Risk Assessment in Forensic Learning Disability Psychology

1. Risk Assessment in Forensic Learning Disability Psychology Dr Jeremy Tudway Consultant Forensic Clinical Psychologist/Lecturer-Practitioner in Clinical Psychology

2. Risk Assessment: Context �Justice is now very much less important than risk, as a preoccupation of criminal justice/law and order policy; the politics of safety have overwhelmed attachment to justice in the institutions of late-modern demographic polities� Gray, et.al., (2001)

3. What should risk assessments do? i Identify likely outcome Estimate: Likelihood Probability Identify factors Increase Decrease Reliable and valid

4. What should risk assessments do? ii Probability NOT Possibility The LIKELIHOOD of a given event Not the HYPOTHETICAL chance It is possible that anyone could set a fire, whereas the probability of someone with no fire-setting history, no interest in fires and few interpersonal problems deliberately setting fires is small in comparison to someone who has previously set fires, shows a long-term interest and has gross interpersonal problems

5. Problems with Risk Assessment �Hired guns� Ill-informed appraisal Poorly researched instruments Inadequate base-rate estimates Not really knowing the �true� rate of occurrence Actuarial versus Clinical data But, crime detection and conviction does not necessarily equate to the �true� rate at which offending has taken place.

6. Clinical versus Actuarial i Clinician accuracy is low (violent behaviour). Monahan (1981) Clinical Not systematic or standardized, based on judgment. Evidence suggests that �professional judgement� is generally very poor and fraught with biases Mental health professionals often make critical decisions in the first couple of seconds of a contact and then direct questions towards �proving� their assumptions! Actuarial Systematic, standardized, rule-governed. Empirical literature Clinical tends to be more conservative.

7. Clinical versus Actuarial ii All risk assessment procedures still require professional judgment Choice of scale, or critical variables Interpretation of �scores� Using idiosyncratic or clinical judgement No systematic empirical support Low agreement (unreliable) and accuracy (unvalidated) Actuarial risk assessment by trained assistants has stronger predictive validity than therapists� assessment. Grove & Meehl (1996) 40% of better, 40% no difference, 10% poorer Beech & Ward (2003)

8. Six risk assessment methods(Doren, 2002) Unstructured/unguided clinical judgment Case material without any structured assessment. Decisions are intuitive or experiential. �Purely actuarial approach Straightforward algorithmic procedures and risk-prediction instruments. Reliant on historical/static risk factors and coding to arrive at a probability of reconviction. �Guided clinical judgment Clinical judgment without relating to current theories of risk. �Anamnestic risk assessment Life history examined in relation to dispositional & contextual factors. Current circumstances examined for presence of particular identified risk factors �Research-guided clinical judgment. Apriori set of research-informed factors (e.g., SVR-20) as a guide to assessment. �Clinically adjusted actuarial approach. Initial employment of one or more actuarial instruments is followed by potential adjustments based on clinical/dispositional considerations.

9. What is a predictor? �� it is often said that the best predictor of future violence is past violence, and anyone with a history of violence needs to be managed with care�.. Unless there are good reasons to believe that major changes have occurred, it is always wise to assume that violence is a possibility, and to take steps accordingly� Sellars, (2002)

10. In other words�.

11. Structured Risk Assessment Many different devices SACJ (Thornton) RM2000 (Thornton) VRAG (Quinsey, Harris, Rice & Cormier) HCR-20 (Webster) RRASOR (Hanson) SVR-20 (Boer, Hart, Kropp & Webster) OASYS (Home Office) Approximately 26 specific sex offender risk assessments to date! (Doren, 2002)

12. Static Variables �Tombstone� factors Always additive Usually negative Although the long-term probability may reduce evidence is unclear Tend to out-weigh other sources of data (eg: PCL-r)

13. Dynamic Variables Indicators of change Dynamic Can reduce and increase Can be both positive and negative Linked to harm-reduction or relapse-prevention models Can be linked to intervention Ideally lend themselves to psychometric data (eg: SOAP)

14. Acute & Stable Dynamic Variables Acute dynamic factors Indicate high likelihood of an offense in the near future. (Hanson & Harris, 2001) �Integrated model that includes: Distal factors (developmental) Historical and stable dynamic (vulnerability or trait) Triggering events (contextual factors) that combine with static and stable dynamic factors and this drive Acute dynamic (state) factors. (Beech & Ward, 2003)

15. Base Rates The frequency of the target behaviour in any given population Accurately know the prevalence Offending behaviour is grossly underestimated by conviction The rate at which acts occur in the population of interest is critical to the predictive ability of any instrument.

16. The Base Rate Fallacy Failure to take base rates into account when judging probability. When considering a particular case, the statistics for that population should also be considered. People tend to ignore the population base rate People are not good statisticians!

17. Problems with Prediction i

18. Problems with Prediction ii False positives Ethical Restriction and impingement of human rights Creating more problems (vicarious learning, institutionalisation) Alarm and concern to systems Cost False negatives Ethical Exposing people to unnecessary potential harm Undermining public confidence in services Damage to public perceptions of PwLD Cost

19. Problems with Prediction iii The most widely research tools report good sensitivity using the Receiver Operator Characteristic (ROC) curve Estimates the accuracy of the instrument by calculating how much data can be accounted for using: Sensitivity True positive rate Hits Specificity True negative rate Misses

20. Problems with Prediction iv No matter how good the ROC curve, with low frequency, high impact behaviour. Over estimation of risk and false positives Positive predictive value (the proportion of positive predictions that turn out correct) (eg Szmukler, 2001a,b)

21. Problems with Prediction v Statistical significance is not necessarily linked to clinical significance. The probability of a given number arising on an analysis may be remote but this does not suggest that this figure is of particular significance to an individual

22. New ways of thinking? Combining both Actuarial & Clinical data (Beech & Ward, 2003) Dynamic & Static factors Situational & Accidental triggers �The dichotomy between clinical and actuarial is unhelpful and being replaced by hybrid models, such as the HCR-20� (Johnston, 2002) �Despite this increasing sophistication of research in mainstream forensic psychiatry, the ability to predict future offending behaviour remains very limited.� Turner (2000)

23. How appropriate are existing schemes? Can actuarial predictors developed in psychiatric or prison populations be valid for individuals with intellectual disabilities? (Green et.al., 2002; Johnston, 2002; Turner, 2000) Adapted SACJ significantly inflates risk estimate in an LD group Under reporting and variable legal responses to offending behaviour in PwLD. Conviction not associated with: Volume Victim range Risk status (Green et.al., 2002)

24. Challenges i Methodological problems Sampling (range, borderline) Definitions (Violence, Sexual offence) Convicted versus non-convicted (Cautions) (eg: Green et.al., 2002; Lindsey, 2002) Rationale for �good practice� is ill-defined (eg: Johnstone, 2002)

25. Challenges ii Difficult to make reasonable comparisons Reported re-offending rates vary from study to study 0-28% & 31.3-85% (Barron et.al., 2002) Difficult to establish predictive factors Few differences on factors associated with sexual recidivism (Green et.al., 2002) Actuarial measures share a common theoretical heritage How appropriate to use multiple forms?

26. The Future i Co-ordinated approach to research and data gathering Initiate inclusive collaborative research to retrospectively identify key predictor variables using robust technologies Initiate a longitudinal prospective study using identified factors Develop culturally sensitive reference groups Ethnicity, Social expectations

27. The Future ii Develop meaningful normative models for comparing key behaviours: Sexual (eg; beliefs, behaviours), Interpersonal (eg; �assertion�), Motivational (eg; �impulsivity�), Social (eg; gender roles) etc Include cautions and systematically detailed reports of problem behaviours in addition to convictions Develop functional formulation Develop Offence Parallel Behaviour models Make the technologies available Establish government funding Communicate the findings and change practice as a result!

28. A final point? Despite all the complications there are, however, two things one can predict with certainty:

29. References Barron, P., Hassiotis A. & Banes, J. (2002) Offenders with intellectual disability: the size of the problem and therapeutic outcomes. Journal of Intellectual Disability Research. 46,�6,�454 �463. Beech, A.R. & Ward T. (2003) The integration of etiology and risk in sexual offenders: A theoretical framework. Aggression and Violent Behavior. 295 In Press Doren, D. M. (2002). Evaluating sex offenders: A Manual for Civil Commitments and Beyond. London: Sage. Gray, N.S., Laing, J. & Noakes, L. (Eds) (2001) Criminal Justice, Mental Health and the Politics of Risk. London: Cavendish Press. Green, G., Gray, N.S. & Willner, P. (2002) Factors associated with criminal convictions for sexually inappropriate behaviour in men with learning disabilities. The Journal of Forensic Psychiatry. 13, 3, 578-607. Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical statistical controversy. Psychology, Public Policy, and Law, 2, 293�323. Hanson, R. K., & Harris, A. (2001). The sex offender need assessment rating (SONAR): A method for measuring change in risk levels. Available: www.sgc.gc.ca/epub/corr/e200001a/e200001b/e200001b.htm. Johnston, S. J. (2002) Risk assessment in offenders with intellectual disability: the evidence base. Journal of Intellectual Disability Research. 46: Sup1, 47-56. Lindsay, W.R. (2002) Research and literature on sex offenders with intellectual and developmental disabilities. Journal of Intellectual Disability Research. 46: Sup1, 74-85. Monahan, J. (1981) Predicting Violent Behavior: An Assessment of Clinical Techniques. Beverley Hills: Sage. Sellars, C. (2002) Risk Assessment in People with Learning Disabilities. BPS Books, Blackwell. Szmukler, G. (2001a) The mathematics of risk assessment for serious violence. Psychiatric Bulletin 25: 359 Szmukler, G. (2001b)Violence risk prediction in practice. The British Journal of Psychiatry. 178: 84-85 Turner, S. (2000) Forensic Risk Assessment in Intellectual Disabilities: The Evidence Base and Current Practice in One English Region. Journal of Applied Research in Intellectual Disabilities. 13, 4, 239