Actuarial versus SPJ Risk Instruments with SOMMI

Transcription

1 Actuarial versus SPJ Risk Instruments with SOMMI DAVID THORNTON PRESENTATION AT THE ATSA 36 TH ANNUAL RESEARCH AND TREATMENT CONFERENCE, OCTOBER 26 TH 2017, KANSAS CITY, MISSOURI

2 Financial Disclosure I have no direct financial interests in the topics covered by this presentation I may on occasion be paid fees for providing training on related topics

3 Goals / Objectives Participants will be able to describe the predictive accuracy of the Static-99/Static- 99R with SOMMI by using the Area Under the Curve (AUC) statistic and the prevalence of SOMMI in the Static-99R normative samples. Participants will be able to provide the AUC range for structured and SPJ measures Participants will be able to describe three ways MMI symptoms can affect sexual offending risk.

4 Background Professionals may be called to assess the risk presented by men with a history of sexual offending and major mental illness (SOMMI) for a number of reasons To facilitate concentrating resources on those who present the greatest risk To determine whether some absolute risk threshold is met To facilitate risk management The task is challenging because existing tools do not fit very well SOMMI are only a very small part of the population of men adjudicated for sex offending so tools designed for that group may not fit SOMMI Similarly, tools designed for those with MMI, even those with MMI and a history of crime, may not fit well since the kinds of crime involved are more usually non-sexual violence

5 Potential Influence of MMI on Risk Following Kelley & Thornton Kelley, S. & Thornton, D. (2015) Assessing risk of sex offenders with major mental illness: integrating research into best practices. Journal of Aggression, Conflict and Peace Research, 7, We can distinguish three potential influences of MMI on sexual recidivism risk MMI symptoms reduce risk by disrupting ability to pursue offending intentions MMI symptoms are irrelevant MMI symptoms can worsen risk Actuarial instruments designed to assess sexual recidivism risk presently Do not take into account the presence of MMI Do not distinguish the different kinds of influence it may have

6 Implications This suggests that actuarial instruments may be less useful with SOMMI and raises the possibility of SPJ allowing better prediction SPJ allows more flexibility and SPJ instruments like the SVR-20 include an item for MMI This line of thought may explain why SPJ is more commonly used in forensic mental health settings But how well founded is this?

7 Outline Review of Static-99 / R properties with SOMMI SOMMI in normative samples? AUC in samples with more SOMMI Review of SPJ properties with SOMMI AUC in samples with more SOMMI AUC generally Implications

8 Prevalence of MMI in 2009 Static-99R Normative Samples: Routine

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11 STATIC Measures in Normative Samples ØOriginal Static-99 (Hanson & Thornton, 2000): Based on 4 samples; one psychiatric sample included psychosis as primary diagnosis Frequencies of dx not described, but Static-99 predicted risk in psychiatric sample equally when compared with the other three samples (AUC=.67 vs AUCs = ) ØCurrent Static-99R: Nature of included psychiatric disorders was not specified or reported Prevalence of SOMMI in normative sample of Static-99R was either unspecified or known to be low in some HRHN samples (under 15%). Static-99R (as well as Static-2002R) both predictive of recidivism (AUCs in 0.7s) in individuals with psychiatric history as defined by DSP project (Helmus, 2012)

12 Summary of Actuarial Results In populations identified as MI or having a higher concentration of MI: Static-99 AUC 0.67 (original) Static-99 AUC 0.65 Craissati & Blundell (2013) mentally disordered sex offenders placed in community STABLE-2007 AUC 0.63 Craissati & Blundell (2013) Static-99R 0.74 DSP (Helmus, 2012) psychiatric hospitalization Static-2002R 0.73 (Helmus, 2012) psychiatric hospitalization AUCs ranged from 0.63 to 0.74 for static and dynamic actuarial instruments

13 SPJ Three SPJ measures that may be relevant to assessing risk in the SOMMI population: ØSexual Violence Risk Management- 20 (SVR-20) (Boer, et al, 1997) ØRisk for Sexual Violence Protocol (RSVP) (Hart, et al, 2003) Both of above integrate dynamic and static risk ; allow for an explicit assessment of individual s mental illness; and allows clinician to give weight to mental illness in making final risk assessment ØAssessment and Risk Manageability for Individuals who Offend Sexually (ARMIDILO-S) (Boer, et al, 2004) First of its kind to identify and assess individual as well environmental factors

14 Numbers of SPJ Recidivism Studies for SOMMI SVR-20 3 samples with substantial proportion of SOMMI (56%, 19%+, 43%; 2 of the 3 studies involved cases referred for NGI assessment) RSVP No studies but is highly correlated with SVR-20 so properties probably transfer ARMIDILO-S No studies with SOMMI but seems to work well with DD samples

15 AUCs for SPJ Instruments

16 Summary of SPJ Results Range of AUCs for SVR-20 with SOMMI Median 0.52 Median AUC for all SVR-20 Studies is , , , 0.66, , 0.58, 0.52, , 0.49, SPJs seem to give highly variable results in general and with SOMMI

17 Concluding Thoughts We properly have concern about the value of existing instruments with the most severe MMI presentations where symptoms impact risk indirectly or directly To date static actuarial instruments seem to do okay in samples with significant numbers of SOMMI but studies of severe SOMMI are absent SPJ instruments (or at least the SVR-20), although they explicitly take into account MI, seem to be less successful in assessing sexual recidivism risk, even in MI pops We may well need to add something to static actuarials for the more severe SOMMI but SPJ instruments like the SVR-20 are more likely to mislead than to assist us in this

18 Introduction to SOMMI Project

19 Goals Characterize the SOMMI population Defined as individuals with a history of sexual offending and Major Mental Illness meaning Bipolar I or Psychotic Disorders Find ways of reliably identifying differences between SOMMI so that risk management can be better individualized This includes specifically: Determine ways of reliably identifying LTVs in SOMMI population Studying the relationship of MMI symptoms to sexual offending Developing ways of reliably characterizing the different forms this relationship can take Ultimate goal is to be helpful to clinicians working with this population

20 SOMMI Population(s) We will need good sized samples drawn from SOMMI populations We will need to draw from SOMMI populations accumulated in the different institutional contexts in which clinicians might encounter them This is an ongoing project

21 Selection of Individuals with a history of SOMMI from WI SVP Program All WI SVPs are re-evaluated every year and these evaluations include DSM diagnoses. The SRSTC Research Unit maintains spreadsheets containing this diagnostic information. We identified individuals for the study based on DSM-IVTR / DSM-5 diagnoses indicating Bipolar I or Psychotic Disorders. We tried to be more inclusive (some individuals had diagnoses that varied over the years) When the case files were being rated the rating psychologist identified cases where there was question about whether they really were SOMMI (about a fifth were questionable) Some 55 cases have been rated with 30 of these cases being rated by two independent raters Note that exact Ns may vary for specific analyses depending on missing data

22 Selection from the Bridgewater Pool These were a pool of individuals who received treatment at Bridgewater and for whom Ray Knight had accumulated electronic file data DSM-III Axis 1 diagnoses had been made and we reviewed those to determine which particular diagnoses best corresponded to modern conceptions of Psychosis or Bipolar I When the case files were being rated the rating psychologist identified cases where there was question about whether they really were SOMMI (about a third were questionable) Some 25 cases have been rated so far with 20 of these cases being rated by two independent raters

23 What follows are progress reports on the different foci of the project