Prevalence and network structure of post-traumatic stress symptoms and their association with suicidality among Chinese mental health professionals immediately following the end of China's Dynamic Zero-COVID Policy: a national survey

The COVID-19 pandemic prompted China’s Dynamic Zero-COVID Policy, which, while effective at controlling transmission, carried psychosocial costs, including increased risks of PTSD-related symptoms. Mental health professionals (MHPs) faced heightened exposure to stressors (e.g., marginalized psychiatric services, training gaps for public health emergencies, exposure to infected inpatients). Traditional approaches treat PTSD symptoms as interchangeable, whereas network analysis highlights interconnections and central symptoms that may be intervention targets. After China’s policy termination in December 2022, it remained unclear how PTSS prevalence, correlates, and symptom network structure manifested in MHPs, and which symptoms linked to suicidality. This study aimed to estimate PTSS prevalence, identify demographic/clinical correlates, model the PTSS symptom network post-policy termination, and determine specific PTSS directly associated with suicidality among Chinese MHPs.

Prior outbreaks showed elevated PTSS in healthcare workers (HCWs), with estimates during COVID-19 ranging approximately 13–25.6%, influenced by measurement tools, exposure, and timing. Risk factors include female gender, younger age, less experience, quarantine, economic burdens, and heavy workloads. Network studies during early COVID-19 among MHPs identified avoidance/numbing symptoms as central, and hyperarousal symptoms as strongly tied to quality of life decrements. Evidence consistently links PTSD with suicidality. Gaps remained regarding post–Dynamic Zero-COVID period prevalence, correlates, and network structure of PTSS among MHPs, and which specific symptoms most strongly connect to suicidality.

Design and participants: National cross-sectional online survey conducted January 22–February 10, 2023, immediately after termination of China’s Dynamic Zero-COVID Policy. Recruitment used snowball convenience sampling via WeChat-based Questionnaire Star; QR codes were distributed to public psychiatric hospitals nationwide. Eligibility: age ≥18; MHPs (psychiatrists, nurses, technicians) working in psychiatric hospitals or psychiatric departments of general hospitals during the pandemic; able to understand Chinese; provided informed consent. No exclusion criteria. Ethics approval: Beijing Anding Hospital Ethics Committee. Measures: Socio-demographics included age, gender, marital status, education, years of clinical work, living status, perceived economic and health status, COVID-19 infection, and quarantine experience. PTSS: Chinese PCL-C (17 items; DSM-IV), referencing COVID-19 and related measures; 5-point Likert (1–5); total 17–85; cutoffs: 38–49 some PTSS, ≥50 significant PTSS; cutoff 38 used to define PTSS. Depression: PHQ-9 (0–27). Suicidality: three standardized items on serious suicidal ideation, plan, and attempt in past week; any endorsement classified as suicidality. Statistical analysis: SPSS 26.0 for univariate and multivariate analyses. Normality: one-sample Kolmogorov-Smirnov. Group comparisons: t-tests or Mann-Whitney U for continuous variables; chi-square for categorical variables. Binary logistic regression examined independent correlates of PTSS (dependent: PTSS yes/no; predictors: variables differing in univariate comparisons), Enter method; alpha 0.05 (two-tailed). Network analysis: R 4.2.2. PTSS network estimated via Graphical Gaussian Model (GGM) using EBICglasso (LASSO regularization) with bootnet’s estimateNetwork; visualized with qgraph and ggplot2. Nodes: individual PCL-C items; edges: partial correlations (green positive, red negative), thickness reflects strength. Node properties: expected influence (EI) and predictability (mgm). Stability/accuracy: bootnet case-drop bootstrapping for correlation stability coefficient (CS-C; ≥0.25 acceptable, ≥0.5 preferable), bootstrapped 95% CIs of edge weights, and bootstrapped edge-weight difference tests. Network Comparison Test (NCT) compared quarantined vs non-quarantined subgroups for global strength and structure. PTSS–suicidality association: Mixed Graphical Model (MGM) with mgm; qgraph flow function to identify direct connections between suicidality and specific PTSS.

Sample: 10,647 MHPs (participation rate 98.0%); psychiatrists 5.5%, nurses 91.3%, other professionals 3.3%; mean age 34.85 (SD 8.40); 18.0% male; 94.8% college or above; 72.5% married. Prevalence: PTSS 6.7% (n=715; 95% CI 6.2–7.2%); significant PTSS (PCL-C ≥50) 2.9% (n=304). Suicidality 7.7% (n=821; 95% CI 7.2–8.2%). Univariate differences (PTSS vs non-PTSS): PTSS subgroup was older, more often male, more often married, had longer working years, poorer perceived economic and health status, more quarantine experience, higher suicidality in past week, and higher PHQ-9 scores; less likely to have college-or-above education; no occupation-type differences. Independent correlates of PTSS (logistic regression): Married (OR 1.523; 95% CI 1.201–1.933; p=0.001); quarantine ≥1 week (OR 1.288; 95% CI 1.065–1.557; p=0.009); any suicidality in past week (OR 3.750; 95% CI 3.039–4.627; p<0.001); PHQ-9 total (per point) (OR 1.229; 95% CI 1.209–1.248; p<0.001). Lower odds with better perceived status: economic fair vs poor (OR 0.710; 95% CI 0.557–0.905; p=0.006); economic good vs poor (OR 0.324; 95% CI 0.165–0.638; p=0.001); health fair vs poor (OR 0.612; 95% CI 0.477–0.784; p<0.001); health good vs poor (OR 0.456; 95% CI 0.300–0.692; p<0.001). PTSS network: Most central symptoms by expected influence: PCL6 Avoiding thoughts (EI 1.189), PCL7 Avoiding reminders (EI 1.157), PCL11 Feeling emotionally numb (EI 1.074) — all Avoidance/Numbing dimension. Mean predictability across 17 nodes: 0.682. Network stability: CS-C 0.75; bootstrapped edge CIs narrow; many edge-weight differences significant. Quarantine subgroup network comparison: No significant differences in global strength (S=0.048; p=0.543) or network structure (M=0.07; p=0.943) between quarantined (n=5,873) and non-quarantined (n=4,774) groups. PTSS–suicidality flow network: Strongest direct positive associations with suicidality were PCL12 Negative beliefs (average edge weight 0.319), PCL16 Hypervigilance (0.070), and PCL14 Irritability (0.054).

The study demonstrates that soon after cessation of China’s Dynamic Zero-COVID Policy, MHPs exhibited a notable prevalence of lingering PTSS (6.7%), higher than some general-population estimates but lower than some prior reports for HCWs during earlier, more acute pandemic phases. Independent correlates highlight psychosocial vulnerabilities: being married (possibly due to concern for family health), poorer economic and health status, quarantine experience, suicidality, and depressive symptom severity. The PTSS network identified Avoidance/Numbing symptoms—Avoiding thoughts, Avoiding reminders, and Emotional numbing—as central hubs, suggesting they play a key role in maintaining the symptom network among MHPs. Direct links from Negative beliefs about the future and hyperarousal symptoms (Hypervigilance, Irritability) to suicidality underscore these as particularly salient targets for suicide prevention. The network’s stability and the lack of differences between quarantined and non-quarantined subgroups indicate a robust symptom structure across exposure groups. Clinically, results support monitoring and prioritizing interventions aimed at avoidance and hyperarousal processes to mitigate PTSS burden and reduce suicidality risk in MHPs.

Among Chinese MHPs immediately post-termination of the Dynamic Zero-COVID Policy, PTSS were relatively prevalent. Risk was elevated among married individuals, those with quarantine experience, poorer perceived economic and health status, suicidality, and higher depressive symptoms. Avoidance/Numbing symptoms were most central in the PTSS network, while Negative beliefs and hyperarousal symptoms showed the strongest direct associations with suicidality. These findings highlight avoidance and hyperarousal as key targets for screening and intervention to alleviate PTSS and prevent suicide in at-risk MHPs. Interventions such as cognitive processing therapy and strategies reducing avoidance and hyperarousal warrant consideration in this population.

Cross-sectional design precludes causal inference. Reliance on self-report measures (PCL-C, PHQ-9) may introduce recall and social desirability biases. PTSS were assessed rather than PTSD diagnoses due to absence of structured clinical interviews. Convenience sampling via online survey may introduce selection bias. Gender imbalance (82% female) may limit generalizability, potentially making results more applicable to female MHPs.