Group Risk Assessment Model Monitoring trends in re-offending among convicted offenders in adult and children’s court Fourth National Justice Modelling Workshop 10-11th July 2008 Dr Nadine Smith and Craig Jones

Background  Justice agencies aim to reduce re-offending  NSW target is to reduce re-offending by 10% by 2016  Re-offending is usually measured by reconviction in court

The problem  Rates of reconviction are affected by  policy to reduce re-offending  characteristics of offenders in court  Must account for offender characteristics when measuring changes in reconviction

The solution  Build a statistical model that predicts re- offending based on offender characteristics  Use the model to ‘adjust’ for changes in offender characteristics when assessing effectiveness of policy to reduce re-offending

The samples Convicted offenders given a non- custodial sanction in 2003/04 in a NSW  ADULT COURT Local, District and Supreme  CHILDREN’S COURT Children’s Court and Youth Justice Conferences

Data source  The Re-Offending Database (ROD) from the NSW Bureau of Crime Statistics and Research (BOCSAR)  ROD court data  routinely collected  linked over time  demographics  criminal history

The outcome RECONVCITION  Counted from date of first conviction in 2003/04 (index offence)  Reconviction occurring within 24 months of the index offence, and  Proven in court within 27 months of the index offence

2003/04 reconviction rates Per cent reconvicted N 3265,562 ADULT COURT 563,706 CHILDREN’S COURT

Possible predictors of reconviction Indigenous status DEMOGRAPHICS Age Sex INDEX OFFENCE Jurisdiction Principal offence type Number of concurrent offences OFFENDER HISTORY Number of prior convictions in the past 8 years

The modelling process  Logistic regression  Identify which possible predictors yield the best model  Examine the accuracy of predicting reconviction

Actual predictors of reconviction Area under the curve indicated acceptable fit for the adult court (c=0.728) and children’s court (c=0.768) models

ADULT COURT Adjusted odd ratios of reconviction or more vs no priors yrs vs 40 or olderIndigenous vs non-Indigenous 2 or more vs no concurrentProperty vs drivingViolent vs drivingFemale vs maleUnknown vs non-Indigenous Odds ratios (95% confidence interval)

CHILDREN’S COURT Adjusted odd ratios of reconviction or more vs no priors Indigenous vs non-Indigenous10-14 yrs vs 18 or older 2 or more vs no concurrent Female vs maleUnknown vs non-Indigenous Odds ratios (95% confidence interval)

Change in reconviction rate ADJUSTED 2003/04 vs 2004/05 OBSERVED  Calculate 2004/05 observed rate from data PREDICTED  Apply 2003/04 coefficients to 2004/05 data to obtain predicted probability for each offender  Calculate the mean predicted probability of reconviction for 2004/05 DIFFERENCE  Predicted - Observed  Using confidence intervals to assess differences

ADULT COURT Change in reconviction rate

CHILDREN’S COURT Change in reconviction rate

Progress towards target Percentage progress towards target = (Predicted-Observed)/Observed*100 ADULT COURT 1.3%* increase in reconviction rate CHILDREN’S COURT 2.3%* decrease in reconviction rate *No statistically significant progress towards target was identified

Caveats of the models  Do not give perfect predictions, particularly for small sub-groups  Should not be only source of information when making decisions about offenders  Can identify individuals for consideration on programs to reduce re-offending

Summary  Developed methodology to account for offender characteristics when measuring change in reconviction  Fairly accurate models of reconviction can be built with routinely collected data  Highlighted practical uses of these or similar models  One limitation is that macro changes, such as the economy, may also impact reconviction rates and are not considered here