The new UK National Statistics Socio-Economic Classification (NS-SEC); investigating social class differences in self-reported health status

Transcription

1 Journal of Public Health Medicine Vol. 22, No. 2, pp Printed in Great Britain The new UK National Statistics Socio-Economic Classification (NS-SEC); investigating social class differences in self-reported health status Tarani Chandola and Crispin Jenkinson Abstract Background The new UK National Statistics Socio-Economic Classification (NS-SEC) is theoretically based on differences in employment relations and conditions. Differences in employment relations could account for some of the often observed social class differences in health in the United Kingdom. This study investigates the associations of the NS- SEC with a well-validated health outcome measure the Short Form health survey (SF-36). Methods Data from the Oxford Healthy Lifestyles Survey III (OHLS III, n ¼ 6454), a cross-sectional survey of adult men and women aged randomly selected from the counties of Berkshire, Buckinghamshire, Northamptonshire and Oxfordshire, were analysed. The associations of the NS- SEC with the SF-36 physical and mental summary scores are investigated in a series of regression models controlling for age, lifestyle factors, housing and neighbourhood conditions. Results There are significant social class differences in the SF-36 physical and mental summary scores after controlling for age. When lifestyle, housing and neighbourhood conditions are controlled for, these differences reduce to non-significance. Conclusion The NS-SEC shows significant social class differences in health, further evidence for its construct validity. Social class differences in housing, neighbourhood and lifestyle factors appear to have a large role in understanding class differences in health. As it is grounded in theory, the NS-SEC is likely to prove a valuable tool for explanations of inequalities in health. Keywords: health status, social class, National Statistics Socio-Economic Classification, SF-36 Introduction The Office for National Statistics (ONS) has commissioned a new socio-economic classification, the National Statistics Socio-Economic Classification (NS-SEC), which will be used in the 2001 UK Census. It is intended that this will replace the existing Registrar General s Social Class (RGSC). In comparison with the RGSC, the NS-SEC has been designed with a clearer theoretical foundation the basis for classifying people s occupations is explicitly based on employment relations and conditions. 1,2 As a result of its recent nature, there are relatively few studies of the associations of the NS-SEC with health outcomes. It would therefore be useful to investigate the associations of the NS-SEC with a wellvalidated health outcome measure such as the Short Form health survey (SF-36). The NS-SEC assigns people to social classes based on their occupational title and responsibilities over the workforce. It distinguishes between employers, employees and the unemployed. Within the category of employers, large-scale employers (employing 25 or more employees) are differentiated from smaller employers and own account workers (the self employed with no employees). Employees are further differentiated on the basis of their service relationship and labour contracts. 3 Managers and professionals have a service relationship with their employers characterized by a high degree of trust and delegated authority by their employers. Such occupations are generally long term and compensation for service to the employer is not only through salaries and salary arrangements (such as company cars or homes) but also through important prospective elements such as salary increments, pension rights, job security and career opportunities. On the other hand, employees in the working class are involved in routine work and have labour contracts specifying discrete amounts of labour under close supervision in return for wages calculated on a piece or time basis. Intermediate occupations are characterized by a mixed form of employment regulation between the service relationship and the labour contract. Differences between the employee classes have been validated in terms of the forms of remuneration (hourly or weekly wages versus monthly or annual salaries, payments for overtime, whether on an incremental pay scheme), job prospects (opportunities for promotion and notice period) and work autonomy (deciding the pace, the timing and/or the planning of tasks). 4 Service classes (managers and professionals) have Nuffield College, Oxford OX1 1NF. Tarani Chandola, Prize Research Fellow Health Services Research Unit, University of Oxford, Institute of Health Sciences, Headington, Oxford OX3 7LF. Crispin Jenkinson, Deputy Director Address correspondence to Dr C. Jenkinson. Faculty of Public Health Medicine 2000

2 SOCIAL CLASS DIFFERENCES IN HEALTH STATUS 183 better forms of remuneration, job prospects and higher work autonomy. These favourable employment characteristics are least represented in the routine employee class, and the intermediate classes have intermediate levels of these characteristics. Part of the debate around social inequality in health in the United Kingdom is due to the lack of explanatory value of the RGSC. 5,6 There is a lack of clarity over what the RGSC actually measures and how well it measures it. 1,7,8 This in turn prevents the development of useful hypotheses and causal narratives that could potentially explain the associations between social class and health. If the NS-SEC is associated with the SF-36, some of the association could potentially be explained by the differences in employment relations and conditions between the social classes. Bartley et al. 9 argued that by analysing the relationships between the NSSC and health indicators, we can reveal how different types of employment relations and conditions affect health outcomes. There is considerable evidence that occupational factors such as autonomy and control over work and physical working conditions have a strong effect on health, cardiovascular disease and sickness absence It has been hypothesized that differences in employment conditions between social classes explain some of the observed social class differences in health. Perhaps the strongest evidence for this view comes from the study by Marmot et al., 14 who found that there were no significant differences among hierarchical employment grades of the civil service in heart disease outcomes after controlling for job control. Low job control is characteristic of labour contract occupations and so one may expect the working classes to have high rates of heart disease and ill health. High job control is characteristic of service relationships so one may expect those classes to have relatively better health. It is also possible that self employment implies a higher degree of job control and consequently, the possibility that the self employed have better than average health. However, the differences between the NS-SEC classes cannot be simply interpreted in terms of differences in job control or other employment relations. The NS-SEC has been designed to capture basic structuring principles of society such as income, housing and consumption, which condition and shape the lives and the life chances of people in different social classes. 1 Hence, other factors, apart from employment relations, could explain any observed social class differences in health. A number of studies have identified factors such as material living conditions (housing and neighbourhood conditions), health behaviours, early childhood conditions and psychosocial stress as important mechanisms underlying the association between social class and health An analysis of the relative contribution of these various mechanisms in explaining social class differences in health could potentially increase our understanding of how inequalities in health are generated. This study also examines the role of health behaviours, housing and neighbourhood conditions in explaining social class differences in health. Other potential explanatory factors such as psychosocial stress and childhood conditions could not be examined because of limitations in the data. There has been extensive research on the effect of behaviours such as smoking, alcohol consumption, exercise and diet on health. Furthermore, a number of studies have shown that healthy behaviours are more prevalent among more socially advantaged groups Poor housing conditions may also directly affect health. For example, cold and damp housing is partly responsible for the increased incidence of cardio-respiratory disease in winter. 22 Cold, directly, and damp, indirectly, increase both the heart s work load and respiratory tract secretions. The strength of the association between housing tenure and health 23 could be partly attributed to the problems of living in poorly maintained local authority rented housing, such as dampness and mould 24 and inadequate heating. Meltzer et al. 25 found that symptoms of psychological distress were more prevalent in people in rented accommodation than owner occupiers. Neighbourhood problems of burglaries, vandalism and poor public transportation can contribute to the stress of living in deprived neighbourhoods, which has been associated with higher risks of morbidity. 26 The RGSC often displays a hierarchical association with health and mortality outcomes those in higher classes have lower rates of mortality and morbidity than those in lower classes. 15,27 We cannot expect such a linear association between the NS-SEC and health because the NS-SEC is not hierarchically or linearly ordered. 1 Although some class categories are superordinate with respect to others, for example, managers in large establishments vis-à-vis intermediate employees and all of the working class, the relationship between the intermediate, self-employed and lower supervisory classes (NS-SEC classes 3, 4 and 5, respectively) is not implicitly hierarchical. The SF-36 is a 36-item health status measure designed to assess self-reported functioning and well-being. 28 Two summary scores can be derived from this measure: the Physical Component Summary Score (PCS) and the Mental Component Summary Score (MCS). 29,30 The PCS assesses self-reported functional ability and the capacity to carry out everyday activities. The MCS assesses emotional well being and mental health. The version of the SF-36 utilized in this study was the SF-36 Version 2, which contains minor alterations in wording and response categories and is more sensitive to variations in health than its predecessor. 31 The physical and mental summary scores were derived from the dataset presented here using the procedures recommended by the developers. Methods Data The results reported here are based on data gained from the third Oxford Healthy Life Survey (OHLS III), undertaken

3 184 JOURNAL OF PUBLIC HEALTH MEDICINE Table 1 Multiple regression of physical and mental SF-36 scores in the OHLS III data for men and women separately: estimates (with SE in parentheses) and F test of age, economic activity status, health behaviours, and housing and neighbourhood conditions Explanatory variables... Men Women Physical Mental n Physical Mental n Age a (2.21) (2.58) (2.09) (2.42) (0.56) 0.61 (0.66) (0.55) 0.49 (0.63) ¹0.73 (0.57) 0.58 (0.66) 710 ¹0.40 (0.56) 1.53 (0.65) ¹1.54 (0.58) 1.14 (0.68) 738 ¹2.41 (0.58) 3.12 (0.67) and over ¹4.38 (0.65) 2.81 (0.76) 521 ¹3.94 (0.70) 4.64 (0.81) 469 F test 21.87** 4.95** 20.23** 15.16** Economic activity status Full time a (2.21) (2.58) (2.09) (2.42) 1583 Part time (<30 h/week) ¹2.01 (0.77) 0.53 (0.90) (0.34) 0.58 (0.39) 1008 Caring for someone ¹1.51 (2.10) ¹8.90 (2.46) 13 ¹0.64 (0.43) 0.75 (0.50) 484 Unemployed ¹1.73 (0.90) ¹3.47 (1.06) 73 ¹0.93 (1.03) ¹0.92 (1.19) 68 Unable to work ¹24.90 (0.90) ¹8.64 (1.06) 76 ¹27.13 (0.90) ¹7.64 (1.04) 94 Retired ¹2.28 (0.76) 2.17 (0.89) 122 ¹3.41 (0.77) 2.05 (0.90) 180 Student ¹0.70 (1.01) ¹1.90 (1.18) (0.87) ¹0.13 (1.00) 109 F test ** 16.82** 158.9** 11.97** Vigorous exercise Never or less than 1 day a month a (2.21) (2.58) (2.09) (2.42) 1716 <1 time a week 1.54 (0.41) 0.56 (0.48) (0.40) 0.93 (0.47) times a week 1.49 (0.37) 0.49 (0.44) (0.36) 1.64 (0.41) times a week 2.15 (0.49) 2.22 (0.58) (0.53) 2.31 (0.62) times a week 1.72 (0.73) 3.33 (0.85) (0.96) 1.10 (1.11) 78 F test 7.80** 6.45** 13.03** 5.93** Alcohol consumption Non-drinker a (2.21) (2.58) (2.09) (2.42) 484 Moderate (<14/<21) 0.82 (0.55) 0.63 (0.65) (0.42) 0.30 (0.49) 2720 Heavy (14/21+) 1.16 (0.61) 0.08 (0.72) (0.61) ¹1.15 (0.71) 322 F test ** 3.30* Is your diet harmful? Yes a (2.21) (2.58) (2.09) (2.42) 410 No 1.34 (0.43) 3.22 (0.50) (0.45) 2.77 (0.52) 2837 Don t know 0.28 (0.57) 0.74 (0.67) 320 ¹0.57 (0.64) 0.82 (0.75) 279 F test 6.55** 27.22** 3.84* 17.43** Cigarette smoking Current smoker a (2.21) (2.58) (2.09) (2.42) 827 Ex smoker 0.43 (0.38) 0.40 (0.45) 875 ¹0.34 (0.41) 0.86 (0.47) 886 Never smoker 1.29 (0.36) 0.60 (0.42) (0.36) 1.27 (0.41) 1813 F test 7.19** * 4.78* Body Mass Index Underweight a (2.21) (2.58) (2.09) (2.42) 389 Desirable 0.29 (0.69) 0.46 (0.81) 1370 ¹0.49 (0.47) 0.49 (0.54) 1859 Overweight ¹0.73 (0.70) 0.37 (0.82) 1370 ¹1.92 (0.50) 0.50 (0.58) 1155 Obese ¹3.50 (1.26) 2.70 (1.47) 50 ¹6.30 (0.87) 0.44 (1.01) 123 F test 7.03** ** 0.29 Cannot keep your house warm enough Most of the time a (2.21) (2.58) (2.09) (2.42) 85 Quite often 0.20 (1.29) ¹0.23 (1.51) 100 ¹0.27 (1.08) 1.47 (1.25) 196 Only occasionally 2.24 (1.11) 1.40 (1.30) (0.96) 4.39 (1.11) 1021 Never 3.07 (1.11) 2.42 (1.29) (0.95) 6.58 (1.11) 2224 F test 6.91** 4.72** 4.93** 29.08**

4 SOCIAL CLASS DIFFERENCES IN HEALTH STATUS 185 Table 1 continued Is damp a problem? Serious problem a (2.21) (2.58) (2.09) (2.42) 35 More a nuisance ¹2.43 (1.61) 5.07 (1.88) (1.49) 0.43 (1.73) 326 No problem ¹2.53 (1.58) 5.89 (1.84) (1.44) 1.30 (1.67) 3165 F test ** 7.66** 1.32 Index of neighbourhood problems None to serious problems 0.12 (0.03) 0.16 (0.04) (0.03) 0.26 (0.04) 3526 F test 12.22** 16.29** 17.82** 42.49** R *p < 0.05; **p < a Reference category. during The dataset was selected because it is the only one that contains the most up-to-date version of the SF-36, which has higher degrees of precision. This was a postal survey in which the SF-36 together with questions on lifestyle and demographics were incorporated into a booklet. A covering letter, explaining the purpose of the study, was sent with the questionnaire. For those who did not respond to the initial questionnaire a reminder postcard was mailed approximately 3 weeks later. If this elicited no response within 3 weeks then another questionnaire and covering letter were sent. The questionnaire booklet contained, in addition to the SF-36, questions on the occupation of the respondent. The questionnaire booklet was mailed to randomly selected subjects, unstratified by age or sex, between the ages of years, inclusive, from the general practitioner records held by the health authorities for Berkshire, Buckinghamshire, Northamptonshire and Oxfordshire. A total of 1068 respondents were removed from the denominator because of incorrect postal addresses (n ¼ 1004), death (n ¼ 6), being outside of the specified age range (n ¼ 42) or because they were unable to read the questionnaire (n ¼ 16). Completed questionnaires were obtained from 8889 of the people originally contacted, giving a response rate of 64.4 per cent. Of those who did return questionnaires, 8801 (99.0 per cent) of respondents answered the question relating to sex, of whom 3863 (43.4 per cent) were male and 4938 (55.6 per cent) were female. The analysis, however, was based on 6454 respondents, as any cases of missing data on any of the variables in the analysis were excluded. Analysis of the data in terms of distribution of age, sex and social class suggests that they are likely to be representative, as the distributions resemble those of the general population. 31 Variables The SF-36 scores To gain the physical and mental summary scores for the SF-36, the data were factor analysed using principal components analysis and orthogonal rotation. Each of the eight individual scales of the SF-36 was then standardized using a Z-score transformation (using means and standard deviations calculated for all respondents in the OHLS III dataset). Each Z-score was calculated by subtracting the OHLS mean from each individual respondent s scale score and dividing the difference by the corresponding scale s standard deviation from the OHLS. After the Z-scores had been calculated for each scale the aggregate scores for the physical (PCS) and mental (MCS) component scores were computed. In the case of the PCS, this involved multiplying each SF-36 scale Z-score by its respective factor score coefficient. Similarly, in the case of the MCS, this involved multiplying each SF-36 scale Z-score by its respective factor score coefficient. Finally, these scores were standardized to a T-score where the mean was set to 50 and the standard deviation to 10. Those scoring over 50 had better than average health and those scoring below 50 had worse than average health. Social class, age and economic activity status The NS-SEC was derived from the respondent s stated occupational title and employment responsibilities large or small employer, self employed, supervisor, foreman or manager. The respondent s previous occupation was used if they were currently unemployed. Age was coded into five categories: years, 25 34, 35 44, and 55 years and above. Respondents were also categorized on the basis of their current economic activity status into those who had fulltime occupations, part-time occupations, those who were caring for someone full time, the unemployed, those who were unable to work, the retired and students. Health behaviours Respondents were categorized into never smokers, current smokers (who smoke tobacco at least once a day) and ex smokers. Respondents were grouped into those who consumed within sensible drinking limits (1 14 units per week for women and 1 21 units per week for men), above sensible drinking limits and those who said they never drank alcohol. Respondents were classified on the basis of the amount of vigorous exercise they did (sport or recreational activity that made them

5 186 JOURNAL OF PUBLIC HEALTH MEDICINE Table 2 Multiple regression of SF-36 physical and mental scores in the OHLS III data for men and women separately: estimates (with SE in parentheses) and F test of NS-SEC in different models Model I Model II Model III Model V n NS-SEC SF-36 physical score men Higher manager a (0.55) (1.05) (2.13) (2.26) 1009 Lower manager ¹0.39 (0.45) ¹0.10 (0.45) ¹0.29 (0.45) ¹0.04 (0.44) 407 Intermediate employee ¹0.14 (0.57) 0.09 (0.57) 0.05 (0.57) 0.25 (0.57) 225 Small employer ¹1.87 (0.49) ¹1.34 (0.49) ¹1.65 (0.49) ¹1.18 (0.49) 330 Lower supervisor ¹1.30 (0.48) ¹0.76 (0.48) ¹1.00 (0.48) ¹0.54 (0.48) 344 Semi-routine employee ¹1.97 (0.43) ¹1.33 (0.43) ¹1.59 (0.43) ¹1.06 (0.43) 507 Routine employee ¹1.74 (0.80) ¹0.89 (0.79) ¹1.37 (0.79) ¹0.65 (0.79) 106 R F test for NS-SEC 5.63** 2.74* 4.01** 2.03 SF-36 physical score women Higher manager a (0.57) (0.89) (2.10) (2.13) 747 Lower manager ¹0.13 (0.50) 0.02 (0.48) ¹0.10 (0.49) 0.02 (0.48) 493 Intermediate employee ¹0.50 (0.42) ¹0.07 (0.41) ¹0.39 (0.41) 0.00 (0.40) 1058 Small employer ¹1.38 (0.70) ¹0.85 (0.68) ¹1.25 (0.69) ¹0.78 (0.68) 192 Lower supervisor ¹1.43 (0.92) ¹0.32 (0.90) ¹1.04 (0.91) ¹0.10 (0.89) 98 Semi-routine employee ¹1.30 (0.48) ¹0.37 (0.48) ¹0.85 (0.48) ¹0.08 (0.48) 638 Routine employee ¹2.08 (0.60) ¹0.66 (0.60) ¹1.46 (0.60) ¹0.27 (0.60) 300 R F test for NS-SEC 3.15** SF-36 mental score men Higher manager a (0.65) (1.24) (2.50) (2.64) 1009 Lower manager ¹0.05 (0.53) 0.16 (0.52) 0.04 (0.52) 0.18 (0.52) 407 Intermediate employee ¹0.03 (0.68) 0.27 (0.67) 0.26 (0.67) 0.49 (0.66) 225 Small employer 0.07 (0.58) 0.50 (0.58) 0.32 (0.57) 0.67 (0.57) 330 Lower supervisor 0.09 (0.57) 0.60 (0.57) 0.54 (0.56) 0.92 (0.56) 344 Semi-routine employee ¹0.59 (0.50) ¹0.07 (0.51) 0.02 (0.50) 0.38 (0.51) 507 Routine employee 1.28 (0.94) 2.11 (0.94) 1.80 (0.93) 2.44 (0.93) 106 R F test for NS-SEC SF-36 mental score women Higher manager a (0.67) (1.05) (2.40) (2.47) 747 Lower manager 0.66 (0.58) 0.90 (0.57) 0.76 (0.56) 0.93 (0.56) 493 Intermediate employee ¹0.93 (0.48) ¹0.69 (0.48) ¹0.73 (0.47) ¹0.57 (0.47) 1058 Small employer 0.23 (0.81) 0.55 (0.80) 0.49 (0.79) 0.69 (0.78) 192 Lower supervisor ¹0.52 (1.07) 0.31 (1.06) 0.27 (1.04) 0.80 (1.03) 98 Semi-routine employee ¹0.99 (0.56) ¹0.25 (0.56) ¹0.06 (0.54) 0.40 (0.55) 638 Routine employee ¹1.88 (0.70) ¹0.78 (0.71) ¹0.64 (0.68) 0.06 (0.69) 300 R F test for NS-SEC 2.89* Model I includes age and economic activity status. Model II includes age, economic activity status and lifestyles. Model III includes age, economic activity status, and housing and environment conditions. Model IV includes age, economic activity status, lifestyles, and housing and environment conditions. *p < 0.05; **p < breathless). They were classed into those who never did any vigorous exercise or less than once a month, less than once a week, 1 2 times a week, 3 4 times a week and 5 or more times a week. Dietary behaviour was classified by respondent s own perception of how healthy their diets were: those who identified their diet as harmful, those who did not and those who did not know. Respondent s Body Mass Index was calculated as weight (kg)/height (m) 2 and classified as underweight ( 20 or less), desirable (>20 to 25), overweight (>25 to 30) and obese (>30). Housing and neighbourhood problems Respondents reported if they had problems with damp housing and how often they could not keep their house warm enough. An index of neighbourhood problems was created from a list of potential problems in the local environment. Respondents identified whether speeding traffic, poor public transport, burglaries, nuisance from dogs, litter and rubbish, noise and vandalism, disturbances from children, smells and fumes, lack

6 SOCIAL CLASS DIFFERENCES IN HEALTH STATUS 187 of open spaces, assaults and muggings, and discarded syringes and needles were serious problems in their local environment. Each of these items that was a serious problem was assigned a score of one. The scores were added up to obtain an index with a low score implying low neighbourhood problems and a high score implying high neighbourhood problems. Analysis The associations between the SF-36 physical and mental scores and the explanatory variables were examined. Linear regression models of the SF-36 scores with age, economic activity status, health behaviours, and housing and neighbourhood problems as explanatory variables were examined for men and women separately. The F test calculates the simultaneous equality of all regression coefficients equalling zero and is used to determine the combined significance of dummy variables. The association of the NS-SEC with health was examined in linear regression models of the SF-36 scores separately for men and women. In Model I, age, economic activity status and the NS-SEC were included in the model. It is important to control for age and economic activity status because increasing age and some categories of employment such as people who are unable to work or those who are retired have higher risks of morbidity and mortality. In Model II health behaviours were added to Model I. In Model III, housing and neighbourhood problems were added to Model I. In Model IV, health behaviours, and housing and neighbourhood problems were added to Model I. The F test calculated the significance of the reduction in the size of the F statistic with the removal of the NS-SEC from each model. Results from the OHLS III data Table 1 examines the results of the association between the SF- 36 physical and mental scores and various explanatory variables. Increasing age is significantly associated with decreasing SF-36 physical scores (or worse health) in both men and women. However, the association between age and the SF-36 mental scores are in the opposite direction. The estimates of the SF-36 mental scores for those in older age groups are higher compared with the younger age groups, implying that the mental health of men and women improves with age. Economic activity status is also significantly associated with physical and mental health. Those who are unable to work (including the disabled) have the worst physical and mental health. Those who are retired have worse physical health and better mental health compared with those employed full time. This association is similar to the associations of the SF-36 physical and mental scores with age. Unemployed men have worse health compared with men in full-time employment. Vigorous exercise is significantly associated with the SF-36 physical and mental scores in both men and women (Table 1). People who exercise more often have better physical and mental health than people who do little vigorous exercise. Alcohol consumption is significantly associated with the SF-36 physical and mental score in women but not so among men. Women who are heavy drinkers have better physical health but worse mental health compared with non-drinkers. Men and women who consider their diets to be harmful have worse physical and mental health compared with those who do not believe their diets to be harmful. Cigarette smoking is associated with worse physical health in men and women as well as worse mental health in women. Being obese (i.e. BMI > 30) is associated with worse physical health but the association between the BMI and mental health is not significant. Men and women who can never keep their house warm enough most of the time have worse physical and mental health compared with those who can keep their houses warm enough (Table 1). Women living in housing with serious damp problems have worse physical health compared with those who do not have problems with damp. Similarly, men living in damp conditions have worse physical health compared with those who do not have problems with damp. Those with fewer neighbourhood problems have better mental and physical health compared with those with more problems (such as problems with burglaries, assaults and noise). Table 2 examines the results of the association of the NS- SEC with the SF-36 physical and mental scores in men and women in different regression models. Model I includes age, economic activity status and the NS-SEC. The association between the NS-SEC and the SF-36 physical score is significant in both men and women. However, the association between the NS-SEC and the SF-36 mental score is significant only among women. In general, the higher and lower managerial social classes have the highest estimated scores (or the best health) whereas the semi-routine and routine employee class have the lowest estimated scores (or the worst health). There is a linear pattern of decreasing physical health from the managerial to the routine employee class. Small employers have worse physical health than managers but better physical health compared with routine employees. Women in the intermediate employee class have worse physical and mental health than managers but better physical and mental health than routine employees. Model II includes age, economic activity status, health behaviours (exercise, alcohol, diet and smoking) and the NS- SEC. The F statistic reduces in size from Model I, implying that healthy behaviours account for some of the social class differences in the SF-36 scores. Furthermore, the social class differences in the SF-36 physical and mental scores reduce to non-significance in women. Model III includes age, economic activity status, housing and neighbourhood factors, and the NS-SEC. As in Model II, the F statistic reduces in size from Model I, implying that housing and neighbourhood factors also account for some of the variation between social classes in their average SF-36 scores. The social class differences in the SF-36 physical and mental scores reduce to non-significance in women when housing and neighbourhood conditions are adjusted for.

7 188 JOURNAL OF PUBLIC HEALTH MEDICINE Model IV includes age, economic activity status, health behaviours, housing and neighbourhood factors, and the NS- SEC. The significant associations between the NS-SEC and the SF-36 physical and mental scores in Model I are reduced to non-significance among both men and women. The standard errors of age, economic activity status, health behaviours, and housing and neighbourhood factors remain relatively small in comparison with their estimates (analysis not shown). This implies that although there is some correlation between the explanatory variables, it is not a serious problem. Discussion The new NS-SEC demonstrates social class differences in the SF-36 physical and mental health scores. This provides further evidence for the construct validity of the NS-SEC it shows expected associations with health variables. A number of studies have demonstrated inequalities in health in the United Kingdom using different measures of socio-economic status. Measures of social position in the United Kingdom may be expected to show inequalities in health. The significant association of the NS-SEC with the SF-36 scores thus provides some evidence for its construct validity. Men and women in the managerial class have the best physical and mental health whereas those in the routine employee class have the worst health. There appears to be a linear pattern of decreasing physical health from the managerial to routine employee classes. A linear pattern in the association between the NS-SEC and mortality was also found by Bartley et al.: 9 the intermediate and self-employed classes had lower risks of mortality compared with the routine employee class and higher risks of mortality compared with the higher managerial class. Although the NS-SEC is not implicitly hierarchical, a linear association between measures of socio-economic status and health is often observed when examining inequalities in health. 33,34 The results also suggest that these social class differences can, to large extent, be understood in terms of differences between social classes in material and lifestyle factors. Health behaviours and material factors as measured by housing and neighbourhood problems appear to have a large role in explaining social class differences in health. These results agree with much of the literature on explanations of social inequalities in health. 15,16,34 Social class differences in smoking behaviour, the amount of alcohol consumed, in exercise and in healthy dietary behaviour have been shown to explain some of the social class differences in health and mortality. Poor housing has direct effects (through damp and cold living conditions) and indirect effects (through psychosocial stress) on health. Stronks et al. 18 found that psychosocial stressors with a material base (such as financial problems) in particular contribute to social inequalities in health. Bartley et al. 9 hypothesized that the examination of the relationship of the NS-SEC to mortality tests the hypothesis that occupations having employment relations and conditions characterized by a wage rather than salary, little or no prospect for promotion and lower levels of autonomy would experience lower life expectancy. The reduction of the social class differences in physical and mental health to non-significance when adjusted for health behaviours and housing and neighbourhood factors suggests the possibility that differences in employment relations and conditions may not have a large explanatory role in relation to inequalities in health. However, Rose and O Reilly 1 argued that the NS-SEC also captures basic structuring principles of society, such as income, housing and consumption, which condition and shape the lives and the life chances of people in different social classes. The explanatory power of health behaviours and housing and neighbourhood conditions needs to be understood in terms of the life chances that flow from people s work and market situations (or employment relations ), including housing and income, as well as other aspects of material circumstances. This study also found that differences in age, economic activity status, health behaviours, and housing and neighbourhood problems were important predictors of physical and mental health. Other studies have shown results similar to these findings. Increasing age has been associated with decreasing physical scores and increasing mental scores of the SF Stronks et al. 36 found that economic activity status and health outcomes are closely linked. Wallace et al. 37 found health promotion campaigns involving lifestyle-based interventions (smoking, exercise, alcohol and dietary behaviours) increased the SF-36 scores of older people. A number of studies have shown that poor housing and neighbourhood conditions are related to poor health. 38,39 Gloag 40 found that noise, particularly unpredictable and uncontrollable noise, such as from noisy neighbours or traffic, can have deleterious psychological effects. Hyndman 24 and Brown and Harris 41 found damp housing to be related to depression in women. Smith et al. 42 found that several housing stressors such as noise, cold and state of disrepair were sources of psychological distress. There are a few potential limitations to this study. Ziebland 43 suggested that the SF-36 might not be sensitive to social class differences in health in the general population. Although the results showed statistically significant associations between the NS-SEC and the SF-36 physical and mental scores, it is possible that social class differences using other health outcome measures such as mortality may reveal significantly greater class differences. If there are greater social class differences in other health outcomes, differences in employment relations may play a more important role in explaining social inequality in health than may be inferred from this study. Another limitation of this study is the cross-sectional nature of the OHLS III data. Although this study has considered material and lifestyle factors to be intervening variables between social class and health outcomes, the associations between social class, material and lifestyle factors and health outcomes may be more complex. 34

8 SOCIAL CLASS DIFFERENCES IN HEALTH STATUS 189 The results in this paper provide further construct validity for the NS-SEC by finding significant social class differences in the SF-36 physical and mental scores, which are well-validated health outcome measures. The evidence reported here suggests that these social class differences may be understood to a large extent in terms of class differences in health behaviours and housing and neighbourhood conditions. The NS-SEC has been developed on a clear theoretical and conceptual basis, which was lacking in the most commonly used measure of social class in health research, the RGSC. The results presented here suggest that the NS-SEC will be useful in describing social inequalities in health. Furthermore, as it captures basic structuring principles in society, it is likely to prove a valuable tool for explanations of inequalities in health. References 1 Rose D, O Reilly K. Constructing classes. Swindon: ESRC ONS, Rose D, O Reilly K. The ESRC review of government social classifications. London: ONS, Erikson R, Goldthorpe JH. The constant flux. Oxford: Clarendon Press, O Reilly K, Rose D. Criterion validation of the interim revised social classification. In: Rose D, O Reilly K, eds. Constructing classes. Swindon: ESRC ONS, Strong P. Black on class and mortality: theory, method and history. J Publ Hlth Med 1990; 12: Bartley M, Carpenter L, Dunnell K, Fitzpatrick R. Measuring inequalities in health: an analysis of mortality patterns using two social classifications. Sociol Hlth Illness 1996; 18: Szreter S. The genesis of the Registrar General s social classification of occupations. Br J Sociol 1984; 35: Marshall G, Rose D, Newby H, Vogler C. Social class in modern Britain. London: Unwin Hyman, Bartley M, Lynch K, Sacker A, Dodgeon B. Social variations in health: relationship of mortality to the ONS socio-economic class (SEC) schema. SEC Validation Workshop, Essex, Karasek RA, Theorell T, Schwartz J, et al. Job characteristics in relation to the prevalence of myocardial infarction in the US Health Examination Survey (HES) and the Health and Nutrition Examination Survey (NHANES). Am J Publ Hlth 1988; 78: Marmot M. Work and other factors influencing coronary health and sickness absence. Work Stress 1994; 8: Bosma H, Marmot MG, Hemingway H, et al. Low job control and risk of coronary heart disease in Whitehall II (prospective cohort) study. Br Med J 1997; 314: Stansfield S, Bosma H, Hemingway H, Marmot M. Psychosocial work characteristics and social support as predictors of SF-36 health functioning: the Whitehall II study. Psychosom Med 1998; 60: Marmot MG, Bosma H, Hemingway H, Brunner E, Stansfeld S. Contribution of job control and other risk factors to social variations in coronary heart disease incidence [see comments]. Lancet 1997; 350: DHSS. Inequalities in health, report of a research working group. London: DHSS, Whitehead M. The health divide. London: Penguin, Barker D. The foetal and infant origins of inequalities in health in Britain. J Publ Hlth Med 1991; 13: Stronks K, Mheen HV, Looman WN, Mackenbach JP. The importance of psychosocial stressors for socio-economic inequalities in perceived health. Social Sci Med 1997; 46: Blaxter M. Health and lifestyles. London: Tavistock Routledge, Cox BD, Huppert FA, Whichelow MJ. The health and lifestyle survey: seven years on. Aldershot: Dartmouth, OPCS. Health survey for England London: HMSO, Hunt S. Housing-related disorders. In: Charlton J, Murphy M, eds. The health of adult Britain London: The Stationery Office, Woodward M, Shewry MC, Smith CS, Tunstall-Pedoe H. Social status and coronary heart disease: results from the Scottish Heart Health Study. Prev Med 1992; 21: Hyndman S. Housing dampness and health among British Bengalis in East London. Social Sci Med 1990; 8: Meltzer H, Gill B, Petticrew M, Hinds K. The prevalence of psychiatric morbidity among adults living in private households, OPCS surveys of psychiatric morbidity in Great Britain, Report 1. London: HMSO, Curtis S. Use of survey data and small area statistics to assess the link between individual morbidity and neighbourhood deprivation. J Epidemiol Commun Hlth 1990; 44: Drever F, Whitehead M, eds. Health inequalities. London: ONS, Ware J, Sherbourne C. The MOS 36 item short form health survey: conceptual framework and item selection. Med Care 1992; 30: Ware J, Kosinski M, Keller S. SF-36 physical and mental summary scales: a user s manual. Boston, MA: The Health Institute, New England Medical Center, Ware J, Kosinski M, Bayliss M, et al. Comparison of methods for scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care 1995; 33: AS264 AS Jenkinson C, Stewart-Brown S, Petersen S, Paice C. Evaluation of the SF-36 Version II in the United Kingdom. J Epidemiol Commun Hlth 1999; 53: Petersen S, Stewart-Brown S, Peto V. Health and lifestyles in four counties. Oxford: Health Services Research Unit, Adler N, Boyce T, Chesney M, et al. Socioeconomic status and health: the challenge of the gradient. Am Psychol 1994; 49: Macintyre S. The Black report and beyond: what are the issues? Social Sci Med 1997; 44: Jenkinson C, Layte R, Wright L, Coulter A. The UK SF-36: an analysis and interpretation manual. Oxford: Health Services Research Unit, Department of Public Health and Primary Care, Stronks K, van de Mheen H, van den Bos J, Mackenbach J. The interrelationship between income, health and employment status. Int J Epidemiol 1997; 26: Wallace J, Buchner D, Grothaus L, et al. Implementation and effectiveness of a community-based health promotion program for older adults. J Gerontol 1998; 53A: M301 M Keithley J, Byrne D, Harrisson S, McCarthy P. Health and housing conditions in public sector housing estates. Publ Hlth 1984; 98:

9 190 JOURNAL OF PUBLIC HEALTH MEDICINE 39 Blackman T, Evason E, Melaughs M, Woods R. Housing and health: a case study of two areas in West Belfast. J Social Policy 1989; 18: Gloag D. Noise and health: public and private responsibility. Br Med J 1980; 18: Brown G, Harris T. Social origins of depressions: a study of psychiatric disorder in women. London: Tavistock, Smith C, Smith C, Kearns R, Abbott M. Housing stressors, social support and psychological distress. Social Sci Med 1993; 37: Ziebland S. The short form 36 health status questionnaire: clues from the Oxford region s normative data set about its usefulness in measuring health gain in population surveys. J Epidemiol Commun Hlth 1995; 49: Accepted on 1 October 1999