Abstract
Objectives To describe the association of diet and socioeconomic position and to assess whether two different indicators, education and occupation, independently contribute in determining diet.
Methods A community-based random sample of men and women residents of Geneva canton, aged 35 to 74, participated in a survey of cardiovascular risk factors conducted annually since 1993. Lifetime occupational and educational history and a semi-quantitative food frequency questionnaire were obtained from 2929 men and 2767 women.
Results Subjects from lower education and/or occupation consumed less fish and vegetables but more fried foods, pasta and potatoes, table sugar and beer. Iron, calcium, vitamin A and vitamin D intake were lower in the lower educational and occupational groups. Both indicators significantly contributed to determining a less healthy dietary pattern for those from low social class. The effects of education and occupation on dietary habits were usually additive and synergistic for some food groups.
Conclusion Assessing both education and occupation, improves the description of social class inequalities in dietary habits, as they act, most of the time, as independent factors.
Keywords Socioeconomic position, social class, education, occupation, diet, food intake, nutrient intake
Accepted 21 August 2000
It is well established that there are socioeconomic inequalities in health.1,2 Some of these inequalities are mediated by different exposures to risk factors such as poor diet. Indeed, in economically developed countries, most37 but not all8,9 studies have reported healthier diet among subjects with higher socioeconomic status. However, differences in the amount of food or in nutrient intake among social classes are often small and can hardly explain the major inequalities observed in morbidity and mortality in these countries.
The measures of socioeconomic position classify individuals in groups of similar status or prestige, power, knowledge and resources.10 Often, educational, occupational and income level are used to characterize socioeconomic groups. The availability of these indicators is sometimes limited to those routinely collected by national statistics. For historical and cultural reasons, Europe traditionally uses education and occupation (information on occupation is available in Britain from death certificates) while the US mainly relies on income and education (since 1991, years of educational attainment is recorded in the death certificate of almost all states).11,12
The correlation between these three indices as measures of socioeconomic position in developed countries is relatively weak (0.30.6).10,1315 These results suggest that each index explains a different component of social class variability which contributes differently to health inequalities. In addition, the interpretation of a given indicator might differ among subgroups of the population, such as women, older people or different ethnicities.16
The objective of this study is to describe the association of diet with socioeconomic position in a large population-based survey, and to assess whether the results differ depending on which indicator, education or occupation, is used.
Materials and Methods
The Bus Santé 2000 Survey is an ongoing, community-based survey of cardiovascular risk factors conducted annually since 1993.17 Geneva (city and surroundings) has a population of 395 609 distributed over 242 km2 of land.18 Data reported here comprise subjects randomly selected throughout 1993 to 1998, to represent the 89 000 men and 98 000 women non-institutionalized residents aged 3574 years.
Subjects were randomly identified from the residents' register published each year.18 Random sampling in age-sex-specific strata was proportional to the corresponding frequencies in the population. In the first letter mailed to a potential subject, the selected individual was asked to indicate a convenient day and time to visit a mobile unit. In the case of non-response, up to seven attempts were made to reach the person by phone, at different times of the day and various days of the week, including Saturday and Sunday. Two more mailings were sent when a selected individual could not be reached by phone. A person who could not be contacted after three mailings and seven phone calls was replaced using the same selection protocol. A systematic check in the following year of the population register showed that over 90% of unreachable subjects no longer resided in Geneva. Subjects who were contacted but refused to participate were not replaced. Overall participation rate was 63%.
Participants completed a self-administered, semi-quantitative food frequency questionnaire (FFQ) at home. This FFQ has been developed in the target population. Mean values obtained from a 24-hour recall diary were very similar between the two questionnaires.19 It asked about 100 food items and their serving sizes, and could be converted into daily energy, nutrient and alcohol intakes.19 On the day of the visit to the mobile unit, participants brought back the completed FFQ as well as a self-administered questionnaire covering lifestyle factors, education, reproductive history and classic cardiovascular disease risk factors. Occupational history consisted of current and the two longest past occupations, their duration and workplace characteristics. Trained interviewers checked these questionnaires for completion.
For this analysis we excluded subjects with missing information on education or occupation (18 men and 88 women). Subjects who reported having never worked for pay were also excluded (2 men and 58 women). Women were eight times more often excluded than men. A total of 2929 men and 2767 women were finally included in the analysis.
Variable definition
According to the type and level of schooling, education was categorized as: low (8 years of schooling), medium (912 years of schooling) and high (
13 years and including people who obtained the Swiss baccalaureate).
Occupational level was measured using the respondent's own occupation: current occupation at the time of the survey or the longest occupation ever held for those not currently working. We grouped them in three occupational levels based on the British Registrar General's Scale:20 high (I and II from the original British classification : professional and intermediate professions), medium (III-N: non-manual occupations) and low (III-M, IV and V: manual or lower occupations).
Groups of food items from the FFQ assessed were: regular dairy products (excluding cheese), low-fat dairy products (excluding cheese), cheese, bread and cereals, pasta and potatoes, vegetables, meat (including lamb, beef, chicken and pork), fish, fruits, pastries and desserts, table sugar, fat and oil for cooking or added into the dishes, fried food (included French fries and fried fish), juices, wine and champagne, beer and hard alcohol. The food items and their serving sizes were converted into daily energy, nutrient and alcohol intakes. Micronutrients included iron, calcium, vitamin D and vitamin A.
Country of birth was classified as Switzerland, a group comprising Spain, Italy and Portugal, another group with France, Germany and England, and finally, an amalgam of different countries was grouped as Other countries.
Statistical analyses
Food consumption and nutrient intake were log transformed in order to improve the normality of their distribution. Results were back-transformed and reported as geometric means. Adjusted mean of food consumption and nutrient intake was estimated using multiple linear regression. The effect of education was adjusted for occupation and the effect of occupation for education.
All models were additionally adjusted for age, country of birth and total energy intake. P-values for trend, obtained from linear regression with education and occupation categorized as scores, tested the null hypothesis that the slope equals 0. P-values of each individual level of education and occupation were obtained coding education and occupation as dummy variables. Fitting a product term of the two variables categorized as score assessed interaction between education and occupation. For those food groups and nutrient intake where education and occupation had a significant interaction the results are presented in Figures stratifying for each level of education within each occupational level.
Results
Table 1 shows descriptive characteristics of the participants. Almost half the men were from the high occupational group while more than half of women were from the medium occupational group. Almost 28% of men versus 15% of women were classified in the low occupational group.
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There were no differences in nutrient intake by educational or occupational level with few exceptions (Table 3). Fibre intake decreased with decreasing level of occupation in men and women (men: trend P = 0.03; women: trend P = 0.01). Total protein intake decreased with decreasing education in women (trend P = 0.03). Saturated fat was lowest in the low educational groups in both, men and women and, finally, monounsaturated fat decreased with decreasing occupation in women (trend P = 0.01).
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Discussion
The present results show that lower education and lower occupation independently contribute to determining differences in dietary habits and that the effect of the two indicators is cumulative. Men from lower socioeconomic position consumed less fish and vegetables but more pasta and potatoes, fried foods, table sugar and beer. Women of lower socioeconomic position also consumed less fish and vegetables but more meat, fried foods, table sugar, pasta and potatoes. Lower intake of iron, calcium, vitamin A and vitamin D was present among lower socioeconomic groups. All results were adjusted for total energy intake and therefore the differences in food and nutrient intake among social classes were independent of the actual amount of food consumed. The differences in food intake due to education and occupation, for most food groups, were simply additive and sometimes more than additive.
Our results are consistent with previous reports on diet and social class. Differences are often of small magnitude and for some, but not all, key components of the diet.39,2123 The present results suggest that these reports may underestimate true differences because, when using a single measure of social class, they only assessed one component of the socioeconomic position. Despite differences in food consumption, nutrient intake was similar among socioeconomic groups, as these differences may not be substantial enough to translate into differences in nutrient intake. Vitamin and supplement intake was uncommon in this population compared to the US where about 50% of the population report multivitamin use.24
Some studies use a battery of different indicators in order to describe inequalities and for most situations the results are fairly similar. An occupation-based measure of social class remained significant in predicting diet after adjusting for education and house tenure.4 Race, education, and income were significantly associated with some nutrient intake among 9 and 10 year old girls.25 Education and income remained independently associated with diet in a population of Ontario seniors.26 Other studies have used different indicators of social class but they did not report whether they had an independent effect.21,23,27 Finally, other studies have used composite indices, which preclude differentiating the independent effect of each component of the index.5,28
We measured the effect of education adjusting for occupation and the effect of occupation adjusting for education. Both indicators were consistently associated with similar dietary patterns. Clearly, both indices measure aspects of the same concept, socioeconomic position. Indeed, the educational level determines the occupation and jointly with occupation they determine the income level. On the other hand, there are plausible pathways through which education and occupation could have an independent role in predicting health-related behaviours. The amount of education and knowledge individuals acquire can influence their lifestyle, problem-solving capacity and values,10,14 the importance given to preventive health measures and the capacity to generate behaviours that will bring benefits on a long term basis.29 The occupational level is a measure of social prestige. Occupation is related to differential exposure to environmental risk factors and to psychological stress.30,31 It determines income and therefore, access to certain food products. At the same time, it generates social networks that can greatly influence behavioural health habits. Thus, both indicators measure different pathways through which socioeconomic position can have an independent effect on diet. It is reasonable to conceive that, for example, poor dietary habits acquired in youth can be added to poor dietary choices in the restaurant of an industrial complex where healthy diet may not be promoted. On the contrary, someone with high education may have broader knowledge about diet and health and will probably choose healthier meals at restaurants with colleagues who might also be more predisposed towards healthier habits.
Education and occupation were similarly associated with diet in men and in women although the significance and meaning of these indicators may vary by gender.12,16 Occupational level was obtained measuring the participant's own current or longest occupation held if not currently working. Whether women are better categorized by their own or by their husband/partner's occupation has been long debated. Household socioeconomic class was a better predictor of health than the woman's own position.32 As more women enter the paid work force similar results may apply to men and household position might be more relevant than individual measures, independent of gender. Another important issue is the limitation that occupation-based classifications may have in measuring women's socioeconomic position. Most of the occupational scales were created using men's occupational categories as reference. Whether these scales equally apply to women has not yet been demonstrated.
The differences we found among socioeconomic groups were often small, thus bringing into question their clinical relevance. Nevertheless, it is important to describe and follow behavioural differences over time33,34 as inequalities are likely to increase.1
Reporting bias among higher social classes could explain some of our results if people from this socioeconomic group tended to report food intakes closer to dietary guidelines without reflecting their true consumption. In this instance we would expect to find high consumption of fruits among men and women of high socioeconomic status, along with the observed higher consumption of fish and vegetables, but this is not the case.
Participation in the study was similar in all age groups and by gender with the exception of women older than 65 who had higher non-response rate. Information on smoking status was obtained from all selected subjects and there were no differences between participants and non-participants. Bias due to non-participants cannot be excluded based on this information, but at least non-respondents did not differ on a variable known to influence diet.35
The main strengths of our study were: (1) a food frequency questionnaire developed and tested in the target population, and (2) a detailed occupational history with current and past occupations. A limitation was that other measures of the socioeconomic position such as income, wealth or supra-individual indices, were not available.
In summary, we found an association between education and occupation and diet. Both indices contributed to explaining a dietary pattern that tended to be worse for the low social class groups. The effects of education and occupation for some foods and nutrients were additive or even synergistic. Our results suggest that both indicators should be assessed in order to provide a full description of the social inequalities in dietary habits.
KEY MESSAGES
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Acknowledgments
This study was funded by the Swiss National Fund for Scientific Research (Grants No 32.31.326.91, 3237986.93 and 3249847.96). We are indebted to Moyses Szklo, Ann Sorenson and Mike Constanza for their advice and comments.
Contributions: B Galobardes planned the study, analysed the data and wrote the paper; A Morabia planned the study, supervised data analysis and participated writing the paper; MS Bernstein coordinated and supervised the data collection and participated in writing the paper.
Notes
Division of Clinical Epidemiology, University Hospital of Geneva, 24 rue Micheli du Crest, 1211 Geneva 14, Switzerland. E-mail: bruna.galobardes{at}hcuge.ch
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