Treatment of Sex Offenders

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to other metrics for communicating risk (for an example of different types of risk
ratios and other decisions required in their calculation, see Hanson et al., 2013 ), difficulty
in communicating them to laypeople (e.g., Varela et al., 2014 ), and potential
for misinterpretation. Specifically, risk is generally overestimated if risk ratios are
not properly contextualized with information about base rates (Elmore & Gigerenzer,
2005 ). In the Static-99R example above, knowing that an offender with a score of 4
is twice as likely to reoffend as an offender with a score of 2 has a very different
meaning if the recidivism rate for a score of 2 is 4 or 40 %.
Absolute Recidivism Estimates
Absolute recidivism estimates are by far the most frequent quantitative metric
reported for actuarial risk scales. They are reported in approximately 90 % of assessment
reports for preventative detention in Canada, compared to percentiles and risk
ratios, which are reported in roughly 40 % and 0 % of cases, respectively (Blais &
Forth, 2014 ). In a survey examining Static-99R reporting practices in sex offender
civil commitment evaluations, absolute recidivism estimates were used by 83 % of
respondents, compared to roughly one third who used either percentiles or risk
ratios (Chevalier, Boccaccini, Murrie, & Varela, 2014 ).
Absolute recidivism estimates can be generated in a variety of ways, such as
from observed recidivism rates for a group of scores (ideally requiring large sample
sizes for each score) or using methods such as survival analysis or logistic regression
(for discussion, see Hanson, Helmus, and Thornton, 2010 ). Absolute risk information
is easy to understand but hard to obtain with high levels of confidence.
Recidivism rates vary based on the follow-up length, so this must be specified.
Additionally, there are several practical complications in obtaining good estimates
of recidivism, including underreporting of offences, misclassification (e.g., sexual
offences pled down to nonsexual violent offences), prosecutorial discretion, and
legal/policy/cultural changes over time.
Likely due to the myriad factors that influence recidivism, research has found
that absolute recidivism estimates were unstable across samples for the Static-99R
and Static-2002R (Helmus, Hanson, Thornton, Babchishin, & Harris, 2012 ), as well
as the MATS-1 (Helmus & Thornton, 2014 ) and the Risk Matrix 2000/Violence
scale (but not the Risk Matrix 2000/Sex scale; Lehmann, Thornton, Helmus, &
Hanson, 2015 ). Additional research has also raised concerns about the generalizability
of the recidivism estimates for the VRAG (Mills, Jones, & Kroner, 2005 ;
Snowden, Gray, Taylor, & MacCulloch, 2007 ). Moreover, analyses of two samples
found that violent recidivism rates differed between samples after controlling for
the VRS-SO pretreatment score (Olver, Beggs Christofferson, Grace, & Wong,
2014 ). Some solutions have been proposed for using absolute recidivism estimates
in light of this variability (e.g., Hanson, Thornton, Helmus, & Babchishin, 2015 ),
but the adequacy of these solutions is not yet known. Minimally, these findings of
variability suggest that creating and reporting reliable and generalizable recidivism
estimates for actuarial scales are more complicated than previously believed.