Factors associated with recidivism at a South African forensic psychiatric hospital
Aims: This study examines common factors associated with recidivism among state patients at a South African forensic psychiatric hospital. More specifically, demographic, clinical and criminological factors of a recidivist group are compared to a non-recidivist group with the intention of understanding to what extent these factors might determine the likelihood of re-offending.
Method: A retrospective case file review of 293 inpatients and a random selection of 120 outpatients was conducted. For the purpose of the study, a patient was classified as a recidivist if an additional charge or act of violence was added to the file while the patient was on leave of absence in the community. Of the inpatients, only those who met the criteria for recidivism were included in the study. All 120 randomly selected outpatients were included. Demographic, clinical and criminological data were captured for all patients.
Results: Eighty recidivists were compared with 100 non-recidivists. Using the × 2 and Fischer’s exact test, substance-use disorder, antisocial personality disorder, an index offence of assault and in-ward adverse events were found to be associated with recidivism (p < 0.05). Using logistic regression analysis, the odds of recidivism in a patient with an index offence of assault was 8.4 times of those who did not commit assault as an index offence (95.0% CI 1.6–43.1). The odds of recidivism for patients with cannabis use was 2.8 (95.0% CI 1.3–6.0) and for patients with in-ward adverse sexual behaviour was 17.2 (95.0% CI 2.0–150).
Conclusion: Substance-use disorder and antisocial personality disorder are associated with higher risk for recidivism. This study also highlights that a less serious offence such as assault had a higher association with recidivism. Patients noted to display adverse sexual behaviour in the ward pose a potentially high risk for re-offence. Important criminal history factors and certain clinical factors could not be interpreted because of large amounts of missing data in patients’ files.