Introduction
In the last 40years, several studies have focused on the relationship between socioeconomic status (SES) and health across the life-course [
1],[
2]. Research on this relationship among adults suggest that adults´ mortality and morbidity were significant and in a gradient relationship with a person´s level of education, occupational status, and income [
3],[
4]. This relationship has been found to be significant in both European and American samples; however, the relationship between socioeconomic status (SES) and health is not consistent across the life course [
5],[
6].
Previous empirical studies have demonstrated the importance of examining the relationship between social status and mental health [
7]-[
9]. Adolescent´s social network is undergoing radical changes while at the same time depression and the frequency of psychosomatic symptoms are increasing [
10]. Despite being free of serious physical illness, many adolescents report subjective health complaints [
11]. Frequent psychosomatic symptoms may also increase the risk of disturbed neuro-psychological development since somatization can be a learned reaction to stress and emotional traumas that may also be manifested in physical illness later on [
12].
WHO proposed mental health as ..."a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community" [p.12] [
13]. Well-being is more than the absence of mental illness and can be measured by several psychological and social indicators (e.g. life satisfaction, depression, anxiety, self-esteem, etc.) [
14],[
15]. Adolescents´ low mental health may lead to illnesses such as depression and social phobias [
16] and may have major implications for adult morbidity and mortality [
17].
From the 1960s, until 1989-90, the shifting of the economic system, and the living standard in Hungary was relatively high compared to other East-Central European countries, with lower rates of social inequalities. After this change, socioeconomic inequality increased rapidly and a new meaning of class appeared [
18]. After 1990, the development of a market economy led to important differences between the social classes in several dimensions (income, lifestyle, health, education etc.). Unemployment was also a new phenomenon for Hungarians to face. Accordingly Hungary, as an East-Central European post-socialist country, is a special and unique social environment to study from a socioeconomic perspective [
10].
Several studies have found substantive correlations between low family SES and low mental health among adolescents [
10],[
19],[
20]. These studies use self-administered questionnaires to measure adolescents´ family SES. Psychosomatic symptoms like headache, backache, nervousness, sleeping difficulties and tiredness have been found to be significantly related to both `subjective´ (self-assessment of family´s social class) and `absolute´ (occupation and education of parents and family structure, assessed by the adolescent) measures of SES [
6],[
17]. Adolescents´ shyness is also related significantly to these family SES indicators: a relatively higher percentage of shyness occurs among students from lower socio-economic classes [
21]. Students´ need to belong moderately correlates with psychosomatic symptoms and shyness [
22]. Self-esteem is positively related to family affluence of the adolescents [
23]. Thus, family socioeconomic status is strongly linked with several dimensions of mental health and differences across a wide range of demographic groups, varying by age, gender and different SES measures [
24].
Given these results, we believe it is important to measure adolescents´ mental well-being using a multidimensional approach that includes five indicators.
Psychosomatic symptoms,
self-
esteem and
loneliness have been used as indicators of adolescents´ mental well-being [
10],[
23],[
25].
Shyness is also related to a variety of adverse psychological health outcomes (depression, social phobias, psychosomatic symptoms, loneliness, etc.) [
26], and have been used as a mental health indicator among adolescents in past studies [
27],[
28]. In addition, belongingness has been found to be an essential predictor of mental well-being [
29]. Social rejection of a student is often linked with development of greater
need to belong[
30]. If need to belong is not satisfied, feeling of loneliness increases, which in turn, has a negative effect on the mental well-being of adolescents [
31]. We have decided to use indicators that include both social and individual aspects of mental well-being. This type of measurement is particularly relevant to consider since most adolescents during this age period are free of serious physical illness, yet they report considerable psychosomatic and psychological distress symptomatology [
10].
The goal of our study is to determine whether family SES is associated with mental health status in a sample of Hungarian adolescents. Studies have measured family SES in a number of different ways. `Absolute´ variables have been used, including parents´ education [
32]-[
34], occupational or employment status [
6],[
10],[
32], family income and affluence [
35],[
36], assessed by the adolescent. In addition, `subjective´ SES variable was included in several studies, based on the self-assessment of the adolescents about the class of his/her own family [
6],[
10]. We attempt to describe the strength and pattern of this association by multidimensional measures of family SES with self-administered questionnaires, including both `absolute´ (educational level and occupational status of both parents, assessed by the adolescents) [
14],[
37]-[
39] and `subjective´ (asking the students to rate their own families´ socioeconomic status) [
10],[
14],[
40] variables. We use the SES related terminology of McLaughlin et al. [
41]. According to previous studies, we expect a positive correlation between both SES measures and mental well-being, and expect that `subjective´ SES will act as a more protective mechanism compared to `absolute´ indicators [
6],[
10],[
33],[
41].
Results
Socio-demographic, socioeconomic and mental well-being characteristics for the high-school student sample are presented in Table1
1. The majority of adolescents considered themselves middle class (61.1%), a total of 17.6 percent said they belong to upper or upper-middle class and only 12.6 percent thought they were in a lower or lower-middle class family. Seventy percent of the students´ fathers and 65.7 percent of their mothers had high school or less than a high school education. With regards to occupation, 20.8 percent of fathers worked in non-manual jobs, 37.1 percent manual jobs, 24.6 percent of them were self-employed and 6 percent were unemployed. On the other hand, adolescents´ mothers´ non-manual status was the most frequent category (36.1%); more than a quarter of students (26.5%) reported that their mothers had a manual occupation, 15.2 percent of mothers were unemployed and 13.2 percent were self-employed. Similarly to the rates of Hungarian secondary education [
47], majority of the surveyed adolescents study in grammar school or vocational education and only the minority are at a technical college. Even so technical college students were underrepresented in the sample.
Table 1
General characteristics of Hungarian high school students (n = 471)
Absolute SES
| | | | |
Father:
| | | | |
Schooling | | | | 450 (95.5%) |
High school or below | 71.8% | | | 338 |
College/university | 23.8% | | | 112 |
Occupation | | | | 418 (88.7%) |
Non-manual | 20.4% | | | 96 |
Self-employed | 24.2% | | | 114 |
Manual | 38.2% | | | 180 |
Unemployed | 5.9% | | | 28 |
Mother:
| | | | |
Schooling | | | | 454 (96.4%) |
High school or below | 66.2% | | | 312 |
College/university | 30.1% | | | 142 |
Occupation | | | | 430 (91.3%) |
Non-manual | 35% | | | 165 |
Self-employed | 13.4% | | | 63 |
Manual | 26.8% | | | 126 |
Unemployed | 15.9% | | | 75 |
Subjective SES
| | | | 428 (90.9%) |
Lower class | 1.5% | | | 7 |
Lower-middle class | 11% | | | 52 |
Middle class | 61.1% | | | 288 |
Upper-middle class | 15.5% | | | 73 |
Upper class | 1.7% | | | 8 |
Socio-demographics | | | | |
Gender | | | | 470 (99.8%) |
Male | 32.9% | | | 155 |
Female | 66.9% | | | 315 |
Age | | 16.2 | 1.1 | 471 (100%) |
Type of school | | | | 471 (100%) |
Grammar | 51.2% | | | 241 |
Modern | 42.9% | | | 202 |
Technical | 5.9% | | | 28 |
Mental health
| | | | |
Need to belong | | 34 | 5.7 | 440 (93.4%) |
Loneliness | | 32 | 8 | 402 (85.4%) |
Psychosomatic symptoms | | 14.8 | 4.3 | 450 (95.5%) |
Self-esteem | | 19 | 6.1 | 437 (92.8%) |
Shyness | | 29.5 | 8.3 | 395 (83.9%) |
Table2
2 presents the results of the binary logistic regression analyses, including odds ratios (OR) for the relationship between family SES indicators, gender and age (independent), and the five mental health (dependent) indicators. This type of regression analysis focuses on the importance of each independent variable and their contribution to differences in the odds in adolescents´ mental well-being.
Table 2
Logistic regression estimates (OR) of predictors of mental well-being indicators [OR (95% CI)] (n = 471)
Father´s schooling | | | | | |
College/University degreea | 1 | 1 | 1 | 1 | 1 |
High school level or below | 0.9 (0.6 - 1.4) | 1.7 (1.1 - 2.7)* | 1.4 (0.9 - 2.0) | 0.6 (0.4 - 0.9)* | 1.5 (1.0 - 2.3) |
Mother´s schooling | | | | | |
College/University degreea | 1 | 1 | 1 | 1 | 1 |
High school level or below | 0.9 (0.6 - 1.4) | 1.1 (0.7 - 1.6) | 0.9 (0.6 - 1.3) | 0.7 (0.4 - 0.9)* | 1.7 (1.1 - 2.6)** |
Father´s employment status | | | | | |
Non-manuala | 1 | 1 | 1 | 1 | 1 |
Self-employed | 1.3 (0.7 - 2.2) | 1.1 (0.6 - 2.0) | 1.6 (0.9 - 2.8) | 0.8 (0.5 - 1.4) | 0.9 (0.5 - 1.6) |
Manual | 1.1 (0.7 - 1.9) | 1.6 (1.0 - 2.8) | 1.9 (1.1 - 3.2)* | 0.4 (0.3 - 0.7)*** | 2.3 (1.3 - 3.9)** |
Unemployed | 3.0 (1.2 - 7.7)* | 2.8 (1.1 - 7.6)* | 1.2 (0.5 - 2.8) | 0.4 (0.2 - 0.9)* | 1.6 (0.6 - 4.1) |
Mother´s employment status | | | | | |
Non-manuala | 1 | 1 | 1 | 1 | 1 |
Self-employed | 0.7 (0.4 - 1.2) | 0.7 (0.4 - 1.4) | 0.8 (0.4 - 1.4) | 0.8 (0.5 - 1.5) | 0.8 (0.4 - 1.5) |
Manual | 0.8 (0.5 - 1.2) | 1.1 (0.7 - 1.8) | 1.0 (0.6 - 1.5) | 0.4 (0.2 - 0.6)*** | 1.8 (1.1 - 2.9)* |
Unemployed | 0.9 (0.5 - 1.6) | 2.2 (1.2 - 4.0)* | 1.4 (0.8 - 2.4) | 0.5 (0.3 - 0.8)** | 3.4 (1.8 - 6.5)*** |
Subjective SES | | | | | |
Upper/upper-middle classa | 1 | 1 | 1 | 1 | 1 |
Middle class | 0.5 (0.3 - 1.0) | 4.2 (2.0 - 8.8)*** | 2.0 (1.0 - 3.8)* | 0.1 (0.1 - 0.3)*** | 2.8 (1.4 - 5.7)** |
Lower/lower-middle class | 0.6 (0.4 - 1.1) | 2.3 (1.4 - 3.9)** | 1.4 (0.9 - 2.4) | 0.3 (0.2 - 0.6)*** | 2.3 (1.4 - 4.0)** |
Gender | | | | | |
Malea | 1 | 1 | 1 | 1 | 1 |
Female | 2.1 (1.4 - 3.1)*** | 0.9 (0.6 - 1.3) | 2.6 (1.8 - 4.0)*** | 0.3 (0.2 - 0.5)*** | 1.1 (0.8 - 1.7) |
Age (cont.) | 0.9 (0.8 - 1.0)* | 1.2 (1.0 - 1.4)* | 1.0 (0.9 - 1.2) | 1.2 (1.1 - 1.4)* | 1.0 (0.8 - 1.1) |
Type of school | | | | | |
Technical collegea | 1 | 1 | 1 | 1 | 1 |
Vocational education | 0.5 (0.2-1.1) | 0.9 (0.4-2.2) | 0.7 (0.3-1.6) | 3.2 (1.2-8.2)* | 1.2 (0.5-3.3) |
Grammar school | 0.8 (0.4-1.7) | 0.5 (0.2-1.6) | 0.6 (0.3-1.3) | 3.9 (1.5-9.9)** | 0.5 (0.2-1.3) |
‛Subjective´ SES was the most influential indicator on adolescents´ mental health, four out of five well-being variables showed statistically significant relationships with this indicator. On the other hand, the relationships were not gradient-like. Both middle-class and lower/lower-middle class groups showed higher ORs of high level of loneliness (OR = 4.2; 95% CI = 2.0-8.8 and OR = 2.3; 95% CI = 1.4-3.9) and shyness (OR = 2.8; 95% CI = 1.4-5.7 and OR = 2.3; 95% CI = 1.4-4.0), and lower odds of high self-esteem (OR = 0.1; 95% CI = 0.1-0.3 and OR = 0.3; 95% CI = 0.2-0.6) compared to upper/upper-middle class, but middle class group had the worse ORs (the most increased risk of high loneliness and shyness, and the lowest of high self-esteem).
Father´s occupational status played the most significant role in adolescents´ mental well-being among the ‛absolute´ SES indicators. Unemployment and manual status were influencing mental health in a negative way; father´s unemployment was related to higher loneliness (OR = 2.8; 95% CI = 1.1-7.6) and need to belong (OR = 3.1; 95% CI = 1.2-7.7), and manual employment status was related to higher levels of shyness (OR = 2.3; 95% CI = 1.3-3.9), psychosomatic symptoms (OR = 1.9; 95% CI = 1.1-3.2), and low self-esteem (OR = 0.4; 95% CI = 0.3-0.7) compared to the non-manual group. Relationship between well-being and mother´s employment status reflects the same pattern, but it was statistically significant in fewer categories. Mother´s unemployment may contribute to higher levels of loneliness (OR = 2.2; 95% CI = 1.2-4.0) and shyness (OR = 3.4; 95% CI = 1.8-6.5), and low self-esteem (OR = 0.5; 95% CI = 0.3-0.8). Manual occupational status of adolescent´s mother may decrease the risk of high self-esteem (OR = 0.4; 95% CI = 0.2-0.6) and increase the likelihood of higher levels of shyness (OR = 1.8; 95% CI = 1.1-2.9). Parents´ schooling was also related to mental well-being of adolescents. Lower levels of schooling of both parents were related to lower levels of self-esteem [OR = 0.6; 95% CI = 0.4-0.9 (father´s schooling) and OR = 0.7; 95% CI = 0.4-0.9 (mother´s schooling)] for students. In addition, the lower schooling of the mother increased the likelihood of shyness (OR = 1.7; 95% CI = 1.1-2.6) for students; father´s schooling was associated with higher levels of loneliness (OR = 1.7; 95% CI = 1.1-2.7).
Both age and gender were related to student mental well-being. Adolescents´ age showed positive relationships with loneliness and self-esteem, and an inverse relationship with the need to belong. Female students had higher risks for three mental well-being dimensions (higher ORs for level of need to belong and psychosomatic symptoms, lower OR for high self-esteem). Type of school was significantly related only to Rosenberg´s self-esteem scale. Adolescents have higher level of self-esteem from both vocational education (OR = 3.2; 95% CI = 1.2-8.2) and grammar school (OR = 3.9; 95% CI = 1.5-9.9) compared to technical college students.
Discussion
The ‛subjective´ SES in our data shows similar rates compared to a national survey conducted in 2010 among Hungarian adolescents [
42]. Among 9th and 11th grade students the majority were middle-class members (59.3% and 65.6%), and the second largest group was upper/upper-middle class (34.3% and 24.7%), similarly to our results. Eleven to twenty-seven percent of the fathers have a college/university degree. This rate is higher in the case of mothers (14.7%-30.8%) in every studied region. These rates are also similar to our results. Rosenberg´s self-esteem scale was used with the same average (18.86) and high reliability (0.83) in the national sample. Another national study [
10] used the Psychosomatic Symptom Scale, with similar mean score (12.8) and high reliability coefficient (0.75).
Our results suggest that ‛absolute´ SES indicators may play a very limited role in adolescents´ mental health; only manual and unemployment status is associated with some mental well-being indicators. Meanwhile the ‛subjective´ SES indicator significantly correlates with four of five mental well-being scales, but this association is also not gradient-like. These findings support previous results, which suggest that ‛subjective´ SES is a relatively stronger predictor of mental health than ‛absolute´ measures [
10],[
48]. The association between ‛subjective´ SES and mental well-being does not appear to be linear, because middle class groups actually had the highest risk of low mental well-being. Interestingly, previous findings suggest that adolescents that considered themselves to be mostly middle or lower class (as compared to those from upper/upper-middle classes) reported a higher likelihood of psychosomatic and depressive symptoms in a gradient-like way [
6]. A possible explanation could be, those middle class adolescents´ higher aspirations and expectations for future social mobility as compared to upper class students (who have higher self-esteem for the future aspirations) or lower class students (who accept their status instead of high aspirations) [
49]-[
51]. All in all, more research is needed to detect the motives behind this finding.
Among ‛absolute´ indicators, father´s employment status was the less inconsistent predictor of student mental well-being. Students with manual worker or unemployed fathers have significantly higher odds of self-reporting low mental well-being, but the results were still limited and inconsistent. Mother´s occupation shows the same pattern, with even less consistency. Parents´ education was the worse predictor of adolescents´ mental health; these results partially support previous findings [
6],[
10]. For example, a statistically significant positive association was found between adolescents´ mental health and the education of the parents. Among occupational status indicators, the mother´s status played a more influential role in determining the students´ mental health [
6], but in our study, the father´s occupational status seemed to be slightly more important. According to our results both parents´ manual occupational status may increase the risk of some aspects of poorer mental well-being, similar to a previous research [
6]. On the other hand, parents´ unemployed status was not clearly established as a negative predictor of adolescent psychosocial health. Although the negative role of father´s unemployment is well established, earlier studies from this region suggest that mother´s unemployment has a positive influence on adolescents´ psychosocial health, because of the substantial overlaps between the status of an unemployed women and a housewife. An unemployed women becoming a housewife can have a positive role in her children´s psychosomatic health [
10]. Other studies established that mothers´ inactive status may contribute to their children´s increasing psychosomatic and depressive symptoms, mentioning attitudinal and the lack of financial resources as possible explanations [
6]. Our results support this association in the case of mental well-being. During the period of socialism, the majority of Hungarian women were full-time employees, but with the dramatic economic shifts, an increasing number of them became full-time housewives [
10]. We believe that during the recent economic crisis this situation has changed and there now appears to be a greater need for both parents´ to be working than ever before. However, it is certainly more difficult for these full-time housewives to return to the labor market-this might be a possible explanation for our results. In relation to this, a recent paper also suggests that children with unemployed parents usually have more health problems [
52]. All in all, the problem of "being housewives or not" is more complex and there may be several other factors, e.g., control and support influencing it, therefore, further research is needed for clarification.
In the case of age and gender, previous studies found statistically significant relationships with adolescents´ psychosocial health, psychosomatic symptoms, and depressive symptoms [
10],[
17]. Our study (with several limitations and inconsistency) supports these earlier reported relationships.
Conclusions
Overall, our results suggest the following: (1) Occasionally positive, but non gradient-like and inconsistent association between adolescents´ family SES and mental well-being; (2) ‛Subjective´ SES was a better, but also inconsistent predictor of adolescents´ mental health compared to `absolute´ SES measures; (3) Among occupational statuses, only manual employment and unemployment of both parents correlated with some aspects of mental well-being; (4) Parents´ education was the weakest predictor among family SES variables; and (5) Both gender and age were significantly correlated with mental well-being in adolescence.
This population-based study may lead to a deeper and more differential understanding of the relationship between mental well-being and socioeconomic factors of adolescents in post-socialist countries. Our research focuses on a less frequently studied age group. Among health related researchers, only a few have focused on adolescents [
1],[
2]. Another strength of our study is the multidimensional measurement of mental health, including five separate indicators. Statistically significant associations were found between four of five indicators and minimally two of the family SES measures. These indicators all had moderately high internal consistency (>0.77). Furthermore, family SES was measured multidimensionally in attempt to capture families´ social status differentially and extensively.
While there are a number of strengths to this study, we should note several limitations. We cannot provide a cause-and-effect relationship, because our study is cross-sectional. In addition, we used self-reported data on adolescents´ socioeconomic status, without any objective source and mental health, or without any clinical diagnoses. Among mental well-being measures the need to belong scale has a reliability coefficient of 0.6 in our data, and statistically significant association with only one family SES indicator (fathers´ unemployment). Accordingly we suggest that this scale, although widely used with high reliability internationally [
22], needs further adaptation and validation to measure mental well-being among adolescents in Hungary. Finally, our findings may have limited generalizability, because of the study´s specific cultural context, sample size, and imbalance of the sample (only Hungarian adolescents were surveyed in one town, boys and technical college students were underrepresented comparing to the Hungarian adolescent population).
More research needs to focus on the context of mental health and family SES in population based studies, using different non-western societies to map features of these associations [
53]-[
58]. More extensive studies, using a longitudinal design should be conducted for a better understanding of progresses and cause-and-effect associations. In addition, other indicators should be considered in determining what precisely is accounting for variation in mental health status among adolescents, e.g., social network, lifestyle, social support and familial factors.
These findings support previous results [
14], which emphasize the need for intervention to reduce poverty and social inequality among adolescents. Growing social inequalities can strengthen mental health disadvantage of low SES youth in post-socialist countries. In addition, we emphasize the importance of mental health promotion programs for adolescents, particularly for socially disadvantaged groups of adolescents. Based on a World Health Organization publication, effectiveness of a wide range of exemplary mental health promotion programs and policies seem justified. Their outcomes show that mental health promotion is a realistic option within a public health approach across e.g., parental care, schools and work settings. Effective and well-designed interventions can contribute to better mental health and well-being of the population, especially of adolescents in many fields of life [
59]. In addition, the association between mental and physical health is widely recognized, they share many of the same social, environmental and economic determinants [
60]. Accordingly, mental health promotion is an inseparable factor of health promotion in adolescence [
61]. Promoting mental health in this age group also has a positive effect on future physical health status, like in the case of children [
62]. According to our results, health policy may want to think about paying more attention to these adolescents who have parents with manual occupational status or who are unemployed. We also recommend professionals of school-based mental health programs to consider these adolescents as a high risk group with potentially low mental well-being and pay special attention to high schools of socio-economically disadvantaged regions.
Competing interests
The authors declare that they have no competing interests.