ArticlePDF Available

Measuring adolescents' exposure to victimization: The Environmental Risk (E-Risk) Longitudinal Twin Study


Abstract and Figures

This paper presents multilevel findings on adolescents' victimization exposure from a large longitudinal cohort of twins. Data were obtained from the Environmental Risk (E-Risk) Longitudinal Twin Study, an epidemiological study of 2,232 children (1,116 twin pairs) followed to 18 years of age (with 93% retention). To assess adolescent victimization, we combined best practices in survey research on victimization with optimal approaches to measuring life stress and traumatic experiences, and introduce a reliable system for coding severity of victimization. One in three children experienced at least one type of severe victimization during adolescence (crime victimization, peer/sibling victimization, Internet/mobile phone victimization, sexual victimization, family violence, maltreatment, or neglect), and most types of victimization were more prevalent among children from low socioeconomic backgrounds. Exposure to multiple victimization types was common, as was revictimization; over half of those physically maltreated in childhood were also exposed to severe physical violence in adolescence. Biometric twin analyses revealed that environmental factors had the greatest influence on most types of victimization, while severe physical maltreatment from caregivers during adolescence was predominantly influenced by heritable factors. The findings from this study showcase how distinct levels of victimization measurement can be harmonized in large-scale studies of health and development.
Content may be subject to copyright.
Measuring adolescents’ exposure to victimization:
The Environmental Risk (E-Risk) Longitudinal Twin Study
aKing’s College London; bDuke University; cSouth London & Maudsley NHS Foundation Trust; and dUniversity of Exeter
Medical School
This paper presents multilevel findings on adolescents’ victimization exposure from a large longitudinal cohort of twins. Data were obtained from the
Environmental Risk (E-Risk) Longitudinal Twin Study, an epidemiological study of 2,232 children (1,116 twin pairs) followed to 18 years of age (with 93%
retention). To assess adolescent victimization, we combined best practices in survey research on victimization with optimal approaches to measuring life stress
and traumatic experiences, and introduce a reliable system for coding severity of victimization. One in three children experienced at least one type of severe
victimization during adolescence (crime victimization, peer/sibling victimization, Internet/mobile phone victimization, sexual victimization, family violence,
maltreatment, or neglect), and most types of victimization were more prevalent among children from low socioeconomic backgrounds. Exposure to
multiple victimization types was common, as was revictimization; over half of those physically maltreated in childhood were also exposed to severe physical
violence in adolescence. Biometric twin analyses revealed that environmental factors had the greatest influence on most types of victimization, while
severe physical maltreatment from caregivers during adolescence was predominantly influenced by heritable factors. The findings from this study showcase
how distinct levels of victimization measurement can be harmonized in large-scale studies of health and development.
Children exposed to various forms of victimization during
childhood have repeatedly been shown to have a range of ad-
verse physical, social, cognitive, and mental health outcomes
(Gilbert et al., 2009), and we are now beginning to appreciate
that such early life stressors have implications for health and
well-being across the life span (Cicchetti & Tucker, 1994;
Currie & Widom, 2010; Danese et al., 2009; Moffitt & the
Klaus Grawe ThinkTank, 2013; Norman et al., 2012; Taki-
zawa, Maughan, & Arseneault, 2014; Widom, Czaja, Bent-
ley, & Johnson, 2012). Although much research has focused
on victimization exposure during the early stages of child-
hood, adolescence is also a time of major emotional, physical,
social, and neurodevelopmental change (Cromer, 2011; Luci-
ana, 2013), suggesting that victimization during this period
could have equally important ramifications for development.
Moreover, as adolescents spend an increasing proportion of
their time outside of the home environment, compared to
when they were children, they are also likely to experience a
greater variety of victimization exposures. Thus, it is important
to measure exposure to a range of possible victimization experi-
ences during this key transition period and examine their im-
mediate and long-term consequences in affected individuals.
However, there is no current consensus about the optimal
method for assessing exposure to such victimization experi-
ences. Reports obtained from social services and medical or
police records are advocated to be the most objective source
of information on victimization exposure (Widom, 1988).
However, these capture only a fraction of victimization cases
because most cases do not come to the attention of such ser-
vices (Widom, Czaja, & DuMont, 2015), and those that do
may overrepresent children from poorer backgrounds (Pelton,
1978) or more extreme cases of physical abuse or neglect
(Groeneveld & Giovannoni, 1977). Therefore, in order to ob-
tain more complete rates of victimization, it is necessary to
question individuals directly. There is an ongoing tension
in research utilizing self-report measures of victimization be-
tween employing self-report checklists or questionnaires, or
more comprehensive interview measures (Monroe, 2008).
Interview-based assessments of stressful or traumatic events
are considered to be superior to self-report checklists or
Address correspondence and reprint requests to: Louise Arseneault, MRC
SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience,King’s Col-
lege London, 16 De Crespigny Park, London SE5 8AF, UK; E-mail:
We aregrateful to thestudy mothers and fathers, thetwins, and the twins’ teach-
ers for theirparticipation.Our thanks to members of the EnvironmentalRisk (E-
Risk) Longitudinal Twin study team for their dedication, hard work, and in-
sights; to Renate Houts for statistical assistance; and to Benjamin Bleiberg,
Perry Dinardo, and Stephanie Gandleman for their research assistance. The
E-Risk Study is funded by the Medical Research Council (UKMRC) Grant
G1002190. Additional support was provided by National Institute of Child
Health and Development Grant HD077482, Economic and Social Research
CouncilGrant RES-177-25-0013, the JacobsFoundation, UKMRC Population
Health Scientist fellowship Grant G1002366 (to H.L.F.), and MQ Fellows
Award MQ14F40 (to H.L.F.).
Development and Psychopathology 27 (2015), 1399–1416
#Cambridge University Press 2015. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence
(, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is
properly cited. doi:10.1017/S0954579415000838
structured questionnaires (Brown, 1989; Dohrenwend, 2006;
Gorman, 1993; Monroe, 2008; Paykel, 2001), because inter-
views are believed to be less influenced by respondent bias
and subjective interpretation of questionnaire items (Dohren-
wend, 2006; Grant, Compas, Thurm, McMahon, & Gipson,
2004; Hepp et al., 2006; Monroe & McQuaid, 1994), thus
providing more precision and reliability when measuring rel-
evant exposures. Nonetheless, published research into child-
hood and adolescent stress exposure has predominantly uti-
lized self-report checklists or questionnaires (Grant et al.,
2004). Resistance to using interviews has mainly revolved
around the considerable time and resources required to ad-
minister these forms of assessment (Dohrenwend, 2006).
Thus, interview-based measures have often been considered
unfeasible for large-scale epidemiological studies. Hence, in
order for the field to move forward, a compromise needs to
be reached between these two levels of measurement: self-re-
port questionnaires and checklists derived from large-sample
survey methodology, with their greater cost-effectiveness and
scalability; and interview-based measures of victimization de-
rived from small-sample clinical methodology, with their
more in-depth coverage and investigator-based rating systems.
In this paper, we describe adolescent victimization in a
large-scale epidemiological twin study using a method that
combined a standardized survey with a more in-depth con-
textual coding system. Specifically, we started by adapting
the Juvenile Victimization Questionnaire (JVQ; Finkelhor,
Hamby, Turner, & Ormrod, 2011; Hamby, Finkelhor, Orm-
rod, & Turner, 2004) as a clinical interview. The JVQ is a
standardized questionnaire that has been used to obtain a
wide range of self-reported victimization experiences from
large samples of adolescents in both the United Kingdom
(Radford, Corral, Bradley, & Fisher, 2013) and the United
States (Finkelhor, Hamby, Ormrod, & Turner, 2005). How-
ever, the data derived from the JVQ are essentially a count
of the items the respondent endorses and may lack sufficient
detail to determine the severity of victimization experienced
or to evaluate the context in which the victimization occurred.
These missing features are considered to be desirable to grasp
the fuller picture of victimization exposure (Barnett, Manly,
& Cicchetti, 1993; Brown, 1974; Brown & Harris, 1978). Se-
verity of exposure, in particular, has been shown to predict la-
ter psychopathology (Clemmons, Walsh, DiLillo, & Mess-
man-Moore, 2007; Fergusson, Boden, & Horwood, 2008;
Schilling, Aseltine, & Gore, 2008). Therefore, we utilized
the existing questions from the JVQ but administered these
as part of an interview in which respondents provided detailed
descriptions of their victimization experiences. These de-
scriptions were coded by an independent panel of expert
raters using a coding system adapted from the Childhood Ex-
perience of Care and Abuse Interview (Bifulco, Brown, &
Harris, 1994; Bifulco, Brown, Neubauer, Moran, & Harris,
1994), which provides standardized anchor points for deter-
mining severity of exposure within the relevant context. It
is hoped that our pragmatic approach to blending two leading
traditions in the assessment of victimization will enable
future researchers to more fully explore the role of multiple
types of victimization in the etiology of physical and mental
This paper hastwo main aims. First, we detail this combined
approach to assessing adolescent victimization and present the
reliability of its implementation in a longitudinal cohort of twin
children. Second,we report initial findings concerning the prev-
alence of different types of severe victimization using this com-
bined assessment method, along with their co-occurrence
(polyvictimization), and recurrence from childhood to adoles-
cence (revictimization), and we examine how demographic fac-
tors influence variability in exposure. We also exploit the twin
design of our study to explore the relative genetic and environ-
mental influences on exposure to each type of adolescent vic-
timization experience.
Study cohort
Participants were members of the Environmental Risk (E-
Risk) Longitudinal Twin Study, which tracks the develop-
ment of a birth cohort of 2,232 British children. The sample
was drawn from a larger birth register of twins born in Eng-
land and Wales in 1994–1995 (Trouton, Spinath, & Plomin,
2002). Full details about the sample are reported elsewhere
(Moffitt & the E-Risk Study Team, 2002). Briefly, the E-
Risk sample was constructed in 1999–2000, when 1,116 fam-
ilies (93% of those eligible) with same-sex 5-year-old twins
participated in home-visit assessments. This sample com-
prised 55% monozygotic (MZ) and 45% dizygotic (DZ)
twin pairs; sex was evenly distributed within zygosity (49%
male). Families were recruited to represent the UK population
of families with newborns in the 1990s, on the basis of resi-
dential location throughout England and Wales and mother’s
age. Teenaged mothers with twins were overselected to re-
place high-risk families who were selectively lost to the regis-
ter through nonresponse. Older mothers having twins via as-
sisted reproduction were underselected to avoid an excess of
well-educated older mothers.
At follow-up, the study sample represents the full range
of socioeconomic conditions in the United Kingdom, as re-
flected in the families’ distribution on a neighborhood-level
socioeconomic index (A Classification of Residential Neigh-
bourhoods [ACORN], developed by CACI Inc. for commer-
cial use in Great Britain; Odgers, Caspi, Russell, et al., 2012).
ACORN uses census and other survey-based geodemo-
graphic discriminators to classify enumeration districts
(150 households) into socioeconomic groups ranging
from “wealthy achievers” (Category 1), with high incomes,
large single-family houses, and access to many amenities,
to “hard-pressed” neighborhoods (Category 5), dominated
by government-subsidized housing estates, low incomes,
high unemployment, and single parents. ACORN classifica-
tions were geocoded to match the location of each E-Risk
study family’s home (Odgers, Caspi, Bates, Sampson, &
H. L. Fisher et al.1400
Moffitt, 2012). E-Risk families’ ACORN distribution closely
matches that of households nationwide: 25.6% of E-Risk
families live in “wealthy achiever” neighborhoods compared
to 25.3% nationwide; 5.3% versus 11.6% live in “urban pros-
perity” neighborhoods; 29.6% versus 26.9% live in “comfort-
ably off” neighborhoods; 13.4% versus 13.9% live in “mod-
erate means” neighborhoods; and 26.1% versus 20.7% live in
“hard-pressed” neighborhoods. E-Risk underrepresents “ur-
ban prosperity” neighborhoods because such households
are likely to be childless.
Follow-up home visits were conducted when the children
were aged 7 (98% participation), 10 (96% participation), 12
(96% participation), and, most recently in 2012–2014, 18
years (93% participation). There were 2,066 children who
participated in the E-Risk assessments at age 18, and the pro-
portions of MZ (55%) and male same-sex (47%) twins were
almost identical to those found in the original sample at age 5.
The average age of the twins at the time of the assessment
was 18.4 years (SD ¼0.36); all interviews were conducted
after the 18th birthday. There were no differences between
those who did and did not take part at age 18 in terms of so-
cioeconomic status (SES) assessed when the cohort was ini-
tially defined (x2¼0.86, p¼.65), age 5 IQ scores (t¼0.98,
p¼.33), or age 5 internalizing or externalizing behavior
problems (t¼0.40, p¼.69 and t¼0.41, p¼.68, respec-
tively). Home visits at ages 5, 7, 10, and 12 years included
assessments with participants as well as their mother (or pri-
mary caretaker); the home visit at age 18 included interviews
only with the participants. Each twin participant was assessed
by a different interviewer.
The Joint South London and Maudsley and the Institute of
Psychiatry Research Ethics Committee approved each phase
of the study. Parents gave informed consent and twins gave
assent between 5 and 12 years and then informed consent
at age 18.
Assessment of victimization exposure between ages
12 and 18
JVQ interview. At age 18, participants were interviewed face-
to-face about exposure to a range of adverse experiences be-
tween 12 and 18 years using the JVQ (Finkelhor et al., 2011;
Hamby et al., 2004), adapted as a clinical interview. The JVQ
has good psychometric properties (Finkelhor et al., 2005) and
was used in the United Kingdom National Society for the Pre-
vention of Cruelty to Children (NSPCC) national survey
(Radford et al., 2011, 2013), thereby providing important
benchmark values for comparisons with our cohort. Our
adapted JVQ comprised 45 questions covering different
forms of victimization grouped into seven categories: crime
victimization, peer/sibling victimization, Internet/mobile
phone victimization, sexual victimization, family violence,
maltreatment, and neglect. The interview schedule used in
this study is provided in supplementary materials Part I.
Within each pair of twins in our cohort, cotwins were in-
terviewed separately by a different interviewers and were as-
sured of the confidentiality of their responses. The partici-
pants were advised that confidentiality would only be
broken if they told the interviewers that they were in immedi-
ate danger of being hurt, and in such situations, the project
leader would be informed and would contact the participant
to discuss a plan for safety.
Each JVQ question was asked for the period “since you
were 12.” Age 12 is a salient age for our participants because
it is the age when British children leave primary school to en-
ter secondary school. Participants were given the option to
say “yes” or “no” as to whether each type of victimization
had occurred in the reporting period. Interviewers could
rate each item “maybe” if the participant seemed unsure or
hesitant in his or her response or the interviewer was not con-
vinced that the participant understood the question or was
paying attention. Items rated as “maybe” were recoded as
“no” or “yes” by the rating team based on the notes provided
by the interviewers. When insufficient notes were available,
then these responses were recoded conservatively as a “no.”
Consistent with the JVQ manual (Finkelhor et al., 2011;
Hamby et al., 2004), participants were coded as 1 if they re-
ported any experience within each type of victimization cate-
gory or 0 if none of the experiences within the category were
endorsed. If an experience was endorsed within a victimiza-
tion category, follow-up questions were asked concerning
how old the participant was when it (first) happened, whether
the participant was physically injured in the event, whether
the participant was upset or distressed by the event, and
how long it went on for (by marking the number of years
on a Life History Calendar; Caspi et al., 1996). In addition,
the interviewer wrote detailed notes based on the participant’s
description of the worst event. If multiple experiences were
endorsed within a victimization category, the participant
was asked to identify and report about his or her worst expe-
Victimization dossiers. All information from the JVQ inter-
view was compiled into victimization dossiers. Using these
dossiers, each of the seven victimization categories was rated
by an expert in victimology (Dr. Helen Fisher) and 3 other
members of the E-Risk team who were trained on using the
rating criteria. Ratings were made using a 6-point scale:
0¼not exposed, then 1–5 for increasing levels of severity.
The anchor points for these ratings were adapted from the
coding system used for the Childhood Experience of Care
and Abuse interview (CECA; Bifulco, Brown, & Harris,
1994; Bifulco, Brown, Neubauer, et al., 1994), which has
good interrater reliability (Bifulco, Brown, & Harris, 1994;
Bifulco, Brown, Lillie, & Jarvis, 1997). The CECA is a com-
prehensive semistructured interview whose standardized cod-
ing system attempts to improve the objectivity of ratings by
basing them on the coder’s perspective (rather than relying
on the participant’s judgment) and focusing on concrete de-
scriptions rather than perceptions or emotional responses to
the questions, together with considering the context in which
the adverse experience occurred. This standardization of
Measuring adolescents’ exposure to victimization 1401
victimization definitions and severity ratings is also crucial to
ensure consistency across research groups and disciplines to
improve the comparability of findings from different studies
(Barnett et al., 1993).
In our adapted coding scheme, the anchor points of the
scale differ for each victimization category, with some fo-
cused more on the severity of physical injury that is likely
to have been incurred during victimization exposure (crime
victimization, family violence, and maltreatment), while oth-
ers are more focused on the frequency of occurrence of vic-
timization (peer/sibling victimization and Internet/mobile
phone victimization), the physical intrusiveness of the event
(sexual victimization), or the pervasiveness of the effects of
victimization (neglect). This reflects the different ways in
which severity has previously been defined for different types
of victimization (Barnett et al., 1993; Bifulco, Brown, & Har-
ris, 1994). (Given that our sample comprises twins, we also
coded if any of the victimization events experienced by
each twin had been perpetrated by their cotwin because it is
possible that growing up with a genetically related, same-
age child could increase or decrease sibling victimization
rates.) Finally, we evaluated whether each participant was ex-
posed to any physical violence, whether in the family, by
peers, or by people in the wider environment, based on the en-
tire dossier of victimization experiences. This “any physical
violence” exposure variable was also rated on a 6-point scale:
0¼not exposed, then 1–5 for increasing levels of severity,
with severity linked to frequency of occurrence of noninjur-
ious physical attacks at the lower end of the scale (1–3) and
the likelihood of incurring an injury and the seriousness of
this injury indicating severity at the upper end of the scale
(4–5). This rating included violence directed toward the par-
ticipants themselves as well as violence they witnessed be-
tween other people. A copy of our coding scheme is provided
in supplementary materials Part II. Each twin’s dossier was
evaluated separately, and we did not use information provided
in the cotwin’s dossier about his or her own or shared victim-
ization experiences to rate direct or witnessed violence expo-
sure for the target twin.
Reliability. The first 26 victimization dossiers were coded by
all raters to provide training on the use of the severity rating
scales and to develop consistency in the application of the an-
chor points to real-life experiences. Rating discrepancies were
discussed by the group and consensus ratings agreed upon.
Interrater reliability was conducted on the next 90 dossiers
(approximately 4% of the total sample) with four raters inde-
pendently scoring all of these dossiers. High levels of interra-
ter reliability were achieved for the severity ratings for all
forms of victimization: crime victimization (intraclass corre-
lation coefficient [ICC] ¼0.89, p,.001), peer/sibling vic-
timization (ICC ¼0.91, p,.001), Internet/mobile phone
victimization (ICC ¼0.90, p,.001), sexual victimization
(ICC ¼0.87, p,0.001), family violence (ICC ¼0.93,
p,0.001), maltreatment (ICC ¼0.90, p,.001), neglect
(ICC ¼0.74, p,.001), and any physical violence exposure
(ICC ¼0.82, p,.001). The remaining dossiers were divided
between the four raters in a series of batches of around 100
each, and the raters met at the end of rating each batch to dis-
cuss difficult ratings and to reach consensus about any uncer-
tain cases. A random selection of 20 dossiers from each batch
of 400 rated (5 per rater per batch) was independently rated by
a psychologist with expertise in interviewing children and
adolescents. Any discrepancies of two points or more on
any of the scales were discussed with the rating team and a
consensus reached about the final ratings. In addition, all dos-
siers with ratings of 4 or 5 for any physical violence exposure
were independently reviewed by a child psychologist and any
discrepancies discussed with the main rating team and a con-
sensus reached about the final any physical violence exposure
For reporting purposes in this article, the ratings for each
type of victimization were then grouped into three classes:
0¼no exposure (score of 0), 1 ¼some exposure (score of
1, 2, or 3), and 2 ¼severe exposure (score of 4 or 5) due to
small numbers for some of the rating points. Combining rat-
ings of 4 and 5 is also consistent with previous studies using
the CECA, which have collapsed comparable scale values to
indicate presence of “severe” abuse (e.g., Bifulco, Brown, &
Harris, 1994; Bifulco et al., 1997; Bifulco, Brown, Moran,
Ball, & Campbell, 1998; Fisher, Bunn, Jacobs, Moran, & Bi-
fulco, 2011).
Childhood SES
Participants’ family SES was defined using a standardized
composite of parents’ income, education, and social class as-
certained at childhood phases of the study, which loaded sig-
nificantly onto one latent factor (Trzesniewski, Moffitt,
Caspi, Taylor, & Maughan, 2006). The latent factor was di-
vided in tertiles. Thus, of participants who were interviewed at
age 18, 33% were characterized as living in a low-SES situa-
tion during childhood.
Measures of victimization exposure up to age 12
Exposure to several types of victimization was assessed re-
peatedly when the children were 5, 7, 10, and 12 years of
age, and dossiers have been compiled for each child with cu-
mulative information about exposure to domestic violence
between the mother and her partner; frequent bullying by
peers; physical maltreatment by an adult (including sexual
abuse); and physical neglect. The E-Risk team has previously
reported evidence on the reliability and validity of the mea-
sures of domestic violence (Moffitt et al., 1997), bullying
(Arseneault et al., 2006; Shakoor et al., 2011), and physical
maltreatment (Jaffee, Caspi, Moffitt, Polo-Tomas, et al.,
2004; Jaffee, Caspi, Moffitt, Polo-Tomas, & Taylor, 2007),
and all the measures are outlined briefly below.
Physical domestic violence. Mothers reported about perpetra-
tion of and victimization involving 12 forms of physical vio-
H. L. Fisher et al.1402
lence (e.g., slapping, hitting, kicking, and strangling) from
the Conflict Tactics Scale (Straus, 1990), on three assessment
occasions during the child’s first decade of life (when the
children were 5, 7, and 10 years of age). Reports of either per-
petration or victimization constituted evidence of physical
domestic violence. Families in which no physical violence
took place were coded as 0 (55.2%); families in which phys-
ical violence took place on one occasion were coded as 1
(28.0%); and families in which physical violence took place
on multiple occasions were coded as 2 (16.8%).
Bullying by peers. Experiences of victimization by bullies
were assessed using both mothers’ and children’s reports.
During the interview, the following standard definition of
bullying was read out:
Someone is being bullied when another child (a) says mean and hurt-
ful things, makes fun, or calls a person mean and hurtful names; (b)
completely ignores or excludes someone from their group of friends
or leaves them out on purpose; (c) hits, kicks, or shoves a person, or
locks them in a room; (d) tells lies or spreads rumors about them; and
(e) other hurtful things like these. We call it bullying when these
things happen often, and when it is difficult to make it stop. We
do not call it bullying when it is done in a friendly or playful way.
Mothers were interviewed when children were 7, 10, and 12
years old and asked whether either twin had been bullied by
another child, responding never, yes,orfrequently. We com-
bined mothers’ reports at child age 7 and 10 to derive a mea-
sure of victimization during primary school. Mothers’ reports
when the children were 12 years old indexed victimization
during secondary school. During private interviews with
the children when they were 12 years old, the children indi-
cated whether they had been bullied by another child during
primary or secondary school. When a mother or a child re-
ported victimization, the interviewer asked her to describe
what happened. Notes taken by the interviewers were later
checked by an independent rater to verify that the events re-
ported could be classified as instances of bullying operation-
ally defined as evidence of (a) repeated harmful actions, (b)
between children, and (c) where there is a power differential
between the bully and the victim (Shakoor et al., 2011). Al-
though interrater reliability between mothers and children
was only modest (k¼0.20–0.29), reports of victimization
from both informants were similarly associated with chil-
dren’s emotional and behavioral problems, suggesting that
each informant provides a unique but meaningful perspective
on bullying involvement (Shakoor et al., 2011). We thus
combined mother and child reports of victimization to capture
all instances of bullying victimization for primary and sec-
ondary school separately: reported as not victimized by
both mother and child; reported by either mother or child as
being occasionally victimized; and reported as being occa-
sionally victimized by both informants or as frequently vic-
timized by either mother or child or both (Bowes et al.,
2013). We then combined these primary and secondary
school ratings to create a bullying victimization variable for
the entire childhood period (5–12 years). Children who
were never bullied in primary or secondary school or occa-
sionally bullied during one of these time periods were coded
as 0 (55.5%); children who were occasionally bullied during
primary and secondary school, or frequently bullied during
one of these time periods were coded as 1 (35.6%); and chil-
dren who were frequently bullied at both primary and second-
ary school were coded as 2 (8.9%).
Physical harm by an adult. When the twins were aged 5, 7,
10, and 12, their mothers were interviewed about each twin’s
experience of intentional harm by an adult. At age 5 we used
the standardized clinical protocol from the MultiSite Child
Development Project (Dodge, Bates, & Pettit, 1990; Lansford
et al., 2002). At ages 7, 10, and 12 this interview was modi-
fied to expand its coverage of contexts for child harm. Inter-
views were designed to enhance mothers’ comfort with re-
porting valid child maltreatment information, while also
meeting researchers’ responsibilities for referral under the
UK Children Act. Specifically, mothers were asked whether
either of their twins had been intentionally harmed (physi-
cally or sexually) by an adult or had contact with welfare
agencies. If caregivers endorsed a question, interviewers
made extensive notes on what had happened, and indicated
whether physical and/or psychological harm had occurred.
Under the UK Children Act, our responsibility was to secure
intervention if maltreatment was current and ongoing. Such
intervention on behalf of E-Risk families was carried out
with parental cooperation in all but one case. No families
left the study following intervention.
Over the years of data collection, the study developed a cu-
mulative profile for each child, comprising the caregiver re-
ports, recorded debriefings with interviewers who had coded
any indication of maltreatment at any of the successive home
visits, recorded narratives of the successive caregiver inter-
views, and information from clinicians whenever the study
team made a child-protection referral. The profiles were re-
viewed at the end of the age 12 phase by two clinical psychol-
ogists. Initial interrater agreement between the coders ex-
ceeded 90%, and discrepantly coded cases were resolved by
consensus review. These were coded as follows: 0 ¼no mal-
treatment at any age;1¼probable maltreatment at any age;
and 2 ¼definite maltreatment at any age. There were 15.4%
of the children coded as probably being exposed to physical
harm and 5.7% as definitely physically harmed by 12 years
of age.
Physical neglect. The cumulative observations of the physical
state of the home environment documented by the interview-
ers during home visits to the twins at ages 5, 7, 10, and 12
were reviewed by two raters for evidence of physical neglect.
This was defined as any sign that the caretaker was not pro-
viding a safe, sanitary, or healthy environment for the child.
This included the child not having proper clothing or food,
as well as grossly unsanitary home environments. (However,
Measuring adolescents’ exposure to victimization 1403
this did not include a family living in a crime-ridden neigh-
borhood for economic reasons.) Initial interrater agreement
between the coders exceeded 85%, and discrepantly coded
cases were resolved by consensus review. Children with no
evidence of physical neglect were coded as 0 (90.9%), those
for whom there was an indication of minor physical neglect
were coded as 1 (7.1%), and where there was evidence of
severe physical neglect the children were coded as 2 (2.0%).
Prevalence of victimization in adolescence
Table 1 presents the rates of victimization between ages 12
and 18 years, as reported by the E-Risk study participants
at age 18. Over half of the participants reported exposure to
crime victimization (51.7%) and to some form of peer/sibling
victimization (58.7%). However, only 15.4% (N¼318) re-
ported that their cotwin had been one of the perpetrators. Ap-
proximately one in five participants reported exposure from
age 12 to 18 to Internet/mobile phone victimization (20.6%)
and to family violence (19.3%) and slightly less reported sex-
ual victimization (16.4%) or maltreatment (14.8%). A smaller
percentage of participants reported neglect (6.4%).
For comparison purposes, Table 1 also presents the life-
time rates of victimization for 11- to 17- year-olds across the
United Kingdom collected by the NSPCC in 2009 (Radford
et al., 2011). Although the two studies differed in important
ways (e.g., we conducted face-to-face interviews whereas the
NSPCC survey used a computerized self-report version of the
JVQ; we inquired about victimization over the past 6 years
whereas the NSPCC survey provides lifetime rates; and our
study participants are 18 years old whereas the NSPCC survey
involved 11- to 17-year-olds), the prevalence rates of victimiza-
tion are broadly comparable. The exceptions to this were mal-
treatment and neglect, which were reported less often by our
participants. This is likely to be due to our prevalence rates
being limited to adolescence, while the NSPCC survey cap-
tured both childhood and adolescent exposure, which could
account for the higher rates in that survey.
Table 1 documents high rates of victimization according to
the participants’ reports, but these experiences are difficult to
interpret without any discrimination as to whether they were
mild or severe. Moving beyond the survey responses in
Table 1,Table 2 presents information about the severity of
victimization experiences as derived from evaluation and
coding of the victimization dossiers compiled for our study
participants. All findings presented in the remainder of this
article refer to the severity-coded victimization experiences.
Approximately a quarter (24.3%) of adolescents were ex-
posed to severe levels of violence (“any physical violence”)
between ages 12 and 18 years; that is, to incidents directed to-
ward themselves or that they witnessed involving other peo-
ple that were likely to result in physical injuries or were life
threatening. Turning to specific types of severe victimization
experiences, just under one fifth reported exposure to severe
forms of crime victimization (19.3%) and 15.6% reported
being frequently victimized by a peer or sibling. Exposure
to severe and/or frequent family violence was reported by
12.1% of the participants, and Internet/mobile phone victim-
ization by 6.5% of the adolescents. Severe maltreatment
(3.3%), contact sexual abuse (2.6%), and extreme neglect
(2.2%) were the least common types of victimization in this
sample during adolescence. The rates of exposure to victim-
ization were almost identical among MZ and DZ twin partic-
ipants in our cohort (see supplementary materials Part III).
Table 2 also presents the prevalence of each victimization
type by gender and the association between gender and expo-
sure to severity of victimization during adolescence. When
calculating these associations using ordinal logistic regres-
sion, we accounted for the nonindependence of the twin ob-
servations using the Huber–White variance estimator (Wil-
liams, 2000). This adjusts the estimated standard errors in
each test to account for the dependence in the data. Overall,
males were exposed to more severe levels of physical vio-
lence than were females (see Table 2) and there were sex dif-
ferences in the other types of victimization experienced by
adolescents. Whereas exposure to crime and peer/sibling vic-
timization was more common among males, exposure to In-
ternet/mobile phone victimization, sexual victimization, and
maltreatment was more common among females. Adolescent
males and females did not differ in their exposure to family
violence or neglect.
Participants from low-SES backgrounds were more like to
experience severe exposure to nearly all types of victimiza-
Table 1. Prevalence of adolescent victimization
experiences among E-Risk participants and respondents
in the NSPCC survey
NSPCC National
(N¼2066) (N¼2275)
Type of Victimizationan(%) n(%)d
Crime 1067 (51.7) 1437 (62.2)
Peer/sibling 1212 (58.7) 1471 (64.9)
Internet/mobile phone 425 (20.6) 319 (13.3)
Sexual 338 (16.4) 285 (16.5)
Family violence 398 (19.3) 342 (19.8)
Maltreatment 306 (14.8) 358 (20.7)
Neglect 132 (6.4) 229 (13.3)
Note: E-Risk, Environmental Risk Longitudinal Twin Study; NSPCC,
National Society for the Prevention of Cruelty to Children.
aWe report whether any victimization experience was reported, within each
type of victimization.
bVictimization between ages 12 and 18 reported by 18-year-olds in the
E-Risk Study.
cLifetime victimization reported by 11- to 17-year-olds from across the
United Kingdom, taken from Radford et al. (2011).
dThese percentages are weighted back to the UK population to compensate
for unequal sampling probabilities, and unequal responses by age group, gen-
der, housing tenure, working status, region, and ethnic group (see Radford
et al., 2011, for full details).
H. L. Fisher et al.1404
tion, including crime, family violence, maltreatment, and ne-
glect (Table 3). However, they were only slightly more likely
to be victimized by peers or siblings, and there was no social
gradient in exposure to Internet/mobile phone or sexual vic-
Polyvictimization: Are adolescents likely to be exposed
to multiple types of severe victimization?
We tested the co-occurrence of different types of severe vic-
timization during adolescence by estimating the odds of being
exposed to one type of severe victimization given exposure to
another type of severe victimization. The positive manifold in
Table 4 reveals that participants who were exposed to one
type of severe victimization in adolescence were much
more likely to have been exposed to multiple other types of
severe victimization. Four findings stand out. First, adoles-
cents who experienced intrafamilial types of victimization
were more likely to also experience victimization outside of
the family home. Second, participants who were cyber/mo-
bile-technology victims were also more likely to be exposed
to victimization in the physical world. Third, the experience
of neglect was not only strongly associated with physical mal-
treatment by adults but also linked to multiple other types of
victimization. Fourth, these associations were independent of
socioeconomic disparities in victimization exposure. That is,
polyvictimization was not simply a function of concentrated
exposure to violence among adolescents growing up in socio-
economically deprived circumstances.
To investigate the level of polyvictimization (i.e., of expe-
riencing multiple types of severe victimization) during ado-
lescence, we summed the number of different types of severe
victimization experiences encountered by each participant.
Around a third of participants experienced at least one of
the seven types of severe victimization in adolescence
(35.4%). Of these, almost half (45.7%) were exposed to mul-
tiple different types of severe victimization (Figure 1a). Fe-
male participants (17.8%) were slightly more likely to have
Table 2. Distribution of the severity of adolescent victimization experiences among males and females in the E-Risk study
Severity Rating
Type of
Never Some/Occasional Severe/Frequent Males Vs. Females
n(%) n(%) n(%) OR (95% CI) p
Any physical violence
All 984 (47.6) 580 (28.1) 502 (24.3)
Boys 379 (38.6) 316 (32.2) 286 (29.2) 0.53 (0.44–0.65) ,.001
Girls 605 (55.8) 264 (24.3) 216 (19.9)
All 998 (48.3) 670 (32.4) 398 (19.3)
Boys 378 (38.5) 369 (37.6) 234 (23.9) 0.50 (0.41–0.60) ,.001
Girls 620 (57.1) 301 (27.7) 164 (15.1)
All 872 (42.2) 870 (42.1) 323 (15.6)
Boys 365 (37.2) 484 (49.4) 131 (13.4) 0.82 (0.68–0.99) .040
Girls 507 (46.7) 386 (35.6) 192 (17.7)
Internet/mobile phone
All 1644 (79.7) 286 (13.9) 133 (6.5)
Boys 825 (84.2) 125 (12.8) 30 (3.1) 1.79 (1.41–2.27) ,.001
Girls 819 (75.6) 161 (14.9) 103 (9.5)
All 1808 (87.8) 198 (9.6) 53 (2.6)
Boys 893 (91.3) 76 (7.8) 9 (0.9) 1.94 (1.44–2.62) ,.001
Girls 915 (84.6) 122 (11.3) 44 (4.1)
Family violence
All 1676 (81.3) 136 (6.6) 250 (12.1)
Boys 798 (81.5) 62 (6.3) 119 (12.2) 1.02 (0.79–1.32) .851
Girls 878 (81.1) 74 (6.8) 131 (12.1)
All 1783 (86.4) 213 (10.3) 67 (3.3)
Boys 872 (89.0) 76 (7.8) 32 (3.3) 1.50 (1.13–2.00) .006
Girls 911 (84.1) 137 (12.7) 35 (3.2)
All 1936 (93.9) 80 (3.9) 46 (2.2)
Boys 927 (94.7) 34 (3.5) 18 (1.8) 1.31 (0.86–1.99) .208
Girls 1009 (93.2) 46 (4.3) 28 (2.6)
Note: OR, Proportional odds ratios derived from ordinal logistic regression adjusted for the nonindependence of twin observations; CI, confidence interval.
Measuring adolescents’ exposure to victimization 1405
been classified as experiencing polyvictimization than were
male participants (14.4%; Figure 1b).
We further examined the phenomenon of polyvictimiza-
tion by adopting a person-centered approach to adolescents’
victimization experiences. Latent class analysis is a person-
centered analytical approach that classifies individuals into
groups or classes based on a profile of variables, in this
case exposure versus no exposure to the seven types of severe
Table 3. Distribution of the severity of adolescent victimization experiences as a function of socioeconomic status (SES)
Severity Rating
Type of
Never Some/Occasional Severe/Frequent Low Vs. Medium/High SES
n(%) n(%) n(%) OR (95% CI) p
Any physical violence
Low 274 (39.7) 187 (27.1) 230 (33.3) 1.77 (1.44–2.18) ,.001
Medium/high 710 (51.6) 393 (28.6) 272 (19.8)
Low 290 (42.0) 220 (31.8) 181 (26.2) 1.59 (1.30–1.96) ,.001
Medium/high 708 (51.5) 450 (32.7) 217 (15.8)
Low 266 (38.6) 299 (43.3) 125 (18.1) 1.27 (1.05–1.55) .016
Medium/high 606 (44.1) 571 (41.5) 198 (14.4)
Internet/mobile phone
Low 539 (78.1) 99 (14.4) 52 (7.5) 1.17 (0.91–1.49) .224
Medium/high 1105 (80.5) 187 (13.6) 81 (5.9)
Low 594 (86.2) 66 (9.6) 29 (4.2) 1.27 (0.94–1.71) .114
Medium/high 1214 (88.6) 132 (9.6) 24 (1.8)
Family violence
Low 535 (77.5) 47 (6.8) 108 (15.7) 1.46 (1.12–1.89) .005
Medium/high 1141 (83.2) 89 (6.5) 142 (10.4)
Low 570 (82.5) 82 (11.9) 39 (5.6) 1.66 (1.24–2.22) .001
Medium/high 1213 (88.4) 131 (9.6) 28 (2.0)
Low 624 (90.4) 37 (5.4) 29 (4.2) 2.34 (1.53–3.56) ,.001
Medium/high 1312 (95.6) 43 (3.1) 17 (1.2)
Note: OR, Proportional odds ratios derived from ordinal logistic regression adjusted for the nonindependence of twin observations; CI, confidence interval.
Table 4. The co-occurrence of different types of severe victimization experienced by adolescents
Type of Victimization
Experienced in
Crime Peer/Sibling
Phone Sexual
Violence Maltreatment Neglect
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Crime — 3.84
Peer/sibling 4.17
— 5.10
Internet/mobile phone 3.53
— 3.31
Sexual 4.20
— 2.34
Family violence 2.03
— 3.40
Maltreatment 4.35
— 7.44
Neglect 3.20
Note: The victimization types listed in the first column are the predictor variables. OR, Proportional odds ratios derived from ordinal logistic regressions are
adjusted for sex, socioeconomic status, and the nonindependence of twin observations; CI, confidence interval.
H. L. Fisher et al.1406
victimization. To ensure replicability of results, we randomly
selected one twin from each family to include in initial anal-
yses; the remaining twin was reserved for replication. In addi-
tion, because we were interested in the profiles of individuals
who were victimized, we excluded participants who did not
experience any severe forms of victimization. These selection
criteria resulted in two subsamples of N¼365 and N¼365
individuals. Latent class analysis was conducted in MPlus
v7.3. In each subsample, we examined fit statistics for two
to six groups (Table 5). In the first subsample, the four-class
solution was the preferred solution; in the second subsample,
both the four- and five-class solutions were acceptable. Upon
further examination of the profiles in the second subsample,
we opted to retain the more parsimonious solution. Final
membership classification was determined using the com-
plete sample of N¼730 individuals in a four-class model
that simultaneously accounted for twin clustering. The model
fit well: entropy ¼0.85; Lo–Mendell–Rubin adjusted likeli-
hood ratio test for three versus four classes ¼113.85, p,
.001; Lo–Mendell–Rubin adjusted likelihood ratio test for
four versus five classes ¼38.70, p,.001.
The four groups were defined by unique victimization pro-
files (Table 6). The first group was defined by exposure to
crime; 84% of individuals classified into this group experi-
enced exposure only to crime victimization, and the remain-
ing 16% experienced one other type of victimization (mal-
treatment, sexual victimization, or Internet/mobile phone
victimization). The second group was defined by exposure
to family violence; 59% of individuals classified into this
group experienced exposure only to family violence; 22% ex-
perienced family violence and crime victimization; and 19%
experienced family violence and other forms of victimiza-
tion. The remaining groups were defined by profiles of poly-
victimization. Specifically, the third group was defined by
peer/sibling victimization in which the vast majority of ado-
lescents also experienced at least one other form of victimiza-
tion, and the fourth group of adolescents experienced multi-
ple varieties of victimization.
Revictimization: Are victimized children likely to be
revictimized in adolescence?
Cohort members who were exposed to domestic violence,
bullied by peers, physically harmed by an adult, or neglected
as a child were significantly more likely (a) to be exposed to
severe violence in adolescence, (b) to experience each of the
different types of severe victimization in adolescence, and (c)
to experience polyvictimization (i.e., they were exposed to a
greater variety of different types of severe victimization;
Table 7). Victimization in early life did not simply show
homotypic continuity, a term that we apply here to refer to
the continuity of similar experiences. Rather, every type of se-
Figure 1. Number of different types of severe victimization experienced by adolescents in (a) the full sample (N¼2,059) and (b) among males
(N¼978) and females (N¼1,081).
Measuring adolescents’ exposure to victimization 1407
vere victimization in childhood was broadly related to both
the same and other, different, types of severe victimization
throughout adolescence.
Turning to exposure to severe physical violence in adoles-
cence, we observe remarkable continuityin the lives of children
and adolescents, whether looking forward or looking back-
ward. In a follow-forward longitudinal analysis, Figure 2a
showsthat 36.6% of children who were exposed to repeated epi-
sodes of domestic violence, 40.4% who were frequently bul-
lied, 55.7% who were physically harmed, and 51.4% who
were neglected, grew up to be exposed to severe physical vio-
lence in adolescence. In a follow-back longitudinal analysis,
Figure 2b shows thatof adolescents who were exposed to severe
physical violence, 25.9% had experienced repeated exposure to
domestic violence during childhood, 14.8% were frequently
bullied during childhood, 13.6% were physically harmed, and
3.6% were neglected. Only a small proportion (12.9%) of those
exposed to severe physical violence in adolescence had not
been bullied or experienced, even mild forms of, maltreatment,
physical domestic violence, or neglect in childhood. Broadly
speaking, victimization is not something that goes away with
time and is not something that often comes out of nowhere;
it is a stable experiential pattern in the lives of many young
Genetic and environmental contributions to adolescent
Table 8 shows the polychoric within-pair correlations for MZ
twins and DZ twins separately for their exposure to each type
of victimization during adolescence. Comparing MZ and DZ
correlations allows us to estimate the relative contributions of
Table 5. Fit statistics for the latent class analysis in two random subsamples of victimized adolescents in the Environmental
Risk Longitudinal Twin Study
Likelihood Ratio x2LMR Adj. LRT
No. of Classes Log Likelihood AIC BIC Estimate df p Entropy Estimate p
Subsample 1 (N¼365)
221170.517 2371.003 2429.532 256.982 112 ,.001 1.000 69.853 ,.001
321131.452 2308.903 2398.601 178.852 104 ,.001 0.757 76.509 .034
421093.750 2249.500 2370.397 103.449 96 .284 0.881 73.838 ,.001
521081.108 2240.984 2392.312 77.164 88 .764 0.947 24.760 .540
621070.501 2235.003 2418.298 56.951 80 .976 0.946 20.773 .237
Subsample 2 (N¼365)
221150.149 2330.298 2388.796 211.678 111 ,.001 0.758 91.751 ,.001
321102.563 2251.126 2340.824 117.589 102 .139 0.866 93.197 ,.001
421077.613 2217.227 2338.123 87.700 96 .715 0.857 48.864 .007
521067.970 2213.940 2366.036 68.413 88 .940 0.775 18.886 .037
621060.565 2215.131 2398.426 53.604 80 .990 0.927 14.502 .216
Note: AIC, Akaike information criterion; BIC, Baysian information criterion; LMR Adj. LRT, Lo–Mendell–Rubin adjusted likelihood ratio test.
Table 6. Percentage of adolescents experiencing each type of victimization in the four classes/groups of
Family Violence
Victimization Polyvictimization I Polyvictimization II
Type of Victimization (N¼190) (N¼158) (N¼329) (N¼53)
Crime 100% 26% 39% 86%
Peer/sibling 0% 0% 86% 35%
Internet/mobile phone 7% 4% 31% 13%
Sexual 2% 1% 9% 27%
Family violence 0% 100% 18% 66%
Maltreatment 3% 6% 1% 76%
Neglect 0% 6% 2% 45%
H. L. Fisher et al.1408
genetic and environmental factors to variation in participants’
exposure to adolescent victimization. We examined the ge-
netic and environmental influences on severity of victimiza-
tion by decomposing variation in each form of victimization
into that explained by additive genetic (A), shared environ-
mental (C; environmental effects common to both twins), and
nonshared environmental (E; environmental effects unique
to each twin) factors. Victimization severity was treated as
ordinal, and we used the threshold model for liabilities (Neale
& Cardon, 1992) to parameterize the model. In this case, the
measured ordered categorical variables have no freely esti-
mated residual variances and are represented as normally
distributed latent response variables underlying the categori-
cal outcome; the standard ACE variance and covariance
restrictions are placed on these latent variables. ACE models
were estimated in MPlus v7.3.
The parameter estimates varied by type of victimization
(see Table 8). We observed the largest genetic influences
on maltreatment and neglect, followed by peer/sibling victim-
ization and crime victimization. Nonshared environmental in-
fluences were pronounced on all forms of victimization, sug-
gesting that unique events and experiences were likely to put
a given adolescent in harm’s way; this was especially the case
with regard to peer, cyber, and sexual victimization. In con-
trast to what is often reported in behavioral genetics, we de-
tected consistent and substantial shared environmental influ-
ences on adolescents’ victimization experiences, suggesting
that growing up in certain families and communities does
contribute to increased victimization risk.
The aim of this report was to integrate best practices in survey
research with optimal approaches to measuring human life
stress, in the service of advancing the study of children’s
and adolescents’ victimization experiences. Specifically, we
combined a widely used self-report questionnaire (Finkelhor
et al., 2011; Hamby et al., 2004) with an investigator-based
rating system adapted from a comprehensive interview about
stressful and traumatic childhood events (Bifulco, Brown, &
Harris, 1994). This combined method was successfully
implemented with 2,066 18-year-olds who are participants
in a nationally representative longitudinal twin cohort study.
We demonstrated that our pragmatic approach of combining
the brevity of a self-report questionnaire with the relative ob-
jectivity of a standardized investigator-based coding system
can be successfully applied to characterize the severity of
multiple types of adolescent victimization experiences with
high levels of interrater reliability. By utilizing the optimal
features of both assessment traditions, we are hopeful that
this combined tool will bring both greater rigor and richness
to the measurement of victimization in a manner that is scal-
able to large population-based studies.
Our approach is not without limitations. First, we relied on
adolescents to tell us about their own victimization experi-
ences, and such self-reports may be biased (Dohrenwend,
Table 7. Continuity of victimization from childhood to adolescence
Type of Victimization in Adolescence
Type of Victimization
in Childhood
Any Physical
Violence Crime Peer/Sibling
Phone Sexual
Violence Maltreatment Neglect Polyvictimization
RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI)
Physical domestic violence 1.23
Bullying by peers 1.26
Physical harm by an adult 1.30
Physical neglect 1.22
Note: Relative risks (RR) are adjusted for sex, socioeconomic status, and the nonindependence of twin observations. Every type of victimization in childhood is broadly related to both the same and other types of
victimization throughout adolescence. CI, Confidence interval.
Measuring adolescents’ exposure to victimization 1409
2006; Grant et al., 2004; Hepp et al., 2006). However, we
tried to minimize subjectivity by (a) conducting a face-to-
face interview, which provided the opportunity for the re-
spondent and interviewer to clarify that the questions were
understood as they were intended to be (Hardt & Rutter,
2004; Schwarz, 2007), and (b) using an investigator-based
standardized coding system to rate the severity of victimiza-
tion experiences (Brown, 1974; Brown & Harris, 1978). Sec-
ond, given time constraints, our interviewonly queried details
about each participant’s “worst experience” within each vic-
timization category. Third, it should be noted that even more
fine-grained information could be elicited in the context of
our combined method. For instance, more details about the
victim’s relationship with the perpetrator and the number of
reoccurrences of the same victimization exposure may be
important to evaluate in developmental psychopathology
(Barnett et al., 1993; Fisher et al., 2010; Matta Oshima, Jon-
son-Reid, & Seay, 2014; Thornberry, Ireland, & Smith,
2001). Fourth, we used a face-to-face interview, and it is pos-
sible that some individuals may be less likely to disclose
when questioned in person due to embarrassment (Della
Femina, Yeager, & Lewis, 1990). However, similar rates of
abuse have been reported for self-report and interview-based
versions of the same measure (Bifulco, Bernazzani, Moran, &
Jacobs, 2005). We welcome application of this assessment
method in other studies to further test its feasibility, reliability
and ease of use, further refine the anchor points (as advocated
by Barnett et al., 1993), and extend its scope.
Figure 2. Violence exposure in adolescence (a) looking forward and (b) looking backward in the lives of victimized youth. Associations between
childhood victimization and adolescent violence exposure are expressed as relative risks (RR) and between adolescent violence exposure and
previous childhood victimization as odds ratios (OR) with 95% confidence intervals adjusted for sex, socioeconomic status, and the noninde-
pendence of twin observations.
H. L. Fisher et al.1410
Prevalence of adolescent victimization
Traditionally each type of victimization measured by the JVQ
is considered to have been experienced if a participant en-
dorses at least one item in the relevant section (Finkelhor
et al., 2011; Hamby et al., 2004). Using this scoring method,
we found that the estimated prevalence rates of victimization ex-
posure between 12 and 18 years in our cohort of 18-year-olds
were broadly comparable to rates reported by the UK NSPCC
in a cross-sectional survey of 11- to 17-year-olds (Radford
et al., 2011). We found slightly lower rates of maltreatment
and neglect in our sample, which likely reflects the fact that
the NSPCC survey inquired about victimization experiences
from birth onward, whereas we only inquired about experi-
ences occurring between ages 12 and 18 years. We found
higher rates of Internet/mobile phone victimization in our
sample, which could be accounted for by the fact that this
type of victimization occurs more often in adolescence than
in childhood and that it may be on the rise (our data collection
period occurred a few years after the NSPCC survey was con-
ducted; Jones, Mitchell, & Finkelhor, 2012). Despite dis-
crepancies in study design, the reported rates of the different
types of victimization in E-Risk and in the UK NSPCC sur-
vey are reassuringly similar.
As others have commented (Gilbert et al., 2009), a con-
cerning proportion of children and adolescents are victimized.
According to the survey administered to our research partici-
pants, close to 73% of adolescents have been exposed to some
form of victimization. According to our rating system of se-
verity, approximately one in three adolescents were classified
as having experienced at least one type of severe victimization
between ages 12 and 18 years (crime victimization, peer/sib-
ling victimization, Internet/mobile phone victimization, sex-
ual victimization, family violence, maltreatment, or neglect).
These results imply that victimization surveys capture both se-
vere as well as more minor experiences and that there is con-
siderable heterogeneity in the victimization experiences cap-
tured in surveys of youth. This discrepancy does not imply
that one estimate is better or worse. Both the survey and the
clinical interview parent measures that we drew upon were
themselves designed to be fit for their original purpose, and
had documented reliability and validity. Our offspring mea-
sure showed “hybrid vigor,” blending the best advantages of
both established approaches to documenting and studying vic-
timization. The higher survey count is required for estimating
the size of the problem for prevention planning. At the preven-
tion stage, before an incident occurs, it is not possible to know
how serious the incident will become, and all forms of victim-
Table 8. Within-pair polychoric correlations and genetic and environmental parameter estimates for univariate models
of severity of adolescent victimization
Correlation Standardized Variance Components
Type of Victimization r95% CI A295% CI C295% CI E295% CI
Any physical violence 0.22 0.00–0.46 0.35 0.14–0.56 0.43 0.35–0.51
Monozygotic .57 0.49–0.65
Dizygotic .46 0.36–0.56
Crime 0.30 0.04–0.56 0.23 0.01–0.46 0.47 0.39–0.55
Monozygotic .53 0.45–0.62
Dizygotic .39 0.28–0.49
Peer/sibling 0.34 0.07–0.61 0.12 0.00–0.35 0.54 0.45–0.62
Monozygotic .47 0.38–0.55
Dizygotic .29 0.19–0.40
Internet/mobile phone 0.25 0.00–0.65 0.17 0.00–0.51 0.57 0.45–0.70
Monozygotic .42 0.30–0.55
Dizygotic .30 0.14–0.46
Sexual 0.28 0.00–0.77 0.19 0.00–0.59 0.53 0.37–0.69
Monozygotic .47 0.30–0.63
Dizygotic .33 0.14–0.51
Family violence 0.17 0.00–0.52 0.42 0.12–0.72 0.41 0.30–0.52
Monozygotic .59 0.47–0.70
Dizygotic .50 0.36–0.64
Maltreatment 0.71 0.61–0.80 0.30 0.20–0.39
Monozygotic .72 0.62–0.82
Dizygotic .25 0.05–0.45
Neglect 0.47 0.00–0.96 0.29 0.00–0.73 0.25 0.13–0.37
Monozygotic .76 0.63–0.88
Dizygotic .52 0.30–0.75
Note: r, Polychoric correlation; CI, confidence interval; A, additive genetic; C, shared environment; E, nonshared environment.
Measuring adolescents’ exposure to victimization 1411
ization should be prevented. After an incident occurs, and
when researchers seek to carry out research to inform postvic-
timization treatment, it is necessary to discriminate the sever-
ity of the victimization. Such refinement to exposure measure-
ment is fundamental to a better understanding of the sequelae
of stress and to studies that seek to reliably identify and repli-
cate resiliency factors, whether in the genome, in supportive
relationships, or in the wider community (Barnett et al.,
1993; Monroe & Reid, 2008; Wertz & Pariante, 2014).
Revictimization from childhood through adolescence
A striking finding from our longitudinal analysis is that vic-
tims of childhood maltreatment are victimized again in ado-
lescence. This phenomenon of revictimization has been doc-
umented before (e.g., Widom, Czaja, & Dutton, 2008;
Radford et al., 2013), but much of the evidence about revic-
timization comes from cross-sectional studies that collect vic-
tims’ retrospective reports about their childhood experiences
or from studies that rely on cases identified by child protec-
tion agencies. Moreover, many studies tend to focus narrowly
on victims of specific experiences (e.g., sexual abuse or as-
sault). Using prospective data and guarding against single-
source reporting biases, here we show that victimization ex-
periences in childhood, as reported by primary caregivers,
are strongly predictive of self-reports of experiences of vic-
timization occurring in adolescence. Two findings stand
out. First, children who suffer one type of victimization are
likely to experience diverse forms of victimization at later
points in adolescence. Second, adolescents who are exposed
to severe physical violence are likely to have experienced di-
verse forms of victimization at earlier points in development.
These results point to three conclusions: (a) victimization
shows considerable continuity in the lives of children and
adolescents; (b) it does not remain true to type during the first
two decades of life; and (c) there does not appear to be a spe-
cific “gateway” via certain types of childhood victimization
to revictimization (Finkelhor, Ormrod, & Turner, 2007).
In terms of theory, the phenomenon of revictimization de-
mands more vigorous attention, not only so that we may un-
derstand how it comes about, but also so that we may be
better able to predict and prevent it. Multiple explanations
have been invoked to account for the phenomenon of revic-
timization, ranging from ecological systems theory (e.g., envi-
ronmental factors that create victimization vulnerability are
stable over victims’ lives) to psychological theories (e.g.,
learned helplessness), but few studies directly test multiple
and competing explanations.
In terms of research design, evidence of revictimization
calls attention to the need to reliably assess victimization ex-
periences across multiple developmental periods. For exam-
ple, much research attention is currently focused on biologi-
cal pathways linking early life stress to later health (Moffitt &
the Klaus Grawe ThinkTank, 2013). However, if revictimiza-
tion is as ubiquitous as our data show, it suggests that theoriz-
ing about sensitive periods needs to be balanced by the pos-
sibility that many of the effects attributable to the biological
embedding of early life stress may actually operate by in-
creasing exposure to additional forms of victimization and
creating a greater cumulative stress load.
In terms of preventing revictimization, our findings sug-
gest that efforts should be focused on children physically
abused by their caregivers. In our sample, over half of those
physically maltreated in childhood were exposed to severe
physical violence in adolescence. This is consistent with pre-
vious studies that have suggested maltreated children are par-
ticularly vulnerable to being victimized again and to experi-
ence other forms of victimization including physical assault
(Radford et al., 2013; Widom et al., 2008). Targeting inter-
ventions at children identified as having been maltreated by
an adult may thus reduce revictimization rates in adolescence,
but this hypothesis requires testing.
Polyvictimization among adolescents
Victimologists have noted that polyvictimization is a ne-
glected component of children’s victimization experiences,
and our findings amplify the call to broaden the focus in de-
velopmental psychopathology to the wider range of experi-
ences in which victimization events occur (Finkelhor et al.,
2007; Finkelhor, Turner, Ormrod, & Hamby, 2009). Evi-
dence of polyvictimization complicates research that seeks
to specify the effects of victimization and to develop interven-
tion and treatment programs. Most research that traces the
consequences of victimization, and most theoretical explana-
tions of such putative effects, are organized around a single
type of victimization (e.g., bullying, physical abuse, sexual
victimization, or neglect). However, the majority of severe
victimization experiences in our cohort occurred to youth
who experienced at least one other type of victimization.
Whereas some youth were only victimized by crime and other
youth were only exposed to violence between family mem-
bers, only a minority of youth solely experienced victimiza-
tion by peers, and practically no youth experienced instances
of maltreatment, sexual victimization, or neglect that were not
embedded in the context of other victimization experiences.
We see parallels between the challenge posed by polyvic-
timization and the now-familiar challenge posed by comor-
bidity. A persistent challenge in mental health research and
treatment is the coexistence of two or more conditions or dis-
orders; in community samples, almost half of individuals
who meet diagnostic criteria for one disorder meet diagnostic
criteria for another disorder, and the rates are higher in clinical
samples (Clark, Watson, & Reynolds, 1995; Newman et al.,
1996). Among the implications of such high rates of comor-
bidity is that (a) studying “pure” cases may offer an unrepre-
sentative picture of the disorder; (b) it is very difficult to iden-
tify disorder-specific causes, correlates, and sequelae when
disorders co-occur at such high rates; and (c) transdiagnostic
approaches to mental health deserve more attention (Caspi
et al., 2014). In parallel, we suggest three implications for vic-
timization research. First, most “pure” instances of victimiza-
H. L. Fisher et al.1412
tion may not represent the developmental landscape or ex-
periential history of victimization. Second, it will be chal-
lenging for researchers to identify correlates and sequelae
that are unique to particular victimization experiences.
When claims of specificity are sought, empirical verification
may need to include not only unexposed controls but also
controls that have been exposed to other forms of victimiza-
tion. Third, treatment options must attend to the ubiquitous
experience of polyvictimization, on top of pervasive revictim-
ization. It may be that the most expedient approach to interro-
gating the psychological and physical consequences of vic-
timization and to developing treatment options is to attend
to the cumulative experience of such exposures.
Genetic and environmental influences on victimization
Traditional research treats victimization as an environmental
exposure that threatens healthy development. However, the
unidirectional environment !person connection has been
challenged by evidence that many putative environmental ex-
posures are, to some extent, under genetic control (Plomin &
Bergeman, 1991). Specifically, genetically informative de-
signs (twin and adoption studies) that have been used to de-
compose variation in measures of the environment suggest
that differences between individuals in their child-rearing ex-
periences, in their relationships with peers, and even in their
risk of being exposed to stressful life events are partially heri-
table (Kendler & Baker, 2007; Plomin, 1994). Models in vic-
timology (e.g., lifestyle-exposure theory) recognize that vic-
timization is not evenly distributed in the population and
that there exist both high-risk places and high-risk persons.
Initial research into the genetic origins of adolescent victim-
ization showed that genetic factors may account for up to 50%
of the variation in criminal victimization (e.g., being threat-
ened with a knife; Beaver, Boutwell, Barnes, & Cooper,
2009; Beaver, Boutwell, Barnes, DeLisi, & Vaughn, 2013),
but victimization was difficult to disentangle from perpetra-
tion. Although some believe that seeking to identify the
causes of victimization is tantamount to blaming the victim,
we think that our twin study provides a unique vantage point
for exploring the causes of victimization by systematically
surveying multiple types of victimization. The results pointed
to a highly nuanced view of adolescent victimization, with
meaningful differences in the origins of different types of ex-
periences and with implications for understanding genetic
and environmental influences on adolescent development.
Turning to genetic effects, we found that peer/sibling and
crime victimization showed significant, but modest heritabil-
ities. Maltreatment was under stronger genetic influence, sug-
gesting that heritable characteristics of the victims influence
their likelihood of being physically maltreated in adoles-
cence, because these characteristics either evoke maltreat-
ment from adults or lead victims to end up in risky situations.
This is intriguing because a previous report from our group
found that maltreatment up to 12 years of age in the current
sample was largely influenced by environmental factors
shared between the twins, with almost no effect of genetic
factors (Jaffee, Caspi, Moffitt, & Taylor, 2004). It is difficult
to know whether this difference reflects a developmental shift
or a methodological difference. On the one hand, it is con-
ceivable that these forms of victimization during adolescence
are more often an adults’ response to adolescent instigation
than are such experiences in childhood. On the other hand,
our analysis of maltreatment up to age 12 years relied on inter-
views conducted with the twins’ mothers, whereas our analy-
sis of maltreatment in adolescence relied on interviews with
the twins themselves. This could potentially have led to the
different results we found regarding genetic influence on mal-
treatment in these two developmental periods. We are not
able to disentangle these alternative interpretations. Finally,
and in marked contrast to other forms of victimization, sexual
victimization in adolescence was not under significant ge-
netic influence and was mostly explained by unique environ-
mental risk factors, specific to each twin in a pair.
Turning to the environment, our findings contribute to a
nascent reassessment of socialization research pointing to the
importance of shared environmental influences on adolescent
development (Burt, 2009). A long-standing controversy set
in motion by behavioral geneticists is the claim that most of
the similarity between siblings is due to genetic effects and
that most of the differences between siblings are due to environ-
mental effects; that is, shared environmental experiences do not
create similarities between siblings. At its extreme, this evi-
dence has been used to challenge the notion that families and
communities are the most important factors in children’s devel-
opment (Harris, 1998). Our data suggest otherwise, and show
that family-wide shared risk factors accounted for approxi-
mately 10%–40% of the variance in adolescents’ victimization
experiences. One possible reason for our evidence that shared
environmental factors give rise to similar victimization experi-
ences may have to do with our measurement approach. Most
studies that have examined genetic influences on environ-
mental experiences have relied on self-reports of the environ-
ment. Estimates of genetic influence on measures of the envi-
ronment are much smaller in studies that have measured the
environment via more direct means (e.g., using observational
measures). This has led to the suggestion that the so-called na-
ture of nurture may to some extent reflect individuals’ heritable
propensity to perceive the environment in particular ways (Plo-
min, 1994). In our research, we have taken a middle course,
using an investigator-based system to determine and interpret
the environmental experience. A second possibility relates to
the fact that we measured severe victimization experiences.
Whereas mild exposures may be less influenced by variation
in the “average expectable” environment, grossly substandard
or dysfunctional environments may shape extreme experiences
(Scarr, 1993). Finally, and relatedly, whereas historically most
twin studies have not deliberately or adequately sampled the ex-
treme ranges of adversity, we sought to ensure that the full
range of contemporary Western children’s environments was
adequately sampled. Restriction of environmental range may
Measuring adolescents’ exposure to victimization 1413
have led previous studies to underestimate the importance of
shared environmental experiences.
The findings about genetic and environmental effects on
victimization have two implications. First, it appears that
much of the variation in victimization experiences is the result
of being exposed to risky environments, and not the product of
victims’ heritable characteristics. Identifying these environ-
mental risk factors should be a priority in order to hasten pre-
ventative interventions. Second, many adolescents growing
up in the same households and neighborhoods are differen-
tially exposed to victimization. Such discordance offers an
important opportunity for basic science. It is clear that etio-
logic studies that seek to understand environmental effects
on mental and physical health need to account for genetic
influences on environmental experiences. Studies of differ-
entially exposed twins provide unique purchase on uncon-
founded genetic and environmental effects on health (van
Dongen, Slagboom, Draisma, Martin, & Boomsma, 2013),
especially in relation to better understanding the conse-
quences of violence victimization.
Many young people who are mistreated by an adult, victim-
ized by bullies, criminally assaulted, or who witness domestic
violence react by developing behavioral, emotional, or learn-
ing problems. Increasingly, it is emerging that such adverse
experiences can lead to hidden physical alterations inside a
child’s body, alterations that may have adverse effects on
life-long health. This evidence has encouraged stress-biology
research and intervention science to join forces to tackle the
problem of victimization (Moffitt & the Klaus Grawe Think-
Tank, 2013). Both basic and translational science will be ad-
vanced by flexible and rich measurement tools that can be
widely applied in different settings. Here we have documented
the feasibility of combining a widely used self-report ques-
tionnaire with an investigator-based rating system to character-
ize victimization experiences. Too often research on important
problems is stymied by measurement impasses created by dis-
ciplinary preferences (survey researchers vs. clinical scien-
tists) and imposed by practical considerations (e.g., insuffi-
cient time). We hope that the combined approach that we
have introduced, borrowing on two valuable traditions for
measuring victimization, will offer converging opportunities
for scientists and practitioners of all stripes to coordinate their
Supplementary Material
To view the supplementary material for this article, please
Arseneault, L., Walsh, E., Trzesniewski, K., Newcombe, R., Caspi, A., &
Moffitt, T. E. (2006). Bullying victimization uniquely contributes to ad-
justment problems in young children: A nationally representative cohort
study. Pediatrics,118, 130–138.
Barnett, D., Manly, J. T., & Cicchetti, D. (1993). Defining child maltreat-
ment: The interface between policy and research. In D. Cicchetti &
S. L. Toth (Eds.), Child abuse, child development, and social policy
(pp. 7–74). Norwood, NJ: Ablex.
Beaver, K. M., Boutwell, B. B., Barnes, J. C., & Cooper, J. A. (2009). The bio-
social underpinnings to adolescent victimization: Results from a longitu-
dinal sample of twins. Youth Violence and Juvenile Justice,7, 223–238.
Beaver, K. M., Boutwell, B. B., Barnes, J. C., DeLisi, M., & Vaughn, M. G.
(2013). Exploring the genetic origins of adolescent victimization in a lon-
gitudinal sample of adoptees. Victims and Offenders,8, 148–163.
Bifulco, A., Bernazzani, O., Moran, P. M., & Jacobs, C. (2005). The Child-
hood Experiences of Care and Abuse Questionnaire (CECA.Q)—Valida-
tion in a community series. British Journal of Clinical Psychology,44,
Bifulco, A., Brown, G. W., & Harris, T. O. (1994). Childhood Experience of
Care and Abuse (CECA): A retrospective interview measure. Journal of
Child Psychology and Psychiatry,35, 1419–1435.
Bifulco, A., Brown, G. W., Lillie, A., & Jarvis, J. (1997). Memories of child-
hood neglect and abuse: Corroboration in a series of sisters. Journal of
Child Psychology and Psychiatry,38, 365–374.
Bifulco, A., Brown, G. W., Moran, P., Ball, C., & Campbell, C. (1998). Pre-
dicting depression in women: The role of past and present vulnerability.
Psychological Medicine,28, 39–50.
Bifulco, A., Brown, G. W., Neubauer, A., Moran, P., & Harris, T. (1994).
Childhood Experience of Care and Abuse (CECA) training manual. Lon-
don: University of London, Royal Holloway College.
Bowes, L., Maughan, B., Ball, H., Shakoor, S., Ouellet-Morin, I., Caspi, A.,
et al. (2013). Chronic bullying victimization across school transitions:
The role of genetic and environmental influences. Development and Psy-
chopathology,25, 333–346.
Brown, G. W. (1974). Meaning, measurement, and stress of life events. In
B. S. Dohrenwend & B. P. Dohrenwend (Eds.), Stressful life events: Their
nature and effects (pp. 217–243). New York: Wiley–Interscience.
Brown, G. W. (1989). Life events and measurement. In G. W. Brown & T. O.
Harris (Eds.), Life events and illness (pp. 3–45). London: Guilford Press.
Brown, G. W.,& Harris, T. O. (1978). Social origins of depression: A study of
psychiatric disorder in women. New York: Free Press.
Burt, S. A. (2009). Rethinking environmental contributions to child and ado-
lescent psychopathology: A meta-analysis of shared environmental influ-
ences. Psychological Bulletin,135, 608–637.
Caspi, A., Houts, R., Belsky, D. W., Goldman-Mellor, S., Harrington, H. L.,
Israel, S., et al. (2014). The “p factor”: One general psychopathology fac-
tor in the structure of psychiatric disorders? Clinical Psychological Sci-
ence,2, 119–137.
Caspi, A., Moffitt, T. E., Thornton, A., Freedman, D., Amell, J. W., Harring-
ton, H., et al. (1996). The life history calendar: A research and clinical as-
sessment method for collecting retrospective event-history data. Interna-
tional Journal of Methods in Psychiatric Research,6, 101–114.
Clark, L. A., Watson, D., & Reynolds,S. (1995). Diagnosis and classification
of psychopathology: Challenges to the current system and future direc-
tions. Annual Review of Psychology,46, 121–153.
Clemmons, J. C., Walsh, K., DiLillo, D., & Messman-Moore, T. L. (2007).
Unique and combined contributions of multiple child abuse types and abuse
severity to adult trauma symptomatology. Child Maltreatment,12, 172–181.
Cicchetti, D., & Tucker, D. (Eds.) (1994). Neural plasticity, sensitive periods,
and psychopathology [Special Issue]. Development and Psychopathol-
ogy,6, 531–814.
Cromer, B. (2011). Adolescent development. In R. M. Kliegman, R. E. Behr-
man, H. B. Jenson, & B. F. Stanton (Eds.), Nelson textbook of pediatrics
(19th ed.). Philadelphia, PA: Saunders Elsevier.
Currie, J., & Widom, C. S. (2010). Long-term consequences of child abuse
and neglect on adult economic well-being. Child Maltreatment,15,
Danese, A., Moffitt, T. E., Harrington, H., Milne, B. J., Polanczyk, G., Par-
iante, C. M., et al. (2009). Adverse childhood experiences and adult risk
factors for age-related disease: Depression, inflammation, and clustering
of metabolic risk markers. Archives of Pediatric and Adolescent Medi-
cine,163, 1135–1143.
Della Femina, D., Yeager, C. A., & Lewis, D. C. (1990). Child abuse: Adoles-
cent records versus adult recall. Child Abuse and Neglect,14, 227–231.
H. L. Fisher et al.1414
Dodge, K. A., Bates, J. E., & Pettit, G. S. (1990). Mechanisms in the cycle of
violence. Science,250, 1678–1683.
Dohrenwend, B. P. (2006). Inventorying stressful life events as risk factors
for psychopathology: Toward resolution of the problem of intracategory
variability. Psychological Bulletin,132, 477–495.
Fergusson, D. M., Boden, J. M., & Horwood, L. J. (2008). Exposure to child-
hood sexual and physical abuse and adjustment in early adulthood. Child
Abuse and Neglect,32, 607–619.
Finkehor, D., Hamby, S. L., Ormrod, R. K., & Turner, H. A. (2005). The Ju-
venile Victimization Questionnaire: Reliability, validity, and national
norms. Child Abuse and Neglect,29, 383–412.
Finkehor, D., Hamby, S. L., Turner, H. A., & Ormrod, R. K. (2011). The Ju-
venile Victimization Questionnaire (JVQ-R2) (2nd rev.). Durham, NH:
Crimes Against Children Research Center.
Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2007). Polyvictimization and
trauma in a national longitudinal cohort. Development and Psychopathol-
ogy,19, 149–166.
Finkelhor, D., Turner, H., Ormrod, R., & Hamby, S. L. (2009). Violence,
abuse, and crime exposure in a national sample of children and youth. Pe-
diatrics,124, 1411–1423.
Fisher, H. L., Bunn, A., Jacobs, C., Moran, P., & Bifulco, A. (2011). Concord-
ance between mother and offspring retrospective reports of childhood ad-
versity. Child Abuse and Neglect,35, 117–122.
Fisher, H. L., Jones, P. B., Fearon, P., Craig, T. K., Dazzan, P., Morgan, K.,
et al. (2010). The varying impact of type, timing and frequency of expo-
sure to childhood adversity on its association with adult psychotic disor-
der. Psychological Medicine,40, 1967–1978.
Gilbert, R., Widom, C. S., Browne, K., Fergusson, D., Webb, E., & Janson, S.
(2009). Burden and consequences of child maltreatment in high-income
countries. Lancet,373, 68–81.
Gorman, D. M. (1993). A review of studies comparing checklist and inter-
view methods of data collection in life event research. Behavioral Medi-
cine,19, 66–73.
Grant, K. E., Compas, B. E., Thurm, A. E., McMahon, S. D., & Gipson, P. Y.
(2004). Stressors and child and adolescent psychopathology: Measure-
ment issues and prospective effects. Journal of Clinical Child and Ado-
lescent Psychology,33, 412–425.
Groeneveld, L. P., & Giovannoni, J. M. (1977). Disposition of child abuse
and neglect cases. Social Work Research Abstracts,13, 24–30.
Hamby, S., Finkelhor, D., Ormrod, D., & Turner, H. (2004). The comprehen-
sive JV administration and scoring manual. Durham, NH: University of
New Hampshire, Crimes Against Children Research Centre.
Hardt, J., & Rutter, M. (2004). Validity of adult retrospective reports of ad-
verse childhood experiences: Review of the evidence. Journal of Child
Psychology and Psychiatry,45, 260–273.
Harris, J. R. (1998). The nurture assumption. New York: Free Press.
Hepp, U., Gamma, A., Milos, G., Eich, D., Ajdacic-Gross, V., Ro
¨ssler, W.,
et al. (2006). Inconsistency in reporting potentially traumatic events. Brit-
ish Journal of Psychiatry,188, 278–283.
Jaffee, S. R., Caspi, A., Moffitt, T. E., Polo-Tomas, M., Price, T. S., & Taylor,
A. (2004). The limits of child effects: Evidence for genetically mediated
child effects on corporal punishment but not on physical maltreatment.
Developmental Psychology,40, 1047–1058.
Jaffee, S. R., Caspi, A., Moffitt, T. E., Polo-Tomas, M., & Taylor, A. (2007).
Individual, family, and neighborhood factors distinguish resilient from
non-resilient maltreated children: A cumulative stressors model. Child
Abuse and Neglect,31, 231–253.
Jaffee, S. R., Caspi, A., Moffitt, T. E., & Taylor, A. (2004). Physical maltreat-
ment victim to antisocial child: Evidence of an environmentally mediated
process. Journal of Abnormal Psychology,113, 44–55.
Jones, L. M., Mitchell, K. J., & Finkelhor, D. (2012). Trends in youth Internet
victimization: Findings from three youth Internet safety surveys 2000–
2010. Journal of Adolescent Health,50, 179–186.
Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the
environment: A systematic review. Psychological Medicine,37, 615–626.
Lansford, J. E., Dodge, K. A., Pettitm, G. S., Batesm, J. E., Crozier, J., & Kap-
low, J. (2002). Long-term effects of early child physical maltreatment on
psychological, behavioral, and academic problems in adolescence: A 12-
year prospective study. Archives of Pediatrics and Adolescent Medicine,
156, 824–830.
Luciana, M. (2013). Adolescent brain development in normality and psycho-
pathology. Development and Psychopathology,25, 1325–1345.
Matta Oshima, K. M., Jonson-Reid, M., & Seay, K. D. (2014). The influence
of childhood sexual abuse on adolescent outcomes: The roles of gender,
poverty, and revictimization. Journal of Child Sexual Abuse,23, 367–
Moffitt, T. E., Caspi, A., Krueger, R. F., Magdol, L., Margolin, G., Silva, P.
A., et al. (1997). Do partners agree about abuse in their relationship? A
psychometric evaluation of interpartner agreement. Psychological As-
sessment,9, 47–56.
Moffitt,T. E., & the E-RiskStudy Team. (2002). Teen-aged mothers in contem-
porary Britain. Journal of Child Psychology and Psychiatry,43, 723–742.
Moffitt, T. E., & the Klaus Grawe ThinkTank. (2013). Childhood exposure to
violence and lifelong health: Clinical intervention science and stress-biology
research join forces. Development and Psychopathology,25, 1619–1634.
Monroe, S. M. (2008). Modern approaches to conceptualizing and measuring
human life stress. Annual Review of Clinical Psychology,4, 33–52.
Monroe, S. M., & McQuaid, J. R. (1994). Measuring life stress and assessing
its impact on mental health. In W. R. Avison & I. H. Gotlib (Eds.), Stress
and mental health: Contemporary issues and prospects for the future (pp.
43–73). New York: Plenum Press.
Monroe, S. M., & Reid, M. W. (2008). Gene-environment interactions in de-
pression: Genetic polymorphisms and life stress polyprocedures. Psycho-
logica Science,19, 947–956.
Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of
twins and families. Dordrecht: Kluwer Academic.
Newman, D. L., Moffitt, T. E., Caspi, A., Magdol, L., Silva, P. A., & Stanton,
W. R. (1996). Psychiatric disorder in a birth cohort of young adults: Prev-
alence, comorbidity, clinical significance, and new case incidence from
ages 11 to 21. Journal of Consulting and Clinical Psychology,64,552
Norman, R. E., Byambaa, M., De, R., Butchart, A., Scott, J., & Vos, T.
(2012). The long-term health consequences of child physical abuse, emo-
tional abuse, and neglect: A systematic review and meta-analysis. PLOS
Medicine,9, e1001349.
Odgers, C. L., Caspi, A., Bates, C. J., Sampson, R. J., & Moffitt, T. E. (2012).
Systematic social observation of children’s neighborhoods using Google
Street View: A reliable and cost-effective method. Journal of Child Psy-
chology and Psychiatry,53, 1009–1017.
Odgers, C. L., Caspi, A., Russell, M. A., Sampson, R. J., Arseneault, L., &
Moffitt, T. E. (2012). Supportive parenting mediates neighborhood so-
cioeconomic disparities in children’s antisocial behaviour from ages 5
to 12. Development and Psychopathology,24, 705–721.
Paykel, E. S. (2001). The evolution of life events research in psychiatry. Jour-
nal of Affective Disorders,62, 141–149.
Pelton, L. H. (1978). Child abuse and neglect: The myth of classlessness.
American Journal of Orthopsychiatry,48, 608–617.
Plomin, R. (1994). The nature of nurture. Newbury Park, CA: Sage.
Plomin, R., & Bergeman, C. S. (1991). The nature of nurture: Genetic influ-
ence on “environmental” measures. Behavioral and Brain Sciences,14,
Radford, L., Corral, S., Bradley, C., & Fisher, H. L. (2013). The prevalence
and impact of child maltreatment and other types of victimization in the
UK: Findings from a population surveyof caregivers, children and young
people and young adults. Child Abuse and Neglect,37, 801–813.
Radford, L., Corral, S., Bradley, C., Fisher, H., Bassett, C., Howat, N., et al.
(2011). Child abuse and neglect in the UK today. London: NSPCC.
Scarr, S. (1993). Biological and cultural diversity: The legacy of Darwin for
development. Child Development,64, 1333–1353.
Schilling, E. A., Aseltine, R. H., Jr., & Gore, S. (2008). The impact of
cumulative childhood adversity on young adult mental health: Measures,
models, and interpretations. Social Science and Medicine,66, 1140–1151.
Schwarz, N. (2007). Cognitive aspects of survey methodology. Applied Cog-
nitive Psychology,21, 277–287.
Shakoor, S., Jaffee, S., Andreou, P., Bowes, L., Ambler, A. P., Caspi, A.,
et al. (2011). Mothers and children as informants of bullying victimisa-
tion: Results from an epidemiological cohort of children. Journal of Ab-
normal Child Psychology,39, 379–387.
Straus, M. A. (1990). Measuring intrafamily conflict and violence: The Con-
flict Tactics (CT) scales. In M. A. Straus & R. G. Gelles (Eds.), Physical
violence in American families: Risk factors and adaptations toviolence in
8,145 families (pp. 403–424). New Brunswick, NJ: Transaction Press.
Takizawa, R., Maughan, B., & Arseneault, L. (2014). Adult health outcomesof
childhoodbullying victimization:Evidence froma five-decadelongitudinal
British birth cohort. American Journal of Psychiatry,171, 777–784.
Thornberry, T.P., Ireland, T. O., & Smith, C. A. (2001).The importance of tim-
ing: The varyingimpact of childhood and adolescent maltreatment on mul-
tiple problem outcomes.Development and Psychopathology,13, 957–979.
Measuring adolescents’ exposure to victimization 1415
Trouton, A., Spinath, F. M., & Plomin, R. (2002). Twins Early Development
Study (TEDS): A multivariate, longitudinal genetic investigation of lan-
guage, cognition and behaviour problems in childhood. Twin Research,
38, 444–448.
Trzesniewski, K. H., Moffitt, T. E., Caspi, A., Taylor, A., & Maughan, B.
(2006). Revisiting the association between reading achievement and anti-
social behaviour: New evidence of an environmental explanation from a
twin study. Child Development,77, 72–88.
van Dongen, J., Slagboom, P. E., Draisma, H. H. M., Martin, N. G., &
Boomsma, D. I. (2013). The continuing value of twin studies in the omics
era. Nature Reviews Genetics,13, 640–653.
ience: Longitudinal twin study. British Journal of Psychiatry,205, 281–282.
Widom, C. S. (1988). Sampling biases and implications for child abuse re-
search. American Journal of Orthopsychiatry,58, 260–270.
Widom, C. S., Czaja, S. J., Bentley, T., & Johnson, M. S. (2012). A prospec-
tive investigation of physical health outcomes in abused and neglected
children: New findings from a 30-year follow-up. American Journal of
Public Health,102, 1135–1144.
Widom, C. S., Czaja, S. J., & DuMont, K. A. (2015). Intergenerational trans-
mission of child abuse and neglect: Real or detection bias? Science,347,
Widom, C. S., Czaja, S. J., & Dutton, M. A. (2008). Childhood victimization
and lifetime revictimization. Child Abuse and Neglect,32, 785–796.
Williams, R. L. (2000). A note on robust variance estimation for cluster-cor-
related data. Biometrics,56, 645–646.
H. L. Fisher et al.1416
... En este sentido, se ha demostrado que el observar violencia entre los padres se relaciona con la violencia en la pareja (Solanke, 2018), con problemas escolares (Hong et al. 2021;Santoyo & Frías, 2014), con conductas de riesgo como el consumo y la adicción al juego (Li et al. 2021) y, en general, con la victimización o perpetración de violencia durante la adolescencia (Forke et al. 2018;Forke et al. 2021). Así, desde la perspectiva de la literatura sobre las experiencias adversas y el trauma durante la niñez (Abbassi & Aslinia, 2010;Fagan, 2020;Fisher et al. 2015;Lapshina & Stewart, 2021), las experiencias de VF (directa o indirecta) en adolescentes podrían aumentar la probabilidad de involucrarse en conductas delictivas, pero también de sufrir victimización fuera del hogar. En otras palabras, la VF experimentada como un evento traumático en la niñez y adolescencia, tiene el potencial de generar efectos en la vida de las personas, replicando los modelos de violencia como perpetradores y/o como víctimas. ...
... Este resultado es interesante pues al ser delitos de distinta naturaleza podría pensarse que no hay una conexión directa; sin embargo, es justo este hallazgo lo que fortalece la concepción de polivictimización utilizado en este trabajo. Como ya se mencionaba, para ser polivíctima no es necesario sufrir el mismo tipo de victimización, partiendo, por un lado, de que todas las violencias se encuentran conectadas (Azaola, 2012;Hamby et al. 2012) y, por otro lado, de que la violencia que sufren los jóvenes suele provenir de múltiples perpetradores y ocurrir en múltiples escenarios (Frías & Finkelhor, 2017;Fry et al. 2021;Turner et al. 2016;Fisher et al. 2015), lo cual es justamente una señal de una condición de mayor vulnerabilidad (Larraín & Fuentealba, 2021). Si se tiene en consideración que el delito de mayor incidencia en México es el robo sin violencia, los resultados pueden interpretarse en el sentido de que la conexión entre VF y victimización, y en consecuencia la polivictimización, tiene que ver con una mayor exposición a las experiencias traumáticas que ocurren de por sí en el entorno (Finkelhor et al. 2011). ...
Full-text available
En el presente trabajo se analizan los efectos de la violencia familiar (VF) en adolescentes. Concretamente, se evalúa si existen diferencias entre los que la han experimentado y los que no, en relación con la probabilidad de ser víctima fuera del hogar y, en consecuencia, de ser polivíctima. El análisis se realiza con datos obtenidos mediante la aplicación de la tercera edición del instrumento International Self-Report Delinquency Study, ISRD-3, en una muestra de adolescentes de nivel secundaria en Guadalajara, México. Los resultados muestran que la violencia familiar directa (VFD) se relaciona con una mayor probabilidad de experimentar victimización fuera del hogar, mientras que los efectos de la violencia familiar indirecta (VFI) se observan sólo en las mujeres. Asimismo, al valorar si existen diferencias en la probabilidad de consumo de sustancias entre no víctimas, víctimas de VF, víctimas de delitos fuera del hogar y polivíctimas, se confirma la importancia de identificar la condición de víctima y los efectos diferenciados en función del género. Finalmente, se discuten las implicaciones de los resultados y se realizan algunas recomendaciones de intervención para las víctimas de VF.
... Vulnerability to interpersonal victimization appears to be transferred to the next generation and various mechanisms could be at play in this transfer, including vicarious learning, heritability, and environmental factors. As such, a longitudinal twin study (Fisher et al., 2015) reported significant but modest heritability for maltreatment and neglect, crime, and peer and siblings victimization in their sample. However, victimization experiences appeared to be more strongly explained by nonshared risky environments than by heritable characteristics (Fisher et al., 2015). ...
... As such, a longitudinal twin study (Fisher et al., 2015) reported significant but modest heritability for maltreatment and neglect, crime, and peer and siblings victimization in their sample. However, victimization experiences appeared to be more strongly explained by nonshared risky environments than by heritable characteristics (Fisher et al., 2015). Again, the long-lasting impacts of CM on mental health, relationships, parenting, and the parenting context, could explain this transfer of risk from mothers to EA (Marshall et al., 2022), but studies directly exploring mechanisms of transfer in diverse populations are needed to confirm these hypothesized pathways. ...
Intergenerational continuity of child maltreatment (CM) is a well-documented phenomenon of concern; however, its effects on the child’s level of exposure to CM, as well as subsequent trauma exposure and adult functioning remain undocumented. The present study aimed to further explore the intergenerational effects of CM by comparing emerging adults (EA; ages 18–25) on their exposure to CM, adult victimization, and psychological functioning according to their mother’s CM histories. One hundred and eighty-five mothers and their EA completed independently an online survey measuring sociodemographics, material deprivation, CM, adult victimization, and psychological functioning. The participating dyads (primarily White and female-identifying) were recruited online through social media, universities, and advertisements in non-profit organizations throughout Canada. Findings revealed that maternal histories of CM were associated with increased neglectful and physically abusive acts endured in childhood for maltreated EA. Maternal histories of CM, regardless of the EA’ victimization status, were associated with a higher EA’ number of adulthood interpersonal—but not non-interpersonal—traumas experienced. While a maternal history of CM was a risk factor for intimate partner violence (IPV) in maltreated EA, it was protective for non-maltreated EA. Maltreated EA with maltreated versus non-maltreated mothers presented more psychological difficulties, but only if they also reported material deprivation. Practitioners working with children at-risk or exposed to CM should document parents’ histories of CM and take that into account in their assessments and intervention practices. This study also provides further evidence to support social policies targeting the family system as a whole.
... Poly-victimization is exposure to multiple forms of victimization and was originally defined as experiencing at least one victimization more than the mean number among the victim group as a whole (Finkelhor et al., 2005b(Finkelhor et al., , 2007, but there are various methods to construct poly-victimization, which are noted in the "poly-victimization" section later. Limited previous research on adolescent poly-victimization in the United Kingdom has explored the phenomenon at different geographical levels such as county (e.g., Jackson et al., 2016Jackson et al., , 2017 and country (e.g., Fisher et al., 2015;Matthews et al., 2020;Radford et al., 2013Radford et al., , 2014, but previous research at the country level is still scarce and tends to use cross-sectional data (Fisher et al., 2015;Matthews et al., 2020;Radford et al., 2013Radford et al., , 2014. Many countries, including the United Kingdom, have experienced major drops in crime since the 1990s (Tseloni et al., 2010) and there is no previous research in the United Kingdom that explored the trends in poly-victimization of adolescents over time using longitudinal datasets. ...
... Poly-victimization is exposure to multiple forms of victimization and was originally defined as experiencing at least one victimization more than the mean number among the victim group as a whole (Finkelhor et al., 2005b(Finkelhor et al., , 2007, but there are various methods to construct poly-victimization, which are noted in the "poly-victimization" section later. Limited previous research on adolescent poly-victimization in the United Kingdom has explored the phenomenon at different geographical levels such as county (e.g., Jackson et al., 2016Jackson et al., , 2017 and country (e.g., Fisher et al., 2015;Matthews et al., 2020;Radford et al., 2013Radford et al., , 2014, but previous research at the country level is still scarce and tends to use cross-sectional data (Fisher et al., 2015;Matthews et al., 2020;Radford et al., 2013Radford et al., , 2014. Many countries, including the United Kingdom, have experienced major drops in crime since the 1990s (Tseloni et al., 2010) and there is no previous research in the United Kingdom that explored the trends in poly-victimization of adolescents over time using longitudinal datasets. ...
Full-text available
This study examined the change in the prevalence of adolescent poly-victimization and individual and area predictors of poly-victimization in England and Wales. The national representative longitudinal Offending, Crime and Justice Survey (2003–2006) was analyzed with data from 2,066 adolescents, aged between 10 and 18 years (mean ± SD at Time 1 = 13.08 ± 2.01), using multilevel multinomial logit models. Findings revealed that the majority of the adolescents (41.6%, 48.5%, 54.6%, 61.6%, respectively) did not experience victimization between 2003 and 2006. However, 28.3%, 25.9%, 19.5%, and 14.5% of the adolescents experienced poly-victimization (experiencing more than or equal to two types of victimizations), with a decrease of 13.8% over the 4-year period. Furthermore, some adolescents were consistent poly-victims, meaning they were poly-victims in all years that they participated in the survey. In particular, 3.57% of the adolescents who participated in the four waves of the survey were poly-victims in all years; 7.41% of the adolescents who participated in three of the four waves of the survey were poly-victims in all years; and 25.79% of the adolescents who participated in two of the four waves of the survey were poly-victims in both years. Statistically significant predictors of poly-victimization included having parents who have been in trouble with the police, offending, participating in community-related activities, being a boy, not managing income well, and living in an urban or deprived area. Offending had the greatest impact on poly-victimization. Findings highlight that adolescent poly-victimization in England and Wales decreased between 2003 and 2006 but some adolescents were more likely to experience poly-victimization due to individual, familial, and area characteristics. The findings therefore indicate that a holistic approach is needed to reduce adolescent poly-victimization and suggest that targeting area deprivation should be the priority.
... [1][2][3][4][5][6] Over the past several years in particular, the pervasiveness of sexual violence has received a great deal of research and media attention, illuminating the deleterious effects of such experiences on survivors. More specifically, women with a history of nonconsensual sexual experiences (NSEs) are more likely to be revictimized, [7][8][9] experience future life stressors, 7,8,10 engage in risky sexual behaviors, 11 be diagnosed with mental or physical health disorders, 10,[12][13][14] and experience difficulty with sexual function and relational intimacy. [15][16][17][18] NSEs have also been shown to negatively impact survivors' views of themselves as sexual beings and their conceptualization of sex and sexuality more generally. ...
... [1][2][3][4][5][6] Over the past several years in particular, the pervasiveness of sexual violence has received a great deal of research and media attention, illuminating the deleterious effects of such experiences on survivors. More specifically, women with a history of nonconsensual sexual experiences (NSEs) are more likely to be revictimized, [7][8][9] experience future life stressors, 7,8,10 engage in risky sexual behaviors, 11 be diagnosed with mental or physical health disorders, 10,[12][13][14] and experience difficulty with sexual function and relational intimacy. [15][16][17][18] NSEs have also been shown to negatively impact survivors' views of themselves as sexual beings and their conceptualization of sex and sexuality more generally. ...
Introduction Sexual violence (SV) has been a prevalent issue on college campuses for decades. Researchers, universities, and legislators have tried to understand and prevent it. Despite these efforts, 25% of female and 6% of male undergraduate students will experience a nonconsensual sexual experience (NSE) as a student. An immense amount of research has been conducted on the prevalence, effects, resources for, and outcomes of SV over the last few decades. Objectives The current paper aims to compile and summarize the extant literature on undergraduate student disclosures of sexual violence. The objective is to provide a comprehensive review of the research. Methods A literature search was performed using the terms sexual violence, NSE, undergraduate students, informal and formal reporting, and disclosure. Results Disclosure patterns and outcomes for survivors vary widely based on individual factors including type of disclosure source (ie, informal or formal reporting), disclosure recipient response, previous history of NSEs, and personal identity (ie, gender identity, sexual orientation, race/ethnicity). Though there are many formal resources (ie, police, Title IX), the majority of survivors report to informal sources (ie, family or friends). In addition to researching survivors’ experiences and rates of disclosures, research also evaluates how disclosure recipients perceive their response to a survivor's disclosure, their likelihood of receiving a disclosure based on their own individual identities, and how the disclosure impacts the recipient and their relationship with the survivor. Conclusion The individualized response and decision to report SV has made prevention and the creation of effective resources difficult. As there are so many individual factors to consider when evaluating how or whether a NSE will be disclosed, future research should consider these individual differences and use them to create more effective reporting sources and resources.
... In this regard, the fact that CTQ, but not ITEC, subscales cross-loaded onto Deprivation may suggest that interview measures that assess multiple features of maltreatment are better able than self-reports to differentiate between the domains of abuse and neglect. This possibility is in line with several researchers' contention that indepth interview measures that allow for probing and clarification offer greater precision in their assessment of environmental experience (Bifulco & Schimmenti, 2019;Fisher et al., 2015;Lobbestael et al., 2009). ...
Full-text available
Background: Investigating different approaches to operationalizing childhood adversity and how they relate to transdiagnostic psychopathology is relevant to advance research on mechanistic processes and to inform intervention efforts. To our knowledge, previous studies have not used questionnaire and interview measures of childhood adversity to examine factor-analytic and cumulative-risk approaches in a complementary manner. Objective: The first aim of this study was to identify the dimensions underlying multiple subscales from three well-established childhood adversity measures (the Childhood Trauma Questionnaire, the Childhood Experience of Care and Abuse Interview, and the Interview for Traumatic Events in Childhood) and to create a cumulative risk index based on the resulting dimensions. The second aim of the study was to examine the childhood adversity dimensions and the cumulative risk index as predictors of measures of depression, anxiety, and psychosis-spectrum psychopathology. Method: Participants were 214 nonclinically ascertained young adults who were administered questionnaire and interview measures of depression, anxiety, psychosis-spectrum phenomena, and childhood adversity. Results: Four childhood adversity dimensions were identified that captured experiences in the domains of Intrafamilial Adversity, Deprivation, Threat, and Sexual Abuse. As hypothesized, the adversity dimensions demonstrated some specificity in their associations with psychopathology symptoms. Deprivation was uniquely associated with the negative symptom dimension of psychosis (negative schizotypy and schizoid symptoms), Intrafamilial Adversity with schizotypal symptoms, and Threat with depression, anxiety, and psychosis-spectrum symptoms. No associations were found with the Sexual Abuse dimension. Finally, the cumulative risk index was associated with all the outcome measures. Conclusions: The findings support the use of both the empirically-derived adversity dimensions and the cumulative risk index and suggest that these approaches may facilitate different research objectives. This study contributes to our understanding of the complexity of childhood adversity and its links to different expressions of psychopathology.
... Longitudinal twin studies have shed light on the mechanisms underlying these pathways by showing that genetic and environmental factors independently contribute to the associations between emotional symptoms, social isolation and peer victimisation (e.g., Matthews et al., 2016;Schaefer et al., 2018). Social isolation and peer victimisation do not occur randomly and are both heritable (30% to 70%), suggesting that genetic factors contribute to individual differences in these social difficulties (Fisher et al., 2015;Johansson et al., 2022;Matthews et al., 2016;Veldkamp et al., 2019). Evidence suggests a genetic overlap between emotional symptoms and negative peer experiences, meaning that a genetic disposition for emotional symptoms enhances the risk of being exposed to negative social experiences via gene-environment correlations (Brendgen, Zheng, Vitaro, Dionne, & Boivin, 2022). ...
Background: Emotional symptoms, such as anxiety and depressive symptoms, are common during adolescence, often persist over time, and can precede the emergence of severe anxiety and depressive disorders. Studies suggest that a vicious cycle of reciprocal influences between emotional symptoms and interpersonal difficulties may explain why some adolescents suffer from persisting emotional symptoms. However, the role of different types of interpersonal difficulties, such as social isolation and peer victimisation, in these reciprocal associations is still unclear. In addition, the lack of longitudinal twin studies conducted on emotional symptoms during adolescence means that the genetic and environmental contributions to these relationships during adolescence remain unknown. Methods: Participants (N = 15,869) from the Twins Early Development Study completed self-reports of emotional symptoms, social isolation and peer victimisation at 12, 16 and 21 years old. A phenotypic cross-lagged model examined reciprocal associations between variables over time, and a genetic extension of this model examined the aetiology of the relationships between variables at each timepoint. Results: First, emotional symptoms were reciprocally and independently associated with both social isolation and peer victimisation over time, indicating that different forms of interpersonal difficulties uniquely contributed to emotional symptoms during adolescence and vice versa. Second, early peer victimisation predicted later emotional symptoms via social isolation in mid-adolescence, indicating that social isolation may constitute an intermediate pathway through which peer victimisation predicts longer-term emotional symptoms. Finally, individual differences in emotional symptoms were mostly accounted for by non-shared environmental factors at each timepoint, and both gene-environment and individual-specific environmental mechanisms were involved in the relationships between emotional symptoms and interpersonal difficulties. Conclusions: Our study highlights the necessity to intervene early in adolescence to prevent the escalation of emotional symptoms over time and to consider social isolation and peer victimisation as important risk factors for the long-term persistence of emotional symptoms.
... There is also evidence that prenatal exposures act cumulatively to predict childhood psychopathology (Roffman et al., 2021), as do neighbourhood risk factors (Theall, Drury, & Shirtcliff, 2012). It is therefore important to understand the cumulative impacts of a range of both proximal (direct) and distal (indirect) risk factors that commonly co-occur, and which may be causally linked or reflect shared background factors (e.g. both maternal complications and childhood victimisation are more common in individuals from socioeconomically deprived areas; Fisher et al., 2015;Kim et al., 2018). Further, the impact of both proximal and distal risk factors may be mediated through the same or similar biological pathways (e.g. ...
Background and hypothesis: Psychotic disorders are associated with a growing number of recognized environmental exposures. Cumulative exposure to multiple environmental risk factors in childhood may contribute to the development of different patterns of schizotypy evident in early life. Hypotheses were that distinct profiles of schizotypy would have differential associations with a cumulative score of environmental risk factors. Study design: We prospectively examined the relationship between 19 environmental exposures (which had demonstrated replicated associations with psychosis) measured from the prenatal period through to age 11 years, and 3 profiles of schizotypy in children (mean age = 11.9 years, n = 20 599) that have been established in population data from the New South Wales-Child Development Study. Multinomial logistic regression was used to examine associations between membership in each of 3 schizotypy profiles (true schizotypy, introverted schizotypy, and affective schizotypy) and exposure to a range of 19 environmental risk factors for psychosis (both individually and summed as a cumulative environmental risk score [ERS]), relative to children showing no risk. Results: Almost all environmental factors were associated with at least 1 schizotypy profile. The cumulative ERS was most strongly associated with the true schizotypy profile (OR = 1.61, 95% CI = 1.52-1.70), followed by the affective (OR = 1.33, 95% CI = 1.28-1.38), and introverted (OR = 1.32, 95% CI = 1.28-1.37) schizotypy profiles. Conclusions: Consistent with the cumulative risk hypothesis, results indicate that an increased number of risk exposures is associated with an increased likelihood of membership in the 3 schizotypy profiles identified in middle childhood, relative to children with no schizotypy profile.
Over the past 10 years, the general factor of psychopathology, p, has attracted interest and scrutiny. We review the history of the idea that all mental disorders share something in common, p; how we arrived at this idea; and how it became conflated with a statistical representation, the bifactor model. We then leverage data from the Environmental Risk Longitudinal Twin Study to examine the properties and nomological network of different statistical representations of p. We found that p performed similarly regardless of how it was modeled, suggesting that if the sample and content are the same, the resulting p factor will be similar. We suggest that the meaning of p is not to be found by dueling over statistical models but by conducting well-specified criterion-validation studies and developing new measurement approaches. We outline new directions to refresh research efforts to uncover what all mental disorders have in common.
Child sexual exploitation and abuse (CSEA) affects all children, but research on the needs and experiences of boys is lacking, support services are limited, and workers lack specialized training to meet their specific needs. This paper explores the perspectives and experiences of 404 Frontline Support Workers providing services to children with CSEA experiences in seven countries, considering trends and implications for boys. A mixed-methods online survey of 121 questions explores characteristics of cases, perceived vulnerabilities, and gender-based challenges in CSEA response. A descriptive analysis of survey data, disaggregated by country, was conducted, allowing for a discussion of broad themes and trends. Despite differences, participants described similar vulnerabilities for boys across these contexts, including poverty as well as sex and sexuality-related taboos, stigmas, and other gendered beliefs, which were perceived to not only increase vulnerability to CSEA but also complicate disclosure in all countries. The implications of these findings on service delivery and recommendations are discussed.
Preface. List of Figures. List of Tables. 1. The Scope of Genetic Analyses. 2. Data Summary. 3. Biometrical Genetics. 4. Matrix Algebra. 5. Path Analysis and Structural Equations. 6. LISREL Models and Methods. 7. Model Fitting Functions and Optimization. 8. Univariate Analysis. 9. Power and Sample Size. 10. Social Interaction. 11. Sex Limitation and GE Interaction. 12. Multivariate Analysis. 13. Direction of Causation. 14. Repeated Measures. 15. Longitudinal Mean Trends. 16. Observer Ratings. 17. Assortment and Cultural Transmission. 18. Future Directions. Appendices: A. List of Participants. B. The Greek Alphabet. C. LISREL Scripts for Univariate Models. D. LISREL Script for Power Calculation. E. LISREL Scripts for Multivariate Models. F. LISREL Script for Sibling Interaction Model. G. LISREL Scripts for Sex and GE Interaction. H. LISREL Script for Rater Bias Model. I. LISREL Scripts for Direction of Causation. J. LISREL Script and Data for Simplex Model. K. LISREL Scripts for Assortment Models. Bibliography. Index.
Men and women throughout the ages have often felt overwhelmed by life’s demands. They have long ruminated about the implications these adversities have for their physical health and mental well-being (Rosen, 1959). Interestingly, the words of people expressing such concerns across the different ages portray similar themes of “stress, distress, and dis-ease” (Rees, 1976; see also Hinkle, 1977; Lazarus & Folkman, 1984; Monroe & Johnson, 1992). Overall, it seems clear that people are prone to perceive their worlds as filled with almost constant, if not excessive, demands, and that they frequently employ such perceptions to explain a great variety of psychological and physical phenomena.