Trajectories of anxiety in oncology patients and family caregivers during and after radiation therapy
Introduction
Approximately 50% of cancer patients receive radiation therapy (RT), either alone or in combination with surgery or chemotherapy. Clinical experience suggests that RT is associated with significant levels of anxiety that is often underdetected and undertreated (Stiegelis et al., 2004). In fact, findings from a recent review of studies on psychological distress during RT (Stiegelis et al., 2004) suggest that 10%–20% of patients experience clinically significant levels of anxiety at the initiation of RT.
Stiegelis et al. (2004) highlighted the considerable heterogeneity in patients’ levels of anxiety at the initiation of RT. In addition, because of the paucity of longitudinal studies, little is known about changes in levels of anxiety during and after the completion of RT. Finally, virtually no information is available on the factors that predict which patients are at greatest risk for high levels of anxiety at the initiation of and during RT. Therefore, the present report used growth mixture modeling (GMM) to identify groups of oncology patients and their family caregivers (FCs) with distinct profiles of anxiety symptoms during and after the completion of RT.
Only three studies have evaluated for changes in anxiety symptoms in patients who underwent RT (Andersen and Tewfik, 1985, Chen et al., 2009, Munro and Potter, 1996). Andersen and Tewfik (1985) reported that in patients with low anxiety prior to RT, anxiety increased during RT. In contrast, in patients with high baseline anxiety, anxiety scores decreased during RT. Patients with moderate anxiety before treatment experienced little change in anxiety. These findings suggest that distinct subgroups of patients with anxiety symptoms may exist. However, this study had only two measurement points, did not evaluate patients after RT, and had a small sample size. Munro and Potter (1996) assessed 110 patients with breast, lung, or head and neck cancer before, during, and 4 weeks after RT. At RT initiation, 62% endorsed anxiety, whereas only 42% endorsed anxiety post-RT. However, the severity and impact of anxiety was not reported. In 40 patients with head and neck cancer, Chen et al. (2009) found that 23% reported mild anxiety, 10% moderate anxiety, and 7% severe anxiety at the initiation of RT; anxiety scores remained constant during and following RT. Given the paucity of longitudinal studies on anxiety in patients undergoing RT (Andersen and Tewfik, 1985, Chen et al., 2009, Munro and Potter, 1996), these studies’ limitations, and advances made in RT since the two earlier studies were published, a need exists to examine changes in anxiety symptoms over time in patients undergoing RT.
Family caregivers (FCs) provide tremendous amounts of care and support (Yabroff and Kim, 2009) and are themselves at risk for psychological distress and negative physical outcomes (Institute of Medicine, 2007). In fact, some studies have reported higher rates of depression and anxiety in FCs than in patients (Couper et al., 2006). Despite the prevalence and impact of these symptoms in both groups, most studies have examined patients’ and FCs’ symptoms separately, under the assumption that the type and severity of stressors faced by the two groups are different.
However, for several reasons, patients and FCs ought to be studied in combination in a subset of studies. First, while a traditional disease model would view cancer as a primary contributor to anxiety symptoms, because of disease- and treatment-related factors, more recent evidence has found that other variables, including demographic, dispositional, and personality-related factors, appear to be robust predictors of distress, generally, and depressive symptoms more specifically (Bower, 2008, Deshields et al., 2006). On the other hand, in these studies, disease-related variables explained a relatively modest amount of variance in levels of these symptoms (Bower, 2008, Deshields et al., 2006).
Second, as described in both the cancer and non-cancer literature (Gottlieb and Rooney, 2004, Kurtz et al., 1997), FCs’ unique personal backgrounds, resources, coping styles, and chronic medical conditions influence the caregiving experience. As described by the Stress Process model of caregiver stress (Pearlin et al., 1990), these individual FC characteristics—beyond disease- and caregiving-related stressors (e.g., physical and logistic demands)—should be considered in studies of caregiver stress.
Third, the approach described here reflects and is consistent with the “allostatic load” hypothesis of stress and adaptation (McEwen and Wingfield, 2010). In this model, stress, regardless of its source, is mediated through common biological pathways. If these pathways are perturbed repeatedly or over a long duration, and if coping mechanisms cannot help the individual adapt, any individual (i.e., a patient or a FC) may manifest what would be viewed as distress, depressive symptoms, or anxiety (McEwen, 2003). The allostatic load hypothesis incorporates genetic factors that affect traits and behaviors that relate to the individual’s ability to adapt to stressors.
Finally, support for combining patients and FCs comes from recent data that suggests that patients and FCs face similar levels of overall symptom burden (Fletcher et al., 2009, Fletcher et al., 2008, Miaskowski et al., 2008). For example, in an evaluation of fatigue in patients with prostate cancer (Miaskowski et al., 2008) and their FCs (Fletcher et al., 2009), we found that while the specific predictors of fatigue differed among patients and their FCs, fatigue severity levels over six months (i.e., the overall outcome) were equivalent in both groups. Thus, our approach to understanding symptoms views cancer as one among many stressors that may influence the incidence, course, and severity, of symptoms, including anxiety in both patients and their FCs.
In most of the psychological distress literature, group means are used to characterize changes in symptoms. However, examination of changes in group means over time (for example, with repeated measures analysis of variance (ANOVA), multilevel regression, or latent growth models), are not sensitive to unobserved systematic (i.e., “latent”) patterns of change in symptom severity. Individuals’ symptom experiences may resemble those of some individuals more than they resemble those of others. In other words, a closer examination of changes in symptoms over time may reveal subgroups of individuals whose symptom experiences are more similar to those of other individuals within that subgroup than to those of individuals in other subgroups.
Recent advances in longitudinal data analysis (e.g., Muthen and Muthen, 2000) permit an examination of such underlying or latent patterns of change in symptom severity over time. One such analytic method, growth mixture modeling (GMM), enables identification of subgroups of individuals, referred to as latent growth classes, whose symptoms share a similar trajectory over time. Although this type of analysis has been used to identify latent classes in the general population with distinct trajectories of anxiety and depression (Das-Munshi et al., 2008, Hunter et al., 2010), as well as in oncology patients with distinct distress trajectories (Helgeson et al., 2004, Henselmans et al., 2010, Lam et al., 2010), GMM has not been used to evaluate differences in anxiety trajectories in oncology patients and FCs.
To better characterize anxiety symptom trajectories of patients and FCs, the purposes of this study were to determine whether we could identify distinct latent classes of oncology patients and FCs based on self-reported anxiety symptoms from prior to the initiation of to four months after the completion of RT and to examine differences in demographic and clinical characteristics among these latent classes. Because depressive symptoms are common in patients and FCs (Swore Fletcher et al., 2008), and because anxiety and depression commonly co-occur in the general population (Das-Munshi et al., 2008) and in cancer patients (Frick et al., 2007), differences in depressive symptom levels over the course of the study were also evaluated among the latent anxiety classes. As the primary purpose of this GMM analysis was exploratory, the focus of this report is not on hypothesis testing. However, we did hypothesize that the distinct latent classes for anxiety that were identified would not be dependent on patient or FC status.
Section snippets
Participants
This study, which focuses on anxiety symptoms, is part of a large, longitudinal study of symptoms in patients and their FCs (Aouizerat et al., 2009, Fletcher et al., 2008, Fletcher et al., 2009, Miaskowski et al., 2008). A total of 167 oncology outpatients with breast, prostate, lung, or brain cancer and 85 FCs participated in this longitudinal study. Patients were eligible to participate if they were >18 years of age; were able to read, write, and understand English; had a self-reported
Participant characteristics
As summarized in Table 1, the majority of participants were female, Caucasian, and well educated. Patients and FCs differed only in terms of gender, marital status, and pain. Compared to the patients, a greater proportion of the FCs was female and married or partnered and a smaller proportion had pain.
Results of GMM analysis
Three distinct latent classes of anxiety symptom trajectories were identified using GMM (see Fig. 1). The fit indices for the various models are shown in Table 2. As shown in Table 2, a
Discussion
To our knowledge, this study is the first to use GMM to identify latent classes of oncology patients and FCs with distinct anxiety symptom trajectories during and following the patient’s RT. Our hypothesis that distinct latent classes would be independent of whether one was a patient or FC was supported. These findings have a number of implications for clinical practice and for symptom management research in oncology patients and FCs.
Conflict of interest statement
None declared.
Acknowledgments
This research was supported by a grant from the National Institute of Nursing Research (NR04835). Dr. Aouizerat is funded through the National Institutes of Health Roadmap for Medical Research Grant (KL2 RR624130). Dr. Dunn received funding from the Mount Zion Health Fund and the UCSF Academic Senate.
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