Prevalence and biopsychosocial indicators of fatigue in cancer patients.

INTRODUCTION: Symptoms of cancer-related fatigue (CRF) can have a significant impact on patients' quality of life and treatment adherence. We aimed to investigate the relationship between CRF and multiple psychosocial and somatic indicators within a large mixed cancer sample. METHODS: In this cross-sectional study, N = 1787 outpatients with cancer were assessed for CRF, pain, anxiety, and depression using validated screening instruments. We further obtained clinical parameters (Hb, CRP, creatinine, leukocytes, ASAT, and ALAT), sociodemographic data (age, gender, income, education level, marital status, parenthood, and living area), and lifestyle factors. Multivariate linear regression models were applied to estimate the impact of each indicator on CRF. RESULTS: Overall, 90.6% of patients experienced some CRF, with 14.8% experiencing severe CRF. No gender difference was found in the prevalence of CRF. Patients with higher levels of pain, depressive symptoms, and lower Hb levels had significantly higher levels of CRF (ps <0.001). Lower levels of CRF were observed in patients who had children (p = 0.03), had less education (p < 0.001), and were physically active for more than 2 h per week before their oncological diagnosis (p = 0.014). The latter was only a significant indicator in the male subsample. CONCLUSION: The present results demonstrate a high prevalence of CRF and highlight that not only somatic and psychosocial factors, but also lifestyle factors prior to diagnosis appear to be associated with the etiology and persistence of CRF. To effectively treat CRF, a biopsychosocial, personalized approach is recommended.

The impact of COVID-19 and socioeconomic status on psychological distress in cancer patients.

Objective: We aimed to investigate the impact of the COVID-19 pandemic on psychological symptom burden against the socioeconomic background of cancer patients using data from routine assessments before and during the pandemic. Method: In this cross-sectional study, standardised assessment instruments were applied in N = 1,329 patients to screen for symptoms of anxiety, depression, post-traumatic stress, and fatigue from 2018 to 2022. Two MANOVAs with post-hoc tests were computed. First, only time was included as predictor to examine the isolated impact of the pandemic. Second, income level and education level were included as further predictors to additionally test the predictive power of socioeconomic factors. Results: In the final model, only income had a significant impact on all aspects of psychological symptom burden, with patients with low income being highly burdened (partial η² = .01, p = .023). The highest mean difference was found for depressive symptoms (MD = 0.13, CI = [0.07; 0.19], p < .001). The pandemic had no further influence on psychological distress. Conclusions: Although the pandemic is a major stressor in many respects, poverty may be the more important risk factor for psychological symptom burden in cancer outpatients, outweighing the impact of the pandemic.

Risk as a pattern over time: Delineation of time-dependent risk factors in biological, psychological, and social variables in cancer patients.

BACKGROUND: Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time. METHODS: Patients who attended the authors' outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model. RESULTS: The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0-4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term. CONCLUSIONS: The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.