Commentary
Workplace climate, employee actions, work injury and structural equation modelling Peter M Smith The paper by Swedler et al1 provides us with new insights into the relationship between workplace factors, safety performance (in this case, the probability of employees wearing slip-resistant shoes), and risk of slip injuries over a 12-week follow-up period. While one could get caught up with potential sample selection issues (eg, the 38% response rate) or measurement issue (if wearing slip-resistant shoes, which required an out-of-pocket purchase by the employee, accurately represents safety performance), the positive aspects of this paper are more worthy of comment. In particular, the use of structural equation modelling (SEM) allows insights into the relationship between the main independent variables and the outcome, which would likely have been missed using a more traditional regression approach.2 In addition to practical issues of model identification, the SEM process requires the one to distinguish between variables that confound the relationship between independent variable(s) and the outcome, and variables that mediate the relationship, with these choices based on a good theoretical justification.3 In the current paper, for example, poor safety climate and not wearing slip-resistant shoes are both risk factors for slip injuries. However, the figure presented by the authors suggests that wearing slip-resistant shoes mediates part of the relationship between safety climate and risk of slip injuries. In other words, wearing slip-resistant shoes is more proximal to the outcome than safety climate. Conversely, age, gender and other factors are positioned as confounders in the relationship between slip-resistant shoes and risk of slip injuries (ie, they are not part of the causal path between wearing slip-resistant shoes and slip injuries). The resulting model demonstrates that while safety climate does not have a direct impact on slip injuries, it is associated Correspondence to Dr Peter M Smith, Institute for Work & Health, 481 University Avenue, Suite 800, Toronto, Ontario, Canada M5G 2E9; [email protected]
with wearing slip-resistant shoes which is directly associated with slip injuries. Ideally, the authors might also have reported whether this indirect pathway (from safety climate to slip resistant shoes to slip injuries) is also statistically significant. The take-away message is, however, that poor safety climate and not wearing slip-resistant shoes are important risk factors for slip injuries, with safety climate being a more distal measure in the relationship. Had the authors used a single regression model with all the study variables included, based on the estimates presented at the bottom of table 3, they may have erroneously concluded that not wearing slip-resistant shoes and being younger were risk factors for slip injuries, while safety climate was not.4 There are at least two other advantages of SEM and the statistical package used by the authors. First, the building and modification of the model is guided by theoretical norms and achieving a good model fit. Good model fit indicates that in the final model, the relationships specified between variables (including where no relationship is specified) is consistent with the data used.5 Second, SEM (and Mplus) circumvents problems encountered when using change in regression estimates for the independent variable in models without and with the mediating variable as a measure of the amount of mediation when the outcome is not normally distributed,6 as is often the case in occupational studies.7 8 SEM (and path models, where there are no latent constructs present) can also be extended to include multiple pathways between an independent variable and an outcome so that the relative importance of each pathway can be examined.9 One of the paper’s more concerning findings was the lack of awareness of the risks associated with slip injuries in the study’s fast-food establishments, along with the lack of prevention efforts to address these risks. Over the 12-week period, study participants averaged 3.08 slips each, which would equate to more than 13 slips per employee per year. Given the effectiveness of slip-resistant shoes on
Smith PM. Occup Environ Med July 2015 Vol 72 No 7
preventing slip injuries, it is unfortunate that only 6 of the 36 restaurants provided slip-resistant shoes at no cost to their employees1 (and these six establishments were removed from analysis in the current paper). Since the employees in the remaining 30 workplaces had to pay for their own personal protective equipment, it is not surprising that groups associated with lower socioeconomic status (such as nonwhite ethnic groups and those working fewer hours) were least likely to wear slip-resistant shoes. Yet, the majority of employees in these 30 establishments agreed with the statement that ‘the management team provides all the equipment necessary to do the job safely’ (see appendix 1, where the mean scores for this item were 4.46 of 5). The effective primary prevention of workplace injuries requires that employees and employers work together to make the workplace safer. This should include employers providing personal protective equipment to protect workers from hazards that exist in their workplace. Based on the results of this study, it is alarming that fast-food employees have such low expectations of their employers in helping to protect them from slip injuries. Competing interests None. Provenance and peer review Commissioned; internally peer reviewed.
To cite Smith PM. Occup Environ Med 2015;72:465– 466. Received 17 February 2015 Accepted 22 February 2015 Published Online First 10 March 2015
▸ http://dx.doi.org/10.1136/oemed-2014-102496 Occup Environ Med 2015;72:465–466. doi:10.1136/oemed-2015-102850
REFERENCES 1
2
3
Swedler DI, Verma SK, Huang Y-H, et al. A structural equation modelling approach examining the pathways between safety climate, behaviour performance and workplace slipping. Occup Environ Med 2015;72: 476–81. Victora CG, Huttly SR, Fuchs SC, et al., The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. Int J Epiemiol 1997;26:224–7. Kline RB. Principles and practice of structural equation modeling. New York: The Guilford Press, 1998.
465
Commentary 4
5
6
466
Weitkunat R, Wildner M. Exploratory causal modeling in epidemiology: are all factors created equal? J Clin Epidemiol 2002;55:436–44. Bollen K, Lennox R. Conventional wisdom on measurement: a structural equation perspective. Psychol Bull 1991;110:305–14. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research:
7
8
conceptual, strategic, and statistical considerations. J Personal Soc Psychol 1986;51:1173–82. Lange T, Vansteelandt S, Bekaert M. A simple unified approach for estimating natural direct and indirect effects. Am J Epidemiol 2012;176:190–5. Kaufman JS, Maclehose RF, Kaufman SA. A further critique of the analytic strategy of adjusting for
9
covariates to identify biologic mediation. Epidemiol Perspect Innov 2004;1:5573. Smith P, Bielecky A, Ibrahim S, et al., Impact of pre-existing chronic conditions on age differences in sickness absence after a musculoskeletal work injury: a path analysis approach. Scand J Work Env Health 2014;40:167–75.
Smith PM. Occup Environ Med July 2015 Vol 72 No 7
Workplace climate, employee actions, work injury and structural equation modelling Peter M Smith The paper by Swedler et al1 provides us with new insights into the relationship between workplace factors, safety performance (in this case, the probability of employees wearing slip-resistant shoes), and risk of slip injuries over a 12-week follow-up period. While one could get caught up with potential sample selection issues (eg, the 38% response rate) or measurement issue (if wearing slip-resistant shoes, which required an out-of-pocket purchase by the employee, accurately represents safety performance), the positive aspects of this paper are more worthy of comment. In particular, the use of structural equation modelling (SEM) allows insights into the relationship between the main independent variables and the outcome, which would likely have been missed using a more traditional regression approach.2 In addition to practical issues of model identification, the SEM process requires the one to distinguish between variables that confound the relationship between independent variable(s) and the outcome, and variables that mediate the relationship, with these choices based on a good theoretical justification.3 In the current paper, for example, poor safety climate and not wearing slip-resistant shoes are both risk factors for slip injuries. However, the figure presented by the authors suggests that wearing slip-resistant shoes mediates part of the relationship between safety climate and risk of slip injuries. In other words, wearing slip-resistant shoes is more proximal to the outcome than safety climate. Conversely, age, gender and other factors are positioned as confounders in the relationship between slip-resistant shoes and risk of slip injuries (ie, they are not part of the causal path between wearing slip-resistant shoes and slip injuries). The resulting model demonstrates that while safety climate does not have a direct impact on slip injuries, it is associated Correspondence to Dr Peter M Smith, Institute for Work & Health, 481 University Avenue, Suite 800, Toronto, Ontario, Canada M5G 2E9; [email protected]
with wearing slip-resistant shoes which is directly associated with slip injuries. Ideally, the authors might also have reported whether this indirect pathway (from safety climate to slip resistant shoes to slip injuries) is also statistically significant. The take-away message is, however, that poor safety climate and not wearing slip-resistant shoes are important risk factors for slip injuries, with safety climate being a more distal measure in the relationship. Had the authors used a single regression model with all the study variables included, based on the estimates presented at the bottom of table 3, they may have erroneously concluded that not wearing slip-resistant shoes and being younger were risk factors for slip injuries, while safety climate was not.4 There are at least two other advantages of SEM and the statistical package used by the authors. First, the building and modification of the model is guided by theoretical norms and achieving a good model fit. Good model fit indicates that in the final model, the relationships specified between variables (including where no relationship is specified) is consistent with the data used.5 Second, SEM (and Mplus) circumvents problems encountered when using change in regression estimates for the independent variable in models without and with the mediating variable as a measure of the amount of mediation when the outcome is not normally distributed,6 as is often the case in occupational studies.7 8 SEM (and path models, where there are no latent constructs present) can also be extended to include multiple pathways between an independent variable and an outcome so that the relative importance of each pathway can be examined.9 One of the paper’s more concerning findings was the lack of awareness of the risks associated with slip injuries in the study’s fast-food establishments, along with the lack of prevention efforts to address these risks. Over the 12-week period, study participants averaged 3.08 slips each, which would equate to more than 13 slips per employee per year. Given the effectiveness of slip-resistant shoes on
Smith PM. Occup Environ Med July 2015 Vol 72 No 7
preventing slip injuries, it is unfortunate that only 6 of the 36 restaurants provided slip-resistant shoes at no cost to their employees1 (and these six establishments were removed from analysis in the current paper). Since the employees in the remaining 30 workplaces had to pay for their own personal protective equipment, it is not surprising that groups associated with lower socioeconomic status (such as nonwhite ethnic groups and those working fewer hours) were least likely to wear slip-resistant shoes. Yet, the majority of employees in these 30 establishments agreed with the statement that ‘the management team provides all the equipment necessary to do the job safely’ (see appendix 1, where the mean scores for this item were 4.46 of 5). The effective primary prevention of workplace injuries requires that employees and employers work together to make the workplace safer. This should include employers providing personal protective equipment to protect workers from hazards that exist in their workplace. Based on the results of this study, it is alarming that fast-food employees have such low expectations of their employers in helping to protect them from slip injuries. Competing interests None. Provenance and peer review Commissioned; internally peer reviewed.
To cite Smith PM. Occup Environ Med 2015;72:465– 466. Received 17 February 2015 Accepted 22 February 2015 Published Online First 10 March 2015
▸ http://dx.doi.org/10.1136/oemed-2014-102496 Occup Environ Med 2015;72:465–466. doi:10.1136/oemed-2015-102850
REFERENCES 1
2
3
Swedler DI, Verma SK, Huang Y-H, et al. A structural equation modelling approach examining the pathways between safety climate, behaviour performance and workplace slipping. Occup Environ Med 2015;72: 476–81. Victora CG, Huttly SR, Fuchs SC, et al., The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. Int J Epiemiol 1997;26:224–7. Kline RB. Principles and practice of structural equation modeling. New York: The Guilford Press, 1998.
465
Commentary 4
5
6
466
Weitkunat R, Wildner M. Exploratory causal modeling in epidemiology: are all factors created equal? J Clin Epidemiol 2002;55:436–44. Bollen K, Lennox R. Conventional wisdom on measurement: a structural equation perspective. Psychol Bull 1991;110:305–14. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research:
7
8
conceptual, strategic, and statistical considerations. J Personal Soc Psychol 1986;51:1173–82. Lange T, Vansteelandt S, Bekaert M. A simple unified approach for estimating natural direct and indirect effects. Am J Epidemiol 2012;176:190–5. Kaufman JS, Maclehose RF, Kaufman SA. A further critique of the analytic strategy of adjusting for
9
covariates to identify biologic mediation. Epidemiol Perspect Innov 2004;1:5573. Smith P, Bielecky A, Ibrahim S, et al., Impact of pre-existing chronic conditions on age differences in sickness absence after a musculoskeletal work injury: a path analysis approach. Scand J Work Env Health 2014;40:167–75.
Smith PM. Occup Environ Med July 2015 Vol 72 No 7
