Disability pensions related to heavy physical workload: a cohort study of middle-aged and older workers in Sweden

The study is based on the recently established Swedish Work, Illness, and labor-market Participation (SWIP) cohort, which includes all individuals 16–64 years of age and registered as living in Sweden in 2005, i.e. 5.4 million. We used a sub-population consisting of the 946,523 women and 888,032 men born during the years 1942–1961 and with occupational code available for the baseline year of the study, 2005. This corresponds to 76.9% of the almost 2.4 million individuals in total within the selected age range, which includes non-workers such as those already on early retirement at baseline. Follow-up regarding date and diagnosis of disability pension was made prospectively for the years 2006–2016. At baseline, the individuals in the study were aged 44–63 years; younger individuals were excluded, because, among them, disability pensions are less likely to be caused by work-environment factors.

Data on occupations, classified on a four-digit level according to SSYK96 (Sweden’s classification of occupations based on ISCO-88), were obtained from Statistics Sweden’s ‘LISA’ register (‘Longitudinal integrated database for health insurance and labour market studies’) (Ludvigsson et al. 2019). The register starts in the year 1990 and contains information on every individual in Sweden aged 16–64, but occupational data are only available from 2005. The data on occupations were linked to the cohort and used for classification of work-environmental exposure using job-exposure matrices (see below for description of variables). Further data, used for multivariable analyses (age, gender, education, marital status, history of unemployment, and registered sickness absence), were obtained either from the LISA register, the Total Population Register at Statistics Sweden (Ludvigsson et al. 2016), or the Swedish Social Insurance Agency’s MIDAS register (‘Micro Data for Analysis of the Social Insurance System’, see forsakringskassan.se 2020a). An overview of the utilization of register data in the study is shown in Table S1.

Among the 1.834,555 individuals, a minor proportion lacked information on level of education (816) and history of unemployment (221). Individuals with a disability pension before the start of the follow-up were excluded from the analyses.

To characterize the women and men with different degrees of physical workload, we calculated the distribution of covariates across the quintiles of physical workload. We also estimated the crude associations between covariates and all-cause disability pension.

To answer the question of whether and how heavy physical work may increase the rate of disability pension, we used Cox proportional-hazards regression, which estimates hazard ratios (HR). Person-time was calculated from January 1, 2006 up to the date of emigration, early (non-disability) pension, death, disability pension or the end of follow-up on December 31, 2016. Associations with disability pension due to any diagnosis or a musculoskeletal, psychiatric, cardiovascular or respiratory diagnosis were analyzed in crude and covariate-adjusted regression models (i, ii), and separately for women and men; the covariates were (i) level of education, marital status, and history of unemployment and (ii) plus job control. To investigate potential over-adjustment, we also carried out analyses stratified by education level and job control, respectively (both dichotomised).

Granting of disability pension is in most cases preceded by a longer period of sickness absence, and due to this structural relation, sickness absence before baseline was not included as a confounding variable. However, as a sensitivity analysis, we reran our analyses on a population excluding individuals with sickness absence registered at the Swedish Social Insurance Agency between 2001 and 2005.

We also checked whether the above-mentioned reduction in granted disability pensions, which was fully implemented by the year 2010, may have impacted on the associations. In this sensitivity analysis, physical workload was related to disability pensions (any) occurring in 2006–2009 and 2010–2016, respectively.

For calculation of HR attenuations (%) in multivariable-adjusted models, the following formula was used: (crudeHR-adj.HR)/(crudeHR-1)*100. In a previous study (Kjellberg et al. 2016), we found large changes in estimates after adjusting for confounding variables.

The association between heavy physical workload and disability pension with a musculoskeletal diagnosis has also been demonstrated in previous studies. A few of these studies had the opportunity to compare several different work-environmental factors and found that the risk estimates for physical workload appeared to be particularly high (Karkkainen et al. 2013; Lahelma et al. 2012). Most of the previous studies have been conducted in Finland and all have used a cohort design with several years or sometimes decades of follow-up data on disability pension. Study populations have varied: middle-aged men in Kuopio (Karpansalo et al. 2002), women and men in Helsinki (Ervasti et al. 2019; Lahelma et al. 2012), and Finish twins born in the first half of the twentieth century (Karkkainen et al. 2013). The sets of variables used for confounding control have also differed.

The largest gender-specific study on disability pension with musculoskeletal diagnoses before the present one is Swedish and was done on cohort data from just under 12,000 workers who were followed over a number of years in middle age with regard to disability pension and diagnosis (Kjellberg et al. 2016). For both women and men, the analyses showed a gradual increase in the risk of disability pension with a musculoskeletal diagnosis in relation to higher long-term physical workload. In multivariable models of the associations, childhood socio-economic position, late childhood cognitive ability, level of education, and degree of control at work were used for confounding control.

Associations between heavy physical workload and disability pension with diagnoses other than musculoskeletal have only been investigated in a small number of studies. One of these studies showed an association between heavy physical workload and increased risk of disability pension with a cardiovascular diagnosis (Karpansalo et al. 2002), although with a statistically uncertain risk estimate after multivariable adjustment. Thus, the present study goes further than previous studies by showing that disability pension with a cardiovascular or a respiratory diagnosis increases with higher degrees of physical workload in multivariable-adjusted models, among both women and men. We have not found any other studies that examined physical workload in relation to disability pension with a cardiovascular diagnosis. However, some studies have investigated disability pension with such a diagnosis in relation to psychosocial work-environmental factors, job strain (Mantyniemi et al. 2012) and effort–reward imbalance (Juvani et al. 2014) but did not show any robust associations.

In several of the studies referred to above, both crude and multivariable-adjusted estimates were reported, and we note that attenuation after adjustments was often large. In Karpansalo et al. (2002), the association between heavy physical workload and disability pension with a musculoskeletal diagnosis was greatly attenuated after adjustments for education level, BMI, alcohol consumption, smoking, maximum oxygen uptake and disease diagnoses at baseline and, in Lahelma et al. (2012), adjustment for occupational social class also had a considerable attenuating effect. In Kjellberg et al. (2016), the estimated hazard ratios were significantly smaller in models that adjusted for differences in late childhood cognitive ability, subsequent level of education, and job control, including disability pension with a musculoskeletal diagnosis. Similar effects were shown in the respective studies by Kärkkäinen et al. (2013) and Ropponen et al. (2014) of older twins, which could also adjust for differences in BMI, education, socio-economic position, marital status, and shift work. The present study adds to this by showing that the statistical association between higher physical workload and disability pension is further attenuated or even eliminated (for disability pension with a psychiatric diagnosis) when low job control is added to other covariates.

A decision at the Swedish Social Insurance Agency to grant disability pension is based on an assessment of full or partial long-term reduction of the individual's work ability (forsakringskassan.se 2020b). In turn, work ability is a function of both health and capacity of the individual and of the requirements and conditions in the individual's work, for example the degree of physical workload.

One likely explanation for the association between heavy physical workload and disability pension with a musculoskeletal diagnosis is that high biomechanical loading affects the musculoskeletal system negatively and decreases physical capacity. There is extensive evidence to support this explanation (da Costa and Vieira 2010; Punnett and Wegman 2004). Furthermore, the association with disability pension with a cardiovascular diagnosis may possibly be explained by overload and deleterious effects of prolonged and intense heavy physical work on the cardiovascular system, possibly involving exertion-related elevation of heart rate and blood pressure, and inflammatory processes manifested in arteriosclerosis (Petersen et al. 2012). However, there is a limited number of studies investigating the associations between heavy physical workload and cardiovascular diseases, and the results are inconsistent (Li et al. 2013; Petersen et al. 2012).

Another explanation may be that heavy manual occupations provide less opportunity for adaptations for older workers with physical illness and reduced physical capacity, which should have a bearing on disability pensions with both musculoskeletal, cardiovascular, and respiratory diagnoses. Furthermore, low decision latitude is a risk factor for disability pension even with musculoskeletal diagnoses (Lahelma et al. 2012). Conversely, occupations without physical strain are often non-manual occupations which allow more flexibility for the workers in terms of how and when tasks are to be performed (Johansson and Lundberg 2009). A hindering effect in physically strenuous jobs could give rise to the more general association with poor self-rated health (Burr et al. 2017), long-term sick leave (Andersen et al. 2016; Sommer et al. 2016; Sundstrup et al. 2018), all-cause disability pension (Emberland et al. 2017; Friis et al. 2008; Halonen et al. 2019; Jarvholm et al. 2014; Karpansalo et al. 2002; Labriola et al. 2009; Lahelma et al. 2012; Sommer et al. 2016) and, consequently, loss of work–life expectancy (Schram et al. 2020) demonstrated in previous studies. However, low education and a history of unemployment also seem to contribute to the explanation. We (Falkstedt et al. 2014; Kjellberg et al. 2016) and others (Schram et al. 2019) have shown in previous studies that the large amount of disability pension cases and loss of work–life expectancy among workers with physically heavy occupations may partly be explained by circumstances that relate to this group's weaker position in the labor market. It has also recently been shown on data from 26 European countries that the gap in work participation between workers with and without chronic diseases is substantially larger and increasing over time for workers with short education as compare to higher educational levels (Schram et al. 2019). To some extent, this could be related to limited opportunities for skills development and broadening of competence within manual occupations. Many years in a job that primarily involves routine task execution could lead to employment problems when health or physical capacity decline.

The lack of association with disability pension with a psychiatric diagnosis, after multivariable adjustment, indicates that physical workload does not create significant obstacles to work in the event of mental illness. The slightly higher prevalence of psychiatric disability pensions among women and men with physically heavy occupations seems instead to be explained by the co-occurrence of low job control in these occupations. There are some previous studies (see Knardahl et al. 2017) indicating that the probability of disability pension with a psychiatric diagnosis is increased at lower degrees of control at work.

One of the main strengths of the present study is the much larger size of the study population compared to all other previous studies focusing on a similar research question. This size allowed uniquely clear analyses of associations for disability pension with a cardiovascular or respiratory diagnosis, in women and men separately. The size and comprehensiveness also enabled analyses of the full range of exposure to physical workload in the labor market and, thus, hypothetical dose–response relations. Another strength is the use of JEMs for ranking of individuals within the study population in terms of physical workload and job control. The use of JEMs reduces differential misclassification and spurious risk estimates, which may arise from the use of self-reported data on physical workload and psychosocial factors at work (Fredlund et al. 2000). Initial differences in capacity and health do not affect the differences in physical and psychosocial workloads attributed by JEMs, as can be the case in self-reported data. An additional strength is the ability to account for the co-occurrence of physical workload and low job control.

Of course, using JEMs also has a downside. Through the construction of JEMs, differences in exposure between individuals within different occupations are eliminated, which can result in non-differential misclassification and downward bias of risk estimates. In addition, we know from previous research that questionnaire data on ergonomic factors, on which the JEMs are based, do not have the same precision as measurement and observation data (Teschke et al. 2009), but are the reasonable alternative for larger study populations. However, both recent (Madsen et al. 2018) and older studies (Bosma et al. 1997; Theorell et al. 1998) on physical workload or job-strain factors have found comparable results when using job-exposure matrices and self-reported exposures side by side. Furthermore, the JEMs in our study are indices that consist of mean value calculations based on multiple questionnaire items, which is a commonly used method in the field. An alternative is statistical modeling for the construction of JEMs, which Madsen et al. used recently and that possibly reduces misclassification to some extent. Finally, like these authors, we emphasize that analytical results based on JEM data refer to the occupational level and thus cannot be directly extrapolated to individuals.

We did not have access to data that could measure differences in lifestyle-related risk factors, such as tobacco smoking, physical inactivity, and obesity, and we cannot rule out the possibility of residual confounding. However, the studies that have had access to such information about the study participants have obtained approximately the same results as we did in this study, that is, remaining although attenuated associations. Furthermore, the adjustment for differences in age, education level, marital status, and history of unemployment before baseline should reduce potential confounding from lifestyle-related risk factors in the present study. As in other countries, low levels of education are known to be associated with several such factors (Falkstedt et al. 2016).

The multivariable models also did not include health status at baseline as a covariate. We chose not to adjust for registered sickness absence at baseline, as disability pension among workers is almost always preceded by long-term sickness absence, but the sensitivity analysis excluding individuals with sickness-absence periods in the years up to the baseline of the study gave results similar to those reported in the tables.

In conclusion, this study of the entire Swedish working population aged 44–63 years at baseline and followed for up to 11 years (censored at age 65) showed that, among both women and men, a higher degree of occupational physical workload is associated with a higher rate of disability pension with a musculoskeletal, cardiovascular or respiratory diagnosis. These associations, shown in multivariable-adjusted models, may reflect mechanisms reducing the chances of continued labor-force participation among older workers. An increased rate of disability pension with a psychiatric diagnosis was explained by confounding variables, including low job control.