Translation and validation of simplified Chinese version of the pain catastrophizing scale in chronic pain patients: Education may matter

Objective: Pain catastrophizing is linked to many aspects of pain perception and defines a unique dimension in predicting pain intensity and physical disability. Pain Catastrophizing Scale (PCS) is an effective, validated,self-report measure, commonly used in clinical trials. Here, we present a Simplified Chinese PCS (SC-PCS) version developed in Chinese patients suffering from chronic pain. Methods: The SC-PCS was generated in five steps and tested on an initial patient cohort (N ¼ 30). A convenience sample (N ¼ 200) of in-hospital patients with non-malignant pain lasting for more than 12 weeks were recruited for the study, of which 81 completed 5 additional pain questionnaires. A subset (N ¼ 24) of the patients completed an additional SC-PCS, 10 days after the initial query to assess test–retest validation. Results: Intra-class correlations coefficient indicated high reproducibility and temporal consistency, (0.97), for the total score. Cronbach’s alpha determined high internal consistency across the SC-PCS total score and its three subscales (0.87, 0.85, 0.62, and 0.65). The SC-PCS total score moderately or weakly (R ¼ 0.2 to 0.49), but significantly, correlated with other measurements, such as pain Visual Analog Scale, Beck Depression Inventory, Pain Anxiety Symptoms Scales, Positive and Negative Affect Schedule, and education. We used exploratory factor analysis to examine the dimensionality of the SC-PCS, which indicated instability of the current three-factor model. However, a confirmatory factor analysis indicated that the three-factor model had the best goodness-fitting. Conclusions: We demonstrate the successful translational adaptation from English to Simplified Chinese as well as the reliability and validity of SC-PCS. An important discovery was education level significantly correlated with SC-PCS, identifying a future consideration for other cross-cultural development of self-reported measures.