Construct Validity of the COVID-19 Peritraumatic Distress Index (CPDI) in a Peruvian Sample During COVID-19 Lockdown and its Relationship With Anxiety and Depression Using Structural Equation Modeling.

BACKGROUND

The main aims of this study were to adapt the COVID-19 peritraumatic distress index (CPDI) to the Peruvian population and to establish a model explaining depression using CPDI values and anxiety symptoms during the COVID-19 lockdown. Finally, we sought predictive values of the obtained CPDI factors for depression and anxiety as a secondary aim.

MATERIALS AND

METHODS

An exploratory factor analysis (n=300) was performed, followed by confirmatory factor analysis in a second phase (n=1135). To explain depression scores during the COVID-19 lockdown, we performed structural equation modeling (SEM). Finally, we performed a hierarchical regression model (HRM) to evaluate the amount of explained variance of the CPDI factors above depression, anxiety, and socio-demographic variables.

RESULTS

A 2-factor solution (rumination and stress) for the CPDI (p<0.001; CFI=0.99) was found. Concerning the SEM, our model was able to explain 81% of the depression scores (p<0.001; CFI=0.98). Finally, in the HRM, rumination could explain 17% additional variance in depression (p<0.001) and 28% in anxiety (p<0.001). However, stress showed collinearity with depression and anxiety, not continuing for further HRM analysis.

CONCLUSIONS

Our results showed a 2-factor solution for the CPDI. Moreover, our SEM model showed that female sex, younger age, and incomplete education (with high COVID-related stress and anxiety) lead to more depression symptoms during the COVID-19 lockdown. Finally, our HRM showed that people who frequently ruminate during the COVID-19 lockdown are more afraid and negatively affected.