Findings from this study provide additional evidence of four distinct distress trajectories in oncology patients when newer methods of longitudinal analysis are employed(Helgeson, et al., 2004
; Henselmans, et al., 2010
; Hou, Law, Yin, & Fu, 2010
; Lam, Bonanno, et al., 2010
; Lam, Shing, et al., 2010
). However, several important differences between this study and previous research warrant discussion.
In Deshields and colleagues’ longitudinal study (2006)
, five distinct subgroups of women were identified based on cutpoint analyses of CES-D scores (i.e., Never Depressed, Recover, Become Depressed, Stay Depressed, Vacillate) at three timepoints following the completion of RT. Compared to our findings (i.e., 38.9% in the Resilient class), a higher percentage of women were classified in the “Never Depressed” group. This difference may be attributed to the fact that variation within and between subgroups was minimized by the use of dichotomous categorizations. In addition, it is possible that a number of women in their “Never Depressed” group would be classified in our Subsyndromal class because the mean CES-D score in the Deshields study was 12.7 (at the end of treatment) which is slightly lower than the mean CES-D of 13.7 for the entire sample in our study. Finally, depressive symptoms were assessed at the completion of RT compared to our study that enrolled women prior to surgery for breast cancer. Consistent with our study findings, compared to the “Never Depressed” group, the other four depression groups had significantly higher state anxiety scores.
In a study that evaluated psychological adjustment, using the Mental Health Component Score of the SF-36, in women with breast cancer (n=287) over a four year period (Helgeson, et al., 2004
), four distinct subgroups were identified. Consistent with our findings (i.e., 38.9% in the Resilient class), approximately 43% of their sample were classified as having low levels of distress across the four years. In addition, 12% of their sample had a deteriorating pattern of psychological distress which is similar to the 11.3% of patients classified in the Delayed class in our study. Personal and social resources distinguished among the four subgroups which suggest that these factors need to be evaluated as potential predictors of mental distress.
In another longitudinal study that followed breast cancer patients (n=171) over a period of twelve months (Henselmans, et al., 2010
), four subgroups with distinct distress trajectories were identified using the General Health Questionnaire (GHQ; i.e., no distress (36%), distress during the active treatment phase only (33%), distress in the re-entry and survivorship phase (15%), and chronic distress (15%)). The five assessment points were selected to correspond with clinical events (i.e., soon after diagnosis (T1), after surgery (T2), after adjuvant therapy (T3), in the re-entry phase (T4), and in the short-term survivorship phase (T5)). Unlike our study, women who had received neoadjuvant chemotherapy were excluded from this study. Despite differences in measurement points and sample inclusion and exclusion criteria, the assessments in our study appear to provide a more in-depth view of the initial period prior to and following surgery for breast cancer. An examination of their trajectories from T1 to T3 suggests an intriguing yet speculative possibility—namely, correspondence of their No-distress (36%) group to our Resilient class (39%); their Recover group (33%), which experienced “distress only right after diagnosis and in the active treatment phase” to our Subsyndromal (45%) class; their “Late” group (15%) to our Delayed class (11%), and their Chronic group (15%) to our Peak class (5%). The latter two comparisons are the most speculative, given the small sample sizes of these groups in both studies. Further work is warranted in independent samples of patients with a variety of cancer diagnoses to confirm these subgroups with distinct depressive symptom or distress trajectories.
In the study by Henselmans and colleagues (2010)
, lower levels of neuroticism, higher levels of mastery and optimism, and fewer physical complaints from adjuvant treatment distinguished the no distress group from the other three groups. Consistent with their previous work, in the multivariate analyses, mastery was the only unique predictor of group membership (Helgeson, et al., 2004
). Consistent with our findings discussed below, their findings support the notion that personality and trait-based variables play key roles in women’s experiences of distress after a breast cancer diagnosis.
Finally, in a recent study that reported on psychological distress (using the Chinese Health Questionnaire) over an 8 month period in a sample of Chinese women (n=285) (Lam, Bonanno, et al., 2010
), four subgroups with distinct distress trajectories were identified (i.e., resilience (66%), chronic distress (15%), recovered (12%), and delayed-recovery (7%)). Although their assessment points (i.e., 5 days, 1 month, 4 months, and 8 months) are not directly comparable to those in the present study, it is interesting that two of the subgroups were similar to those identified in our study (i.e., minimal distress and delayed-recovery that had a parabolic trajectory). In addition, the largest changes in distress within the subgroups occurred over the first three assessments (4 months after surgery) which corresponds to the first five assessments in our study and suggests that our findings may provide more detailed information on the trajectories of psychological distress in the 6 months following breast cancer surgery. The large proportion of women classified as resilient (66%) in Lam et al. study is somewhat smaller than the Resilient class (39%) identified in our study. This difference may be due to the different measures used to assess psychological distress versus depressive symptoms. Optimism and less physical symptom distress at 1 month following surgery distinguished the resilient group from the other groups. In addition, Lam and colleagues reported that their chronic distress group had the poorest outcomes, in terms of distress and social adjustment, at six years follow up (Lam, Shing, et al., 2010
Taken together, these findings suggest several conclusions and point to important directions for future research. First, subgroups of patients with breast cancer with distinct trajectories of distress or depression can be identified using newer methods of longitudinal data analysis. Second, personal characteristics (e.g., optimism, mastery) and higher levels of physical symptoms post-treatment, but not disease and treatment characteristics, appear to distinguish among these subgroups. Third, levels of trait and state anxiety, which differed significantly among the subgroups in our study and the study by Deshields and colleagues (2006)
, need to be evaluated as a risk factor for elevated depressive symptoms. Fourth, the specific measures chosen to assess psychological distress leads to different proportions of women classified as resilient.
This final point deserves particular mention. In our study, the largest latent class (Subsyndromal; 45%) had depressive symptom scores that on average were just above the clinically significant CES-D cutpoint of 16. Because a psychiatric evaluation was not done on these patients, definitive conclusions cannot be drawn about whether or not these patients met diagnostic criteria for specific clinical syndromes (e.g., major depression, dysthymic disorder, mixed anxiety and depression). However, it is possible that at least some portion of these patients experienced subsyndromal depression (i.e., symptoms of depression that do not meet the full threshold criteria for a major depressive episode). This hypothesis is supported by the finding that the Subsyndromal class exhibited significantly higher baseline trait and state anxiety scores and higher anxiety scores over time than the Resilient class. In addition, this class reported a lower functional status score than the Resilient class. Both the size and characteristics of this class are important because subsyndromal depression is associated with decreased functional status and QOL in the general population (Das-Munshi, et al., 2008
; Forsell, 2007
; Judd, Paulus, Wells, & Rapaport, 1996
). Additional research is warranted, ideally using in-depth psychological and psychiatric assessments, to characterize women’s experience of depressive and anxiety symptoms during and after treatment for breast cancer.
The Delayed and Peak classes together accounted for 16% of the sample. Although no specific disease or patient characteristics differentiated between these two classes, the Peak class had mean depressive symptoms at baseline that were below those of the Subsydromal class and reached a peak at three months post-surgery. It is possible that this class may have experienced an increase in depressive symptoms related to variables not described here (e.g., higher levels of treatment-related physical symptoms). Both of these classes had higher state anxiety at baseline compared to the Resilient class, which underscores the role of anxiety in predisposing to higher levels of depressive symptoms after surgery. Moreover, the different longitudinal symptom patterns of these classes highlight the heterogeneity of the symptom experience and the need to evaluate patients at different time points rather than simply before or after surgery.
As predicted, the distinct latent classes differed from one another in terms of age, with the Resilient group being significantly older than the Subsyndromal group. This finding is consistent with previous reports that found that, on average, older cancer patients, including those with breast cancer, have lower levels of depressive symptoms and better overall QOL compared to younger patients (Helgeson, et al., 2004
; Kroenke et al., 2004
; Parker, Baile, de Moor, & Cohen, 2003
). Various explanations are offered for why younger adults are more likely to have elevated depressive symptoms in the context of cancer (Compas, et al., 1999
; Kroenke, et al., 2004
). Proposed factors include differences in the types of treatments received, the severity of side effects (e.g., abrupt menopause, infertility, sexual dysfunction), and the use of more adaptive coping mechanisms (Compas, et al., 1999
; Mosher & Danoff-Burg, 2005
). However, in at least one study of older women with breast cancer (Ganz et al., 2003
), the older age groups had higher study refusal rates. Therefore, it is possible that older patients who agree to participate in psychosocial or symptom-related research, particularly studies that require multiple assessments over a prolonged period of time, may be relatively healthier and less distressed than non-participating counterparts.
The majority of the patients in the study had relatively high functional status scores. The lower functional status score reported by patients in the Subsyndromal class compared to the Resilient class, while statistically significant and clinically meaningful (effect size, d=0.43), may be explained by the lower functional status noted in population-based studies between subsyndromal depression and functional status (Judd, et al., 1996
; Judd, Schettler, & Akiskal, 2002
The finding that state anxiety distinguished the Resilient class from the other three classes at baseline through six months of follow-up highlights the important relationship between these two symptoms. However, levels of state anxiety did not change in the same way as depression, demonstrating that the symptoms are not synchronous. In the general population, anxiety can occur independently or jointly with depressive symptoms (Stein, Kirk, Prabhu, Grott, & Terepa, 1995
). Deshields and colleagues (2006)
reported that scores on the STAI-S differed among the identified subgroups at the first study timepoint. Moreover, women in their sample who were categorized as having depression initially but then “recovered” had greater reductions in anxiety over time. Taken together, these findings underscore the need for longitudinal assessments of both anxiety and depression, as well as the need for further work to clarify distinct predictors of each symptom.
This study has several clinical implications. First, the finding that nearly half of the patients (Subsyndromal class, 45%) had slightly elevated or subsyndromal levels of depressive symptoms suggests that identification of these patients may be as important as identifying those patients with clearly elevated symptoms. Given that most screening instruments (e.g., Distress Thermometer (Jacobsen, et al., 2005
)) use fewer items to screen for depression, it is unclear whether these patients would have been identified with such brief screeners. Thus, this substantial subgroup, may remain unidentified, yet may need referral or intervention. Little research exists on the effects of subsyndromal depressive symptoms on treatment outcomes, functional status, and QOL in cancer patients. Future research needs to clarify the prevalence, correlates, and outcomes of subsyndromal levels of depressive as well as anxiety symptoms.
Second, the consistent finding of a resilient class of patients across several studies suggest that resilience is common in adults who experience significant trauma, including a life-threatening illness (Bonanno, 2004
). Additional research is needed to identify factors that protect individuals from the stressful effects associated with a cancer diagnosis and its treatment. From both clinical and research perspectives, distress interventions should be designed and tailored for individuals at highest risk. In contrast, resilient individuals may not need an intervention or may need a different type of intervention to maintain their resilience.
Third, these trajectories may represent underlying traits, which, at times of increased stress, predispose individuals to different trajectories of psychological symptoms. As mentioned above, previous studies reported on the relationship between a number of putative predisposing, personality-related factors (e.g., optimism, neuroticism, trait anxiety) and distress or depressive symptoms in women with breast cancer (Den Oudsten, et al., 2009
; Henselmans, et al., 2010
). Further work is needed to understand the degree to which traits influence psychological distress (both depression and anxiety) in cancer patients, and to develop and test interventions to improve coping skills in those predisposed to greater psychological distress by virtue of their underlying traits.
Finally, further work is needed to understand the longitudinal relationships among depressive symptoms and other prevalent symptoms in cancer patients, particularly fatigue, pain, and sleep disturbance. Research on the underlying neurobiology of depression in cancer suggests that these symptoms may share a common underlying basis (Raison & Miller, 2003
Several limitations must be acknowledged. While information on depressive and anxiety symptoms were obtained through valid self-report measures, future studies need to include a clinical evaluation of previous and concurrent psychiatric comorbidities. The fact that the major reasons for refusal were being too overwhelmed with their cancer treatment or too busy may have led to an underestimation or overestimation of depressive symptoms in this sample. It is possible that the four latent classes may reflect some unique characteristics of this sample.
Finally, while other studies have used GMM to identify distinct latent classes of patients with and without cancer based on self-reports of depressive symptoms or distress (Carragher, Adamson, Bunting, & McCann, 2009
; Colman, Ploubidis, Wadsworth, Jones, & Croudace, 2007
; Henselmans, et al., 2010
; Hunter, Muthen, Cook, & Leuchter, 2010
), the findings from this study must be interpreted with caution until they are replicated in future studies. Ideally future studies should be done with sample sizes that are large enough to allow for confirmatory analyses of both the number and trajectories of the latent classes, as well as the phenotypic and genotypic characteristics that are unique to each class.
In addition to demographic and clinical variables, investigators should examine coping styles, personality traits, and other pre-existing individual characteristics as potential mediators and moderators of latent class membership. Research with other seriously or chronically ill populations would assist investigators and clinicians to understand how individual differences manifest in response to illness. If these latent classes with distinct symptom trajectories are reproduced, these findings would strengthen the notion that underlying traits are essential to understanding the incidence and course of distress or resilience in individuals affected by chronic and serious illnesses like cancer.