Objective To construct a model for predicting recidivism in violence in community-based schizophrenia spectrum disorder patients (SSDP) by adopting a joint modeling method.
Methods Based on the basic data on severe mental illness in Southwest China between January 2017 and June 2018, 4565 community-based SSDP with baseline violent behaviors were selected as the research subjects. We used a growth mixture model (GMM) to identify patterns of medication adherence and social functioning. We then fitted the joint model using a zero-inflated negative binomial regression model and compared it with traditional static models. Finally, we used a 10-fold training-test cross validation framework to evaluate the models’ fitting and predictive performance.
Results A total of 157 patients (3.44%) experienced recidivism in violence. Medication compliance and social functioning were fitted into four patterns. In the counting model, age, marital status, educational attainment, economic status, historical types of violence, and medication compliance patterns were predictive factors for the frequency of recidivism of violence (P<0.05). In the zero-inflated model, age, adverse drug reactions, historical types of violence, medication compliance patterns, and social functioning patterns were predictive factors for the recidivism in violence (P<0.05). For the joint model, the average value of Akaike information criterion (AIC) for the train set was 776.5±9.4, the average value of root mean squared error (RMSE) for the testing set was 0.168±0.013, and the average value of mean absolute error (MAE) for the testing set was 0.131±0.018, which were all lower than those of the traditional static models.
Conclusion Joint modeling is an effective statistical strategy for identifying and processing dynamic variables, exhibiting better predictive performance than that of the traditional static models. It can provide new ideas for promoting the construction of comprehensive intervention systems.
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