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Is Violence Bad for Your Health?An Assessment of ChronicDisease Outcomes in a NationallyRepresentative SampleJennifer M. Reingle, Wesley G. Jennings, Alex R. Piquero &Mildred M. Maldonado-MolinaPublished online: 22 May 2012.

To cite this article: Jennifer M. Reingle, Wesley G. Jennings, Alex R. Piquero & Mildred M.Maldonado-Molina (2014) Is Violence Bad for Your Health? An Assessment of Chronic DiseaseOutcomes in a Nationally Representative Sample, Justice Quarterly, 31:3, 524-538, DOI:10.1080/07418825.2012.689315

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Is Violence Bad for Your Health? AnAssessment of Chronic Disease Outcomesin a Nationally Representative Sample

Jennifer M. Reingle, Wesley G. Jennings, Alex R.Piquero and Mildred M. Maldonado-Molina

Longitudinal offending research has grown substantially in the last two dec-ades. Despite this increased scholarly attention, longitudinal investigations ofthe effects of offending on physical health have not kept pace. Acknowledgingthe intersections of criminology, criminal justice, and public health, this studyexamines the relationship between violent offending and chronic diseasesamong a nationally representative longitudinal sample of young adults. Resultssuggest that variation across offender typologies (i.e. adolescence-limited,adult-onset, and consistent violence during youth and young adulthood) signifi-cantly predicts experiencing chronic disease in early adulthood, with the risk

Jennifer M. Reingle is a post-doctoral research associate in the Department of Epidemiology at theUniversity of Florida. She earned her doctoral degree in epidemiology from the University of Flor-ida in August 2011. She has published more than 20 peer-reviewed articles, and her major researchinterests include the relationship between prescription drug use and violence, longitudinal dataanalysis, and health disparities in substance use. Wesley G. Jennings is an assistant professor in theCollege of Behavioral and Community Sciences in the Department of Criminology and has a courtesyassistant professor appointment in the Department of Mental Health Law and Policy at the Univer-sity of South Florida. He received his doctorate degree in criminology from the University of Floridain 2007. He has published over 60 peer-reviewed articles, and his major research interests includelongitudinal data analysis, semi-parametric group-based modeling, sex offending, gender, andrace/ethnicity. He is also currently a co-investigator on a National Institute of Justice funded pro-ject examining sex offender recidivism and collateral consequences. In addition, he is a recentrecipient of the 2011 William S. Simon/Anderson Publishing Outstanding Paper Award from theAcademy of Criminal Justice Sciences. Alex R. Piquero is ashbel smith professor in the Program inCriminology in the School of Economic, Political, and Policy Sciences at the University of Texas atDallas, Adjunct Professor Key center for Ethics, Law, Justice, and Governance, Griffith UniversityAustralia, and the co-editor of the Journal of Quantitative Criminology. His research interestsinclude criminal careers, criminological theory, and quantitative research methods. He hasreceived several research, teaching, and service awards and is a fellow of both the American Soci-ety of Criminology and Academy of Criminal Justice Sciences. Mildred M. Maldonado-Molina is anassociate professor in the Department of Health Outcomes and Policy and the Institute for ChildHealth Policy at the University of Florida. Her research interests include examining health dispari-ties in alcohol and drug use among adolescents, alcohol policy research, and longitudinal meth-ods. Correspondence to: J. Reingle, Department of Epidemiology, University of Florida, 101 S.Newell Rd., Room 3101, Gainesville, FL 32610, USA. E-mail: jreingle1@ufl.edu

JUSTICE QUARTERLY, 2014Vol. 31, No. 3, 524–538, http://dx.doi.org/10.1080/07418825.2012.689315

� 2012 Academy of Criminal Justice Sciences

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being the most pronounced among those individuals, who demonstrateviolence continuity. Study limitations and policy implications are discussed.

Keywords offending; violence; chronic disease; longitudinal

Introduction

Despite significant growth in longitudinal research on offending in an array of

disciplines, most notably in criminology and public health (Laub & Vaillant,2000; Odgers et al., 2007; Piquero, Daigle, Gibson, Piquero, & Tibbetts, 2007;

Piquero, Farrington, Nagin, & Moffitt, 2010; Samuelson, Hodgins, Larsson,Larm, & Tengstrom, 2010; Shepherd, Farrington, & Potts, 2002, 2004), longitu-

dinal examinations of the adverse effects of life-course offending on physicalhealth remain the exception (Piquero, Shepherd, Shepherd, & Farrington,

2011). This is unfortunate since the long-term effects of involvement in vio-lence on individual health constitute a serious public health problem. In fact,

Healthy People 2010 has identified adverse health outcomes related to violenceand exposure to violence as a health promotion priority (USDHHS, 2000). Fur-ther, although the importance of violence prevention is widely acknowledged

(Prothrow-Stith, 1995), only a relatively small body of literature examining therelationship between violent behavior and chronic disease exists (Ben-Shlomo

& Kuh, 2002). Recognizing this deficiency in the literature, a number of schol-ars have called for research exploring the potential for a behavior (e.g. vio-

lence) to (biologically) influence serious chronic health problems (e.g.diabetes, heart disease, and cancer, etc.) (Ben-Shlomo & Kuh, 2002; Crofford,

2007; McEwen, 1998). As such, the current study offers an investigation intohow the continuity of violence during an individual’s juvenile and young adultcriminal career influences the prevalence and frequency of chronic diseases in

early adulthood.

Prior Research on the Violence-Disease Relationship

There is reason to believe that chronic disease is biologically linked to violent

behavior. Prior research has demonstrated that the cumulative effect of vio-lence, and the inevitably related risk of victimization (Jennings, Piquero, &

Reingle, 2012), is associated with physiological changes in brain and body func-tion (Crofford, 2007; McEwen, 1998). Assuming that violence is a stressfulexperience, the likelihood of stress-related symptoms (e.g. in the form of

chronic disease) is elevated (Crofford, 2007). Specifically, Crofford (2007) hasindicated that cumulative lifetime stress affects hormonal and autonomic

activity, or in other words experiencing violence may amplify the potential forengendering a general state of vulnerability or susceptibility for manifesting

somatic syndromes that affect physical health. In addition, Heim et al. (2000)

VIOLENCE & CHRONIC DISEASE 525

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have provided evidence suggesting that violence may be linked with neuroen-docrine dysregulation.

Continual exposure to stressful situations has also been demonstrated toincrease levels of the hormone cortisol (McEwen, 1998), and these repeated

stressful experiences have been linked to a number of physical diseases(Bjorntorp, 1990; McEwen, 1998; Raikkonen, Keltikangas-Jarvinen, Adlercreutz,& Hautenen, 1996). For example, Bjorntorp (1990) reported that socioeco-

nomic factors and stress may act as “secondary” risk factors that elevate thelikelihood of heart disease resulting from an interaction with or simultaneous

manifestation of more immediate triggers (e.g. hyperglycemia or high bloodpressure). In another study, Raikkonen et al. (1996) examined the relationship

between stress-related variables and physical health problems among a sampleof 90 middle-aged males. Their results indicated that behavioral indicators of

a stress-inducing lifestyle (e.g. type A personality, hostility, and anger) weredetermined to be significant predictors of hyperinsulinemia, hyperglycemia,

dyslipidemia, hypertension, and increased abdominal obesity. Hostility, specifi-cally, was a robust predictor of high blood pressure and diabetes.

Violence has been linked to hospitalization (Piquero et al., 2011), disability

(Piquero et al., 2011; Ratcliffe, Enns, Belik, & Sareen, 2008), cardiovascularillness (Shepherd, Farrington, & Potts, 2002), self-reported poor health in

adolescence (Boynton-Jarrett, Ryan, Berkman, & Wright, 2007), and variousother physical and mental health conditions (McEwen, 1998; Repetti, Taylor, &

Seeman, 2002; Shepherd et al., 2004). For instance, Hertzman, Power, Mat-thews, and Manor (1999) explored the utility of an integrated model evaluating

the factors that predict physical-health outcomes at age 33 among membersof the 1958 British Birth Cohort. The results from a series of multivariateregression models revealed that self-reported physical health at age 33 was

predicted by both early and later stage life-course factors. Similarly, Shepherdet al. (2002) analyzed data from male participants in the Cambridge Study in

Delinquent Development (CSDD) and reported an association between respira-tory tract illnesses and convictions specifically and antisocial behavior more

generally. Most recently, using the same CSDD study participants as Shepherdet al. (2002), Piquero et al. (2011) investigated the relationship between

offending trajectories and registered disabilities and hospitalization in middleage (age 48). Piquero et al.’s results suggested that high-rate chronic offend-

ers exhibited the greatest likelihood for experiencing both outcomes, and thatthis relationship maintained its significance after accounting for individual andenvironmental risk. Acknowledging these important findings, most of the prior

studies in this limited body of literature have evaluated the cumulative risk ofviolence throughout adolescence and early adulthood on chronic disease out-

comes later in life (Hertzman et al., 1999; Piquero et al., 2011). Thus, consid-ering the fact that the cumulative effects of stress have an immediate effect

on the brain and the body (McEwen, 1998), there is potential for an earlyemergence of chronic disease among those involved in violence.

526 REINGLE ET AL.

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Theoretical Relevance of Violence Typologies

Violent youth are not a homogeneous group, and risk behaviors are known tocluster and change as adolescents’ age. This differential participation in vio-

lence could result in different outcomes. As such, it was necessary to catego-rize offenders based upon their cumulative experience in violent behavior. In

this vein, Moffitt’s (1993) dual taxonomy provides a potentially useful theoreti-cal framework for the current study’s focus on typologies.

Moffitt (1993) posits that there are two qualitatively distinct offendingtypologies: life-course-persistent (LCP) offenders and adolescence-limited (AL)offenders. LCP offenders are characterized by neuropsychological deficits and

neural abnormalities, which may explain the difficult temperament and behav-ioral issues exhibited among members of this group. According to Moffitt

(1993), children with neuropsychological problems initiate a negative cycle ofcumulative risk, affecting the function of the family unit, peer groups, and

lifestyle choices. Antisocial behavior becomes a persistent characteristic ofthese individuals. In contrast, AL offenders do not enter life with neurological

deficits and because of this absence of neurological risk ALs are able todevelop social skills, prosocial behaviors, and basic academic skills. AL youthgenerally participate in crime due to the “maturity gap”, which results from a

conflict in biological and social maturity levels that is met with similarly situ-ated peer contexts. In many instances, antisocial behavior among AL youth is

learned from LCP youth in a process referred to as social mimicry. Neverthe-less, AL offenders eventually mature and desist from offending in early

adulthood, whereas LCP offenders remain on a stable antisocial path that per-sists over the life-course. A third typology has also been proposed (e.g. adult-

onset offending), which suggests that, among a small group of individuals,criminal behavior initiates in adulthood (Kratzer & Hodgins, 1999; Zara &

Farrington, 2009, 2010). The inherent link proposed in Moffitt’s taxonomybetween neurological and biological functioning and criminal behavioralongside the prior studies examining the association between offending and

adverse physical health (Piquero et al., 2011; Shepherd et al., 2002) suggestschronic disease as a potential outcome for LCP violent offenders (see also

Moffitt, 2006; Piquero et al., 2007).

The Current Study

With attention to the increasing interdisciplinary cross pollination of crimino-logical, criminal justice, and public health research (Akers & Lanier, 2009), the

purpose of the current study is to examine how the continuity of violenceduring an individual’s juvenile and young adult criminal career affects chronic

diseases in early adulthood. To our knowledge, only a few studies have exam-ined the health outcomes of criminal behavior involvement, but these studies

tend to employ some combination of official records for medical and offending

VIOLENCE & CHRONIC DISEASE 527

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information and/or have not examined health outcomes that could be, in part,due to involvement in violent behavior. Because violent, property, and drug

offenders may have different outcomes, we investigated self-reported chronicdisease outcomes for violent behavior involvement only in young adulthood,

among a nationally representative sample from the USA. In accordance withthe findings from Piquero et al. (2011), we hypothesize that chronic offenders(operationalized as those who offend continuously from adolescence into

young adulthood) will be most likely to have reported chronic diseases in theirlate 20s and early 30s.

Methods

Data

The National Longitudinal Study of Adolescent Health (Add Health) panel studyconducted from 1994 (Wave I) through 2008 (Wave IV), when participant ages

ranged from 11 to 32 (ages 11-19 at baseline), is used for the current study.Eighty communities were selected to insure demographic representativeness

(ethnic composition, region of the country, urbanicity, school size, and schooltype) of students in the USA. All students who were enrolled in the school and

were present on the survey day were eligible for participation in the study.Approximately 200 students were randomly selected from strata of grade and

sex, resulting in a final cohort sample of 9,421 adolescents. After excludingthose who did not respond to the violence measures at all four waves, 8,273

participants remained in the dataset for the current analysis.Descriptive information is detailed in Table 1. The vast majority of partici-

pants were nonviolent at all four waves (87%). Of those who reported violence,

AL violence was the most common (13% of the full sample), followed by adult-onset violence (4%), and consistent violence (e.g. involvement in violence

during their juvenile and young adult criminal careers, 1%). Furthermore,although the majority of participants did not report experiencing any chronic

diseases, 38% did report at least one of the following chronic diseases: heartdisease (.8%); cancer (1.3%); diabetes (2.6%); high cholesterol (7.6%); hyper-

tension (10.2%); asthma, chronic bronchitis, or emphysema (15.4%); andmigraine headaches (14.3%). Regarding the sample demographics, 45% weremale and 56% were white, black (20%), or Hispanic (15%). The mean age at

Wave I was 15.3 (SE = .02).

Measures

Violence

Violence was measured using three items assessed across each of the fourwaves of data collection. The three items included: In the past 12 months,

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how many times have you (1) hurt someone badly enough that he or she

needed care from a doctor or nurse?; (2) pulled a knife or gun on someone?;and (3) shot or stabbed someone? Response options for the “hurting someone

badly enough to need care from a doctor or nurse” item included, “0 times,”“1-3 times,” and “4 or more times,” while having shot or stabbed someone

and pulling a knife or gun on someone were measured dichotomously (“yes” or“no”) at each wave. For standardization purposes across the three items the

first item, “hurting someone badly enough to need care from a doctor ornurse,” was dichotomized. Following this re-coding procedure, the three

dichotomous items were combined at each wave and then dichotomized inorder to yield one binary violence measure (e.g. reported violence, or did notreport violence) for each of the four waves. Specifically, those who were non-

violent at all four waves were categorized as “non-violent, all waves.” Thosewho reported violence at Wave I or II (or both), but not Waves III or IV (when

participants were young adults, ages 21-26), were categorized as “adoles-cence-limited violence” offenders. Those who reported violence at Waves III

or IV (ages 21-26), but not Waves I or II (ages 15-16), were categorized as“adult-onset violence” offenders. Finally, those who were violent at all four

waves, or reported violence at either Wave I or II (or both) as well as Waves IIIor IV were categorized as “consistently violent” offenders.

Table 1 Sample description, add health (n = 8,273)

Measures n (%)

Waves I-IV

Nonviolent, all waves 6,879 (87.0)

AL violence 1,049 (12.7)

Adult-onset violence 287 (3.5)

Consistent violence 58 (.7)

Chronic disease (Wave IV) 3,592(38.13)

Cancer 123 (1.31)

High cholesterol 718 (7.62)

Hypertension 964 (10.23)

Diabetes 248 (2.63)

Heart disease 71 (.75)

Asthma, chronic bronchitis, or emphysema 1,447 (15.36)

Migraine headaches 1,346 (14.29)

Demographics (Wave I)

Male 4,279(45.42)

White or Caucasian 5,336 (56.64)

African-African or black 1,969 (20.90)

Hispanic or Latino 1,448 (15.37)

Other race 663 (7.04)

Age (mean, SE) 15.26 (.02)

VIOLENCE & CHRONIC DISEASE 529

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Chronic disease diagnosesAt Wave IV, respondents were asked to report illnesses, medications, and phys-

ical disabilities. The following measure was used to assess each respondent’sdiagnosis of several chronic diseases, “Has a doctor, nurse, or other health

practitioner ever told you that you have or had [a disease]?” The specific dis-eases included: (1) cancer or lymphoma or leukemia (not skin cancer, exceptmelanoma); (2) high blood cholesterol or triglycerides or lipids; (3) high blood

pressure or hypertension (if female, when you were not pregnant); (4) highblood sugar or diabetes (if female, when you were not pregnant); (5) heart dis-

ease; (6) asthma, chronic bronchitis, emphysema; and/or (7) migraine head-aches. Each specific disease diagnosis was coded dichotomously. A “number of

chronic diseases” variety index was also created by summing the individualdiagnosis variables. Informed by prior research (Bjorntorp, 1990; McEwen,

1998; Raikkonen et al., 1996) and due to the relative absence of literatureexamining the relationship between various chronic diseases and violence

typologies, all physical chronic disease outcomes provided in the data wereincluded in the models to explore their relationship with violence typologies.

Covariates. BMI, smoking, and physical activity

Current smoking, Body Mass Index (BMI) (P30 as identified by height andweight measurements), and physical activity were included as covariates due

to their strong association with multiple chronic diseases (Blair & Brodney,1999; Nelson et al., 1994). The physical activity measure was derived from a

series of self-reported physical activities including: bicycling, skateboarding,dancing, hiking, hunting, skiing, roller blading, playing racquet sports, gymnas-

tics, weight lifting, aerobics, football, hockey, walking, rugby, basketball,lacrosse, or doing yard work in the past week. Those whose physical activitywere in the “moderate” to “high” range for any of these activities were con-

sidered to be “physically active.” Those who self-reported infrequent partici-pation in these activities were considered “non-active”.

Analytic Strategy

Logistic regression

Logistic regression was used to estimate the effect of violence typologies oneach chronic disease. This procedure compares membership in each violence

typology to a reference category (e.g. consistently nonviolent). Clusteredrobust standard errors were estimated to produce error estimates that takeinto account the autocorrelation due to the sampling design. STATA 11 was

used to conduct all regression analyzes.

Poisson regressionTo test a dose-response relationship between violence typologies and the num-

ber of chronic diseases a participant has been diagnosed with, a Poisson

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regression model was used.1 The categorical measures representing the variousviolence typologies were included as the primary independent variables of

interest, controlling for race, age, and gender.The first stage of model selection involved a bivariate test of the association

of the violence typologies with the chronic disease variables. All variables thatwere not marginally related (p < .10) to any chronic disease in the bivariateanalyses were not included in the multivariate model. Post-hoc significance

tests were conducted using ANOVA and Chi-Squared analyses.

Results

The bivariate logistic regression model demonstrated that consistent violence

(OR = 2.55; 95% CI 1.18-5.52) and AL violence (OR = 1.23; 95% CI 1.00-1.51)were significantly associated with any chronic disease diagnose. Similarly, the

bivariate Poisson regression model also indicated that consistent violence (inci-dence rate ratio [IRR] = 1.90; 95% CI 1.31-2.75) and AL violence (IRR = 1.20; 95%CI 1.05-1.37) were significantly associated with the number of chronic disease

diagnoses. Based upon the bivariate models (Table 2), cancer, high cholesterol,and asthma were not significantly associated with violence and were therefore

excluded from the final model. In the bivariate models, consistent violence sig-nificantly increased the risk for diabetes by more than five times (OR = 5.35;

95% CI 1.42-20.02) and significantly increased the risk for hypertension by morethan two times (OR = 2.35; 95% CI 1.00-5.49). AL violence was significantly

associated with migraine headaches (OR = 1.73; 95% CI 1.38-2.18).As detailed in Table 3, AL and consistent violence were predictive of chronic

disease diagnoses (OR = 1.16; 95% CI 1.02-1.32 for AL; OR = 1.68; 95% CI 1.18-

2.41 for consistent violence), after adjusting for demographics (including race/ethnicity, age, and gender), smoking, physical activity, and BMI. AL violence

was predictive of migraine headaches (OR = 1.59; 95% CI 1.25-2.03), and adult-onset violence was significantly associated with diabetes risk (OR = 2.08; 95% CI

1.00-4.31). Furthermore, consistent violence emerged as the strongest corre-late of diabetes (OR = 5.04; 95% CI 1.44-17.74).

As expected, obesity significantly increased the risk of any chronic diseasediagnosis (OR = 1.62; 95% CI 1.50-1.77), but especially diabetes (OR = 3.98; 95%

CI 2.66-5.95) and hypertension (OR = 3.28; 95% CI 2.72-3.95). Compared towhites, blacks were at 25% reduced risk for migraine headaches (OR = .75; 95%CI .58-.97). In addition, males were at lower risk for migraine headaches

(OR = .33; 95% CI .27-.40) compared with females, but they were more likely tohave been diagnosed with hypertension (OR = 1.47; 95% CI 1.19-1.81). Age was

1. The Poisson model provided a better fit to the data when compared with a negative binomialmodel based on a likelihood ratio test for significance of overdispersion, which indicated that thePoisson distribution assumption was reasonable.

VIOLENCE & CHRONIC DISEASE 531

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Table

2Bivariate

relationshipsbetw

eenviolence

typologiesan

dch

ronic

disease/s

Chronic

disease

Anych

ronic

disease

Numberofch

ronic

diseasesa

Diabetes

Hyp

ertension

Migraine

head

aches

OR

95%CI

IRR

95%CI

OR

95%CI

OR

95%CI

OR

95%CI

Violence

typologies

Nonviolent

1.00

(reference

group)

–1.00

(reference

group)

1.00

(reference

group)

–1.00

(reference

group)

–1.00(reference

group)

–

ALviolence

1.23

⁄1.00

-1.51

1.20

⁄⁄1.05

-1.37

1.18

.70-1.98

1.22

.89-1.68

1.73

⁄⁄⁄

1.38

-2.18

Adult-onset

violence

1.08

.78-

1.51

1.11

.87-1.42

1.97

.98-3.96

1.09

.61-1.95

1.13

.70-

1.82

Consistent

violence

2.55

⁄1.18

-5.52

1.90

⁄⁄1.31

-2.75

5.35

⁄1.42

-20.02

2.35

⁄1.00

-5.49

1.59

.69-

3.67

Note.Models

aread

justedforrace

,ag

e,an

dge

nder.⁄ p

<.05.

⁄⁄p<.01.

⁄⁄⁄ p

<.001

.aThese

valuesrepresentIRRsestim

atedusingaPoissonregressionmodel.

532 REINGLE ET AL.

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Table

3Multivariate

logistic

regressionmodels

eva

luatingviolence

typologiesas

risk

factors

forch

ronic

disease/s

Chronic

disease

Anych

ronic

disease

Diabetes

Hyp

ertension

Migrainehead

aches

OR

95%CI

OR

95%CI

OR

95%CI

OR

95%CI

Violence

typologies

Nonviolent

1.00

(reference

group)

–1.00

(reference

group)

–1.00

(reference

group)

–1.00

(reference

group)

–

ALviolence

1.16

⁄1.02

-1.32

1.08

.64-1.84

1.16

.83-1.61

1.59

⁄⁄⁄

1.25

-2.03

Adult-onset

violence

1.13

.88-1.44

2.08

⁄1.00

-4.31

1.14

.62-2.08

1.13

.70-1.84

Consistent

violence

1.68

⁄⁄1.18

-2.41

5.04

⁄1.44

-17

.74

1.78

.74-4.31

1.55

.67-3.61

Cova

riates

Physical

activity

1.00

.89-1.13

1.15

.72-1.84

1.15

.88-1.49

1.03

.81-1.32

Currentsm

oking

1.05

.96-1.15

1.32

.85-2.03

.98

.78-1.23

1.23

.99-1.52

Obese

BMI

1.62

⁄⁄⁄

1.50

-1.77

3.98

⁄⁄⁄

2.66

-5.95

3.28

⁄⁄⁄

2.72

-3.95

1.11

.91-1.36

Demograp

hics

white

1.00

–1.00

–1.00

–1.00

–

black

.90

.79-1.02

1.34

.83-2.17

1.23

.93-1.61

.75⁄

.58-.97

Hispan

ic.93

.81-1.02

1.06

.54-2.07

.96

.69-1.32

.91

.70-1.19

Otherrace

1.06

.92-1.21

1.25

.66-2.37

1.01

.72-1.42

.87

.64-1.18

Male

.75⁄⁄

⁄.69-.82

.83

.55-1.24

1.47

⁄⁄⁄

1.19

-1.81

.33⁄

⁄⁄.27-.40

Age

1.01

.99-1.04

1.16

⁄1.02

-1.32

1.05

.98-1.11

.99

.95-1.05

⁄ p<.05.

⁄⁄p<.01.

⁄⁄⁄ p

<.001

.

VIOLENCE & CHRONIC DISEASE 533

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also significantly associated with a small increase in diabetes (OR = 1.16; 95% CI1.02-1.32).

As shown in Table 4, Chi-Squared post-hoc analyses were conducted to char-acterize the prevalence of chronic disease diagnoses across violence typolo-

gies. A number of significant differences emerged. First, consistently violentoffenders were more likely to have been diagnosed with any chronic disease

(p < .01) compared with nonviolent individuals, and they were also more likelyto be diagnosed with a greater number of diseases (p < .001). Second, ALoffenders also had a greater number of diagnoses (p < .01) compared with non-

violent individuals. Third, all offender typologies were more likely to be diag-nosed with diabetes compared with nonviolent individuals. Fourth, AL

offenders and consistently violent offenders were more likely than nonviolentindividuals to have reported hypertension (p < .001 for both groups). Finally, no

significant differences across typologies were observed for migraine headachediagnoses. A Fisher’s Exact test (not shown) found no significant differences

between cancer, high cholesterol, hypertension, or heart disease and violencetypologies.

Discussion

This study set out to examine the relationship between typologies of violent

offending as a juvenile and adult and chronic disease in early adulthood.Results suggest that consistently violent offenders during their juvenile and

young adult criminal careers had the greatest number of and the greatest riskfor any chronic disease diagnosis in general and for diabetes specifically. Fur-

ther, AL offenders were also at elevated risk for chronic diseases in generalwhen compared with nonviolent individuals; however, migraine headaches

Table 4 Post-hoc chi-squared examination of disease prevalence by violence typology

Chronicdisease

Any chronicdisease

Number of chronicdiseasesa Diabetes Hypertension

Migraineheadaches

Nonviolent .39 .51 (.01) .02 .10 .15

AL violence .40 .56 (.02)⁄⁄ .03⁄ .14⁄⁄⁄ .17

Adult-onsetviolence

.37 .51 (.05) .04⁄ .12 .11

Consistentviolence

.57⁄⁄ .79 (.12)⁄⁄⁄ .11⁄⁄⁄ .27⁄⁄⁄ .15

Note. The nonviolent group serves as the reference category for the post-hoc comparisons. A Fish-er’s exact test found no significant differences between cancer, high cholesterol, hypertension, orheart diseases and violence typologies.⁄p < .05.⁄⁄p < .01.⁄⁄⁄p < .001.aThese values represent means (standard error).

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were the only specific health problem that emerged among this group. Adult-onset offenders demonstrated an increased risk for diabetes only.

In comparing our results to Piquero et al.’s (2011) recent analysis among theCambridge males, our findings suggest that involvement in violence in adoles-

cence and continuing this involvement into young adulthood elevates the riskof chronic disease in early adulthood. These findings are consistent with Pique-ro et al. (2011), who reported that chronic offenders had the highest risk for

being registered disabled and having been hospitalized. The similarities areparticularly noteworthy considering that Piquero et al.’s study was based on a

cohort of South London males at age 48, whereas our study utilized a nation-ally representative sample of young adult from the USA.

Findings are also consistent with previous literature on violent behavior andchronic disease outcomes. For instance, the finding that diabetes was the most

common chronic disease outcome is consistent with the biological literaturesuggesting the degenerative effect of cortisol (the stress hormone) on the body

(Bjorntorp, 1990; Raikkonen et al., 1996). Given the relatively low prevalenceof diabetes in the sample, the consistent emergence of this as a risk factor isparticularly important. The finding that AL violence increases the risk for

migraine headaches may also be explained using the stress mechanism(Wacogne, Lacoste, Guillibert, Hughes, & Le Jeunne, 2003). Specifically,

chronic and cumulative stress (which has been demonstratively related toviolence) triggers the release of stress hormones into the brain and body

(Crofford, 2007; McEwen 1998).To be sure, the relationship between violence, diabetes, and migraine head-

aches may also be attributable to other factors, such as drug and alcoholabuse. For instance, substance abuse has been consistently associated withLCP offending (Odgers et al., 2007; Pulkkinen, Lyra, & Kokko, 2009). Similarly,

Bergman and Andershed (2009) found that 58% of males who were LCPoffenders were diagnosed with alcohol abuse, as opposed to 4% of nonviolent

offenders. Odgers et al. (2008) also found that even AL offenders were morelikely than non-offenders to be users of drugs and alcohol. Therefore, the co-

existence of violence and alcohol (or other drug use) may explain the higherprevalence of chronic diseases within a group of people who are consistently

violent.The findings from the current study have several implications for a public

health approach for violence prevention. Specifically, involvement in violencegenerally, as well as involvement in violence across different phases of the lifecourse (e.g. adolescence, adulthood, during adolescence and young adulthood),

increases the risk for chronic diseases in early adulthood. Therefore, interven-tions should target both childhood health-related risk factors and early onset

offending, because a combined approach is more likely to end up reducing andpreventing chronic disease during early adulthood. Should interventions be

implemented that have been empirically shown to be successful in this regard,such as early family-parent training programs and nurse-family partnerships

(Piquero, Farrington, Tremblay, Welsh, & Jennings, 2009), it is likely that these

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efforts will reduce the rates of violence, lessen the burden of chronic offendingon the criminal justice system, improve public health, and, ultimately yield sig-

nificant cost savings to society (Cohen, Piquero, & Jennings, 2010).Notwithstanding these policy implications, these findings should be inter-

preted in light of their limitations. First, we were unable to assess the precisetemporality of the relationship between violent behavior and chronic disease.Nevertheless, because an extremely small proportion of youth under the age

of 20 are diagnosed with diabetes (<1; National Diabetes Information Clearing-house, 2011), we can safely assume that the majority of the 2.6% of young

adults who reported diabetes were not diabetic prior to age 20 (National Dia-betes Information Clearinghouse, 2011). Relatedly, earlier measures of all of

the chronic diseases were not included in the data. Future research shouldexamine these relationships as data become available. Finally, the use of self-

reported diagnoses may be considered somewhat problematic. However, priorresearch examining the validity of self-reported chronic diseases found a kappa

of .85 in the concordance of self-report and official records of diabetes diagno-ses (Kriegsman, Penninx, Van Eijk, Boeke, & Deeg, 1996). As such, these find-ings suggest that self-reports of chronic diseases are relatively accurate but

we encourage corroboration across raters in subsequent research.Given these limitations, this study had a number of strengths. First, because

the Add Health data provided measures of several chronic disease outcomes,we were able to look beyond the most severe outcomes of violence (e.g.

death, hospitalization, and disability), and focus on physiological outcomes.Second, we investigated the effects of violence on a younger population than

is generally included in studies of chronic disease, which may illuminate apoint of intervention before their health deteriorates even further with time.Third, our results include a nationally representative sample across the USA,

which is important given that previous studies tended to use geographicallylimited or international populations. Finally, we categorized violent offenders

into typologies based upon their onset and continuity of violence during ado-lescence and young adulthood. Overall, this study provides a unique crimino-

logical and public health perspective on the relationship between violenceinvolvement and physical health outcomes. Future research should investigate

the inter-relationships between criminological, criminal justice, and publichealth research to increase our knowledge of the relationship between criminal

behavior and health and in so doing unpack the mechanisms that link offendingto chronic health problems such that appropriate intervention efforts can bedeveloped and implemented. Consideration of other theoretical mechanisms

linking violence and chronic disease should also be a top priority.

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