Cluster analysis of social isolation in gynecologic cancer patients: A cross-sectional survey.

Abstract

To identify the distinct clusters of social isolation among gynecologic cancer patients and analyze the predictive factors associated with each cluster. A total of 463 patients diagnosed with gynecologic cancer were recruited from three tertiary hospitals between November 2021 and March 2023. Using a two-step cluster analysis, participants were categorized into clusters based on social isolation scales. Multinomial logistic regression was then employed to predict factors influencing the identified clusters. Social isolation in gynecologic cancer patients manifested in four distinct clusters: mild social isolation subgroup (13.8%), moderate social isolation subgroup (32.0%), severe isolation subgroup (33.5%), and high social isolation (20.70%). Multivariate logistic regression analysis revealed that cognitive emotional regulation, social support, negative emotions, endometrial cancer, and disease recurrence or metastasis were significant predictive factors for the identified social isolation clusters (P < 0.05). The study underscored the heterogeneity in the social isolation characteristics of gynecologic cancer patients. Consequently, healthcare professionals should prioritize the identification of potential high-risk groups and devise personalized interventions to prevent and mitigate the occurrence of social isolation.