Drug Use and Recidivism Among Young Offenders on Supervised Community Orders

Drug use and recidivism among young offenders on supervised community orders

Paul Kenneth Nelson

BA, MHSc (Hons)

A thesis submitted in accordance with the requirements for admission to the degree of Doctor of Philosophy

National Drug and Alcohol Research Centre School of Public Health and Community Medicine Faculty of Medicine University of New South Wales

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Surname or Family name: NELSON

First name: Paul Other name/s: Kenneth

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School: Public Health and Commumty Medicine Faculty: Medicine

Title: Drug use and recidivism among young offenders on supervised community orders

Abstract 350 words maximum: (PLEASE TYPE)

Drug use is a highly prevalent and modifiable risk factor for recidivism. Drug use and recidivism are complex phenomena, as reflected in the diverse drug-crime relationships reported in the research literature. These relationships have not been studied in detail among community-supervised youths, who comprise the majority of supervised young offenders and differ in important ways from detainees and adults. This thesis examined drug-crime relationships among 800 Australians in community-based juvenile justice supervision. Extensive baseline health surveys were linked with lifetime court data, giving four years' prospective observation of recidivism.

Two cross-sectional studies showed that the sample was characterised by family disruption, Conduct Disorder (58%), distress (59%), cognitive deficits (67%), delinquent peers (77%), school failure (79%) and weekly drug use (69%). Psychosocial dysfunction was concentrated among frequent binge drinkers (10%), daily cannabis users (35%) and weekly users of amphetamines (9%) or opioids (5%). Frequent binge drinking was linked with five-fold higher odds of self-harm but did not affect recidivism.

Three prospective studies modelled predictors of the prevalence (79%), timing (median: eight months after baseline), frequency (mean: six convictions) and severity of recidivism (58% committed less serious offences). Frequent drug users had poorer recidivism outcomes but drug use did not predict recidivism for most drug users. However, daily cannabis use predicted more rapid violence and weekly opioid use predicted a four-fold increase in theft outcomes. Static factors including prior detention were the strongest predictors of recidivism, and with the exception of opioid use, risk factors varied substantially by gender, ethnicity and age.

The original contribution of this thesis was to show that young offenders' drug-crime relationships are characterised by their variation. Drug use per se did not affect recidivism; rather, certain patterns of frequent use increased the risk of some offences. Research, policy and prevention efforts must disaggregate drug use and recidivism measures when modelling their relationships, assessing recidivism risk, and triaging offenders into education and treatment. These findings provide a clear imperative to reduce progression to frequent drug use, and to provide demographically-tailored responses that address the health and criminogenic implications of frequent drug use.

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Copyright Act 1968. I retain all proprietary rights, such as patent rights. I al so retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted

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Dec 31,2013

ii Abstract Drug use is a highly prevalent and modifiable risk factor for recidivism. Drug use and recidivism are complex phenomena, as reflected in the diverse drug-crime relationships reported in the research literature. These relationships have not been studied in detail among community-supervised youths, who comprise the majority of supervised young offenders and differ in important ways from detainees and adults. This thesis examined drug-crime relationships among 800 Australians in community-based juvenile justice supervision. Extensive baseline health surveys were linked with lifetime court data, giving four years' prospective observation of recidivism.

Three prospective studies modelled predictors of the prevalence (79%), timing (median: eight months after baseline), frequency (mean: six convictions) and severity of recidivism (58% committed less serious offences). Frequent drug users had poorer recidivism outcomes but drug use did not predict recidivism for most drug users. However, daily cannabis use predicted more rapid violence and weekly opioid use predicted a four-fold increase in theft outcomes. Static (unmodifiable) risk factors including prior detention were stronger predictors of recidivism than dynamic (modifiable) risk factors. With the exception of opioid use, risk factors varied substantially by gender, ethnicity and age.

iii Dedication

For Bec, who is the ultimate travel companion, saathi, swasni, Sherpa ra sirdar.

For Skye, who made my toughest year the best. Happy 1st Birthday, monster.

iv Acknowledgements

The first word must go to my supervisors. Dianna Kenny entrusted me with the baseline survey and has been an unflagging and uncompromising mentor ever since, for which I am extremely grateful. Dhanyabad, Dianna. Louisa Degenhardt enabled me to pursue this PhD and provided an intensive internship in epidemiology. Thank you, Louisa, for constantly raising the bar and keeping it in my sights. Wendy Swift dove in mid-PhD and worked with me to clear a path of my choosing. This has been arduous but hugely empowering, Wendy; I owe you one. Chris Lennings inspired me to pursue forensic psychology and materialised at the right times en route.

The baseline health survey succeeded through the combined efforts of a talented field team, especially Natalie Lyall, Di Ison and Istvan Schreiner. My thanks to all those who contributed: the students, investigators, Justice Health and Juvenile Justice staff across NSW. To the young people who shared their time and their stories: I wish you well. Many clinicians and academics have helped shape my understanding of issues in this thesis and beyond, including Shelley Turner, Andrew McGrath, Anna Ferrante, Tara McGee, Tony Thompson, Claudia Vecchiato, Gilbert Whitton and John Kasinathan.

Data linkage was made possible by Don Weatherburn, and Craig Jones, Tracy Painting and Nadine Smith at BOCSAR graciously resolved my queries. Barbara Toson, Tim Slade, Fiona Shand, and Natacha Carragher led me into and out of the statistical woods. Shane Darke told it how it is and stuck with me when times were tough. Mary Kumvaj, Eva Congreve, Kristy Martire, Michael Farrell, Kate Dolan and Anthony Shakeshaft went beyond the call in supporting my efforts. Capers academic and extra- curricular were made memorable and enjoyable by the NDARC diaspora: Sarah, Ed, Bianca, Briony, Emma, Alys, Elizabeth, Courtney, Fi, Marion, Tim, Caitlin, Jenny, Annie, Amy, Anna, John, Chiara, Jo, Amanda, Hammad, Ansari, Nat, Michelle, Mark, Julie et al.

The belief and benevolence of my Nan, Mum, Graham, Chris, Liz, Dad, Deborah and all my family and friends sustained me while I was away with this thesis. They cheered me on, gave me space, housed and nourished me, made comments and proofed at short notice, and kept me connected with the ‘real’ world. Finally, my partner Bec gave so much to make this happen, and was her beautiful self throughout. B… we made it!

v Table of contents Declarations ...... ii Abstract ...... iii Acknowledgements ...... v Table of contents ...... vi List of tables ...... ix List of figures ...... xii List of abbreviations ...... xiii 1 Young offenders, drug use and recidivism ...... 1 1.1 Overview of juvenile offending ...... 2 1.2 Review of recidivism studies ...... 7 1.3 Review of explanations of offending ...... 14 1.4 Review of risk factors for offending ...... 21 1.5 Review of drug use among young offenders...... 40 1.6 Conclusion ...... 52 2 Drug-crime relationships ...... 53 2.1 Explanations of drug-crime relationships ...... 54 2.2 Empirical evidence of drug-crime relationships ...... 62 2.3 Conclusion ...... 74 2.4 Thesis structure ...... 75 3 Methods ...... 77 3.1 Overall research design ...... 77 3.2 Sampling and recruitment ...... 79 3.3 Baseline measures and variable definitions ...... 82 3.4 Linkage to conviction data...... 92 3.5 Data analysis ...... 96 3.6 Conclusion ...... 99 4 Description of the sample and correlates of specific offences ...... 100 4.1 Correlates of specific offending...... 101 4.2 Aims ...... 102 4.3 Method ...... 102

vi 4.4 Results: sample characteristics ...... 103 4.5 Prior criminal justice involvement ...... 113 4.6 Discussion ...... 124 5 Patterns and correlates of drug use ...... 130 5.1 Aims ...... 130 5.2 Method ...... 131 5.3 Results ...... 132 5.4 Discussion ...... 144 6 Patterns and correlates of recidivism over two years after baseline ...... 150 6.1 Prevalence of two year recidivism ...... 151 6.2 Aims ...... 151 6.3 Method ...... 152 6.4 Results ...... 153 6.5 Discussion ...... 162 7 Patterns and correlates of the timing of recidivism ...... 168 7.1 Survival analysis and the recidivism hazard ...... 168 7.2 Correlates and predictors of survival ...... 172 7.3 Aims ...... 178 7.4 Method ...... 179 7.5 Results ...... 182 7.6 Discussion ...... 194 8 Patterns and correlates of the frequency and severity of recidivism ...... 204 8.1 Frequency of offending ...... 205 8.2 Severity and escalation of offending ...... 207 8.3 Aims ...... 208 8.4 Methods ...... 208 8.5 Results ...... 211 8.6 Discussion ...... 225 9 General discussion ...... 228 9.1 Summary of key findings by theme ...... 228 9.2 Explaining recidivism and drug-crime links ...... 234 9.3 Practical implications ...... 239

vii 9.4 Limitations ...... 245 9.5 Research implications ...... 249 9.6 Conclusion ...... 253 References ...... 254 Appendix A: awards and publications during candidature ...... 308 Appendix B: analyses of demographic variation ...... 312 Appendix C: additional tables and figures ...... 318

viii List of tables Table 1.1 Australian studies of juvenile recidivism urbane to this thesis ...... 13 Table 1.2 Meta-analytic predictors of juvenile and adult recidivism ...... 24 Table 3.1: Demography of the sample and target population ...... 80 Table 3.2: Time from sentencing to baseline survey, and recidivism observation time 93 Table 3.3: Jurisdiction of hearings ...... 93 Table 4.1: Demographic characteristics of the sample and target population...... 103 Table 4.2: Family history, living characteristics, and maltreatment ...... 104 Table 4.3: Mental and physical health ...... 106 Table 4.4: Cognitive ability, academic indicators and employment...... 107 Table 4.5: Peers, victimisation, and sexual history ...... 109 Table 4.6 Frequency of drug use in the past year ...... 111 Table 4.7 Unadjusted and adjusted odds ratios for independent correlates of female gender ...... 112 Table 4.8: Baseline conviction and court orders ...... 113 Table 4.9: Prevalence and frequency of prior conviction type by gender ...... 115 Table 4.10 Prior offending by demographic and other risk factors ...... 117 Table 4.11 Prior conviction type by drug use and drug-related offending variables ... 119 Table 4.12 Unadjusted and adjusted odds ratios for correlates of violent convictions ...... 121 Table 4.13 Unadjusted and adjusted odds ratios for correlates of theft convictions .. 122 Table 4.14 Unadjusted and adjusted odds ratios for correlates of robbery convictions ...... 123 Table 5.1 Problems with drug use, drug treatment and other problems...... 133 Table 5.2 Adjusted odds ratios for correlates of frequency of binge drinking ...... 135 Table 5.3 Adjusted odds ratios for correlates of frequency of cannabis use ...... 138 Table 5.4 Adjusted odds ratios for correlates of frequency of amphetamine use ...... 141 Table 5.5 Adjusted odds ratios for correlates of opioid use ...... 143 Table 6.1 Prevalence of recidivism among Australian young offenders ...... 151 Table 6.2 Descriptive and bivariate statistics for continuous predictors of recidivism 155 Table 6.3 Descriptive and bivariate statistics for categorical predictors of recidivism 156

ix Table 6.4 Unadjusted and adjusted odds ratios for predictors of general recidivism . 158 Table 6.5 Unadjusted and adjusted odds ratios for predictors of violent recidivism .. 159 Table 6.6 Unadjusted and adjusted odds ratios for predictors of theft recidivism ...... 160 Table 6.7 Unadjusted and adjusted odds ratios for predictors of robbery recidivism . 161 Table 7.1 Key Australian survival analysis studies referenced in this chapter ...... 170 Table 7.2 Abbreviated life table: new offences after baseline by the entire sample ... 182 Table 7.3 Unadjusted and adjusted time ratios for predictors of general recidivism .. 188 Table 7.4 Unadjusted and adjusted time ratios for predictors of theft recidivism ...... 190 Table 7.5 Unadjusted and adjusted time ratios for predictors of violent recidivism ... 193 Table 8.1 Model fit indices for alternative count models ...... 210 Table 8.2 Mean, variance and maximum for conviction outcomes ...... 211 Table 8.3 Unadjusted and adjusted incident rate ratios for predictors of recidivism .. 216 Table 8.4 Unadjusted and adjusted incident rate ratios for theft recidivism predictors ...... 218 Table 8.5 Unadjusted and adjusted incident rate ratios for violent predictors of recidivism ...... 220 Table 8.6 Adjusted odds ratios for predictors of violence severity among recidivists . 222 Table 8.7 Prevalence, unadjusted and adjusted odds ratios for predictors of escalation in seriousness of offending ...... 223

Appendix Table A: Missing data and coding for multivariate models ...... 318 Appendix Table B: Studies frequently referenced in this thesis ...... 319 Appendix Table C: Summary of predictors across all studies ...... 320 Appendix Table D: Prevalence and unadjusted odds ratios for variables in the binge drinking model (Table 5.2) ...... 324 Appendix Table E: Prevalence and unadjusted odds ratios for variables in the cannabis use model (Table 5.3) ...... 325 Appendix Table F Prevalence and unadjusted odds ratios for variables in the amphetamine use model (Table 5.4) ...... 326 Appendix Table G: Prevalence and unadjusted odds ratios for variables in the opioid use model (Table 5.5) ...... 327

x Appendix Table H Relationship of escalation and violence severity to frequency of recidivism, expressed as incidence rate ratios (Figure 8.5) ...... 328 Appendix Table I: Prevalence and unadjusted odds ratios for predictors of violence severity among recidivists (Table 8.6)...... 329

xi List of figures Figure 4.1 Prevalence of prior convictions by offence type ...... 114 Figure 4.2 Overlap between prior conviction types ...... 115 Figure 5.1 Number of drugs including alcohol used in past year ...... 132 Figure 5.2 Relationships between patterns of weekly drug use ...... 132 Figure 6.1 Relationship between conviction patterns in the two years after baseline 153 Figure 7.1 Baseline hazard for general recidivism ...... 183 Figure 7.2 Bivariate hazard ratios: recidivism type by drug use ...... 184 Figure 7.3 Failure estimates for general recidivism by frequency of cannabis use ...... 185 Figure 7.4 Failure estimates for violent recidivism by age group ...... 186 Figure 7.5 Adjusted theft survival curves by frequency of opioid use ...... 191 Figure 7.6 Adjusted theft hazard curves by frequency of opioid use ...... 191 Figure 8.1 Distribution of recidivism counts ...... 211 Figure 8.2 Predicted count of general, violent and theft convictions by drug type ..... 212 Figure 8.3 Predicted count of convictions with 95% confidence intervals, by age ...... 213 Figure 8.4 Unadjusted odds ratios for violence severity by drug use ...... 221 Figure 8.5 Relationship of escalation and violence severity to frequency of recidivism ...... 224

Appendix Figure I Prevalence of prior convictions by offence type (Figure 4.1 in landscape format) ...... 322 Appendix Figure II Bivariate hazard ratios: recidivism type by drug use (Figure 4.2 in landscape format) ...... 323

xii List of abbreviations AOR Adjusted odds ratio ASB Antisocial behaviour ATR Adjusted time ratio AIRR Adjusted incidence rate ratio APS-SF Adolescent Psychopathology Scale – Short Form ASOC Australian Standard Offence Classification AUD Alcohol Use Disorder BOCSAR Bureau of Crime Statistics and Research CALD Culturally and linguistically diverse CD Conduct Disorder CI Confidence interval CJS Criminal justice system DJJ NSW Department of Juvenile Justice DSM Diagnostic and Statistical Manual of Mental Disorders ESB English-speaking background ESB/CALD English-speaking background, with one or more CALD parents IDU Injecting drug use IRR Incidence rate ratio IQ Intelligence quotient JJ NSW Juvenile Justice NSW NB Negative binomial NSW New South Wales (Australian state) OR Odds ratio PDU Problem drug use SD Standard deviation SES Socioeconomic status SSH Suicidal and self-harming behaviour SUD Substance use disorder TR Time ratio TSI Time spent incarcerated UK United Kingdom US United States VIF Variance inflation quotient VIQ Verbal IQ YLS/CMI:AA Youth Level of Service/Case Management Inventory: Australian adaptation YLSI Youth Level of Service Inventory

xiii 1 Young offenders, drug use and recidivism

Young people who offend are at high risk of recidivism (repeat offending) (Gatti, Tremblay, & Vitaro, 2009) including serious, violent and chronic offending (Kempf- Leonard, Tracy, & Howell, 2001; Lipsey & Derzon, 1998). Recidivists account for the majority of juvenile crime, a disproportionate share of crime across the lifespan, and the most severe offending (Kempf-Leonard et al., 2001). Recidivism burdens health, welfare and justice systems (Chitsabesan et al., 2012), through trauma to victims and the community, lost productivity (Bailey & Scott, 2009), and law enforcement and correctional costs (McCollister, French, Sheidow, Henggeler, & Halliday-Boykins, 2009; Welsh et al., 2008). Hence, recidivism reduction is perennially on the policy agenda. Incarceration disrupts adolescent development, does not produce long-term reductions in recidivism and can increase recidivism risk (Gatti et al., 2009). Supervised community orders give juvenile justice agencies a mandate to address modifiable risk factors for recidivism and an opportunity to address offender health and welfare without removing youths from their community.

Juvenile recidivism arises from a wide range of risk factors, amongst which drug use is one of the most prevalent and modifiable (Prichard & Payne, 2005; Simpson, Howard, Copeland, & Nelson, 2009). Treatment for drug problems can significantly reduce recidivism (Holloway, Bennett, & Farrington, 2006) and improve well-being (Lennings, Kenny, Howard, Arcuri, & Mackdacy, 2007). Drug-crime research has focused largely on adult offenders and may not generalise to juveniles, given their unique developmental and legal characteristics (Richards, 2011c; Steinberg 2009). This research has indicated that drug use and recidivism vary widely by type, frequency and severity, and their relationships are accordingly diverse (Bennett & Holloway, 2007). However, there is little relevant research on young offenders sentenced to community supervision, who outnumber young detainees by four to one (Australian Institute of Health and Welfare, 2012), or about how these relationships vary by age, gender or ethnicity. Wide legal and demographic variation between jurisdictions precludes easy generalisation from international studies to the Australian context, yet no Australian studies have systematically assessed the impact of drug use on recidivism in this group.

1 A greater understanding of drug-crime relationships among specific offender groups can inform criminal justice responses and service planning and may in turn reduce the health and psychological burden on offenders and the economic and social burden on the community. Well-controlled models are necessary to identify the independent effects of drug use and other potential drivers of recidivism. Disaggregated measures of drug use and recidivism allow for a nuanced assessment of drug-crime relationships that may reveal more precise and clinically appropriate intervention targets.

This thesis represents the first systematic assessment of drug-crime relationships among community-supervised young offenders in Australia’s most populous state, New South Wales (NSW). This thesis explores the correlates of binge drinking, cannabis, amphetamine and opioid use by this group, and assesses how drug use and other recidivism risk factors relate to prior offending and recidivism outcomes including the prevalence, timing, and frequency of general, violent and theft offences. This chapter provides overviews of juvenile offending (focussing on recidivism) and of drug use (focussing on drug use by young offenders). The characteristics of the population under study are described, as are the prevalence, correlates, and explanations for recidivism and drug use. This provides a platform from which to review explanations and evidence of drug-crime relationships in detail (Chapter Two).

1.1 Overview of juvenile offending

Juvenile offending refers to antisocial behaviour (ASB) by persons under the age of majority (18 years of age in the state of New South Wales), that, if detected, could lead to a conviction (Bailey & Scott, 2009). The prevalence of juvenile offending is difficult to ascertain, in part because definitions used in studies vary widely. A large self-report study suggested that offending was normative among NSW high-school students (61% lifetime prevalence, 48% past year prevalence, 29% past year prevalence of assault) and reported few gender differences (Baker, 1998). Australian national data and a US/Australian cross-national study reported figures of 8-12% for violence and other ASB among youths aged 13-17 (Hemphill et al., 2009; Sawyer et al., 2010). In Australia and globally, the prevalence of Conduct Disorder (CD; severe ASB diagnosed in persons under 18) is 3% (4% males, 2% females, Erskine et al., 2013).

2 Participants in this thesis had court convictions, that is, they were proven offenders in a legal sense. Most juveniles self-report that they offend, but only a minority have criminal justice system (CJS) contact, and most of these are diverted from the courts. Of those who are convicted, one in three are sentenced to supervision, and approximately 80% of these are supervised in the community (Australian Institute of Health and Welfare, 2012). The term, young offender, used throughout this thesis, refers to youths under 25 years of age who are adjudicated within a juvenile justice system, in this thesis Juvenile Justice NSW (JJ NSW).

1.1.1 Criminal justice system involvement

Estimates of juvenile offending prevalence vary widely by CJS agency. In Australia, a minority of youths who self-report offending have recorded CJS contact (Smart et al., 2005). In 2007-08, 9% of juveniles in NSW were dealt with by police, and one in four of these were referred to court, usually due to more serious offending (Richards, 2009). Among court-sentenced young offenders in NSW in 2002, 30% were sentenced for violent offences and 36% for theft (Smith & Jones, 2008).

Most juvenile matters are heard in the Children’s Court and result in convictions; JJ NSW also supervises adults sentenced by this court. Legislation encourages diversion from formal CJS involvement (e.g. to conferencing, Luke & Lind, 2005), and most convictions lead to fines (36%) or unsupervised orders (34%) rather than community supervision (23%) or detention (5%) (Richards, 2009). Unsupervised offenders are considered less serious offenders and may have ‘attenuated’ problems (Ross & Graham, 2011) compared with supervised offenders. Across Australia, 0.5% of Australians aged 10-17 were under juvenile justice supervision in 2010-11, with 29% of these in NSW (Australian Institute of Health and Welfare, 2012). This reflects a marginal increase over the past decade, and is comparable to the UK, but far lower than in the US (Australian Institute of Health and Welfare, 2012) (see caveat on international comparisons below). Most (80%) community-supervised juveniles are on a probation-related order, 10% are on bail (pending sentencing), and 10% are on parole or a suspended sentence.

3 Although community-supervised offenders outnumber detainees by four to one, more research has been conducted with detainees (Bickel & Campbell, 2002; Dixon, Howie, & Starling, 2004; Lennings et al., 2007; Putninš, 2005) due largely to the greater ease of accessing detained populations. The two groups are not mutually exclusive (for example, many detainees are ultimately paroled) but differ in key ways. Community supervision permits continuity of family, education, employment, and pro-social leisure activities (Kessler & Kraus, 2008; Lowe, Dawson-Edwards, Minor, & Wells, 2008) and to ongoing criminal and drug use opportunities. Detention restricts these activities but also makes detainees more accessible to treatment providers (Myers & Farrell, 2008), and some custodial treatments are more effective than community-based treatments (Lee et al., 2012). Detainees tend to have more extensive prior offending and more heterogeneous risk characteristics (Indig et al., 2011; Kenny & Nelson, 2008; Lemke, 2009). One NSW study found detainees were more likely to intend to reoffend (Vignaendra, Viravong, Beard, & McGrath, 2011); close confinement with antisocial peers may explain why detention can be iatrogenic of recidivism (Bayer, Hjalmarsson, & Pozen, 2009; Dishion, McCord, & Poulin, 1999). However, both groups tend to be repeat offenders (Smith & Jones, 2008; Weatherburn, Cush, & Saunders, 2007) at high risk of recidivism (Chen, Matruglio, Weatherburn, & Hua, 2005).

Juveniles are overrepresented in the CJS. For example, juveniles comprise 11% of the NSW population but 26% of persons proceeded against by police (Australian Bureau of Statistics, 2007; Richards, 2009). This over-representation has been linked to juveniles’ more visible, incompetent, opportunistic and group-based offending (Cunneen & White, 2011). Males and Indigenous youths are heavily overrepresented in the CJS and this is particularly true for supervised offenders. In 2010-11, there were four times as many males as females under juvenile justice supervision in NSW and 15 times as many Indigenous than non-Indigenous youths (Australian Institute of Health and Welfare, 2012). Overrepresentation is less extreme among community-based than detained offenders; nationally, males accounted for 92% of detainees compared to 82% of community-supervised offenders, and Indigenous youths accounted for 50% of detainees compared to 41% of community-supervised offenders (Australian Institute of Health and Welfare, 2012).

4 Cross-jurisdictional comparisons of CJS involvement are unreliable (Richards, 2009) because of the wide variation within and between countries in policing methods, sentencing policies (e.g. mandatory detention), criminal justice processes, and philosophies of offender classification (e.g. age of criminal responsibility) and management (Australian Institute of Health and Welfare, 2012). Sociodemographic, structural and cultural differences also complicate cross-jurisdictional comparisons. For example, the ethnic composition of juvenile offender populations varies widely: Black and Hispanic youths are prevalent in the US but not Australia; Indigenous youths are highly prevalent in Australia and Canada but not the UK. Access to firearms, drugs, treatment services and treatment funding also come to bear on offending but vary from country to country.

1.1.2 Recidivism and associated harms

In essence, recidivism refers to recurrent criminal behaviour (Maltz, 1984 [2001]), and is typically operationalised as a return to the criminal justice system (e.g. reconviction) in studies that use official measures of offending. Detailed explanations of the concept of recidivism are provided at Section 3.4.2 and in the recidivism Chapters Six to Eight.

Most youths reoffend after formal CJS involvement, which infers that the CJS is ineffective in preventing reoffending. Thus as Lennings (2008) notes, a focus on early intervention and prevention is needed in addition to rehabilitation. Second, many juvenile offenders reoffend as adults, so studies should follow youths into the adult CJS. Third, the time period between recidivism events varies widely. Distinguishing those who reoffend sooner is essential for resource and service planning. Fourth, offence frequency distributions have a strong positive skew: many offenders commit few crimes, and few commit many crimes. Distinguishing recidivists and non-recidivists is therefore inadequate for addressing recidivism, and at the same time the volume of recidivism can be reduced without affecting its prevalence. Finally, there is also marked heterogeneity in the type (and severity) of offending among recidivists, as discussed in the next section.

5 Recidivism incurs direct and indirect costs to the community through fear of crime and actual trauma. The costs of CJS involvement (McCollister et al., 2009; Welsh et al., 2008) are also substantial. There is also a considerable loss to the community and offenders through lost productivity (Bailey & Scott, 2009). Schubert, Mulvey, and Glasheen (2011), for example, demonstrate that more extensive prior ASB and negative peer influence predict lower levels of educational/employment participation among serious young offenders, not only recidivism. Recidivists especially experience poor health and psychosocial outcomes and are marginalized and traumatised in the course of their criminal involvement (Tucci 2008, in Richards, 2009).

This thesis focuses on general recidivism, but also separately assesses violent and acquisitive offending, because their prevalence, harms, and associated risk factors (including drug use) vary widely. Definitions of offence types are provided at Section 3.4.1. Clearly, variations in the nature of recidivism have major clinical and policy relevance. This thesis assesses the frequency of offending over time, and thus the rate of recidivism. The timing and rapidity of recidivism is also assessed. Offence type (see next section) is assessed, allowing the severity of recidivism and escalation to be observed. This subset of ‘criminal career’ dimensions (Blumstein & Cohen, 1987) is addressed more fully in later empirical chapters. Criminal career research has a long history of study Ellis (1897), and is exhaustively reviewed in Blumstein, Cohen, Roth, and Visher (1986a); DeLisi and Piquero (); Piquero, Farrington, and Blumstein (2003).

Understanding risks is the first step towards reducing related harms and costs. Well- designed, appropriately targeted interventions to reduce juvenile recidivism have been proven to be effective (Borum, 2003). Most offender treatment studies show modest effects on recidivism (McGuire, 2008), with greater effects for higher risk offenders (Andrews et al., 1990), and effective community-based programs can cut recidivism by violent juveniles by 40% (Lipsey & Wilson, 1998). Recidivism reduction is not the sole goal of CJS intervention. Restorative justice (for example) is no more effective in reducing recidivism than court orders but can offer closure and relief to victims and offenders (Smith & Weatherburn, 2012). Drug treatment programs are more effective in reducing drug use than crime (Prendergast, Podus, Chang, & Urada, 2002).

6 1.2 Review of recidivism studies A systematic search strategy (updated 2012) identified all studies of recidivism among Australian juvenile offenders. Search strings were developed and revised with specialist drug and alcohol, medical and legal librarians. Medline, EMBASE, CINCH, APAIS, NCJRS, and the Sociological Abstracts databases were consulted. Grey literature was sourced through Google Scholar, research and justice agency websites (Australian Institute of Criminology, Bureau of Crime Statistics and Research, Rand Corporation, UK Home Office, US Office of Juvenile Justice and Delinquency Prevention, JJ NSW, and the New Zealand Ministry of Justice). References of relevant articles were searched and unpublished material was sought from local and international experts. Reviews and meta-analyses were also included in the search.

Three main types of recidivism study were considered. Most used retrospective or cross-sectional designs that preclude solid conclusions about causality or sequencing of risk factors for recidivism (Hill, 1965; Liberman, 2008; Rutter & Tienda, 2005); recall of prior offending can be very poor (Morris & Slocum, 2010; Roberts & Wells, 2010). Data linkage enables excellent case ascertainment of prospective recidivism data, but this method has been underutilised with Australian juveniles (Ferrante, 2008) and has often been limited to administrative data that are very limited in scope. Few studies have included extensive psychosocial data and relevant Australian studies have not explored demographic variation in such factors. A second design, the prospective cohort study, provides vital information on the development of juvenile offending, prevalent risk factors, and the effects of individual change on offending. However, prospective cohorts of offenders are rare, and attrition is high due to offenders’ transience, mortality, incarceration and poor compliance (Kenny & Nelson, 2008). Thirdly, interventions to reduce recidivism were also considered. The studies reviewed varied widely in their sampling, design, analyses and location (as described in more detail below); results from any one study should not be generalised to all offenders.

Recidivism studies typically distinguish ‘static’ risk factors (fixed, historical or otherwise unmodifiable, e.g. age) from ‘dynamic’ risk factors (modifiable, such as drug use). Dynamic risk factors may affect multiple outcomes; when the outcome is recidivism, dynamic risks are also referred to as criminogenic needs (Andrews & Bonta, 2007).

7 1.2.1 Recidivism of at-risk or general population youths

Several studies from at-risk and general population samples have provided high quality information about the characteristics of juvenile recidivists, and have clarified developmental links between drug use and offending. These are summarised in Appendix Table B. General population studies are limited by the low base rates of many serious offences and very problematic behaviours (e.g. injecting drug use). Offenders also tend to be under-sampled unless the study comprises a birth cohort; those that have clarified developmental links between drug use and offending include the Mater University Study of Pregnancy (Australia), the Dunedin Multidisciplinary Health and Development Study, and the Christchurch Health and Development Study. At-risk cohorts, such as socioeconomically deprived youths (e.g. West & Farrington, 1973) overcome this to some extent by including youths among whom the base rates are much higher.

Appendix Table B also lists prospective studies of international samples with repeated follow-up and low levels of attrition, cited frequently in this thesis, including the Pathways to Desistance study which directly compared predictors of male and female recidivism, and gender-specific moderation effects. Schubert et al. (2011) recently reported gender differences in predictors of rearrest and self-reported ASB rates over six years. Gender specific predictors of both outcomes were identified: parental ASB for males and lower psychosocial maturity for females. Negative peer influence significantly predicted both outcomes for males, but only ASB for females. By contrast, prior ASB predicted both outcomes for females but only ASB for males. However, association with peers increased male rearrest for those with a substance use disorder (SUD) or comorbid SUD and mental health problems, while prior ASB predicted male rearrest only for those with no diagnosis. These results suggest SUD moderates the link between males’ prior ASB and risk of recidivism, and that interventions should focus particular attention on drug-using males’ peers. International studies have greatly shaped knowledge of juvenile offending, but their findings may not apply to Australian offenders (see Section 1.1.1).

8 1.2.2 Recidivism of Australian juvenile offenders

There is a paucity of literature profiling Australian community-based offenders. This section describes the design and findings of major relevant Australian juvenile recidivism studies that are urbane to this thesis. The prevalence and basic sampling details of these studies are summarised in Table 1.1. The table shows that around two- thirds of community-supervised youths recidivate within four years.

McGrath (2007) compiled the first mixed custodial and community-based sample of juvenile offenders in NSW, including 200 male and 43 females in the community. This work focused on the sentencing process and deterrent effects of custodial sanctions, with subsequent analyses expanding the sample and exploring correlates and prospectively-assessed recidivism outcomes for each gender (McGrath, 2009b; McGrath, 2010). Detailed drug use data were collected but were not a focus of the research (A McGrath, personal communication, 11 December 2009), and so were aggregated in the analyses. Half (52%) of the sample were reconvicted within the maximum 37 month observation period. Recidivists were more often male, Indigenous, had more prior CJS contacts, and earlier onset of offending (McGrath, 2009b; McGrath, 2010). Frequency of alcohol use and extent of past year drug use were significantly associated with recidivism, but past month drug use was not. Other significant correlates of recidivism included low parental supervision (but not most parenting factors, or socioeconomic status), truancy, and delinquent peer associations (but not peer drug use). Strikingly, the only independent predictors of recidivism were having a prior court appearance, and having received a custodial rather a community- based sanction. This is encouraging of a focus on recidivists and of separate consideration of these two offender populations. Most interviews were conducted in court following sentencing to a community or custodial sanction, or later in custody (Weatherburn et al, 2009). Although apt for the aims of that particular study, this design presents challenges that interviewers might otherwise seek to avoid, such as a non-neutral interview setting (rather than a community location), proximity to a stressful event (sentencing), different mood states (presumably negative for those receiving following a harsher penality), and two conditions for timing and location. Such factors complicate comparisons with studies conducted in other settings. 9 One other Australian study has prospectively assessed community-supervised youths’ recidivism. Denning and Homel (2008) modelled rearrest (prevalence: 71%) among 190 Queensland youths. Independent predictors included peer delinquency and ‘any drug use’, but type of drug use was not explored, strong static predictors of recidivism were not considered (demographics, criminal history; see Section 1.4), and females were excluded. Such design features are deterrents to wider generalisation of these results.

Several retrospective studies have been conducted in NSW. In a study of all juvenile offenders appearing in the Children's Court from 1986-94, 30% re-appeared during this time with half of these re-appearing only once (Cain, 1996); this was labelled a ‘good news story’ (Cain, 1997). Carcach and Leverett (1999) and Coumarelos (1994) reported similar recidivism rates. Consistent predictors across these studies included male gender, younger age, and criminal histories (more prior offences, prior theft offences, and prior supervised orders). Later studies that tracked juveniles into the adult CJS reported much higher recidivism rates, for example, 68% over eight years after a first Children’s Court appearance, with 84% of recidivists progressing to adult court (Chen et al., 2005). Recidivism outcomes were worse for male, Indigenous, early onset youths, and youths with more prior appearances. These factors also predicted two- year reconviction by juveniles with non-custodial sanctions (Smith & Jones, 2008).

Holmes (2011) included recidivists when calculating reconviction rates for all juveniles convicted in 1994: 79% were reconvicted within 15 years (vs. 65% of adults aged 18- 25), and the probability of reconviction decreased over time: 40% were reconvicted within one year and 67% within four years. This pattern is typical of offender samples; Chapter Seven reviews the literature on the timing of offending and its correlates in detail. Specific recidivism was more common among youths convicted for assault (52% were reconvicted for assault) and for theft (54%) than for robbery (27%). However, among youths reconvicted in the Children’s Court (Smith, 2010), 30% were convicted for theft, which was nearly twice the number convicted for violence; and only 3% were convicted of robbery. Other Australian studies of offender and birth cohorts offer similar findings about prevalence, demographics and the significance of prior CJS involvement to recidivism (Brame, Mazerolle, & Piquero, 2010; Ferrante, Loh, &

10 Maller, 2004; Lynch, Buckman, & Krenske, 2003; Skrzypiec, 2005; Victorian Department of Human Services, 2001). These studies show that early-onset offenders are more likely to be frequent offenders and that a small group of frequent offenders contribute disproportionately to the total volume of crime, e.g. 12% of apprehended Victorian juveniles committed 53% of all offences by age 18 (Payne, 2009).

The foregoing studies drew solely on administrative data with only a few, static risk factors. Weatherburn et al. (2007) compiled dynamic and additional static factors from government data to profile 392 youths first commencing community-supervised orders in 2000-01. Within four years, 71% were reconvicted and younger age, Indigeneity, CJS contacts and prior theft were associated with reconviction while gender and other ethnicity were not. Other correlates included broken homes, foster care, maltreatment, and peer delinquency; academic skills and illicit drug use were not (however, type or frequency of use was not distinguished). As with earlier studies, recidivism was predicted by younger age and prior contacts (more than two prior convictions increased the risk of recidivism ten-fold). Early school leaving and school exclusion both doubled the risk, but these may be proxy risk factors for peer delinquency, which was excluded. This study showed that administrative data can improve recidivism prediction, but also highlighted limits of such data and of manual linkage (the small N reflects the complexity of manually integrating such data).

Recent data on juvenile recidivism in NSW (McGrath & Thompson, 2012) provides insights into the dynamic risk factors that distinguish more established offenders, by mapping the major criminogenic risk domains (as measured by the Youth Level of Service Inventory) onto recidivism. All domains were correlated with recidivism, with offence history (static) and peer relations (dynamic) equally so. In the final multivariate model, these factors were equally strong predictors, with substance abuse and other dynamic domains (attitudes/beliefs, education/employment) half as strong again. By using more complete data and more robust measures this study revealed a different risk profile for recidivists than the previous studies did. Some dynamic risk domains did not predict recidivism (family/housing, personality/behaviour and leisure/recreation) but as the authors note, these factors still inform needs assessment and case planning.

11 Most recidivism studies of former Australian detainees have limited their assessment of risk factors to administrative data. Along with studies of young adult prisoners, these indicate a comparable but more substantial demographic bias to that reported for mixed samples, e.g. greater male, early onset, prior offending, and Indigenous over-representation. Putninš (2005) identified age, age at first offence and prior offences as correlates of recidivism, along with two weaker dynamic correlates taken from a standardised psychosocial assessment: frequency of recent alcohol and inhalant use, and signs of ADHD (e.g. impulsivity). Although multivariate results were not reported, all five of these variables were retained in a risk index with solid predictive validity, underscoring the value in including dynamic factors for explaining recidivism.

Rates of recidivism over five years among young offenders internationally are comparable to Australian rates, e.g. 72% for NZ detainees (Wilson, 2004 in Lennings, 2008) and 79% in a large US sample (Trulson, Marquart, Mullings, & Caeti, 2005). Recidivism rates of offenders at different stages in the CJS reflect the severity of their CJS involvement. For example, five-year reconviction rates were 42% for youths receiving cautions and 58% for youths diverted to a restorative justice process in NSW (Vignaendra & Fitzgerald, 2006), whereas 66% of young detainees in South Australia recidivated within just six months (Putninš, 2005). Two Australian studies have shown that for young offenders matched on several characteristics (e.g. age,) recidivism rates did not differ by penalty type (McGrath & Weatherburn, 2012; Smith & Weatherburn, 2012), which suggests that more punitive intervention does not deter recidivism.

Basic design features of major Australian juvenile recidivism studies from the past 15 years are summarised in Table 1.1; the review of other empirical evidence for recidivism risk factors follows in Section 1.4. Cross-sectional studies of Australian offenders are not reviewed here, but the most important of these are listed in Appendix Table B, including the comprehensive surveys of young detainees in NSW (Allerton et al., 2003; Indig et al., 2011) and the Drug Use Careers of Offenders (DUCO) juvenile study (Prichard & Payne, 2005) which retrospectively assessed the correlates of offending among juvenile detainees in Australian states other than NSW. The tables show wide variation in study design including population, size, setting, outcomes, and

12 duration. Meta-analyses (see 1.4.2 & 2.2.1) can quantify how such factors may affect the nature and strength of risk factor relationships. Bennett et al. (2008), for example, found drug-crime associations have increased over time and are twice as strong in studies of adults than juveniles (however, associations also varied widely between studies). Such findings provide an important caveat against uncritical generalisation across studies, and encourage scrutiny of study methodologies.

Table 1.1 Australian studies of juvenile recidivism urbane to this thesis

Study Sample (N) State Outcome Recidivism (observation period) prevalence (Cain, 1998) Juveniles (53000) NSW Children’s Court 30% appearance (1-7 years) (Chen et al., 2005) Cohort, intake age 10- Court (re)appearance 68% any 18, (5476) NSW (<8 years) 57% adult (Holmes, 2011) Court-convicted Reconviction 67% (4y) juveniles (8454) NSW (15 years) 79% (15y) (Smith & Jones, 2008) Young convicted non- Reconviction 59-61% detainees (3709) NSW (<2 years) (Weatherburn, Court-sentenced youths Reconviction 52% Vignaendra, & (152 custodial/243 (6-37 months) McGrath, 2009)* community) NSW (Weatherburn et al., Community-supervised Reconviction 71% 2007) youths (392) NSW (<4 years) (McGrath & Community-supervised Court conviction 51% Thompson, 2012)* youths (3568) NSW (<1 year) (34% <6m) (Ferrante et al., 2004) Youths in JJ contact Any juvenile justice 39% (46410), West. Australia contact (<2 years) (Denning & Homel, Community-supervised Rearrest 71% 2008) youths (~350) QLD (<18 months) (Lynch et al., 2003) Supervised juveniles Adult CJS supervision 79% (1503) QLD (to a max. age of 25) (Putninš, 2005)* Young detainees Recidivism 66% (697) South Australia (6 months) (Victorian Dept. of Young offenders (1527) Subsequent orders 49% (61% Human Services, 2001) Victoria (2 years) recidivists) *Considers drug-crime issues. NSW: New South Wales.QLD: Queensland

13 1.3 Review of explanations of offending 1.3.1 Criminological explanations

Criminologists have put forward diverse explanations for offending. Control theory and its offshoots locate the cause of crime in failures to control individuals’ inherent antisocial drives. Control may relate to self (e.g. ego depletion, Muraven, Pogarsky, & Shmueli, 2006), others (e.g. parental monitoring, peer pressure), and society (e.g. threat of CJS intervention). The ‘general theory of crime’ (Gottfredson & Hirschi, 1990) emphasised the role of self-control and related this to early childhood experience, following early observations of the chronic effects of problematic infant attachment on psychological well-being (Cooper, May, Soderstrom, & Jarjoura, 2009). More recent extensions of control theory (Sampson & Laub, 2005) draw parallels with later workplace and romantic dysfunction. Jobs, schools and partners can provide controls against crime, but low self-control is also a precursor to problems in these areas, such as domestic abuse (Payne, Triplett, & Higgins, 2011).

Merton’s (1938) strain theory attributes offending to social exclusion (from wealth and other esteemed sources of gratification), rather than to individual criminality. Illegitimate means become necessary when individuals perceive a gap between their realistic and aspirational goals. The higher prevalence of robbery among juveniles than adults (Richards, 2011c) could relate to less privileged juveniles being unable to afford the goods they are being encouraged to possess (by advertising, etc). Were satisfaction available elsewhere, crime would not occur. Lennings (2008) argues that crime becomes a ‘consensual’ way for youths to meet their needs in poor communities.

Labelling theory (Becker, 1963) argues that an individual who deviates from social rules becomes ‘labelled’ (i.e. seen and described by others) as deviant, and that if individuals adopt this label further deviance will result. Children with behavioural difficulties may have a long history of labelling that contributes to their initiation and persistence with offending. Criminal charges and more punitive CJS responses may stigmatise offenders and reinforce perceptions of deviance, and this can increase the risk of juvenile recidivism (McGrath, 2009a). Formal labels also affect others with whom offenders have contact; Conduct Disorder (CD) diagnoses can negatively affect clinicians’ 14 approaches to young offenders (Kasinathan, 2009a). Moffitt and Scott (2009) caution that the clinical label of psychopathy could lead to some children being ‘written off’. These notions have special relevance to the study of drug-crime relationships given the strong stigma against heavy drug use by juveniles.

Other theories emphasise different sources of reinforcement for offending. Social learning/differential association theory (Sutherland, Cressey, & Luckenbill, 1992) focuses on others who teach, model and reinforce criminal skills and attitudes. Family members are prominent influences, and peers become more so for adolescents; early exposure to antisocial influences increases the likelihood of involvement with delinquent peers and lower parental supervision. These propositions have found strong empirical support (Warr, 2005).

Situational theories emphasise short-term risk factors (Van Der Laan, Blom, & Kleemans, 2009), including transient emotional states, offender-victim, and features of the environment (e.g. lighting, bystanders). Criminality need not be inherent to an individual; an opportunistic offence may be more likely given a ‘soft’ target and surroundings (such as a lone commuter, and no surveillance). However, the likelihood of offending is increased by the presence of delinquent peers and by being disinhibited due to drug use (Felson, Teasdale, & Burchfield, 2008), and is particularly increased when these two factors co-occur (White, 2004). Victim characteristics also are important for understanding how individuals behave in certain situations, and thus inform forensic treatment and crime prevention efforts. Unfortunately, while such information can be found in police statements and judges’ remarks they are not readily available for recidivism analyses.

1.3.2 Typological and pathological explanations

Age of onset is a major feature of typological theories of offending (Loeber, 1982; Moffitt, 1993; Patterson, DeBaryshe, & Ramsey, 1989). Moffitt proposed two distinct groups of antisocial youths. The first is a small group who initiate ASB in childhood and persist with this behaviour (life-course persistence); their neuropsychological problems interact with their environments to produce personality pathology (Moffitt, 1993). A

15 second, larger group initiate offending during adolescence, and desist in adulthood (adolescence-limited); this group were theorised to imitate ASB by life-course persistent offenders, motivated by a ‘maturity gap’ , or frustration with the disparity between their current (age-mandated) social status and their desire for maturity and “its consequent power and privilege” (Moffitt, 1993: 686).

Various revisions have identified additional, smaller subgroups including one offending only in childhood (Moffitt et al., 2008), and one initiating in adulthood (Zara & Farrington, 2009). However, the life-course persistent and adolescence-limited trajectories have been confirmed by analyses of several longitudinal cohorts (Moffitt et al., 2008). Distinguishing features of the life-course persistent group include parental ASB and attendant genetic risk, early family adversity, neurocognitive deficits, low IQ, impulsivity, and early school and peer difficulties (Moffitt et al., 2008). The life-course persistent group evince the most serious subsequent offending, psychosocial and physical health problems (Moffitt, 2003; Odgers et al., 2007). The adolescence-limited group do not show problems during childhood, and their ASB does arise through delinquent peer interactions, including a desire for status (Moffitt et al., 2008). They show lower levels of adult ASB that go undetected by the CJS (Odgers et al., 2008).

The diagnoses of CD and Anti-Social Personality Disorder (ASPD) reflect a pathological conceptualisation of ASB, rather than a criminological or legal conceptualisation. ASPD is an adult personality disorder so will not be considered here. CD is a childhood behavioural disorder characterized by a persistent pattern of ASB that violates rules or the rights of others. CD should reflect individual pathology rather than a response to the social environment (Wakefield, Pottick, & Kirk, 2002); thus diagnoses must not be made with symptom (behavioural) data only. Revisions to CD criteria with each edition of the major psychiatric diagnostic manuals document changes in the prevailing understanding of serious adolescent ASB (Moffitt et al., 2008). Distinctions have been made between aggressive and non-aggressive CD (DSM-III) and group-based and solitary CD (ICD-9/DSM-III-R). DSM-IV distinguishes childhood onset (no symptoms before age 10) from adolescent onset. These distinctions reflect attempts to characterise subtypes of ASB of different etiologies, courses, behaviours and

16 prognoses (Lahey, Loeber, Quay, Frick, & Grimm, 1997). DSM-V will distinguish youths with callous-unemotional traits (Moffitt & Scott, 2009).

1.3.3 Other approaches to explaining offending

Jessor and Jessor (1977) considered that the proclivity to engage in many co-occurring adolescent risk behaviours reflects an underlying problem behaviour syndrome. This view is supported by research that shows a large overlap between the risk factors for numerous problems, including recidivism and heavy drug use. Several studies (e.g. Sullivan, Childs, & O’Connell, 2010) have identified qualitatively different risk behaviour subgroups. Many others have found support for a dimensional syndrome (a constellation of risk behaviours loading on one unifying latent construct) (LeBlanc, 2003 in Sullivan et al., 2010). The most rigorous studies, however, show that such a construct explains very little involvement in most risk behaviours (Krueger et al., 2002).

Farrington’s ‘Integrated Cognitive Antisocial Potential’ theory draws on strain, control, labelling, and learning theory. The theory posits a central dimensional construct (individual potential to offend) that is influenced by both long term (e.g. physiology, poor attachment) and short term (e.g. intoxication, co-offenders) factors. Long-term potential is stable but short term potential fluctuates. Offending arises from an interaction between these two aspects of individual potential to offend and the social environment – for example if an impulsive individual encountered an opportunity to offend, while intoxicated. Feedback effects are also considered, for example, peer approval may reinforce offending, while a criminal record may hinder employment prospects; both increase offending potential (Farrington, 2003a).

Risk-needs-responsivity theory (RNR; Andrews and Bonta, 2007) is a sophisticated and empirically validated model of offender rehabilitation that outlines primary causes for recidivism and principles for reducing recidivism (Polaschek, 2012). It is social learning and social cognition based , and suggests offending is learned through “complex interactions between cognitive, emotional, personality, and biological factors, along with environmental reward-cost contingencies (rational choice)” (Lennings, 2008). RNR recognises that offending may arise from qualitatively different processes (such as the

17 adolescence-limited/life-course persistent distinction made earlier) but also that the greater an offender’s risk exposure, the more likely they are to offend (Lennings, 2008). RNR sets out three core principles for working with offenders: the risk principle states that recidivism risk differs between offenders and treatment should be focused on higher risk offenders; the need principle outlines the most useful risk factors to target; the ‘responsivity’ principle is concerned with maximising offenders’ treatment engagement and response (Polaschek, 2012). Additional general principles encourage structured risk assessments and community-based treatment that addresses multiple risk factors (Polaschek, 2012).

The RNR brings together risk factors into eight ‘central’ risk/needs domains including the ‘big four’ which are strongly linked to recidivism, and four that are moderately linked with recidivism. RNR assesses the inclusion of these domains based on their relationship to recidivism, as evident in meta-analytic evidence (see following section; Polaschek, 2012). As modified for juveniles, the big four are: antisocial personality, antisocial attitudes/beliefs/cognitions/values, antisocial peer relationships, and criminal history; the smaller factors relate to problematic family/living circumstances (including marriage for adults), education/employment problems, low prosocial leisure/recreation activities, and substance abuse (McGrath & Thompson, 2012; Andrews & Bonta, 2007). Apart from criminal history, which is inherently unmodifiable, these are dynamic risk factors or ‘criminogenic needs’, reflecting the hope that reducing them will lead to reduced recidivism risk (Andrews & Bonta, 2007). RNR also acknowledges risks/needs that are less promising rehabilitation targets and may be ‘non-criminogenic’ (e.g. social status, housing, health, mood problems); improvements in these areas may increase treatment responsivity (Andrews & Bonta, 2007).

The most widely used actuarial risk assessment tools for general recidivism, the Level of Service Inventory family, are modelled on the RNR principles outlined above and the Australian Adaptation of the YLSI (Thompson & Pope, 2005) is the standard for young offenders in NSW (Weatherburn et al., 2007). Similarly, this thesis uses the principles and concepts of RNR for selecting variable, interpreting results and drawing recommendations. As the thesis does not evaluate or design rehabilitation efforts, it

18 does not seek to test the overarching RNR model (which is far more complex than illustrated above, Polaschek, 2012),

1.3.4 Critiques of theories of offending

This thesis does not test theories, however theories are reviewed in this thesis because they inform variable selection and are informative at a contextual level. In their critique of juvenile offending theory, Cunneen and White (2011) explain that theories about serious juvenile offending are largely built using data from criminal justice agencies. This presents two key issues. First, some individuals (e.g. Indigenous youths) are more likely to be charged and convicted for a given offence than non-Indigenous youths, which may inflate the apparent seriousness of Indigenous offending. Second, by their nature, justice agencies collect data relating directly to agency operation and aims, foremost of which is the prevention of recidivism. There is greater emphasis on factors implicated in programming and service planning (e.g. modifiable factors, such as drug use), than on social and structural determinants of offending (such as poverty and low community efficacy, Section 1.3.1).

A related issue is the heavy influence of a few prospective cohort studies (especially the Cambridge and Dunedin studies) (Case & Haines, 2009; Day & Wanklyn, 2012). The theories they have put forward have been tested in innumerable replications, and the risk factors on which they have focused and explanations for the role of these risk factors have gained primacy in the wider literature. These are not experimental studies and so the correlations they report invite multiple explanations (Case & Haines, 2009). The result is a more homogeneous research base which meta-analysis is more likely to accommodate, thereby reinforcing those studies’ conceptualisations of offending. Likewise, the RNR model dominates the offending literature (Polaschek, 2012).

The theories discussed in this section have variously emphasised a wide range of factors and mechanisms linked to offending. There is now a strong emphasis on neuroscientific mechanisms in juvenile offending (Cauffman & Steinberg, 2012) as there is in drug treatment research with offenders (Chandler, Fletcher, & Volkow, 2009). However, theories of offending have also emphasised inequities that underpin

19 disadvantaged young offenders’ disproportionate CJS involvement. These go to deeper causes of offending and drug use including existential factors (e.g. disaffection) and social exclusion (Hagan, Gillis, & Simpson, 1985). Attempts to address specific behaviours without addressing underlying issues are unlikely to engage offenders or radically alter these behaviours.

20 1.4 Review of risk factors for offending 1.4.1 Risk factor concepts and terminology

In common parlance, risk factors indicate an increased probability of an undesirable outcome (e.g. recidivism). More precise definitions are required to identify the nature of the relationship between variables and outcomes. A wide range of terminological and conceptual approaches have been put forward to this end, and consensus is still wanting. The most widely cited approach defines ‘correlate’ as a variable associated with the outcome, and ‘risk factor’ as a correlate that precedes the outcome (Kraemer et al., 1997); correlates that do not precede the outcome may be consequences or causally unrelated to the outcome. A risk factor that affects the outcome when modified may be called a ‘causal risk factor’ (Kraemer et al., 1997). Few factors are said to cause crime because of the difficulty in fully isolating the effects of any given factor from the effects of other factors that are at play (Kazdin, Kraemer, Kessler, Kupfer, & Offord, 1997). However, in practice, many criminological studies describe relationships in causal terms (e.g. Bennett & Holloway, 2009), without inferring that stringent epidemiological standards of proof have been met (see Anthony & Forman, 2003).

Bivariate analyses are important for describing the characteristics of recidivists, and these show that risk factors are numerous and often co-occur. Multivariate models estimate the contribution made by one risk factor to recidivism independent of other risk factors. This estimate is always imperfect, as it relies on a list of imperfectly measured factors (Achen, 1982) that is in practice incomplete and varies from study to study (Wundersitz, 2010). Thus, models may distinguish ‘predictors’, ‘independent correlates’ or ‘independent risk factors’ from ‘proxy risk factors’ (Kraemer et al., 1997; Wundersitz, 2010) that are not causal but relate to an outcome because they correlate strongly with another risk factor; these may suggest useful areas to search for causal risk factors (Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001). This is a central concept in this thesis. To paraphrase Kraemer et al. (2001), if only some patterns of drug use are risk factors for recidivism, they should be disaggregated to clarify etiologies and potential intervention points; if all patterns of use are risk factors, they may be proxies for a global factor that should be studied in aggregate (Kraemer et al., 2001).

21 Haines and Case (2008) argues that recidivism research is too ‘risk-focused’ and that this reinforces negative stereotypes about young offenders. Rutter (1987) reflected on an earlier trend to invert risk factors and reframe them as protective factors, to “inject some hope… into the dispiriting story of stress and adversity” (Rutter, 1987). This thesis uses ‘risk factor’ and ‘protective factor’ in a purely statistical sense, as two sides of the same coin (Kraemer et al., 1997) and is not concerned with the social implications of these terms. Females typically have lower odds of recidivism, thus, female gender may be described as a protective factor. However, to facilitate interpretation of odds ratios (ORs), it is common to report odds for the group more likely to experience the outcome (i.e. males, Benda, Flynn Corwyn, & Toombs, 2001). The substantive relationship is unaltered. An alternative approach is to define a protective factor as one which reduces the effect of other risk factors or predicts a low probability of offending in high risk groups (Farrington, 2011).

A distinction is commonly drawn between fixed or historical risk factors (e.g. gender, prior behaviour) and variable or dynamic factors (e.g. drug use) (Kraemer et al. 1997; Andrews & Bonta, 2007). Meta-analysis suggests that static factors make a greater contribution to recidivism (Andrews & Bonta, 2007; Cottle, Lee, & Heilbrun, 2001). After static factors were accounted for, dynamic factors (including drug abuse) contributed weakly to recidivism for NSW juveniles (McGrath & Thompson, 2012), and not at all for US juveniles (Van Der Put et al., 2012). Static factors consistently predicted recidivism by Australian prisoners across demographic groups, but the effect of dynamic factors was inconsistent (Hsu, Caputi, & Byrne, 2011). However, dynamic factors are more relevant to offender rehabilitation.

A final distinction relates to the proximity of risk factors to outcomes. Factors may be more proximal (in time and space) or distal, and failing to consider this complicates the understanding of risk relationships (Weatherburn, 2001). Questionnaire wording can also affect whether a risk factor is proximal (e.g. drinks) or distal (e.g. ever drank). Proximal factors tend to be more predictive of recidivism. One meta-analysis reported that risk factors measured at ages 12 to 17 were better predictors of adult recidivism than factors measured in childhood (Leschied, Chiodo, Nowicki, & Rodger, 2008).

22 1.4.2 Meta-analyses of recidivism predictors

Meta-analyses combine results of different studies into a composite measure of effect size (ES), weighting individual results by sample size. Meta-analyses are atheoretical and do not reveal relationships between risk factors. They can, however, identify common and significant risk factors, and invite speculation about why other hypothesised risk factors are not significant. The sole meta-analysis of general recidivism among juveniles (Cottle et al., 2001) included 22 studies of CJS-recorded and self-reported recidivism among youths with at least one prior arrest. The samples were primarily aged 14 to 18, and mean follow-up was nearly four years. Cottle et al. (2001) found significant predictors across multiple domains, and wide variation within domains: substance abuse (ES .149) but not use (ES .014) was predictive. Earlier criminal justice contact was the strongest predictor of recidivism. The large number of predictors and relatively small effect sizes are notable: few predictors exceeded an ES of 0.2. This suggests that many factors play a role in recidivism. Significant predictors (p<.01) from Cottle et al’s (2001) meta-analysis are presented in Table 1.2, with comparable predictors (p<.05) of adult recidivism (Gendreau, Little, & Goggin, 1996).

Other meta-analyses of juvenile recidivism have focused on specific outcomes or samples (usually sexual recidivism (McCann, 2006)), or aspects of criminal justice intervention (Lipsey, 2009; Olver, Stockdale, & Wormith, 2009). Psychopathy has yielded moderate effect sizes for general and violent recidivism (Asscher et al., 2011; Edens, Campbell, & Weir, 2007), but other risk factors including substance use have not been subjected to specific meta-analytic treatment. Bennett, Holloway, and Farrington (2008)’s meta-analysis of drug-crime associations (not necessarily recidivism) found that compared to non-users, juvenile drug users were twice as likely to offend, while adult users were around four times more likely to offend. Lipsey and Derzon (1998)’s meta-analysis of mainly general population studies revealed age- variance in predictors. Child (age six to 11) and early adolescent (age 12 to 14) predictors of violence or major theft at age 15 to 25 were compared. Offending was a strong predictor in both groups and substance use was the next best predictor in childhood (ES .30) but a weak predictor in early adolescence (ES .06), during which time social ties and antisocial peers were preeminent (ES .36-.37). 23 Table 1.2 Meta-analytic predictors of juvenile and adult recidivism

Juvenile (Cottle et al., 2001) Adult (Gendreau et al., 1996)

Effect Size Studies N Effect Size Studies N

Substance abuse .149 6 1111 .10 60 54838 DEMOGRAPHICS Age .11 56 61312 Male gender .111 3 9671 .06 17 62021 Race (minority) .067 6 10121 .17 21 56727 Socioeconomic status -.065 3 10363 FAMILY/PEERS Family problems .227 5 1054 Out-of-home placements .184 2 9949 Victim of abuse .112 5 9949 Single parent .070 5 10501 Effective use of leisure time -.233 2 588 Delinquent peers .204 7 1525 .21 27 11962 COGNITIVE Non-severe pathology .305 7 953 Conduct problems .255 7 1667 .18 63 13469 Risk assessment instruments .118 6 10353 Achievement score -.153 3 506 Verbal IQ score -.111 4 716 Full-scale IQ score -.142 5 1756 .07 32 21369 History of special education .130 2 432 CRIMINAL HISTORY Age at first commitment -.346 3 720 Age first contact with the law -.341 8 1225 Length of first incarceration .187 3 641 Number of prior .174 3 585 commitments Type of crime .159 7 10267 Number of prior arrests .058 7 10155 Adult criminal history .17 164 123940 Juvenile antisocial behaviour .16 119 48338

Substance use (ES.014), parental psychopathology, performance IQ, school attendance, severe pathology, and treatment history were non-significant in Cottle et al. (2001); weak predictors in Gendreau et al. (1996) include personal distress.

24 A recent meta-analysis suggested that family factors predicted problem behaviours (e.g. drug use) more strongly than offending, including out of home care (ES .38 for problem behaviour vs. ES .13 for offending) and parental offending (ES .29 vs. ES .14) (Derzon, 2010). Many family predictors are not amenable to intervention but denote prevention targets and identify youths needing special attention (Derzon, 2010).

Adult meta-analyses draw on a larger number of studies and provide insight into more specific risk factors. While Gendreau et al. (1996) found a general measure of substance abuse was the weakest dynamic predictor of recidivism (ES .10), other meta- analyses reported unequal effects of alcohol abuse and drug abuse on general and violent recidivism (ES .06-.14) (Bonta, Law, & Hanson, 1998; Dowden & Brown, 2002). Effects were stronger in studies with a shorter follow-up which suggests that drug use makes declining contribution (over time) to recidivism. A more recent meta-analysis (Collins, 2010) reported that drug use strongly predicted violent recidivism, and alcohol use also predicted violent recidivism, but only among males.

Meta-analyses give some indication about the universality of risk factors when identified relationships are consistent across studies, but meta-analyses face a number of limitations. They are disproportionately influenced by US studies as these tend to be more numerous and have larger sample sizes, so results may not generalise to Australian offenders. Heterogeneity between studies is also often substantial (Bennett et al., 2008). This may be reduced by applying stringent study inclusion criteria (Murray, Farrington, & Eisner, 2009), but such criteria lead to the exclusion of many informative studies (Erskine et al., 2013; Nelson et al., 2011). Meta-analysis may also aggregate diverse factors within a single domain, concealing the variation therein (e.g. Hubbard & Pratt, 2002). Meta-analyses are atheoretical but variable operationalisation and selection in the original studies may not be. Risk factors are also assessed in isolation thus ignoring potential confounding (Lipsey, 2003). Meta-regression (Thompson & Higgins, 2002) overcomes some of these issues but has not been applied to juvenile recidivism. Thus, it is necessary to consider individual studies, and Australian studies in particular, to establish the role of many risk factors for community-based young offenders.

25 1.4.3 Demographic correlates

Demographic factors are static factors and can be ‘risk markers’ (US Surgeon General, 2001). They are frequently used as control terms in recidivism studies but demographic variation in risk factors is less often considered (Geis, 2009). Age. Most individuals who become involved in offending do so during adolescence; aggressive offending peaks at about age 17 and non-aggressive offending slightly earlier (Cauffman & Steinberg, 2012). These patterns reflect normal developmental processes including boundary-testing, neurological maturation, individuation and attendant conflict with authority figures, drug use initiation, and endocrinological changes (e.g. Hemphill et al., 2010; Steinberg 2009). Lynch et al. (2003) reported peak involvement in offending by Australian males at age 16 (15 for females), and peak involvement in CJS contact slightly later (around 18 for police contact and 18 to 19 years for court contact) (Chen et al., 2005; Kazemian & Farrington, 2005). Despite some contradictory findings (Tittle & Ward, 1993), there is strong support for the notion that many factors are especially relevant to offending at specific developmental stages (Childs, Sullivan, & Gulledge, 2011; Derzon, 2010; Van Der Put et al., 2011). This thesis focuses on adolescent risk for factors to recidivism.

Gender. Risks for participation in offending are largely gender-neutral (Hubbard & Pratt, 2002; Mazerolle, Brame, Paternoster, Piquero, & Dean, 2000), but males comprise increasing proportions of offenders at more serious stages of the criminal justice process (Section 1.1.1). This suggests that females in criminal justice supervision may be relatively more troubled than supervised male offenders. Two consistent gender differences are females’ earlier involvement in and higher prevalence of dependent drug use, and experiences of sexual victimisation (Steffensmeier & Allan, 1996). Lennings et al. (2007) also noted young Australian female offenders’ higher risks of mental health problems, including co-morbid presentations, as well as strong links between drug use and offending. In a young treatment sample, females’ high levels of distress were related partly to prior abuse and psychosocial dysfunction but were maintained by problematic offending lifestyles (Lennings & Collins, 2008). In contrast, males in the general population tend to report more problematic drug use (Lennings et al., 2007). This suggests gender-specific etiological paths and treatment needs. Male 26 gender is a weak but significant predictor of juvenile (Cottle et al., 2001) and adult recidivism (Gendreau et al., 1996), and males tend to offend more frequently than females (Brame et al., 2010). This supports Moffitt’s hypothesis that life-course persistence is an almost exclusively male phenomenon (Moffitt, 1993). However, the predictors of female recidivism are less well established than males’, and there is mixed evidence as to whether recidivism risk factors are gender-invariant (e.g. Penney, Lee, & Moretti, 2010) or unique (e.g. Trulson et al., 2005).

Ethnicity. Indigenous youths in NSW were 17 times more likely to be in community supervision than non-Indigenous youths (and 23 times more likely to be in detention) (Eldridge & Macdonald, 2008). This over-representation, which increases with CJS involvement, is long-standing, fairly stable, and is observed in international offender samples (Marie, Fergusson, & Boden, 2009; Yessine & Bonta, 2009). McGrath and Thompson (2009) compared recidivism risk ratings of Indigenous status for young offenders in NSW, and their relationship to recidivism over two to five years using reoffending data. Indigenous youths scored higher on all subscales and were more likely to recidivate (reoffending much sooner than non-Indigenous youths). Strikingly, however, the disparity between Indigenous and non-Indigenous recidivism rates was minimal for high risk offenders but was very pronounced for low risk offenders (the recidivism rate for low-risk Indigenous offenders was more than 1.5 times that of similarly rated non-Indigenous youth). It is possible that the average risk score was higher for the Indigenous youths (these data were not published), but this does suggest there are factors not collected by risk assessment tools that can significantly affect recidivism and which vary demographically. The differential effects for low and high risk ratings might also suggest differences in policing and detection rates. Some authors (e.g. Cunneen & White, 2007) have argued that rates of Indigenous contact with the CJS (and particularly incarceration) are confounded by policing and sentencing effects. Minority ethnicity was a much weaker predictor of juvenile recidivism (Cottle et al., 2001) than adult recidivism (Gendreau et al., 1996), but ethnicity requires a more refined measurement (Seidler, 2010). Crime and violence are generally less prevalent among youth in immigrant families than would be expected, given the social disadvantages they often face (MacDonald & Saunders, 2012).

27 1.4.4 Offending and criminal history

Juvenile offending and CJS involvement is one of the strongest predictors of recidivism (see Table 1.2) among juveniles (Cottle et al., 2001) and adults (Gendreau et al., 1996; Leschied et al., 2008) (ES .38), including young female offenders (Dowden & Andrews, 1999). This link partly reflects continuity in offending behaviour, but system effects are also relevant. Recorded offending is a unique risk factor in that it can directly determine the nature of an offender’s current CJS involvement, including monitoring (e.g. parole conditions) and support to refrain from offending. For these reasons, offending and self-reported conduct problems are considered separately.

Early onset of offending is consistently and negatively related to recidivism, regardless of how onset is measured (Watt, Howells, & Delfabbro, 2004). Early onset was the strongest predictor reported in Cottle et al. (2001), with an ES of -.34. This has been shown for juveniles in NSW (Carcach & Leverett, 1999). Early onset is also a powerful marker of other poor life outcomes (McGee et al., 2011). A cut-off of offending by age 14 is typically used to classify early onset (Mazerolle et al., 2000). The event that is classified is important, as onset of self-reported offending by serious offenders tends to be around age 12 with police contact, court appearance and sentencing occurring much later (Nagin, Farrington, & Moffitt, 1995). Early onset offenders follow different trajectories (Van Domburgh, Loeber, Bezemer, Stallings, & Stouthamer-Loeber, 2009) and early onset is more strongly predictive of recidivism in certain subgroups, e.g. young Indigenous males in NSW (Chen et al., 2005). However, age of onset does not predict all aspects of recidivism including frequency, (Chapter Eight, Loeber & Snyder, 1990); many chronic offenders are not arrested before age 14 (DeLisi, 2009). Early onset is probably not causal of recidivism: Bacon, Paternoster, and Brame (2009), for example, found later onset was more predictive of recidivism once factors accompanying early onset were controlled. Further, many serious violent juvenile offenders do not show childhood warning signs; late-onset violence is not well understood (US Surgeon General, 2001).

Meta-analysis shows that more serious CJS involvement is a stronger predictor (e.g. incarceration, compared to arrest, Cottle et al., 2001). Prior incarceration may be a

28 marker of higher criminogenic need and more severe offending history, and may also disrupt normal developmental processes – isolating youths from supports, and concentrating their involvement with delinquent peers. Cottle et al. (2001) found that longer incarceration was a solid predictor of juvenile recidivism and adult meta- analyses show recidivism is elevated among those incarcerated for more than six months (Lemke, 2009). Prior incarceration was the strongest predictor of two-year recidivism by serious adolescent offenders in the study by Benda et al. (2001). A recent NSW study, however, found that after matching custodial and community-sentenced offenders on background characteristics, custodial sentences did not significantly affect the risk of juvenile recidivism (McGrath & Weatherburn, 2012). Incarceration may not be an effective deterrent (Weatherburn et al., 2009) but CJS sanctions (e.g. intensive drug court supervision, Jones, 2012) can reduce recidivism risk.

More extensive prior CJS contact is a consistent predictor of juvenile recidivism (Watt et al., 2004), whereas current offence type is less predictive (Cottle et al., 2001). This was the case for index offence (those attracting the most serious penalty) among NSW juveniles given non-custodial sanctions (Smith & Jones, 2008). Offence type history may be predictive of specific recidivism, however. Violent recidivism approached 50% among violent juveniles in the Cambridge Study in Delinquent Development, while few non-violent juveniles initiated violent offending in adulthood (Farrington 1995, in Day & Wanklyn, 2012). Offenders with multiple violent convictions tend to have convictions for other offences (US Surgeon General, 2001). McSherry (2002) observed that past violence was the best predictor of future violence. However, violence may be moderated by other factors; Kenny and Press (2006) found severe violence to be more common amongst offenders with head injuries.

29 1.4.5 Individual risk factors

Aggression. Aggression encompasses personality characteristics and behaviours that intend to harm others (psychologically, physically or otherwise). These may manifest as anger, violence, bullying or threats; aggression is a possible feature of CD. While extreme violence is more likely to appear in criminal records, much aggression is not easily observed, so self-report data are valuable (Day & Wanklyn, 2012). Longitudinal cohort studies find that early and persistent aggression in childhood is one of, if not the strongest, predictors of ASB and offending in adolescence (Bor, McGee, & Fagan, 2004), but Australian studies have so far not assessed its impact on juvenile recidivism. Among 730 young Dutch offenders (Mulder, Brand, Bullens, & van Marle, 2010 ABS), aggression correlated with but did not predict two-year violent recidivism. As Lemke (2009) concludes for adult recidivism, whether aggression adds to recidivism risk over and above related risk factors is unclear. Furthermore, aggression may be a stronger predictor for males than females (Day & Wanklyn, 2012).

Impulsivity/self-control. This tenet of most criminal propensity theories is very inconsistently operationalised, and appears roughly half as predictive of crime for juveniles as for adults (Pratt & Cullen, 2000). Low self-control predicts CJS involvement (Beaver, DeLisi, Mears, & Stewart, 2009) and various measures of it have predicted recidivism among juvenile detainees over a four year period (self-restraint in Steiner, Cauffman, & Duxbury, 1999; impulsivity/reactivity in Taylor, Kemper, Loney, & Kistner, 2009). Notably, in Taylor’s study, prevalence and incidence of recidivism (and CJS histories) were comparable for impulsive and psychopathic personality types, and both were nearly double that of the anxious/inhibited type. Self-regulation was also the joint strongest predictor of sexual recidivism (ES .39) in meta-analysis, however, ahead of a host of factors including psychopathy (Hanson & Morton-Bourgon, 2005). Australian studies have not assessed links between impulsivity and juvenile recidivism. Rey, Sawyer, and Prior (2005) reported strong links between impulsive/hyperactive ADHD and aggressive, but not other delinquent behaviour. Low self-control has a special significance for community-supervised offenders, because as Lemke (2009) points out, not only must they avoid offending but also comply with conditions of supervision or risk breaching their existing orders. 30 Psychological distress. Psychological distress as measured by some screening tools (e.g. the Kessler-10) correlates strongly with mood disorder, but does not necessarily indicate the presence of such a diagnosis (Andrews & Slade, 2001). Distress was not a strong predictor of adult recidivism in Gendreau et al. (1996)’s meta-analysis and internalising problems were less predictive (ES .21) than externalising problems (ES .52) of adult offending (Leschied et al., 2008). Suicidal and self-harming behaviour are specific indicators of distress, are more prevalent among offenders than non- offenders, and are a major cause of death among Australian young offenders (Coffey, Veit, Wolfe, Cini, & Patton, 2003). Violence to self may also denote recidivism risk.

Psychiatric disorders. The prevalence of mental health problems among offenders, whether self-reported or diagnosed, greatly exceeds that of the general population. Rates are highest among young detainees (Fazel, Doll, & Langstrom, 2008), and higher among young female than male offenders (Teplin, Abram, McClelland, Dulcan, & Mericle, 2002), especially for depression. Nearly all female (90%) and most male (78%) prisoners in NSW suffered from a psychiatric disorder on entry to prison (Butler & Allnutt, 2003). Mental disorders also frequently co-occur and are compounded by drug problems (Swendsen et al., 2010). Prospective studies indicate that serious mental illness (bipolar and major depression and psychotic disorders) increases the risk of recidivism (Kasinathan, 2009b). This effect is independent, although smaller than the impact of drug use and other predictors (Hodgins, Cree, Alderton, & Mak, 2007) (meta- analyses described above). Associations with recidivism vary by disorder, and this may connote diagnostic features, such as acting on delusions or hallucinations (schizophrenia), and intensified indifference to personal and other well-being issues (severe depression) (Lemke, 2009). Bizarre and public behaviour may result in mentally disordered offenders being more easily detected by police than other offenders who are better able to conceal their offending (Hodgins, 1998). Substance use disorders and comorbidity are discussed in Chapter Two.

Conduct Disorder and other disruptive behaviour. Certain aspects of CD (see Section 1.3.3) show high continuity with adult antisocial personality disorder, but conduct problems per se are not analogous to antisocial personality. Conduct problems were a

31 strong meta-analytic predictor of juvenile recidivism (Cottle et al., 2001) and to a lesser extent adult recidivism (Gendreau et al., 1996). The number of CD symptoms present by age 15 has predicted later violent crime independent of drug use (Hodgins et al., 2007). Several caveats accompany CD-crime links reported in the literature. CD symptoms include offending behaviours and so may inflate the association with recidivism (Day & Wanklyn, 2012). There are also problems with diagnostic issues; CD should reflect internal dysfunction and not social context (American Psychiatric Association, 1994), but this is often overlooked (Wakefield et al., 2002). Thus, CD-crime links may well reflect contextual elements, such as dysfunctional parenting and maltreatment (Kenny & Nelson, 2008); running away from sexual abuse may be an adaptive response, not CD per se. CD also frequently co-occurs with other conditions, and may not predict adult criminality in the presence of internalising disorder (Mordre, Groholt, Kjelsberg, Sandstad, & Myhre, 2011).

Other disruptive disorders have been less consistently linked with offending. Mordre et al. (2011)’s recent study adds to evidence that ADHD does not predict recidivism among young offenders (Grieger & Hosser, 2011). Other studies have linked ADHD to delinquency (Sibley et al., 2011) and violent adult offending (Eklund & af Klinteberg, 2003), but outcomes are far worse for youths with comorbid CD (Sibley et al., 2011; Von Polier, Vloet, & Herpertz-Dahlmann, 2012). Most studies that find CD and/or ADHD but not Oppositional Defiant Disorder predict juvenile recidivism; Plattner et al. (2009) were surprised to find to the contrary, and suggested that this is because Oppositional Defiant Disorder is by definition an early onset disorder which is linked with poorer prognosis (Section 1.4.4).

Cognitive functioning, education, and truancy. Low general and verbal (but not performance) IQ and academic problems predict juvenile recidivism (Cottle et al., 2001), more so than adult recidivism (Gendreau et al., 1996). These factors show a dose relationship; high school attainment and intelligence has protected against involvement in offending (Farrington, Loeber, & Ttofi, 2012) and school performance has been correlated negatively with violence (Blum, Ireland, & Blum, 2003). However, lower verbal IQ among youths aged 16 distinguished offenders from non-offenders but

32 did not predict recidivism at ages 17 to 20 (Loeber, Pardini, Stouthamer-Loeber, & Raine, 2007). Intellectual disability has been inconsistently measured in offender studies, but appears strongly linked with juvenile recidivism (Cottle et al., 2001; Lipsey & Derzon, 1998). Frize, Kenny, and Lennings (2008) have shown intellectual disability is correlated with more frequent offending among non-Indigenous but not Indigenous young people on supervised community orders in NSW. Low verbal IQ may impair social cognition (Herrnstein & Murray, 1994), and reflect neurological impediments to self-regulation and communication (Moffitt, 1993).

IQ may also be adversely affected by poor educational experiences as reported by many young offenders. Academic failure during adolescence is a risk factor for serious delinquency and violence (Herrenkohl, Lee, & Hawkins, 2012), both for males and females (Fagan, Van Horn, Hawkins, & Arthur, 2007). In combined analyses of the three Sibling Study cohorts (Fagan & Mazerolle, 2011) found school dropout was associated with both recidivism and repeat victimisation (see below). School dropout was the strongest predictor of ASB in the Mater University Study of Pregnancy (McGee, 2011). Variation in academic performance may be attributable to IQ (Maguin & Loeber, 1996), but only the former is realistically amenable to change. Poor literacy may increase recidivism by impeding employment opportunities (Putninš, 1999). Underachievement may cause frustration that leads on to ASB, but ASB may also jeopardize school involvement (Putninš, 1999). Truancy is a correlate of violence (Herrenkohl et al., 2012), offending (Lipsey & Derzon, 1998) and general recidivism (Chang, Chen, & Brownson, 2003), perhaps related to unsupervised time and disengagement from school (Henry, Thornberry, & Huizinga, 2009), and associating with delinquent peers (discussed below).

Employment, housing and socioeconomic status. Workforce participation can provide structured time and pro-social peers that might offset some of the risk associated with early school leaving. During adolescence, unemployment becomes increasingly financially relevant to young offenders. Rates of property crime amongst Cambridge study participants aged 15 to 16 were strongly correlated with their employment status (Farrington, Gallagher, Morley, St. Ledger, & West, 1986); Chapman,

33 Weatherburn, Kapuscinski, Chilvers, and Roussel (2002) showed a strong correlation between long-term unemployment and property crime rates among young males in NSW; unemployment also predicted property recidivism in the Christchurch study (Fergusson, Lynskey, & Horwood, 1997).

Accommodation problems including homelessness and housing instability have not shown clear relationships with recidivism and are no longer included in the adult version of the Level of Service Inventory (Lemke, 2009). For juveniles, unstable housing is less likely to arise from personal financial strain (than for adults), and running away may be adaptive (explained earlier in this Section). However, youth housing problems have been related to problem behaviour (Derzon, 2010) including drug use and victimisation (Martin et al., 2009).

Low socioeconomic status (SES) is consistently correlated with offending (Derzon, 2010), but was a very weak predictor of juvenile recidivism (Cottle et al., 2001). Low SES infers financial strain, motivating individuals to offend (Weatherburn, 2001). However, there is also a clear socioeconomic gradient in population health, and low SES is a proxy for multiple indicators of disadvantage including reduced access to services and education, and poorer child and family functioning (University of New South Wales Research Consortium, 2002). SES is thus better understood as a distal factor moderated by proximal factors, such as parenting (Weatherburn, 2001) and peers (Fergusson, Swain-Campbell, & Horwood, 2004).

1.4.6 Family, peer and social factors

Although not the subject of this thesis, evidence regarding the contribution of family and peer factors to recidivism supports social control and social learning explanations of offending. Bonds can and do change, and this will affect criminal behaviour (e.g. Fergusson, Horwood, & Nagin, 2000; Sampson & Laub, 2005). Over adolescence, peer relationships become more important and proximal influences on offending, while the influence of family declines (Farrington et al., 2012; US Surgeon General, 2001).

Parenting. Parenting factors have been strongly related to delinquency, and the consistent success of programs, such as the Triple P in reducing criminal involvement

34 (Weatherburn, 2001) indicates that parenting style is a causal risk factor. Meta- analyses (Derzon, 2010; Hoeve et al., 2009; Hoeve et al., 2012) find modest but significant effects of poor attachment, parental rejection, harsh discipline, and neglect in particular (ES 0.3) on juvenile delinquency, and of family conflict (ES .38) during adolescence on adult offending (Leschied et al., 2008). The impact of these problems on recidivism is less clear. Family problems in aggregate were strongly related to juvenile recidivism (Cottle et al., 2001), but specific parenting factors did not predict recidivism in the Pittsburgh Youth Study (Loeber et al., 2007). Family structure during adolescence has a strong impact on adult offending (ES .67, Leschied et al., 2008), but single parenting is only weakly linked to juvenile recidivism (Cottle et al., 2001).

Maltreatment and child welfare placements. Child maltreatment is a major risk for juvenile offending (Farrington, 1994; Jonson-Reid & Barth, 2000; Stewart, Waterson, & Dennison, 2002) and a weak predictor of juvenile recidivism (Cottle et al., 2001). Its effects may differ by type of maltreatment: of the five types assessed by the Childhood Trauma Questionnaire, only emotional neglect predicted juvenile recidivism in one US study (Kingree, Phan, & Thompson, 2003). Maltreatment is also more strongly related to non-violent offending (Derzon, 2010). Maltreatment and sexual abuse in particular has been strongly linked with females’ offending (Day & Wanklyn, 2012; Mazerolle & Legosz, 2007), as has early initiation of drug use and problem drug use (Mazerolle & Legosz, 2007).

Youths from the child welfare system are highly over-represented in Australian offender populations (Kenny & Nelson, 2008) and a history of out of home care may serve as a proxy for recidivism risk (Weatherburn et al., 2007). However, not all studies have observed a link with out of home care and juvenile recidivism (Cottle et al., 2001). Further, Derzon (2010) found that child welfare placements weakly predicted offending, but strongly predicted other problem behaviours.

Family criminality and pathology. Familial involvement in crime, drug use and other psychopathology have been linked to serious juvenile offending, but the nature of these effects vary. Parents, and fathers in particular, appear to have the strongest effects (Farrington, Jolliffe, Loeber, Stouthamer-Loeber, & Kalb, 2001), although these

35 may not significantly impact recidivism once prior offending is considered (Huan, Ang, & Lim, 2010). Parental incarceration predicts serious juvenile offending, but the effects are relatively weak (Kjellstrand & Eddy, 2011), may be limited to theft and are heavily moderated by peer delinquency (Murray, Loeber, & Pardini, 2012). Fagan and Najman (2003) reported independent correlations between siblings’ delinquency in the Mater University Study of Pregnancy that were stronger for males, but also for youths with criminally-involved parents, reinforcing the notion that offending is concentrated within families. Western, Lynch, and Ogilvie (2003) found a negative relationship between siblings’ delinquency in the Sibling Study offender cohort, but their speculation about a deterrent effect for serious offenders appears unfounded (De Rakt, Nieuwbeerta, & Apel, 2009).

Parental mental health did not predict lifetime violent or other offending (Derzon, 2010) or adult offending (Leschied et al., 2008) in meta-analyses, but did predict other problem behaviour (Derzon, 2010). Parental acceptance and use of drugs was a weak (ES<0.1) predictor of offending (and of other problem behaviour, as was parental mental health) (Derzon, 2010). Recent studies of the effects of parental incarceration on drug use found evidence both for (Roettger, Swisher, Kuhl, & Chavez, 2011) and against (Murray et al., 2012) a predictive effect. In contrast to parental criminality, early drug use (alcohol and tobacco) is more strongly affected by older siblings’ than parents’ use (Fagan & Najman, 2005).

Victimisation. Richards (2009) observes that public, political and media coverage of juvenile's criminal justice contact focuses on their offending, recidivism and progression to the adult CJS. In Australia, however, juveniles are more frequently victimised than adults and a large proportion are fearful of crime, abuse and other assault (Tucci, Mitchell & Goddard 2008: 31, in Richards, 2009). Victimisation is heavily implicated in the development of offending; exposure to violence (which also includes domestic violence) increases the likelihood of acting violently, perhaps pre-emptively against real or perceived threats (US Surgeon General, 2001). Young offenders’ contact with the CJS as victims of crime is worthy of independent study, but this phenomenon

36 is not addressed here. Rather, this thesis addresses victimisation as it pertains to offending and recidivism.

NSW police data indicate up to 4% of NSW juveniles were victims of offending during 2007 to 2008; precise data are not reported (Drabsch, 2006; Richards, 2009). Most were victims of violent offences, predominantly assault, half were female (in stark contrast with the wider gender ratio for offending) and females were overrepresented as sexual offence victims (Richards, 2009). NSW child protection data estimate that nearly 1% of children under 17 and known to child protection services have been physically or sexually abused, and more commonly, emotionally abused or neglected (Richards, 2009). The gender ratio was even, but Indigenous youths were over- represented by at least 7:1 (Richards, 2009).

Peers. Peers are hypothesised to be strongly related to adolescents’ behaviour through selection, modelling and/or reinforcement (Loeber, Farrington, Stouthamer-Loeber, & Van Kammen, 1998). As Weatherburn (2001) notes, involvement with delinquent peers improves the knowledge and skill required to successfully commit offences. Delinquent peers was a strong meta-analytic predictor of juvenile (Cottle et al., 2001) and adult recidivism (Gendreau et al., 1996). Recent analysis of Pittsburgh Youth Study data finds peer delinquency the strongest risk factor for offending (Farrington, 2011) and there is strong meta-analytic evidence that the risk of recidivism carries across gender and ethnicity (Hubbard & Pratt, 2002; Lipsey & Derzon, 1998; Wilson, Lipsey, & Soydan, 2003) – although the effect may be stronger for boys (Fagan et al., 2007). The salience of peer delinquency may increase over adolescence; it predicted recidivism amongst offenders aged 16 to 17 but not younger offenders among 1400 US juveniles (Van Der Put et al., 2012). Peer ASB also has a stronger effect on offending in the short-term (Lemke, 2009), and Dowden and Andrews (1999)’s meta-analysis found that interventions that address antisocial peers reduced recidivism while others did not. Delinquent peers were a risk factor for both recidivism and repeat victimisation in the Sibling Study (Fagan & Mazerolle, 2011). Delinquent peers also influence the initiation of drug use (Trucco, Colder, & Wieczorek, 2011).

37 Low involvement with peers was hypothesised by Moffitt (1993) to be a feature of those who abstain from ASB, but this was not borne out in later analyses (Lennings, 2008). Peer rejection shows associations with externalising behaviour and delinquency (Van lier & Koot 1998, in Day & Wanklyn, 2012), but low peer involvement may indicate peer avoidance, thus tapping into internalising problems and anxiety which are associated with lower levels of offending.

1.4.7 Correlates of specific offence types

Offences are defined at Section 3.4.1. So far, the review has focused on risk factors for general recidivism, and to a lesser extent violent recidivism; these categories are not mutually exclusive so it is not surprising to have found factors common to both. Most convicted young offenders commit a variety of offences, so as Weatherburn (2001) suggests, the risk factors for one offence type are likely to be relevant for understanding others. Involvement in one offence type may also increase the risk of others: Lipsey and Derzon (1998)’s meta-analysis found non-violent offences were among the best predictors of serious (including violent) recidivism in adolescence (along with peer delinquency).

For example, studies that specifically compare the correlates of non-violent and violent offending observe similarities including heavy alcohol use (Fergusson & Horwood, 2000a; Komro, Williams, & Forster, 1999) and higher levels of CD (Lacourse et al., 2010). However, violent offenders tend to be older (e.g. Latimer, 2003), have lower verbal abilities (e.g. Kennedy, Burnett, & Edmonds, 2011), and have had low parental supervision (Derzon, 2010). Non-violent offenders meanwhile tend to be more prolific (Myner, Santman, Cappelletty, & Perlmutter, 1998) and are more likely to report peer drug use. Schoenfield (2010) discriminated violent from non-violent US juvenile offenders in a multivariate model, with violent offenders more likely to reoffend and to have a psychiatric disorder and intellectual disability, and non-violent offenders characterised by prior drug offences (suggesting problem drug use) and learning disorders. Correlates of theft and robbery are addressed in Chapter Four.

38 There is evidence that chronic/ life-course persistent offenders consist of a violent and predominantly non-violent subgroup (Kempf-Leonard et al., 2001). Capaldi and Patterson (1996) found that violent and chronic non-violent offenders were substantially different to other at-risk youths but did not differ significantly from each other. Weak distinguishing features of the chronic violent group were low SES and paternal ASB, while chronic non-violent offenders had lower self-esteem, suggesting emotional neglect. Similarly in another study, adult recidivism was identified in just 32% of juvenile offender population but 48-59% of three overlapping subgroups: chronic (several police encounters), violent and serious (violent and serious property crime) (Kempf-Leonard et al., 2001). Males were twice as likely to be chronic or serious offenders as females. Holding race and SES constant, adult recidivism was predicted for serious male offenders (adjusted odds ratio/AOR 1.3-1.5); violent female offenders (AOR 1.8); male and female chronic offenders (AOR 2.2); and serious female property offenders (AOR 2.5). The authors concluded that chronic and serious female offending cannot be ignored. Correlates of frequency and severity are reviewed in Chapter Eight.

The foregoing review has been restricted to factors in the baseline dataset of this thesis. Other potentially important factors include psychopathy (which tends to be an enduring trait), criminal attitudes and beliefs, treatment responsivity, genetic and physiological markers (e.g. low resting heart rate). The implications of not including these factors in later recidivism models are considered in Chapter Nine.

39 1.5 Review of drug use among young offenders

Psychoactive drugs, or simply ‘drugs’ in this thesis, refers to a vast range of substances that, through their effects on the central nervous system (CNS), can alter a person’s thoughts, mood, motivations, or consciousness (World Health Organization, 2004). Based on their primary effects, however, drugs are broadly classified as depressants (e.g. alcohol, opioids), stimulants (e.g. amphetamines), or hallucinogens (e.g. LSD); cannabis exhibits hallucinogenic effects but is typically classified as a distinct drug type (Ghodse, 2010; World Health Organization, 2004). Effects can vary between individuals and within individuals across time, being related to physiology, mood, dose and context (Ghodse, 2010).

In the psychiatric nomenclature, drug abuse and dependence are patterns of ongoing use that result in significant distress or impairment. Dependence is typically distinguished by tolerance or withdrawal (or drug use to avoid withdrawal); other features of dependence include failure to reduce drug use, heavy time investment in drug use, and drug use despite ongoing ill health (American Psychiatric Association, 1994). Drug-crime research with juvenile offender research has rarely had access to information about psychiatric diagnoses (for exceptions, see Indig et al., 2011; Teplin, Welty, Abram, Dulcan, & Washburn, 2012b). It is more common for these studies to analyse involvement in drug use and to use ad-hoc measures (Cave et al., 2010) that differentiate patterns of ‘problem drug use’ linked with more harmful outcomes, such as ‘weekly use’ or use of specific drugs (European Monitoring Centre for Drugs and Drug Addiction, 2011). Literature on relationships between drug use and offending is reviewed in Chapter Two.

1.5.1 Description of drugs studied in this thesis

Alcohol (ethanol) is a CNS depressant typically consumed in beverages including beer, spirits, and wine. Australian guidelines recommend against drinking before age 18 and very strongly against drinking before age 15 (National Health and Medical Research Council, 2001). Alcohol absorbs rapidly into the blood and drinking may initially be accompanied by confidence and arousal due to reduced inhibition, with impairments

40 to judgement and coordination increasingly apparent at higher doses. High-level, single episode consumption (binge drinking) places drinkers and others at risk of injury and commonly results in headache; anxiety and concentration problems may also be evident in the period following bingeing. Alcohol use may promote depression by reducing serotonin levels and disrupting sleep, and chronic use can damage most major organs and bodily systems.

Cannabis refers to marijuana, hashish, and other preparations derived from the cannabis sativa plant that is smoked or eaten (Calabria et al., 2010). Its main psychoactive ingredient is delta-9-tetrahydrocannabinol (THC), which typically produces feelings of relaxation and well-being but may also disrupt the senses, memory and concentration (Ghodse, 2010). These effects may be unpleasant, leading to anxiety and agitation, and large doses can result in psychosis (Ghodse, 2010). Ongoing, heavy use (typically, several weeks of increasing frequency and quantity) can lead to dependence and in Australia, weekly cannabis use marks a threshold for increased risk of later dependence (Coffey, Carlin, Lynskey, Li, & Patton, 2003). Withdrawal may result in irritability, restlessness, and sleeping problems (Budney & Hughes, 2006; Jones, Benowitz, & Herning, 1981). Other problems linked to cannabis use include motor accidents and lung disease (Hall, 2009). Niveau and Dang (2003) reviewed forensic cases in which cannabis intoxication precipitated violent, impulsive and unpredictable behaviour, as a response to acute anxiety and feelings of persecution. Chronic use may also promote violence (Pacula & Kilmer, 2003) , perhaps due to cannabis withdrawal (Hoaken & Stewart, 2003).

Amphetamines are CNS stimulants that include illicitly-manufactured amphetamine and methamphetamine and diverted pharmaceutical amphetamines (Degenhardt, Calabria, et al., 2010). These are chemically and toxicologically distinct (Barceloux, 2012) but sufficiently similar to be grouped together in most drug-crime research (e.g. Bennett & Holloway, 2007). Methamphetamine accounts for most positive amphetamine tests among young Australian arrestees (Australian Institute of Criminology, 2010). Amphetamines come in several physical forms which can be ingested, inhaled, smoked or injected; the crystalline form is the most potent

41 (Australian Crime Commission, 2011). Illicit amphetamines vary widely in purity (1- 78%, at the time of baseline data collection). Amphetamine use produces a ‘fight or flight’ effect similar to adrenalin (Ghodse, 2010). Individuals who engage in prolonged, regular use of amphetamines (usually twice weekly or more) are at particular risk of becoming dependent (Degenhardt, Calabria, et al., 2010). Heavy use is associated with sleep disturbances, risky sexual behaviour, brief psychosis, and blood-borne infections among injectors (McKetin, McLaren, & Kelly, 2005).

Opioids are narcotic analgesics and include natural opiates (e.g. opium from poppies), synthetic analogues (e.g. morphine, heroin/diacetylmorphine), and pharmaceutical opioids (e.g. codeine, methadone) (Nelson et al., 2010; World Health Organization, 2004). The immediate effects of opioid use are pain relief, euphoria, and detachment (Ghodse, 2010; Nelson et al., 2010); opioids also sedate the CNS causing drowsiness and respiratory failure at high doses (Ghodse, 2010). Abstinence can be extremely distressing for the user and psychological dependence may continue well beyond physical withdrawal (Ghodse, 2010). Dependence is linked with elevated morbidity and mortality, especially through overdose and blood-borne infections if the drug is injected (Degenhardt, Hall, Warner-Smith, & Lynskey, 2004; Nelson et al., 2010).

1.5.2 Onset and prevalence of drug use

Drug use amongst adolescents typically relates to curiosity, peer pressure or mood regulation (Australian Institute of Health and Welfare, 2011). Users typically commence with tobacco and alcohol, with around one in four progressing to cannabis, and few going on to use other drugs. Drugs, once initiated, tend to be maintained (Labouvie & White, 2002), so poly-drug use is common among users. Earlier use is prognostic of ongoing, problematic use (Grant & Dawson, 1997). The mean age of alcohol onset among young Australians is 15, with bingeing and other drugs initiated later, e.g. 18 for amphetamines (Australian Institute of Health and Welfare, 2011; Chartier, Hesselbrock, & Hesselbrock, 2010). Offenders initiate much earlier; alcohol intoxication and cannabis at 13 years, amphetamines and heroin at age 15 for juvenile detainees in NSW (Indig et al., 2011). There are similar patterns in the UK (Drugs Prevention Advisory Service, 2000; Hoare & Moon, 2010).

42 Rates of use among offenders, particularly for less prevalent drugs, are much higher than among non-offenders. Past year use amongst young detainees in NSW approaches 95% for alcohol and 80% for any other drug (Indig et al., 2010). Overall drug use by offenders in wealthy Western jurisdictions tends to be similarly high, but prevalence of specific drug use varies. Binge drinking is less prevalent amongst young US offenders (40%, Mulvey, Schubert, & Chassin, 2010) than their Australian and British peers, around 80% (Hammersley, Reid, & Marsland, 2003; Indig et al., 2010). Cannabis use, however, is a prominent feature among young offenders wherever its use is prevalent (typically around 80% prevalence) (Andrade, Assumpção Junior, Teixeira, & Fonseca, 2011; Indig et al., 2010).

Amphetamine use among US and Australian young offender samples range from 15% to 48% compared to 6% to 7% of students and general population youths (Centre for Epidemiology and Research, 2008; Indig et al., 2010; Maxwell, 2008; NSW Health, 2005). Young offenders’ drug use is more comparable to adult prisoners and youths in treatment (e.g. amphetamine use 39-57%, Lennings et al., 2007). Amphetamine and opioid use are more prevalent amongst young Australian offenders than in the US, where cocaine is far more prevalent (Dembo & Sullivan, 2009). The prevalence of heroin use among young Australian offenders has decreased substantially in the past decade (e.g. lifetime prevalence among young detainees decreased from 20% in 2003 to 6% in 2009, Allerton et al., 2003; Indig et al., 2011). This reflects the nation-wide trend following the 2001 Australian heroin shortage (Degenhardt, Day, & Hall, 2004). However, recent data suggests a ‘recovery’ of some Australian heroin markets (Fetherston & Lenton, 2013).

Problem drug use, however defined, is highly elevated among young offenders, with rates approaching those of youths in drug treatment and exceeding those of other vulnerable youth populations (Aarons, Brown, Hough, Garland, & Wood, 2001). Among Australians aged 14 to 19, 5% use cannabis weekly (Australian Institute of Health and Welfare, 2005), compared with two-thirds of Australian, UK and US young offenders. Prevalence is typically higher among detainees than community-based offenders (Hammersley et al., 2003; Indig et al., 2011; Mulvey et al., 2010). In a systematic

43 review of drug use disorders among young detainees, prevalence was 39% for cannabis use disorders, 26% for alcohol use disorders (AUDs) and 6% for other drug disorders (Colins et al., 2010). However, for detainees, drug use measures pertain to their use before entering custody (e.g. Indig et al., 2011) and the restricted availability of drugs in detention and greater access to treatment mean that drug problems are often more prevalent in community-based offenders (Chitsabesan et al., 2006; Teplin et al., 2012b). For most drugs and problem drug use, prevalence increases more sharply during early adolescence among offenders than in the general population (McClelland, Elkington, Teplin, & Abram, 2004; Putninš, 2001) but declines from age 15 to 20, unlike in the wider population (although it remains far higher, Teplin et al., 2012b).

1.5.3 Correlates, risk factors, harms of drug use

The correlates of Australian young offenders’ drug use have not been thoroughly documented. Studies of these youths have tended to consider drug use as a correlate, rather than as the dependent variable. This may reflect a bias towards the ‘drugs cause crime’ model rather than its alternatives (Stevens, Trace, & Bewley-Taylor, 2005) and greater political concern regarding offenders’ harm to others, rather than to themselves. Australian studies of young offenders’ drug use have aggregated drug types or been limited to bivariate analyses; this is also common in international studies (e.g. Vaughn, Howard, Foster, Dayton, & Zelner, 2005). Analyses of specific drugs are important, given their divergent properties and because drug use per se is ubiquitous among young offenders (therefore its correlates are of little interest). Factors that affect progression to or maintenance of problem drug use (e.g. frequent use) are also centrally important in offender rehabilitation (Kenny & Nelson, 2008); less frequent use is a risk factor for progression to frequent use, so its correlates are also of interest.

Risk factors for problem drug use are found in all domains (Hawkins, Catalano, & Miller, 1992). Prominent risks include early conduct problems, early drug use, and poor mental health; familial separation, parental drug use, parental offending, and maltreatment. They also include peer drug use, peer offending, educational disengagement and victimisation; and community disadvantage, policing and drug availability (Curcio, Mak, & George; Dembo, Williams, Schmeidler, Wish, et al., 1991;

44 Hawkins et al., 1992; Holloway, Bennett, & Farrington, 2005; Loeber et al., 2001; Spooner, 1999). Dynamic individual factors (including offending, Copeland, Howard, Keogh, & Seidler, 2003; Lennings et al., 2007) are often more prominent correlates of drug problems, partly because they are more easily measured than the complex developmental processes from which they often derive (Lloyd, 1998). However, static factors also have treatment and etiological relevance. For example, socioeconomic deprivation and perinatal complications (Alati et al., 2008; Farrington, 1994; Hawkins et al., 1992) interact with conduct problems and family environment (Collishaw, Goodman, Pickles, & Maughan, 2007; Lloyd, 1998; Windle, 1994) to affect the development of drug problems (Caspi et al., 2002; DeLisi, Beaver, Vaughn, & Wright, 2009). The correlates of problem drinking (Curcio et al.), other drug use (Spooner, 1999), and offending overlap substantially but not entirely. This review considers the correlates of problem drug use among young offenders, and then discusses correlates of specific problem drug use.

1.5.3.1 Correlates of problem drug use among young offenders

Young female and male offenders are often distinguished through females’ higher prevalence of drug problems. Among NSW juvenile detainees, female gender has been correlated with a need for help with drug use (Copeland et al., 2003), heavy tobacco use (Indig & Haysom, 2012) and also with AUDs, in contrast with the general population (Indig et al., 2011; Mewton, Teesson, Slade, & Grove, 2011). Problem drug use was most likely among young male offenders in a large US study (Mulvey et al., 2010), and alcohol problems were more prevalent among young UK offenders (Chitsabesan et al., 2006). Ethnicity also has a strong impact on drug use in offender (Mulvey et al., 2010) and non-offender samples (Armstrong & Costello, 2002). Among NSW young detainees, drug use disorders were more common for Indigenous youths (Indig et al., 2011). However, Caucasian young offenders in the US and UK tend to have a higher rate of drug problems than other ethnic groups (Chitsabesan et al., 2006; Mulvey et al., 2010), especially compared with Black youths (Teplin, Welty, Abram, Dulcan, & Washburn, 2012a). Interactions between gender and ethnicity are also apparent. Drug problems were most prevalent among females and least prevalent for

45 Indigenous males in Putninš (2001)’s South Australian detainee sample. Such variations emphasise the importance of local data and of considering demographic factors.

Research conclusively shows effects of the family environment on adolescent drug problems (Nation & Heflinger, 2006). Childhood abuse and disrupted family relationships have predicted problem drug use among young offenders internationally (Uchida, 1995; Ybrandt, 2010). Prichard and Payne (2005) described bivariate correlates of early onset and weekly drug use among Australian juvenile detainees, which included familial substance use and childhood maltreatment. Parental heroin use and serious drug-related health problems were highly elevated among female juvenile detainees in NSW (Copeland et al., 2003). The link between child maltreatment and problem drug use can vary by drug type, maltreatment type and gender (Begle et al., 2011; Danielson et al., 2009). For example, in a US national study, physical abuse predicted substance abuse in early adulthood for males, whereas sexual abuse predicted drug abuse only for females (Danielson et al., 2009).

Among psychosocial correlates of problem drug use, one of the most consistent is peer delinquency, both for young offenders (Contreras Martinez, Molina Banqueri, & Cano Lozano, 2012) and male and female adolescents in the general population (Barnes, Welte, Hoffman, & Dintcheff, 2005). Peer delinquency has been linked with more frequent binge drinking, cannabis use, and opioid use among youths in treatment (Nation & Heflinger, 2006). The influence of substance abusing peers appears to be more prominent than that of substance abusing parents (Windle, 2000). Associating with peers who use and have access to drugs increases risk of use which in turn predicts later drug problems, although this process is subtle, complex and not easily captured (Brière, Fallu, Descheneaux, & Janosz, 2011; Lloyd, 1998). Drug problems have been linked with school failure; early onset drug use was correlated with early school leaving and truancy among Australian juvenile detainees (Prichard & Payne, 2005). However, the independent contribution of school failure to young offenders’ drug problems is unclear, especially given that conduct problems tend to co-occur and predate school failure (Hawkins, 1992, in Lloyd, 1998). Unsettled housing has also been

46 linked with problem drug use; poly-drug use was more likely among Australian juvenile detainees living out of home (Prichard & Payne, 2005).

Externalising psychological problems (conduct and attention problems) are commonly linked with problem use of a variety of drugs and alcohol (Nation & Heflinger, 2006; Ybrandt, 2010); internalising psychopathology less so. Among US community-based juvenile offenders, externalising behaviour was strongly correlated with more problematic alcohol, cannabis and other drug use (Feldstein Ewing, Venner, Mead, & Bryan, 2011). (Putninš, 2006) found a drug use frequency/diversity measure correlated with conduct and attention problems and depression among young South Australian detainees. However, only attention problems predicted later drug problems, suggesting that conduct and drug problems may be manifestations of general deviance. Similarly, Zeitlan (1999, in Nation & Heflinger, 2006) concluded that psychological problems could reflect an underlying problem behaviour syndrome or vulnerability to drug problems. The following sections describe the specific patterns of use under investigation in this thesis: binge drinking, cannabis, amphetamine and opioid use, and their correlates.

1.5.3.2 Correlates of binge drinking

Drinking is more problematic for young people than adults as youths have less tolerance and experience with alcohol and typically take greater risks than adults (National Health and Medical Research Council, 2001). Their cognitive and social development (including decision-making abilities) is incomplete until at least age 25 (National Health and Medical Research Council, 2001) and they tend to drink in a less controlled environment (Kenny & Nelson, 2008). Hence, their risk of alcohol-related harms, such as risky sexual behaviour, neurological damage, injury and violence to self and others, is greater (National Health and Medical Research Council, 2001). Adolescent alcohol dependence has been associated with cognitive impairment (Brown, Tapert, Granholm, & Delis, 2000). Alcohol use is also correlated with other drug use amongst adolescents (Best et al., 2000) and treatment data suggest this correlation may be increasing in Australia (Victorian Department of Health, 2012). For young US offenders, poly-drug use is more prevalent among drinkers than cannabis

47 users (Mulvey et al., 2010), and AUDs are also far more comorbid with other SUDs, especially cannabis use disorders (McClelland et al., 2004).

Several factors show distinct associations with problem use of alcohol. Among NSW young detainees, females were more likely to have AUDs than males (Indig et al., 2011). Gender did not predict high risk drinking among community-supervised youths; maltreatment and father absence did (Kenny & Schreiner, 2009). Being out of school was a risk factor for more frequent use of alcohol but not of cannabis (Feldstein Ewing et al., 2011) among young US community-based offenders. Other US studies have linked Caucasian ethnicity with alcohol problems (Danielson et al., 2009; Teplin et al., 2012a). Familial alcohol dependence is a risk factor with a strong genetic element (Haber, Jacob, & Heath, 2005). Familial alcohol problems correlated strongly with alcohol dependence among Australian female detainees (Johnson, 2004). Sacks et al. (2009) reported that heavy alcohol use was linked to self-harm by US adult offenders.

1.5.3.3 Correlates of cannabis use

Cannabis use prevalence tends to be higher among young male offenders (Childs, Dembo, Belenko, Wareham, & Schmeidler, 2011; Wei, Makkai, & McGregor, 2003) and among Australian Indigenous young offenders (Indig et al., 2011). Cannabis problems, however, are less strongly linked to gender and ethnicity (e.g. Teplin et al., 2002). This contrasts with the general population, in which male gender is the strongest predictor of cannabis use disorder by age 21, but in some samples does not predict use (Hayatbakhsh, Najman, Bor, O'Callaghan, & Williams, 2009). Psychosocial risk factors for cannabis problems among young offenders are not well documented. One reason may be that cannabis use is not typically thought to be an important contributor to offending (White & Gorman, 2000), Chapter Two, and thus has received less attention in the juvenile offending literature.

Among youths in drug treatment, antisocial peers and delinquent behaviour shared similar correlations with frequency of cannabis use as with other drugs, and no psychosocial correlates were unique to cannabis use (Nation & Heflinger, 2006). However, frequency of cannabis use contributes to poorer social adjustment in the

48 general population: more frequent use at ages 15 to 16 led to changes in youths’ social contexts and behaviours that increased their vulnerability to drug problems and offending at ages 16 to 18 (Fergusson & Horward 1997, in Lloyd, 1998).

One important point of difference from other drugs is that problem use of cannabis is less strongly correlated with poly-drug use. Whereas 80% of young detainees with AUDs or non-cannabis drug use disorders had multiple SUDs, 52% of those with cannabis use disorder did not (McClelland et al., 2004). Nevertheless, poly-drug problems are becoming more common among Australian cannabis users entering treatment (Victorian Department of Health, 2012).

1.5.3.4 Correlates of amphetamine use

The high dependence potential and lower prevalence of amphetamine use means that the correlates of both use and heavy use are of interest in this thesis. Available data suggest a higher prevalence of amphetamine use and problem use among young female offenders. Young female detainees in NSW were much more likely than males to have used amphetamines (any form) and to have used crystalline methamphetamine at least weekly (Indig et al., 2011). Female gender uniquely predicted amphetamine use among young detainees and problem drug users but not ‘low-risk’ youths in one systematic review (Russell et al., 2008). However, it was only a correlate and not independently predictive for young Australians in drug treatment (Dean, McBride, Macdonald, Connolly, & McDermott, 2010). Among US detainees, non-cannabis SUDs (primarily amphetamines or cocaine) were three times as likely at age 15 among females (Teplin et al., 2002); whether such a disparity exists in late adolescence is unclear in the available data, as specific SUDs were not disaggregated in the age 18 or age 20 follow-up. Young Indigenous detainees showed similar elevations in amphetamine use to young females (see above, Indig et al., 2011). Hispanic, but not African-American ethnicity strongly predicted amphetamine use among young US detainees (Steinberg, Grella, Boudov, Kerndt, & Kadrnka, 2011). Minority ethnic variation is yet to be documented for young offenders in Australia.

49 In their systematic review, Russell et al. (2008) found family drug use and psychiatric problems predicted amphetamine use among low-risk (no history of drug problems) and high-risk youth samples (detainees/drug problems), while other drug use and risky behaviour were predictive only for low-risk samples, and family criminality was predictive only for high-risk samples. Out of home care may distinguish both use and problem use of amphetamines (and opioids), given the much higher prevalence of these behaviours among youth in out of home care. However, at least one study found that it did not differentiate alcohol and cannabis (Braciszewski & Stout, 2012). Sexual risk-taking has also been closely correlated with amphetamine use (Steinberg et al., 2011). Problems with other drugs are typical of individuals with amphetamine problems. Alcohol use doubled the odds of amphetamine use among young female US detainees, which in turn was strongly correlated with cannabis and poly-drug use (Steinberg et al., 2011); 71% of persons seeking amphetamine treatment had poly- drug problems (Victorian Department of Health, 2012). However, a large Australian cohort study found that the correlation between adolescent amphetamine use and later poly-drug use and mental health problems was almost completely attributable to early use of cannabis (Degenhardt, Coffey, Moran, Carlin, & Patton, 2007).

1.5.3.5 Correlates of opioid use

Opioids have even higher dependence potential and lower prevalence than amphetamines. Adolescent heroin use tends to be accompanied by diverse problems including family conflict, unstable housing, ill-health, low education and unemployment (as well as offending) (Hopfer, 2002 in Fagan, Naughton, & Smyth, 2008). Given its low prevalence, information on problem use by young offenders is scarce. Among young offenders and youths in drug treatment, female gender is a common correlate of opioid use including heroin (Copeland et al., 2003; Dean et al., 2010), and in these groups, females are more likely to report overdoses (Copeland et al., 2003; Fagan et al., 2008). Ethnic variation in adolescent opioid problems is not well documented. Among 121 NSW adolescents in drug treatment, heroin use was not associated with cannabis use and was negatively associated with alcohol use (Spooner, Mattick, & Noffs, 2000). However, poly-drug problems are common among heroin users entering treatment (Victorian Department of Health, 2012).

50 1.5.4 Explanations of problem drug use

This thesis does not test theories of offending or of drug use; a survey of theories of drug use is beyond the scope of this thesis. Theories are not directly relevant to the analyses in this thesis but provide context for their interpretation.

Fagan and Western (2005) cover at least twenty distinct approaches of varying merit and relevance for different stages of drug use. A number of these have been applied to both drug use and juvenile offending. For example, social learning theory suggests that parental or peer drug use affects youths’ attitudes, expectations and knowledge about how to engage in drug use, and social control theory holds that youths with poor social attachments and unsupportive families may be more inclined towards unconventional behaviours and peers including drug use (Petraitis, Flay, & Miller, 1995).

Theories of problem drug use draw less on notions of ‘rational choice’ and more on habituation, compulsion, and impaired self-control (West & Hardy, 2005). Problem behaviour theory has been applied to problem drug use (e.g. riskier dispositions lead to a wider range of drug use, Sneed, Morisky, Rotheram-Borus, Lee, & Ebin, 2004). There is also a strong argument that at least some problematic patterns of drug use (especially opioid dependence) arise through a process of self-medication of psychological distress that arises from early trauma (Darke, 2013; Khantzian, 1997). Further explanations are considered in the review of drug-crime theory (Section 2.1).

51 1.6 Conclusion

This section has described the prevalence and correlates of drug use among young offenders, and risk factors for juvenile recidivism. Drug use and cannabis use in particular typify young offenders. However, regular problem drug use is atypical and a substantial minority show sustained abstinence (Mulvey et al., 2010). Demographic characteristics and offence histories are powerful but static predictors. Other relatively stable predictors were found in the individual (e.g. IQ) and family domain (e.g. neglectful parenting). Dynamic factors including peer delinquency and problem drug use are related to recidivism, although their contribution to recidivism risk over and above static factors is modest. However, there is solid evidence that well-implemented treatment programs targeting dynamic factors including substance use can reduce recidivism.

The overlap between predictors of involvement and persistence in criminal behaviour was substantial but incomplete. A dose relationship was often but not always apparent: drug use predicts involvement and problem drug use predicts recidivism, but low SES predicts initiation only. Important differences were also observed between predictors of involvement and persistence, although much more evidence exists for the former. Much of the evidence on predictors of juvenile recidivism is drawn from overseas samples and studies using administrative data. This underscores the need for detailed, local research. The literature also suggests, but is far less clear on, risk factors with particular relevance to females, young people and to Indigenous youths.

Many risk factors for juvenile recidivism are also risk factors for drug use problems. Many are also risk factors for psychopathology and psychosocial dysfunction. Efforts to understand and reduce these outcomes must focus on a wide range of static and dynamic risk factors and their interplay (Van Der Laan et al., 2009; Weatherburn, 2001). To assess the significance of any single risk factor, models must control for other risk factors, or consider the consequences of their exclusion. Risk factors must also be clearly operationalised. This thesis aims to fill some of these gaps relating to the relationship of drug use to recidivism.

52 2 Drug-crime relationships

Drug use is strongly associated with involvement in juvenile offending and most young offenders attribute their offending to their drug use (Hammersley et al., 2003). Recidivism tends not to be predicted by drug use per se (drug use is so common among offenders it is not a useful predictor), and only weakly predicted by problem drug use (Cottle et al., 2001). However, the diverse nature of drug use and offending suggests many drug-crime relationships, not just one (Bennett & Holloway, 2007). Young offenders report a multiplicity of relationships between their drug use and offending (Hammersley et al., 2003). Some drug-related crime serves to support drug use (Prichard & Payne, 2005) but a substantial amount involves violence unrelated to income-generation (Grann, Danesh, & Fazel, 2008). Drug-crime relationships need to be assessed in the context of other risk factors (Brownstein & Crossland, 2003), which cluster differently in juvenile, adult, community and detainee populations.

The costs of drug-related offending (Collins & Lapsley, 2008) are enormous, and treatment of drug problems generally has positive effects on recidivism (Holloway et al., 2005). Treatment places are limited and young offenders rarely seek treatment (Lennings, Kenny, & Nelson, 2006). Agencies need to understand which patterns of drug use are linked with offending, for which drug-using offender subgroups, so treatment can be targeted. This is particularly relevant given the harmful effects of more coercive CJS involvement. Australia’s Chief Justice has submitted that judges also need this information so that it can inform the sentencing process (French, 2009). The assessment of specific drug types and demographic variation has been identified as a priority for drug-crime research (Ashcroft, Daniels, & Hart, 2003; Brownstein & Crossland, 2003) but there is a paucity of such research for serious juvenile offenders.

This chapter will describe explanations of drug-crime relationships, review Australian studies of drug-crime relationships among young offenders, and describe key international studies and explanations of these relationships. It will focus on links from alcohol, cannabis, amphetamine, and opioids to recidivism, as assessed in Chapters Six to Eight. The chapter will conclude with an outline of the thesis structure.

53 2.1 Explanations of drug-crime relationships

Five basic structural models have been proposed to explain the association between drug use and offending: 1) drug use influences offending; 2) offending influences drug use; 3) drug use and offending mutually influence each other; 4) drug use and offending arise from a shared process; 5) drug use and offending do not influence each other but co-occur coincidentally. These models are tested by establishing temporal precedence between drug use and offending, and controlling for shared and unique risk factors for both behaviours. The thesis dataset contains prospective measures of offending (Section 3.4) and cross-sectional measures of drug use and other risk factors (Section 3.3), so it is suitable for testing ‘model 1’ above. In practice drug use is usually assumed to be a risk factor, but it may also be protective (as with antipsychotic drugs and violence, Swanson et al., 2000). This chapter will also touch briefly on the other models, however causal inferences cannot be drawn for those models from the available data. However, this thesis does not undertake theory-testing.

Drug-crime research was initially dominated by theories concerned with which behaviour came first (Nurco, Kinlock, & Hanlon, 1995). The addiction or ‘enslavement’ model suggested that drug use leads to offending because users become dependent and are unable to support their drug use through legitimate means (Goode, 1998). By extension, reducing drug use or dependence would lead to lower levels of offending (Fry, Smith, Bruno, O’Keefe, & Miller, 2007). The ‘criminality’ model argued individuals with a predisposition to deviance also become drug users (Goode, 1998), thus suggesting that addressing drug use would not prevent crime (Fry et al., 2007). A composite model proposes a shift from criminality to addiction as drug use intensifies (Goode, 1998). Therefore, some offending will be drug-related and could be prevented by reducing progression to dependence. According to Bennett and Holloway (2005c), the theoretical literature is now extensive, complex and divided with conflicting and contradictory elements. There are explanations for the initiation of crime or drug use, for progression (to regular drug use, or to recidivism as explored in this thesis), for specific offending incidents, and for longer-term patterns of offending (Bennett & Holloway, 2005c).

54 Inconsistent operational definitions account for some inconsistent findings in risk factor research, and measures of recidivism and drug use are particularly diverse (see Chapter One). This variation affects the nature of observed drug-crime relationships (e.g. Bennett et al., 2008). For example, there are incongruities between arrestee’s self-reports and urinalysis-based estimates of drug use (typically under-reporting). Sentenced offenders may be less inclined to misrepresent their drug use given that disclosure does not present an obvious threat (e.g. to sentencing outcomes). A related problem is that many studies (including juvenile recidivism meta-analyses) have used aggregate measures of drug use and have not disaggregated specific types of recidivism other than violence; this has concealed potential variation in the relationship. Drug-crime theory has largely focused on specific drug-crime relationships (heroin and cocaine with acquisitive crime, and alcohol with violence) and other relationships are grossly under-represented among empirical studies (Bennett & Holloway, 2005c). Cannabis-crime links have received very little attention.

Various theories have proposed that drug use may have both proximal and distal effects on offending, and that contextual factors may affect how the drug-crime relationship manifests (Bennett & Holloway, 2005c). Common-cause theories (e.g. Problem Behaviour Syndrome) offer an integrated explanation of involvement in drug use and/or offending (as environmentally-mediated manifestations of underlying impulsivity or deviance), but do not explain more specific elements of drug-crime relationships (such as how heroin dependence drives acquisitive crime). A grand theory of all drug-crime relationships, has yet to be developed (Bennett & Holloway, 2005c). Given the diversity of possible relationships, this seems a quixotic venture, but thorough empirical study may reveal commonalities between specific drug-crime relationships (Bennett & Holloway, 2005c; Farabee, Joshi, & Anglin, 2001).

Goldstein (1985) developed a tripartite explanation of drug-related violence in the context of the 1980s crack epidemic in New York City (MacCoun, Kilmer, & Reuter, 2003), classifying this offending as psychopharmacological, economically-motivated, or systemic in nature (see descriptions below). Brownstein and Crossland (2003) suggest that Goldstein’s model is less useful for studying non-violent offending and the effects

55 of drugs other than cocaine and heroin. However, the model has been extended to property offending (Goode, 1998) and remains popular for exploring a wide range of drug-crime relationships (Payne & Gaffney, 2012).

2.1.1 Psychopharmacological explanations

Psychopharmacological explanations propose that drug use or withdrawal affects offence risk by altering mood, cognition (including judgement and attention deficit), metabolism, and disinhibition (Stevens et al., 2005; White & Gorman, 2000) which is particularly related to violence (Fagan, Weis, & Cheng, 1990; Goode, 1998). These effects are drug-and dose-dependent and so offending should vary by pattern of drug use. Compared with adults, juveniles’ shorter duration of drug use and lower drug tolerance suggests they will experience fewer effects relating to dependence. Psychopharmacological effects are typically invoked to explain acute increases in offence risk pertaining to intoxication, but may also be chronic, protective, or dose- dependent (e.g. arousal at low doses and sedation at high doses). Withdrawal and other factors related to dependent drug use, such as sleep deprivation, poor nutrition, and precipitated psychosis may also exacerbate offending and violence risk (Virkkunen & Linnoila 1993, in White & Gorman, 2000). Alcohol is the drug most commonly linked with psychopharmacological violence (White & Gorman, 2000), and this is related to alcohol intoxication rather than alcohol use per se (Felson et al., 2008).

Psychopharmacological explanations typically do not directly attribute offending to a specific psychoactive effect of drug use (see Section 1.5), but describe a mediated process (Wright & Jacques, 2010). Thus, alcohol use may impair social comprehension and verbal skills and thereby increase one’s chance of misinterpreting an ambivalent cue as a threat and of responding aggressively to the perceived threat, while decreasing capacity to de-escalate ensuing violence. In this example there may be a psychopharmacological link to victimisation and offending. Psychopharmacological explanations may be used to explain uncharacteristic behaviour (Wright & Jacques, 2010), but drugs are used to alter conscious experience (e.g. to suppress pain, or to increase confidence). Thus, attitudes, motivations, mood, expectations and beliefs

56 regarding drug use may also mediate psychopharmacological links. Physiological differences (e.g. mass) are also relevant (Livingston & Room, 2009).

In addition to these individual differences, offenders’ routine activities and social circumstances may mediate psychopharmacological links. Many young offenders reside in neighbourhoods with high concentrations of offending and drug use, low supervision and social control, and social disadvantage (Weatherburn, 2001). This combination is likely to facilitate drug-related offending, and this is exacerbated by juveniles’ tendency to offend in groups. White (2004) describes a ‘group psychopharmacological effect’ whereby the offending frequency of individual youths is highest when amongst a group of intoxicated, delinquent peers. Given the above, efforts to explain offending in terms of psychopharmacology should consider ‘direct’ (e.g. immediate neurological) effects and ‘indirect’ effects mediated by social and environmental factors (Bennett & Holloway, 2005c).

2.1.2 Economic explanations

Economic motivation is the most commonly used explanation of the relationship between drug use and recidivism, and the ‘enslavement’ model is the most common form. This model suggests that offenders who are drug-dependent engage in crime that enables them to sustain their dependence. Income-generating theft and drug dealing and are the most common of these crimes. Unlike psychopharmacologically- linked crime, economic motivations are thought to motivate covert offending to minimise detection (Goldstein 1985, in Bennett & Holloway, 2005c) which would jeopardise continuity of drug use. Evidence of the sale of prescriptions (Larance, 2012) and sex work (Roxburgh, Degenhardt, & Breen, 2005) to support drug use are consistent with this position. Violence may arise during such offences but is less likely to be premeditated (e.g. theft may escalate into robbery when a victim resists).

Given that enslavement relates to drug-dependent offenders, one would expect psychopharmacological elements to such offending. Withdrawal is particularly relevant here, as this may result in agitation and slower cognitive processing that increases an offender’s likelihood of violence. At the same time it impairs execution of a planned

57 acquisitive crime. Strong evidence of a withdrawal syndrome for cannabis (Allsop et al., 2012) was not apparent at the time of Goldstein’s original writing, but could also explain some offending among dependent cannabis users, in particular violence (Hoaken & Stewart, 2003).

Despite young offenders’ low rates of dependence on high-value drugs, such as heroin, economic explanations are still highly relevant to this group. By definition, economic explanations apply especially to offenders with low economic power. These are not restricted to drug-related offending (see Chapter One), but the expense of drug use is likely to be considerably greater than basic needs (i.e. survival crime) or even status items (such as sneakers). Young offenders’ low economic power means they will turn much sooner than adults to illegitimate sources of income or drugs to maintain dependent drug use. This includes dependent and/or high frequency cannabis use, which is very common and incurs significant expense (Wilkins & Sweetsur, 2010).

As Brunelle, Brochu, and Cousineau (2000) and colleagues explain, youths do not need to be dependent to be economically motivated to offend. Drug use is a normative and central aspect of delinquent peer affiliation for many young offenders, and the considerable pressure to conform to drug use may well involve using at unaffordable levels. A binary measure of dependence may therefore be less effective at identifying an economic link than a graded measure of frequency of use (also see Hunt 1991, in Bennett & Holloway, 2005c). The more frequently an individual uses, the more this use may outstrip their legitimate income, thus necessitating more frequent offending. Measurements of recidivism frequency are necessary to assess this dose relationship.

2.1.3 Systemic explanations

The use of illicit drugs necessitates involvement in an illicit drug market, and increasingly greater involvement at higher levels of drug use. Goldstein (1985) described a systemic link between drug use and offending, especially violent offending; he considered drug markets to be inherently violent, citing (for example) territorial disputes between dealers and the forceful theft of drugs Wright 2009 (Wright & Jacques, 2010). Moeller and Hesse (2013) question whether this explanation

58 generalises to other drug markets. Some characteristics of the crack market are violent-prone (e.g. street-level dealing of a high frequency commodity) (Moeller & Hesse, 2013). By contrast, cannabis markets are typically indoor-based and between peers (Moeller & Hesse, 2013). Similarly, many young Australian recreational drug users (mean age 24) report involvement in dealing and most exchanges of cannabis and amphetamines (the drugs most prevalent among young offenders) occur in residences and between acquaintances (Sindicich & Burns, 2013). Moeller and Hesse (2013) explain that cannabis markets have not traditionally been linked with violence, but show that cannabis is a valuable enough commodity that it may elicit violence among dealers to protect market share.

The drug markets within which drug-using young Australian offenders engage have not been described. However, drug markets are concentrated in disorganised communities (Weatherburn & Lind, 2000) and these may facilitate juvenile offending by undermining community security (White & Gorman, 2000) and by providing opportunities for drug market participation. NSW communities with high concentrations of young offenders do report more drug-related crime than other areas of the state (Moffatt, Goh, & Fitzgerald, 2005). Drug market involvement also provides a direct link between drug use and offending through drug law violations (e.g. drug use or possession), but this link is inherent rather causal (Bennett & Holloway, 2005c).

The above explanations strongly suggest that drug use may affect juveniles’ likelihood of recidivism, and highlight potential links between violent offending and heavy drinking, more frequent offending and more frequent drug use, and rapid acquisitive crime among frequent users of high value drugs. However, these explanations may be inadequate for juvenile offenders in the Australian context, and given the diversity of drug use among young offenders (Chapter One), multiple explanations may apply to any given drug-using offender.

2.1.4 Other relationships between drug use and crime

The relationship between drug and crime may also operate in reverse, with offending impacting on drug use. ‘Reverse’ psychopharmacological explanations include

59 offenders using drugs to facilitate offending (Brochu 2001 in Bennett & Holloway, 2005c), or to celebrate their offending (Menard 2001 in Bennett & Holloway, 2005c) and this is confirmed by some offenders (Wright & Decker 1997 in Bennett & Holloway, 2005c). Offenders may also use illegal income to buy drugs as a way of disposing of this income (Collins 1985 in Bennett & Holloway, 2005c), without being ‘compelled’ to buy drugs, suggesting some nuance to economic drug-crime explanations. There is also a clear systemic explanation arising from control and social learning theories (Chapter One) for how offending can lead to drug use. Young offenders frequently liaise with delinquent peers and are disengaged from pro-social structures of control; the ‘offending lifestyle’ is conducive to drug and heavy alcohol use (White & Gorman 2000, in Bennett & Holloway, 2005c).

The ‘intensification’ model offers one example of a bi-directional understanding of drug-crime relationships: e.g. offending leads to drug use, which leads to dependence, which leads to greater offending. Hough (1996, in Bennett & Holloway, 2005c) explains another bi-directional relationship, in which drugs are used to mentally escape from sex work, which in turn funds drug use, without any escalation necessarily occurring. A link between drug use and illegal sex work has been established among street-involved youths (Spittal et al., 2012), but this is unlikely to emerge in studies reliant on court records, given that most such offences are dealt with earlier in the CJS.

Drug use is one of many potentially causal risk factors for recidivism, and these correlates overlap extensively with risk factors for drug use. Common-cause models (see Section 1.3.3) suggest that drug use and offending arise from shared external factors, for example, a general deviance factor (e.g. the ‘criminality’ model described above), low self-control and sensation-seeking (per Edgework Lyng, 1990) and the General Theory of Crime (Gottfredson & Hirschi, 1990). Shared roots may also lie in poor parenting, delinquent peer affiliation, and social disadvantage, as described in social learning and control theories (see Sections 1.3.1 and 1.3.3). More sophisticated theories admit both common causes and bi-directional relationships (Lynne-Landsman, Bradshaw, & Ialongo, 2010). Spurious explanations meanwhile suggest that crime and drug use arise through unrelated causal processes. Bennett and Wright (1984, in

60 Bennett & Holloway, 2005c) suggested that incidental crime-related social conversations among burglars may influence decisions about further offending. The typically social context of young offenders’ cannabis use may be conducive to recidivism in this regard.

Drug-crime theory, influenced heavily by US scholarship, has been particularly concerned with the links between drug use (especially alcohol use) and violence, and between dependent heroin or cocaine use and acquisitive crime. Less attention has been given to explaining other drug-crime relationships, including the role of amphetamines and cannabis especially. Hoaken and Stewart (2003) offers explanations for the association between stimulants (including amphetamines) and violence. Most of these overlap with general explanations: e.g. a subset of users has a propensity for violence; withdrawal causes aggression; aggression facilitates access to the drug. A further possibility that has specifically been proposed for cannabis is that stimulant use increases psychotic symptoms and thus violence (Niveau & Dang, 2003). Such differences highlight the value in disaggregating drug types in drug-crime research.

Notwithstanding the differences in explanations for links between specific drug use and crime, general explanations are important, because users rarely engage in one constant pattern of drug use (Tyner & Fremouw, 2008). Poly-drug use could be spuriously linked with offending if it arises from a more deviant underlying personality, or it may require more extensive drug market involvement (to acquire multiple drugs), or crime to support a higher spend (Bennett & Holloway, 2005a). Leri et al (2003, in Bennett & Holloway, 2005a) suggest that dependent heroin users may use amphetamines for the alertness needed to sustain their heroin use.

It must also be acknowledged that in some cases, drug use should be negatively correlated with offending (Anthony & Forman, 2003). For example, intoxication can impede coordination and ability to execute an offence. In particular it can impede the ability to offend without being detected. The multiplicity of drugs, crimes, and social contexts render inevitable that evidence about the drugs-crime relationship is not wholly consistent (Anthony & Forman, 2003). This evidence is explored in the remainder of this chapter.

61 2.2 Empirical evidence of drug-crime relationships

In evaluating empirical findings regarding drug-crime relationships, the methodological issues raised in the risk factor review (Section 1.4) are also relevant. Firstly, drug use and offending are diverse phenomena, but are often aggregated in recidivism studies, and there is scarce prospective research into young Australian community-supervised offenders. Secondly, cross-sectional data do not permit causal conclusions. Thirdly, detainee studies do not generalise to community offenders. Lastly, general population studies rarely assess serious drug use and offending (Bennett et al., 2008), and adult studies may generalise poorly to juvenile populations because adult offenders’ drug use tends to be more entrenched, thus altering the nature of the drug-crime dynamic.

2.2.1 Prior reviews and meta-analyses

In a wide-reaching but United States and adult-focused review, (White & Gorman, 2000) identified “substantial variation in all issues surrounding drug use and crime”, including the extent and nature of offending and drug use, and relationships between drug use and offending. For example, this included the fact that most drug-using offenders did not specialise in one type of crime. The authors concluded that drug use tended not to cause the onset of offending, and that while there was greater support for the common-cause explanation, heterogeneous paths led to both behaviours (White & Gorman, 2000). The Australian Institute of Criminology (2004) has also reported that “the best research has generally concluded that the relationship is extremely complex and defies attempts to sort out directionality”. As the current chapter will show, these conclusions still appear to hold.

Colins’ systematic review showed that cannabis use disorders (39%) are more prevalent than alcohol (26%) or other drug disorders (6%) among young detainees (Colins et al., 2010). In Colins’ review, AUDs were unrelated to violence, in contrast to young NSW detainee (Whitton, Indig, Vecchiato, & Kumar, 2010) and general population studies (Fergusson, Lynskey, & Horwood, 1996; White, Tice, Loeber, & Stouthamer-Loeber, 2002).

62 There are no meta-analyses of detailed relationships between drug use and juvenile recidivism. Cottle et al. (2001) (see Chapter One) found juvenile recidivism was weakly predicted by substance abuse, but not by substance use; Derzon and Lipsey (1999, see below) used meta-analysis to assess links between cannabis use and delinquency (but not recidivism and not among serious offenders per se). Otherwise, meta-analyses have included only aggregate relationships between drug use and juvenile recidivism (rather than by specific pattern of use) or have included such relationships incidentally (i.e. in studies including adult and juvenile samples, e.g. Bennett et al., 2008).

A number of adult meta-analyses have separately analysed links between alcohol, drugs, general recidivism and violent recidivism. Bonta et al. (1998) found that drug and alcohol abuse weakly predicted general recidivism. Dowden and Brown (2002) reported similar results for general and violent recidivism, and further that drug abuse predicted general recidivism equally for males and females, while alcohol abuse was relevant only for males. A later meta-analysis found a small association between drug use and violent recidivism compared to non-recidivism, but there was no association when compared to non-violent recidivism (Collins, 2010). Findings for alcohol use were inconclusive, and only one relevant study was identified for females. Collectively these studies support disaggregating drug and crime type and focussing on problem use.

There are few meta-analyses of links between use of specific illicit drugs and recidivism. One reason for this can be drawn from Derzon and Lipsey (1999)’s discussion of longitudinal studies that assess temporal associations between cannabis use and juvenile offending (delinquency); they describe them as cumbersome, diverse and potentially incommensurable (Derzon & Lipsey, 1999). In their meta-analysis of 30 studies, overall associations with cannabis were moderate, slightly stronger for property than violent offending, and they also weakened over adolescence. The association was strongest when use was concurrent with offending, and weakest (ES≤0.1) when use preceded offending. This finding suggests that cannabis use correlates most strongly with property crime among younger adolescents and is not causal of offending. This highlights the importance of juvenile-specific and ideally age- stratified analyses - at least for links with offending.

63 A broader, cross-sectional meta-analysis assessed many more links between specific patterns of drug use and specific offences (Bennett et al., 2008), across 30 studies that were published prior to mid-2003. The meta-analysis sought to clarify and empirically confirm some of the explanations outlined in the previous section. Statistical associations varied by type of drug and type of offence. Opioid use (mean odds ratio/OR 3.1-3.6) was more strongly related to offending overall than cannabis (mean OR 1.5), amphetamines (mean OR 1.9), or cocaine use (mean OR 2.6), with the strongest link for crack (mean OR 6.1-6.2) (Bennett et al., 2008). Considerable variation was observed between individual studies, however: half the associations with cannabis and amphetamines were non-significant, and significant ORs ranged up to 4.7 for cannabis, and 20.9 for heroin. Distinct differences were also observed between specific offences: the link was strongest for shoplifting, around half as strong for burglary, and weaker still for robbery. These associations are at the higher end of those observed in the literature, as the associations selected from individual studies preferentially included those hypothesised to most strongly display an association with offending (for example, for a study reporting links to theft from cannabis and heroin, only the heroin link was used).

Several reviews have focused on links between drug use and aggression or violence, particularly relating to alcohol use. Bushman and colleagues found the evidence was strongest for central nervous system depressants (including alcohol), with low doses having a small to medium effect on aggressive behaviour independent of other predictors (Bushman, 1993). Alcohol has pharmacological and psychological effects (e.g. expectancies) but appears to be an indirect predictor of aggression, with provocation, frustration and other facilitating factors having a greater effect on aggression by intoxicated than non-intoxicated persons (Anderson & Bushman, 2002). Similar results emerge from other reviews (eg Hoaken & Stewart, 2003). Thus, social setting is important in understanding alcohol-related aggression. Hoaken and Stewart (2003) found the drugs/aggression relationship was interactional and multifactorial, and differed by drug type and dose. Hoaken and Stewart (2003) suggest that withdrawal is the most likely explanation for a cannabis/violence link, as irritation and aggression is a feature of withdrawal occurring a few days into abstinence (Allsop et

64 al., 2012). Cannabis may be indirectly associated with violence as it is a precursor or ‘gateway’ to drug use including stimulants that have been linked with violence. However, progression to these drugs is better attributed to causes other than cannabis use per se (Degenhardt, Dierker, et al., 2010; Swift et al., 2012).

Results regarding other drugs have not permitted firm conclusions. Hoaken and Stewart (2003) described the literature on the stimulant/aggression link (including amphetamines) as particularly ‘idiosyncratic’, and better explained by pre-existing interpersonal factors than intoxication (Hoaken & Stewart, 2003). Boles and Miotto (2003) concur that amphetamines and violence are related but confounded by individual and social factors, with evidence for pharmacological and systemic links. Tyner and Fremouw (2008)’s critical review of studies published to 2005 concluded that the link between amphetamines and violence was indirect and correlational. The impact of amphetamine on other offences has received less scrutiny. While occasional opioid use increases feelings of well-being, chronic use affects complex behavioural and emotional changes, and unmanaged withdrawal has undesirable consequences (Hoaken & Stewart, 2003). Dependent opioid users tend to have premorbid psychopathology which may include aggressive tendencies (Hoaken & Stewart, 2003) and it remains unclear whether opioid dependence causes aggression.

Meta-analyses provide insight into methodological factors that affect the size and nature of drug-crime relationships – as with Derzon and Lipsey (1999)’s finding of stronger relationships between cannabis and crime in early adolescence. Dowden and Brown (2002) found that the mean effect size of studies with follow-up periods of less than two years was 50% higher than studies with longer follow-ups. This suggests a decreasing relevance over time of the effect of drug problems on recidivism, perhaps because drug use can change over time. Bennett et al. (2008) showed that overall drug-crime associations were much weaker for samples of offenders and drug users than samples drawn from the general population, and likewise for studies comparing users to other users rather than to non-users (these two findings are likely to be related, given that offender studies rarely compare users with non-users). This supports a focus on problem drug use rather than drug use among offenders.

65 Further variations in the size and nature of drug-crime relationships were observed in Bennett et al. (2008)’s meta-analysis: drug use increased juveniles’ likelihood of offending two-fold, but by a factor of three to five for adults; there was inconclusive evidence of a stronger association for females; the association was stronger in more recent studies and in UK versus US samples. It follows that the drug-crime relationship is influenced by sample type and methodology. Attention to such variation is important, and a greater evidence base is needed to facilitate systematic investigation.

2.2.2 Australian studies

No prospective analyses of a young Australian offender cohort have been published. Drug-crime associations were reported in several recidivism studies of Australian juveniles (Section 1.2.2) but these studies typically used aggregated measures of drug use (e.g. Weatherburn et al., 2007). In a two-wave study of around 1000 detainees, Putninš (2006) found drug use and conduct problems were significantly correlated at both time points and were mutually predictive from time one to two, but not after baseline correlations were adjusted. Earlier, Putninš (2003) found alcohol and inhalant use (but not other drugs) at arrest was associated with recidivism. Vignaendra et al. (2011) aggregated illicit drugs into a continuous measure of past year frequency of use, and this measure was significantly associated with recidivism risk (p=.03), as was weekly drinking (p<.001). However, neither measure predicted recidivism among community-based offenders in a multivariate model (McGrath, 2009b).

Prichard and Payne (2005) retrospectively studied 371 Australian detainees aged 11 to 17 (93% male). The sample was typified by heavy and persistent drug use and diverse offending behaviours (Prichard & Payne, 2005). Drug use and offending commenced earlier for regular violent and property offenders. Frequency of offending and drug use were strongly correlated: 72% of daily users offended on most days, compared to 50% of weekly users and 34% of monthly users. Similar dose relationships were observed with drug-related attributions, and 33% of crimes were attributed to drug use, and 29% to intoxication. Alcohol and amphetamine use were more prevalent among regular violent offenders, as were psychopharmacological reasons for offending, whereas economic reasons were more common for regular property offenders.

66 However, other attributions (e.g. anger and revenge for assault, peer associations and other financial need for burglary) were more common. Other, largely historical risk factors (e.g. maltreatment) were also common but multivariate analyses were not performed, so independent drug-crime associations were unknown. Interestingly, drug use had little impact on Indigenous youths’ offending (perhaps because their risk profiles were so extreme), but regular drug use clearly preceded regular offending by non-Indigenous offenders (Prichard & Payne, 2005).

In the most detailed analysis of Australian juvenile arrestees (n=493, Wei et al., 2003), 57% of arrestees reported past month drug use (cannabis 54%, amphetamines 16%, opioids 13%, and 28% more than one illicit drug). Users of illicit drugs other than cannabis (i.e. with positive amphetamine, opioid or cocaine tests) were more likely to be female, less likely to be on violent charges, and were more frequent offenders than other youths. Juvenile arrestees reported a much quicker progression from cannabis to other illicit drugs compared to adult arrestees (approximately two versus six years) (Wei et al., 2003). Payne (2006) later showed that among adult male property offenders, those who had progressed rapidly from first to regular drug use were the most frequent offenders. These studies highlight the small window within which progression of drug use occurs among juveniles, and suggest that even delaying progression to regular use may assist in reducing the volume of crime.

Among adult arrestees, heroin is more likely to be implicated in recent offending by heroin users (54%) than is alcohol among alcohol users (41%), amphetamines among amphetamine users (33%), and cannabis among cannabis users (14%) (Payne & Gaffney, 2012). By extension, 6% of arrestees attributed their offending to each of cannabis, amphetamine, and heroin use whereas 30% attribute their offending to alcohol (Payne & Gaffney, 2012). Cannabis and amphetamine users were most likely to report intoxication by those particular drugs during their last offence, and least likely to report offending to support their drug use; the reverse was true for heroin. Withdrawal during offending was reported by a minority of users, typically heroin users, and ‘drug lifestyle’ explanations (e.g. being caught using drugs) were common (Payne & Gaffney, 2012). Alcohol was implicated in offending to the same extent as all

67 illicit drugs combined. It also accounted for more violent offences (34% vs. 12%) but fewer property offences (21% vs. 37%) (Payne & Gaffney, 2012). Earlier national analyses showed that dependent heroin users accounted for nearly half of property crime arrestees and 10% of violent arrestees, whereas dependent amphetamine users comprised one in three property arrestees and 12% of violent arrestees; they were also more likely to be alcohol dependent (Weierter & Lynch, 2002).

Salmelainen (1995) assessed correlates of frequent versus non-frequent theft offending among 247 NSW juvenile detainees. Cannabis users were more likely to be high rate theft offenders than were non-users of cannabis. Very frequent cannabis users were also far more likely to be frequent offenders than were less frequent users, occasional and non-users (Salmelainen, 1995). The need to support drug use was also correlated with a high frequency of both offences. A study of NSW detainees (Stevenson & Forsythe, 1998; Trimboli & Coumarelos, 1998) reported a strong association between heroin use and acquisitive crime. However, among juveniles who did not use heroin, cannabis expenditure correlated significantly with the frequency of burglary (Trimboli & Coumarelos, 1998) and more so than for other drugs (Stevenson & Forsythe, 1998). These findings suggest that young Australian offenders may commit property crime to support their drug use, including cannabis use (Trimboli & Coumarelos, 1998). However, the studies could not determine the temporal ordering of crime and drug use.

2.2.3 Longitudinal studies

Several large international longitudinal studies (Dembo, Williams, Getreu, et al., 1991; Loeber et al., 2001; Moffitt, Caspi, Rutter, & Silva, 2001) show that drug abuse and early onset of drug use affect onset of offending, and delay desistence from offending. Teplin et al. (2012b) suggests the nature and course of substance user disorders (SUDs) among serious young offenders differs from that of non-offenders and adult offenders. Young offenders’ persistence of SUDs is higher than in the general population, higher among males than females, and higher among white than minority males (for alcohol use disorders especially). Young white male offenders appear to make a particularly poor transition to adult roles (Teplin, Abram, McClelland, Washburn, & Pikus, 2005),

68 and one contributing factor may be their low levels of drug treatment involvement as noted by Lennings et al. (2006).

Hakansson and Berglund (2012) assessed recidivism (return to the CJS; prevalence 69%) among 4152 Swedish prisoners with lifetime drug problems over a mean of 2.7 years. Amphetamine and heroin use predicted a substantially higher risk of recidivism, particularly when injected, suggesting that injecting itself warrants separate attention. In contrast, binge drinking more than twice weekly predicted a lower risk of recidivism, independent of psychiatric problems and criminal history. Among Swedish drug treatment clients (half of whom were recent offenders), opioid and stimulant dependence independently predicted general or theft recidivism, while stimulant dependence weakly predicted violent recidivism, and cannabis dependence predicted lower levels of theft and violent recidivism (but not general recidivism) (Fridell et al., 2008). In a small adult psychiatric population, Mulvey et al. (2006) reported a higher likelihood of violence on days following alcohol or poly drug use but not vice versa.

Cartier, Farabee, and Prendergast (2006) studied reincarceration among 640 US adult parolees, half of whom had completed prison-based drug treatment. Adjusting for ethnicity and other background factors, the frequency of methamphetamine use one year after baseline was the strongest correlate of self-reported violence; alcohol use severity also correlated with this outcome. Methamphetamine use also had a small impact on general recidivism. Drug dealing explained minimal additional variance in both outcomes (i.e. recidivism and violence were more closely related to drug use than to drug market involvement), but the users’ higher rates of violence were not reflected in the recidivism data.

Zamble and Quinsey (1997) interviewed Canadian male prisoners, including recidivists (n=311) and first-time prisoners (mean age 30), about the lead-up to their latest offence. Recidivists were significantly more likely to drink and use drugs, to drink at higher quantities and to use drugs more often (Zamble & Quinsey, 1997). This disparity with non-recidivists was most pronounced in the immediate pre-offence period: 42% reported binge drinking in the 24hrs prior to re-offending, compared with 3% of non- recidivists. For some offenders, drug use represented the final response to a problem,

69 signalling a breakdown in coping. The offence process was typically impulsive and not easily explained by earlier risk factors. The authors concluded that in most cases drug abuse was inseparable from other maladaptive behaviours (Zamble & Quinsey, 1997). Their implications are that drugs are important antecedents to recidivism, and that attempts to address coping strategies without addressing drug use, or vice versa, would be ‘foolish’ (Zamble & Quinsey, 1997). This study reveals a complex relationship between drug use and offending that varies by drug, offence and offender.

Peer deviance has been empirically and theoretically linked with drug use and offending. In the general population Christchurch cohort (see Appendix Table B), youths with antisocial behaviour (ASB; drug use and delinquency) tended to select more antisocial peers. Involvement with these peers increases ASB, but much of this association is non-causal (Fergusson, Swain-Campbell, & Horwood, 2002). Peer effects for both genders were stronger for property than violent offending, and stronger for cannabis than alcohol disorders. Peer effects were also strongest among young adolescents (aged 14 to 15), and decreased sharply over adolescence (Fergusson et al., 2002) For serious offenders, peer effects may be more persistent as they tend to remain in delinquent peer groups into adulthood, but weaker as they experience more and more serious risk factors for both behaviours (including other drug use not studied in the Christchurch Health and Development Study).

2.2.4 Cross-sectional studies

Information about disaggregated drug-crime relationships is more readily available in cross-sectional than longitudinal studies, as they often study larger groups of offenders and so can identify far more specific drug-crime associations. Dawkins (1997) assessed associations between self-reported frequency of alcohol, cannabis and heroin use, and 20 different offences among 312 US male juvenile detainees. Alcohol use frequency showed moderate correlations with all offences, and holding ethnicity and criminal history constant, was predominantly correlated with serious violence and property damage. Cannabis use was moderately correlated with group violence, police contacts and theft. None of the factors assessed were independently correlated with heroin, perhaps because prevalence of use was negligible.

70 Brunelle, Cousineau, and Brochu (2005) asked Canadian juvenile offenders to explain the link between their drug use and offending. Explanations were akin to those described in Section 2.1, but with some important differences. For some juveniles, an economic drug-crime link was apparent before they became drug-dependent, even for inexpensive drugs (i.e. other than heroin), because of their very low economic power (Brunelle et al., 2000; Brunelle et al., 2005). This finding echoes the Australian studies by Salmelainen reviewed above. A study of 231 Belgian young male detainees reported a strong link between cannabis problems (dependence more so than abuse) and property offending, as opposed to other offence patterns (Colins, 2009). This is also suggestive of an economic link between cannabis use and offending.

Juveniles in Brunelle et al. (2005)’s study also gave ‘crime leads to drug use’ explanations (Section 2.1.4). Some used drugs to facilitate their offending: intoxication was subsequent to their intention to offend (Brunelle et al., 2005). Others bought drugs (rather than goods) to covertly dispose of their illegal income. Expensive drugs offer more opportunity to dispose of earnings but tend also to have greater dependence potential, so more lucrative or successful crime may inadvertently lead offenders to heavier drug use. Brunelle et al. (2005) concluded that crime is more often attributed to drug use amongst entrenched users.

A study of 188 US adult prisoners with drug problems (Kinlock, O'Grady, & Hanlon, 2003) reported well established links between opioid use and offence frequency, and between frequent opioid use and drug dealing to support their drug use. More surprisingly, exclusive use of cannabis was associated with violence by a small group of young males who dealt drugs for profit. This group sought to avoid the expense and psychosocial impairment of other drug dependence, that would hinder their control of their market, which sometimes involved violence (Kinlock et al., 2003). The authors inferred that cannabis use does not lead to dependence, which is a persistent misconception (Allsop et al., 2012; Jones et al., 1981). Opioid use was also independently correlated with violent offending. Although violence constitutes very little of heroin users’ offending, the incidence of violence among heroin users can still be substantial due to their overall high volume of offending (Nurco et al., 1995).

71 The most comprehensive drug-crime analyses in an offender population arose from the 1999-2002 ‘NEW-ADAM’ program (Bennett & Holloway, 2005a, 2005b; Bennett & Holloway, 2009; Bennett, Holloway, & Farrington, 2008; Holloway & Bennett, 2008), and focused on income-generating offending by 4675 UK arrestees, including 24% adolescents (age 17-19) and 26% young adults (20-24) (Bennett & Holloway, 2007). Other offences (e.g. violent offending) and alcohol use were outside the study scope. Most (63%) drug-using past year offenders causally related drug use to their offending, but this was much less common among adolescents (47%) than young (63%) or older (72%) adults (Bennett & Holloway, 2007). Economic explanations (offending to support drug use) were by far the most common causal attributions made by offenders but much less likely among adolescents (70%) than young adults (87%), as was opioid use was (15% vs. 32% young adults). Drug users were more likely to offend and to do so more often than non-users, and for most drugs, drug use was linked to a roughly two- fold higher risk of multiple offence types, controlling for demographics and other drug use (Bennett & Holloway, 2007). More frequent users offended more, and more often, than other users but few drugs were independently linked to frequent offending; further, without the strong link between heroin use and (frequent) shoplifting “there would [have been] no drug-crime connection” (Bennett & Holloway, 2007 p. 129).

The findings of the NEW-ADAM analyses highlight the diversity and specificity of associations within the overall drug-crime relationship. Substantive gender and ethnic differences were also identified (drug-crime links were generally stronger for females and Caucasians) but were not broken down for the adolescent subsample.

2.2.5 Intervention studies

Evidence about drug-crime relationships also comes from studies of prevention, diversion and treatment programs for drug-related offenders. Primary prevention is supported through social policy and community-level efforts, including policing partnerships and situational crime prevention (Stevens, Trace, & Bewley-Taylor, 2005). Schubert et al. (2010) showed that increasing employment opportunities is likely to lead to reductions in offending and drug use. For secondary prevention of drug-related offending among young offenders, family support is cost-effective (Stevens et al.,

72 2005). A drug education program based on one demonstrated to reduce drug use progression among students may also prove effective (Teesson, Newton, & Barrett, 2012; Vogl, Roberts, Liang, & Horvath, 2011).

Court-mandated treatment is as effective in reducing recidivism (and improving social functioning) as non-CJS treatment (McSweeney, Stevens, Hunt, & Turnbull, 2007). Drug treatment programs in community and custodial settings can be effective in reducing recidivism and drug use, but their effectiveness varies widely. Effective treatments include drug courts, functional family therapy and diversion to community-based treatments based on principles of effective intervention (Bostaph, Cooper, & Hatch, 2008). The most thorough analysis of programs assessing impacts of CJS treatments on drug-related crime (Holloway et al., 2005) found treatment groups were 41% better off than no-treatment groups, and treatment was more effective for juveniles than adults. However, within juvenile populations, outcomes may be poorer for younger adolescents (Luchansky, He, Longhi, Krupski, & Stark, 2006). Drug type has also been associated with treatment outcome. Luchansky et al. (2006) showed that youths treated primarily for alcohol problems went on to commit fewer serious offences than other youths, while recidivism did not differ for users of other drugs. The adult literature also suggests drug treatment reduces general and violent recidivism. Treatment episodes lead to similar reductions in the risks of both these outcomes (Gumpert et al., 2010). However, Evans, Huang, and Hser (2011) found reductions in offending frequency, but not in overall participation, in recidivism among high-risk offenders in court-supervised drug treatment.

A final point about drug treatment is that the long-term effects on recidivism (i.e. beyond early adulthood) among young offenders are not well understood. Complicating the study of these long-term effects is the dynamic nature of adolescent drug use, the chronic and often relapsing nature of dependent use, and the multiple, incomplete treatment episodes that characterise youths with treatment histories (e.g. Lennings et al., 2006). Few treatment studies (and none of Australian young offenders) have compiled follow-up data on drug use, recidivism and treatment readmissions.

73 2.3 Conclusion

This chapter has highlighted the diversity of potential relationships between drug use and offending and the explanations provided for these relationships. Aggregating drug types and offence types masks this diversity. It is clear that some offending occurs to support expensive patterns of drug use (e.g. Bennett & Holloway, 2007; Dobinson & Ward, 1986). This explanation may apply to a wider range of drug-using juvenile offenders than adults, given juveniles’ lower earning power (Brunelle et al., 2000). Evidence for other associations is more mixed. There is almost certainly a psychopharmacological link between heavy alcohol use and violence (Bennett & Holloway, 2009; Lennings, Copeland, & Howard, 2003), but this appears to be conditional on individual and contextual factors, rather than invariant. Variation by gender and ethnicity in these relationships is also evident (Bennett & Holloway, 2007). To paraphrase Weisburd, Lum, and Petrosino (2001), evidence suggests some of these explanations will apply to some offenders, some of the time.

There is an abundance of detailed evidence for unmodifiable correlates of juvenile recidivism, but relatively little for drug use, despite the availability of interventions to reduce drug use and drug-related offending. Few Australian recidivism studies have disaggregated measures of recidivism and drug use and considered demographic differences; none so far have explored this variation among community-supervised young offenders and the contribution of cannabis use and binge drinking is especially unclear. This thesis will address these gaps by assessing the independent and prospective contribution of specific patterns of drug use to specific patterns of recidivism. Analyses may enable causal inferences regarding the ‘drug use leads to crime’ model as it applies to existing offenders. This is needed to build the evidence base from which generalisations may be drawn, and to provide precise evidence to policy makers about drug-crime relationships within the local jurisdiction. In NSW, explorations along these lines have been hindered by the shortcomings of administrative data, and barriers to data linkage. In overcoming these challenges, the research described in this thesis has produced the first Australian study of drug-crime relationships among young offenders serving orders in the community.

74 2.4 Thesis structure

This thesis assesses the correlates of specific patterns of drug use, offending and recidivism, focusing on the impact of drug use on recidivism independent of other static and dynamic risk factors, among 800 community-supervised young offenders in NSW, Australia.

Chapter Three describes the empirical plan for this thesis, the data sources used, and the measurement, classification and analysis issues that come to bear on the empirical chapters. The chapter explains the rationale, recruitment, and conduct of the baseline survey, and the author’s role in this process. The data linkage process is then outlined, with reference to the logistical and ethical challenges that were faced. The strengths and weaknesses of the methodological options available for modelling drug use and recidivism are reviewed and the proposed analysis plan is outlined.

Chapter Four reviews the sample’s demographic, familial and psychosocial characteristics, and criminal histories. It also provides a detailed assessment of the sample’s prevalence of drug use. Gender differences and the correlates and predictors of prior violent and theft offending are assessed using logistic regression.

Chapter Five describes the patterns of drug use, problematic aspects of drug use and drug treatment among the sample. It assesses the correlates and independent predictors of frequency of binge drinking, cannabis, amphetamine and opioid use. Multinomial logistic regression models are used to compare frequent, non-frequent and non-users.

Chapter Six investigates the prevalence and predictors of conviction within two years of the baseline survey. Associations between drug use and general (overall), violent, theft, and robbery recidivism are reviewed and the predictors of these four outcomes in the current sample are assessed in logistic regression models. The generalisability of each model to females, Indigenous, younger and older offenders is also assessed; this approach is also employed Chapters Seven and Eight.

75 Chapter Seven assesses predictors of survival time (duration from baseline to the first new offence). The survival analysis literature is reviewed and the methodology is described. Various techniques are used to ascertain median time between baseline and subsequent offending, the relationship of drug use to survival time, and the impact of drug use and other covariates on timing of general, violent and theft recidivism.

Chapter Eight assesses three final recidivism outcomes. The impact of drug use and other factors on the frequency or rate of recidivism is modelled using negative binomial regression, while escalation (increasing severity from pre-baseline to post- baseline offending), and severity of violent recidivism are modelled in multinomial logistic regression.

Chapter Nine draws together these findings and considers implications for understanding drug-crime relationships, assessing drug use and recidivism risk, interventions, and research into drug-crime relationships among young offenders.

76 3 Methods

Two main data sources were accessed for the conduct of this research. The first was a large, state-wide survey of young offenders (2003-2006) who had received court- mandated supervision orders to be served in the community for offences committed as juveniles (Kenny & Nelson, 2008). This was part of a collaborative project led by Professor Dianna Kenny from the University of Sydney, in partnership with the (then known) New South Wales (NSW) Department of Juvenile Justice (DJJ), and Justice Health, that aimed to profile the health, welfare and criminogenic needs of this population. Participants completed a comprehensive baseline assessment including a structured interview, psychometric test battery, and physical health assessment. Offence and supervision histories from the DJJ client database were appended to the survey dataset. The author managed the field research team, oversaw recruitment, and contributed to the psychometric testing and preparation of the baseline data files. Team contributions are in Kenny and Nelson (2008). In 2009, the author negotiated linkage of the baseline dataset to lifetime court adjudication histories held in the NSW Bureau of Crime Statistics and Research (BOCSAR) Re-Offending Database.

As a consequence of this earlier role, Sections 3.3 (sampling) and 3.4 (baseline measures) of the current chapter include edited portions of the candidate’s work as published in the descriptive report (Kenny & Nelson, 2008). Some descriptive data included in Section 4.4 (sample characteristics) were also reported by Kenny and Nelson (2008) but all bivariate analyses in that Section are new, original work for this thesis. In addition, the description and analyses of prior convictions (Section 4.5), all analyses of drug use (Chapter Five), and all analyses of recidivism (Chapters Six to Eight) were also designed and conducted as original new work by the thesis candidate, subsequent to the publication of the descriptive report by Kenny and Nelson (2008).

3.1 Overall research design

Most existing studies of drug-crime relationships among young offenders have included highly selected samples, e.g. detainees or non-violent offenders in drug treatment (Allerton et al., 2003; Lennings et al., 2007). This thesis presents the first

77 detailed analysis of relationships between patterns of drug use and prospective recidivism outcomes amongst community-based young offenders in Australia. These comprise 80% of supervised young offenders (the remaining 20% are detainees). The sample is larger and more demographically diverse than previous studies. Face-to-face interviews were essential to complete physical health assessments, monitor participants’ distress when discussing sensitive information, administer the psychometric tests and provide feedback. Reliance on phone contact would have excluded the large majority of young offenders without reliable access to a telephone.

Cross-sectional data do not allow conclusions to be drawn about causality, timing, sequencing, individual change over time and the impact of drug use on later offending (Hill, 1965; Liberman, 2008; Rutter & Tienda, 2005). Prospective data are required to assess the impact of drug use on recidivism. Attrition from tracking surveys is high amongst offenders due to their transience, use of multiple aliases, incarceration and poor compliance (Kenny & Nelson, 2008). Further, prior offending (especially its timing) can be poorly recalled (Morris & Slocum, 2010; Roberts & Wells, 2010). Data linkage ensures maximum case ascertainment of recidivism data, but has been underutilised in Australian juvenile justice research (Ferrante, 2008).

3.1.1 Ethics approval and funding

Ethics approval was granted for the baseline survey by the Human Research Ethics Committee of the University of Sydney, DJJ, Justice Health and the Aboriginal Health and Medical Research Council in 2003. Ethical approval for the linkage study was granted by the University of New South Wales (UNSW) Human Research Ethics Committee (HREC) B on 27/8/08 (HREC number 08231), and ratified by the University of Sydney HREC on 1/11/08. The baseline dataset was released in 2008 per a licensing arrangement with Sydnovate, the commercial arm of the University of Sydney, for and on behalf of Professor Dianna Kenny, who was the Principal Investigator. Data linkage was also approved by BOCSAR’s director Dr Don Weatherburn. The baseline survey was funded by Australian Research Council Linkage Grant LP0347017, awarded to Prof Kenny as Chief Investigator (with co-investigators Dr Chris Lennings, Professor Tony Butler, Mr Mark Allerton, and Ms Una Champion). This doctoral research was funded

78 by an Australian Postgraduate Award and National Drug and Alcohol Research Centre (NDARC) top-up scholarship (2008-11), with subsequent salary support from NDARC.

3.2 Sampling and recruitment

The studies in this thesis used a linked, de-identified dataset, and no direct recruitment was undertaken. There were 4036 unique youths serving community-based supervision orders in NSW between October 2003 and December 2005; the sampling frame consisted of the 2822 youths (70% of the total population) who were in the geographic catchment area of the baseline survey (Kenny & Nelson, 2008). This area covered all administrative regions of DJJ, with twelve regional and rural communities attended at different times during the study period, and urban areas attended for its entirety. Urban areas contain the large majority of young offenders, and were of special interest to the thesis as they are the areas in which drug use is most prevalent and diverse. Effective recruitment methods included home visits, direct approaches in DJJ offices, phone calls and text messages to potential participants by survey staff and case workers; indirect methods, such as advertising in youth centres; and snowballing (word-of-mouth). Recruitment sought youths from a range of demographic backgrounds and was not intended to be precisely representative.

3.2.1 Inclusion criteria and sample

Youths were included in the baseline survey if they satisfied the following criteria: 1. Under community-based supervision within two months of the study period (or in rare instances and on request of supervising workers, awaiting sentencing). 2. Ability to comprehend spoken English. 3. Signed consent, including from parents or guardians if aged less than 14 years. 4. Neither in substance withdrawal nor acutely intoxicated. 5. No serious mental health problems, i.e. acutely suicidal, psychotic or sentenced under the Mental Health (Criminal Procedure) Act 1990 or relevant amendment.

The final sample of 800 represented 28% of the sampling frame. Approximately 18% refused, 35% did not respond, 14% could not be contacted, and 4% were excluded for 79 mental health concerns and concerns for interviewer safety (Kenny & Nelson, 2008). All 800 participants were included in the data linkage. Table 3.1 shows that the sample matched the gender distribution of the community-supervised young offender population during the study period (NSW Department of Juvenile Justice, 2006, 2007). The mean age (17 years, SD 1.3; range 12-21) was slightly lower than that of the population (17 years 11 months, SD 1.8, range 11-25) (NSW Department of Juvenile Justice, 2006). Indigenous males (but not females) were under-represented and CALD offenders were over-represented compared with the population. Participants aged under 16 and Indigenous youths were more likely to live in regional areas, while older and CALD youths were more likely to live in Sydney, reflecting population trends.

Table 3.1: Demography of the sample and target population

Sample % Population % (n=800) (n=4036)i Male gender 85 85 Aged under 16 22 15 Ethnicity: English-speaking background (ESB) 66 56 Indigenous 19 33ii ESB/CALD (ESB with CALD parents ) 15 7ii CALD (Culturally and Linguistically Diverse) 14 Region: Sydney 75 45 Other metropolitan 12 14 Regional (other) 13 41iii i All youths on supervised community orders during the study period. Sample frame N=2400. ii 4% had no ethnicity data; Indigenous status was unclear for a further 5%. iii Includes 6% from remote areas not part of the baseline survey.

3.2.2 Baseline data collection including health survey

Interviews were conducted in confidential settings, typically private rooms in DJJ community offices or youth-friendly community facilities (e.g. drop-in centres). Participants completed a physical assessment with a registered nurse, psychometric assessments with a registered or supervised psychologist, and a structured questionnaire with either professional. Most sessions took three to four hours to

80 complete. Limits of confidentiality were explained to participants prior to interview and at the time of any potential breach. Participants were encouraged to answer honestly and completely, but were assured that there would be no penalty for refusing to answer questions. After the session, participants were offered feedback and debriefing and reimbursed for expenses. Those serving Community Service Orders also had eight hours deducted from their remaining order. Many participants reported that their primary motivation for completing the survey was the chance to discuss health issues with a health worker.

An information sheet detailing survey procedures was provided to all participants (and read out, in the case of low literacy). The interview process placed demands on participants and covered issues of a sensitive and personal nature. Interviewers were provided with a clear and comprehensive protocol for the management of potential adverse events. The unpublished manual prepared by the current author is reproduced in part in Kenny and Nelson (2008). Adverse events included participant distress, aggression or intoxication, information disclosure requiring a breach of confidentiality, and interviewer distress. Reports were made with participants’ knowledge when acute and specific risk of suicidal ideation, crime or harm to a child was disclosed. Youths who tested positive to physical health problems were referred to appropriate services. Quality control checks were embedded in the data collection, coding and data entry procedures. These included standardised scales to assess consistency and accuracy of responses, random checks of data entry, consensus (group-based) coding, and reviews of psychometric test scoring by senior clinical psychologists (Kenny & Nelson, 2008).

3.2.3 Validity and reliability of self-report

There is ongoing debate about the validity of offender and drug-user self-report with persuasive evidence for their use (Darke, 1998; Kim, Fendrich, & Wislar, 2000) tempered by cautions about geographic and racial variation (Golub, Johnson, Taylor, & James, 2002; Rosay, Najaka, & Herz, 2007). Overall response patterns of NSW juvenile detainees on a similar survey instrument to that used for this thesis indicated very high internal consistency (Kenny & Grant, 2007). In terms of drug use, analysis of NSW

81 juvenile arrestee data (Australian Institute of Criminology, 2010) finds moderate to high concurrence between positive self-report of drug use and positive urine testing.

3.3 Baseline measures and variable definitions

The baseline survey covered more than 30 distinct aspects of young offenders’ physical health, social background and psychological functioning. This interview included several standardised measures from general population, school, offender and at-risk youth surveys. Drug use and offending variables were used as outcome and dependent variables in the various empirical studies in this thesis. The function of these variables is described in the methods of each empirical chapter, as are data modifications (e.g. transformations) performed for specific analyses. All variables described in this chapter are taken from the baseline survey or linked court data. Self-evident variables (e.g. gender) are not defined.

3.3.1 Demographics

Age was recorded as a continuous variable. Categorical coding of age was also used, for example to compare youths above and below the median (17 years).

Coding ethnicity is a complex challenge (Rutter & Tienda, 2005). For this thesis, ethnicity was defined on the basis of each participant’s country of birth, parents’ countries of birth, main language spoken at home and identification with Aboriginal or Torres Strait Islander culture. Self-reports of ethnicity were checked against DJJ client records. Prior research with juvenile offenders in NSW (Allerton et al., 2003) has distinguished three broad ethnic groups: Indigenous, English-speaking Background (ESB) and Other/Culturally and Linguistically Diverse (CALD). The current research distinguished children of immigrants from within the ESB subgroup, whose differential criminal involvement from native-born youths has long been recognised (Hart, 1896), but who have received little attention in Australian juvenile justice research.

Participants were allocated to one of three geographic regions based on their interview location: the Sydney metropolitan area, outer metropolitan (Gosford, Newcastle, Wollongong) and regional (other locations across NSW). Of young people in

82 NSW aged 15-24, 59% reside in Sydney, 19% in outer metropolitan areas, and 22% in other areas (Australian Bureau of Statistics, 2007). Geographic region is a close proxy for residential region, as few participants were interviewed far from their residence.

Socioeconomic status (SES) was derived using mean advantage-disadvantage index scores for participants’ residential postcodes (Australian Bureau of Statistics, 1998). These scores incorporate local area disadvantage based on education, occupation and income and are imprecise indicators of disadvantage as postcodes may contain a wide SES range (Australian Bureau of Statistics, 2006). Scores were collapsed into quintiles (lowest 20%, etc).

3.3.2 Criminal history

Criminal history data were collected from three sources. The baseline survey provided self-report information on incarceration history, drug and alcohol-related offending, and recent antisocial behaviours (ASB) covered by various scales in the Adolescent Psychopathology Scale – Short Form (APS-SF, see 3.3.5). Incarceration history included experiences of detention on both control and remand orders, and overnight lock-up in police custody. This was recoded as none/once/two or more times, due to extreme outliers and positive skew. A second variable coded duration of incarceration before baseline as none/less than six months/more than six months. Additional self-report data from the baseline survey was superseded by BOCSAR’s Re-Offending Database (see Section 3.4.1 below).

Information on participants’ offending and juvenile justice supervision histories and Australian Adaptation of the Youth Level of Service/Case Management Inventory (YLS/CMI:AA) risk assessment scores were extracted from the DJJ Client Information Management System. At the time of extraction, this information had not yet been fully digitised, so these data were incomplete for many participants. Physical files were unable to be accessed, and in any case other research has reported problematic levels of incomplete or missing data in these files (Weatherburn et al., 2007). YLS/CMI:AA administration data were unavailable for one in six participants, and most administrations had been completed two or more months before or after the baseline

83 survey. Thus, the available risk assessment ratings were incomplete, time-varying, and a mix of prospective and retrospective. As the YLS/CMI:AA includes dynamic risk factors, the available data did not confidently or consistently reflect baseline risk ratings. Further, the YLS/CMI:AA ratings aggregate data that were collected in greater detail at baseline (e.g. drug use) or during linkage (e.g. criminal history), and so are likely to have introduced multicollinearity to the recidivism models containing such data. Following expert consultation, YLS/CMI:AA data were dropped from models in this thesis. The remaining variables overlapped at least partially with all YLS/CMI:AA domains, but important examples of factors exclusive to the YLS/CMI:AA were inadequate parental supervision, poor time usage, low frustration tolerance, empathy deficits, and authority defiance (which has implications for risk and responsivity).

3.3.3 Family history and maltreatment

Questions drawn mainly from the Young Offender Risk and Protective Factor Survey (Carroll, 2002) and Inmate Health Survey (Butler & Milner, 2003) collected information on primary caregivers, family structure and separation, incarceration histories of parents and relatives, current housing status, and history of out of home care. Additional family history questions modelled on the National Longitudinal Survey of Children and Youth (Willms, 2002) assessed the physical and mental health of participants’ immediate family, and the impact of this on participants. Drug use of parents and relatives was also assessed. Unstable housing included current homelessness, out of home care, or sharing with friends (but not renting). Father absence was coded ‘yes’ if participants reported that their father had not been responsible for raising them, as per Kenny and Schreiner (2009). Emotional support referred to the presence of close friends or others (including family) with whom participants indicated that they could discuss personal problems.

Measures of childhood maltreatment were taken from the Childhood Trauma Questionnaire (Bernstein & Fink, 1998), a retrospective self-report tool that produces classification scales for emotional, physical and sexual abuse, and emotional and physical neglect. Trauma ratings were attached to the total scores for each five item scale: none to minimal; low to moderate; moderate to severe; and severe to extreme.

84 The Childhood Trauma Questionnaire is internally consistent (.66-.92; Cronbach’s α=0.95), has high test-retest reliability (.79-.86) and robust construct validity, with clinically-referred groups reporting higher levels of trauma than those not in treatment (Furlong & Pavelski, 2001).

3.3.4 Drug use and treatment

The main methods of measuring drug use by individual offenders are self-report and urinalysis. Self-report measures use widely varying time-frames and operationalisations across different studies, are more direct, sensitive, and complete than official records (White & Gorman, 2000) and typically show modest agreement with urinalysis data in high-risk populations (Feucht, Stephens, & Walker, 1994; Magura & Kang, 1996). However, self-report/urinalysis concordance among Australian arrestees (including juveniles; see Drug Use Monitoring in Australia study, Chapter Two) exceeded 80%, and was higher still for younger, more frequent users and repeat arrestees (Wei et al., 2003), suggesting that self-report is a sufficient proxy for drug use in this thesis sample.

The operational definition of ‘drug use’ employed in this thesis is ‘alcohol, illicit drug, or extra-medical use of other psychoactive substances’. Unless specified, drug use refers to past year rather than lifetime use to ensure that use is recent. Tobacco and legitimate prescription medication use were excluded from this definition, as were drugs that affect physical but not mental functioning (e.g. penicillin). Age of initiation and tobacco use questions were drawn from the National Drug Strategy Household Survey and 2001 Inmate Health Survey (Butler & Milner, 2003), with heavy smoking coded as ten or more cigarettes per day.

Alcohol questions were based on the National Drug Strategy Household Survey and the Young Offender Risk and Protective Factor Survey. These covered the amount, frequency, and type of alcohol consumed and problems related to use. Coding of the total amount of use and risk levels relating to this use amongst adolescents is complex (Kenny & Nelson, 2008). This thesis used a measure of frequency of binge drinking

85 (males drinking more than six standard drinks and females drinking more than four standard drinks, in a single session).

Drug use questions were adapted from the Young Offender Risk and Protective Factor Survey, National Drug Strategy Household Survey and Young People in Custody Health Survey and assessed age of onset of drug use, recency of use, route of administration and frequency of use in the past year for: cannabis, amphetamines, heroin, methadone (prescribed and unprescribed), other opioids, extra-medical use of painkillers, benzodiazapines, cocaine, ecstasy and inhalants. One question recorded past year history of injecting drug use. The APS-SF (see Section 3.3.5) asked questions about the frequency of past six month use of several drugs, and these were used in the absence of questionnaire data.

Drug use variables analysed in this thesis included information about frequency of use. This allowed the form of their relationship with offending and other correlates to be explored, and a focus on the contribution of more frequent use; dose relationships, such as that between cannabis use frequency and initiation of other drugs (Fergusson & Horwood, 2000b) cannot be assessed with dichotomous measures. Analysing frequency allows comparisons with important young offender studies (Dembo, Wareham, Greenbaum, Childs, & Schmeidler, 2009; Welte, Barnes, Hoffman, Wieczorek, & Zhang, 2005). Frequency is a simple and explicit behavioural measure.

Frequency cut-offs were informed by clinical advice, relevant precedents in the research literature (e.g. Bowden, 1975; Hakansson & Berglund, 2012; Nunes-Dinis & Weisner, 1997; Swift, Hall, & Copeland, 1997; Taxman, 1998) and the distribution of use in the current sample; resulting subgroups were limited to a minimum of 5% of the sample to reduce the likelihood of data separation due to low cell sizes. The reference groups for illicit drugs were non-users, while for binge drinking, the reference category included non-binge drinkers and those who reported binge drinking rarely (i.e. less than monthly, per earlier research (Lightowlers, 2011). The dependent variables were:

Binge drinking: none (or rarely), monthly, weekly, more than twice weekly Cannabis use: none, less than weekly, weekly, daily

86 Amphetamine use (excludes ecstasy): none, less than weekly, weekly Opioid use (heroin and other opiates): none, less than weekly, weekly

Extensive questions about experience of drug and alcohol treatment and help-seeking, were modelled on the 2001 Inmate Health Survey (Butler & Milner, 2003) and covered any history of treatment, type of treatment received, referral source and treatment completion. Due to the low rates of treatment involvement, history of drug treatment in this thesis was coded as a single binary item (yes/no). Similarly, a binary item coded participants’ treatment history for other psychological and behavioural problems.

3.3.5 Psychosocial and other health data

Psychopathology. Current psychological functioning was assessed with the APS-SF, a multidimensional measure of a range of psychological and psychiatric symptoms, as well as defensiveness and consistency of responding to test items. It has been extensively standardised, correlates significantly with other psychometric measures (including the Minnesota Multiphasic Personality Inventory/MMPI), has high internal consistency and test-retest reliability (Carlson, 2003; Reynolds, 2000) and demonstrates that scores are not purely related to external factors.

Six clinical scales in the APS-SF were included, which focused on the DSM-IV (American Psychiatric Association, 1994) symptoms associated with externalising disorders (Conduct Disorder (CD), Oppositional Defiant Disorder, and Substance Abuse Disorder) and internalising disorders (Post-Traumatic Stress Disorder, Generalised Anxiety Disorder, Major Depressive Disorder). Six further scales assessed domains of adolescent psychosocial problems and competencies including anger/violence proneness, and Academic Problems (which ostensibly checks DSM-IV symptoms of Attention Deficit Hyperactivity Disorder). Based on a mean T score of 50 and standard deviation (SD) of 10, scores were categorised as None (below 60T), Subclinical (60T to 64T), Mild (65T to 69T), Moderate (70T to 79T) or Severe (80T and above). Scores are not diagnostic of DSM-IV disorders but those above T=65 indicate problems that may require intervention.

87 Fewer than 5% (n=38) of APS-SF protocols were potentially invalid (11 severe and 27 moderate scores on either validity scale). The only observed demographic trend for validity was CALD participants’ overrepresentation on moderate/severe defensiveness scores, suggesting potential under-reporting of psychopathology for this group. Concerns have been raised about the representativeness of the United States (US) standardisation sample to US adolescents (Pfeiffer, 2003), and there are no norms for Australian adolescents. This thesis overcame this concern for scales that conform closely to DSM-IV symptom criteria, by extracting individual items and developing symptom profiles. This is particularly relevant for CD.

Psychological distress, suicide and self-harm. The Kessler-10 is a 10-item scale of non- specific psychological distress that examines the level of anxiety and depressive symptoms in the past four weeks. It was designed to detect the most prevalent mental disorders but may also detect less common ones, such as schizophrenia (Deady, 2009). Potential levels of distress are grouped as follows: 10-15 (low/none); 16-21 (moderate distress); 22-29 (high); 30-50 (very high). Levels of distress are indicative of potential diagnoses of anxiety or depressive disorder(s) (Andrews & Slade, 2001).

Suicidal and self-harming behaviour (SSH) is elevated in the young offender population as a whole (Kenny, Lennings, & Munn, 2008), and especially amongst drug-using young offenders (Sanislow, Grilo, Fehon, Axelrod, & McGlashan, 2003). Although extensive detail on lifetime history and past year involvement and ideation was collected, due to the low prevalence of actual attempts, a single item ‘past year history of SSH’ was selected for analysis in this thesis.

Physical illness, disability, and functional health. Much of the baseline survey covered physical health issues. Measures were mainly adapted from the 2001 Inmate Health Survey. Models in this thesis included binary items for limiting health problems or disabilities lasting six months or more, and other health problems associated with problem drug use: history of sexually transmitted or blood-borne infection derived from self-report and serological testing and current symptoms (e.g. pain, headaches) (Butler et al., 2008; Kenny, Denney-Wilson, Nelson, & Hardy, 2008; Van Der Poorten, Kenny, Butler, & George, 2007; Van Der Poorten, Kenny, & George, 2008).

88 Functional health was assessed using the SF-12 Health Survey (Ware, Kosinski, & Keller, 1996), a brief measure covering physical and mental health problems and sequelae including pain and role limitations. Test-retest reliability is sound (Physical Component Summary 0.89, Mental Component Summary 0.76) and Australian studies provide evidence of good construct validity (Sanderson & Andrews, 2002). It has been extensively validated in a range of contexts (Deady, 2009) and used with young offenders in NSW (Allerton et al., 2003). The SF-12 generates Physical and Mental Component Summary scores with a mean of 50, SD of 10, and range of 0 (lowest) to 100 (highest) functioning. Conventional interpretation considers that SF-12 scores of 50 or above reflect no disability, 40-49 reflect mild, 30-39 moderate, and less than 30 severe disability (Andrews, 2002). Young people tend to report higher mean Physical Component Summary scores and slightly higher mean Mental Component Summary scores, so results should be interpreted in this light.

Norms for young offenders were not available so it was of research interest to create sample-relative ratings using the sample mean and SD. Such an approach has been recommended elsewhere (Centre for Health Data, 2001) and may allow better identification of those most in need. A binary classification was applied to participants’ scores with those more than one SD below the sample mean classified as poor physical or mental functioning, and all others as normal functioning.

Cognitive ability and academic achievement. Cognitive ability was assessed with the Wechsler Abbreviated Scale of Intelligence (WASI) (The Psychological Corporation, 1999). The WASI yields verbal (VIQ), performance and full scale IQ scores, and has excellent psychometric properties (Keith, 2001; Lindskog & Smith, 2001; Sattler, 2001) that far exceed other available brief intelligence measures (Lindskog & Smith, 2001). VIQ is a measure of acquired knowledge, verbal reasoning and attention to verbal information. Performance IQ measures fluid reasoning, spatial processing, attention to detail, and visual-motor integration. Scores have a mean of 100 and SD of 15. Test- retest stability coefficients for these scales is above .85 (Johnson, 2003) and .95 for Full Scale IQ amongst 12-16 year olds. Qualitative labels are suggested for IQ deciles (e.g. ‘borderline’ for a score from 70-79) (The Psychological Corporation, 1999) but to

89 increase cell sizes and power, this thesis recoded scores into four levels: more than two SD below the mean (IQ less than 70), between one and two SD below the mean (IQ 70 to 84), less than one 1 SD below the mean (IQ 85 to 99), and 100 or more (as few participants scored over 115).

Achievement scores were collected using the Wechsler Individual Achievement Test – Second Edition – Abbreviated (WIAT-II-A) (The Psychological Corporation, 2001). The WIAT-II-A is a normative expansion of the WIAT (Johnson, 2003) that identifies basic skills in word reading; numerical operations and spelling, with an Australian adaptation of language and metrics. Standardised scores are generated for each of those scales as well as a composite standard score. The distribution of these scores parallels the WASI. Technical data reported by Johnson (2003) is positive, including test-retest reliabilities above 0.9 in the normative student sample.

Schooling, peers and sexual behaviour. Participants provided detailed educational histories based on questions from the 2001 Inmate Health Survey (Butler & Milner, 2003) and National Longitudinal Survey of Children and Youth (Willms, 2002). The variables analysed in this thesis are: frequent truancy (skipping school more than three days per week on average); history of special schooling (attended a school or class for intellectually or behaviourally disturbed youth); in school or graduated (i.e. in full-time education and/or completed year 12).

Questions examining peer relations were based on the Young Offender Risk and Protective Factor Survey (Carroll, 2002) and the National Longitudinal Survey of Children and Youth (Willms, 2002). Participants reported the number of close friends, how influential their peers were, and the proportion of peers engaging in various forms of drug use, educational disengagement, and criminal behaviour. Those reporting no close friends were given the modal scores for these variables.

This thesis used three indicators of sexual risk. Age of first sex was included as a binary item ‘had sex before age 14’ (yes/no). Those with more than 20 lifetime sexual partners were classified as highly promiscuous. Information on prophylactic practices was used to code a single binary indicator ‘use condom less than half of the time with

90 casual partners’ (those reporting no casual partners were coded ‘no’). Additional binary items coded other aspects of sexual history: sexual assault (having sex against one’s will), history of sex work, pregnancy, or fathering children.

Physical victimisation, injuries, fighting and bullying. Questions on drug and alcohol- related victimisation were drawn from the National Drug Strategy Household Survey. These asked about experiences of interactions with intoxicated people in the year prior to the survey. Binary items were coded for physical victimisation, verbal victimisation, and being put in fear by drunk and drug-affected persons (six items). Victimisation was assessed because of its correlation with drug use and crime (Chapters One and Two).

Complex data on physical injury was collapsed to produce items recording physical victimisation-related injury (intentional injury due to fights, riots, etc). One variable recorded history of head injury leading to loss of consciousness (none/once only/more than once). Aggression and heavy alcohol use have been implicated in the association between head injury and violent offending among juvenile detainees (Kenny & Lennings, 2007). Separate items recorded the number of physical fights in the six months prior to baseline (the maximum recorded was six), as well as whom participants fought with, and whether medical treatment was required. Based on school bullying experiences, participants were coded as bullies, victims, or both.

91 3.4 Linkage to conviction data

Most recidivism studies are based on data from administrative datasets. These tend only to contain information operationally relevant to the collecting organisation. Hence, criminal sentencing databases contain information on court outcomes and basic demographics, but not on medical history or psychosocial functioning, e.g. BOCSAR’s Re-Offending Database (Smith & Jones, 2008). Although such variables are amongst the most predictive of recidivism, they account for only a modest proportion of the variation in recidivism, and offer limited theoretical insight into the drivers of recidivism. Moreover, they rarely contain information on aspects of offender behaviour that might be modified to reduce recidivism risk (dynamic variables).

Linking multiple datasets allows a broader set of explanatory variables to be considered, but also presents considerable technical and ethical challenges (Baldry, 2010), so such studies are rare (see Baldry, Dowse, Snoyman, Clarence, & Webster, 2008; Ferrante, 2008; Jutte, Roos, & Brownell, 2011, for exceptions). Manual record linkage (Makkai, Ratcliffe, Veraar, & Collins, 2004; Weatherburn et al., 2007) is extremely labour intensive (Weatherburn et al., 2007) which poses a barrier to the establishment of a large cohort.

Data linkage outcomes are presented in Table 3.2. All except seven participants were linked correctly to court records (n=793). Two false positives were detected when one set of court records was linked to two participants. False positives involving linkage of baseline data to non-participants’ offending data could not be assessed but are likely to have been rare; the total false positive rate in a large BOCSAR study was .05% (Hua & Fitzgerald, 2006). There were five possible false negatives involving unlinked participants or linkage to empty court records; this compares favourably to the 4.4% reported by Hua and Fitzgerald (2006).

Court data included finalised court appearances prior to October 2008, permitting a mean follow-up duration of three years and ten months, and within the range of 3-5 years for 95% of participants. This equates to 3025 total person-years of prospective observation. Median time from participants’ index (most recent prior) conviction date

92 to the baseline survey was 98 days; 75% participants were surveyed within six months of their index conviction and 15% were surveyed within one month of this date. Most (54%) hearings occurring after baseline were in adult courts (Table 3.3).

Table 3.2: Time from sentencing to baseline survey, and recidivism observation time

% (N=800) Time from sentencing to survey Less than one month 15 One to less than three months 29 Three to less than six months 25 Six to less than twelve months 19 More than twelve months 7 No data 5 Recidivism observation time 2.8 to 3 years 5 3 to 3.5 years 27 3.5 to 4 years 26 4 to 4.5 years 31 4.5 to 5.1 years 11

Table 3.3: Jurisdiction of hearings

Jurisdiction All hearings (N=5636) Recidivism only (N=3067) Children’s 68% 46% Local court 30% 52% Higher court 2% 2%

3.4.1 Linked data variable coding and definitions

Information on plea, verdict, and outcome were collapsed. Proven offences leading to convictions were counted in this thesis, and offences that were not finalised in court, or that resulted in a non-guilty verdict were not counted. Convictions for different offence types were counted individually, to ensure that the diversity of offending was captured. Convictions leading to multiple penalties were counted as one conviction.

93 Conviction outcomes included dismissal without penalty, fines, supervised and unsupervised community orders (bond, community service, probation and parole), suspended sentences, and detention orders (home, weekend, full-time). Information on penalty duration was considered only for detention orders. BOCSAR’s re-offending database includes detail on parole and non-parole periods for adult sentences, but only imprecise information on juvenile detention. The estimation of time-at-risk, and the analysis and adjustment for incarceration time is a complex methodological matter most relevant to Chapter Seven (where it is addressed more fully) and Chapter Eight.

Australian Standard Offence Classification (ASOC, Australian Bureau of Statistics, 1997) codes in BOCSAR’s Reoffending Database were used to categorise offences as violent, robbery, sexual, theft, drug, and other offences. These were revised in line with the substantial revision to ASOC in 2008. Theft was defined as non-aggressive acquisitive crimes and thus included break and enter (burglary), steal from persons, vehicle and other property theft (e.g. shoplifting), fraud and deception (e.g. fare evasion), and excluded robbery and property damage. Robbery involves the use or threat of force during or immediately preceding theft/attempted theft (Home Office), i.e. it has both violent and acquisitive components, although some authors argue that unarmed robbery is nonviolent. As suggested by Zamble and Quinsey (1997), this thesis sought to analyse robbery as a separate offence type; where this was not possible, it was aggregated with violent rather than property offences, given that violence is considered more serious than property offences (Australian Bureau of Statistics, 1997).

Violence is a highly varied phenomenon and consensus about violence coding (e.g. whether to include sexual offences) is lacking (Kenny & Press, 2006; McGuire, 2008). In this thesis, violence was defined as non-acquisitive crime against persons, including sexual offences. Some offences that involve violence are not classified as such by ASOC, including ‘riot and affray’ which involves “...acts or threats of violence for a common purpose…” (Australian Bureau of Statistics, 2011). ASOC also aggregates violent offences of varying severity, e.g. assault with weapon, and assault police (Australian Bureau of Statistics, 2011). Thus, Law Part codes (Judicial Commission of New South Wales, 2010) were used to provide more precise classification of violence

94 and severity of violence (Chapter Eight). Law Part codes were provided by BOCSAR, and the definitions (which are publically available) were provided to the author by the NSW Judicial Commission.

Offence dates and finalisation (sentencing) dates were also used, as was the age of first offence leading to a conviction (which is the best available proxy for age of offending onset, in lieu of comparable self-report data). The Reoffending Database includes extensive additional information including court, jurisdiction, postcode of offender at time of offence, and fine amounts; see www.bocsar.nsw.gov.au.

3.4.2 Defining recidivism

Recidivism is “the reversion of an individual to criminal behaviour” (Maltz, 1984 [2001]). This definition implies that an offender must have a distinct period of non- offending between offences. Traditionally, incarceration would create that non- offending period (through removal of the offender from the community and offending opportunity), but formal CJS involvement is now taken as a proxy of non-offending. Thus, recidivism may be operationalised as rearrest, reconviction, and reincarceration. Most studies express recidivism as a dichotomous event (Friendship, Beech, & Browne, 2002), often measuring this retrospectively (e.g. Weatherburn et al., 2007), and usually over a 24-month period, though longer periods reveal a higher prevalence of recidivism (Holmes, 2011). Many studies use conviction data, thereby excluding offences occurring during the observation period but for which convictions occurred after the observation period (due to delays in offence detection and CJS processing). Criminally ambiguous outcomes, such as a breach of parole are inconsistently included.

Recent recommendations to the Australasian Juvenile Justice Administrators (Richards, 2011a) argue for prospective measurement of recidivism, using dates of offences (rather than court dates); that juveniles should be tracked into the adult CJS; that only new offences (not new convictions relating to historical offences) be counted, and that minor offences and supervision breaches be excluded; and that prevalence, frequency and severity of recidivism be considered over multiple time periods (Richards, 2011a). These matters are addressed in Chapters Six to Eight.

95 3.5 Data analysis

Analyses were conducted in SPSS 18 (SPSS Inc, 2009), STATA 10SE (StataCorp, 2009a), Excel 2007 (Microsoft Corporation, 2006) and their subsequent revisions. Specific analytic procedures are detailed in the relevant empirical chapters.

3.5.1 Descriptive and bivariate analyses

Univariate data cleaning followed procedures outlined in Tabachnick and Fidell (2001). Most skewed variables were exponentially distributed or showed strongly positive skew; logarithmic transformations were applied to four significantly skewed variables in the multivariate models (p114). Inexplicable univariate outliers were truncated to the highest rational value or treated as missing values. Bivariate relationships between binary and categorical variables were tested with the likelihood ratio chi-square statistic. Logistic regression was used to assess bivariate relationships between continuous variables and binary or categorical outcomes. No continuous by continuous variable analyses were conducted. The Wald and likelihood ratio chi-square test are asymptotically equivalent so are increasingly similar as sample size increases (www.ats.ucla.edu/stat/mult_pkg/faq/general/nested_tests.htm); they did not differ substantially in this sample. Prevalence and bivariate data are reported for variables in the final models. Confidence intervals for ratio statistics (e.g. odds) provide the range in which estimates reported for this sample are likely to hold for the wider population.

3.5.2 Multivariate models and missing data

Multivariate analyses involving binary or multinomial logistic regression were conducted in SPSS and STATA. Survival analysis (regression modelling of time-to-event data; Chapter Seven) and negative binomial regression (regression modelling of conviction frequency data; Chapter Eight) were conducted in STATA. In this thesis, ‘time’ began at the baseline survey and ended when participants committed a new offence, or on 30/9/2008 (the last observation date in the Reoffending Database).

Different model-building strategies were employed in each empirical chapter (and are discussed therein), but some general strategies were also used. First, an alpha level of

96 p<0.15 was used to screen bivariate associations to identify variables for inclusion in multivariate models. A cut-off as high as p<0.25 has been recommended (Hosmer & Lemeshow, 2000), but a more conservative cut-off was used given the large number of comparisons undertaken. All multivariate models involving the total sample controlled for gender and age. Parsimonious models were achieved by removing variables from the model that did not improve model fit (using backwards elimination). Interaction terms were included to assess if associations between drug use and recidivism were moderated by gender or age (Holmbeck, 2002). Mediation analyses (Kraemer et al, 2001) were beyond the scope of the thesis.

Categorical variable cells had expected frequencies above five for at least 80% of cases, with small cell sizes addressed by recoding or removing variables as necessary. Variables with more than 10% missing values were excluded from multivariate analyses. Regression diagnostics adhered to Tabachnick and Fidell (2001): variables were tested for multicollinearity using ordinary least squares regression (variance inflation factors below 2, tolerance above 10). Multivariate outliers were not removed. Standard error (SE) thresholds for detecting multi-collinearity may be set as high as 5.0 (Chan, 2004) but this thesis used the more common, conservative value of 2.0; variables with greater SE were excluded from models.

The recidivism models (Chapters Six to Eight) were respecified for female, Indigenous, younger (under 17) and older (17 and above) subgroups in the sample. This approach involved running the full sample model on a subsample, then removing the least significant term one at a time until only terms that significantly improved model fit remained. These data are discussed in Appendix B: analyses of demographic variation.

The samples in the final multivariate models throughout this thesis ranged in size from 539 to 761 cases, with most ranging from 641-740. Generally, participants missing from one model were missing from multiple, or most models. For example, the samples in each of the models in Chapter Five differed by only 5-10% of cases. Missing cases were found to differ from included cases, but not in ways likely to affect the observed relationships. Missing data for variables in multivariate models are presented in Appendix Table A: Missing data and coding for multivariate models.

97 3.5.3 Theoretical and statistical considerations for models

Baseline data were collected for descriptive epidemiological purposes. The survey instrument (Section 3.3) assessed a subset of variables specified as predictors in current theoretical models of offending behaviour and drug-crime relationships (Section 2.1), while other known predictors were available only in the YLS/CMI:AA data, which was excluded (Section 3.3.2). Testing of these theories was therefore avoided. Rather, the thesis develops statistical models (e.g. to isolate the independent contributions of drug use to recidivism, controlling for other bivariate correlates). Theory does not dictate the complete and necessary set of variables to describe variance in an outcome (Achen, 1982), so the analyses in this thesis drew upon variables specified in theory (e.g. risk-needs-responsivity, Section 1.3.3) as well as others linked to the outcomes of interest by meta-analyses (e.g. Bennett et al, 2008; Cottle et al, 2001; see Sections 1.4.2 & 2.2.1) and significant studies of recidivism and drug-crime links (e.g. McGrath, 2007; Prichard & Payne, 2005). The statistical selection criteria (see 3.5.2) then restrict further modelling to variables showing at least a weak association with the outcomes in the current sample.

Model building in regression analysis has been the topic of robust and enduring debate (Achen, 1982; Hosmer & Lemeshow 2001; Tabachnick & Fidell, 2007). Choosing between the wide range of available approaches has been referred to as an art (e.g. Harrell, 2001) and each has benefits and limitations. The approach in this thesis is to discard variables (other than age or gender) that do not improve model fit. This simplifies the model, reduces the size of the confidence intervals around the estimates, and makes the models easier to generalise. However, it also means that confounding factors may be overlooked (e.g. if factors that account for some of the relationship between drug use and recidivism do not improve model fit). Substantively unimportant variables that account for unique variance in the outcome may also be privileged over variables of greater theoretical relevance. The final statistical solution (including variables therein) will be at least partly affected by random error in measurement and sampling (Tabachnick & Fidell, 2007). Thus, the model may reflect the characteristics of the current sample rather than the wider population from which it is drawn. 98 3.6 Conclusion

This thesis is unique in its combination of a sample of community-supervised offenders, an extensive set of baseline variables, linkage to official offending data with more than a million days of prospective observation, the disaggregration of specific patterns of drug use, and the assessment of multiple recidivism outcomes with multiple statistical techniques. This sample is more heterogeneous and representative of the wider population of criminally-involved youth than previous studies. It is also sufficiently large to permit the analysis of patterns of behaviour with relatively low base rates (e.g. weekly opioid use, serious violent offending), and includes a sufficient subsample of females to allow gender-informed models to be developed.

The study’s controls are relevant and wider ranging than those included in many previous efforts, which allows more accurately specified predictive models. The completeness of the data linkage and generous observation period also allow a more nuanced assessment of relationships between drug use and recidivism. The study is also one of the first to use data linkage with juvenile offenders in NSW and adds important evidence for the utility and efficiency of this non-intrusive technique. The analytic techniques used are more sophisticated, and being used in combination, can provide a much more thorough picture than previous studies, which typically utilise binary logistic recidivism, or only one of the more refined methods. In summary, this is the most comprehensive analysis of drug use and recidivism in a non-incarcerated Australian young offender population to date.

99 4 Description of the sample and correlates of specific offences

Chapters One and Two noted that juvenile offenders experience a wide range of mental, physical and social problems in addition to drug use. Profiling participants’ background characteristics and baseline functioning can highlight potential etiological and maintaining factors for their criminal involvement. Offenders experience other problematic outcomes in addition to recidivism (Odgers et al., 2008), and baseline profiling may identify non-criminogenic needs (i.e. factors that do not predict recidivism) which may be better addressed outside of the criminal justice setting. Profiling participants’ offence histories is essential because these are strongly predictive of subsequent offending, and provide a point of comparison for recidivism models. This thesis explores violence and theft (not just offending per se) because these are both of major community concern, however, but have different risk profiles. Neither variation by offence type nor gender variation has been explored in detail among Australian youths in community supervision. This chapter provides the most detailed description of these variations. Specifically, it examines the risk profiles associated with violence and theft (the two most prevalent offence classes, each of which has different associations with drug use) and with robbery.

Although 80% of supervised young offenders are community-based, research has focused on detainees (Indig et al., 2011) because they pose a far lighter methodological challenge. Chapter One described key points of difference between the two groups: males and minority ethnic groups are more heavily over-represented among detainees, who are the most serious offenders and have higher rates of recidivism (Hamilton, Sullivan, Veysey, & Grillo, 2007); community-supervised youths experience less social disruption but have ongoing access to drug use and offending opportunities. Young community-based offenders also have more internally located explanations for their criminal involvement (Nair, 1994) and greater treatment responsivity (Tsytsarev, Manger, & Lodrini, 2000), however they also have less access to treatment services.

100 The major risks for participation in offending are largely gender-neutral (Mazerolle et al., 2000), but females are found in decreasing proportions at each step in the criminal justice process, and comprise one in seven community-supervised offenders. Community-supervised young females offend less frequently than males (Brame et al., 2010), but the relative scarcity of females among supervised offenders suggests the female offenders’ risk profiles may be more problematic than males’. Female offenders are equally or more likely to use drugs than male offenders, in contrast with the general population pattern (Lennings et al., 2007). It seems likely that the treatment needs of Australian community-based offenders may differ by gender, however statistical differences between these groups have not yet been assessed.

4.1 Correlates of specific offending

This thesis explores violence and theft (not just offending per se) because these are both of major community concern, but have different risk profiles. The correlates of violent offending were discussed in Chapter One (see Section 1.4). The correlates of theft and violent offending tend to overlap with those for offending per se, and include heavy alcohol use (Fergusson & Horwood, 2000a; Komro et al., 1999) and Conduct Disorder (Lacourse et al., 2010). This overlap may reflect the fact that many offenders are involved in both offence types. However, violent offenders tend to be older (e.g. Latimer, 2003) and have lower verbal abilities (e.g. Kennedy et al., 2011), whereas theft offenders tend to have more drug-using peers (e.g.Latimer, 2003) and be more prolific offenders (Myner et al., 1998). Risk factors more closely linked with violence have included earlier age of offending (DeLisi, 2009) and victimisation (e.g. Australian Institute of Criminology, 2006; Shaffer & Ruback, 2002).

Robbery (the use or threat of violence to take someone’s property, Salmelainen, 1996) warrants separate analysis to other violent and acquisitive crime. In NSW, the highest rates of robbery are among juveniles aged 16-17; robberies typically occur on the street and do not involve weapons (Salmelainen, 1996) and are concentrated in the Sydney metropolitan area (Australian Bureau of Statistics, 2007). Nugent et al, 1989 (in Salmelainen, 1996) found most Australian robberies were driven by economic need, often due to drug dependence. A study of 330 male prisoners also supports an

101 economic but not a psychopharmacological explanation for robbery (Bowen, 2011). Explanations for juvenile robbery may differ, however, given that drug dependence is less prevalent than among older offenders. Ethnic minorities are over-represented as robbery offenders and as victims. In the US, robbery is more highly concentrated among Black youths than any other crime for any other ethnic group (O’Flaherty & Sethi, 2008), and robbery of acquaintances is common, particularly among poor, Black youths (Felson, Baumer, & Messner, 2000). Victimisation is also associated with socioeconomically depressed communities in Australia (Lennings, 2008).

4.2 Aims 1. Describe the characteristics of the sample including drug use and other factors linked with health, welfare and offending; 2. Assess differences between male and female participants on these factors; 3. Describe the sample’s history of criminal justice involvement; 4. Identify the independent correlates of violent, robbery, and theft convictions.

4.3 Method

General methods were outlined in Chapter Three. Data for Section 4.4 comes from the baseline survey, and for Section 4.5 from BOCSAR’s Reoffending Database. Offence categories were based on Australian Standard Offence Codes. Drug use is used as an independent variable in this chapter, however the method by which drug use was coded is explained in Section 5.2; drug use is the dependent variable in Chapter Five.

Multivariate models were developed using stepwise binary logistic regression. An alpha value of p<.15 was used to identify variables for inclusion. Variables with more than 10% missing values were excluded from multivariate analyses. Ordinal variables were modelled as linear variables in the multivariate models, with Box-Tidwell tests assessing linearity (Hilbe, 2011). Gender differences are reported using odds ratios (OR) for categorical variables and t-tests for continuous variables. Some discrete variables were dichotomised to simplify the interpretation of odds (per Farrington & Loeber, 2000) and conviction counts were log transformed to improve model fit, as noted in each model.

102 4.4 Results: sample characteristics 4.4.1 Demographics

Participants’ demographic characteristics are presented in Table 4.1. Males’ mean age was significantly higher than females (17 years 1 month vs. 16 years 8 months; F=7.3, p=.007), but females were less likely to be under 16. One in 12 (8%) participants was aged less than 15 (minimum 12.9 years), and 4% were 19 or above (maximum 22.0 years). Most were of English-Speaking Background, (ESB), one in five was Indigenous, and the remainder were from Culturally and Linguistically Diverse (CALD) backgrounds, or were ESB with at least one CALD parent. CALD and ESB/CALD groups primarily comprised youths from Middle Eastern, Asian, or Pacific Island heritage. Most (55%) participants lived in socioeconomically disadvantaged areas with 35% from the most disadvantaged areas (usually in Sydney); 18% lived in relatively advantaged areas including 8% in the most advantaged areas. See Table 3.1 for representativeness data.

Table 4.1: Demographic characteristics of the sample and target population.

Total % Male % Female % OR Female

(n=800) (n=682) (n=118) (95%CI) Male 85 - - - Age under 16 years (reference) 22 20 31 1.0* 16 22 21 25 0.8 (0.5-1.3) 17 32 33 28 0.6 (0.3-0.9)* 18 and above 24 26 16 0.4 (0.2-0.7)** Ethnicity: ESB (reference) 52 52 52 1.0*** Indigenous 19 17 31 1.8 (1.2-2.9)** ESB/CALD (ESB; CALD parents) 15 17 3 0.2 (0.1-0.6)** CALD 14 14 14 1.0 (0.5-1.8) Region: Sydney (reference) 75 75 79 1.0 Other metropolitan 12 12 6 0.5 (0.2-1.1) Regional 13 14 15 1.1 (0.6-1.8) Lowest socioeconomic status quintile 35 36 31 0.8 (0.5-1.2) *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; No missing values; p- value for reference category is for the overall likelihood ratio chi-square test; Ref. reference.

103 4.4.2 Family history and living circumstances

Table 4.2 describes participants’ family environments. Most were from ‘broken homes’: fathers were either minimally or not involved in raising most participants (58%); 10% reported being raised by neither parent. Most participants had criminally- involved family members; females were twice as likely to have parents who had been in prison. Nearly all Indigenous participants had relatives with a history of incarceration, 6.7 times the rate of other youths (95% CI 3.9-11.6, p<.001). Substance use was common in participants’ families; half had relatives who had abused substances, and parental substance abuse was twice as likely for females as males. Females were also twice as likely to have been placed in care and more likely to live away from their families (not shown); one in ten participants were in unstable housing (e.g. homeless). One in three females had been pregnant and 10% had a child (males: 5%), mean age at delivery 15.5 years. One in three participants reported at least moderate maltreatment, and females were more likely to report all forms, particularly sexual abuse, for which they provided half (46%) of all reports.

Table 4.2: Family history, living characteristics, and maltreatment

Total % Male % Female % OR Female (95%CI) Father absence 58 56 68 1.7 (1.1-2.6)** Out of home care before age 16 24 22 36 2.2 (1.5-3.4)** Unstable housing 11 11 14 1.2 (0.7-2.2) Relatives ever in prison 62 61 72 1.6 (1.1-2.5)* Parents ever in prison 27 25 39 1.9 (1.2-2.8)* Parental substance abuse 22 20 33 1.9 (1.2-3.0)** Relatives substance abuse 45 43 55 1.6 (1.1-2.4)* Emotional abuse (CTQ) 21 18 34 2.3 (1.5-3.5)*** Physical abuse (CTQ) 14 13 25 2.3 (1.4-3.7)*** Sexual abuse (CTQ) 10 6 30 6.5 (3.9-10.9)*** Emotional neglect (CTQ) 21 20 30 1.7 (1.1-2.6)* Physical neglect (CTQ) 21 19 34 2.7 (1.5-3.5)*** *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; N>760 parental substance abuse N=691, relatives substance abuse N=732; CTQ Childhood Trauma Questionnaire moderate/severe

104 4.4.3 Mental and physical health

Mental and physical health characteristics of the sample are presented in Table 4.3. Scores in the severe range of the Adolescent Psychopathology Scale - Short Form (APS- SF) are indicative of DSM-IV (American Psychiatric Association, 1994) psychiatric diagnoses, and scores in the moderate range are also clinically significant (Reynolds, 2000). Overall, two in five participants reported severe psychopathology, typically on the externalising dimension. The most prevalent were Substance Use Disorder and Conduct Disorder (CD): two in five (41%) youths reported clinically significant levels of CD (the extent of involvement in a range of antisocial behaviours in the past six months). Males and females did not differ in their prevalence or severity of CD. Internalising disorders were much less prevalent but showed marked gender differences at higher symptom levels. Females were also more likely to show clinically significant anger/violence proneness (28% vs. 17% males; OR 1.9, 95%CI 1.2-3.0, p<.01).

Females were more likely to report higher levels of distress (scores on the Kessler-10) and more than twice as likely to report self-injurious behaviour, one in ten having attempted suicide in the past year. Levels of treatment were lower than for males. Mean SF-12 (functional health) scores were higher for males, significantly so for mental functioning. Against US norms, almost one in five females may be labelled as having had moderate to severe mental disability.

The sample reported numerous specific health issues and the following were reported by at least one in three participants: problems with sleep, memory, appetite, fatigue and/or headaches, and one third were overweight/obese. The majority of offenders completed serology testing for blood-borne viruses including hepatitis B & C; positive results were returned by 5% of participants and one in eight females.

105 Table 4.3: Mental and physical health

Total % Male % Female % OR Female (95%CI) Any severe psychopathology (APS-SF) 40 40 38 n.s. Severe Substance Use Disorder 27 26 29 n.s. Severe externalising psychopathology 39 39 37 n.s. Severe internalising psychopathology 3 2 7 3.2 (1.3-7.8)** Conduct Disorder (APS-SF): None 42 41 44 1.0 Subclinical/low 18 17 21 n.s. Moderate 22 23 17 n.s. High 19 19 18 n.s. Distress (K-10): Low 41 44 27 1.0 Moderate 34 33 38 1.8 (1.1-3.0)* High 18 17 23 2.2 (1.3-4.0)** Severe 7 6 13 3.2 (1.6-6.5)** Suicide attempt/self-harm (SSH) 11 9 18 2.1 (1.2-3.6)** Prior psychiatric treatment 26 26 21 n.s. Health concerns 84 83 90 n.s. Limiting disability 12 13 10 n.s. Poor physical functioning (SF-12 <40) 3 2 4 n.s. Poor mental functioning (SF-12 <40) 9 7 20 3.4 (1.9-5.9)*** Blood-borne virus seropositive 5 4 13 3.5 (1.5-8.2)** *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; K-10 Kessler-10 Psychological Distress Scale; N>760, except blood-borne virus seropositive N=495. Externalising: Conduct Disorder, Oppositional Defiant Disorder, Substance Abuse Disorder, and Academic Problems. Internalising: Major Depression, Generalised Anxiety Disorder, Post Traumatic Stress Disorder, and Eating Disorder. SF-12 <40 = >1 standard deviation below the US population mean.

106 4.4.4 Cognitive ability, academic indicators, employment

Cognitive performance for most participants was at least one standard deviation (SD) below the population average (i.e. less than 85; Table 4.4). Mean full-scale IQ was 83 (SD 13), with 15% in the very low range (less than 70). Mean performance (non-verbal) IQ was 91 (SD 14; not shown). Mean verbal IQ was 79 (SD 14). Overall academic achievement was comparably poor, most participants scoring in the below average range for reading and spelling and lower still for numeracy (not shown). Just 5% achieved average or higher scores overall, and one third of males and one fifth of females showed extremely poor academic achievement. One in ten participants (11%) scored less than 70 for both academic achievement and IQ; this combination suggests intellectual disability (Frize et al., 2008). These results accompany very limited schooling involvement. Most participants had dropped out by year nine, and had regularly truanted (females in particular). Additionally, 39% of both males and females had been in special education. Most youths, especially females, were unemployed (only 9% in total employed full-time, and half received welfare benefits (46% male, 53% female).

Table 4.4: Cognitive ability, academic indicators and employment.

Total % Male % Female % OR Female (95%CI) Verbal IQ (VIQ) under 70 23 23 19 1.0 VIQ 70-84 44 44 44 n.s. VIQ 85-99 26 26 32 n.s. VIQ 100 or above 7 7 5 n.s. Academic achievement (WIAT-II-A) <70 30 32 20 1.0 WIAT-II-A 70-84 43 42 51 n.s. WIAT-II-A 85 or above 27 26 29 n.s. Student or graduate 21 22 19 n.s. Truancy >3 days per week 32 30 42 1.7 (1.1-2.5)** Employed 25 27 16 0.5 (0.3-0.8)* *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; WIAT-II-A Wechsler Individual Achievement Test, Second Edition, Abbreviated, Composite Standard Score

107 4.4.5 Peers, victimisation and sexual history

Sample characteristics relating to peers, victimization, bullying, fighting, injury, and sexual behaviour are presented in Table 4.5. Almost all (93%) participants reported having close friends and three quarters reported other people with whom they could discuss their problems. Two thirds of participants reported that most or all of their peers smoked cannabis, with a similar figure for alcohol. More frequent users reported that more of their peers used these drugs, as did older youths. Half the sample had peers who used other drugs, and this was more likely for females (p<.05). Half of the amphetamine and opioid users said most peers used other drugs. Females were much more likely to report that most or all peers used other drugs. However, males were much more likely to report that most of their peers had committed crime or had been excluded from school.

One third of participants had been bullied at school, and most of these victims had bullied others. Most bullies reported negative emotional responses to bullying others but one in ten enjoyed bullying or felt superior to their victims. Frequent fighting was much more common amongst males but a similar proportion of males and females (one in six) had needed medical treatment because of fighting-related injuries. Head injury was also common, with a quarter of the sample experiencing a single event and one in seven reporting a history of multiple head injuries; head injuries were often, but not exclusively due to victimisation.

More than a third of participants had been recently (in past year) victimised by intoxicated persons. Participants were more likely to attribute their perpetrators’ intoxication to alcohol than other drugs (not shown), and rates of all types of victimisation were higher for female participants, particularly being threatened.

One third of the sample were sexually active before 14 years, one in five rarely used condoms with casual sexual partners, and among youths tested for sexually transmissible infections, one in three males and half of all females tested positive. Few youths disclosed having sex for drugs or money, but nearly one in three females reported having sex against their will – something rarely reported by males.

108 Table 4.5: Peers, victimisation, and sexual history

Total % Male % Female % OR Female (95%CI) Most peers break the law 45 47 31 0.5 (0.3-0.8)** Most peers use cannabis 66 66 62 n.s. Most peers use other drugs 30 29 40 1.6 (1.1-2.5)* Victim of bullying 30 29 37 n.s.~ Bullied others 56 55 60 n.s. >5 fights in past six months 14 16 5 0.3 (0.1-0.7)** Multiple head injuries 14 15 9 n.s.~ Victimisation by intoxicated person 37 35 47 1.5 (1.0-2.3)* Verbally abused 33 32 42 1.6 (1.1-2.4)* Physically assaulted 23 22 32 1.7 (1.1-2.6)* Threatened (put in fear) 11 9 22 3.0 (1.8-5.0)*** Low condom use in casual sex 20 20 21 n.s. More than 20 sexual partners 9 10 5 n.s. Initiated sex before age 14 34 33 39 n.s. Unwanted sex (ever) 8 4 30 10.7 (6.1-18.8)*** *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; N>760

109 4.4.6 Drug use

Lifetime use of any drug was 95% and most lifetime users reported past year use. Most (81%) participants had used an illicit drug in the past year. Table 4.6 details past year frequency of drug use. Cannabis and tobacco use were by far the most common (76- 77%), one third had used amphetamines, one third ecstasy, and one in four had used another drug (typically cocaine, 13%). Almost all opioid users had used heroin.

Almost all past year drinkers had binged; non-binge drinkers were grouped with non- drinkers for further analyses. Bingeing frequency increased with drinking frequency: half of weekly drinkers binged weekly or more; three quarters of more than twice weekly drinkers also binged more than twice weekly. Daily drinking was rare (3%), but half of all cannabis users smoked cannabis daily (35%). Two in five heroin users used weekly. Weekly use of other drugs was less common (less than 5% for cocaine and benzodiazapines, less than 1% for hallucinogens, steroids and other drugs).

Gender differences were generally not significant. Fewer females drank weekly (OR 0.7, 95% CI 0.5-0.99, p<.05) but females who drank were more likely to binge, and a greater proportion of females binged more than twice weekly. Females were also over- represented amongst weekly users of amphetamines and opioids. Females were three times as likely to have a history of injecting drug use (OR 2.9, 95% CI 1.6-5.1, p<.001) but were otherwise comparable to males.

Two-thirds of participants initiated drug use by age 14, including one third by age 13. Males initiated alcohol and cannabis use at around age 13, and other drugs at around 15; females initiated drug use significantly earlier (e.g. heroin at age 13.5).

The correlates of drug use are examined more thoroughly in Chapter Five.

110 Table 4.6 Frequency of drug use in the past year

Total % Male % Female % OR Female FREQUENCY OF USE IN PAST YEAR (%) (N=800) (N=682) (N=118) (95%CI) BINGE DRINKING (N=786) None/rarely 18 18 19 1.0 Less than weekly 50 52 46 0.8 (0.5-1.4) Weekly (once or twice per week) 22 22 20 0.9 (0.5-1.6) More than twice weekly 10 9 15 1.6 (0.8-3.3) CANNABIS (N=796) None 23 22 28 1.0 Less than weekly 24 25 21 0.7 (0.4-1.2) Weekly 18 19 15 0.7 (0.4-1.2) Daily 35 35 35 0.8 (0.5-1.3) AMPHETAMINES (N=795) None 64 66 55 1.0* Less than weekly 27 27 30 1.3 (0.9-2.1) Weekly 9 8 15 2.3 (1.3-4.3)* OPIOIDS (N=786) None 86 87 80 1.0~ Less than weekly 9 9 12 1.5 (0.8-2.8) Weekly 5 4 8 2.0 (0.9-4.3)~ ECSTASY (N=783) None 68 69 66 1.0 Less than weekly 26 26 29 1.1 (0.6-1.9) Weekly 5 5 5 1.0 (0.5-2.0) OTHER DRUGS (N=796) Monthly use, any 7 7 10 1.5 (0.8-2.9) Monthly cocaine use (N=796) 4 4 6 1.5 (0.7-3.6) Monthly benzodiazepine use (N=794) 3 2 7 3.5 (1.4-8.5)** TOBACCO (N=788) None 24 20 28 1.0 <10 cigarettes/day 25 29 24 1.1 (0.6-2.0) ≥10 cigarettes/day 51 51 48 0.9 (0.5-1.6) INJECTING (N=784) 6 4 9 2.5 (1.2-5.2)**

Weekly drug use: None 31 31 28 1.0 One 39 40 35 1.0 (0.6-1.6) Two or more 30 29 37 1.5 (0.9-2.4) *p<.05, ** p <.01, ***p<.001, ~p<.15; OR odds ratio

111 4.4.7 Factors distinguishing males and females

All gender-specific correlates (p<.15) described above were included in a logistic regression model, with only variables significant at p<.05 retained in the final model. Although gender cannot be causally affected by these factors, this approach identifies the factors that most strongly distinguish females from males. Table 4.7 presents unadjusted odds ratios (ORs) and adjusted odds ratios (AORs) for these factors. The association between female gender and sexual victimisation remained extremely strong after controlling for other correlates; females were also characterised by less frequent physical fighting. Specific drugs were not independently associated with gender but females were twice as likely to report past year injecting drug use (IDU), and more of their friends used drugs other than alcohol or cannabis.

Table 4.7 Unadjusted and adjusted odds ratios for independent correlates of female gender

OR (95%CI) AOR (95%CI) Age (years) 0.8 (0.7-0.9)** 0.7 (0.6-0.9)** Ethnicity: English-speaking background (ESB) 1.0** 1.0**

Indigenous 1.8 (1.2-2.9)** 1.9 (1.0-3.2)**

ESB/CALD (ESB with CALD parents) 0.2 (0.7-0.6)** 0.2 (0.1-0.8)*

CALD (Culturally/Linguistically Diverse) 1.0 (1.0-1.0) 1.1 (0.6-2.3) Sexual abuse (0 ‘none’ to 4 ‘severe’) 2.2 (1.8-2.7)*** 1.8 (1.4-2.3)*** Unwanted sexual encounter 10.7 (6.1-19)*** 6.9 (3.5-13)*** Fighting (0 ‘none’ to 4 ‘several’) 0.8 (0.7-0.9)** 0.7 (0.5-0.8)*** Past year injecting drug use (IDU) 2.9 (1.6-5.1)*** 2.2 (1.0-4.6)* Peers use drugs other than cannabis (0 ‘none’ to 4 ‘all’) 1.2 (1.0-1.3)* 1.2 (1.0-1.4)* *p<.05 **p<.01 ***p<.001; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; N=749.

112 4.5 Prior criminal justice involvement 4.5.1 Patterns of prior criminal justice involvement

Participants’ court records began a mean of six years before the baseline survey. Mean age of first conviction was 15 years nine months (SD 1.6, range 10-21). Most (54%) had first appeared in court less than one year prior to baseline; 13% first appeared three years prior. One in five participants were less than 14 when they committed the offence for which they were first convicted; there were no gender differences for this ‘early onset’ offending. The mean number of convictions was 6.2 (SD 7.0, range 0-64); 76% participants had multiple convictions and 5% were awaiting their hearing (Table 4.8). Participants had received an average of 2.5 (SD 2.2, max. 17) court orders. One in eight had received control orders; male, Indigenous and non-urban residence doubled the odds of prior incarceration (AOR 1.7-2.1, all p<.05).

Table 4.8: Baseline conviction and court orders

Total % Male % Female % OR Female (95%CI) Prior convictions: None (reference) 5 4 7 1.0 One conviction 19 19 19 n.s. Two or three convictions 23 21 30 n.s. Four or five convictions 16 16 13 n.s. More than five convictions 37 38 31 n.s. Prior unsupervised order 47 49 37 0.6 (0.4-0.9)* Prior community supervision order 86 86 87 n.s. Prior custodial order 12 13 7 0.5 (0.2-1.1)~ *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; n.s. not significant

The mean frequency of past year convictions was 3.5 (SD 4.0, max. 28); 25% had no past year convictions, and 28% had at least five past year convictions. Past year conviction frequency was negatively correlated with age but varied widely at all ages.

Participants’ self-reports of their prior criminal justice involvement indicated that most (61%) had spent at least six months in community-based supervision, but few (13%) had spent six months or more in detention. However, 73% had been detained overnight on multiple occasions. Gender differences were observed on these variables.

113 4.5.2 Patterns of prior convictions

The sample had convictions in all major offence categories (Figure 4.1; presented in landscape format for easier viewing in Appendix Figure I), however prevalence ranged from less than 1% to 51%. Half (49%) had received conviction for violence, 33% for robbery, and 57% for theft. Most violent offences were for non-homicidal, non-sexual, physically assaultive behaviour. These varied greatly in seriousness (see Chapter Eight) for most violent offences had not involved serious injury to others (e.g. grievous bodily harm). One in four had committed ‘dangerous acts’ usually involving driving. Robbery typically involved interpersonal confrontation, and half of the robbery offenders had committed aggravated robbery (serious threat to victim); 10% had committed non- confrontational offences, such as extortion. Theft encompassed a wide range of non- violent acquisitive crimes. One in three theft offenders had a burglary conviction, and shoplifting, vehicle theft and handling stolen goods were also common; just 10% had committed non-material theft (e.g. fraud).

One in eight participants had an illicit drug conviction, mostly for possession or use of cannabis, and rarely for high level dealing or manufacture. Other offences were diverse and broadly distributed. ‘Victimless’ public order offences were as common as robbery or assault; 24% participants had a conviction for unlicensed or unregistered driving, often in conjunction with vehicle theft, and 27% had convictions relating to criminal justice processes, usually involving a breach of supervision conditions.

Figure 4.1 Prevalence of prior convictions by offence type

114 Figure 4.2 displays the co-occurrence of prior offence types. Violence and theft tended to co-occur, and not to co-occur with robbery. Most participants with any of these convictions had convictions for ‘other’ offences; 6% of participants had convictions only for ‘other’ offences. Participants had convictions for a mean of two offence types.

Figure 4.2 Overlap between prior conviction types

Table 4.9 presents data on specific prior convictions. Violence and robbery (71% prevalence combined) constituted 25% of the samples’ 8500 convictions prior to baseline. One third of these offences was theft-related, typically burglary or vehicle theft. Females were half as likely to have robbery or drug convictions and twice as likely to have breached orders. There were no gender differences for violence or theft overall, but nearly all sex convictions and a greater proportion of burglaries were attributable to males, and females were more likely to have shoplifted.

Table 4.9: Prevalence and frequency of prior conviction type by gender

Total % Male % Female % OR Female (95%CI) Mean (SD) max. Violent 49 48 55 n.s. 1.0 (1.5) 14 Robbery 33 35 25 0.6 (0.4-1.0)* 0.5 (1.0) 8 Theft 57 57 56 n.s. 2.1 (3.4) 27 Other offences (all) 63 63 61 n.s. 2.6 (3.6) 37 Other: drug 12 12 7 0.5 (0.2-1.1)~ 0.2 (0.6) 6 Other: breach 14 12 21 1.9 (1.1-3.1)* 0.3 (0.9) 8 *p<.05 **p<.01 ~p<.15; OR odds ratio; CI confidence interval; SD standard deviation; N=793

115 All conviction count variables included in later multivariate models were log transformed due to significant skew (Section 3.5). Skewness values were 2.83 (total), 2.74 (violent), 2.79 (theft), 2.04 (past year convictions), with .087 standard error (SE). Skewness/SE far exceeded 3.09 for all variables, therefore p-values for all were <.001.

4.5.3 Bivariate correlates of prior convictions

Table 4.10 presents the prevalence of demographic, psychosocial and criminal history risk factors for the full sample, violent, robbery, and theft offenders; these offence types are not mutually exclusive. Significance values were from F-tests for continuous variables, Pearson chi-square tests for dichotomous variables, and adjusted Pearson residuals for polychotomous variables with overall chi-square tests of p<.05. For example, females comprised 16% of violent offenders (not significantly different to males); significantly fewer participants from Sydney and significantly more participants from regional areas had violent convictions than were expected; and, violent offenders were significantly older than offenders with no violent convictions. The largest demographic contrast was that robbery offenders were more likely to be from CALD backgrounds and to live in the Sydney area (two closely correlated factors).

Differences were also apparent for all risk domains. Violent offenders showed the greatest elevations on several family risk factors (these were generally lower for robbery offenders). Cognitive abilities and school involvement were generally worse for violent offenders and much worse for theft offenders (again, in contrast to robbery offenders). Frequent fighting was highest among robbery rather than violent offenders; it was less common among theft offenders. Violent and theft offenders were comparable in other risk domains and their conviction histories, with the exception of drug offences. Compared with other offenders, they tended to have offended before age 14, and have more convictions for other offences.

Associations between drug use and offence type are considered in Section 4.5.4. Other risk factors (including anger/violence proneness, head injury, criminal peers, distress, maltreatment except physical neglect, bullying, suicidal and self-harming behaviour, and victimisation by intoxicated persons) did not differ significantly by offence type.

116 Table 4.10 Prior offending by demographic and other risk factors

CATEGORICAL VARIABLES Total % Violent % Robbery % Theft % Female 15 16 11 14 Ethnicity: English-speaking background 52 57** 35** 56 Indigenous 19 22** 16 25 ESB/CALD (ESB with CALD parents) 15 13 24** 9 Culturally/linguistically diverse (CALD) 14 8** 25** 10 Region: Sydney 75 70** 92** 71** Outer metropolitan 11 13 4** 12 Regional 14 17** 4** 16** FAMILY AND HOUSING Parents ever in prison 26 32** 20** 32** Parents abuse substances 22 26* 17* 24 Ever in out of home care 24 29** 20~ 26 Unstable housing 12 15* 9~ 14* SCHOOL AND PSYCHOSOCIAL Ever in special education 39 41 36~ 43** In school/graduated 21 16** 27** 15** Employed 25 26 32** 25 Frequent fighting past 6 months 14 14 25** 10** Current limiting disability 12 10* 13 10* CONVICTION HISTORY Proven guilty before age 14 22 28** 19 32** Violent 49 - 33** 53** Robbery 33 23** - 28** Theft 57 62** 47** - Other (total) 65 77** 56** 81** Other (drug) 12 14~ 11 16** Other (breach) 14 19** 15 19** CONTINUOUS VARIABLES mean (SD) Age 17.0 (1.3) 17.2 (1.3)** 17.2 (1.3)* 17.0 (1.3) Socioeconomic status quintile 2.4 (1.3) 2.5 (1.3)~ 2.2 (1.3)* 2.4 (1.2) Physical neglect scale 1.7 (1.0) 1.7 (1.0) 1.7 (1.0) 1.8 (1.0)** Verbal IQ 79.1 (14) 78.6 (14)* 80.5 (14)** 77.3 (13)** Performance IQ 90.9 (14) 90.3 (15) 93.4 (14)** 89.0 (14)** Academic achievement 76.8 (13) 75.9 (13)~ 79.1 (13)** 75.1 (13)** Conduct Disorder scale 1.7 (1.6) 1.6 (1.6) 1.6 (1.6) 1.8 (1.6)* Proportion of peers who drink 3.8 (1.4) 3.9 (1.4) 4.0 (1.3)* 3.8 (1.3) Number of prior convictions 6.2 (7) 8.7 (8.4)** 6.4 (7.8) 9.1 (7.9)** *p<.05 **p<.01 ***p<.001 ~p<.15; SD standard deviation.

117 4.5.4 Prior convictions by pattern of drug use

Violent offenders’ drug use was very similar to non-violent offenders’, although violent offenders were more likely to report being under the influence of alcohol during their last offence (Table 4.11) and less likely to use ecstasy weekly (29% of weekly users had a violent conviction, compared to 50% of other participants).

Robbery offenders were comparable to the wider sample, except in regards to cannabis use; daily cannabis use was significantly less likely among robbery offenders in Sydney (28% vs. 38%, p<.05) but higher for offenders in other areas (47% vs. 37%).

Theft offenders reported a significantly higher prevalence of daily cannabis use, weekly opioid use and past year injecting. Theft offenders also reported the highest prevalence of offending to support drug use and offending whilst drug-affected. There were no significant differences across these offence types for amphetamine use.

One in two (52%) participants were affected by drugs or alcohol during their last offence (19% by alcohol only, 17% by other drugs but not alcohol, and 16% by both), and this was increasingly likely with age. Nearly half (45%) had offended to support their use. No significant or substantive gender differences were observed for these variables.

118 Table 4.11 Prior conviction type by drug use and drug-related offending variables

Total % Violence % Robbery % Theft % Age of first drug use: mean (SD) 13.2 (1.9)* 13 (2)* 13.4 (1.8)** 12.8 (1.9)** BINGE DRINKING (N=786) None 18 16 17 18 Monthly 51 50 48 51 Weekly (1-2 days) 21 24 26~ 20 More than two days per week 10 9 8 11 CANNABIS (N=796) no use 23 24 25 19** Less than weekly 24 24 27 22 Weekly 18 17 19 18 Daily 35 35 29~ 41** AMPHETAMINES (N=795) no use 64 66 64 63 Less than weekly 28 27 28 28 Weekly 8 7 8 9 OPIOIDS (N=786) no use 86 86 89 84 Less than weekly 9 10 8 9 Weekly 5 4 4 7* Injecting drug use 8 8 6~ 10* Any crime to obtain drugs 45 45 44 55** Last crime alcohol affected 36 40* 38 35 Last crime drug affected 33 33 31 39** *p<.05 **p<.01 ***p<.001 ~p<.15; SD standard deviation

119 4.5.5 Models of prior violent, theft and robbery convictions

Multivariate models assessed the correlates of prior violent convictions (Table 4.12), theft convictions (Table 4.13), and robbery convictions (Table 4.14). A hierarchical approach was used to assess the unique contribution of drug use variables. Age, gender and ethnicity served as control terms. All models were dominated by the total count of prior convictions: as these increased, associations between different conviction types decreased markedly despite their strong correlation, which suggests statistical suppression (Cohen, Cohen, West, & Aiken, 1983).

The first violence model accounted for 43% of variance in violence and correctly classified 75% of cases. Adding drug use variables (Model Two) improved model fit, and produced a small improvement change in r2 and classification, but did not substantively affect the parameters for other covariates. Odds of violence were 40% lower amongst weekly amphetamine users than non-users. Participants who were affected by alcohol during their last offence were 63% more likely to have a violent conviction. Odds of violence increased by 21% with each year of age, were higher for females (p=.06), lower for those with a disability, and much lower for robbery. CALD ethnicity, parental imprisonment, and early offending were amongst the bivariate but not independent correlates of violence. The association of prior theft with a history of prior violence conviction appears to be suppressed by the count of prior convictions. A theft by violent interaction term was dropped due to its excessive standard error.

The second theft model was the most accurate prior conviction model, accounting for 59% of variation in prior theft. Adjusted odds of theft were lower amongst CALD youth. Youths who offended before age 14 were twice as likely to have a theft conviction, and odds were also higher for those with multiple detention episodes. The count of prior convictions was again the strongest independent predictor, and this suppressed the otherwise positive association between any ‘other’ (non-violent/non-acquisitive) conviction and theft. Adjusted odds of theft were greatly increased by weekly opioid use (AOR 4.9, p<.01). Model Two for theft shows a small reduction in these odds (to AOR 4.2, p=.01) due to the inclusion of offending to support drug use (AOR 1.5, p=.07).

120 Table 4.12 Unadjusted and adjusted odds ratios for correlates of violent convictions

Total % No violent % Violent % OR (95%CI) AOR (95%CI) Model 1 AOR (95%CI) Model 2 Female 15 13 16 1.30 (0.87-1.93) .19 1.59 (0.92-2.73) .09~ 1.69 (0.98-2.92) .06~ Age: mean (SD) 17.0 (1.3) 16.9 (1.3) 17.2 (1.3) 1.23 (1.11-1.38) *** 1.21 (1.03-1.42) .02* 1.21 (1.02-1.42) .03* Ethnicity: ESB (reference) 52 48 57 1.0*** 1.0, .48 1.0, .48 Indigenous 19 16 22 1.15 (0.79-1.67) .47 0.96 (0.58-1.57) .87 0.90 (0.54-1.48) .67 ESB/CALD 15 17 13 0.64 (0.42-0.97) .03 1.18 (0.67-2.09) .56 1.18 (0.66-2.10) .59 CALD 14 19 8 0.36 (0.23-0.57) *** 0.68 (0.37-1.24) .21 0.63 (0.35-1.17) .14~ Parents ever in prison 26 21 32 1.76 (1.27-2.44) *** 1.16 (0.74-1.80) .52 1.16 (0.75-1.82) .50 Unstable housing 12 10 15 1.60 (1.04-2.48) .03* 1.21 (0.68-2.15) .51 1.40 (0.78-2.52) .26 Limiting disability 12 15 10 0.61 (0.40-0.95) .03* 0.54 (0.31-0.96) .04* 0.53 (0.30-0.95) .03* Proven guilty by age 14 22 22 28 1.93 (1.37-2.73) *** 1.36 (0.79-2.32) .27 1.39 (0.81-2.39) .23 Prior convictions: mean (SD) ln 6.2 (7.0) 3.7 (4.1) 8.7 (8.3) 3.37 (2.72-4.18) *** 8.09 (5.59-11.7) *** 8.21 (5.64-12.0) *** Prior robbery conviction 33 44 23 0.38 (0.28-0.51) *** 0.21 (0.14-0.32) *** 0.20 (0.13-0.31) *** Prior theft conviction 57 48 62 1.45 (1.10-1.93) *** 0.14 (0.08-0.24) *** 0.14 (0.08-0.24) *** Amphetamine use: None (ref.) 64 62 66 1.0, .15~ - 1.0 .02* Less than weekly 27 28 27 0.88 (0.64-1.21) .44 0.66 (0.43-1.02) .06~ Weekly 9 10 7 0.61 (0.36-1.02) .06~ 0.40 (0.20-0.81) ** Alcohol affected during last crime 36 32 40 1.41 (1.04-1.90) .03* 1.63 (1.10-2.41) .02* Classification / pseudo r2 / -2LL 75.4% / .430 / 706.0 76.2% / .445 / 692.9 *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; ln Log transformed; N=743.

121 Table 4.13 Unadjusted and adjusted odds ratios for correlates of theft convictions

Total % No theft % Theft % OR (95%CI) AOR (95%CI) Model 1 AOR (95%CI) Model 2

Female 15 15 14 0.94 (0.63-1.41) .78 0.97 (0.55-1.72) .92 0.89 (0.49-1.59) .69 Age: mean (SD) 17.0 (1.3) 17.0 (1.3) 17.0 (1.3) 1.01 (0.91-1.13) .82 0.85 (0.71-1.01) .07 0.84 (0.70-1.01) .06~ Ethnicity: ESB (reference) 52 47 56 1.0*** 1.0* 1.0* Indigenous 19 12 25 1.77 (1.18-2.67) ** 1.17 (0.66-2.08) .59 1.16 (0.65-2.07) .63 ESB/CALD 15 23 9 0.34 (0.22-0.52) *** 0.41 (0.23-0.73) *** 0.46 (0.25-0.83) .01 CALD 14 18 10 0.47 (0.31-0.72) ** 0.60 (0.34-1.06) .08~ 0.60 (0.33-1.08) .09~ Proven guilty by age 14 22 8 32 5.03 (3.28-7.72) *** 2.21 (1.17-4.19) .02* 2.04 (1.05-3.94) .04* Prior convictions: mean (SD) ln 6.2 (7.0) 2.4 (2.4) 9.1 (7.9) 10.9 (7.88-15.2) *** 20.6 (12.4-34. 5) *** 20.2 (11.9-34.2) *** Prior other/drug conviction 65 45 81 5.17 (3.77-7.09) *** 0.34 (0.19-0.58) *** 0.34 (0.20-0.60) *** Multiple prior incarcerations 73 61 83 3.02 (2.17-4.20) *** 1.68 (1.08-2.60) .02* 1.57 (0.99-2.48) .06~ Opioid use: None (reference) 86 88 84 1.0 1.0* 1.0* Less than weekly 9 10 9 1.01 (0.62-1.64) .97 1.14 (0.57-2.29).71 1.18 (0.57-2.45) .65 Weekly 5 2 7 2.96 (1.34-6.55) ** 4.94 (1.63-14.9) ** 4.23 (1.37-13.1) .01* Committed crime to obtain drugs 45 31 55 2.67 (1.97-3.61) *** - 1.48 (0.96-2.28) .07~ Classification / pseudo r2 / -2LL 81.6% / .584 / 582.6 81.3% / .587 / 579.4 *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; ln Log transformed

122 Only one model for robbery is presented because drug use did not improve model fit for this offence type. The model accounted for 34% of variance and correctly classified 76% of cases. Robbery offenders were more likely to be male and older, but these factors were irrelevant in the final model. CALD ethnicity and living in Sydney, however, remained very strong predictors, increasing odds of robbery three-fold over ESB participants and non-metropolitan locations respectively. Odds of robbery increased with the prevalence of prior convictions. As with the violence model, the count of prior offences suppressed the effects of specific offences (not shown); odds were much lower for those with prior theft or violent convictions, although their inclusion greatly increased the pseudo r2 to 34% and improved classification accuracy.

Table 4.14 Unadjusted and adjusted odds ratios for correlates of robbery convictions

No Robbery OR (95%CI) AOR (95%CI) robbery % % Female 16 11 0.64 (0.41-1.00) .05* 0.91 (0.54-1.53) .72 Age: mean (SD) 16.9 (1.3) 17.2 (1.3) 1.16 (1.03-1.30) .01* 1.00 (0.87-1.16) .96 Ethnicity: ESB (ref.) 61 35 1.0*** 1.0*** Indigenous 21 16 1.29 (0.84-1.98) .24* 1.49 (0.91-2.43) .12 ESB/CALD 10 24 4.10 (2.67-6.30) *** 2.92 (1.77-4.81) *** CALD 8 25 5.31 (3.40-8.32) *** 3.57 (2.13-5.97) *** Region: Sydney (ref.) 67 92 1.0 1.0*** Regional 14 4 0.19 (0.10-0.38) *** 0.28 (0.13-0.58) ** Rural 19 4 0.16 (0.09-0.31) *** 0.19 (0.09-0.39) *** Employed 22 32 1.71 (1.22-2.38) ** 1.68 (1.12-2.50) .01 Convictions mean(sd)ln 6.1 (6.6) 6.4 (7.8) 1.06 (0.89-1.26) .53 3.71 (2.66-5.16) *** Prior violent conviction 57 33 0.38 (0.28-0.51) *** 0.19 (0.13-0.30) *** Prior theft conviction 62 47 0.56 (0.42-0.75) *** 0.20 (0.13-0.33) *** Classification / pseudo r2 / -2LL 76.1% / .339 / 772.9 *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; SD standard deviation; ln Log transformed

123 4.6 Discussion

This chapter described the prevalence of, and gender differences in, the health, welfare and criminogenic needs of community-supervised young offenders, including the first detailed estimates of the frequency of specific drug use in this population. It also described their prior conviction patterns and the risk profiles linked with a history of violent, theft and robbery convictions. Risk exposures were far greater than for the general population, particularly for the most problematic behaviours, and were comparable (not identical) to those in previous young offender studies. Many risk factors co-occurred, pointing to the need for multivariate analyses to identify potentially causal risk factors for recidivism. The sample was heterogeneous with respect to reported recidivism risk factors. For example, 24% had one or no prior convictions but 37% had more than five. Males and females were highly similar in most respects, but there were also some profound gender differences.

4.6.1 Patterns of drug use

These data highlight the extremely elevated rates of drug use in this sample, particularly for the least prevalent drugs. Past year prevalence approached 95%, and most used weekly and/or multiple drugs. This is typical of juvenile offender samples (e.g. McClelland et al., 2004). Rates of alcohol use were double that of youths in the general population: 91% reported past year alcohol use versus 54% of Australian teens (NSW Health, 2005); 82% reported binge drinking compared to 40% of students aged 16-17 in NSW (40% NSW Health, 2005). Young offenders initiated drug use much earlier than youths in the general population; by age 16, 88% of participants had used illicit drugs compared with 33% of Australian adolescents (Australian Institute of Health and Welfare, 2008). Binge drinking also occurred at least weekly for one in three drinkers, and was usually at a level well above the minimum cut-off for ‘binge drinking’. Binge drinking prevalence was comparable to that of community-supervised young offenders in the UK (85%, Hammersley et al., 2003) but twice that of young US offenders (40%, Mulvey et al., 2010); young detainees in NSW reported a similar prevalence of past year bingeing (89%) but double the rate of weekly bingeing (60% vs. 32%, Indig et al., 2011).

124 As in the wider adolescent population (Wu, Woody, Yang, Pan, & Blazer, 2011), cannabis was the most prevalent illicit drug (89%, vs. 27% NSW students, NSW Health, 2005) and by far the most frequently used, with nearly half of all users reporting daily use (35% vs. 5% weekly users in general population youth samples, Australian Institute of Health and Welfare, 2008). The rates of use (81-89%) and weekly use (53-64%) of cannabis varied little across this sample, young detainees in NSW, and young UK and US offenders. Cannabis is a remarkably consistent feature of young offenders in cultures where cannabis is prevalent (e.g. Lebeau-Craven et al., 2003; Parry, Plüddemann, Louw, & Leggett, 2004); by extension, its acquisition and intoxication are entrenched aspects of young offenders (Simpson, Howard, Copeland, & Nelson, 2009).

The fact that cannabis was used far more often than alcohol could also relate to price and availability. Alcohol is regulated (supply restricted, taxed) and juveniles tend to rely on irregular sources of supply: parents or older friends (Hearst et al, 2007) or in the case of young offenders, theft (Jennings et al, 2011). Cannabis is unregulated, widely available, and frequently traded between peers (e.g. Moeller & Hesse, 2013). Frequent use incurs substantial cost (Wilkins & Sweetsur, 2010), which implies a high economic burden for juveniles (Brunelle, Brochu, & Cousineau, 2000). Thus, dealing could play an important role in facilitating regular supply and use (see Section 2.3.2).

Amphetamine use and opioid use were much less prevalent than other drugs, but even more discrepant from non-offenders, e.g. 46% used amphetamines compared with 6- 7% of students and general population teens in NSW and the US (Australian Institute of Health and Welfare, 2005; Maxwell, 2008; NSW Health, 2005). This discrepancy was even more stark for weekly use of these drugs and for injecting drug use (8% vs. 0.5% of Australians over 14, Australian Institute of Health and Welfare, 2008). Participants’ rates of use of these drugs were comparable to young detainees and UK offenders, at least double those of young US offenders (whose rate of cocaine are much higher, Dembo & Sullivan, 2009) but were lower than for adult prisoners, for example 14% for opioid use versus 41% (Indig et al., 2010).

Patterns of drug use were unevenly distributed in the sample; weekly amphetamine and opioid use was much more likely amongst female, urban and older youths.

125 Females were twice as likely to report injecting, and to have peers who use drugs other than cannabis. Female dependent heroin users were more likely to have initiated with a partner (Shand, Degenhardt, Slade, & Nelson, 2011) and for young females, injecting is often tied up with intimacy in relationships (Lenton, Fraser, Moore, & Treloar, 2011).The fact that most offenders use drugs and many have progressed to regular use necessitates a different mix of prevention/intervention strategies to other youths (Lennings et al., 2006). The higher rates of opioid use among prisoners reflects the cumulative risk of initiation and progression through ongoing criminal involvement (Makkai & Payne, 2003) and points to opportunities to prevent progression of injectable drugs by the sample. Chapter Five analyses correlates of specific drug use with a view to identifying possible precursors and appropriate intervention targets.

4.6.2 Risk factors other than drug use

Most participants reported clinically significant psychopathology, and a large minority were severely distressed or likely to have been eligible for a DSM-IV diagnosis. Female participants had particularly dysfunctional backgrounds, and were significantly more likely than males to report most family risk factors. Participants’ schooling careers were short and suggest very early problems with learning and the mainstream school environment. Their cognitive and academic abilities were far poorer than the general population average. Together, these further marginalise young offenders and reduce their access to meaningful and rewarding means of employment and civic participation. Early school disengagement truncates academic progress and facilitates contact with disengaged and older youth (Putninš, 2004). Most participants’ peers engaged in a range of delinquent behaviours including drug use and offending, and in the absence of strong positive parental relationships these are likely to have a substantial influence on adolescent behaviour. Participants also reported many adverse social interactions with intoxicated persons and were both perpetrators and victims of violence. Females were disproportionately exposed to sexual violence.

Participants’ court records revealed a sample of predominantly serious offenders with diverse criminal histories. A minority of participants had been incarcerated (12%), had convictions pertaining to offences committed before age 14 (22%), had conviction

126 histories of more than one year’s duration, and (as noted above) were ‘high-rate’ offenders. This contrasts with juvenile detainees who tend to be characterized by frequent, early onset, persistent offending. However, most participants were proven recidivists (multiple prior convictions; 74%), had been detained overnight at least twice (73%) and had committed violence or robbery (71%). There was wide demographic variation in patterns of criminal justice system (CJS) involvement, including that male and Indigenous participants were more likely to have been incarcerated. This is congruous with prior research and illustrates the importance of a demographically diverse sample, and of distinguishing community-supervised youths from detainees. Females were more likely than males to have breached previous orders, suggesting problems with compliance, but criminal involvement was otherwise largely gender- invariant. Violence and theft were comparably prevalent, although violence occurred much less frequently.

4.6.3 Correlates of specific offending history

The multivariate models showed that although the correlates of violence, theft and robbery varied in many regards, there were few distinguishing factors. Each offence was predicted by aspects of criminal history, but theft was far more strongly correlated with offending frequency, and violence was correlated with more extensive offence histories, as is consistent with prior literature. One might assume robbery profiles to be a combination of theft and violent profiles (given robbery’s violent acquisitive nature), but robbery was the least strongly associated with prior offending.

Violent offenders were 63% more likely to have been alcohol-affected at the time of their last offence (AOR 1.63, p=.02), which might suggest a psychopharmacological effect, as reported in prior literature. This was also the only easily modifiable independent correlate of violence, which might make it an attractive intervention target given the considerable policy interest in reducing alcohol-related youth violence (Calabria, Shakeshaft, & Havard, 2011; NSW State Government, 2006). However, the model could not assess whether alcohol contributed to violence, so this will need to be clarified in the prospective models (Chapters Six through Eight). Violence was negatively associated with amphetamine use, in contrast with prior research (Hoaken

127 & Stewart, 2003, Chapter Two). Also contrary to expectations, low verbal IQ did not correlate independently with violence.

Theft was independently and negatively related to current age and age of onset; it was rare for youths commencing their offending by age 14 not to have accrued a theft conviction (17%, vs. 50% of later-onset offenders). Theft offenders were less likely to be from CALD heritage, which contrasted sharply with the robbery model (see below). This suggests a strong tendency for CALD youth towards violence (threats, or actual violence) rather than nonviolence in their acquisitive offending. Theft was strongly associated with weekly opioid use (AOR 4.2, p<.001), but not with less than weekly use; a dose relationship with frequency was not at all apparent. Adjusted odds were higher than the unadjusted odds which confirms what prior literature has consistently shown: this is a direct association. By contrast, prior offending to support drug use was much less strongly related to theft after adjustment for other factors.

Robbery had a distinct risk profile to other offences, including the link with CALD ethnicity, and was not associated with drug use. Efforts to address robbery offenders’ needs should be culturally-sensitive (e.g. involving consultation with CALD communities) and as ethnicity necessarily precedes robbery, there may also be opportunities to prevent robbery initiation. Robbery was linked with urban areas, reflecting the geographic distribution of robberies in NSW. Together the results suggest that robbery should be analysed separately from other offences.

These results show that drug use and offending were independently associated, but accounted for less variation in offending than other aspects of criminal history, and associations were drug- and crime-specific.

4.6.4 Limitations

The offence types modelled in this chapter were not mutually exclusive categories (for example, most violent offenders were also theft offenders), so the risk profiles for each offence type overlap to some extent. There may be some theoretical appeal to studying offenders who solely commit one type of offence, but such ‘pure’ offenders are relatively rare (Chapter One); only 7% of participants had violent convictions only.

128 The temporal association between these offences and most of their correlates could not be ascertained in the cross-sectional models. However, the weight of prior evidence suggests that although offending tends to commence before drug use, heavy use intensifies and alters this connection (e.g. providing a motivation for theft to support an otherwise unaffordable habit, thereby averting withdrawal and exposure to psychic pain). Most prospective studies support this link and rarely observe increases in drug use due to offending, although some offenders celebrate their offending by using drugs (the ‘life as a party’ explanation, Bennett & Holloway, 2009).

4.6.5 Conclusion

This chapter has outlined the prevalence of drug use and other risk exposures experienced by the sample. With the exception of cannabis, most offenders are users but not frequent (weekly) users. Their rates of use were far in excess of general population youths, and comparable to other offender samples, but the drug mix differed in important ways. Notably, cocaine use was rare compared with US and UK offender samples (Bennett & Holloway, 2007; Dembo & Sullivan, 2009). Other major differences from international samples included the high proportion of Indigenous and non-urban youths, and unlike detainee samples participants had very limited custodial experience. Generalising between these samples is problematic.

Cognitive impairment, psychological distress, socioeconomic disadvantage, peer delinquency, and maltreatment were common experiences for the majority of participants and most youths had convictions for multiple offence types. However, even within this serious young offender sample, there was wide variance in demographic backgrounds, risk exposures and criminal history, and significant and substantive differences between offenders with different conviction histories. For example, some participants were as yet unconvicted while others had extensive conviction histories; injecting drug use and victimisation were strongly characteristic of young females; opioid use was a strong independent correlate of a history of theft. However, other patterns of drug use were of little consequence to the multivariate models. These differences would be obfuscated in models of general offending, which underscores the importance of specific recidivism models (Chapters Six to Eight).

129 5 Patterns and correlates of drug use

Juvenile drug-crime research has strongly focused on how drug use is linked with recidivism (e.g. Putninš, 2003). This has been far less thoroughly investigated among young offenders (see the review of young offenders’ drug use, Section 1.5), despite urgent calls for such studies (Lloyd, 1998). This information is important because problematic drug use that develops during adolescence predicts ongoing problems other than offending, including illness and injury (Toumbourou et al., 2007). Drug use is also among the most prominent risk factors for death among youths aged 15 to 19 (Lim et al., 2012). For young Australian offenders, this is primarily due to overdose (Coffey, Veit, et al., 2003) and for young offenders in the US, to gun violence related to drug markets (Teplin, McClelland, Abram, & Mileusnic, 2005).

Drug-using young offenders experience significant harms relating to preferential use (Copeland et al., 2003; Lennings et al., 2007; Putninš, 2006) but prior studies have tended to aggregate drug type (e.g. Golder, 2007) or address only one aspect of use (e.g. heavy drinking Kenny & Schreiner, 2009). A comprehensive analysis of young Australian community-based offenders’ drug use has not yet been published. Juvenile justice contact presents a rare opportunity to address the drug-related needs of youths often heavily involved in drug use who rarely seek treatment, and to reduce their involvement in problematic drug use (Lennings et al., 2006).

Chapter Four of this thesis described the prevalence of participants’ drug use (and gender differences therein). This chapter describes problems relating to this drug use, and assesses the correlates of frequency of binge drinking, cannabis, amphetamine and opioid use.

5.1 Aims 1. Describe participants’ self-reported drug problems and treatment histories 2. Assess the correlates of binge drinking, cannabis, amphetamine and opioid use. 3. Model the independent correlates of these four patterns of drug use

130 5.2 Method

Section 3.3.4 described how drug use was measured and coded in this thesis. Measures were based on self-reports by participants, preferentially to questions in the baseline survey, or items in the Adolescent Psychopathology Scale – Short Form. The dependent variables in this chapter were independent variables in Chapter Four: Binge drinking: none/rarely, less than weekly, weekly, more than twice weekly Cannabis use: none, less than weekly, weekly, daily Amphetamine use (excludes ecstasy): none, less than weekly, weekly Opioid use (heroin and other opiates): none, less than weekly, weekly

Multivariate analyses were conducted using multinomial logistic regression (MLR), which accommodates polychotomous dependent variables (i.e. multiple outcome categories), performing binary logistic regressions for each outcome. MLR quantifies the size and significance of the odds (coefficients) of any one category versus any other (e.g. weekly use vs. no use). MLR does this more conveniently and is more statistically efficient than manually running separate logistic regressions for each outcome. MLR was selected a priori over ordinal regression, to ensure that the form of the relationship between risk factor and drug use frequency could be assessed. Non-linear relationships have been reported between frequency of drug use and other factors (Pape & Hammer, 1996; Raby et al., 2009), e.g. an exponential relationship with frequent juvenile offending (Hammersley et al., 2003) and a U-shaped relationship with psychosocial adjustment (better adjustment among moderate users than abstainers and frequent users, Shedler & Block, 1990).Ordinal regression can however provide a more parsimonious model for ordered outcomes than MLR (Section 5.4.2).

Bivariate associations of p<.15 in a particular domain (e.g. family) were entered into a domain-specific model, and backwards elimination removed covariates of p≥.15. Those remaining in each domain-specific model were input to a single model and retained at p<.05 to produce a model containing only the strongest independent predictors. ‘Prediction’ refers to statistical rather than causal prediction, as the models are cross- sectional. This process produced a more parsimonious model with less collinearity. Age, gender, and other logically necessary terms were included as controls.

131 5.3 Results 5.3.1 Patterns of drug use, drug problems and treatment

The prevalence and gender differences in past year drug use were presented in Table 4.6 and Section 4.4.6: 82% of participants binge drank (10% more than twice weekly); 77% used cannabis (35% at least daily), 34% used amphetamines (9% at least weekly) and 14% used opioids (5% at least weekly); females were over-represented among the most frequent users except for cannabis. By extension, poly-drug use was common. Participants had used a mean of three drugs in the past year (SD 1.9, max. 10); 78% were poly-drug users (two or more drugs; Figure 5.1) and 30% were weekly poly-drug users. Females used more drugs on a weekly basis than males. Figure 5.2 illustrates, to scale, the substantial overlap between patterns of weekly use.

None 33% One Two 17% Three 78% 15% Poly drug use Four 13% 5% Five 8% More than five 9%

Figure 5.1 Number of drugs including alcohol used in past year

binge + cannabis

binge + cannabis + other cannabis binge + other other + other Figure 5.2 Relationships between patterns of weekly drug use

132 Half of all drug-using participants said that their drug use had caused them significant health, social, family or legal problems in the past year (Table 5.1); problems were more likely with increasing age. However, only 11% of participants reported a need for help with drug problems. Participants’ drug problems also largely untreated; one in four past-year users had received drug treatment (including rehabilitation centres, detox clinics, and outpatient counselling); under 5% of participants had undertaken or completed high quality programs. Males and female did not differ in this regard.

Table 5.1 Problems with drug use, drug treatment and other problems

TOTAL % Male % Female %

(n=800) (n=682) (n=118) Problems due to drug use 42 42 44 Police problems due to drug use 25 26 23 Health problems due to drug use 9 8 11 Needs help with drug problems 11 11 10 Any history of drug treatment 19 19 19 Ever been in detoxification 7 7 11 Even been in rehabilitation 9 9 8 *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; CI confidence interval; N=752.

5.3.2 Bivariate correlates of drug use

All patterns of drug use were strongly associated (p<.01) with all other patterns of use (e.g. binge drinking with cannabis, amphetamine and opioid use) and frequent users were more likely to use other drugs frequently. Users tended to be older and to report poorer functioning and greater risk exposure than non-users. All patterns of use were associated with variables from domains of functioning, but several variables were linked with only one pattern of use. Disability was linked only with heavy binge drinking; parental imprisonment, parental separation, unemployment, and special education only with cannabis use; and bullying only with amphetamine use. Prior violent convictions were unrelated to drug use. Frequent users were much more likely to experience other drug-related problems and Conduct Disorder, while peer deviance was most strongly linked with infrequent drug use. Bivariate results for variables retained in the final multivariate models are presented in Appendix Tables D-G.

133 5.3.3 Binge drinking model

The final binge drinking model accounted for 43% of the variance in the frequency of binge drinking and significantly improved classification accuracy. Compared to the bivariate results (Appendix Table D), multivariate associations (Table 5.2) were much smaller in magnitude, indicating a high degree of shared variance. Nonetheless, there were several strong independent correlates across multiple domains of functioning.

Frequency of bingeing showed strong dose-response relationships with alcohol- affected offending, peer drinking, fighting, smoking and opioid use, with the highest risks amongst heavy binge drinkers (more than twice weekly). For example, less than weekly binge drinkers were twice as likely to have been alcohol affected during their last offence (AOR 1.9, p<.05), while weekly binge drinkers were four times as likely (AOR 4.1, p<.001) and heavy binge drinkers eight times as likely (AOR 8.6, p<.001). In fact, alcohol-affected offending was significantly more likely amongst heavy binge drinkers than amongst weekly binge drinkers (AOR 2.1, p<.05). In contrast, drug- affected offending did not predict binge drinking (p>.15). Disability was no longer associated with heavy binge drinking once other factors were considered and so was removed from the final model.

Females were three times as likely as males to binge heavily (than not at all), but no more likely to binge less often (e.g. weekly). Likewise, adjusted odds of past year suicidal or self-harming (SSH) behaviour was strongly related to heavy but not less frequent bingeing; it was four times as likely amongst heavy than weekly binge drinkers (AOR 4.2, 95% CI 1.8-9.6, p<.001). One in three (32%) heavy binge drinkers reported SSH compared to fewer than 10% of other participants. Head injury was a non-specific correlate, being equally associated with all frequencies of bingeing. Amphetamine and cannabis use had strong positive associations with bingeing frequency at the bivariate level that were not apparent in the final model.

134 Table 5.2 Adjusted odds ratios for correlates of frequency of binge drinking

More than twice More than twice weekly (Reference category: no/rare bingeing) Monthly Weekly Model weekly vs. weekly AOR (95%CI) AOR (95%CI) AOR (95%CI) χ2 AOR (95%CI) Female 1.23 (0.62-2.44) 1.56 (0.69-3.56) 2.90 (1.07-7.82)* .170 1.85 (0.78-4.39) Age (scale) 1.17 (0.98-1.40)~ 1.27 (1.01-1.59)* 1.26 (0.93-1.72)~ .197 1.00 (0.75-1.33) Father absent 0.86 (0.53-1.40) 0.48 (0.27-0.86)* 0.47 (0.22-1.00)* .014 0.97 (0.50-1.88) Head injury (0-2) 1.79 (1.20-2.67)** 1.79 (1.14-2.79)* 1.89 (1.12-3.2)* .022 1.06 (0.71-1.57) Self-harm/suicidal behaviour 1.39 (0.52-3.71) 1.27 (0.41-3.88) 5.33 (1.69-16.82)** .002 4.21 (1.84-9.62)*** Any friends 2.87 (1.33-6.22)** 2.23 (0.78-6.41)~ 2.32 (0.53-10.1) .069 1.04 (0.23-4.59) Peer drinking (1-5) 1.46 (1.24-1.72)*** 2.11 (1.68-2.65)*** 2.25 (1.60-3.17)*** .000 1.07 (0.76-1.50) Fighting frequency (1-5) 1.06 (0.88-1.28) 1.18 (0.95-1.47)~ 1.37 (1.03-1.83)* .097 1.16 (0.91-1.49) Smoking (0-2) 1.51 (1.12-2.04)** 1.83 (1.26-2.66)** 2.80 (1.62-4.85)*** .001 1.53 (0.92-2.54)~ Cannabis use: none 1.0 1.0 1.0 .012 1.0 less than weekly 2.39 (1.29-4.41)** 1.96 (0.91-4.26)~ 0.71 (0.22-2.30) 0.36 (0.12-1.09)~ weekly 2.69 (1.31-5.52)** 2.38 (0.98-5.78)~ 1.45 (0.44-4.80) 0.61 (0.20-1.80) daily 3.49 (1.69-7.18)*** 2.32 (0.96-5.59) 2.50 (0.82-7.63)~ 1.08 (0.40-2.90) Amphetamine use: none 1.0 1.0 1.0 .061 1.0 less than weekly 1.78 (0.81-3.89) 2.67 (1.13-6.32)* 1.90 (0.68-5.30) 0.71 (0.33-1.53) weekly 0.33 (0.12-0.90)* 0.53 (0.17-1.66) 0.49 (0.13-1.82) 0.92 (0.32-2.64) Opioid use^ 2.12 (0.72-6.22) 3.16 (1.00-9.98)* 6.67 (1.96-22.7)** .004 2.11 (0.98-4.51)~ Last offence alcohol-affected 1.88 (1.01-3.50)* 4.13 (2.08-8.20)*** 8.60 (3.69-20.1)*** .000 2.08 (1.06-4.11)* Six months or more in custody 0.54 (0.27-1.05)~ 1.19 (0.54-2.61) 0.90 (0.32-2.50) .040 0.76 (0.31-1.82) *p<.05 **p<.01 ***p<.001 ~p<.15; N=708; AOR adjusted odds ratio. CI confidence interval; Nagelkerke r2 .426; ^All opioid users combined as all users binge drank 135 5.3.4 Cannabis model

The cannabis model (Table 5.3) explained 56% of the variance in frequency of use and was twice as effective in classifying cases as the null model. Most bivariate associations were non-significant after adjustment, but other covariates were robust to adjustment. Bivariate results for variables in the final model are presented in Appendix Table E. Males and indigenous participants had higher odds of more frequent use; use was positively related to peer cannabis use, negatively to first age of drug use; it was strongly associated with other drug use and drug-related offending, and much less likely amongst the employed.

Females were less than half as likely to report cannabis use as males. Ethnic differences were most evident for daily use: Indigenous participants were two to three times more likely to use weekly or daily (than not at all) than Australian non- Indigenous participants. Employment was rare amongst weekly and daily users; they were around three times as likely to be unemployed as less frequent users. The proportion of cannabis using peers was significantly correlated with cannabis use frequency; daily users were more than twice as likely as weekly users to report that all their peers used cannabis. High risk sexual behaviour was less common amongst cannabis users in the final model, in contrast with bivariate analyses.

Age of first drug use was negatively associated with frequency of cannabis use. For each year later that participants began drug use, their odds of cannabis use decreased by 21% for weekly use (AOR 0.79, p<.01), and 32% for daily use (AOR 0.68, p<.001) and their odds of daily use compared to weekly use decreased by 13% (AOR 0.87, p<.05). Tobacco and amphetamine use were strongly related to cannabis use frequency. Less than weekly amphetamine use was much more likely amongst weekly or daily cannabis use, while weekly amphetamine use was only more likely amongst daily cannabis users.

Daily users were much more likely to indicate a need for assistance with drug problems, compared to weekly users, both before and after multivariate adjustment.

136 The prevalence and likelihood of drug-intoxication at last offence and drug-related police problems increased greatly with frequency of cannabis use. Committing crime to support drug use showed a similar pattern, (16% of non-users and up to 73% for daily users). For males, this behaviour was twice as likely amongst daily than weekly users; for females, weekly and daily users’ odds were comparable. Bingeing was more likely amongst cannabis users in the unadjusted model only.

Parental smoking and parental drug use were strong domain-specific correlates but due to excessive missing data for these variables were removed from the final model. Self-reported need for help with drug problems was retained (despite listwise deletion of 31 cases) as this is an indicator of current functioning, rather than a static (background) factor. Finally, age and gender interactions were tested for all variables, but none were significant at p<.01 (per Hosmer & Lemeshow, 2000).

137 Table 5.3 Adjusted odds ratios for correlates of frequency of cannabis use

(Reference category: no use) Less than weekly Weekly Daily Model Daily vs. weekly AOR (95%CI) AOR (95%CI) AOR (95%CI) χ2 AOR (95%CI) Female 0.45 (0.22-0.95)* 0.27 (0.11-0.64)** 0.34 (0.14-0.78)* .020 1.24 (0.59-2.62) Age (scale) 0.86 (0.69-1.06) 0.85 (0.66-1.08) 0.89 (0.69-1.14) .479 1.05 (0.84-1.31) Ethnicity: English-speaking 1.0 1.0 1.0 .006 1.0 Indigenous 1.09 (0.50-2.36) 2.40 (1.06-5.43)* 2.89 (1.31-6.39)** 1.21 (0.63-2.29) ESB/CALD 1.66 (0.82-3.37) 0.97 (0.38-2.49) 1.07 (0.42-2.72) 1.10 (0.45-2.72) CALD 1.30 (0.63-2.65) 1.73 (0.75-4.02) 0.62 (0.24-1.64) 0.36 (0.15-0.85)* Employed 0.93 (0.53-1.61) 0.30 (0.15-0.62)** 0.28 (0.14-0.57)*** .000 0.93 (0.48-1.81) Peer cannabis use (1-5) 1.13 (0.95-1.35) 1.21 (0.98-1.50)~ 1.56 (1.25-1.95)*** .001 1.29 (1.05-1.59)* Assaulted by intoxicated person 1.04 (0.52-2.07) 2.30 (1.11-4.77)* 1.56 (0.75-3.26) .047 0.68 (0.39-1.17) High risk sexual behaviour 0.41 (0.20-0.83)* 0.25 (0.11-0.57)*** 0.49 (0.24-1.02)~ .005 1.94 (1.03-3.66)* Age first used drugs (≤9 to ≥15 years) 0.88 (0.76-1.01)~ 0.79 (0.67-0.93)** 0.68 (0.58-0.80)*** .000 0.87 (0.76-0.98)* Smoking (0-2) 1.33 (1.07-1.65)** 1.34 (1.03-1.73)* 1.51 (1.16-1.97)** .012 1.13 (0.89-1.44) Amphetamine use: none 1.0 1.0 1.0 .000 1.0 less than weekly 3.47 (1.53-7.90)** 8.17 (3.45-19.4)*** 8.44 (3.60-19.8)*** 1.03 (0.59-1.80) weekly 1.51 (0.32-7.18) 2.11 (0.40-11.2) 7.51 (1.69-33.5)** 3.56 (1.28-9.89)* Needs help with drug problems 0.22 (0.05-1.04)~ 0.72 (0.20-2.55) 1.95 (0.59-6.42) .000 2.73 (1.24-5.98)* Committed crime to obtain drugs 2.18 (1.12-4.24)* 2.32 (1.11-4.83)* 4.01 (1.97-8.18)*** .001 1.73 (0.98-3.07)~ Drug-intoxicated during last offence 1.27 (0.59-2.73) 2.01 (0.90-4.49)~ 3.21 (1.50-6.90)** .003 1.60 (0.93-2.75)~ Drug-related police problems 2.30 (0.89-5.94)~ 3.98 (1.49-10.6)** 3.53 (1.36-9.15)** .024 0.89 (0.49-1.59) *p<.05 **p<.01 ***p<.001 ~p<.15; N=646; AOR adjusted odds ratio. CI confidence interval; Nagelkerke r2 .559; Classification accuracy 60% (96.3% over chance).-

138 5.3.5 Amphetamines model

The final model (Table 5.4) explained 55% of the variance in frequency of amphetamine use, and improved classification accuracy by 36%. Amphetamine use was strongly correlated with peer drug use, other forms of drug use (especially past year injecting), and drug-related problems with police. Weekly and less than weekly users were distinguished on several covariates, including higher odds of unstable housing, emotional neglect and many sexual partners.

Neither gender nor age predicted amphetamine use frequency in the multivariate models (as they had in bivariate analyses, see Appendix Table F), but use remained negatively correlated with socioeconomic status in the final model. All measures of child maltreatment were associated with amphetamine use at the bivariate level, and the final model showed severe emotional abuse was more likely and severe physical neglect less likely amongst weekly versus less than weekly users. Weekly users also had far higher odds of unstable housing. Parental drug use was linearly related to amphetamine use and doubled the odds of weekly use in the domain-specific model, but was excluded from the final model due to excessive missing data (n=100).

No measures of psychological functioning remained in the multivariate model, although all showed significant bivariate and domain-specific associations with amphetamine use, notably past year suicidal and self-harming behaviour (SSH), Conduct Disorder (CD) and anger/violence proneness. Similarly several school/cognitive domain variables (including early school leaving and regular truanting) dropped out of the final model. By contrast, even a small increase in the proportion of peers who use drugs other than cannabis and alcohol was linked with higher (nearly double) adjusted odds of amphetamine use; frequency of fighting was also an independent predictor. Amphetamine users were in poor health; one in four weekly users reported multiple head injuries (compared to 12% of non-users, p<.01), and half typically did not use condoms with casual partners. Many sexual partners was the only multivariate health domain predictor, with weekly users’ odds far higher than less than weekly users’.

139 Offending to support drug use, and drug-related police problems increased greatly with frequency of use; the latter remained so in the multivariate model. Weekly users were less likely to have a violent conviction. Users consistently reported higher levels of other drug use and use-related problems. Weekly users had initiated drug use earlier, and more reported a need for help with drug problems; they were more likely to have received treatment, but more than half had not. The final model found users were several times more likely to use cannabis daily, and injecting was very strongly associated; weekly users were four times as likely as less than weekly users to have injected. Less than weekly (but not weekly) use of opioids was positively related to amphetamine use.

140 Table 5.4 Adjusted odds ratios for correlates of frequency of amphetamine use

Reference category Weekly vs. less Less than weekly Weekly use Model : no use than weekly AOR (95%CI) AOR (95%CI) χ2 AOR (95%CI) Female 1.08 (0.55-2.09) 1.49 (0.56-4.02) .728 1.39 (0.55-3.50) Age (scale) 1.05 (0.87-1.27) 1.22 (0.89-1.67) .451 1.16 (0.86-1.57) Socioeconomic quintile 1.26 (1.07-1.49)** 1.25 (0.95-1.65)~ .019 0.99 (0.76-1.29) Unstable housing 1.58 (0.78-3.21) 3.56 (1.31-9.97)* .050 2.25 (0.91-5.58)~ Emotional abuse (1-5) 0.83 (0.65-1.07)~ 1.39 (0.96-2.02)~ .011 1.68 (1.18-2.38)** Physical neglect (1-5) 1.24 (0.98-1.55)~ 0.73 (0.49-1.08)~ .008 0.59 (0.40-0.86)** Fighting (1-5) 1.23 (1.05-1.45)* 1.39 (1.05-1.84)* .013 1.13 (0.86-1.48) Any friends 3.57 (1.28-9.91)* 1.45 (0.41-5.12) .030 0.41 (0.10-1.61) Peer other drug use (1-5) 1.85 (1.59-2.15)*** 1.83 (1.42-2.35)*** .000 0.99 (0.78-1.26) More than 20 sex partners 0.46 (0.21-1.00)* 1.43 (0.53-3.87) .030 3.15 (1.18-8.37)* Cannabis use: none 1.0 1.0 .000 1.0 less than weekly 3.13 (1.40-6.97)** 1.65 (0.32-8.56) 0.53 (0.09-2.96) weekly 6.43 (2.83-14.6)*** 2.36 (0.45-12.5) 0.37 (0.06-2.07) daily 7.21 (3.28-15.9)*** 8.22 (1.89-35.9)** 1.14 (0.24-5.40) Opioid use: none 1.0 1.0 .096 1.0 less than weekly 1.96 (0.87-4.41)~ 3.67 (1.34-10.1)* 1.88 (0.80-4.38)~ weekly 2.16 (0.64-7.30) 1.60 (0.36-7.17) 0.74 (0.22-2.53) Injecting drug use 4.13 (1.21-14.1)* 15.2 (3.92-58.9)*** .000 3.68 (1.45-9.30)** Drug-related police 1.76 (1.07-2.89)* 3.28 (1.56-6.92)** .005 1.87 (0.93-3.77)~ problems Prior violent offences 0.67 (0.43-1.04)~ 0.32 (0.15-0.67)** .007 0.48 (0.23-0.97)* *p<.05 **p<.01 ***p<.001 ~p<.15; AOR adjusted odds ratio. CI confidence interval; N=698; Nagelkerke r2 .547; Classification accuracy: 77.4% (35.9% improvement over chance)

141 5.3.6 Opioids model

Opioids presented a very different multivariate profile to the other drugs. The model explained much less variance than for the other drugs (25%). Half of the 12 factors in the final model were significant, although others improved model fit. Opioid use was much less prevalent than other drugs (10% less than weekly; 5% weekly). This low prevalence and uneven subgroup distribution means that associations above conventional significance levels may still indicate a meaningful relationship. Only one factor showed a clear dose-relationship with opioid use frequency: living in Sydney was a strong predictor of use, but especially of weekly use. Adjusted odds of weekly opioid use for females were high but just outside the alpha level (AOR 2.5, 95%CI 0.96-6.3).

Amphetamine use frequency and needing help with drug problems were strong predictors of weekly opioid use but equally strong predictors of less than weekly use. Drug-intoxication during the last offence showed a similar but non-significant relationship with opioid use. Offending to support drug use was a much more precise predictor of weekly use (AOR 2.9), being unrelated to less than weekly use (AOR 1.1), however this result was not significant (p=0.1). By contrast, a number of correlates predicted less than weekly opioid use only, i.e. showed a ‘U-shaped’ relationship with opioid use. This included emotional abuse, the sole bivariate correlate of familial dysfunction that was retained in the final model; this predicted less than weekly use over weekly use. A similar effect was found for academic achievement (higher achievement predicted less than weekly but not weekly use), prior psychiatric care, and binge drinking frequency.

Bivariate results for variables in this model are presented in Appendix Table G.

142 Table 5.5 Adjusted odds ratios for correlates of opioid use

Weekly vs. less Reference category: no use Less than weekly Weekly Model than weekly AOR (95%CI) AOR (95%CI) χ2 AOR (95%CI) Female 1.23 (0.54-2.82) 2.45 (0.96-6.31) .172 1.99 (0.63-6.26) Age (scale) 1.00 (0.76-1.32) 1.16 (0.81-1.65) .716 1.16 (0.76-1.77) Regional or rural residence 0.66 (0.31-1.42) 0.26 (0.07-0.92)* .075 0.40 (0.10-1.63) Emotional abuse (1-5) 1.32 (1.00-1.74)* 0.71 (0.47-1.08) .022 0.54 (0.34-0.86)** Student or graduate 0.42 (0.17-1.05) 0.52 (0.14-1.95) .132 1.24 (0.27-5.79) Academic achievement (scale) 1.06 (1.03-1.08)** 1.00 (0.96-1.03) .000 0.94 (0.91-0.98)** Prior psychiatric treatment 2.56 (1.37-4.79)** 1.41 (0.60-3.34) .013 0.55 (0.21-1.45) Binge drinking (1-4)^ 1.95 (1.37-2.77)** 1.13 (0.73-1.75) .001 0.58 (0.35-0.97)* Amphetamine use (0-2)^ 2.04 (1.33-3.13)** 2.04 (1.20-3.47)** .000 1.00 (0.54-1.87) Need help with drug problem 3.08 (1.46-6.50)** 3.15 (1.31-7.55)* .002 1.02 (0.37-2.82) Offending to support drug use 1.08 (0.54-2.15) 2.94 (1.07-8.04)* .110 2.73 (0.84-8.85) Drug-affected at last offence 1.87 (0.96-3.67) 2.03 (0.88-4.67) .066 1.08 (0.40-2.95) *p<.05 **p<.01 ***p<.001 ~p<.15; AOR adjusted odds ratio. CI confidence interval; N=718; Nagelkerke r2 .21; Classification accuracy: 88% (1% improvement over chance); ^ Modelled as ordinal terms due to zero cell counts for some combinations of drug use

143 5.4 Discussion

This chapter described problems relating to drug use by the sample and provided the first detailed assessment of correlates and independent covariates of drug use by Australian youths on community supervision orders. The most frequent users tended to report the highest levels of criminal involvement, peer drug use, health and other psychosocial problems. Risk profiles varied by drug. Unlike in the models of prior offending (Chapter Four), the strongest independent correlates were not criminal history factors. Few of these factors were independently correlated with more than one drug type.

5.4.1 Drug specific findings

Risk factors were not exclusive to the most frequent drug users. For example, peer drug use was higher amongst opioid users, but did not differ by level of use; binge drinking was most frequent amongst less than weekly amphetamine users. However, in the majority of cases, higher levels of use were associated with higher levels of risk. Further, the multivariate models showed that heavy use was associated with a) the most risks, and b) in some cases, significantly higher levels of risk than the next most frequent users. This has three implications. First, risks were more heavily concentrated amongst the heaviest users. Second, the majority of the sample who were not heavy users also experienced risk relating to their drug use (at least in the multivariate model). Third, users appear to be qualitatively different to non-users; some risks were largely unobserved amongst non-users. This supports the decision to use multinomial, rather than ordinal regression.

Risks tended to increase additively rather than exponentially with use in this sample, suggesting more of a dimensional than categorical relationship to frequency of use. It may be that whereas in the general population, the burden of harm is highly concentrated amongst the heaviest users, drug use incurs less impairment for young people from highly disadvantaged backgrounds with pre-existing trauma and low premorbid functioning. Assistance should be targeted to the most frequent users, but available to all drug-using young offenders.

144 Use was positively and independently associated with offence-related problems. Levels of drug-related offending and police contact were highest amongst the most frequent users. This is a familiar finding that has previously been used to illustrate the drug- crime nexus e.g. Prinz and Dumas (2004). The models in this chapter show that for most drugs, these associations remained after adjustment for other predictors. Whether drug use precedes or causes these problems cannot be ascertained with the available data, but the relationship does not appear to be spurious. At the same time, drug use was not necessarily accompanied by offence-related problems. A substantial proportion of users, including the most frequent users, did not report such problems. Thus, as established in the Pittsburgh Study (Elliott, Huizinga, & Menard, 1989), drug use is not synonymous with offending.

Of course, heavy drug users may experience risks but not perceive or report them as such. For example, drug use by definition brings individuals into more regular criminal contact. The following chapter’s recidivism models offer a chance to assess the link between drug use and offending data collected administratively, and therefore not subject to the limitations of self-report. Further, users reported other experiences that are likely to indirectly impact on recidivism. For example, users reported consistently higher rates of victimisation than non-users, which has been linked with violent offending (Barrett, Mills, & Teesson, 2011). These risks are likely to continue whilst trauma remains untreated.

One of the most important findings, again consistent with the literature, is the strong and independent association between peers’ and participants’ drug use. A considerable proportion of offences were committed with peers, often facilitated by drug use (the ‘group psychopharmacological effect’, White, 2004). Close attention should be given to peer affiliation and the potential for peer drug use to promote or sustain participants’ use with detrimental criminal justice outcomes. Conditions of court orders and preventative/treatment interventions designed to restrict young person’s use may be undermined by a return to a social environment that condones and supports drug use.

145 There was variation in the demographic profiles associated with drug type, but in most cases demography did not make an independent contribution to frequency of use. Each drug was significantly associated with different demographic predictors, suggesting that drug-specific intervention/prevention efforts will be more efficient and effective if tailored to the demographic profile rather than across the young offender population. Alternatively, further investigation into demographically unusual patterns may illuminate underlying risks or protective factors, for example weekly opioid users outside the Sydney region.

All drugs were independently associated with use of at least one other drug. Even adjusting for the use of other significant covariates, at least one drug emerged as a significant predictor of use in each model. This supports the early descriptive findings in this study: poly-drug use was more common amongst the more frequent users; different patterns of use presented somewhat overlapping risk profiles, particularly for the more frequent users; and, therefore, they shared similar risk factors.

Drug use was independently associated with non-criminogenic health problems. Drug users experienced higher levels of health-related harms, which may have no causal link to offending, but nonetheless present an additional challenge and opportunity to criminal justice service providers. For example, use of the most prevalent drugs (cannabis and alcohol) was independently associated with cigarette smoking – particularly amongst the most frequent users of these drugs. Smoking jeopardises adolescents’ physical development and longer-term health. Juvenile justice contact represents a prime opportunity to deliver preventative health messages and interventions (Indig & Haysom, 2012), particularly during periods of incarceration (Richmond et al., 2012).

A large proportion of young offenders were at high risk of progression to heavy use. Whilst only a small proportion of young offenders used stimulants, opioids, or benzodiazapines on a weekly basis, nearly half had done so in the past year – usually on at least a few occasions.

146 Amphetamine use and risky sexual behaviour were strongly associated. The association with many sex partners appears to be well-established (Steinberg et al., 2011), although the strength of this result in the final model is interesting. Iritani, Hallfors, and Bauer (2007) found amongst young US adults that despite the strong, expected associations with risky sexual behaviours, adjusted odds were much lower, and close to one for young males. These results suggest the relationship was largely accounted for by other drug use and personality factors, and also, that amphetamine use is an independent risk factor for risky sexual behaviour amongst young women only.

The high prevalence of drug use and distress suggests that the unmet need for treatment is significant, especially among females. Intervention should focus on offenders in the greatest distress and at the highest risk of harm because the greatest treatment effects are observed for this group (Andrews et al., 1990). However, greater population-level effects may be achieved by addressing harms incurred by offenders with less severe but still clinically significant problems, and preventing progression to more serious dysfunction among this group.

The binge drinking, cannabis and amphetamine models each accounted for around half of the variance in drug use frequency (43%, 55% and 56% respectively) – at least twice that reported by Nation and Heflinger (2006). This reflects the more extensive set of predictors used in the current models, including other drug use which was a strong predictor of all outcomes, and which Nation and Heflinger (2006) did not account for. Nonetheless, half of the variance was not explained, and far more for opioid use.

5.4.2 Limitations and future research

Analyses in this chapter utilised multinomial logistic regression (MLR). The rationale (see Section 5.2) was that levels of risk may vary across levels of use, and may differ qualitatively for users and non-users. While it was possible to visually observe trends in odds with increasing frequency of use, MLR cannot test the significance of that trend (Bender & Grouven, 1997). Significant trends may have gone undetected for variables without significant categorical differences, however this is most likely where the effect

147 is small. Ordinal logistic regression can detect monotone trends but was inappropriate because it assumes that drug use is an ordered outcome and that odds are proportional across outcome categories. Future research could consider the rarely used cumulative logistic regression, which allows for incremental but differing odds to be reported (see Patton and colleagues, in Bonomo et al., 2001).

While the models accounted for a relatively large proportion of drug use frequency (Nation & Heflinger, 2006), prediction was far from perfect. The literature review suggests that such variance might have been accounted for by some variables that were not included in the dataset: quantity of illicit drug use (Swift, Hall, Didcott, & Reilly, 1998), attitudes regarding drug use (Spooner, 1999), and genetic liability (Meyers & Dick, 2010). Similarly, all models contained at least 80% of cases and were sample-representative, but several variables exceeding 5% of missing cases were excluded. Notable among these was parental drug use, which is implicated in genetic liability, drug availability and learned attitudes to drug use. Many participants had not been raised by both parents, and were unsure about non-contact parents’ drug use. Future research would ideally, as in clinical assessment, seek such information from parents or other informants (e.g. Erskine et al., 2013). To a lesser extent, unmeasured social and structural factors may have influenced drug use by these youths, including drug pricing, availability, laws and their implementation (Weatherburn, Jones, Freeman, & Makkai, 2003). These factors may be useful in responding to adolescent problem drug use (PDU), for example, access to drug treatment is inhibited by low income as well as stigma surrounding the provision of treatment (Spooner & Hetherington, 2005).

It is possible to describe frequency of drug use as an ordinal term (i.e. ordered categories), and ordinal regression can provide a more parsimonious model for ordered outcomes than MLR. Ordinal regression quantifies how odds differ with each one-unit increase in the outcome (e.g. as frequency increases from monthly to weekly, or weekly to daily), assuming that each one-unit increase in a predictor variable brings the same change in odds for all proximate outcome categories. This ‘proportional odds assumption’ is suitable for quantitatively different outcome categories (e.g. frequency

148 of use) but not for those qualitatively different categories (e.g. use vs. no use). Odds may decrease and then increase in a U-shaped distribution. When models in this chapter were tested in ordinal regression, many odds ratios (ORs) were found to violate the proportional odds assumption. This does not rule out all ordinal models, but the alternative (Ordinal Generalised Linear Model) involves an unofficial STATA command (Williams, 2010) and has no precedent in the offending or drug use literature, and so was not employed here.

This study has contributed novel information about the correlates of frequent use of specific drugs among young Australian offenders, but the correlates of specific substance use disorders (SUDs) remain unexplored. Finally, as with Chapter Four, the models are cross-sectional, so temporal and causal relationships could not be assessed.

5.4.3 Conclusion

This chapter has assessed and described the correlates of binge drinking, cannabis, amphetamine and opioid use frequency by the sample. Diverse drug use and distinct risk profiles associated with this use were revealed. Many correlates of drug use are not directly criminogenic; health and welfare implications are considered above. Of particular relevance to the remaining empirical chapters, and this thesis overall, drug- crime associations were partial and inconsistent. Generally, however, there was a strong and positive association between frequency of use and drug-related offending (such as intoxicated offending). The substantial differences between heavy and less heavy users, and between users of different drugs, suggests that any attempt to model drug use in this sample should retain disaggregated and nuanced coding. Finally, given the high levels of poly-drug use and independent correlations between patterns of use, the models in Chapters Six to Eight will assess the contributions of each pattern of use on recidivism, adjusting for other patterns of use.

149 6 Patterns and correlates of recidivism over two years after baseline

Whereas Chapter Four found drug use was weakly linked with conviction history, Chapter Five revealed that drug use was strongly linked with self-reported offending. Both prior convictions and drug use showed substantial demographic variation and were linked to a raft of health and welfare issues. There was also substantial variation in risk profiles by drug and offence type. Thus, consistent with the conclusions of Chapters One and Two, drug use and criminal involvement share a complex and non- exclusive relationship. The importance of disaggregating measures of both phenomena, and considering a wide range of co-occurring risk factors is clear. The thesis now turns to examining the sample’s subsequent criminal involvement (recidivism) and the prospective effects of drug use and other factors on recidivism.

Chapter Six assesses the prevalence, correlates, and predictors of recidivism, defined in this chapter as any conviction (the event), within two years (the observation period) of the baseline health survey (the start point). Recidivism is the primary measure of success in the criminal justice system’s efforts to rehabilitate young offenders and deter future offending. The simple, fixed-time operationalisation used in this chapter is closely comparable with that used in most existing studies, and Australian advice on measuring juvenile recidivism (Richards, 2011c). First, the prevalence of recidivism is measured to reveal the scale of the problem in the sample. Next, the characteristics of general recidivists and the characteristics of specific (violent, theft and robbery) recidivists are considered in detail. Finally, multivariate recidivism models assess the independent impacts of drug use (including frequency of binge drinking, cannabis, amphetamine, and opioid use) and other risk factors on recidivism.

150 6.1 Prevalence of two year recidivism

Measurement issues, jurisdictional differences in policing and sentencing, cohort effects (Smith & Jones, 2008) and age differences in sampling partly account for varying recidivism rates between samples. Nonetheless, some broad inferences can be drawn from studies of recidivism over two years or a similar duration (see Table 6.1). Most juveniles who are convicted return to the CJS. Recidivism rates are generally higher among supervised offenders and detainees. Most jurisdictions attempt to divert youth from the CJS, so those sentenced to supervision tend to have more extensive offence histories. Prevalence is higher in studies that track offenders into the adult CJS.

Table 6.1 Prevalence of recidivism among Australian young offenders

Study Recidivism Years Sample Event Notes % (Hua, Baker, & Poynton, 2006) 60 3 Court Appearance 1st offence (Weatherburn et al., 2009) 52 <2.8 Mixed Conviction Survival analysis (Denning & Homel, 2008) 68 ~3 Supervised Conviction Survival analysis (Victorian Department of 61 2 Supervised Conviction 49% if 1st Human Services, 2001) offence (Coumarelos, 1994) 67 3 Sentenced Conviction (Cain, 1996) 50 ~2 Detainee Conviction Juvenile data only (Putninš, 2005) 91 2 Detainee Detention ~approximately

6.2 Aims 1. Describe the prevalence of convictions within two years of the baseline survey, for general recidivism, and for specific recidivism (violence, theft, and robbery). 2. Assess the correlates of total and specific recidivism. 3. Model the predictors of recidivism, focusing on the impact of drug use independent of demographics, criminal history and other covariates.

151 6.3 Method

In this chapter binary indicators of recidivism were used, based on those created by the Bureau of Crime Statistics and Research (BOCSAR, see Chapter Five): general, violent, theft, or robbery conviction within two years of baseline. Under this definition, offences committed prior to baseline but convicted within two years (i.e. ‘pseudo- recidivism’) were included. Offences committed within two years of baseline but convicted later than this time were not included. The discussion argues that for the binary outcomes of this chapter, pseudo-recidivism does not present a major threat to validity. As noted in Chapter Four, robbery is a far more specific offence category than violence and theft.

Binary logistic regression (LR) was used to develop the multivariate models in Chapter Six. This is a robust and well-tested method that is easy to interpret (Britt & Weisburd, 2010) and common in the juvenile and adult recidivism literature (Benda, Toombs, & Whiteside, 1996; Hser, Evans, Teruya, Huang, & Anglin, 2007). Although more sophisticated methods offer greater insight into recidivism (see Chapters Eight and Nine), LR offers maximum comparability with prior studies and remains an attractive option when seeking to inform government policy (as evidenced by recent population studies of offenders in New South Wales (Hsu, Caputi, & Byrne, 2009; Smith & Jones, 2008)). In this chapter, LR results are expressed as odds ratios (ORs), which describe the strength of the association between a given predictor and recidivism.

Multivariate models were evaluated in part using Nagelkerke’s/Cox-Snell’s pseudo r- squared. Classification accuracy was also assessed, with a 25% improvement over the chance model considered a significant improvement (per Schwab, 2003).

152 6.4 Results 6.4.1 Prevalence of recidivism

Three quarters (73%) of participants were convicted within two years of baseline. Theft (42%) and violence (34%) were much more common than robbery (11%), and many youths recorded multiple conviction types (see Figure 6.1). For example, 52% of theft recidivists were also violent recidivists and 80% of violent recidivists also committed theft recidivism, but only 37% of robbery recidivists were violent or theft recidivists. Two in three recidivists (half of the sample) were convicted for at least two offence categories (violent, theft, robbery, other).

% 15 Other

12 Robbery Violent & theft 19 Theft Violent Theft 3 Violence 17 Violent and theft 1 Violent and robbery (V+R) VT+R VTR 2 Theft and robbery (T+R) V+R T+R 3 Violent, theft and robbery (VT+R) Other 73 TOTAL Robbery er None

Figure 6.1 Relationship between conviction patterns in the two years after baseline

The correlates of recidivism were numerous and often intercorrelated (Chapter One), so bivariate results are presented only for variables retained in the final models. Descriptive and bivariate statistics for these variables are presented in Table 6.2 (continuous variables) and Table 6.3 (categorical variables).

153 6.4.2 Bivariate correlates of two year recidivism

Prevalence of all recidivism outcomes was lower for females, significantly so for general recidivism (65% vs. 74% males), and with non-significant trends (p<.15) for theft and robbery but not for violence (p=.5). Indigenous and regional youth were nearly twice as likely to be reconvicted, especially for theft; culturally and linguistically diverse (CALD) youth had the opposite profile and were more likely to recidivate with robbery.

Age was negatively related to general and specific recidivism other than robbery. This was most pronounced for theft recidivists, who were five months younger on average than other participants (16.8 vs. 17.2, p<.01). Adult participants (those aged 18 plus) were half as likely to accrue theft convictions as youths under 16 (27% vs. 51%). While the prevalence of most recidivism outcomes peaked among youths aged below 15 (n=61), the age/recidivism relationship was not precisely linear; prevalence tended to be slightly higher among youths aged 15 than those aged 16.

Recidivists on the whole had more extensive self-reported and official criminal histories. They were more likely to have been convicted before age 14, have theft convictions, more prior convictions, and to have reported multiple detention episodes, but less likely to have had violent convictions. Associations with specific recidivism were inconsistent. Prior violence was associated with lower odds of theft recidivism, and higher odds of robbery recidivism; prior robbery was associated only with robbery recidivism.

General recidivism typically increased with more frequent drug use, and was much higher in daily cannabis and weekly opioid users (82-84%) than other youths. Amphetamine use was unrelated to any form of recidivism (and was therefore excluded from the table). Violent recidivism appeared more common among those who used drugs other than opioids; opioid use was by far the most strongly associated with theft. Robbery recidivists differed somewhat in their heavy amphetamine and occasional cannabis use. General recidivism was more prevalent amongst participants reporting heavy drug use (71%) or high psychological distress (76%) and highest

154 amongst those reporting both (80%), compared to those reporting neither (60%). Recidivists also tended to have initiated drug use earlier and to have reported problems with use. Recidivists were also more likely to provide drug-related explanations for offending: prior economic-compulsive offending was linked with most types of recidivism, and psychopharmacological offending with overall and violent recidivism.

Child maltreatment and parenting variables showed few substantive associations with two year recidivism, with the exception of parental imprisonment (general/theft recidivism). Unstable housing was correlated with theft but not with other outcomes (OR 1.6, 95%CI 1.0-2.4, p<.05). Conduct Disorder (CD) and fighting were associated with all outcomes, peer criminality was positively associated with most outcomes, and several other variables were associated with at least one outcome. Factors that were generally associated with lower prevalence of recidivism included being a student/graduate, higher verbal IQ (VIQ) and greater academic performance. Some factors were associated with only one outcome, for example, past year assault by intoxicated persons was more common amongst violent recidivists only (29% vs. 21%).

Table 6.2 Descriptive and bivariate statistics for continuous predictors of recidivism

n General Violent Theft Robbery Mean (SD) Age (12-21) 800 17.0 (1.3)~ 16.9(1.3)* 16.8(1.3)** 16.9(1.3) VIQ 781 77.5(12)*** 76.9(12)** 75.2(12)*** 77.7(9.5) Prior convictions (1-64) 793 7.1(7.6)*** 7.6(8.3)*** 8.2(8.3)*** 6.2(6.0) Past year convictions (1-18) 793 2.3(2.8) 2.3(2.8)* 2.4(2.9)*** 2.2(2.7) *p<.05, **p<.01, ***p<.001, ~p<.15. SD: standard deviation

155 Table 6.3 Descriptive and bivariate statistics for categorical predictors of recidivism

n General Violent Theft Robbery Female 115 65.2* 30.4 33.9~ 7~ Ethnicity: ESB (reference) 415 75.2 34.7 43.4 8.7 Indigenous 151 85.4*** 40.4* 58.9*** 10.6 ESB/CALD 118 58.5*** 28.8* 29.7*** 13.6 CALD 109 63.3*** 23.9* 22*** 15.6 Student/graduate 166 55.4*** 24.1** 29.5*** 9.6 Any peers offend 602 75.7*** 36.1** 43.9* 10.9 CRIMINAL HISTORY Convicted before 14 127 85*** 39.9* 61.8*** 8.1 Multiple detention episodes 567 79.9*** 37.7*** 47.8*** 12.2* Prior violent conviction 390 70* 32 38** 12* Prior robbery conviction 264 71 33 32 17*** Prior theft conviction 451 81*** 38** 54*** 12 Conduct disorder: none (ref) 323 63.8*** 27.5** 32.2*** 7.7~ Low 137 70.8*** 35.0** 42.3*** 13.1~ Moderate 170 82.9*** 34.7** 52.4*** 11.2~ Severe 146 83.6*** 43.2** 48.6*** 14.4~ DRUG USE Binge drinking: none (ref) 142 64.1~ 26.8* 42.3 9.2 Less than weekly 399 73.7~ 31.3* 42.7 9.3 Weekly (once or twice) 169 77.1~ 39.8* 38.6 14.5 More than twice weekly 75 76.0~ 41.3* 40.0 13.3 Cannabis use: none (ref) 181 61.9 28.7 34.3 6.6 Less than weekly 193 69.4*** 28.5** 36.8* 15.5* Weekly 143 75.5*** 28.7** 43.4* 10.5* Daily 273 81.7*** 42.5** 48.4* 10.3* Opioid use: none (ref) 669 72.8 32.6 40.1 10.6 Less than weekly 73 68.5 38.4 42.5** 11 Weekly 38 84.2** 39.5 65.8** 13.2 Needs help with drug use 81 85.2** 46.9** 58** 12.3 TOTAL 800 73.0 33.4 41.4 10.7 *p<.05, **p<.01, ***p<.001, ~p<.15

156 6.4.3 General recidivism model

The general recidivism model is presented in Table 6.4. Cannabis use was rendered non-significant as a predictor of general recidivism after adjustment for other predictors. Removing cannabis from the model did not alter model fit significantly, confirming the irrelevance of cannabis to general recidivism risk.

Only ten of the many bivariate associations remained significant in this model; age and ethnicity were not amongst these. Females had nearly half of males’ odds of recidivism, but younger females were more likely to recidivate than males (see female specific model in Appendix B: analyses of demographic variation). Current/completed schooling also halved the odds of recidivism. Exploratory analyses indicated that this protective effect was limited to males: schooling increased the risk of recidivism for females, but the term revealing this interaction rendered the model unstable and thus was excluded; (also see Appendix B: analyses of demographic variation).

Of the criminal history correlates of recidivism, only prior convictions and multiple detention episodes were independent predictors, the latter more than doubling the odds of recidivism. Peer criminality weakly predicted recidivism, while fighting frequency, and moderate or severe CD were stronger predictors. Higher VIQ remained strongly protective, with those youths scoring above average four times less likely to recidivate than below average participants. Participants with limiting disabilities were also less likely to recidivate.

The final model accounted for a modest proportion (21%) of variance in recidivism. Classification accuracy showed a modest (10%) improvement over the chance model. The Hosmer-Lemeshow test was sound (p>.05) and multi-collinearity was not an issue (tolerance and VIF close to 1). Issues with missing cases and outliers (discussed below) were not significant, which indicates that this model provides a reliable assessment of the independent correlates of recidivism.

157 Table 6.4 Unadjusted and adjusted odds ratios for predictors of general recidivism

OR (95%CI) AOR (95%CI) Females 0.65 (0.43-0.99)* 0.58 (0.35-0.96)* Age (scale) 0.89 (0.79-1.00)~ 0.93 (0.77-1.11) Ethnicity: ESB (reference) 1.0*** Indigenous 1.94 (1.17-3.2)* 1.30 (0.73-2.33) ESB/CALD 0.46 (0.30-0.71)*** 0.54 (0.31-0.94)** CALD 0.57 (0.36-0.89)* 0.71 (0.41-1.23) Limiting disability 0.47 (0.31-0.74)** 0.39 (0.23-0.66)*** Student/graduate 0.36 (0.25-0.52)*** 0.52 (0.33-0.84)** Any peers offend 1.79 (1.25-2.56)** 1.54 (0.99-2.40)* Fighting frequency (1-5) 1.35 (1.20-1.52)*** 1.20 (1.03-1.39)* Multiple detention episodes 3.54 (2.51-4.98)*** 2.42 (1.61-3.63)*** Prior convictions (ln) 1.96 (1.59-2.41)*** 1.40 (1.00-1.96)* Past year convictions (ln) 1.70 (1.35-2.14)*** 1.27 (0.92-1.76) Conduct disorder: none (reference) 1.0*** 1.00 low 1.38 (0.89-2.12)~ 1.29 (0.77-2.18) moderate 2.76 (1.74-4.37)*** 2.41 (1.37-4.22)** severe 2.89 (1.76-4.73)*** 1.84 (1.03-3.30)* VIQ: <70 (reference) 1.0*** 1.0*** 70-84 0.98 (0.63-1.52) 1.04 (0.62-1.74) 85-99 0.61 (0.38-0.98)* 0.66 (0.38-1.15)~ 100+ 0.17 (0.09-0.33)*** 0.23 (0.11-0.50)*** *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; VIQ verbal IQ; ln log transformed; N=737; pseudo-r2 .21; Hosmer-Lemeshow test p=.28. Classification accuracy: 78.5%.

158 6.4.4 Violent recidivism model

Cannabis use was the only pattern of drug use that predicted violent recidivism (Table 6.5) but there were no significant differences between users and non-users. Rather, daily cannabis users’ odds of violent recidivism were significantly higher than weekly (AOR 2.0, 95%CI 1.2-3.2, p<.01), and less than weekly users (AOR 1.6, p<.05). Further, including cannabis increased pseudo r2 by .01, a significant but inconsequential change. Fighting, multiple detention episodes and lower VIQ were strong predictors of violent recidivism; youths with below average VIQ (less than 85) were twice as likely to be violent recidivists as those with average or higher VIQ. Age, schooling and victimisation showed non-significant, negative trends (p<.15), and ethnicity, prior convictions, theft and CD were removed from the final model (p>.15). The model did a poor job of predicting violent offending (pseudo r2 .12).

Table 6.5 Unadjusted and adjusted odds ratios for predictors of violent recidivism

OR (95%CI) AOR (95%CI) Females 0.85 (0.56-1.31) 0.98 (0.61-1.58) Age (scale) 0.89 (0.80-1.00)* 0.89 (0.77-1.01) Student or graduate 0.57 (0.38-0.84)** 0.66 (0.42-1.04) Physical victimisation 1.53 (1.08-2.16)* 1.36 (0.91-2.03) Fighting frequency (1-5) 1.68 (1.12-2.53)* 1.23 (1.08-1.39)** Custody more than once 2.23 (1.54-3.24)*** 1.57 (1.05-2.34)* Verbal IQ (ref: VIQ<70) 1.0** 1.0* VIQ 70-84 0.84 (0.58-1.22) 0.73 (0.48-1.10) VIQ 85-99 0.66 (0.43-1.01) 0.60 (0.37-0.98)* VIQ 100+ 0.34 (0.16-0.69)** 0.36 (0.16-0.78)* Binge drink: none (reference) 1.0* 1.0^ (p=.197) monthly 1.25 (0.81-1.91) 1.14 (0.70-1.86) weekly 1.81 (1.11-2.93)* 1.69 (0.97-2.93) more than twice weekly 1.93 (1.07-3.48)* 1.15 (0.58-2.31) Cannabis use: none (reference) 1.0** 1.0* less than weekly 0.99 (0.63-1.55) 0.84 (0.51-1.39) weekly 1.00 (0.61-1.62) 0.70 (0.40-1.20) daily 1.83 (1.23-2.74)** 1.41 (0.89-2.23) *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; N=753; pseudo r2 .117; Hosmer-Lemeshow p.75. ^Significantly improved model fit.

159 6.4.5 Theft recidivism model

The model for theft (Table 6.6) accounted for 34% of the variance in recidivism. Opioid use was a strong predictor; weekly opioid use increased odds of theft recidivism five- fold compared with non-use and three-fold compared with less than weekly use. These parameters were not affected by injecting drug use, which was not significant, and thus removed from the final model. Prior theft convictions and multiple detention episodes were the strongest predictors of theft recidivism. There were also strong, negative relationships between age, VIQ, CALD ethnicity and theft recidivism. Weaker predictors were out of home care and having delinquent peers.

Table 6.6 Unadjusted and adjusted odds ratios for predictors of theft recidivism

OR (95%CI) AOR (95%CI) Female 0.69 (0.46-1.05)~ 0.63 (0.38-1.05)~ Age (scale) 0.78 (0.70-0.87)*** 0.74 (0.64-0.86)*** Ethnicity: ESB (reference) 1.00** 1.00* Indigenous 1.87 (1.28-2.73)** 1.29 (0.82-2.04) ESB/CALD 0.55 (0.35-0.85)** 0.84 (0.49-1.45) CALD 0.37 (0.23-0.60)*** 0.46 (0.26-0.81)** History of out of home care 1.81 (1.29-2.52)** 1.56 (1.03-2.36)* Prior theft conviction 3.71 (2.72-5.05)*** 2.57 (1.63-4.04)*** Multiple detention episodes 3.10 (2.15-4.46)*** 2.15 (1.40-3.30)*** Any peers offend 1.54 (1.08-2.18)* 1.47 (0.96-2.24)~ Conduct disorder: none (reference) 1.00* 1.00~ low 1.54 (1.02-2.33)* 1.24 (0.76-2.03) moderate 2.31 (1.58-3.39)*** 1.75 (1.10-2.79)* severe 1.99 (1.36-2.97)** 1.33 (0.82-2.17) VIQ: <70 (reference) 1.00*** 1.00*** 70-84 0.73 (0.51-1.06)~ 0.75 (0.48-1.16) 85-99 0.37 (0.24-0.56)*** 0.32 (0.19-0.53)*** 100+ 0.11 (0.05-0.25)*** 0.15 (0.06-0.37)*** Opioid use: none (reference) 1.0** 1.00*** Less than weekly 1.10 (0.68-1.80) 1.26 (0.70-2.27) Weekly 2.88 (1.45-5.72)** 4.80 (2.02-11.4)*** *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; OOHC out of home care; VIQ verbal IQ; N=717; pseudo r2 .336

160 6.4.6 Robbery recidivism model

Robbery results contrasted sharply with the other models. Less than weekly cannabis use emerged as a possible predictor, although only in comparison to non-users. Very low VIQ was protective, compared to those with slightly higher VIQ, while prior robbery and multiple detention episodes were predictive. Explanatory power for this model was low (pseudo-r2 .09), however, and significant associations were less likely given the low prevalence of this outcome (11%).

Table 6.7 Unadjusted and adjusted odds ratios for predictors of robbery recidivism

OR (95%CI) AOR (95%CI) Females 0.58 (0.27-1.24) 0.64 (0.29-1.41) Age (scale) 0.94 (0.79-1.12) 0.92 (0.77-1.11) Previous robbery 2.51 (1.59-3.96)*** 2.63 (1.63-4.25)*** Multiple detention episodes 2.06 (1.11-3.81)* 1.91 (1.00-3.63)* VIQ: <70 (reference) 1.0* 1.0* 70-84 2.33 (1.21-4.50)* 2.41 (1.17-4.95)* 85-99 1.45 (0.68-3.08) 1.50 (0.67-3.39) 100+ 0.46 (0.10-2.12) 0.44 (0.09-2.14) Cannabis use: none (reference) 1.0* 1.0~ less than weekly 2.59 (1.28-5.24)** 2.44 (1.17-5.05)* weekly 1.65 (0.75-3.65) 1.39 (0.61-3.18) daily 1.61 (0.80-3.25) 1.60 (0.77-3.33) *p<.05 **p<.01 ***p<.001 ~p<.15; OR odds ratio; AOR adjusted odds ratio; CI confidence interval; VIQ verbal IQ; N=760; pseudo r2 .09 Hosmer-Lemeshow p.36

6.4.7 Subsample models

The full sample models described above were respecified in the female, Indigenous, younger (under 17) and older subsamples. Models generalised poorly to these subsamples; for example, none of the full sample predictors of general recidivism predicted general recidivism by Indigenous youths. Unlike the full sample models, daily cannabis use did not predict violent recidivism in older participants, nor did weekly opioid use predict theft recidivism among females. Detailed results are presented in Appendix B: analyses of demographic variation.

161 6.5 Discussion

Recidivism is the norm for young offenders. Even with a stringent definition of recidivism, three in four (73%) participants recidivated within two years of the baseline survey. This is at the high end of rates reported by similar studies (Table 6.1), and well above average for court-recorded offenders (68% within eight years, Chen et al., 2005). Recidivism was not characterised by any one specific offence category. Roughly half of all recidivists (47%) were reconvicted for a violent offence and slightly more (58%) for a theft offence. Chapter Four showed that similar proportions of the whole sample had prior violent and theft convictions (Chapter Four). Only one in seven recidivists (16%) was reconvicted for robbery, although nearly one third of the sample had a prior robbery conviction. These results are consistent with Weatherburn et al. (2007) who observed that young offenders’ index offence and their subsequent offence are largely concordant, with the exception of robbery. Given its low prevalence, subsequent chapters will combine robbery with other violent recidivism.

Recidivists also tended to be generalists, that is, they accrued convictions for multiple offence types: 68% had two or more of violent, robbery, theft or other convictions. While this is consistent with prior conviction patterns, (Chapter Four showed that most offenders had diverse conviction histories), and continuity between prior and subsequent offending varied by offence type. Two thirds of recidivists with prior theft convictions also had subsequent theft convictions, but only half with prior violent convictions were violent recidivists (46%); the figure was lower still for robbery (24%). Prospective specialisation was even less common. One third (32%) of recidivists with a prior theft conviction had subsequent convictions only for theft, while specialisation was less common for violence (15%) and robbery (8%). The relative rarity of specialisation is consistent with recent work (Sullivan, McGloin, Ray, & Caudy, 2009) and suggests that the characteristics of specific recidivism types will not be mutually exclusive. Different offences require different causal explanations, particularly given the variation in their correlates, including drug use. Chapter Four showed that this was so for prior offending, and the current chapter suggests that variation was even greater in prospective models.

162 6.5.1 Bivariate correlates of recidivism

Many variables were associated at a bivariate level with recidivism, consistent with previous research (Table 6.3). Recidivism was generally lowest amongst non-drug using offenders, and highest amongst heavy users, although this was offence-specific. Significant dose relationships were found for binge drinking and violence, and cannabis use and general recidivism; other drug-crime relationships were clearly non-linear. Theft risk was concentrated amongst weekly opioid users, and robbery was most prevalent amongst occasional cannabis users. These strong but varied associations contrast an earlier finding that prior illicit drug use did not predict recidivism (Weatherburn et al., 2007). Such a measure offers little value to predictive models because the variance is so low (few young offenders have not used drugs). Those authors argue for more detailed measurement of both risk factors and outcomes, as provided by this thesis.

Across offender subgroups, recidivism prevalence varied. No one factor comprehensively accounted for recidivism risk, although the prevalence of specific recidivism varied substantially by subgroup: violence ranged from 24% for students and CALD participants to 47% for those needing help with drug problems, and theft ranged from 22% for CALD to 66% for weekly opioid users. The magnitude of associations differed greatly: recidivism was 50% more likely for males than females, but 350% more likely for participants with multiple prior incarcerations than other youths. The literature is replete with reports that prior incarceration is a strong predictor of recidivism (e.g. Andrews and Bonta, 2007; Gatti et al., 2009). Prior violent offending and CALD ethnicity were associated with lower rates of recidivism, the reverse result to that reported by Weatherburn et al. (2007). Correlates of general recidivism tended to be correlated with specific recidivism, with gender and intoxicated offending notable exceptions. Few associations were common to all types of recidivism, for example cannabis use was associated with violence but with neither theft nor robbery. In rare instances, factors promoted one outcome and protected against another: prior robbery was associated with high odds of robbery recidivism, but low odds of theft. Other factors were associated with only one specific type of recidivism (e.g. out of home care with theft). 163 6.5.2 Predictors of recidivism

Few variables predicted recidivism, reflecting the overlap between many of these risk factors. General and theft recidivism were quite well explained by their respective models, while violence and robbery recidivism were not. Independent associations between drug use and offending were highly specific, e.g. daily cannabis use predicted violence and weekly opioid use predicted theft. Drug use was not, however, a universal risk factor for recidivism: no pattern of drug use predicted general recidivism, and neither binge drinking nor amphetamine use predicted any recidivism outcome.

A history of multiple detention episodes was a strong predictor of all types of recidivism. It increased recidivism risk independently of prior convictions and rendered bivariate associations, such as past year offending frequency insignificant. This association is well established (Benda & Tollett, 1999) and may be causal (Gatti et al., 2009). Multiple detention episodes increased general recidivism risk in this community-based sample (independent of having peers who offended), and may do so through increasing the risk of more punitive subsequent intervention, or by denoting greater social instability. While prior theft increased risk of theft recidivism, and prior robbery increased the risk of robbery recidivism, prior violence did not predict violent recidivism and in fact protected against general recidivism. This may reflect the lower rate at which violent offences tend to be committed, compared to other crimes (Weatherburn & Lind, 2000).

Although males were significantly more likely to recidivate than females (AOR 1.7), holding other predictors constant, absolute rates were quite comparable (74% vs. 65%). Hua et al found similar unadjusted odds for multiple court reappearances in NSW by age 21 – whilst the baseline odds of ANY appearance in court were nearly three times greater for males (OR 4.7, Hua et al., 2006). Although serious criminal involvement was a predominantly male phenomenon, gender was a far less salient factor in recidivism. However, the recidivism predictors for males and females may differ. Schooling, for example, was a risk factor for girls but protective for boys. Future studies should attempt to attain larger female subsamples to study such effects.

164 Age was not an important predictor for males and the total sample. Whilst risk may have been fairly consistent in the short term (two years after baseline), some differences were found for its correlates amongst younger and older clients. Notably, peer criminality was predictive of recidivism only for participants under 17. A larger sample would allow closer examination of this ‘shifting salience’ (Childs, Dembo, et al., 2011) of predictors over time. Higher adjusted odds of recidivism were not observed for Indigenous clients, which conflicts with most prior research (e.g. Chen et al., 2005). This may reflect the coding of ethnicity in this model, with English-speaking background (ESB) participants as the reference group and CALD participants as a second contrast group.

Previous models of Indigenous recidivism have identified gender, incarceration and age of first offence as important predictors, and the probability of recidivism amongst young Indigenous males with a history of juvenile detention approached certainty in the study by Chen et al. (2005). None of these factors were predictive for the current sample. It may be that there was too little variance to study the predictors of Indigenous recidivism in this sample; 85% of Indigenous youths recidivated within two years compared to 70% of non-Indigenous youths. Further, a number of factors that inordinately contribute to Indigenous criminal justice involvement, including direct and indirect effects of the ‘stolen generation’, such as inadequate culturally appropriate parenting (Dodson & Hunter, 2006; Hunter, 2001), were not assessed in this study. These results serve as a reminder of the limitations of predictive models for such a diverse sample, and that results from one sample may not necessarily generalise to another. Discussion of other predictors common to multiple recidivism outcomes (such as VIQ, CD) is continued in Chapter Nine, along with the comparison of prior and prospective participation in offending.

The small size of the regression coefficients in the violence model and their poor overall ability to predict recidivism may reflect the absence of important predictors of youth violence. Several risk factors (including early onset of violence, low empathy, impulsivity, peer rejection and community disorganisation), and all protective factors (e.g. prosocial involvement, resilience and positive attitude) assessed by one major

165 violence risk assessment tool (the Structured Assessment of Violence Risk in Youth/SAVRY; Borum, 2006) were not present in the thesis dataset. A systematic review of research on the accuracy of risk assessment instruments in predicting violence). However, meta-analytic evidence indicates that such specialised tools predict juvenile violence little better than general risk assessment tools (Olver, Stockdale, & Wormith, 2009) and have at best moderate predictive accuracy (Fazel et al, 2012). Similarly weak predictive results have been observed for an additional construct that was not available in the thesis dataset: juvenile psychopathy (Asscher et al, 2011). Nonetheless, future research should consider the effect of including these theoretically-specified factors on the prediction of violence by young offenders.

6.5.3 Limitations

Recidivism may have occurred at any time within the two year period after baseline. Knowledge of when recidivism occurs may be informative to those planning interventions for young offenders. The window of opportunity to prevent recidivism amongst offenders who reoffend very quickly admits different strategies than those who reoffend only intermittently. Chapter Seven addresses this by assessing the rapidity of offending and its predictors. A second limitation is that many offenders committed multiple offences; only the first conviction was included in these logistic models, thereby failing to distinguish predictors of infrequent and chronic recidivism. Chapter Eight thus assesses the predictors of frequency of new convictions. Individual binary logistic regression models are also unable to predict multiple outcomes (e.g. different offence types). Future research could consider alternative strategies for exploring mutually exclusive outcomes. Multinomial logistic regression (Chapter Five) is not ideal because it would require several exclusive categories (e.g. ‘violent and theft but not robbery’). Latent class analysis may identify a smaller number of homogeneous subgroups of recidivists (e.g. ‘multiple offence types but primarily violent’) but as latent subgroups do not actually exist in the population they are far less easily applied to juvenile justice practice (as reflected by the scant Australian and international evidence). Finally (and as illustrated in the previous paragraph), a number of known risk factors for recidivism including attitudinal and personality factors were not available for analysis; this may have reduced the explanatory power of the models. 166 6.5.4 Conclusion

This chapter showed that most young offenders recidivated within a two year period. Recidivism was highly varied, and was not characterised by any particular offence. There was a significant association between drug use and recidivism but this varied by drug type and offence type. Daily cannabis use was the most consistent marker of recidivism risk, and even so this was not apparent for all offences (i.e. for robbery). The specificity of relationships between drug use and recidivism was drawn into sharper focus in the multivariate models, which showed that frequent drug use was not a predictor of general recidivism. Thus, the association between drug use and general recidivism would appear to be largely accounted for by other common risk factors. Weekly opioid use, however, emerged as powerful predictor of theft.

Recidivism proved hard to predict, even for specific offence types. One reason for this unpredictability is that even specific types contain diverse offences, and the rapidity and frequency of these offences was not considered. Later chapters will look at these aspects of offending, with a view to more precise prediction. The lack of gender differences stands in contrast to the gender mix of the criminal justice population (roughly seven males to every female). This may reflect the higher aggregate level of risk amongst young female than male offenders (Chapter Four). Subsequent chapters will explore whether the relative equivalence in recidivism risk extends to other aspects of recidivism, such as frequency. As many prior studies have found, the strongest independent predictors were ethnicity and criminal history. Although not directly modifiable, they should be retained in predictive models. Ethnicity can be a marker of enduring social disadvantage or antisocial subcultural values, and draws attention to subgroups for which more social and development responses are required, rather than criminal justice intervention. Criminal history is a marker of criminal propensity and suggests some continuity in offending. Both help clarify the impact of modifiable factors, such as drug use. The models also confirm the need for disaggregated measures of drug use to differentiate recidivists. Crude indicators would not be useful for policy or potential interventions, and may result in some drug-using offenders being inappropriately labelled at risk.

167 7 Patterns and correlates of the timing of recidivism

Survival models provide important information about the timing of recidivism and the factors that affect how soon recidivism occurs. Models of survival, or time to recidivism, are less common than logistic models in the juvenile recidivism literature, but have become more widely used since the publication of early United States (US) (Gruenewald & West, 1989) and New South Wales (NSW) studies (Carcach & Leverett, 1999). Survival time should be at its shortest during adolescence when juvenile offending frequency reaches its peak, and age does significantly affect recidivism hazards (Gruenewald & West, 1989). Jurisdictional differences may also affect survival time. Most juvenile studies have focused on predictors of survival, rather than its duration. There is little information on the underlying distribution of recidivism, and few studies have considered how the effects of predictors on recidivism vary over time. Specific patterns of drug use have rarely been disaggregated in these models, and specific recidivism has rarely been addressed. The current study attempts to fill these gaps, adding to key Australian studies (Carcach & Leverett, 1999; Chen et al., 2005; McGrath, 2009b).

7.1 Survival analysis and the recidivism hazard

Survival analysis is a family of statistical techniques (also referred to as failure, duration, or event history analysis) concerned with the amount of time between two events or states of being, and the predictors of this duration and change (Blossfeld & Golsch, 2007; Bradburn, Clark, Love, & Altman, 2003; Carcach & Leverett, 1999). Recidivism survival analyses typically model the time between two criminal justice events, such as release from secure custody and subsequent ‘failure’ (e.g. first re- offence or re-incarceration). Compared with logistic regression, survival analysis provides a greater understanding of recidivism patterns and the impact of time-varying factors (e.g. age) (Cunneen & Luke, 2007). Its ability to use all available follow-up data makes it more powerful than logistic regression, which is restricted to modelling recidivism within the shortest follow-up for a given sample (three years in the case of

168 this thesis). This also means that hard-won data (observations of participants from three to five years after baseline in this thesis) are not wasted (McGrath, 2009b). Survival analysis does, however, require precise data on offence timing.

Three main statistical methods are used in survival studies (see Section 7.4 for more detail). Non-parametric Kaplan-Meier (KM) analysis provides information on the timing of recidivism and subgroup differences in timing, with log-rank tests for significance. Semi-parametric Proportional Hazards (PH) models are the most popular for multivariate survival analyses; these assess the impact of covariates on recidivism. Exponentiated beta coefficients in PH models are called hazards, and hazard ratios compare the instantaneous risk of recidivism for a one-unit change in a covariate at any point in time (per odds ratios (ORs) in logistic regression). Finally, parametric models make testable assumptions about the underlying survival distribution that enable more rigorous inferences about covariates (Carroll, 2003). Change in median survival (i.e. when the probability of survival reaches 0.5) for a one-unit change in a covariate is known as a time ratio (TR). Thus, whereas hazard ratio 2.0 indicates a doubling of the risk of recidivism, TR 2.0 indicates a doubling of the time to recidivism (Bradburn et al., 2003).

The rate at which a sample reoffends tends be highest soon after sentencing (or release from detention), declining thereafter. Thus, the longer an offender ‘survives’ the less likely they are to reoffend. Many studies report that the risk of recidivism initially increases (Gruenewald & West, 1989; Huebner & Berg, 2011; Visher, Lattimore, & Linster, 1991). This may reflect re-adjustment to life in the community, or conditions of sentencing; non-compliance with these conditions may initially trigger a warning, but not be punished for several weeks after. This pattern (initial increase then monotone decline) has been well approximated by log-logistic and log-normal distributions (i.e. parametric survival models) in large studies of Australian juvenile (Carcach & Leverett, 1999) and adult offenders (Broadhurst & Loh, 1995), and serious US juvenile offenders (Gruenewald & West, 1989). This pattern cannot be observed in the PH models that predominate in the literature.

169 Early Australian recidivism studies (e.g. Carcach & Leverett, 1999) were restricted to juvenile recidivism. Chen et al. (2005) followed NSW Children’s Court attendees into the adult system and for up to eight years after their first court appearance; their median time to second court appearance was 21 months. As offences precede subsequent criminal justice system (CJS) processing, survival time will be shorter to offending (Carcach & Leverett, 1999), especially for serious young offenders who tend already to have multiple prior appearances. Median time to re-arrest was just seven months among community-supervised youths in Queensland, although with only a short 18 month follow-up (Denning & Homel, 2008). For sentenced young offenders observed up to three years (McGrath, 2009b), median time to first proven offence was nearly four months, and far longer for youths on non-custodial orders (mean eight months; median not reported). These studies, like most, observed recidivism from last date of sentencing. Commencing observation at a later date, as in the current study (Section 7.4.1) may mean that some post-conviction offences are not counted, especially for rapid recidivists. Such youths tend to be prolific, however, and are thus likely to be detected for a later offence. The studies described above are summarised in Table 7.1.

Table 7.1 Key Australian survival analysis studies referenced in this chapter

Study Sample Outcome (Carcach & 5509 NSW juveniles convicted Time between Children’s Court Leverett, 1999) 1992-93 appearances (6-60 months observation) (Chen et al., NSW cohort, N=5476, Intake Time from first to second court 2005) age 10-18 appearance (Denning & 190 community-supervised Time to re-arrest after index court date; Homel, 2008) youths, Queensland 18 months observation (McGrath, 2009b) Youths receiving custodial and Time to reconviction; 3 year non-custodial orders, NSW observation (McGrath & 4238 young offenders in NSW Time to court conviction Thompson, 2009)

170 Information on survival time to specific recidivism has not been reported for Australian juveniles, and most adult and international studies have been concerned with sexual and driving offences. Available data suggest substantially longer time to property recidivism than general recidivism, e.g. twice as long for subgroups of Queensland drug court participants (full sample medians not reported, Payne, 2008). The rate of violence is far lower than for general offending (Ellis & Marshall, 2000), which suggests that survival will be longer for violence, and theft offenders have been shown to reoffend sooner than violent offenders (Bowles & Florackis, 2007).

Survival patterns among young Australian community-based offenders cannot be easily generalised from detainee or adult samples. Detainees face acute social and economic stress on release from detention, which may affect their survival patterns (Larney, 2011). Juveniles’ offence patterns may also result in their more rapid detection and apprehension than adults (Cunneen & White, 2011). More rapid offences tend to be less serious than average offences (Carcach & Leverett, 1999). If property offences occur more frequently and thus sooner than violent offences, variance in general recidivism will be disproportionately explained by predictors of property crime. Hence, predictors of offence severity are modelled separately in Chapter Eight.

Among juvenile offenders in NSW (McGrath & Thompson, 2012) most who reoffended within one year did so within six months; identifying predictors of rapid recidivism can also inform case planning, as non-intensive community-based interventions may be unlikely to reduce recidivism.

171 7.2 Correlates and predictors of survival

Factors that increase the risk of recidivism should reduce survival times, so the earlier risk factor reviews (e.g. Chapter One) are relevant to this chapter. Over similar observation periods, the relationship of independent (IV) and dependent variables (DV) in logistic and survival models should be closely comparable, given that the DV in both cases is a dichotomous event, e.g. reconviction. This is supported by recidivism studies that compare these two approaches (e.g. Jones, 2009) .

7.2.1 Drug use and recidivism

The majority of young offenders use drugs but not heavily (Chapter Five). Thus, relatively few are economically compelled to steal to fund their drug use, and most are unlikely to be heavily exposed to the ‘systemic’ violence of drug markets (Chapter Two). Frequent drug use may account for more rapid offending by disinhibiting behaviour and fostering individual and group aggression (Bushman, 1997; White, 2004). Such effects may be apparent in occasional users too, given their lower tolerance levels. Drug use may also indirectly shorten survival, by making users more visible (particularly those using in public places and in groups) and increasing the frequency of police contacts.

Numerous local and international studies have reported shorter times to recidivism among drug-using offenders (e.g. Denning and Homel, 2008; McGrath 2009b; Stoolmiller & Blechman, 2005; McGrath and Thompson, 2009) but the independent effect of drug use are more modest, and closely tied to the extent of statistical control for other survival predictors. McGrath (2009b) found several drug use variables (weekly past year binge drinking, extent of last binge, and total past year drug use) were loosely related to survival (p<.25) among young offenders in NSW, but none were independent predictors. Static risk factors accounted for most of the variation in survival time; failure to account for these may mean the role of drug use is overstated. The paper by Denning and Homel (2008) presents a similar example: ‘any’ drug use indepdently doubled the recidivism hazard, but only two of the ‘central eight’ risk factors (Andrews and Bonta, 2007; Section 1.3.3) were present in the model.

172 Problematic or frequent drug use is more robust predictor of shorter survival in multivariate models. A large NSW study that assessed the relative contributions of these eight factors (via the Youth Level of Service Inventory subscales) concluded that the substance abuse score (a loose proxy for severity of drug problems) contributed modestly to the recidivism hazard for young offenders McGrath and Thompson (2009). Recidivism occurred sooner among frequent drug users a study of US juvenile arrestees in a well-controlled model including prior delinquency and demographics (Stoolmiller & Blechman, 2005). Drug treatment has also been associated with reduced recidivism and longer survival (e.g. among 279 US probationers, Broome, Knight, Hiller, & Dwayne Simpson, 1996) and a US study of opioid using ex-prisoners linked longer time to reincarceration with higher methadone doses, holding demographics constant (Bellin et al., 1999). These effects lend support to a focus on heavier users, and the possibility of a dose relationship between drug use and recidivism.

The effects of specific illicit drugs on survival or of drugs on specific recidivism are unclear. Among Swedish community-based offenders with psychiatric histories (Grann et al., 2008), substance use disorders (SUDs) independently doubled the hazard of violence, with good adjustment for potential confounders. By contrast, Lattimore, Visher, and Linster (1995) found that heavy drug and alcohol use did not predict violent recidivism among juvenile parolees in the US, and in a later study of young US adult offenders drug dependency had no effect on violent or property recidivism (Lo, Kim, & Cheng, 2008). The latter study did find that drug dependency increased the hazard of drug-related recidivism. It is difficult to draw conclusions across these studies given their diverse samples, locations, data collection eras, as well as the measures used to assess recidivism. A more consistent finding is that cannabis appears to have a weaker effect on survival than other illicit drugs. For example, among 1200 Australian drug-using offenders (Larney & Martire, 2010), those with cannabis as their principal drug survived for longer after commencing drug treatment than other offenders. Cannabis initiation also increased the risk of escalation to regular property offending among young Australian detainees but less so than for initiation to other illicit drug use (Payne, 2006).

173 (Gruenewald & West, 1989) concluded from several studies that the impact of drug use on recidivism is highly time-dependent: it is strongest close to baseline (e.g. sentencing) and dissipates quickly thereafter. This is consistent with the observation that drug use is highly dynamic, particularly in adolescence. Changes in drug use are of high relevance to offending behaviour. Payne (2006) found offenders who progressed to frequent drug use during their first year of use also progressed more quickly to frequent property offending. Changes in drug use (e.g. desisting, progressing) affect predictive models of antisocial behaviour in community samples (e.g. Loeber et al., 2012), but time-varying effects of heavy use have not been modelled among juveniles.

A few studies offer the tantalising suggeston of wode demographic variation in the drug use/survival relationship. McGrath and Thompson (2009) found that the YLSI substance abuse subscale predicted recidivism in a survival model for non-Indigenous but not Indigenous young offenders in NSW. Variation in drug-crime relationships between other ethnic groups has been documented in the US (Geis, 2009), and in a study of adult US parolees: alcohol abuse predicted shorter survival for males but not females (Benda, 2005). Drug use might be expected to have a greater impact on recidivism in younger offenders (Childs, Sullivan, et al., 2011; Van Der Put et al., 2012) as they are less likely to use drugs and thus drug use has greater discriminatory power. Further research is needed to clarify whether relationships reported in survival studies hold for younger offenders and other less prevalent sub-groups (e.g. females).

7.2.2 Demographic variation in survival

Demographic variation in the effects of drug use on survival amongst Australian juveniles is poorly documented, with no gender or age comparisons to date. Age is a powerful predictor of survival, but as with the prediction of participation in juvenile recidivism, the impact of age is not linear. Carcach and Leverett (1999) reported a quadratic relationship whereby survival times increase from childhood, and then decrease in mid-adolescence. For this reason, it is useful to model age categorically. Chen et al. (2005) found youths first appearing in court before age 14 had comparable survival to those aged 15-16, but significantly shorter survival than those aged 17-18, indicating a non-linear relationship.

174 Gender does little to explain differences in time between juvenile court appearances (Carcach & Leverett, 1999, pp. 16-17), but females’ offending frequency declines more quickly than males’ in early adulthood. Studies of juveniles that incorporate adult recidivism data find that male gender predicts shorter survival. For young NSW offenders, males’ median survival was approximately two years compared with eight years for females (Chen et al., 2005), and their recidivism hazard was 70% higher than females, controlling for general recidivism risk rating (McGrath & Thompson, 2009) (study details in Table 7.1). In adult studies, male gender did not predict survival for Indigenous offenders (Broadhurst & Loh, 1995), nor violence or property recidivism (Lo et al., 2008; Makkai et al., 2004), and Benda (2005) found gender-specific predictors of survival among US adult parolees (see Section 7.2.4). Gender variation among Australian juveniles has not been profiled in detail.

Holding other risk factors constant, young Australian Indigenous offenders reoffend (Broadhurst & Loh, 1995), reappear in court (Chen et al., 2005) and are reconvicted (McGrath & Thompson, 2009) far sooner than non-Indigenous youths (e.g. median recidivism of 8-10 vs. 14-36 months in these studies). Young Indigenous and non- Indigenous offenders’ survival durations were comparable for youths rated at high risk of recidivism, but divergent at low levels, with much shorter survival for low-risk Indigenous youths (McGrath & Thompson, 2009). Reports of variation in recidivism and specific recidivism hazards for other ethnicities were not located.

7.2.3 Criminal history

More extensive criminal histories are associated with higher odds of recidivism (Chapter Six) and more rapid recidivism (e.g. Huebner & Berg, 2011); CJS involvement may stigmatise offenders, complicate their reconciliation with family and lead to social exclusion. McGrath and Thompson (2009) found criminal history was the YLSI domain that most strongly predicted recidivism. Incidence rates (which reflect rapidity) of general, violent and non-violent recidivism were significantly higher among Canadian juvenile arrestees who were processed in court compared with matched youths who were diverted from court (Petitclerc, Gatti, Vitaro, & Tremblay, 2012). The most predictive factor of recidivism among criminal history variables is prior incarceration,

175 both locally (Jones, Hua, Donnelly, McHutchison, & Heggie, 2006) and worldwide (Gatti et al., 2009; Holleran & Spohn, 2004). Similarly, NSW juveniles receiving custodial sentences had significantly shorter times to reconviction than those receiving community-based sentences (approximate median survival 8 vs. 12 months) (Weatherburn et al., 2009).

Quantitative measures of criminal history appear to relate logarithmically to recidivism, with differences most relevant at low levels of prior offending. Broadhurst and Loh (1995) concluded that the probability of recidivism amongst Australian prisoners “[approaches] certainty after very few offences”. Among NSW juveniles, the most frequent offenders reappeared in court soonest, but the greatest differences in survival times were between those with none vs. one prior offence (Carcach & Leverett, 1999). As for the US studies of specific recidivism, prior offending was a strong predictor of violent rearrest (Lattimore et al., 1995) while the number of violent offences predicted time to rearrest for violence, and likewise for property offences (Lo et al., 2008). Prior offence rate should strongly predict the recidivism rate (see Chapter Eight), but is rarely considered in the survival literature. Similarly, frequent fighting is an intuitive risk factor for rapid violent recidivism including resisting arrest and so is worth examining. Index offence type, meanwhile, predicts recidivism in some studies (e.g. Chen et al., 2005) but not others (e.g. Bowles & Florackis, 2007). However, as specialisation tends to be short rather than long term (McGloin, Sullivan, & Piquero, 2009), index offences may be more relevant for survival analysis. Lo et al. (2008) provides evidence of such sequential specialisation with shorter times to violent and property recidivism for those with violent and property index convictions, respectively.

7.2.4 Individual, peer, and family factors

The prevalence of recidivism varies for different psychiatric profiles, but links between juvenile ASB, other common psychopathology and time to recidivism have received little research attention. Studies of psychiatric contributions to violence have focused on the ‘visible minority’ (Peterson, Skeem, Hart, Vidal, & Keith, 2010) of adults with psychosis, serious mental illness or personality disorders (Grann et al., 2008). The juvenile literature has also tended to aggregate measures, which may obscure

176 variation in relationships. Among NSW young offenders, survival was unrelated to personality and attitude problems (McGrath & Thompson, 2009), but substantially shortened by ‘intent to reoffend’ (Vignaendra et al., 2011). Neither Conduct Disorder (CD) nor ADHD independently affected the recidivism hazard among young male German detainees, and counterintuitively, CD reduced the violence hazard (Grieger & Hosser, 2011). Severe CD should shorten survival to general and specific recidivism, because with increasing severity CD incorporates more diverse ASB including violent and property violations. However, as yet there is no empirical evidence to support this contention. Links with psychopathology may be gender-specific. Severe anxiety greatly increased the female recidivism hazard for young Austrian detainees (Plattner et al., 2009), as did distress for female US parolees (Benda, 2005), while aggression predicted shorter survival for males but not females (Benda, 2005).

‘Peer relationships’ was the strongest dynamic domain of the YLSI (McGrath & Thompson, 2009) in predicting the recidivism hazard. McGrath (2009b)’s earlier study found several aspects of peer deviance (friends’ contact with police, history of suspension, truanting) were correlated with but did not predict survival times. Denning and Homel (2008) reported that delinquent peer influence nearly doubled the recidivism hazard, but did not shorten survival among recidivists. Thus, it may be that delinquent peers are a marker for recidivism but do not drive its rapidity. The influence of peers on deviant behaviour is greatest during mid-late adolescence (Carcach & Leverett, 1999), especially for those who disengage most from schools (Dishion, Véronneau, & Myers, 2010). Peer influence may also be greater for adolescent-onset offenders (rather than persistent offenders who tend to be convicted by early adolescence). Peer effects are not limited to adolescence, but may vary by gender. Benda (2003) showed that for US parolees, criminal peer association independently increased recidivism hazards for males but not females (Benda, 2005).

Family factors are more salient to the initiation than persistence of adolescent offending, consistent with the declining influence of family during this time. However, early family problems are associated with early onset delinquency (e.g. Alltucker, Bullis, Close, & Yovanoff, 2006), and so may indirectly increase the risk of more

177 frequent and thus more rapid recidivism. It is important to disaggregate aspects of specific domains; while overall family/living circumstances were unrelated to NSW young offenders’ survival times (McGrath & Thompson, 2009), poor supervision was a strong correlate of the recidivism hazard (but not an independent predictor, Weatherburn et al., 2009). Similarly, unstable housing strongly predicted time to rearrest amongst US parolees (Wood, 2011). Benda (2005)’s gender-specific models identified maltreatment as a predictor of shorter survival for females but not males.

Education tends to protect against recidivism, and better YLSI education/employment scores predict longer survival (McGrath & Thompson, 2009). US adult studies suggest some variation in this link; Lo et al. (2008) found school failure increased the hazard for property but not violent crime, and education had a greater protective effect on survival for males than females (Benda, 2005). Intellectual disability has been linked with more extensive histories of offending among young community-based offenders (Frize et al., 2008). This concords with Snow and Powell (2012)’s argument that cognitive problems hinder youths’ capacity to understand and comply with forensic guidance (i.e. increasing the risk of supervision breaches). Relationships between IQ and survival are yet to be explored with Australian juveniles.

7.3 Aims 1. Describe the baseline hazard and prevalence of general, violent and theft recidivism over the total observation period (mean: 3.8 years). 2. Assess the correlates of survival to general, violent and theft recidivism. 3. Model the impact of drug use on survival to general, violent and theft recidivism, independent of other covariates.

178 7.4 Method 7.4.1 Variables introduced in this chapter

This chapter uses similar covariates to previous chapters, disaggregating aspects of individual domains where possible. Median survival time was used to contrast the time at which two or more groups reached a 0.5 probability of survival. This chapter uses event- and person-level data. Recidivism was defined as the first proven offence after the baseline survey (i.e. the first offence committed after baseline that subsequently led to a conviction). The specific recidivism outcomes assessed were theft and violence, with robbery recidivism subsumed under violent recidivism due to the very low prevalence of robbery (11%).

7.4.2 Non-parametric survival analysis (bivariate)

Kaplan-Meier (KM) analyses assess the bivariate impact of categorical variables on survival. Scale variables were dichotomized or split at clinically meaningful points to facilitate KM analysis, or analysed in bivariate parametric models (see Section 7.4.3). KM makes no assumptions about the distributional form of recidivism over time (i.e. the shape of the survival curve). Log-Rank tests were used to assess the equality of survival distributions. Log-Rank tests weight all time points equally and are robust to extensive or unequal censoring between groups. Participants were observed until the date of the first new offence for which they were convicted after baseline, or for those not convicted for any new offence, until the date of linkage (e.g. 30 September 2008). Participants who had not been convicted by this time were considered to be censored in the survival analyses.

7.4.3 Parametric survival analysis (multivariate)

Multivariate survival analysis was also necessary given that many covariates affect the timing of juvenile recidivism (Gruenewald & West, 1989). Parametric survival analysis was used to model the independent relationships of covariates to survival time, by fitting a survival regression model to a specified distribution. This approach is preferable to PH methods when the hazard is not monotonic. Parametric survival

179 analysis assesses the effect of covariates on the scale of the selected distribution (i.e. the length of survival time), as well as its shape. Associations in parametric models are tested by likelihood ratio tests (LRTs). Providing its strict statistical assumptions are met, including identification of an appropriate distribution, parametric (unlike PH) analysis also provides for multivariate prediction of median survival times where survival exceeds 50% (as it does for violent and theft recidivism).

In the simplest parametric model, the hazard has a constant distribution over time (Kleinbaum & Klein, 2012). Hazards that vary over time, as is typical for recidivism (Gruenewald & West, 1989), must be specified using ancillary or ‘shape’ parameters (Kleinbaum & Klein, 2012, pp. 278, see following paragraph). Monotonically decreasing hazards follow a Weibull distribution (Kleinbaum & Klein, 2012); hazards that increase and then decrease may fit a log-logistic or log-normal distribution (Kleinbaum & Klein, 2012); more complicated distributions require additional parameters. Theoretically strong precedents for distribution choice are rare in the social sciences (Blossfeld & Golsch, 2007). Different distributions employ similar approaches and are only approximated by most data, so there will often be more than one tenable match (Allison, 1995). If models using different distributions produce similar results this can bolster one’s conclusions (Allison, 1995). In this chapter, model selection was informed by model fit indices (-2LL, AIC and BIC) and minimal departures of Cox-Snell residuals from the horizontal (Gruenewald & West, 1989).

Weibull and log-logistic models can have ‘accelerated failure time’ properties. Covariates in accelerated failure time models affect the scale of the distribution by accelerating or decelerating survival time (Kleinbaum & Klein, 2012). These ‘scale covariate’ effects are multiplicative and are quantified as ‘time ratios’ (TR); a TR of 2.0 indicates that a one-unit increase in the risk factor doubles the predicted survival time, at any point in time. However, this ‘proportional odds’ (PO) assumption is breached if a covariate is found to affect the shape of the distribution, regardless of whether it affects its scale. The significance of ‘shape covariates’ can be tested in parametric models (unlike PH or logistic models) by linking to the ancillary parameter. For monotonic distributions, shape covariates with positive values increasingly affect

180 survival over time; for unimodal (e.g. log-logistic) distributions, shape covariates may also affect when the hazard peaks.

In this study, shape covariates were included if these improved model fit, however their inclusion meant that accelerated failure time no longer held (Kleinbaum & Klein, 2012), as TRs are conditional on the shape of the now covariate-linked hazard. TRs can still be calculated for a given covariate value, time, and shape but this is a complicated process that is not yet implemented in Stata command. These specific values were not of particular interest to this study, so for models with shape covariates, results focused on the relative strength of predictors, rather than their actual values.

Model building proceeded as per Chapter Six. The initial model consisted of age, gender and covariates associated with survival at p<.15. Variables presenting problems with collinearity or perfect prediction were then removed. Model reduction involved the removal of individual variables, starting with those with the largest p-values, until model fit deteriorated significantly (based on LRTs). Drug use variables removed during model reduction were then re-entered to the reduced model and retained in a final model if they improved overall model fit.

181 7.5 Results 7.5.1 Recidivism prevalence and hazard function

Participants were observed for a minimum of 1023 days (2.9 years, mean 3.8, SD 0.5). Over 1861 days (five years) of observation, 79% were convicted for a new offence. The proportion convicted was slightly higher (83%) when including ‘pseudo-recidivists’ convicted solely for offences committed prior to baseline. Life tables report survivorship and the survival probability of a population at varying ages, and are used to examine time to first new proven offence after baseline (summary in Table 7.2). Median time to recidivism for recidivists was 227 days (eight months after baseline); 33% reoffended within three months, 50% within 12 months and 68% within 24 months. One in three participants who had not offended by 24 months were subsequently reconvicted, most within 36 months of baseline. Recidivism peaked shortly after baseline before declining monotonically. Of note, 80% of recidivists had their first new offence finalised in the juvenile jurisdiction. Regarding specific recidivism, median time to theft was 246 days (8 months) and to violence was 328 days (11 months), among youths convicted of those offences.

Table 7.2 Abbreviated life table: new offences after baseline by the entire sample

Enter Censored Reoffending Month Hazard rate n n n proportion Total % 1 782 0 44 .06 6 .0019 2 738 0 43 .06 11 .0020 6 574 0 49 .09 33 .0030 12 401 0 13 .03 50 .0011 24 263 0 11 .04 68 .0014 36 190 5 3 .02 76 .0005 48 85 7 2 .02 79 .0008 60 2 1 0 .00 79 .0000

182 7.5.2 Baseline recidivism hazard

Baseline hazard functions (the distribution of recidivism risk in the entire sample over time) for general, violent, and theft recidivism were assessed using methods outlined in Kleinbaum and Klein (2012). Log-logistic distributions were appropriate for all outcomes, with peak hazards determined using Equation 7.1 where p (ancillary/ shape parameter) is greater than 0 and gamma (λ; model parameters) is greater than 0.

( )

Equation 7.1 Log-logistic distribution

In summary, in the general recidivism model p=1.0783, which exceeds 1.0 indicating that the hazard first rose then fell (i.e. is unimodal). Solving for all values in Equation 7.1 gives 34.8, with days as the metric. Thus, the maximum hazard (the highest instantaneous risk of recidivating) in the loglogistic distribution was reached 34.8 days or five weeks after baseline. Figure 7.1 displays this hazard function, or the instant probability of recidivism over time.

Loglogistic distribution peaks at 35 days

Days since baseline

Figure 7.1 Baseline hazard for general recidivism

Solutions for the Weibull and log-normal distributions provide larger log-likelihood values and are thus less appropriate approximations than the log-logistic. The log- logistic assumption was also graphically evaluated by plotting the log odds of recidivism, i.e. the log of 1- (t)/ (t) (where (t) is the Kaplan-Meier survival estimate) against log time (Kleinbaum & Klein, 2012). Straight lines confirmed the choice of distribution. 183 7.5.3 Bivariate correlates of recidivism

Bivariate associations are presented in Table 7.3, Table 7.4, and Table 7.5. Given that Chapter Six reviewed bivariate associations with two-year recidivism in detail, the following review of bivariate relationships with time to reoffending is kept brief.

Bivariate hazard ratios from PH models (Figure 7.2, presented in landscape format in Appendix Figure II for easier viewing) are presented to facilitate comparisons with bivariate odds in Chapter Six. These show a linear relationship between bingeing and cannabis use frequency and violence. Weekly opioid users’ theft hazard was double that of less than weekly and non-users’. Trends (p<.15) were apparent for violence among weekly amphetamine users and theft among daily cannabis users.

Reference category: none (no use) Any Violent Theft 1.75 1.5 1.25

Hazard ratio (HR) ratio Hazard 1 0.75

Figure 7.2 Bivariate hazard ratios: recidivism type by drug use

Cannabis was the sole drug or drug-related variable significantly associated with general recidivism. Survival decreased with frequency of use (Kaplan-Meier failure estimates are presented in Figure 7.3), but the early portion of the curves (inset) shows that in year one after baseline, this was apparent only for daily users; the remaining groups of cannabis users were indistinguishable.

184

---- daily ---- weekly ---- less than weekly

---- none Proportion reconvicted Proportion

Days since baseline

Figure 7.3 Failure estimates for general recidivism by frequency of cannabis use

The bivariate log-logistic model found predicted median time to recidivism for daily users was 283 days. The time ratio (TR) for daily users was 0.75 (p=.07) compared to weekly users, 0.67 (0.49-0.90, p=.007) compared to less than weekly users, and 0.61 (0.45-0.83, p=.002) compared to non-users. Thus, daily cannabis users offended 25% faster than weekly users, 33% faster than less than weekly users and 39% faster than non-users.

Demographic correlates. Associations varied by gender, ethnicity and age. Males were reconvicted twice as quickly as females (p<.001) across the study period for general recidivism and theft, but not violence. One in three females were unconvicted at the end of the study period. The figure for Indigenous youth was one in eight, and they had shorter times to all outcomes compared to English-speaking background (ESB) youths, particularly violence and theft (TR 0.5, p<.001). Culturally and linguistically diverse (CALD) groups survived for longer than ESB, particularly for theft.

Age had a complex, non-linear relationship with recidivism, with significant differences in failure between younger and older participants. Median survival also varied widely for older participants. Cumulative hazard curves show the hazard for those aged 16 was initially steep but tapered off after year two after baseline; for younger participants it climbed into year three. Curves for youths aged 16 and under 16 differed, but did not differ significantly. Age was strongly related to theft: compared to under 16s, survival was similar for age 16, double for age 17 and four times longer for adults, but was proportional over time. The violence hazard differed by age, as

185 reflected by the diverging failure curves, initial overlap and ultimately clear separation in CIs (Kaplan-Meier failure estimates are presented in Figure 7.4). The ratio of young to older participants at risk increased over time and the proportional odds assumption was breached. When linked to the ancillary parameter, age showed a significantly

increasing effect on survival over time.

Age under 17

Age 17 plus Proportion reconvicted Proportion

Days since baseline Figure 7.4 Failure estimates for violent recidivism by age group

Other correlates. More extensive criminal histories were generally associated with shorter survival times, particularly earlier age at first offence and total convictions before baseline. Fighting and higher levels of conduct disorder were also strongly related to faster recidivism, and participants with criminal peers recidivated sooner (TR 0.67). Schooling was protective and higher VIQ even more so. Time elapsed since the index court date was unrelated to survival. Psychological distress (Kessler-10 scores) did not affect survival for either gender.

Different patterns of associations were found for specific recidivism. In contrast with general recidivism, out of home care was predictive of shorter times to theft. Criminal history variables were particularly predictive: youths with prior theft convictions committed subsequent thefts four to eight times as quickly as other participants (TR 0.18, 95%CI 0.12-0.26, p<.001). CD was more strongly predictive of shorter times to theft and VIQ of longer times to theft. Prior violence, fighting and disability, were unrelated to theft. Most associations were weaker for violence. Neither disability nor prior violence was predictive of violent recidivism, however fighting was more predictive, and victimisation uniquely predictive of this outcome.

186 7.5.4 General recidivism model

Adjusted time ratios for variables in the final model for general recidivism are presented in Table 7.3, alongside the unadjusted time ratios for these variables. Cannabis use was removed from the final model, as it neither predicted time to first offence (p=.4) nor improved model fit. No other drug use variables were retained. Variables excluded from the final model are not shown in the table.

Having prior convictions was the strongest predictor of recidivism (shorter survival). Having multiple detention episodes halved survival times, even after controlling for prior custodial sentences and length of incarceration, which were both removed from the final model. Fighting and high levels of CD shortened survival, while prior violence and disability extended survival. Criminal peers shortened survival, particularly for younger participants. Schooling extended survival for males but shortened it for females (median survival 509 days for female students, 839 for non-students). Several variables were removed without significantly affecting model fit, including days since index court date.

Two factors significantly affected the shape of the recidivism hazard, therefore they were also included as ancillary terms in the model. Age differences in survival were smallest at baseline and increased over time. Further, an initial increase in the baseline hazard was observed for younger and older participants, but was most pronounced for the younger group. In these ways, age affected the shape of the recidivism hazard. The hazard for participants with average or greater VIQ declined over time, whereas the hazard for participants with low VIQ increased steeply in the first year after baseline. Thus, verbal IQ also affected the shape of the recidivism distribution. Graphical illustrations of the effects of variables on the shape and scale of recidivism are provided in Section 7.5.5.

187 Table 7.3 Unadjusted and adjusted time ratios for predictors of general recidivism

TR 95%CI p ATR 95%CI p Male 0.51 0.37-0.71*** <.001 0.52 0.37-0.72*** <.001 Student/graduate 1.81 1.37-2.40*** <.001 0.55 0.29-1.05 .069

Male x student/grad (not shown) .001 2.84 1.41-5.72** .003 Age 17 plus 1.29 1.02-1.61* .031 0.89 0.58-1.38 .605 Criminal peers 0.67 0.51-0.87** .003 0.59 0.42-0.85** .004

Age 17+ x Crim. peers (not shown) .190 1.72 1.06-2.80* .030 Limiting disability 1.48 1.04-2.12* .029 1.38 1.00-1.92 .052 VIQ (0-3) 1.50 1.31-1.72*** <.001 1.39 1.22-1.58*** <.001 Prior convictions (ln) 0.57 0.50-0.65*** <.001 0.64 0.56-0.74*** <.001 Multiple custody 0.40 0.31-0.51*** <.001 0.55 0.43-0.70*** <.001 Any prior violence 1.22 0.96-1.56~ .111 1.28 1.01-1.61* .037 Fighting (0-5) 0.84 0.77-0.91*** <.001 0.93 0.86-1.01 .075 Conduct disorder: nil 1.0 *** <.001 1.0 * .038

low 0.82 0.59-1.14 .242 0.82 0.61-1.10 .178

moderate 0.57 0.43-0.77*** <.001 0.70 0.53-0.92* .012

high 0.54 0.40-0.73*** <.001 0.70 0.53-0.94* .019 ANCILLARY TERMS Aged 17 plus 1.11 0.97-1.27~ .128 1.18 1.02-1.35* .025 VIQ (0-3) 1.12 1.03-1.22*** <.001 1.14 1.05-1.24** .003

*p<.05 **p<.01 ***p<.001 ~p<.15; TR time ratio; ATR adjusted time ratio; CI confidence interval; N=728; ‘x’ indicates interaction term. ln=log transformed. ATRs are precise at baseline, but at later timepoints are conditional on ancillary parameters which change over time. Model constants for calculating time-specific ATRs: scale (8.25, p<.001), shape (- .61,p<.001).

188 7.5.5 Theft model

The final model for theft is presented in Table 7.4. Non-significant variables were excluded from the final model and are not shown in the table. Survival curves (Figure 7.5 and Figure 7.6) illustrate how covariates affected recidivism over time, i.e. the scale and shape of the recidivism hazard. Opioid use was by far the strongest predictor of survival time. Weekly users reoffended much sooner than other youths (median survival amongst recidivists was 1.5 vs. nearly five years) but the impact varied with time, and was highest one year after baseline (also see Figure 7.6). Youths with histories of out of home care had shorter survival times, but these differed little in year one after baseline. The reverse was true for past year convictions, which did not denote a higher prevalence of recidivism, but did affect the shape of the hazard: offenders with more past year convictions recidivated more quickly than others.

Gender and schooling interacted strongly, with female students and male non- students offending far sooner than other participants. Survival times were lowest for participants aged 16 and became longer with age. CALD groups survived longer than other participants, particularly those from non-English-speaking families. Higher VIQ strongly predicted longer survival, with a marked disparity at one standard deviation (SD) below the mean (VIQ 85). Participants with prior theft convictions were convicted nearly three times faster than others; the number of convictions was non-significant after this adjustment. Multiple detention episodes, criminal peers, and higher CD were more modest predictors.

189 Table 7.4 Unadjusted and adjusted time ratios for predictors of theft recidivism

TR 95%CI p ATR 95%CI p Male 0.61 0.36-1.03~ .066 0.42 0.25-0.70*** .001 Student/graduate 1.68 1.07-2.62* .023 0.21 0.08-0.54** .001 Male x student/grad 6.37 1.78-22.6** .004 5.57 2.00-15.5** .001 Age <16 1.0 *** <.001 1.0 *** <.001 16 1.03 0.63-1.70 .895 0.74 0.47-1.17 .201 17 1.91 1.19-3.05** .007 1.50 0.94-2.38 .089 18+ 3.96 2.33-6.73*** <.001 2.00 1.14-3.50* .016 Ethnicity: ESB (reference) 1.0 *** <.001 1.0 ~ .112 Indigenous 0.51 0.33-0.78** .002 0.96 0.65-1.41 .826 ESB/CALD 2.70 1.55-4.72*** <.001 1.44 0.84-2.47 .181 CALD 3.09 1.72-5.53*** <.001 1.78 1.05-3.04* .033 Out of home care 0.45 0.30-0.67*** <.001 0.57 0.40-0.81** .002 Verbal IQ (0-3) 2.07 1.68-2.56*** <.001 1.75 1.43-2.13*** <.001 Age at first offence 1.57 1.41-1.75*** <.001 1.15 1.02-1.31* .022 Multiple detention epi. 0.27 0.17-0.42*** <.001 0.5 0.33-0.76** .001 Prior theft 0.18 0.12-0.26*** <.001 0.37 0.26-0.54*** <.001 Any criminal peers 0.48 0.31-0.74** .001 0.64 0.44-0.94* .022 Conduct disorder: nil 1.0 ** .001 1.0 * .016 low 0.61 0.37-1.03~ .063 0.81 0.52-1.26 .350 moderate 0.30 0.19-0.47*** <.001 0.53 0.35-0.81** .003 severe 0.38 0.24-0.62*** <.001 0.61 0.40-0.93* .023 Opioid use: none 1.0 *** <.001 1.0 *** <.001 less than weekly 1.00 0.54-1.86 .999 0.96 0.54-1.68 .874 weekly 0.28 0.16-0.51*** <.001 0.26 0.16-0.42*** <.001 ANCILLARY TERMS Prior care 0.86 0.71-1.06~ .150 0.77 0.62-0.94* .011 Past year lambda (ln) p=.4 1.17 1.05-1.31** .005 Weekly opioid use 0.71 0.51-0.99* .045 0.67 0.48-0.95* .023

*p<.05 **p<.01 ***p<.001 ~p<.15; TR time ratio; ATR adjusted time ratio; CI confidence interval; N=731; ‘x’ indicates interaction term. ln=log transformed. ATRs are precise at baseline, but at later timepoints are conditional on ancillary parameters which change over time. Model constants for calculating time-specific ATRs: scale 7.08 (p<.001), shape .003 (p=.99). Past year lambda was not a bivariate correlate of survival but significantly improved model fit. Multiple detention epi: Multiple detention episodes. 190 Figure 7.5 estimates survival functions for each level of opioid use, adjusting for other covariates in the theft model. Opioid use clearly affected the scale of the hazard.

— no use ····· less than weekly use --- weekly use

Days since baseline Figure 7.5 Adjusted theft survival curves by frequency of opioid use

In Figure 7.6, the shape parameter for non-users is positive (γ=1.1, p=.05); their hazard declined at a decreasing rate. The hazard for weekly users (γ=0.6, p=.002) peaked one year after baseline before declining. (Less than weekly users were non-significant)

less than weekly use

Days since baseline Figure 7.6 Adjusted theft hazard curves by frequency of opioid use

191 7.5.6 Violence model

The violence model is presented in Table 7.5. The effect of daily cannabis use was robust to control for type and frequency of other drug use. Infrequent use was unrelated to violence, but amongst violent recidivists, median survival ranged from 3.1 years for daily users, 4.3 for weekly, 5.3 for less than weekly and 5.9 for non-users.

Males, younger participants, Indigenous and non-ESB CALD participants reoffended more quickly than other participants. Age also had ‘increasing relevance with tenure’ (Blossfeld & Golsch, 2007): older participants’ median times to recidivism increased more over time than did younger participants’. Inspection of time-specific hazards showed minimal difference between age groups in the first six months after baseline, but significantly longer TRs for older participants after this time. Multiple detention episodes, criminal peers, physical victimisation, fighting and daily cannabis use were linked with more rapid reoffending. As for theft, there was little difference in rapidity of violent recidivism at lower levels of VIQ but higher VIQ was predictive of far longer times to violence.

7.5.7 Subsample models for general and specific recidivism

The results of models run exclusively with the female, younger (under 17), older and Indigenous subsamples are presented in Appendix B: analyses of demographic variation. Few predictors of general or specific recidivism in the full sample predicted these outcomes for all subgroups; weekly opioid use and daily cannabis use were the notable exceptions to this pattern.

192 Table 7.5 Unadjusted and adjusted time ratios for predictors of violent recidivism

TR 95%CI p ATR 95%CI p Male 0.53 0.34-0.83** .005 0.59 0.38-0.90* .015 Age 17 plus 1.87 1.35-2.58*** <.001 1.64 1.19-2.26** .003 Ethnicity: ESB (reference) 1.0 *** <.001 1.0 ** .002

Indigenous 0.48 0.33-0.69*** <.001 0.55 0.38-0.80** .002

ESB/CALD 1.51 0.95-2.42~ .082 1.09 0.68-1.77 .718

CALD 0.79 0.51-1.24 .309 0.56 0.36-0.88* .011 VIQ: <70 (reference) 1.0 *** <.001 1.0 *** <.001

70-84 1.06 0.73-1.54 .746 1.01 0.70-1.46 .960

85-99 1.90 1.23-2.92** .004 1.71 1.11-2.62* .014

100+ 3.93 1.96-7.89*** <.001 3.08 1.53-6.18** .002 Prior convictions (ln) 0.65 0.54-0.77*** <.001 0.56 0.39-0.81** .002 Multiple detention 0.37 0.26-0.53*** <.001 0.81 0.67-0.98* .026 episodes Criminal peers 0.51 0.36-0.74*** <.001 0.60 0.41-0.88** .008 Several fights 0.51 0.37-0.70*** <.001 0.70 0.50-0.96* .029 Physical victimisation 0.56 0.40-0.79** .001 0.57 0.41-0.81** .002 Cannabis use: none 1.0 ** .005 1.0 *** <.001

Less than weekly 0.88 0.56-1.39 .590 0.99 0.63-1.56 .967

Weekly 0.73 0.45-1.18 .198 1.09 0.68-1.76 .715

Daily 0.47 0.31-0.70*** <.001 0.61 0.41-0.92* .019 ANCILLARY TERMS Age 17 plus 1.28 1.07-1.53** .007 1.27 1.06-1.52* .011

*p<.05 **p<.01 ***p<.001 ~p<.15; TR time ratio; ATR adjusted time ratio; CI confidence interval; VIQ verbal IQ; N=736; ‘x’ indicates interaction term. ln=log transformed. ATRs are precise at baseline, but at later timepoints are conditional on ancillary parameters which change over time. Model constants for calculating time-specific ATRs: scale 9.64 (p<.001), shape -.103 (p=.12).

193 7.6 Discussion

In this sample, just 21% ‘survived’ (i.e. had not been convicted for a new offence at the end of the study period). Most recidivism occurred within a year of baseline (50% within nine months), but one in seven recidivists did not recidivate until at least two years after baseline. The incidence of recidivism after 3.8 years (the mean observation period for this group) was almost zero, suggesting that a longer time frame offers little to the measurement of prevalence or time to general recidivism. This observation period produced robust models of general recidivism but longer observation periods would be preferable for specific offences.

Identifying the non-monotone baseline hazard for general recidivism helped select the best modelling approach (log-logistic, not PH). Understanding why the hazard is shaped this way is less straight forward. Peak offending frequency for both persistent and adolescence-limited offenders occurs at age 17 to 18, slightly later for violence, and slightly earlier for theft. As age was normally distributed in this sample, with a mean of 17, the increasing hazard for general and violent, but not theft recidivism, may simply reflect the age-crime curve. While this increase has been observed in post- release samples (Gruenewald & West, 1989) this may not reflect the population trend, as participants in this study were arguably better functioning than detainees. It may also be that participants were functioning relatively well at the baseline interview, (which typically coincided with a juvenile justice supervision session) and thus some deterioration in functioning (regression to the mean propensity to reoffend) might be expected. This cannot be tested using the available data but is something for a cohort study to investigate. Delayed peak hazard occurred as the sample approached the peak recidivism time for the broader population of young people on supervised community orders.

194 7.6.1 Covariates: comparing survival and participation

Covariates of survival over the total observation period (mean 3.8 years) resembled, but were distinct from, the correlates of two year recidivism. The similarities follow from the fact that most recidivists reoffended within the first two years of baseline. Correlates showing similar associations in both Chapter Six and Seven are suggestive of stable, persistent risk factors. Such was the case for Indigenous status, which doubled the odds of recidivism in two years, and doubled the recidivism hazard over 3.8 years. This is unsurprising given the literature reviewed earlier, and yet it is also provocative: the juvenile justice system has half as much time to intervene with Indigenous participants before recidivism is likely to occur. A similarly short window exists for intervention to reduce theft recidivism in younger participants.

Some of the reasons for differences in survival and participation were also apparent in the bivariate analyses. The effects of covariates on the hazard may change over time, and logistic regression is insensitive to such changes. The survival curves for cannabis, for example, revealed a much higher risk of general recidivism experienced by daily users in year one after baseline. If short term recidivism reduction is the aim, it would make sense to focus resources towards this group. Daily cannabis users’ overall hazards differed little from other users, however, and were only 50% higher than non- users. Over the longer term, then, there may be more appropriate foci of intervention than cannabis use.

Daily cannabis use was more strongly related to two year recidivism (Chapter Four) than recidivism over 3.8 years (this Chapter). Dynamic covariates (such as drug use) are likely to have decreasing effects over the long term. Longer observation means more opportunity to change: for heavy users to reduce use, for non-users to initiate, and for other dynamic factors to emerge (e.g. greater influence of peers as parental influence declines over time). Meanwhile, static factors (such as Indigenous status) continued to contribute to risk. The sparse associations between drug use and the mean 3.8 year recidivism hazard suggest that other effects of drug use observed in Chapter Six faded with time. This could not be assessed but may indicate reductions in drug use (Teplin et al., 2012b) or changes in other risk factors (Childs, Sullivan, et al., 2011).

195 7.6.2 Predictors of survival

7.6.2.1 Drug use

Cannabis was the sole drug associated with all recidivism outcomes, but was independently predictive only of violent offending. There was no apparent dose- response relationship. Rather, daily users had much shorter times to recidivism than other users and non-users. This subgroup was also one of only two for whom the first offence was more likely to be violent. Daily users constituted 43% of all violent recidivists; it is clearly worth exploring possible explanations for this link. Direct and indirect explanations can be considered. Clearly, more precise measurement of frequency of use (or of dependence) will be necessary to assess whether the risk is concentrated amongst those who use even more frequently (e.g. several times daily).

One indirect link is suggested by Goldstein’s systemic hypothesis: frequent use necessitates more contact with illicit drug markets, which are inherently violent. The other predictor of violence but not of other recidivism was victimisation by other drug users (which is another feature of Goldstein’s hypothesis). If this were an appropriate explanation for this sample, one would expect similar associations for daily users of other drugs. Daily use of other drugs was too rare (3%) to assess this possibility statistically, however controlling for daily use (or even weekly use) of other drugs made no difference to the impact of daily cannabis use on the violence hazard. However, it seems implausible that violence would be inherent to the cannabis market but not others; Goldstein originally described this link in the context of cocaine markets.

Perhaps daily drug use serves as a marker of other unmeasured risk factors. Frequent use means a considerable amount of time spent using, being affected by and recovering from a drug. This renders many activities including full-time education or employment much less viable, weakening pro-social bonds. Daily use may have explained some of the variation in violence that schooling, employment, leisure and family characteristics would otherwise explain, were they included in the final model. Those variables were not significant, however, and were also largely irrelevant to the prediction of general recidivism among NSW young offenders in a study by McGrath

196 and Thompson (2012). Finally, one probably would expect a graded relationship with recidivism, not the concentration of risk amongst daily users that exists here.

Given that the risk amongst daily users was independent of many other risk factors (and that many others were considered but found to be inconsequential to violent recidivism) direct explanations may apply. Frequent cannabis use may increase the risk of psychosis (Barkus & Murray, 2010), which has been directly linked with interpersonal violence (Douglas, Guy, & Hart, 2009). Unfortunately, data on psychosis, and parental schizophrenia, which is an important marker of vulnerability (Degenhardt & Hall, 2006) could not be explored. However, the contribution of cannabis use to psychosis is small (pooled odds of around two for weekly versus less frequent use in various meta-analyses), so the fraction of violence attributable to cannabis-related psychosis would be insubstantial in any case. Cannabis-precipitated psychosis would also not explain the different survival patterns in year one versus later years after baseline.

As in the participation models, cannabis did not have a significant impact on general or theft recidivism. A positive but non-significant relationship with theft contrasts Salmelainen (1995)’s study (Chapter Two), which found frequent use was a crucial determinant of theft amongst juveniles in NSW. (More frequent offending tends also to be more rapid). However, relationships varied by theft type, and the strongest link was with vehicle theft.

The link between regular opioid use and non-violent acquisitive recidivism is well understood and so requires less contemplation. It was only weekly users, who constituted 5% of the sample, amongst whom the theft hazard was elevated. A further 9% of the sample had used in the past year, and are at risk of transitioning to weekly use (and thus are at greater risk of theft recidivism). Treatments for opioid dependence (e.g. buprenorphine, Larance, 2012) are discussed in Chapter Nine. Whether coerced or voluntary, it is essential that these are made available to opioid using young offenders.

197 Amphetamine use explained little variation in survival times, at even the bivariate level. The attention granted to the potentially criminogenic effects of amphetamine use (Chartier et al., 2010; Fleming, Gately, McGregor, & Morris, 2012) did not find support in these data. Other drug-related problems made a minor contribution to the prediction of recidivism, but did not improve model fit beyond the variables mentioned above. Prior offending to support drug use, offending whilst intoxicated, and need for treatment were generally associated with shorter survival, but none were independent predictors.

7.6.2.2 Predictors of multiple outcomes

The effect of age differed slightly for specific recidivism types, but in general age extended survival. Older participants’ median times to recidivism were longer than younger participants’, and for general and violent recidivism this differential increased over time. The important context for this increasingly protective effect of older age is that almost all (92%) participants were aged 18 to 22 at data linkage (i.e. when observation of their offending ended). Holding other factors equal, the decline in risk will be steeper for participants observed from ages 17 to 20 than those observed from ages 15 to 18, given incidence peaks around age 16. Age may be a marker of maturational processes not captured by the variables in this model, arising from intrinsic factors (e.g. improvements in risk perception, Steinberg, 2008) and life events, such as employment and parenthood that increase psychosocial stability (Sampson & Laub, 1990). Prompt intervention appears especially warranted for younger offenders, particularly those under 15 years, who were the most rapid recidivists. This intervention should also address the impact of criminal peers.

Striking contrasts were found between the different ethnic groups. Indigenous status predicted recidivism compared with all other offenders but did not predict recidivism compared only with ESB (Australian-born) participants. There were also clear differences in specific recidivism between the two CALD groups: CALD offenders were convicted much sooner for violence than ESB/CALD, while both CALD groups avoided theft for longer than other participants. These findings do not suggest an obvious explanation. Both groups were heterogeneous: ESB/CALD were predominantly of

198 Maori, Pacific Island and Lebanese background; other CALD were a mix of ethnicities including Asian. Such differences most likely reflect a suite of unmeasured parenting and peer issues that vary by ethnicity (Marie et al., 2009). Qualitative evidence of such differences is available (Seidler, 2010), but robust quantitative evidence is lacking in the Australian juvenile offending literature. Implications for coding of ethnicity are addressed in Chapter Nine.

Multiple episodes of detention predicted shorter survival to all recidivism outcomes. Although prevalence was nearly 75%, this was the only custodial predictor (duration, and number of custodial orders were not significant). Youths who avoid being detained (or perhaps are deterred by detention) had distinctly better outcomes. In most cases, the most powerful predictor was total prior offences. The relationship between Conduct Disorder symptoms and survival has been observed elsewhere (Kjelsberg, 2005) and is of interest because it shows that self-reported offending adds considerably to the prediction of rapid recidivism, over and above conviction data.

Verbal IQ (VIQ) strongly predicted longer survival in all models, especially theft, and the nature of this relationship has implications for programming. First, despite a broadly inverse relationship, recidivism outcomes for those with very low VIQ (below 70) were only minimally lower than those with VIQ 70-84. Even mild deficits in verbal reasoning have a detrimental impact on recidivism risk. Meanwhile, recidivism was rare amongst those with above average VIQ. Adjustment for academic performance and performance IQ did not substantially alter these relationships. Although low VIQ is traditionally associated with violence risk, the effect was also clearly apparent in the theft model. One explanation is that youths with lower VIQ have more difficulty comprehending supervision requirements (Snow & Powell, 2012; Snow, Powell, & Sanger, 2012).

7.6.2.3 Predictors of theft or violence only

The theft model provided evidence of offence continuity, with both prior theft and total theft convictions more strongly related to theft than to general recidivism (and prior theft also increasing the likelihood that the first offence would be theft). This

199 suggests that interventions which focus specifically on the instrumental motivations for theft (such as using theft to generate income to support drug use), rather than addressing a general propensity to offending are worth considering. The impact of out of home care (and its increasing relevance over time) on theft is less easily interpreted. Young offenders with foster care experience do tend to initiate their offending earlier (Alltucker, 2006, in Zhang, Barrett, Katsiyannis, & Yoon, 2011), which is in turn another predictor of rapid recidivism. However, age of first offence was controlled for in the theft model in this chapter (it was inversely related to theft risk).

Prior violent offending did not predict violence which suggests that violent offenders tend to be generalist (diverse) offenders. Recurrent violence is more likely to result from a diverse accumulation of factors that interact over the life course (Volavka & Swanson, 2010). Drug use is only one of these, but also impacts on many others including mental health, education, and the risk of victimisation (Volavka & Swanson, 2010). Whether or not cannabis use is causal of violence, a focus on daily users’ needs can be justified on the grounds that it is a risk factor for other more recognised contributors to recidivistic behaviour.

7.6.3 Limitations

The modelling approach used here was chosen to extend earlier studies, more precisely to ascertain the recidivism hazard function, and allow for time-varying effects to be noted. The complexity of analysis and need for elaboration is a drawback of this approach (Allison, 1995) that may partly explain the scarcity of such models in the literature; parametric analysis is also unsupported by basic statistical packages (e.g. SPSS). Analysts have also opted for the status quo (PH models) to facilitate comparisons with prior studies (Bradburn et al., 2003). However, the parametric models used here were justified by their ability (unlike PH models) to examine changes in the baseline hazard over time and to calculate predicted median survival times (Allison, 1995). Adding to the scant literature base may encourage further comparisons using these arguably superior modelling alternatives.

200 Few Australian studies have provided comparably rigorous analyses of recidivism hazards, but nonetheless these analyses were not exhaustive. All reported models used the log-logistic distribution, which unlike PH models, assumes all offenders ultimately recidivate (i.e. it has an asymptote of 1, Hepburn & Albonetti, 1994). As the results of PH models were comparable, this suggests that the reported results were not unduly affected by this erroneous assumption. The log-normal distribution could have been used for the specific recidivism models, but results from this approach differed little for the study factors (drug use), and this would have complicated cross- model comparisons. More complex parameterizations were beyond the scope of this thesis but could be considered in future analyses of these data. ‘Frailty’ models have an additional parameter to account for unobserved heterogeneity between individual participants’ risks of recidivism over time (StataCorp, 2009b), akin to random effects regression (Wan, Moore, & Moffatt, 2013). Extant studies offer inconsistent evidence as to the interpretability or relevance (e.g. improvement to model fit) of the frailty parameter to recidivism (Carvalho & Bierens, 2002; Sirakaya, 2006).

These analyses focused on each participant’s first new offence, which tended to occur relatively close to baseline, and not to be their sole new offence. The models also found some predictors to be time-varying; older age became more protective against violence as time elapsed. Together, this means that the results will not generalise precisely to subsequent (2nd, 3rd,… nth) offences. Options for exploring multiple events, which must account for intra-individual correlation of these events, include frailty (Wan et al., 2013) and recurrent event survival models. These can provide more precise hazard estimates than single event models (Ezell, Land, & Cohen, 2003) but require accurate time-at-risk data (Larney, 2011) which were not available in this dataset. Further, as the event being predicted becomes more distal, baseline values for dynamic covariates become less relevant. Chapter Eight explores the predictors of total offence counts.

Precise TRs were not reported for multivariate models containing covariates linked to the ancillary (shape) parameter. By definition, these TRs vary over the observation period, but TRs were observed to be the same at baseline as at later times for most

201 covariates, except for those linked to both scale and shape. The calculation of precise, time-specific TRs is complex (Section 7.4.3) and their reporting would greatly extend table sizes. Accordingly, this study emphasised the relative strength of predictors (which was precisely reported) rather than the magnitude of temporal variation.

There are also phenomena relating to drug use that may affect recidivism but that were not measured in the current data. For example, serious offending also predicted later cocaine use by US youths (Doherty, Green, & Ensminger, 2008). This offers an indirect pathway from prior offending to recidivism via later problem drug use, and underscores the need for cautious interpretation of dynamic factors at baseline. If more heavily criminally involved participants initiate sooner than others, this confounds the impact of baseline drug use on recidivism. Drug-related mortality presents a similar issue. Young Australian offenders’ mortality is ten times higher than age-matched peers for males and 40 times higher for females; and the most common cause of death is drug-related (Coffey, Veit, et al., 2003; Coffey et al., 2004). Half of all post-release deaths in Larney (2011)’s NSW opioid-dependent prisoner cohort were drug-related and mostly involved opioids; mortality was highest close to release. In the current study, participants who died before recidivating were ‘censored’ (i.e. still at risk of recidivating, but no longer observed) at the date of data linkage, whereas they are in fact ‘immune’ (i.e. not at risk of recidivating) (Carcach & Leverett, 1999). If heavy drug use predicts mortality, models will have underestimated its impact on recidivism. As most recidivism occurred soon after baseline, however, this should be less of a concern for survival models than models giving equal weight to offences committed across the full observation period (i.e. Chapter Eight).

7.6.4 Conclusion

This chapter has shown that as in other offender samples, the timing of recidivism by young offenders on community orders varied widely. However, most recidivism occurred within a one year period. The recidivism hazard increased initially, but became very low by around three years. The potential for reductions in the overall prevalence of involvement in recidivism diminished rapidly with time, slightly less so for violence.

202 Survival time decreased with frequency of drug use, but associations were specific: time to all recidivism outcomes was shorter for daily cannabis users, time to violence was shorter for most weekly drug users, and time to theft was much shorter for opioid users. The effect of cannabis on violence, and of opioids on theft, were apparent across demographic sub-groups and persisted after adjustment for the other mostly demographic and static risk factors. Thus, drug-survival associations may be largely attributable to non-drug factors, but there are still opportunities for targeted intervention with certain heavy user groups that may prevent or delay some specific offence types. Violence and theft risk should be assessed among all offenders reporting these patterns of use.

The models of time to specific recidivism were the first for Australian juveniles, and reveal distinct differences in the risk profiles of theft and violent offenders. The models also showed that these models do not generalise to all offender subgroups (especially female and Indigenous offenders). The models also revealed how hazards change over time and differ for younger and older offenders. These results suggest the need for offence, sub-group, and age-specific approaches to rehabilitation and recidivism modelling (e.g. Plattner et al., 2009).

With regard to planning and prioritising interventions with offenders, knowledge of the timing of recidivism (and how this differs by offence and subgroup) is more useful than the overall associations with recidivism reported in Chapter Six. However, the predictors in both chapters were similar in number and nature. Factors that predict more rapid recidivism should also predict a higher volume of offences (although rapidity can reflect short-term intensity, while volume is more related to persistence). Neither Chapter Six or Seven speaks to the persistence of offending beyond the first new offence, or the severity of this offending (especially as serious offences tend to be infrequent). Distinguishing more persistent and severe offenders would help policy and program staff target their efforts to youths who present the greatest community concern and CJS burden. This would also identify those youths who recidivate, but might not require intensive intervention. Thus, Chapter Eight considers predictors of frequency and severity of recidivism.

203 8 Patterns and correlates of the frequency and severity of recidivism

Previous chapters have addressed the prevalence (i.e. participation) and timing of recidivism. This final empirical chapter explores three further dimensions of recidivism: the frequency of recidivism, the severity of violence, and escalation in offence seriousness from pre-baseline offending. It begins by focusing on the frequency (incidence, rate, or lambda, Blumstein et al., 1986) of recidivism, as this reveals the total volume of recidivism by the sample, and the intensity of this offending (Petras, Nieuwbeerta, & Piquero, 2010). The relevance of this measure to the thesis sample was explained in Section 3.4: participants (N=793) made 3067 new court appearances. It is critical to assess the nature of this variation, because the frequency of offending typically varies widely within large offender samples (Loeber & Snyder, 1990), and it is common for studies to identify a small number of high rate offenders. Focusing on these offenders may be a more efficient means of reducing the volume of recidivism than broad efforts to reduce recidivism by low rate offenders.

The rationale for modelling lambda and the other severity measures follows, with descriptions of key studies that have used the same method to study drug-crime relationships. The chapter then focuses on models of the incidence of general, theft and violent recidivism. The chapter aims to clarify the contribution to these outcomes of various patterns of drug use.

204 8.1 Frequency of offending

There are several reasons to study the correlates of offence frequency in this sample. The first is to identify appropriate strategies for dealing with high-rate offenders. The second is to address treatment matching and resource allocation. The ‘risk’ principle of the risk-needs-responsivity model (Chapter One) and broader principles of juvenile justice encourage a focus on high-risk offenders and the use of the least restrictive measures available. It would be prudent to link the intensity of interventions with frequency and not just participation in offending. This is clinically appropriate and could reduce undue intervention with youths who reoffend infrequently. Third, the correlates of frequency and participation may differ. Despite calls to investigate this (Blumstein, Cohen, Roth, & Visher, 1986b; Loeber & Snyder, 1990) relatively few studies have done so (Brame et al., 2010). Such analyses may have theoretical implications, as some theories predict differences in the characteristics of high and low rate offenders (e.g. Moffitt, 1993) whilst others do not (Lattimore, MacDonald, Piquero, Linster, & Visher, 2004). Finally, problem drug use has been linked with offending frequency (e.g Bennett & Holloway, 2007), but few studies have examined this link across a range of drugs and none so far have attempted this for young Australian offenders.

The major reviews of the correlates of offending frequency have called for studies of the predictors of high and low rate offending, variation in lambda across offence types, and disaggregation by gender and ethnicity in a longitudinal prospective study (Blumstein et al., 1986a, 1986b; Piquero et al., 2003, pp. 479-480). These reviews total more than 1000 pages in length but provided little information on relationships between drug use and lambda among young offenders. An early study of US male prisoners showed that drug use was a far better predictor of self-reported offence frequency (and the seriousness of offending) than criminal records. Moreover, a particularly strong relationship was observed between problem drug use and high rate serious offending (Chaiken, Chaiken, & Peterson, 1982). The authors’ later review of relationships between drug use and predatory (violent) crime (Chaiken & Chaiken, 1990) revealed a dose relationship between drug use and offending frequency that

205 was strongest for dependent users. In a retrospective analysis of UK arrestees, Bennett and Holloway (2007) later found that drug use was associated with more frequent income-generating offences and moreover that more frequent drug use was linked with more frequent offending.

As explained in Chapter One, conclusions from studies of drug use among adult offenders are particularly difficult to generalise to juvenile populations. However, amongst the few thorough accounts of the frequency of recidivism and its predictors (e.g. Lattimore et al., 2004) drug use has received little focused attention. For example, Schubert et al. (2011) aggregated different drug types. Studies have also generally controlled poorly or not at all for time at risk, despite its impact on count modelling conclusions (Ferrante, 2009; Piquero et al., 2001).

One Australian study (Salmelainen, 1995) examined the correlates of high versus low theft offending frequency among 247 NSW juvenile detainees. This study found factors influencing criminal involvement differed from those affecting offence frequency. Cannabis users were more likely to be high rate break and enter or vehicle theft offenders, and amphetamine users were more likely to be break and enter offenders. The risk of offending was higher among more frequent users. In fact, there was a striking difference between youths who smoked more than 40 ‘cones’ per week, and less frequent, occasional and non-users for whom frequent theft was equally likely (Salmelainen, 1995). Self-reported need to support drug use was also correlated with high frequency of both offences, as were truancy and living out of home. This study suggests a strong link from frequent drug use and a need to support this use to frequent theft. Interestingly, drug use was not related to shoplifting, and opioid use was uncorrelated with any offence, although perhaps due to the small sample size. Again, the sample contained too few females to assess separately.

206 8.2 Severity and escalation of offending

The first aspect of offence severity considered in this chapter is the severity of violence among recidivists. Violent offences, in aggregate, are considered more serious than other offences (Australian Bureau of Statistics, 2011), but violent offending is an extremely broad category and there are reasons to consider the severity of offences therein. Assessing violence severity provides a further measure of the burden of offending by the sample. This is an important companion to the models of offending frequency, because many of the most serious offences (e.g. homicide) are extremely rare. Distinguishing the characteristics of more and less severe violence may also help clarify the etiology of violence. For example, if the frequency of drug use was consistently associated with the severity of violent recidivism, this could suggest that drug use exacerbates the severity of offending behaviour. In studying violence severity in the sample, It is important to look beyond the purely legal classification of the Australian and New Zealand Standard Offence Classification system (Australian Bureau of Statistics, 2011) and consider other elements of violence (Kenny & Press, 2006). The question has other practical implications, too. In NSW, the severity of violence affects whether juveniles are dealt with in the adult or juvenile justice system (Kenny, 2013), which in turn has a bearing on the diversionary and therapeutic options available to offenders. Variation in severity of violence by pattern of drug use has received little attention to date in the Australian juvenile offending literature.

The second measure of severity of offending considered in this chapter examines differences between the seriousness of participants’ offending prior to baseline, and subsequent (recidivist) offending. Whereas other models of recidivism in this thesis distinguish the correlates of persistence in offending, this approach provides insights into two subtle but substantively important changes, namely ‘escalation’ (to more serious offending after baseline) and de-escalation (recidivism, but due to less serious offending). Drug use was found to influence escalation in the frequency of offending by Australian detainees (Payne, 2006) but so far its relationships with changes in severity of offending have not been studied.

207 8.3 Aims  Describe the frequency of total, violent and theft convictions for new offences by the sample after baseline, and the annual rate for these outcomes, adjusting for time at risk (time not incarcerated);  Assess the relationship between drug use, other factors and the rate of total, violent and theft recidivism;  Assess the severity of violent recidivism, using forensically-informed severity coding, and the relationship of drug use and other factors to the level of severity of violent recidivism;  Assess escalation in seriousness from pre-baseline offending to post-baseline offending (i.e. recidivism), using an objective legal measure of seriousness, and assess the relationship of drug use and other factors to this escalation.

8.4 Methods 8.4.1 Inputs and outputs of count models

This chapter employs negative binomial (NB) regression to model individual offence frequencies during the observation period (time between baseline and the date of data linkage). Offence frequency is denoted by the Greek λ (lambda, Blumstein et al., 1986a), and was operationalised as the number of convictions for new offences committed after baseline. The incidence rate is the number of convictions divided by the length of time at risk of offending in the community (see Section 8.4.4). Counting started from the baseline survey date, as per Chapter Seven, rather than the most recent (index) conviction, to ensure that models predicted behaviour prospectively. The primary output is the incidence rate ratio (IRR) which reflects the change in conviction rate for a one-unit change in a predictor variable (e.g. the predicted rate of convictions among males, compared with females). Margins were also calculated; in these models, margins are the number of convictions predicted for a given variable in the model, holding other terms constant.

208 8.4.2 Variables introduced in this chapter

The dependent variables (counts of new convictions) were calculated only for this Chapter. These were total counts (general recidivism), counts of new theft convictions, and counts of new violent convictions. As in Chapter Seven, violence included non- acquisitive violent offences and robbery. A dichotomous covariate ‘any problem drug use’ (any PDU) was coded ‘1’ if participants reported any of the following: more than twice weekly binge drinking, daily cannabis use, weekly amphetamine use, and/or weekly opioid use).

8.4.3 Model building

This chapter aimed to assess variation between patterns of drug use and offending frequency and used a more simplified approach to model-building than in previous chapters. Models of offending frequency were built initially using the predictors of more rapid recidivism (Chapter Seven) for the corresponding outcome (i.e. the base model for general recidivism frequency began with the survival model for general recidivism). Terms with excessively large standard errors (above 2.0) were removed, as were non-significant terms, starting with the least significant term, until removal of a term significantly affected model fit. Patterns of drug use that were associated (p<.15) with the outcome in bivariate analysis were added to the model and retained if they improved model fit. Gender and age were retained in all models as control terms. Models were rerun in different demographic subgroups to assess their generalisability and these are described in Appendix B: analyses of demographic variation.

No problems were detected with multicollinearity across the NB models in this chapter; variance inflation factors (VIFs) were below two, well under the recommended maximum of 4 (Belsley, 1991), and tolerance was below 0.5.

Alternatives to the negative binomial (NB) model were evaluated, including zero- inflated NB, but none were superior to the NB model, that is they did not substantively improve model fit. To illustrate, Table 8.1 lists the model fit indices for the general recidivism model.

209 Table 8.1 Model fit indices for alternative count models

Model N df -2LL AIC BIC Poisson 718 24 5900.02 5947.98 6057.82 NB 718 25 3927.482 3977.48 4091.89 G NB 718 27 3897.126 3951.13 4074.69 G LM (gamma) 718 27 3897.126 3951.13 4074.69

GNB Generalised negative binomial; GLM Generalised linear model; df degrees of freedom; -2LL -2 Log Likelihood; AIC Akaike Information Criterion; BIC Bayesian Information Criterion

8.4.4 Time at risk

Offenders are ‘at risk of offending’ when they are not incarcerated. Time at risk is the total observation time minus time spent incarcerated (TSI). In this thesis, TSI was calculated as the total of the non-overlapping, non-parole periods of control orders. This measure was used because data on remand periods were not available (unlike in Weatherburn et al., 2009). Given the high rates of juvenile remand, the measure used is probably most conservative for younger participants as these youths were observed for longer periods in the juvenile jurisdiction, where a higher proportion of detainees are on remand rather than control orders.

One in three (31%) participants served one or more control orders after baseline, and mean TSI was one year (367 days). Mean time at risk was three years six months, with a minimum of five months, and 95% of participants were at risk for at least two years. In total, the sample was at risk for 92% of the observation period. This is equivalent to the estimate by Ferrante (2009) for adults with prison experience, but lower than their estimate for the wider convicted population (98%).

210 8.5 Results

Four in five participants were reconvicted (79%, Chapter Seven). The sample accrued 4562 new convictions in total, and the frequency distribution showed an extreme positive skew (Figure 8.1). Nearly 70% of participants accrued multiple convictions, 40% (one in two recidivists) accrued more than five convictions, and the 5% most frequent offenders accrued more than 20 convictions (to a maximum of 45).

20% Non-recidivists Median convictions amongst recidivists = 5 15% 10% >20 convictions (5% of recidivists) 5% 0% 0 5 10 15 20 25 30 35 40 45 Number of convictions for new offences after baseline Figure 8.1 Distribution of recidivism counts

Mean counts and rates are listed in Table 8.2. Rates adjusted for time spent incarcerated (TSI) were much higher. Recidivists accrued 2.7 convictions per year of time at risk (including 0.5 for violence and 0.7 for theft). The most prevalent recidivist accrued 11 convictions per year before TSI-adjustment, but 92 per year after TSI- adjustment. Rates of specific convictions were much lower. Theft recidivists accrued 1.3 thefts and violent recidivists 0.9 violent convictions per year at risk (not shown).

Table 8.2 Mean, variance and maximum for conviction outcomes

Convictions Total Violent Theft N=793 Mean Variance Max. Mean Variance Max. Mean Variance Max. Count 5.75 41.1 45 1.13 2.8 12 1.37 7.5 24 Unadjusted rate 1.52 2.9 11 0.30 0.2 3 0.36 0.5 7 Adjusted rate (λ) 2.11 21.4 92 0.42 1.1 19 0.57 3.8 38 λ recidivists only 2.68 25.7 0.54 1.3 0.72 4.7

Variance greatly exceeded the mean for all TSI-adjusted outcomes, including for recidivists only. This suggests that neither Poisson nor zero-inflated Poisson regression are appropriate for dealing with these data.

211 8.5.1 Bivariate correlates of frequency of recidivism

Figure 8.2 presents predicted conviction counts of each recidivism outcome, by drug type; counts are labelled for the subgroups that differ significantly from non-users.

8 Binge drinking 6 * 4 lower 2 0 convictions none less than weekly once/twice weekly >twice weekly Predicted count count Predicted of Total 7.9 6.3 7.9 7.3 Violent 1.4 1.1 1.6 1.5 Theft 2.6 1.5 1.8 1.9 * 8 Cannabis use 6 4 * lower * 2 0 convictions none less than weekly weekly daily

Predicted count of of count Predicted Total 6.8 5.3 6.7 8.7 Violent 1.1 1 1.2 1.7 Theft 2.4 1.3 1.5 1.9 8 Amphetamine use 6 4 2 0 convictions none less than weekly weekly

Predicted count of of count Predicted Total 7.5 6.2 6.4 Violent 1.3 1.2 1.6 Theft 2 1.4 1.5 * 10 Opioid use 8 * 6 4 *

2 convictions 0 Predicted count of of count Predicted none less than weekly weekly Total 6.8 7.1 11 Violent 1.3 1.4 1.1 Theft 1.6 2 5.5 Figure 8.2 Predicted count of general, violent and theft convictions by drug type

212 Recidivism rates generally increased with frequency of drug use amongst users, but this relationship varied by drug and recidivism type. Weekly opioid users had significantly more predicted convictions (11.0) than non-users (6.8). The ratio of these counts, or incident rate ratio (IRR), was 1.63 (unadjusted IRR column, general recidivism model, Table 8.3). This disparity was larger for theft. By contrast, infrequent drinkers and cannabis users had significantly lower counts of theft convictions than non-users of these drugs. Predicted counts of violence fell within a narrow range (1.0- 1.7) and were linked with daily cannabis use, but no other drug.

In addition, any problem drug use (PDU) shared a similar relationship to violence as daily cannabis use did. Daily cannabis use was reported by 67% of youths who reported PDU. Injecting drug use (IDU) shared a similar relationship to theft as weekly opioid use did. Weekly opioid use was reported by 40% of youths who reported IDU.

Demographics. Predicted conviction counts decreased by 17% with each year of age (IRR 0.83, Table 8.3) but this relationship was not linear. From age 15 upwards, there was a strong curvilinear trend with predicted counts increasing with age to age 16.5, and then decreasing steadily among increasingly older participants (Figure 8.3). As shown in the figure, predicted counts varied little at each year from 15 to 19. However, predicted counts were higher among participants aged less than 15 and the count varied widely in this group. Similar patterns were observed for violence and theft.

20 Vertical lines: 95%CI 15 Predicted count 12.6 10 8.9 6.6 5 6 5.3 2.5 0 Under 15 15 16 17 18 19 plus Age group Figure 8.3 Predicted count of convictions with 95% confidence intervals, by age

213 This pattern suggests that at a population level, intensity of offending among youths on supervised community orders peaks in mid-adolescence, but is particularly intense among youths in supervision at a very young age. Under 15s were too few (8%) and too correlated with ‘early onset’ offending (having been proven guilty for an offence committed before age 14) to include as a covariate in the models in this thesis.

Males accrued 50% more convictions and nearly twice as many violent convictions as females, but showed no difference on theft. Compared to English-speaking background (ESB) youth, overall rates and theft rates in particular were much higher for Indigenous youth. Rates were lower for culturally and linguistically diverse (CALD) youth. However, violent lambda did not differ by ethnicity. Rates decreased by around 10% with each socioeconomic status (SES) quintile for all outcomes (p=.04-.08).

Other risk factors. Total prior convictions had a strong, positive relationship with lambda, as did prior violence with violent lambda, and prior theft with theft lambda. Youths convicted before age 14 recidivated two to three times as often as other youths for all outcomes (all p<.001). A similar pattern was found for youths with multiple detention episodes. Most participants had multiple detention episodes (73%) and this was a strong risk factor for total and violent recidivism. Self-reported antisocial behaviour (ASB) was correlated with increased recidivism. Higher rates (more than 50%) of all outcomes were evident at even mild levels of ASB. Violence rates were more than double at high levels of ASB (IRR 2.2, p<.001). Frequent fighting was modestly correlated with all outcomes. Verbal IQ (VIQ) was increasingly protective at higher levels, particularly for theft; differences at low and very low levels were negligible. Youths with a history of care had recidivism rates 52-65% higher than others for all outcomes (p<.01).

214 8.5.2 General recidivism model

Table 8.3 presents the unadjusted and adjusted rates of general recidivism for variables in the final model. Weekly opioid users (5% prevalence) incurred convictions twice as frequently as non-users, and nearly twice as frequently as less than weekly users (AIRR 1.8, p=.016). A different pattern emerged for amphetamines: weekly amphetamine users had lower rates of recidivism than non-users but these groups did not differ significantly. However, less than weekly amphetamine users had significantly lower rates of recidivism than non-users (AIRR 0.7, p=.001). AIRRs for opioid use differed by very little when amphetamine use was excluded from the model, indicating that the relationship of each of these drugs with lambda was largely independent. Including IDU reduced weekly opioid users’ AIRR to 1.8, but did not improve model fit, so was not retained in the model. Binge drinking and cannabis use were not significant in bivariate analyses so they were excluded from the final model.

Age had a negative effect on recidivism. Offenders of CALD backgrounds had substantially lower rates of recidivism. Schooling interacted significantly with gender: schooling predicted a lower rate of recidivism among males than among females. Participants with VIQ below 85 recidivated at twice the rate of offenders with VIQ of 85 or above (i.e. in the average or higher range). No other psychosocial factors contributed substantively to the model. From criminal history, more extensive convictions, incarcerations, and conduct disorder predicted higher rates of recidivism. 2 Pseudo r was very low (.066), indicating that the model explained little variance.

215 Table 8.3 Unadjusted and adjusted incident rate ratios for predictors of recidivism

Covariate IRR 95%CI p AIRR 95%CI p Male 1.51 1.14-1.98 .003** 1.89 1.43-2.5 *** School 0.70 0.52-0.86 .001** 1.66 0.92-3 .092~ Male X school 0.70 0.35-1.41 .328 0.50 0.27-0.93 .028* Age (scale) 0.83 0.77-0.89 *** 0.83 0.77-0.89 *** Ethnicity: ESB 1.0 - *** 1.0 - *** Indigenous 1.70 1.33-2.18 *** 1.15 0.92-1.44 .215 ESB/CALD 0.57 0.43-0.76 *** 0.62 0.48-0.8 *** CALD 0.56 0.42-0.75 *** 0.67 0.52-0.87 .003** Prior total (ln) 1.70 1.54-1.87 *** 1.53 1.38-1.69 *** Multiple detention episodes 2.63 2.12-3.27 *** 1.84 1.5-2.25 *** Conduct Disorder (1-4) 1.23 1.14-1.34 *** 1.15 1.07-1.24 *** Verbal IQ<70 1.0 - *** 1.0 - *** 70-84 0.95 0.75-1.20 .661 1.00 0.81-1.25 .972 85-99 0.66 0.5-0.86 .002** 0.68 0.53-0.88 .003** 100+ 0.27 0.18-0.40 *** 0.42 0.29-0.61 *** Amphetamines: no use 1.0 - .167 1.00 - .029* Less than weekly 0.82 0.66-1.02 .072~ 0.70 0.57-0.86 .001** Weekly 0.85 0.60-1.20 .357 0.86 0.62-1.18 .353 Opioids: no use 1.0 - .097~ 1.0 - *** Less than weekly 1.05 0.76-1.47 .762 1.19 0.88-1.60 .254 Weekly 1.63 1.04-2.53 .031* 2.09 1.41-3.09 *** A/IRR adjusted/incident rate ratio; p<~.15 *.05 **.01 ***.001; 95CI 95% Confidence interval; ln = log transformed. X interaction

Indicator AIC BIC -2LL pseudo r2 N DF SEmax VIFmax Value 4112.8 4209.7 4070.8 .066 746 17 0.4 1.3 A/BIC Akaike/Bayesian information criterion; -2LL -2 log likelihood; DF degrees of freedom; SEmax max standard error; VIF variance inflation factor maximum

216 8.5.3 Theft recidivism model

The final theft model (Table 8.4) shows effects of amphetamine and opioid use similar to but stronger than those in Model 1. Predicted rates of theft for amphetamine users were nearly half those of non-users, a relationship that was not apparent in the unadjusted results. A strong trend was evident for opioid use, with weekly users’ rates 2.4 times less than weekly users’ rates (p=.004), and nearly four times those of non- users. IDU also increased theft, by 80%. These data suggest weekly amphetamine users committed more thefts if they used opioids weekly, and more still if they injected drugs. Injecting interacted strongly with opioid use: nearly half the predicted convictions for weekly opioid users related to injecting (relating to predicted counts of 6.3 before adjusting for injecting, and 3.3 after adjusting for injecting). Frequency of cannabis was included as an ordinal variable, because its relationship with theft recidivism was linear. That is, increasing frequency of cannabis use predicted decreasing rates of theft. Binge drinking was not significant in bivariate analyses and so was excluded from the final model.

Age and CALD ethnicity remained strongly protective against theft in the final model, while Indigenous status proved to be only a bivariate correlate of theft. More extensive prior convictions, multiple prior detention episodes, more severe ASB, and a history of theft predicted rates of theft recidivism at least 50% higher. Verbal IQ showed a linear, protective effect against theft recidivism.

Model fit statistics showed no problem with collinearity or model fit. Pseudo r2 was the highest of all three models but still modest at 0.3.

217 Table 8.4 Unadjusted and adjusted incident rate ratios for theft recidivism predictors

IRR 95%CI p AIRR 95%CI p Male 0.91 0.6-1.38 .644 1.37 0.94-2 .103~ Age (scale) 0.77 0.69-0.87 *** 0.73 0.65-0.81 *** Ethnic: ESB 1.00 - *** 1.0 - .001** Indigenous 2.00 1.38-2.89 *** 1.09 0.79-1.51 .599 ESB/CALD 0.33 0.21-0.52 *** 0.52 0.34-0.8 .003** CALD 0.39 0.25-0.61 *** 0.56 0.36-0.85 .007** Prior total (ln) 1.94 1.69-2.24 *** 1.52 1.26-1.83 *** Prior theft 2.46 2.1-2.87 *** 1.76 1.24-2.49 .002** Multiple detention epis. 2.73 1.93-3.86 *** 1.51 1.09-2.09 .013* Conduct Disorder: No 1.00 - *** 1.0 - .031* Low 1.77 1.15-2.74 .010* 1.02 0.69-1.5 .926 Moderate 1.94 1.32-2.87 .001** 1.48 1.05-2.09 .027* Severe 1.89 1.26-2.83 .002** 1.59 1.08-2.36 .020* Verbal IQ<70 1.00 1-1 *** 1.0 - *** 70-84 0.89 0.62-1.27 .510 0.8 0.58-1.1 .171 85-99 0.62 0.41-0.94 .023* 0.5 0.34-0.72 *** 100+ 0.14 0.07-0.27 *** 0.23 0.12-0.44 *** Cannabis use (1-4) 0.97 0.86-1.1 .844 0.87 0.77-0.98 .021* Amphetamines: no use 1.00 - .130~ 1.0 - .004** Less than weekly 0.72 0.51-1.01 .059~ 0.58 0.41-0.81 .001** Weekly 0.74 0.43-1.26 .266 0.58 0.33-1.01 .052~ Opioids: no use 1.00 - *** 1.0 - *** Less than weekly 1.27 0.77-2.08 .352 1.63 1.03-2.58 .036* Weekly 3.50 1.85-6.64 *** 3.85 1.98-7.47 *** Injecting in past year 2.93 1.75-4.89 *** 1.80 1-3.23 .048* A/IRR adjusted/incident rate ratio; p<~.15 *.05 **.01 ***.001; 95CI 95% Confidence interval; ln = log transformed. X interaction. Epis: episodes

Indicator AIC BIC -2LL pseudo r2 N DF SEmax VIFmax Value 2186.9 2288.4 2142.9 .297 745 20 1.3 1.5x A/BIC Akaike/Bayesian information criterion; -2LL -2 log likelihood; DF degrees of freedom; SEmax max standard error; VIF variance inflation factor maximum

218 8.5.4 Violent recidivism model

Table 8.5 presents the results of the final model for violence. No specific pattern of drug use predicted the rate of violent recidivism. However, participants reporting any PDU had a higher rate (AIRR 1.52, p<.001) of violent recidivism than other youths. Therefore, this was the only drug use variable included in the model. The adjusted and unadjusted IRR for PDU differed little, indicating that the effect of PDU was largely unrelated to other predictors of violent recidivism, such as Conduct Disorder.

Males and younger participants accrued violent convictions at a higher rate than other participants. However, male gender predicted violent recidivism only for younger males; young males had higher rates of violent recidivism than females and older males. Indigenous youths had a higher rate of violence and CALD youths a lower rate than ESB youths. However, this relationship was almost completely accounted for by other variables. Ethnicity did not improve the fit of the final model and so was excluded from the model and from Table 8.5.

Total and violent convictions and multiple detention episodes independently predicted higher rates of violence. ASB and fighting had an impact on recidivism only at the most severe levels. Violence decreased with higher VIQ (although less sharply than for other outcomes) and schooling was unrelated to violent recidivism (in contrast to theft and general recidivism). Victimisation weakly predicted violent recidivism but did not affect model fit, and so was removed from the model. Model fit indices show the model was preferable to considered alternatives, with pseudo r2 slightly lower than the theft model.

8.5.5 Subsample models

Appendix B: analyses of demographic variation presents the results of the general, theft and violent lambda models, respecified for demographic subgroups. As in Chapters Six and Seven, the full sample modes did not generalise well across gender, ethnicity or age.

219 Table 8.5 Unadjusted and adjusted incident rate ratios for violent predictors of recidivism

IRR 95%CI p AIRR 95%CI p Male 1.92 1.35-2.73 *** 2.42 1.52-3.85 *** Age ≥17 0.62 0.49-0.79 *** 1.28 0.68-2.42 .445 Male X Age≥17 0.43 0.21-0.86 .017* 0.45 0.23-0.88 .020* Verbal IQ<70 1.0 - *** 1.0 - *** 70-84 0.91 0.68-1.22 .521 0.92 0.70-1.22 .576 85-99 0.61 0.44-0.85 .003** 0.66 0.48-0.91 .011* 100+ 0.34 0.20-0.57 *** 0.49 0.30-0.81 .020* Conduct Disorder: no 1.0 - *** 1.0 - *** Low 1.45 1.03-2.04 .035* 1.13 0.83-1.56 .437 Moderate 1.56 1.14-2.13 .005** 1.09 0.81-1.47 .549 Severe 2.2 1.60-3.02 *** 1.57 1.15-2.13 .004** Frequent fighting 1.89 1.46-2.43 *** 1.52 1.20-1.92 .001** Prior convictions (ln) 1.55 1.37-1.77 *** 1.21 1.03-1.41 .019* Prior violent (ln) 1.22 1.14-1.30 *** 1.13 1.04-1.22 .004** Multiple detention 2.32 1.74-3.08 *** 1.68 1.27-2.21 *** Problem drug use 1.58 1.24-2.00 *** 1.52 1.21-1.90 *** Cannabis use: none 1.0 - *** Less than weekly 0.91 0.64-1.29 Weekly 1.17 0.81-1.70 Daily 1.64 1.19-2.25 .002 A/IRR adjusted/incident rate ratio; p<~.15 *.05 **.01 ***.001; 95CI 95% Confidence interval; ln = log transformed. X interaction

Indicator AIC BIC -2LL Pseudo r2 N DF SEmax VIFmax Value 2152.4 2226.1 2120.4 .192 740 14 .57 1.52 A/BIC Akaike/Bayesian information criterion; -2LL -2 log likelihood; DF degrees of freedom; SEmax max standard error; VIF variance inflation factor maximum

220 8.5.6 Severity of violence

Consensus coding for violence severity (developed with forensic clinicians and informed by Kenny & Press, 2006) classified 20% of the sample as low-level violent recidivists (e.g. common assault), 27% as moderate (e.g. aggravated assault), 5% high (e.g. assault occasioning grievous bodily harm), and 27% as non-violent recidivists (theft or other recidivism); the remaining 21% were not reconvicted.

Figure 8.4 displays unadjusted odds of severity of violence among recidivists, by pattern of drug use; the reference category for each drug is ‘no use’. For each drug type, levels of use that differed significantly from those of non-users are marked (*). For example, recidivists who binged more than twice weekly were three times as likely to commit low level violence, and 2.7 times more likely to commit moderate level violence, compared to non-drinkers (and these differences were significant). Odds of severe violence were 2.3 times greater among less than weekly opioid users and 2.9 times greater for weekly opioid users than nonusers. Appendix Table I presents the prevalence and unadjusted odds ratios for the predictors of violence severity among recidivists, which are reported overleaf.

Figure 8.4 Unadjusted odds ratios for violence severity by drug use

221 The final multinomial logistic model (Table 8.6) accounted for little variance in severity of violence (pseudo r2 .07), and confidence intervals were wide. In this model the reference group was ‘non-violent recidivism’. Weekly and less than weekly opioid use was combined into ‘any opioid use’ due to small cell sizes. Recidivists who used opioids were three times more likely to be convicted of a high severity violent offence than a non-violent offence (AOR 3.1, p<.05). However, they were no more likely to be convicted of a low or moderate violent offence than a non-violent offence. Use of other specific drugs did not predict severity of violence. Recidivists who reported one or more other pattern of problem drug use (i.e. daily cannabis use, weekly amphetamine use, and/or more than twice weekly binge drinking) were (in aggregate) 2.3 times more likely to be convicted of a moderately violent offence, than a non- violent offence. CALD and Indigenous recidivists had much higher odds of high level violent recidivism than ESB (Australian) youths. Having been detained on multiple occasions was a consistent predictor of all levels of violence, and frequent fighting showed a linear relationship with severity of violence.

Table 8.6 Adjusted odds ratios for predictors of violence severity among recidivists

Reference category: Low severity Moderate severity High severity LRT Non-violent recidivism AOR (95%CI) AOR (95%CI) AOR (95%CI) χ2 Male 1.24 (0.66-2.34) 2.00 (1.04-3.84)* 1.90 (0.58-6.21) .170 Age 17 plus 0.73 (0.46-1.14) 0.64 (0.42-0.99)* 0.46 (0.22-1.02)~ .114 Ethnicity: ESB 1.0 1.0 1.0 .002 Indigenous 1.25 (0.69-2.26*) 1.74 (1.01-2.99)* 3.78 (1.47-9.69)** .023 ESB/CALD 0.72 (0.37-1.40) 0.61 (0.31-1.17) 1.61 (0.51-5.11) .268 CALD 1.11 (0.56-2.19) 1.23 (0.65-2.33) 4.00 (1.41-11.3)** .089 Multiple detention 2.39 (1.39-4.09)** 1.82 (1.12-2.94)* 2.65 (0.95-7.35)* .004 Past year assault 2.33 (1.36-4.00)** 1.86 (1.10-3.15)* 1.00 (0.38-2.67) .008 Frequent fighting 1.53 (0.92-2.55)* 2.15 (1.34-3.45)** 2.87 (1.32-6.25)** .005 Any opioid use 0.76 (0.39-1.47) 0.75 (0.39-1.39) 3.11 (1.23-7.87)* .018 PDU non-opioid 1.53 (0.96-2.44)~ 2.30 (1.48-3.59)*** 1.50 (0.67-3.31) .003 p*<.05 **p.01 ***p.001 ~p.15. CI: confidence interval. LRT: likelihood ratio test N=582; AOR Adjusted Odds Ratio. PDU non-opioid: more than twice weekly binge drinking, daily cannabis use and/or weekly amphetamine use.

222 8.5.7 Escalation in severity of offending

Escalation in severity from pre-baseline to post-baseline offending was coded using Median Sentence Ranking (MacKinnell, Poletti, Holmes, & Wales, 2010; Smith & Weatherburn, 2012), which measures severity using average sentence lengths given to first time offenders. Prevalence of escalation was 25%, de-escalation 46%, no change 9%, and no recidivism 21%. Daily cannabis use was associated with escalation (OR 1.5, p=02), and PDU even more so (OR1.6, p<.01). Escalation was regressed on predictors of two-year recidivism (Chapter Six), using non-escalation as the reference category. Overall prediction was poor (r2 = .07). PDU, criminal peers and multiple detention episodes predicted escalation (AORs 1.6-1.8, p<.05), while CALD ethnicity, prior convictions and victimisation were protective (p<.05). Results were not sensitive to the inclusion or exclusion of non-recidivists. Subsample analyses (Appendix B: analyses of demographic variation) revealed that PDU did predicted escalation only among participants under 17 years of age.

Table 8.7 Prevalence, unadjusted and adjusted odds ratios for predictors of escalation in seriousness of offending

Not escalate Escalate Escalate vs. not escalate (%) (%) OR (95%CI) AOR (95%CI) Male 88 87 0.97 (0.60-1.62) 1.06 (0.60-1.87) Age mean 17.0 mean 16.8 0.88 (0.77-1.00) 0.98 (0.85-1.14) Ethnicity: ESB 51 59 1.0* 1.0* Indigenous 20 24 1.04 (0.67-1.58) 1.07 (0.67-1.71) ESB/CALD 15 9 0.53 (0.29-0.94)* 0.51 (0.27-0.96)* CALD 15 8 0.47 (0.24-0.82)** 0.41 (0.21-0.79)** Multiple detention 76 83 1.47 (0.95-2.29)~ 1.68 (1.04-2.70)* Prior convictions mean 1.8 mean 1.6 0.71 (0.58-0.87)** 0.59 (0.47-0.75)** Past year assault 26 19 0.66 (0.43-1.02)~ 0.57 (0.36-0.91)* Limiting disability 13 8 0.59 (0.33-1.08)~ 0.57 (0.29-1.12)~ Criminal peers 75 83 1.15 (0.99-1.34)~ 1.76 (1.07-2.88)* Problem drug use 41 53 1.63 (1.15-2.30)** 1.61 (1.10-2.36)*

A/OR: adjusted/odds ratio *p<.05 **p.01 ~p.15. CI: confidence interval. r2=.07.; N=582

223 8.5.8 Relationships between frequency of offending, escalation in seriousness and severity of violence

Figure 8.5 presents the annual conviction rate adjusted for time spent incarcerated (TSI) after baseline for recidivists (N=628), by change in offence seriousness (left side of figure) and by seriousness of violent recidivism (right side of figure). Seriousness and violence severity were correlated with rate of recidivism (Appendix Table H).

Youths whose offending escalated in seriousness accrued 71% more convictions than those whose offending de-escalated (became less serious). The highest rates of recidivism were amongst youths with moderate level violent offences (4.2 total annual convictions, but with wide variation: SD 7.8). The rate of violent offending was twice as high among youths whose offending escalated or remained stable as among those who de-escalated. Likewise, the rate of violent offending was twice as high among youths committing moderate or severe level violent offences compared with those committing low level violence. Moderate level violent offenders also had the highest rates of theft, double those of other groups including non-violent recidivists.

Total Violent Theft

3 2 1

(adjusted for TSI) for (adjusted Annual rate of offending offending of rate Annual

Figure 8.5 Relationship of escalation and violence severity to frequency of recidivism

Among recidivists, odds of escalation increased with the severity of violent recidivism: OR 1.8 (95%CI 1.1-2.9, p=.02) for low, 3.0 (1.9-4.7, p<.001) for moderate, and 3.5 (1.7- 7.3, p=.001) for high level violence.

224 8.6 Discussion

This chapter revealed the frequency of recidivism by the sample and the wide variation in frequency across individuals. The norm for this sample was not simply recidivism but recurrent recidivism. Consistent with prior studies, conviction counts were skewed, with most recidivists responsible for only a small proportion of the total volume of offending and a few responsible for a grossly disproportionate share. However, there were no clear statistical demarcations between low and high rate offenders that might suggest qualitatively different groups. Adjustment for time at risk impacted significantly on conviction rates, and the predictors of this risk.

Violent recidivism was slightly more prevalent than theft, but the rate of recidivism (and theft, specifically) amongst theft recidivists was higher than the rate of recidivism (and violent recidivism) among violent recidivists. Thus, predictors of general lambda were more strongly influenced by the predictors of theft than of violence. However, most convictions after baseline were for other (non-violent, non-acquisitive) offences.

Drug-crime associations varied by pattern of drug use and type of crime. Recidivism was once again higher amongst opioid and cannabis users, but only among those who used heavily. Weekly opioid use was one of the strongest independent predictors, and amphetamine use – which was unrelated to all forms of recidivism at the bivariate level – emerged as mildly protective for non-regular users. However, these results may be an artefact of far stronger associations between these patterns of use and theft. For other drugs, associations with theft were contrary to expectations: theft rates were lower amongst users than non-users, especially for two prevalent patterns (monthly binge drinking and cannabis use). Also unexpected was the absence of any apparent associations between binge drinking, amphetamine use and recidivism. However, the scale of these associations was relatively small (all differences in conviction rates varied between 0.5 and 2.0), with the exception of opioid use and theft.

At the bivariate level, drug use was not a powerful marker of lambda over the time- frame considered in this chapter. Outcomes for participants reporting any problem drug use were broadly comparable to daily cannabis users’ outcomes. Similarly,

225 injecting drug users’ outcomes were comparable to weekly opioid users’. These similarities were evident despite the moderate overlap between patterns of use. The relationship of drug use to lambda was robust to controls for age, gender and numerous other predictors. Lambda for all outcomes showed significant demographic variation. Most notably, lambda was negatively correlated with age: there were distinct peaks amongst the youngest participants and at the mean sample age, and very low rates amongst the oldest participants.

Criminal history and antisocial behaviour were correlated with higher levels of all outcomes. There was clear continuity of prior offending and Conduct Disorder with subsequent offending (including for specific offences, e.g. violence predicting violence), and youths with histories of multiple detention were more frequent offenders. Verbal IQ was also a consistent correlate, and protected against all outcomes. Most other correlates were differentially associated with violence and theft (as for drug use). For example, lambda for theft was higher for Indigenous youths, but this was not the case for lambda for violence. The importance of nuanced ethnicity coding was clear, as Indigenous youths differed from CALD but not ESB youths.

Frequency of recidivism was significantly correlated with the change in seriousness from pre-baseline to post-baseline offending, and with the severity of violence (among recidivists), but the correlation between these measures of severity was imperfect. Thus, each measure provided different insights into the burden of recidivism. Although most (67%) recidivists committed at least one offence involving violence (as coded using offence descriptions from the NSW Law Parts), two in every five violent recidivists committed very minor violent offences, and one in ten committed very serious violence. Thus, violent offending, as an aggregate offence category, is a very non-specific measure of offence seriousness. The second measure of severity indicated that most (58%) of recidivists also de-escalated; that is, although most participants recidivated, the overall trend was to less serious offending.

Frequent drug use was implicated in violent rather than non-violent recidivism, and in escalating offence seriousness. However, the relationship was drug-specific: opioid use was linked with severe violence and other drugs with less serious violence.

226 8.6.1 Limitations

The major limitation of this study was the imprecise control for time spent incarcerated. The absence of remand data means that lambda will have been underestimated for many participants. This is discussed in Chapter Nine. Variables in the final models contained data for at least 92% of participants. Some differences were observed between included cases (participants in the model) and excluded cases (participants missing data for any variable in the model). A higher proportion of excluded cases reported weekly opioid use (10%, vs. 5% included cases). Excluded cases were also more likely to inject drugs or have severe Conduct Disorder, and less likely to be in school or of CALD ethnicity. It is likely therefore that the regression parameters for these variables underestimate their true impact on lambda. Finally, very high rate offenders and extremely violent offenders may be better studied using descriptive case analysis (e.g. Piquero, Sullivan, & Farrington, 2010) than with standard regression techniques, particularly where levels of predictor variables show a complex relationship with these outcomes.

8.6.2 Conclusion

This chapter has revealed the extremely wide range in participants’ rates of recidivism. Most participants accrued multiple convictions but the distribution was highly skewed, with few youths offending at an extremely high rate. Drug-crime and other bivariate associations were largely, although not completely consistent with previous recidivism chapters. Notably, binge drinking was not related to higher rates of violence. Opioid use increased the rate of overall offending and theft in particular, while amphetamine use had a weaker and protective effect on these outcomes. Cannabis use reduced the rate of theft recidivism and increased the rate of violence, although violence was more powerfully predicted by overall involvement in problem drug use. Lambda was particularly high among younger clients and showed strong continuity (i.e. frequent pre-baseline offending was linked with frequent recidivism). The chapter also considered escalation in offence severity, and severity of violent recidivism, and the impact of drug use on these outcomes. Most recidivists committed less serious offences after baseline and most committed either low or non-violent offences.

227 9 General discussion This thesis examined patterns and correlates of drug use and recidivism in a sample of young community-supervised Australian offenders. In a linked series of five empirical studies, it systematically and prospectively assessed variation in relationships between specific patterns of drug use, and their associations with a range of co-occurring recidivism risk factors. Together, the recidivism studies comprise the most comprehensive profile of recidivism in this population to date. This was made possible by the near-complete linkage of lifetime court data to an extensive baseline dataset. The sample included 800 youths from across NSW, and was larger and more demographically diverse than many juvenile drug-crime studies.

The cross-sectional studies assessed baseline risk factors and correlates of specific offence types (violence, theft and robbery) (Study 1), and patterns and correlates of the frequency of binge drinking, cannabis, amphetamine and opioid use (Study 2, multinomial logistic regression). The prospective studies assessed patterns, correlates and demographic variation in general and specific recidivism: prevalence of reconviction within two years of baseline (Study 3), timing of new offences during the four year follow-up period (Study 4), frequency of new offences (Study 5), level of violence and escalation in severity (Study 5).

This chapter presents and synthesises key findings across five cross-cutting themes: patterns of offending, drug-crime relationships, recidivism risk factors, health/welfare, and demographics; these are summarised in Appendix Table C. The chapter then discusses limitations and draws implications for theory, practice and research.

9.1 Summary of key findings by theme 9.1.1 Offending and recidivism

The prevalence of recidivism (i.e. conviction for any new offence) was 79% at the end of follow-up (mean 3.8 years), and 65% two years after baseline. Time to recidivism was typically short (median duration eight months), and recidivism risk peaked one month after baseline and declined thereafter. Recidivists accrued an average of six

228 new convictions. The 5% of recidivists who accrued at least 20 convictions contributed one in six new convictions. Recidivists accrued 2.7 convictions per year during follow- up after adjusting for time spent incarcerated (TSI; 1.9 before adjustment). Violence (49%; excluding robbery) and theft (44%) occurred at a TSI-adjusted rate of 0.9 and 1.3 per year, respectively. Violence severity was rated moderate for 52% and high for a further 10% of violent recidivists. One in four youths also committed more serious crime after baseline than before (i.e. escalated). One in three youths (31%) received a control order during follow-up, however remand orders were not recorded. Thus, recidivism was not a foregone conclusion, but was typical for this sample. This is troubling for the community and the criminal justice system (CJS), but consistent with figures from similar studies in Table 1.1. This suggests that recidivists were not underrepresented, which is reassuring as the sample was primarily constructed by convenience sampling. The prevalence of recidivism was also more similar to detainee samples (Indig et al., 2011) than unsupervised young offenders or adults.

Recidivism involves a continuation of criminal involvement and is best understood in this context. The sample’s offending was well established at baseline. Offence histories were typified by multiple convictions (77% prevalence), multiple detention episodes (73%, though only 12% had been sentenced to detention) and violence (71%). Theft (57%) was the most frequent offence (one in three prior convictions). Offence histories were also diverse: the mean number of prior convictions was six, but 19% of participants had only one conviction while 19% had more than 10. Such variation was evident in self-report data: 19% reported severe antisocial behaviour (ASB), 42% minimal ASB and 60% multiple types of ASB including aggression. One in five youths had been convicted by age 14 (i.e. ‘early onset’).

These results reveal the substantial overall contribution of community-supervised young offenders to the total volume of juvenile recidivism. This group is more numerous but less accessible than young detainees; by definition, efforts to reduce their criminal involvement must involve non-custodial interventions. The diversity of the sample’s offending highlights the need for nuanced measures (see research implications at Section 9.5) and explanations of offending).

229 9.1.2 Patterns of drug use and links with offending

Drug use was ubiquitous (95%) and typified by at-least-weekly binge drinking and/or cannabis use. Half of the sample had tried other drugs and 19% used these weekly.  82% binge drank; 22% binged weekly and 10% binged more often  77% used cannabis; 18% used weekly and 35% used daily  36% used amphetamines; 9% used these weekly or more often  14% used opioids (primarily heroin); 5% used these weekly or more often  Patterns of drug use were also strongly inter-correlated (see Section 9.1.4)

Frequency of use typically showed independent dose relationships with drug-crime attributions: alcohol-affected crime with binge drinking (AOR 4.1 weekly/8.6 more often); drug-affected crime with daily cannabis or weekly opioid use (AOR 3.1); crime supporting drug use with cannabis use (AOR 2.3 weekly/4.1 daily); drug-related problems involving police with weekly amphetamine use (AOR 3.3). Recorded crime was usually greater for more frequent users, e.g.: recidivism prevalence (85% daily and 79% weekly cannabis versus 71% nonusers), time to violence (50% sooner for heavy versus non-drinkers), rate of theft (3.5 times higher for weekly opioid users) and prior convictions (41% more for daily cannabis or weekly opioid users). However, once other recidivism risk factors (see Section 9.1.3) were held constant, a different pattern emerged:  Binge drinking did not affect any measure of offending.  Daily cannabis use – but not weekly – predicted more rapid violence.  Amphetamine use – weekly or less than weekly – predicted fewer thefts.  Weekly opioid use predicted multiple aspects of recidivism, especially theft.

Thus, frequency of drug use was an inconsistent, drug-and-crime-specific and generally weak predictor of recidivism. Two very different patterns (daily cannabis and weekly opioid use) were independent markers of recidivism risk reported by a substantial minority of participants. This has direct implications for offender rehabilitation (Section 9.3.2). It is also clear that disaggregated measures of drugs and crime are vital in recidivism risk assessment (Section 9.3.1) and in research (Section 9.5.1).

230 9.1.3 Recidivism risk factors

Drug use was one of many correlates of offending that contributed little to prediction. Many predictors (including cannabis use and drug-crime attributions) differentiated offence types, prior offending from recidivism, or aspects of recidivism (e.g. rate vs. prevalence). These cross-cutting findings reveal the complex and co-occurring nature of participants’ risk profiles, and show that prospective, multivariate analyses of multiple recidivism outcomes can help identify intervention targets.

Demographic factors strongly predicted recidivism. Female gender and older age generally protected against recidivism, whereas Indigenous status predicted more rapid and severe violence, and culturally and linguistically diverse (CALD) status predicted fewer convictions but more severe violence. The factors associated with recidivism by these demographic subgroups varied widely (see 9.1.5). Multiple detention episodes strongly predicted all recidivism outcomes, and prior convictions were strong predictor of general, violent and escalating recidivism. Some continuity in offending was evident: prior theft offenders were much more likely to commit further thefts (but not other offences), and violent offenders accrued more violent convictions. Notably, early onset offending was not an important factor in these models, but more severe conduct disorder predicted a range of outcomes. and fighting was strongly linked with rapid, frequent and severe violence.

Among psychosocial predictors of recidivism, at least average verbal IQ strongly protected against all types of recidivism, and predicted longer survival and less frequent recidivism. Schooling also protected against all forms of recidivism, but was moderated by gender in some models; employment, however, had little relevance to recidivism. Having criminal peers predicted general and theft recidivism, more rapid offending (all forms), and escalation. Other psychosocial variables predicted specific recidivism only: physical victimisation predicted more rapid violent recidivism and moderately severe violence; a history of care predicted higher odds and more rapid theft recidivism. Few strong predictors predicted multiple outcomes.

231 9.1.4 Health and welfare issues

The sample was characterised by pervasive problems that varied widely in prevalence and severity, and that may jeopardise participants’ health and social welfare (Chapter One). These included drug use, predictors of recidivism, such as early school leaving (21%, versus 71% of all NSW youths in 2004, Australian Bureau of Statistics, 2004) and poor verbal ability (mean verbal IQ 79, 21 points below the population mean), as well as factors that did not predict recidivism, e.g.:  childhood maltreatment (74%; 33% moderate/severe)  emotional distress (59%; 11% with recent suicidal or self-harm behaviour/SSH)  socio-economic disadvantage (55% below average SES; 18% above average)  physical health concerns (84%; 5% with Hepatitis C, 12% with disabilities).

Psychosocial problems were experienced by many non drug users, but usually to a greater extent by users and frequent users in particular. For example:  Frequency of smoking predicted frequency of binge drinking and cannabis use.  Opioid use predicted weekly and more frequent binge drinking.  Injecting drug use (IDU) predicted weekly amphetamine and opioid use.  Peer drug use predicted heavy use of all drugs.

In addition to criminal history, other predictors differentiated patterns of drug use. Important drug-specific associations included:  any binge drinking with head injury, but only heavy binge drinking with SSH  cannabis use with males, weekly use with Indigenous and unemployment  amphetamine use with higher SES, weekly use with unstable housing  opioid use with urban residence, weekly use with drug treatment.

These results echo implications raised by the recidivism models by showing the need to differentiate patterns of drug use, focus on the most frequent drug users, and to prevent progression to frequent use. However, drug use also presents many non- criminogenic problems, even for non-frequent use (which was more common). Thus, public health and developmental perspectives can and should augment the actions of the CJS to reduce harms associated with juvenile offending.

232 9.1.5 Demographic variation in risk profiles

The full sample models above were dominated by non-Indigenous juvenile males (51% of the sample), but 15% were female; 24% aged 18 or over; 19% Indigenous and 29% CALD (minority ethnicity); and 25% from non-metropolitan areas. Subgroup comparisons revealed demographic variation in drug-crime relationships. Females, older (over 17) and CALD youths had significantly better recidivism outcomes than other youths (Section 9.1.3). Demographic factors were inconsistently linked with drug use, but female gender predicted injecting, Indigenous status predicted daily cannabis use, and weekly amphetamine and opioid use were urban phenomena. Females were also distinguished from males by greater non-cannabis drug use among peers, sexual victimisation, and less frequent fighting.

Full sample models generalised poorly to female, Indigenous, older and younger participants. Criminal history was consistently implicated in recidivism and weekly opioid use was a highly consistent predictor (of theft), unlike most other factors:  Daily cannabis use predicted violence, and criminal peers predicted more rapid theft and violence, for younger but not older participants.  Younger age predicted recidivism (AOR 3.2) and physical victimisation predicted violence (AOR 3.1) for females, but not for males.  Schooling predicted shorter survival for females (ATR 0.45), longer for males.  Verbal IQ predicted recidivism for non-Indigenous but not Indigenous youths.

There was further complexity in ethnic-crime linkages. Compared with English- speaking background (majority) status, Indigenous status was not a general predictor of recidivism, but did predict more rapid and serious violence. CALD status was a general protective factor (e.g. 33-38% fewer convictions than ESB); first generation CALD status predicted more rapid and serious violence, however, no effect was observed for youths born to CALD parents. These results have implications for understanding and responding to the needs of minority subsamples, comparing demographically different samples, measuring and sampling (e.g. of ethnicity), and for drawing theoretical, practical and research implications from the full sample results.

233 9.2 Explaining recidivism and drug-crime links

The thesis did not aim to test or develop theories of drug-crime links or offending; the baseline data were compiled for descriptive epidemiological purposes, not for theory testing. Nonetheless, the findings illuminate potential mechanisms for recidivism and the role of drug use therein.  There was no evidence of a general drug-crime association. Drug-crime relationships were characterised by their variation (Anthony & Forman, 2003; Blumstein et al., 1986b), and more than one drug-crime theory could be invoked to explain the specific associations identified (Bennett & Holloway).  Drug use was not intrinsic to recidivism. Drug use and recidivism were strongly correlated but drug use was a weak predictor of recidivism. Drug use and recidivism also shared many predictors.  Recidivism arose from manifold causes. There were stable (e.g. gender) and modifiable predictors (e.g. peer offending) in multiple domains of functioning, yet much of the variance in recidivism remained unexplained.  Risk profiles varied by offence type and demographic subgroup, but distinct subtypes of recidivists did not emerge.

9.2.1 Binge drinking and concurrent violence

In contrast with much prior research, binge drinking was not linked with violent offending or recidivism (e.g. White et al., 2002) but was linked with self-harm (Harford, Yi, & Grant, 2013), fighting, and recent alcohol-affected offending. This may reflect a disinhibiting (i.e. psychopharmacological) relationship between alcohol and violence (Wei, Loeber, & White, 2004). However, as these variables were measured contemporaneously, causal inferences cannot be drawn; the relationship could also reflect a predisposition to violence among heavy binge drinkers. A cross-sectional relationship between binge drinking and violence in the absence of a prospective relationship was also reported by Lightowlers (2011). It may be that binge drinking is less causal of violence by offenders than by people in the general population, because offenders experience a wider range of risk factors.

234 9.2.2 Cannabis use and violent recidivism

There was a striking concentration of violent recidivism among daily cannabis users (prevalence: 35%), with almost no variation in the prevalence or frequency of violence among less frequent users; daily use was also a strong predictor of more rapid violent recidivism. Cannabis and violence have been inconsistently associated in prior studies (Loeber & Pardini, 2008; White et al., 2002). No one explanation is likely to apply to such a large subgroup of offenders. One possibility is systemic violence arising through conflict within cannabis markets (Brunelle et al., 2000). However, even if a large subgroup of daily users were involved in drug dealing (which was not evident in the data), this explanation seems incomplete for the Australian context, where cannabis is usually dealt in ‘closed’ markets, often among acquaintances in a non-monetary economy (Moeller & Hesse, 2013).

Participants’ cannabis use was strongly linked with both individual (e.g. age of first drug use) and social factors (e.g. extent of cannabis use among peers). Frequent cannabis use increases the risk of psychosis (Zammit et al., 2008) although a causal link has not been established (Degenhardt & Hall, 2006); psychosis is linked with violence among drug-using offenders (Fazel et al., 2008). This can explain only a small proportion of violence by daily cannabis users, however, as the prevalence of psychosis is very small (Grann et al., 2008). For a greater proportion of daily cannabis users, drug use is less likely to cause violence than to be an element of an antisocial lifestyle in which both drug use and offending are accepted or normative among peers (Hammersley et al., 2003; Rutter, Giller, & Hagell, 1998). Violence may also occur in the context of other, less frequent drug use (e.g. occasional binge drinking among peers who use cannabis on a regular basis; Chapter Five).

9.2.3 Amphetamine use and recidivism

The absence of a positive association (and in some cases the presence of a negative association) between amphetamine use and recidivism was surprising, given links drawn to both violent and acquisitive crime in earlier literature (Boles & Miotto, 2003; Gately, Fleming, Morris, & McGregor, 2011). It is unclear why this was the case. One

235 possibility is that at the time of data collection, use of more pure (e.g. crystalline) forms of amphetamine was less prevalent than in US studies reporting the amphetamine-crime association (Nation & Heflinger, 2006). Another is that few amphetamine users in the sample reported injecting, which is the pattern of use most strongly correlated with offending for amphetamines and other drugs (e.g. cocaine, Bennett & Holloway, 2007) and is also more prevalent among adult users. The higher mean socioeconomic position of amphetamine users in this sample could also be a proxy for lower exposure to other unmeasured recidivism risk factors or could suggest a different social context to their drug use. These possibilities should be explored in later studies.

9.2.4 Opioid use, theft, and serious violent recidivism

Weekly opioid use showed a strong, dose response relationship with prior drug offending and all theft recidivism outcomes. Opioid use was also linked with serious violence, but neither the prevalence nor frequency of violence per se. The opioid-theft relationship also held across different demographic subgroups, which suggests that this relationship was not due to sampling error or unmeasured variables. This is consistent with prior juvenile detainee and adult offender research (Dobinson & Ward, 1986) and strongly suggests an economic explanation (e.g. White, Loeber, & Farrington, 2008), i.e. dependent opioid users preferentially commit income- generating crime to support their drug use. This pattern, and the overlap between injecting drug use and serious violence among weekly opioid users, suggests that violence may arise during routine acquisitive offending, perhaps due to victim non- compliance. Violence may arise through a psychopharmacological mechanism, including aggression linked with withdrawal (Ghodse, 2010) or use of other drugs, especially benzodiazepines (Boles & Miotto, 2003). These possibilities could be explored in detailed qualitative interviews (see Section 9.5.4).

9.2.5 Other risk factors for recidivism

Drug use was a relatively weak predictor among many predictors of recidivism that came from multiple domains of functioning. This is consistent with multi-causal

236 hypotheses, such as the social development model (Hawkins et al., 2007). Conduct disorder was strongly correlated with all measures of drug use, while other predictors of recidivism overlapped substantially but incompletely with the correlates of drug use. These findings offer partial evidence of a problem behaviour syndrome (Jessor, Donovan, & Costa, 1991).

Few factors robustly predicted recidivism across demographic subgroups and specific types of recidivism. There was a clear, protective effect of increasing age, which suggested powerful unmeasured maturational processes that may include reductions in drug use (Teplin et al., 2012b), a decreasing influence of criminal peers (Childs, Sullivan, et al., 2011), and family and job role commitments (Sampson & Laub, 1990). The other consistent predictors of recidivism were Conduct Disorder, prior convictions and multiple detention episodes. These findings suggest continuity in offending behaviour and also that CJS involvement is ineffective in deterring recidivism among many serious young offenders. Incarceration may promote recidivism through the development of criminal peer associations (Gatti et al., 2009). Incarceration may also be a marker of unmeasured risks including social instability and inadequate guardianship (Kenny & Nelson, 2008).

The relationship of other risk factors to recidivism was inconsistent. Being in school or having graduated was protective against most outcomes but did not hold among older offenders, as reported by White et al. (2008). Having criminal peers was also predictive of recidivism among younger offenders only. This is consistent with the notion of schools and peers having a declining influence into adulthood, as intrapersonal factors become more relevant (Childs, Sullivan, et al., 2011; Van Den Bree & Pickworth, 2005). Verbal IQ was generally protective against recidivism, but not for Indigenous offenders. This may indicate that neurocognitive deficits are not a universal risk factor for recidivism, but may also reflect the need for Indigenous-specific measures (see Section 9.5.3). Family factors and community-level factors did not contribute to recidivism. As measured in this thesis, these factors were distal; more proximal factors exerted more influence during adolescence (Schulenberg & Maslowsky, 2009), including the effects of peers as noted above (Childs, Sullivan, et al., 2011).

237 9.2.6 Subgroups and specific offences

Evidence for typological theories of offending was mixed. There were no clear statistical demarcations to suggest qualitatively different low and high rate offenders, and most participants were diverse offenders (i.e. had committed at least two major offence types). Recidivism was predicted by factors characteristic of adolescence- limited (e.g. criminal peers, Moffitt, 1993) and life-course persistent offenders (e.g. low verbal IQ, Raine et al., 2005), while early onset offending had little bearing on recidivism. However, the data were unable to assess trajectories (Section 9.4.2).

The full sample models generalised poorly to the female, Indigenous, younger and older participants, and both static and dynamic risk factors had varying impacts on recidivism among these subgroups. The results strongly suggest different etiological pathways and social contexts for recidivism; comprehensive models of recidivism among these subgroups are needed to build gender, age and ethnic-specific theory (Chesney-Lind & Okamoto Scott, 2001).

There were also marked differences between the predictors of general and specific recidivism, but implications regarding the drivers of specific offence types must be drawn tentatively because of the low degree of offence specialisation. Fighting and physical victimisation differentiated violent recidivists, but prior violent convictions did not, which suggests some continuity in violent behaviour that is less evident in the recorded offending data. Predictors of robbery recidivism were distinct from both violent and theft recidivism. The over-representation of ethnic minority youths among robbery offenders is consistent with UK research. The reasons behind CALD offenders’ involvement in robbery were not assessed in this thesis but key motivations noted in the literature include gaining material and social status (Bennett & Brookman, 2010). There may be cultural drivers to robbery, including the use of robbery to dominate and command respect from victims by black US offenders (Anderson, 1999 in Bennett & Brookman, 2010) To move past such speculation, future research should continue to explore robbery separately from other offence types.

238 9.3 Practical implications

This thesis has revealed the extent and complexity of drug use among young community-based offenders, the complex links between drug use and offending, and the extensive and varied recidivism patterns. Recidivism was predicted by both static and dynamic factors (including drug use). Only some patterns of drug use increased recidivism, but drug use was typically linked with poorer health. The link with poorer health was strongest for weekly drug users and the prevalence of weekly drug use was several times higher than that of adolescents in the general population. Thus, young offenders present a burden to public health through their drug use behaviours in addition to drug-related offending; all young offenders should be screened for drug problems. The need for intervention to reduce recidivism is clear, and intervention must also be targeted at those at highest risk. The results have implications for: recidivism risk assessment (and in particular, the role of drug use therein), rehabilitation and recidivism reduction, screening and intervention for drug problems, and improving offender well-being. Practical implications are drawn carefully because the thesis did not develop comprehensive theoretical models of drug use or recidivism. 9.3.1 Drug use and risk/needs assessment

Internationally and in Australia, juvenile offender risk/needs assessment inventories tend to use binary (e.g. ‘any drug abuse’) or simple quantitative measures of drug use (e.g. total substance abuse score, per the Youth Level of Service Inventory or YLSI). These measures conceal much of the variation in the relationship between drug use and recidivism. It follows that careful consideration be given to what information about drug use is collected routinely from young offenders, and how this information is reported and used. Each item in the Australian YLSI (YLS/CMI:AA) contributes equally to the recidivism risk score, e.g. ‘any illicit drug use’, which is not a predictor of juvenile recidivism (Cottle et al., 2001). This thesis suggests that minor patterns of drug use (e.g. infrequent cannabis use) may denote psychosocial problems but not recidivism risk. Risk prediction might be improved by excluding this item from the risk calculation, or by weighting items in terms of the strength of their relationship with recidivism, without degrading the needs assessment function of the YLS/CMI:AA. The YLS/CMI:AA also aggregates different illicit drugs, yet this thesis showed distinct recidivism patterns 239 for users of different illicit drugs. Even if drug-specific questions do not improve risk prediction, they can inform needs assessment and case planning. Using risk assessment tools including the YLSI leads probation staff to focus more on dynamic risk factors and refer more appropriately to further services (Vincent, Paiva-Salisbury, Cook, Guy, & Perrault, 2012). However, actuarial tools should support, not supplant clinical judgement (Buchanan, 2008); best practice combines risk assessment with case formulation (Day & Casey, 2012). Treatment matching is an important factor underpinning the effectiveness of intervention (Andrews & Bonta, 2007).

Daily cannabis use was a robust risk factor for violent recidivism. However, it is unlikely that daily cannabis use increases violent behaviour in most users, so it is better conceived as a marker of violent recidivism. Daily users should be screened for further factors that may exacerbate their risk of violence, including mental health issues, such as psychosis (Hyshka, 2013). Enquiring about how daily users support their drug use could identify users who are at risk of violence in the course of drug dealing.

Binge drinking showed concurrent but not prospective links with offending, and was largely unrelated to prior or subsequent offending. However, binge drinking was strongly correlated with recent self-harm. Prior research has emphasised the risk of self-harm among detainees as well as the role of incarceration in increasing this risk (Kenny, Lennings, et al., 2008). In this sample, self-harm was not related to prior incarceration. This strongly suggests that community-based offenders should be screened for self-harm, that screening should assess frequency of drug use, and that screening should occur regularly for frequent binge drinkers. During risk/needs assessment, trained staff should enquire directly about SSH because it tends not to be volunteered by suicidal youths (Pompili et al., 2009). Further, a focus on self-harming heavy alcohol users may have benefits for violence reduction, because this group have been previously shown be more aggressive and impulsive (Harford, Yi, & Grant, 2013).

The strong links between weekly opioid use and theft found in this thesis are consistent with earlier adult and detainee studies, and suggest that opioid use is a driver of offending even among less serious young offenders. Pharmacotherapy is one approach to reducing opioid use and is discussed below. Less frequent opioid use was

240 weakly or unrelated to recidivism, but with the high dependence potential of opioid use, efforts to address opioid use should be extended to all opioid using offenders.

9.3.2 Interventions for community-based offenders

A range of interventions are effective in reducing juvenile recidivism, including drug treatment (Holloway et al., 2006). Across 70 meta-analyses of Australian and international treatment studies, most had small/moderate effects on recidivism (McGuire, 2008); effect sizes of drug treatment studies are in a similar range (Holloway et al., 2006). With a range of effective treatments to choose from, the policy challenges include deciding how much to spend and on whom (Weatherburn et al., 2007), and in which setting (e.g. corrections, education, health).

Community supervision without treatment is ineffective in reducing juvenile recidivism (consistent with Lee et al., 2012; MacKenzie, 2006). Aos and colleagues (Lee et al., 2012) maintain a database of international evidence-based interventions and services for juvenile offenders, with information about the costs, effectiveness, and return on investment. A range of treatments, therapeutically-oriented supervision, and restorative justice measures are highly effective but vary widely in their set-up costs and return on investment. For example, supervision that adheres to risk-needs- responsivity (RNR) principles delivers similar savings (i.e. through prevented crime) to mediation-based interventions, which are much cheaper to implement, and to multi- systemic therapy (MST) which is more expensive. The RNR model emphasises more intensive intervention for high-risk offenders, thorough assessment of offenders’ criminogenic needs (as provided by this thesis, Section 9.1.3), and tailoring interventions to suit offenders’ learning styles (Andrews & Bonta, 2007).

This thesis showed the low levels of verbal IQ among participants, and the strong inverse relationships between verbal IQ and recidivism. Low intellectual functioning affects responsivity (Frize et al., 2008). Poor cognitive and oral language ability has implications for selecting interventions; for example, mediation is highly conversational and thus may be unsuccessful for offenders lacking such skills (Snow & Powell, 2012). Given that verbal IQ was unrelated to Indigenous recidivism, these

241 implications should not be applied to Indigenous offenders. Recidivism risk factors that relate to low IQ are suitable targets of intervention. For example, school failure was also a consistent predictor of recidivism in the sample and reduces legal earning potential. This can be offset by remedial education (Bailey & Scott, 2009), while social cognition deficits (which increase the risk of violence) can be moderated by social skills training (Bailey & Scott, 2009). Reducing or preventing heavy cannabis use and early onset use would seem to be an important target given its links with lower educational engagement (Arcuri, Howard, Norberg, Copeland, & Toson, 2011) and long-term reductions in IQ (Moffitt, Meier, Caspi, & Poulton, 2013).

Criminal peers were a related, robust factor for recidivism in the sample. Interventions need to address these factors and other aspects of their social environment that support drug use. Family factors were somewhat weak predictors of recidivism but have the potential to facilitate or undermine rehabilitative interventions. Not all family factors are easily modifiable (e.g. parental offending and drug use) (Bailey & Scott, 2009), but poor parenting styles that can be inferred from the high levels of abuse and neglect reported by young offenders, are amenable to treatment (Bailey & Scott, 2009). MST is an intensive, family-focused supervision program that can reduce recidivism with serious and recidivist offenders, including those with cognitive deficits (Randall, Henggeler, Pickrel, & Brondino, 1999). Juvenile Justice NSW is trialling an MST-based intensive supervision program (Juvenile Justice NSW, 2012).

9.3.3 Addressing drug use among young offenders

In this thesis, problem drug use was a prominent criminogenic need for participants and there was a significant unmet need for treatment for drug problems. Most effective interventions (Lee et al., 2012) will address the role of drug use in offending, but two focus specifically on this issue: drug courts and pharmacotherapy. Recidivism among juveniles who successfully complete juvenile drug court is 33-50% lower than for non-graduates (Stein, Deberard, & Homan, 2013). Key features of juvenile drug courts are that the magistrate and case workers are informed about the nature of drug dependence and relapse, relevant behaviour management principles, and the social milieu of young offenders’ drug use (King, 2009). Unfortunately, after ten years of

242 promising progress, but no formal evaluation, the NSW Youth Drug and Alcohol Court (Turner, 2012) was terminated in 2012.

For some entrenched drug users, including those who fail to comply with the conditions of drug court supervision, opioid substitution treatment (OST) can reduce offending (Fagan et al., 2008; Lee et al., 2012) and reduce other harms associated with dependence (Minozzi, Amato, & Davoli, 2009). This may be an appropriate strategy for reducing the burden of theft offending in the sample, given the very strong link between weekly opioid use and frequent theft. The evidence base for pharmacotherapies for cannabis, alcohol, amphetamines, and tobacco (which were used by far more participants in this thesis), is less well established (Elkashef et al., 2008; Lingford-Hughes, Welch, Peters, Nutt, & With expert reviewers : Ball D, 2012; Vandrey & Haney, 2009). In this thesis, only one in ten youths who had received drug treatment reported OST. Treatment should not be limited to pharmacotherapy, however, as drug use was one of many risk factors for recidivism. Young offenders should also be offered psychosocial support, harm reduction, and family interventions (Lingford-Hughes et al., 2012).

The majority of drug-using offenders in this sample did not report levels of drug use indicative of dependence, and so would not be suitable candidates for pharmacotherapy. Drug courts are not restricted to dependent users but can serve only a small number of offenders. A promising approach to reducing problem drug use with a far wider reach is ‘X-Roads’ (Vogl et al., 2011). This cognitive behaviour therapy- based skill-building and goal-focused program addresses drug-related offending behaviour among young offenders in NSW. The program has not yet been evaluated but is based on the first drug use prevention program for secondary students to show prospective and sustained impacts on drug prevention in a number of randomised- controlled trials (Teesson et al., 2012). Many drug-using participants did not report patterns of use linked with recidivism, but are nonetheless at risk of progression (e.g. to more regular or problematic use). It is important to prevent or delay uptake of illicit drugs by this group and the minority of participants who had not yet initiated drug use. A non-coercive approach (e.g. motivational interviewing) is indicated for these youths.

243 9.3.4 Improving young offenders’ welfare

This thesis has highlighted the high level of health and welfare problems experienced by young offenders (in particular drug-using offenders), that were not necessarily linked with recidivism but are important nonetheless. Addressing these broader needs can reduce later impairment in health, interpersonal relationships and parenting (Raudino, Woodward, Fergusson, & Horwood, 2012). For example, one in two participants smoked tobacco daily, a behaviour not thought to affect recidivism risk. Smoking receives less attention than other drugs in young offender populations but reductions in smoking have been linked with reductions in other drug use among young people (Myers 2007 in Chassin, 2008). Criminal justice supervision orders provide an opportunity to assess and address these needs, which may be overlooked given the focus of juvenile justice agencies on reducing recidivism. Some needs that are unrelated to offending may be more appropriately addressed outside of the criminal justice system, so referral pathways should be established.

Many participants were victims of violence and more frequent drug use was strongly correlated with victimisation by other drug users. This also reflects young offenders’ violent social environments; addressing structural determinants of recidivism and welfare (including social disorganisation, poverty and unemployment) requires broader policy responses and these are not easily implemented. Social policies and welfare programs (e.g. community nursing for disadvantaged mothers) that are designed to prevent the onset of serious juvenile offending may be the most effective recidivism reduction technique (Homel, 1999; Weatherburn & Lind, 1997). A final, cross-cutting implication arises from the demographic differences in recidivism risk profiles described in Section 9.1.5. These findings support targeting specific subgroups, as suggested by (Bennett & Holloway, 2007) or tailoring generic programs to these groups. For example, robbery recidivism was concentrated among urban, non- Indigenous minority ethnic youths, which suggests targeting the drivers of this particular crime within CALD communities. Two factors that should be addressed in violent offending programs among young offenders, that did not appear to drive violence by older offenders, are daily cannabis use and criminal peers. Female offenders, meanwhile, were at much higher risk of injecting drug use than males. 244 9.4 Limitations 9.4.1 Sampling

The analyses used the largest and most representative sample of community- supervised offenders to date, but this sample was not precisely representative of the wider population of community-supervised offenders (notably, male Indigenous offenders and youths from rural areas were under-sampled). Although the analyses of drug-crime relationships were more detailed than in previous Australian research, there was insufficient power to robustly assess relationships among demographic subgroups (e.g. females) or drug types (e.g. benzodiazepines) with low prevalence. Thus, the results should not be generalised to these subgroups or drug types. The sample also excluded offenders with severe psychiatric disturbance, which may have led to a slight underestimation of the prevalence of recidivism (Kasinathan, 2009b). These sampling problems could be resolved by acquiring a large sample, and targeting minority subgroups. These are costly methods, however, especially for those youths who are most difficult to reach (e.g. in remote communities or in unstable housing). Constructing a sample that is adequately powered to explore subgroup variation is further hindered by the relatively small population in Australia. Weighting cases to the population characteristics would provide a cheaper method of overcoming these issues, but requires detailed population statistics (i.e. gender by ethnicity by location) that were not publicly available for the NSW community-supervised offender population (Broadbent, 2001). Data linkage is a cost-effective alternative.

9.4.2 Measurement

The measures in the baseline dataset were broad and included most major risk factors. After adjusting for criminal history, other factors made small contributions to recidivism, which suggests that exclusion of any one factor is unlikely to change the major conclusions of this thesis. Nonetheless, measurement issues could affect the interpretation of the results. Drug problems are predicted by quantity and frequency of use (Walden & Earleywine, 2008; Zeisser et al., 2012), but quantity data were only available for alcohol. Drug use data were self-reported and could not be validated by urinalysis. Urinalysis results show that under-reporting of drug use is common in

245 offender samples, particularly for infrequent users (Feucht et al., 1994), although this is less common among younger offenders (Wei et al., 2003). Further, young NSW offenders’ self-reports in general have been found to be consistent and reliable (Kenny & Grant, 2007). Substance Use Disorder (SUD) data were collected using non- diagnostic tools that over-estimate prevalence as they do not apply impairment criteria (Erskine et al., 2013), and do not provide information on drug-specific disorders (e.g. cannabis dependence). The correlation between frequency of drug use and SUDs among young offenders has not yet been assessed (L. Teplin, personal communication, 29 January 2013; Indig et al., 2011). Drug-crime modelling was also hindered by a lack of data on drug expenditure (Salmelainen, 1995), progression to regular drug use (Payne, 2006), and specific drug-crime attributions (Payne & Gaffney, 2012).

Adjusting for time spent incarcerated (TSI) revealed a much greater rate of recidivism among some groups of offenders including weekly opioid users. However, TSI data included non-parole periods of control orders and excluded periods of remand, which account for the majority of juveniles’ detention (Richards, 2011b). Recidivism rates will therefore have been underestimated for the most serious offenders who are the most likely to be denied bail. More precise TSI-adjustment should be a goal of future recidivism research (Ferrante, 2009), although custodial admission and discharge data in NSW are reportedly imprecise and difficult to access (Larney & Martire, 2010). Future research should also consider the quality of criminal supervision, which could not be assessed by this thesis. Independent of their level of recidivism risk, NSW young offenders supervised by more skilled probation staff had lower recidivism rates than those under standard supervision (Trotter & Evans, 2012).

The proxy used for early-onset antisocial behaviour (ASB) (court-proven offending by age 14) is supported by prior research (Mazerolle et al., 2000), but there were no data on persistent childhood ASB which is highly prognostic of recidivism severity and duration. Measures of lifetime exposure (e.g. maltreatment) do not allow speculation about temporal relationships with drug use or offending. Parental drug abuse has also been implicated in juveniles’ drug use and offending but was excluded from the models because of excessive (>10%) missing values; including this measure would have

246 reduced the sample in each model and the power to assess key relationships. Other unexamined factors included romantic relationships, which may protect against recidivism (Laub & Sampson, 2001), situational factors (per most recidivism studies, e.g.White et al., 2008); genetic factors, which may moderate drug-crime associations (Vaske et al., 2009); and structural variables including neighbourhood poverty that can combine to provide epidemic conditions for offending (Weatherburn & Lind, 1997).

9.4.3 Analysis

By focusing on some specific aspects of the drug-crime relationship, others were necessarily omitted from consideration. Drug use and many other factors were analysed as categorical variables, despite their continuous nature. This enabled comparisons between users and non-users, and between different levels of use. Some information was lost as a result of collapsing these continuous terms, but the approach is supported by prior research (Farrington & Loeber, 2000).

Analyses focused on use of specific drugs, rather than on diversity of involvement in drug use. Poly-drug use has been linked with frequent drug use (Teesson et al., 2005), and has predicted general and specific recidivism (Hakansson & Berglund, 2012; Hayhurst et al., 2013), but its contribution to recidivism over and above frequent use was not tested. There are many combinations of poly-drug use, including six among the four drugs considered in this thesis. Latent class analysis (LCA) could identify homogenous and orthogonal patterns of poly-drug use and of offending, given that most participants committed multiple offence types (specific recidivism outcomes were not mutually exclusive). LCA could also reduce the number of terms in future models, thereby reducing the number of associations appearing significant by chance.

While most analyses assessed individual drug types, models could not disaggregate heroin from other prescribed and non-prescribed opioids due to problems with statistical power. These drugs are pharmacologically coherent and their use often co- occurs (Larance, 2012), but use of opioids in treatment is associated with reductions in offending; results may not generalise to youths in opioid substitution treatment. Recidivism data were limited to finalised court appearances, which are a small subset

247 of detected offences (minor and most drug offences). General recidivism models may therefore underestimate the links between drug use and recidivism. It would be useful to assess these links using additional sources of offence data (e.g. police contacts), and the 99% case ascertainment in data linkage for this thesis encourages further linkage.

Prospective data were limited to recidivism data, so changes in behaviour, functioning or service use could not be analysed. For example, heavy drug users are more likely to receive treatment and this can reduce recidivism (Larney, 2011) which would confound the drug use/recidivism relationship. However, the prior treatment experiences of participants in this sample were insubstantial (Lennings et al., 2006), and the treatment environment did not change profoundly during the observation period (Noetic Solutions, 2010); confounding is likely to have been minor.

Changes in drug use present a greater limitation than unmeasured treatment involvement, however; developmental trajectories of drug use and offending are known to covary (Sullivan & Hamilton, 2007). While frequent use of cannabis (Swift, Coffey, Carlin, Degenhardt, & Patton, 2008) and opioids (Nelson et al., 2010) tends to be persistent, infrequent users were at risk of progression to more frequent use after baseline. Links between infrequent drug use and recidivism may reflect progression, rather than direct effects of infrequent use on recidivism. However, prospective drug use data have only been reported by one Australian juvenile recidivism study (Putninš, 2003). Young offenders’ mortality is extremely high relative to non-offenders (Coffey, Veit, et al., 2003), and is more likely among heavy drug users. Mortality data were unavailable in this thesis, so deceased individuals in this sample were assumed to be alive and not offending, which would have underestimated drug-crime associations.

Finally, it is important to acknowledge the limitations of nomothetic (group-based) data in explaining potentially causal contributions to offending behaviour (Viljoen, McLachlan, & Vincent, 2010). One of the core principles of forensic mental health assessment is to incorporate nomothetic and idiographic (case-specific) evidence (Heilbrun et al., 2003). Similarly, drug-crime research requires qualitative explorations of drug-crime dynamics unique to individuals, to help establish underlying mechanisms (Section 9.5.4) as well as large-scale quantitative studies such as this thesis.

248 9.5 Research implications 9.5.1 Measurement of drug use and offending

This thesis has validated calls for disaggregated measurement and reporting of drug use and offending in the study of drug-crime relationships (Anthony & Forman, 2003; Bennett & Holloway, 2005b). Unfortunately, most juvenile recidivism studies aggregate drugs and focus on the prevalence of general recidivism, while cross-sectional and unadjusted analyses also remain popular. The latter approaches may have been justified historically on the basis of difficulty with data access, data collection, or statistical complexity. Such barriers are rapidly diminishing, in part due to administrative data linkage as undertaken by this thesis.

Cannabis is the most prevalent drug among young offenders but has received less attention than other drugs in juvenile recidivism research (see Salmelainen, 1995 for an Australian exception). This thesis showed that cannabis must be considered separately in future studies. The variation observed between different drugs suggests that other patterns of drug use should be explored. Adult studies have found strong links between heavy benzodiazepine use, serious health problems (Jones, Mogali, & Comer, 2012) and offending (Fleming et al., 2012; Hall, Howard, & McCabe, 2010), but this remains unexplored among Australian juveniles. Data linkage (see below) will be required to ensure a sufficient sample size to explore this relationship. Future research should examine whether poly-drug use or particular poly-drug use patterns can more effectively differentiate recidivism outcomes than specific drug use. The association between anabolic steroid use and interpersonal violence appears to be largely attributable to problematic poly-drug use, suggesting that violence reduction efforts should not focus on steroid use per se (Lundholm, Frisell, Lichtesnstein, & Långström, 2013). It would be helpful to clarify whether young offenders’ drug use should also be assessed more broadly.

Similarly, it would be useful to profile specific offences, given that distinct differences were identified between the risk profiles of general, violent and theft recidivists. Offenders report diverse motivations for different theft offences (Brookman, Maguire, Pierpoint, & Bennett, 2010) so understanding more specific drug-crime relationships 249 may identify opportunities to tailor policing or supervision strategies (Bennett & Holloway, 2007). For example, frequent cannabis use predicted frequent break and entry (burglary) but was unrelated to shoplifting among young offenders in NSW (Salmelainen, 1995).

9.5.2 Data linkage and data analysis

Data linkage facilitated the drug-crime analyses undertaken in this thesis and will underpin future efforts. Linkage and re-analysis of existing datasets offers a more efficient and cost-effective means of extending drug-crime and recidivism research than primary data collection. Young offenders in NSW are routinely assessed with the YLSI, which covers several aspects of drug use. Links between recidivism and the drug use subscale have been explored (McGrath & Thompson, 2012; Thompson & McGrath, 2012); intra-scale variation (e.g. the differential contribution of drug type or severity of use) could be assessed with these existing data.

Similarly, much criminal justice data held in administrative databases has not been incorporated in juvenile recidivism studies. Comparing the links between drug use and police contact with links between drug use and convictions could indicate whether drug use affects the risk of detection more or less than the risk of conviction. If self- reported offending could be linked to these data, the contribution of drug use to offending (as opposed to law enforcement or criminal justice responses) could also be explored. The current dataset could also be linked to recent recidivism data, which would enable identification of youths on a ‘life-course persistent’ offending trajectory, who pose a greater criminal justice and public health burden than other recidivists. Lastly, in addition to assessing recidivism outcomes, Australian young offender research needs to assess broader aspects of well-being and harmful outcomes (Day & Casey, 2012), to quantify the burden associated with offending and to encourage a holistic, welfare-based (rather than a punitive offence-focused) approach to offender rehabilitation. This approach is imbued in the recently implemented Victorian juvenile justice case management tool (Day & Casey, 2012). Inter-agency data linkage provides another means of exploring these outcomes (Jutte et al., 2011).

250 9.5.3 Exploring demographic and local variation

The sample differed in important ways from young detainees, young offenders internationally, and adult offenders. This underscores the importance of studying recidivism and drug-crime relationships in a variety of contexts, and to be wary of extrapolating between studies of different populations. It would be appropriate for research to shift its focus from detainees to the far larger group of community-based offenders. This thesis includes the first detailed drug-crime analyses in this population, so replication is needed in a more recent sample or in another Australian state.

The thesis also showed clear variation in recidivism risk profiles between different demographic subgroups. Data linkage and population weighting (see 9.4.1) could be used to construct samples in which subgroups (in particular, female, younger, and Indigenous offenders) are sufficiently represented to explore this variation without rendering models unstable through inadequate cell counts. Measures may need to be tailored to specific subgroups, for example, a culture-fair measure of intellectual functioning could be more appropriate for Indigenous young offenders (Frize et al., 2008).

Among the subgroup variations identified in this thesis, the most novel contribution relates to ethnicity. Future research should not simply dichotomise ethnicity as Indigenous/non-Indigenous (Broadhurst & Loh, 1995; Indig et al., 2011; Jones et al., 2006; Weatherburn et al., 2007). Indigenous participants’ drug-crime relationships may be more clearly assessed against other Australian-born, English-speaking, Caucasian offenders. This suggestion is supported by the disparity in one year recidivism among NSW young offenders (63% Indigenous, 49% Australian, 41% minority), and the finding that Indigenous young offenders’ drug use problems was significantly greater than other minority but not than Australian-born offenders (Thompson & McGrath, 2012). Although ethnic-specific findings cannot be generalised to other countries, international studies using more nuanced coding of ethnicity (Teplin et al., 2012b) also reveal significant variation in drug use among offenders that is otherwise concealed by dichotomous coding (Stoolmiller & Blechman, 2005).

251 Ethnic variation in drug-crime relationship was shown clearly in the NEW-ADAM study(Holloway & Bennett, 2008): arrestees were most likely to report use of heroin and alcohol, black arrestees were most likely to report use of cannabis, and mixed race arrestees were most likely to report use of crack cocaine. Asian arrestees were least likely to report use of any illicit drug. These findings highlight how important it is to disaggregate findings by ethnic group. It is not the case that ethnic minority group arrestees have the same drug use patterns as white arrestees. It is also not the case that one ethnic group is necessarily the same as another ethnic group in terms of drug misuse. Hence, it does not help understanding of the nature and problems of drug misuse to aggregate them as if they were the same (Holloway & Bennett, 2008).

The distributions of offending frequency and violence severity were strongly skewed, and youths at the extremes of these distributions were not simply caricatures of less extreme offenders. They differed qualitatively (e.g. opioid use predicted severe violence but not violence per se). These groups might be explored through in-depth case reviews. More broadly, qualitative methods should be used to explore aspects of drug-crime relationships that are difficult to capture with statistical models, including offenders’ perceptions of drug-crime dynamics (Bennett & Holloway, 2009).

9.5.4 Further implications for research

The fact that drug-crime relationships showed wide variation (large confidence intervals) in multivariate models invites questions about this variation. Future research could explore how opioid use relates to severe violence, for example whether there is a subgroup of violent offenders who use opioids (Torok, Darke, Kaye, & Ross, 2011). Research should also explore current patterns of drug use by community-based young offenders, which may differ from those reported by this sample. Among other offenders in NSW, since 2003 there has been a decrease in heroin use while cannabis use has remained largely stable (Australian Institute of Criminology, 2010; Indig et al., 2011; Simpson, Howard, Copeland, & Arcuri, 2009), and use of new drugs including synthetic cannabinoids has been reported (Payne & Sweeney, 2011).

252 9.6 Conclusion This thesis has presented the first detailed analyses of drug-crime relationships and predictors of recidivism among young Australian community-supervised offenders. Recidivism was highly prevalent (79%) in this sample, and the burden of recidivism was distributed unevenly in the sample. Drug use was correlated with recidivism but made only modest contributions to recidivism; drug-crime relationships were drug- and crime-specific and were concentrated among frequent users. Young offenders, especially frequent drug users, also experienced pervasive psychosocial problems.

A strong link was observed between weekly opioid use (5%) and theft recidivism, suggesting an economic and potentially causal relationship, as reported in previous studies. Cannabis was the most prevalent illicit drug, as in other young offender samples, and daily cannabis use (35%) was linked with violent recidivism. A range of explanations were considered, including that cannabis use is a feature of a pervasive delinquent lifestyle. Daily users should be screened for other violence-related factors including psychosis, poly-drug use and drug distribution. Binge drinking did not predict recidivism, but frequent binge drinking (12%) was linked with self-directed violence.

Despite its modest contribution to recidivism, drug use is one of the few predictors that can be readily addressed through interventions. Reducing demand for drugs among frequent users could lead to direct reductions in violent and theft recidivism among serious young offenders. Delaying or preventing progression to frequent use also has important implications for recidivism reduction, public health and offender well-being. However, recidivism by community-based offenders was driven by a complex set of issues, confirming the need for broad-based interventions.

This thesis has revealed that frequent drug use predicted recidivism by one in three youths serving community supervision orders. The thesis has also confirmed the importance of disaggregating measures of drug use and offending, and of assessing demographic variation in these relationships. The patterns identified differed from detainee, adult and international samples, underscoring the importance of local research. Studies are now needed to assess the interaction of drug use and offending over time, and to assess the nature of drug-crime relationships at the individual level.

253 References

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307 Appendix A: awards and publications during candidature

Awards during candidature

1. Aileen Plant Memorial Prize in Infectious Diseases Epidemiology, 2012 2. Australian Postgraduate Award, 2008-2011 3. National Drug and Alcohol Research Centre top-up scholarship, 2008-2011 4. National Drug and Alcohol Research Centre travel scholarship, 2010

The following output arises from or relates to this thesis:

Publications

1. Nelson, P., Swift, W., Toson, B., Kenny, D., & Degenhardt, L. (2011). Binge drinking and violent juvenile offending (abstract). Drug and Alcohol Review, 30(s1), 69.

2. Nelson, P., Degenhardt, L., Kenny, D., & Lennings, C. (2010). Patterns of drug use and conduct problems that independently predict reoffending: Results from a prospective cohort study of serious young offenders (abstract). Drug and Alcohol Review, 29(s1), 54.

3. Kenny, D.T., & Nelson, P.K. (2008). Young offenders on community orders: Health, welfare and criminogenic needs. Sydney: Sydney University Press.

4. Kenny, D.T., Lennings, C.J., & Nelson, P. (2008). Mental health of young offenders serving orders in the community: Implications for rehabilitation. In D. W. Phillips III (Ed.). Mental health issues in the criminal justice system. New York: Hawthorne.

Presentations

1. Nelson, P., (2013, September). Links between drug use and recidivism among young offenders: what are the implications for practice? Invited seminar, Justice Health Seminar Series, Malabar.

308 2. Nelson, P., Degenhardt L., Kenny, D., Lennings, C. & Carragher, N. (2010, July). Conduct disorder severity and recidivism of adolescent offenders. Paper presented at the World Psychiatric Association Section on Epidemiology and Public Health Meeting: Prediction in Psychiatric Epidemiology, Lisbon.

3. Nelson, P., Swift, W., Kenny, D.T., & Degenhardt, L. (2011, September). How does drug use affect recidivism of young offenders? Paper presented at the NDARC Annual Symposium, Kensington.

4. Nelson, P., Degenhardt L., Kenny, D. Swift, W. (2010, October). The impact of psychiatric comorbidity on youth justice outcomes. Paper presented at the School of Public Health and Community Medicine: 8th Postgraduate Research Student Conference, Kensington.

5. Nelson, P. (2010, September). Self-reported antisocial behaviour and conviction outcomes for adolescents. Paper presented at the Australia New Zealand Society of Criminology Postgraduate & Early Career Researcher Conference, Alice Springs.

6. Nelson, P., Degenhardt, L., Kenny, D., Carragher, N. (2010, September). Examining approaches to the classification of antisocial behaviour in a high risk cohort. Paper presented at the Australian and New Zealand Society of Criminology: 23rd Annual Conference, Alice Springs.

7. Nelson, P., Degenhardt, L., Kenny, D., Lennings, C., Toson, B. (2010, August). What information do we need to predict recidivism by serious young offenders? Paper presented at the Australian and New Zealand Association of Psychology, Psychiatry and Law: 30th Annual Congress, Surfers Paradise.

8. Nelson, P., Degenhardt, L., Kenny, D. & Lennings, C. (2009, November). Predictors of drug use by young offenders in the community. Poster presented at the School of Public Health and Community Medicine Postgraduate Conference, UNSW.

9. Nelson, P., Degenhardt, L., Kenny, D. & Lennings, C. (2009, September). Diff’rent Strokes? Predicting drug use by young male and female offenders. Poster presented at the National Drug and Alcohol Research Centre Symposium, Sydney.

309 10. Nelson, P. (2008, November). Improved screening for drug and alcohol problems in juvenile offenders. Presentation at the School of Public Health and Community Medicine 6th Annual Research Student Conference, Kensington.

11. Nelson, P. (2009, May). Drug crime research with a highly vulnerable and neglected population: methodological challenges and first longitudinal results. Presentation at the National Drug and Alcohol Research Centre Seminar Series, Randwick.

12. Nelson, P. (2009, May). Researching drug-crime relationships: Innovative methods for elusive populations. Poster presented at the Advances in Public Health and Health Services Research Symposium, Kensington.

Selected additional output during candidature

1. Nelson, P., Mathers, B.M., Cowie, B., Hagan, H., Des Jarlais, D., Horyniak, D., & Degenhardt, L. (2011). Global epidemiology of hepatitis B and hepatitis C in people who inject drugs: Results of systematic reviews. The Lancet, 378(9791), 571-583.

2. Nelson, P., Sindicich, N., Barrett, E., Marel, C., Sutherland, R., Rodas, A., & Simpson, M. (2011). NDARC crime research network: Increasing capacity to undertake drug and alcohol research with offenders and criminal justice agencies (abstract). Drug and Alcohol Review, 30(s1), 69.

3. Nelson, P., McLaren, J., Degenhardt, L., Bucello, C., Calabria, B., Roberts, A., … Wiessing, L. (2010). What do we know about the extent of opioid use and dependence? Results of a global systematic review. Technical Report (309). Sydney, Australia: NDARC. (Co‐authored reports 307‐10: cannabis, amphetamine, cocaine).

4. Erskine, H., Ferrari, A., Nelson, P., Polanczyk, G., Flaxman, A., Vos, T., … Scott, J. (2013). Research review: Epidemiological modelling of attention- deficit/hyperactivity disorder and conduct disorder for the Global Burden of Disease Study 2010. Journal of Child Psychology and Psychiatry. doi: 10.1111/jcpp.12144

310 5. Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., … Nelson, P., … Ezzati, M. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859), 2224-2260.

6. Murray, C.J.L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A.D., Michaud, C., … Nelson, P., … Lopez, A.D. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859), 2197-2223.

7. Vos, T., Flaxman, A.D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., … Nelson, P., … Murray, C.J.L. (2012). Years lived with disability (YLDS) for 1160 sequelae of 289 diseases and injuries 1990-2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859), 2163-2196.

8. Degenhardt, L., Bucello, C., Calabria, B., Nelson, P., Roberts, A., Hall, W., … Wiessing, L. (2011). What data are available on the extent of illicit drug use and dependence globally? Results of four systematic reviews. Drug and Alcohol Dependence, 117(2-3), 85-101.

9. Horyniak, D., Nelson, P., Cowie, B., Hagan, H., Jarlais, D.D., Degenhardt, L., & Kinner, S. (2011). P2-416 estimating the prevalence of hepatitis b infection among people who inject drugs: Results from a global systematic review (abstract). Journal of Epidemiology and Community Health, 65(Suppl 1), A336-A337.

10. Degenhardt, L., Mathers, B., Nelson, P., & Horyniak, D. (2010). Levels of HIV, HCV, and HBV among people who inject drugs: Global overview (abstract). Drug and Alcohol Review, 30(s1), 26.

11. Simpson, M., Howard, J., Copeland, J., & Nelson, P. (2009, November). Recent trends in the consumption of cannabis use by young offenders. Paper presented at the ANZAPPL Annual Congress, Fremantle.

311 Appendix B: analyses of demographic variation

Models of recidivism developed with the full sample were rerun with female (n=118), Indigenous (n=151), younger (under 17; n=344) and older participants (n=449). This approach has been employed elsewhere (Barnes & Beaver, 2010) and helps established whether models generalise to different demographic subgroups in a sample.

Chapter Six (two year recidivism) subsample models

General recidivism. When rerun with the male subsample, results differed minimally from the full sample model. A model was developed for females by removing non- significant terms and those with overly large standard errors, leaving just three terms and a poor fit (pseudo-r2=.09). Unlike males, females under 17 were more likely to recidivate (AOR 3.2, 95%CI 1.3-7.7, p=.01), and schooling was weakly predictive (AOR 3.1, 0.9-10.3, p=.06) while prior convictions were also a strong predictor (p<.01). However, variance was large because cell sizes were very small (e.g. only 21 females were in school).

When rerun with the Indigenous subsample, none of the full sample predictors were significant. In a reduced model, cannabis use, Conduct Disorder (CD), peer offending and prior convictions were weakly predictive (p<.15) but had excessively large confidence intervals or standard errors. Notably, however 70% of Indigenous youths without CD were reconvicted (the prevalence as all non-Indigenous youth), whereas 100% of Indigenous youths with severe CD were reconvicted (prevalence among non- Indigenous youths with severe CD 77%).

Violent recidivism. Only one variable from the final model was predictive of female violent recidivism: physical victimisation (AOR 3.1, p=.04; not predictive for males), but an independent association was apparent for any cannabis use (AOR 4.4, p=.03). For males, odds for cannabis odds were comparable to the full sample model. Victimisation and daily cannabis use were the only predictors for Indigenous youths

312 (AOR 5.3, p<.01; AOR 2.4, p=.03). Daily cannabis use was predictive for younger participants only (AOR 2.3, p=.002), while non-cannabis PDU was a weak predictor for older participants (AOR 1.9, p=.08). Schooling was protective for younger (AOR 0.5, p=.047) but not older participants (p=.8). Interactions between cannabis use, binge drinking and Indigenous status were non-significant, although it is noteworthy that Indigenous youths who binged more than twice weekly were less likely to reoffend violently than non-Indigenous youths.

Theft recidivism. Weekly opioid use was not predictive of theft recidivism for females (AOR 1.9, p=.4) but was strongly predictive for males (AOR 8.0, p<.001). Only two variables were significant in the female model: prior theft (AOR 2.7, p=.05), and higher VIQ (p=.04) (both similarly predictive for males). The sole predictors for Indigenous youths were (younger) age (AOR 0.7, p=.02) and any Conduct Disorder (AOR 4.0, p=.01). Weekly opioid use was predictive of theft recidivism by both younger (under 17) and older participants, and the major difference between the age-specific models was that prior theft was twice as predictive for younger participants (AOR 4.4, p<.001) as for older youths (AOR 2.2, p=.01).

Robbery recidivism. Female and Indigenous-specific models were unstable due to the inadequate cell sizes, and so are not discussed here. Cannabis use was predictive only for older youths (approximate AOR 4.0, p<.05), while prior robbery was the only common predictor for both younger and older participants.

313 Chapter Seven (survival analysis) subsample models

General recidivism. Subsample models revealed demographic variation in predictors. Only schooling (ATR 0.45, p=0.3), prior convictions (ATR 0.6, p=.04) and prior custody (ATR 0.5, p=.03) shortened female survival; VIQ was protective (ATR 1.4, p=.04). VIQ was irrelevant for Indigenous participants (p=0.9), as was prior violence. Peer offending (ATR 0.58, p=.003) and disability (ATR 1.9, p=.007) were significant for younger participants only; prior violence was significant only for older offenders.

Theft recidivism. Weekly opioid use was a comparably strong predictor of shorter time to theft for all demographic subgroups, but other predictors varied by subgroup. VIQ was twice as strongly predictive for females as males, and schooling was predictive of theft only for females (per the interaction discussed above). The interaction and the effect of criminal peers held for younger but not older participants. For Indigenous participants, VIQ was only loosely associated with survival (p=.11) while age of first offence, prior theft and criminal peers were not at all (p>.4). CD was twice as predictive for Indigenous than other participants. Prior theft and conduct disorder were more predictive of recidivism in younger than older participants.

Violent recidivism. Daily cannabis use was comparably predictive of shorter time to violent offending in all subsample models (ATR 0.6, p<.05). Cannabis use at all levels of use shortened survival time for females. The female-specific model also differed in other ways from that of the full sample: higher VIQ actually reduced survival time, while criminal history and prior victimization were not predictive. For Indigenous youths, age and VIQ were not predictive of violence; criminal history was less predictive while physical victimisation was more predictive (ATR 0.4, p=.01). Criminal peers were predictive of violence only for younger participants (ATR 0.5, p=.01) while male gender predicted violence only for older participants. Prior convictions were more predictive for younger and recent convictions for older participants. VIQ was also much more protective for older youths.

314 Chapter Seven subsample models discussion

The contrast of the subsample-specific models to those of the full sample highlights the risk in extrapolating from studies of predominantly white males to female and Indigenous offenders, or even from young adult to adolescent offenders. Drug use was something of an exception: theft occurred sooner among weekly opioid users and violence occurred sooner among daily cannabis users in all subgroups, not only the full sample. This gives support to a claim that these patterns of use are a robust and independent marker of risk. Even so, it was cannabis use per se rather than frequency of cannabis use that differentiated violence risk for females.

Other aspects of the risk profiles of different subsamples differed more clearly. The interaction with schooling (whereby this was protective for males but a risk factor for females) was consistent with Chapter Six. Why this is so remains unclear; as the finding has emerged in more than one chapter, explanations are considered in Chapter Nine. Other than criminal history, most predictors did not generalise to females, nor to Indigenous youths who comprise a much larger proportion of the offender population. The stronger effect of prior victimisation on reducing time to violence by Indigenous youths, and the weaker effect of peer criminality on reducing time to theft suggest different drivers of offending by this group. The most notable feature of the Indigenous models, as observed in the previous chapter, was the irrelevance of verbal IQ to Indigenous youths’ recidivism. This finding is addressed in Chapter Nine.

Differences in the predictors of juvenile versus adult recidivism (see Chapter One) provide a priori evidence of age-varying predictors, which were also seen in the younger and older offender-specific models in this chapter. The interaction between criminal peers and age was significant in the overall model and criminal peers predicted shorter time to both theft and violence for younger participants. This fits expectations about adolescent individuation: social factors (e.g. peers, schooling) are less influential later in adolescence as intrapersonal factors become more relevant.

315 Chapter Eight (frequency of recidivism) subsample models

General recidivism. In the subsample-specific models, weekly opioid use was found to be a particularly strong risk factor for females and Indigenous youths, while amphetamine use was not protective for these groups. Other notable differences were that more severe CD predicted much higher rates of recidivism by females than males, and VIQ was largely irrelevant to Indigenous participants. For younger participants the impacts of amphetamine use were stronger and opioid use weaker; the reverse was true for older participants. Gender also interacted with schooling only amongst the younger group; otherwise, age did not greatly affect the regression parameters.

Theft recidivism. The impacts of drug use were similar for females, although non- significant due to very wide variation, with the exception of injecting which was a very strong risk factor for this subgroup. Age was also a stronger (protective) predictor for females while CD and prior theft were more strongly predictive. The effects of drug use were comparable for Indigenous youths, with the notable exception of less than weekly amphetamine use which was unrelated to their theft lambda. Gender, prior theft, and VIQ were not predictive. Notable differences in the result for older offenders were the greater protective effect of cannabis use, stronger effects of opioid use and injecting, and irrelevance of amphetamine use, age and gender. For younger offenders, amphetamine use was strongly protective whilst other drugs had little or no relevance.

Violent recidivism. In the subsample models, violence rates were higher for females reporting PDU (AIRR 2.0, p=.03) and this was due to non-cannabis PDU rather than daily cannabis use; incarcerations were also unrelated to female violence. PDU was loosely associated with Indigenous violence (AIRR 1.5, p=.09), but not associated with cannabis or other PDU; VIQ was irrelevant as in prior chapters, as was fighting. PDU was more predictive for older participants (AIRR 1.7, p=.001) due to non-cannabis PDU only; prior violence was also strongly predictive. For younger participants, PDU was less predictive (AIRR 1.4, p=.04) due to daily cannabis (AIRR 1.6, p=.01) but not other PDU (AIRR 0.8, p=.4); male gender was predictive for this subgroup (per the interaction in the full model).

316 Escalation. In subsample models, PDU and criminal peers were more predictive of escalation amongst females than males. PDU predicted escalation only among participants under 17; criminal peers were predictive only for older participants. PDU was not predictive for Indigenous youth. Incarceration was predictive and prior convictions were protective for all sub-groups.

317 Appendix C: additional tables and figures

Appendix Table A: Missing data and coding for multivariate models

LABEL Notes/coding (if not binary) N Miss % Academic achievement (WIAT-II-A) Scale 779 21 2.6 Age first used drugs/alcohol Range: 4-15 777 23 2.9 Amphetamine use frequency None/less/weekly 795 5 .6 Any friends 790 10 1.3 None/monthly/weekly/more than Binge drinking frequency 786 14 1.8 twice weekly Cannabis use frequency None/less/weekly/daily 796 4 .5 Childhood maltreatment (all types) 1-4 (none/low/mod/severe) 795 5 .6 Committed crime to get drugs 755 45 5.6 Conduct disorder 1-4 (none/low/moderate/severe) 783 17 2.1 Employed 784 16 2.0 Father absent 790 10 1.3 Fighting frequency 1-5 (none/1-2/3-4/5-6/7+) 783 17 2.1 Frequently truant from school 785 15 1.9 Had more than 20 sex partners 770 30 3.8 Head injury 0-2 (none/once/two or more) 785 15 1.9 High risk sexual behaviour 770 30 3.8 History of drug treatment 777 23 2.9 Needs help with drug problems 746 541 6.8 Offended whilst alcohol-affected 752 48 6.0 Offended whilst drug-intoxicated 756 44 5.5 Opioid use frequency None/less/weekly 786 14 1.8 Parental alcohol/drug use excluded from final models 700 100 12.5 Past year injecting drug use 786 14 1.8 Past year self-harm/suicide attempt 777 23 2.9 Police-related drug problems 747 53 6.6 Prior psychiatric treatment 775 25 3.1 Prior convictions 793 7 .9 Problems relating to drug use 755 45 5.6 Proportion of peers drink alcohol 1-5 (likert: none-all) 785 15 1.9 Proportion of peers use cannabis 1-5 (likert: none-all) 782 18 2.3 Proportion of peers use other drug 1-5 (likert: none-all) 778 22 2.8 Proportion of peers who smoke 1-5 (likert: none-all) 788 12 1.5 Six months or more in detention 774 26 3.3 Student (or graduated) 789 11 1.4 Symptoms of illness/injury Range: 0-33 790 10 1.3

N=800 for demographic variables (gender, region, ethnicity, SES, age). 1 Missing analysed as a response

318 Appendix Table B: Studies frequently referenced in this thesis

Study Reference Population Age Location Cross-sectional

Young People in (Indig et al., 2011) Juvenile 14-20* NSW Custody Health Survey detainees (Australia) Drug Use Careers of (Prichard & Payne, Male juvenile 11-17* Australia Offenders ^ 2005) detainees (not NSW) Drug Use Monitoring (Wei et al., 2003) Juvenile 10+* Australia Australia arrestees Arrestee Drug Abuse (Bennett & Adult arrestees 17+ UK Monitoring program^ Holloway, 2007) Prospective

Pathways to desistance^ (Mulvey & Convicted 14-18+ Two US cities Schubert, 2012) offenders Florida studies (Dembo, Williams, Arrestees and 12+ Miami Schmeidler, detainees (USA) Getreu, et al., 1991) Cambridge Study in (Farrington, 2003b) Low SES males Cohort, London Delinquent age 8+ (UK) Development Pittsburgh Youth Study (Loeber et al., At risk youths Pittsburgh 2002) (USA) Cohort studies

Mater study (Bor et al., 2004) General Birth+ Queensland (Australia) Dunedin (Moffitt et al., General Birth+ Dunedin Multidisciplinary Health 2011) (NZ) and Development Study Christchurch Health and (Fergusson & General Birth+ Christchurch Development Study Horwood, 2001) (NZ) SES socioeconomic status; NZ New Zealand; ^Detailed coverage of drug-crime issues.

319 Appendix Table C: Summary of predictors across all studies Predictors GENERAL THEFT VIOLENT# ROBBERY Severity Escalate 2yr Time Rate Prior 2yr Time Rate Prior 2yr Time Rate Prior 2yr Mid/High Females O+s T+*s R+* O+~ T+*s P~ T+ R+* Age scale R+* P+~ O+* R+* P O+~ Age 17+ T+p T+^ T+*^ Age 17 (ref: age<16) T+~ Age 18 (ref: age<16) T+ Ethnicity (ref: ESB) Indigenous T* M H* ESB/CALD O~ R+* P+ R+* P* E+ CALD R+* P+~ O+~ R+* T P* H~ E+* Region: Non-urban P+* Age at first offence T+ Offended before age 14 P Multiple detention episodes O* T* R* P~ O T* R O R* P MH E Prior theft O* T* R* P+* P+* Prior violent T+ R* P+* Prior robbery P+* P* Prior other/drug P+* P* Prior total convictions O T* R* P* R+* P* T* R P* E* Conduct disorder (0-3) R* Conduct disorder (ref: none) CD low CD moderate O* T O~ T* R CD severe O T T R R* Fighting frequency (0-5) O T~ O M*H* Several fights T R* Student/graduated O+ T~ R~g T* O+~

320 Predictors GENERAL THEFT VIOLENT# ROBBERY Severity Escalate 2yr Time Rate Prior 2yr Time Rate Prior 2yr Time Rate Prior 2yr Mid/High Employed P* History of care O T*^ Limiting disability O+ T+~ P+ E+~ Any peers offend O~ T*a O T T* E Physically victimised T* M E+ Verbal IQ (range: 0-3) T+* T+*^ Verbal IQ (ref: VIQ<70) P VIQ 70-84 VIQ 85-99 R+* O+* R+ O+ T+ R+ VIQ 100+ O+* R+* O+* R+ O+ T+ R+ Offended while intoxicated P~ P* Offended to support drug use P~ Any problem drug use R* M not opi E Cannabis use (0/none to 3/daily) R+ Cannabis use (ref: nil) Less than weekly P~ Weekly Daily O~ T Opioids (ref: no use) Less than weekly R H Weekly R* P* O* T* R* Amphetamine use: none Less than weekly R+* R+* P+~ Weekly R+~ P+* Variables that that did not predict recidivism are excluded from the table, including: binge drinking, parental incarceration, distress, and child maltreatment. Interactions: sschool ggender aage17 ppeers, ^Ancillary, #Violence excludes robbery in prior offending and 2-year recidivism models. *<.01 ~<.1. + protective (odds/rate <1, time >1). P: Prior convictions, Odds: O/AOR, Time: T/ATR, Rate: R/AIRR, M/H: medium/high violence (reference: non-violent recidivism). E: escalation.

321

Appendix Figure I Prevalence of prior convictions by offence type (Figure 4.1 in landscape format)

322 2 Reference category: none (no use) Any Violent Theft

1.75

1.5

1.25 Hazard ratio (HR) ratio Hazard

0.75

Appendix Figure II Bivariate hazard ratios: recidivism type by drug use (Figure 4.2 in landscape format)

323 Appendix Table D: Prevalence and unadjusted odds ratios for variables in the binge drinking model (Table 5.2)

Variables in multivariate model % or Mean (Standard Deviation) Odds ratio (95% Confidence Interval)p 2 (range; χ test if p>.05) Never 2x wkly 2x wkly vs never >2x wkly vs weekly Female (p=.19) 15.4 13.0 13.6 22.7 0.82 (0.48-1.41) 0.87 (0.46-1.63) 1.61 (0.80-3.27) 1.86 (0.93-3.73)~ Age (scale) 16.7(1.6) 17.0(1.3) 17.2(1.2) 17.2(1.2) 1.23 (1.06-1.42)** 1.40 (1.18-1.67)*** 1.40 (1.13-1.75)** 1.00 (0.80-1.24) Father absent (p=.15) 59.2 59.7 49.7 60.0 1.02 (0.69-1.51) 0.68 (0.43-1.07)~ 1.04 (0.59-1.83) 1.52 (0.87-2.64)~ Head injury (0-2) 0.3(0.6) 0.5(0.7) 0.6(0.7) 0.8(0.8) 1.93 (1.39-2.68)*** 2.16 (1.51-3.1)*** 2.72 (1.80-4.12)*** 1.26 (0.89-1.78) Self-harm/suicidal behaviour 4.2 9.7 8.5 32.0 2.43 (1.01-5.89)* 2.10 (0.79-5.62)~ 10.7 (4.12-27.6)*** 5.08 (2.44-10.6)*** Any friends 85.9 93.5 95.3 94.5 2.35 (1.27-4.36)** 3.30 (1.41-7.74)** 2.91 (0.96-8.85)~ 0.88 (0.26-3.02) Peer drinking (1-5) 2.9(1.5) 3.8(1.5) 4.4(1.0) 4.4(1.0) 1.56 (1.36-1.79)*** 2.30 (1.90-2.79)*** 2.35 (1.82-3.03)*** 1.02 (0.78-1.33) Fighting frequency (1-5) 2.2(1.3) 2.5(1.4) 2.9(1.4) 3.3(1.4) 1.22 (1.05-1.41)** 1.45 (1.23-1.72)*** 1.79 (1.45-2.21)*** 1.24 (1.02-1.50)* Smoking (0-2) 0.9(0.8) 1.3(0.7) 1.5(0.7) 1.7(0.6) 1.90 (1.49-2.42)*** 2.39 (1.77-3.21)*** 4.09 (2.61-6.41)*** 1.71 (1.09-2.68)* Cannabis use: none 50.0 18.1 16.1 16.0 1.0 1.0 1.0 1.0 less than weekly 21.8 26.6 26.8 10.7 3.37 (2.01-5.66)*** 4.22 (2.29-7.76)*** 6.41 (3.68-11.2)*** 0.14 (0.07-0.27)*** weekly 12.7 19.4 20.8 16.0 3.82 (2.02-7.22)*** 5.11 (2.49-10.5)*** 7.29 (3.77-14.1)*** 0.88 (0.52-1.50) daily 15.5 35.9 36.3 57.3 1.53 (0.57-4.11) 3.94 (1.52-10.2)** 11.6 (5.20-25.7)*** 1.59 (0.72-3.47) Amphetamine use: none (reference) 85.8 65.7 53.6 42.7 1.0 1.0 1.0 1.0 less than weekly 8.5 27.8 36.3 37.3 4.29 (2.28-8.08)*** 6.83 (3.47-13.4)*** 8.82 (4.04-19.3)*** 1.29 (0.71-2.36) weekly 5.7 6.3 10.1 20.0 1.45 (0.64-3.31) 2.86 (1.18-6.91)* 7.09 (2.76-18.2)*** 2.48 (1.11-5.54)* Opioid use: none (reference) 96.5 87.7 82.1 65.3 1.0 1.0 1.0 1.0 less than weekly 0.0^ 6.8 17.3 22.7 3.82 (1.49-9.79)** 5.91 (2.23-15.7)*** 14.4 (5.25-39.7)*** 2.44 (1.32-4.53)** weekly 3.5 5.5 0.6 12.0 Last offence alcohol-affected 13.5 31.2 50.3 67.6 2.89 (1.68-4.97)*** 6.47 (3.60-11.6)*** 13.3 (6.60-26.9)*** 2.06 (1.15-3.7)* Last offence drug-affected 14.4 33.9 35.9 54.3 3.05 (1.81-5.12)*** 3.33 (1.87-5.90)*** 7.07 (3.62-13.8)*** 2.13 (1.2-3.76)* Six months+ in custody (p=.07) 16.1 10.0 16.9 16.0 0.58 (0.33-1.02)~ 1.06 (0.58-1.95) 1 (0.46-2.15) 0.94 (0.45-1.97) 2x wkly: more than twice weekly; p*<.05 **<.01 ***<.001 ~<.15. ^Opioid users combined as all binge drank. 324 Appendix Table E: Prevalence and unadjusted odds ratios for variables in the cannabis use model (Table 5.3)

Variables in multivariate model % or Mean (Standard Deviation) Odds ratio (95% Confidence Interval)p 2 (range; χ test if p>.05) Never

325 Appendix Table F Prevalence and unadjusted odds ratios for variables in the amphetamine use model (Table 5.4)

Variables in multivariate model % or Mean (Standard Deviation) Odds ratio (95% Confidence Interval)p (range; χ2 test if p>.05) Never

326 Appendix Table G: Prevalence and unadjusted odds ratios for variables in the opioid use model (Table 5.5)

Variables in multivariate model % or Mean (Standard Deviation) Odds ratio (95% Confidence Interval)p (range; χ2 test if p>.05) Never Twice weekly# 7.3 23.3 24.3 - - - Amphetamine use: none 70.4 27.0 26.3 1.0 1.0 1.0 less than weekly 23.9 47.3 50.0 5.15 (2.89-9.18)* 5.59 (2.55-12.3)* 1.09 (0.42-2.79) Weekly 5.6 25.7 23.7 11.9 (5.83-24.1)*** 11.2 (4.30-29.3)*** 0.95 (0.32-2.84) Need help with drug problems 7.4 28.2 37.8 4.91 (2.70-8.91)*** 7.62 (3.68-15.7)*** 1.55 (0.67-3.60) Offending to support drug use 40.3 64.8 84.2 2.73 (1.64-4.55)*** 7.91 (3.26-19.2)*** 2.90 (1.07-7.87)* Drug-intoxicated during last offence 28.3 57.1 64.9 3.37 (2.04-5.58)*** 4.67 (2.33-9.37)*** 1.38 (0.61-3.16)

327 Appendix Table H Relationship of escalation and violence severity to frequency of recidivism, expressed as incidence rate ratios (Figure 8.5)

Escalation IRR (95%CI) Violence severity IRR (95%CI) Recidivism type (vs. non-escalation) (none-low-moderate-high) General recidivism 1.5 (1.2-1.9) 2.1 (1.9-2.4)^ Violent recidivism 1.7 (1.3-2.2) 3.3 (2.7-3.9) Theft recidivism 1.2 (0.8-1.7) p=.379 1.8 (1.5-2.2)^ p<.001 unless specified. ^Violence severity had a non-monotone relationship with general and theft recidivism rates: moderate violent recidivists had the highest rates of these offences (Figure 8.5). IRR Incidence rate ratio, CI confidence interval.

328 Appendix Table I: Prevalence and unadjusted odds ratios for predictors of violence severity among recidivists (Table 8.6)

Non-violent Low Moderate High Low Moderate High Prevalence (%) Odds ratio (95% confidence interval) reference: non-violent LRT χ2 Male 85 86 91 89 1.1 (0.61-1.98) 1.71 (0.94-3.1)~ 1.48 (0.49-4.47) .331 Age 17 plus 62 52 51 45 0.66 (0.44-1.01)~ 0.62 (0.42-0.91)* 0.49 (0.24-0.99)* .043 Ethnicity: ESB 55 57 52 34 1.0 1.0 1.0 .146 Indigenous 16 21 25 32 1.23 (0.71-2.13) 1.59 (0.96-2.62)~ 3.12 (1.3-7.47)** ESB/CALD 16 11 11 13 0.67 (0.35-1.26) 0.69 (0.38-1.24) 1.3 (0.43-3.91) CALD 12 12 13 21 0.92 (0.48-1.77) 1.06 (0.58-1.92) 2.72 (1.02-7.24)* Multiple detention 68 85 81 87 2.63 (1.55-4.45)*** 2.04 (1.29-3.22)** 3.08 (1.15-8.27)* .004 Assaulted in past year 15 31 27 19 2.54 (1.53-4.23)*** 2.1 (1.29-3.43)** 1.29 (0.52-3.21) .002 Frequent fights 19 32 38 43 1.93 (1.19-3.14)** 2.58 (1.65-4.04)*** 3.18 (1.52-6.66)** .001 Any opioid use 14 13 13 29 0.96 (0.52-1.76) 0.95 (0.54-1.67) 2.56 (1.14-5.74)* .085 PDU non-opioid 30 40 46 39 1.54 (1.00-2.38)~ 1.99 (1.33-2.97)*** 1.49 (0.73-3.06) .011 p*<.05 **p.01 ***p.001 ~p.15. LRT: likelihood ratio test; PDU non-opioid: more than twice weekly binge drinking, daily cannabis use and/or weekly amphetamine use

329