Rapid screening for suicide risk: An algorithmic approach

In the United States, primary medical care settings are the first accessed resource for both medical and behavioral health care. Thus, there is a clear need for accurate and efficient behavioral health screening in this setting, including routine surveillance screening for suicide risk. The Multidimensional Behavioral Health Screen (MBHS), a broadband but very brief screening tool developed specifically for primary care, has been updated to include an algorithm that classifies suicide risk based on the interpersonal-psychological theory of suicide, and associated interview and decision framework. This study aims to evaluate the predictive accuracy of the new MBHS 2.0 suicide risk algorithm, with actual risk determined by clinical suicide risk interview.

Data were collected as part of a larger study that, overall, included 551 college student participants. Of these, 309 completed the MBHS 2.0 and the clinical suicide risk interview, the two measures reported here. The final participant count was 299 following the removal of incomplete or invalid cases. Predicted suicide risk as determined by the MBHS 2.0 (Low, Mild, At least Moderate) was compared to actual risk as determined by clinical interview (Low, Moderate, Severe, Extreme).

Utilizing chi-square analyses, data show a significant association between the predicted suicide risk category based on the MBHS 2.0 algorithm and the actual risk category based on the semi-structured clinical interview. Furthermore, classification analyses suggest that primary care providers will be able to confidently assess the suicide risk level for the majority of their patients when using the MBHS.

Findings suggest that the MBHS 2.0 can be an accurate and efficient tool for use by primary care providers in classifying suicide risk. Future research will be useful to evaluate the utility of the suicide risk algorithm among primary care populations.