ArticleRisk management and healthcare policy2026
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.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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