Exploring the interconnectedness between health-related quality of life factors among long-term adolescent and young adult cancer survivors (AYAs): a network analysis

Author:

Bootsma Tom I.ORCID,van de Wal DeborahORCID,Vlooswijk CarlaORCID,Roos Daniëlle C.,Drabbe CasORCID,Tissier RenaudORCID,Bijlsma Rhodé M.ORCID,Kaal Suzanne E.J.ORCID,Kerst Jan Martijn,Tromp Jacqueline M.,Bos Monique E.M.M.ORCID,van der Hulle TomORCID,Lalisang Roy I.ORCID,Nuver JanineORCID,Kouwenhoven Mathilde C.M.ORCID,van der Graaf Winette T.A.ORCID,Janssen Silvie H.M.ORCID,Husson OlgaORCID

Abstract

Abstract Purpose Adolescent and young adult cancer survivors (AYAs) are at increased risk of long-term and late effects, and experience unmet needs, impacting their health-related quality of life (HRQoL). In order to provide and optimize supportive care and targeted interventions for this unique population, it is important to study HRQoL factors’ interconnectedness on a population level. Therefore, this network analysis was performed with the aim to explore the interconnectedness between HRQoL factors, in the analysis described as nodes, among long-term AYAs. Methods This population-based cohort study used cross-sectional survey data of long-term AYAs, who were identified by the Netherlands Cancer Registry (NCR). Participants completed a one-time survey (SURVAYA study), including the EORTC survivorship questionnaire (QLQ-SURV111) to assess their long-term HRQoL outcomes and sociodemographic characteristics. The NCR provided the clinical data. Descriptive statistics and a network analysis, including network clustering, were performed. Results In total, 3596 AYAs (on average 12.4 years post diagnosis) were included in our network analysis. The network was proven stable and reliable and, in total, four clusters were identified, including a worriment, daily functioning, psychological, and sexual cluster. Negative health outlook, part of the worriment cluster, was the node with the highest strength and its partial correlation with health distress was significantly different from all other partial correlations. Conclusion This study shows the results of a stable and reliable network analysis based on HRQoL data of long-term AYAs, and identified nodes, correlations, and clusters that could be intervened on to improve the HRQoL outcomes of AYAs.

Publisher

Springer Science and Business Media LLC

Subject

Oncology

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