1. A statistical model for predicting risk of re-imprisonment
The Criminal Re-imprisonment Estimate Scale (CRES)
A statistical model for predicting risk of
re-imprisonment

2. CSNSW Vision

3. LSI-R

4. Percentage of Recidivists and Non-Recidivists Administered an LSI-R
Current Practice
Percentage of Recidivists and Non-Recidivists Administered an LSI-R
False Positive
True Positives
False Positives

5. Risk Screening Tools
LSI-R: SV
OASys (ORGS)
RoR-PV
GRAM
ROC ROI

6. The Criminal Re-imprisonment Estimate Scale (CRES)

7. Model Development 23,000 DYNAMIC STATIC EMPLOYMENT EDUCATION HOUSING
SUBSTANCE
MENTAL HEALTH
STATIC
GENDER
INDIGENOUS
AGE
SENTENCE LENGTH
TIME IN COMMUNITY
ADAPTED COPAS RATE
PRIOR INCARCERATION
OFFENCE
23,000

8. Adapted Copas rate n = # full-time custodial sentences
t = age at end current imprisonment – age first adult imprisonment
+5 makes the distribution closer to normal and makes it comparable for those offenders who are at the beginning of their criminal career.

9. Final Model Adjusted Odds Ratios of Re-imprisonment

10. Area under the curve indicated acceptable fit for the model
Model Adequacy ROC AUC
Area under the curve indicated acceptable fit for the model
auc = 0.79
CRES LSI-R (Watkins, 2011)
Female Indigenous Australian
Female Non-Indigenous Australian
Male Indigenous Australian
Male Non-Indigenous Australian

11. Reimprisonment by predicted probability
% not re-imprisoned
% re-imprisoned

12. Application of the Criminal Re-imprisonment Estimate Scale (CRES) to CSNSW Offender Management

13. Model Thresholds
Optimal Threshold
True positives vs False Positives

14. Model Thresholds

15. Classification Accuracy
Screening Tool
Number of Released Inmates with an LSI-R %
Sensitivity (True Positives)
Specificity
(True Negatives)
Not Applied
15317
(67) 68 34
>=.15
19337
(84) 97 25
>=.25
15322 89 50
>=.35
12972
(56) 81 62
False Positive

16. Classification Accuracy
Current Practice
CRES >=.25
Total Inmate Population
23,000
Total Inmate Population
23,000
LSI-R Administration
15,317
LSI-R Administration
15,317
Recidivists
9,826
Recidivists
9,826
True Positive
68%
False Positive
64%
True Positive
89%
False Positive
50%

17. Reductions in re-imprisonment
Conclusions and Implications
Application of the CRES model to practice
Redistribution of resources to higher risk offenders
Reductions in re-imprisonment

18. Questions?

19. Classification Accuracy
Screening Tool
Number of LSI-R Administrations %
Sensitivity
Specificity
Positive predictive value
Negative predictive value
Not Applied
15317
(100) 94 24 49 85
>=.10
13975
(91) 93 36 51 89
>=.15
12901
(84) 92 38 53 86
>=.2
11793
(77) 45 55
False Positive