. The social class distribution of the women in our study is similar to that found in the 1991 census (52% in manual social class in our study ν 55% older adults in the 1991 census). Responders were younger and less likely to have diabetes and stroke but had similar prevalences of coronary heart disease and cancer to non-responders. Our cohort may therefore have been healthier, but this would bias the associations observed only if they were in a different direction or markedly different in the non-responders, which seems unlikely.

Women without social class data were more likely to have fathers or husbands, or both, who were long term unemployed, and they were more likely to smoke. If we had included them the degree of clustering observed might have been increased, making our estimates conservative. Finally, most of the women examined were of white ethnic background (99.6%) and possibly risk factors may cluster in socially determined patterns more in men and ethnic minority groups than in white British women. Replication of these analyses in men and in ethnic minority groups would be of interest.

Implications

Clustering of risk factors—in distinction to the co-occurrence of risk factors—implies that they are not independent of each other and may therefore reflect an underlying causal or pathogenetic mechanism. The clustering we observed was similar in each social class group and, unlike the clustering observed in insulin resistance syndrome,8 does not seem to be particularly associated with causal mechanisms operating in childhood. Clustering of risk factors may be of relevance in explaining observed variations in risk for coronary heart disease. If clustering is more pronounced in geographically or socially defined groups and clusters of risk factors operate synergistically—that is, with greater than additive effect—to increase risk for coronary heart disease, then much of any “unexplained” variation may be explained by risk factor clustering. However, the lack of any social class patterning of clustering observed here suggests that, for these risk factors at least, this is not a plausible explanation for social inequalities in women's risk for coronary heart disease.

Simply including socioeconomic position into risk factor scoring systems would be an effective means of identifying population subgroups in whom co-occurrence of risk factors is more likely to occur and in whom need for treatment is greater.

The British women's heart and health study is codirected by Shah Ebrahim, Peter Whincup, and Goya Wannamethee. We thank Rita Patel, Carol Bedford, Alison Emerton, Nicola Frecknall, Karen Jones, Mark Taylor, and Katherine Wornell for collecting and entering data; all of the general practitioners and their staff who have supported data collection; and the women who have participated in the study. We thank Margaret May for advice on statistical methods and George Davey Smith for comments on an earlier draft of the manuscript.