Offending Patterns of Young Homicide Offenders

A Prospective Study of Offending Patterns of Youth Homicide Offenders into Adulthood: An

Examination of Offending Trajectories and The Crime Mix Post-Homicide

Evan C. Mccuish, Jesse Cale and Raymond R. Corrado Youth Violence And Juvenile Justice

2018, Vol. 16(1) 18-36

Youth Violence And Juvenile Justice

Publication History Established in 2003

Frequency of Publication Quarterly

Editor-in-Chief (Co-Editors) Chad R. Trulson and Jonathan W. Caudill

Impact Factor 1.79 (2017)

Abstracting and Indexing Scopus and the Social Science Citation Index

Publication House Sage Publications

INTRODUCTION

• A prevailing assumption is that youth homicide offenders (YHOs) represent the most serious type of young offender, even at the “deep end” of the justice system.

• The fact that they have committed one of the most serious crimes, combined with the high costs associated with homicide offenses, has prompted political leaders and policy makers to support efforts to identify childhood and early adolescent risk factors that help predict which youth will be involved in a homicide offense.

• However, there is minimal evidence of meaningful distinctions in the developmental risk factor profiles of YHOs and other serious and violent youth offenders that would allow for reliable predictions of risk for involvement in a homicide offense

• For Example, although some studies have identified certain risk factors that were more common among homicide offenders compared to other offenders, false positives occurred in upward of 90% of cases.

• In other words, developmental risk factors common among young homicide offenders also tend to be common for other serious and violent youth.

• Being a YHO is too rare, and the risk factors associated with YHOs are too common among other serious and violent offenders to reliably designate particular youth as being at-risk for involvement in a homicide offense.

• The assumption that YHOs have a more serious risk factor profile compared to other serious and violent youth is problematic because of the situational and often unplanned nature of most homicide offenses.

• Many homicide offenses arise in contexts that are similar to other crimes such as assaults and only differ in the outcome to the victim.

• Furthermore, even in cases of “planned” homicide offenses where intent to murder is present, the premature development of executive functions combined with lower social and cognitive skills common for adolescents mean that many of them do not fully understand the consequences of their actions.

• This is particularly true for adolescents with neuropsychological deficits.

• Nonetheless, the penalties and interventions associated with homicide offenses are severe and may lead to negative consequences that result from social processes such as labelling.

• A neglected dimension of research on YHOs is the nature and extent of offending patterns post homicide beyond strictly recidivism.

• Although involvement in the juvenile justice system is a key precursor to involvement in the adult justice system, especially for serious and violent youth, a key question is whether and how a homicide offense in youth impacts the unfolding of offending in adulthood.

• Given that developmental risk factors are not likely sufficient to meaningfully differentiate most YHOs from those involved in serious nonlethal violence, a prospective approach was used to examine YHO and youth non homicide offender (YNHO) involvement in new crimes as they transitioned through the early stages of adulthood.

• First, authors examined changes in criminal versatility, or the crime mix, pre and post homicide among YHOs who were interviewed in adolescence as part of the Incarcerated Serious and Violent Young Offender Study (ISVYOS).

• Second, authors compared offending trajectories measured from ages 12–28 across male and female YHOs (n ¼ 26), violent youth non homicide offenders (VYNHOs; n ¼ 358), and nonviolent youth non homicide offenders (NVYNHOs; n ¼ 139).

Offending Patterns of Young Homicide Offenders

• Studies have produced mixed results in terms of comparisons of criminal histories prior to a young person’s involvement in homicide.

• This is likely related, at least in part, to the timing in adolescence of the homicide offense.

• For Example, DiCataldo and Everett (2008) found that YNHOs had a higher frequency of delinquency and violence in their criminal histories compared to YHOs.

• On the other hand, they found that YHOs were slightly younger (approximately 1 year) than YNHOs at the age of their first violent conviction.

• Using data from the Pittsburgh Youth Study, Loeber et al. (2005) observed that virtually all (31 of 33) of their young male homicide offenders (defined as participants that committed a homicide up to age 26) were previously involved in violent crimes.

• However, using the same data, Farrington, Loeber and Berg (2012) found that homicide offenders were less likely to be chronic offenders relative to other offenders.

• Using retrospective data on 455 habitual adult male offenders, DeLisi, Hochstetler, Jones- Johnson, Caudill, and Marquart (2011) observed that chronic offending, measured by arrests, was not associated with prior homicide offending.

• Similarly, in their study of the population of YHOs in England and Wales, Rodway et al.

(2011) found that only 52% of YHOs were previously convicted.

• One possible explanation that can be drawn from these findings is that young homicide offenders spend long periods of time incarcerated, which reduces their offending opportunities and impacts the unfolding of offending trajectories.

• The lengthy sentences served by YHOs also make it challenging to examine offending patterns following their homicide offense.

• The few studies that examined post homicide offending patterns of YHOs typically relied on recidivism outcomes, most often measured either as a reconviction or return to custody for any offense.

• Hagan (1997) compared the prevalence of recidivism between YHOs (n ¼ 20) and YNHOs (n ¼ 20) over a follow-up period of at least 5 years post release.

• Using a return to prison as the definition of reoffending, no differences were evident in the prevalence of recidivism between the groups; 60% of YHOs reoffended during the follow-up period compared to 65% of YNHOs.

• However, those convicted of first-degree murder were never released from custody during the follow-up period.

• Considering that those involved in first-degree murder may be the most serious offenders, the prevalence of recidivism among YHOs may have been underestimated.

• In a larger study from the Netherlands, Vries and Liem (2011) examined the prevalence of recidivism among a sample of 137 YHOs.

• Like the study by Hagan (1997), approximately 60% of YHOs reoffended, though the follow-up period was from 1 to 16 years.

• Of those followed at least 10 years, nearly three quarters (71%) reoffended. In effect, the prevalence of recidivism increased with the length of follow-up period, with the prevalence of recidivism nearly doubling between the first and fifth year of follow-up in the study by Vries and Liem (2011).

• Differences in time until recidivism among YHOs reflected differences in the frequency of offending for this group. YHO recidivists averaged nearly eight new crimes after their homicide, with a range of 1–42 new offenses.

• Seventy-five percent of recidivists committed at least two new crimes. In effect, among recidivists, most were multiple recidivists.

• Taken together, a strict emphasis on recidivism outcomes may mask substantial within-group heterogeneity in the offending patterns of this group.

• Other studies have produced comparable estimates; in a sample of 59 YHOs given adult sentences for their involvement in murder, manslaughter, or attempted murder, Heide, Spencer, Thompson, and Solomon (2001) indicated that 60% of YHOs reoffended (defined as a subsequent incarceration), most of whom reoffended in the first 3 years following their release from custody.

• Similar patterns were also observed in a recent and large American study. Trulson, Haerle, Caudill, and DeLisi (2016) examined the prevalence and timing of recidivism among a sample of approximately 1,400 felony youth offenders, including 277 YHOs.

• Approximately two thirds (64%) of YHOs were considered recidivists and the prevalence of recidivism among YHOs did not statistically differ from other youth in the sample.

• Of those followed for at least 5 years post release into the community, almost two thirds (approximately 60%) of YHOs were rearrested at some point over the 5-year period; one half of whom were rearrested in the first 3 years post release.

• They also identified characteristics of YHOs that increased the risk of recidivism. On the one hand, being male, African American ethnicity, having a history of institutional assaults, and a shorter incarceration period were all related to an increased likelihood of reoffending post release among YHOs.

• On the other hand, participation in a specialized treatment program for YHOs significantly reduced the risk of recidivism.

• Given that the sample consisted of particularly serious and violent youth, and that, overall, three quarters of the sample went on to commit subsequent felony (i.e., serious) offenses post release, these findings are important because they highlight potential within-YHO variability in the severity of future offending patterns.

• In another effort to capture within-YHO variability, Khachatryan, Heide, and Hummel (2016) examined the prevalence of recidivism across two types of YHOs followed over 33 years.

• The first type were YHOs involved in homicide during the commission of another crime and the second were those involved in homicide as part of some ongoing conflict with the victim.

• These two groups did not differ in terms of age at homicide offense, presence of a prior criminal offense, presence of previous violence, age of onset of general offending, number of prior arrests, and length of time incarcerated for their homicide offense.

• Like the study by Vries and Liem (2011), these YHOs were incarcerated for approximately 8 years before release. The prevalence of recidivism (defined as a rearrest) was high (88% of the full sample), over half of the sample reoffended violently, and nearly 40% were rearrested at least 7 times.

• Clearly, YHOs were not a homogenous group in terms of their recidivism patterns, as the frequency of rearrest ranged from 0 to 30 for general offending and 0 to 23 for violent offending specifically.

• Recognizing that general recidivism outcomes are quite prevalent among serious offenders. Caudill and Trulson (2016) examined the seriousness (i.e., felony compared to non-felony) of post homicide offenses.

• Among a sample of 221 YHOs followed 10 years after release from custody, 58% committed a subsequent felony offense with approximately 70% of recidivists reoffending within the first 2 years of release. The authors observed that lengthier periods of incarceration were associated with a decreased likelihood of felony (i.e., serious) recidivism post release.

• Across studies, differences in the definition of recidivism and the length of follow-up period, failure to control for exposure time in some studies, and strict focus on a reoffense as a post-release outcome of YHOs inhibits a more detailed understanding of their adult offending patterns.

• Given that the prevalence of recidivism among YHOs increases with the length of the follow-up period, a reoffense, whether measured by subsequent arrests, convictions, or incarceration alone, is not an optimal measure of seriousness and gives the impression that all YHOs remain at risk of reoffending for long periods of time. Indeed, some studies have already shown that YHO recidivists vary in the frequency and seriousness of reoffending .

• From a risk-needs-responsivity perspective, making distinctions in the level of severity of YHOs has important implications for the efficacy of criminal justice system intervention strategies.

• From this perspective, the assumption that all YHOs will become prolific or serious adult offenders can result in increasing the risk that would be non-recidivists or YHOs characterized by a low-rate offending pattern will develop into more frequent and serious adult offenders.

• In effect, understanding offending trajectories and the crime mix post homicide among YHOs has important implications for which YHOs warrant more intensive intervention from the justice system and the allocation of additional resources for this group.

Study Aims

• For various arms of the criminal justice system, whether YHOs are likely to recidivate, how often they recidivate, and the types of crimes that they commit after their release from custody has implications for the development of policy concerning this group.

• As noted by others, apart from the handful of studies discussed above, empirical descriptions of the post incarceration behaviours of YHOs are limited compared to research on the retrospective, pre homicide characteristics and behaviours of this group.

• This is particularly true when considering measures of offending beyond strictly recidivism outcomes. The current study addressed whether the offending patterns of male and female YHOs differed from VYNHOs and NVYNHOs to better understand whether YHOs represent a distinct category of offender warranting particular attention from the justice system.

• To accomplish this, three research questions were posed:

a) Research Question 1: Are there differences in the prevalence of adult recidivism across YHOs, VYNHOs and NVYNHOs?

b) Research Question 2: Are there differences in the crime mix of YHOs prior to versus after their homicide offense?

c) Research Question 3: Are YHOs associated with a specific offending trajectory that differs from that of other serious and violent youth?

METHOD

Sample

• Data for the current study were derived from ISVYOS, which was conducted in British Columbia, Canada and has been ongoing since 1998.

• As part of this study, male and female youth between ages 12 and 19 were interviewed in open and secure custody facilities within the Greater Vancouver Regional District and surrounding areas.

• The focus of the current study is only on youth from the first wave of data collection that were followed prospectively into adulthood (n ¼ 523).

• The initial sample included 527 youth, but criminal records were not found for 0.8% (n ¼ 4) of participants. Indigenous persons are overrepresented in detention facilities in Canada.

• Likewise, the percentage of Indigenous participants in the current study (23.9%) was substantially higher than within the general population of British Columbia (6.2%; Statistics Canada, 2013).

• The sample is by no means representative of all youth involved in crime in the province of British Columbia.

• However, it is representative of offenders in the Canadian youth justice system sentenced to detention and of youth with more serious patterns of offending.

• Youth included in the sample were convicted of, on average, 12.17 offenses (standard deviation [SD] ¼ 8.74) between age 12 and 17 and averaged about 1 year incarcerated during this same period (353.99 days; SD ¼ 294.92). Additional descriptive information about the sample is presented in Table 1.

Demographic Characteristics N (%) Mean (SD) Gender

Male 404 (77.2)

Female 119 (22.8)

Ethnic Origin

White 320 (61.2)

Indigenous 124 (23.7)

Non-indigenous Minority 76 (14.5)

Criminal Career Measures

Age Of Onset 14.34 (1.51)

Days In Custody (12–28) 1085 (1071)

Offending Frequency (12–28) 22.50 (17.27)

Nature of Homicide Offense

Murder 13 (2.5)

Manslaughter 6 (1.1)

Attempted Murder 7 (1.3)

Note. n ¼ 523.

Table 1. Descriptive Characteristics of the Sample

Procedure

• The purpose of the ISVYOS was to obtain information on risk factors associated with various criminal career parameters.

• Self-report interviews were conducted and file-based information was collected on a sample of youth incarcerated in various detention centres throughout British Columbia.

• The British Columbia Ministry of Child and Family Development acts as the legal guardian to all youth in custody, and their consent allowed the research team to ask incarcerated youth if they wished to participate in the study.

• Youth were eligible to participate in the study if all the following criteria were met:

a) were English-speaking,

b) demonstrated an understanding of interview questions (e.g., had no noticeably severe learning disability)

c) were willing to provide accurate information.

• Data concerning refusal rates were not collected during the entire course of the study, but in the time that such data were collected, approximately 5% refused.

• Research assistants (RAs) interviewed youth in an isolated interview room to help ensure confidentiality.

• All participants were read and given a copy of an information sheet which explained the purpose of the study, how information would be collected (i.e., interview and file information), and that all information would be kept confidential unless the participant made a direct threat against themselves or someone else.

• To improve the reliability of self-reported information, RAs accessed case management files, which contained presentence reports and other information, to help detect discrepancies between interview responses and official records.

Measures

Measures of Offending:

• Offending was measured using data from British Columbia Corrections’ computerized system, Corrections Network (CORNET), which contains information pertaining to each offender’s movement in and out of custody as well as the exact criminal offense, date of conviction, and sentence type.

• Criminal convictions were coded for the entire sample from age 12 to age 28.The start of the follow-up period, age 12, represents the minimum age of criminal responsibility in Canada.

• From that point on, every criminal charge that resulted in a conviction was coded. The average number of charges that resulted in conviction between ages 12 and 28 was 22.50 (SD ¼

17.28).

• The median number of convictions was 18, highlighting that the high mean number of convictions found was not an artefact of a small subgroup of individuals (i.e., chronic offenders).

• Although it was possible for offenders to commit new offenses while outside the province, the current study only had access to records of offenses committed within the province of British Columbia and 21 participants (4.0%) moved outside of the province during the study period. An additional 26 offenders died (5.0% of the sample).

• For these participants, convictions after the age of death or age of move were coded as missing rather than as “zero.” Although official data have the disadvantage of underestimating offending, the current study is focused on more serious crimes that are less likely to be self-reported (e.g., rape, homicide, assault), and this also minimizes memory recall bias issues.

• In line with prior studies, to be categorized as a YHO (n ¼ 26), an offender must have been charged with first-degree murder, second-degree murder, man-slaughter, or attempted murder.

• Attempted murder was included in this conceptualization of homicide because of its fundamental similarity to murder. Indeed, Shumaker and McKee (2001) noted that young persons charged with attempted murder did not differ from other homicide offenders regarding demographic characteristics, familial background, use of co-offenders and prior offending behaviour.

• Similarly, Myers, Scott, Burgess, and Burgess (1995) asserted that progression from attempted murder to murder was primarily the result of chance. Reiterating this assertion, Heide et al. (2001) noted that what often distinguished attempted murder from murder was the health of the victim at the time of the offense .

• Although ages 12–17 represent the definition of a young offender in Canada, homicide charges through age 19 were used to define YHOs because of growing recognition that adulthood begins later than initially conceptualized.

• This approach was similar to prior definitions of the age range of YHOs.

• Excluding YHOs that died, moved, or were never released (n ¼ 6), the group was followed for an average of 10.35 years (SD ¼ 2.32; range of 4–14) after being released from custody.

• VYNHOs (n ¼ 358) were defined as participants convicted of a violent offense between ages 12 and 19; all other participants were NVYNHOs (n ¼ 139).

Analytic Strategy

• The current study examined whether criminal career parameters varied between YHOs, VYNHOs and NVYNHOs.

• This comparison begins with an examination of traditional offending outcomes (e.g., recidivism) across the three groups of offenders.

• Based on public and policy makers’ concerns about YHOs following their release, YHO offending patterns up to and including their homicide offense were compared to their offending patterns after their homicide offense.

• Specific interest was in the qualitative nature of YHO offending patterns following their re-entry to the community.

• To examine this, seven offense categories were used to create a variable representing the crime mix of YHOs: violent, property, violation of court orders, weapon, miscellaneous, drug, and sexual offenses.

• Given that YHOs committed their homicide offenses at different ages, the length of time after the homicide offense and time before the homicide offense varied across the sub sample.

• As such, rather than examine total crimes pre versus post homicide, the analysis concerned the average number of each crime type committed per year prior to and including the homicide versus the average number of each crime type committed following their homicide offense.

• This allowed for an interpretation of whether the crime mix of YHOs changed after their homicide offense. Thus, the crime mix reflects the relative importance of seven different crime types and whether more serious crimes became more prominent in the post homicide criminal careers of YHOs.

• Following this line of analysis, Proc TRAJ for SAS 9.4 was used to conduct Semi Parametric Group-based Modelling (SPGM) to identify the number and shape of offending trajectories, measured from ages 12 to 28, that best fit the sample.

• The SPGM analysis in the current study included a measure of exposure time, number of days in the community for each year of age, to control for time at risk.

• Piquero et al. (2001) illustrated that desistance within their sample of youth from the California Youth Authority study was substantially overestimated when studies did not account for time incarcerated.

• The current study used Piquero et al.’s (2001) original formula to calculate exposure time for each participant at each person-period observation:

• where j is the respondent and i is the year of observation.

• Number of days incarcerated was divided by 367 as:

a) exposure must be a nonzero value

b) offenders, on occasion, spent 366 days incarcerated due to leap years

RESULTS

Offender-Type and Offending Outcomes

• In Table 2, offending parameters were compared across the three offender types. In adolescence (ages 12–17), YHOs and NVYNHOs averaged significantly fewer convictions compared to VYNHOs (p < .001).

• With respect to time incarcerated, YHOs and VYNHOs averaged a significantly greater number of days incarcerated compared to NVYNHOs.

• In adulthood (ages 18–28), YHOs averaged significantly fewer convictions compared to VYNHOs (p < .01) but spent a significantly greater number of days incarcerated compared to both VYNHOs and NVYNHOs (p < .001).

• Individuals incurring a new conviction between ages 20 and 28 were defined as adult recidivists.

Offender Type (n ¼ 523)

YHO (n ¼ 26)

VYNHO (n ¼ 358)

NVYNHO (n ¼ 139)

w²/F, p, F/Z² Demographic Characteristics M (SD)/n (%) M (SD)/n (%) M (SD)/n (%)

Gender

¼ 1.1, p ¼ .577, F ¼ .05

Male 20 (76.9) 281 (78.5) 103 (74.1) w²(2)

Ethnicity

¼ 9.9, p < .05, F ¼ .14

White 12 (46.2) 223 (62.5) 85 (62.0) w²(4)

Indigenous 7 (26.9) 77 (21.6) 40 (29.2)

Non-Indigenous minority 7 (26.9) 57 (16.0) 12 (8.8

Measures of offending

¼ 6.8, p < .01, Z² ¼ .02

Age of onset 14.73 (1.40) 14.18 (1.40)^a 14.69 (1.74)^b F(2)

Offending frequency (12–17) 4.73 (5.94)^a,b 13.20 (8.96)^a,c 10.91 (7.74)^b,c F^d(2) ¼ 19.8, p < .001, Z²¼ .05 Offending frequency (18–28) 5.18 (6.57)^b 11.24 (11.82)^c 8.82 (11.87) F^d(2) ¼ 6.2, p < .01, Z² ¼ .02

Time incarcerated (12–17) 500.89 (296.91)^a 380.50 (297.51)^a 258.41 (262.6)^b,c F(2) ¼ 12.5, p < .001, Z²¼ .04 Time incarcerated (18–28) 2004.82 (1482.02)^a

,b 733.42 (850.83)^a,c 526.53 (805.16)^b,c F^d(2) ¼ 14.7, p < .001, Z²¼ .11 Recidivism in adulthood 17 (70.8) 264 (74.6) 89 (65.0) w²(2) ¼ 4.5, p ¼ .100, F ¼ .09

Note.YHO ¼ youth homicide offender; VYNHO ¼ violent youth non-homicide offender; NVYNHO ¼ nonviolent youth non-homicide offender.

aSignificantly different from NVYNHO.^bSignificantly different from VYNHO.^cSignificantly different from YHO.^dAsymptotically F distributed; Browne-Forsythe statistic used.

Table 2. Comparison of Demographic Characteristics and Criminal Career Parameters Across Offender Type

• The lower bound of adulthood was defined as age 20 rather than age 18 to ensure that there was no overlap between receiving a charge or conviction for a homicide offense between ages 18 and 19 and the measure of recidivism in adulthood.

• As shown in Table 2, excluding study participants who died and did not recidivate (n ¼ 6) or moved and did not recidivate (n ¼ 2), offender type was unrelated (p >.05) to the prevalence of recidivism in adulthood, with YHOs (70.8%) and VYNHOs (74.6%) trending toward a higher prevalence of recidivism compared to NVYNHOs (65.0%).

• After excluding from the recidivism analyses the group of YHOs that were never released from custody and did not recidivate¹ (n ¼ 4), there was a marginally significant association between offender type and recidivism, w²(2) ¼ 6.0, p ¼ .05.

• Specifically, YHOs had the highest prevalence of recidivism in adulthood (84.2%, n ¼ 16) compared to VYNHOs and NVYNHOs. In other words, once exposure time/opportunity was better accounted for, YHOs trended toward a greater likelihood of recidivism compared to other offenders.

• To explore this relationship further, a logistic regression analysis was performed with the latter recidivism outcome regressed on offender type, gender, and ethnicity included as covariates. The overall model was significant, 2 log likelihood ¼ 569.18, w²(4) ¼ 24.44, p < .001, but offender type was unrelated to recidivism. Being male increased the odds of recidivism by a factor of 2.72 (p>.001).

• At the bivariate level, when another w² analysis was performed after removing from the sample all female YHOs (n ¼ 6), VYNHOs (n ¼ 77), and NVYNHOs (n ¼ 36), the association observed between offender type and any of the above mentioned measures of adult recidivism disappeared.

Pre-Homicide and Post-Homicide Offending Patterns

• The aggregate crime mix of YHOs was measured up to and including each YHO’s homicide offense and then measured for each YHO following their homicide offense (see Table 3).

• This comparison did not include YHOs that were never released from custody following their conviction for their homicide offense.

• A paired-sample t-test was used to compare frequency of convictions across both measurement periods.

• After their homicide offense, YHOs averaged 0.43 total convictions per person-period observation (i.e., per year), an offense rate that was significantly lower (p<.05) compared to average total convictions per person-period observation in the period leading up to and including their homicide offense.

Pre-Incarceration Post-Incarceration

Frequency of Offending Outcomes M (SD) M (SD) t, p

Number of general offenses 1.03 (0.94) 0.43 (0.53) t(20) ¼ 2.72, p < .05 Number of violent offenses 0.38 (0.30) 0.07 (0.12) t(20) ¼ 4.38, p < .001 Number of property offenses 0.22 (0.33) 0.04 (0.10) t(20) ¼ 2.22, p < .05 Number of violation offenses 0.20 (0.34) 0.16 (0.26) t(20) ¼ 0.46, p ¼ .653 Number of weapons offenses 0.04 (0.78) 0.04 (0.09) t(20) ¼ 0.22, p ¼ .832 Number of miscellaneous offenses 0.14 (0.25) 0.08 (0.14) t(20) ¼ 1.58, p ¼ .131

Number of drug offenses 0.03 (0.11) 0.04 (0.09) t(20) ¼ 0.31, p ¼ .762 Number of sexual offenses 0.01 (0.03) 0.00 (0.00) t(20) ¼ 1.00, p ¼ .329 Number of days incarcerated 115.85 (66.02) 101.00 (104.46) t(20) ¼ 0.71, p ¼ .486

Note. n ¼ 21. Pre-incarceration refers to all crimes committed prior to incarceration for the homicide offense. Post-incarceration refers to all crimes committed after release from custody following the homicide offense. Paired-samples t-test used to compare pre-incarceration to post-incarceration levels of offending.

Analyses excluded youth homicide offenders that were never released from custody. Number of offenses refers to crimes per year of observation as the number of years of observation varied between the pre-incarceration period and the post-incarceration period

Table 3. Comparing the Offending Patterns of Youth Homicide Offenders Before and After Their Incarceration for Their Homicide Offense

• Overall, a significant (p < .05) and decreasing pattern of offending frequency was observed for total crimes, violent crimes, and property crimes. In inspecting the average frequency of each conviction type included in Table 3, when YHOs did commit new offenses following release, they tended to commit relatively minor crimes such as violations of court orders and miscellaneous offenses (e.g., drinking and driving, dangerous driving).

• The crime mix of YHOs is depicted in Figure 1 to help visually illustrate differences in pre and post release offending patterns.

• Importantly, in comparing the pre and post release crime mix of YHOs, time incarcerated was not accounted for. However, as shown in Table 3, there was no significant difference in the number of days incarcerated per person-period observation across the two time periods.

Figure 1. Aggregate crime mix showing the average frequency of conviction per year of observation for each crime type. The left-hand side reflects convictions per person-period observation up to and including the homicide offense whereas the right-hand side reflects convictions per person-period observation after the homicide offense.

Offending Trajectories

• With recidivism in adulthood common for all offender types, offending trajectories were examined to help capture within-group differences in offending patterns among the sample.

• Model identification was the first stage of the SPGM analysis, which involved specification of the number and shape of the offending trajectories that best fit the data.

• A zero-inflated Poisson model with quadratic functional form was used to estimate the distribution of the offending trajectories. Bayesian Information Criteria (BIC) values are typically used to identify the number of trajectories that best fit the data, with values closer to 0 indicating improved fit.

• A four-group solution showed a BIC value closer to 0 ( 16,106.83) compared to both a three- group model ( 16,245.12) and a five-group model ( 17,918.25).

• Jeffrey’s scale of the evidence of the Bayes factor was used to validate the retention of the four- group solution. Using the formula e^BICi ^BICj , where the values of B_ij greater than 10 indicate strong evidence for model “i” (see Nagin, 2005), there was strong evidence for a four-group model over a five-group model (B_ij >10) but not for a three-group model over a four-group model (B_ij < 10).

Parameters of the four-group model are shown in Table 4.

• Classification accuracy was determined by the average posterior probability of accurately assigning individuals to a particular trajectory, and values were high for each trajectory (range of .94–.99).

Odds of correct classification (OCC) provided additional confidence that individuals were assigned to the appropriate trajectory and was calculated as:

Offending Trajectories

w²/F, p

Characteristics of the Trajectories AL Stable Low HRD HRP

n (%) 214 (40.9) 98 (18.7) 98 (18.7) 113 (21.6)

Estimated model parameters

Intercept 18.37 (1.16) 1.99 (0.48) 16.56 (0.83) 5.69 (0.33)

Linear 2.29 (0.14) 0.27 (0.05) 2.21 (0.10) 0.77 (0.04)

Quadratic 0.07 (0.00) 0.01 (0.00) 0.07 (0.00) 0.02 (0.00)

Model fit characteristics

Peak age 16 16 16 17

Range 0.57–1.00 0.51–1.00 0.51–1.00 0.55–1.00

Mean probability—AL 0.99 (0.05) 0.01 (0.03) 0.02 (0.09) 0.00 (0.00)

Mean probability—stable low 0.01 (0.03) 0.95 (0.11) 0.02 (0.05) 0.01 (0.05)

Mean probability—HRD 0.01 (0.05) 0.01 (0.04) 0.94 (0.12) 0.01 (0.03)

Mean probability—HRP 0.00 (0.00) 0.03 (0.09) 0.02 (0.09) 0.98 (0.07)

OCC value 98.54 18.81 25.74 48.57

Offending characteristics

334.62) ¼ 143.29, p < .001 Offending frequency (12–17) 5.97 (4.29)^a,b,c 10.34 (6.00)^b,c,d 18.42 (6.63)^b,d 20.16 (8.79)^b,d F^e(3,

Offending frequency (18–28) 1.44 (1.63)^a,b,c 12.83 (7.03)^a,b,c 8.26 (6.30)^a,c,d 26.92 (11.00)^a,b,d F^e(3, 245.32) ¼ 271.28, p < .001 Time incarcerated (12–17) 186.22 (187.57)^a,b,c 311.18 (228.44)^b,c,d 540.24 (281.07)^a,d 549.02 (318.16)^a,d F^e(3, 356.63) ¼ 67.03, p < .001 Time incarcerated (18–28) 202.11 (628.79)^a,b,c 798.76 (711.55)^b,c,d 461.82 (465.83)^a,c,d 1932.72 (712.70)^a,b,d F^e(3, 369.69) ¼ 176.65, p < .001

Offender type

¼ 12.38, p ¼ .06

Youth homicide offender 14 (53.8%) 5 (19.2%) 4 (15.4%) 3 (11.5%) w²(6)

Youth violent offender 130 (36.3%) 70 (19.6%) 69 (19.3%) 89 (24.9%)

Youth nonviolent offender 70 (50.4%) 23 (16.5%) 25 (18.0%) 21 (15.1%)

Table 4. Fit Statistics for a Zero-Inflated Poisson Model of Trajectories of General Offending

Note.n ¼ 523. Analysis of variance was used to compare offending characteristics across the four trajectories. Tamhane used for all post hoc tests as Levene’s test was violated. Fisher’s exact test used for the w² analysis due to two cells with expected counts less than five. AL ¼ adolescent limited; HRD ¼ high-rate desisting; HRP ¼ high-rate persisting; OCC ¼ odds of correct classification.

aSignificantly different from stable low.^bSignificantly different from HRD.^cSignificantly different from HRP.^dSignificantly different from AL.^eAsymptotically F distributed; Browne-Forsythe statistic used.

• OCC values for each of the four trajectories were greater than 5 (range of 18.81–98.54), which indicates high classification accuracy. Based on their shape, the trajectories (see Figure 2) were labelled: adolescent limited (40.9% of the sample; n ¼ 214), stable low (18.7%; n ¼ 98), high-rate desisting (HRD; 18.7%; n ¼ 98), and high-rate persisting (HRP; 21.6%; n ¼ 113).

• For all four trajectories, offending peaked in late adolescence. From ages 12 to 17, the trajectories resembled one another in shape but not in height. After this period, however, each trajectory took on a unique shape.

• As its name implies, the adolescent-limited trajectory reached a near-zero level of offending shortly into adulthood. In contrast, the stable low trajectory showed no decline in level of offending from age 18 onward.

Figure 2. General offending trajectories measured from age 12 to 28 for the sample (n ¼ 523)

• The HRD trajectory reached a near-zero level of offending that was similar to the adolescent-limited trajectory, but did so at a much later age period.

• Finally, although the HRP trajectory showed a declining frequency of offending

from ages 15 to 28, this trajectory averaged the greatest number of convictions at

each person-period observation relative to all other trajectories.

Offending trajectories and sample characteristics:

• At the bivariate level, both ethnicity, w²(6) ¼ 22.42, p < .01, and gender, w²(3) ¼ 42.66, p

< .001, were associated with trajectory group, with non-Indigenous minorities least likely to be associated with a chronic offending trajectory compared to other ethnic groups and males more likely to be associated with a chronic offending trajectory compared to females.

• Using Fisher’s exact test, as shown in Table 4, offender type (YHO, VYNHO, NVYNHO) was marginally related to trajectory association, w²(6) ¼ 12.38, p ¼ .06.

• The prevalence of each offender type within each trajectory is shown at the bottom of Table 4.

• In total, only approximately 27% of YHOs were in the two chronic offending trajectories, compared to approximately 50% of VYNHOs and 33% of VYNHOs.

• Over half of all YHOs were in the adolescent-limited trajectory. From ages 12 to 17 and ages 18 to 28, this trajectory averaged the lowest frequency of offending and averaged the least amount of time spent incarcerated of the four trajectories identified (see Table 4).

• To look more closely at the relationship between offender type and trajectory association, specific comparisons were made between YHOs and YNHOs across each of the four trajectories.

• As shown in Table 5, there was no statistically significant difference between the prevalence of YHOs and YNHOs within each trajectory.

YHOs YNHOs

w², p

General Offending Trajectory n (%) n (%)

2

¼ 1.9, p ¼ .169

AL trajectory a 14 (53.8) 200 (40.2) w₂ (1)

Stable low trajectory a 5 (19.2) 93 (18.7) w₂ (1) ¼ 0.0, p ¼ .999

High-rate desister trajectory 4 (15.4) 94 (18.9) ^w2 (1) ¼ 0.2, p ¼ .800 High-rate persistent trajectory 3 (11.5) 110 (22.1) w (1) ¼ 1.6, p ¼ .201

Note. Comparisons across individual trajectories were also made between YHO and VYNHO and between YHO and NVYNHO.

None of these comparisons resulted in a statistically significant association (p > .05). YHO ¼ Youth homicide offender; YNHO ¼ Youth non-homicide offender; AL ¼ adolescent limited. ^aFisher’s exact test used for the w² analysis due to one cell with expected counts less than 5.

Table 5. Comparisons Between Trajectory Association, YHOs and YNHOs

DISCUSSION

• In prior studies, minimal differences have been observed in terms of the prevalence of recidivism between YHOs and YNHOs, as well as between types of YHOs. Furthermore, the bulk of studies on this issue demonstrate that the prevalence of recidivism among YHOs increases with the length of follow-up period.

• The current study examined three key questions to shed further light on the post homicide offending patterns of YHO:

a) Are there evident differences in the adult offending outcomes of YHOs, VYNHOs and NVYNHOs?

b) Are there differences in the severity of offending patterns of YHOs prior to and after their homicide offense?

c) Are YHOs associated with a specific offending trajectory that differs from that of other serious and violent youth?

• In line with prior studies, YHOs had fewer convictions in their criminal histories prior to their homicide compared to other serious and violent offender groups and spent comparably more time incarcerated in both adolescence and adulthood.

• Furthermore, up to age 28, there were no evident differences in the prevalence of recidivism between YHOs, VYNHOs and NVYNHOs in the current study, echoing previous between-group comparisons.

• Finally, the prevalence of adulthood recidivism among YHOs was high (70.8%), especially after controlling for those that moved, died, or were never released (84.2%), which was also in line with studies with comparable follow-up periods .

• To explore these relationships further, authors employed a measure of the crime mix of YHOs pre and post homicide offense to describe the qualitative aspects of their offending patterns. Using seven different crime types, compared to the pre-homicide period, it was evident that YHOs averaged fewer crimes and were more likely to commit less serious crimes following their homicide offense.

• The crime mix of YHOs revealed a tendency for this group to be involved in miscellaneous crimes (e.g., mischief, vandalism, drinking and driving) and violations of court orders (e.g., breaching curfew, violating no-contact orders) following their homicide offense.

• This stands in contrast to early clinical conceptions of YHOs as a homogenous and uniquely dangerous group with a propensity for serious violence that involved a lack of sensitivity to the pain and suffering of others.

• These clinical assertions continue to characterize some descriptions of YHOs as a group of persistent violent offenders.

• For Example, Woodworth, Agar, and Coupland (2013) suggested that YHOs would be exposed to increasing levels of violence that would contribute to continued aggressive behaviour. Although this may be the case for some, the post release crime mix of YHOs in the current study showed that violence was not necessarily the norm for this group.

• The findings are also in line with DeLisi et al. (2014) explanation that many homicides resulted from the same types of fights that were committed by violent non-homicide offenders.

• Taken together with the current finding that YHOs, especially following their homicide offense, were typically involved in less serious crimes, it may be necessary to revisit the clinical notion of YHOs as group characterized by a propensity for serious violence.

• Nonetheless, it is also evident that key indicators not examined in the current study (e.g., psychopathy, gang membership) can potentially differentiate these more serious YHOs;

YHOs with a history of severe violence prior to and while incarcerated are at an increased likelihood of serious violence post release.

• Caudill and Trulson (2016) found a similar recidivism pattern when examining the likelihood of felony (i.e., serious) offenses of YHOs post homicide in adulthood.

Importantly, there are likely competing explanations for these patterns.

• On the one hand, they suggested that lengthier prison sentences and involvement in specialized treatment reduced the likelihood of recidivism among YHOs post release.

• At the same time, Baay, Liem, and Nieuwbeerta (2012) showed that lengthier prison sentences adversely affected homicide offenders’ connections to their intimate partners, the latter being an important form of social support that promotes desistance.

• Taken together, these findings point to the potential for specialized treatment in custody to reduce the likelihood of recidivism and the need for support upon community re-entry to help promote desistance.

• Although it was not possible to investigate the impact of treatment and degree of post release social support for YHOs in the current study, heterogeneity in long-term offending patterns (i.e., offending trajectories) of YHOs can potentially inform the allocation of resources along these lines.

• Accounting for exposure time, four offending trajectories measured from ages 12 to 28 were identified. These trajectories included patterns of adolescent-limited, stable-low, HRD, and high- rate persistent offending.

• Critically, YHOs were neither overrepresented nor underrepresented in any specific offending trajectory. Although YHOs are routinely identified as a group warranting great attention and resources from the criminal justice system compared to other serious and violent youth, the heterogeneity in their criminal careers, at least through age 28, seems to suggest that a more individualized and tailored approach to assessment and treatment is required.

• Although much has been learned about YHOs in terms of their personal characteristics, victim selection, and use of weapons in the commission of their offense, less is understood about the course of offending leading up to, and following, involvement in a homicide offense.

• Currently, a young person’s involvement in a homicide offense seems to reveal more about how the criminal justice system will respond than about how this offender will remain in contact with this system. YHOs can vary in the nature and seriousness of their criminal careers.

• A greater appreciation of criminal career patterns (e.g., age of onset, versatility) and key risk factors (e.g., psychopathy, gang membership) is likely to be more informative of which offenders remain at a continued risk for involvement in frequent/serious offending.

• For Example, McCuish, Corrado, Hart, and DeLisi (2015) found that youth with higher symptoms of psychopathy were more likely to show a pattern of persistent violence in adulthood. This persistent involvement in violence may increase the likelihood that any given act of violence will result in the death of a victim.

• Similarly, Corrado, DeLisi, Hart, and McCuish, (2015) noted that higher symptoms of psychopathy would increase the likelihood of involvement in especially serious, premeditated forms of offending such as homicide.

• Following from this, one aspect of homicide offending authors were not able to assess in the current study relates to gang involvement and other within-group differences regarding the nature of homicide events.

• In the Pittsburgh Youth Study, Farrington et al. (2012) noted that 30% of young homicide offenders were gang involved.

• DeLisi, Spruill, Vaughn, and Trulson (2014) found that gang-involved homicide offenders were approximately a decade younger at the age of their homicide offense compared to other homicide offenders and Trulson, Caudill, Haerle, and DeLisi (2012) showed that gang-affiliated YHOs were more likely to recidivate compared to other YHOs.

• More broadly, Khachatryan, Heide, Rad, and Hummel (2016) observed that YHOs that offended in a group had more pre homicide arrests and were also more likely to reoffend following release compared to solo offenders.

• The fact that group offenders were more frequent offenders may be reflective of access to a larger pool of potential criminal accomplices that can help provide opportunities and collaboration for future criminal offenses.

• At the same time, Khachatryan et al. (2016) reported no differences in the prevalence of violent rearrest across the two types of YHOs (i.e., group vs. solo).

• This suggests that if group-based YHOs did have access to more opportunities to offend, these were not necessarily opportunities for violent crimes.

• A contrasting finding was observed by Trulson et al. (2012) using retrospective data on serious and violent offenders (n ¼ 1,804), including a subsample that were involved in gang-related homicide.

• Gang-affiliated YHOs were more likely to recidivate in general and were more likely to commit a new serious (i.e., felony) offense compared to other serious and violent offenders, even after controlling for a variety of demographic characteristics and risk factors.

• In terms of frequency of offending, being a gang-affiliated YHO was unrelated to frequency of rearrest

LIMITATIONS

• The current study relied on a small sub sample of YHOs (n ¼ 26), all of whom were incarcerated. It is typically the case that community-based studies are more generalizable than studies using offender-based samples, and it could be argued that the current study includes a highly specific sample of offenders.

• Although this is true of the YNHOs in the current study, as this subsample represented only those YNHOs who were incarcerated for their crimes (i.e., a small proportion of all offenders), when it comes to studying YHOs, offender-based samples are advantageous because they are more likely to capture a fuller range of YHOs compared to community studies.

• Specifically, community-based studies often consist of children or adolescents from higher risk neighbourhoods or lower socio-demographic areas.

• Consequently, YHOs from lower risk neighbourhoods or more affluent areas are excluded from such samples. Given the typically punitive response to homicide offenses (Hagan, 1997), it is likely that all YHOs will serve time in youth detention regardless of neighbourhood status.

• Therefore, sampling from a youth detention population is likely to provide the most representative sample of YHOs, even if this population does not represent the typical offender.

CONCLUSION

• An important policy implication to address in future research is the impact of a YHO’s sentence in preventing future offending.

• Although this type of offender is likely to receive a lengthier period of incarceration to help prevent future offending, from a state dependence perspective, lengthier periods of incarceration absent of specialized treatment would decrease or inhibit the acquisition of informal social controls and thus might contribute to continued offending.

• Future research would benefit from considering Baay et al.’s (2012) research examining the relationship between time incarcerated and the breaking down of informal social controls. This is not to say that lengthy sentences are never justified.

• Balance is needed when considering the importance of protecting the public and ensuring the public’s perception of the justice system does not fall into disrepute given the grievous nature of the offense and other costs.

• Critically, the deterioration of informal social controls for these youth is also a consequence that jeopardizes public safety.

• Although it is true that not all YHOs are involved in future serious and violent offending, it is also true that some of these offenders will commit similar crimes in the future.

• What is clear from the current state of research is the necessity for precise assessment of risk and protective factors of YHOs, closer attention to their behaviour and treatment progress while incarcerated and the nature and extent of social support post release.

• In other words, how an offender’s homicide offense is situated within their broader criminal career may be informative of their likelihood of continued offending.

• At this point, a homicide appears to be a distinct type of crime, but being involved in a homicide does not appear to reflect a distinct type of offender involved in more serious crimes or offending more frequently relative to other serious and violent youth.

STATISTICS USED

PROSPECTIVE STUDY

• A prospective study is a longitudinal study that follows over time a

group of similar individuals (cohorts) who differ with respect to

certain factors under study, to determine how these factors affect rates

of a certain outcome.

LOGISTIC REGRESSION ANALYSIS

• In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick.

• This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc.

• Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one.

Assumptions of Logistic Regression

• First, binary logistic regression requires the dependent variable to be binary and ordinal logistic regression requires the dependent variable to be ordinal.

• Second, logistic regression requires the observations to be independent of each other.

• Third, logistic regression requires there to be little or no multi-collinearity among the independent variables.

• Fourth, logistic regression assumes linearity of independent variables and log odds.

• Finally, logistic regression typically requires a large sample size.

BIVARIATE ANALYSIS

• Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them.

• Bivariate analysis can help determine to what extent it becomes easier to know and predict a value for one variable (possibly a dependent variable) if we know the value of the other variable (possibly the independent variable).

PAIRED SAMPLE t-TEST

• The paired sample t-test, sometimes called the dependent sample t-test,

is a statistical procedure used to determine whether the mean

difference between two sets of observations is zero. In a paired

sample t-test, each subject or entity is measured twice, resulting

in pairs of observations.

Assumptions of Paired Sample t-Test

• The paired sample t-test has four main assumptions:

a) The dependent variable must be continuous (interval/ratio).

b) The observations are independent of one another.

c) The dependent variable should be approximately normally distributed.

d) The dependent variable should not contain any outliers.

POISSON REGRESSION

• In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables.

• Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modelled by a linear combination of unknown parameters.

• A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.