European Journal of Clinical Nutrition (2010) 64, 231–238
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ORIGINAL ARTICLE
Dietary patterns and socioeconomic position
P Mullie1,2, P Clarys3, M Hulens1,4 and G Vansant1
1
Department of Nutrition, Preventive Medicine and Leuven Food Science and Nutrition Research Centre (LFoRCe), Catholic University
Leuven, Leuven, Belgium; 2Unit of Epidemiology and Biostatistics, Queen Astrid Military Hospital, Brussels, Belgium; 3Faculty of
Physical Education and Physiotherapy, Laboratory for Human Biometrics and Biomechanics, Vrije Universiteit Brussel, Brussels,
Belgium and 4Department of Rehabilitation Sciences, Research Centre for Musculoskeletal Rehabilitation, Catholic University Leuven,
Heverlee, Belgium
Background/Objectives: To test a socioeconomic hypothesis on three dietary patterns and to describe the relation between
three commonly used methods to determine dietary patterns, namely Healthy Eating Index, Mediterranean Diet Score and
principal component analysis.
Subjects/Methods: Cross-sectional design in 1852 military men. Using mailed questionnaires, the food consumption frequency
was recorded.
Results: The correlation coefficients between the three dietary patterns varied between 0.43 and 0.62. The highest correlation
was found between Healthy Eating Index and Healthy Dietary Pattern (principal components analysis). Cohen’s kappa
coefficient of agreement varied between 0.10 and 0.20. After age-adjustment, education and income remained associated with
the most healthy dietary pattern. Even when both socioeconomic indicators were used together in one model, higher income
and education were associated with higher scores for Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary
Pattern. The least healthy quintiles of dietary pattern as measured by the three methods were associated with a clustering of
unhealthy behaviors, that is, smoking, low physical activity, highest intake of total fat and saturated fatty acids, and low intakes
of fruits and vegetables.
Conclusions: The three dietary patterns used indicated that the most healthy patterns were associated with a higher
socioeconomic position, while lower patterns were associated with several unhealthy behaviors.
European Journal of Clinical Nutrition (2010) 64, 231–238; doi:10.1038/ejcn.2009.145; published online 20 January 2010
Keywords: dietary pattern; nutritional assessment; nutritional epidemiology; public health; socioeconomic status; prevention
Introduction
Dietary pattern analysis, based on the concept that foods
eaten together are as important as a reductive methodology
characterized by a single food or nutrient analysis, has
emerged more than a decade ago as an alternative approach
to study the relation between nutrition and disease (Schwerin
et al., 1982; Randall et al., 1991). As reviewed by Hu (Hu,
2002), dietary pattern analysis is a better method to examine
Correspondence: Drs P Mullie, Department of Nutrition, Preventive Medicine
and Leuven Food Science and Nutrition Research Centre (LFoRCe), Catholic
University Leuven, Kapucijnenvoer 35 bus 7001, B-3000 Leuven, Belgium.
E-mail: [email protected]
Contributors: PM conceived the original idea together with GV, performed the
study, analyzed the data and wrote the first draft; PC and MH conceived and
refined the original idea, advised on study design and data analysis and
critically appraised the paper.
Received 10 March 2009; revised 24 October 2009; accepted 6 November
2009; published online 20 January 2010
the effect of overall diet: food and nutrients are not eaten
in isolation, and the ‘single food or nutrient’ approach will
not take into account the complex interactions between
food and nutrients. Two major methods are used to reduce
complex dietary data: a hypothesis-oriented approach
using previous information to stratify a dietary pattern
and a statistical approach using study-specific data to rank
individuals, that is, principal component analysis or reduced
rank regression models (Hoffmann et al., 2004; Schulze and
Hoffmann, 2006).
The Healthy Eating Index and the Mediterranean Diet
Score are two frequently used hypothesis-oriented approaches (Waijers et al., 2007; Arvaniti and Panagiotakos, 2008;
Fransen and Ocke, 2008; Kourlaba and Panagiotakos, 2009).
The Healthy Eating Index represents the degree to which a
dietary pattern conforms to official guidelines summarized
in the United States Department of Agriculture Food Guide
Pyramid (Kennedy et al., 1995). The Mediterranean Diet
Score, according with the Mediterranean dietary pattern, has
Education, income and nutrition
P Mullie et al
232
received a lot of attention because of the associated
reduction in mortality (Sofi et al., 2008).
An example of commonly used exploratory approach is
principal component analysis identifying foods that are
consumed together. This statistical technique may be able to
detect correlations between foods or food groups contained
in an array of nutritional data.
Few publications have reviewed the different methods
to determine a dietary pattern (Hu, 2002; Kourlaba and
Panagiotakos, 2009). A first aim of this study was to
compare the degree of accordance of Healthy Eating Index,
Mediterranean Diet Score and principal component analysis
on the ranking of individuals according to their dietary
patterns.
The second aim of this study was to describe the relation
between Healthy Eating Index, Mediterranean Diet Score
and principal component analysis versus education and
income as indicators for socioeconomic position. A healthy
dietary pattern has been consistently associated with a
higher socioeconomic position (Dynesen et al., 2003; Huot
et al., 2004; Robinson et al., 2004; Park et al., 2005; Kant and
Graubard, 2007).
To our knowledge, the relation between socioeconomic
position and different methods to describe a dietary pattern
has seldom been studied.
Materials and methods
In February 2007, air and terrestrial components of the
Belgian army totaled 33 053 men. After stratification for
military rank and age, 5000 men were selected representative
for the total army structure. The selection consisted of 598
officers; 2103 non-commissioned officers and 2299 soldiers.
This population has the advantage to limit the influence of
occupation as socioeconomic determinants allowing us to
focus on the influence of income and education.
A semi-quantitative food frequency questionnaire with
150 food items was mailed to the participants. The following
categories of consumption frequency were used: never, one
to three times a month, once a week, two to four times a
week, five to six times a week, once a day, two to three times
a day, four to six times a day and more than six times a
day. Portion sizes were predefined using familiar measuring
devices (teaspoon, glass, cup...). The validity of the questionnaire was tested on a sample of 100 men representative
for the participants to the cross-sectional study (Mullie et al.,
2009).
A second questionnaire was used to register health-related
and lifestyle characteristics. This questionnaire was selfreporting regarding smoking, marital status, main occupation, age, weight, height, number of children and knowledge
of cardiovascular risk factors. This questionnaire was used in
previous research (Autier et al., 2003). Yearly gross salary was
obtained from administrative services, taking into account
the rank and years of active duty.
European Journal of Clinical Nutrition
The individual characteristics of the responders were
categorized in age-category (20 to 29 years, 30 to 39 years,
40 to 49 years and 50 to 59 years); Body mass index (BMI)
classified according to the World Health Organization in
normal weight, 18.5pBMI o25.0 kg/m2, overweight, 25.0p
BMI o30.0 kg/m2 and obesity, BMIX30.0 kg/m2 (World
Health Organisation, 2003); physical activity (stratified in
low, moderate and high according to the International
Physical Activity Questionnaire) (Hallal and Victora, 2004);
use of vitamin supplements (yes or no); actual smoking
(yes or no); educational level (low for vocational education,
moderate for secondary level and high for bachelor or master
level); and income (low for lowest tertile of yearly gross
income of the group, moderate for intermediate tertile of
income and high for highest tertile of yearly income).
Participation was on a voluntary basis and without
incentives. An informed consent was signed by all
participants.
Statistical analysis
Using a w2 test, we assessed the differences in the proportion
of officers, non-commissioned officers and soldiers that
responded. Using data from military records, that is, age
and rank, the differences between responders and nonresponders were tested with the same test. For descriptive
statistics, mean and s.d. were calculated for the individual
characteristics, according to quintiles of dietary patterns.
Differences between quintiles were tested with w2 and
analysis of variance.
The Healthy Eating Index and the Mediterranean Diet
Score were computed as described earlier (Basiotis et al.,
2002; Sofi et al., 2008). The possible scores for Healthy Eating
Index ranged from 0 to 100 and for Mediterranean Diet Score
from 0 to 9, with a high score for the most healthy pattern.
Principal component analysis was applied to the data of the
semi-quantitative food frequency questionnaire. First, 150
food items were classified into 34 predefined food groups
with similar nutrient profile, according to Hu et al. (2000).
Principal components analysis was used to derive dietary
patterns based on the 34 food groups. Varimax transformation was effectuated to achieve uncorrelated factors with a
greater interpretability. Components with eigenvalues more
than 1.5, interpretability of the factors and Scree plot were
used to determine the number of selected factors. The
eigenvalues of the factors dropped after the second factor
(from 2.44 to 1.77) and after the third factor (from 1.77 to
1.44). The remaining factors were more similar after the
fourth factor (ranging from 1.38 for the fifth factor to 1.10
for the tenth factor). Three major dietary patterns were
clearly identified for further analysis. The factor scores for
each pattern were constructed by summing up the observed
intakes of the component food items, weighted by the
individual factor loadings. Those factor scores rank individuals according to their agreement with each dietary
pattern. The most healthy dietary pattern was selected, that
Education, income and nutrition
P Mullie et al
233
Table 1 Characteristics of the participants
Characteristics
Total
Age (in years)
Military rank
Body mass indexa
Physical activitya
Regularly use of vitamin supplementation
Actual smoking
Educational levela
Incomea
Categorization
Responders
20–29
30–39
40–49
50–59
Officers
Non-commissioned officers
Soldiers
Normal (o25.0)
Overweight (X25.0–o30.0)
Obesity (X30.0)
Missing
Low
Moderate
High
Missing
Low
Moderate
High
Low
Moderate
High
Non-responders
n
%
n
%
1852
119
358
1064
311
217
936
699
744
836
244
28
365
383
1016
88
283
447
789
811
252
618
616
618
100.0
6.4*
19.3*
57.5*
16.8*
11.7*
50.5*
37.7*
40.2
45.1
13.2
1.5
19.7
20.7
54.9
4.8
15.3
24.1
42.6
43.8
13.6
33.4
33.3
33.4
3148
461
753
1439
495
381
1167
1600
100.0
14.6*
23.9*
45.7*
15.7*
12.1*
37.1*
50.8*
a
Body mass index (in kg/m2) was classified according to the World Health Organization (World Health Organisation, 2003); physical activity was stratified in low,
moderate and high according to the International Physical Activity Questionnaire (Hallal and Victora, 2004); educational level is low for vocational education,
moderate for secondary level and high for bachelor or master level; income is low for lowest tertile of yearly gross income, moderate for intermediate tertile of
income and high for highest tertile of yearly income. *Po0.001.
is, the Healthy Dietary Pattern (principal components
analysis), because a high factor score is associated with the
most healthy pattern, which is also the case for Healthy
Eating Index and Mediterranean Diet Score. This Healthy
Dietary Pattern was associated with a high intake of
fruits, vegetables, nuts, fish, whole grain and low-fat diary
products. Participants were divided in quintiles according
to the scores for Healthy Eating Index, Mediterranean
Diet Score and factor scores for the Healthy Dietary Pattern
(principal components analysis). Spearman correlation
coefficients, percentages of participants classified into the
same and opposite quintiles of intake, and Cohen’s kappa
coefficient of agreement were calculated for the three dietary
patterns.
Age-adjusted and BMI stratified linear regression was
executed to separately estimate the independent effect of
education and income categories on Healthy Eating Index,
Mediterranean Diet Score and principal components analysis
dietary pattern as continuous dependent variable. Tolerances
were checked for all variables. Plots of the residuals versus
the predicted values were examined to ascertain that basic
model assumptions were met. Correlation between education and income was 0.4, which excluded multicollinearity
problems. A two-sided level 0.05 level of significance was
defined. SPSS 16.0 (SPSS Inc., Chicago, IL, USA) statistics
software was used. The Bioethical Committee of the
University of Leuven approved the complete research
protocol.
Results
Table 1 presents the demographic and lifestyle characteristics
of the participants. Out of the 5000 selected men, only 37%
participated to the study. The most prevalent age-category
was 40–49 years, 76% were non-smokers. Approximately
58% had a BMIX25.0 kg/m2 while 42.6% had a low level of
education. Responders to the mailing tended to be older than
non-responders (74.3% were older than 40 years compared
with 61.4% for the non-responders) (Po0.001). Moreover,
soldiers were less incline to participate to the study than
officers and non-commissioned officers (Po0.001).
Tables 2 and 3 present the factor groupings used in
the principal component analysis and the factor-loading
matrix for the three major factors identified by using food
consumption data from the food frequency questionnaire.
The greater the factor loading for a specific food or food
item, the greater the effect of that food or food item to a
specific factor. The first factor was heavily loaded with red
meats, processed meats, beer, garlic, tomatoes, wine, eggs,
poultry, liquor, organ meats and vegetables. This factor
explained 7.4% of the total variance. This was labelled Meat
European Journal of Clinical Nutrition
Education, income and nutrition
P Mullie et al
234
Table 2 Factor groupings used in the dietary pattern analysis
Food or
food groups
Food items
Processed meats
Processed meats, bacon, hot dogs, salami,
sausage, ham
Beef, pork, lamb, hamburger
Liver
Fish
Chicken or turkey
Eggs and all types of preparations with eggs
Butter
Margarine
Skim or low-fat milk or yoghurt or chocolate
milk, buttermilk, low-fat cheese
Whole milk or yoghurt or chocolate milk,
half-and-half milk, cream, ice cream, all types
of cheese
Liquor
Red and white wine
Beer
Tea
Coffee
Oranges, grapefruit, raisins, grapes, bananas,
fresh apples or pears, strawberries, apricots,
nectarines, cherries, kiwi, pineapple, peaches,
plums
Orange juice, other fruit juice
Broccoli, coleslaw and uncooked cabbage,
cooked cabbage, cauliflower, Brussels sprouts,
kale, sauerkraut, carrots, yams, spinach,
iceberg or other lettuce, celery, mushroom,
green pepper, eggplant, all other vegetables
Beans, peas, lentils, soybeans
Tomatoes, tomato juice
Garlic
Potatoes
French fries
Cooked breakfast cereals, dark bread, brown
rice, other grains, wheat germ
Cold breakfast cereals
White bread, biscuits, white rice, pasta,
sandwiches
Potato chips, pancakes
Nuts
Cola or other beverages with sugar
Cola or other beverages without sugar
Oil, vinegar
Mayonnaise, dressings
Home-made or ready-made soup
Chocolate, candy bars, cookies, cake,
pie, pastry, sugar, jam, waffles
Red meats
Organ meats
Fish
Poultry
Eggs
Butter
Margarine
Low-fat diary products
High-fat diary products
Liquor
Wine
Beer
Tea
Coffee
Fruit
Fruit juice
Vegetables
Legumes
Tomatoes
Garlic
Potatoes
French fries
Whole grain
Cold breakfast cereals
Refined grains
Snacks
Nuts
High-energy drinks
Low-energy drinks
Oil, vinegar
Mayonnaise
Soup
Sweets and desserts
Dietary Pattern. The second factor, explaining 7.2% of the
total variance, was more loaded for tomatoes, fruit, low-fat
diary products, whole grain, vegetables, cold breakfast
cereals, fruit juice, fish, tea and nuts. This was labelled
Healthy Dietary Pattern. The last factor, explaining 6.2% of
the total variance, was heavily loaded with red meats,
processed meats, sweets, desserts, snacks, high-energy drinks,
high-fat diary products, refined grains, mayonnaise and
potatoes. This was labelled Sweet Dietary Pattern.
Table 4 presents the distribution of lifestyle and nutritional
exposure in function of the quintiles of Healthy Eating Index,
European Journal of Clinical Nutrition
Table 3 Factor-loading matrix for the major factors identified by using
food consumption data from the food frequency questionnairea
Factor 1
(meat dietary
pattern)
Red meats
Processed meats
Beer
Garlic
Tomatoes
Wine
Eggs
Poultry
Liquor
Organ meats
Fruit
Low-fat diary products
Whole grain
Vegetables
Cold breakfast cereals
Fruit juice
Fish
Tea
Nuts
Sweets and desserts
Snacks
High-energy drinks
High-fat diary products
Refined grains
Mayonnaise
Potatoes
Factor 2
(healthy dietary
pattern)
Factor 3
(sweet dietary
pattern)
0.60
0.58
0.47
0.43
0.43
0.40
0.38
0.37
0.37
0.33
0.58
0.47
0.43
0.39
0.38
0.37
0.36
0.32
0.30
0.53
0.45
0.42
0.40
0.36
0.30
0.30
a
Absolute values o0.30 were excluded from the table for simplicity. Foods or
food groups with factor loadings o0.30 for three factors were excluded; see
Table 2 for food groupings. The percentage of explained variance was 7.4%
for factor 1, 7.2% for factor 2 and 6.2% for factor 3.
Mediterranean Diet Score and Healthy Dietary Pattern
(principal components analysis). The range of the scores was
for the lowest and the highest quintiles of Healthy Eating
Index, Mediterranean Diet Score and Healthy Dietary Pattern,
respectively 22–45 and 68–95; 0–2 and 6–9; 3.6 to 0.8 and
0.7–4.8. There was no relation between the quintiles of dietary
patterns and age or BMI. The highest quintiles of Healthy
Eating Index, Mediterranean Diet Score and Healthy Dietary
Pattern were systematically associated with higher physical
activity (all Po0.001), general use of vitamin supplements
(all Po0.001), non-smoking (all Po0.001), high education
(all Po0.05), high income (Po0.001, except for the Healthy
Eating Index). The highest quintiles of the three dietary
patterns were associated with the lowest daily intake of total
fat (Po0.001), saturated fatty acids (Po0.001), mono-unsaturated fatty acids (Po0.001), poly-unsaturated fatty acids
(Po0.001 except for the Mediterranean Diet Score) and with
the highest intake of carbohydrates (Po0.001), all expressed in
energy-percent.
In Table 5, the Spearman correlation coefficients, percentages of participants classified into the same and opposite
quintiles of intake, and Cohen’s kappa coefficient are
presented. The highest correlation was found between
Education, income and nutrition
P Mullie et al
235
Table 4 Baseline characteristics according to Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern (principal components
analysis) (n ¼ 1852)a
Healthy eating
index a
Q1
n
Range scores
Age (mean s.d.)
Body mass indexa (mean s.d.)
Mediterranean diet
score a
Q3
Q5
362
362
362
22–45
53–60
68–95
43.5 (7.2) 43.2 (6.9) 42.8 (7.2)
26.3 (3.9) 25.9 (3.4) 26.4 (3.5)
Q1
Q3
Healthy dietary pattern
(principal components analysis) a
Q5
344
379
406
0–2
4–4
6–9
43.0 (7.2) 42.6 (7.3) 43.4 (7.2)
26.4 (4.0) 26.0 (3.4) 26.0 (3.3)
Q1
Q3
Q5
362
362
362
3.6– 0.8 0.3–0.1
0.7–4.8
43.0 (7.4) 43.5 (6.6) 43.3 (6.8)
25.8 (3.7) 26.3 (3.4) 26.5 (3.6)
Subject characteristics (%)
a
Physical activity
Low
Moderate
High
Use of vitamin supplements
Smokinga
Educational categoriesa
Low
Moderate
High
Income categoriesa
Low
Moderate
High
30.1
22.5
47.4
11.0
34.8
22.2
21.3
56.5
11.3
24.0
10.4***
21.6***
68.0***
20.4***
10.8***
25.2
23.4
51.4
7.8
32.6
20.9
19.8
59.3
14.2
21.4
14.9**
21.3**
63.8**
25.6***
14.5***
30.8
26.9
42.3
8.0
37.6
22.8
20.5
56.7
15.7
21.3
12.3***
17.9***
69.8***
24.6***
11.9***
47.8
43.6
8.6
40.3
43.6
16.0
37.0*
47.2*
15.7*
48.8
42.4
8.7
41.4
44.1
14.5
33.7***
46.6***
19.7***
45.0
46.1
8.8
45.9
42.3
11.9
40.3**
45.6**
14.1**
32.9
31.5
35.6
24.6
36.5
39.0
24.0
35.9
40.1
32.8
36.6
30.5
28.8
33.8
37.5
19.0***
33.0***
48.0***
32.6
34.5
32.9
22.1
40.9
37.0
23.2**
35.1**
41.7**
Nutrient intakes-mean (s.d.) in energy_percent
Protein
Total fat
Saturated fat
Monounsaturated fat
Polyunsaturated fat
Carbohydrates
15.4
46.0
18.8
17.4
8.5
35.0
(3.6)
(5.9)
(3.8)
(2.8)
(3.1)
(5.9)
16.7
36.8
14.2
14.3
7.0
42.5
(3.2)
(4.9)
(3.1)
(2.6)
(2.6)
(5.7)
17.3
28.7
10.2
11.5
5.8
49.8
(3.1)***
(4.9)***
(2.0)***
(2.5)***
(1.9)***
(6.3)***
16.4
40.7
17.2
15.0
7.3
39.9
(3.3)
(7.7)
(4.1)
(3.1)
(2.6)
(7.5)
16.2
37.0
14.1
14.5
7.2
43.0
(3.4)
(7.8)
(4.0)
(3.3)
(2.9)
(7.9)
16.9
33.8
11.9
13.7
7.0
44.8
(3.1)*
(7.1)***
(3.4)***
(3.1)***
(2.4)
(7.4)***
15.0
42.8
17.3
16.2
8.4
37.5
(3.4)
(7.6)
(4.2)
(3.0)
(3.2)
(6.9)
16.7
37.1
14.1
14.7
7.0
42.5
(3.2)
(6.3)
(3.5)
(3.1)
(2.6)
(6.4)
17.7
30.7
11.3
12.1
5.9
48.3
(3.3)***
(6.3)***
(3.2)***
(2.9)***
(1.9)***
(7.0)***
a
Healthy Eating Index, Healthy Dietary Pattern (principal components analysis) (both expressed in 0 to 100 scale of agreement) and Mediterranean Diet Score
(expressed in 0 to 9 scale of agreement); Body mass index measured in kg/m2; physical activity was measured in metabolic equivalent task (as calculated by the
International Physical Activity Questionnaire); smoking expressed as actual smokers versus non-smokers or ex-smokers; educational categories were low (for
vocational level), medium (for secondary level) and high (for bachelor or master level); income categories were low (for lowest tertile of yearly gross income),
medium (for middle tertile of yearly gross income) and high (for highest tertile of yearly gross income). Physical activity, use of vitamin supplements, smoking,
educational and income categories are expressed in percents. *Po0.05; **Po0.01; ***Po0.001.
Healthy Eating Index and Healthy Dietary Pattern, the
lowest between Healthy Eating Index and Mediterranean
Diet Score. Surprisingly, the kappa coefficients expressed
only slight agreements between the three dietary patterns.
Table 6 expresses the relation between the socioeconomic
indicators education and income and the three dietary
patterns, stratified in normal weight, overweight and
obesity. All the age-adjusted linear regressions showed the
same relation, that is, a higher education or income level was
associated with the most healthy dietary pattern, independently of the weigth-category. When the models were
adjusted for age and for both indicators, the socioeconomic
relation is attenuated but still present. In summary, a higher
socioeconomic position was associated with an increasing
score for Healthy Eating Index, Mediterranean Diet Score
and Healthy Dietary Pattern: in the category normal weight,
the score increased respectively with 1.59, 0.25 and 0.10 with
increasing education; and with 1.38, 0.28 and 0.11 for
increasing income (Table 6).
Discussion
The first aim of this work was to study the relation between
three commonly used methods to determine dietary patterns,
namely Healthy Eating Index, Mediterranean Diet Score and
Healthy Dietary Pattern (principal components analysis). The
low correlation and Cohen’s kappa coefficient of agreement
did not influence the hypothesis, that is, the relation between
socioeconomic indicators and dietary patterns.
After age-adjustment, education and income remained
associated with the most healthy dietary pattern. Even when
both socioeconomic indicators were used together in a
model, a higher income and education were associated
European Journal of Clinical Nutrition
Education, income and nutrition
P Mullie et al
236
with a higher score for Healthy Eating Index, Mediterranean
Diet Score and Healthy Dietary Pattern. Stratification in
normal weight, overweight and obesity did not influence
Table 5 Correlation coefficients, percentages of participants classified
into the same and opposite quintiles of intake, and Cohen’s kappa
coefficient (K) for Healthy Eating Indexb, Mediterranean Diet Scoreb and
Healthy Dietary Pattern (principal components analysis) (n ¼ 1852)b
Dietary pattern
Percentage
classified by in
Spearman
correlation
coefficients
Same Adjacent
quintile quintile
Healthy eating index—
Mediterranean diet score
Healthy eating index—
Healthy dietary pattern
(principal components
analysis)
Mediterranean diet score—
Healthy dietary pattern
(principal components
analysis)
K
Non-adjacent
quintile
0.43a
28.5
39.1
32.4
0.10
0.62a
36.0
40.1
23.9
0.20
0.49a
33.5
36.9
29.6
0.17
a
Correlation is significant at the 0.01 level (two-tailed).
Healthy Eating Index and Healthy Dietary Pattern (principal components
analysis) (both expressed in 0 to 100 scale of agreement) and Mediterranean
Diet Score (expressed in 0 to 9 scale of agreement).
b
the relation between socioeconomic indicator and dietary
pattern. Although socioeconomic differences in prevalence
of obesity have been described, that is, higher prevalence
for the lowest socioeconomic positions (Mullie et al., 2008),
dietary pattern analysis seems not to be able to detect a
specific dietary pattern explaining this socioeconomic
occurrence of obesity.
The positive association between socioeconomic position
and dietary pattern has been confirmed by research carried
out on different populations, using different indexes or
statistical techniques to determine dietary patterns. As
reviewed by Darmon et al. (2008), higher values of Healthy
Eating Index (Loughley et al., 2004; Angelopoulos et al.,
2009; Manios et al., 2009), Diet Quality Index (Patterson
et al., 1994; Lallukka et al., 2006), dietary diversity scores
(Kant and Graubard, 2007), and other diet-quality measures
(Groth et al., 2001; Dynesen et al., 2003; Robinson et al.,
2004) have all been associated with a higher socioeconomic
position, usually estimated by education level. Using
principal components analysis to determine dietary patterns,
Robinson et al. (2004) found that educational attainment
was the most important determinant of a healthy eating
pattern. The general observed socioeconomic nutritional
gradient can be mediated by food costs, meaning that lowest
cost diets mainly consumed by the lowest socioeconomic
positions are generally unhealthy. People who have less
Table 6 Age-adjusted linear regression with dietary pattern as continuous dependent variable: effects of educational categories and income categories
unadjusted for each other (Model 1), and simultaneous adjustment for both socioeconomic indicators (Model 2) (n ¼ 1852)
Healthy eating index—unstandardized linear regression coefficient (95% confidence interval)
Model 1
b
Body mass index
Normal (n ¼ 744)
Educational
1.59 (0.29–2.89)*
categoriesa
Income categoriesa 1.38 (0.15–2.61)*
Model 2
Overweight (n ¼ 836)
1.50 (0.26–2.75)*
Obesity (n ¼ 244)
Normal (n ¼ 744)
Overweight (n ¼ 836)
2.27 (0.26 to 4.79) 1.19 (0.26 to 2.65) 1.04 (0.36 to 2.44)
Obesity (n ¼ 244)
1.90 (0.89 to 4.64)
1.37 (0.23–2.52)*
1.58 (0.74 to 3.90) 0.83 (0.55 to 2.20) 0.94 (0.34 to 2.20)
0.89 (1.64 to 3.41)
Mediterranean Diet Score—unstandardized linear regression coefficient (95% confidence interval)
Model 1
b
Body mass index
Normal (n ¼ 744)
Educational
0.25 (0.06–0.44)*
categoriesa
Income categoriesa 0.28 (0.10–0.46)**
Model 2
Overweight (n ¼ 836)
0.43 (0.26–0.60)***
Obesity (n ¼ 244)
Normal (n ¼ 744)
Overweight (n ¼ 836)
0.18 (0.15 to 0.51) 0.15 (0.06 to 0.36) 0.33 (0.14–0.52)***
Obesity (n ¼ 244)
0.08 (0.28 to 0.44)
0.34 (0.18–0.49)***
0.24 (0.06 to 0.54) 0.22 (0.01–0.42)*
0.21 (0.12 to 0.54)
0.20 (0.03–0.37)*
Healthy Dietary Pattern (principal components analysis)—unstandardized linear regression coefficient (95% confidence interval)
Model 1
Model 2
Body mass indexb
Normal (n ¼ 744)
Overweight (n ¼ 836)
Obesity (n ¼ 244)
Normal (n ¼ 744)
Overweight (n ¼ 836)
Obesity (n ¼ 244)
Educational
0.10 (0.01 to 0.20) 0.06 (0.04 to 0.15)
0.05 (0.13 to 0.24) 0.06 (0.06 to 0.18) 0.01 (0.09 to 0.12)
0.07 (0.13 to 0.27)
categoriesa
Income categoriesa 0.11 (0.01–0.21)*
0.09 (0.01–0.18)*
0.01 (0.18 to 0.16) 0.08 (0.03 to 0.19) 0.08 (0.02 to 0.18) 0.03 (0.22 to 0.15)
a
All analyses were adjusted for age as continuous variable; operationalization of variables: the continuous dependent variable Healthy Eating Index, Healthy Dietary
Pattern (principal components analysis) (both expressed in 0 to 100 scale of agreement) and Mediterranean Diet Score (expressed in 0 to 9 scale of agreement);
educational categories were low (for vocational level), medium (for secondary level) and high (for bachelor or master level); income categories were low (for lowest
tertile of yearly gross income), medium (for middle tertile of yearly gross income) and high (for highest tertile of yearly gross income).
b
Body mass index (in kg/m2) was classified according to the World Health Organization (World Health Organisation, 2003) in normal (o25.0 kg/m2), overweight
(X25.0–o30.0 kg/m2) and obesity(X30.0 kg/m2). *Po0.05; **Po0.01; ***Po0.001.
European Journal of Clinical Nutrition
Education, income and nutrition
P Mullie et al
237
money, choose to buy cheaper foods, and these cheaper
foods are less healthy (Drewnowski, 2003; Drewnowski and
Darmon, 2005; Darmon and Drewnowski, 2008). In this
study, the least healthy quintiles of dietary pattern measured
by the three methods were associated with a clustering of
unhealthy behaviors, that is, smoking, low physical activity,
highest intake of total fat and saturated fatty acids, and low
intakes of fruits and vegetables.
The correlation between Healthy Eating Index and Mediterranean Diet Score was 0.43, a rather moderate correlation
coefficient. This may be explained by the fact that the two
hypothesis-based indexes have different foundations: the United
States Department of Agriculture Food Guide Pyramid for the
Healthy Eating Index and the Mediterranean dietary pattern for
the second. Both indexes had a rather comparable score system
for vegetables, fruits, milk and meat but the other components
will differ. The Mediterranean Diet Score focused on the ratio
monounsaturated fatty acids on saturated fatty acids, legumes
and alcohol; the Healthy Eating index on total fat, saturated fat,
cholesterol, sodium and diet variety. A major drawback of the
Healthy Eating Index is that it is unable to distinguish between
whole grains and refined grains, which will limit the capacity
to assess dietary fibers (Arvaniti and Panagiotakos, 2008).
Second, in the Mediterranean Diet Score the median intake of
each component serves as cut-off value. This approach does not
automatically means that a high score is associated with a
healthy level of intake. The consequence of the binary method
used to categorize intakes in two groups is that people with
moderate high or low intake of a component are classified in the
same category as people with a very high or very low intake. The
advantage of using the median is that half of the participants
will score positively and half negatively, creating enough
contrast for further research (Waijers et al., 2007). The Healthy
Eating Index is a scoring system based on current views of
healthy eating, which allows more comparisons between
populations because the scoring system remains the same. A
major disadvantage of the Healthy Eating Index is the low
discriminative power of the components if all the participants
have a low score for a component. In Western diets with a high
intake of saturated fatty acids, the discriminative power of those
fatty acids could be very low. Moreover, the fact that energy
intake may be a confounder, that is, participants with a high
intake will more easily meet the guidelines, can not be excluded
for the Mediterranean Diet Score. The scoring system of the
Healthy Eating Index depends on the recommended energy
intakes: the adequate number of servings is expressed according
to energy intake level, based on sex and age.
The high correlation coefficient between Healthy Eating
Index and Healthy Dietary Pattern (principal components analysis) can be the consequence of overlapping
components: high scores for Healthy Eating Index and for
Healthy Dietary Pattern (principal components analysis)
are characterized by high intake of fruits, vegetables, cereals
and low intake of meat and diary products, with low
intake of total fat, cholesterol and saturated fatty acids as
a consequence.
The use of principal component analysis involves that
several arbitrary decisions must be taken, such as the number
of retained factors and the labels of the factors. The value of
the labelling can be judged from the presented factor
loadings. Moreover, the percentage of the variance explained
by the factors in this study (20.8%) is comparable to other
studies using comparable statistical methods (Hu et al., 2000;
van Dam et al., 2003; Slattery, 2008). Three distinct dietary
patterns were identified; similar factor loadings were extracted in other studies when two or three major patterns
were selected (Hu et al., 2000; Schulze et al., 2001; Kim et al.,
2004; Park et al., 2005).
Some limitations of this study are worth noting. The
response in this study was only 37%, but information could
be gathered regarding non-responders. The responders were
older than non-responders. A military population was selected
for this study. This population has the advantage of limiting
the influence of occupation as socioeconomic determinant,
which allowed us to restrain our investigations to the influence
of income and education as socioeconomic indicators. A
second advantage is that we could have exact figures regarding
income from administrative services. The sample can be
observed as representative for Belgian army men. However,
because of the different manual and non-manual tasks,
occupations and education levels present in an army, our
sample can be observed as a representative sample for men with
an occupation. Moreover, our nutritional and lifestyle results
match with the results of a recent cross-sectional study on a
representative Belgian male population (Devriese et al., 2006).
The low correlation between education and income in this
study (r ¼ 0.40) indicate that each indicator involve different
components of exposition variability. The correlation was
comparable to other publications (Liberatos et al., 1988;
Winkleby et al., 1992; Galobardes et al., 2001; Braveman
et al., 2005). Colinearity and unstable models did not
influence, because the correlation between education and
income is below 0.50 (Winkleby et al., 1992; Turrell et al.,
2003; Braveman et al., 2005).
In conclusion, in this study, a higher socioeconomic
position as measured by education and income was systematically associated with a more healthy dietary pattern,
independently of the method to determine the dietary
pattern. Healthy Eating Index, Mediterranean Diet score
and Healthy Dietary Pattern (principal components analysis)
obtained a comparable ranking. From a practical point
of view, the choice of the Healthy Eating Index or the
Mediterranean Diet Score seems to be obvious, because those
two methods are less time consuming than principal
component analysis. Moreover, principal component analysis involves that several arbitrary decisions must be taken.
Conflict of interest
The authors declare no conflict of interest.
European Journal of Clinical Nutrition
Education, income and nutrition
P Mullie et al
238
Acknowledgements
The authors are indebted to the participants of this study.
The authors thank Ms Jeanine De Leeuw for her valuable
assistance in realizing this study.
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