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Physical health conditions and intimate partner violence: A gendered issue

    Intimate partner violence (IPV) is a widespread global public health issue with serious and long-lasting consequences. While much research has focused on the mental health consequences of IPV, such as depression and PTSD, there is limited evidence on its association with physical health.

    This study explored how different types and number of types of IPV are linked to specific physical health conditions, and whether these associations differ between men and women. VISION researchers Dr Ladan Hashemi, Dr Anastasia Fadeeva and Professor Sally McManus, with Nadia Khan, City St George’s UoL, examined this using data from the 2014 Adult Psychiatric Morbidity Survey.

    Key findings include:

    • Women were more likely to experience IPV and a higher number of IPV types than men.
    • Women’s experience of lifetime and 12-month IPV were significantly associated with 12 and 11 different physical health conditions, respectively, while men’s experience of lifetime and 12-month IPV were significantly associated with 4 and 1 conditions, respectively.
    • Different types of IPV types were associated with different types of physical health condition, particularly among women.
    • A cumulative association between experiencing a greater number of IPV types and an increased risk of physical health conditions was evident for women but not for men.

    The research concludes that IPV is a gendered issue, with stronger associations between IPV and physical health evident in this data for women than for men. This may be because women are more likely to experience more and multiple types of IPV, more frequently, and more often with injury. Healthcare systems must recognise IPV as a priority issue, ensuring support is tailored to those affected.

    Recommendation

    • Healthcare systems need to address IPV as a priority health issue for the female population. Gender-informed approaches in IPV intervention strategies and healthcare provision are required. This means emphasising the development of IPV-responsive healthcare systems and comprehensive IPV curricula in medical and health training.

    To download the paper: Intimate partner violence and physical health in England: Gender stratified analyses of a probability sample survey – Ladan Hashemi, Anastasia Fadeeva, Nadia Khan, Sally McManus, 2025

    To cite: Hashemi L, Fadeeva A, Khan N, McManus S. Intimate partner violence and physical health in England: Gender stratified analyses of a probability sample survey. Women’s Health. 2025;21. doi:10.1177/17455057251326419

    For further information, please contact Ladan at ladan.hashemi@citystgeorges.ac.uk

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    Synthetic datasets enable linkage and a longitudinal understanding of experiences of violence and health impacts and consequences

      Violence is a complex social problem and a public health issue, with implications for the health and social care systems, police and justice systems, as well as significant productivity losses for those who experience it. Analysing data collected by these systems can aid understanding of the problem of violence and how to respond to it. In social research, analysing administrative records together with survey data has already enabled better measurements of violence and its costs, capturing experiences of both victim-survivors and perpetrators across multiple points in time and social and economic domains.

      Ideally, data from the same individuals would enable linkage and a longitudinal understanding of experiences of violence and their (health) impacts and consequences. However, most studies in violence-related research analyse data in silo due to difficulties in accessing data and concerns for the safety of those exposed. This is particularly the case for data from third sector specialist support services for victims or perpetrators of violence which has, to VISION’s knowledge, not been linked or combined with other datasets. Because these services provide person-centred trauma-informed care and there is a risk that information on their service users may be used against them in courts or by immigration authorities, direct data linkage is not possible and alternatives are needed.

      With this research, VISION researchers Dr Estela Capelas Barbosa, Dr Niels Blom, and Dr Annie Bunce provide a proof-of-concept synthetic dataset by combining data from the Crime Survey for England and Wales (CSEW) and administrative data from Rape Crisis England and Wales (RCEW), pertaining to victim-survivors of sexual violence in adulthood. Intuitively, the idea was to impute missing information from one dataset by borrowing the distribution from the other.

      The researchers borrowed information from CSEW to impute missing data in the RCEW administrative dataset, creating a combined synthetic RCEW-CSEW dataset. Using look-alike modelling principles, they provide an innovative and cost-effective approach to exploring patterns and associations in violence-related research in a multi-sectorial setting.

      Methodologically, they approached data integration as a missing data problem to create a synthetic combined dataset. Multiple imputation with chained equations were employed to collate/impute data from the two different sources. To test whether this procedure was effective, they compared regression analyses for the individual and combined synthetic datasets for a variety of variables.

      Results show that the effect sizes for the combined dataset reflect those from the dataset used for imputation. The variance is higher, resulting in fewer statistically significant estimates. VISION’s approach reinforces the possibility of combining administrative with survey datasets using look-alike methods to overcome existing barriers to data linkage.

      Recommendations

      • Imputing missing information from one dataset by borrowing the distribution from the other should be applicable for costing exercises as it permits micro-costing. 
      • Compared to traditional research, VISION’s proposed approach to data integration offers a cost-effective solution to breaking (data-related) silos in research.

      To download the paper: Look-alike modelling in violence-related research: A missing data approach | PLOS One

      To cite: Barbosa EC, Blom N, Bunce A (2025) Look-alike modelling in violence-related research: A missing data approach. PLoS ONE 20(1): e0301155. https://doi.org/10.1371/journal.pone.0301155

      For further information, please contact Estela at e.capelasbarbosa@bristol.ac.uk

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      Call for Frontiers in Sociology abstracts: Enhancing data collection and integration to Reduce health harms and inequalities linked to violence

        Frontiers in Sociology is currently welcoming submissions of original research for the following research topic: Enhancing Data Collection and Integration to Reduce Health Harms and Inequalities Linked to Violence.

        This edition is guest-edited by Dr Estela Capelas Barbosa (University of Bristol and the UKPRP VISION research consortium), Dr Annie Bunce (City St. George’s, UoL and the UKPRP VISION research consortium), and Katie Smith (City St. George’s, UoL / University of Bristol).

        Submissions should focus on any of the following:

        • advancing measurement approaches which emphasise cross-sector harmonisation to better evaluate interventions, address health inequalities, and reduce violence
        • addressing any form of violence (e.g., physical, non-physical, technology-facilitated) and its impacts on health, social and economic well-being, and marginalised groups, considering intersections of age, gender, ethnicity, disability, and religion

        Research using existing datasets or primary data (quantitative or qualitative), cross-sectoral and cross-disciplinary approaches (e.g., sociology, criminology, public health), and lived experience perspectives is encouraged.

        Contributions may include conceptual reviews, methodological innovations, empirical studies and systematic reviews on themes such as health inequalities, intervention effectiveness, outcome measurement, data harmonisation, and linkage strategies.

        Abstracts are due by 7th April 2025, and the deadline for manuscripts is 28th July 2025.

        For details of the different article types accepted and associated costs, please follow this link https://www.frontiersin.org/journals/sociology/for-authors/publishing-fees.

        For more information and to submit an abstract or manuscript, please use the “I’m interested” link below or visit the Research Topic page here https://www.frontiersin.org/research-topics/67291/enhancing-data-collection-and-integration-to-reduce-health-harms-and-inequalities-linked-to-violence. 

        This special edition provides an excellent opportunity to advance knowledge in this critical area. Please do reach out and contact us if you have any questions: annie.bunce@city.ac.uk

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        Implications of changing domestic abuse measurement on the Crime Survey for England & Wales

          The Office for National Statistics (ONS) is making a major decision this month on the future of Crime Survey for England and Wales (CSEW) Domestic abuse measurement and monitoring.

          Last year, ONS ran an experiment where half of the CSEW sample got the domestic abuse module used since 2005, and the other half got a new module that is not comparable with the previous one. ONS intend to move over entirely to the new module in the next data collection (2025/26).

          Loss of the existing module has major implications: it is world-leading, uses globally comparable items, and with trend data going back to 2005. Without consistently administered core items from that module, it will no longer be possible to:

          • Produce long-term trends over time in domestic abuse for England and Wales.
          • Group a decade of survey years together to have enough cases to robustly examine domestic abuse in particular regions, minoritised groups, and by other protected characteristics for many years. This is essential for understanding inequalities in violence and subsequent service contact, and whether these are changing.

          The new module is problematic for many reasons:

          • Is not a standardised measure, has undergone little validation or psychometric testing, and is not comparable with anything used previously or in any other country or study.  
          • It separates data collection between former and current partner based on relationship status at the time of the interview, not at the time of abuse. This distinction creates confusion for interpretation of analysis and may be misinterpreted. The distinction is also problematic for classification of casual and other relationship types.
          • The overhaul of the module was intended to align measurement with the Domestic Abuse Act 2021 definition, but it appears that domestic abuse as recognised by that Act cannot be identified by this module.

          We urgently recommend that before losing this world-leading time series and relying on an untested, not comparable, and flawed new approach to DA measurement in England and Wales, that ONS:

          1. Pause: continue the split-sample data collection for one more year.
          2. Test the new approach: fully compare data collected using the new and old modules data so the validity and utility of the new measures can be evaluated appropriately, and its impact on inequalities assessed.
          3. Publish these results publicly: and fully consult once stakeholders understand all the implications of having data collected in each way before the decision to roll out new data collection is finalised.
          4. With this information, then compare all options: such as maintaining some of the existing questions alongside adding new coercive control items. This straightforward approach would ensure the utility of the survey for national trends (in both England and Wales) and analysis of inequalities and minoritised groups, while also improving the measurement of coercive control.

          We urge others who feel similarly to contact ONS at CrimeStatistics@ons.gov.uk  or contact us at VISION_Management_Team@city.ac.uk if you would like to discuss.

          Note that ONS is planning a raft of further changes with similar implications for trends and analysis of minoritised groups, including:

          • Removal of the sexual victimisation module from next data collection (2025/26), with redevelopment at some future date.
          • Removal and redevelopment of the nature of partner abuse questions, which cover DA survivors service use and police contact and are essential to understanding whether some groups are underserved by services.

          These will further undermine continuity of data for trends and the ability to analyse minoritised groups or by protected characteristics.

          For researchers interested in combining CSEW waves to enable robust analysis of inequalities by protected characteristics and for minoritised groups, VISION researcher Niels Blom has published syntax: https://vision.city.ac.uk/news/new-possibilities-created-by-crime-survey-wave-integration/.

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          Natural Language Processing: Interrogating free text in mental healthcare records to capture experiences of violence

            Violence can be categorised in a variety of ways for example physical, sexual, emotional, and domestic but all cause significant physical and mental morbidity within general populations. Individuals with a severe mental illness have been found to be significantly more likely to experience domestic, physical, and sexual violence compared to the general population. For these individuals, experiences of violence are important risk factors however, this is not routinely collected by mental health services.

            In general data on all forms of violence has been inadequately available from healthcare records. This is partly due to the lack of routine enquiry by professionals at points of clinical contact, and partly because instances of violence are difficult to identify in healthcare data in the absence of specific coding systems.

            A general challenge for using health records data for research is that the most valuable and granular information is frequently contained in text fields (e.g., routine case notes, clinical correspondence) rather than in pre-structured fields; this includes mentions of violence whether experienced as a victim or perpetrated. Capturing violence experiences across mental healthcare settings can be challenging because most instances are likely to be recorded as unstructured text data. Therefore, natural language processing (NLP), is increasingly in use to extract information automatically from unstructured text in electronic health records, particularly in mental healthcare, on clinical entities.

            Dr Ava Mason from Kings College London and VISION researchers Professor Robert Stewart, Dr Angus Roberts, Dr Lifang Li, and Dr Vishal Bhavsar worked with colleagues to apply NLP across different clinical samples to investigate mentions of violence. They ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category.

            Results showed that patients with a mood disorder, personality disorder, schizophrenia spectrum disorder or PTSD had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups for Black and Asian had significantly higher likelihood of recorded violence victimisation compared to White groups. Males were significantly less likely to have reported recorded violence victimisation than females.

            The researchers demonstrated the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse.

            To download the paper: Frontiers | Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis

            To cite: Mason AJC, Bhavsar V, Botelle R, Chandran D, Li L, Mascio A, Sanyal J, Kadra-Scalzo G, Roberts A, Williams M, Stewart R. Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis. Frontiers in Psychiatry 2024 Sep 10. doi:103389/fpsyt.2024.1181739

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            Deputy Chief Constable awarded Practitioner in Residence at Violence and Society Centre

              Katy Barrow-Grint, Deputy Chief Constable, Gloucestershire

              City St George’s, UoL, offers a Practitioner in Residence programme at the School for Policy and Global Affairs. It is for mid-level and senior policy practitioners within the UK and provides a platform to grow and explore their practice in partnership with the school.

              Katy Barrow-Grint, Deputy Chief Constable in Gloucestershire and an executive leader in national policing, became aware of the opportunity via her work with VISION Senior Research Fellow, Dr Ruth Weir,  on the VISION adolescent domestic abuse (ADA) research programme. Having recently written a book entitled ‘Policing Domestic Abuse’ with Ruth and others, the research identified a national gap academically and in policing with how ADA is understood.

              Katy’s focus will be on how police constabularies document ADA and developing a better understanding of the impact of the statutory age limitations on the practical work police officers do on the front line.

              Forces do not routinely record ADA as the statutory guidance states that domestic abuse occurs in relationships where both parties are aged 16 or over. As a result, whilst crimes against young people will be recorded and investigated, they are not necessarily classified as domestic abuse, and it may be that child protection, domestic abuse or front-line response teams deal with the case.

              Her project work will seek to understand how forces are recording such incidents, and what type of officer and role is investigating. Katy will work with policing nationally through the National Police Chief‘s Council (NPCC) domestic abuse and child protection portfolios and collate an up-to-date picture across all forces in England and Wales to understand how they are recording and who is investigating ADA.

              Katy is also undertaking specific localised work in Oxfordshire, Gloucestershire and Northumbria, hosting roundtables with Dr Ruth Weir and  practitioners from all relevant agencies to gain a qualitative understanding of the problems staff encounter when dealing with ADA.

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              VAWG data dashboard consultation highlights usefulness of tool

                The UK Office for National Statistics (ONS) developed a prototype violence against women and girls (VAWG) data dashboard in 2022. The webpages presented statistics and charts on violence against women and girls in England and Wales, drawing on multiple sources. However, due to reprioritisation at ONS, maintenance of the dashboard stopped and as of April 2024 it was no longer accessible.

                VISION developed a consultation to ascertain the usefulness of a VAWG data dashboard as a result. The call was open from March to May 2024, and 102 responses were received. Most participants responded in their capacity as individuals (n=61), although 25 stated that they were responding on behalf of an organisation and four on behalf of a group. Some participants both responded as an individual and on behalf of an organisation or group.

                Consultation participants responded as people from across a variety of roles and sectors. The
                most commonly cited were working in research or education (n=40) and in policy or planning
                (n=28), 27 people responded as someone with lived experience and 13 as members of the
                public. The remainder comprised those in service provision (n=17), a campaign role (n=10) or
                some other capacity (8).

                Consultation results

                Many participants had heard of the data dashboard before the VISION consultation (n=51), although 28 reported that they had not. Of those who had heard of the dashboard before, most had made use of it (n=39).

                Most participants reported that having a dashboard that brought together data on violence
                and abuse would help them either ‘somewhat’ (n=12) or ‘a lot’ (n=39).

                Participants were asked what they found to be useful about the data dashboard:

                • Data discovery
                • Finding data and finding it faster
                • Breakdowns and local profiles
                • Comparisons
                • Authoritative context
                • Source material

                Participants were asked for their thoughts on the limitations or what was missing from the dashboard:

                • Wider coverage in relation to topics (health, disability, suicide, law, family courts, policy), types of violence and abuse (homicide, forced marriage, sexual violence), and particular groups (men, perpetrators)
                • Deeper context in terms of much more nuanced contextual discussion of what the data means, ‘data without context is misleading’
                • Critical interrogation such as the highlight of methodological limitations
                • Interactive functionality with more scope for further breakdowns by local authority areas and police forces for example
                • Improved search function
                • Positive action such as a ‘section about work being done to support victims/reduce cases’
                • Human stories meaning to go beyond numbers and to tell the human stories that underpin them

                Recommendations

                Through this consultation, approximately 100 people told VISION that a VAWG data dashboard is
                something that they valued having and wish to have again. These included people with lived
                experience of violence and abuse, people working in health, justice, specialist and other
                services, researchers and academics across disciplines, and members of the wider public.

                A violence and abuse data dashboard is needed because it has:

                • Symbolic value: indicating that violence against women and girls matters to the
                  Government, and
                • Practical value: as a functional and easy to use tool facilitating access to high
                  quality data spanning a range of types of violence, groups, areas and years.

                In April 2024, ONS’ prototype VAWG data dashboard was withdrawn due to reprioritisation of
                resources within that organisation. Since then, a new Government has come into office with a
                stated mission to halve violence against women and girls within the decade. How progress towards this commitment is monitored will be essential to its success. General population health and crime surveys, alongside other data sources, will be key and that includes a revitalised, fit for purpose VAWG data dashboard. To instill trust and collective investment in this goal, a public platform for transparent monitoring is needed and the dashboard could be an effective, useful tool.

                Next steps

                VISION is a cross-sectoral consortium of academics and government and service partners
                working with UK data on violence and abuse. We are aware that further development and relaunching of a data dashboard will require a collaborative effort from relevant departments of
                state, data providers (not least ONS) and external funding. Drawing on our work in this area
                we aim to coordinate this effort, with three initial objectives:

                • Resource: Identify partners and funding source(s)
                • Define: Agree clear definitions to best capture and monitor subgroup and temporal
                  trends in VAWG and violent crime in the population
                • Design and test a revised violence and abuse data dashboard with people from
                  across sectors

                To download the report:

                Consultation: Is there a need for a Violence Against Women and Girls (VAWG) data dashboard

                Or for further information, please contact Sally at sally.mcmanus@city.ac.uk

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                The story so far: Co-production in Lambeth

                  By Elizabeth Cook, Senior Lecturer in Criminology & Sociology at City St George’s, University of London

                  As the VISION consortium approaches the end of its third year, work continues on consolidating the learning from various large datasets in crime and justice, health, and specialist services.

                  What we know is that these datasets are structured in different ways, collected by different agencies, and curated for quite different purposes. They represent particular ways of knowing about violence and abuse: they can help to identify patterns (e.g., what determines whether victim-survivors of sexual violence and abuse access support), prevalence (e.g., of workplace bullying and harassment), trends over time, and associations (e.g., between intimate partner violence, suicidality, and self-harm). However, we also know that large datasets struggle to capture the complex, and sometimes messy, realities of violence and abuse experienced by communities, especially those that are marginalised and minoritised.

                  Peer action research in Lambeth

                  In Lambeth, working in collaboration with peer researchers has made visible the evidence gaps that emerge at the intersection of multiple systems of inequality, including racism and misogyny.

                  We are lucky to be partnered with Lambeth Peer Action Collective (LPAC), High Trees and Partisan as part of a peer action research project. The aim of the project is to explore the role that trusted adults and trusted spaces can play in protecting young people from exposure to violence. Currently, there are 11 peer researchers that work as part of the LPAC: a collective of young people and youth organisations campaigning for change in their community. They are supported by High Trees, a Community Development Trust in Tulse Hill, eight partner youth organisations, and Partisan, a Black-led Community Interest Company providing culturally sensitive mental health support.

                  What has been achieved so far?

                  The project builds upon research conducted by the previous cohort of LPAC researchers conducted between December 2021 and August 2022. This project identified the impacts of violence on young people in Lambeth and the structural conditions of poverty, housing, education, urban regeneration, and public safety that were experienced unequally across the community.

                  Developing these findings further, the second cohort of peer researchers have been participating in weekly research training sessions led by High Trees and supported by VISION. The group has been learning everything they need for the next stage: from safeguarding and finances, to developing research questions, critical thinking skills, and how to evaluate research methods. This month, the LPAC researchers are getting ready to put into practice the interview skills that they have been learning each week in preparation for the next stage of the project – recruitment.

                  There has been amazing progress so far – not only in forming a research question and defining key concepts, but in developing a shared space for researchers to feel like change is possible and to collaborate with others who want the same.

                  What have we learned?

                  There are ongoing conversations about how peer action research can work to redress the imbalance between ‘researcher’ and ‘researched.’ These conversations seem even more relevant to research on violence and abuse, where the issue of power is central to both.

                  So far, the weekly sessions with peer researchers as well as our meetings with High Trees have taught us a lot about how power operates within institutions and the ways that it can be shared if there is a will to share it. This can be reflected in adequate resourcing, decision-making, access, and sharing skills and knowledge. The project has underlined the importance of respect in research: for different forms of expertise, within spaces, and within research relationships. The project has also challenged adult-centric assumptions about what we suppose that young people need to live better lives.

                  As mentioned previously, this project highlights the evidence gaps that occur at the intersection of multiple inequalities. In doing so, peer action research can also shape how we utilise large datasets, recognising how different social realities are reflected within existing data (or not).

                  In this sense, this collaboration has also made hyper-visible the question of: what and who is research for? As others have suggested, action research is not so much a methodology, but a way of thinking about research: it is a way of approaching a specific problem through community, participation, and curiosity. It is not necessarily driven by knowing more about something, but by wanting to change something with what you know.

                  We hope that this research continues in that spirit!

                  Further information

                  Do check out the LPAC’s manifesto for change and their previous report!

                   Photograph is copyrighted to Lambeth Peer Action Collective and not for use.

                  Natural Language Processing: Improving Data Integrity of Police Recorded Crime

                    By Darren Cook, Research Fellow in Natural Language Processing at City, University of London

                    Did you know that police recorded crime data for England and Wales are not accredited by the UK’s Office for Statistics Regulation (OSR)? This decision, made by the OSR after an audit in 2014, was due to concerns about the reliability of the underlying data.

                    Various factors affect the quality of police-recorded data. Differences in IT systems, personnel decision-making, and a lack of knowledge-sharing all contribute to reduced quality and consistency. Poor data integrity leads to a lack of standardisation across police forces and an increase in inaccurate or missing entries. I recently spoke about this issue at the Behavioural and Social Sciences in Security (BASS) conference at the University of St. Andrews, Scotland.

                    Correcting missing values is no small feat. In a dataset of 18,000 police recorded domestic violence incidents, we found over 4,500 (25%) missing entries for a single variable. Let’s assume it takes 30 seconds to find the correct value for this variable – that’s 38 hours of effort – almost a full working week. Given that there could be as many as twenty additional variables, it would take over four months to populate all the missing values in our dataset. Expanding such effort across multiple police forces and for multiple types of crime highlights the inefficiency of human-effort in this endeavour.

                    In my talk, I outlined an automated solution to this problem using Natural Language Processing (NLP) and supervised machine learning (ML). NLP describes the processes and techniques used by machines to understand human language, and supervised ML describes how machines learn to predict an outcome based on previously seen examples. In this case, we sought to predict the relationship between the victim and offender – an important piece of demographic information vital to ensuring victim safety.

                    The proposed system would use a text-based crime ‘note’ completed by a police officer to classify the victim offender relationship as either ‘Ex-Partner”, “Partner”, or “Family” – in keeping with the distinction made by Women’s Aid. Crime notes are an often-overlooked source of information in police data, yet we found they consistently referenced the victim-offender relationship. The goal of our system, therefore, was to extract the salient information from the free-form crime notes and populate the corresponding missing value in our structured data fields.

                    Existing solutions based on keywords and syntax parsing are used by multiple UK police forces. While effective, they require manual effort to create, update, and maintain the dictionaries, and they don’t generalise well. Our supervised ML system, however, can be automatically updated and monitored to maintain accuracy.

                    When tested, our system achieved 80% accuracy, correctly labelling the relationship type in four out of five cases. In comparison, humans performed this task with approximately 82% accuracy – an arguably negligible difference. Moreover, once trained, our system could classify the entire test set (over 1,000 crime notes) in just sixteen seconds.

                    However, we noted some limitations, the biggest of which was a high linguistic overlap in crime notes between ‘Ex-Partner’ and ‘Partner’ that caused several misclassifications. We believe more advanced language models (i.e., word embeddings) will improve discrimination between these relationships.

                    We also discovered a potential prediction bias against minorities. Although victim ethnicity wasn’t included in our training setup, we observed reduced accuracy for Black or Asian victims. The source and extent of this bias are subjects of ongoing research.

                    Our findings highlight the promise of automated solutions but serve as a cautionary tale against assuming these systems can be applied carte blanche without careful consideration of their limitations. Several outstanding questions remain. Is a system with 80% accuracy good enough? Is it better to leave missing values rather than predict incorrect ones? Incorrectly identifying a perpetrator as a current partner rather than an ex-partner could significantly impact the victim’s safety. Additionally, a model biased against certain ethnicities risks overlooking the specific needs of minority groups.

                    The conference sparked lively and engaging conversation about many of these issues, as well as the role that automation can be play within the social sciences more broadly. A research article describing these results in full is the focus of ongoing work, and the presentation slides are available below as a download.

                    For further information please contact Darren at darren.cook@city.ac.uk or via LinkedIn @darrencook1986

                    Dr Darren Cook, An application of Natural Language Processing (NLP) to free-form Police crime notes – 1 download

                    D Cook_NLP and PoliceDownload

                    Photo by Markus Spiske on Unsplash

                    Uncovering ‘hidden’ violence against older people

                      By Dr Anastasia Fadeeva, VISION Research Fellow

                      Violence against older people is often overlooked. As a society, we often associate violence with young people, gangs, unsafe streets, and ‘knife crime’. However, violence also takes place behind front doors, perpetuated by families and partners, and victims include older people. 

                      Some older people may be particularly vulnerable due to poorer physical health, disability, dependence on others, and financial challenges after retirement. Policy rarely addresses the safety of this population, with even health and social care professionals sometimes assuming that violence does not affect older people. For example, doctors may dismiss injuries or depression as inevitable problems related to old age and miss opportunities to identify victims (1). In addition, older people may be less likely to report violence and abuse because they themselves may not recognise it, do not want to accuse family members, or out of fear (2). 

                      Given victims of violence often remain invisible to health and social services, police, or charities, the most reliable statistics on violence often come from national surveys such as the Crime Survey for England and Wales (CSEW) conducted by the Office for National Statistics. However, for a long time the CSEW self-completion – the part of the interview with the most detail on violence and abuse – excluded those aged 60 or more, and only recently extended to include those over 74. Some national surveys specifically focus on older people, but these ask very little about violence and abuse. Additionally, despite people in care homes or other institutional settings experiencing a higher risk of violence, it can be challenging to collect information from them. Therefore, many surveys only interview people in private households, which excludes many higher-risk groups.

                      We need a better grasp of the extent and nature of violence and abuse in older populations. First, reliable figures can improve the allocation of resources and services targeted at the protection of older people. Second, better statistics can identify the risk factors for experiencing violence in later life and the most vulnerable groups.

                      In the VISION consortium, we used the Adult Psychiatric Morbidity Survey (APMS 2014) to examine violence in people aged 60 and over in England (3). While we found that older people of minoritised ethnic backgrounds are at higher risk of violence (prevalence of 6.0% versus 1.7% in white people in 12 months prior to the survey), more research needs to be done to distinguish the experiences of different ethnic groups. Our research also showed that loneliness and social isolation were strongly related to violence in later life. Older people may experience social isolation due to limiting health issues or economic situations, and perpetrators can exploit this (4). Moreover, isolation of victims is a tool commonly used by perpetrators, especially in cases of domestic abuse (5).  Knowing about these and other risk factors can help us better spot and protect potential victims.

                      Additionally, more needs to be learnt about the consequences of life course exposure to violence for health and well-being in later life. This is still a relatively unexplored area due to limited data and a lack of reporting from older victims and survivors. It is sometimes more difficult to establish the link between violence and health problems because the health impacts are not always immediate but can accumulate or emerge in later life (6). Also, as people develop more illnesses as they age, it is more challenging to distinguish health issues attributable to violence. Therefore we are also using the English Longitudinal Study of Ageing (ELSA) to examine temporal relationships between lifetime violence exposure and health in older age.

                      Dr Sophie Carlisle, Evaluation Researcher at Health Innovation East Midlands, and former VISION researcher, also reflects on violence against older people and includes an analysis of our study’s strengths and weaknesses in her 10 December 2024 blog on the Mental Elf website, Violence against older people – linked to poor mental health #16DaysOfActivism2024. Sophie highlighted how the study reported that violence against older people is often perpetrated by an intimate partner and is strongly associated with poor mental health.

                      In an inclusive society, every member should be able to lead a life where they feel safe and respected. We are delighted that the CSEW has removed the upper age limit to data collection on domestic abuse, which is one step towards making older victims and survivors heard. Continuous work on uncovering the ‘hidden’ statistics and examining the effects of intersectional characteristics on violence is crucial in making our society more inclusive, equal, and safe for everyone. For example, one VISION study (7) has demonstrated that the risks of repeated victimisation in domestic relationships had opposite trends for men and women as they aged. We are committed to support the Hourglass Manifesto to end the abuse of older people (8), and are willing to provide decision makers with evidence to enable a safer ageing society.

                      For further information, please see: Violence against older people and associations with mental health: A national probability sample survey of the general population in England – ScienceDirect

                      Or please contact Anastasia at anastasia.fadeeva@city.ac.uk

                      Footnotes

                      • 1.  SafeLives U. Safe later lives: Older people and domestic abuse, spotlights report. 2016.
                      • 2.  Age UK. No Age Limit: the blind spot of older victims and survivors in the Domestic Abuse Bill. 2020.
                      • 3.  Fadeeva A, Hashemi L, Cooper C, Stewart R, McManus S. Violence against older people and mental health: a probability sample survey of the general population. forthcoming.
                      • 4.  Tung EL, Hawkley LC, Cagney KA, Peek ME. Social isolation, loneliness, and violence exposure in urban adults. Health Affairs. 2019;38(10):1670-8.
                      • 5.  Stark E. Coercive control. Violence against women: Current theory and practice in domestic abuse, sexual violence and exploitation. 2013:17-33.
                      • 6.  Knight L, Hester M. Domestic violence and mental health in older adults. International review of psychiatry. 2016;28(5):464-74.
                      • 7.  Weir R. Differentiating risk: The association between relationship type and risk of repeat victimization of domestic abuse. Policing: A Journal of Policy and Practice. 2024;18:paae024.
                      • 8.  Hourglass. Manifesto A Safer Ageing Society by 2050. 2024.

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