Introduction
People’s position in the social hierarchy is strongly linked to health in a graded way; the higher the position the better the health. The resulting socioeconomic inequalities in health, the social gradient in health, have been widely observed [
1‐
8]. The burden associated with socioeconomic inequalities is immense as each year millions of deaths and years of potential life lost across the world are attributed to the unequal distribution of social and economic resources and its individual, community and societal implications [
9,
10]. Research has focused on explaining socioeconomic inequalities in health and identifying causal pathways that might constitute targets for prevention [
10]. Various explanations have been put forward about what might explain the graded association between socioeconomic position (SEP) and risk of ill-health and death [
11‐
18], while empirical research has offered evidence on many different mediating factors ranging from unhealthy behaviours to health insurance and from control over life to work stress [
1,
10,
19‐
21].
Subjective social status (SSS), a concept that refers to self-perceptions of one’s own social position, has received less attention in epidemiological research and its role in socioeconomic inequalities in health remains poorly understood. This is despite its potential to add to the current understanding of socioeconomic inequalities in health when used in conjunction with conventional SEP measures. SSS is a measure of SEP as it is perceived by the individuals themselves; one’s personal translation of objective SEP. Thus, it is a measure of SEP as experienced and internalised by individuals and for that reason it is expected to be closely related to health and a series of personal attributes including behaviours, attitudes, values and worldviews. Further, SSS captures personal individualised aspects of one’s social identity and socioeconomic position [
22] such as lifetime achievement and recognition by others, prestige and a successful family life that conventional SEP measures do not [
23]. For that reason its use in epidemiological research can broaden our ability to understand socioeconomic inequality beyond conventional SEP measures. In addition, unlike commonly used measures that tap into specific SEP dimensions, SSS is a summary measure of SEP that is easy to measure and thus appealing to survey designers.
Previous research has used SSS to predict various health outcomes [
23‐
26], but paradoxically SSS has only rarely been used to predict mortality [
27]. At the moment it remains unclear how strongly SSS is associated with mortality and what is its role in the associations between objective SEP measures and mortality. We aimed to cover this gap in the literature by examining whether and how SSS might be associated with mortality at older ages. To provide a fuller picture of this association we examined both all-cause and cause-specific mortality. Because evidence suggests that SSS might partially mediate the associations between objective SEP measures and different health outcomes, we also explored whether SSS mediated the associations between paternal occupational class when respondents were 14 years old, education, occupational class, income, wealth and mortality. The broad age range of our sample, that is ≥ 50 years, allowed for an exploration of age differences in the association between SSS and mortality that can substantially add to the limited literature on socioeconomic inequalities in health in old age [
28].
Results
In both age groups, male, married, non-smokers, non-obese and physically active participants as well as those who did not report elevated depressive symptoms on average scored higher on the SSS scale (Table
1). As expected, there were strong positive associations between SSS and measures of objective SEP. The wealth differences in SSS score were the greatest observed in our data. In the younger age group, there was difference of 2 points between participants in the highest and lowest wealth tertile, while in the older age group this difference was 1.7 points.
Table 1
The baseline characteristics of the sample by age, English Longitudinal Study of Ageing 2002–2013
N
| 5275 | – | – | 4697 | – | – |
Mean age (95% CI)
| 56.8 (56.7–56.9) | – | – | 73.8 (73.6–73.9) | – | – |
Sex
| | | < 0.001 | | | 0.010 |
Male | 2454 (46.5) | 6.0 (5.9–6.1) | | 2138 (45.5) | 5.6 (5.5–5.7) | |
Female | 2821 (53.5) | 5.8 (5.7–5.8) | | 2559 (54.5) | 5.5 (5.4–5.5) | |
Marital status
| | | < 0.001 | | | < 0.001 |
Married | 3949 (74.9) | 6.0 (6.0–6.1) | | 2738 (53.8) | 5.7 (5.6–5.8) | |
Other | 1326 (25.1) | 5.4 (5.2–5.5) | | 1959 (41.7) | 5.3 (5.2–5.4) | |
Smoking
| | | < 0.001 | | | < 0.001 |
Current smoker | 1189 (22.5) | 5.3 (5.2–5.4) | | 587 (12.5) | 4.9 (4.8–5.1) | |
Former smoker | 2191 (41.5) | 6.0 (6.0–6.1) | | 2463 (52.4) | 5.6 (5.5–5.7) | |
Never smoker | 1895 (35.9) | 6.1 (6.0–6.1) | | 1647 (35.1) | 5.6 (5.6–5.7) | |
Physical activity at least once a week
| | | < 0.001 | | | < 0.001 |
Vigorous-intensity | 1798 (34.1) | 6.3 (6.2–6.4) | | 964 (20.5) | 5.9 (5.8–6.0) | |
Moderate-intensity | 2552 (48.4) | 5.9 (5.8–6.0) | | 2224 (47.3) | 5.6 (5.6–5.7) | |
Mild-intensity | 597 (11.3) | 5.2 (5.0–5.3) | | 827 (17.6) | 5.2 (5.1–5.4) | |
Physically inactive | 328 (6.2) | 4.7 (4.5–4.9) | | 682 (14.5 | 5.0 (4.8–5.1) | |
Body mass index
d
| | | < 0.001 | | | 0.003 |
< 25 kg/m2 | 1493 (28.3) | 6.0 (5.9–6.1) | | 1195 (25.5) | 5.5 (5.4–5.6) | |
25 to < 30 kg/m2 | 2154 (40.8) | 6.0 (5.7–6.1) | | 1922 (40.9) | 5.7 (5.7–5.7) | |
≥ 30 kg/m2 | 1254 (23.8) | 5.7 (5.6–5.8) | | 1053 (22.4) | 5.4 (5.6–5.8) | |
Missing | 374 (7.1) | 5.6 (5.4–5.8) | | 527 (11.2) | 5.3 (5.2–5.5) | |
Elevated depressive symptoms
| | | < 0.001 | | | < 0.001 |
No | 4484 (85.0) | 6.1 (6.0–6.1) | | 3907 (83.2) | 5.7 (5.6–5.7) | |
Yes | 791 (15.0) | 4.7 (4.5–4.8) | | 790 (16.8) | 4.8 (4.7-5.0) | |
Education
| | | < 0.001 | | | < 0.001 |
A-level or higher | 1910 (36.2) | 6.6 (6.5–6.7) | | 944 (20.1) | 6.5 (6.4–6.6) | |
GCSE/O-level/other qualification | 1721 (32.6) | 5.8 (5.7–5.9) | | 1244 (26.5) | 5.7 (5.6–5.8) | |
No educational qualifications | 1644 (31.2) | 5.1 (5.0–5.2) | | 2509 (53.4) | 5.1 (5.0–5.2) | |
Occupational class
e
| | | < 0.001 | | | < 0.001 |
Managerial and professional occupations | 1748 (33.1) | 6.7 (6.6–6.8) | | 1215 (25.9) | 6.3 (6.2–6.4) | |
Intermediate occupations | 1231 (23.3) | 6.0 (5.9–6.1) | | 1110 (23.6) | 5.7 (5.6–5.8) | |
Semi-routine and routine occupations | 2246 (42.6) | 5.2 (5.1–5.3) | | 2269 (48.3) | 5.1 (5.0–5.2) | |
Other/never worked | 50 (1.0) | 5.2 (4.6–5.9) | | 103 (2.2) | 5.5 (5.1–6.0) | |
Paternal/carer’s occupational class when respondent was 14 years olde | | | < 0.001 | | | < 0.001 |
Managerial and professional occupations/run own business | 1617 (30.7) | 6.4 (6.3–6.5) | | 1204 (25.6) | 6.1 (6.0–6.2) | |
Intermediate occupations | 1700 (32.2) | 5.8 (5.8–5.9) | | 1509 (32.1) | 5.5 (5.4–5.6) | |
Routine occupations/casual jobs/unemployed/disabled | 1764 (33.4) | 5.4 (5.4–5.5) | | 1689 (36.0) | 5.2 (5.1–5.3) | |
Other (incl. Armed Forces) | 194 (3.7) | 5.9 (5.6–6.2) | | 295 (6.3) | 5.4 (5.2–5.7) | |
Weekly household income tertiles
| | | < 0.001 | | | < 0.001 |
Highest (≥ £262.79) | 2453 (46.5) | 6.5 (6.4–6.6) | | 994 (21.2) | 6.6 (6.5–6.7) | |
Middle (< £262.79 to ≥ £155.19) | 1616 (30.6) | 5.6 (5.6–5.7) | | 1714 (36.5) | 5.6 (5.5–5.7) | |
Lowest (< £155.19) | 1206 (22.9) | 4.9 (4.8–5.0) | | 1989 (42.3) | 5.0 (4.9–5.0) | |
Total net non-pension household wealth tertiles
| | | < 0.001 | | | < 0.001 |
Highest (≥ £203,000) | 1981 (37.6) | 6.7 (6.7–6.8) | | 1392 (29.7) | 6.5 (6.4–6.5) | |
Middle (< £203,000 to ≥ £76,020) | 1821 (34.5) | 5.9 (5.8–6.0) | | 1552 (33.2) | 5.5 (5.4–5.6) | |
Lowest (< £76,020) | 1473 (27.9) | 4.7 (4.6–4.8) | | 1735 (37.1) | 4.8 (4.8–4.9) | |
We observed 402 and 1861 deaths in the younger and older age groups, respectively (Table
2). In the younger age group, all-cause mortality risk increased by 24% per unit increase in the SSS score after adjustment for age, sex, and marital status, while in the older age group, this increase was smaller at 8%. SSS appeared to be associated more strongly with CVD-related and other mortality than with cancer-related mortality. As in all-cause mortality, these associations were stronger in the younger age group compared with older age group. Adjustments for unhealthy behaviours, BMI and elevated depressive symptoms fully explained the association between SSS and other (in participants aged ≥ 65 years) and cancer mortality and partially the associations between SSS and all-cause, CVD and other mortality (in those aged 50–64 years).
Table 2
The association between subjective social status and all-cause and cause-specific mortality by age, English Longitudinal Study of Ageing 2002–2013
All-cause mortality
|
No of deaths | 402 | 1861 |
Deaths/1000 person years | 7.5 (6.8–8.3) | 46.3 (44.2–48.6) |
Model 1 HR (95% CI) | 1.25 (1.18–1.31) | 1.08 (1.06–1.11) |
Model 2 HR (95% CI) | 1.24 (1.18–1.31) | 1.08 (1.05–1.11) |
Model 3 HR (95% CI) | 1.14 (1.07–1.20) | 1.04 (1.01–1.06) |
Model 4 HR (95% CI) | 1.11 (1.05–1.18) | 1.03 (1.00–1.06) |
Cardiovascular mortality
|
No of deaths | 99 | 663 |
Deaths/1000 person years | 1.9 (1.5–2.3) | 16.5 (15.3–17.8) |
Model 1 HR (95% CI) | 1.36 (1.22–1.51) | 1.11 (1.06–1.17) |
Model 2 HR (95% CI) | 1.36 (1.22–1.51) | 1.11 (1.05–1.16) |
Model 3 HR (95% CI) | 1.18 (1.06–1.32) | 1.07 (1.02–1.12) |
Model 4 HR (95% CI) | 1.15 (1.03–1.29) | 1.06 (1.01–1.11) |
Cancer mortality
|
No of deaths | 193 | 514 |
Deaths/1000 person years | 3.6 (3.1–4.2) | 12.8 (11.7–13.9) |
Model 1 HR (95% CI) | 1.14 (1.05–1.23) | 1.06 (1.00–1.11) |
Model 2 HR (95% CI) | 1.13 (1.05–1.23) | 1.06 (1.01–1.12) |
Model 3 HR (95% CI) | 1.07 (0.98–1.16) | 1.03 (0.98–1.09) |
Model 4 HR (95% CI) | 1.05 (0.97–1.15) | 1.03 (0.97–1.08) |
Other mortality |
No of deaths | 110 | 684 |
Deaths/1000 person years | 2.1 (1.7–2.5) | 17.0 (15.8–18.3) |
Model 1 HR (95% CI) | 1.35 (1.22–1.49) | 1.07 (1.03–1.12) |
Model 2 HR (95% CI) | 1.32 (1.20–1.46) | 1.06 (1.01–1.11) |
Model 3 HR (95% CI) | 1.21 (1.09–1.34) | 1.02 (0.97–1.07) |
Model 4 HR (95% CI) | 1.17 (1.05–1.31) | 1.00 (0.96–1.05) |
Sample sizes
|
No of participants | 5275 | 4697 |
Person years of follow-up | 53431 | 40196 |
In both age groups, the association between SSS and all-cause mortality was little affected by adjustment for most objective SEP measures, except for the adjustment for wealth, which explained a considerable part of it (Table
3). The associations between measures of objective SEP and all-cause mortality were partially explained, to a varying extent, after adjustment for SSS (Table
4). In the younger age group, SSS explained a large part of the associations between education and adult occupational class and all-cause mortality, and a smaller part of the associations between childhood occupational class, income and wealth and all-cause mortality. In the older age group, SSS explained a smaller part of these associations.
Table 3
The association between subjective social status and all-cause mortality by age, English Longitudinal Study of Ageing 2002–2013
All-cause mortality
|
No of deaths | 402 | 1861 |
Deaths/1000 person years | 7.5 (6.8–8.3) | 46.3 (44.2–48.6) |
Model 1 HR (95% CI) | 1.24 (1.18–1.31) | 1.08 (1.05–1.11) |
Model 2 HR (95% CI) | 1.22 (1.15–1.29) | 1.06 (1.03–1.09) |
Model 3 HR (95% CI) | 1.21 (1.14–1.28) | 1.06 (1.03–1.09) |
Model 4 HR (95% CI) | 1.22 (1.16–1.29) | 1.07 (1.04–1.10) |
Model 5 HR (95% CI) | 1.19 (1.13–1.26) | 1.06 (1.03–1.09) |
Model 6 HR (95% CI) | 1.14 (1.08–1.21) | 1.04 (1.01–1.07) |
Sample sizes
|
No of participants | 5275 | 4697 |
Person years of follow-up | 53,431 | 40,196 |
Table 4
The associations between each of the objective socioeconomic position measures and all-cause mortality by age, English Longitudinal Study of Ageing 2002–2013
Predictor: Education
|
No of deaths | 121 | 103 | 178 |
No of participants | 1910 | 1721 | 1644 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.00 (0.77–1.31) | 1.73 (1.36–2.18) |
Model 2 HR (95% CI) | 1.00 (reference) | 0.86 (0.66–1.13) | 1.28 (1.00–1.65) |
Predictor: Occupational class
a
|
No of deaths | 94 | 89 | 213 |
No of participants | 1748 | 1231 | 2246 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.46 (1.09–1.95) | 1.79 (1.41–2.29) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.26 (0.94–1.69) | 1.33 (1.03–1.73) |
Predictor: Paternal/carer’s occupational class when respondent was 14 years oldb |
No of deaths | 85 | 139 | 162 |
No of participants | 1617 | 1700 | 1764 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.48 (1.13–1.94) | 1.66 (1.27–2.15) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.30 (0.99–1.71) | 1.36 (1.04–1.77) |
Predictor: Equivalised weekly household income tertiles
|
No of deaths | 115 | 149 | 138 |
No of participants | 2453 | 1616 | 1206 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.85 (1.45–2.36) | 2.20 (1.71–2.84) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.59 (1.24–2.05) | 1.67 (1.27–2.19) |
Predictor: Total net non-pension household wealth tertiles
|
No of deaths | 92 | 116 | 194 |
No of participants | 1981 | 1821 | 1473 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.43 (1.08–1.88) | 3.01 (2.34–3.89) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.26 (0.96–1.67) | 2.30 (1.73–3.06) |
Predictor: Education |
No of deaths | 308 | 430 | 1123 |
No of participants | 944 | 1244 | 2509 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.07 (0.92–1.24) | 1.33 (1.17–1.51) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.02 (0.88–1.18) | 1.23 (1.08–1.40) |
Predictor: Occupational class
a
|
No of deaths | 441 | 404 | 965 |
No of participants | 1215 | 1110 | 2269 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.12 (0.98–1.29) | 1.32 (1.18–1.48) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.09 (0.95–1.25) | 1.25 (1.11–1.40) |
Predictor: Paternal/carer’s occupational class when respondent was 14 years oldb |
No of deaths | 445 | 594 | 702 |
No of participants | 1204 | 1509 | 1689 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.14 (1.01–1.29) | 1.25 (1.11–1.41) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.10 (0.97–1.24) | 1.18 (1.05–1.33) |
Predictor: Equivalised weekly household income tertiles
|
No of deaths | 292 | 658 | 911 |
No of participants | 994 | 1714 | 1989 |
Model 1 HR (95% CI) | 1.00 (reference) | 1.25 (1.09–1.43) | 1.34 (1.17–1.53) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.18 (1.02–1.36) | 1.23 (1.06–1.41) |
Predictor: Total net non-pension household wealth tertiles
|
No of deaths | 415 | 548 | 898 |
No of participants | 1392 | 1552 | 1753 |
Model 1 HR (95% CI | 1.00 (reference) | 1.17 (1.03–1.33) | 1.72 (1.53–1.95) |
Model 2 HR (95% CI) | 1.00 (reference) | 1.13 (0.99–1.28) | 1.63 (1.44–1.86) |
Discussion
In a national sample of people aged ≥ 50 years, we found subjective social status, one’s perceptions of their own social status, to be inversely associated with all-cause and cause-specific mortality. These associations were stronger in participants aged 50–64 years compared with those aged ≥ 65 years and were explained to a varying extent by unhealthy behaviours, obesity and elevated depressive symptoms. SSS partially mediated the associations between objective SEP measures such as education and occupational class and mortality, especially in participants aged 50–64 years. SSS appears to explain a unique part of mortality that no single objective SEP measure could explain. Nevertheless, in both age groups, wealth partially explained the association between SSS and mortality; a strong indication that the association between SSS and mortality can partially be attributed to SSS reflecting one’s wealth and being a product of assets ownership and material deprivation.
Despite the importance of SSS to better understand socioeconomic inequalities in health and an expanding literature on its associations with morbidity [
24‐
26,
32], very little research has focused on the association between SSS and mortality. We are aware of only one individual-level study on the association between SSS and mortality [
27]. Their findings partially concur with ours; they examined separately men and women aged 40–65 years and found SSS to predict mortality over 3.5 years of follow-up in men, but not in women. Other studies have explored the associations between self-perceptions of specific dimensions of SEP such as self-perceived income and wealth [
33,
34], relative deprivation [
35,
36], occupational prestige [
37], and perceptions about own work trajectory [
38] and all-cause mortality. Notwithstanding methodological differences, our findings concur with those of most previous studies [
34‐
38].
Our study has strengths and limitations that need to be acknowledged. The use of data from a survey that is designed to be nationally representative is a strength and makes our findings more generalizable to community-dwellers aged ≥ 50 years. The novelty of our findings should also be stressed. Our study is the first to examine the association between SSS and mortality in people aged ≥ 65 years and the first to examine the association between SSS and cause-specific mortality. It is also the first systematic attempt to explore the interrelationships between SSS and commonly used objective SEP measures in relation to mortality. Finally, the comprehensive assessment of SEP and the 10-year long follow-up make our study a thorough investigation of the association between SSS, SEP and mortality. A weakness of our study is our inability to fully control for non-response bias. We were able to impute missing at random SSS values and link almost all participant data with mortality records, but our sample remained to some extent selected as at baseline it included community-dwellers who have survived at least to age ≥ 50 years. Further, the baseline household response rate was very good at 70%, but nevertheless left some room for non-response bias. Another weakness of our study is its purely exploratory character. Our work neither proposed nor tested any theoretical model of the associations between objective SEP measures, SSS, and mortality. However, it generated basic evidence about these associations, which can then be used to build a well-defined testable model of socioeconomic inequalities in mortality. The mediation analysis presented in Table
4 is based on the conceptual argument that SSS is most likely a product of objective SEP and thus a good candidate mediator of the associations between each one of the objective SEP measures and all-cause mortality. Our approach was simple and based on a three-variable system with a single mediator, which is expected to be associated with both the predictor and the outcome and explain to a varying extent the effect of the predictor on the outcome [
39]. This approach neither allows a simultaneous examination of direct and indirect effects nor fully accounts for confounding [
40].
Our findings indicate that SEP has a substantive subjective dimension that is strongly related to all-cause mortality in three different ways. First, SSS mediates to a varying degree the associations between objective SEP measures and mortality. Second, SSS to some extent appears to be an independent predictor of mortality, possibly as a measure of facets of social position not captured by objective SEP measures. Third, SSS is partially associated with mortality as a product of wealth and material circumstances.
In people aged 50–64 years, SSS explained to a considerable extent the associations between objective SEP measures and mortality. On the basis that objective SEP is expected to shape people’s perceptions of their standing on the societal hierarchy and influence their social identity, our findings likely suggest that self-perceptions of own social status as captured by SSS is an important channel through which objective SEP exerts a considerable part of its effect on mortality. In people aged 50–64 years, SSS appears to be explaining to a greater extent the associations between education and adult social class and mortality. We can only speculate that this might happen because social comparisons among working age people are typically made on the basis of education and adult occupational class and thus these two SEP measures might be more important for the formation of perceptions of own social status, that is SSS, than other SEP measures in this age group. Further, education and childhood and adult social classes are in a sense historic SEP markers and thus expected to exert their impact on mortality mostly indirectly via more contemporary SEP measures such as SSS, income and wealth.
In people aged ≥ 65 years, SSS continues to be a significant predictor of mortality. Nevertheless, the importance of SSS as a mediator of the associations between SEP measures and mortality is somewhat decreased. This change in the role of SSS in socioeconomic inequalities in mortality likely can be attributed to its dynamic and age-dependant character. Past the age of 65 years, where most people are retirees and no longer financially active, SSS might be less about education and adult occupational class and more about more dimensions of social position that are perhaps more meaningful in this age group such as lifetime achievement, successfulness in family life, prestige and recognition within one’s local community. These more individualised dimensions of SEP can also be important for survival in old age because of their connection with the provision of key resources such as emotional support, care and practical help.
The observed age differences in the association between SSS and mortality are expected. It is known that the effect of most risk factors on mortality decreases with age partially as a result of survivor bias. Nevertheless, the public health importance of SSS inequalities in people aged ≥ 65 years should not be underestimated. Most deaths occur past the age of 65 years and that means that even small differences in the relative risk of mortality according to SSS in this age group correspond to great differences in the number of deaths.
Regarding specific causes of death, in accordance with previous evidence suggesting a inverse association between objective SEP measures and CVD [
6], we found that SSS is strongly associated with CVD-related mortality in our participants. The strength and persistence of this association underline the importance of the subjective dimension of SEP for cardiovascular mortality. The same applies to the association between SSS and other mortality in participants aged 50–64 years, which is indicative of a strong association between the subjective aspects of SEP and death from respiratory and other causes including suicide and accidents. The association between SSS and cancer-related mortality was strong, especially among participants 50–64 years, but fully explained after adjustment for unhealthy behaviours and obesity.
Conclusions and public health implications
In summary, our study provides substantial evidence for an inverse association between SSS and mortality. SSS appears to partially mediate the associations between objective SEP measures such as education and occupational class and mortality—especially in people aged 50–64 years. To some extent SSS appears to be associated with mortality independent of objective SEP measures likely because it captures facets of socioeconomic position that no objective SEP measure does. Nevertheless, our findings suggest that SSS is partially associated with mortality as a product of wealth.
The implications of our work for public health are considerable. Our findings contribute to a better understanding of socioeconomic inequalities in health and expand the knowledge basis for prevention strategies aiming to reduce socioeconomic inequalities in health. It is important to know that feelings of disadvantage and low social status may lead to increased mortality on the top of the pernicious effect of material disadvantage. This knowledge can be used to fine-tune prevention strategies so that they include empowerment as an additional target next to the main ones of alleviation of material disadvantage and reduction of socioeconomic inequalities in health. Our findings also highlight the existence of important socioeconomic inequalities in people aged ≥ 65, which need to be targeted by prevention strategies, and point out the need to take into account age differences when designing prevention strategies to tackle socioeconomic inequalities in health in adult population.