Validation of patient health questionnaire (PHQ) for major depression in Chinese Outpatients with multiple somatic Symptoms: A multicenter cross-sectional study

Abstract

Background: Despite the high co-morbidity of depressive symptoms in patients with multiple somatic symptoms, the validity of the 9-item Patient Health Questionnaire (PHQ-9) has not yet been investigated in Chinese patients with multiple somatic symptoms. Methods: The multicenter cross-sectional study was conducted in ten outpatient departments located in four cities in China. The psychometric properties of the PHQ-9 were examined by confirmative factor analysis (CFA). Criterion validation was undertaken by comparing results with depression diagnoses obtained from the Mini International Neuropsychiatric Interview (MINI) as the gold standard. Results: Overall, 491 patients were recruited of whom 237 had multiple somatic symptoms (SOM+ group, PHQ-15 ??? 10). Cronbach’s ?? of the PHQ-9 was 0.87, 0.87, and 0.90 for SOM+ patients, SOM- patients, and total sample respectively. All items and the total score were moderately correlated. The factor models of PHQ-9 tested by CFA yielded similar diagnostic performance when compared to sum score estimation. Multi-group confirmatory factor analysis based on unidimensional model showed similar psychometric properties over the groups with low and high somatic symptom burden. The optimal cut-off point to detect depression in Chinese outpatients was 10 for PHQ-9 (sensitivity=0.77, specificity=0.76) and 3 for PHQ-2 (sensitivity=0.77, specificity=0.74). Limitations: Potential selection bias and nonresponse bias with applied sampling method. Conclusions: PHQ-9 (cut-off point=10) and PHQ-2 (cut-off point=3) were reliable and valid to detect major depression in Chinese patients with multiple somatic symptoms.

Publication
Journal of Affective Disorders, (174), pp. 636 - 643
Sandra Nolte
Sandra Nolte
Psychometrics and Health Outcomes
Felix Fischer
Felix Fischer
Group Leader Psychometrics and Health Outcomes

Psychologist with a weakness for quantitative data analysis

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