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Causal discovery for studying sexual abuse and psychotic phenomena

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).

This is a repost of a blog available on LinkedIn: https://www.linkedin.com/pulse/causal-discovery-studying-sexual-abuse-psychotic-phenomena-moffa

Paper available open access: https://www.cambridge.org/core/journals/psychological-medicine/article/sexual-abuse-and-psychotic-phenomena-a-directed-acyclic-graph-analysis-of-affective-symptoms-using-english-national-psychiatric-survey-data-erratum/CF603075EBBD5D75E60F327CE01C4050

For further information about the approach: giusi.moffa@unibas.ch

VISION member awarded UKDS Impact Fellow focused on the socioeconomics of violence

We’re delighted that one of VISION’s core researchers, Dr Niels Blom, has been awarded a prestigious UK Data Service (UKDS) Fellowship.

The award will be used to improve the reach and impact of Niels’ research on violence and abuse and its relationship with job loss, health, and wellbeing. He is using several UKDS datasets, including the UK Longitudinal Household Survey and the Crime Survey for England Wales, to understand the link between violence, particularly intimate partner violence, and its socioeconomic, wellbeing, and health impact.

For more information about Niels, his work, and what he hopes to get out of the Fellowship scheme, see his blog on the UKDS website.

The UKDS is funded by the UKRI and houses the largest collection of economic, social and population data in the UK. Its Data Impact Fellowship scheme is for early career researchers in the academic or the voluntary, community, and social enterprise (VSCE) sector. The focus in 2023 is on research in poverty, deprivation, the cost of living crisis, housing and homelessness, using data in the UK Data Service collection. The purpose of the programme is to support impact activities stemming from data-enhanced work.

For further information on the UK Data Service please see: UK Data Service

To read Niels’ blog please see: UK Data Service Data Impact Fellows 2023: Niels Blom – Data Impact blog

Or contact Niels at niels.blom@city.ac.uk

Photo by Alina Grubnyak on Unsplash

Measuring violence using administrative data collected by specialist domestic and sexual violence and abuse support services

Interpersonal violence, which can include various forms of domestic and sexual violence and abuse (DSVA) is a leading cause of death, particularly among young adults. In the UK, specialist DSVA services provide much-needed support to victim-survivors of these types of violence, and some provide support for perpetrators to change their behaviour. To monitor and support their work, specialist services collect data on violence. This data has the potential to improve understanding of violence but presents unique challenges.

In this review, VISION researchers Dr Annie Bunce, Dr Sophie Carlisle and Dr Estela Capelas Barbosa describe and discuss some of the key challenges facing the data collected by specialist services.

Inconsistencies in data collection arise due to the differing remits and priorities of specialist services, which mean violence and abuse are defined and measured in slightly different ways by these organisations. Particularly, the review highlights the significant variation in outcomes and outcome measurement tools used to evidence the effectiveness of services and interventions.

Specialist support services collect valuable data on many and multiple types of violence, the wide impacts of violence on victim-survivors’ lives, and information about perpetrators. As the data are not collected for research purposes, a considerable amount of work is often required to make the data suitable for statistical analysis. Critically, the piecemeal and insecure funding of specialist services limits their capacity to collect and analyse data.

Together these issues make it challenging to collate data from specialist services and use it to inform measurements of violence.

The researchers recommend the development of a core outcomes framework, exploration of methods for linking specialist services data with other sources of administrative data on violence, and sustainable funding for third sector specialist support services.

For further information please see: Social Sciences | Free Full-Text | The Concept and Measurement of Interpersonal Violence in Specialist Services Data: Inconsistencies, Outcomes and the Challenges of Synthesising Evidence (mdpi.com)

Or contact Dr Annie Bunce at annie.bunce@city.ac.uk

Photograph by Claudio Schwarz on Unsplash

Varying definitions and measurements of violence limit reduction strategies

Violence reduction is a United Nations (UN) sustainable development goal (SDG) and is important to both the public health and criminology fields. The collaboration between the two has the potential to create and improve prevention strategies but has been hampered by the usage of different definitions and measurements.

In this paper, VISION researchers Dr Niels Blom, Dr Anastasia Fadeeva and Dr Estela Capelas Barbosa explore the definitions and measurements of violence by the World Health Organization, UN, and Council of Europe to arrive at a harmonized framework aligned with the SDGs.

Violence and abuse are defined by these organizations as intentional actions that (are likely to) lead to harm, irrespective of physicality or legality. When recording violence and abuse, health- and justice-based administrative systems use different codes which cannot directly be translated without resorting to broad overarching categories.

The researchers propose a framework to record violence that includes individual and event identifiers, forms of violence and abuse (including physical, sexual, and psychological), harm, and individual and event characteristics.

For further information please see: Social Sciences | Free Full-Text | The Concept and Measurement of Violence and Abuse in Health and Justice Fields: Toward a Framework Aligned with the UN Sustainable Development Goals (mdpi.com)

Or contact Niels at Niels.Blom@city.ac.uk

Photo by Parsa on Unsplash

Unlocking violence information from clinical text

Blog by Dr Lifang Li, Research Associate with UKPRP VISION, Kings College London

Clinical Record Interactive Search (CRIS)

In 2008, the Clinical Record Interactive Search (CRIS) system was launched. CRIS removes personal identifiers from the health records of the South London and Maudsley NHS Trust, making them available for use in mental health research. The platform operates under a governance framework that prioritises patient anonymity and places patients at the centre of its operations. The use of exceptionally large volumes of records with unprecedented levels of detail has the potential to revolutionise mental health research.

The CRIS Violence application

The CRIS violence application is computer software that finds clinical text that refers to interpersonal violence, including the presence of violence, patient status (i.e. as perpetrator, witness or victim of violence) and violence type (domestic, physical and/or sexual) using Natural Language Processing (NLP). NLP uses pattern matching and statistical techniques to automatically process natural human language. It was developed by Riley Botelle, Professor Robert Stewart and their colleagues to the identification and classification of experiences of violence in narrative records, described in their 2022 paper “Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study”. Recently, after a thorough validation process, the CRIS team has started to run the violence application routinely, alongside many other NLP applications (e.g., to find suicidality, agitation, medications, anxiety) that are available for CRIS. Structured output from these, now including various violence-related variables, is saved back into the CRIS database, from where authorised health researchers can access it.

How does it serve the researchers and clinicians?

By accurately identifying the presence of violence, different types of violence, and patient status, the application is enabling researchers to examine how experiences of violence are correlated with various mental health problems, outcomes and treatment trajectories, and how these relate to patients’ characteristics (such and age, gender, and ethnic group), and account for health inequalities.

Future work

Given the possibility that psychological abuse and economic abuse may also occur in patients and are recorded in the health record by clinicians, our work as part of the VISION consortium involves updating the current violence application to identify mentions of these, allowing us to extend the violence research possible using CRIS.

Illustration: Nina Rys / Shutterstock.com

Mental health and wellbeing data – webinar for researchers

This webinar focused on quantitative analysis of secondary data, to provide insight into population mental health and its social determinants. It took place on Teams Monday, 6 March 2023, at 14:00-15:30.

Speakers included VISION researcher Sally McManus, who discussed England’s main mental health survey, the Adult Psychiatric Morbidity Survey (APMS). The survey series covers anxiety and depression, alongside items on violence and abuse.

This webinar formed part of a series organised by Understanding Society, UK Data Service, Centre for Longitudinal Studies (CLS) and the National Centre for Research Methods (NCRM). The Data Resource Training Network is a collaboration between a number of ESRC-funded resource centres working together to promote the value and use of social science data.

Photo credit: Photo by Erol Ahmed on Unsplash