Comparing Symptom Clusters in Cancer Survivors by Cancer Diagnosis: A Latent Class Profile Analysis

Abstract

Abstract Purpose: Research on symptom clusters in oncology is progressing, but knowledge gaps remain. One question is whether the number and types of Symptom Subgroups differ based on cancer diagnosis. However, no research has compared Symptom Subgroups in heterogeneous populations based on cancer diagnosis. The purpose of this study was to: (1) identify the clustering of four highly prevalent symptoms (pain, fatigue, sleep disturbance, and depression), and (2) compare symptom clusters across the seven populations of cancer survivors (prostate, non-small cell lung, non-Hodgkin’s lymphoma, breast, uterine, cervical, and colorectal cancer). Methods: This study is a cross-sectional secondary analysis of data obtained from the My-Health study in partnership with four Surveillance, Epidemiology, and End Results (SEER) cancer registries located in California (two), Louisiana, and New Jersey. The sample included 4,762 cancer survivors 6-13 months following diagnosis of one of the seven cancer types mentioned. Latent class profile analysis was used. Results: Subjects were primarily young (59% age 21-64 years), Caucasian (41%), married/cohabitating (58%) and unemployed (55%). The number of symptom subgroups varied across these seven cancer populations: (1) four-class solution in prostate, lung, non-Hodgkin’s lymphoma, and breast cancer survivors; (2) three-class solution in uterine and cervical cancer survivors; and (3) two-class solution in colorectal cancer survivors. Conclusion: Identifying symptom subgroups by cancer diagnosis has the potential to develop innovative and effective targeted interventions in cancer survivors. Further research is needed to establish extensive knowledge in symptom clustering between treatment regimens, and short-term and long-term cancer survivors.