Illness uncertainty in individuals with gynecologic and breast cancer: A latent profile analysis and structural equation modeling
- 1Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China
- 2College of Nursing, Hebei University, Hebei, China
- 3Department of Breast Surgery, Peking University People’s Hospital, Beijing, China
Abstract
Background: Gynecologic and breast cancers pose a significant global health burden for women. Illness uncertainty is a common psychological challenge among patients with gynecologic and breast cancers, particularly in settings with limited supportive care resources. This study aimed to identify latent classes of illness uncertainty, examine their associated factors, and investigate its mediating role between social support and depressive symptoms. Methods: A cross-sectional study was conducted from December 2024 to June 2025, enrolling 413 patients from a tertiary hospital in Beijing using convenience sampling. Data were collected with a general information questionnaire, the Mishel Uncertainty in Illness Scale (MUIS), the Social Support Rating Scale (SSRS), the 9-item Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder-7 scale (GAD-7). Latent profile analysis was applied to identify subgroups of illness uncertainty. Univariate analysis and multinomial logistic regression were used to examine influencing factors. Structural equation modeling was employed to test the mediating effect. Results: Three latent classes were identified: low uncertainty-psychological adaptation (8.0%), moderate uncertainty-complexity distress (37.6%), and high uncertainty-cognitive ambiguity (54.4%). Educational level, caregiver type, time since diagnosis, social support, and depressive symptoms were significantly associated with class membership. Mediation analysis revealed that illness uncertainty partially mediated the relationship between social support and depressive symptoms, with a significant indirect effect of −0.033 (95% CI: −0.056 to −0.016), accounting for 22.3% of the total effect. Conclusions: This study revealed significant heterogeneity in illness uncertainty among patients with gynecologic and breast cancers, with class membership associated with multiple factors including caregiver type and time since diagnosis. Illness uncertainty appeared to mediate the relationship between social support and depressive symptoms. These findings may inform the development of stratified psychosocial interventions tailored to distinct uncertainty profiles, though further longitudinal research is needed to establish causal relationships.
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BibTeX
@article{zeng2026illness,
title = {{Illness uncertainty in individuals with gynecologic and breast cancer: A latent profile analysis and structural equation modeling}},
author = {Hui Zeng and Yan Lu and Quanping Zhao and Jingjing Gong and Li Mao and Yuxuan Wei and Xiaodan Li},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358366},
url = {https://doi.org/10.1371/journal.pone.0358366},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Illness uncertainty in individuals with gynecologic and breast cancer: A latent profile analysis and structural equation modeling
AU - Hui Zeng
AU - Yan Lu
AU - Quanping Zhao
AU - Jingjing Gong
AU - Li Mao
AU - Yuxuan Wei
AU - Xiaodan Li
PY - 2026
DA - 2026/09/18
JO - PLOS One
DO - 10.1371/journal.pone.0358366
UR - https://doi.org/10.1371/journal.pone.0358366
AB - Background: Gynecologic and breast cancers pose a significant global health burden for women. Illness uncertainty is a common psychological challenge among patients with gynecologic and breast cancers, particularly in settings with limited supportive care resources. This study aimed to identify latent classes of illness uncertainty, examine their associated factors, and investigate its mediating role between social support and depressive symptoms. Methods: A cross-sectional study was conducted from December 2024 to June 2025, enrolling 413 patients from a tertiary hospital in Beijing using convenience sampling. Data were collected with a general information questionnaire, the Mishel Uncertainty in Illness Scale (MUIS), the Social Support Rating Scale (SSRS), the 9-item Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder-7 scale (GAD-7). Latent profile analysis was applied to identify subgroups of illness uncertainty. Univariate analysis and multinomial logistic regression were used to examine influencing factors. Structural equation modeling was employed to test the mediating effect. Results: Three latent classes were identified: low uncertainty-psychological adaptation (8.0%), moderate uncertainty-complexity distress (37.6%), and high uncertainty-cognitive ambiguity (54.4%). Educational level, caregiver type, time since diagnosis, social support, and depressive symptoms were significantly associated with class membership. Mediation analysis revealed that illness uncertainty partially mediated the relationship between social support and depressive symptoms, with a significant indirect effect of −0.033 (95% CI: −0.056 to −0.016), accounting for 22.3% of the total effect. Conclusions: This study revealed significant heterogeneity in illness uncertainty among patients with gynecologic and breast cancers, with class membership associated with multiple factors including caregiver type and time since diagnosis. Illness uncertainty appeared to mediate the relationship between social support and depressive symptoms. These findings may inform the development of stratified psychosocial interventions tailored to distinct uncertainty profiles, though further longitudinal research is needed to establish causal relationships.
SN - 1932-6203
ER -