Addressing Social Inequality in Aging by the Danish Occupational Social Class Measurement

Abstract

Objective: To present the Danish Occupational Social Class (DOSC) measurement as a measure of socioeconomic position (SEP) applicable in a late midlife population, and to analyze associations of this measure with three aging-related outcomes in midlife, adjusting for education. Method: Systematic coding procedures of the DOSC measurement were applied to 7,084 participants from the Copenhagen Aging and Midlife Biobank (CAMB) survey. We examined the association of this measure of SEP with chronic conditions, self-rated health, and mobility in logistic regression analyses, adjusting for school education in the final analysis. Results: The measure of SEP showed a strong social gradient along the social classes in terms of prevalence of chronic conditions, poor self-rated health, and mobility limitations. Adjusting for school education attenuated the association only to a minor degree. Discussion: The DOSC measure was associated with aging-related outcomes in a midlife Danish population, and is, thus, well suited for future epidemiological research on social inequalities in health and aging.

Keywords

occupational social class measurement social gradient aging

Introduction

Epidemiological research has consistently shown a graded association between socioeconomic position (SEP) and health in both men and women and throughout the life course (Commission on Social Determinants of Health, 2008; Diderichsen, Andersen, & Manuel, 2012; Due et al., 2011; Mackenbach et al., 2008; Marmot, 2004; Marmot et al., 2010; Stringhini et al., 2011). Thus, various aging-related health outcomes show an unequal distribution across social class, including mortality (Bronnum-Hansen & Baadsgaard, 2012), health expectancy (Bronnum-Hansen & Baadsgaard, 2008), self-rated health (Sacker, Worts, & McDonough, 2011), mobility, and functional decline (Avlund, Damsgaard, & Osler, 2004; Nilsson, Avlund, & Lund, 2010b).

SEP has been defined as a generic term that refers to economic and social factors, which influence social positions of individuals and groups within the structure of society (Lynch & Kaplan, 2000). In social epidemiology, numerous terms, conceptualizations, and measures of SEP are used in studies of the association between SEP and health outcomes. In the study of health inequalities, attention has been drawn to the importance of conceptual clarification in relation to each level of biological, ecological, and social organization that is relevant for elucidating population patterns of health, disease, and mortality (Krieger, 2001). Measures of education, income, and occupational social class are frequently used interchangeably despite differences in conceptualization and potential causal processes in their relation to health (Galobardes, Lynch, & Smith, 2007; Geyer, Hemström, Peter, & Vågerö, 2006). Associations between SEP and health may vary during the life course. Level of education usually reflects characteristics acquired early in life representing the transition from a social position largely received from parents to a person’s own social position as an adult (Lynch & Kaplan, 2000). Occupation influences living conditions in adult life and represents the major structural link between education and income. Income relates directly to the material conditions that may influence health, regardless of employment status.

In older age groups, typically defined as age 65 years and above, financial assets, material wealth, or home ownership may be particularly appropriate measures of SEP (Avlund et al., 2003; Robert & House, 1996). Several studies have shown that older individuals with low income, low financial assets, and individuals who do not own their housing have an increased risk of mobility disability compared with more privileged individuals (Avlund et al., 2004; Koster et al., 2006; Lantz et al., 2001; Robert & House, 1996; Sainio, Martelin, Koskinen, & Heliovaara, 2007). It has been argued that measures of material wealth reflect a cumulative influence of social circumstances throughout life, and therefore inequalities in mobility among older people might be correlated to the accumulated risk factors from childhood to old age.

In middle-aged individuals, occupation-based indicators of SEP reflect a combination of material resources and social standing, which relate health outcomes to certain privileges and better opportunities in life (Galobardes et al., 2007). This standpoint has been supported by several studies. Bartley and Plewis (2002) found that unemployment and a disadvantaged social class position are associated with increased risk of long-term illness 20 years later. Volkers, Westert, and Schellevis (2007) showed that Dutch workers in low occupational position were at significantly higher risk of suffering from chronic diseases and the risk gradually decreased in relation to those in higher positions. In addition, Lahelma, Martikainen, Rahkonen, Roos, and Saastamoinen (2005) found occupational social class inequality in self-rated health, pain, chronic illnesses, physical health functioning, angina symptoms, and circulatory diseases, but not in mental health in employees aged 40 to 60 years.

Strength and limitations of the different measures of SEP have been tested and discussed in a number of empirical studies. For example, Davey Smith et al. (1998) analyzed how the socio-economic indicators, education, and occupational social class were associated with health-related behaviors, physiological risk factors, health status, and mortality among 5,749 men aged 35 to 64 who were followed up for 21 years. They found occupational social class to be a better discriminator of socioeconomic differentials in overall and non-cardiovascular mortality and smoking behavior compared with education. Marmot and Shipley (1996) showed that among men aged 40 to 69 years in the Whitehall study, occupational grade was a strong predictor of mortality before retirement. After retirement, the ability of occupational grade to predict mortality decreased, while the predictive value of SEP measured by car-ownership declined less. However, also based on the Whitehall study, Breeze et al. (2001) showed that occupational grade both in middle age and at age of retirement was associated with four self-reported morbidity outcomes in old age. In the latter study, neither car ownership nor housing tenure in midlife added to the predictive power of occupational grade.

The study of social inequalities in health is relevant for all periods of life, but little is known about how occupational social class is related to aging-related health outcomes in a late midlife population. Occupations vary in terms of exposures in the physical and psychosocial working environments that may affect health and aging. Likewise, occupational social status also includes competencies and control, which may be reflected in health behavior and differential access to health care service. This is particularly relevant because social inequalities in aging-related outcomes may be present prior to retirement (House, Kessler, & Herzog, 1990; House, Lantz, & Herd, 2005; House et al., 1994) and persist into old age (Schöllgen, Huxhold, & Tesch-Römer, 2010).

We argue that occupational social class is a particularly relevant measure of SEP in a population that is transitioning from working age in mid-adulthood to old age when the focus is on aging-related health outcomes such as chronic conditions, self-rated health, and mobility. These health indicators represent aspects of the disablement process described by Verbrugge and Jette (1994), as chronic conditions are suggested to precede functional limitations and subsequent disability, which in turn are closely associated with self-rated health (Idler & Benyamini, 1997). This model has been applied to the study of functional decline among older people, but it is likely that a similar process applies to the earlier stages of the disablement that may occur in middle age.

A commonly used measurement of occupational social class in studies on health inequalities in working populations is the British Registrar-General’s social classification. The conceptual basis for this classification is unclear, but it does reflect a general social standing in the community and the occupational skills associated with each class (Macintyre, McKay, Der, & Hiscock, 2003). This classification has been widely used not only in Britain but also in other European countries, even though it has been criticized for inconsistent coding procedures and slow updating of occupations and positions (Breen & Rottman, 1995; Carr-Hill, 1987; Liberatos, Link, & Kelsey, 1988). In Denmark, a social class measurement developed in the late 1960s by the Danish National Institute of Social Research has been widely used in its original or adapted versions. This measure resembles the British Registrar-General’s social classification in that it is based on levels of prestige, although in contrast to the British classification it also includes categories for those individuals who are economically inactive. During the 1990s, the classification was further modified to develop a measure that could be used in ongoing research on social inequality in health. To overcome the problems of changing occupations over time, it requires thorough coding procedures and regular updating of occupations and positions. Thus, the first aim of this article is to present the Danish Occupational Social Class (DOSC) coding as a measure of SEP applicable in a late midlife population of Danish men and women. The second aim is to analyze associations of occupational social class with three aging-related outcomes in midlife: chronic conditions, self-rated health, and mobility limitations, adjusting these associations for educational gradients.

Materials and Methods

A total of 17,937 cohort members living in the Eastern parts of Denmark were invited to participate in the Copenhagen Aging and Midlife Biobank (CAMB) study (7,750 from the Metropolit [MP] cohort, 5,282 from the Copenhagen Perinatal Study [CPC], and 4,906 from the Danish Longitudinal Study on Work, Unemployment, and Health [DALWUH]). In total, 7,190 (40%) of the invited persons answered the postal questionnaire (for further details and description of each of the cohorts, see Avlund et al., 2014). Items necessary for coding participants into occupational social classes included self-reported job description, information on vocational training, and information on the number of subordinates in the present job position. We had full information on all three items for 7,084 participants.

DOSC Measure

The measure is based on the assessments of the occupational skills and competencies necessary for the job as well as the power and control associated with the position (E. J. Hansen, 1984). The measure refers to Neo-Weberian theory of social class and the concept of credentialism, which is considered to be an important inequality-generating mechanism. Credentialism refers to the assignment of “social value” to particular skills and knowledge, which are unevenly distributed in society across categories of educational certification (Muntaner et al., 2010). The DOSC includes three special categories: (a) people on transfer income, including unemployment benefits, sickness benefits, and disability pension; (b) individuals who are economically active (self-employed or having a job) but with insufficient information to categorize the job according to the social class classification; and (c) housewives and students. The second and third categories were added to assign occupation-based social class statuses to usually the small percentage of members of a given population that may not have work, to avoid missing data. The same systematic coding procedures have been applied to all studies using the DOSC, including basic principles for social class coding. The list of job positions is continuously revised following the ongoing changes in the job market, including new job positions, changes in credentials needed to qualify for new jobs, and changes in credentials for existing jobs. This means that the updated manual and coding lists are exchanged between studies to secure that all studies use the most up-to-date version.

The main criterion for coding is the nature of the job, not what the participant is trained to do according to the responses with regard to vocational training. A highly educated person whose job is garbage collector is coded as garbage collector, that is, manual worker (Social Class V); a person with low educational attainment who owns a large factory is coded as owner of a big business (Social Class I). Information on vocational training is only included to qualify the occupational social class coding of generic job titles such as engineer, ranging from a Bachelor (Social Class II) to a Master in engineering (Social Class I). The coding procedures are as follows: Each self-reported job title is categorized according to the educational requirements and/or the management skills requested to perform the job, and/or the amount of control over subordinates or economic assets, for example, management responsibilities, ownership, self-employment. During the coding procedure, the requirements for each job are scrutinized in national qualification profiles, guides, and books for different job positions and related educational programs. The DOSC measurement includes the following eight groups: Social Class I: (a) jobs which require top-level educational attainment (at least 4 years of university or a similar training); (b) white-collar jobs that imply management control of big organizations (top manager in a big company, top level civil servant, government advisor, or other executives and professionals within government, management and administration; owners of large-scale companies in both rural and urban trades with more than 50 subordinates, and self-employed with more than 20 subordinates. Social Class II: (a) white-collar jobs that require approximately 3 years of theoretical training (e.g., nurse, primary school teacher, social worker, medium level civil servant, journalist); (b) white-collar jobs which imply management responsibilities for 11 to 50 subordinates; (c) owners of medium-scale companies (i.e., companies with 6+ employees). Social Class III: (a) non-manual white-collar jobs which demand expertise, based on approximately 1½ years of theoretical training and practical skills (e.g., accountant, police detective); (b) white-collar jobs which demand expertise at basic level but with management responsibilities for 1 to 10 subordinates; (c) self-employed with small-scale business (e.g., small-scale farmer, self-employed craftsmen, and tradesmen with 0-5 employees). Social Class IV: (a) manual white-collar jobs which require some theoretical training up to 1 year as well as practical training (e.g., technicians, nurse assistants, office worker, sales assistant); (b) craftsmen and tradesmen and other blue-collar workers with up to 1 year of theoretical training and practical training (e.g., carpenter, bricklayer, blacksmith, plumber). Social Class V: manual jobs which require little theoretical and practical training including semi- or unskilled workers (e.g., lorry-driver, factory worker, construction worker, farm-worker). Social Class VI: individuals who are economically inactive and rely primarily on transfer income (e.g., disability pensioner, unemployed, or long-term sick). Social Class VII: This is a special category that includes individuals who are economically active (self-employed or have a job), but with insufficient information to categorize the job according to the Social Class Classification I to V. Social Class VIII: This special category includes students and housewives.

The coding procedure in CAMB was based on the lists that have been used in previous surveys of the MP and the DALWUH cohorts in 2004 (n = 6,292) and 2006 (n = 6,151). For the coding in CAMB, we added social class codes for another 205 jobs that had not appeared in earlier surveys, mainly job titles reflecting new developments in the labor market. An inter-rater agreement analysis between the coding performed by three of the authors (U.C., P.W.L., and R.L.) based on a random selection of 202 participants showed a percentage agreement of 79.7% and a weighted kappa coefficient of .82 (95% CI = [0.77, 0.88]), indicating a high consensus. Missing item response on the items necessary for social class coding (questions on job title, vocational training, and number of subordinates/employees) was observed for 1.1% (n = 79) of the 7,190 eligible respondents. The modest participation rate in the CAMB study (40%) may account, in part, for the proportion of missing occupational data. The low missing item response equals the response in the baseline survey of the DALWUH cohort (age 40 and 50) in 2000, which had a higher response rate (69%; Avlund et al., 2014) and a missing item response rate of 1.8% for the same questions. Because of the limited number of participants in social classes VII (n = 31) and VIII (n = 34), we only included Social Classes I to VI for the analyses on the association between social class and aging-related health outcomes.

Education

School education was recorded in nine categories, which were combined into four categories: (a) less than 9th grade, (b) 9th grade, (c) lower secondary school/10th grade, and (d) higher preparatory examination/higher commercial examination/higher technical examination/upper secondary school/other educational background.

Health Measures

Number of chronic conditions was assessed by questions on whether the study participants currently or previously had a range of different health problems and diseases: “Do you have or have you had any of the following diseases?” From the list of 16 health problems and diseases, we chose 10 conditions which are likely to affect the level of everyday functioning in midlife and left out the 6 conditions less likely to affect everyday functioning. The 10 conditions included diabetes, myocardial infarction or angina pectoris, stroke, chronic bronchitis or emphysema, osteoarthritis or rheumatoid arthritis, osteoporosis, cancer including leukemia, chronic anxiety or depression, other mental disorders, and back disease. We used the cut points 0-1 versus 2+ conditions, to have a rough measure of morbidity and because very few of our participants had more than two diseases.

Self-rated health was assessed by the question “In general, would you say your health is?” with the response categories excellent, very good, good, fair, or poor, dichotomized into excellent, very good or good versus fair or poor.

Mobility limitations were assessed by three measures derived from questions on lower extremity functions: (a) “Does your health limit you in running 100 m and climbing stairs to the 2nd floor?” with the response categories yes very limited, yes somewhat limited, no not limited at all, dichotomized into very/somewhat limited versus not limited; (b) “Are you usually able to walk 400 m (0.25 miles) without resting?” with the response categories yes with no difficulty, yes with minor difficulty, yes with major difficulty, and no not able to, dichotomized into able to with minor/major difficulty and not able to versus no difficulty. Register-based measures of age and gender were included as covariates.

Statistical Analyses

Logistic regression analyses were performed to study the association between social class and chronic conditions, self-rated health, and the three measures of mobility limitations, respectively (Table 2). Similarly, the association between school education and the same outcomes was assessed by logistic regression. Model 1 was unadjusted, Model 2 was adjusted for gender and age, and in Model 3 social class and school education were added as control variables. Each of the regression analyses was restricted to those with full records on social class and school education (N = 7,084) in addition to complete data on the dependent variable in question.

Results

The social class distribution in the study population is shown in Table 1. Whereas 14%, 24%, and 22% of the population were categorized into Social Classes I, II, and III, respectively, 27% were in Social Classes IV and V, representing participants with manual jobs. Finally, 12% were economically inactive and relying primarily on transfer income (Social Class VI). Gender differences were seen and a higher proportion of male participants were in Social Class I compared with women, and overall more women than men in the total CAMB cohort were coded into Social Class IV (data not shown). Thirty-nine percent of the population had received a high level of school education. whereas 9% had achieved less than 9 years of mandatory school education. Also shown in Table 1 is the distribution of chronic conditions, self-rated health, and the three measures of mobility limitations. A higher proportion of participants (29%) were very or somewhat limited in running 100 m, whereas only 17% and 7% experienced limitations or difficulties in climbing stairs or walking 400 m, respectively.

Table 1.

The Study Population Distributed by Gender, Age, Social Class, School Education, Number of Chronic Condition, Self-Rated Health, and Mobility Limitations (N = 7,084).

	n (%)
Gender
Men	4,880 (69)
Women	2,204 (31)
Year of birth
1948	252 (3)
1949	780 (11)
1953	3,102 (44)
1958	205 (3)
1959	917 (13)
1960	996 (14)
1961	832 (12)
Social class
I	1,014 (14)
II	1,710 (24)
III	1,531 (22)
IV	1,210 (17)
V	699 (10)
VI	855 (12)
VII	31 (0.5)
VIII	34 (0.5)
School education
Higher examination/upper secondary school	2,803 (39)
Lower secondary school/10th grade	2,601 (37)
9th grade	1,043 (15)
Less than 9th grade	637 (9)
Number of chronic conditions
0-1	5,370 (76)
2<	1,670 (24)
Self-rated health
Excellent/very good/good	6,198 (88)
Fair/poor	870 (12)
Running 100 m
Very/somewhat limited	2,056 (29)
Not limited	4,983 (71)
Climbing stairs to the second floor
Very/somewhat limited	1,212 (17)
Not limited	5,840 (83)
Walk 400 m
Not able to/minor or major difficulties	516 (7)
No difficulty	6,442 (93)

Table 2 shows clear social gradients in health outcomes, with greater odds of having ≥2 chronic conditions, poorer self-rated health, and mobility limitations when descending from higher to lower social classes and when descending from higher to lower school educational level. These findings were not affected when adjusting for age and gender in Model 2. However, when simultaneously adjusting for social class and school education in Model 3, the estimates were somewhat attenuated, but the clear social gradient along social class gradients remained highly significant. Especially Social Class VI, the economically inactive, had a markedly higher odds of chronic conditions, poor self-rated health, and mobility limitations compared with Social Class I.

Table 2.

Odds Ratios (95% CI) for >2 Chronic Conditions, Fair/Poor Self-Rated Health, and Three Measures of Mobility Limitations, by Social Class and School Education (N = 7,084).

		Odds ratio (95% CI)
	n/number of cases	Model 1^a	Model 2^b	Model 3^c
>2 chronic conditions
Social class
I	999/111	1.00	1.00	1.00
II	1,660/268	1.54 [1.22, 1.95]	1.52 [1.19, 1.92]	1.38 [1.08, 1.76]
III	1,505/291	1.92 [1.52, 2.43]	1.92 [1.51, 2.43]	1.61 [1.25, 2.06]
IV	1,157/249	2.19 [1.72, 2.79]	2.14 [1.67, 2.73]	1.70 [1.31, 2.21]
V	669/174	2.81 [2.16, 3.66]	2.81 [2.16, 3.66]	2.14 [1.61, 2.85]
VI	780/462	11.62 [9.11, 14.83]	11.49 [8.99, 14.67]	9.16 [7.05, 11.88]
Higher examination/upper secondary school	2,723/452	1.00	1.00	1.00
Lower secondary school/10th grade	2,523/588	1.53 [1.33, 1.75]	1.53 [1.34, 1.76]	1.22 [1.05, 1.43]
9th grade	991/305	2.23 [1.89, 2.64]	2.20 [1.86, 2.61]	1.51 [1.24, 1.83]
Less than 9th grade	592/224	3.07 [2.53, 3.73]	2.95 [2.42, 3.60]	1.71 [1.37, 2.15]
Fair/poor self-rated health
Social class
I	1,011/36	1.00	1.00	1.00
II	1708/103	1.74 [1.18, 2.56]	1.73 [1.17, 2.55]	1.60 [1.08, 2.38]
III	1,529/122	2.35 [1.61, 3.44]	2.35 [1.61, 3.44]	2.03 [1.36, 3.02]
IV	1,208/115	2.85 [1.94, 4.18]	2.83 [1.93, 4.16]	2.29 [1.52, 3.44]
V	698/79	3.46 [2.30, 5.19]	3.45 [2.30, 5.19]	2.59 [1.68, 3.99]
VI	849/405	24.70 [17.26, 35.37]	24.80 [17.31, 35.54]	19.68 [13.48, 28.75]
Higher examination/upper secondary school	2,800/227	1.00	1.00	1.00
Lower secondary school/10th grade	2,593/295	1.46 [1.21, 1.75]	1.46 [1.22, 1.75]	1.07 [0.87, 1.31]
9th grade	1,040/181	2.39 [1.94, 2.95]	2.46 [1.99, 3.04]	1.46 [1.14, 1.86]
Less than 9th grade	635/167	4.05 [3.24, 5.06]	4.29 [3.41, 5.41]	2.13 [1.63, 2.79]
Limited in running 100 m
Social class
I	1,011/145	1.00	1.00	1.00
II	1,702/370	1.66 [1.34, 2.05]	1.62 [1.31, 2.00]	1.43 [1.15, 1.78]
III	1,524/363	1.87 [1.51, 2.31]	1.85 [1.50, 2.30]	1.48 [1.18, 1.86]
IV	1,203/376	2.72 [2.19, 3.36]	2.61 [2.10, 3.24]	1.96 [1.55, 2.48]
V	693/246	3.29 [2.60, 4.16]	3.26 [2.58, 4.13]	2.31 [1.79, 2.99]
VI	844/533	10.24 [8.18, 12.81]	10.10 [8.06, 12.67]	7.61 [5.98, 9.67]
Higher examination/upper secondary school	2,792/583	1.00	1.00	1.00
Lower secondary school/10th grade	2,585/269	1.60 [1.42, 1.82]	1.60 [1.42, 1.82]	1.29 [1.12, 1.48]
9th grade	1,035/400	2.39 [2.04, 2.79]	2.36 [2.02, 2.76]	1.62 [1.36, 1.94]
Less than 9th grade	627/304	3.57 [2.98, 4.28]	3.45 [2.86, 4.16]	2.07 [1.68, 2.56]
Limited in climbing stairs to second floor
Social class
I	1,009/50	1.00	1.00	1.00
II	1,705/170	2.12 [1.53, 2.94]	2.03 [1.46, 2.81]	1.70 [1.22, 2.37]
III	1,524/199	2.88 [2.09, 3.97]	2.82 [2.04, 3.89]	2.07 [1.48, 2.90]
IV	1,204/203	3.89 [2.82, 5.37]	3.64 [2.63, 5.03]	2.46 [1.75, 3.48]
V	695/158	5.64 [4.04, 7.89]	5.50 [3.93, 7.70]	3.42 [2.39, 4.90]
VI	850/416	18.38 [13.42, 25.18]	18.03 [13.14, 24.73]	12.16 [8.72, 16.96]
Higher examination/upper secondary school	2,790/278	1.00	1.00	1.00
Lower secondary school/10th grade	2,594/456	1.93 [1.64, 2.26]	1.91 [1.63, 2.25]	1.44 [1.21, 1.72]
9th grade	1,034/243	2.78 [2.30, 2.36]	2.81 [2.32, 3.41]	1.73 [1.39, 2.15]
Less than 9th grade	634/235	5.32 [4.34, 6.52]	5.45 [4.41, 6.74]	2.91 [2.29, 3.69]
Difficulties walking 400 m
Social class
I	1,004/13	1.00	1.00	1.00
II	1,690/52	2.42 [1.31, 4.47]	2.34 [1.27, 4.33]	1.97 [1.06, 3.66]
III	1,509/47	2.45 [1.32, 4.55]	2.41 [1.30, 4.78]	1.75 [0.92, 3.32]
IV	1,188/53	3.56 [1.93, 6.57]	3.37 [1.82, 6.23]	2.24 [1.18, 4.25]
V	687/67	8.24 [4.51, 15.05]	8.03 [4.39, 14.68]	4.90 [2.60, 9.22]
VI	816/275	38.75 [22.00, 68.24]	38.08 [21.59, 67.15]	25.34 [14.06, 45.66]
Higher examination/upper secondary school	2,771/104	1.00	1.00	1.00
Lower secondary school/10th grade	2,563/175	1.88 [1.47, 2.41]	1.86 [1.45, 2.39]	1.37 [1.04, 1.80]
9th grade	1,002/123	3.59 [2.73, 4.71]	3.62 [2.75, 4.77]	2.08 [1.53, 2.84]
Less than 9th grade	622/114	5.76 [4.34, 7.63]	5.81 [4.33, 7.79]	2.57 [1.84, 3.58]

Crude model: social class/school education → outcomes.

Social class/school education + gender + age → outcomes.

Social class + gender + age + school education → outcomes.

School education + gender + age + social class → outcomes.

Discussion

Non-participation in the CAMB study was higher among economically inactive persons than among employed persons, indicating that the CAMB study population may represent a socially selected group (Avlund et al., 2014). Compared with the social class distribution in the general Danish study population during the same years, the CAMB study population represents more people from Social Classes I to II (38% vs. 10% in the general population) and less people with manual job positions (27% vs. 47%; Schytz Juul, 2012). The proportion of people in Social Class III in the CAMB study was similar to that in the general population (22% vs. 23%), whereas the proportion of people in Social Class VI in the study population was only 12% compared with 20% in the general Danish population. A general finding was that more men than women were categorized in Social Class I and more women than men were categorized in Social Class IV. These differences are in line with a similar pattern observed for all men and women in the Danish workforce in 2010 (Statistics Denmark, 2012). During the past 50 years, the level of vocational training among women has increased steadily across birth cohorts. While contemporary cohorts of women seem to have overcome any disadvantages in the level of education, women from the generations included in the CAMB did not achieve the same level of training as men. This explains why women in the CAMB study were disproportionally represented in the lower occupational social classes. Differences in prevalence in several measures of mobility limitations are in accordance with the disablement process, showing that limitations in running come before limitations and difficulties in climbing stairs and walking.

We found a strong social gradient in the prevalence of chronic conditions, poor self-rated health, and mobility limitations along the social classes. Compared with Social Class I, we found social gradients in all health outcomes along Social Classes II to VI, also when adjusting for age and gender. As expected, given the classification of the disabled and long-term sick into Social Class VI, the economically inactive stands out with markedly higher odds of poorer health outcomes compared with Social Class I. Our findings are in accordance with other studies on occupational social class and health (Bartley & Plewis, 2002; Lahelma et al., 2005; Volkers et al., 2007). Also the finding on a social gradient in social class is in line with other studies on midlife populations, especially with regard to mobility limitations in midlife populations (Adamson, Hunt, & Ebrahim, 2003; Grundy & Glaser, 2000; Melchior, Lert, Martin, & Ville, 2006; Nilsson, Avlund, & Lund, 2010a; Osler et al., 2009).

We found that school education was associated with the same set of aging-related outcomes as was occupational social class. However, when adjusting for school education in the analyses, the associations between occupational social class and the selected health outcomes were still highly significant. Although education acquired early in life is predictive of the onset of a range of adverse health outcomes in midlife (Herd, Goesling, & House, 2007), occupational social class is independently and strongly associated with aging-related outcomes in a midlife population, still eligible for the workforce.

In the present study, we used data on three measures of mobility limitations of various grades of severity. Stair-climbing ability and ability to walk 400 m have previously been applied in midlife populations (Adamson et al., 2003; Clark, Stump, & Wolinsky, 1998; Koster et al., 2005; Rosow & Breslau, 1966; Sainio et al., 2006, 2007), and have been shown to be predictive of mobility limitations and disability in old age (Fried, Bandeen-Roche, Chaves, & Johnson, 2000; Guralnik & Ferrucci, 2003; Manty et al., 2007). Chronic diseases of high prevalence in midlife such as cardiovascular disease, hypertension, diabetes, and chronic obstructive pulmonary disease are often related to poor health behaviors and high body mass index (BMI). This may have an additional impact on self-rated health and physical functioning such as mobility in midlife (Kuh, Bassey, Butterworth, Hardy, & Wadsworth, 2005; LaCroix, Guralnik, Berkman, Wallace, & Satterfield, 1993), in addition to being socially unequally distributed (Koster et al., 2006). Even though the direction of causality cannot be established in this study, health behaviors such as smoking, use of alcohol, low levels of physical activity, and high BMI might function as mediators of the association between social class and the aging-related health outcomes in the present study. The identification of explanatory factors for the social gradients was not a purpose of this study, therefore health behaviors and BMI were not included in the logistic regression models.

The DOSC classification is a theoretically derived measurement based on a Weberian sociological theory of the society. As such, it contributes to hypotheses on pathways between social conditions and health outcomes, which may be rooted in the social structure, and incorporates class characteristics related to status, competencies, control, and privileges. Although Weber suggested that not all differentials in power could be understood exclusively in terms of economic determinants, he did recognize that the combination of status and wealth was more important to the distribution of power in a society than merely social status by itself (Lynch & Kaplan, 2000). This combination is exactly what the DOSC measurement is intended to measure. Application of the DOSC measurement enables analysis of the detrimental effects of lack of material resources combined with skills and knowledge linked to social standing. Credentialism is an essential element as educational certificates contribute to the individual’s possibility to achieve high social ranking in the labor market. In this regard, the DOSC resembles some of the American measurements of occupational status with the emphasis on educational attainment required for job positions (Galobardes, Shaw, Lawlor, Davey Smith, & Lynch, 2006; Oakes & Rossi, 2003).

Work is an important structural link between educational training and income, and the health consequences of socioeconomic stratification can hardly be understood without insight into how work (or lack thereof) structures people’s lives (Lynch & Kaplan, 2000). Thus, a serious limitation of measures of occupational social class is the lack of a category for non-employed individuals, which may lead to erroneous conclusions on social class differences in health (Martikainen & Valkonen, 1999). In the DOSC classification, we have included all individuals representing people on transfer income, including unemployment benefits, sickness benefits, and disability pension. This strengthens the validity of the DOSC as a measurement of occupational social class.

Grundy and Holt (2001) identified five criteria for valid measures of SEP in a population of older adults, that is, the measure should be theoretically grounded, easily collected, sensitive enough to allow identification of gradients and of the most disadvantaged and should ideally not be an outcome of health status. Based on these criteria, they found that educational qualification or occupational social class paired with a deprivation indicator had the greatest explanatory power when studying social inequality in self-reported health among adults aged 55 to 69. The DOSC measurement fulfills many of these criteria: It has a very low missing item response value for the items necessary to code social class categories. Thus, it is feasible, in the context of a large-scale, epidemiological study, to assess information on a job title, vocational training, and number of employees. Also, our well-described, systematic, and regularly updated coding procedures resulted in consistent coding of similar job titles, which was confirmed by the high inter-rater agreement.

Due to the inclusion of the economically inactive, the classification shows good sensitivity regarding the identification of social gradients in health outcomes in an adult population. This includes mobility disability, physical performance, depression, and health behavior (Andersen, Thielen, Bech, Nygaard, & Diderichsen, 2011; Christensen et al., 2004; Nilsson et al., 2010b). The DOSC classification has also been included in several of the articles in the present special issue of this journal (e.g., A. M. Hansen et al., 2014; Mortensen, Flensborg-Madsen, Molbo, Christensen, et al., 2014a, Mortensen, Flensborg-Madsen, Molbo, Fagerlund, et al., IN PRESS-b). Specifically, social gradients along Social Classes I to VI were found in physical performance and in the measures which reflect muscle strength in central parts of the body, explosive power, and reaction time reflecting the coordination between brain and muscles (A. M. Hansen et al., 2014).

Because of non-response after the merger of the three original cohorts, the CAMB population inevitably represents a socially selected group, which may potentially limit our study. Patterns of non-participation were relatively similar across the three cohorts. The percentage of participants and non-participants was similar for persons with upper secondary education, whereas the participating group included more persons with higher education, compared with non-participants. In addition, employment was associated with participation, so that higher proportions of employed persons and fewer persons out of work took part in the study (Avlund et al., 2014). In conclusion, we found clear occupational social gradients in chronic diseases, self-rated health, and mobility. Thus, the DOSC is associated with aging-related outcomes in a midlife Danish population, and is well suited for future epidemiological research on social inequalities in health and aging. As such, the measurement will be an important component in the future CAMB studies of the socially differential patterns of long-term aging.

Footnotes

Acknowledgements

The authors thank the staff of Institute of Public Health and the National Research Center for the Working Environment who undertook the data collection. Further thanks to Helle Bruundgaard, Nils-Erik Fiehn, Åse Marie Hansen, Poul Holm-Pedersen, Erik Lykke Mortensen, and Merete Osler who initiated and established the Copenhagen Aging and Midlife Biobank from 2009 to 2011. The authors acknowledge the crucial role of the initiators and steering groups of the Metropolit Cohort, The Copenhagen Perinatal Cohort, and The Danish Longitudinal Study on Work, Unemployment, and Health.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Copenhagen Aging and Midlife Biobank has been supported by a generous grant from the Velux Foundation, grant number VELUX26145.

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