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. 2025 Jan 23;16(1):2448385. doi: 10.1080/20008066.2024.2448385

Gender differences in early posttraumatic stress disorder symptoms: a network analysis

Diferencias de género en los síntomas tempranos del trastorno de estrés postraumático: Un análisis de redes

Line Rønning a,*, Rachel L Zelkowitz b,c,*, Marilyn L Piccirillo d,e, Jianlin Liu f, Jordan L Thomas g, Jessy Guler h, J Joana Kyei i, Chris M Hoeboer j, Jeanet F Karchoud k, Miranda Olff j,m, Anke B Witteveen n, Mirjam van Zuiden o,p,CONTACT
PMCID: PMC11758801  PMID: 39846426

ABSTRACT

Background: Despite known gender/sex differences in the prevalence of posttraumatic stress disorder (PTSD), potential differences in the associations among PTSD symptoms between men and women in the early post-trauma period are not well-characterized.

Objective: This study utilized network analysis to assess potential differences in the associations among PTSD symptom clusters between men and women during the early post-trauma period.

Method: We included n = 475 participants (57.5% self-identified women) who recently (≤2 months) experienced an interpersonal or motor vehicle potential traumatic event in the Netherlands. Past month PTSD symptoms were measured with the PTSD Checklist for DSM-5 (PCL-5) and composited according to the five-node PTSD symptom cluster dysphoric arousal model. We estimated the network as well as indices of centrality (strength and predictability) and assessed the stability of the modelled networks in subsamples of men (n = 202) and women (n = 273). We compared network structures using the Network Comparison Test (NCT).

Results: Results largely demonstrated adequate correlation stability for the estimated network structures for women and men. For both men and women, avoidance symptoms were among the strongest nodes with greatest predictability in the networks. In men, anxious arousal additionally showed high strength whereas re-experiencing showed high predictability. In women, re-experiencing symptoms demonstrated both high strength and predictability. The NCT demonstrated nonsignificant differences in global network structure (M = 0.08, p = .054) and strength (S = .073, p = .067). Post hoc comparisons showed an association of re-experiencing symptoms with negative alterations in cognitions and mood in men but not women (E = .038, p = .005).

Conclusion: Results demonstrated possible modest gender differences in aspects of network structure although most elements of the network structure were similar across genders. These results help to characterize gender differences in associations among PTSD symptom clusters during the early post-trauma period, which may inform the potential relevance of future gender-sensitive early intervention strategies to ameliorate the risk for long-term PTSD.

KEYWORDS: PTSD, trauma, network analysis, gender, stress

HIGHLIGHTS

  • Individuals in the early post-trauma period (≤2 months since trauma) self-reported PTSD symptoms.

  • Networks based on the 5-factor dysphoric arousal model of PTSD were compared across self-identified men and women.

  • Avoidance showed high strength in both networks, whereas re-experiencing also showed high strength in women; anxious arousal was among the strongest nodes for men.

  • There were modest, possible differences in aspects of network structure and no differences in network strength between women and men.

1. Introduction

Women are twice as likely to develop posttraumatic stress disorder (PTSD) following a traumatic event compared to men (Tolin & Foa, 2008), and gender differences in rates of PTSD typically manifest soon after trauma exposure. Accordingly, the role of gender (and sex) in the experience of trauma and manifestation of PTSD symptoms has been a central question for the field (Langeland & Olff, 2024). Gender and biological sex have distinct meanings, each with potential relevance to our understanding of the experience of trauma (Langeland & Olff, 2024). Gender encompasses social, cultural, and behavioural characteristics linked to particular gender identities whereas sex refers to the biological status of male, female, or intersex (American Psychological Association, 2023). Although it is recommended to assess these separately (except in circumstances when it might be culturally inappropriate or unethical to do so), disentangling the unique effects of sex vs gender can be quite challenging (Langevin et al., 2024). Accordingly, we refer to sex- and gender-related mechanisms throughout the manuscript. Meta-analytic evidence from prospective and longitudinal cohorts demonstrates that women on average exhibit higher PTSD symptom severity than men during the early post-trauma period (i.e. one to three months post-trauma; Diamond et al., 2022; Haering et al., 2024b). However, there is conflicting evidence on whether these sex- and gender-related differences in early PTSD symptom severity fully explain the differences in longer-term PTSD prevalence between women and men (Haering et al., 2024a; Shalev et al., 2019). Examining the interrelationships among PTSD symptoms may help to advance understanding of sex- and gender-related differences in early PTSD symptom manifestation, given the significant heterogeneity in PTSD (Galatzer-Levy & Bryant, 2013). However, research has largely focused on total symptom severity; thus, additional research evaluating associations among clusters of symptoms is needed.

Previous studies on sex- and gender-related differences in PTSD symptom manifestation, regardless of time frame, have predominantly adopted a latent variable framework to modelling PTSD (e.g. Chung & Breslau, 2008). This framework posits that mental health problems or symptoms are caused by an underlying (i.e. latent) variable or factor that results in symptom covariation. Network analysis offers an alternative framework to conceptualizing mental health symptoms which may provide new insights as to how symptoms interact with one another to maintain PTSD (Birkeland et al., 2017; Borsboom & Cramer, 2013; Gay et al., 2020). The network perspective on mental health suggests that mental health concerns may develop and persist due to interrelationships, i.e. associations among individual symptoms (Borsboom & Cramer, 2013). For instance, within the context of PTSD, nightmares may be directly associated with sleep disturbances, which may in turn be linked to impaired concentration (McNally et al., 2015). Additionally, symptoms that are more central to the network demonstrate stronger and/or more numerous associations with other symptoms within that network, suggesting potential avenues for improved assessment or potential treatment targets.

Examining symptom interrelationships during the early post-trauma period may offer insights to advance understanding of how symptoms cluster together during this critical window. A previous large study of adults with traumatic injuries found that symptom networks during the acute post-trauma period differed from those examined one year later and identified re-experiencing symptoms (i.e. intrusive memories) and physiological reactivity as most central in networks derived from the acute post-trauma period (Bryant et al., 2017). In a follow-up study with an independent sample, Haag et al. (2017) found that re-experiencing symptoms, identified as high-centrality symptoms in Bryant et al.'s (2017) acute symptom networks, measured two weeks post-trauma predicted PTSD diagnosis at six-month follow-up, while PTSD symptoms with lower centrality in these networks had less predictive value. A recent longitudinal study investigating both contemporaneous and temporal networks of PTSD symptoms of intrusions, avoidance and sense of current threat and functional impairments in UK healthcare workers during the COVID-19 pandemic also observed that intrusion symptoms were the most predictive for later avoidance and sense of threat symptoms, as well as various functional impairments, whilst intrusion and avoidance symptoms were mutually reinforcing (Freichel et al., 2024).

The importance of particular PTSD symptoms for subsequent PTSD outcome is supported by additional non-network analytic research demonstrating that re-experiencing as well as hyperarousal symptoms often precede the development of symptoms from other PTSD symptom clusters (Pietrzak et al., 2014; Solberg et al., 2016; Solomon et al., 2009). Collectively, these findings underscore the significance of early PTSD symptom manifestation in response to trauma as predictive for and even potentially driving or reinforcing subsequent PTSD symptom course.

Previous research has suggested potential differences within early post-trauma PTSD symptom manifestation across genders. For example, women reported higher levels of re-experiencing symptoms and physiological reactivity one month following a traumatic event, particularly when encountering reminders of the trauma or during recollection of the event (Fullerton et al., 2001), as compared to men. However, previous work constructing symptom networks has revealed mixed findings regarding sex- and gender-related differences (men vs. women) in PTSD symptom networks and has not focused explicitly on symptom interrelationships during the early post-trauma period. Specifically, two previous studies found that associations between PTSD symptoms, particularly those symptoms labelled most central within the networks, were similar across adult women and men (Birkeland et al., 2017; Gay et al., 2020). A study conducted in disaster-exposed adolescents with significant PTSD symptomatology revealed that girls exhibited stronger overall associations in their network of PTSD symptoms compared to boys (Cao et al., 2019). Additionally, the study found gender-specific patterns in the connectivity of individual symptoms, with intrusions, flashbacks, avoidance, and detachment exhibiting higher centrality (i.e. stronger or more numerous associations with other symptoms) for girls. Conversely, flashbacks, psychological cue reactivity, diminished interest, and foreshortened future demonstrated higher centrality in the boys’ network (Cao et al., 2019).

Investigating potential sex- and gender-related differences in PTSD symptom networks early post-trauma may help to further characterize differences in PTSD symptom manifestation at various stages following trauma exposure between women and men. This could help to inform the necessity of the development or tailoring of sex/gender-sensitive early intervention strategies to prevent chronic PTSD (e.g. provide directions for potential sex/gender-specific intervention targets; Roberts et al., 2019).

Therefore, the aim of the current study was to investigate PTSD symptom cluster-level associations reported by adult self-identified men and women during the early post-trauma period (i.e. within two months of their traumatic experience). We constructed psychological networks of PTSD symptom clusters for men versus women (following the recent approach by Blekić et al., 2023). We utilized a five-node PTSD symptom cluster model, containing the clusters ‘re-experiencing’, ‘avoidance’, and ‘negative alterations in cognition and mood’ as specified in the DSM-5 whilst separating the DSM-5 ‘alterations in arousal and reactivity’ symptom cluster into separate dysphoric arousal and anxious arousal symptom nodes.

We examined dysphoric and anxious arousal separately given evidence that these two arousal clusters play different roles in PTSD, each correlating with unique aspects of current comorbid mental health symptoms and functioning (Pietrzak et al., 2015). More specifically, dysphoric arousal is associated with current depression and generalized anxiety, physical and mental functioning, as well as overall quality of life; whereas anxious arousal does not show these same correlations (Pietrzak et al., 2015). A network study found that symptoms of dysphoric arousal and anxious arousal independently increased over time following a traumatic event. However, no associations between these two symptom clusters were found to emerge (Bryant et al., 2017). This emphasizes the importance of considering anxious arousal and dysphoric arousal as distinct constructs when studying PTSD networks.

In addition to the PTSD symptom cluster networks, we also constructed psychological networks for men versus women using all 20 PTSD symptoms individually. For both network types, we evaluated sex-and gender-related differences by comparing the overall network strength and structure, as well as node strength and predictability. Our aim in the present study was to use network analyses to characterize the data-driven associations between PTSD symptom clusters (or individual symptoms) and to examine whether associations in symptom clusters meaningfully differed between men and women.

2. Methods

2.1. Participants

Data for the present study were selected from an ongoing larger prospective cohort study (Towards Accurate Screening and Prevention [2-ASAP]; Karchoud et al., 2024). The current participants of the 2-ASAP cohort were invited to enrol in the study upon recent contact with Slachtofferhulp Nederland (Victim Support Netherlands), the largest organization in the Netherlands offering emotional and practical support to victims of accidents, crimes, and disasters. Participants in this study were individuals aged 18 years or older who had recently experienced a traffic accident involving injuries or acts of violence such as assault, threat, robbery, or theft by force, fulfilling Criterion A of the DSM-5 (American Psychiatric Association, 2013). Exclusion criteria included exposure to (1) homicidality, suicidality, or injuries resulting from intentional self-harm, (2) ongoing or repeated trauma exposure, or (3) inability to understand study procedures, risks, or otherwise unable to provide informed consent or follow the study protocol. Participants included in the present study were n = 475 individuals self-identifying as either women (n = 273; 57.5%) or men (n = 202, 52.5%). Almost all (99.6%) participants reported gender identifications consistent with their sex assigned at birth (see Table 1). All participants completed a self-report baseline assessment within 2 months of their traumatic experience.

Table 1.

Descriptive characteristics of participants.

  Full sample (N = 475) Women (n = 273) Men (n = 202)  
  % (n) or M(SD) % (n) or M(SD) % (n) or M(SD) p-value
Sex assigned at birth        
 Female 57.9 (275) 100 (273) 1.0 (2) <.001
 Male 42.1 (200) 0 (0) 99.0 (200)  
Age (mean, SD) 46.50 (17.97) 45.44 (17.66) 47.93 (18.34) .110
Relationship status        
 Committed relationship 72.0 (342) 69.6 (190) 75.2 (152) .181
 Not in a committed relationship (single, divorced, or widowed) 28.0 (133) 30.4 (83) 24.8 (50)  
Highest level of education attained       .739
 Less than secondary 1.9 (9) 2.2 (6) 1.5 (3)  
 Secondary or greater 98.1 (466) 97.8 (267) 98.5 (199)  
Employment status        
 Employed 65.5 (311) 63.0 (172) 68.8 (139) .333
 Student 8.2 (39) 9.5 (26) 6.4 (13)  
 Unemployed/not working 26.3 (125) 27.5 (75) 24.8 (50)  
Trauma type       <.001
 Interpersonal 30.9 (147) 24.2 (66) 40.1 (81)  
 Accidental 69.1 (328) 75.8 (207) 59.9 (121)  
PCL-5 total score (mean, SD) 21.98 (17.33) 25.04 (18.14) 17.85 (15.27) <.001
Re-experiencing cluster (mean, SD) 5.27 (5.11) 6.29 (5.37) 3.90 (4.38) <.001
Avoidance cluster (mean, SD) 1.90 (2.37) 2.19 (2.51) 1.51 (2.11) <.001
Negative alterations in cognitions and mood cluster (mean, SD) 7.44 (6.20) 8.13 (6.50) 6.50 (5.63) <.001
Dysphoric arousal (mean, SD) 4.32 (3.66) 4.82 (3.79) 3.65 (3.38) <.001
Anxious arousal (mean, SD) 3.06 (2.56) 3.61 (2.60) 2.31 (2.30) <.001
Time since trauma at PCL-5 assessment (days) 46.87 (8.39) 46.38 (8.44) 47.52 (8.29)  .113
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Note: Group differences in categorical variables were tested with Fisher’s exact tests; group differences in continuous variables were tested using Mann-Whitney U tests.

2.2. Procedure

All study procedures were approved by the ethical review board at the Amsterdam University Medical Center, location AMC. Potential participants were first approached by Slachtofferhulp Nederland by sending a support letter and information flyer to trauma-exposed individuals between 18 and 24 days after their traumatic experience. Interested individuals contacted the research team at Amsterdam University Medical Center and were screened for study inclusion and exclusion criteria. Eligible participants provided informed consent before completing an online self-report baseline questionnaire within 2 months after exposure to their traumatic event. Participants received a total incentive of 50 euros via gift cards for completing all 2-ASAP procedures including multiple follow-up assessments until one-year post-trauma.

2.3. Measures

Participants provided their demographic characteristics (i.e. sex assigned at birth, gender, age, educational level, marital status, occupational status, and children and living situation). Researchers classified trauma type as interpersonal (physical assault) or motor vehicle accident (MVA) based upon participants’ verbal descriptions during screening. The validated Dutch version of the PTSD Checklist for DSM-5 (PCL-5; Blevins et al., 2015; Hoeboer et al., 2024; Weathers et al., 2013) was used to measure past month PTSD symptoms in relation to the index trauma (i.e. interpersonal or MVA) at baseline. The PCL-5 is widely used and has demonstrated high reliability and validity metrics (Blevins et al., 2015; Hoeboer et al., 2024). In the present sample, Cronbach's alpha was .943 for the whole questionnaire, and ranged from .757 (dysphoric arousal) to .897 (re-experiencing) across the five-node symptom cluster PTSD model.

2.4. Data analysis

Power analysis for network analysis was calculated using the R package powerly, in line with recent recommendations for cross-sectional network analyses (Constantin et al., 2023). For a randomly generated Gaussian graphical model (GGM) with five nodes (reflecting the five-cluster/node dysphoric arousal model) and the following parameters: .9 density, .6 sensitivity, and .80 power, a sample size of 315 was required. Nodes in the network consisted of average composite scores reflecting the five-cluster PCL-5 dysphoric arousal model calculated by summing the designated PCL-5 items within each cluster (see Note Figure 1). We first examined the distribution of composite scores, which were non-Gaussian. Thus, we constructed a mixed graphical model (mgm), to model each cluster using a Poisson distribution for gender subsamples. The mgm computed regularized generalized regressions for all edges. The graphical Least Absolute Shrinkage and Selection Operator algorithm with the Extended Bayesian Information Criteria (EBICglasso) and γ = 0.5 was used to identify the most parsimonious pathways in the network by shrinking estimates from potentially spurious edges.

We calculated two centrality metrics to describe the impact of each node within the network: (1) strength and (2) predictability. Strength indices were presented as standardized z-scores, with higher scores reflecting stronger (or more numerous) associations with other nodes in the network (Bringmann et al., 2019). Predictability indices were presented as R2 values, with higher scores reflecting a greater amount of variance in that node explained by other components of the network (Haslbeck & Waldorp, 2018).

We evaluated the stability of node centrality indices and edge weights using the estimatenetwork function from the R package bootnet package (Epskamp et al., 2018), which can be used to calculate correlation stability coefficients. These can range from 0 to 1; as a guideline, stability estimates of at least .25 are desired (Epskamp et al., 2018). We tested whether there were differences in the overall network structure, as well as the global strength between men and women using the networkcomparisontest function with alpha set at .05 (Van Borkulo et al., 2023). Overall network structure refers to the constellation of edge weights in the network (Van Borkulo et al., 2023), whereas global strength refers to the overall connectivity or the weighted absolute sum of all edges in the network (Opsahl et al., 2010; Van Borkulo et al., 2023). Thus, differences in the overall network structure between men and women would suggest that any edges in one gender’s network are significantly different from edges in the other gender network (Van Borkulo et al., 2023). Differences in global strength would suggest that the absolute sum of edge weights in one gender’s network is significantly different from the absolute sum of edge weights in the other gender’s network (Van Borkulo et al., 2023). We applied the Benjamini and Hochberg (1995) correction for post hoc comparisons of individual nodes and edges across men and women. Results of all bootstrap analyses from both the estimatenetwork and networkcomparisontest functions were based on 10,000 iterations. We visualized networks using qgraph.

We also constructed an exploratory item-level network using PCL-5 data to contextualize our results with those conducted in prior literature (e.g. Gay et al., 2020). Power analyses and stability estimates from case-dropping bootstrapping were low, thus we did not interpret these networks (see Supplementary Material). All analyses were conducted using R (R: The R Project for Statistical Computing, 2023).

3. Results

Participant and trauma characteristics are presented in Table 1. There were no significant gender differences across demographic characteristics. However, interpersonal trauma was significantly more frequent in men than in women, whilst women had significantly higher PCL-5 total scores and scores on all 5 clusters than men.

The estimated network structure for women generally demonstrated adequate stability (see Figures 1 and 2, left panels; CSstrength = .21, CSedge = .59). The re-experiencing and avoidance cluster were the strongest nodes in the network and demonstrated highest predictability (see Table 2 and Figure 3, left panel). The edge between re-experiencing and avoidance was significantly stronger than edges between avoidance and negative alterations in cognitions and mood, re-experiencing and dysphoric arousal, dysphoric arousal and anxious arousal, and negative alterations in cognitions and mood and anxious arousal (see Table 3 and Figure 4, left panel).

Figure 1.

Figure 1.

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Network Models Using 5-Cluster PTSD Model Subscales for Women (left) and Men (right).

Notes: Reexp = re-experiencing symptoms (PCL-5 B1, B2, B3, B4, B5); Avoid = avoidance symptoms (PCL-5 C1, C2); NACM = negative alterations in cognition and mood symptoms (PCL-5 D1, D2, D3, D4, D5, D6, D7); DA = dysphoric arousal symptoms (PCL-5 E1, E2, E5, E6); AA = anxious arousal symptoms (PCL-5 E3, E4). Green lines show associations (edges) between symptom clusters (nodes) in the networks; inset numbers refer to estimates (bootstrapped edge weights). Visualizations of edge weights were determined within each gender-stratified network separately, thus edge weights of similar values may have slightly different thicknesses across networks. Navy blue rings denote the degree to which a given node is predicted by other symptom clusters in the network (predictability).

Figure 2.

Figure 2.

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Stability of Node Strength (top) and Edge Weight Estimates (bottom) for Women (left) and Men (right).

Notes: Stability plots demonstrate the average correlation between strength and edge weight estimates calculated from the original networks with strength and edge weight estimates calculated from 10,000 bootstrapped networks sampled with increasingly smaller subsets of data. Shading around the line plot reflects the 95% confidence interval around the average correlation estimate.

Table 2.

Node-wise strength and predictability estimates from MGM networks by gender.

  Men Women
Node Strength Predictability Strength Predictability
Avoidance cluster 0.24 0.57 0.20 0.63
Anxious arousal cluster 0.27 0.44 0.18 0.47
Dysphoric arousal cluster 0.12 0.46 0.14 0.56
Negative alterations in cognitions and mood cluster 0.19 0.41 0.15 0.59
Re-experiencing cluster 0.20 0.59 0.21 0.66
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Figure 3.

Figure 3.

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Node Strength for PTSD Cluster in Women (left) and Men (right).

Notes: Reexp = re-experiencing symptoms (PCL-5 B1, B2, B3, B4, B5); Avoid = avoidance symptoms (PCL-5 C1, C2); NACM = negative alterations in cognition and mood symptoms (PCL-5 D1, D2, D3, D4, D5, D6, D7); DA = dysphoric arousal symptoms (PCL-5 E1, E2, E5, E6); AA = anxious arousal symptoms (PCL-5 E3, E4). Plots depict raw values.

Table 3.

Edge-weight estimates from MGM networks by gender.

  Men Women
Edge (Node 1 – Node 2) Estimate
M (SD) [95% CI]		 Estimate
M (SD) [95% CI]
Avoid. – Anx. arousal 0.14 (0.03) [0.07, 0.20] 0.05 (0.03) [-0.004, 0.11]
Avoid. – Dys. arousal 0.00 (0.02) [-0.04, 0.04] 0.00 (0.01) [-0.03, 0.03]
Avoid. – NACM 0.05 (0.02) [0.01, 0.10] 0.05 (0.01) [0.02, 0.07]
Dys. arousal – Anx. arousal 0.02 (0.02) [-0.02, 0.07] 0.03 (0.02) [-0.01, 0.07]
NACM – Anx. arousal 0.02 (0.02) [-0.01, 0.06] 0.03 (0.01) [-0.001 0.06]
NACM – Dys. arousal 0.07 (0.01) [0.05, 0.10] 0.07 (0.01) [0.06, 0.09]
Reexp. – Anx. arousal 0.09 (0.02) [0.05, 0.12] 0.07 (0.01) [0.05, 0.10]
Reexp. – Avoid. 0.05 (0.03) [-0.01, 0.10] 0.10 (0.02) [0.07, 0.13]
Reexp. – Dys. arousal 0.03 (0.02) [-0.01, 0.07] 0.04 (0.01) [0.02, 0.06]
Reexp. – NACM 0.04 (0.01) [0.01, 0.06] 0.00, (0.01) [-0.01, 0.01]
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Notes: Avoid = Avoidance cluster; Anx. arousal = Anxious arousal cluster; Dys. arousal = Dysphoric arousal cluster; NACM = Negative alterations in cognitions and mood cluster; Reexp = Re-experiencing cluster. M and SD refers to the mean and standard deviation of the estimates for that parameter obtained from bootstrapping. 95% CI refers to the range of likely values of the parameter, calculated with 95% certainty.

Figure 4.

Figure 4.

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Edge weight differences between PTSD Cluster for Women (left) and Men (right).

Note: Reexp = re-experiencing symptoms (PCL-5 B1, B2, B3, B4, B5); Avoid = avoidance symptoms (PCL-5 C1, C2); NACM = negative alterations in cognition and mood symptoms (PCL-5 D1, D2, D3, D4, D5, D6, D7); DA = dysphoric arousal symptoms (PCL-5 E1, E2, E5, E6); AA = anxious arousal symptoms (PCL-5 E3, E4). Pairs of edges that differ significantly within the subsamples (i.e. women vs. men) are denoted via a black box. Grey shading denotes nonsignificant differences between pairs of edge weights.

The estimated network structure for men (Figure 1, right panel) also demonstrated adequate stability (see Figure 2, left panels; CSstrength = .36, CSedge = .36). The anxious arousal and avoidance clusters were the strongest network nodes and the re-experiencing and avoidance clusters demonstrated highest predictability (see Table 2 and Figure 3, right panel). The edge connecting avoidance and anxious arousal was significantly stronger than edges connecting re-experiencing and negative alterations in mood and cognitions, re-experiencing and dysphoric arousal, negative alterations in mood and cognitions and anxious arousal, and dysphoric and anxious arousal (see Table 3 and Figure 4, right panel).

The Network Comparison Test revealed a marginally statistically significant difference in global network structure (M = 0.085, p = .054), but not strength (S = 0.073, p = .067) across men and women. We proceeded with post-hoc comparisons of edge-weights, but results should be interpreted cautiously in light of the marginally significant global test. Post-hoc comparisons of the edge weights showed a significantly stronger edge between re-experiencing and negative alterations in cognitions and mood in men versus women (E = .038, p = .005). As the stability coefficients for both the edge weight and node strength estimates in the item-level models were demonstrably low for men and women (CS < .01); we did not interpret these exploratory item-level models further. We present the visualizations for item-level networks by gender, along with the respective stability plots, in the Supplementary Material.

4. Discussion

The current study applied network analytic techniques to examine potential gender differences in associations between PTSD symptom clusters in a trauma-exposed Dutch adult sample during the early post-trauma period following direct exposure to interpersonal violence or motor vehicle accidents. This is consistent with broader efforts in the field to characterize gender differences and similarities in the experience and course of PTSD symptoms following trauma exposures (e.g. Langeland & Olff, 2024). Notably, our study employed a five-node symptom cluster model, which separates the DSM-5 ‘alterations in arousal and reactivity’ symptom cluster into distinct dysphoric arousal and anxious arousal symptom nodes. This approach allowed us to more thoroughly characterize PTSD symptom interrelationships, specifically associations between facets of arousal and other PTSD symptom clusters.

Our results revealed marginally significant sex/gender differences in the global structure but not global strength of the networks. This means that the constellation of edge weights in the network for women differed somewhat from the constellation of edge weights in the network for men; however, the summed absolute strength of the edge weights (reflecting overall connectivity) across the two networks did not differ significantly. These findings are largely congruent with previous literature on sex/gender comparisons in PTSD symptom network structure and strength (Birkeland et al., 2017; Cao et al., 2019; Gay et al., 2020). Prior studies, investigating trauma-exposed adults in a later post-trauma period than the current investigation, found no significant sex/gender differences in global network structure, nor significant gender differences in strength between male and female networks (Birkeland et al., 2017; Gay et al., 2020). Participants in the current study were surveyed, on average, less than two months following trauma exposure. Taken collectively, results of these investigations might suggest that any variations in early post-trauma PTSD symptom networks between women and men diminish over time (e.g. Gay et al., 2020). Longitudinal data will be required to test this, however.

For women, re-experiencing and avoidance emerged as the strongest PTSD symptom cluster nodes in the network, with the edges between re-experiencing and avoidance being significantly stronger than other edges in the network. A previous study of survivors of the Utøya Island terrorist attack in Norway found that re-experiencing symptoms were more commonly endorsed among women, although interrelationships of re-experiencing with other PTSD symptoms were not examined (Glad et al., 2023). The findings from our network analyses are in line with previous network studies of PTSD symptoms among a combined sample of men and women which has found re-experiencing symptoms to be highly central to other symptoms in the peri-traumatic and acute post-trauma period (Bryant et al., 2017) and later post-trauma period (Armour et al., 2017; McNally et al., 2015). The results are also in line with network studies exploring gender-specific PTSD symptoms networks approximately 2.5 years following natural disaster exposure, finding intrusive recollections, flashbacks, avoidance, and detachment to be among the most strongly connected symptoms in disaster-exposed adolescent girls (Cao et al., 2019). It also seems consistent with the understanding that intrusive memories are often avoided because of their distressing content (e.g. Ehlers & Clark, 2000). These findings also align with theories suggesting that maladaptive traumatic memories, manifesting as re-experiencing symptoms, form the link between traumatic experiences and other PTSD symptoms (Rubin et al., 2008). In line with this notion, a temporal network analysis in UK healthcare workers during the COVID-19 pandemic revealed that intrusive symptoms were most predictive for later PTSD symptoms, and that intrusive and avoidance symptoms were mutually reinforcing over time (Freichel et al., 2024).

For men, the anxious arousal and avoidance nodes emerged as strongest PTSD symptom cluster nodes, although we note again that the network strength did not differ significantly from that of women. Edges connecting anxious arousal and avoidance were significantly stronger than the other edges within the network for men. Tentatively, this finding in men may have been influenced by societal gender norms and roles influencing the way men and women perceive and express distress. For example, in reaction to a stressful or traumatic event, men may demonstrate increased physical restlessness (included in the anxious arousal node) and engage in numerous activities or consuming alcohol to diminish this (Kaiser et al., 2020).

Notably, the edge connecting re-experiencing symptoms and negative alterations in cognitions and mood differed significantly across genders (present for men, absent for women). If replicated in further studies, this could suggest unique associations of re-experiencing symptoms and posttraumatic cognitions and mood disturbances among men. A recent investigation of gender differences in a sample of cannabis users with trauma histories showed that among men only, sexual assault survivors endorsed significantly higher negative alterations in cognitions and mood than those exposed to other traumas (Stewart et al., 2024). Thus, specific traumatic experiences such as sexual assault might amplify the association between the re-experiencing and negative alterations in cognitions and mood clusters among men, possibly due to stigma that amplifies feelings of self-blame, guilt, and negative beliefs. Important research questions include determining the direction of influence (i.e. what is the direction of the association between re-experiencing symptoms and negative alterations in cognitions and mood?), and identifying if re-experiencing symptoms relate differently to negative cognitions versus mood.

The results of this study should be considered in light of certain limitations. Firstly, we examined cross-sectional associations between symptoms; future research using longitudinal data is critical in order to characterize whether and how symptom-symptom associations manifest and change over time. Second, these data were obtained via self-report. Future network analysis research should use clinical interviews to capture information on PTSD symptom presentation. Thirdly, the relatively low sample size for our sex/gender subsets may have limited the statistical power available to detect sex- and gender-related differences and the accuracy of the estimated symptom node networks (see e.g. Epskamp et al., 2018), as well as contributed to the lower stability of the node strength estimates in the women’s network and reliability of the item-level networks in both sexes/genders. Relatedly, our sample was limited to self-identified women and men, and almost all participants were cisgender. This precluded any comparisons with gender-diverse individuals or efforts to parse the impact of sex versus gender. These are limitations of the literature more broadly (Langeland & Olff, 2024) and reflect an important gap to address in subsequent work.

Thus, future research using larger epidemiological samples collected during the early post-trauma phase are needed to examine interrelationships between PTSD symptom clusters and at the item level using network analytic techniques with adequate statistical power. A future prospective study focusing on individual variation seems promising, potentially through an individual patient data approach (e.g. individual participant data meta-analyses; Tierney et al., 2023) that leverages large-scale prospective data. Such an approach could significantly enhance our understanding of PTSD symptom dynamics, potentially allowing us to predict treatment response by examining early improvements in specific PTSD symptoms at the individual level, as well as interactions between early symptom changes and gender.

Such epidemiology-focused efforts would also allow researchers to disentangle the effects of gender and type of trauma experienced, which we were unable to accomplish in this present work due to sample size limitations. Previous research has shown that type of trauma is associated with the presentation and severity of PTSD symptoms across (Benfer et al., 2018; Birkeland et al., 2022; Ditlevsen & Elklit, 2012; Ferreira et al., 2022; Stefanovic et al., 2022) and within sexes/genders (Søegaard et al., 2021), and as such may further interact with sex- and gender-related factors in the development of PTSD symptoms. This is particularly pertinent in our study, where men more frequently reported interpersonal violence as the index trauma compared to women. Therefore, it is possible that the effects currently attributed to gender in our study may be at least partially influenced by gender differences in the type of trauma experienced. Additionally, meta-analytic evidence shows that differences in PTSD symptom severity between men and women emerges within the first month post-trauma (Haering et al., 2024). In line with this, the women in our sample reported significantly higher total PTSD symptom severity than the men, and we cannot conclusively disentangle the impact of gender from that of overall symptom severity. However, selecting our sample based on symptom severity could have induced Berkson’s bias (de Ron et al., 2021), and we were thus unable to account for these differences in the current study.

Women face greater prevalence and conditional risk of PTSD relative to men (Tolin & Foa, 2008), suggesting the possible value of sex-/gender-informed PTSD (prognostic) assessment and (early) intervention efforts, although any clinical decision-making should be driven by an individual’s unique presentation. The results of this study demonstrated possible modest sex/gender differences in the overall network structure of PTSD symptom clusters during the early post-trauma phase, although the associations between specific PTSD symptom clusters were largely consistent across women and men in our sample. In women, the re-experiencing symptom cluster demonstrated particularly high strength whereas in men, anxious arousal was among the most strongly connected symptom clusters. Furthermore, avoidance emerged as a particularly central cluster in both men and women. Thus, our findings underscore the importance of understanding both gender similarities and possible differences in the development of early PTSD symptoms. Such understanding may provide valuable insights into the mechanisms driving later development of PTSD. Although clinical decision-making should always be based on careful assessment of an individual’s unique symptom presentation and circumstance, understanding these mechanisms may ultimately aid in creating more effective PTSD prevention and intervention strategies for self-identified men and women.

Supplementary Material

figure_s1_layout.tif
ZEPT_A_2448385_SM5251.tif (2.5MB, tif)
fig_s2_layout.tif
ZEPT_A_2448385_SM5250.tif (1.3MB, tif)

Acknowledgements

We acknowledge the 2-ASAP consortium partners and advisors for their collaboration and input, in particular our recruitment site Victim Support Netherlands (Slachtofferhulp Nederland) for their efforts in participant recruitment for the current study.

Funding Statement

The 2-ASAP study was funded by the Netherlands Organization for Health Research and Development (ZonMw; #636340004). Marilyn L. Piccirillo received support by a grant from National Institute of Health (K99AA029459, R00AA029459). Rachel L. Zelkowitz received support from the Career Development Award – Veterans Affairs (VA CSR&D IK2CX002439).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on reasonable request from the corresponding author and will be made available on OSF after completion of the 2-ASAP cohort study.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

figure_s1_layout.tif
ZEPT_A_2448385_SM5251.tif (2.5MB, tif)
fig_s2_layout.tif
ZEPT_A_2448385_SM5250.tif (1.3MB, tif)

Data Availability Statement

The data that support the findings of this study are available on reasonable request from the corresponding author and will be made available on OSF after completion of the 2-ASAP cohort study.

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