The influence of factors identified in adolescence and early adulthood on social class inequities of musculoskeletal disorders at age 30: a prospective population-based cohort study

Masuma Khatun1,*, Christina Ahlgren2 and Anne Hammarström1

1 Family Medicine, 2 Occupational Medicine, Department of Public Health and Clinical Medicine, Umeå University, SE-901 85 UMEÅ, Sweden.

* Corresponding author. Masuma Khatun, Family Medicine, Department of Public Health and Clinical Medicine, Umeå University, SE-901 85 Umeå, Sweden. E-mail: masuma.khatun{at}fammed.umu.se


    Abstract
 Top
 Abstract
 Population and Methods
 Results
 Discussion
 References
 
Background Social class inequities have been observed for most measures of health. A greater understanding of the relative importance of different explanations is required. In this prospective population-based cohort study we explored the contribution of factors, ascertained at different stages between adolescence and early adulthood, to social class inequities in musculoskeletal disorders (MSD) at age 30.

Methods We used data from 547 men and 497 women from a town in north Sweden who were baseline examined at age 16 and followed up to age 30. Using logistic regression models, we estimated the unadjusted odds ratios (OR) for MSD for blue-collar versus white-collar workers in men and women separately. We assessed the contribution of different factors identified between adolescence and early adulthood by comparing the unadjusted OR for social class differences with OR adjusted for these explanatory factors.

Results We found significant class differences at age 30 with higher MSD among blue-collar workers (OR = 2.03 in men [95% CI: 1.42, 2.90] and 1.98 in women [95% CI: 1.29, 3.02]). After adjustment for explanatory factors, class differences decreased and were no longer significant, with OR of 1.20 in men (95% CI: 0.76, 1.95) and 1.18 in women (95% CI: 0.69, 2.03). School grades at age 16; being single and alcohol consumption at age 21; having children, restricted financial resources, physical activity, alcohol consumption, smoking, and working conditions at age 30 were important for men; parents' social class, school grade, smoking and physical activity at age 16; being single at age 21; and working conditions at age 30 were important for women.

Conclusion The accumulation of adverse behavioural and social circumstances from adolescence to early adulthood may be an explanation for the class differences in MSD at age 30. Interventions aimed at reducing health inequities need to consider exploratory factors identified at early and later stages in life, also including structural determinants of health.


Keywords Social class, inequity, neck pain, low back pain, adolescence, adulthood, longitudinal, prospective study

Accepted 3 February 2004

During the last decades, class inequities have been observed for most measures of health. They continue to be one of the most consistent findings in public health research.1–4 For most age groups, men and women from lower social classes have higher morbidity than those from higher social classes.

Persistent class inequities in health are believed to be mainly related to health selection or social causation, with most empirical studies supporting the latter explanation.5 The health selection hypothesis suggests that social mobility is a major factor. According to this hypothesis, individuals with poor health tend to end up in lower social strata in later life, i.e. health affects social class. The social causation hypothesis conversely suggests that class differences in health result from different accumulations of and exposures to risk factors throughout the life course, with more unfavourable conditions experienced by lower social classes, i.e. social class affects health.

In previous studies different aspects of life circumstances such as economic deprivation,6–8 psychosocial environment at home, social support, social network,9 physical and psychosocial environment at work8–10 and unhealthy behaviours8 have been commonly found to be associated with class inequities in health. However, most previous studies on health inequities tend to focus on recent life factors only or consider limited explanatory factors. Given the importance of unfavourable exposure throughout the life course in generating health inequity, studies that ascertained exploratory factors at only one point in time may not adequately explain the observed health differences.11–13 With few exceptions,8,14,15 however, there is a lack of research considering different aspects of life circumstances together with behavioural factors throughout the life course as explanations of class inequity in health.

In Sweden, musculoskeletal disorders (MSD) are the most reported causes of ill health and the leading causes of work absence, long-term work disability and early retirement, especially among women.16 Since 1990, class differences in MSD among men have decreased, mainly due to decreased prevalence among blue-collar men, while class differences have remained unchanged among women. A better understanding is needed of the potential risk factors which contribute to these gender-related class differences. We used data from a prospective cohort study to examine the relative importance of factors identified at ages 16, 21, and 30 in explaining social class inequities in MSD among men and women at age 30. Factors previously established to have adverse effects on health were selected as possible explanations for class inequities.


    Population and Methods
 Top
 Abstract
 Population and Methods
 Results
 Discussion
 References
 
Data source
This study is based on longitudinal data from a 14-year follow-up study which was carried out in Luleå, an industrial town in the north of Sweden. The baseline survey was conducted in 1981, with all the 16 year old pupils of the last year of compulsory school completing a comprehensive self-administered questionnaire (577 boys and 506 girls). The same questionnaire was used for the follow-up at ages 18, 21, and 30. The total response rate was 96.4% throughout the 14-year follow-up. Full details of the study are reported elsewhere.17 For the purpose of this study, data from ages 16 (adolescence), 21 (youth), and 30 (early adulthood) as well as school register data were used.

Musculoskeletal disorders
MSD were ascertained based on a previously validated18 question: ‘Have you in the last 12 months had any of the following illnesses or ailments?’ Three items in the list of illnesses or ailments concerned aches and pains in the back/hip, neck/shoulder, and hand/elbow/knee regions. For each item 0 indicated no pain, 1 mild and 2 severe pain. We used factor analysis to select items for an index of MSD and included factors loading higher than 0.50. This resulted in the inclusion of items on back/hip and neck/shoulder pain only. The composite index ranged from 0 to 4 and internal consistency was 0.81. Because of the low prevalence of severe disorders (4.9%), we combined mild and severe disorders and dichotomized the MSD index: a value of 0 on the MSD index indicated the absence and values between 1 and 4 the presence of a disorder.

Social class
Social class was categorized using the Swedish socio-economic classification (SEI) of occupational categories.19 Manual workers were grouped into blue-collar and non-manual into white-collar workers. At age 16, the classification was based on parents' occupation, primarily the father's occupation, but in single-mother households the mother's occupation was used. At ages 21 and 30, the subjects' own occupation was used. Some 106 of 21 year old participants were still studying at university and they were included in the white-collar group.

Explanatory factors from different age periods
Socio-economic condition
In addition to the social class variable from the three age periods, family size at age 16 and restricted financial resources at ages 21 and 30 were addressed. Family size was categorized as small (<3 siblings) versus large (≥3 siblings). At age 21, restricted financial resources was assessed based on a question reading ‘can you raise a sum of US$ 1000 in a week by any means?’ At age 30, the amount asked about was approximately US$ 1860.

School qualifications
The school grades at age 16 (i.e. last year of compulsory school) were retrieved from the school register. They ranged from 0 to 5 and were derived from the arithmetic mean of the scores from 14 different topics.

Earlier health status
For each age, two different indicators, namely individual level of psychological distress and MSD were determined. Psychological distress was assessed using a composite index based on depression, nervousness, sleeping problems, restlessness, concentration problems, and anxiety, as used and validated in Scandinavian research.20 The index ranged from 0 to 9, with higher values indicating more distress. The internal consistency using Cronbach's alpha was 0.77.

Health behaviour
The health behaviour measures used were smoking (never or stopped versus yes), alcohol consumption (cl pure alcohol/year),21 physical activity in terms of sports and body mass index (BMI). We assessed the frequency of physical activity using a score from 0 to 2, with 0 indicating regular, 1 infrequent and 2 no activity. BMI (BMI, kg/m2) was calculated from measured weight and height data recorded in school health records at baseline, and from self-reported information on weight and height at ages 21 and 30.

Family factors and social relations
Parental divorce or separation versus living with both parents at age 16 and own family formation at ages 21 and 30 were assessed. Family formation was reported as single versus married or cohabiting, and the presence or absence of own children. At age 30, the perceived availability of social network and social support was assessed using indices that had previously been validated:22 the social network index representing the number of people with whom links are recognized (range: 4–24) and the social support index reflecting the degree of emotional and material support received when needed (range: 6–23).

Work environment
Work environment was assessed based on physical and psychosocial characteristics of the current employment at age 30. Respondents were asked if their work was physically heavy (yes/no). Psychosocial job characteristics were assessed using two six-item scales adopted from Karasek and Theorell on job demand (referring to quantity of work, intellectual requirements, and time constraints of the job) and on job control (referring to the possibilities of making decisions, being creative, and using and developing own abilities), both ranging from 6 to 24.23 The scales were dichotomized according to the median split.

Data analysis
We calculated the prevalence of MSD for ages 16, 21, and 30 and estimated the differences in prevalence between blue- and white-collar workers and between men and women. We fitted separate univariable logistic models to estimate the effect of each explanatory factor on class differences in MSD at age 30, using the difference between the unadjusted and adjusted odds ratio (OR) to estimate the contribution of each variable. The OR of MSD of blue-collar workers compared with white-collar workers at age 30 was the base model from which the contributions of individual factors were estimated. First, we determined whether the adjustment for an individual factor resulted in a reduction in the OR of MSD of blue-collar workers compared with white-collar workers in bivariable models. Factors that resulted in a reduction of the OR were considered for inclusion in a subsequent multivariable model. We used a stepwise approach to construct this model, following a temporal sequence: first, we included factors ascertained at age 16, then factors identified at age 21, finally we included factors determined at age 30. This method allowed for the timing of particular events and circumstances, providing an indication of the relative and cumulative effects of particular explanations.24 In the final model we only included factors that had contributed to a decrease in the OR using this stepwise approach.

For exploratory purposes, we performed separate analyses for neck/shoulder and back/hip pain and found similar results. We also explored the impact of collinearity between the independent variables and found results unaffected.25 The internal non-response rate was 6.2% for all the variables. Analyses were performed in SPSS version 11.5 (SPSS Inc., Chicago, IL, USA) and Epicalc (Brixton Health, UK).


    Results
 Top
 Abstract
 Population and Methods
 Results
 Discussion
 References
 
No social class gradient was observed at ages 16 or 21, but a significant gradient existed in men and women at age 30, with higher prevalence of MSD among blue-collar than white-collar workers. Within each social class, gender differences in MSD were found among blue-collar workers at 21 and both social classes at 30, with women reporting significantly more MSD than men. With the exception of white-collar males at ages 16 to 21, the prevalence of MSD increased with age independently of social class. The gradual increase was more pronounced in blue-collar workers and in women (Table 1).


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Table 1 Prevalence of musculoskeletal disorders stratified by age, gender and social class, including differences (Diff.) in prevalences between different social classes and between males and females along with 95% CI

 
The OR for MSD among blue-collar workers relative to white-collar workers at age 30, adjusted separately for each explanatory factor identified at ages 16, 21 and 30, are shown in Table 2. Adjustment for school grade, family size, alcohol consumption, smoking, physical activity, and psychological distress at age 16 reduced the OR for both men and women. Parents' social class, parental divorce, and BMI were of additional importance in women. Adjustment for MSD at 16 did not reduce the class differences for either men or women.


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Table 2 Odds ratios (OR) for social class differences in musculoskeletal disorders (MSD) at age 30 unadjusted (top) and adjusted in separate univariable logistic regression analyses. Each estimate indicates the OR for MSD resulting from an adjustment for the specified variable in a bivariable model. An OR > 1 indicates that MSD are more likely in blue-collar workers than in white-collar workers

 
Adjustment for being single, having children, restricted financial resources, alcohol consumption, smoking, MSD, and psychological distress at age 21 decreased the OR for both men and women. Own social class, physical activity, and BMI at age 21 contributed additionally in women.

Having children, restricted financial resources, alcohol consumption, smoking, physical activity, job control, heavy working conditions, social network, and psychological distress at age 30 decreased the OR for men. For women, restricted financial resources, job control, heavy working conditions, and social network were important. Table 3 indicates that many of the selected factors from the three age periods were also significantly associated with MSD in univariate analysis.


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Table 3 Association between musculoskeletal disorders (MSD) at age 30 and explanatory factors at ages 16, 21 and 30 years (odds ratios [OR] and 95% CI from univariable analyses). An OR of 1.91 for smoking at age 30, for example, indicates that MSD are 1.9 times more likely in smokers as in non-smokers

 
The combined effect of the variables from adolescence to early adulthood which showed a reduction in class differentials for men and women at age 30 is presented in Table 4. Without adjustment, the risk of MSD for blue-collar workers was 2.03 (95% CI: 1.42, 2.90) for men and 1.98 (95% CI: 1.29, 3.02) for women. From the adolescence period, only the school grade contributed to a reduction in class differentials for men. For women, important factors besides high school grade were parents' social class, smoking, and physical activity. Adolescent factors contributed to a higher reduction in class differentials for women than for men (for women 13.1% [OR from 1.98 to 1.72] and for men 3.5% [OR from 2.03 to 1.96]). After adding factors from age 21 together with 16, the class differentials were reduced by 10.3% (OR from 2.03 to 1.82) in men and 16.2% (OR from 1.98 to 1.66) in women. Contributory factors from age 21 were being single for both men and women, and alcohol consumption for men. Further addition of factors from age 30 reduced the class differences substantially to a non-significant level for both men (OR = 1.20, 95% CI: 0.76, 1.95) and women (OR = 1.18, 95% CI: 0.69, 2.03). From age 30 years, several factors were important for men, which included having children, restricted financial resources, physical activity, alcohol consumption, smoking, job control, and heavy work. For women only heavy work and low job control were important. Health status in terms of MSD during early years and psychological distress during any age period did not have any additional influence on class differences for either men or women.


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Table 4 The combined effect of the variables from age 16 to 30 that contributed to a reduction in class differentials in musculoskeletal disorders (MSD) among men and women at age 30 in a multivariable logistic regression model. An odds ratio (OR) >1 indicates that MSD are more likely in blue collar workers than in white collar workers

 

    Discussion
 Top
 Abstract
 Population and Methods
 Results
 Discussion
 References
 
A social class gradient in MSD existed in this cohort for both men and women at age 30, with MSD being twice as likely in blue-collar workers as compared with white-collar workers. No significant class differences were observed at ages 16 or 21. These findings support the view that class gradients in health are less apparent during adolescence or early youth.26,27 In Sweden, school is compulsory up to age 16 and at age 21 a number of participants were still studying (total 106). It has been suggested that the influences of the school, the peer groups, and the youth culture reduce class differences in health independent of family background.26,27 In order to understand how such class inequities developed at the age 30, this paper focused upon the contribution of adolescence to early adulthood factors related to social environmental, health and behavioural aspects. Our study indicates that both early and recent life factors contribute to class gradients. Our results might be interpreted mainly in terms of a life course accumulation or a chain of risk model hypothesis.28 The dominant factor that contributed to class inequity was recent work conditions. Most of the contributing factors were similar for men and women. However, several factors from adolescence appeared to be more important for women while the recent factors were more important for men. The mechanism behind these gender-specific associations is not clearly understood, however. One possible explanation could be chance due to multiple testing.

The longitudinal data used in this study are quite unique for the extremely low non-response rate after a follow-up duration of 14 years (3.6%). Considering that the baseline questionnaire at age 16 was sent to all the pupils of a certain age group in a specified municipality, our cohort is likely to be representative of young people of middle-range industrial towns in Sweden during the 1980s and 1990s.

Comparing the prevalence rate in this study with other studies may be problematic because it applies to a specific age group. An epidemiological study29 in central Sweden reported, however, that 72.5% of respondents aged 35–45 had neck and back pain in the previous year as compared with 68.8% in our study among people aged 30.

The common analytical techniques used for testing life course models are multilevel models, latent models, and graphical chain models.30,31 We have used multivariate logistic regression analysis because our main study objective was to assess the relative importance of factors in explaining the class difference in MSD. We included factors in temporal sequence, including factors ascertained at age 16 first, followed by factors observed at age 21, including factors determined at age 30 last. This data analysis approach provides an indication of the relative and cumulative effects of particular explanations.24 The data analysis approach we have applied here has been used in previous studies.8,14

As shown by others, poor socio-economic conditions during early age (measured as parents' social class) contribute to class differences in adults' health.6,15 In this study, poor socio-economic conditions during adolescence appeared to affect the class differentials in women. For men, restricted financial resources was important. Adulthood risk factors for class differences in ill health are believed to have their origin in early life rather than appearing exclusively in the adult period.7 For instance, worse economic circumstances during adulthood were found among participants with lower class origins.7

A positive association between level of education and health has been well recognized.32 According to the 1958 British birth cohort study, the level of education (measured from school qualification) was also found to be associated with class inequities in health at age 33 for both men and women.8 Instead of educational level, school grade was used in this study and found to be equally important for men and women in contributing to class inequities during adulthood. School grade may be viewed as a marker of social class rather than a possible causal factor.

Health behaviour during adolescence for women and during adulthood for men was also an important contribution to class inequities. The effect of smoking for both men and women is consistent with the 1958 British birth cohort study, which showed that smoking at age 16 and more recently is an important factor for health inequities at age 33.8 For men, adult alcohol consumption was of additional importance. Alcohol consumption was also found to be a major contributing factor to class differentials in mortality among Swedish men.33 We suggest, however, that alcohol consumption may not be directly related to MSD in our study. Rather, alcohol consumption was associated with other health-damaging behaviour, such as a lack of physical activity, which in turn may result in an increased risk for MSD.

Being single at age 21 contributed to inequities in both men and women, with individuals living alone being less likely to experience MSD. Living alone may reflect a more career-oriented lifestyle and a higher education and professional training. This in turn, could result in a more secure professional situation later in life and less exposure to risk factors associated with class differences in working conditions and health behaviour. A strong positive influence of educational achievement and skills on both occupational position13 and better health behaviour32 is well documented.

Family formation, i.e. having children by the age of 30, appeared to be associated with social class gradient in men only. This contrasts with the findings of the British birth cohort study.15 In this study, having children by age 23 was found to contribute to class differences in psychological ill health in women only. In Sweden, having children might be irrelevant for social class gradients particularly in women because childcare benefits and child support are provided by the welfare state to all socio-economic groups. Since women still take the major responsibility for child care,34 they are more likely to receive these benefits.

Among working factors, physical working conditions and job control were the most important factors in reducing class differentials for both men and women. These findings are consistent with numerous earlier studies.8,9,35,36 However, it has been suggested that the associations between subjective job stress and subjective health outcome are a result of reporting bias.37,38 People reporting high levels of distress may be more likely to feel ill in the absence of any specific disorder than individuals reporting lower levels of distress. For instance, the Whitehall II study35 showed that self-reported job stress was associated only with self-reported symptoms (angina) but not with an objective measure of heart disease (ischaemia). The index of MSD used in our study is derived from items considered to be less influenced by mood or spirit.39 Effect-dependent misclassification was not found in a longitudinal study on musculoskeletal disorders.40 Therefore, we consider it unlikely that our results were strongly affected by reporting bias. The modest increase in class inequities associated with the adjustment for job demand could be explained by the fact that high job demand was more common in white-collar employees in our study.

In conclusion, the accumulation of adverse behavioural and social circumstances from adolescence to early adulthood might be an explanation for the class differentials in MSD for both men and women. Interventions aimed at reducing health inequities need to consider exploratory factors identified at early and later stages in life, also including structural determinants of health.


KEY MESSAGES

  • In this Swedish cohort, social class inequities in musculoskeletal disorders (MSD) were not evident at ages 16 and 21.
  • At age 30, blue-collar workers were twice as likely to have MSD as white-collar workers irrespective of gender.
  • Both early and recent life factors contributed to the class inequities.
  • Much of the observed class gradient in MSD seemed to be due to differences in physical and psychosocial work conditions and health behaviours.
  • Interventions aimed at reducing health inequities need to consider exploratory factors identified at early and later stages in life, also including structural determinants of health.

 


    Acknowledgments
 
This study was financed by the Swedish Council for Working Life and Social Research and the Swedish National Institute of Public Health. The authors wish to thank Leif Nilsson for statistical help during data analysis. The authors are indebted to Abbas Bhuiya for his contribution and support during the revision of this article.


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 Population and Methods
 Results
 Discussion
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