Abstract
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.
Highlights
1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Introduction This article reports on a study that applies computational causal discovery (CCD) methods to a unique longitudinal observational data set, to determine causal factors for post-traumatic stress (PTS) related to police duty critical incident exposure
There is an extensive literature validating the capacity of CCD methods, including an empirical literature demonstrating that these methods can accurately detect true
We present a visualization of all second-degree neighbors of the PTS Symptom Severity (PTS Sev) variable, including a subset of these variables that are defined by the Local Causal Network Model. These Markov boundary variables reflect the minimal set of information required to predict the value of the target as accurately as the whole of data support this prediction. Conditioned on these nodes, PTS Sev is rendered independent of the rest of the nodes in the network
Summary
This article reports on a study that applies computational causal discovery (CCD) methods to a unique longitudinal observational data set, to determine causal factors for post-traumatic stress (PTS) related to police duty critical incident exposure. Advances in intervention requires research that identifies causal factors[1,2], but the scientific literature that would inform the identification of causes are almost exclusively based on the application of correlational methods to observational data. Saxe et al Translational Psychiatry (2020)10:233 causes within observational data sets, when true causes were previously known: and a literature providing rigorous mathematical proofs of these methods for causal inference[1,2,11,12,13,14,15,16]. CCD methods have yielded important advances in non-psychiatric medical fields and have been successfully applied in psychiatric research, to large extent by our group[17,18,19,20,21,22,23,24,25,26,27]. A more detailed description is provided in Supplementary Material 1
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