As an early career researcher in Criminology, I am interested in violent crime, domestic violence and threats to kill. Specifically, my research has focused on the measurement and outcomes of violence and how the harms of violence differ for different types of victims.
In Comparing Single Perpetrator and Multiple Perpetrator Violent Events in the Crime Survey for England and Wales (CSEW) I look at the complex structure of violent events reported by CSEW participants. My aim was to compare the needs of victims of violence perpetrated by groups, with the needs of victims of violence perpetrated by a single offender.
Victimisation surveys are the gold standard in measuring crime (Tilley and Tseloni 2016). They supplement police data. While police data can only capture crimes that are reported to the police, the CSEW captures up to 50% more by also including those events not reported to the police (ONS, 2020). We can use this to understand which types of crime and which victims are not appearing in police data.
My analyses of CSEW data have revealed that victims of multiple perpetrator violent events more often report their experiences to the police than victims of single perpetrator violent events. They were also more likely to receive medical attention and treatment at hospital after the violent incident and were also more likely to have contact with victims’ services.
These findings highlight how victims of violent events with one perpetrator may well be underrepresented in records drawn from police, health, and specialist services. It is important that research based on such data sources are aware of this issue in coverage.
Further research is needed to investigate why some victims do not access services and how access to services can be improved for those who are currently underrepresented.
Tilley, N., & Tseloni, A. (2016). Choosing and Using Statistical Sources in Criminology: What Can the Crime Survey for England and Wales Tell Us? Legal Information Management, 16(2), 78-90. https://doi.org/10.1017/S1472669616000219
On Thursday 19th October 2023, Dr Elizabeth Cook was invited to contribute to an event organised by Public Policy Exchange on Combatting Knife Crime in the UK. With contributions from Professor Lawrence Sherman, Professor Kevin Browne, Bruce Houlder CB KC DL, Nathaniel Levy, Dr Sue Roberts, and Sammy Odoi, the event examined current government strategy and policy responses to knife crime. Applying Carol Bacchi’s (1999; 2009) ‘What’s the problem represented to be?’ (WPR) approach, Elizabeth made the case for a gender analysis of ‘knife crime’, a summary of which is provided below.
What’s the problem represented?
Knife crime is a policy priority that ranks consistently high on the government agenda, appearing in key strategic areas such as serious violence, ‘gang’ involvement and exploitation, and children, young people, and vulnerability. Cutting across these strategic areas is a particular attention to tackling county lines and the misuse of drugs, restrictions on weapon-carrying and possession, early intervention and prevention programmes with young people, and community partnership responses and safeguarding.
What are the assumptions underpinning these representations?
There are key assumptions that underpin these representations of knife crime in public policy, each linked to specific ideas about:
who exactly is at risk,
where is considered to be safe,
who is vulnerable to harm,
and, on the whole, what forms of violence are deemed to be ‘serious’.
Constructions of knife crime as they currently stand, depict the problem as one committed primarily by and against men, occurring in public spaces, often between young people, and as an issue that is increasingly racialised in media and public discourse. The evidence base for each is not to be ignored and there are key takeaways from each policy approach which contribute one piece of a puzzle.
However, taking a WPR approach, there are questions to be asked: What is left unproblematic and what harms and whose voices are missed as a result?
There are key elements that are omitted from current policy approaches to knife crime and lessons to be learned from the violence against women and girls sector which have been relatively absent so far.
What is left unproblematic? Can the problem be thought about differently?
Various sources of evidence highlight that knives are consistently the most frequent method of killing in the context of intimate partner homicide by men against women. While the proportions fluctuate (e.g., ONS 2023; Femicide Census, 2020; VKPP, 2023), it stands that when women are killed by men, they are most likely killed using a knife.
What effects are produced by this problem representation?
Considering that up to 1 in 3 victims of homicides using a knife are women, it is problematic that there is so little analysis of sex/gender in policy responses (see, MOPAC 2017, for an exception). This has serious implications for how interventions are identified.
For example, efforts to regulate offensive weapons through legislation hit a wall when it comes to domestic abuse committed within the home. There have been several proposals over the years to either blunt kitchen knives or confiscate particular knives in the possession of known domestic abuse perpetrators – the assumption here being that the removal of the weapon is the removal of risk. However, the fundamental issue in domestic abuse is that anything is a weapon.
These raise questions about what (or who) is considered to be a source of risk and what can be done to reduce it.
How can we disrupt the problem representation?
While public health approaches to violence frequently invoke the need for multi-agency and partnership working, this must also translate to policy and implementation in strategy as well as practice. This means further work to avoid and break down policy siloes and assumptions in problem representations.
The Crime Survey for England and Wales (CSEW) and its predecessor, the British Crime Survey (BCS), are widely used by both academics and government to assess the level of crime and its impact on society. While the survey has run since 1982, combining the multiple years of the survey can be complex and mistakes are easily made. As a researcher in criminology who frequently uses the CSEW and its predecessor, I have produced detailed Stata code to combine data from multiple survey years to support other researchers who also analyse the CSEW in Stata (or would like to start). I worked with the UK Data Service (UKDS) and the Office for National Statistics (ONS) to share the code and develop guidance for its use.
With this code, you can specify what you need, namely, which years of the Crime Survey you want to merge and if you want the adolescent and young adult panels, the bolt-on datasets that provide uncapped codes, and/or if you want to use the ethnic minority booster samples. As a result, the code can be easily tailored for each researcher’s needs.
By combining multiple survey sweeps, analysts can examine temporal trends. A combined file also enables analysts to look at low prevalence offences, population groups, or consequences, that do not have a high enough frequency in a single year.
Two examples are given below on how this integrated dataset provides new and exciting opportunities.
Example 1: Revealing gender and age differences in trends in experiencing violence
We used our integrated crime survey dataset to examine temporal trends in different types of violence, and whether these varied by gender and age.
After a rise in violent crime in the 1980s, there was a decade of steady decline followed by a decade of stability (blue line, Figure 1a). However, for other crimes, which can also be considered violent, the patterns observed are different. After a short period of decline in the 1990s, sexual violence against women remained relatively stable until around 2010 when it began to increase, reaching the 20 years high by 2020. Additionally, there has been a sharp rise in threats reported by women in the last 5 years of data, making threats almost as prevalent as at its peak in the late 1990s.
The trends in violent crime for men follow a broadly similar pattern as for women, but at a higher rate. Unlike women, however, men did not experience an increase in threats in the more recent period.
Figure 1. Prevalence of violence by type of violence and gender, 1982 to 2020
a) Proportion of women experiencing violence by type of violence
b) Proportion of men experiencing violence by type of violence
Source: Authors’ analysis using CSEW/BCS data from 1982 to 2019/2020.
Notes: Weighted proportions. Violent crime includes the following offences: Serious wounding, other wounding, common assault, attempted assault, serious wounding with sexual motive, other wounding with sexual motive. Sexual violence includes the following offences: rape, attempted rape and indecent assault. Due to low frequencies, sexual violence is not reported here for men.
Figure 2 reveals that there has been major change in the age profile of victims over the past 40 years. 16- to 19-year-olds were almost 3 times as likely to become a victim of violence as people aged 30 to 39 in the mid-1990s. But violence against this group has declined rapidly since then: while they continue to be the group that is most likely to be victim of violence with 7.2% annual victimization in 2020, this used to be over 28% in the mid-1990s. While risk of violence has declined for all the ages under 40, the shift has been the largest for the younger groups.
Relatively few people over 50 become victims of violence compared to younger age groups in each time period. However, closer inspection reveals there is a significant increase in the risks of violence among the older age groups (60-69 and 70 and older) since the late 1990s, and particularly since 2015.
Overall, the age profile of victims has shifted massively over the decades, there is now much less variation in rates between age groups.
Figure 2 Prevalence of violence (including violent crime, threats, robberies, and sexual violence) by age group, 1982 to 2020.
Source: Authors’ analysis using CSEW/BCS data from 1982 to 2019/2020.
Example 2: Investigating smaller groups: Differences in wellbeing impact between intimate partner perpetrators
Our integrated crime survey dataset allows for the study of minority groups that are relatively small or forms of violence that are not often reported.
For example, only by combining twenty years of the crime survey (2001 to 2020) do we have sufficient sample size to study the impact physical intimate partner violence has on wellbeing and health, and how it differs between various types of intimate partner perpetrator.
Firstly, it is important to note that physical violence by any type of intimate partner has a higher risk of high emotional impact (Figure 3a) and a higher risk of injury (Figure 3b) than violence by other types of perpetrators.
Figure 3a below shows that the emotional impact reported by female victims is higher when the violence was committed by a current or former spouse/partner compared to if it was done by a current or former boy/girlfriend. Women were more likely to say they were ‘very much’ affected by the violence when it was committed by a current or former spouse/partner. It could be that the proximity of spousal relationships, which are often cohabitating, and their average longer duration account for some of the greater report impact. However, in contrast to emotional impact, figure 3b (below) shows that women are more likely to get an injury(ies) by violence by current spouses than by former spouses.
Overall, this study highlighted that physical violence by an intimate partner has a more severe wellbeing and health impact than violence by others, but also the need to differentiate intimate partner violence and abuse by not only the type of violence/abuse but also the type of intimate partner.
Figure 3 Estimated emotional wellbeing and risk of injuries for women following physical intimate partner violence, differences between intimate partner perpetrators.
a) Respondent’s reported emotional impact (showing the highest category).
b) Respondent’s reported physical health impact (showing the risk of injury)
Source: Authors’ analysis using CSEW/BCS data from 2001 to 2019/2020.
Notes: Respondent’s self-assessed emotional impact measured in four categories: not impacted, little impact, quite a lot, very much impacted. Respondent’s self-assessed risk of injuries is measured in three categories: no force was used, force was used but no injury was sustained, force was used that led to an injury. Figures are based on average marginal effects following ordered logit models controlling for key (socio)demographics. Significance was tested in additional models.
What the merger code does and doesn’t do
The Stata code enables users to merge the raw CSEW/BCS datasets. Consequently, at the moment, this code does not harmonize variables that change (slightly) over different years. Considering the measurement of many variables changes over the years, the users of this combined file need to make their own decisions on what operationalisations work best for their research and for the years they use.
Most of the time new variable names are used when a new measurement is used. However, for a few variables, different measurements seem to be used in different years, but they have the same variable name (for instance for household income variables such as tothhin2). In the current code, these variables are treated as being the same. Therefore, users need to carefully check the variables that they use for the relevant years.
Next, this code does not work in the secure researcher environment as provided by UKDS or ONS because the datasets in these environments have different names and the structure of the folders is different.
Overall, the merger code will save researchers precious time in combining the surveys that they want to use. As we have shown here, combining survey sweeps can benefit the study of trends in victimisation. The code can also be used for studying groups or crimes that are too rare to study using only a single sweep, therefore, this code may provide an incentive for studying marginalised groups and specific crimes, contributing to new insights into victimisation.
Citation for merged code
Blom, Niels (2023). Code for Merging Waves of the Crime Survey of England and Wales and the British Crime Survey, 1982-2020. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-856494
Examples in this blog are from
Blom, N., Obolenskaya, P., Phoenix, J., and Pullerits M. (2023, September 11-13). Differentiating intimate partner violence by perpetrator relationship type. Types of crimes committed and consequences for victims’ health and wellbeing by different types of intimate partner perpetrators [Conference Presentation]. European Conference on Domestic Violence, Reykjavik, Iceland.
Obolenskaya, P. & Blom, N. (2023, September 6-9). The rise, fall and stall of violence in England and Wales: how have risks of violence changed for different groups? [Conference Presentation]. EuroCrim 23rd Annual Conference of the European Society of Criminology, Florence, Italy.
Office for National Statistics. Crime Survey for England and Wales, 2001-2002 to 2019-2020 and British Crime Survey 1982 to 2001 [data collections]. UK Data Service SN: 8812, 8608, 8464, 8321, 8140, 7889, 7619, 7422, 7252, 6937, 6627, 6367, 6066, 5755, 5543, 5347, 5324, 5059, 4787.
At UK Prevention Research Partnership (UKPRP) funded consortium VISION we have dedicated a workstream to studying the data gaps, analytical biases, and systemic blind spots that arise around questions of race, ethnicity and migration. We acknowledge that all research data is socially constructed. Asking questions about that process of construction can help researchers be aware of the biases and distortions that will arise when data are used uncritically and unreflectively.
Over the first two years of the project, we have considered the gaps in our own data, drawn on expertise and insight from our research team and associates, and drawn on a diverse array of methodological and disciplinary backgrounds. This research has allowed us to develop a tool to support researchers on our project and beyond to mitigate the risk of introducing or reproducing bias regarding ethnicity and migration in data, analysis and reporting findings. The tool initially responds specifically to administrative and survey data but can be adapted for use with any dataset.
The tool was conceived following consultation with VISION colleagues regarding the construction of ethnicity and migration data across the consortium. The objective was to produce the best quality data and analysis possible that could account for diversity and diverse experiences in the population. The VISION consortium is concerned with measuring violence: ethnicity and migration status are key areas where violence is underreported, which creates gaps in data. We found that existing datasets are unable to properly reflect diversity and inequality in the population and therefore cannot fully explain different experiences. Thus, we sought ways to prevent reproducing biases and data gaps within our analyses.
A collaborative workshop held by the Ethnicity and Migration research group initiated an iterative process of tool development. A meeting with the UKPRP Community of Practice for Race and Ethnicity specified feedback to the tool design. Extensive research of existing literature took place between September 2022 and March 2023 and was summarised into a companion document, both providing citations and further information.
A draft tool and companion document was shared with specialist service IMKAAN (a by-and-for service for minoritized women and girls at risk of violence) in July 2023 for consultative feedback.
I am pleased to announce the tool is now ready for circulation and use.
The tool comprises three parts:
The first part offers guiding questions to assess the quality of a data set at the adoption stage, with regard to how well the dataset mitigates the potential biases that may be produced during data collection regarding ethnicity and migration. Researchers can apply the questions within this section to any dataset they are adopting, although it was designed particularly with survey data and administrative data in mind because these are the main datasets used in the VISION project.
The second part of the tool guides the researcher in a reflective process that is intended to allow the researcher to assess the potential for internally held biases or structural and systemic biases to which they have been exposed affecting the data analysis.
The third part asks researchers to consider the impact of reporting findings. The wording of publications might affect how the finds are interpreted or reported in the media, cited by other researchers, or circulated. Findings might also be misused. This final section of the tool offers some techniques for mitigating the misinterpretation or misuse of findings (although we acknowledge that this is often outside of researcher control).
This document will evolve over time, therefore, we are keen for your feedback. If you download the tool and guide and use them, please let us know how you get on! Is the tool easy to use? Is the guide clear? How effective were they in helping you mitigate bias when working with your data? Please let us know by contacting Andri at Alexandria.email@example.com. We look forward to hearing from you!
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).
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.
by Dr Leonie Tanczer, Associate Professor, University College London, and Co-Investigator, UKPRP VISION
The growth of digital technologies in our lives creates new habits, practices, and expectations. We need better public awareness and debate about the “new normal” we are experiencing in a society where the misuse of digital technologies has become widespread.
I don’t know about you, but there used to be a time when I was excited and thrilled by technology. I remember how ecstatic I was when I got my first mobile – and, later, my first smartphone. How unbelievably neat it felt “browsing” the web to research a school assignment. And how empowering and beneficial I once perceived platforms such as Twitter.
That’s sadly no longer how I think and feel. And I must confess, I’ve become quite pessimistic.
You can blame my dreary outlook on living through my 20s in a world where digital innovations became entrenched in daily life and now constantly demand our attention. Alternatively, you may say that my perspective has changed since I started to study technology-facilitated domestic violence (tech abuse). My interest in tech abuse emerged in 2018 when I set out to examine how smart, Internet-connected devices – such as Amazon’s Ring Doorbell or the Google Home smart speaker – impacted domestic abuse victims and survivors. It should have been only a short, six-month research project, but it developed a life of its own. Since then, my research focus and team have steadily grown and we are researching tech abuse as part of the VISION project. As the research grows, so has the scale and awareness of tech abuse.
Tech can exacerbate societal problems
I never fully bought into the narrative that tech can solve all societal ills. If anything, my research on tech abuse has shown how the misuse of digital technologies can exacerbate societal problems. The boundaries have started to blur around what is and isn’t acceptable online and where one can draw the line around what may or may not be abusive when handling digital tech.
Tech abuse is the misuse of “everyday” digital systems to alter, amplify, and accelerate coercive and controlling behaviour in the context of intimate partner violence (IPV). Tech abuse is a major concern because it offers perpetrators of domestic abuse new and powerful tools to monitor and harass. And let’s be clear: domestic abuse is an epidemic. It is widespread (approximately 1 in 5 UK adults aged 16 years and over had experienced domestic abuse since the age of 16 years); harmful (it impacts victims’/survivors’ mental, emotional, physical, social and financial wellbeing); as well as gendered and deadly ( Homicide Index data for the year ending March 2019 to the year ending March 2021 show that 72.1% of victims of domestic homicide were female).
To date, our research group has investigated numerous angles related to this expanding abuse form, from usability tests of digital devices and the analyses of legal tools to tech abuse’s interconnection with mental health. We have been outspoken about shortcomings in policy debates and the wider cybersecurity sector and collaborated with and been informed by the efforts of key stakeholders that represent the voice and lived experience of victims and survivors, as well as those working with perpetrators.
What is “normal” and “acceptable”?
The functionalities and abilities many digital services offer (and for which consumers actively pay!) create a slippery slope towards their misuse. For example, I am all up for the remote control of my heater from my UCL office, the sharing of geolocation data whilst in an Uber, and the exchange of streaming service passwords with family and friends. I mean as a white, privileged, tech-savvy woman in a consensual partnership and with supportive colleagues and friends, these features frequently benefit me.
But, what if they don’t? What if I wasn’t the legal owner and account holder of the systems I use? What if I had to think of the inferences corporations and governments will make based on my data profile? And what if it were down to my coercive partner to control the temperature, to know my whereabouts, and to set up/maintain my Netflix or email account?
At present, many concerns that digital systems cause are addressed along the principle of informed consent, which is technically quite simple: once something happens without the awareness and approval of all parties involved, a line has been breached. But what are we doing when ensuring informed consent is impossible or doesn’t go far enough to protect someone from abuse?
More profoundly, I believe we must start to ask ourselves important questions around the “new normal” that is looming and that I don’t think we have begun to unpack: is it OK for my partner to know my email password? Is it OK for my partner to check who I’ve been texting? And is it OK for my partner to ask for nudes via text? Plus, what if we bring children into the mix? Is it OK for parents to overtly install parental control software on devices legitimately purchased and gifted to their kids? And can – and should – a 15-year-old reject?
We need a public debate
Undoubtedly, I don’t have definite answers to any of the above-posed questions. But they have been in my mind for some time, and I’d love to see them addressed. Relationships – whether with our children, parents, friends, or romantic partners – are not always pure bliss. They can be overshadowed by conflict, but in the worst case, they can be toxic and outright destructive and harmful. Our digital systems must be capable to account for this. I, thus, believe a public debate or a re-evaluation on what we should accept as ‘normal’ is urgently needed. This then may hopefully lead to safeguards put into place so that everyone – independent of their situation – can make conscious choices on tech’s impact on their lives as well as partnerships.
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.
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.