Lifestyleobesity Women Men

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RISK FACTORS

The contribution of lifestyle factors to socioeconomic differences in obesity

in men and women a population-based study in Sweden

Anu MolariusCentre for Public Health Research, Karlstad University, Karlstad, Sweden

Accepted in revised form 4 October 2002

Abstract. Background: The objective was to investi-

gate whether and to what extent the association be-

tween socioeconomic status and obesity can be

explained by lifestyle factors. Methods: The rela-

tionship between socioeconomic status (SES) and

obesity, and the role of lifestyle factors such assmoking, physical activity, heavy alcohol use, avoi-

dance of dietary fat and propensity to eat fiber-rich

food, was studied in a cross-sectional population-

based study consisting of 6394 men and women aged

2574 years in Va rmland County in Sweden. Edu-

cational level was used for measuring SES. The

contribution of the measured lifestyle factors was

assessed using logistic regression models. Results:

12% of men and 14% of women were obese. Subjects

with high education were leaner than subjects with

low education, except among elderly women (65

74 years). Although many lifestyle factors were re-

lated to obesity and SES in this study, only a part

(1829%) of the association between educational

level and obesity could be explained by the measured

lifestyle factors. Physical inactivity and heavy alcohol

use were the main factors contributing to this asso-ciation, whereas smoking and the measured dietary

attitudes towards fat and fiber had little additional

effect. Conclusions: The findings of this study are

consistent with the view that socioeconomic differ-

ences in obesity and its consequences can only partly

be reduced by changes in lifestyle. Longitudinal

studies, a more detailed investigation of the role of

dietary factors and more studies including elderly

subjects are, however, recommended to further elu-

cidate the association between SES and obesity.

Key words: Lifestyle, Obesity, Population studies, Socioeconomic status

Background

An inverse association between socioeconomic status

(SES) and relative body weight has been observed

among women in affluent societies [1, 2]. The asso-

ciation is less consistent among men, although in

most western countries an inverse association has

also been found in men. Likewise, in Sweden persons

with high SES are leaner than persons with low SES

[35] and this association has been rather stable

during the last decade [5].

Socioeconomic inequalities in self-perceived health,

morbidity and mortality exist in many countries [6].

Because obesity is related to the incidence of several

chronic diseases and mortality [710], socioeconomic

differences in the prevalence of obesity may act as one

factor through which these inequalities emerge [11].

Therefore it is important to investigate possible ex-

planations for socioeconomic differences in obesity.

Lifestyle factors, such as physical activity, dietary

habits, smoking and alcohol intake are related to

relative weight [1215] and also differ between so-cioeconomic groups in such a way that the most

privileged often have healthier lifestyle [6, 1618].

Because these behavioural factors are potentially

modifiable, it is of interest to know whether and to

what extent the association between SES and obesity

can be explained by these factors. Some studies have

suggested that differences in health behaviour only

partly explain the association between SES and rel-

ative weight [11, 1921]. These studies have, however,

not been population-based and they have studied

subjects only within a limited age range e.g. they have

not included elderly subjects. There are also meth-

odological shortcomings in measuring lifestyle factors

in these studies. Therefore the evidence remains in-

conclusive.

In the present study the relationship between SES

and obesity, and the role of lifestyle factors in this

relationship, was investigated in a cross-sectional

population-based study consisting of 6394 men and

women aged 2574 years in the Va rmland County in

Sweden. Educational level was used for measuring

SES.

Methods

The data were derived from a random sample of the

adult population aged 1879 years in Va rmland

European Journal of Epidemiology18: 227234, 2003. 2003Kluwer Academic Publishers. Printed in the Netherlands.


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County in western Sweden. The data were gathered

between 21 March and 19 May 2000 using a postal

survey questionnaire. The overall response rate was

70%. A total of 8288 subjects answered the ques-

tionnaire. Because most of those aged 1824 years

had not completed their education and the informa-tion on education was missing for most of those aged

75 years or over, these age groups were excluded

from the study. 6394 men and women aged 25

74 years with data on body mass index (BMI) and

educational level were included in the study.

Relative weight was measured using BMI. BMI

was calculated from self-reported weight and height

as weight divided with height squared (kg/m2). The

participants were categorised in concordance with the

WHO guidelines [22] as obese when BMI was equal

to or over 30 kg/m2.

Educational level was obtained through recordlinkage from a national education register. The re-

cord linkage was carried out by means of personal

identification codes. Educational level refers to the

end of year 1999 and it was categorised into three

classes: low (elementary school), medium (upper

secondary school), and high (at least 3 years of uni-

versity or corresponding education).

Data on lifestyle factors were obtained using the

questionnaire. Physical activity was measured with a

question: How much do you exercise physically in

your leisure time? with the options little exercise

(walking, bicycling or other light exercise less than

2 hours a week), moderate exercise (walking, bicy-cling or other light exercise more than 2 hours a

week), moderate regular exercise (exercising 12

times a week at least for half an hour at a time in

jogging, playing tennis, bicycling, exercising at a gym

or other moderate exercise that makes one to sweat)

and vigorous exercise and training (exercising or

competing at least three times a week at least for half

an hour at a time in team sports, jogging, playing

tennis, swimming or other vigorous exercise). Because

the survey was carried out between late March and

May, the possibilities for example for walking are not

affected by winter circumstances in this area ofSweden.

Smoking was asked with a question: Do you

smoke? with the answer options No, I have never

smoked regularly, No, I have stopped smoking,

Yes, occasionally, and Yes, daily. Alcohol use was

obtained with a question asking how often it hap-

pened that the subject drank at the same occasion

alcohol corresponding to at least half a bottle of

strong liquor (with giving the corresponding amount

in wine and beer). The options ranged from At least

five times a week to Never, and were for the anal-

ysis grouped into three categories Once a month or

more often, More seldom and Never.

There was no information about actual intake of

fat and fiber, but dietary attitudes towards fat and

fiber were asked with a question: Do you try to avoid

fat food? and Do you try to eat fiber-rich food (such

as wholemeal bread, mu sli or root vegetables)?. The

answer options were Not at all, Yes, to some ex-

tent, and Yes, I am very particular about it.

Statistical methods

As background information the prevalence of obesi-

ty, low education and the measured unhealthy life-

style factors are reported by three age categories

(2544, 4564, and 6574 years), and the prevalence

of obesity and unhealthy lifestyle factors by educa-

tional level. The association between educational

level and relative weight was assessed by calculating

the difference in mean BMI between the educational

levels in the three age categories (with further ad-

justment for age) in a general linear model.

The contribution of lifestyle factors to the associ-

ation between educational level and obesity was

studied using logistic regression models where obesity

was the dependent variable. The contribution of each

lifestyle factor was assessed by adding these factors

one by one into the model containing obesity and

educational level. Smoking was added first, then

physical activity and heavy alcohol use, and finally

dietary attitudes. Smoking was added first, because

an inverse association was assumed [14] and dietary

attitudes last, because only an indirect association

was assumed. Only those factors that were associated

with obesity were included in the final model. Because

age was strongly related to both obesity and educa-tional level, all models were adjusted for age. Since

the association between age and obesity was non-

linear, age was used as a categorical variable (5-year

age classes). Because there was no association be-

tween educational level and relative weight among

women aged 6574 years, these were excluded from

further analysis. All analyses were performed sepa-

rately for men and women.

To confirm the results between obesity, SES and

lifestyle factors obtained in the logistic regression

analysis, the same analyses were repeated using gen-

eral linear models where BMI was the continuousdependent variable.

The contribution of lifestyle factors to the associ-

ation between SES and obesity was calculated as the

proportion (percentage) of the difference in the odds

ratios between the crude model (C) and the model

including the lifestyle factors (final model, F) in re-

lation to the increased risk in the crude model:

100 ORC ORF

ORC 1 %

In the corresponding general linear model the con-

tribution of lifestyle factors to the association be-

tween SES and BMI was calculated as the

proportional change in the difference in mean BMI

between the educational categories before and after

adjustment for the measured lifestyle factors.

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Results

Table 1 shows the number of participants and the

prevalences of obesity and low education by gender

and age group. Obesity was relatively common in the

study population, 12% of men and 14% of womenwere obese. Obesity increased with age, except among

men in the oldest age group. The proportion of

subjects with low education increased strongly with

age. The table also shows the proportion of physi-

cally inactive, daily smokers, heavy alcohol users and

subjects with unhealthy dietary attitudes. About one-

fifth of the population was physically inactive and

physical inactivity was most common among young

(2544 years) men. It was the moderate exercise

which increased with age in men, whereas vigorous

exercise decreased with age. Women were more often

smokers than men, except among the elderly (6574 years). Heavy use of alcohol was very common

among men, especially among young men, almost

half of them consumed alcohol corresponding to half

a bottle strong liquor at least once a month. Dietary

attitudes showed a similar pattern as heavy alcohol

use. Women were more often than men trying to

avoid fat food and to eat fiber-rich food. Older sub-

jects had healthier dietary attitudes than younger

subjects.

Obesity was more common among subjects with

low education than among subjects with high edu-

cation (Table 2). Also lifestyle habits differed con-

siderably between the educational levels. Those with

low education were more often physically inactive,

smokers, used more often alcohol and had un-healthier dietary attitudes than those with high edu-

cation. Those with middle level education were,

however, even more often heavy alcohol users and

had unhealthier dietary attitudes (among men) than

those with low education. Because also age differed

between the educational levels (Table 2) and it was

related to lifestyle factors (Table 1) the subsequent

analyses were adjusted for age.

Figure 1 shows the differences in mean BMI be-

tween the high and low educational levels in men and

women in the three age categories. Subjects with high

education were leaner than subjects with low educa-tion in all age groups among men. A similar inverse

association was observed among women aged 25

64 years, this association was particularly strong

among women aged 4564 years. There was, how-

ever, no association between educational level and

relative weight among elderly (6574 years) women.

Subjects with high educational level were also leaner

than subjects with middle level education in all age

groups except in elderly women (now shown).

Table 1. Number of subjects, prevalence of obesity, low education, physical inactivity, daily smoking, heavy alcohol use,

and unhealthy dietary attitudes by age group in men and women (crude percentages)

Men Women

Age group 2544 4564 6574 2544 4564 6574

n 1033 1124 872 1233 1285 847

Obese (%) 10.9 13.2 12.7 10.6 14.2 17.5

Low education (%) 14.8 38.4 59.6 12.7 27.7 57.4

Physically inactive (%) 27.1 22.7 17.3 19.0 21.2 18.3

Daily smokers (%) 13.2 17.2 13.3 21.0 22.0 10.7

Heavy alcohol use at least once a month (%) 43.9 28.3 19.5 13.0 9.0 3.2

Does not try to avoid fat food (%) 54.1 37.8 32.0 27.3 12.9 13.2

Does not try to eat fiber-rich food (%) 47.2 28.6 17.7 22.7 11.6 8.9

Table 2. Number of subjects, mean age, prevalence of obesity, physical inactivity, daily smoking, heavy alcohol use, and

unhealthy dietary attitudes by educational level in men and women (crude percentages)

Men Women

Educational level Low Medium High Low Medium High

n 1155 1716 208 991 2061 313

Mean age (SD) 59.5 (12.4) 47.1 (14.3) 52.7 (13.7) 59.4 (13.2) 47.0 (13.9) 48.8 (13.3)

Obese (%) 14.8 11.5 5.3 17.2 13.0 7.7

Physically inactive (%) 21.5 24.8 12.7 23.9 18.2 16.6

Daily smokers (%) 17.6 13.6 8.7 20.3 19.8 7.7Heavy alcohol use at least once a month (%) 28.1 35.0 15.5 7.7 10.4 3.9

Does not try to avoid fat food (%) 38.2 44.9 33.8 18.5 18.8 14.3

Does not try to eat fiber-rich food (%) 28.8 35.4 18.3 16.1 15.3 9.7

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Table 3 shows the results from the logistic regres-

sion analysis among men aged 2574 years. Physical

inactivity and alcohol use were positively associated

with obesity. The association between obesity and

smoking was not statistically significant in men

(p 0:41). Dietary attitudes were not associated withobesity in men when adjusted for the other lifestyle

factors (p 0:28 for avoidance of dietary fat andp 0:48 for propensity to eat fiber-rich food). Phys-ical inactivity had the strongest association with

obesity. Adjustment for physical exercise and heavyalcohol use attenuated the risk of obesity between

high and low educational levels by 25% and between

high and medium educational levels by 29%.

Results for women aged 2564 years were to some

extent similar to those for men (Table 4). Adjustment

for lifestyle factors attenuated the risk of obesity

between high and low educational levels by 18%, and

by 21% between high and medium educational levels

in women. Physical inactivity was even more strongly

associated with obesity in women than in men.

Smoking was not related to obesity in women

(p 0:19). Avoidance of dietary fat was related toobesity in women but in an unexpected way. Those

who did not try to avoid fat food were less often

obese than those who tried to avoid fat food. This

suggests that avoidance of fat food can be interpreted

as a consequence of rather than a risk factor for

obesity in women, i.e. that obese women try to avoid

fat food more often than normal weight women.

Propensity to eat fiber-rich food was not associatedwith obesity in women (p 0:55).

The results using BMI as a continous dependent

variable (general linear model, not shown) were in

concordance with the results obtained from the lo-

gistic regression analysis. The proportions explained

by the measured lifestyle factors were slightly lower

than for obesity. Twenty-two percent of the difference

in mean BMI between high and low educational

levels and 24% between high and medium educa-

Figure 1. Difference in mean BMI (kg/m2) in subjects with low educational level compared with subjects with high edu-

cational level in three age categories in men and women (adjusted for age).

Table 3. Odds ratios (95% confidence interval in parenthesis) for risk of obesity by educational level adjusted for lifestylefactors associated with obesity (all models adjusted for age) among men aged 2574 years (n = 2814)

Crude Adj. for physical activity

Adj. for physical activity

and alcohol use

Education, high 1 (ref.) 1 (ref.) 1 (ref.)

Medium 2.4 (1.3, 4.5) 2.2 (1.1, 4.1) 2.0 (1.1, 3.8)

Low 3.0 (1.6, 5.7) 2.7 (1.4, 5.1) 2.5 (1.3, 4.8)

Physical activity, vigorous exercise 1 (ref.)

Moderate regular 1.0 (0.6, 1.7)

Moderate 1.3 (0.8, 1.9)

Inactive 2.4 (1.6, 3.8)

Heavy alcohol use, never 1 (ref.)

More seldom than monthly 1.2 (0.9, 1.6)

Monthly 1.5 (1.1, 2.1)

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tional levels was explained by the measured lifestyle

factors in men. In women the corresponding pro-

portions were 12 and 20%.

Discussion

As in most other western populations, subjects withhigh educational level were leaner than subjects with

low educational level in the present study population

of men and women aged 2564 years. Among the

elderly aged 6574 years, however, there was an in-

verse association between educational level and rel-

ative weight among men only. Although many

lifestyle factors were related to obesity and SES in

this study, only a part (1829%) of the association

between educational level and obesity could be ex-

plained by the measured lifestyle factors. The con-

tribution of the measured lifestyle factors was

somewhat bigger in men than in women. Physicalinactivity and heavy alcohol use were the main fac-

tors contributing to the association between educa-

tional level and obesity, whereas smoking and dietary

attitudes towards fat and fiber had little additional

effect on this association.

The total caloric intake, the actual intake of fat and

other dietary components were not measured in the

present study. There were only two questions about

diet and these are not sufficient to measure unhealthy

dietary habits. Measuring dietary intake has been

found to be difficult since there is a considerable

underreporting of caloric intake among the obese

subjects and the underreporting seems to be food-

specific [23, 24]. This might also be the reason why

evidence on the association between dietary habits

and SES is mixed [2527]. In addition, dietary fiber,

but not fat, has been found to some extent explain

differences in prevalences of obesity between popu-

lations [28]. In the present study, there were socio-

economic differences in avoiding fat food and trying

to eat fiber-rich food. These factors were however not

related to obesity when adjusted for the other mea-

sured lifestyle factors, except trying to avoid fat food

which was inversely related to obesity in women. Thelatter implies that obese women are more often trying

to avoid fat food than non-obese women. Whether

their actual fat intake is lower that of non-obese

women cannot be assessed from the present data.

Wamala et al. [21] found that unhealthy dietary

habits were the second strongest factor after repro-

ductive history accounting for socioeconomic differ-

ences in obesity among middle-aged women. A more

careful study about the role of the dietary habits in

the general population is therefore warranted, espe-

cially an investigation how dietary habits contribute

to deliberate weight control.Because alcohol has high caloric density, alcohol

intake is usually associated with obesity. In some

studies alcohol intake has not been associated with

relative weight which might be due to difficulties in

measuring alcohol intake accurately [2931]. In ad-

dition, the association has been found to differ be-

tween men and women, with a positive association

often found in men but a negative association in

women [30]. This may be due to the fact that the same

cut-off points for alcohol intake have usually been

used both for men and women whereas the actual

intake is much lower in women than in men. In this

study the actual alcohol intake was not measured but

the subjects were instead asked about the frequency

of heavy alcohol use. Heavy alcohol use was related

to both obesity and low SES and explained part of

Table 4. Odds ratios (95% confidence interval in parenthesis) for risk of obesity by educational level adjusted for lifestyle

factors associated with obesity (all models adjusted for age) among women aged 2564 years (n = 2406)

Crude

Adj. for physical

activity

Adj. for physical

activity and

alcohol use

Adj. for physical activ-

ity, alcohol use and

avoidance of dietary fat

Education, high 1 (ref.) 1 (ref.) 1 (ref.) 1 (ref.)

Medium 2.4 (1.4, 4.1) 2.2 (1.3, 4.0) 2.2 (1.3, 3.8) 2.1 (1.2, 3.7)

Low 2.7 (1.5. 4.8) 2.4 (1.3, 4.4) 2.3 (1.3, 4.2) 2.4 (1.3, 4.2)

Physical activity, vigorous

exercise

1 (ref.)

Moderate regular 2.8 (1.3, 6.1)

Moderate 4.8 (2.3, 9.9)

Inactive 9.6 (4.6, 20.3)

Heavy alcohol use, never 1 (ref.)

More seldom than monthly 1.2 (0.9, 1.6)

Monthly 2.0 (1.1, 2.3)

Tries to avoid fat food, veryeagerly

1 (ref.)

To some degree 0.6 (0.5, 0.9)

Not at all 0.4 (0.2, 0.6)

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the differences in obesity by SES. This effect was

more marked in men than in women.

Smoking was not associated with obesity in the

present study. Even though an inverse association

between smoking and obesity has been reported for

most countries, there are studies that have shown alack of association between smoking and obesity in

other populations with low prevalences of smoking

[14]. This might be due to that in countries with very

low smoking prevalence smokers are a selected group

of people with also other unhealthy lifestyle habits.

The finding that there was an inverse association

between SES and obesity among elderly men but not

among elderly women has no immediate explanation.

Most studies on socioeconomic differences in obesity

have investigated middle-aged or working-age popu-

lations [2, 5, 11, 1921, 32] and those which have

included elderly have not studied them separately [3,4, 33]. The increase in obesity after the age of 65 years

in women (but not in men), changes in the educa-

tional system over the past decades, the small number

of elderly women with high educational level, and

possible overrepresentation of diseases which lead to

weigth reduction among low educated subjects are

factors that may have contributed to the lack of as-

sociation between SES and obesity in elderly women.

The association between SES and obesity among el-

derly requires further research.

In the present study data on weight and height

were self-reported. Self-reported data have been

found to underestimate the actual prevalence ofoverweight and obesity [34, 35]. This would, however,

more likely lead to an underestimation of the

relationship between SES and obesity than to over-

estimate it, since the distribution of BMI using self-

reported data becomes more limited. Socioeconomic

differences in self-report bias cannot, however, be

excluded. For example, high SES subjects (who are

more health conscious) may underestimate their

weight and overestimate their physical activity more

often than low SES subjects. In that case there would

be an overestimation of the association between SES

and obesity and the contribution of physical activityto this association. Similarly, there is a possiblity of

underreporting of heavy alcohol use in the high SES

subjects. This would overestimate the contribution of

alcohol intake on the relationship between SES and

obesity. The data on educational level were obtained

from a national register and the quality of these data

is considered to be good.

The present study was cross-sectional in design and

cannot therefore be used for drawing causal infer-

ences about the association between SES and obesity,

and the role of lifestyle factors in this association.

Even though many lifestyle factors are known to be

determinants of obesity, sometimes the reverse may

also be true. For example, even if physical activity is

known to be a strong determinant of obesity, obese

subjects may find it difficult to carry out physical

exercise, especially strenuous physical activity [36].

The same applies to the findings concerning dietary

attitudes in the present study: it is likely that it is the

obese women who are most often trying to avoid fat

food and not those trying to avoid fat food who are

most often obese.The response rate in our study was relatively high

(70%) for a postal questionnaire. When the respon-

dents were compared with non-respondents, it was

found that the respondents had slightly higher edu-

cational level. Response rate among subjects with

high educational level was 78% and among subjects

with low educational level 65%. The response rate

was also higher among older (77% in age group 65

79 years) than younger subjects (62% in age group

1834 years), and higher among women (75%) than

men (66%). It is, however, unlikely that the associa-

tions between SES, obesity and lifestyle factors ob-served among the respondents would differ to such an

extent among the non-respondents that this would

considerably affect the results.

The results of this study are in concordance with

results from other studies where lifestyle factors have

been found to only partly explain the socioeconomic

differences in obesity [19, 20]. It also suggests that the

finding holds in population-based studies and among

elderly men. Similar findings have been observed for

weight gain. For example, Martikainen and Marmot

[11] found that adjustment for behavioural factors

attenuated the association between SES and weight

gain by about 20%. This is quite near our estimatedeffect of the role of the measured lifestyle factors in

socioeconomic differences in obesity. It cannot,

however, be excluded that inaccuracies in the mea-

surement of lifestyle factors in this and the other

studies may have contributed to the low contribution

of lifestyle factors in explaining socioeconomic dif-

ferences in obesity.

One possible explanation for the fact that lifestyle

factors only partly explain the socioeconomic differ-

ences in obesity prevalence is that higher SES subjects

may to a greater extent deliberately control their

weight. A recent study among British adults [33]found that especially high SES women had lower

levels of perceived overweight, monitored their

weight more closely and were more likely to be trying

to loose weight than did low SES women. Another

factor that has been found to explain the excess

overweight among low SES women is reproductive

history (higher parity and earlier age at menarche) [4,

21]. These factors were, however, not measured in the

present study. Also unfavourable psychosocial fac-

tors have been found to be associated with obesity

[32] and factors such as poor quality of life, low self-

esteem and job strain have been mentioned as factors

partly explaining the low SESobesity relationship

[21]. However, although these factors are related to

SES, they can also be interpreted as consequences of

rather than risk factors for obesity and therefore their

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association with obesity should be investigated in

longitudinal studies.

The contribution of lifestyle factors to the socio-

economic differences in obesity is important from a

public health point of view. If lifestyle factors would

explain a large part of the association between SESand obesity, changes in lifestyle would be important

for reducing socioeconomic differences in obesity.

Furthermore, changes in lifestyle factors would even

be important for reducing the socioeconomic differ-

ences in the consequences of obesity. Since the con-

tribution of lifestyle factors such as physical inactivity

and heavy alcohol use on the basis of this and other

studies seems to be limited, other factors should be at

least as important as lifestyle factors when trying to

reduce the socioeconomic differences in obesity. Ex-

amples of such possible other factors are reproductive

history in women, deliberate weight control practisesand cultural factors such as what is considered as

desirable weight.

In conclusion, although many lifestyle factors were

related to obesity and SES in men and women, only a

part of the association between educational level and

obesity could be explained by the measured lifestyle

factors. Longitudinal studies, a more detailed inves-

tigation of the role of dietary factors and more

studies including elderly subjects are recommended to

further elucidate the association between SES and

obesity and the contribution of lifestyle factors.

Acknowledgement

The population survey in Va rmland was financed by

the Va rmland County Council.

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Address for correspondence: A. Molarius, Va stmanland

County Council, Department of Community Medicine, 721

51 Va stera s, Sweden

Phone:46-21-174583; Fax: 46-21-174515E-mails: [email protected], anu.molarius@

kau.se

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