DETERMINANTS OF SELF-REPORTED HEALTH
Nevenka Černigoj Sadar, Alenka Brešar Iskra
DETERMINANTS OF SELF-REPORTED HEALTH
Abstract
The theoretical background of this article is based on the biopsy-
chosocial model of health and the resource theory . The basic hypoth-
esis is that socio-economic status has an impact on risk behaviour as
well as on health status . The respondents have been asked about
various aspect of their physical fitness, risk behaviour and the fre-
quency of perceived symptoms of illness experienced during the last
year Gender, age, education and average household income per capi-
ta have been used as indicators of socio-economic status .
Data has been obtained with the questionnaire `Quality of Life in
Slovenia - 1994' on a representative sample of the Slovenian popula-
tion. The hypothesis has been tested on a sample of respondents
aged from 18 to 75 years, using the chi-square test and multiple
regression analysis .
Results demonstrate that most women (87%) and men (85%) have at
least one habit that can be considered harmful to the health . Men
tend to have more health endangering habits than women . Gender,
followed by age and education, has the highest explanatory power for
engaging in high risk behaviour as well as for the incidence of self-
reported symptoms of illness. Average household income per capita
has the lowest weight although, in most cases, it is statistical signifi-
cant. The relations between socio-economic status and health indica-
tors are greater for women than for men .
Keywords: health, risk behaviour, symptoms of illness, socio-eco-
nomic status, gender, quality of life
INTRODUCTION
Health is one of the most important dimensions of the overall quality of
life . It is an inexchangeable personal resource necessary for the creation of
positive living conditions and it is also among the most holistic indicators
which can be used to evaluate the general level of living . The first studies of
life standards and of quality of life have taken into account various aspects
and factors of the overall health of the population. In addition to positive
indicators, such as accessibility of health care, so-called negative indica-
tors have predominated in these studies . 'Negative' indicators measure the
absence of health as reflected by the incidence of certain illnesses and vari-
ous mortality indexes. Such data are systematically gathered by govern-
ment agencies in most countries and are used as standard indicators for
international comparison. We will use standard data to illustrate the posi-
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Nevenka Černigoj Sadar, Alenka Brešar Iskra
tion of Slovenia compared to other European countries . In 1990 Slovenia
ranked 13th among 35 countries (between Italy and Ireland) as measured
by infant deaths per 1000 live births (8 .4) . The life expectancy of women is
77.3 years, 20th place among 39 countries (after Denmark and Croatia) .
The life expectancy of men is 69 .4 years, the 24th place following Bosnia
and Herzegovina and Albania (source: Statistični letopis Republike Slovenije
1994: 594-596). In terms of infant mortality, Slovenia is in the top half of
European countries ; life expectancy of women ranked Slovenia in the mid-
dle and life expectancy of men in the bottom half .
However, the available statistical data measures only very limited aspects
of the health status of the population as most of these indicators are based
on a negative concept of health . The negative concept of health treats health
merely as the absence of illness . Such a notion of health is inadequate for
policy planning, the priority of which should be investments in improving
and preserving health For this reason, the World Health Organisation
(WHO) supports endeavours for asserting both a positive and holistic con-
cept of health which is defined as total physical, psychic and social well-
being (WHO, 1951) . Although in a different context, these same terms are
used in the operationalisation of the quality of life concept . Among the pre-
requisites for health and healthy living are the following : being free from
threats to life, having meaningful role and function in society, a good edu-
cation, a decent home and sufficient income to meet basic needs
(WHO, 1994 : 1) . The latter is, to a great extent, consistent with the three
groups of needs defined by Allardt (1993) : having (physiological needs and
needs to be safe), loving (the need to belonging), being (the need for self-
realisation) .
The WHO's definition of health is essentially universal and therefore it is
difficult to operationalise. This is an important reason why this concept of
health is not used by statistical offices . Okolski argues that "in spite of the
central position occupied by health care in various areas of societal activi-
ties and extensive research in various areas of health, the very notion of
'health' remains unclear and value-loaded" (Okolski, 1993 :127) . The con-
cept of health used most often which assumes that health is the absence
of disability, i .e. restriction of activity ' in the manner or within the range
considered normal for a human being' is, according to Okolski and many
other authors, vague and subjective . Such a train of thought could lead to
the conclusion that people cannot be healthy unless they are living in ade-
quate social, political and economic conditions or unless they are capable of
love, work and creativity (Selič, 1994) . On the one hand this definition
exceeds the prevailing narrow comprehension of health because it considers
not only the absence of illness and negative feelings but considers the
whole life situation . On the other hand, it invites misinterpretation because
on this basis everyone who is not satisfied could be considered sick .
The WHO's programme `Health for all by the year 2000' is a new develop-
ment in the direction of health policy which emphasises the biopsychoso-
cial model of health . This model emphasises the interaction of biological,
psychological and social factors important for health . (Polič, 1995) Thus,
health is not a state which is a given, but rather one which has to be creat-
ed in accordance with the biological characteristics and specific needs of
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the human being. When it is created, it functions as a source for the reali-
sation of personal and social potentials . WHO (WHO, 1994) considers the
following life-styles as healthy : non-smoking, moderate drinking of alcohol,
no use of drugs, healthy nutrition, regular physical activity and safe sexual
behaviour.
Our analysis focuses on a single dimension of health : namely, self-report-
ed physical well-being . Thus, the term health is operationalised as the
absence of symptoms of illness . In some of the indicators of health, a physi-
cal dimension is revealed (for example, physical fitness) while in others, a
psychic dimension is also revealed (for example, restlessness or tiredness) .
In all cases, what was registered was the subjective perceptions of the
respondents .
The following activities which endanger health have also been analysed :
smoking, drinking alcohol and the absence of regular activities for main-
taining psycho-physical well being . However, the authors are aware that
these are only a few of the risks that can contribute to a worsened health
status . Following the example of other studies focused on the influence of
socio-economic factors on health (Gross, 1989 ; Ross & Bird, 1994 ; Davis &
Fine-Davis, 1991) the following socio-economic indicators were also taken
into account : gender, age, education and average household income per
capita .
All analyses were conducted on a sample of 1717 respondents aged 18 to
75 years .
DETERMINANTS OF HEALTH
It is a known fact that diseases which occur frequently in developed coun-
tries are closely connected with unhealthy behaviour such as smoking,
insufficient physical activity, harmful eating habits and inadequate nutri-
tion. Such behaviour patterns can be changed regardless of socio-economic
status. However, the chances for change decrease if the pattern of activity
becomes a habit; this is especially true when the habit is not the direct
cause of obvious negative consequences . Unhealthy behaviour patterns
usually develop in early life or in early adulthood when most people do not
yet have any serious problems with their health . Thus, most of the negative
consequences appear after a long period . Usually, such unhealthy habits
serve some social function or enhance `short-term satisfaction' and equilib-
rium . For this reason an exceptionally high level of motivation is required
in order to stop the unhealthy habit before related disease appears .
The analysis of risk factors has demonstrated that physical activity is the
most critical area related to health status . To be physically passive (as com-
pared to smoking or drinking alcohol) is more acceptable in society, espe-
cially for women who are usually double-burdened with paid work and with
care for the family . Only a minority of respondents is engaged in a regular
activity intended to maintain good physical and mental shape . This minori-
ty is mainly represented by men as well as by young and well-educated
people. The greatest differences are to be found between different age and
DR, Vol.XII(1996)22-23	 117
Nevenka Černigoj Sadar, Alenka Brešar Iskra
Table 1 :
RISK FACTORS
Smoking ( 1 ) (%)
	
Consumption of Irregular physical
	 alcohol(2) (%)	activity(3)(%)
Total sample	29.7	12.7 78.8	
Males 33.9 22 .0 74 .8
Females 25 .7 3 .9 82 .6
Chi-square	13.9	***	208 .4 "" 15 .5 ""
15-24 years 30 .4 6 .7 61 .6
25-34 years 44 .5 8 .5 75 .2
35-44 years 38 .3 12 .1 82 .0
45-54 years 25 .9 14 .7 82 .4
55-75 years 12 .9 18 .3 86 .0
Chi-square	113.8	"' 45.9 "' 64.2 '*
Less than
primary school 23.5 19 .4 90.7
Primary school 27.3 10 .1 86.9
Vocational training 36 .0 12 .7 80 .9
Secondary school 31 .5 10 .6 67 .2
More than
secondary s . 23 .6 16 .2 64 .1
Chi-square	17.2 57.2 "" 94 .1 "`
In the table are presented % of respondents who answered that they :
(1) smoke ;
(2) drink alcohol every day or nearly every day ;
(3) exercise less than twice each week .
Statistical significance : 0 .05 < p<_ 0 .1
0 .01 < p <_ 0 .05
p<_ 0 .01 ""
education groups . The number of physically active respondents significantly
decreases after the age of 24 . According to education, the critical difference
is between the group with vocational education or less and other groups .
Although the differences between men and women are statistically signifi-
cant, they are much smaller than the differences revealed in education and
age categories when expressed as a percentage .
Smoking is ranked in second place among negative habits . More than
every third men and every fourth women are smokers . The majority of them
(approximately 70%) smokes more than 10 cigarettes per day . The relation-
ship between smoking and age group is not linear. The greatest share of
smokers is to be found in the age group from 25 to 45 years which is also
the period when working and family burdens are the greatest . After the 45
years, the number of smokers decreases drastically as the negative effects
of smoking become clearly visible . Only a minority continues to smoke after
the age of 55. The relationship between the level of education and smoking
is not linear either. There are a relatively low share of smokers in lower edu-
cational categories, probably due to the age structure of that category
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DETERMINANTS OF SELF-REPORTED HEALTH
(there are mostly older people)' . The share of smokers declines with comple-
tion of secondary school . The difference in numbers of smokers between
sexes is also statistically significant .
More than 10% of respondents drink alcohol every day or nearly every
day and one-quarter of them drink heavily (for example more than 1/2 1 of
wine per day) . There are substantial gender differences in alcohol consump-
tion . Yet because drinking alcohol is socially more unacceptable for women,
the question arises whether these results shows the real situation . Men
usually solve every day problems and discomfort with alcohol while women
are more likely to take some types of medication (Cernigoj Sadar, 1987) .
Consumption of alcohol increases with age as well as with the level of edu-
cation . The differences are greater between age groups than between
groups with different education level .
We took a special interest in whether risk factors were cumulative or
whether they functioned separately. Analysis revealed the cumulation of
risk factors in the case of men in lower educational categories. Men have a
greater tendency to experience a cumulative effect in risk factors than
women . In the case of women, one risk factor tends to be dominant and this
is usually the absence of physical activity .
The share of men and women who do not engage in any of the above-
mentioned risk factors increases with the level of education . The greatest
share of respondents who live healthy lives (i .e . have no analysed risk fac-
tors) is among the young and more among men than women . With age the
gender difference increases . The greatest number of persons who do not
smoke or drink and who exercise at least twice a week is among the young
and more among man than women. In terms of cumulative risk factors, the
gender difference increases with age .
As we discovered that age and education are related (in general, older
people have lower education) and that household income per capita relates
to education (r=0 .23), we used multiple regression analysis to find out
which factor is the strongest determinant on a total number of risk factors .
In the case of women, education and age determine the number of risk
factors to a larger extent than in the case of men while just the opposite
holds true for income . Women's behaviour is generally influenced more by
social milieu and life period . The same results were observed in research
which focused on patterns of leisure activity (conducted in the mid-80s by
Cernigoj Sadar, 1989) . The two sets of results undoubtedly prove that
women are more influenced by social milieu and its expectations . The role
that age plays in the two genders is also interesting : the older the man is
the more likely he is to live unhealthy while the older the woman is the less
risk factors she tends to engage in .
These differences can be explained in the same way as the differences in
the number of risk factors . Men who have more negative habits that most
probably do not change in their old age, while at the same time physical
activity decreases markedly in older age groups . In contrast, it is possible
that middle-aged women give up negative habits more often then men due
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Nevenka Černigoj Sadar, Alenka Brešar Iskra
to a greater sensitivity to the visible signs of ageing . Such signs of age
appear sooner and more drastically in smokers and in those who are physi-
cally passive .
Table 2 :
RELATION BETWEEN SOCIO-ECONOMIC STATUS AND THE NUMBER OF RISK FACTORS
Independent variables	Subsample	B	Beta	T-test	
Household Males -0 .0027 -0 .077 -2 .10 * `
income
	
Females -0 .0017 -0 .065 -1 .67 "
per capita	 All	-0.0026 -0.084 -3 .23	
Males 0.0033 0.063 1 .73
Age Females -0.0060 -0.164 -4 .21
All -0.0017 -0.038 -1 .49	
Males -0.0142 -0.115 -3 .13 "'
Education Females -0.0287 -0.152 -3 .64 "
All -0.0135 -0.096 -3.65 *"
Males 2.4158 22.56
Constant Females 2.7413 23.72
All	2.5169	34.87 ""
Males 0.027
R2 Females 0.044
	 All	0.02	
Males 7.03 *'
F-test Females 12.25
	 All 10.84 "'
Dependent variable is the number of risk factors (smoking, consumption of alcohol, irregular physical
activity) .
Legend : B . . . regression model parameters
Beta . . . estimations of regression coefficients (in the case of standardised variables
these are partial coefficients)
R2 . . square of multiple correlation coefficient
Gender differences which relate to the number of risk factors in different
age groups may also be the result of certain socio-economic influences to
which some generational groups were exposed and others were not . For
example, older generations were under strong social pressure regarding
gender roles and related patterns of behaviour . Smoking, not to mention
alcohol consumption, were viewed negatively in the case of women, while
accepted in the case of men . Another example is that of good physical
shape which is very highly valued in younger generations . One cannot con-
trol for all of these influences unless the behaviour patterns of different
generations are longitudinally followed which is not the case in this study .
The proportion of explained variance in risk factors is statistically signifi-
cant, yet very low. This can be explained by the fact that the persistence of
some health-endangering habits is related to certain personality character-
120 DR, Vol .XII(1996)22-23
DETERMINANTS OF SELF-REPORTED HEALTH
istics and to certain socio-economic indicators (such as family cycle and
employment status) were left out of our analysis . It is also possible that
socio-economic status effects the intensity of a particular habit more than
the number of health-endangering habits engaged in . Due to the greater
awareness and higher expectations of their social milieu, those from higher
social strata control the intensity of the negative habit more than those
from a lower social strata .
HEALTH STATUS
In the analysis of health status we studied the following indicators : physi-
cal fitness, perception of troubles caused by the poor functioning of various
body systems, chronic tiredness and frequency of visits to the doctor .
Table 3 :
HEALTH STATUS INDICATORS
Poor physical
	
Chronic Visits to
	 condition (%)	tiredness (%)	doctor (%)
Total sample	 11 .1	52.5 54 .4
Males 8 .2 44 .6 49 .0
Females 13 .8 59 .9 59 .4
Chi-square 51 .6 *** 51 .9 * * * 18 .9 ***
15-24 years 0.8 40.6 47 .2
25-34 years 2 .2 45 .0 44 .8
35-44 years 5 .6 49 .0 47 .6
45-54 years 15 .5 59.9 59.1
55-75 years 25 .3 62 .9 68 .4
Chi-square	560.3 *** 102 .4 *** 63 .8 ***
Less than primary school 26.7 68.9 61 .1
Primary school 15 .5 59 .4 60 .2
Vocational training 7 .7 51 .6 50 .8
Secondary schoo 4 .9 44.3 50.6
More than secondary s . 2 .3 34 .0 48 .1
Chi-square	221 .0 *** 94 .6 * * * 17 .9 ***
Poor physical condition : have problems walking 100 m, running 100 m and walking up and down stairs .
Chronic tiredness : often felt tired in the past 14 days, or had difficulties getting started in the morning,
were especially tired during the day and felt very tired in the evening .
Visits to doctor : Visited doctor due to health problems in the past 12 months (visits to dentists and
gynaecologists not included) .
More than every tenth respondent is in a very bad physical shape and
thus number sharply increases after the age of 45 . Well-educated respon-
dents (those with a secondary level education or more) take better care of
their health which is reflected in their over-all physical fitness . The greater
number of women in poor physical shape, as compared to men, is not sur-
prising when one considers the empirical evidence regarding the dominant
patterns of women's activities (Černigoj Sadar, 1993) . Differences between
the sexes in this area are statistically significant regardless of age group .
DR, Vol.XII(1996)22-23 121
Nevenka Černigoj Sadar, Alenka Brešar Iskra
The data on chronic tiredness 2 are especially alarming: namely, more
than half of the respondents reported that they are chronically tired . More
women than men report chronic fatigue and such fatigue appears to
increase with age . Nevertheless, the proportion of chronically-tired among
the young is also extremely high - two-fifths of younger respondents report-
ed chronic fatigue during the late spring in 1994 . The perception of chronic
fatigue is related more to the level of education than to age ; the ratio
between the lowest and the highest age category is 1 :2. The number of
respondents who reported chronic fatigue is approximately the same as the
number which visited the doctor at least once in the past year . However, the
number of visits to the doctor does not decrease as drastically with a higher
level of education than does the number of chronically tired . More than half
of the respondents visited the doctor due to an illness or health problems
during the past year, the highest number being among older respondents
and among those with lower levels of education . The differences between
genders, age groups and education categories are statistically significant .
Women tend to have more symptoms of illness and visit doctors more often
than do men .
RELATIONSHIP BETWEEN SOCIO-ECONOMIC STATUS,
RISK FACTORS AND PERCEPTION OF ILLNESS
Among socio-economic indicators, age explains the greatest part of ill-
ness symptoms . Age is followed by household income per capita and educa-
tion, both of approximately the same weight . The effect of the number of
risk factors is not statistically significant, for which there are several possi-
ble explanations . In the previous discussion it was already indicated that
the consequences of health-endangering habits often become visible only
after a longer period of time . Furthermore, many respondents probably gave
up their 'negative' habits after having fallen seriously ill . Moreover, those
who have just started to live healthier lives may not have perceived an
improvement in their health status at the time of the study and only data
on current situation was analysed in the present study . Lastly, the measure
of risk factors used in this study is relatively rough as it does not take into
account the intensity of risk factors .
Among all determinants of symptoms of illness included in our study, the
influence of age appears to be the greatest for both sexes . However, the
influence of other determinants is gender-differentiated . Education has
much greater effect on health in women than it does in men, while just the
opposite is true for the number of risk factors . The latter is statistically sig-
nificant factor associated with the reported symptoms of illness of men but
not of women. As regards the effect of household income per capita, no dif-
ference between men and women was found .
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Table 4 :
RELATIONSHIP BETWEEN SOCIO-ECONOMIC STATUS, RISK FACTORS AND PERCEIVED
SYMPTOMS OF ILLNESS(1)
Independent variables	Subsample	B	Beta	T-test	
Household
	
Males -0 .01 -0 .11 -3 .2 "'
income Females -0.01 -0 .11 -3 .1
per capita	 All	-0.02 -0 .13 -5 .3	""	
Males 0.07 0.40 12 .0
Age Females 0.06 0.32 8 .8
	 All	0.07	0.38	16.3	""	
Males -0.03 -0.06 -1 .9
Education Females -0.16 -0.18 -4 .6 ""
All -0 .06	-0.11	-4.6	
Number of Males 0.20 0.06 1 .9
risk Females -0.07 -0.02 -0 .5
factors	 All	0.01	0.002	0.1	
Males 0.65 1 .6
Constant Females 4.19 6 .2 ""
	 All	2.06 5.9	'"	
Males 0.185
R2 Females 0.200
	 All	0.191	
Males 43 .1 **'
F-test Females 47 .9 "'
	 All	91 .9 "`	
(1) The perceived symptoms index (dependent variable) is a sum of subjective perception of single symp-
toms of illness classified in the following groups :
headache, migraine ;
pains in shoulders, back, breast, chest or hands, rheumatism, hip trouble and the like ;
heart conditions, low or high blood pressure, extended veins, swollen legs, difficulty in breathing, dizzi-
ness ;
indigestion, gastric trouble, trouble with blisters, kidney trouble, gall bladder or liver trouble ;
general tiredness ;
nervous problems, anxiety, tension ;
depression
There were three possible answers :
1 - no troubles or illness in the past 12 months
2 - minor troubles
3 - serious troubles or serious illness
Estimates for single groups were totalled and transformed into a scale 1 to 15 . Cronbach alpha for the
index of symptoms is 0 .72, standarised item alpha is 0 .73 .
Considering the clear differences which emerged in the study between
men and women, we took a greater interest in the role of gender. Results of
multiple regression analyses where gender was included as a dummy vari-
able demonstrate, that gender has the highest weight among socio-econom-
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Nevenka Černigoj Sadar, Alenka Brešar Iskra
is indicators in explaining self-perceived health status . Women report sta-
tistically significant more symptoms of illness than men . The importance of
gender is followed by age (which positively correlates with the number and
intensity of symptoms reported) and education (which negatively correlates
with reported symptoms) . The importance of household income per capita
(which negatively correlates with reported symptoms) is the lowest but is
still statistically significant at the level 0 .001 . Education has a greater
importance in the case of women while the importance of age does not differ
significantly between the sexes. These results correspond with the results of
the two-dimensional and multi-dimensional analyses already discussed .
CONCLUSION
The results of the study show that most women (87%) and men (85%)
have at least one habit that is bad for the health . The most frequently
reported negative habit is the absence of regular activity for maintaining
psycho-physical fitness, followed by smoking and consumption of alcohol .
Men tend to have more health-endangering habits than women and the
accumulation of risk factors is higher for men than for women . Almost
three-fifths of men reported two or three risk factors while only one-fifth of
women reported a similar level . Conversely, more women than men report
poor physical condition, more women report chronic fatigue and experience
a higher number of symptoms of illness (the average number of reported
symptoms in our sample is 2 .56 for women and 1 .61 for men) . It is difficult
to ascertain whether this `contradiction' is the result of women's greater
sensitivity or is also the partial result of not including all relevant risk fac-
tors in the analysis. For example, being overburdened with both paid and
unpaid work was not included as a risk factor . Namely, even if one does not
smoke or consume alcohol and does exercise regularly, one can still feel
poorly if one cannot stand the pressure of social and work obligations . Such
a situation can also lead to chronic tiredness and other illnesses .
Moreover, we must also be cautious when interpreting data on risk fac-
tors as answers may be influenced by the desire to present a positive image
of oneself. The number of respondents who admitted health-endangering
habits, with the exception of irregular physical activity, seems relatively
low. In addition, only data on current status were analysed . It is clear that
data on past behaviour is also relevant . However, this would require a dif-
ferent methodological approach (specifically, event history analysis) . Most
likely, these shortcomings in the methodology reduced the statistical signif-
icance of the number of risk factors on perceived symptoms of illness .
Gender, followed by age and education, proved to be the most important
determinant of risk factors as well as of the frequency of perceived symp-
toms. Household income per capita has the lowest weight although, in most
cases, it is statistically significant .
The study showed that women's health status is more related to social
position than is men's . This means that not only are women in higher social
strata more aware of the importance of a healthy way of living than are
men, but also that they utilize various strategies to overcome the burdens
of everyday life which may (or may not) result in a better health status .
According to all of the analysed health status indicators, the groups
which have a distinctively worse health status are as follows : older people,
women, lower educated respondents, and those with lower household
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income per capita . These findings are particularly concerning because these
are the characteristics of groups which are already at the margins of the
society . Our results are also consistent with the results of research studies
which used the subjective indicator of health status (Gross, 1989, Ross &
Bird, 1994, Davis & Fine-Davis, 1991) as well as with the results of studies
which were based on aggregate statistical data (Okolski, 1993) . Davis and
Fine-Davis (1991) stated that already in 1952 Myrdal had pointed out the
complexity of the relationship between health and socio-economic status .
The author explicitly emphasised the problem of `cumulative social causa-
tion' in social systems, whereby low income causes sickness and sickness
increases poverty . In short, what is referred to is the vicious cycle of bad
health and worsening social status .
NOTES
1) Correlation between years of education and age is -0.20 .
2) Reports of chronic tiredness were also influenced by the season when the
inquiry was conducted (May, June) and by the ending of the school year which is a
burden for parents as well as for students .
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