Using the QIDS-SR16 to Identify Major Depressive Disorder in Primary Care Medical Patients

Abstract

Major depressive disorder (MDD) is a serious and prevalent mental health issue. As the majority of MDD cases are identified and treated by one's primary care physician, it is imperative that care providers utilize accurate and efficient methods for diagnosing MDD in primary care settings. The present study is the first to investigate the accuracy of the Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR(16)) as a screen for MDD. A heterogeneous sample of 155 primary care patients completed the QIDS-SR(16) prior to attending a primary care appointment. Participants were then assessed for psychopathology using the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID) by clinicians who were blind to QIDS-SR(16) scores. Scores on the QIDS-SR(16) were compared to clinician-assessed current and lifetime diagnoses derived from the SCID, which represented the gold-standard criterion measure. Receiver operator characteristic analysis was utilized to determine the optimal QIDS-SR(16) cut score to correctly classify participants based on their MDD status as assessed by the SCID. The test revealed a robust area under the curve (.82, p<0.00001) and suggested that a cut score of 13 or 14 provided the best balance of sensitivity (76.5%) and specificity (81.8%) in this primary care sample. Over 80% of participants were correctly classified. Separate analyses by race were conducted to address the possibility that different cut scores may be more accurate for African American and Caucasians. Findings from the present study provide support for the use of the QIDS-SR(16) as a screening measure for identifying primary care patients who will meet diagnostic criteria for MDD based on clinician assessment.