Suicide is a significant public health issue in the US. Despite national and international prioritization since 1996, little definitive progress has been made in terms of identification and intervention in cases of elevated suicide risk. Forty percent of those who died by suicide attended an emergency department within a year of death. Therefore, universal suicide risk screening in emergency departments could prove a vital component to a national suicide prevention strategy. The present study empirically evaluated the universal suicide risk screening program recently implemented at Parkland Health and Hospital System. The sample consisted of patients over 18 years of age (N=333,855; Mage=42.7, 32% male) screened as part of routine clinical care from May 4th, 2015, through November 3rd, 2015. The Parkland Algorithm for Suicide Screening (PASS) is part of a clinical decision support system for responses to Columbia - Suicide Severity Rating Scale Clinical Practice Screener (C-SSRS) items, leading to an automated clinical response via three suicide risk stratification levels: no action for no risk identified, psychiatric social worker assessment for moderate risk identified, and psychiatrist/psychologist interview for high risk identified. The present study used receiver operating characteristic (ROC) curve analysis, which found the PASS predicted disposition (z=30.46, p<.001, AUC=.78, …

Mexican Americans face a significant health disparity related to the development of Mild Cognitive Impairment (MCI) when compared to other ethnic groups. Recent work has documented the utility of utilizing blood-based biomarkers in the detection of amnestic MCI among this population. Efforts to enhance the utility of biomarkers in detecting disease through the inclusion of select neuropsychological measures, an approach termed Molecular Neuropsychology, has shown promise. The present study sought to utilize the molecular neuropsychology approach and examine biobanked serum samples as well as neuropsychological assessments from the Health and Aging Brain among Latino Elders (HABLE) study. Random Forest analyses were conducted to determine the proteomic profile of MCI. Then separate linear regression analyses were conducted to determine the variance accounted for by the biomarkers within the select neuropsychological measures. Trail Making Test Part B was identified as having the least amount of variance and was combined with top five biomarkers within the MCI proteomic profile to create a biomarker-cognitive profile for detecting disease presence. This same method was applied to the amnestic and non-amnestic forms of MCI. The overall biomarker-cognitive profile was shown to be 90% accurate in the detection of MCI, with no significant increase when demographic variables …