Validation of the Arabic Version Post-COVID-19 Symptom Scale (PCSS-Ar) for Assessing Long COVID-19 Severity Among Arabic-Speaking Populations: A Factor Analysis and Rasch Analysis Study

Purpose: The COVID-19 pandemic has led to long COVID-19, a condition characterized by persistent, multisystemic symptoms. This study validated the Arabic version of the Post-COVID-19 Symptom Scale (PCSS-Ar) to assess long COVID-19 severity in Jordan. Patients and Methods: A cross-sectional online survey was conducted with 582 Jordanian adults (aged ≥18 years), recruited via social media. The PCSS-Ar underwent content validity evaluation by an expert panel, followed by confirmatory factor analysis (CFA) and Rasch analysis to assess its psychometric properties. Results: The final 24-item, five-factor model demonstrated an excellent fit (CFI = 0.95, TLI = 0.95, SRMR = 0.02) and strong internal consistency (Cronbach's α ≥ 0.97). Rasch analysis confirmed the tool's ability to differentiate symptom severity levels effectively. Key findings indicated that higher frequencies of COVID-19 infection were significantly associated with more severe long COVID-19 symptoms, whereas mild initial infections were linked to lower symptom severity. Notably, lower income was associated with higher PCSS-Ar scores, suggesting socioeconomic disparities in post-COVID-19 recovery. Female participants had lower PCSS-Ar scores, contrasting with previous studies, indicating a potential population-specific effect. Conclusion: The PCSS-Ar is a validated and reliable tool for assessing long COVID-19 symptoms in Arabic-speaking populations. Its application in both clinical and research settings can help monitor symptom progression and guide targeted interventions.