In 2022, the UK Office for National Statistics (ONS) developed a prototype violence against women and girls (VAWG) dashboard. The tool presents statistics and charts on violence against women and girls in England and Wales, drawing on multiple sources. However, due to re-prioritisation at ONS, maintenance of the dashboard was halted and from 1st April 2024 it will no longer be accessible.
The VISION consortium is consulting on whether there is need for a VAWG data dashboard. This consultation is seeking views on:
Whether the dashboard was useful
Who used it and why
If the dashboard was to continue, what aspects should be kept, dropped or added.
Anyone interested in the idea of a VAWG data dashboard is welcome to respond to the survey, particularly if interested in using one in the future.
Answer as many questions as you like. You can provide contact details or complete this anonymously. The findings will be used to draft a report and provide recommendations on whether the dashboard should continue. The report will include a list of the groups and organisations that participated (where details are provided). Individuals will not be named, although quotes may be taken from the text provided. The report may be published, for example on the VISION website.
The ONS VAWG dashboard was available online until 31 March 2024. Therefore, if you would like to participate in this consultation, please view the sample screenshots of the tool below.
Despite violence being recognised as a harm to health, it is not consistently or adequately captured in healthcare data systems. Administrative health records could be a valuable source for researching violence and understanding the needs of victims, but such datasets are currently underutilised for this purpose.
Anastasia worked with Hospital Episode Statistics Accident and Emergency (HES A&E) and the Emergency Care Data Set (ECDS) while on secondment at the Department of Health and Social Care (DHSC), with helpful review provided by researchers in the department.
Among the datasets reviewed in the study, the South Wales Violence Surveillance dataset (police and emergency department data linked by Public Health Wales) had the most detail about violent acts and their contexts, while the Clinical Practice Research Datalink (CPRD) provided the more extensive range of socioeconomic factors about patients and extensive linkage with other datasets. Currently, detailed safeguarding information is routinely removed from the ECDS extracts provided to researchers, limiting its utility for violence research. In the HES A&E, only physical violence was consistently recorded.
Addressing these limitations and increasing awareness of the potential utility of health administrative datasets to violence-related research has the potential to provide insight into the health service needs of victims.
VISION researchers Dr Annie Bunce and Dr Estela Capelas Barbosa have been working with administrative data provided by specialist domestic and sexual violence and abuse (DSVA) support services.
Whilst the wealth and breadth of the data collected creates exciting opportunities for improving our understanding of patterns in experiences of violence and service use, the process of preparing the data for analysis has its challenges. Such challenges- and potential strategies for overcoming them- are not well documented, creating missed opportunities for improving the utilisation of specialist services’ data.
In their new publication, Annie and Estela, along with City, University of London PhD student, Katie Smith, and Dr Sophie Carlisle, a former VISION researcher, reviewed the scope and merits of administrative data generally, and that collected by specialist DSVA services specifically, and the evidence to date for its use by researchers.
They found that the extent to which new insights on violence from specialist services’ data can be used to inform policy and practice is limited by three interrelated challenges: different approaches to the measurement of violence and abuse; the issue of disproportionate funding and capacity of services, and the practicalities of multi-agency working.
Nonetheless, the authors maintain the unique contribution to knowledge on violence that can be provided by DSVA services’ administrative data, and are hopeful that the paper will encourage further discussion about how to better utilise it. Additional resources, collaboration between multiple agencies, service providers and researchers, and the integration of specialist services’ data with other sources of data on violence are needed to maximise policy impact. Given the benefits individuals and society stand to gain, this is a worthwhile endeavour.
VISION researchers Dr Polina Obolenskaya, Dr Elouise Davies and Dr Niels Blom will present at the Crime Surveys User Conference 2024 on 6 February 2024 in Islington, London.
The event brings data producers and data users together to share updates on the development of the surveys and to showcase research that is being carried out using the data. It is organised by the UK Data Service in collaboration with the Office for National Statistics, Scottish Government and the Home Office.
Polina – The rise, fall and stall of violence in England and Wales: How have risks of violence changed for groups in the population?
Elouise – When there’s more than one assailant: Understanding variation in victims’ needs
Niels – New Crime Survey for England and Wales integration code: Impact for investigating rare events such as different intimate partner perpetrator types
However, recently, ‘Understanding Society’, the United Kingdom Household Panel Survey (UKHLS), began fielding a small battery of questions relating to violence experience. Here, we examined the strengths and weaknesses of these UKHLS measures with similar indices from the Crime Survey for England and Wales (CSEW), a widely used and regarded but cross-sectional survey.
Vanessa and Niels empirically assessed the extent to which the UKHLS variables are comparable with those in the CSEW to determine the viability of the UKHLS for the longitudinal study of (fear of) violence and its consequences.
Overall, they regarded the UKHLS to provide an important resource for future panel research on the consequences of victimisation. They found the indicators measuring physical assault to be similar in both sets of data, but also noted differences in prevalence and/or different distributions by socioeconomic group for the indices relating to being threatened and of feeling unsafe.
Nonetheless, Vanessa and Niels maintain their utility for researchers in this field, allowing researchers to uncover new inequalities in violence exposure.
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.
Data reference
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.innes@city.ac.uk. We look forward to hearing from you!
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).
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
