Cumulative Prevalence of Arrest From Ages 8 to 23 in a National Sample
Robert Brame, Michael G. Turner, Raymond Paternoster and Shawn D. Bushway
Pediatrics; originally published online December 19, 2011;
DOI: 10.1542/peds.2010-3710

The online version of this article, along with updated information and services, is
located on the World Wide Web at:
http://pediatrics.aappublications.org/content/early/2011/12/14/peds.2010-3710

PEDIATRICS is the official journal of the American Academy of Pediatrics. A monthly
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ARTICLE

Cumulative Prevalence of Arrest From Ages 8 to 23 in
a National Sample
AUTHORS: Robert Brame, PhD,a Michael G. Turner, PhD,a
Raymond Paternoster, PhD,b and Shawn D. Bushway, PhDc
aDepartment of Criminal Justice and Criminology, University of
North Carolina at Charlotte, Charlotte, North Carolina;
bDepartment of Criminology and Criminal Justice, University of
Maryland at College Park, College Park, Maryland; and cSchool of
Criminal Justice, University at Albany, State University of New
York, Albany, New York

KEY WORDS
crime, criminals, arrest, health risk factors
ABBREVIATIONS
MAR—missing at random
NLSY97—National Longitudinal Survey of Youth 1997
Each of the authors made substantive intellectual contributions
to this study. Dr Turner procured the data with the approval of
the University of North Carolina-Charlotte Institutional Review
Board; and Drs Turner, Paternoster, and Bushway collaborated
closely on the data analysis with Dr Brame who assumed
primary responsibility for that analysis. All authors participated
in writing and revising the article through multiple drafts. All
authors concur in their approval of the article’s contents and
the findings reported herein.
www.pediatrics.org/cgi/doi/10.1542/peds.2010-3710
doi:10.1542/peds.2010-3710
Accepted for publication Sep 9, 2011
Address correspondence to Robert Brame, PhD, Department of
Criminal Justice and Criminology, University of North Carolina at
Charlotte, 9201 University City Blvd, Charlotte, NC 28223. E-mail:
rbrame@uncc.edu
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2012 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have
no financial relationships relevant to this article to disclose.

WHAT’S KNOWN ON THIS SUBJECT: Although there is some older
literature examining how arrest prevalence accumulates through
adolescence and adulthood, there is no contemporary research
examining the arrest histories of a representative sample of
American youth.
WHAT THIS STUDY ADDS: Using a contemporary US sample of
adolescents and young adults, we estimated the cumulative
arrest prevalence through age 23. The results suggest
a substantial increase in the cumulative prevalence of arrest
since the 1960s.

abstract
OBJECTIVE: To estimate the cumulative proportion of youth who selfreport having been arrested or taken into custody for illegal or
delinquent offenses (excluding arrests for minor traffic violations)
from ages 8 to 23 years.
METHODS: Self-reported arrest history data (excluding arrests for
minor traffic violations) from the National Longitudinal Survey of
Youth 1997 (N = 7335) were examined from 1997 to 2008.
RESULTS: By age 18, the in-sample cumulative arrest prevalence rate
lies between 15.9% and 26.8%; at age 23, it lies between 25.3% and
41.4%. These bounds make no assumptions at all about missing
cases. If we assume that the missing cases are at least as likely to
have been arrested as the observed cases, the in-sample age-23
prevalence rate must lie between 30.2% and 41.4%. The greatest
growth in the cumulative prevalence of arrest occurs during late
adolescence and the period of early or emerging adulthood.
CONCLUSIONS: Since the last nationally defensible estimate based on
data from 1965, the cumulative prevalence of arrest for American youth
(particularly in the period of late adolescence and early adulthood) has
increased substantially. At a minimum, being arrested for criminal activity signifies increased risk of unhealthy lifestyle, violence involvement,
and violent victimization. Incorporating this insight into regular clinical
assessment could yield significant benefits for patients and the larger
community. Pediatrics 2012;129:21–27

PEDIATRICS Volume 129, Number 1, January 2012

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For American youth, involvement in
criminal behavior is a well-known risk
factor for adverse health, social, academic, occupational, and economic outcomes.1–10 Youth with arrest records
have lower levels of earnings, longer
bouts with unemployment, greater
work instability, diminished educational levels, and a greater risk of
destructive family conflicts.11,12 Youth
with arrest records are also at greater
risk of both violence involvement and
violent victimizations.13,14 Once youth
are formally processed by the criminal justice system, they may find
themselves at an early age with diminished personal, social, human, and
cultural capital, a process described by
Hagan15,16 as “criminal embeddedness.” Often ensnared in criminal social networks as the result of 1 or
more arrests, youth may find it hard to
escape, leading to additional crime as
adults. Indeed, there is a risk that the
collateral social and personal damage
created by an arrest mortgages the
futures of young people as they make
the transition to adulthood.7,17–19
Moreover, rates of involvement with
the criminal justice system after conviction, from probation to incarceration,
have dramatically increased (three- to
fourfold) since the 1970s (S. Shannon,
PhD, C. Uggen, PhD, M. Thompson, PhD,
J. Schnittker, PhD, M. Massoglia, PhD,
unpublished data). What is not clear
is whether the prevalence of arrest for
nontraffic offenses has changed over
time. The best available benchmark
for assessing this question was provided by Christensen20 in the 1960s.
Christensen’s20 work forecast the future prevalence of arrest for nontraffic offenses assuming that 1965
conditions remained stable. Based on
this work, the best available estimate
of the percent of the population arrested at least once for a nontraffic
offense by age 23 is 22%. Yet there has
never been a national estimate of the
22

BRAME et al

cumulative prevalence rate of arrest
for criminal activity of adolescents and
young adults by using individual-level
survey data from a nationally representative sample.1,21,22

METHODS
The National Longitudinal Survey of
Youth 1997 (NLSY97) is a prospective
household-based longitudinal study of
American youth between the ages of
12 and 16 years in 1997. It is based on
a stratified multistage cluster probability sampling design. The initial stage
of data collection included a screening interview to determine whether
age-eligible youth resided in selected
households. These screening interviews were successfully completed in
94% of the targeted households. Based
on the information obtained from the
screening interviews, a total of 9808
youth were included in the NLSY97
targeted sample, and a subset of 8984 of
these youth actually participated in the
first interview.23
The targeted sample of 9808 youth
comprised 2 mutually exclusive groups:
(1) a “cross-sectional” sample (N =
7335) and (2) a supplemental oversample of minority youth (N = 2473).
The cross-sectional sample was designed to be a self-weighting representation of US households with
adolescents between the ages of 12
and 16 on December 31, 1996. Because
6% of the selected households did not
complete an initial screening interview, it is not possible to definitively
say that this representativeness was
actually achieved but the sample certainly includes a broad cross-section of
American youth in the late 1990s.
We examined self-reports of arrest
from the cross-sectional NLSY97 sample of 7335 youth. The vast majority of
these youth (N = 6748; 92%) participated in the first round of surveys
that were conducted in 1997 and 1998.
This leaves a residual sample of 587

individuals who did not participate at
the first round. Since the first round of
data collection, 11 additional (approximately annual) surveys have been
conducted (through 2008) and made
publicly available. The NLSY97 survey
methodology allowed individuals who
participated at the first wave to miss
1 or more waves of data collection and
still remain in the study, but individuals who did not participate at the first
wave did not participate at any of the
subsequent waves.
At the first wave of data collection, each
study participant was asked the following question: “Have you ever been
arrested by the police or taken into
custody for an illegal or delinquent offense (do not include arrests for minor
traffic violations)?” Participants who answered “yes” were asked a follow-up
question about their age (in years) at
the time of their first arrest. Our analysis
makes the assumption that these arrest
events occurred at the midpoint of the
age reported by the respondent.
At each of the follow-up interviews,
study participants were asked a slightly
different question: “Since the date of
last interview on [date of last interview], have you been arrested by the
police or taken into custody for an illegal
or delinquent offense (do not include
arrests for minor traffic violations)?”
Participants who answered “yes” were
also asked to recall the month and year
of the first arrest since the last interview. Based on the participants’
answers to the first-wave and follow-up
questions, we compiled a history of
what is known about each individual’s
“ever-arrested” status at each age
from age 8 until 2008 when the most
recent available round of data were
collected. At each age, we divided the
NLSY97 cross-sectional sample (N =
7335) into 3 groups: (1) those who have
not been arrested yet; (2) those who
have been arrested; and (3) those whose
arrest status cannot be determined at

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ARTICLE

that age (because of missing data).
Table 1 summarizes this information
from ages 8 to 23 (beginning at age 23,
the youngest participants’ arrest status cannot be determined; therefore,
after age 23, respondents are progressively censored).
As Table 1 shows, some of the arrest
data are missing at each age covered
by the survey. It is possible, however,
to obtain interval estimates that definitely contain the in-sample prevalence
rate at each age.24 At each age, we estimated the upper bound of the insample prevalence rate by a 2-step
process: (1) add the number of people
who had been arrested by that age to
the number of missing cases at that
age, and (2) divide that sum by the total
number of people (N = 7335). This calculation is based on the extreme assumption that all of the missing people
have been arrested. The lower bound of
the in-sample prevalence rate is estimated by dividing the number of people who had been arrested by that age
by the total number of people (N =
7335). Symmetrically, this calculation
makes the assumption that none of the
missing people have been arrested.
It is, of course, unlikely that the missing
cases behave in such uniform ways. For
this problem, many researchers would

be willing to invoke the assumption that
the missing cases are missing at random (MAR).24–26 The MAR estimates are
obtained by dividing the number of
cases who have ever been arrested at
a particular age by the number of valid
cases at that age (ever arrested + never
arrested, excluding the missing cases).
The final step of the analysis involves the
estimation of 95% confidence limits
around the outer bound estimates and
the MAR point estimates. We estimated
these intervals by drawing 1000 bootstrap samples and calculating the SD of
each parameter’s bootstrap distribution.26 One potential problem with this
approach is that the NLSY97 is based
on a cluster sampling design. Although
the point and interval estimates obtained from the cross-sectional sample
are valid, the SEs and confidence intervals obtained under the assumption
of simple random sampling will generally be too small. To correct for this
problem, we conservatively assumed
a SE design effect multiplier of 2.0.23
The resulting design-effect corrected
confidence intervals are only trivially
different from those obtained by conventional bootstrap SEs. Confidence
intervals presented in this article are
based on the design-effect corrected
SEs.

TABLE 1 Summary of Ever-Arrested Status (N = 7335)
Ages 8–15

Ages 16–23

Age, y

Yes

No

Missing

Age, y

Yes

No

Missing

8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5

0
4
4
10
10
25
25
60
60
146
151
262
286
448
495
638

6716
6712
6712
6706
6706
6691
6691
6656
6656
6569
6555
6438
6408
6232
6167
6006

619
619
619
619
619
619
619
619
619
620
629
635
641
655
673
691

16.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
21.5
22.0
22.5
23.0
23.5

731
837
929
1050
1164
1270
1374
1458
1539
1598
1660
1719
1778
1816
1858
1895

5890
5765
5647
5498
5369
5237
5102
4978
4871
4773
4665
4571
4474
4388
4299
4193

714
733
759
787
802
828
859
899
925
964
1010
1045
1083
1131
1178
1247

PEDIATRICS Volume 129, Number 1, January 2012

RESULTS
Figure 1 displays the main analysis
results from the NLSY97 (ages 8–23)
alongside the 1965 point estimates
(indicated by black squares) presented
by Christensen 20 (ages 10–23). It is
important to note that Christensen20
graphed the population male and female growth curves separately. We obtained his point estimates by visually
inspecting his graph and assuming
that male and female participants each
comprise 50% of the population. The
MAR point estimate of the cumulative
prevalence of arrest is documented by
a solid black circle at each age. Moving
outward, the next set of lines represents the 95% confidence interval for
the MAR estimate (our uncertainty due
to sampling error if the usual MAR assumption is valid). The next set of lines
represents the outer bounds of the insample prevalence estimates based
on the assumptions that the missing
cases were either all arrested (upper
bound) or all not arrested (lower
bound). Finally, the interval endpoints
represent the design-effect corrected
95% confidence limits for the outer
bound prevalence estimates.
It is useful to compare the MAR estimates
from the NLSY97 to Christensen’s 20
estimates from 1965. The 1965 estimates are higher than those in the
NLSY97 during early adolescence
(through age 15). At ages 16 to 18, there
is considerable parity between the 2
measurements; the 1965 estimates lie
within the 95% confidence limits of the
MAR estimate. From ages 19 to 23, the
1965 estimates appear to increase at
a much slower pace than the NLSY97
MAR estimates. By age 23, the NLSY97
MAR estimate of 30.2% is over a third
higher than the 1965 estimate of 22%.
And the 1965 estimate lies clearly outside the 95% confidence interval for the
MAR estimate.
There are 2 potential concerns with this
comparison: (1) we do not know what

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23

FIGURE 1
Analysis results from the NLSY97 and the 1965 point estimates.

the confidence intervals for the 1965
estimates are, and (2) the MAR assumption is untestable.25,26 Concerning the first issue, although we know
Christensen’s20 estimates are subject
to some degree of error, we are uncertain how much error there is. It is
worth noting, however, that if the error
in Christensen’s20 estimates was as
large as the sampling error for the
MAR estimate in the NLSY97, there

would still be a significant difference
between the 2 estimates.
Considering the validity of the MAR
assumption, it is clearly possible that
individuals who are missing or who
choose not to answer some or all of the
arrest questions may not be comparable to those who provide valid data. To
address this concern, we consider the
assumption that the MAR estimate is
a lower bound measure of the true

arrest prevalence rate. Because research suggests that active offenders
are underrepresented in broad population surveys such as the NLSY97, this
assumption seems plausible.14,22,27,28
Figure 2 presents this comparison assuming that the MAR estimate and
lower 95% confidence limit capture the
lower bounds for what we would accept as a credible arrest prevalence
rate in the NLSY97. From age 19 on, the

FIGURE 2
Arrest Rates and 95% confidence intervals assuming the lower bound is equal to MAR.

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NLSY97 is clearly pointing to a higher
arrest prevalence rate than what was
estimated in 1965.
A second feature of both Figs 1 and 2 is
the growth in the cumulative prevalence of arrest that occurs during adolescence and into early adulthood. An
observable pattern of growth begins
around age 12 when the MAR estimate
is virtually 0. By age 18, the MAR rate
is 17.8% and is very similar to the
1965 estimate. This is a steep rate of
growth and reflects the relatively common experience of criminal involvement
and arrests for criminal involvement
during the adolescent years in the
United States (both in the 1960s and
today).
Finally, although growth continues into
adulthood, the rate of growth compared
with the adolescent years becomes
slower. Visually, the figure appears to
be taking on a flatter trajectory as
individuals enter their 20s. In fact, the
rate of change of the MAR estimate
between ages 16 and 17 [(14.1 2 11.0)/
11.0 = 28.2%] is greater than the rate of
change of this same estimate between
ages 22 and 23 [(30.2 2 28.4)/28.4 =
6.3%]. But the rate of growth in early
adulthood appears to be on steeper
trajectory in recent years than it was
in 1965.

DISCUSSION
Early arrest research conducted by
Christensen20 in the 1960s estimated
that if 1965 conditions remained stable,
∼22% of the US population would be
arrested for a nontraffic offense by age
23.20 More recent research has been
based on individual criminal history
searches for populations in particular
locales.29–32 But estimation of cumulative arrest prevalence rates based on
these methods are problematic. Calculations based on aggregate statistics from government agencies rely on
strong and hard-to-test assumptions.20
And, criminal history searches also
PEDIATRICS Volume 129, Number 1, January 2012

have well-documented ambiguities and
difficulties including record accuracy,
satisfactory name-matching, jurisdictional boundaries, change in jurisdiction as a result of a family move, and
confidentiality of juvenile records.33,34
There are, therefore, 3 significant gaps
in the extant literature: (1) lack of contemporary evidence about cumulative
arrest prevalence among young people;
(2) estimation of cumulative arrest
prevalence for a national sample of
specific individuals rather than aggregated data sources; and (3) estimates
based on measurement methods that
are not marred by the inadequacies of
criminal history searches.
This study examined self-reports of
arrest from a broad, contemporary US
sample during adolescence and young
adulthood to address these gaps. Arrest
experiences were measured via selfreports of study participants. The analysiswasdesignedtoanswer thefollowing
2 questions: (1) what proportion of
American youth is arrested by age 23,
and (2) how does that proportion accumulate as the population moves
through adolescence and into early
adulthood? Of course, the self-report
method is accompanied by its own difficulties including whether perceptions
of arrest are accurate, whether timing
can be adequately recalled, and whether
respondents are making an effort to
be truthful in reporting their experiences.14,28
Our primary conclusion is that arrest experiences are common among
American youth (most likely on the order
of ∼1 out of 3 by age 23). In fact, our MAR
estimate of 30.2% is substantively
higher than the 22% previously forecast by Christensen20 in the 1960s. And
there are a number of compelling reasons to believe that the prevalence of
arrest may have increased over this
time period. The criminal justice system
has clearly become more aggressive
in dealing with offenders (particularly

those who commit drug offenses and
violent crimes) since the 1960s (S.
Shannon, PhD, C. Uggen, PhD, M.
Thompson, PhD, J. Schnittker, PhD, M.
Massoglia, PhD, unpublished data,
2011).35 In addition, there is some evidence that the transition from adolescence to adulthood has become
a longer process; more youth are involved in postsecondary education,
whereas marriages, childbearing, and
the beginning of careers are occurring later in life, perhaps contributing
to a longer period of “adolescence”
today than in years past.36 Because
adolescence has traditionally been a
period of greater offending activity, a
lengthening of adolescence (a period
psychologists refer to as “emerging
adulthood”) might be partially responsible for an increase in the prevalence of arrest experiences.

CONCLUSIONS
Given the considerable risks signaled
by arrest experiences and the developmental handicaps that arrest may
create for youth and young adults, these
findings raise important questions
about consequences and opportunities
for intervention by pediatricians. For
example, there is little doubt that
pediatricians are concerned about the
long-term developmental health of
youth, including their involvement in
violent and antisocial behavior.37,38 We
have identified more than 400 articles
published in Pediatrics since 1948 that
pertain to delinquency and problem
social behaviors (cigarette smoking,
drinking, drug use, bullying, and various forms of abuse). However, although we have prevalence estimates
for the risk of victimization,39 exposure
to violence,40 insomnia,41 and sun exposure,42 until now there simply was
no contemporary national prevalence
estimate of the risk of a criminal arrest
for American youth.35 This is in spite of
the fact that having an arrest record is

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25

known to be an important risk marker
for violence involvement, violent victimizations, and an unhealthy and unsafe lifestyle.
In addition, the experience of being
arrested may be more than a marker
insofar as it has its own effects both at
the time of the arrest and in the months
and years after. The primary routes for
youth to be successful today are to
cultivate conventional social networks
and social capital through education
and securing stable employment.
Youth with an arrest record, however,
may fail to secure this long-term
beneficial form of capital and as a result may be effectively shut out of
educational and employment opportunities. Youth with arrest records
have been shown to have unstable and
abbreviated employment histories,
are less likely to stay in high school and
enroll in college, are at greater risk of
failing to obtain other markers of adult
success such as having their own
home and a stable relationship with
a partner, and are more likely to have

These are the very types of risk factors
that any pediatrician could easily
identify with a risk assessment examination in any normal office visit,
and physicians may be the first

noncaregiver to view both the composite landscape of risk factors confronting a child and the medical
consequences of those factors.37,38 The
early efforts of pediatricians could play
a pivotal role as early intervention has
repeatedly been shown to be the most
effective avenue for dealing with delinquent behavior. What Yancy45 noted
15 years ago is no less true today:
“Because the pediatrician has a continuous relationship with children and
their families, he or she can direct
them to the appropriate facility and
encourage and support them in carrying out the treatment.” Based on our
findings, a significant percentage of
American youth will experience at least
1 arrest for a nontraffic offense by age
23, and the greatest increase in the risk
of this experience occurs during late
adolescence. Timely intervention by
pediatricians in the lives of these youth
may be an important opportunity to
move young people onto a path toward
safer, healthy, productive, and successful lives.

8. Piquero AR, Daigle LE, Gibson C, Piquero
NL, Tibbetts SG. Are life-course-persistent
offenders at risk for adverse health outcomes? J Res Crime Delinq. 2007;44(2):
185–207
9. Laub JH, Vaillant GE. Delinquency and mortality: a 50-year follow-up study of 1,000
delinquent and nondelinquent boys. Am J
Psychiatry. 2000;157(1):96–102
10. Sweeten G, Bushway SD, Paternoster R.
Does dropping out of school mean dropping into delinquency? Criminology. 2009;47
(1):47–91
11. Wiesner M, Kim HK, Capaldi DM. History of
juvenile arrests and vocational career outcomes for at-risk young men. J Res Crime
Delinq. 2010;47(1):91–117
12. Nagin DS, Farrington DP, Moffitt TE. Lifecourse trajectories of different types of
offenders. Criminology. 1995;33(1):111–139
13. McCord J, Widom CS, Crowell NA. Juvenile
Crime, Juvenile Justice. Washington, DC:
National Academy Press; 2001

14. Blumstein A, Cohen J, Roth JA, Visher CA.
Criminal Careers and “Career Criminals”.
Washington, DC: National Academy Press;
2001
15. Hagan J. The social embeddedness of crime
and unemployment. Criminology. 1993;31(4):
465–492
16. Hagan J, Dinovitzer R. Collateral consequences of imprisonment for children,
communities, and prisoners. Crime Justice.
1999;26:121–162
17. Sampson RJ, Laub JH. Crime in the Making:
Pathways and Turning Points Through Life.
Cambridge, MA: Harvard University Press;
1993
18. Bernburg JG, Krohn MD. Labeling, life
chances, and adult crime: the direct and
indirect effects of official intervention in
adolescence on crime in early adulthood.
Criminology. 2003;41(4):1287–1318
19. Sweeten G. Who will graduate? Disruption of
high school education by arrest and court
involvement. Justice Q. 2006;23(4):462–480

medical problems and adult drug and
alcohol abuse.43,44
There are early risk factors that appear
for delinquency (risk factors that can
easily be identified by pediatricians).
For example, we know that the following
factors put youth at risk for laterand ongoing delinquent behavior if they appear before age 12: hyperactivity or
poor concentration; delayed language
development; low academic performance; poor relationships with parents
and general home discord; antisocial
parents; a broken home; harsh, punitive, or inconsistent discipline from
parents; family violence; child abuse or
neglect; diminished executive function
or other cognitive deficits; sleep disorders; low birth weight or other
perinatal complications; teenage parenthood; and early aggressive or bullying behavior.13,37,38

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27

Cumulative Prevalence of Arrest From Ages 8 to 23 in a National Sample
Robert Brame, Michael G. Turner, Raymond Paternoster and Shawn D. Bushway
Pediatrics; originally published online December 19, 2011;
DOI: 10.1542/peds.2010-3710
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