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Women, work and musculoskeletal health
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Social Science & Medicine 58 (2004) 997–1005
Women, work and musculoskeletal health Lyndall Strazdins*, Gabriele Bammer National Centre for Epidemiology and Population Health, The Australian National University, Canberra ACT 0200, Australia
Abstract Why are employed women at increased risk for upper limb musculoskeletal disorders and what can this tell us about the way work and family life shape health? Despite increases in women’s labour force participation, gender differences in work-related health conditions have received little research attention. This appears be the first study to examine why employed women are much more likely than men to experience upper body musculoskeletal disorders. A mailed self-report survey gathered data from 737 Australian Public Service employees (73% women). The majority of respondents were clerical workers (73%). Eighty one per cent reported some upper body symptoms; of these, 20% reported severe and continuous upper body pain. Upper body musculoskeletal symptoms were more prevalent and more severe among women. The gender difference in symptom severity was explained by risk factors at work (repetitive work, poor ergonomic equipment), and at home (having less opportunity to relax and exercise outside of work). Parenthood exacerbated this gender difference, with mothers reporting the least time to relax or exercise. There was no suggestion that women were more vulnerable than men to pain, nor was there evidence of systematic confounding between perceptions of work conditions and reported health status. Changes in the nature of work mean that more and more employees, especially women, use computers for significant parts of their workday. The sex-segregation of women into sedentary, repetitive and routine work, and the persisting gender imbalance in domestic work are interlinking factors that explain gender differences in musculoskeletal disorders. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Gender differences in health; Upper body musculoskeletal disorders; Repetitive strain injuries; Women’s health; Australia
Introduction Musculoskeletal disorders, particularly those of the upper body, are increasingly prevalent in Western societies (Muggleton, Allen, & Chappell, 1999; Yassi, 1997). Although some studies have found levels as low as 10%, in the working population levels of around 50% and as high as 80% are more common (Ariens, Borghouts, & Koes, 1999; Bammer & Blignault, 1988; Blyth et al., 2001; LeResche, 1999; Putz-Anderson, 1988; Unruh, 1996; van der Windt & Croft, 1999). Upper body disorders cover a wide range of clinical symptoms and conditions, grouped under general terms such as repetitive strain injuries, cumulative trauma disorders *Corresponding author. Tel.: +61-2-6125-2837; fax: +61-2-6125-0740. E-mail addresses: [email protected] (L. Strazdins), [email protected] (G. Bammer).
and occupational overuse syndrome. These disorders occur gradually, have a chronic course, and often go untreated (Polanyi et al., 1997; Yassi, 1997). Typically, pain becomes progressively severe and loss of function occurs. The pain and disability may persist for many years, and in some cases become intractable (Bammer & Blignault, 1988; Keogh, Nuwayhid, Gordon, & Gucer, 2000; Putz-Anderson, 1988). Employed women are two to five times more likely than men to report these sorts of problems (Ariens et al., 1999; LeResche, 1999; Unruh, 1996). Why are employed women at increased risk for upper limb musculoskeletal disorders, and what can this tell us about the way health differences are shaped between men and women? In developed economies, the relationship between work and health is changing for both men and women. Now, most women are in paid employment (Hatch & Moline, 1997), and the nature of work itself is changing. These two social changes are likely to affect
0277-9536/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0277-9536(03)00260-0
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both men’s and women’s lives, but in differing ways. Like other western nations, the Australian labour market is characterised by an increase in part-time jobs, reduced job security, contingent and uneven workforce attachment, increased pace and workload, and, an increased reliance on computer technology (ACIRRT, 1999). These changes have brought with them an array of potentially new factors that could affect women’s health. On the one hand, gender gaps in some health outcomes might narrow as women’s participation in the labour force comes nearer to men’s. On the other, women and men may have vastly different experiences of paid employment and its effects on family life, especially once they become parents (Blane, Berney, & Montgomery, 2001). The sex-segregation of the labour force and the persistent gender imbalance in domestic work means that employed women’s exposure to risk factors from both work and family could differ widely from employed men’s (Hunt & Annandale, 1999). This exposure is likely to be daily and chronic. Musculoskeletal disorders may thus reflect the accumulation of difference in exposures at work and at home, providing an opportunity to tease out the relationships between work-related factors, domestic load and underlying biological differences. Prevailing explanations of women’s excess health risk revolve around two basic propositions. Greater prevalence or severity of symptoms may be due to the higher demands and constraints that women face, or because women are more affected by, or vulnerable to, the health impact of particular demands and constraints (McDonough & Walters, 2001). Thus the difference between women and men in exposure, at work and at home, to risk factors for musculoskeletal disorders is one model that may explain the markedly higher prevalence of these disorders in women compared to men. We call this the work and family demand model. An alternative explanation is that women may be more prone to developing musculoskeletal disorders due to sex-linked biological factors such as hormones or physiology, because the meaning of work and family demands are different, or because women have less resources to cope with these demands. We refer to this as the vulnerability model. And, of course, both models may be involved. Gender differences in work and family demands As we have outlined above, changes in technology, and the emergence of the information and service economy have altered the sorts of work women typically do (Webster, 1996). Women tend to be clustered into lower status jobs, often sedentary and involving repetitive work and static load (holding the body in one position for extended periods), with less job control and less substantive complexity. These comprise the key
biomechanical and psychosocial risk factors for upper body musculoskeletal disorders (Ariens et al., 1999; Bernard, 1997; Carayon, Smith, & Haims, 1999; Latko et al., 1999; Putz-Anderson, 1988). Many women work with computers where these risk factors can be exacerbated (Torgen & Kilbom, 2000; Webster, 1996). Interlinked with the sex-segregation of the labour force is the gender division of domestic work (Baxter, Gibson, & Lynch-Blosse, 1990). Women’s domestic work, especially once they become mothers, affects their ability to participate in the labour market by imposing time constraints that men generally do not share. This in turn channels women into increasingly casualised, lowskilled jobs (Webster, 1996), the sorts of jobs where routine and repetitive work predominate. Furthermore, the gender division of domestic work may mean that employed women have less time outside of work for activities that might ameliorate the health costs of their jobs. Compared to fathers, mothers invest more time and energy caring for children, helping others, and doing housework (Bird, 1999). The resultant ‘time poverty’ means that employed mothers have less time for leisure, relaxation and exercise (Firestone & Shelton, 1988; Hildebrandt, Bongers, Dul, van Dijk, & Kemper, 2000; Lundberg, 1999; Ross & Bird, 1994). Thus domestic work, per se, may be a risk factor for musculoskeletal problems, and, more importantly, it may constrain women’s ability to protect themselves from the effects of their paid work. For example, Lundberg (1999) argues that lack of relaxation impairs recovery of the musculoskeletal system from repetitive work demands. He proposes that domestic work and stress from overload further exacerbate musculoskeletal disorders because they interfere with the recovery process. The health hazard of domestic work, in this context, is time constraint, and this combines with the hazard of repetitive and sedentary duties that many women encounter in the workplace. Gender differences in vulnerability Pain severity and chronic duration of pain are central symptoms of musculoskeletal disorders. Women appear to be more sensitive to pain and consistently report greater pain when it is experimentally induced (Fillingham, Edwards, & Powell, 1999). Furthermore, pain sensitivity varies across stages in the menstrual cycle suggesting that sex-linked hormones are involved in pain perception (Fillingham et al., 1999; Fillingham & Ness, 2000; LeResche, 1999). Women also tend to see their pain problems as more threatening, and to cope differently compared to men, suggesting that there may also be some gender differences in the meaning of pain and in the resources that men and women bring to bear in coping with pain (Jensen, Nygren, Gamberale, Goldie, & Westerholm, 1994; Keefe et al., 2000). Thus
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the gender gap in musculoskeletal disorders is possibly due to women’s greater pain sensitivity, or women’s tendency to perceive and cope with pain differently. This study provided an opportunity to test both the work and family demands and vulnerability models for explaining the gender difference in musculoskeletal disorders. If women are at greater risk because of the combination of their work and family demands, then the association between gender and symptom severity should disappear once these demands are included in the analysis. If a gender excess persists, or if work and family risk factors affect women more that they affect men, this suggests that sex-linked vulnerabilities are exerting an influence. Methodological problems studying work conditions and health Studying work conditions and health poses particular methodological problems. Associations found between work and health from cross-sectional studies have been challenged because having a health condition could predispose workers to perceive or report on their work conditions negatively (Emslie et al., 1999). The present study is also unique in addressing this limitation in cross-sectional research, which was possible because workers were organised into teams. We compared individuals with their team’s aggregated ratings of the shared workplace environment to see the extent of consensus within teams, and whether individuals with poorer health perceived the workplace differently. General features of the work environment that all team members shared were rated, such as overall building and equipment design. Then comparisons were made within work teams of team members with high or low musculoskeletal conditions. If workers with and without a musculoskeletal condition rate general characteristics of the workplace differently, it would suggest that their health status is influencing their perceptions of work. On the other hand, if there are no differences in these general workplace ratings, we can be more confident that associations found between individual’s specific work conditions (e.g., degree of control, repetitive workload) and their symptom severity reflects the influence of work conditions on health, and not the reverse.
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conditions and to sample enough men and women to make meaningful comparisons. Focus groups were held initially to refine the questionnaire. Then, surveys were distributed to randomly selected branch offices. Participation was voluntary and responses were anonymous. The response rate was 50%, resulting in 737 questionnaires returned. Occupational status was coded using the Australian Standard Classification of Occupations (ASCO 1st Edition, ABS, 1986). As expected from the nature of the organisations, which primarily administered and delivered government benefits to the public, clerical and service jobs predominated; 77% worked in clerical and customer service jobs, 16% in managerial jobs, 7% in professional jobs, and 1% in unskilled jobs. The mean age was 36 years (SD=9.55). Most employees were Australian born (73%), 12% were of European origin, and 9% were of Asian origin. Just under a third of the sample (32%) had completed 10 years or less education, another 32% had 12 years of completed education, 17% had obtained trade or technical qualifications or an undergraduate diploma, 13% a Bachelor degree and 5% a post graduate diploma or higher degree. Nearly 60% of the sample were either married or living with a partner. Forty three per cent of employees were parents with at least one dependent child living at home. Women and men differed on several demographic variables. Men were more likely to be older (the average age of men was 37710 years, while the average age of women was 3579 years, (t (642)=2.65, po0:01). Men were also likely to be better educated (w2=38.89, po0:01). Only 16% of men had attained 10 years or less of education, compared to 38% of women. In terms of tertiary qualifications, 25% of men compared to 15% of women had attained a Bachelors Degree or higher. Men also worked in higher status occupations (30% worked in professional or managerial positions compared to 17% of women, w2=21.57, po0:01). Women were more likely to be parents of dependent children (47% women, 34% men, w2=10.65, po0:01) and to be employed part-time rather than full-time (20% of women were employed part-time compared with 5% of men, w2=30.05, po0:01). The differences in occupational status and part-time employment reflect the sexsegregated nature of work (Labour Force Statistics, ABS, 1997).
Method Measures Sample Upper body musculoskeletal symptoms Respondents were sampled from two government organisations spread across three eastern states of Australia. We sampled across the organisational hierarchy to include a range of occupational levels and work
Following Browne, Nolan, and Faithfull’s (1984) description of upper body repetitive strain injury, the 17 item measure assessed a variety of symptoms, their
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location and degree of pain persistence. Symptoms included pain, aching, stiffness, cramp, swelling, soreness, weakness, tingling and numbness, and respondents rated the extent symptoms were present while they worked (response categories ranged from never to continuously). Respondents were then asked to rate the extent of pain and pain persistence in their upper back, neck, shoulders, arms, elbows, forearms, wrists, and fingers. The response categories were no pain (scored 0), pain present but gone by the next morning (scored 1), pain present at work, persists to the following morning, but is gone after the weekend (scored 2), and pain is present and continuous all the time which is the most severe category (scored 3). Scores were summed to form the scale (a ¼ 0:92). A zero score indicated no symptoms in the upper body, whereas high scores indicated more severe symptoms. For example, neck pain that persisted after work but was gone by the next morning would be given a score of 2. If respondents described their neck pain as continuos the score would be 4. Scores would be higher if multiple upper body areas were involved. While the measure uses criteria developed for treatment and rehabilitation of upper body musculoskeletal disorders, high scores are not diagnostic of specific clinical conditions.
Poor ergonomic equipment: A single item assessed comfort and ergonomic design of work equipment and work environment. Response categories ranged from 1=very comfortable and well designed to 5=very uncomfortable and poorly designed. Job control: A four item scale measured control over work load, pace, duties and decisions (e.g., ‘how often does your workplace give you control over the pace of your work?’). Response categories ranged from 1=very seldom to 4=almost always. Items were summed and averaged to form the scale (a ¼ 0:82). Relaxation and exercise: Two items assessed time spent on exercise, leisure and relaxation over the last six months (‘yhow often have you set aside time just for physical activities (leisure or exercise)’ and ‘yhow often have you set aside time just to relax?’). Response categories were 1=not at all, 2=some or a little of the time (about once a month or more), 3=occasionally or a moderate amount of time (about once a week or more), 4=often or a lot of the time (about once a day) and 5=frequently (more than once a day). These items were highly intercorrelated (r ¼ 0:66) and were summed and averaged to form the measure.
Results General workplace rating Gender differences in musculoskeletal health This was assessed using two items; ‘Compared to other workplaces, do you think your workplace provides employees with comfortable and well-designed equipment, where the work is not too repetitive, not too stressful, not too boring, and the amount and pace of work is not too much?’ and; ‘In general, how physically stressful is your workplace? Is it likely that your workplace is the sort of place where people could develop muscle tensions, strains and overuse problems?’ Response categories, respectively, ranged from very comfortable (scored 1) through intolerably uncomfortable (scored 6); and not at all (scored 1) through extremely physically stressful (scored 6). The two items were summed and averaged with low scores representing positive evaluations of the workplace and high scores negative evaluations (item r ¼ 0:65). Risk factors Repetitive work demands: Seven items assessed the extent work duties involved repetitive movements, keyboarding, computer use, and holding an uncomfortable posture or body position (e.g., on a routine day how often do you have to make repetitive movements with your head, neck, arms, hands or fingers). Response categories ranged from 1=never to 5=all the time. Items were summed and averaged to form the scale (a ¼ 0:80).
Age adjusted means for musculoskeletal health for men and women are presented in Table 1. Proportionally more women reported at least one symptom (83% of women compared to 77% of men, w2=13.38, po0:01). Women also reported more severe symptoms (23% of women reported continuous pain in their upper body compared to 12% of men). Gender differences in work and family demands According to the demands hypothesis, the gender gap in musculoskeletal health occurs because women encounter more risk factors. To test for this, age adjusted mean differences in risk factors for men and women were compared (see Table 1) and statistically significant differences were found for all but one risk factor. Women’s work conditions were more likely to involve physically repetitive work demands. For example 34% of women compared to 21% of men sat in the same position for long periods of time, 81% of women compared to 73% of men worked longer than 5 hours per day on a computer, and 30% of women compared to 16% of men reported that their job involved making repetitive movements all of the time. Women were also more likely to work in poorly designed and uncomfortable environments. Fifteen per cent of women, compared to 10% of men described their work environments
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Table 1 Age-adjusted gender differences in mean scores for upper body musculoskeletal symptoms and risk factors Variable
Gender
Adj. mean
95% confidence interval
Difference
p
Upper body symptoms
Men Women Men Women Men Women Men Women Men Women
7.14 9.88 3.47 3.77 2.36 2.55 2.20 2.131 3.01 2.68
(5.97, (9.16, (3.37, (3.71, (2.23, (2.47, (2.08, (2.06, (2.88, (2.61,
2.74
0.001
0.30
0.001
0.18
0.022
0.06
0.326
0.32
0.001
Repetitive work Poor ergonomic equipment Job control Relaxation and exercise time
as either uncomfortable or very uncomfortable. In addition, women spent considerably less time than men exercising or relaxing. Twenty per cent of women did no exercise at all and 14% did not spend time relaxing, over the previous month. This compares to 12% and 10%, respectively, for men. There was, however, no evidence that women experienced less job control than men. For example, 51% of men compared to 60% of women reported that they often or almost always made decisions about their work, 65% of men and 62% women reported that they often or almost always had control over the pace that they worked, and 67% of men compared to 64% of women reported they often or almost always had control over the amount of work that they did. Multiple regression analyses testing the vulnerability and demand models A hierarchical regression equation (OLS) tested the model that gender differences in upper body musculoskeletal symptoms were due to women’s poorer work conditions (repetitive work demands and poor ergonomic equipment) and greater constraints on time to relax or exercise. A reduction in the size of the standardised b coefficient or beta weight for gender after entry of the risk factors would provide support for this model (Baron & Kenny, 1986). Pearson product– moment correlations among predictor variables are appended and show that they are not highly intercorrelated. Table 2 summarises the change in association between gender and upper body musculoskeletal problems after the exposure variables were included in the equation. The beta weight for gender dropped to become nonsignificant, indicating that the gender gap is due to women encountering more risk factors for musculoskeletal problems at work and at home. Following Karasek’s model of demand and control (Theorell & Karasek, 1996) interactive effects of repetitive work
8.31) 10.61) 3.60) 3.84) 2.50) 2.63) 2.31) 2.20) 3.13) 2.76)
demands and job control were also tested, and were nonsignificant. However, vulnerability factors may also be important and may modify the effects of work conditions and time to relax and exercise for men and women. After centering all predictor variables to reduce multi-collinearity (Aiken & West, 1991), cross-product terms for gender with each of the predictor variables were entered into the equation. Only one significant interactive effect for the predictor variables was found, namely time to relax and exercise, which was more strongly associated with women’s upper body musculoskeletal symptoms than men’s. Beta weights in separate sex regressions were b 0.18, po0:01; for women and b 0.00, ns, for men.1 In summary, we found no evidence that women are more affected than men by repetitive work or poor work conditions, nor was there evidence of a persisting gender difference due to unmeasured sex-linked factors such as pain sensitivity or pain perception. Instead, the gender gap appears to be explained by employed women’s greater exposure to risk factors at work and the difficulty these women face finding time to relax or exercise outside of work. Parenthood and time The gender difference in time to exercise or relax may be due to the gender division of domestic work, which becomes most marked for mothers. If this is the case, then being a parent should have different effects on men’s and women’s time. Simultaneous regression analyses (OLS) with centred variables provided support 1
The sample was also stratified (upper quartile compared to lower three quartiles) in terms of symptom severity. Both groups showed similar patterns of associations between risk factors and symptoms, with one exception. Time to relax or exercise showed a stronger association in the more symptomatic group (beta coefficient 0.25, po0:001) compared to the lower symptom group (beta coefficient 0.03, ns).
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Table 2 Summary of hierarchical regression analyses testing demand and vulnerability models of gender differences in upper body musculoskeletal symptoms (n ¼ 665) Predictor Step 1 Gender Step 2 Gender Job control Repetitive work Poor ergonomics Relaxation and exercise time
B
SE B
ba
0.17**
0.04
0.16**
0.07 0.04 0.27** 0.09** 0.07**
0.04 0.11 0.08 0.02 0.02
0.07 0.08 0.43** 0.19** 0.13**
Adjusted R2 ¼ 0:20; po0:01: R2 change 0.03, po0:01 for set one (gender only), R2 change 0.29, po0:01; for set 2 (all predictors). Gender scored 0=male, 1=female. Age, ethnicity, education, occupational status, workhours and parent status were entered in a second set after gender to control for possible confounding (coefficients not shown). This set of variables did not contribute significantly to the prediction of upper body musculoskeletal pain (R2 change 0.02, ns) or affect the beta coefficient for gender. Missing data deleted listwise. **po0:01: a b standardized beta coefficients.
for this hypothesis. Gender and parent status interacted in their effect on time relaxing and exercising (see Table 3). On average, women were less likely than men to exercise or relax regularly. However, this gender difference varied between parents and non-parents. The gender difference was relatively narrow among non-parents and widened among parents. Mothers were the least likely to take time to relax or exercise, whereas fatherhood made little difference to men’s time.
Comparing ratings of the workplace by respondents with and without musculoskeletal disorders A possible explanation for the associations we have found is that severe musculoskeletal symptoms affected the way respondents viewed their workplaces. To see if such a perceptual bias was present, we dichotomised respondents on the basis of their symptom levels; those who had no or mild musculoskeletal symptoms, and those with severe or continuous pain. We compared the ratings of respondents within work teams because they were likely to share the same workplace environment. Twenty-five teams, ranging in size from 7 to 31 members were identified. Workplace ratings of team members with and without severe symptoms were compared using t-tests (because of the number of comparisons, the significance level was set to 0.01). Of the 25 comparisons, only one significant difference between ratings was observed (M low symptoms=2.80, SD=0.27, M high
Table 3 Summary of simultaneous regression analyses testing for gender differences in the effects of parent status on time to relax and exercise (n ¼ 665) Predictor
B
SE B
ba
Gender Age NESB Education Occupation Workhours Parent status Gender parent status
0.23** 0.00 0.32** 0.02 0.03 0.00 0.25** 0.32*
0.08 0.00 0.08 0.02 0.02 0.01 0.07 0.16
0.11** 0.05 0.15** 0.05 0.05 0.06 0.14** 0.08*
Adjusted R2 ¼ 0:09; po0:01: Gender scored 0=male, 1= female, NESB scored English speaking background=0, non-English speaking background=1, Parent scored nonparent=0, parent=1. Missing data deleted listwise. po0:05; po0:01: a b standardized beta coefficients.
symptoms=4.50, SD=0.87, tð7Þ ¼ 4:25; po0:01). Even though the small size of some teams would have reduced power to detect significant differences, it is noteworthy that no differences in how the workplace environment was viewed were found in the larger teams. These analyses suggest that the effects of workplace factors on health are unlikely to be greatly confounded by the effects of health status on the reporting of workplace conditions.
Discussion This is one of the first studies to explain why women have an excess risk for upper body musculoskeletal disorders. We compared men and women employed in white collar clerical and service occupations—the occupations where women tend to predominate, and where upper body musculoskeletal problems are prevalent. Women in our sample were more likely to report symptoms in their upper body, with nearly a quarter reporting continuous pain suggestive of advanced disorder. We tested the possibility that women were prone to musculoskeletal problems, either because of a propensity to report more pain, or because risk factors affected them more than men. We found that women’s excess risk was due to the different demands that they faced at work and at home. At work, women spent more time using computers, did more repetitive movements, and reported poorer and less comfortable equipment. Outside of work, women bore the brunt of the unpaid work involved in parenting, and to accommodate the time squeeze, cut back on their exercise and relaxation. In this way, they lost access to activities that protect
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against, or at least may help ameliorate, upper body musculoskeletal disorders. This pressure on women’s time is likely to continue as western economies respond to globalised labour markets and the pace of work intensifies. This study is also one of the first to address a common concern with cross-sectional research on work and health. If the presence of musculoskeletal symptoms systematically alters perceptions of work conditions, it might suggest that the associations we found between work conditions and symptoms reflect a perceptual bias rather than a causal relationship. Thus we compared, within work teams, the way employees with high and low symptoms rated the teams’ shared workplace environment. We found no evidence that having a musculoskeletal disorder systematically alters the way workplaces are perceived, lending more confidence to our interpretation that work conditions act as risk factors. However, longitudinal studies are needed, especially because upper body musculoskeletal disorders are cumulative, likely to evolve over time and intensify during particular life course stages. Study limitations More studies are needed to test the model in different countries, sectors and organisations. The sample was white-collar government employees, limiting generalisability to private companies and organisations, or to other industries and occupations with different types of repetitive work demands, such as manufacturing, packing, etc. In addition, the relatively small sample size may have affected our power to detect differences, especially for the men in the sample. The response rate may also mean that some self-selection bias occurred. Perhaps employees who volunteered were more likely to have a
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concern with their health or dissatisfaction with their workplace and possibly some gender-based bias in responding occurred. We do not have information on non-respondents and cannot assess the extent to which self-selection may have biased our sample. These limitations may have inflated the prevalence of upper body problems, or meant that the workplace was rated more negatively. We checked for the possibility of an effect on the nature of the association between symptoms and risk factors by stratifying the sample by gender and symptom severity, and found little difference in effects, with one exception. Constraints on time to relax or exercise was more important for those respondents reporting relatively more severe symptoms, thus our results may be most applicable to high symptom groups. Finally, the study was cross sectional in design, therefore the associations that we report cannot be considered causal. Changes in the nature of work, the sex-segregation of women into sedentary, repetitive and routine work, and the persistent gender imbalance in domestic work, are interlinking factors affecting the health of women in contemporary Western economies. This study illuminates one way that these factors combine to explain gender differences in musculoskeletal health.
Acknowledgements We would like to thank the men and women who participated in this study, as well as Monika Reinhart and Brian Wilson for their help in conducting the study, Comcare Australia for access to the data, Dr. Dorothy Broom, Dr. Rennie D’Souza, Dr. Mark Clements, Karen Lees and Marluce Silva for their helpful comments.
Table 4 Intercorrelations among independent variables (n ¼ 665) Predictors
1
2
3
4
5
6
7
8
9
10
11
1. Gender 2. Age 3. NESB 4. Education 5. Occupation 6. Workhours 7. Parent 8. Repetitive work 9. Equipment 10. Job control 11. Time
—
0.09 —
0.05 0.10 —
0.17 0.02 0.12 —
0.16 0.14 0.04 0.15 —
0.21 0.04 0.04 0.13 0.14 —
0.12 0.22 0.05 0.13 0.02 0.29 —
0.19 0.08 0.03 0.10 0.23 0.10 0.05 —
0.10 0.02 0.04 0.01 0.05 0.01 0.02 0.31 —
0.03 0.05 0.01 0.08 0.27 0.15 0.07 0.27 0.15 —
0.17 0.05 0.17 0.08 0.11 0.14 0.17 0.06 0.12 0.08 —
Note: Gender scored 0=male, 1=female, NESB scored English speaking background = 0, non-English speaking background=1, Parent scored non-parent=0, parent=1. ‘Time’ denotes relaxation and exercise time. Bolded correlation coefficients are significant at the po0:05 level (two tailed). Missing data deleted listwise.
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Appendix A Bivariate intercorrelation matrix of independent variables predicting upper body musculoskeletal pain is given in Table 4.
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Torgen, M., & Kilbom, A. (2000). Physical load between 1970 and 1993—did it change? Scandinavian Journal of Work and Environmental Health, 26(2), 161–168. Unruh, A. M. (1996). Gender variations in clinical pain experience. Pain, 65, 123–167. van der Windt, D. A. W. M., & Croft, P. R. (1999). Shoulder pain. In I. K. Crombie, P. R. Croft, S. J. Linton, L. LeResche, & M. Von Korff (Eds.), Epidemiology of pain (pp. 257–282). Seattle, USA: International Association for the Study of Pain. Webster, J. (1996). Shaping women’s work: Gender, employment and information technology. London: Longman. Yassi, A. (1997). Repetitive strain injuries. Lancet, 349, 943–947.
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Women, work and musculoskeletal health Lyndall Strazdins*, Gabriele Bammer National Centre for Epidemiology and Population Health, The Australian National University, Canberra ACT 0200, Australia
Abstract Why are employed women at increased risk for upper limb musculoskeletal disorders and what can this tell us about the way work and family life shape health? Despite increases in women’s labour force participation, gender differences in work-related health conditions have received little research attention. This appears be the first study to examine why employed women are much more likely than men to experience upper body musculoskeletal disorders. A mailed self-report survey gathered data from 737 Australian Public Service employees (73% women). The majority of respondents were clerical workers (73%). Eighty one per cent reported some upper body symptoms; of these, 20% reported severe and continuous upper body pain. Upper body musculoskeletal symptoms were more prevalent and more severe among women. The gender difference in symptom severity was explained by risk factors at work (repetitive work, poor ergonomic equipment), and at home (having less opportunity to relax and exercise outside of work). Parenthood exacerbated this gender difference, with mothers reporting the least time to relax or exercise. There was no suggestion that women were more vulnerable than men to pain, nor was there evidence of systematic confounding between perceptions of work conditions and reported health status. Changes in the nature of work mean that more and more employees, especially women, use computers for significant parts of their workday. The sex-segregation of women into sedentary, repetitive and routine work, and the persisting gender imbalance in domestic work are interlinking factors that explain gender differences in musculoskeletal disorders. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Gender differences in health; Upper body musculoskeletal disorders; Repetitive strain injuries; Women’s health; Australia
Introduction Musculoskeletal disorders, particularly those of the upper body, are increasingly prevalent in Western societies (Muggleton, Allen, & Chappell, 1999; Yassi, 1997). Although some studies have found levels as low as 10%, in the working population levels of around 50% and as high as 80% are more common (Ariens, Borghouts, & Koes, 1999; Bammer & Blignault, 1988; Blyth et al., 2001; LeResche, 1999; Putz-Anderson, 1988; Unruh, 1996; van der Windt & Croft, 1999). Upper body disorders cover a wide range of clinical symptoms and conditions, grouped under general terms such as repetitive strain injuries, cumulative trauma disorders *Corresponding author. Tel.: +61-2-6125-2837; fax: +61-2-6125-0740. E-mail addresses: [email protected] (L. Strazdins), [email protected] (G. Bammer).
and occupational overuse syndrome. These disorders occur gradually, have a chronic course, and often go untreated (Polanyi et al., 1997; Yassi, 1997). Typically, pain becomes progressively severe and loss of function occurs. The pain and disability may persist for many years, and in some cases become intractable (Bammer & Blignault, 1988; Keogh, Nuwayhid, Gordon, & Gucer, 2000; Putz-Anderson, 1988). Employed women are two to five times more likely than men to report these sorts of problems (Ariens et al., 1999; LeResche, 1999; Unruh, 1996). Why are employed women at increased risk for upper limb musculoskeletal disorders, and what can this tell us about the way health differences are shaped between men and women? In developed economies, the relationship between work and health is changing for both men and women. Now, most women are in paid employment (Hatch & Moline, 1997), and the nature of work itself is changing. These two social changes are likely to affect
0277-9536/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0277-9536(03)00260-0
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both men’s and women’s lives, but in differing ways. Like other western nations, the Australian labour market is characterised by an increase in part-time jobs, reduced job security, contingent and uneven workforce attachment, increased pace and workload, and, an increased reliance on computer technology (ACIRRT, 1999). These changes have brought with them an array of potentially new factors that could affect women’s health. On the one hand, gender gaps in some health outcomes might narrow as women’s participation in the labour force comes nearer to men’s. On the other, women and men may have vastly different experiences of paid employment and its effects on family life, especially once they become parents (Blane, Berney, & Montgomery, 2001). The sex-segregation of the labour force and the persistent gender imbalance in domestic work means that employed women’s exposure to risk factors from both work and family could differ widely from employed men’s (Hunt & Annandale, 1999). This exposure is likely to be daily and chronic. Musculoskeletal disorders may thus reflect the accumulation of difference in exposures at work and at home, providing an opportunity to tease out the relationships between work-related factors, domestic load and underlying biological differences. Prevailing explanations of women’s excess health risk revolve around two basic propositions. Greater prevalence or severity of symptoms may be due to the higher demands and constraints that women face, or because women are more affected by, or vulnerable to, the health impact of particular demands and constraints (McDonough & Walters, 2001). Thus the difference between women and men in exposure, at work and at home, to risk factors for musculoskeletal disorders is one model that may explain the markedly higher prevalence of these disorders in women compared to men. We call this the work and family demand model. An alternative explanation is that women may be more prone to developing musculoskeletal disorders due to sex-linked biological factors such as hormones or physiology, because the meaning of work and family demands are different, or because women have less resources to cope with these demands. We refer to this as the vulnerability model. And, of course, both models may be involved. Gender differences in work and family demands As we have outlined above, changes in technology, and the emergence of the information and service economy have altered the sorts of work women typically do (Webster, 1996). Women tend to be clustered into lower status jobs, often sedentary and involving repetitive work and static load (holding the body in one position for extended periods), with less job control and less substantive complexity. These comprise the key
biomechanical and psychosocial risk factors for upper body musculoskeletal disorders (Ariens et al., 1999; Bernard, 1997; Carayon, Smith, & Haims, 1999; Latko et al., 1999; Putz-Anderson, 1988). Many women work with computers where these risk factors can be exacerbated (Torgen & Kilbom, 2000; Webster, 1996). Interlinked with the sex-segregation of the labour force is the gender division of domestic work (Baxter, Gibson, & Lynch-Blosse, 1990). Women’s domestic work, especially once they become mothers, affects their ability to participate in the labour market by imposing time constraints that men generally do not share. This in turn channels women into increasingly casualised, lowskilled jobs (Webster, 1996), the sorts of jobs where routine and repetitive work predominate. Furthermore, the gender division of domestic work may mean that employed women have less time outside of work for activities that might ameliorate the health costs of their jobs. Compared to fathers, mothers invest more time and energy caring for children, helping others, and doing housework (Bird, 1999). The resultant ‘time poverty’ means that employed mothers have less time for leisure, relaxation and exercise (Firestone & Shelton, 1988; Hildebrandt, Bongers, Dul, van Dijk, & Kemper, 2000; Lundberg, 1999; Ross & Bird, 1994). Thus domestic work, per se, may be a risk factor for musculoskeletal problems, and, more importantly, it may constrain women’s ability to protect themselves from the effects of their paid work. For example, Lundberg (1999) argues that lack of relaxation impairs recovery of the musculoskeletal system from repetitive work demands. He proposes that domestic work and stress from overload further exacerbate musculoskeletal disorders because they interfere with the recovery process. The health hazard of domestic work, in this context, is time constraint, and this combines with the hazard of repetitive and sedentary duties that many women encounter in the workplace. Gender differences in vulnerability Pain severity and chronic duration of pain are central symptoms of musculoskeletal disorders. Women appear to be more sensitive to pain and consistently report greater pain when it is experimentally induced (Fillingham, Edwards, & Powell, 1999). Furthermore, pain sensitivity varies across stages in the menstrual cycle suggesting that sex-linked hormones are involved in pain perception (Fillingham et al., 1999; Fillingham & Ness, 2000; LeResche, 1999). Women also tend to see their pain problems as more threatening, and to cope differently compared to men, suggesting that there may also be some gender differences in the meaning of pain and in the resources that men and women bring to bear in coping with pain (Jensen, Nygren, Gamberale, Goldie, & Westerholm, 1994; Keefe et al., 2000). Thus
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the gender gap in musculoskeletal disorders is possibly due to women’s greater pain sensitivity, or women’s tendency to perceive and cope with pain differently. This study provided an opportunity to test both the work and family demands and vulnerability models for explaining the gender difference in musculoskeletal disorders. If women are at greater risk because of the combination of their work and family demands, then the association between gender and symptom severity should disappear once these demands are included in the analysis. If a gender excess persists, or if work and family risk factors affect women more that they affect men, this suggests that sex-linked vulnerabilities are exerting an influence. Methodological problems studying work conditions and health Studying work conditions and health poses particular methodological problems. Associations found between work and health from cross-sectional studies have been challenged because having a health condition could predispose workers to perceive or report on their work conditions negatively (Emslie et al., 1999). The present study is also unique in addressing this limitation in cross-sectional research, which was possible because workers were organised into teams. We compared individuals with their team’s aggregated ratings of the shared workplace environment to see the extent of consensus within teams, and whether individuals with poorer health perceived the workplace differently. General features of the work environment that all team members shared were rated, such as overall building and equipment design. Then comparisons were made within work teams of team members with high or low musculoskeletal conditions. If workers with and without a musculoskeletal condition rate general characteristics of the workplace differently, it would suggest that their health status is influencing their perceptions of work. On the other hand, if there are no differences in these general workplace ratings, we can be more confident that associations found between individual’s specific work conditions (e.g., degree of control, repetitive workload) and their symptom severity reflects the influence of work conditions on health, and not the reverse.
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conditions and to sample enough men and women to make meaningful comparisons. Focus groups were held initially to refine the questionnaire. Then, surveys were distributed to randomly selected branch offices. Participation was voluntary and responses were anonymous. The response rate was 50%, resulting in 737 questionnaires returned. Occupational status was coded using the Australian Standard Classification of Occupations (ASCO 1st Edition, ABS, 1986). As expected from the nature of the organisations, which primarily administered and delivered government benefits to the public, clerical and service jobs predominated; 77% worked in clerical and customer service jobs, 16% in managerial jobs, 7% in professional jobs, and 1% in unskilled jobs. The mean age was 36 years (SD=9.55). Most employees were Australian born (73%), 12% were of European origin, and 9% were of Asian origin. Just under a third of the sample (32%) had completed 10 years or less education, another 32% had 12 years of completed education, 17% had obtained trade or technical qualifications or an undergraduate diploma, 13% a Bachelor degree and 5% a post graduate diploma or higher degree. Nearly 60% of the sample were either married or living with a partner. Forty three per cent of employees were parents with at least one dependent child living at home. Women and men differed on several demographic variables. Men were more likely to be older (the average age of men was 37710 years, while the average age of women was 3579 years, (t (642)=2.65, po0:01). Men were also likely to be better educated (w2=38.89, po0:01). Only 16% of men had attained 10 years or less of education, compared to 38% of women. In terms of tertiary qualifications, 25% of men compared to 15% of women had attained a Bachelors Degree or higher. Men also worked in higher status occupations (30% worked in professional or managerial positions compared to 17% of women, w2=21.57, po0:01). Women were more likely to be parents of dependent children (47% women, 34% men, w2=10.65, po0:01) and to be employed part-time rather than full-time (20% of women were employed part-time compared with 5% of men, w2=30.05, po0:01). The differences in occupational status and part-time employment reflect the sexsegregated nature of work (Labour Force Statistics, ABS, 1997).
Method Measures Sample Upper body musculoskeletal symptoms Respondents were sampled from two government organisations spread across three eastern states of Australia. We sampled across the organisational hierarchy to include a range of occupational levels and work
Following Browne, Nolan, and Faithfull’s (1984) description of upper body repetitive strain injury, the 17 item measure assessed a variety of symptoms, their
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location and degree of pain persistence. Symptoms included pain, aching, stiffness, cramp, swelling, soreness, weakness, tingling and numbness, and respondents rated the extent symptoms were present while they worked (response categories ranged from never to continuously). Respondents were then asked to rate the extent of pain and pain persistence in their upper back, neck, shoulders, arms, elbows, forearms, wrists, and fingers. The response categories were no pain (scored 0), pain present but gone by the next morning (scored 1), pain present at work, persists to the following morning, but is gone after the weekend (scored 2), and pain is present and continuous all the time which is the most severe category (scored 3). Scores were summed to form the scale (a ¼ 0:92). A zero score indicated no symptoms in the upper body, whereas high scores indicated more severe symptoms. For example, neck pain that persisted after work but was gone by the next morning would be given a score of 2. If respondents described their neck pain as continuos the score would be 4. Scores would be higher if multiple upper body areas were involved. While the measure uses criteria developed for treatment and rehabilitation of upper body musculoskeletal disorders, high scores are not diagnostic of specific clinical conditions.
Poor ergonomic equipment: A single item assessed comfort and ergonomic design of work equipment and work environment. Response categories ranged from 1=very comfortable and well designed to 5=very uncomfortable and poorly designed. Job control: A four item scale measured control over work load, pace, duties and decisions (e.g., ‘how often does your workplace give you control over the pace of your work?’). Response categories ranged from 1=very seldom to 4=almost always. Items were summed and averaged to form the scale (a ¼ 0:82). Relaxation and exercise: Two items assessed time spent on exercise, leisure and relaxation over the last six months (‘yhow often have you set aside time just for physical activities (leisure or exercise)’ and ‘yhow often have you set aside time just to relax?’). Response categories were 1=not at all, 2=some or a little of the time (about once a month or more), 3=occasionally or a moderate amount of time (about once a week or more), 4=often or a lot of the time (about once a day) and 5=frequently (more than once a day). These items were highly intercorrelated (r ¼ 0:66) and were summed and averaged to form the measure.
Results General workplace rating Gender differences in musculoskeletal health This was assessed using two items; ‘Compared to other workplaces, do you think your workplace provides employees with comfortable and well-designed equipment, where the work is not too repetitive, not too stressful, not too boring, and the amount and pace of work is not too much?’ and; ‘In general, how physically stressful is your workplace? Is it likely that your workplace is the sort of place where people could develop muscle tensions, strains and overuse problems?’ Response categories, respectively, ranged from very comfortable (scored 1) through intolerably uncomfortable (scored 6); and not at all (scored 1) through extremely physically stressful (scored 6). The two items were summed and averaged with low scores representing positive evaluations of the workplace and high scores negative evaluations (item r ¼ 0:65). Risk factors Repetitive work demands: Seven items assessed the extent work duties involved repetitive movements, keyboarding, computer use, and holding an uncomfortable posture or body position (e.g., on a routine day how often do you have to make repetitive movements with your head, neck, arms, hands or fingers). Response categories ranged from 1=never to 5=all the time. Items were summed and averaged to form the scale (a ¼ 0:80).
Age adjusted means for musculoskeletal health for men and women are presented in Table 1. Proportionally more women reported at least one symptom (83% of women compared to 77% of men, w2=13.38, po0:01). Women also reported more severe symptoms (23% of women reported continuous pain in their upper body compared to 12% of men). Gender differences in work and family demands According to the demands hypothesis, the gender gap in musculoskeletal health occurs because women encounter more risk factors. To test for this, age adjusted mean differences in risk factors for men and women were compared (see Table 1) and statistically significant differences were found for all but one risk factor. Women’s work conditions were more likely to involve physically repetitive work demands. For example 34% of women compared to 21% of men sat in the same position for long periods of time, 81% of women compared to 73% of men worked longer than 5 hours per day on a computer, and 30% of women compared to 16% of men reported that their job involved making repetitive movements all of the time. Women were also more likely to work in poorly designed and uncomfortable environments. Fifteen per cent of women, compared to 10% of men described their work environments
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Table 1 Age-adjusted gender differences in mean scores for upper body musculoskeletal symptoms and risk factors Variable
Gender
Adj. mean
95% confidence interval
Difference
p
Upper body symptoms
Men Women Men Women Men Women Men Women Men Women
7.14 9.88 3.47 3.77 2.36 2.55 2.20 2.131 3.01 2.68
(5.97, (9.16, (3.37, (3.71, (2.23, (2.47, (2.08, (2.06, (2.88, (2.61,
2.74
0.001
0.30
0.001
0.18
0.022
0.06
0.326
0.32
0.001
Repetitive work Poor ergonomic equipment Job control Relaxation and exercise time
as either uncomfortable or very uncomfortable. In addition, women spent considerably less time than men exercising or relaxing. Twenty per cent of women did no exercise at all and 14% did not spend time relaxing, over the previous month. This compares to 12% and 10%, respectively, for men. There was, however, no evidence that women experienced less job control than men. For example, 51% of men compared to 60% of women reported that they often or almost always made decisions about their work, 65% of men and 62% women reported that they often or almost always had control over the pace that they worked, and 67% of men compared to 64% of women reported they often or almost always had control over the amount of work that they did. Multiple regression analyses testing the vulnerability and demand models A hierarchical regression equation (OLS) tested the model that gender differences in upper body musculoskeletal symptoms were due to women’s poorer work conditions (repetitive work demands and poor ergonomic equipment) and greater constraints on time to relax or exercise. A reduction in the size of the standardised b coefficient or beta weight for gender after entry of the risk factors would provide support for this model (Baron & Kenny, 1986). Pearson product– moment correlations among predictor variables are appended and show that they are not highly intercorrelated. Table 2 summarises the change in association between gender and upper body musculoskeletal problems after the exposure variables were included in the equation. The beta weight for gender dropped to become nonsignificant, indicating that the gender gap is due to women encountering more risk factors for musculoskeletal problems at work and at home. Following Karasek’s model of demand and control (Theorell & Karasek, 1996) interactive effects of repetitive work
8.31) 10.61) 3.60) 3.84) 2.50) 2.63) 2.31) 2.20) 3.13) 2.76)
demands and job control were also tested, and were nonsignificant. However, vulnerability factors may also be important and may modify the effects of work conditions and time to relax and exercise for men and women. After centering all predictor variables to reduce multi-collinearity (Aiken & West, 1991), cross-product terms for gender with each of the predictor variables were entered into the equation. Only one significant interactive effect for the predictor variables was found, namely time to relax and exercise, which was more strongly associated with women’s upper body musculoskeletal symptoms than men’s. Beta weights in separate sex regressions were b 0.18, po0:01; for women and b 0.00, ns, for men.1 In summary, we found no evidence that women are more affected than men by repetitive work or poor work conditions, nor was there evidence of a persisting gender difference due to unmeasured sex-linked factors such as pain sensitivity or pain perception. Instead, the gender gap appears to be explained by employed women’s greater exposure to risk factors at work and the difficulty these women face finding time to relax or exercise outside of work. Parenthood and time The gender difference in time to exercise or relax may be due to the gender division of domestic work, which becomes most marked for mothers. If this is the case, then being a parent should have different effects on men’s and women’s time. Simultaneous regression analyses (OLS) with centred variables provided support 1
The sample was also stratified (upper quartile compared to lower three quartiles) in terms of symptom severity. Both groups showed similar patterns of associations between risk factors and symptoms, with one exception. Time to relax or exercise showed a stronger association in the more symptomatic group (beta coefficient 0.25, po0:001) compared to the lower symptom group (beta coefficient 0.03, ns).
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Table 2 Summary of hierarchical regression analyses testing demand and vulnerability models of gender differences in upper body musculoskeletal symptoms (n ¼ 665) Predictor Step 1 Gender Step 2 Gender Job control Repetitive work Poor ergonomics Relaxation and exercise time
B
SE B
ba
0.17**
0.04
0.16**
0.07 0.04 0.27** 0.09** 0.07**
0.04 0.11 0.08 0.02 0.02
0.07 0.08 0.43** 0.19** 0.13**
Adjusted R2 ¼ 0:20; po0:01: R2 change 0.03, po0:01 for set one (gender only), R2 change 0.29, po0:01; for set 2 (all predictors). Gender scored 0=male, 1=female. Age, ethnicity, education, occupational status, workhours and parent status were entered in a second set after gender to control for possible confounding (coefficients not shown). This set of variables did not contribute significantly to the prediction of upper body musculoskeletal pain (R2 change 0.02, ns) or affect the beta coefficient for gender. Missing data deleted listwise. **po0:01: a b standardized beta coefficients.
for this hypothesis. Gender and parent status interacted in their effect on time relaxing and exercising (see Table 3). On average, women were less likely than men to exercise or relax regularly. However, this gender difference varied between parents and non-parents. The gender difference was relatively narrow among non-parents and widened among parents. Mothers were the least likely to take time to relax or exercise, whereas fatherhood made little difference to men’s time.
Comparing ratings of the workplace by respondents with and without musculoskeletal disorders A possible explanation for the associations we have found is that severe musculoskeletal symptoms affected the way respondents viewed their workplaces. To see if such a perceptual bias was present, we dichotomised respondents on the basis of their symptom levels; those who had no or mild musculoskeletal symptoms, and those with severe or continuous pain. We compared the ratings of respondents within work teams because they were likely to share the same workplace environment. Twenty-five teams, ranging in size from 7 to 31 members were identified. Workplace ratings of team members with and without severe symptoms were compared using t-tests (because of the number of comparisons, the significance level was set to 0.01). Of the 25 comparisons, only one significant difference between ratings was observed (M low symptoms=2.80, SD=0.27, M high
Table 3 Summary of simultaneous regression analyses testing for gender differences in the effects of parent status on time to relax and exercise (n ¼ 665) Predictor
B
SE B
ba
Gender Age NESB Education Occupation Workhours Parent status Gender parent status
0.23** 0.00 0.32** 0.02 0.03 0.00 0.25** 0.32*
0.08 0.00 0.08 0.02 0.02 0.01 0.07 0.16
0.11** 0.05 0.15** 0.05 0.05 0.06 0.14** 0.08*
Adjusted R2 ¼ 0:09; po0:01: Gender scored 0=male, 1= female, NESB scored English speaking background=0, non-English speaking background=1, Parent scored nonparent=0, parent=1. Missing data deleted listwise. po0:05; po0:01: a b standardized beta coefficients.
symptoms=4.50, SD=0.87, tð7Þ ¼ 4:25; po0:01). Even though the small size of some teams would have reduced power to detect significant differences, it is noteworthy that no differences in how the workplace environment was viewed were found in the larger teams. These analyses suggest that the effects of workplace factors on health are unlikely to be greatly confounded by the effects of health status on the reporting of workplace conditions.
Discussion This is one of the first studies to explain why women have an excess risk for upper body musculoskeletal disorders. We compared men and women employed in white collar clerical and service occupations—the occupations where women tend to predominate, and where upper body musculoskeletal problems are prevalent. Women in our sample were more likely to report symptoms in their upper body, with nearly a quarter reporting continuous pain suggestive of advanced disorder. We tested the possibility that women were prone to musculoskeletal problems, either because of a propensity to report more pain, or because risk factors affected them more than men. We found that women’s excess risk was due to the different demands that they faced at work and at home. At work, women spent more time using computers, did more repetitive movements, and reported poorer and less comfortable equipment. Outside of work, women bore the brunt of the unpaid work involved in parenting, and to accommodate the time squeeze, cut back on their exercise and relaxation. In this way, they lost access to activities that protect
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against, or at least may help ameliorate, upper body musculoskeletal disorders. This pressure on women’s time is likely to continue as western economies respond to globalised labour markets and the pace of work intensifies. This study is also one of the first to address a common concern with cross-sectional research on work and health. If the presence of musculoskeletal symptoms systematically alters perceptions of work conditions, it might suggest that the associations we found between work conditions and symptoms reflect a perceptual bias rather than a causal relationship. Thus we compared, within work teams, the way employees with high and low symptoms rated the teams’ shared workplace environment. We found no evidence that having a musculoskeletal disorder systematically alters the way workplaces are perceived, lending more confidence to our interpretation that work conditions act as risk factors. However, longitudinal studies are needed, especially because upper body musculoskeletal disorders are cumulative, likely to evolve over time and intensify during particular life course stages. Study limitations More studies are needed to test the model in different countries, sectors and organisations. The sample was white-collar government employees, limiting generalisability to private companies and organisations, or to other industries and occupations with different types of repetitive work demands, such as manufacturing, packing, etc. In addition, the relatively small sample size may have affected our power to detect differences, especially for the men in the sample. The response rate may also mean that some self-selection bias occurred. Perhaps employees who volunteered were more likely to have a
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concern with their health or dissatisfaction with their workplace and possibly some gender-based bias in responding occurred. We do not have information on non-respondents and cannot assess the extent to which self-selection may have biased our sample. These limitations may have inflated the prevalence of upper body problems, or meant that the workplace was rated more negatively. We checked for the possibility of an effect on the nature of the association between symptoms and risk factors by stratifying the sample by gender and symptom severity, and found little difference in effects, with one exception. Constraints on time to relax or exercise was more important for those respondents reporting relatively more severe symptoms, thus our results may be most applicable to high symptom groups. Finally, the study was cross sectional in design, therefore the associations that we report cannot be considered causal. Changes in the nature of work, the sex-segregation of women into sedentary, repetitive and routine work, and the persistent gender imbalance in domestic work, are interlinking factors affecting the health of women in contemporary Western economies. This study illuminates one way that these factors combine to explain gender differences in musculoskeletal health.
Acknowledgements We would like to thank the men and women who participated in this study, as well as Monika Reinhart and Brian Wilson for their help in conducting the study, Comcare Australia for access to the data, Dr. Dorothy Broom, Dr. Rennie D’Souza, Dr. Mark Clements, Karen Lees and Marluce Silva for their helpful comments.
Table 4 Intercorrelations among independent variables (n ¼ 665) Predictors
1
2
3
4
5
6
7
8
9
10
11
1. Gender 2. Age 3. NESB 4. Education 5. Occupation 6. Workhours 7. Parent 8. Repetitive work 9. Equipment 10. Job control 11. Time
—
0.09 —
0.05 0.10 —
0.17 0.02 0.12 —
0.16 0.14 0.04 0.15 —
0.21 0.04 0.04 0.13 0.14 —
0.12 0.22 0.05 0.13 0.02 0.29 —
0.19 0.08 0.03 0.10 0.23 0.10 0.05 —
0.10 0.02 0.04 0.01 0.05 0.01 0.02 0.31 —
0.03 0.05 0.01 0.08 0.27 0.15 0.07 0.27 0.15 —
0.17 0.05 0.17 0.08 0.11 0.14 0.17 0.06 0.12 0.08 —
Note: Gender scored 0=male, 1=female, NESB scored English speaking background = 0, non-English speaking background=1, Parent scored non-parent=0, parent=1. ‘Time’ denotes relaxation and exercise time. Bolded correlation coefficients are significant at the po0:05 level (two tailed). Missing data deleted listwise.
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Appendix A Bivariate intercorrelation matrix of independent variables predicting upper body musculoskeletal pain is given in Table 4.
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