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Brain Behav. Sep 2012; 2(5): 576–589.
Published online Jul 26, 2012. doi:  10.1002/brb3.76
PMCID: PMC3489810
Sensitivity, specificity, and predictive power of the “Brief Risk-resilience Index for SCreening,” a brief pan-diagnostic web screen for emotional health
Leanne M Williams,1,2 Nicholas J Cooper,3,4 Stephen R Wisniewski,5 Justine M Gatt,2 Stephen H Koslow,1 Jayashri Kulkarni,6 Savannah DeVarney,3 Evian Gordon,2,3 and Augustus John Rush6
1BRAINnet Foundation, 71 Stephenson Street, Suite 400, San Francisco, California, 94105
2University of Sydney Medical School and Westmead Millennium Institute, Sydney, New South Wales, 2145, Australia
3Brain Resource, Level 12, 235 Jones Street, Ultimo, Sydney, 2007, Australia
4Brain Resource, 1000 Sansome Street, San Francisco, California, 94111
5Department of Epidemiology, University of Pittsburgh, 127 Parran Hall, Pittsburgh, Pennsylvania, 15261
6Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore, 169857
Leanne M. Williams, Westmead Hospital, University of Sydney Medical School, Sydney, NSW 2145, Australia. Tel: +61 2 9845 8195; Fax: +61 2 9845 8190; E-mail: lea.williams/at/
Funding Information This research received no specific grant from any funding agency in the public, commercial, or not for profit sectors. It was supported in part by grants DP0773994 and LP0883621 from the Australian Research Council. Brain Resource was the industry partner on LP0883621.
Received September 14, 2011; Revised May 2, 2012; Accepted May 16, 2012.
Few standardized tools are available for time-efficient screening of emotional health status across diagnostic categories, especially in primary care. We evaluated the 45-question Brief Risk-resilience Index for SCreening (BRISC) and the 15-question mini-BRISC in identifying poor emotional health and coping capacity across a range of diagnostic groups – compared with a detailed clinical assessment – in a large sample of adult outpatients. Participants 18–60 years of age (n = 1079) recruited from 12 medical research and clinical sites completed the computerized assessments. Three index scores were derived from the full BRISC and the mini-BRISC: one for risk (negativity–positivity bias) and two for coping (resilience and social capacity). Summed answers were converted to standardized z-scores. BRISC scores were compared with detailed health assessment and diagnostic interview (for current psychiatric, psychological, and neurological conditions) by clinicians at each site according to diagnostic criteria. Clinicians were blinded to BRISC scores. Clinical assessment stratified participants as having “clinical” (n = 435) or “healthy” (n = 644) diagnostic status. Receiver operating characteristic analyses showed that a z-score threshold of −1.57 on the full BRISC index of emotional health provided an optimal classification of “clinical” versus “healthy” status (sensitivity: 81.2%, specificity: 92.7%, positive predictive power: 80.2%, and negative predictive power: 93.1%). Comparable findings were revealed for the mini-BRISC. Negativity–positivity bias index scores contributed the most to prediction. The negativity–positivity index of emotional health was most sensitive to classifying major depressive disorder (100%), posttraumatic stress disorder (95.8%), and panic disorder (88.7%). The BRISC and mini-BRISC both offer a brief, clinically useful screen to identify individuals at risk of disorders characterized by poor emotion regulation, from those with good emotional health and coping.
Keywords: Depression and anxiety, emotional well-being, Internet, mental health screen, risk and resilience, sensitivity and specificity
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