Post-traumatic stress and future substance use outcomes: leveraging antecedent factors to stratify risk

Background: Post-traumatic stress disorder (PTSD) and substance use (tobacco, alcohol, and cannabis) are highly comorbid. Many factors affect this relationship, including sociodemographic and psychosocial characteristics, other prior traumas, and physical health. However, few prior studies have investigated this prospectively, examining new substance use and the extent to which a wide range of factors may modify the relationship to PTSD. Methods: The Advancing Understanding of RecOvery afteR traumA (AURORA) study is a prospective cohort of adults presenting at emergency departments (N = 2,943). Participants self-reported PTSD symptoms and the frequency and quantity of tobacco, alcohol, and cannabis use at six total timepoints. We assessed the associations of PTSD and future substance use, lagged by one timepoint, using the Poisson generalized estimating equations. We also stratified by incident and prevalent substance use and generated causal forests to identify the most important effect modifiers of this relationship out of 128 potential variables. Results: At baseline, 37.3% (N = 1,099) of participants reported likely PTSD. PTSD was associated with tobacco frequency (incidence rate ratio (IRR): 1.003, 95% CI: 1.00, 1.01, p = 0.02) and quantity (IRR: 1.01, 95% CI: 1.001, 1.01, p = 0.01), and alcohol frequency (IRR: 1.002, 95% CI: 1.00, 1.004, p = 0.03) and quantity (IRR: 1.003, 95% CI: 1.001, 1.01, p = 0.001), but not with cannabis use. There were slight differences in incident compared to prevalent tobacco frequency and quantity of use; prevalent tobacco frequency and quantity were associated with PTSD symptoms, while incident tobacco frequency and quantity were not. Using causal forests, lifetime worst use of cigarettes, overall self-rated physical health, and prior childhood trauma were major moderators of the relationship between PTSD symptoms and the three substances investigated. Conclusion: PTSD symptoms were highly associated with tobacco and alcohol use, while the association with prospective cannabis use is not clear. Findings suggest that understanding the different risk stratification that occurs can aid in tailoring interventions to populations at greatest risk to best mitigate the comorbidity between PTSD symptoms and future substance use outcomes. We demonstrate that this is particularly salient for tobacco use and, to some extent, alcohol use, while cannabis is less likely to be impacted by PTSD symptoms across the strata

The AURORA Study: a longitudinal, multimodal library of brain biology and function after traumatic stress exposure

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions

A network-based maximum link approach towards MS identifies potentially important roles for undetected ARRB1/2 and ACTB in liver cancer progression

10.1504/IJBRA.2012.048967International Journal of Bioinformatics Research and Applications83-4155-17

Regulatory Requirements Traceability and Analysis Using Semi-Formal Specifications

Abstract: Information systems are increasingly distributed and pervasive, enabling organizations to deliver remote services and share personal information, worldwide. However, developers face significant challenges in managing the many laws that govern their systems in this multi-jurisdictional environment. In this paper, we report on a computational requirements document expressible using a legal requirements specification language (LRSL). The purpose is to make legal requirements open and available to policy makers, business analysts and software developers, alike. We show how requirements engineers can codify policy and law using the LRSL and design, debug, analyze, trace, and visualize relationships among regulatory requirements. The LRSL provides new constructs for expressing distributed constraints, making regulatory specification patterns visually salient, and enabling metrics to quantitatively measure different styles for writing legal and policy documents. We discovered and validated the LRSL using thirteen U.S. state data breach notification laws

WUENIC – A Case Study in Rule-based Knowledge Representation and Reasoning

Abstract. WUENIC is a rule-based system implemented as a logic program, developed by WHO and UNICEF for estimating global, country by country, infant immunization coverage. It possesses many of the characteristics of rulebased legislation, facilitating decisions that are consistent, transparent and replicable. In this paper, we focus on knowledge representation and problemsolving issues, including the use of logical rules versus production rules, backward versus forward reasoning, and rules and exceptions versus argumentation