Reaction to trauma: A cognitive processing model

Integrated existing cognitive processing models of posttrauma reactions into a longitudinal model. Data were obtained after a multiple shooting in a city office block. The S group comprised 158 office workers who were in the building at the time of the shootings. The methodology of this research was a repeated measures survey, with data collection at 4, 8, and 14 mo posttrauma. Measures included the Impact of Events Scale (IES) and the SCL-90--Revised. A path analysis was performed with the IES as an indication of cognitive processing. Intrusion and avoidance were shown to mediate between exposure to trauma and symptom development. Intrusion was also found to be negatively related to subsequent symptom levels. The findings provide provisional support for a cognitive processing model. (PsycINFO Database Record (c) 2008 APA, all rights reserved

Modelling a disease-relevant contact network of people who inject drugs

This study uses social network analysis to model a contact network of people who inject drugs (PWID)relevant for investigating the spread of an infectious disease (hepatitis C). Using snowball sample data, parameters for an exponential random graph model (ERGM) including social circuit dependence and four attributes (location, age, injecting frequency, gender) are estimated using a conditional estimation approach that respects the structure of snowball sample designs. Those network parameter estimates are then used to create a novel, model-dependent estimate of network size. Simulated PWID contact networks are created and compared with Bernoulli graphs. Location, age and injecting frequency are shown to be statistically significant attribute parameters in the ERGM. Simulated ERGM networks are shown to fit the collected data very well across a number of metrics. In comparison with Bernoulli graphs, simulated networks are shown to have longer paths and more clustering. Results from this study make possible simulation of realistic networks for investigating treatment and intervention strategies for reducing hepatitis C prevalence

Computational and Mathematical Organization Theory Affiliation of Authors

January 2004: Special Issue on Mathematical representations for the analysis of social networks within and between organization

The role of anger and ongoing stressors in mental health following a natural disaster

Objective: Research has established the mental health sequelae following disaster, with studies now focused on understanding factors that mediate these outcomes. This study focused on anger, alcohol, subsequent life stressors and traumatic events as mediators in the development of mental health disorders following the 2009 Black Saturday Bushfires, Australia’s worst natural disaster in over 100 years. Method: This study examined data from 1017 (M = 404, F = 613) adult residents across 25 communities differentially affected by the fires and participating in the Beyond Bushfires research study. Data included measures of fire exposure, posttraumatic stress disorder, depression, alcohol abuse, anger and subsequent major life stressors and traumatic events. Structural equation modeling assessed the influence of factors mediating the effects of fire exposure on mental health outcomes. Results: Three mediation models were tested. The final model recorded excellent fit and observed a direct relationship between disaster exposure and mental health outcomes (b = .192, p < .001) and mediating relationships via Anger (b = .102, p < .001) and Major Life Stressors (b = .128, p < .001). Each gender was compared with multiple group analyses and while the mediation relationships were still significant for both genders, the direct relationship between exposure and outcome was no longer significant for men (p = .069), but remained significant (b = .234, p < .001) for women. Conclusions: Importantly, anger and major life stressors mediate the relationship between disaster exposure and development of mental health problems. The findings have significant implications for the assessment of anger post disaster, the provision of targeted anger-focused interventions and delivery of government and community assistance and support in addressing ongoing stressors in the post-disaster context to minimize subsequent mental health consequences

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease