Case identification of depression with self-report questionnaires

Area of Science:

• Psychiatry
• Psychometrics
• Clinical Psychology

Background:

• Self-report depression measures often use cutoff scores for diagnosis, leading to inconsistent results across studies.
• Variability in optimal cutoff scores complicates cross-study comparisons of depression prevalence and severity.

Purpose of the Study:

• To evaluate the diagnostic performance of the Diagnostic Inventory for Depression (DID) scale.
• To compare a standard cutoff scoring approach with a DSM-IV symptom-summation algorithm for the DID scale.

Main Methods:

• The study utilized a semi-structured interview for clinical diagnosis as the gold standard.
• Receiver operating characteristic (ROC) analysis was employed to compare the diagnostic accuracy of two DID scoring methods.
• The Diagnostic Inventory for Depression (DID) scale was assessed using both cutoff scoring and an algorithmic approach.

Main Results:

• Both the cutoff scoring method and the algorithmic approach demonstrated comparable performance in identifying depression cases.
• Contrary to previous research, algorithmic scoring did not significantly outperform cutoff scores for the DID scale in this study.
• The findings suggest that cutoff scores may be as effective as algorithmic approaches for DID-based depression case identification.

Conclusions:

• The Diagnostic Inventory for Depression (DID) scale shows comparable diagnostic utility whether scored by cutoff or algorithm.
• The study highlights the need to consider the context and user when selecting a scoring method for depression screening scales.
• Further research may explore factors influencing the choice between cutoff and algorithmic scoring for improved depression diagnosis.