ArticleBMC cancer2026
Perceived stigma among lung cancer patients: a cross-sectional study with latent profile analysis.
Article in BMC cancer, 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
backgroundPerceived stigma is a significant psychosocial issue among lung cancer patients. Understanding the characteristics of lung cancer stigma, along with its influencing factors, is crucial for providing effective support.
objectivesWe aimed to identify stigma profiles, examine associated demographic/clinical factors, and propose a clinically usable Short-form Cataldo Lung Cancer Stigma Scale (CLCSS-C-SF) cut-off for screening in Chinese lung cancer patients.
methodsWe conducted a secondary analysis of data from 441 lung cancer patients who completed the CLCSS-C-SF. Latent profile analysis (LPA) was applied to analyze stigma. Both univariate analysis and multinominal logistic regression were adopted to investigate the influencing factors. Receiver operating characteristic (ROC) curve analysis was utilized finally to establish the cut-off value of the CLCSS-C-SF.
resultsThe LPA analysis revealed three stigma profiles: low (6.09%), moderate (44.31%), and severe (49.60%). Factors associated with lower perceived stigma included primary education (odds ratio [OR] = 0.273, p = 0.007), annual household income over 100,000 Chinese yuan (OR = 6.642, p = 0.019), and absence of metastasis (OR = 8.231, p = 0.028). Those with diagnosis duration longer than 12 months (OR = 0.156, p = 0.043), retirees (OR = 1.268, p < 0.001) and those with two or more children (OR = 0.716, p = 0.011) were more likely to belong to the moderate and severe stigma profiles. The ROC curve analysis identified a 49 as the optimal cut-off value of the CLCSS-C-SF.
conclusionThis study highlights the heterogeneity and influencing factors of perceived stigma in lung cancer patients. The findings support the need for targeted psychological interventions to address stigma in this patient population.
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