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.
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The QIDS-SR16 is a reliable screening instrument for the diagnosis of major depressive disorder.
“A comparison of QIDS-SR16 scores with current and lifetime diagnosis based on Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID) as a measure criterion showed that QIDS-SR16 is a reliable screening instrument for the diagnosis of MDD.68”
A QIDS-SR16 cut-off score of 13 yields sensitivity of 76.5%, specificity of 81.8%, and correct classification of 80.7% for major depressive disorder.
“The cut-off scores of 13 and 14 yielded best results for sensitivity (76.5%) and specificity (81.8%), leading to correct classification of over 80% of the participants.68”
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- Authors
- Lamoureux, B. E., Linardatos, E., Fresco, D. M., Bartko, D., Logue, E., & Milo, L.
- Affiliations
- Kent State University (US)
- Summa Health System (US)
- Corresponding author
- Fresco, David M.
- ORCID
- Fresco, David M.
- Venue
- Behavior Therapy
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 41
- Issue
- 3
- Pages
- 423-431
- ISSN
- 0005-7894 (ISSN-L)
- OpenAlex
- W2047245565
- Cited by
- 58
- References
- 31
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 1 · 2023: 6 · 2022: 12 · 2021: 5 · 2020: 8 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 2 · 2014: 5 · 2013: 2 · 2012: 1
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Behavior Therapy (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- major depressive disorder, QIDS-SR16, depression screening, primary care, screening accuracy, cut-off values, sensitivity and specificity, African American patients
- MeSH terms
- Adolescent, Adult, Aged, Mental Disorders, Black or African American, Major Depressive Disorder, Female, Humans, Interview, Psychological, Male, Middle Aged, Primary Health Care, Psychiatric Status Rating Scales, ROC Curve, Self-Assessment, Sensitivity and Specificity, United States, Comorbidity, Prevalence, Area Under Curve, White People, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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