Morgan Byrne1, Jaclyn Leiser2, Sandra A Mitchell3, Erin E Kent4, Elizabeth J Siembida5, Tamara Somers6, Hannah Arem7. 1. Biostatistics and Epidemiology Consulting Service, George Washington University Milken Institute School of Public Health, Washington, DC, USA. 2. Department of Epidemiology, George Washington University Milken Institute School of Public Health, Washington, DC, USA. 3. Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA. 4. Department of Health Policy and Management, University of North Carolina, Chapel Hill, NC, USA. 5. Center for Health Innovations and Outcomes Research, The Feinstein Institutes for Medical Research, Northwell Health, NY, Manhasset, USA. 6. Department of Psychiatry and Behavioral Sciences, Duke School of Medicine, Durham, NC, USA. 7. Healthcare Delivery Research, Medstar Health Research Institute, Washington, DC, USA. Hannah.Arem@medstar.net.
Abstract
PURPOSE: Fatigue is one of the most common and distressing symptoms experienced by cancer survivors. Understanding fatigue trajectories from pre- to post-diagnosis could inform fatigue prevention and management strategies. METHODS: We used the Surveillance, Epidemiology and End Results Medicare Health Outcomes Survey (SEER-MHOS) linked data resource to characterize fatigue trajectories and their predictors 1214 older adult survivors of breast, colorectal, or prostate cancer. Fatigue was measured prior to the cancer diagnosis (T0) and at two timepoints after diagnosis (T1: mean = 20 months and T2: mean = 39 months post-diagnosis). Latent growth curve modeling and mixed effects models for repeated measurements were used to investigate fatigue experiences before and after a cancer diagnosis. RESULTS: Overall, mean fatigue T-scores declined (T0 = 50, T1 = 46, and T2 = 45) indicating worsening fatigue over time. Four latent trajectory subgroups were identified: severe fatigue worsening over time (8.2% of sample), severe fatigue persisting over time (14.4%), no fatigue pre-diagnosis and mild fatigue post-diagnosis (44.4%), and not fatigued (33%). Age, cancer stage, comorbidities, and depressed mood predicted membership in the two trajectory groups experiencing severe fatigue that persisted or that worsened post-diagnosis. Older age, advanced cancer stage at diagnosis, and depressed mood were significantly associated with worsening fatigue from T1 to T2 (all p < 0.0001). CONCLUSIONS: Evaluating cancer patients for depressive symptoms and considering prior fatigue levels, age, comorbid conditions, and cancer stage may help providers anticipate fatigue trajectories and implement pre-emptive strategies to lessen fatigue impact.
PURPOSE:Fatigue is one of the most common and distressing symptoms experienced by cancer survivors. Understanding fatigue trajectories from pre- to post-diagnosis could inform fatigue prevention and management strategies. METHODS: We used the Surveillance, Epidemiology and End Results Medicare Health Outcomes Survey (SEER-MHOS) linked data resource to characterize fatigue trajectories and their predictors 1214 older adult survivors of breast, colorectal, or prostate cancer. Fatigue was measured prior to the cancer diagnosis (T0) and at two timepoints after diagnosis (T1: mean = 20 months and T2: mean = 39 months post-diagnosis). Latent growth curve modeling and mixed effects models for repeated measurements were used to investigate fatigue experiences before and after a cancer diagnosis. RESULTS: Overall, mean fatigue T-scores declined (T0 = 50, T1 = 46, and T2 = 45) indicating worsening fatigue over time. Four latent trajectory subgroups were identified: severe fatigue worsening over time (8.2% of sample), severe fatigue persisting over time (14.4%), no fatigue pre-diagnosis and mild fatigue post-diagnosis (44.4%), and not fatigued (33%). Age, cancer stage, comorbidities, and depressed mood predicted membership in the two trajectory groups experiencing severe fatigue that persisted or that worsened post-diagnosis. Older age, advanced cancer stage at diagnosis, and depressed mood were significantly associated with worsening fatigue from T1 to T2 (all p < 0.0001). CONCLUSIONS: Evaluating cancerpatients for depressive symptoms and considering prior fatigue levels, age, comorbid conditions, and cancer stage may help providers anticipate fatigue trajectories and implement pre-emptive strategies to lessen fatigue impact.
Authors: Ana Ruiz-Casado; Alejandro Álvarez-Bustos; Cristina G de Pedro; Marta Méndez-Otero; María Romero-Elías Journal: Clin Breast Cancer Date: 2020-07-24 Impact factor: 3.225