This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline—the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)—was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.
Bibliographical noteFunding Information:
The Police Prospective Study was supported by NIMH Grant 5R01MH056350 (Charles R. Marmar, PI). Dr. Marmar receives support from NIAAA, Department of Defense, Cohen Veterans Network, Robin Hood Foundation, McCormick Foundation, Home Depot Foundation, City of New York and Tilray Pharmaceuticals. Dr. Saxe receives funding from NIMH Grant R01MH119114 (Glenn Saxe, PI), and SAMHSA Grants U79SM080049 (Glenn Saxe, PI) and U79SM080013 (Julian Ford, PI).
PubMed: MeSH publication types
- Journal Article
- Research Support, U.S. Gov't, Non-P.H.S.
- Research Support, N.I.H., Extramural