Application of Latent Class Analysis to Examine the Association Between Allostatic Load and Profiles of Perceived Stress and Lack of Support Among Firefighters in the Waterloo Region

Abstract

Background and Objectives Firefighters are often exposed to significant occupational hazards due to the demanding nature of their work. They are repeatedly exposed to trauma, physical strain, and emotional pressure because they are daily faced with fire rescue calls that involves live and properties. Such exposures make firefighters vulnerable to events that cause both physical and psychological stress. The cumulative effect of occupational stress among firefighters over the course of their career leads to wear and tear of their body system, and this has negative impact on their health outcomes. Additionally, their perception of stress may influence how they process and respond to everyday demands, which in turn could change their psychological resilience, quality of sleep, emotional regulation, and overall well-being. The effects of perceived stress may be further compounded when social support, which is meant to be a resiliency factor is lacking. With these points in mind, this thesis proposed a new paradigm of using validated instruments from the domains of psychosocial stress (PSS-10, SOOS-14) and social support (MS-PSS, SSS-FF) to develop a multidimensional profile, which we called Perceived Stress and (lack of) Support. In particular, this thesis used Latent Class Analysis as a means of modeling Perceived Stress and (lack of) Social Support Profiles (PSSP) via the aforementioned variables. This new concept of PSSP was motivated and applied to data gathered from firefighters in the City Waterloo. With these data, we examined the association between our proposed PSSP, and physiological stress captured by an Allostatic Load Profiles (ALP; developed for the same participants by Elliot 2024). This approach provided an understanding of how perceived stress and lack of social support can co-exist and their association with physiological wear and tear in this high-risk population.

Methods This study used male-only data from the firefighter’s study in Waterloo. Latent class analysis (LCA) was applied to identify PSSP based on validated psychosocial indicators. Then, the ALP that was previously developed from physiological biomarkers in the same male-only data was used as the outcome variable in logistic regression models to examine its association with PSSP as the predictor variable. Furthermore, these models sequentially adjusted for relevant occupational and behavioural confounders which included length of service, sleep disturbance, alcohol use, exercise, and smoking. Finally, sensitivity analyses were conducted to assess the stability of the aforementioned objectives when using the pooled study sample, which included both male and female firefighters.

Results For the male-only sample, approximately 50% of firefighters were classified into the elevated-ALP, while 70.1% were classified into the high-PSSP. The pooled sample had about 25.4% of firefighters with elevated ALP and 65.1% high PSSP class. Logistic regression results for the male-only sample indicated a positive association between elevated-PSSP and high-ALP; however, this association was not statistically significant in unadjusted (OR = 1.25, 95% CI: 0.38 – 4.51) and adjusted models (OR range 1.01 – 1.10). Adjusted models controlled for length of service, sleep disturbance, alcohol use, exercise, and smoking. Sensitivity analyses yielded similar results with narrow confidence intervals (CI), but still not statistically significant.

Conclusion This study expands understanding of occupational stress in firefighters by showing that perceived stress and lack of social support co-occur as a distinct psychosocial profile. The association between the ‘high perceived stress and lack of social support’ profile and ‘elevated allostatic load’ profile was consistently positive, regardless of adjustment for confounders. Future research should build on these findings by using longitudinal designs and larger samples to inform occupational health interventions that simultaneously reduce stress and strengthen support systems within this unique sample.