Group Risk Assessment Model

Monitoring trends in re-offending among convicted offenders in adult and children’s

court

Fourth National Justice Modelling Workshop10-11th 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 reconvictedN

3265,562ADULTCOURT

563,706CHILDREN’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

DEMOGRAPHICSAge Sex Indigenous status INDEX OFFENCE Jurisdiction Principal offence type # of concurrent offences OFFENDER HISTORY# of prior convictions

ADULT

COURT

✔✔✔

✔✔✔

✔

CHILDREN'S

COURT

✔✔✔

✔✖✔

✔

ADULT COURT Adjusted odd ratios of reconviction

0 1 2 3 4 5

4 or more vs no priors

10-21 yrs vs 40 or older

Indigenous vs non-Indigenous

2 or more vs no concurrent

Property vs driving

Violent vs driving

Female vs male

Unknown vs non-Indigenous

Odds ratios (95% confidence interval)

CHILDREN’S COURTAdjusted odd ratios of reconviction

0 1 2 3 4 5

4 or more vs no priors

Indigenous vs non-Indigenous

10-14 yrs vs 18 or older

2 or more vs no concurrent

Female vs male

Unknown vs non-Indigenous

Odds ratios (95% confidence interval)

Change in reconviction rateADJUSTED 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 COURTChange in reconviction rate

ADULT

COURTReconviction rate (95% CI)

Observed 2003/04

32.0 (31.7, 32.4)

Observed 2004/05

31.7 (31.4, 32.0)

Predicted 2004/05

31.3 (31.0, 31.6)

Difference between 2003/04 and 2004/05 percent reconvicted

-0.3-0.4

-1

0

1

Unadjusted(Obs0405-Obs0304)

Adjusted(Pred0405-Obs0405)

CHILDREN’S COURTChange in reconviction rate

Difference between 2003/04 and 2004/05 percent reconvicted

0.7 1.3

-4

-3

-2

-1

0

1

2

3

4

Unadjusted(Obs0405-Obs0304)

Adjusted(Pred0405-Obs0405)

CHILDREN'S

COURTReconviction rate (95% CI)

Observed 2003/04

55.5 (53.9, 57.1)

Observed 2004/05

56.2 (54.6, 57.9)

Predicted 2004/05

57.5 (55.9, 59.2)

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