Data Missingness Patterns in Homicide Datasets: An Applied Test on a Primary Data Set.

Research on homicide missing data conventionally posits a Missing At Random pattern despite the relationship between missing data and clearance. The latter, however, cannot be satisfactorily modeled using variables traditionally available in homicide datasets. For this reason, it has been argued that missingness in homicide data follows a Nonignorable pattern instead. Hence, the use of multiple imputation strategies as recommended in the field for ignorable patterns would thus pose a threat to the validity of results obtained in such a way. This study examines missing data mechanisms by using a set of primary data collected in New Jersey. After comparing Listwise Deletion, Multiple Imputation, Propensity Score Matching, and Log-Multiplicative Association Models, our findings underscore that data in homicide datasets are indeed Missing Not At Random.

Triaging Psychiatric Care: Risk Assessment Construction and Validation for Washington's Involuntary Treatment and Forensic Commitment Populations.

There is a growing need in Washington to triage patients being considered for inpatient treatment. There has previously been no risk assessment normed to persons with mental illness in the State of Washington. The Static Risk Assessment (SRA) is used in Washington to assess risk among offenders under State Department of Corrections supervision. The present study sought to create and validate a new risk assessment, the SRA for Mental Health Patients (SRA-MHP), for two mental health populations in Washington. A sample of 16,289 patients involuntarily committed due to being deemed dangerous or gravely disabled, as well as a sample of 8,713 forensic patients undergoing a competency evaluation to stand trial after being charged with a crime, was gathered to assess the utility in applying the SRA-MHP to persons with mental illness. Results indicate that the SRA-MHP has high predictive performance for these samples.