Comparing symptom clusters in cancer survivors by cancer diagnosis: A latent class profile analysis
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View abstract on PubMed
Summary
This summary is machine-generated.Cancer survivors exhibit varied symptom subgroups based on cancer type. Unemployment and low education correlate with higher symptom burden, informing targeted interventions for better patient outcomes.
Area Of Science
- Oncology
- Psychosocial Oncology
- Cancer Survivorship
Background
- Symptom clusters in oncology are increasingly studied, yet knowledge gaps persist regarding variations across cancer diagnoses.
- Understanding these differences is crucial for developing tailored supportive care strategies.
Purpose Of The Study
- To identify and compare latent class subgroups of four prevalent symptoms (pain, fatigue, sleep disturbance, depression) across seven cancer types.
- To examine sociodemographic and clinical factors associated with these symptom subgroups in diverse cancer populations.
Main Methods
- Cross-sectional secondary analysis of the My-Health study data, including 4,762 cancer survivors.
- Utilized latent class profile analysis across seven cancer types: prostate, lung, non-Hodgkin's lymphoma, breast, uterine, cervical, and colorectal.
- Data collected 6-13 months post-diagnosis from SEER cancer registries.
Main Results
- Symptom subgroup composition varied significantly among the seven cancer types.
- Four distinct symptom subgroups were commonly identified in prostate, lung, non-Hodgkin's lymphoma, and breast cancer survivors.
- Unmarried status, lower education, and unemployment were associated with a higher symptom burden across all cancer types.
Conclusions
- Identifying cancer-specific symptom subgroups can facilitate the development of targeted interventions for cancer survivors.
- Further research is needed to explore symptom clustering in relation to treatment regimens and survivorship duration (short-term vs. long-term).
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