# paper.pdf ## Metadata - PDFFormatVersion=1.3 - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - CreationDate=D:20260711145253+05'30' - Creator=Arbortext Advanced Print Publisher 9.1.405/W Unicode - Producer=Acrobat Distiller 8.0.0 (Windows) - ModDate=D:20260723070543+00'00' - Title=Childhood attention-deficit hyperactivity disorder traits and experiences of intimate partner violence up to midlife: evidence from a longitudinal cohort over five decades - xap:creatortool=Arbortext Advanced Print Publisher 9.1.405/W Unicode - xap:createdate=2026-07-23T07:05:43+00:00 - xap:modifydate=2026-07-23T07:05:43+00:00 - dc:format=application/pdf - dc:title=Childhood attention-deficit hyperactivity disorder traits and experiences of intimate partner violence up to midlife: evidence from a longitudinal cohort over five decades - dc:rights=Not for further distribution unless allowed by the License or with the express written permission of Cambridge University Press. - dc:source=https://doi.org/10.1192/bjp.2026.10720 - dc:description=https://doi.org/10.1192/bjp.2026.10720 Published online by Cambridge University Press - pdf:producer=Acrobat Distiller 8.0.0 (Windows) - xapmm:documentid=uuid:1d702e1e-bcad-42fb-9d1c-848b5c5bcf95 - xapmm:instanceid=uuid:5c56ea0a-5290-4dc7-b775-7ff45cedf532 ## Contents ### Page 1 Original ArticleChildhood attention-deficit hyperactivity disorder traits and experiences of intimate partner violence up to midlife: evidence from a longitudinal cohort over five decades Amber John, Elizabeth O’Nions, Lucy Corrigan, Joanne Cotton, Warren J. Donnellan, Henry Shelford, Celine El Baou, Gavin R. Stewart, Roopal Desai, Aphrodite Eshetu, Rob Saunders, Jae Won Suh, William Mandy, Philip J. Asherson, Jennifer Kirton, Georgia Pavlopoulou, Jessica Agnew-Blais and Joshua StottBackgroundEmerging evidence suggests that attention-deficit hyperactivity disorder (ADHD) may be associated with increased likelihood of experiencing interpersonal harm, including from intimate partners.AimsThis study aims to test the long-term associations between ADHD traits in childhood and experiences of intimate partner violence (IPV) up to midlife (age 51 years).MethodData were from the 1970 British Cohort Study, a nationally representative longitudinal study of people born during 1970. ADHD traits were assessed at age 10 years. Lifetime experience of five types of IPV was measured at age 51 years (harassment, economic abuse, physical violence, threats/coercive control, sexual violence). Regression models were used to test associations between childhood ADHD traits and midlife IPV, adjusting for gender, ethnicity and childhood social class.ResultsAdjusted models showed that ADHD traits in childhood were associated with higher risk of experiencing any form of IPV, a greater number of different IPV experiences and all individual subtypes of IPV by age 51 years. Post-estimation showed that around one in seven men and one in four women without high ADHD traits experience some form of IPV by midlife. In people with high ADHD traits (4.40%), these estimates increase to approximately one in five men and one in three women.ConclusionsHigher levels of childhood ADHD traits are associated with an increased risk of experiencing IPV by middle age. These findings highlight that ADHD may be associated with increased vulner- ability to interpersonal harm in relationships. Identifying people with ADHD at greatest risk, as well as addressing the needs of people with ADHD within IPV prevention and response systems is important for reducing harm and addressing inequalities faced by people with ADHD over the life course.KeywordsAttention-deficit hyperactivity disorders; longitudinal data; intimate partner violence; cohort study; ADHD.Copyright and usage© The Author(s), 2026. Published by Cambridge University Press on behalf of Royal College of Psychiatrists. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/ licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.Attention-deficit hyperactivity disorder (ADHD) is a neuro- developmental condition, characterised by traits of inattention, hyperactivity and/or impulsivity. These traits occur across multiple settings and can interfere with day-to-day functioning.1 ADHD is lifelong and traits persist into adulthood for many people.2 Despite this, ADHD is underdiagnosed in adults,3 particularly in margin- alised groups,4 including people of minoritised ethnicities, women and older people. From a young age, children and adolescents with ADHD often face a greater emotional burden than their neuro- typical peers, including heightened experiences of social exclusion and harm.5 These inequalities frequently continue throughout adulthood, extending across multiple settings. Adults with ADHD are disproportionately affected by social exclusion,6 poorer physical and mental health,7 economic instability (e.g. unemployment, financial insecurity and homelessness)8,9 and earlier mortality.10 Intimate partner violence (IPV) is a key public health issue in the UK and worldwide, with serious negative impacts on individuals. Recent statistics of the prevalence of IPV in England and Wales show that 22.9% of women and 9.7% of men report experiencing IPV during their adult lives.11 IPV is a multidimensional construct that can take many forms, including physical violence, sexual violence, coercive control, economic abuse, harassment and more.11,12 IPV can have considerable consequences for the indi- vidual, including poorer mental and physical health,13 as well as lower quality of life.14 Meta-analytical evidence indicates that neurodivergent adults and people with disabilities are more sus- ceptible to different types of violence, including sexual violence, compared with their non-disabled counterparts.15 A recent review based on a small number of studies suggested there are higher rates of IPV among people with ADHD.16 However, current under- standing of IPV experiences in people with ADHD is limited, because the majority of research (seven out of the nine studies on IPV in the identified review) has focused on ADHD as a risk factor for perpetration of IPV, rather than looking at vulnerability to experiencing IPV (only four studies in the identified review) within this population. This imbalance in focus of previous research may potentially reflect and/or reinforce prevailing negative stereotypes that exist about ADHD. It may also be because ADHD is under- researched in women, who may be more likely to experience IPV. In addition to this, the majority of research on this topic has been cross-sectional and has focused on IPV during adolescence and young adulthood only. Of the studies identified in the reviewThe British Journal of Psychiatry (2026) 1–7. doi: 10.1192/bjp.2026.10720 1https://doi.org/10.1192/bjp.2026.10720 Published online by Cambridge University Press ### Page 2 focusing on experiencing IPV, the oldest age group reported was in university students (up to age 21 years).16 Little is known about how ADHD traits in childhood may be associated with long-term risk of IPV experiences. The studies included in the review also varied in the measurement of IPV, with most focusing on a single domain (e.g. physical or sexual violence). This does not capture the full complexity and multidimensional nature of IPV experiences. It is possible that patterns observed in other forms of neurodivergence (e.g. autism) may apply to ADHD. For example, IPV is alarmingly common among autistic adults, with prevalence estimates up to 70%.17 Autistic people often report difficulties recognising abusive behaviours at the time they occur, in part due to experiences of masking and a heightened sense of powerlessness within rela- tionships.18 IPV can also profoundly disrupt autistic people’s sense of self, with participants describing dehumanisation and loss of autonomy. This pattern may also apply to people with ADHD, although this has not yet been systematically tested. This study aimed to test whether childhood ADHD traits are associated with self-reported rates of experiencing IPV by midlife, using data from a large longitudinal birth cohort study.Method DataData were used from the 1970 British Cohort Study (BCS70), a nationally representative longitudinal study that follows the lives of 17 198 people born during a single week in 1970 across England, Scotland and Wales.19 The cohort has been surveyed repeatedly over more than five decades, collecting extensive information on participants’ demographic, health and lifestyle characteristics. The most recent data collection was completed when participants were aged 51 years. Comprehensive descriptions of the study design and data collection procedures have been published elsewhere.19 The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2013. Ethical approval for the most recent wave of data collection for BCS70 was obtained from the South East Coast – Brighton and Sussex MREC (approval number 15/LO/1446). Written informed consent was obtained from all participants.MeasuresADHD traitsA set of 14 items reflecting ADHD traits (nine items corresponding to symptoms of hyperactivity, and five5 to symptoms of inatten- tion) was used from data collected when participants were 10 years old. These items originated from behaviour questionnaires com- pleted by parents and teachers.20,21 Two main indicators of ADHD were derived and validated in previous research that used a data mining framework to map items onto the DSM-5 criteria for ADHD,22 and were replicated in this study: (a) a continuous measure of ADHD traits derived using a zero-inflated item response theory mixture model, and (b) a binary variable indicating whether DSM-5 diagnostic criteria for ADHD were met or not (4.40% of the analytic sample met criteria). This approach showed good model fit and strong discriminative validity. The resulting scale showed expected associations with established correlates of ADHD, such as being male, experiencing social disadvantage, and later educational and behavioural difficulties.22 To maximise power, the primary predictor used in these analyses was the continuous indicator of ADHD traits. The binary measure was used in analyses estimating predicted probabilities of IPV in high versus not high ADHD trait groups. Full methodological details on the ADHD trait construction are reported elsewhere,22 and a user guide is also available from the Centre for Longitudinal Studies. Items included in the measure are presented in Supplementary Materials 1 avail- able at https://doi.org/10.1192/bjp.2026.10720.IPVQuestions about IPV were asked at age 51 years (list of questions are presented in Supplementary Materials 2). Participants were asked to indicate if they have ever experienced five different forms of IPV (yes or no): harassment, economic abuse, physical violence, threats/coercive control and sexual violence. This multidomain approach to investigating IPV is in line with the framework used in the Crime Survey for England and Wales11 and recent UK policy definitions outlined in the Domestic Abuse Act 2021.12 Participants completed these questions as part of a self-completion survey during the interview along with other sensitive topics. In addition to the individual domain measures, a binary measure was derived indicating whether participants reported experiencing any form of IPV, and a sum score was also created indicating the number of different IPV domains each participant reported experiencing (ranging from 0 to 5).CovariatesCovariates were gender, ethnicity and childhood social class at age 10 years. Gender was coded as male or female. Ethnicity was grouped into ‘White’ or ‘Minoritised ethnic background’. Social class was determined using the occupation of the father at age 10 years, or the mother if the father’s information was unavailable. Occupational classifications followed the Registrar General’s Social Class schema, which includes unskilled, partly skilled, manual, non- manual, managerial/technical and professional roles.Statistical analysesLogistic regressions were conducted to test associations between ADHD traits in childhood and the binary measure indicating whether any form of IPV was experienced by age 51 years. Further logistic regressions were used to test associations between child- hood ADHD traits and domains of IPV by age 51 years (harass- ment, economic abuse, physical violence, threats/coercive control or sexual violence). Because of overdispersion, negative binomial regression was used to test associations between ADHD traits and number of IPV domains experienced by age 51 years. Analyses were run adjusted for gender only (model 1) and adjusted for key covariates (gender, ethnicity and social class at age 10 years) (model 2). Given established differences in IPV patterns and rates of ADHD between men and women, unadjusted models were not presented, as these would yield biased estimates. In addition, all analyses were checked for gender interactions. For the sexual violence domain, the analyses were run on women only, because of very small numbers of men reporting this form of IPV in this sample (n = 10, 0.37% of men in the sample). Predicted probabilities of experiencing IPV by age 51 years were estimated with the margins command following adjusted logistic regression in Stata, comparing individuals with and without high ADHD traits, using the binary indicator. This analysis was stratified by gender to provide gender-specific estimates. The sample with missing data was compared with the sample without missing data, using t-tests and χ2-tests to assess differences in key variables and covariates. All analyses were conducted in Stata v18 on Windows (StataCorp LLC, College Station, Texas, USA; https://www.stata.com). People with lived experience contributed to this research through (a) feedback from members of a public and patientJohn et al 2https://doi.org/10.1192/bjp.2026.10720 Published online by Cambridge University Press ### Page 3 involvement group of people with ADHD; and (b) inclusion of multiple neurodivergent researchers on the authorship team, including people with ADHD, whose perspectives informed the design and interpretation of this study.Results Descriptive statistics and missing dataA total of 5770 people (254 with high ADHD traits, 4.40%) were included in the main analyses. Compared with the sample included in analyses, the sample with missing data on at least one key variable or covariate required for analyses were more likely to be men, of minoritised ethnicity and from lower socioeconomic backgrounds. The sample with missing data were also more likely to have higher ADHD traits and to have experienced economic abuse and sexual violence from an intimate partner, compared with the analytic sample. The samples did not differ on other domains of IPV (harassment, physical violence or threats/coercive control), likely because of the timing of assessment of these variables (Supplementary Table 1). The descriptive statistics for the analytic sample are presented in Table 1. Rates of reported IPV by gender are reported in Supplementary Table 2. Overall, 740 (24.24%) women and 388 (14.28%) men in this sample reported experiencing IPV by age 51 years.Associations between childhood ADHD traits and IPV by age 51 yearsThere was no significant interaction term between the continuous ADHD traits score and gender on IPV as a binary variable (odds ratio 0.96, 95% CI 0.82–1.12, p = 0.61) or count (incidence rate ratio (IRR) = 0.93, 95% CI 0.79–1.09, p = 0.37). Logistic regression showed that ADHD traits in childhood were associated with higher risk of experiencing IPV by age 51 years (model 1: odds ratio 1.25, 95% CI 1.16–1.35, p < 0.001; model 2: odds ratio 1.23, 95% CI 1.13–1.33, p < 0.001) (Table 2). Predicted probabilities from a margins analysis from an adjusted logistic regression model using the binary indicator of ADHD traits showed that in men without high ADHD traits, 13.89% (95% CI 12.58–15.21%) reported experiencing IPV by age 51 years, compared with 19.89% (95% CI 15.21–24.57%) of men with high ADHD traits. In women without high ADHD traits, 23.99% (95% CI 22.47–25.51%) reported experiencing IPV, compared with 32.68% (95% CI 26.10–39.27%) of women with high ADHD traits. This suggests that around one in seven men and one in four women without high ADHD traits experience IPV by midlife. This increases to approximately one in five men and one in three women with high ADHD traits. Negative binomial regression showed that ADHD traits were also associated with a greater number of types of IPV experienced by age 51 years (model 1: IRR = 1.20, 95% CI 1.10–1.30, p < 0.001; model 2: IRR = 1.17, 95% CI 1.07–1.27, p = 0.001) (Table 2). Gender-stratified models are presented in Supplementary Table 3.Associations between childhood ADHD traits and IPV domains by age 51 yearsThere were no significant gender interactions for any of the IPV domains (harassment: odds ratio 0.90, 95% CI 0.73–1.10, p = 0.28; economic abuse: odds ratio 0.85, 95% CI 0.65–1.10, p = 0.22; physical violence: odds ratio 0.95, 95% CI 0.79–1.14, p = 0.60; threats/coercive control: odds ratio 0.90, 95% CI 0.72–1.12,p = 0.33; sexual violence: odds ratio 1.74, 95% CI 0.79–3.82,p = 0.17). The analyses for sexual violence were restricted to women because of the very small number of men reporting this form of IPV (n = 10, 0.37% of men) compared with women (n = 211, 6.92% of women). ADHD traits in childhood were associated with higher odds of reporting experiences of harassment (model 1: odds ratio 1.16, 95% CI 1.05–1.28, p = 0.003; model 2: odds ratio 1.14, 95% CI 1.03–1.27, p = 0.01), economic abuse (model 1: odds ratio 1.22, 95% CI 1.08–1.38, p = 0.001; model 2: odds ratio 1.17, 95% CI 1.03–1.34, p = 0.02), physical violence (model 1: odds ratio 1.25, 95% CI 1.14–1.36, p < 0.001; model 2: odds ratio 1.23, 95% CI 1.11–1.35, p < 0.001), threats/coercive control (model 1: odds ratio 1.20, 95% CI 1.09–1.32, p < 0.001; model 2: odds ratio 1.16, 95% CI 1.05–1.29, p = 0.005) and sexual violence (model 1: odds ratio 1.26, 95% CI 1.07–1.47, p = .004; model 2: odds ratio 1.22, 95% CI 1.02–1.45, p = 0.03) (Table 3).Discussion Summary of findingsThis study provides longitudinal evidence showing that prospec- tively identified childhood ADHD traits are associated with a higher risk of experiencing IPV by midlife in a large population- based birth cohort study. Higher ADHD traits at age 10 years were associated with 23% higher odds of reporting IPV experiences by age 51 years, after adjusting for gender, ethnicity and childhood social class. In addition, ADHD traits were associated with a greater number of different IPV experiences by age 51 years, and with higher odds of reporting all domains of IPV included in this study (harassment, economic abuse, physical violence, threats/coercive control and sexual violence). Effect sizes observed are modest, but meaningful on a population scale. In adults without high ADHDTable 1 Descriptive statistics for the analytic sample (N = 5770) Variable Mean (s.d.)/n (%)ADHD traits, mean (s.d.) −0.18 (0.83) Intimate partner violence, n (%) No 4642 (80.45) Yes 1128 (19.55) Harassment, n (%) No 5208 (90.28) Yes 561 (9.72) Economic abuse, n (%) No 5406 (93.72) Yes 362 (6.28) Physical violence, n (%) No 5050 (87.52) Yes 720 (12.48) Threats/coercive control, n (%) No 5147 (89.23) Yes 621 (10.77) Sexual violence, n (%) No 5544 (96.17) Yes 221 (3.83) Gender, n (%) Male 2717 (47.09) Female 3053 (52.91) Ethnicity, n (%) White 5614 (97.30) Minoritised ethnicity 156 (2.70) Social class at age 10 years, n (%) Unskilled 186 (3.22) Partly skilled 686 (11.89) Manual 2208 (38.27) Non-manual 694 (12.03) Managerial and technical 1574 (27.28) Professional 422 (7.31) ADHD, attention-deficit hyperactivity disorder.Childhood attention-deficit hyperactivity disorder 3https://doi.org/10.1192/bjp.2026.10720 Published online by Cambridge University Press ### Page 4 traits, the predicted probabilities of experiencing IPV after adjustment for covariates were around one in seven (14%) for men, and one in four (24%) for women. In adults with high ADHD traits, probabilities increased to approximately one in five (20%) for men, and one in three (33%) for women. Although there were no sex interactions observed in this study, because of the higher prevalence of IPV experiences in women in general, women with ADHD traits are more likely than men with ADHD traits to experience IPV. Findings should be interpreted as relating to dimensional ADHD traits, rather than clinically diagnosed ADHD. These findings support previous research showing links between ADHD and IPV.16 They extend this previous research by showing that this association exists beyond adolescence and young adulthood, persisting through to middle age, and by demonstrating this using a large, nationally representative sample and a multi- domain measure of IPV. These findings align with evidence on the increased prevalence of IPV in populations who experience social exclusion.23 Findings are also in line with emerging evidence on increased rates of trauma and interpersonal harm in neuro- divergent populations, including bullying24 and childhood neglect.25 The prevalence rates of IPV observed in study appear to be broadly consistent with national estimates for women, but are higher for men in this study. According to the 2024 Crime Survey for England and Wales, 22.9% of women and 9.7% of men reported experiences of IPV at some point in their lives after the age of 16 years.11 Some research suggests that men may experience IPV at higher rates than commonly reported in these official statistics. For example, one study using data from the 2014 Adult Psychiatric Morbidity Survey reported that 15.3% of men experienced IPV.26 This is consistent with rates reported in this study in men (14.28%). Taken together, this suggests that IPV experiences may be underestimated in official statistics, particularly for men. This may be related to the conceptualisation of IPV, the age of participants or underreporting in men.Interpretation and potential explanationsFindings are based on observational data, and as such, causality cannot be inferred. The observed association may be attributable to a range of underlying mechanisms and broader pathways. Indeed, there are multiple potential explanations for the association between ADHD and experiences of IPV. First, people with ADHD are more likely to experience co-occurring mental health prob- lems,7 often shaped by broader social determinants and systemic disadvantages across the life course. These include financial difficulties,8 housing instability,9 adverse childhood experiences25 and substance use,7 all of which are independently associated with increased IPV risk. Beyond these structural risks, people with ADHD may face heightened vulnerability because of social and psychological factors. Differences in impulsivity, emotional regulation and social communication can increase peer rejection and make people more susceptible to harm.27 Stigma and ster- eotypes that frame ADHD traits as laziness or irresponsibility can serve to legitimise mistreatment, and intersecting inequalities further compound risk.28 Repeated experiences of rejection may erode self-esteem and self-advocacy,29 and underdiagnosis and limited institutional support leave many adults without adequate safeguards.3,4,30 Observed associations may also reflect compounding dis- advantages faced by people with ADHD across the life course. These include difficulties or delays in accessing diagnosis,3 limited access to appropriate and tailored services,31 challenges with medication access and adherence,32 co-occurring health conditions7 and persistent stigma.28 Together, these factors can reduce the resources available for people with ADHD to protect themselves, seek support or leave harmful relationships. Finally, evidence suggests that people who experience societal exclusion because of disability may be actively targeted by abusive partners,33 a pattern that may also apply to ADHD. Taken together, these intersecting challenges may create conditions of increased vulnerability to IPV among people with ADHD.Strengths and limitationsA key strength of this study is the use of the BCS70, a large birth cohort, with follow-up data available across more than 50 years. The BCS70 is a nationally representative cohort in England, Scotland and Wales, meaning that results from this study extend the existing literature, which is predominantly based in the USA. In addition to this, IPV was measured with a multidomain approach, which enabled a more comprehensive understanding of how ADHD traits are associated with various IPV experience, compared with studies that only considered one form of IPV (e.g. physical or sexual violence only). Additionally, ADHD was assessed by a trait- based approach, rather than relying on clinical diagnoses. This is a strength, given the underdiagnosis of ADHD in people aged≥50 years. The full analytic sample was used, rather than a nested case–control design, to maximise statistical power and preserve the population-based structure of the cohort, as well as enabling inclusion of ADHD traits across their full distribution in the population.Table 2 Associations between attention-deficit hyperactivity disorder traits in childhood and intimate partner violence by midlife Variable IPV, odds ratio (95% CI), p-value Cumulative IPV, IRR (95% CI), p-value Model 1 Model 2 Model 1 Model 2ADHD traits 1.25 (1.16–1.35), <0.001 1.23 (1.13–1.33), <0.001 1.20 (1.10–1.30), <0.001 1.17 (1.07–1.27), 0.001 Gender Male Reference Reference Reference Reference Female 2.20 (1.92–2.51), <0.001 2.05 (1.79–2.36), <0.001 2.68 (2.33–3.09), <0.001 2.51 (2.17–2.91), <0.001 Ethnicity White – Reference – Reference Minoritised ethnicity – 0.90 (0.62–1.36), 0.62 – 0.88 (0.57–1.36), 0.56 Social class at age 10 years Unskilled – Reference – Reference Partly skilled – 1.10 (0.74–1.62), 0.64 – 1.18 (0.77–1.82), 0.45 Manual – 0.91 (0.63–1.30), 0.60 – 1.00 (0.67–1.49), 0.99 Non-manual – 0.87 (0.58–1.30), 0.50 – 0.92 (0.59–1.42), 0.70 Managerial and technical – 0.78 (0.54–1.13), 0.19 – 0.81 (0.54–1.22), 0.31 Professional – 0.74 (0.48–1.15), 0.19 – 0.75 (0.47–1.20), 0.23 ADHD, attention-deficit hyperactivity disorder; IPV, intimate partner violence; IRR, incidence rate ratio.John et al 4https://doi.org/10.1192/bjp.2026.10720 Published online by Cambridge University Press ### Page 5 The study is also subject to some limitations. For example, there was substantial attrition over the 51-year follow-up period, which may have introduced a selective and biased sample. Spe- cifically, participants retained in analyses were more likely to be women, of White ethnicity and from more advantaged socio- economic backgrounds, meaning observed associations may not be fully representative of the UK population. Additionally, only a relatively small set of potential covariates were controlled for in these analyses, to avoid inclusion of factors that may lie on the causal pathway. However, it is possible that unmeasured con- founding may contribute to observed associations. Next, odds ratios are reported for some analyses, which can overstate strength of associations in cases where the outcomes are common. To help with interpretation, predicted probabilities estimated from model margins are additionally reported. Another limitation is that although childhood ADHD traits were assessed prospectively according to DSM-5 criteria, they were based on behavioural questionnaires rather than clinical diagnostic interviews, and therefore may not capture all cohort members who would meet criteria for an ADHD diagnosis and also may include people whose symptoms may not have been at clinical levels or may have been better explained by other conditions. Additionally, follow-up data on ADHD traits into adulthood were not available, meaning stability of traits over time in this sample is not known. IPV experiences were reported retrospectively at a single time point only (age 51 years) and included only a binary response option (yes or no). More comprehensive information about IPV (such as the frequency, severity or timing) were not available in these data. This may introduce recall bias and limits understanding of the nuances of IPV experiences, particularly in relation to other life course factors. This should be considered when interpreting findings, particularly in terms of temporal sequencing across the life course. Finally, data were only available about binary gender, rather than gender identity.Future researchFuture research should focus on understanding the mechanisms that underlie observed associations between ADHD and IPV. This may include mental health problems over the life course and trauma histories. Qualitative research would also be valuable to provide richer insights into the experiences of IPV in people with ADHD. Research should aim to use a participatory approach, ensuring people with ADHD are at the heart of the translational research process.34Table 3 Associations between attention-deficit hyperactivity disorder traits in childhood and domains of intimate partner violence by midlife Variable Model 1, odds ratio (95% CI), pModel 2, odds ratio (95% CI), pHarassment ADHD traits 1.16 (1.05–1.28), 0.003 1.14 (1.03–1.27), 0.01 Gender Male Reference Reference Female 1.74 (1.46–2.08), <0.001 1.64 (1.37–1.97), <0.001 Ethnicity White – Reference Minoritised ethnicity– 0.68 (0.36–1.26), 0.22 Social class at age 10 years Unskilled – Reference Partly skilled – 1.11 (0.65–1.89), 0.70 Manual – 1.05 (0.64–1.72), 0.85 Non-manual – 1.04 (0.61–1.79), 0.87 Managerial and technical– 0.88 (0.53–1.46), 0.61 Professional – 0.70 (0.38–1.29), 0.26 Economic abuse ADHD traits 1.22 (1.08–1.38), 0.001 1.17 (1.03–1.34), 0.02 Gender Male Reference Reference Female 3.11 (2.45–3.93), <0.001 2.83 (2.22–3.62), <0.001 Ethnicity White – Reference Minoritised ethnicity– 1.08 (0.57–2.03), 0.81 Social class at age 10 years Unskilled – Reference Partly skilled – 1.08 (0.58–2.00), 0.82 Manual – 1.01 (0.57–1.79), 0.97 Non-manual – 0.69 (0.36–1.33), 0.27 Managerial and technical– 0.66 (0.36–1.20), 0.17 Professional – 0.68 (0.33–1.39), 0.29 Physical violence ADHD traits 1.25 (1.14–1.36), <0.001 1.23 (1.11–1.35), <0.001 Gender Male Reference Reference Female 2.44 (2.08–2.88), <0.001 2.26 (1.91–2.68), <0.001 Ethnicity White – Reference Minoritised ethnicity– 0.93 (0.57–1.52), 0.77 Social class at age 10 years Unskilled – Reference Partly skilled – 1.05 (0.67–1.65), 0.84 Manual – 0.84 (0.55–1.28), 0.42 Non-manual – 0.84 (0.53–1.34), 0.48 Managerial and technical– 0.68 (0.44–1.05), 0.08 Professional – 0.72 (0.43–1.20), 0.20 Threats/coercive control ADHD traits 1.20 (1.09–1.32), <0.001 1.16 (1.05–1.29), 0.005 Gender Male Reference Reference Female 3.79 (3.14–4.59), <0.001 3.60 (2.95–4.39), <0.001 Ethnicity White – Reference Minoritised ethnicity– 0.85 (0.49–1.47), 0.55 Social class at age 10 years Unskilled – Reference Partly skilled – 1.21 (0.73–2.01), 0.45 Manual – 1.03 (0.64–1.66), 0.89 Non-manual – 0.88 (0.52–1.48), 0.63 Managerial and technical– 0.70 (0.43–1.15), 0.16 Professional – 0.78 (0.44–1.38), 0.39(Continued)Table 3 (Continued ) Variable Model 1, odds ratio (95% CI), pModel 2, odds ratio (95% CI), pSexual violence (women only) ADHD traits 1.26 (1.07–1.47), 0.004 1.22 (1.02–1.45), 0.03 Ethnicity White – Reference Minoritised ethnicity– 1.52 (0.71–3.24), 0.28 Social class at age 10 years Unskilled – Reference Partly skilled – 1.29 (0.55–3.04), 0.56 Manual – 1.14 (0.51–2.55), 0.75 Non-manual – 0.86 (0.35–2.11), 0.75 Managerial and technical– 0.90 (0.39–2.06), 0.80 Professional – 0.72 (0.26–1.96), 0.52 ADHD, attention-deficit hyperactivity disorder.Childhood attention-deficit hyperactivity disorder 5https://doi.org/10.1192/bjp.2026.10720 Published online by Cambridge University Press ### Page 6 In addition, understanding protective factors and sources of resilience, such as social support or ADHD-related strengths, is also an important direction for future research. Research could also test how different ADHD diagnostic categories and presentations may be linked to distinct patterns of vulnerability and risk. Finally, future research should also focus on how intersecting identities (e.g. related to gender, ethnicity, sexual orientation or disability) influence the associations between ADHD and IPV. This could help to identify particular subgroups of people with ADHD who may be at a higher risk.ImplicationsThese findings highlight the importance of recognising long-term outcomes of high ADHD traits beyond childhood and adolescence, particularly related to interpersonal harm. It is important that policy and support (including IPV prevention strategies, public health interventions and support services) take into account the barriers faced by people with ADHD, given the higher rates of IPV experiences in this population. Care should be taken to ensure such services are accessible for people with ADHD. In addition, pro- fessionals working across various settings (health, social care and criminal justice) should be appropriately trained to be able to provide accessible and appropriate support for people with ADHD experiencing IPV. Given increasing clinical and legal focus on coercive control, as well as its profound impacts on mental health and well-being, it is important that clinicians are sufficiently equipped to recognise and respond appropriately to less visible forms of IPV, such as coercive control – particularly in people with ADHD, who may be at increased risk. This study provides longitudinal evidence from a large, pro- spective cohort to show that ADHD traits in childhood are asso- ciated with increased risk of experiencing multiple forms of IPV by midlife. These findings add to a growing body of evidence on the long-term outcomes of ADHD, including vulnerability to inter- personal harm. Addressing the needs of people with ADHD within IPV prevention and response systems is important for reducing harm in this population.Amber John , PhD, Department of Psychology, University of Liverpool, UK; and Clinical, Educational, and Health Psychology (CEHP), University College London, UK;Elizabeth O’Nions, PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UK; and Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK; Lucy Corrigan, BSc, Department of Psychology, University of Liverpool, UK; Joanne Cotton, PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UK; Warren J. Donnellan, PhD, Department of Psychology, University of Liverpool, UK; Henry Shelford, MSc, ADHD UK, London, UK; Celine El Baou , MSc, Clinical, Educational, and Health Psychology (CEHP), University College London, UK; Gavin R. Stewart, PhD, Social, Genetic and Developmental Psychiatry Centre, Kings College London, UK; Roopal Desai, PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UK;Aphrodite Eshetu, MSc, Clinical, Educational, and Health Psychology (CEHP), University College London, UK; Rob Saunders , PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UK; Jae Won Suh, PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UK;William Mandy , PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UK; Philip J. Asherson , PhD, Social, Genetic and Developmental Psychiatry Centre, Kings College London, UK; Jennifer Kirton, PhD, Primary Care and Mental Health, University of Liverpool, UK; Georgia Pavlopoulou , PhD, Group for Research in Relationships and NeuroDiversity (GRRAND), Brain Sciences, University College London, UK; and Anna Freud Centre, London, UK;Jessica Agnew-Blais , PhD, Department of Psychology, Queen Mary University of London, UK; Joshua Stott , PhD, Clinical, Educational, and Health Psychology (CEHP), University College London, UKCorrespondence: Amber John. Email: ajohn@liverpool.ac.uk First received 16 Jan 2026 UTC, final revision 29 Apr 2026 UTC, accepted 31 May 2026 UTCSupplementary materialThe supplementary material is available online at https://doi.org/10.1192/bjp.2026.10720Data availabilityData are available from the UK Data Service. Scripts used to analyse data and any additional research material from this study are available upon request to the corresponding author.AcknowledgementsThe Economic and Social Research Council funds the Centre for Longitudinal Studies (CLS) Resource Centre (ES/W013142/1), which provides core support for the CLS cohort studies. Although the CLS Resource Centre makes these data available, the CLS does not bear any responsibility for the analysis or interpretation of these data by researchers. The CLS cohorts are only possible because of the commitment and enthusiasm of their participants, and their time and contribution is gratefully acknowledged.Author contributionsA.J. contributed to study conceptualisation, analysis, visualisation, funding acquisition and writing the original draft of the manuscript. 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