First Responders (FRs), such as law enforcement officers (LEOs), firefighters (FFs), and emergency medical technicians (EMTs) are frequently exposed to traumatic events as part of their job. As a result, individuals with these jobs often disproportionately present with symptoms of depression, generalised anxiety disorder (GAD) and post-traumatic stress disorder (PTSD) compared to the general population, and are at increased risk of suicide (Tiesman et al., 2021; Walker et al., 2016). Untangling the co-occurrence of these three mental health conditions is of key importance for both clinical and non-clinical researchers, as it can make accurate diagnosis and subsequent treatment difficult, leading to underdiagnosis. Although transdiagnostic approaches have significantly improved our understanding and treatment outcomes, the connections between the symptoms of these overlapping disorders remain unclear, alongside whether commonalities extend to underlying mechanisms beyond negative affectivity, as proposed by the tripartite model (Clark & Watson, 1991).
Understanding these connections is crucial because it will shed light on the shared and the unique characteristics of each disorder, contributing to more precise diagnosis and more effective treatments. To that end, Baker et al. (2023) explored this research gap by using network analysis to identify patterns of symptoms within PTSD, depression, and GAD among FRs. Network analysis is a method that helps researchers create a map of symptoms to see how they connect and influence each other, with symptoms being dots on the map, and lines between them showing how strongly they are related. As First Responders suffer disproportionately from PTSD, depression and anxiety, understanding symptom connections can help unveil intertwined patterns and mechanisms.
Data were collected from 432 US first responders (FRs) seeking treatment, with the majority being law enforcement officers (LEOs), firefighters (FFs), and emergency medical technicians (EMTs). The analysis revealed significant rates of probable PTSD, severe depression, and severe GAD among the participants. Network analysis identified six communities of symptoms, showing how symptoms clustered within and across diagnostic categories. Key symptoms like feeling unable to relax and experiencing intrusive thoughts were found to have strong correlations among First Responders.
Baker et al. (2023)’s network analysis supports the complex interplay between symptoms within and across PTSD, depression, and anxiety in first responders, emphasizing the need for tailored, transdiagnostic interventions that address key symptoms. The findings highlight the interrelated nature of mental health symptoms beyond diagnostic categories, suggesting the importance of addressing influential symptoms to improve treatment outcomes for FRs.
Overall, while the study provides valuable insights, limitations such as reliance on self-report assessments and cross-sectional data, warrant caution in interpreting the results. Future research should explore differences in symptom networks among subgroups of FRs and replicate the study to ensure the reliability of the findings across various samples and populations.
