Socioeconomic differences in dietary patterns among middle-aged men and women

Socioeconomic differences in dietary patterns among middle-aged men and women

Social Science & Medicine 56 (2003) 1397–1410 Socioeconomic differences in dietary patterns among middle-aged men and women Pekka Martikainena,b,*, E...

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Social Science & Medicine 56 (2003) 1397–1410

Socioeconomic differences in dietary patterns among middle-aged men and women Pekka Martikainena,b,*, Eric Brunnera, Michael Marmota a

Department of Epidemiology and Public Health, International Centre for Health and Society, University College London Medical School, 1-19 Torrington Place, London WC1E 6BT, UK b Department of Sociology, Population Research Unit, P.O. Box 18, FIN-00014, University of Helsinki, Finland

Abstract The aim of the study is to (i) identify common dietary patterns, (ii) study socioeconomic differences in these dietary patterns, and (iii) assess whether they contribute to socioeconomic differences in biological risk factors. The data come from the Whitehall II study of London civil servants, who participated in the third phase (1991–1993) and were 39–63years old ðN ¼ 8004Þ: Food frequency questionnaire and socioeconomic background information was from a questionnaire, and biological risk factors from a medical screening. Six dietary patterns were identified. In reference to high employment grade men, the odds ratios of low grade men consuming the ‘unhealthy’ or the ‘very unhealthy’ diet were 1.26 and 3.34, respectively, while the odds for the ‘French’ diet was 0.13. Among women the corresponding odds were 2.98, 6.19 and 0.25. Adjusting for spouse’s socioeconomic status and to a lesser extent smoking and exercise as well as job control attenuate these grade differences somewhat. Among men and women adjusting for dietary patterns accounted for about 25—50 per cent of grade differences in HDL and serum triglyceride levels. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Socioeconomic status; Dietary patterns; Biological risk factors; UK

Introduction Specific food items and nutritional constituents are related to established risk factors for coronary heart disease, diabetes, and are important factors in the causation of many cancers (World Cancer Research Fund, 1997; Shetty & McPherson, 1997). However, it is difficult to isolate these effects and influence the consumption of single food items as the intakes of different foodstuffs are strongly and complexly interrelated. If we can understand these interrelationships, identify the common dietary patterns that individuals consume and are able to specify some of the broad sociodemographic determinants of these patterns, we *Corresponding author. Tel.: +44-171-391-1690; fax: +44171-813-0242. E-mail address: [email protected] (P. Martikainen).

may be closer to being able to prevent disease by focussing not on specific nutrients but on unhealthy and healthy dietary patterns. Earlier evidence shows that common dietary patterns can be identified, and that these dietary patterns are closely related to many micro-nutrient levels and energy intake as well as socioeconomic status; those who consume healthier diets are from higher socioeconomic groups (Whichelow & Prevost, 1996; Wirf.alt & Jeffery, 1997; Pryer et al., 2001). However, we lack a more detailed assessment of the causes of socioeconomic differences in dietary patterns and the possible contribution of these dietary patterns to socioeconomic differences in risk factors for disease. The purpose of this study is: (i) to identify common dietary patterns in a cohort of middle-aged men and women, (ii) to describe socioeconomic differences in these dietary patterns, and study the sociodemographic and behavioural predictors of these differences, and (iii)

0277-9536/03/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 2 ) 0 0 1 3 7 - 5

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to assess whether these dietary patterns contribute to socioeconomic differences in BMI, waist-to-hip-ratio, HDL and serum triglyceride.

Materials and methods Sample The data come from the Whitehall II study, a prospective cohort study of men and women aged 35– 55 years and working in the London offices of 20 civilservice departments at enrolment. Subjects were invited to participate by letter. The overall response rate was 73 per cent, although the true response rate is likely to be higher because around 4 per cent of those listed as employees were not eligible as they had moved before the study began. Altogether 10,308 participants of which 67 per cent are men responded, and subsequently completed a self-administered health questionnaire, and attended an extensive screening examination in 1985–1988. The data and the measurements are described more fully elsewhere (Marmot et al., 1991). After initial participation at phase 1, a further postal questionnaire was carried out in 1989 (phase 2) and in 1991–93 (phase 3), when an additional screening examination also took place ðN ¼ 8104Þ: Men and women participating in the phase 3 screening clinic were asked to take home and complete a food frequency questionnaire (Willett et al., 1985). The population examined in the present study consists of those who completed the questionnaire, altogether 8004 participants of which 70 per cent are men. The 127 item food frequency questionnaire is a machine readable semi-quantitative questionnaire on the consumption of food stuffs, and drinks. The questionnaire is shown to be a valid instrument in these data (Brunner et al., 2001). Grade of employment at phase 3 was obtained by asking all participants for their civil service grade title. On the basis of this information three grades were compiled: administrative (I), professional and executive (II) and clerical and office support (III). These grades differ markedly in salaries; from an annual salary in 1987 of about d3000 to d6000 in grade III to about d18,000–d62,000 in grade I. The grades also differ in respect to educational qualifications, housing tenure and car ownership. Our measure of marital status was classified as (i) married, (ii) not married. Spouse’s socioeconomic status for the married participants followed the Registrar General’s social class classification (Leete & Fox, 1977), and for the purposes of this study we categorised it as (i) I and II, (ii) III N, and (iii) III M, IV and V. Ethnicity was (i) white European, (ii) other. In addition we used questionnaire based self-reports of two health related behaviours. Questions on current

smoking, ever smoking and number of manufactured cigarettes smoked daily were combined to derive a tobacco smoking variable with the following categories: ‘never smoker’, ‘ex smoker’, ‘light smoker’ (1–10 cigarettes daily), ‘medium smoker’ (11–20 cigarettes daily) and ‘heavy smoker’ (21 or more cigarettes daily). Physical activity was assessed using questions on hours of mild, moderate and vigorous activity undertaken each week. These were aggregated into the following three categories: light, moderate (more than an hour of moderate physical activity per week) and vigorous (more than an hour of vigorous physical activity per week). Job decision latitude or job control, a measure combining work decision authority and skill discretion is based on the Karasek job content instrument (Karasek & Theorell, 1990; Bosma et al., 1997). Equal weights were used for the 15 questions. The nine questions and statements on decision authority were: Do you have a choice in deciding how you do your job? Do you have a choice in deciding what you do at work? Others take decisions concerning my work; I have a good deal of say in decisions about work; I have a say in my own work speed; my working time can be flexible; I can decide when to take a break; I have a great deal of say in planning my work environment. Six items for skill discretion were: Do you have to do the same things over and over again? Does your job provide you with a variety of interesting things? Is your job boring? Do you have the possibility of learning new things through your work? Does your work demand a high level of skill or expertise? Does your job require you to take the initiative? In addition, we measured perceived feelings of health control (health locus of control) with three questions on whether keeping healthy depends on things that the participants can do themselves, and whether they can do things that reduce the risk of heart attack and cancer. Response alternatives to these questions and statements varied from often to never, or not applicable. Material problems from phase 2 were based on questions on the availability of money to buy food or clothing and pay the bills as well as problems with housing (small, need of repair, damp) and the neighbourhood (noise, unsafe, few local facilities) (Pearlin, Lieberman, Menaghan, & Mullan, 1981). Obesity and body fat distribution were assessed with the body mass index and waist-to-hip circumference ratio at phase three screening. Body mass index (kg/m2) is a measure of overall or generalised obesity and waist-tohip circumference ratio measures abdominal obesity. Blood was taken to determine the high density lipoprotein and triglyceride concentrations (mmol/l). The laboratory techniques and the questionnaire used have been described elsewhere (Beksinska, Yea, & Brunner, 1995). Although measured, blood pressure and total cholesterol are not included in these analyses as previous

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studies on these data indicate that they are not related to socioeconomic status (Marmot et al., 1991). Cluster analysis We identified dietary patterns for the combined sample of men and women using cluster analysis FASTCLUS in SAS (SAS Institute, 1988), with responses to food frequency questionnaire items (except coffee and tea consumption) as input variables. In the FASTCLUS-procedure clustering is based on minimizing the sum of squared Euclidean distances between the observations and the cluster means. In the final cluster solution each observation (i.e. each individual) is allocated to one cluster. The following considerations were used to decide on the final number of dietary clusters. First, we tried to locate a threshold in the power of predicting the food frequency questionnaire items from the cluster solution as a function of the number of clusters. In these data, allowing for one additional cluster improved the predictive power of the cluster solution markedly until about five or six clusters were included in the cluster solution. Second, having a more limited number of clusters facilitates clarity of interpretation, and previous cluster analysis of dietary patterns in England (Pryer et al., 2001) have distinguished about 4–8 meaningful clusters Thus, in the end we observed six meaningful clusters, these were: ‘Very healthy diet’, ‘Moderately healthy diet’, ‘French diet’, ‘Sweet unhealthy diet’, ‘Unhealthy diet’, ‘Very unhealthy diet’. For each variable we can calculate R2 for predicting the variable from the cluster. In the interpretation of the clusters we focussed on the 22 food frequency questionnaire items with the highest R2 value. The characteristics and the rationale behind the labelling of the clusters are described more fully in the next section. By calculating cluster solutions that included 30–60 clusters we assessed the robustness of the cluster solution for outliers—observations that fall into clusters that only include few observations. 124 (1.5 per cent) outlying observations were excluded by not allowing FASTCLUS to assign distant observations to a cluster. After exclusion of the outliers the cluster solution was robust, and we obtained very similar cluster solutions (on average 91.4 per cent concordance) when using five random halves of the observations. When clusters were calculated separately for men and women, fairly similar clusters were observed. For men concordance—per cent falling in the same cluster on the basis of combined data and data for only men—was about 80 per cent. Among women the corresponding concordance was about 60 per cent. Most of the lack of concordance was due to movement between the ‘healthy’ clusters on the one hand, and between the ‘unhealthy’ clusters on the other hand. However, among women the

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‘sweet unhealthy’ cluster was not observed, but a fourth healthy cluster was obtained and the women who belonged to the ‘sweet’ cluster were incorporated into the ‘very unhealthy’ cluster. For simplicity and to be able to compare sexes we have presented the cluster solution obtained for the combined data of men and women. Bias in food frequency reporting may be considerable, and this may be partly responsible for the dietary patterns we have identified. One way of evaluating this possibility is to assess whether low energy reporting (here defined as energy consumption estimated from the food frequency questionnaire items falling below 1.2 times the age and sex specific basal metabolic rate) is systematically related to the cluster solution, and whether qualitatively different cluster solutions are identified in a sub-sample of participants that are not energy under reporters. Table 1 shows that clear differences between the clusters in the proportion of low energy reporters exist, but that under reporting is not systematically associated with good or bad diets. Furthermore, cluster analyses of participants that are not energy under reporters indicate that relatively similar clusters are identified; almost 70 per cent of non-energy under reporters fall into the same cluster in both cluster solutions. Logistic regression For the analyses of the socioeconomic and behavioural determinants of dietary patterns we used multinomial logistic regression and for the analyses of socioeconomic differences in biological risk factors linear regression. In multinomial logistic regression we estimate models for a discrete dependent variable with more than two categories; here membership of a dietary pattern. As our discrete outcome has no natural ordering we have selected the ‘healthy diet’ as the comparison outcome category or the base category. All choices of the base category are arbitrary and lead to different parameterisations of the same underlying model. The base category of the outcome variable should not be confused with the reference category of each explanatory variable. The results are presented as means, differences in means, odds ratios and their 95 per cent confidence intervals. For odds ratios one category of each explanatory variable was taken as the reference category, with an odds ratio of one. The calculations were carried out in STATA (STATA Computing Resource Center, 1992) separately for each sex. As a summary index—here referred to as the slope— for the grade effects in both multinomial logistic and linear regression models we estimated a continuous regression coefficient for the three category grade. This method gives a more stable effect estimate since data for all grade categories are used, and it has an intuitive

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Table 1 Proportion (%) reporting consumption of selected food frequency questionnaire items more than once a week in six dietary patterns Very healthy Moderately healthy Very unhealthy Sweet Unhealthy French All

P for heterogeneity

Beef Sausages Oily fish White bread Wholemeal bread Chips and fries Full cream milk Single cream French dressing Butter Polyunsaturated margarine Sweet biscuits Fruit pies Added sugar Jam & marmalade Wine Beer Apples Peaches Marrow Peppers Green salad

37 7 49 33 88 16 12 5 26 20 49 42 15 13 53 54 21 91 51 34 50 84

57 18 39 40 95 42 20 13 20 18 94 81 43 41 85 55 46 84 34 27 30 76

65 31 19 80 37 56 48 9 8 50 42 62 24 100 46 39 46 52 17 14 18 46

76 37 33 81 72 64 52 30 28 69 48 88 57 97 86 73 63 79 40 30 34 79

0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

Total (N) (%)

(1339) 17.0

(1376) 17.5

(1220) 15.5

(1053) (1518) 13.4 19.3

(1374) (7880) 17.4 100

Mean age % women % energy under reporters

50 56 58

50 22 28

49 14 10

49 30 21

interpretation as the average change in outcome (in terms of odds ratio or difference in mean) for each step of the grade variable.

Results Description of the dietary clusters Table 1 presents the six dietary clusters obtained from the cluster analyses of food frequency questionnaire items. Each observation is finally allocated to one cluster. The table shows the proportions consuming selected food items for the six clusters. We have only included the 22 items with the highest R2 value. Very healthy diet had low consumption of meat and white bread, high consumption of fish and wholemeal bread, low consumption of full cream milk, cream, butter, sugar, biscuits and pies, moderate consumption of wine and low consumption of beer, and high consumption of fruit and vegetables. Some of the very low average consumption levels (e.g. sausages and

50 23 53

68 28 22 87 42 56 36 13 10 46 42 70 28 8 54 53 49 64 21 14 20 55

49 33 58

60 18 51 59 91 37 27 28 61 57 59 75 40 14 79 92 62 88 59 50 64 92

60 23 36 63 71 45 32 16 25 42 56 69 34 42 67 61 47 76 37 28 36 72

49 30 37

cream) indicate that the followers of this dietary pattern regulate their intakes very consciously. Moderately healthy diet has average levels of meat consumption, high consumption of wholemeal bread, low consumption of full cream milk, and has substituted butter for polyunsaturated margarine, a relatively high consumption of biscuits, pies and jam, average level of alcohol consumption, and slightly below average levels of fruit and vegetable consumption. It differs from the ‘very health dietary regime’ in that it does not as efficiently control for consumption of meat, fat and biscuits and cakes. French diet (modern continental) consists of rather average levels of intake of meat and high consumption of fish. Average consumption of white bread and high consumption of wholemeal bread. Lower than average consumption of full fat milk, but quite a high consumption of cream and butter. Average consumption of biscuits, tarts, and jam, high consumption of wine and beer, and a very high consumption of fruit and vegetables. The sweet unhealthy diet consisted of participants, that had a very high consumption of biscuits, pies, sugar and

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jam, and meat, sausages and savoury pies, as well as white bread. High consumption of full cream milk, butter and alcohol, and average consumption of fruit and vegetables. Unhealthy diet consists of more than average consumption of meat and sausages, and low consumption of fish, high consumption of white bread, and low consumption of wholemeal bread. Higher than average consumption of full cream milk, and average consumption of butter, biscuits, pies and jam, but a low consumption of added sugar. Average consumption of wine and beer, and a very low consumption of fruit and vegetables. Very unhealthy diet is quite similar to the unhealthy diet. The main difference is that those following the very unhealthy diet consume high levels of added sugar, and very little wine. Furthermore, their consumption of fruits and vegetables is consistently lower than among those on unhealthy diets.

Determinants of dietary patterns Very small differences in the age-structure of dietary clusters were observed (Table 2). However, among women there was some tendency for the eaters of the French diets to be a little younger, and eaters of the moderately healthy diets to be somewhat older than women in general. Overall 30 per cent of participants were women. Women were over represented among the very healthy eaters and under represented among the eaters of the sweet diet. Dietary clusters had very large differences in their employment grade structure among both men and women; with low grade participants being relatively more numerous in the two unhealthy diets, while high grade participants were more numerous in the French diet. High grade men were also relatively more common in the sweet and moderately healthy diets, but the opposite was the case among women. Marital status differences were much smaller. However, men consuming unhealthy diets were more likely to be unmarried, but among women moderately healthy diets were more typical among the unmarried. Ethnic differences were quite large among men and women with ethnic minorities being more likely to consume both the very healthy and the very unhealthy diets. On the other hand, the French diet was relatively uncommon among these minorities. Unhealthy diets also tend to go together with other unhealthy behaviours of smoking and little exercise among both men and women. Those with a French diet are vigorous exercisers. Dietary pattern membership is not very strongly related to material problems, with the exception that those with very unhealthy and sweet diets had more material problems.

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Those with unhealthy, very unhealthy and sweet diets more often believe that one can do relatively little to influence one’s health. Followers of French diets have high decision latitude at work and those with unhealthy diets low decision latitude. Understanding employment grade differences in dietary patterns Table 3 shows the number of participants and odds ratios for employment grade by sex. The table includes only participants with full information on the explanatory variables presented in Table 2. The grade differences in this restricted data set are very similar to those observed in the total data. Those in the highest employment grade (grade I) were used as the reference group with an odds ratio of 1.00. Those with very healthy diets were used as the comparison or base outcome, e.g. the odds ratio of 3.34 for grade III men having a very unhealthy diet is obtained from the cross product ratio of the frequencies: (265  129)/(37  277). The corresponding odds ratio for the French diet is 0.13 and is obtained from (265  12)/ (37  661). As already shown in Table 2, dietary patterns differ markedly in their grade structure. Odds ratios of very unhealthy diet in grade III being 3.34 for men and 6.19 for women; the corresponding odds are 0.13 and 0.25 for the French diet. Table 3 also shows summary measures– slopes for the different clusters. These slopes re-affirm the grade differences between clusters and are used in Table 4 for explanatory analyses. Only a small proportion of the grade differences in dietary pattern membership can be attributed to differences in age or the proportion of energy under reporters (Table 4). An exception to this is the grade difference in moderately healthy diet among women, which is halved when adjusting for these factors. Also marital status, material problems and clustering with other health behaviours account for very little of the grade differences in dietary pattern membership. Only grade differences in the very unhealthy diet are somewhat attenuated after adjustment for smoking and exercise among men and women; also after the adjustment of material problems the grade differences in sweet diet is attenuated among women and the reverse grade difference is exacerbated among men. Among men adjusting for ethnicity attenuates grade differences for most dietary patterns, but exacerbates them for both unhealthy diets, especially for the unhealthy diet. Among women exacerbation of grade differences after adjustment for ethnicity is observed for all diets except the French diet. Among men and women adjusting for health control and decision latitude at work affects the unhealthy diet grade differences in different ways; adjusting for health

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Table 2 Univariate distribution (%) of selected background factors by dietary pattern membership All

Very healthy

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

24 24 22 30 100 (578)

27 27 19 27 100 (1063)

27 27 24 22 100 (930)

27 29 19 25 100 (893)

28 33 16 23 100 (1004)

30 28 18 25 100 (948)

Employment grade (P for w2 ¼ 0:001) Grade I (high) 49 46 Grade II 45 47 Grade III (low) 6 6 All 100 100 (N) (5375) (572)

52 44 5 100 (1054 )

30 56 14 100 (926)

53 44 3 100 (885)

39 54 7 100 (997)

70 28 1 100 (941)

Marital status (P for w2 ¼ 0:001) Married 83 84 Not married 17 16 All 100 100 (N) (5375) (572)

85 15 100 (1055)

76 24 100 (925)

86 14 100 (884)

79 21 100 (999)

88 12 100 (940)

Ethnicity (P for w2 ¼ 0:001) White 93 Other 7 All 100 (N) (5412)

84 16 100 (578)

98 2 100 (1062)

85 15 100 (929)

97 3 100 (893)

94 6 100 (1002)

98 2 100 (948)

Smoking (P for w2 ¼ 0:001) Never smoker 49 Ex-smoker 39 Current smoker 11 All 100 (N) (5171)

51 42 6 100 (558)

59 37 4 100 (1021)

42 33 25 100 (872)

48 39 13 100 (840)

45 42 13 100 (961)

50 44 7 100 (919)

Physical activity (P for w2 ¼ 0:001) Mild 14 16 Moderate 47 44 Vigorous 40 41 All 100 100 (N) (5376) (572)

11 49 40 100 (1055)

23 47 30 100 (925)

8 45 47 100 (886)

18 47 35 100 (999)

8 47 45 100 (939)

Material problems (P for w2 ¼ 0:025) Few 70 73 Several 30 27 All 100 100 (N) (4723) (494)

71 29 100 (960)

65 35 100 (776)

68 32 100 (788)

70 30 100 (863)

72 28 100 (842)

Health control (P for w2 ¼ 0:001) No 49 36 Yes 51 64 All 100 100 (N) (5366) (571)

44 56 100 (1054)

56 44 100 (923)

57 43 100 (883)

51 49 100 (998)

46 54 100 (937)

55 45

40 60

46 54

MEN Age (P for w2 ¼ 0:001) 39–44 27 45–49 28 50–54 20 55–63 25 All 100 (N) (5416)

Decision latitude (P for w2 ¼ 0:001) Low 43 42 High 57 58

43 57

30 70

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Table 2 (continued) All

Very healthy

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

All (N)

100 4911

100 522

100 942

100 858

100 794

100 923

100 872

All (N)

(5419)

(579)

(1064)

(930)

(893)

(1004)

(949)

WOMEN Age (P for w2 ¼ 0:001) 39–44 23 45–49 26 50–54 22 55–63 30 All 100 (N) (2360)

22 27 23 28 100 (734)

14 26 21 39 100 (298)

21 25 20 35 100 (272)

27 22 19 31 100 (150)

21 24 23 33 100 (493)

31 27 21 21 100 (413)

Employment grade (P for w2 ¼ 0:001) Grade I (high) 16 18 Grade II 46 47 Grade III (low) 38 34 All 100 100 (N) (2340) (725)

11 51 38 100 (298)

5 35 60 100 (272)

12 42 46 100 (147)

9 43 48 100 (489)

33 51 16 100 (409)

Marital status (P for w2 ¼ 0:075) Married 63 63 Not married 37 37 All 100 100 (N) (2337) (723)

56 44 100 (298)

63 37 100 (271)

68 32 100 (147)

65 35 100 (489)

67 33 100 (409)

Ethnicity (P for w2 ¼ 0:001) White 88 Other 12 All 100 (N) (2359)

81 19 100 (734)

92 8 100 (299)

77 23 100 (273)

89 11 100 (148)

94 6 100 (493)

96 4 100 (412)

Smoking (P for w2 ¼ 0:001) Never smoker 56 Ex-smoker 27 Current smoker 16 All 100 (N) (2263)

64 27 9 100 (697)

59 31 9 100 (293)

50 17 33 100 (265)

58 20 22 100 (143)

48 27 25 100 (470)

54 34 11 100 (395)

Physical activity (P for w2 ¼ 0:001) Mild 35 34 Moderate 48 46 Vigorous 18 21 All 100 100 (N) (2341) (725)

36 52 12 100 (298)

43 45 11 100 (272)

29 50 21 100 (147)

41 45 14 100 (490)

23 54 23 100 (409)

Material problems (P for w2 ¼ 0:115) Few 68 67 Several 32 33 All 100 100 (N) (2051) (644)

68 32 100 (259)

65 35 100 (231)

60 40 100 (133)

69 31 100 (423)

73 27 100 (361)

Health control (P for w2 ¼ 0:001) No 50 43 Yes 50 57 All 100 100 (N) (2337) (725)

45 55 100 (297)

61 39 100 (271)

58 42 100 (147)

56 44 100 (488)

51 49 100 (409)

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1404 Table 2 (continued) All

Very healthy

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

Decision latitude (P for w2 ¼ 0:001) Low 66 62 High 34 38 All 100 100 (N) (2093) (650)

74 26 100 (253)

78 22 100 (241)

73 27 100 (128)

76 24 100 (446)

47 53 100 (375)

All (N)

(299)

(273)

(150)

(493)

(413)

(2363)

(735)

Table 3 Number of observations and odds ratios of dietary pattern membership by grade. Membership in comparison to those with ‘very healthy’ dietary pattern Very healthy

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

Number of observation Men Grade I (high) Grade II Grade III (low)

265 270 37

546 459 49

277 520 129

472 385 28

393 535 69

661 268 12

Women Grade I (high) Grade II Grade III (low)

133 342 250

32 152 114

14 95 163

18 62 67

42 212 235

136 209 64

Odds ratiosa Men Grade I (high) Grade II Grade III (low) Slopeb

—a — — —

1.00 0.83 0.64 0.81(0.68–0.96)

1.00 1.84* 3.34* 1.87(1.58–2.23)

1.00 0.80* 0.42* 0.74(0.62–0.89)

1.00 1.34* 1.26 1.22(1.03–1.45)

1.00 0.40* 0.13* 0.38(0.32–0.46)

Women Grade I (high) Grade II Grade III (low) Slopeb

— — — —

1.00 1.85* 1.90* 1.28(1.05–1.56)

1.00 2.64* 6.19* 2.50(1.99–3.15)

1.00 1.34 1.98* 1.45 (1.11–1.90)

1.00 1.96* 2.98* 1.68 (1.41–2.00)

1.00 0.60* 0.25* 0.50(0.42–0.60)

a

We have selected the ‘healthy diet’ as the comparison outcome category or the base category. This base category of the outcome variable should not be confused with the reference category of each explanatory variable. b Employment grade entered to the logistic regression model as a continuous variable (and 95% confidence interval). * Statistically significant at the 5 per cent level.

control exacerbates the differences and adjusting for decision latitude attenuates the differences. After adjustment for all co-variates clear grade differences are observed for both unhealthy diets and the French diet among men and women, and also sweet diet among women. In Table 5 we analyse the effects of spouse’s social class on grade differences in dietary patterns among the study participants. These analyses are only feasible among the married and cohabiting. Overall grade differences among married men and women are quite similar to those in the total study population. In addition, as for own low grade of employment, low

spouse’s social class is prevalent among those with unhealthy diets, and uncommon among those with French diets. Adjusting for spouse’s class attenuates the grade differences in both unhealthy diets and the French diet. This attenuation is especially strong for the unhealthy dietary pattern. Can employment grade differences in biological risk factors be understood in terms of dietary patterns Table 6 presents dietary pattern differences in selected biological risk factors. For men and women the two unhealthy dietary patterns tend to have higher

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Table 4 Employment grade differencesa in dietary pattern membership, while adjusting for covariates Model

(N)

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

MEN (M1) Gradeb (M2) Gradeb (M3) Grade+age+energy reporting (M3)+marital status (M3)+material problems (M3)+smoking+physical activity (M3)+health control (M3)+decision latitude (M3)+ethnicity Full model

(5375) (4139) (4139) (4139) (4139) (4139) (4139) (4139) (4139) (4139)

0.81* 0.84 0.84 0.84 0.82 0.87 0.86 0.80 0.99 1.00

1.87* 1.80* 1.82* 1.74* 1.76* 1.63* 1.91* 1.71* 1.90* 1.78*

0.74* 0.70* 0.70* 0.71* 0.66* 0.70* 0.74* 0.72* 0.80 0.89

1.22* 1.19 1.19 1.15 1.18 1.14 1.22 1.15 1.36* 1.34*

0.38* 0.38* 0.37* 0.37* 0.35* 0.37* 0.38* 0.40* 0.42* 0.45*

WOMEN (M1) Gradeb (M2) Gradeb (M3) Grade+age+energy reporting (M3)+marital status (M3)+material problems (M3)+smoking+physical activity (M3)+health control (M3)+decision latitude (M3)+ethnicity Full model

(2340) (1787) (1787) (1787) (1787) (1787) (1787) (1787) (1787) (1787)

1.28* 1.21 1.10 1.12 1.11 1.10 1.15 0.91 1.25 1.09

2.50* 2.64* 2.74* 2.74* 2.76* 2.56* 2.90* 2.42* 2.76* 2.44*

1.45* 1.38* 1.46* 1.44* 1.41* 1.45* 1.56* 1.37 1.59* 1.51*

1.68* 1.65* 1.68* 1.68* 1.72* 1.63* 1.82* 1.52* 2.00* 1.87*

0.50* 0.48* 0.49* 0.49* 0.49* 0.50* 0.51* 0.56* 0.56* 0.66*

a

Employment grade differences were assessed by the slope, obtained by entering grade to the logistic regression model as a continuous variable. b Model 1 (M1) has more observations than other models as all other models only include observation with information on all explanatory factors. * Statistically significant at the 5 per cent level.

waist-to-hip-ratios, lower HDL and higher triglyceride levels than participant consuming healthy and French diets. Differences in BMI are more inconsistent, but those with the unhealthy diet have highest BMIs. Significant risk factor differences between dietary patterns exist even if grade and age are adjusted for (results not shown here). Employment grade differences in biological risk factors (Table 7) are in the direction expected on the basis of grade differences in disease. Among men each step down the three category grade ladder is associated on average with a 0.01 unit increase in waist-to-hip-ratio and a 0.27 unit increase in BMI. Corresponding increase for triglycerides is 0.16 mmol/l and HDL declines by 0.025 mmol/l. Similar associations are observed among women, but the associations are stronger for BMI and HDL. Among men adjusting for the six-category dietary pattern in linear regression models reduces the grade differences for most biological risk factors. Particularly, serum triglyceride differences are reduced by about a quarter and HDL differences are about halved. Also among women similar grade differences are attenuated

by about 25 per cent. Table 7 also indicates that adjustment for a finer cluster solution with 18 dietary patterns will provide an even fuller account of grade differences in biological risk factors. Further analyses (not presented here) indicate that alcohol consumption—measured in terms of alcohol consumption patterns in the past year and number of units in the past week—account for a large part (80 per cent) of the effects of dietary patterns on grade differences in HDL among men, but less (40 per cent) among women. For serum triglycerides the effects of dietary clusters on the grade differences are largely independent of alcohol consumption patterns. In addition, separately adjusting for physical activity does not strongly account for the effects of dietary patterns on grade differences in HDL or serum triglycerides.

Discussion Self-reported dietary data are often inaccurate indicators of nutrient intake; correlations of blood plasma or urine measures of nutrients and estimates based on

P. Martikainen et al. / Social Science & Medicine 56 (2003) 1397–1410

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Table 5 Distribution (%) of spouse’sa social class and employment grade differencesb in dietary pattern membership, while adjusting for spouse’s social class All

Very healthy

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

MEN Spouse’s class (P for w2 ¼ 0:001) I–II III N Manual All (N) (M1) Grade+age+energy reporting (M1)+spouse’s class

49 39 12 100 (2329) (2329) (2329)

55 33 12 100 (252) — —

43 46 11 100 (446) 0.88 0.80

39 44 17 100 (386) 1.82* 1.65*

47 39 14 100 (369) 0.77 0.71*

46 42 12 100 (436) 1.16 1.08

66 28 5 100 (440) 0.42* 0.44*

WOMEN Spouse’s class (P for w2 ¼ 0:001) I–II III N Manual All (N) (M1) Grade+age+energy reporting (M1)+spouse’s class

51 16 33 100 (947) (947) (947)

57 17 26 100 (278) — —

44 24 32 100 (105) 1.20 1.04

38 12 50 100 (118) 2.61* 2.24*

51 19 30 100 (63) 1.62* 1.60*

38 13 49 100 (210) 1.54* 1.19

71 13 17 100 (173) 0.60* 0.66*

a

Only married and cohabiting participants included in the analyses of this table. Employment grade differences were assessed by the slope, obtained by entering grade to the logistic regression model as a continuous variable. The ‘healthy diet’ was the comparison outcome category or the base category (see Methods). * Statistically significant at the 5 per cent level. b

Table 6 Crude means of biological risk factors by dietary pattern Very healthy

Moderately healthy

Very unhealthy

Sweet

Unhealthy

French

P for heterogeneity

Men WHR BMI (kg/m2) HDL (mmol/l) Triglyceride (mmol/l) Total (N)

0.906 25.29 1.32 1.52 (539)

0.888 24.44 1.31 1.39 (1002)

0.910 24.83 1.28 1.77 (876)

0.897 24.69 1.31 1.59 (840)

0.916 26.03 1.30 1.75 (939)

0.901 25.40 1.39 1.53 (885)

0.0001 0.0001 0.0001 0.0001 —

Women WHR BMI (kg/m2) HDL (mmol/l) Triglyceride (mmol/l) Total (N)

0.773 25.87 1.69 1.15 (698)

0.771 25.27 1.66 1.26 (278)

0.781 24.48 1.61 1.35 (247)

0.777 24.94 1.61 1.36 (143)

0.782 26.39 1.64 1.28 (461)

0.763 25.30 1.83 1.01 (397)

0.0011 0.0001 0.0001 0.0001 —

dietary information are usually low (Bingham et al., 1997; Bingham & Day, 1997; Brunner et al., 2001). In this paper, we have not set out to measure nutrient levels. Our aim has been to identify common dietary patterns based on food frequency data, and to assess socioeconomic differences in these dietary patterns and their sociodemographic and psychosocial (decision latitude and health control) determinants, and to evaluate whether adjusting for these dietary patterns

attenuates socioeconomic differences in biological risk factors. The data for these analyses are cross-sectional and causal interpretations of some of our results should thus be conducted cautiously. Identification of dietary patterns Our results indicate that meaningful dietary patterns can be identified with cluster analyses of food frequency

P. Martikainen et al. / Social Science & Medicine 56 (2003) 1397–1410 Table 7 Grade differencesa in selected risk factors, while adjusting for dietary pattern membership. Age

+Cluster6b

+Cluster18b

Men WHR BMI (kg/m2) HDL (mmol/l) Triglyceride (mmol/l)

0.011* 0.27* 0.025* 0.16*

0.009* 0.28* 0.012 0.12*

0.008* 0.27* 0.003 0.11*

Women WHR BMI (kg/m2) HDL (mmol/l) Triglyceride (mmol/l)

0.012* 0.72* 0.107* 0.11*

0.012* 0.78* 0.083* 0.08*

0.010* 0.66* 0.070* 0.07*

a Employment grade differences were assessed by the slope, obtained by entering grade to the logistic regression model as a continuous variable (see methods). b We adjusted for both a six category cluster solution (cluster6) as used elsewhere in the analyses, and a finer cluster solution of 18 dietary patterns (cluster18). * Statistically significant at the 5 per cent level.

questionnaires. After the exclusion of 124 outliers— observations that in a 30 or 60 cluster solution fell into clusters with only few observations—the cluster solution was robust, and we obtained very similar cluster solutions when using five random halves of the observations. Furthermore, we observe qualitatively similar dietary patterns when cluster analyses are performed separately for men and women, and for participants who are not energy under reporters. Furthermore, the validity and the meaningfulness of the dietary clusters is also emphasised by the fact that large employment grade—our measure of socioeconomic status—differences in dietary pattern membership can be observed and that biological risk factors vary between these clusters independently of grade. Socioeconomic differences in diet Socioeconomic differences in diet and dietary patterns have been observed in several studies from various populations using various techniques (Whichelow & Prevost, 1996; Pryer et al., 2001; Pr.att.al.a, Karisto, & Berg, 1994; Steele, Dobson, Alexander, & Russell, 1991; Anderson, Macintyre, & West, 1994; Milligan et al., 1998; James et al., 1997). Broadly these studies show that men and women who consume healthier diets are from higher socioeconomic groups. Our results are in accordance with previous studies in documenting this socioeconomic difference—with the exception of reverse differences among men for the moderately healthy and in particular the sweet diet—but is unique in trying to systematically assess its causes and consequences. At

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least the following explanations can be put forwards to explain socioeconomic differences dietary patterns: (1) Aggregation of unhealthy behaviour. Poor diets may be related to other unhealthy life-styles (Pr.att.al.a et al., 1994; Karisto, Pr.att.al.a, & Berg, 1993; Whichelow & Prevost, 1996). For example, Finnish male smokers are also more likely to consume unhealthier diets than non-smokers (Pr.att.al.a et al., 1994). We have also shown that in our data being a current smoker and not taking exercise are related to unhealthy diets. Such aggregation of unhealthy behaviours is grade specific. For example, of those with very unhealthy diets, more than 40 per cent of grade III men and less than 20 per cent of grade I men were current smokers or participated in only mild exercise (results not shown here). However, among men and women the contribution that this aggregation makes to ‘explaining’ socioeconomic differences in dietary patterns is quite small. Further understanding of the factors that contribute to aggregation of unhealthy behaviours, particularly in the lower socioeconomic groups, is important. (2) Material hardship. It has been proposed that lower socioeconomic groups may have both more financial constraints in purchasing healthy foods as well as more limited access to healthy foods in deprived residential areas than higher social classes (Sooman, Macintyre, & Anderson, 1993; Davey Smith & Brunner, 1997). However, consumption of relatively low cost food stuffs, such as sugar or brown bread, in which cost is unlikely to play a major role indicate that higher socioeconomic groups make healthier decisions (Steele et al., 1991). Furthermore, socioeconomic differences in healthy food purchasing behaviours were not explained by structural, material and economic factors in Brisbane, Australia, but food preferences play an important role (Turrell, 1998). Our results also indicate that material hardship, as we are able to measure it in a white-collar cohort, is only weakly related to poorer diets, and its contribution to socioeconomic differences in the dietary patterns is minor. (3) Contextual influences (ethnicity and household effects). Food purchasing choices and decisions on the preparation of meals are not made in a vacuum. Dietary preferences are likely to be influenced by the characteristics of the household and broad ‘cultural’ traditions related to e.g. ethnicity. In these data ethnicity is strongly related to diets, but not in a consistent manner; non-white participants are more likely to consume both the very healthy and the very unhealthy diets. The tendency of non-white participants to be from low socioeconomic statuses and be more likely to consume the very healthy comparison diet somewhat masks the actual grade differences in unhealthy diets among men and women; adjusting for ethnicity increases grade differences in unhealthy and very unhealthy diets. Because of the relatively small number of non-white

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participants (8.5 per cent of all participants) the contribution of ethnicity to the overall socioeconomic differences in dietary patterns is quite small. However, although ethnic differences in our data did not contribute to grade differences in dietary behaviour other broad contextual factors—for example those associated with long standing regional dietary differences as well as household and childhood formation of food preferences may still be important. Our results also indicate that among men—but not among women—being non-married is related to unhealthier diets, and that adjusting for marital status attenuates the socioeconomic differences in dietary patterns somewhat. This finding is in accordance with studies showing non-married men to be less advantaged in terms of socioeconomic position, health and health related behaviours (Kiernan, 1988; Joung, 1996). For married participants lower spouse’s socioeconomic status is related to poorer dietary patterns, particularly among women. Among men adjusting for wife’s class attenuates the grade differences in very unhealthy and unhealthy diets by about 20 and 50 per cent respectively. Among women the corresponding attenuation is 20 and 65 per cent from grade differences that are initially larger than among men. Also grade differences in moderately healthy diet are reduced markedly among women when husband’s socioeconomic status is adjusted for. However, it remains somewhat unclear from these analyses why spouse’s socioeconomic status is related to the participants’ dietary choices. Results discussed above do not strongly support an explanation based on household material hardship in these data. Possibly spouses from higher socioeconomic groups contribute to healthier household food culture, control unhealthy dietary behaviours of their partners more strenuously (Umberson 1992) and support in attempts to modify diets. (4) Perception of control over health. Control over different domains of life, and health in particular, may be an incentive to consume a healthy diet. Accordingly, health control as well as job control were related to dietary patterns. Adjusting for job control made a modest contribution to understanding socioeconomic differences in unhealthy dietary behaviours. However, adjustment for health control increased socioeconomic differences in unhealthy and very unhealthy diets. Overall these results indicate that knowledge and beliefs about what one can do for one’s health and rational choices based on this knowledge may not be major determinants of socioeconomic differences in dietary choices in the general population. It is thus possible that socioeconomic differences in dietary patterns are more determined by food preferences—likes and dislikes of particular foodstuffs. Food items are unlikely to be consumed only because they are healthy. However, it is

uncertain from these data what may contribute to socioeconomic differences in food preferences, but variable impact of dietary promotion programmes or yet unspecified sub-cultural food related prescriptions may play a role (Turrell, 1998). Socioeconomic differences in risk factors and diet Food consumption patterns are known to be related to cardiovascular risk factors (Huijbregts, Feskens, & Kromhouts, 1995; Post, Kemper, Twisk, & van Mechelen, 1997; Appel et al., 1997). However, very little empirical work on the contribution of diet to socioeconomic differences in risk factors, and incidence of disease has been carried out to date. Our results show that a part of the larger waist-to-hip-ratio, higher triglyceride and especially lower HDL levels of the lower socioeconomic groups are partially accounted for by socioeconomic differences in dietary patterns. Calculations based on these data show that in a 5-year followup period the socioeconomic differences observed for HDL are associated with a 5 and 10 per cent difference in the incidence of fatal CHD and non-fatal MI for men and women correspondingly. For triglyceride the corresponding difference is about 3–4 per cent for both men and women. Socioeconomic differences in BMI have little to do with the dietary patterns as we have measured them. Partly the effects of dietary clusters—particularly among men—on grade differences in HDL can be accounted for by alcohol consumption differences between the dietary clusters. This mainly reflects high HDL among male followers of the French dietary pattern, which is characterised by high consumption of wine and beer. From these data it is difficult to assess whether all of this attenuation reflects the causal effects of alcohol consumption on HDL, or whether alcohol consumption patterns also act as a proxy for a broader set of dietary behaviours that increase HDL. Although this issue can not be settled with these data, we observe that relatively high levels of alcohol consumption (sweet unhealthy diet) are not necessarily accompanied with high HDL levels. Although our cross-sectional associations have to be interpreted with caution, these results indicate that dietary patterns may contribute to socioeconomic differences in cardiovascular disease through some of the well established biological risk factors. This possibility will have to be demonstrated in future studies on disease incidence.

Summary These results show that dietary patterns are strongly related to socioeconomic status, and that these dietary

P. Martikainen et al. / Social Science & Medicine 56 (2003) 1397–1410

patterns may be important determinants of socioeconomic differences in some health related risk factors. The causes of socioeconomic differences in dietary choices are not well understood and our data are not ideal to study this. However, these data indicate that dietary choices are less related to material circumstances, and perceptions of control over health, but that factors related to household socioeconomic circumstances, and to a lesser extent clustering of unhealthy behaviours as well as job control play a more important role. A large part of the socioeconomic differences in dietary patterns remain unexplained and thus excluding any specific explanation is unwarranted.

Acknowledgements 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 (RO1-HL36310), US, NIH: National Institute on Aging (RO1-AG13196), US, NIH; Agency for Health Care Policy Research (RO1-HS06516); and the John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health. PM is supported by Academy of Finland (grant 41498, 70631 and 48600) and the Gyllenberg Foundation. MM is supported by an MRC Research Professorship. We also 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.

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