Dr Annie Bunce, VISION Research Fellow, was awarded Best Oral Presentation at the Lancet Public Health Science conference in London this November. She presented on the Prevalence, nature and associations of workplace bullying and harassment with mental health conditions in England: a cross-sectional probability sample survey.
Annie’s research, conducted with VISION colleagues Ladan Hashemi, Sally McManus, and others, presents the first nationally representative findings on the prevalence of workplace bullying and harassment in England for over a decade. Annie analysed data from the 2014 Adult Psychiatric Morbidity Survey (APMS) to demonstrate: the prevalence of workplace bullying and harassment (WBH) in the working population in England; the nature of WBH experienced, who it was perpetrated by and the types of behaviour it involved; and associations between the experience of WBH and indicators of adverse mental health.
The study is unique in that the APMS makes robust assessments of mental health – operationalising diagnostic criteria – which provides an accurate assessment of clinical need. Implications for employers, policymakers, health services and researchers are outlined.
Sexual abuse and bullying are associated with poor mental health in adulthood. Elucidating putative causal relationships between affective and psychotic symptoms may inform the development of therapies. Causal diagrams can help gain insights, but how?
Given a causal diagram, usually represented as a directed acyclic graph (DAG), and observational data from the variables on the graphs, many analytical methods (especially adjustment techniques) allow us to estimate the effect that intervening on a variable is expected to have on another.
In real-world problems, we rarely have a complete picture of an underlying structural mechanism regulating the relationship among different variables. Causal discovery is a technique leveraging statistics and machine learning tools to uncover plausible causal relationships from data, with little to no prior knowledge of them. While learning causal structures from purely observational data relies on unrealistic assumptions (especially causal sufficiency and faithfulness), a causal discovery exercise may help us identify the most promising scenarios to prioritise when designing interventional studies.
In a recent article, now available open access in Psychological Medicine, Dr Giusi Moffa, Statistician affiliated with the University of Basel, Switzerland and colleagues used state-of-the-art sampling methods for inference of directed acyclic graphs (DAGs) on data from the English Adult Psychiatric Morbidity Surveys, to investigate sexual abuse and psychotic phenomena.
The analysis sought to model the interplay among 20 variables, including being a victim of bullying or sexual abuse and a range of psychotic (e.g. paranoia, hallucinations and depression) and affective symptoms (e.g. worry and mood instability) while accounting for the sex of the participant. To respect temporality, we imposed some prior constraints on the DAG structure: childhood sexual abuse and bullying referred to events that were temporally antecedent to the assessment of the psychological variables, and hence they only admit incoming edges from sex and each other.
Contrary to expectations, the procedure favoured models placing paranoia early in the cascade of relationships, close to the abuse variables and generally upstream of affective symptoms. A possible implication is that paranoia follows from early abuse involving bullying or sexual exploitation as a direct consequence. Overall, the results were consistent with sexual abuse and bullying driving a range of affective symptoms via worry. As such, worry may be a salient target for intervention in psychosis.
Check out the paper for a more thorough discussion of the findings (joint work with Jack Kuipers, Elizabeth Kuipers, Paul Bebbington and VISION member Sally McManus).