Background
Numerous studies show that socioeconomic status (SES) affects people’s health status. A social gradient can be identified for different health indicators, for example mortality, morbidity or self-rated health: each step down on the social ladder is associated with increasing health risks [
1‐
3].
As with health, SES has also been found to affect social relations. Different SES indicators have been associated with social relationships in previous studies: poor social relations are more likely to occur in low SES groups [
4,
5]. A recent study using the same data as our present analysis showed that poorer social relations were more frequent among lower status groups [
6]. For example this study revealed that persons with low education had an odds ratio (OR) of 2.1 to report social isolation compared to the group with highest education. When SES was measured by income, low income groups had an OR of 2.4 to report social isolation.
When studying social relations, generally qualitative and quantitative aspects are differentiated. Quantitative or structural aspects of social relations such as number, frequency and intensity of social contacts are widely used in social-epidemiologic research and can be measured by a well-established index, the Social Integration Index (SII) [
7]. It includes information on marital status, number of close contacts and participation in volunteer organisations. Functional or qualitative aspects of social relations are assessed by means of social support. Here, a distinction is especially made between emotional and instrumental support [
8]. Emotional support includes help in decision-making, understanding and someone to discuss daily problems with, while instrumental support focuses on practical help or financial aid.
Both aspects of social relations have repeatedly been associated with different health indicators, including self-rated health [
8‐
16]. Also, social support has been found to be associated with mental health [
4,
17‐
19]. A lack of social ties has repeatedly been found to increase mortality from different causes of death [
20]. A recently published review concludes that the influence of social relationships on mortality risks is highly comparable with biomedical and behavioural risk factors [
21].
Explanatory approaches on health inequalities have so far included behavioural, material and psychosocial factors [
3,
22,
23]. Among these psychosocial factors are social relations. Only few studies systematically examined the explanatory effect of social relationships on the association between SES and health. In an early work on different contributing factors for explaining socioeconomic differences in health, Marmot and colleagues found a slight explanatory contribution of social relations for the social gradient in self-reported health, waist-hip ratio and psychological well-being [
3]. A US study using cross-sectional data from the National Health Interview Survey indicated only little evidence for a contribution of social support and social integration [
24]. Another US study showed that psychosocial factors were independent determinants of health but no explanatory factors for SES effects on subjective health [
25]. A study among older Danes found no mediating effect of social relations on socioeconomic differences in the onset of disability [
26]. A German study revealed weak mediating effects among the aged [
12]. Emotional support contributed only little to explaining educational differences in general subjective health in a European comparison [
27]. Overall, results on mediating effects have been ambiguous as no clear results regarding the possible mediating effect of social relations on the association of SES and health were found in former studies.
The following analyses are drawn upon a population-based cohort study, the Heinz-Nixdorf Recall (HNR) study, which was carried out in the Ruhr Area. The Ruhr Area has several specific characteristics. It is the largest metropolitan area in Germany with several cities, of which three are included in the study. It is an area in transition, which used to be dominated by an industrial productive sector, specialised on coal and steel. With the closing down of most of this industrial sector, a shift to the third sector took place. This process came along with rising unemployment. At the same time, a process of ‘suburbanisation’ set in. High and middle income families moved into the more rural surrounding areas, leaving an older, poorer and mostly childless population behind. The Ruhr Area as the second biggest metropolitan area in Europe will lose about 7% of its population (about 374,000 inhabitants) due to this process of suburbanisation between 1998 and 2015 [
28]. Therefore, we suspect that in areas undergoing such structural changes and in times of greater uncertainty, social relationships become even more important as a source of support and might play an even greater role in explaining negative health effects of low SES.
Against this background we examine whether social relationships can help explain socioeconomic differences in subjective health in a region characterised by structural changes, as stated in the hypothesis of differential exposure [
29]. Because it can be assumed that the inconsistency of the results in former studies may be due to the use of different indicators, SES is measured by three indicators (education, income and occupational status). Also qualitative as well as quantitative aspects of social relations are covered. Moreover, as earlier studies showed that SES and social relationships have different health effects in men and women [
4,
30‐
32], our analyses are based on the overall sample as well as on the subsample of men and women separately.
Results
Descriptive characteristics of the study population are shown in Table
1. As mentioned above, only those cases with information on general subjective health at baseline and follow-up were included in the analyses (N = 4,146). Chi-square statistics were included in order to reveal significant differences between the genders. P-values of the Chi-square tests are displayed. Slight changes between baseline examination and follow-up occurred in self-rated health: while at baseline about 15% rated their health as poor or very poor, 5 years later this number rose to 17%. Changes in subjective health were stronger in men than in women.
Table 1
Sample characteristics (Heinz Nixdorf Recall Study)
Years of Education (5) | <=10 years | 418 (10.1) | 85 (4.2) | 333 (15.9) | 0.000 |
| 11-13 | 2,314 (55.8) | 964 (47.1) | 1,350 (64.4) | |
| 14-17 | 940 (22.7) | 701 (34.2) | 239 (11.4) | |
| = > 18 years | 472 (11.4) | 298 (14.6) | 174 (8.3) | |
Household equivalent income per month (260) | <1,000€ (very low) | 896 (23.0) | 372 (18.9) | 524 (27.3) | 0.000 |
| 1,000-1,500€ (low) | 1,285 (33.0) | 622 (31.6) | 663 (34.5) | |
| 1,500-2,000€ (average) | 923 (23.7) | 515 (26.1) | 408 (21.3) | |
| >2,000€ (high) | 785 (20.2) | 461 (23.4) | 324 (16.9) | |
Occupational Status (754) | Unskilled employees/ workers | 580 (17.1) | 196 (10.0) | 384 (26.9) | 0.000 |
| Qualified employees/ workers | 1,401 (41.3) | 800 (40.7) | 601 (42.1) |
| Technicians and associate professionals | 796 (23.4) | 518 (26.3) | 278 (19.5) |
| Manager and Professionals | 618 (18.2) | 453 (23.0) | 165 (11.6) |
Social Integration Index (68) | Level I (isolation) | 273 (6.7) | 73 (3.6) | 200 (9.7) | 0.000 |
| Level II | 1,656 (40.6) | 757 (37.6) | 899 (43.5) |
| Level III | 1,943 (47.6) | 1.076 (53.4) | 867 (42.0) |
| Level IV | 209 (5.1) | 109 (5.4) | 100 (4.8) |
Instrumental support (30) | Support available but not needed | 1,278 (30.8) | 669 (32.8) | 609 (29.3) | 0.000 |
| Support appropriate | 2,357 (57.2) | 1,165 (57.2) | 1,192 (57.3) |
| Support inappropriate | 171 (4.1) | 55 (2.7) | 116 (5.6) |
| Support needed but not available | 313 (7.5) | 148 (7.3) | 165 (7.9) |
Emotional support (24) | Support available but not needed | 458 (11.0) | 289 (14.2) | 169 (8.1) | 0.000 |
| Support appropriate | 3019 (72.8) | 1,443 (70.7) | 1,576 (75.6) |
| Support inappropriate | 311 (7.5) | 101 (5.0) | 210 (10.1) |
| Support needed but not available | 337 (8.1) | 207 (5.0) | 130 (6.2) |
Subjective Health
|
Baseline (3) : Very good/ good/ moderate | 3,509 (84.6) | 1,810 (88.3) | 1,699 (81.0) | 0.000 |
| Poor/ very poor | 637 (15.4) | 239 (11.7) | 398 (19.0) |
|
Follow-up (0) : Very good/ good/ moderate | 3,426 (82.6) | 1,742 (84.9) | 1,684 (80.3) | 0.000 |
| Poor/ very poor | 723 (17.4) | 310 (15.1) | 413 (19.7) |
Significant gender differences were found in nearly all variables except for age and emotional support. Women reported a lower SES: they had significantly fewer years of education, reported lower household equivalent income per month and also a lower occupational status than men. Furthermore, women reported themselves to be less socially integrated as indicated by the SII score. Also, women were more likely to rate their health as poor or very poor at both baseline examination and follow-up. These differences underline the above mentioned argument for gender-specific analyses.
Multivariate analyses revealed a social gradient for subjective health in a longitudinal perspective: the lower a person’s SES at baseline (measured by education, income, and occupational status), the higher were the odds for reporting poor or very poor subjective health at 5 year follow-up, after controlling for subjective health at baseline in the overall sample (Table
2). Generally, the lowest socioeconomic groups showed highest risks for poor subjective health.
Table 2
Socioeconomic status at baseline and subjective health at follow-up: Odds ratios
1
, 95% confidence intervals (CI) and percentage change
2
(N = 4,146)
Model 1:
| 14-17 years | 1.30 | (0.90-1.89) | | average | 1.04 | (0.77-1.39) | | Technicians |
1.49
| (1.06-2.09) | |
adjusted for age, gender and general health status at baseline | 11-13 years |
1.50
| (1.07-2.10) | low | 1.10 | (0.84-1.45) | Qualified employees |
1.53
| (1.12-2.08) |
≤10 years |
1.79
| (1.19-2.69) | very low |
1.64
| (1.23-2.17) | Unskilled |
1.90
| (1.34-2.69) |
Model 2:
| 14-17 years | 1.27 | (0.88-1.85) | | average | 1.04 | (0.76-1.37) | | Technicians |
1.48
| (1.06-2.10) | −2.0 |
Model 1 additionally adjusted for Social Integration Index | 11-13 years |
1.43
| (1.02-2.01) | −14.0 | low | 1.10 | (0.82-1.42) | | Qualified employees |
1.48
| (1.09-2.03) | −9.4 |
≤10 years |
1.67
| (1.10-2.51) | −15.2 | very low |
1.54
| (1.16-2.05) | −15.6 | Unskilled |
1.77
| (1.24-2.52) | −14.4 |
Model 3:
| 14-17 years | 1.31 | (0.90-1.90) | | average | 1.01 | (0.75-1.36) | | Technicians |
1.46
| (1.04-2.05) | −6.1 |
Model 1 additionally adjusted for instrumental and emotional support | 11-13 years |
1.48
| (1.06-2.09) | −4.0 | low | 1.06 | (0.81-1.40) | | Qualified employees |
1.46
| (1.07-1.99) | −13.2 |
≤10 years |
1.73
| (1.15-2.62) | −7.5 | very low |
1.55
| (1.17-2.06) | −14.1 | Unskilled |
1.82
| (1.28-2.59) | −8.9 |
Model 4:
| 14-17 years | 1.28 | (0.88-1.87) | | average | 1.01 | (0.75-1.36) | | Technicians |
1.46
| (1.04-2.06) | −6.1 |
Model 1 additionally adjusted for all three indicators of social relations | 11-13 years |
1.42
| (1.01-2.01) | −16.0 | low | 1.06 | (0.80-1.41) | | Qualified employees |
1.43
| (1.04-1.95) | −18.9 |
≤10 years |
1.64
| (1.08-2.49) | −19.0 | very low |
1.47
| (1.10-1.96) | −26.6 | Unskilled |
1.71
| (1.20-2.44) | −21.1 |
The mediator analyses indicated a mediating effect of social relations on the association between SES and self-rated health. Percentage reductions were found for all SES indicators after the introduction of social relations, even though these reductions varied in strength. When education was used as SES measure, percentage reductions ranged between 2% and about 18%. Similar results were found for occupational status. The introduction of social relations into the association of equivalent household income and subjective health led to strongest reductions: here social relations explained between 15% and 30% of the association.
Generally, strongest percentage reductions were found in Model 4, when structural and functional aspects of social relations were introduced into the analyses simultaneously. Percentage reductions ranged between 6% and 30%. Percentage reductions in Model 2 varied between 2% and about 16%, while in Model 3 they varied between 2% and 17% in the overall sample.
In men, only the lowest income and occupational categories are significantly associated with poor subjective health in model 1 (Table
3). Therefore, percentage reductions were only calculated for these two SES groups. After the introduction of social relations into the multiple logistic regression models, percentage reductions of up to 15% were observed.
Table 3
Socioeconomic status at baseline and subjective health at follow-up, men: Odds ratios
1
, 95% confidence intervals (CI) and percentage change
2
(N = 2,049)
Model 1:
| 14-17 years | 1.16 | (0.73-1.85) | | average | 0.98 | (0.66-1.47) | | Technicians | 1.45 | (0.97-2.19) | |
adjusted for age and general health status at baseline | 11-13 years | 1.43 | (0.92-2.22) | low | 0.97 | (0.66-1.42) | Qualified employees | 1.33 | (0.91-1.95) |
≤10 years | 1.29 | (0.63-2.66) | very low |
1.84
| (1.24-2.73) | Unskilled |
1.97
| (1.22-3.19) |
Model 2:
| 14-17 years | 1.12 | (0.70-1.78) | | average | 1.01 | (0.68-1.52) | | Technicians | 1.44 | (0.95-2.17) | |
Model 1 additionally adjusted for Social Integration Index | 11-13 years | 1.36 | (0.87-2.11) | | low | 0.98 | (0.66-1.44) | | Qualified employees | 1.26 | (0.86-1.84) | |
≤10 years | 1.19 | (0.58-2.46) | | very low |
1.83
| (1.23-2.73) | −2.4 | Unskilled |
1.85
| (1.13-3.01) | −9.3 |
Model 3:
| 14-17 years | 1.14 | (0.58-2.52) | | average | 0.97 | (0.65-1.45) | | Technicians | 1.41 | (0.94-2.13) | |
Model 1 additionally adjusted for instrumental and emotional support | 11-13 years | 1.38 | (0.89-2.15) | | low | 0.95 | (0.64-1.40) | | Qualified employees | 1.26 | (0.86-1.85) | |
≤10 years | 1.21 | (0.58-2.52) | | very low |
1.76
| (1.18-2.62) | −9.5 | Unskilled |
1.92
| (1.18-3.13) | −5.2 |
Model 4:
| 14-17 years | 1.10 | (0.69-1.76) | | average | 1.00 | (0.67-1.50) | | Technicians | 1.41 | (0.93-2.13) | |
Model 1 additionally adjusted for all three indicators of social relations | 11-13 years | 1.32 | (0.85-2.06) | | low | 0.96 | (0.65-1.42) | | Qualified employees | 1.20 | (0.82-1.77) | |
≤10 years | 1.13 | (0.54-2.35) | | very low |
1.74
| (1.16-2.60) | −11.9 | Unskilled |
1.80
| (1.10-2.96) | −17.5 |
In women, significantly higher risks for poor subjective health at follow-up were found for lowest SES groups (Table
4). Contribution of social relations to explain these health inequalities considerably differed depending on the indicators used. Percentage reductions varied between 2% and 50%.
Table 4
Socioeconomic status at baseline and subjective health at follow-up, women: Odds ratios
1
, 95% confidence intervals (CI) and percentage change
2
(N = 2,097)
Model 1:
| 14-17 years | 1.65 | (0.88-3.07) | | average | 1.11 | (0.72-1.73) | | Technicians | 1.64 | (0.89-3.00) | |
adjusted for age and general health status at baseline | 11-13 years | 1.64 | (0.96-2.79) | low | 1.22 | (0.82-1.82) | Qualified employees |
1.89
| (1.09-3.29) |
≤10 years |
2.00
| (1.11-3.59) | very low |
1.50
| (1.00-2.26) | Unskilled |
2.00
| (1.13-3.54) |
Model 2:
| 14-17 years | 1.65 | (0.88-3.09) | | average | 1.07 | (0.69-1.67) | | Technicians | 1.67 | (0.91-3.09) | |
Model 1 additionally adjusted for Social Integration Index | 11-13 years | 1.59 | (0.93-2.72) | | low | 1.19 | (0.79-1.78) | | Qualified employees |
1.95
| (1.12-3.40) | +6.7 |
≤10 years |
1.92
| (1.07-3.48) | −8.0 | very low | 1.36 | (0.90-2.05) | −28.0 | Unskilled |
1.92
| (1.08-3.43) | −8.0 |
Model 3:
| 14-17 years | 1.76 | (0.93-3.33) | | average | 1.06 | (0.68-1.65) | | Technicians | 1.59 | (0.87-2.93) | |
Model 1 additionally adjusted for instrumental and emotional support | 11-13 years | 1.68 | (0.97-2.91) | | low | 1.15 | (0.77-1.73) | | Qualified employees |
1.81
| (1.04-3.14) | −9.0 |
≤10 years |
2.01
| (1.10-3.67) | +1.0 | very low | 1.40 | (0.93-2.12) | −20.0 | Unskilled |
1.89
| (1.07-3.36) | −11.0 |
Model 4:
| 14-17 years | 1.75 | (0.92-3.32) | | average | 1.03 | (0.66-1.60) | | Technicians | 1.63 | (0.88-3.01) | |
Model 1 additionally adjusted for all three indicators of social relations | 11-13 years | 1.64 | (0.95-2.85) | | low | 1.13 | (0.76-1.70) | | Qualified employees |
1.86
| (1.07-3.25) | −3.4 |
≤10 years |
1.97
| (1.08-3.62) | −3.0 | very low | 1.28 | (0.84-1.94) | −44.0 | Unskilled |
1.84
| (1.03-3.28) | −16.0 |
Discussion
This is one of the first studies that systematically examined the mediating effect of social relationships on the association between SES and subjective health using data from a 5 year follow-up. Our results indicate a mediating effect of social relationships, i.e. social relations contribute to the explanation of socioeconomic inequalities in subjective health. When measures of social relations were introduced as mediators into the regression models, percentage reductions of the odds ratios between 2% and 30% were observed in the overall sample. In most cases percent reductions exceeded 10%. If the associations between indicators of SES and general subjective health were to be independent of social relations, a variation in effect size would not have been found after the introduction of SII, emotional and instrumental support into the regression models.
Former studies have not consistently revealed such a mediating effect of social relationships on health. Some studies found only slight mediating effects of social relations [
3,
12], while others showed no contribution of social relations to the explanation of health inequalities [
24,
25]. However, these studies differ in terms of study design, measurement of social relations and health as well as study region.
Regarding gender differences, SES indicators are differently associated with subjective health in men and women. On the one hand, a low equivalent household income leads to stronger OR for poor subjective health in men than in women. On the other hand, less than 10 years of education are more strongly related to poor subjective health in women than in men (see Tables
3 and
4). Especially with regard to the specific age-group of the study, which is characterised by a lower degree of labour participation in women, one could imagine that income is of higher importance to men, as it might more directly reflect their success as “breadwinners”. The social status of women in this age-group might be more accurately assessed by their educational background. This might help to explain, why these indicators are differently associated with health in men and women. The mediator analyses revealed similar results as mediating effects of social relationships were detected for both men and women, though varying in effect size. For example, when equivalent household income was used as SES measure, a percentage reduction of up to 50% was found in women, while in men the percentage reduction was 15% in the lowest income group (see Tables
3 and
4). Due to a reduced sample size in the gender-specific analyses, the effects in the first basic model rarely reached significance. Therefore, percentage reductions were not calculated in most cases. Generally, the introduction of social relations reduces the association of SES and subjective health in women and in men. Two exceptions can be found in women (see Table
4). The introduction of instrumental and emotional support (Model 3) leads to a small percentage increase of the respective OR in women with less than 10 years of education. Secondly, the introduction of the SII leads to a 7% increase of the OR for poor health among female qualified employees compared to managers and professionals. As these increases are minor and no particular pattern can be observed, we are careful in drawing conclusions from these findings. In our view it is important to further investigate gender differences in the association between SES, social relations and health in future studies, because we are far from understanding the particular mechanisms in men and women [
19].
Regarding the explanatory contribution of social support and social integration, results are inconsistent. Overall, the simultaneous introduction of both aspects of social relations leads to largest percentage reductions. However, it remains unclear, which aspect of social relations contributes most to the explanation of inequalities in subjective health. For example when SES is measured by education, the introduction of the SII alone leads to percentage reductions of about 15%, while the introduction of instrumental and emotional support shows marginal percentage reductions of between 2% and 6% (see Table
2). When SES is measured by occupational status, the introduction of social support leads to stronger percentage reductions in technicians and qualified employees than does the introduction of SII, while for the unskilled the opposite is true. We additionally analysed in which way the two indicators of functional aspects of social relations, emotional and instrumental support, contribute differently to the explanation of socioeconomic inequalities in subjective health. Hence, emotional and instrumental support were introduced separately into logistic regression models (results not shown). The explanatory contribution proved to be very similar, with no clear pattern of differences regarding the explanatory contribution of instrumental and emotional support.
Earlier research has led to a vast body of evidence showing different associations of functional and structural aspects of social relations with different health indicators [
4,
9,
11,
21,
44]. While structural aspects of social relations may facilitate the availability of help, social support might more directly affect health behaviour and psychological mechanisms such as feelings of self-esteem and coping [
9,
15,
45]. It has been highlighted that especially for ones feeling of accessibility of support and its effect on health it is important to distinguish between perceived and actually received support [
44]. Stansfeld and Fuhrer have developed several models to show how different facets of social relations may influence population health [
11]. In a meta-analytic review Holt-Lunstad and colleagues showed that especially a multidimensional assessment of social-relations led to strongest associations with mortality-risks, as they included the different pathways by which social relations influence health and mortality [
21].
Regarding the three SES indicators, similar results in the strength of the associations can be observed. Generally, the lowest SES groups have the highest risks of reporting poor subjective health. Moreover, in this group percentage reductions are largest when social relations are introduced into the logistic regression models. This is true for income, education as well as for occupational status.
In interpreting the presented results, several methodological aspects should be considered. It is a strength of our analyses that they are based on a cohort study. So far no study has been able to draw conclusions on the mediating effect of social relations on the association of SES and subjective health in a longitudinal perspective. Furthermore, special emphasis was put on quality control of data collection and data handling in the HNR study, as evidenced by external certification [
33]. Complex measures of social support indicators were used. When constructing the items for measuring support, both availability as well as adequacy of support were considered, as proposed in earlier research [
18].
On the other hand our results are limited as they are all based on self-reported data and do not include objectively measured health indicators. Therefore, a possible bias can not be ruled out. Also the longitudinal design is limited as we refer to one 5-year follow-up and include only two measurement points. As expected, there is only little variation in subjective health in a 5-year period. Moreover, we did not calculate significances of mediator effects by using the Sobel-Test as multiple mediators were included in our model. Another restriction is the high number of missing information on occupational status, especially in women (N = 669). For those cases, no information on actual job status or occupational status before retirement was available. This might lead to bias in the results for this SES category. Furthermore, analyses draw on a specific study population, namely a sample of residents of the Ruhr Area aged 45 to 75 years. As noted earlier, the Ruhr Area is a region in transition. It is therefore possible, that in such uncertain times of change, social relationships play a particularly important role in protecting individuals from negative health effects of socioeconomic hardship. This may be one reason why mediating effects of social relationships found in our study are more consistent than in former studies. Therefore, our results cannot be generalised to the overall German population but maybe to populations living under similar conditions.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
KHJ, RE, JS are principal investigators of the HNR study; OK is the principal investigator of the DFG-funded project ‘Health inequalities and social relationships`. ND participated in conducting the HNR study and coordinated data acquisition for this project. Statistical analyses were done by NV. NV, JK and OK were responsible for interpreting the data and writing the manuscript. All authors contributed to drafting the manuscript and approved the final version. All authors read and approved the final manuscript.