The demographic and contextual correlates of work-related repetitive strain injuries among canadian men and women
- Disclosure Statement: The authors report no conflicts of interests.
Correspondence to: Dr. F. Curtis Breslin, Institute for Work and Health, 481 University Ave., Suite 800, Toronto, ON, Canada M5G 2E9.
The study sought to identify gender differences in work-related repetitive strain injuries (RSI), as well as examine the degree to which non-work factors such as family roles interact with gender to modify RSI risk. Another aim is to examine whether there are potential provincial differences in work-related RSI risk.
The 2003/2005 Canadian Community Health Survey included over 89,000 respondents who reported working in the past 12 months. Separate multi-level models for men and women were used to identify the correlates of work-related RSIs.
Women reported sustaining more work-related RSIs than men. Also, having one or more children in the household was associated with lower work-related RSI risk for females. Both men and women in British Columbia reported higher work-related RSI rates than in Ontario.
Gender contributes to RSI risk in multiple and diverse ways based on labor market segregation, non-work exposures, and possibly biological vulnerability, which suggests more tailored interventions. Also, the provincial differences indicate that monitoring and surveillance of work injury across jurisdictions can assist in province-wide prevention and occupational health and safety evaluation. Am. J. Ind. Med. 56:1180–1189, 2013. © 2013 Wiley Periodicals, Inc.
Women's participation in work has increased markedly over the last few decades. Currently, Canadian women made up 47% of total employment numbers [Labour Force Survey, 2012]. However, gender segregation in jobs continues to occur in the Canadian labor market [Chung et al., 2000]. For example, when one compares the top ten most common occupations for men and women, only “retail clerk” position appears in both lists [Messing and Stellman, 2006].
Certain types of work-related musculoskeletal (MSK) and repetitive strain injury (RSI) symptoms are reported more frequently by working women than men in industrialized countries. For instance, an early review of 21 studies examining neck and shoulder complaints by occupation and gender showed women having an increased prevalence compared to men [Hagberg and Wegman, 1987]. More recently, women in office settings reported more upper extremity complaints than men [Karlqvist et al., 2002; Janwantanakul et al., 2008]. Also lower extremity MSK complaints are higher among women as well [Messing et al., 2008; Messing et al., 2009]. However, less consistent is research examining back complaints, where some studies indicate increased prevalence for women [Krause et al., 1997; de Zwart et al., 1997], while others report increased prevalence for men [Leino-Arjas, 1998; Hooftman et al., 2009].
Supporting the notion that work exposures are a major contributing factor for gender differences, some studies examining exposure differences tend to find that MSK complaints are not significantly different when comparing men and women in the same occupational categories or performing the same tasks [Silverstein et al., 1987; Coury et al., 2002; European Agency for Safety and Health at Work, 2003]. However, a review of specific exposures and MSK complaints suggests certain vulnerabilities for both men and women [Hooftman et al., 2004]. For example, women completing tasks that required awkward arm postures were at higher risk for neck and shoulder symptoms than men performing similar tasks [Hooftman et al., 2004]. Also, Hooftman and colleagues have found that men were more vulnerable to the effect of lifting than women doing the same activity [Hooftman et al., 2009].
Exposures outside of work such as caring for children and home demands increase the risk of MSK symptoms at work, as well as work absences and work-related disability for women and these non-work exposures are not found to be risk factors for men [McDonough, 1997; Messing et al., 2009; Sandmark, 2009]. This pattern of findings is consistent with role-strain theory which suggests that the increased and conflicting roles of household responsibilities, caregiving, and paid employment have a detrimental effect on women's health [McDonough, 1997]. It should be noted, however, that there is also support for multiple roles enhancing women's health [Baruch and Barnett, 1985; Reid and Hardy, 1999]. Role enhancement theory contends that, for women with young children, roles outside the home put them in touch with more social support and economic resources [Rantanen et al., 2011].
The direction and degree of gender differences in working conditions appear to vary by jurisdiction, raising the possibility that broader contextual factors influence exposures and, in turn, work-related RSI risk for men and women. For example, a European study using Karasek's job demand/control model found significantly lower job demands for men than women in Southern- and Mid-European countries, but the reverse was true in Sweden [de Smet et al., 2005]. In Canada, there are notable legal/regulatory differences between provinces such as required workplace safety committees and such differences may affect the nature/extent of hazard exposure as well as the safety training provided [Tucker, 2003]. Geographic differences in RSI risk may also result from economic policies that affect the industrial mix and socio-economic factors in a jurisdiction [Sullivan, 2000; Breslin et al., 2007]. The aim of this paper is to report on gender differences in work-related RSI, as well as the degree to which non-work factors such as family roles interact with gender to modify RSI risk. Another aim is to examine whether there are potential provincial differences in RSI risk for working men and women.
The Canadian Community Health Survey (CCHS), an ongoing series of annual, cross-sectional surveys, provides estimates of health status and determinants of health at a sub-provincial level called health regions [Statistics Canada, 2003a]. Using a multi-staged, stratified sampling frame, the CCHS target population consists of household residents aged 12 and older who are living in private dwellings in all Provinces and territories. People living on Indian reserves or Crown lands, residents of institutions, full-time members of the Canadian Armed Forces, and residents of certain remote regions were excluded from the sampling frame. The survey design features and core content have remained largely unchanged during the series of surveys starting in 2001.
The CCHS 2.1 cycle respondents were recruited between January and December of 2003. Out of the 166,222 households selected to participate, an initial response regarding basic demographic information of people dwelling in the household was obtained from 144,836 households (87.1%). Among the 144,836 responding households, one individual per household was randomly selected for an in-depth interview. A person-level response rate of 92.6% was obtained at this stage [Statistics Canada, 2003a].
To increase cell counts at the Census Division level, we also included a subsample of respondents who participated in the CCHS 3.1 (completed two years after CCHS 2.1) into the analytic sample [Statistics Canada, 2006a]. This was appropriate for work injury because the period of 2003 to 2005 was one of relatively stable labor market growth in Canada [Statistics Canada, 2011]. Although the 3.1 survey had the same target population and nearly identical question content, the CCHS 3.1 survey asked only one-third of the respondents the detailed questions about their labor force participation (to reduce respondent burden), so only this portion of respondents who were asked in detail about their employment (men n = 8,567, women n = 8,450) could be combined with respondents from CCHS 2.1, where all respondents had been asked the detailed questions about labor force participation. The household and person-level response rates for the CCHS 3.1 survey were 84.9% and 92.9%, respectively.
For the purpose of the current analyses, only respondents 15 years or older who had worked at least 1 week in the previous 12 months were included (n = 99,556) which was 59.4% of the total respondents in that age range. As only a small number of respondents in the Canadian territories (e.g., Northwest Territory) were surveyed (n = 3,923), and that after excluding missing values and applying analytic weights to this sample there were <500 respondents, these Canadian territory respondents were removed. Of the respondents who met our age and employment inclusion criteria, a further 6,092 respondents were missing information on one or more of the predictor variables and were deleted (i.e., 6.4% respondents with missing data), leaving a final analytic sample of 89,541 respondents.
Our analysis of missing data showed that the following age groups: 15–24 (OR = 0.64; 95% confidence interval = (0.59, 0.70)), 25 to 34 (OR = 0.52; 95% CI = (0.48, 0.57)), 35–44 (OR = 0.62; 95% CI = (0.58, 0.67)), and 45–54 (OR = 0.69; 95% CI = (0.64, 0.75)), were significantly less likely to have a missing value than those respondents 55 years and older. Also, those respondents with less than a high school education (OR = 1.74; 95% CI (1.62, 1.87)) and those with a high school diploma (OR = 1.20; 95% CI (1.11, 1.30)) were more likely to have a missing value than those who completed a post secondary degree. In addition, visible minorities (OR = 1.41; 95% CI (1.30, 1.50)), and both immigrants who have been in Canada for <5 years and those immigrants in Canada for 5 or more years (OR = 1.7; 95% CI (1.40, 2.10); OR = 1.3; 95% CI (1.20, 1.40), respectively) were significantly more likely to have a missing value. Also, those respondents working part-time (OR = 1.5; 95% CI (1.40, 1.60)) and those working <6 months out of the year (OR = 2.0; 95% CI (1.90, 2.20)) were more likely to have missing data than full-time workers and those holding jobs the entire year, respectively. The study was approved by the Research Ethics Board of the University of Toronto.
Outcome measure: Work-related repetitive strain injury
To provide a general definition of RSI for respondents, they were first told that RSIs are “caused by overuse or by repeating the same movement frequently (for example, carpal tunnel syndrome, tennis elbow, or tendonitis)” [Statistics Canada, 2005, p. 193]. Next, respondents were asked whether they had any injuries due to repetitive strain which was serious enough to limit their normal activities in the past 12 months. Finally, the respondents were asked, for their most serious RSI in the past 12 months, “what type of activity [they] were doing when [they] got this repetitive strain?” One of the response options for this question was “working at a job or business (excluding traveling to and from work). We operationalized work-related RSI as only those RSIs of respondents who reported having an RSI in the past 12 months which occurred as a result of working at a job or business.
Predictors: Socio-demographic variables
A range of socio-demographic variables were included in the analyses. Age was dummy coded into 10-year blocks, with 55+ serving as the referent group. Education was categorized based on educational milestones that have been completed, with earning a post-secondary degree being the referent group. Marital status was categorized as married/cohabitating versus single/divorced/widowed. Family caretaking responsibilities were operationalized as the presence of at least one child in the household who was younger than 12 years old. Immigrant status was categorized as <5 years in Canada, 5 years or more in Canada, or not an immigrant (referent group). Another indicator of acculturation included was the respondent's perception of their ability to converse fluently in English or French, Canada's two official languages. Visible minority status was obtained in the survey with questions asking about cultural and racial backgrounds. Also included was an indicator of whether the respondent lived in a rural or urban environment, which defined urban using census population counts as a “population concentration of 1,000 or more and population density of 400 or more per square kilometer” [Statistics Canada, 2006b].
Predictors: Work characteristics
Respondents provided details about the position they considered their main job. Respondents reported on whether they worked full time (i.e., 30 hr or more per week) or part-time. They were also asked how many weeks they worked at their main job in the past year. Respondents were also asked whether they were an employee or self-employed. The industrial grouping was derived by asking respondents what kind of business they worked in and providing several examples (e.g., cardboard box manufacturing, retail shoe store). The retail industry was designated as the reference category because it is one of the largest industrial sectors in Canada representing 11.9% of the working population in 2009 [Statistics Canada, 2010]; and tends to have a lower risk of RSI than other groups [Breslin and Smith, 2006]. With regard to work stress, respondents were asked whether most days in their main job in the past 12 months were stressful on a 5 point Likert scale (i.e., not at all, not very, a bit, quite a bit, or extremely stressful). It was transformed into a three-category variable by collapsing the two lowest and the two highest response categories.
Predictors: Census division-level variable
Previous studies suggested that area-level variables within a province such as low socioeconomic status of communities may lead to particular vulnerability for injury and disability [Mustard and Frohlich, 1995; McDonough, 1997]. The purpose of including the proportion of single parents in each census division was to operationalize a proxy variable for a potential relevant contextual variable, in this case social/material deprivation. Conceptually, area-level indicators of socioeconomic status can supplement individual indicators of socioeconomic status because “areas with a high level of socioeconomic disadvantage may also be disadvantaged with respect to transport, retail outlets, leisure facilities, environmental pollution, and social disorganization, in ways that influence health independently of the socioeconomic characteristic of the people living in these areas” [Smith et al., 1998, p. 399]. Proportion of single parents in an area has been used in previous research and is correlated with other indicators of social/material deprivation [Mustard and Etches, 2003]. Therefore we selected and computed from the 2001 Canadian Census as the percentage of households in a census division who reported being a single parent [Statistics Canada, 2003b]. The census division, the level of aggregation for the present study, is a “general term applying to areas established by provincial law, which are intermediate geographical areas between the municipality (census subdivision) and the province. Usually they are created to facilitate regional planning and the provision of services which can be more effectively delivered on a scale larger than a municipality” [Statistics Canada, 1992].
Province referred to the respondent's province of residence. It was a dummy-coded variable, with Ontario as the referent category. Low cells sizes also led us to aggregate across three Atlantic Provinces (i.e., Newfoundland & Labrador, Nova Scotia, and New Brunswick).
In the present study, the probability of a work-related RSI was estimated separately for men and women using MLwin 2.0 [Rasbash et al., 2000]. Multiple membership, multi-level logistic regressions were used to estimate the probability of a work-related RSI [Lawson et al., 2003]. This was a Bayesian analysis and the models were fitted using Markov Chain Monte Carlo techniques [Browne, 2009], with simultaneous entry of all predictor variables described above. The unit of observation was the individual respondent (i.e., level 1 of multi-level model), which was nested within census divisions (i.e., level 2, random effect), with provinces included as a fixed, dummy-coded variable.
Spatial dependence, which arises from a geographic area being correlated with neighboring areas, can occur because of the presence of unobserved environmental conditions that are more similar for geographic units that are closer together [Clayton and Hills, 1993]. To control for the influence of spatial autocorrelation (i.e., spatial random effect), an additional random effect term at the census-division (i.e., subprovincial) level was included in the regression models. Consequently, the regression is described as a multiple membership model because “individuals within an area may be supposed to be influenced by both an effect of that area and the effects of surrounding contiguous areas” (p. 41) [Fielding and Goldstein, ]. In addition, the regressions described below included the cycle that the respondent participated in as a categorical variable in the analyses [Roberts, 2000]. To account for the different probability of selection and nonresponse, we used the survey weights in the descriptive and regression analyses [Lohr, 1999].
To evaluate whether the correlates of work-related RSIs were statistically different between the gender-stratified regressions, we tested for the difference between regression coefficients across men and women for each predictor. We used a method for testing differences between regression coefficients outlined by Allison . Briefly, this method provides a z-test consisting of the difference between the regression coefficients (e.g., children <12 years old at homemen's regression, children <12 years old at homewomen's regression), which form the numerator, and calculates a standard error using the combined standard deviations of the two regression coefficients, which forms the denominator.
When examining the overall sample with regard to self-reported work-related RSIs, a higher proportion of women reported this type of injury than men (7.5% and 6.9%, respectively; OR = 1.1, 95% CI (1.04, 1.15)). Table I shows the frequency of work-related RSI, stratified by gender for this sample of Canadian workers.
Table I. Prevalence of Work-Related Repetitive Strain Injury (WRKRSI) for Demographic and Work Predictors Stratified by Gender (Men: n = 45,102; Women n = 44,439)
|Overall||45,102||3,130 (6.94)||44,439||3,355 (7.55)|
|Year of survey|
|2003||36,535||2,534 (6.94)||35,989||2,763 (7.68)|
|2005||8,567||596 (6.96)||8,450||592 (7.01)|
|15–24||8,284||398 (4.81)||8,831||316 (3.58)|
|25–34||9,209||660 (7.16)||9,261||599 (6.47)|
|35–44||11,759||976 (8.30)||11,452||967 (8.45)|
|45–54||9,558||799 (8.36)||9,980||1,073 (10.75)|
|≥55||6,292||297 (4.73)||4,915||400 (8.14)|
|Married/cohabiting||28,572||2,105 (7.37)||27,034||2,229 (8.25)|
|Single/widowed/divorced||16,530||1,025 (6.20)||17,405||1,126 (6.47)|
|Less than secondary||7,204||480 (6.67)||5,640||436 (7.73)|
|Secondary graduate||8,455||699 (8.26)||8,571||641 (7.48)|
|Other post secondary||3,984||232 (5.83)||4,307||295 (6.84)|
|Post secondary graduate||25,459||1,719 (6.75)||25,921||1,983 (7.65)|
|Children <12 years old at home|
|Yes||12,802||958 (7.49)||12,512||784 (6.27)|
|No||32,300||2,172 (6.72)||31,927||2,571 (8.05)|
|White||38,006||2,798 (7.36)||37,689||2,937 (7.79)|
|Others||7,096||332 (4.69)||6,750||418 (6.19)|
|<5 years||1,086||18 (1.62)||900||28 (3.06)|
|≥5 years||7,896||374 (4.74)||7,407||548 (7.40)|
|Not immigrant||36,120||2,738 (7.58)||36,132||2,779 (7.69)|
|Part-time (i.e., <30 hr/week)||5,008||200 (3.99)||11,538||569 (4.94)|
|Full time||40,094||2,930 (7.31)||32,901||2,786 (8.47)|
|Rural||8,490||743 (8.76)||7,896||671 (8.51)|
|Urban||36,612||2,387 (6.52)||36,543||2,684 (7.34)|
|Low||12,609||624 (4.95)||11,794||622 (5.28)|
|Medium||19,228||1,312 (6.83)||18,255||1,265 (6.93)|
|High||13,265||1,194 (9.00)||14,390||1,468 (10.20)|
|0–26 weeks||5,239||257 (4.91)||6,312||284 (4.51)|
|27–51 weeks||7,626||581 (7.62)||8,180||658 (8.05)|
|52 weeks||32,237||2,292 (7.11)||29,947||2,413 (8.06)|
|Yes||8,535||614 (7.19)||5,334||341 (6.40)|
|No||36,567||2,516 (6.88)||39,105||3,014 (7.71)|
|Converse in English/French|
|Yes||44,652||3,112 (6.97)||43,990||3,323 (7.55)|
|No||450||18 (4.04)||449||32 (7.16)|
|Agriculture/forestry/mining/utilities||3,306||281 (8.50)||1,077||83 (7.69)|
|Whole sale/transport/warehousing||5,001||367 (7.33)||2,088||218 (10.47)|
|Finance/real estate/professional/management||5,556||200 (3.61)||5,877||342 (5.82)|
|Arts/entertainment/accommodation/food service||4,763||185 (3.89)||6,024||401 (6.66)|
|Administration and support/public administration||5,805||384 (6.62)||6,276||427 (6.80)|
|Educational services||2,033||93 (4.56)||4,549||298 (6.54)|
|Health care and social assistance||1,608||93 (5.76)||8,072||691 (8.56)|
|Construction||4,533||514 (11.35)||647||52 (7.91)|
|Manufacturing||7,997||707 (8.84)||3,390||383 (11.31)|
|Retail trade||4,500||306 (6.80)||6,439||460 (7.15)|
|Newfoundland and Labrador/Prince Edward Island/Nova Scotia/New Brunswick||3,297||226 (6.85)||3,332||237 (7.12)|
|Quebec||10,331||702 (6.79)||10,021||724 (7.23)|
|Manitoba||1,577||123 (7.81)||1,624||107 (6.58)|
|Saskatchewan||1,360||105 (7.75)||1,353||127 (9.38)|
|Alberta||4,903||328 (6.70)||4,627||410 (8.86)|
|British Columbia||5,848||477 (8.16)||5,795||480 (8.29)|
|Ontario||17,786||1,169 (6.57)||17,687||1,270 (7.18)|
With regard to age, workers 15–24 years old showed the lowest unadjusted rates of work-related RSIs for both men and women. In terms of work variables, full time-workers as well as those reporting working more than 6 months reported the highest unadjusted work-related RSI rates. Also, workers who reported high stress in their jobs exhibited the highest unadjusted work-related RSI rates. For men, the construction industry showed the highest unadjusted rates of work-related RSI rate, while the manufacturing industry showed the highest unadjusted rates of work-related RSI rate for women. Table I also shows that at the provincial level, British Columbia and Saskatchewan show the highest unadjusted rates of work-related RSI for men and women, respectively.
The coefficients and adjusted odds ratios for the final models are presented in Table II. For work-related RSIs in the past 12 months for men, those in the agriculture/mining/forestry industry, manufacturing, and the construction industry showed an increased risk of work-related RSIs compared to those men in the retail industry. Men in the arts/entertainment/accommodation/food service and men in the finance/real estate/professional services/management and education services industries showed significantly decreased risk of a work-related RSI. The association between engaging in full-time work and work-related RSI approached traditional levels of significance. In terms of provincial differences, men working in British Columbia were significantly more likely to report a work-related RSI than in Ontario, even with other demographic and work-related factors controlled. Having dependent children under 12 years old was not associated with work-related RSI for men.
Table II. Multi-Level Logistic Regressions on Risk of Work RSIs by Gendera (Men: n = 45,102; Women n = 44,439)
|Children <12 years old at home|
|Yes||0.96 (0.88–1.06)||0.79 (0.72–0.87)|
|Full-time||1.19 (0.99–1.44)||1.28 (1.13–1.46)|
|Agriculture/forestry/mining/utilities||1.25 (1.06–1.48)||0.89 (0.71–1.10)|
|Whole sale/transport/warehousing||1.08 (0.92–1.28)||0.95 (0.79–1.14)|
|Finance/real estate/professional/management||0.56 (0.46–0.69)||0.61 (0.53–0.71)|
|Arts/entertainment/accommodation/food service||0.77 (0.63–0.93)||0.99 (0.86–1.13)|
|Administration and support/public administration||0.96 (0.81–1.13)||0.86 (0.75–0.98)|
|Educational services||0.58 (0.45–0.75)||0.59 (0.50–0.69)|
|Health care and social assistance||0.97 (0.77–1.23)||0.87 (0.77–0.99)|
|Construction||1.65 (1.41–1.93)||0.72 (0.52–1.01)|
|Manufacturing||1.50 (1.29–1.75)||1.41 (1.21–1.63)|
|Newfoundland and Labrador/Prince Edward Island/Nova Scotia/New Brunswick||0.92 (0.78–1.09)||0.96 (0.80–1.14)|
|Quebec||1.11 (0.97–1.27)||0.91 (0.79–1.06)|
|Manitoba||1.01 (0.82–1.23)||0.99 (0.80–1.23)|
|Saskatchewan||1.06 (0.87–1.29)||1.11 (0.89–1.38)|
|Alberta||0.97 (0.82–1.16)||1.16 (0.96–1.41)|
|British Columbia||1.29 (1.09–1.53)||1.47 (1.21–1.79)|
|Random effects in model||Variance (std)||Variance (std)|
|Census division random effect||0.016 (0.010)||0.006 (0.007)|
|Neighboring census division random effect||0.025 (0.030)||0.072 (0.039)|
For work-related RSIs in the past 12 months for women, those women having dependent children under 12 years old were at significantly lower risk for work-related RSI than those women with no dependent children under 12. Those women in the manufacturing industry showed an increased risk of RSI compared to those in the retail industry. In contrast, those women in the finance/real estate/professional services/management, health care and social assistance, administration and support/public administration, and educational services showed significantly lower risk of work-related RSI. Women engaging in full-time work also showed significantly higher risk of work-related RSI than those working part-time. In terms of provincial differences, women working in British Columbia were significantly more likely to report a work-related RSI than in Ontario, even with other demographic and work-related factors controlled.
Statistical examination of gender differences
To examine differences between men and women, we tested for the difference between coefficients across men and women for each predictor, [Allison, 1999]. The z tests indicated that having a dependent child under 12 years old showed a significant difference between gender on this variable's impact on the risk of work-related RSI (i.e., factor associated with women's work-related RSIs, but not for men; z = 2.84, P < .01). The agriculture/mining/forestry/utilities (z = 2.56, P < .01) and construction (z = 4.38, P < .01) industries showed a significant difference between genders on the impact of these industries on the risk of work-related RSI, with more impact of these on men's risk. Conversely, men in the arts/entertainment/accommodation/food service industry had a significantly lower risk of work-related RSI than women in this industry (z = 2.10, P < .05), which resulted in a significant between-gender difference. None of the other predictors in the model showed significant between-gender differences in their associations with risk of work-related RSI.
As in previous studies, the present study observed at a descriptive level that proportionally more women report work-related RSIs than men [Hagberg and Wegman, 1987]. The present study adds to the literature by being from a representative sample of Canadian workers from all industrial sectors.
The pattern of findings suggest that gender contributes to RSI risk in multiple and diverse ways based on labor market segregation, non-work exposures, and possibly biological vulnerability. For example, the gendered segregation of the labor market may explain the different industry risk factors for men and women. For men, the construction and agricultural/mining/fishing/forestry industries were associated with elevated work-related RSI risk compared to men in the referent retail industry. However for women, working in the construction and agricultural/mining/fishing/forestry industries was not associated with elevated work-related RSI risk compared to women in the retail industry. In addition, compared to their male counterparts, women in the construction and agricultural/mining/fishing/forestry industries showed significantly lower work-related RSI risk. Though at this broad level of industrial categorization multiple factors could be involved, this pattern may be related to the different jobs, tasks, and, in turn, hazards that men and women encounter even within the same industry. In contrast, working in the manufacturing industry led to increased work-related RSI risk for both men and women, which may suggest similar hazard exposures that lead to comparable risk.
Another key finding was that the demographic and non-work-related correlates of RSI differed for men and women. For example, not having one or more dependent children under 12 years old increased work-related RSI for females, but had no significant association with men's work-related RSI risk. Another study examining the impact of having dependent children to care for on work-related back pain showed that women who stood at work, had more than two children, and worked greater than 40 hr/week were at increased risk for back pain compared to women who stood and did not have this combination of risk factors. Given the differences between the present study and this previous study on the outcome variable and the way the dependent children variable was operationalized and combined with other exposures, this difference in findings should not be considered contradictory. Instead, the comparison between these two studies may suggest that the influence of non-work factors such as having dependent children varies depending on the type of work injury and the exposure to other risk factors. In terms of applying the role-enhancement perspective, it is difficult to identify mechanisms whereby caring for dependent children buffers or protects working women from work-related RSIs. A more generic mechanism might be a kind of “healthy worker” effect associated with females engaging in multiple roles. That is, the healthiest women that are least susceptible to work-related RSI, are more likely to take on and maintain both work and child care responsibilities.
Finally, the present study is the first to report that men and women in BC show elevated work-related RSI risk compared to their counterparts in Ontario, even with demographic and work-related variables controlled. One potential explanation of this provincial difference is contextual factors such as differences in OHS legislation, regulations, or enforcement. For instance, cross-provincial analyses of OHS legislation indicated that western provinces such as Alberta had less stringent methods for protecting workers such as rights of participation in workplace safety systems and the right to refuse unsafe work than Ontario [Tucker, 2003]. With regard to British Columbia (BC), however, in the late 1990's BC instituted ergonomic regulations in the workplace. At first glance it appears counter-intuitive that BC put in place stronger measure in protecting workers from RSI, but the work-related RSI risks were higher than other provinces. One way that this pattern may be explained is that the work-related RSI problem in BC was substantially worse, and that the greater work-related RSI burden led to sufficient motivation to pass specific regulations. Sometimes regulatory initiatives have delayed effects, so a future research question would be to examine more recent work-related RSI trends by province. Another more artifactual explanation is that, through passage of specific work-related RSI regulations, BC workers as a group were more aware of the issue of work-related RSIs and were, in turn, more willing to attribute RSIs to work causes and/or report work-related RSIs on the survey.
There are several limitations to this study that need to be noted when interpreting the present results. The research design was cross-sectional and observational, so the causal relationships between variables cannot be determined. Another limitation is that the outcome variable was a self-reported measure that was operationalized quite broadly—for example some other studies on gender differences and work-related MSK complaints obtained detail on which site (e.g., neck and shoulder) rather than in the aggregate as in the present study. In addition, neither job title nor a measure of specific hazard exposures was available in the present health survey. Such potential misspecification issues for both the predictors and the outcome variables would tend to reduce the strength of the associations observed. Two strengths of the present study are its representative sample and the use of the same measure across all provinces regarding work-related RSIs.
The implications of these data are that a more tailored, gender specific approach to prevention is warranted. Also, there appear to be important between-province differences that indicate that monitoring and surveillance of work injury across provinces is an important practical issue for prevention, as well as providing an opportunity to compare occupational health and safety initiatives and performance in the aggregate to other provinces. Also, a fruitful area for future research would be the causes of these provincial differences.
We would like to acknowledge Albana Çanga for her help in preparing the manuscript and Dr. Karen Messing for her comments on a previous draft. This work was supported by the Canadian Institutes of Health Research grant number #93981. Statistics Canada provided access to the survey data for this study. In terms of competing interests, there are none to declare.