Improving Suicide Risk Screening to Identify the Highest Risk Patients: Results From the PRImary Care Screening Methods (PRISM) Study.

PURPOSE: Over 95% of patients who screen positive on the Patient Health Questionnaire-9 (PHQ-9) suicide risk item do not attempt or die by suicide, which could lead to unnecessary treatment and/or misallocation of limited resources. The present study seeks to determine if suicide risk screening can be meaningfully improved to identify the highest-risk patients. METHODS: Patients eligible to receive medical treatment from the US Department of Defense medical system were recruited from 6 military primary care clinics located at 5 military installations around the United States. Patients completed self-report measures including the PHQ-9 and 16 items from the Suicide Cognitions Scale (SCS) during routine primary care clinic visits. Postbaseline suicidal behaviors (suicide attempts, interrupted attempts, and aborted attempts) were assessed by evaluators who were blind to screening results using the Self-Injurious Thoughts and Behaviors Interview. RESULTS: Among 2,744 patients, 13 (0.5%) engaged in suicidal behavior in the 30 days after screening and 28 (1.0%) displayed suicidal behavior in the 90 days after screening. Multiple SCS items differentiated patients with suicidal behavior less than 30 days after screening positive for suicide risk. Augmenting the PHQ-9 suicide risk item with SCS items improved the identification of patients who were most likely to have suicidal behavior within a month of screening positive without sacrificing sensitivity. CONCLUSION: Among primary care patients who screen positive for suicide risk on the PHQ-9, SCS items improved screening efficiency by identifying those patients who are most likely to engage in suicidal behavior within the next 30 days.

Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study.

BACKGROUND: With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States. OBJECTIVE: The aim of this study, building on previous work qualitatively analyzing depression-related Twitter data, was to describe the development of a comprehensive annotation scheme (ie, coding scheme) for manually annotating Twitter data with Diagnostic and Statistical Manual of Mental Disorders, Edition 5 (DSM 5) major depressive symptoms (eg, depressed mood, weight change, psychomotor agitation, or retardation) and Diagnostic and Statistical Manual of Mental Disorders, Edition IV (DSM-IV) psychosocial stressors (eg, educational problems, problems with primary support group, housing problems). METHODS: Using this annotation scheme, we developed an annotated corpus, Depressive Symptom and Psychosocial Stressors Acquired Depression, the SAD corpus, consisting of 9300 tweets randomly sampled from the Twitter application programming interface (API) using depression-related keywords (eg, depressed, gloomy, grief). An analysis of our annotated corpus yielded several key results. RESULTS: First, 72.09% (6829/9473) of tweets containing relevant keywords were nonindicative of depressive symptoms (eg, "we're in for a new economic depression"). Second, the most prevalent symptoms in our dataset were depressed mood and fatigue or loss of energy. Third, less than 2% of tweets contained more than one depression related category (eg, diminished ability to think or concentrate, depressed mood). Finally, we found very high positive correlations between some depression-related symptoms in our annotated dataset (eg, fatigue or loss of energy and educational problems; educational problems and diminished ability to think). CONCLUSIONS: We successfully developed an annotation scheme and an annotated corpus, the SAD corpus, consisting of 9300 tweets randomly-selected from the Twitter application programming interface using depression-related keywords. Our analyses suggest that keyword queries alone might not be suitable for public health monitoring because context can change the meaning of keyword in a statement. However, postprocessing approaches could be useful for reducing the noise and improving the signal needed to detect depression symptoms using social media.