Prevalence,trends,and predictors of victimisation and polyvictimisation among children in England and Wales

Abstract

Childhood victimisation and polyvictimisation (experiencing two or more distinct crime types) can have lasting developmental, psychological, and social consequences. Yet there is limited research on victimisation and polyvictimisation in England and Wales using robust data sets. This study addresses this gap by investigating prevalence, trends, and individual, household, and area-level predictors of non-sexual non-familial violence, personal theft, household theft, and criminal damage and polyvictimisation using the 10- to 15-year-old Crime Survey for England and Wales (2011/2012–2019/2020; N = 25,415). A series of binary logistic regressions was performed, supplemented by visualisations. The weighted percentage of children experiencing a single type of victimisation ranged from 1.1% (criminal damage) to 5.8% (violence), while 1.1% were polyvictimised. Although most victimisation types and polyvictimisation declined over the 9-year period, reductions in polyvictimisation varied depending on socioeconomic status. Both individual (sex, ethnicity, disability) and area-level (deprivation) factors predicted polyvictimisation and individual victimisation types. Implications of the findings are discussed.

Keywords

victimisation polyvictimisation youth crime disability deprivation

Introduction

Childhood victimisation is linked to a range of adverse outcomes. Harms are particularly acute for children growing up in deprived areas, who are most at risk of being victims of crime, yet have scarce resources to minimise its vast and detrimental effects (Hough, 2008). These harms range from physical and mental health problems (Jackson-Hollis et al., 2017; Wolfe, 2018) to negative economic (Brimblecombe et al., 2018) and academic (Torres et al., 2020) outcomes. Public resources are limited, and the economic and social costs of crime are considerable (Heeks et al., 2018), but little is done to help victimised children recover (Gilad et al., 2019).

Despite the seriousness of childhood exposure to crime and its impact on children in the United Kingdom (Jackson-Hollis et al., 2017) and most other contexts (Ford and Delker, 2018), previous research on child victimisation (experiencing one type of crime once or more) and polyvictimisation (experiencing two or more distinct crime types) on a national scale in the United Kingdom is limited. This lack of evidence hinders the development of effective prevention and intervention strategies. As Palermo et al. (2019) argue, evidencing predictors of child victimisation and polyvictimisation is essential for untangling the complexity of victimisation. Importantly, research that examines only one type of victimisation significantly underestimates the true victimisation experiences of children and, by discounting polyvictimisation, may misidentify the risk profiles associated with victimisation (Turner et al., 2010).

This study analyses 9 years (2011/2012–2019/2020) of the 10- to 15-year-old Crime Survey for England and Wales (CSEW) to investigate the prevalence, trends, and predictors of four individual victimisation types (i.e. non-sexual non-familial violence, personal theft, household theft, and criminal damage) and polyvictimisation in England and Wales. Identifying unique and shared predictors for individual victimisation types and polyvictimisation helps pinpoint the likely points of entry for disruption for different child populations. Focusing on these predictors, child protection professionals can target populations more effectively, improve screening tools, and implement trauma-informed practices that reduce the incidence and impact of victimisation.

Literature review

Previous prevalence studies from the United States and the United Kingdom suggest that around 60% of children and young people experience at least one form of victimisation within a 12-month period (Finkelhor et al., 2015; Jackson-Hollis et al., 2016; Radford et al., 2013). Focusing on specific victimisation types, Jackson-Hollis et al. (2016) report that 16.2% of children experienced property victimisation (including theft, vandalism, and robbery) in the past year, while an identical proportion (16.2%) experienced physical victimisation (including assault, bias attacks, and kidnap or attempted kidnap) in a UK county. In relation to multiple forms of victimisation, Tura et al. (2022) found that between 14.5% and 28.3% of children aged 10–17 experienced polyvictimisation between 2003 and 2006 in England and Wales. Prevalence estimates vary considerably across studies, largely due to differences in definitions, survey design, and methodological approaches (Radford et al., 2013).

Victimisation and polyvictimisation are associated with substantial developmental, psychological, and social harms. Children who experience victimisation exhibit poorer social, emotional, and psychological outcomes, reduced well-being and educational attainment, and increased risks of later criminal engagement compared to both the general population and those experiencing isolated forms of victimisation (Ford et al., 2010; Jackson-Hollis et al., 2017; Schaefer et al., 2018; Tanksley et al., 2020). Risks of victimisation and polyvictimisation are unevenly distributed across the population (e.g. DeCamp et al., 2018) and a minority of children experience repeated or multiple forms of harm. Understanding the predictors of victimisation and polyvictimisation is therefore crucial for effective prevention and targeted support to those most at risk.

Patterns of victimisation are increasingly understood not as random, or episodic, but as clustering within a subset of children exposed to multiple and intersecting disadvantages (Tompson et al., 2026; Tura et al., 2022, 2025, 2026). Research on polyvictimisation demonstrates that such clustering reflects cumulative exposure processes, whereby early victimisation increases vulnerability to subsequent harms across childhood and adolescence (Finkelhor et al., 2007a; Turner et al., 2010). Using a life-course perspective, evidence from England and Wales indicates that childhood abuse/adversity is associated with increased risk of violence victimisation in adulthood, with risk increasing in a graded manner as adversities accumulate (Butler et al., 2020). Together, this body of work situates victimisation clustering within broader processes of cumulative disadvantage across the life course, highlighting how risks compound through key developmental transitions, rather than arising as isolated or time-limited events (Ford and Delker, 2018).

Studies of child victimisation can be split into local- and national-level analyses. This paper focuses on the latter, as the aim is to generate findings that are generalisable to the population of England and Wales. While local studies offer valuable contextual insights, national studies provide a broader picture of prevalence and patterning across diverse populations, supporting the identification of priority groups and informing strategic discussions about child‑safety policy at scale. Despite their volume, however, numerous shortcomings constrain the contributions of existing national studies in the United Kingdom.

First, most studies (e.g. DeCamp et al., 2018; Fisher et al., 2015; Matthews et al., 2020; Radford et al., 2011, 2013) rely on surveys primarily designed to examine offending behaviour, with victimisation treated as a secondary concern. As a result, they capture a limited range of victimisation types and provide restricted insight into frequency and co-occurrence.

Second, much of the evidence on polyvictimisation draws on data collected over a decade ago, during a markedly different social and policy context. For example, Radford et al. (2013) draws on a questionnaire administered in 2009, while Tura et al. (2022) analyse data from the Offending, Crime and Justice Survey (2003–2006). While such studies remain informative, patterns of victimisation may have shifted in response to changes in technology, youth culture, service provision, and socioeconomic conditions. Updated research is therefore required. Consistent with this, Tura et al. (2026) found no studies examining childhood polyvictimisation using contemporary CSEW data.

Third, contemporary studies have important methodological limitations. Some measure victimisation retrospectively, which is vulnerable to recall bias and likely to underestimate victimisation, particularly among children (Finkelhor et al., 2009). Others include a narrow set of predictors, often focusing on factors such as gender, socioeconomic status, and loneliness (e.g. Fisher et al., 2015; Matthews et al., 2020; Office for National Statistics (ONS), 2020a).

More broadly, existing studies neglect a wide range of potential risk and protective factors operating at the individual, household, and area level, as highlighted by victimisation theories such as the Routine Activities Approach (RAA; Cohen and Felson, 1979) and Social Disorganisation Theory (SDT; Sampson and Groves, 1989). While both frameworks are widely used to explain victimisation risk, they operate at distinct analytic levels and offer different explanatory strengths. RAA is particularly effective in accounting for individual- and household-level exposure to risk, emphasising how supervision (guardianship), routine activities and proximity to motivated offenders shape victimisation experiences. In contrast, SDT focuses on area-level structural conditions, such as residential instability, economic deprivation, and weakened collective efficacy, that may concentrate victimisation within particular neighbourhoods (Sampson et al., 1997). However, the applicability of classical SDT mechanisms beyond the US context has been challenged, with UK research suggesting more context-dependent and indirect associations between neighbourhood structure and crime outcomes (Lymperopoulou et al., 2022). Taken together, these frameworks encourage a more holistic understanding of victimisation, one that recognises the role of routine exposure and guardianship while situating these processes within wider structural conditions.

At the individual level, characteristics such as age, gender, ethnicity, and disability or long-term illness shape exposure to victimisation risk in ways that align closely with the RAA and indirectly with SDT. From an RAA perspective, these characteristics influence target suitability by affecting individuals’ physical vulnerability, social visibility, and capacity for guardianship. Younger children with disabilities, for example, may have reduced ability to recognise or avoid risky situations and may depend more heavily on others for protection, increasing their suitability as targets in the absence of effective guardianship (Finkelhor and Asdigian, 1996; Finkelhor et al., 2007a, 2007b; Tompson et al., 2026). Similarly, ethnic minority children may face increased risks of victimisation linked to discriminatory targeting or biased-motivated abuse, reflecting offender perceptions of vulnerability and social marginalisation (Christoffersen, 2019; Lasky et al., 2021; Lauritsen and Rezey, 2018; Tompson et al., 2026)

Household factors such as the occupation of the reference adult, household composition, or housing tenure may further influence levels of guardianship, economic stress and residential stability. For instance, renting rather than owning a house may signal economic insecurity, while frequent household moves may inhibit the formation of community ties. At the area level, neighbourhood deprivation and instability may increase exposure to crime, consistent with SDT’s emphasis on weakened informal social control and increased victimisation risk. Children living in disadvantaged and unstable environments may, therefore, face heightened vulnerability due to reduced social protection and higher exposure to delinquent peer networks (Barnes et al., 2007; Felson, 2002; Graif and Matthews, 2017; Svensson and Oberwittler, 2010; Van Wilsem et al., 2006).

Finally, none of the existing studies included interaction terms, which are crucial for understanding victimisation risk across intersecting identities, nor did they examine changes in the prevalence of victimisation over time. Therefore, there is a large gap in knowledge on child (poly)victimisation patterns in the United Kingdom and what might influence such experiences. Effective responses to child victimisation depend on a solid body of evidence built over time. This study takes a step in that direction by describing patterns of child (poly)victimisation in the United Kingdom and identifying potential risk markers for further investigation.

The current paper

The current paper analyses the 10- to 15-year-old CSEW (2011/2012–2019/2020) to investigate (1) prevalence of individual types of victimisation (i.e. violence, personal theft, household theft, and criminal damage) and polyvictimisation (i.e. experiencing two or more distinct types of crime); (2) the most common victimisation types and most common combinations of victimisation types among polyvictims; (3) change in prevalence of individual types of victimisation and polyvictimisation between 2011/2012 and 2019/2020; (4) predictors of individual types of victimisation and polyvictimisation; and (5) whether the trend in polyvictimisation between 2011/2012 and 2019/2020 varies by sex, age, ethnicity, disability, and socioeconomic status.

Methodology

Data

We analysed a large nationally weighted victimisation survey of children living in private households in England and Wales (10- to 15-year-old CSEW; 2011/2012–2019/2020). As the children’s data collected before 2011/2012 are considered experimental, they were excluded from the analysis. The analysis ends in 2019/2020 due to the COVID-19 pandemic disrupting the 10- to 15-year-olds CSEW (ONS, 2020b, 2020c, 2020d, 2021a, 2021b, 2021c, 2022, 2023, 2024). The CSEW follows a stratified multi-clustered cross-sectional sampling design whereby respondents participate only once and report any crimes they experienced in the 12 months prior to the interview. One child is invited to participate in households where an adult has taken part in the main CSEW. The CSEW follows a consistent sampling methodology and questionnaire over time. The accomplished sample size of each 10–15 CSEW is around 3,000 per annum; therefore, the final unweighted sample size for the merged data set (2011/2012–2019/2020) after data cleaning was 25,415 children (original unweighted N = 26,238).

Dependent variables

The CSEW captures four distinct victimisation types recorded for each child over the 12‑month reference period in the screener questionnaire: non-sexual non-familial violence (as reporting sexual violence would require disclosure to children safeguarding bodies and impede statistical confidentiality), personal theft, household theft, and criminal damage. Each one of these were used as a binary dependent variable in our analysis (0 = not victimised, 1 = victimised one or more times). Violence includes wounding, assault with minor injury, assault with no injury, and robbery; personal theft includes snatch theft, stealth theft, and other theft of personal property; household theft includes theft from a dwelling, theft from outside a dwelling, and bicycle theft. Criminal damage captures whether anything belonging to children has been broken, damaged, or ruined.

Measuring crime among children aged 10–15 presents particular conceptual challenges, as many incidents that constitute an offence in law may reflect low-level, normative interactions that are not perceived by children or others as criminal (Roe and Ashe, 2008). Recognising this, the four approaches to classifying incidents reported by children, including ‘all in law’, ‘norms-based’, ‘all in law outside school’, and ‘victim-perceived’, were published in Millard and Flatley (2010). Following empirical testing and user consultation, two approaches were favoured with regard to estimating levels of victimisation among children: the ‘Broad measure’ and ‘Preferred measure’ approaches (ONS, n.d.). The ‘Broad measure’ (formerly known as the ‘All in law’ approach) is the widest possible count but will include minor offences between children and family members that would not normally be treated as criminal matters. The ‘Preferred measure’ (formerly known as the ‘Norms-based’ approach) is a more focused method which takes into account factors identified as important in determining the severity of an incident, but will still include incidents of a serious nature even if they took place at school. Eventually, the ‘Preferred measure’ was adopted by the ONS, and the present study adopts this ‘Preferred measure’ for non-sexual non-familial violence, personal theft, household theft and criminal damage, as it provides a balanced and policy-relevant estimate of victimisation that avoids inflating prevalence through the inclusion of low-level normative behaviours, while still capturing incidents of substantive harm. Nevertheless, this approach may still undercount some experiences that children do not interpret as sufficiently serious to meet the classification thresholds, a limitation that should be borne in mind when interpreting prevalence estimates.

We also created a polyvictimisation variable by summing the four binary victimisation variables and then coding 0 for children who experienced no victimisation or only one type, and 1 for those victimised by two or more distinct types of crime in the past 12 months. In the child CSEW, the screener questionnaire records whether each offence type occurred at least once in the reference period. Accordingly, our measure reflects the breadth of exposure to distinct offence types as reported at the screener stage.

Importantly, the screener can capture co‑occurring offence features reported by respondents (e.g. an assault during which a bicycle was stolen). In the main CSEW estimation process, such multi‑feature episodes are later classified to a single priority offence for incident counting, which is known to suppress some co‑occurring (often violent) offences in headline counts (Pullerits and Phoenix, 2024). Because our analysis relies on the screener‑level reports (not the post hoc priority allocation used for incident totals), our polyvictimisation variable may include offence‑type combinations that arose within a single episode. This approach aligns with the conceptualisation of polyvictimisation as exposure to multiple forms of victimisation, even when those forms co‑occur. Nevertheless, readers should note that our estimates speak to the diversity of offence types experienced rather than to repeat incidents per se.

Theory-driven independent variable selection

Drawing on the RAA and SDT, we selected a range of personal, household- and area-level characteristics from the CSEW as independent variables. Personal characteristics included sex (male or female – using the survey’s original terminology), age (10–12 or 13–15 – based on CSEW groupings), ethnicity (Asian or Black or Mixed or White or Chinese and Other) and presence of a long-term illness or disability (yes or no). Household-level factors included occupation of the Household Reference Person¹ (HRP; managerial and professional or intermediate or small employer and own account worker or lower supervisory and technical or semi-routine and routine or never worked and long-term unemployed), number of children (1 or 2+) and adults (1 or 2 or 3+), tenure type (homeowners or social renters or private renters) and residential (in)stability (less than 12 months or 1–2 years or 2–5 years or 5+). Area-level factors included inner-city residency (yes or no) and deprivation level, which was measured using the Index of Multiple Deprivation, and was categorised into the 20% most deprived, 20% least deprived, and the middle 60%. Table 1 presents descriptive statistics for the dependent and independent variables.

Table 1.

Descriptive statistics (2011–2019).

Dependent variables	Categories	Unweighted N	Unweighted %	Weighed %
Violence	0	23,899	94.0	94.2
	1 or more incidents^a	1,516	6.0	5.8
Personal theft	0	24,276	95.5	95.6
	1 or more incidents^a	1,139	4.5	4.4
Household theft	0	25,095	98.7	98.8
	1 or more incidents^a	320	1.3	1.2
Criminal damage	0	25,152	99.0	98.9
	1 or more incidents^a	263	1.0	1.1
Polyvictimisation	0	25,120	98.8	98.9
	1 or more incidents^a	295	1.2	1.1
Independent variables	Categories	Unweighted N	Unweighted %	Weighed %
Sex	Male	13,097	51.5	51.1
	Female	12,318	48.5	48.9
Age	10–12	12,528	49.3	49.3
	13–15	12,887	50.7	50.7
Ethnicity	White	20,871	82.1	78.9
	Mixed	902	3.5	3.7
	Asian or Asian British	2,196	8.6	10.3
	Black or Black British	1,021	4.0	5.0
	Chinese and Other	425	1.7	2.1
Long-term illness or disability	Yes	2,367	9.3	9.2
	No	23,048	90.7	90.8
Occupation of HRP	Managerial and professional	10,513	41.4	40.9
	Intermediate	2,407	9.5	9.3
	Small employers and own-account workers	3,339	13.1	13.4
	Lower supervisory and technical	2,173	8.6	8.3
	Semi-routine and routine	6,274	24.7	24.8
	Never worked and long-term unemployed	709	2.8	3.3
Area of residency	Not inner city	23,053	90.7	89.3
	Inner city	2,362	9.3	10.7
Length of residency	Less than 12 months	1,789	7.0	6.9
	1 to 2 years	1,678	6.6	6.7
	2 to 5 years	4,724	18.6	19.1
	5 or more years	17,224	67.8	67.3
Number of children	1 child	10,347	40.7	30.3
	2 or more children	15,068	59.3	69.7
Number of adults	1 adult	4,745	18.7	19.3
	2 adults	14,124	55.6	57.2
	3 or more adults	6,546	25.8	23.5
Tenure type	Homeowner	15,464	60.8	59.1
	Social renter	5,127	20.2	21.9
	Private renter	4,824	19.0	19.0
Index of multiple deprivation	20% least deprived	5,354	21.1	20.2
	20% most deprived	5,240	20.6	22.5
	Middle range (20%–80%)	14,821	58.3	57.3
Region	North East	1,349	5.3	4.4
	North West	3,218	12.7	12.6
	Yorkshire & Humberside	2,498	9.8	9.4
	East Midlands	2,299	9.0	8.1
	West Midlands	2,571	10.1	10.5
	East of England	3,073	12.1	10.9
	London	2,828	11.1	13.9
	South East	3,417	13.4	15.9
	South West	2,356	9.3	9.1
	Wales	1,806	7.1	5.3
Year	2011	3,786	14.9	10.7
	2012	2,705	10.6	10.8
	2013	2,863	11.3	11.0
	2014	2,304	9.1	10.9
	2015	2,728	10.7	10.9
	2016	2,980	11.7	11.0
	2017	2,930	11.5	11.2
	2018	2,791	11.0	11.6
	2019	2,328	9.2	11.9

Victimisation prevalence figures are based on yearly estimated population prevalence (over a 12-month period) rather than lifetime prevalence.

Analytical strategy

We first fitted binary logistic regression models to investigate the relationship between the five dependent variables (four individual victimisation types and polyvictimisation) and independent variables. In all models, we controlled for the year of the CSEW survey and the regions in England and Wales as fixed effects. We then created visualisations. First, we plotted victimisation prevalence for individual victimisation types and polyvictimisation over the 9 years of the study period. Second, we investigated and visualised the most common victimisation types among polyvictims, and the most common combinations of victimisation types among polyvictims. Finally, we investigated interaction effects between year and sex, age, ethnicity, disability, and socioeconomic status variables in the binary logistic polyvictimisation model to check if the trend in polyvictimisation prevalence differed across these characteristics.

All descriptive and multivariable analyses apply the CSEW child individual calibration weight. These weights adjust for the complex, multi-stage clustered sampling design of the survey and correct for unequal selection probabilities, household non-response, and differential probabilities of selection associated with household composition. When applied, the calibration weights produce estimates that are representative of children aged 10–15 living in private households in England and Wales. Children not living in private households, including those in residential care or residential schools, are not covered by the survey design and therefore fall outside the population represented by the weighted estimates. All data cleaning and statistical analyses were conducted using R (version 4.5.1).

Results

Prevalence of (poly)victimisation

Table 1 shows that 1.1% of the children (unweighted N = 295) were polyvictims. Across the four individual victimisation types we investigated, violence victimisation was the most common (5.8%; unweighted N = 1,516), followed by personal theft (4.4%; unweighted N = 1,139). Figure 1 shows line plot that pictures the change in weighted victimisation prevalence of individual victimisation types and polyvictimisation from 2011/2012 to 2019/2020. It shows that, except for criminal damage, the prevalence of individual victimisation types and polyvictimisation decreased from 2011 to 2019.

Figure 1.

Weighted 3-year moving average of victimisation prevalence (2011–2019).

Most common victimisation types among polyvictims

Figure 2 shows the most common victimisation types among polyvictims. It suggests that the top two victimisation types experienced by polyvictims are violence and personal theft. Out of 295 polyvictims, 245 (weighted percentage: 83.4%) experienced violence, while 215 (weighted percentage: 71.5%) of them experienced personal theft.

Figure 2.

Most common victimisation types among polyvictims (unweighted N = 295).

Most common combinations of victimisation types among polyvictims

Figure 3 presents the most common combinations of victimisation types among polyvictims. They are violence and personal theft (weighted percentage: 54; unweighted N = 166) and violence and criminal damage (weighted percentage: 14.6; unweighted N = 37). These combinations are followed by violence and household theft (weighted percentage: 11.3; unweighted N = 33) and personal theft and criminal damage (weighted percentage: 7.6; unweighted N = 22).

Figure 3.

Most common combinations of victimisation types among polyvictims (unweighted N = 295).

Predictors of (poly)victimisation

Figure 4 summarises the findings from five weighted binary logistic regression models predicting the relationship of individual-, household-, and area-level factors with individual victimisation types and polyvictimisation. It presents odds ratios (ORs) with significance shading for the independent variables included in the models (see Supplemental Appendix Table 1 for full model weighted results, and Supplemental Appendix Table 2 for unweighted results²). In the following sections, we first report the independent variables that predicted both polyvictimisation and individual victimisation types, and then the predictors of individual victimisation types only, meaning these independent variables were not correlated with polyvictimisation.

Figure 4.

Odds ratios from (weighted) binary logistic regression models.

Predictors of polyvictimisation and individual victimisation types

Sex

Girls are less likely than boys to experience polyvictimisation (OR = 0.67, 95% CI [0.51–0.88], p < 0.01) and violence (OR = 0.49, 95% CI [0.43–0.55], p < 0.001), personal theft (OR = 0.78, 95% CI [0.68–0.90], p < 0.001), household theft (OR = 0.43, 95% CI [0.32–0.57], p < 0.001), and criminal damage (OR = 0.64, 95% CI [0.48–0.86], p < 0.01).

Ethnicity

Asian children are less likely than White children to experience polyvictimisation (OR = 0.24, 95% CI [0.11–0.52], p < 0.001), and violence (OR = 0.41, 95% CI [0.30–0.55], p < 0.001).

Long-term illness or disability

The striking finding is that disabled children are more likely than non-disabled children to experience polyvictimisation (OR = 2.03, 95% CI [1.46–2.81], p < 0.001), and violence (OR = 1.87, 95% CI [1.59–2.20], p < 0.001), personal theft (OR = 1.67, 95% CI [1.38–2.03], p < 0.001), and criminal damage (OR = 2.36, 95% CI [1.66–3.37], p < 0.001).

Area of residency

Children living in inner city areas are less likely than children living in non-inner city areas to experience polyvictimisation (OR = 0.54, 95% CI [0.32–0.91], p < 0.05).

Multiple deprivation index

Children living in the 20% most deprived areas are more likely than children living in the 20% least deprived areas to experience polyvictimisation (OR = 2.44, 95% CI [1.45–4.10], p < 0.001), and violence (OR = 1.40, 95% CI [1.12–1.74], p < 0.01), household theft (OR = 2.26, 95% CI [1.34–3.8], p < 0.01), and criminal damage (OR = 2.0, 95% CI [1.17–3.40], p < 0.05). Similarly, children living in middle-ranged (20%–80%) deprived areas are more likely than children living in the 20% least deprived areas to experience polyvictimisation (OR = 1.62, 95% CI [1.08–2.41], p < 0.05), and violence (OR = 1.35, 95% CI [1.14–1.59], p < 0.001), personal theft (OR = 1.29, 95% CI [1.07–1.56], p < 0.01), and household theft (OR = 1.77, 95% CI [1.16–2.71], p < 0.01).

Predictors of individual victimisation types only

Age

Children aged 13–15 years are more likely than children aged 10–12 years to experience personal theft (OR = 1.18, 95% CI [1.02–1.35], p < 0.05).

Ethnicity

Black children are less likely than White children to experience violence (OR = 0.53, 95% CI [0.37–0.78], p < 0.001) and household theft (OR = 0.38, 95% CI [0.18–0.79], p < 0.01). Chinese and Other children are less likely than White children to experience violence (OR = 0.54, 95% CI [0.30–0.96], p < 0.05).

Number of adults

Children living in a household with 1 adult are more likely than children living in a household with 2 adults to experience personal theft (OR = 1.27, 95% CI [1.06–1.52], p < 0.01) and household theft (OR = 1.76, 95% CI [1.29–2.40], p < 0.001). In contrast, children living in a household with 3 or more adults are less likely to experience violence (OR = 0.85, 95% CI [0.73–1.00], p < 0.05) and personal theft (OR = 0.81, 95% CI [0.68–0.97], p < 0.05).

Occupation of HRP

Children from households with a reference person who has a semi-routine or routine occupation are more likely than children from households with a reference person who has a managerial or professional occupation to experience violence (OR = 1.28, 95% CI [1.08–1.52], p < 0.01) and criminal damage (OR = 1.51, 95% CI [1.02–2.24], p < 0.05). Children from households with a reference person who has never worked or is long-term unemployed are less likely than children from households with a reference person who has a managerial or professional occupation to experience household theft (OR = 0.20, 95% CI [0.05–0.75], p < 0.05), but more likely to experience criminal damage (OR = 2.40, 95% CI [1.14–5.04], p < 0.05).

Length of residency

Children living in their local area for 2–5 years are less likely than children living in their local area for 5 or more years to experience household theft (OR = 0.69, 95% CI [0.48–0.99], p < 0.05).

Tenure type

Children living in socially rented households are more likely than children living in an owner-occupied household to experience violence (OR = 1.52, 95% CI [1.27–1.81], p < 0.001) and household theft (OR = 1.78, 95% CI [1.27–2.50], p < 0.001). Children living in privately rented households are more likely than children living in an owner-occupied household to experience household theft (OR = 1.49, 95% CI [1.07–2.09], p < 0.05).

Trends in Polyvictimisation by household socioeconomic status

The final aim of the paper was to check whether trends in polyvictimisation between 2011/2012 and 2019/2020 varied across social groups. For this, interaction effects between survey year (modelled as a continuous variable) and key demographic characteristics, such as sex, age, ethnicity, disability, and HRP occupational class variables, were tested in survey-weighted binary logistic regression polyvictimisation models, each fitted separately. Of these interactions, only the interaction between year and HRP occupational class was statistically significant. Specifically, the results suggest that children from households with a reference person who has never worked or was long-term unemployed experienced a faster decline in the likelihood of polyvictimisation (OR = 0.58, 95% CI [0.38–0.87], p < 0.01) compared to others (see Figure 5 and Supplemental Appendix Table 1).

Figure 5.

Predicted prevalence of polyvictimisation by year and HRP occupation.

Figure 5 presents model-based predicted probabilities illustrating this interaction. Because the year is specified as a continuous variable, the figure depicts linear trends in predicted polyvictimisation prevalence across the study period for each HRP occupational group. Predicted values are conditional on other covariates being held at their reference categories and therefore represent relative differences in temporal trends rather than population-averaged prevalence estimates. Predicted probabilities approaching zero for children living in households where the HRP has never worked or is long-term unemployed in later survey years should be interpreted cautiously, as they reflect model-based predictions conditional on the survey design and weighting, derived from relatively few observations, rather than definitive evidence of zero prevalence.

Discussion

This study aimed to investigate (1) prevalence of individual types of victimisation and polyvictimisation (i.e. experiencing two or more distinct types of crime); (2) the most common victimisation types and most common combinations of victimisation types among polyvictims; (3) change in prevalence of individual types of victimisation and polyvictimisation between 2011/2012 and 2019/2020; (4) predictors of individual types of victimisation and polyvictimisation; and (5) whether the trend in polyvictimisation between 2011/2012 and 2019/2020 varies by sex, age, ethnicity, disability, and socioeconomic status.

The percentage of children who experienced victimisation ranged from 1.1% (criminal damage) to 5.8% (violence), and 1.1% were polyvictimised. Children mostly experienced violence, and polyvictimised children mostly experienced combinations of violence and personal theft. The co-occurrence of these victimisations supports the idea of ‘victimisation clustering’ where vulnerability to one form of harm increases risk for others (Finkelhor et al., 2005). This underlines the importance of assessing multiple forms of victimisation simultaneously, particularly among high-risk children (e.g. disabled).

In terms of prevalence trends, there was a downward trend in all individual victimisation types and polyvictimisation except for criminal damage, which aligns with the crime drop literature (Tseloni et al., 2010). Furthermore, a range of individual, household-, and area-level factors significantly predicted risk of individual victimisation and polyvictimisation. Sex, ethnicity, disability and deprivation level predicted both polyvictimisation and individual victimisation types. Age, ethnicity, number of adults, occupation of HRP, length of residency, and tenure type predicted individual victimisation only. Together, these predictors add to a more comprehensive overview of child (poly)victimisation across England and Wales.

At the individual level, girls were less likely than boys to experience polyvictimisation, as well as all individual victimisation types. This gendered pattern may reflect differences in routine activities, unsupervised time, and exposure to risky contexts (Lauritsen and Rezey, 2018). It may well, however, be an artefact of the 10–15 CSEW data victimisation measures which exclude sexual victimisation as girls are more at risk of this offence type than boys (May-Chahal et al., 2018, see also later discussion). Older children (13–15 years old) had increased odds of being victims of personal theft. These findings could reflect developmental shifts in independence and exposure to public spaces. As children grow older, they gain more mobility in their social environments, which might lead to changes in social behaviours and exposures (Pacilli et al., 2013). Older children might have more unstructured time, spend more time in public places without supervision and in public spaces not designed for their age-based needs, which would increase the likelihood of opportunistic crimes like theft (Buil-Gil, 2025). Furthermore, ethnicity was a significant but complex predictor of victimisation risk. Asian children had significantly lower odds of polyvictimisation, and violence compared to White children. Black children were also less likely to experience violence and household theft. These findings are consistent with previous UK-based studies indicating that while some ethnic minority children may face lower risks for certain violent crimes, others may be disproportionately exposed to property-related crimes, potentially due to their higher representation in densely populated urban settings (Lauritsen and Rezey, 2018; Radford et al., 2011).

Disabled children were more than twice as likely to experience polyvictimisation compared to non-disabled children and they faced significantly greater odds of most individual crime types. These results align with previous research (Finkelhor et al., 2007a), which attributes increased risk among disabled children to reduced social protection, greater dependence on caregivers, and limited mobility or communication abilities. It is vital that disabled children are not overlooked and underserved by current and future prevention and safeguarding efforts.

Socioeconomic disadvantages, as captured by living in deprived areas or in socially rented housing, also significantly increased odds of polyvictimisation and individual victimisation types. Children living in the 20% most deprived areas were twice as likely to be polyvictims and significantly more likely to experience violence, household theft, and criminal damage. These areas may be marked by limited collective efficacy, greater exposure to antisocial peers, and weakened institutional oversight, which aligns with contemporary understanding of SDT (Sampson and Groves, 1989). Similarly, children from socially renting households had increased odds of being victims of violence and household theft. When considered together, these environments may indicate housing instability, limited social control, or higher exposure to community violence. At the same time, children from households where the reference person had never worked or was long-term unemployed experienced a faster decline in polyvictimisation. Sure Start, which was introduced in 1999 and the first large government initiative to provide holistic support to families with children under the age of 5 in England, might explain this finding as a recent evaluation of the programme reported larger impacts for those from the poorest backgrounds and those from non-white backgrounds (Carneiro et al., 2024).

Similarly, family structure and residential (in)stability were also important predictors. Children from lone-parent households had significantly higher odds of experiencing personal theft and household theft. These associations may reflect lower levels of adult supervision or economic vulnerability in single-adult homes (Varga, 2021) or fewer guardians to protect the property (Tseloni et al., 2018). Conversely, households with three or more adults appeared to confer some protection, especially against personal and household theft, perhaps due to increased adult supervision acting as a deterrent in the crime commission process. Furthermore, children with moderate residential stability (living in their area for 2–5 years) were less likely to be victims of household theft, compared to those with 5+ years of residence. In addition, children from private rental households faced higher odds of experiencing household theft. These findings align with prior work suggesting that frequent moves disrupt protective social ties and increase exposure to transient peer groups (Finkelhor et al., 2007b; Sampson and Groves, 1989). To understand vulnerability, the local context in its entirety must be considered.

Overall, the results highlight risk markers and are intended to inform policies that can be used to reduce child (poly)victimisation given their detrimental effects on children and societies. Understanding the kinds of crimes children experience, along with the associated characteristics, is crucial for policymakers aiming to reduce the negative psychosocial consequences of (poly)victimisation. It is also highly relevant for academics and practitioners seeking to understand victimisation trajectories during childhood – both to prevent escalation into adult victimisation and to interrupt a potentially lifelong cycle. Schools, local social services and the police can benefit from the findings. For example, Local Authorities and Police Forces could develop holistic strategic-level responses to tackle child victimisation with their limited resources as ‘law enforcement, health care agencies, and CPS [Child Protective Services] all tend to take an incident-specific approach to assessment and intervention, rather than consistently assessing children’s safety in all contexts’ (Hamby et al., 2018: 721). Evidence from Welsh frameworks aimed at preventing violence against children (Snowdon et al., 2023) suggest reducing identified risks – such as gender and racial inequality, poverty and socioeconomic inequality, unemployment and lack of opportunity, discrimination based on protected characteristics, a lack of a nurturing environment, or negative peer norms and social control – requires a public health, whole-system approach. Local authorities should understand and engage their multi-setting communities, including the children they aim to protect, and work alongside them to implement resources and policies that promote safe activities and communities, nurturing environments, trusted adults, prosocial attitudes, opportunities for education and employment, reduced social inequality, and greater social, gender, and racial inclusion. Families, communities, and children should be supported from early stages and throughout developmental stages. At the national level, government bodies (e.g. Ministry of Justice and Department for Education) and civil societies (e.g. Children’s Commissioner for England and The National Society for the Prevention of Cruelty to Children) could benefit from the findings to revise or develop their strategies to tackle crimes against children. For example, the National Society for the Prevention of Cruelty to Children has several national programmes for community-based and direct work with children and families, especially around child sexual abuse and neglect due to mental ill health, financial stressors, and adversity (e.g. the Together for Childhood initiative). Such programmes could benefit from embedding risk markers of individual victimisation and polyvictimisation into their long-term strategies. Similarly, the Department of Education’s (2023) school-based interventions for addressing serious violence would benefit from embedding evidence on contextualised victimisation and polyvictimisation and risk factors which go beyond attitudinal or educational.

Limitations and future research

This study is subject to important measurement limitations that have direct implications for the interpretation of its findings. Although the CSEW provides a well-established and methodologically rigorous source of self-reported victimisation data, the 10–15 CSEW does not capture several key forms of harm that are central to polyvictimisation research, such as sexual violence and abuse, as well as forms of digital or online victimisation. The omission of these victimisation types is likely to result in an underestimation of the overall prevalence and complexity of children’s polyvictimisation experiences. Moreover, because sexual and digital forms of harm are known to be patterned by gender and other social characteristics (May-Chahal et al., 2018), their exclusion may skew observed inequalities, particularly by under-representing the victimisation experiences of girls and certain other groups. As a result, the patterns of risk identified in this study should be interpreted as reflecting a partial profile of children’s victimisation, primarily capturing offline and non-sexual forms of harm. Future research should therefore seek to integrate data sources or employ survey instruments that more comprehensively capture sexual and digital victimisation in order to provide a fuller and more accurate account of children’s (poly)victimisation experiences and to assess whether the patterns observed here persists when these forms of harm are included.

Similarly, there are limitations in terms of sampling. The CSEW does not capture data from institutionalised children living in, for example, secure homes, residential facilities or orphanages. Yet, they are at higher risk of maltreatment, victimisation, and polyvictimisation (Rus et al., 2017; Segura et al., 2017). Future studies should therefore seek to capture prevalence and predictors within these populations. Our analysis also does not consider the potential impact of the suspect–victim relationship and therefore cannot distinguish whether the victimisation took place in intra- or extra-familial contexts. There may be important differences in risk factors and the type and amount of victimisation and polyvictimisation suffered within and outside families, meaning that family- and relationship-centred preventive measures may be needed. In addition, the cross-sectional design limits causal inference. The data set also excludes children under the age of 10 and, while calibration weights adjust for unequal selection probabilities associated with the household-based sampling design, the survey does not capture within-household clustering of victimisation experiences among siblings or children living outside private households. To understand how victimisation risks evolve and persist over the lifespan, future research should investigate contextualised longitudinal trajectories of victimisation, including escalations in frequency, severity and harm, as well as transitions from child to adolescent to adult victimhood. Using child-centric methodologies, qualitative or mixed methods studies could also explore how children make sense of multiple victimisation and what support structures they find effective to ensure that interventions reflect children’s lived experiences, including their experiences at different developmental points, and that long-term prevention and disruption policies are contextualised.

Conclusion

The present findings add nuance to victimisation and polyvictimisation risk markers. Not all children faced the same risks and those with intersecting needs are particularly vulnerable to both individual victimisation and polyvictimisation. Sex, ethnicity, disability/long-term illness, and deprivation predicted both individual victimisation and polyvictimisation, while age, ethnicity, number of adults, occupation of HRP, length of residency and tenure type predicted individual victimisation only. The importance of increasing social equity, equality and stability across family, community, and individual levels cannot be minimised. Contextualised multi-level and multi-agency long-term interventions addressing individual-, household-, and area-level factors are needed for effective prevention. Reducing child (poly)victimisation is not just a criminal justice concern; it is a public health, social justice, and human rights imperative. As such, children at risk of experiencing victimisation and polyvictimisation need to be a clear priority for local and national policies.

Supplemental Material

sj-docx-1-irv-10.1177_02697580261430760 – Supplemental material for Prevalence, trends, and predictors of victimisation and polyvictimisation among children in England and Wales

Supplemental material, sj-docx-1-irv-10.1177_02697580261430760 for Prevalence, trends, and predictors of victimisation and polyvictimisation among children in England and Wales by Ferhat Tura, Ioana Crivatu, Andromachi Tseloni and Lisa Tompson in International Review of Victimology

Supplemental Material

sj-docx-2-irv-10.1177_02697580261430760 – Supplemental material for Prevalence, trends, and predictors of victimisation and polyvictimisation among children in England and Wales

Supplemental material, sj-docx-2-irv-10.1177_02697580261430760 for Prevalence, trends, and predictors of victimisation and polyvictimisation among children in England and Wales by Ferhat Tura, Ioana Crivatu, Andromachi Tseloni and Lisa Tompson in International Review of Victimology

Footnotes

Acknowledgements

The authors thank the Office for National Statistics for providing access to the Crime Survey for England and Wales data.

ORCID iDs

Ferhat Tura

Ioana Crivatu

Lisa Tompson

Ethical Considerations

This study is based on secondary analysis of publicly available data (the Crime Survey for England and Wales) accessed via the UK Data Service. As no primary data collection involving human participants was conducted, ethical approval was not required.

Consent to Participate

As this study used anonymised, publicly available data obtained from the UK Data Service, individual informed consent was not required.

Author contributions

Dr F.T. led the conceptualisation, data analysis, and drafting of the manuscript. Dr I.C. contributed to data interpretation, literature review, and critical revisions of the manuscript. Professor A.T. provided methodological guidance, supervision, and contributed to the interpretation and refinement of the analysis, and manuscript review. Dr L.T. contributed to the study design, contextual interpretation of findings, and manuscript review.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Wellcome Trust (grant number: SRG24\241386).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The authors cannot share the data publicly. However, data citation, Supplemental Appendix Tables 1 and 2 and replication codes can be found here:

Supplemental material

Supplemental material for this article is available online.

Notes

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