The European Journal of Public Health Advance Access originally published online on November 27, 2006
The European Journal of Public Health 2007 17(4):353-360; doi:10.1093/eurpub/ckl257
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Health Inequalities |
Socioeconomic Status, Psychosocial Health and Health Behaviours among Hungarian Adolescents
Bettina F. Piko1 and Kevin M. Fitzpatrick2
1 University of Szeged, Department of Psychiatry, Division of Behavioral Science 6722 Szeged, Hungary
2 University of Arkansas, Department of Sociology and Criminal Justice Fayetteville, AR, USA
Correspondence: Bettina F. Piko, M.D., Ph.D. Department of Psychiatry, Division of Behavioral Science, University of Szeged, 6722 Szeged, Szentharomsag street 5, Hungary, tel./fax: +36 62 420 530, e-mail: pikobettina{at}yahoo.com
Received July 7, 2006, accepted October 19, 2006
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Abstract |
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Background: While socioeconomic differences in health, morbidity, and disability are highest among middle-aged persons, there is a certain level of ‘equalization’ during adolescence and young adulthood. Despite this equalization, however, there still are differences in psychosocial variables or health-related behaviours, often very subtle and sometimes difficult to measure. Methods: Using data (n = 1114) on high school students (aged between 14 and 21 years) from the Southern Plain Region, Hungary, the present study looks at the role of multiple SES indicators (objective and subjective; occupation and education; family structure) in adolescents' psychosocial health (self-perceived health, psychosomatic, and depressive symptomatology) and health behaviour (substance use and sports activity). Results: Based on the results of multivariate logistic regression analyses, findings suggest the following: (i) SES self-assessment proved to be a significant predictor of adolescents' psychosocial health and health behaviours; (ii) family structure (that is, living in a non-intact family) also significantly influenced adolescents' psychosocial health and health behaviours; (iii) parents' employment status and schooling had a limited influence on their children's health outcomes; (iv) in a word, SES gradients in adolescents' psychosocial health and health behaviour were inconsistent and sometimes irregular (that is, inverse). The subjective SES measurement plays an important role (positive association), whereas certain types of parents' inactive status (in terms of labour market, that is, unemployment or retirement) seem to act in a predictable way (negative association). Conclusions: Our results indicate that despite certain level of equalization during adolescence, some important relationships between SES variables and health outcomes may occur.
Keywords: adolescents, health behaviour, health inequalities, psychosocial health
Throughout the world, social scientists continue to pay close attention to the relationship between social inequalities and health.1 One of the greatest achievements of our modern health-focused society has been the dramatic increase in life expectancy. However, life expectancy is significantly lower among persons with lower levels of education, income, and occupational prestige since socioeconomic status (SES) inequalities have profound effects on health status.2 On the other hand, the relationship between SES and health status does not appear to be consistent across the life cycle.3 Socioeconomic differences in health, morbidity, and disability are highest among middle-aged persons, while there is a certain level of ‘equalization’ during adolescence and young adulthood.4,5
This relative ‘SES equality’ might be explained by school factors (achievement orientation of the high school), a growing independence from parental influences, and that youth form a special ‘autonomous field’ (i.e. youth culture) in society with its own internal logic and dynamic.6 In addition, morbidity and mortality are relatively rare during this period, which might also contribute to a relatively low level of social inequality in health status.7
Research on social inequality during adolescence has found few significant differences in morbidity and mortality rates based on SES.6 Findings from the literature indicate the absence of social class gradients in several domains of health, though some findings support the existence of a relationship between SES and health in some aspects of health-related problems.8 However, even if some studies report differences in adolescents' health due to SES, inequalities are inconsistent across different outcomes. Thus, the SES and health gradient is much less pervasive than found among adults, in addition, the relationship is often non-linear, even controversial or irregular.7,9,10
While empirical studies support a certain level of equalization in terms of the relationship between SES and health during adolescence, this does not mean that there are no class-based inequalities among adolescents. Indeed, there are considerable inconsistencies in the SES–health relationship during this period of life compared to other times in the life course. Equalization means three different things: (i) SES differences in morbidity and mortality are the lowest during adolescence compared with adulthood and young childhood; (ii) in adolescence, biological (health) selection may be greater than social selection due to greater equalization of health risks and/or protections; and (iii) SES–health relationship may be more difficult to detect, that is, there may be latent variables and processes. In terms of latent processes, one recent study on SES differences in adolescents' health found that while ‘classical’ SES indicators (e.g. occupational status or schooling) were not significant predictors of adolescents' psychosocial health, a self-reported measure of SES proved to be a strong determinant of adolescents' psychosomatic symptoms, self-perceived health and psychological well-being.7 This subjective SES measurement, as a latent variable, might generate social inequalities in morbidity and mortality in later adult life.
All in all, even in adolescence, when there is a certain level of equalization, psychosocial variables may reflect SES inequalities, or sometimes may generate SES differences in health as latent variables or mediators. Adolescents from lower SES groups usually report lower levels of self-perceived health,11–13 and more psychosomatic health complaints.7,11,14 However, some of the studies do not confirm this relationship.5 In addition, parental economic conditions were found to consistently influence adolescents' depressive symptomatology and psychological health problems.15,16 Finally, results also report the important role of family structure (non-intact family status) in generating inequalities in health.16,17
Findings on health behaviours are even more controversial.18 Some studies report a positive relationship between SES indicators and substance use.19,20 Other studies find an inconsistent or irregular relationship, for example, a higher level of smoking and drinking among adolescents from the higher SES groups.10 This is similar to studies on adults which find higher rates of smoking, heavy drinking and substance use disorders among higher SES groups and self-employed persons.21,22 Findings on physical activity, however, usually report consistent findings regarding SES; adolescents from higher SES tend to engage in more sports activity than their peers from lower SES groups.10 However, differences in lifestyle by SES are not yet reflected in differences in biological risk factors by SES.19
Since there are some inconsistencies regarding health inequalities among youth, there is a need for further investigation with important policy implications that need to be addressed. In addition, previous research also suggests that different SES indicators may have an altering role in determining health outcomes.7,22 Therefore, the main goal of the present study is to attempt to further define the general role of each SES indicator in influencing adolescents' psychosocial health and health behaviours. Based on the work from earlier studies, the current one looks at the role of multiple SES indicators (objective and subjective; occupation and education; family structure) and their influence on these health-related variables. We hypothesize that a certain level of equalization is present in the relationship between SES and health in our sample of adolescents; however we also expect that there should be some difference in the role that SES indicators play among certain health outcomes. Finally, similar to previous studies7,23, we expect that SES self-assessment, expressing the cognitive perception of one's own relative socioeconomic circumstances, will play a significant role in influencing adolescents' health outcomes.
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Participants and procedures
Data were collected in 2004 from students enrolled in the secondary schools of the Southern Plain Region (three counties, namely, Bacs-Kiskun, Bekes, and Csongrad) of Hungary. This representative sample, consisting of 1200 students, was based on randomly selected classes from each randomly selected high school (three schools from each county). Of the 1200 questionnaires sent out, 1114 were returned and analysed, yielding a response rate of 92.8%.
The age range of the respondents was 14–21 years of age (mean: 16.5 years, SD: 1.3), 39.9% were males. Trained public health workers distributed the questionnaires to students prior to the start of class. Parental permissions were obtained prior to the start of the study. Students were given a brief explanation of the objectives of the study and instructions for filling out the questionnaire. Participation in the study was voluntary. Confidentiality of the responses was emphasized and that aggregated data would be used for research purposes only. The response time ranged from 30–40 min. Completed questionnaires were placed in sealed envelopes and collected from each of the participating schools.
Measures
Self-administered questionnaires were used to obtain information from students regarding their family structure, psychosocial health, health behaviours and sociodemographics.
Socioeconomic status: We selected variables reflecting the multidimensionality of SES. This means that both ‘objective’ and ‘subjective’ social status measures were applied.7,24 The objective social status measures were based on employment (occupational) status and the educational level of the parents. Since social structure in Hungary is based on a dual-earning system, both the father's and mother's educational level and occupational status were measured. Employment status was divided into five or six (in case of mother's status) groups: (i) professional, managerial and skilled non-manual; (ii) self-employed or entrepreneur; (iii) skilled or unskilled manual; (iv) unemployed; (v) retired; and (vi) housewife. A four-level classification of education was used to measure father's and mother's schooling: (i) primary education; (ii) apprenticeship; (iii) General Certificate of Education, i.e. high school level; and (iv) University or college degree. In the analyses, a dichotomized variable was applied: (i) high school level or below, and (ii) college/university degree. In additon, a subjective evaluation of SES was used. The subjective SES indicator asked adolescents to respond to the following question: ‘How would you rate your family's socioeconomic status?’ The answer categories included: (i) lower; (ii) lower-middle; (iii) middle; (iv) upper-middle; and (v) upper class. In the present analyses, a three-level classification was used: (i) low/lower-middle; (ii) middle; and (iii) upper/upper-middle. Finally, family structure was measured as a dichotomized (intact/non-intact family) variable in the analyses among the socioeconomic factors.
Self-perceived health: as a global health indicator was measured by asking respondents how they compared their health status to that of their peers. The responses included: (i) poor; (ii) fair; (iii) good; and (iv) excellent.25 The self-perceived health variable was dichotomized and expressed as either poor/fair or good/excellent perceptions of one's own health.
A psychosomatic symptom scale: was constructed and it included the following self-reported symptoms: lower-back pain, tension-headache, sleeping problems, chronic fatigue, stomach pyrosis, tension-diarrhoea and heart palpitation. This measure was used in order to obtain information on the frequency of these symptoms during the last 12 months.25 Respondents were asked: ‘During the past 12 months, how often have you had a back-pain?’...etc. Responses were coded as: (3) often, (2) sometimes, (1) seldom, and (0) never. The final scale had a range of 0–21 and was reliable with a Cronbach's alpha of 0.70. Based on this scale, two groups of adolescents were identified: those who have high symptoms (above the median value) and low symptoms (below the median value).
Depressive symptomatology: was measured by a shortened version of the original 27-item children's depression inventory (CDI), a self-rated depressive symptom scale for young children adapted from the Beck depression inventory for adults26, which was validated in Hungarian samples.27 Each item of the original and shortened versions (containing eight items) assesses a single symptom, such as sadness, and is coded from 1 to 3, e.g., (1) ‘I am rarely sad’; (2) ‘I am often sad’; and (3) ‘I am always sad’. The shortened version of the CDI, based on the present data, was reliable with a Cronbach's alpha of 0.72 (mean = 10.7; SD = 2.5). Again, two groups of adolescents were identified: those who have high symptoms (above the median value) and low symptoms (below the median value).
Health behaviours: The instrument also contained questions on four health behaviours (three forms of substance use and a preventive health behaviour, that is, sports activity) during the last three months.28 Smoking was assessed using a 7-item scale that ranged from not smoking at all (1) to >20 per day (7). Drinking was measured by a 5-item scale that ranged from (1) never drinking to (5) regularly, i.e. 
3 times a week. Marijuana or other illicit drug use was measured using a 5-item scale from (1) never smoking marijuana or taking drugs, to (5) regularly, about every week. Responses for exercise (beyond school physical education, for at least a half hour) varied from (1), never exercising, to (5), 3 times per week. For the purpose of logistic regression analyses, all these variables were dichotomized as follows: smoked/did not smoke; drank/did not drink; used/did not use marihuana; did extra sport at least once a week/did not engage in sports or just occasionally. The main goal of this dichotomization was to analyse possible social inequalities in relation with the presence or absence of the psychosocial health problems and health behaviours in question.
Data analysis
SPSS for MS Windows Release 11.0 program was used in the calculations with a significance level of 0.05. The analysis begins with an examination of the descriptive statistics (frequencies) for both the dependent and independent variables. The primary focus of the analyses are presented as odds ratios (ORs) which helped detect the bivariate relationships between youth's psychosocial health (that is, depressive and psychosomatic symptoms, and self-perceived health), health behaviours (three forms of substances use and sports activity status) and the socioeconomic variables. Gender and age were applied as controlling variables. Finally, multiple logistic regression analyses were calculated for checking interactions between independent variables controlling for gender and age. The results of the binary and mutivariate logistic regression analyses are presented as a series of odds. The baseline odds are set to 1.0. An OR > 1.0 indicates that there is a positive association between the factors of interest to the baseline odds while a value <1.0 indicates the inverse. For statistically significant relationships, 95% confidence intervals (95% CI) were also calculated. In multivariate analyses, the goodness of fit was determined by calculating the deviance of the model. It was defined as twice the difference between the maximum log likelihood and the log likelihood of the model divided by the degrees of freedom. A deviance of 0 would indicate a perfect model fit, whereas a deviance of 1.0 would indicate very little variance explained.
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Regarding the detailed socioeconomic and health profiles of Hungarian adolescents in the sample, 45% of the students reported smoking, 66.5% of them reported alcohol, and 7.6% marijuana use during the past 3 months, whereas 65.6% of them engaged in extra sports activity (besides school) at least once a week. Most students perceived their own health as good or excellent (90.3%). College or university degree was more common among mothers (30.4%) as compared to fathers (25.9%). The frequencies of fathers' employment categories were the following: 33.2% non-manual; 23.8% self-employed; 28.4% manual; 4.9% unemployed; and 9.7% retired. Mothers' employment categories were the following: 42.4% non-manual; 10.3% self-employed; 22.2% manual; 12.6% housewife; 6.7% unemployed; and 5.9% retired. Most of the students considered themselves middle class (62.5%), 18% reported being upper/upper-middle class, and 19.5% said they belonged to the lower/lower-middle class. The majority of adolescents reported living in intact families (68.6%)
Since we anticipate that SES self-assessment might play an important role in adolescents' psychosocial health, we have also analysed the relationship between SES self-assessment and other SES indicators using Chi-squared tests. There are significant differences in SES self-assessment by all other SES variables, that is, parental schooling and employment status, and family structure (P < 0.001). Children whose parents have college degree or above evaluate themselves upper/upper-middle class more frequently. Non-manual or self-employed status of parents goes together with a higher SES evaluation. Finally, children from non-intact families are more likely to assess themselves lower/lower-middle class.
Table 1 shows the results of the calculated ORs for the relationship between SES indicators and psychosocial health variables. The binary logistic regression analyses helps to detect the importance of each SES indicator and their influence on adolescents' health variables. Among the ‘objective’ SES indicators, mother's schooling plays the most important role in (positively) influencing adolescents' psychosocial health, that is, lower levels of mother's schooling is related to depressive and psychosomatic symptoms and a poor/fair evaluation of one's own health. Among the occupational group variables, parental unemployment or retirement seem to be influential in a positive way, particularly mother's status. SES self-assessment, as the subjective evaluation of one's own social circumstances, is the most significant factor impacting the odds of psychosocial health variables. The association is positive, that is, those evaluating themselves as middle or lower class report higher odds as having psychosomatic and depressive symptoms and poor/fair self-perceived health. Likewise, living in a non-intact family may contribute to depressive and psychosomatic symptoms and a poor/fair self-perceived health. This association is also positive.
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Table 2 focuses on the relationship between SES indicators and health behaviours. Again, SES self-assessment is the most influential SES indicator of adolescents' health behaviour; however the relationship is inverse (negative) with regard to drinking and drug use, that is, those evaluating themselves as lower and lower-middle class had a lower likelihood of reporting drinking and marijuana use, whereas they had a higher risk of physical inactivity. In terms of substance use behaviours, their relationships with SES indicators are either non-significant or irregular, that is, inverse (negative). Students whose parents are unemployed, retired or working as a housewife, had a lower likelihood of reporting substance use, particularly alcohol use. The relationship between alcohol use and mother's schooling is also significant and inverse (negative). The relationship between SES and sports activity shows a different pattern; low level of sports activity seems to be more likely among adolescents from lower social classes applying all SES indicators (parental schooling, occupation or SES self-assessment). In addition, living in a non-intact family may contribute to substance use (positive association).
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Table 3 presents the results for multivariate logistic regression analyses for psychosocial health variables where age and gender are also controlled for. There are little change in the role of independent variables as compared to the results of binary logistic analyses. Although some of the significant results disappear (e.g. the role of mother's schooling in adolescents' depression), most independent variables remain significant such as the retired status of parents, SES self-assessment or family structure.
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Table 4 shows the results for multivariate logistic regression analyses for health behaviour variables where age and gender are also controlled for. Again, most significant influences which have been detected using the binary regression analyses remain significant in the final multivariate model, such as the role of variables indicating parents' inactive status in their children's smoking and drinking (negative association, that is, protection) or lower levels of sport (positive association, that is, risk). In addition, SES self-assessment and family structure also remain significant in the final model influencing adolescent health behaviour.
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Discussion |
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The focus of our analyses was detecting the nature of the relationship between adolescents' SES and their psychosocial health and health behaviour. Since previous results suggest that we should use multiple indicators of SES,7 we applied both objective and subjective indicators; occupation and education; and family structure in measuring the influence of SES on health-related variables. Based on our earlier discussion, we hypothesized that there was a certain level of ‘equalization’ during adolescence.4–6 On the other hand, we also hypothesized that despite the dominant effect of equalization, there might be social inequalities in certain aspects of adolescents' health related problems.8 Previous studies also draw our attention to some controversies or irregular (i.e. inverse, negative) relationships in terms of these health behaviour outcomes.9,10 Therefore, we actually concentrated on ‘latent’ variables such as psychosocial health and health behaviour. In addition, we also anticipated that SES self-assessment (as the cognitive perception of one's own relative socioeconomic circumstances) might play the most significant role in influencing adolescents' health outcomes, similar to the findings in previous studies.7,13,23
Since earlier research also suggests that different SES indicators may have an altering role in determining health outcomes7, the current study looked at the role that each SES indicator plays in health outcomes. Based on the results of binary and multivariate logistic regression analyses, our findings suggest the following: (i) SES self-assessment proved to be a significant predictor of adolescents' psychosocial health and health behaviours; (ii) family structure (namely, living in a non-intact family) also significantly influenced adolescents' psychosocial health and health behaviours; (iii) parents' employment status and schooling had a limited influence on their children's health outcomes; (iv) in a word, SES gradients in adolescents' psychosocial health and health behaviour were inconsistent and sometimes irregular (that is, inverse, negative).
It seems that while ‘classical’ SES indicators (e.g. occupation or schooling) are inconsistently related to adolescents' health outcomes5–7, the subjective SES measurement plays a consistent and important role. Our findings suggest that those evaluating themselves as middle or lower class (as compared with those from upper/upper-middle classes) reported a higher likelihood of depressive and psychosomatic symptoms and poor/fair self-perceived health in a gradient-like way. This relationship is in concordance with previous results on self-perceived health7,11–13 and depressive and psychosomatic symptomatology.14–16 Furthermore, those from lower and lower-middle classes are also less likely to engage in sports activity, similar to previous research findings.10 On the other hand, those evaluating themselves as lower class, are less likely to report drinking and marijuana use. This irregular relationship has already been detected among adults21 and adolescents10.
Family structure as an indicator of family environment is an important determinant of youth's lifestyle and health, particularly living in a non-intact family which may serve as a risk factor.16,17 We found that those who reported living in a non-intact family, were more likely to smoke, drink alcohol, and use marijuana, and express worse psychosocial health (a higher likelihood of depressive and psychosomatic symptoms and poor/fair perception of one's own health).
Among parental schooling variables as ‘objective’ SES indicators, mothers' lower level of education seems to be a risk factor in terms of their children's depressive and psychosomatic symptoms, self-perceived health, and sedentary lifestyle. Fathers' schooling is not significant. On the other hand, both mothers' and fathers' schooling variables were significantly but inversely (negatively) related to their children's drinking status. This is consistent with previous results that children living in high SES families report higher frequency of substance use.10 This relationship may stem from adolescents' higher access to financial resources to be able to obtain alcohol (e.g. a higher amount of pocket money) or the possible liberal attitudes of higher SES families.
Among parental employment status variables, certain types of parents' inactive status (in terms of labour market) seem to act in a more predictable way. For example, mothers' unemployment or retired status may contribute to their children's developing depressive and psychosomatic symptoms and a poor/fair evaluation of their own health. The relationship between unemployment and ill health is well established29, however, more research is needed to detect how parents' inactive status may influence their children's psychosocial health. On the other hand, parents' inactive status (e.g., retirement, unemployment, being a housewife) is associated with a lower likelihood of smoking, drinking and drug use among their children. Financial and attitudinal resources may be important to determine this association. Again, more research is needed to clarify the nature of this relationship.
Overall, our results indicate that despite certain level of equalization during adolescence, some important relationships between SES variables and health outcomes may occur. Subjective SES, that is, SES self-assessment, seems to be the most significant and consistent factor besides family structure. Likewise, certain aspects of parents' schooling and employment status may also serve as either a risk or protective factor in predicting adolescents' health and behavioural problems. We should take into account that when we examine the SES–health relationship, this does not automatically imply a positive relationship but in fact, often implies the inverse. All in all, equalization may also stem from contradictory associations: while being upper and upper-middle classes may serve as a risk factor in terms of substance use, it may also serve as a protective factor in terms of depressive and psychosomatic symptoms, self-perceived health and sports activity. Further research should focus on further clarifying these associations.
Limitations to the present study include the cross-sectional nature of analyses. This means that cause and effect relationships cannot be definitively stated. In addition, health measures were based on self-report data without parental information or medical verifications. Although social class evaluation was also self-report and not based on the families' financial background, studies usually note a high level of income non-reporting30, which may be particularly high among adolescents. Finally, it should also be noted that these patterns were found in a sample of Hungarian adolescents. Although SES and health relationships among adolescents have been analysed in a number of countries4,7,10–13, differences across cultures in health and lifestyle practices, belief systems, and health attitudes may result in different patterns of SES and psychosocial health relationships. For example, it may happen that parental inactivity in the labour market is more important in a post-socialist country than in a country with more developed economy. On the other hand, our findings make an unique contribution to the literature on health inequalities by providing data on the role that each SES indicator might play in influencing psychosocial health and health behaviour in an adolescent population. For example, findings suggest that health policy programs should pay attention to the role of parents' inactive status in their children's health problems. Overall, we think that our findings may contribute to a better understanding of SES and health relationship during adolescence. Future research should focus on possible ways of detecting how the results for psychosocial adjustment would translate to social inequalities in health in later adulthood. In addition, a more complex model is needed to map the contexts of this process, e.g. social, educational and familial factors.4
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Acknowledgments |
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This study was supported by the OTKA T 042490 research grant (Hungary). The authors also thank the Editor and three anonymous reviewers for their helpful comments on the paper.
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