Valid group comparisons can be made with the Patient Health Questionnaire (PHQ-9): A measurement invariance study across groups by demographic characteristics

Abstract

Objective: Analyze the measurement invariance and the factor structure of the Patient Health Questionnaire-9 (PHQ-9) in the Peruvian population.

Method: Secondary data analysis performed using cross-sectional data from the Health Questionnaire of the Demographic and Health Survey in Peru. Variables of interest were the PHQ-9 and demographic characteristics (sex, age group, level of education, socioeconomic status, marital status, and area of residence). Factor structure was evaluated by standard confirmatory factor analysis (CFA), and measurement invariance by multi-group CFA, using standard goodness-of-fit indices criteria for interpreting results from both CFAs. Analysis of the internal consistency (α and ω) was also pursued.

Results: Data from 30,449 study participants were analyzed, 56.7% were women, average age was 40.5 years (standard deviation (SD) = 16.3), 65.9% lived in urban areas, 74.6% were married, and had 9 years of education on average (SD = 4.6). From standard CFA, a one-dimensional model presented the best fit (CFI = 0.936; RMSEA = 0.089; SRMR = 0.039). From multi-group CFA, all progressively restricted models had ΔCFI<0.01 across almost all groups by demographic characteristics. PHQ-9 reliability was optimal (α = ω = 0.87).

Conclusions: The evidence presents support for the one-dimensional model and measurement invariance of the PHQ-9 measure, allowing for reliable comparisons between sex, age groups, education level, socioeconomic status, marital status, and residence area, and recommends its use within the Peruvian population.

Fig 1. Flowchart of selected participants included in the study.

Fig 2. Factorial structure of Patient Health Questionnaire-9 (two weeks).

Note: The circular figure indicates a latent variable; the figure of the rectangles indicates observed variables (items); λ = standardized factor loads; θ = error variance; CFI and TLI = comparative adjustment index considers optimal values ≥0.95; SRMR = Mean root of standardized error considers adequate values <0.08; RMSEA = Mean root of the approximation error considers appropriate values <0.08.