Keywords

Depressive symptoms, Head and neck cancer, Hierarchical linear modeling, Radiotherapy

 

Authors

  1. Astrup, Guro Lindviksmoen MSc, RN
  2. Rustoen, Tone PhD, RN
  3. Miaskowski, Christine PhD, RN
  4. Paul, Steven M. PhD
  5. Bjordal, Kristin PhD, MD

Abstract

Background: Although patients with head and neck cancer are at increased risk for depressive symptoms compared with other cancer patients, few longitudinal studies have evaluated changes in and predictors of this symptom over time.

 

Objective: The aim of this study was to determine whether levels of depressive symptoms changed over time and whether specific demographic, clinical, symptom, or psychosocial characteristics were associated with depressive symptoms.

 

Methods: In a longitudinal study of patients with head and neck cancer, depressive symptoms were assessed with the Center for Epidemiologic Studies-Depression scale, from the initiation of radiotherapy and for 6 months after. Hierarchical linear modeling was used to evaluate for changes in as well as for potential predictors of interindividual differences in depressive symptoms.

 

Results: The severity of depressive symptoms increased during radiotherapy and then decreased over time. The portion of patients who reported clinically meaningful levels of depressive symptoms at each assessment ranged from 29% to 42%. Several known predictors of pretreatment severity of depressive symptoms (ie, physical symptoms, less social support, dissatisfaction with looks) were corroborated. In addition, having surgery before radiotherapy was associated with lower levels of depressive symptoms at initiation of radiotherapy.

 

Conclusion: A moderate proportion of patients with head and neck cancer reported levels of depressive symptoms that indicated the need for clinical evaluation. Several patient characteristics were associated with depressive symptoms.

 

Implications for Practice: Knowledge on prevalence, time course, and predictors of depressive symptoms from this study can be used to identify patients at higher risk for more severe depressive symptoms.