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Published on: October 23, 2020
Coping with an Uncertain or Poor Cancer Prognosis as an Adolescent or Young Adult: A Cross-Sectional Cluster Analysis
Milou J P Reuvers1,2, Winette T A van der Graaf1,2, Olga Husson1,3
1Department of Medical Oncology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, 1066 CX Amsterdam, The Netherlands.
Background:
A subgroup of adolescent and young adult patients (AYAs; 18 to 39 years at diagnosis) face an uncertain or poor cancer prognosis (UPCP). Previous qualitative research identified dual coping pathways in this population: engagement in life versus the reality of premature death. This study examines whether similar psychosocial profiles can be identified through quantitative data, aiming to differentiate patient experiences and identify characteristic features of each cluster. Additionally, this study examines the association between cluster membership and social support needs to understand psychosocial disparities.
Methods:
Eligible participants completed questionnaires assessing physical, psychosocial, and existential outcomes related to their disease and prognosis. An ensemble clustering approach was applied, including evaluation of clustering tendency and multiple algorithms, with stable clusters identified through majority voting. Associations with social support needs were analyzed using Fisher's exact test.
Results:
Data from 155 AYAs with a UPCP were included. The mean age at diagnosis was 31.2 years, with glioma (34.8%) and breast cancer (17.4%) as the most common diagnoses. Two distinct clusters were identified: one (22%) characterized by poorer functional outcomes and fewer protective factors (e.g., hope, meaning in life), and another cluster (78%) with better functioning and less frequent needs for social support (p < 0.00043).
Conclusions:
Findings revealed divergent psychosocial profiles within the AYA-UPCP population, highlighting the importance of early identification of vulnerable subgroups. Strengthening protective factors may enhance resilience and reduce unmet support needs. Validation in larger, external datasets is needed to confirm these pathways and guide tailored supportive care strategies.
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