Higher Stress And Symptom Severity Are Associated With Worse Depressive Symptom Profiles In Patients Receiving Chemotherapy

Patients and settings

This study is part of a larger, longitudinal study of the symptom experience of oncology outpatients receiving chemotherapy (Miaskowski et al., 2014). Eligible patients were: ≥18 years of age; had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and gave written informed consent. Patients were recruited from two Comprehensive Cancer Centers, one Veteran’s Affairs hospital, and four community-based oncology programs. The major reason for refusal was being overwhelmed with their cancer treatment.

Study procedures

The study was approved by the Institutional Review Board at each of the study sites. Of the 2234 patients approached, 1343 consented to participate. These patients completed depressive symptom questionnaires a total of six times over two chemotherapy cycles (i.e., prior to chemotherapy administration, approximately 1 week after chemotherapy administration, and approximately 2 weeks after chemotherapy administration). A total of 1327 patients who had complete data on the depression measure were included in this analysis.

Instruments

Data analysis

Descriptive statistics and frequency distributions were generated for sample characteristics at enrollment using the Statistical Package for the Social Sciences (SPSS) version 27 (IBMCorp, 2020). Latent profile analysis (LPA) was used to identify unobserved subgroups of patients (i.e., latent classes) with distinct depressive symptom profiles over the six assessments, using the six CES-D scores. The LPA was performed using MPlus™ Version 8.4 (Muthen and Muthen, 1998–2020).

Estimation was carried out with full information maximum likelihood with standard error and a chi-square test that are robust to non-normality and non-independence of observations (“estimator=MLR”). Model fit was evaluated to identify the solution that best characterized the observed latent class structure with the Bayesian Information Criterion (Mravec et al.), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLRM), entropy, and latent class percentages that were large enough to be reliable (Muthén and Muthén, 2009). Missing data were accommodated with the use of the Expectation-Maximization (EM) algorithm (Muthen and Shedden, 1999).

Differences among the latent classes in demographic and clinical characteristics, stress and resilience measures, and symptom severity scores at enrollment were evaluated using analysis of variance, Kruskal-Wallis, or Chi Square tests. A p-value of <.05 was considered statistically significant. Post hoc contrasts were evaluated using a Bonferroni corrected p-value of <.008 (.05/6 possible pairwise comparisons).

Latent profile analysis

Table 1 displays the fit indices for the one- through five-class solutions. The 4-class solution was selected because the BIC for that solution was lower than the BIC for the 3-class solution. In addition, the VLMR was significant for the 4-class solution, indicating that four classes fit the data better than three classes. Although the BIC was smaller for the 5-class than for the 4-class solution, the VLMR for 5-classes was not significant, indicating that too many classes had been extracted.

In terms of latent class membership, 47.3% of the patients (n=628) were classified as “None”; 33.6% (n=446) as “Subsyndromal”; 13.8% (n=183) as “Moderate”; and 5.3% (n=70) as “High” (Figure 1). Classes were named based on established cutoff scores for the CES-D (Radloff, 1977).

Differences in demographic and clinical characteristics

Compared to the None class, patients in the Subsyndromal class were younger, more likely to be female, and had a lower annual household income. In addition, patients in the Subsyndromal class had a lower KPS score, a higher SCQ score, and were more likely to self-report a diagnosis of depression or back pain (Table 2).

Compared to the None class, patients in the Moderate class were younger, more likely to be female, more likely to live alone, more likely to have elder care responsibilities, had a lower annual household income, were less likely to be married partnered, and less likely to be employed. In addition, patients in the High class had a lower KPS score, a higher number of comorbidities, a higher SCQ score, a higher MAX2 score, a higher AUDIT score, were more likely to self-report a diagnosis of depression or back pain and were more likely to have an antiemetic regimen that included a neurokinin-1 (NK-1) receptor antagonist and two other antiemetics.

Compared to the None class, patients in the High class were less likely to be married or partnered, more likely to live alone, more likely to be Hispanic, and had a lower annual household income. In addition, patients in the High class had a lower KPS score, a higher number of comorbidities, a higher SCQ score, and were more likely to self-report a diagnosis of stomach disease, depression, or back pain. Additional post hoc comparisons are noted on Table 2.

Differences in stress and resilience measures

Significant differences in PSS total, IES-R total and subscale, and LSC-R PTSD sum scores at enrollment were found among the four latent classes in the expected pattern (i.e., None < Subsyndromal < Moderate < High; Table 3). Compared to the None class, patients in the other three classes reported higher LSC-R total and LSC-R PTSD sum scores. In terms of resilience, significant differences in CDRS scores were found among the four latent classes in the expected pattern (i.e., None > Subsyndromal > Moderate > High).

Differences in co-occurring symptom scores

Significant differences in depressive symptoms, trait anxiety, state anxiety, sleep disturbance, and mean pain interference scores at enrollment were found among the four latent classes in the expected pattern (i.e., None < Subsyndromal < Moderate < High; Table 4). Compared to the None class, patients in the other three classes reported higher levels of morning and evening fatigue, lower levels of morning and evening energy, and lower levels of attentional function. Compared to the None class, a lower percentage of patients in the other three classes reported that they did not experience pain. Compared to the None class, a higher percentage of patients in Moderate and High classes reported the occurrence of both non-cancer and cancer pain and higher worst pain severity scores. Differences in the pain interference scores followed the expected pattern (i.e., None < Subsyndromal < Moderate < High).

Demographic and clinical characteristics associated with higher depressive symptom profiles

In terms of age, consistent with previous studies of oncology patients (Saracino et al., 2020a; Yang et al., 2020), patients in the Subsyndromal and Moderate classes were significantly younger than those in the None class. As noted previously (Saracino et al., 2020a), older oncology patients may endorse lower levels of depressive symptoms related to a variety of factors (e.g., a true increased sense of well-being as one ages, differing response styles in different age groups). Of note, in a recent confirmatory factor analysis of the CES-D (Saracino et al., 2020b), the authors concluded that the CES-D can be used to screen oncology patients and does not require scoring adjustments for patients’ age.

Consistent with findings in the general population (Morssinkhof et al., 2020), a higher percentage of women were in the Subsyndromal and Moderate classes. The fact that a higher percentage of patients in the Moderate and High classes reported being single and living alone suggests that the lack of social support contributes to depressive symptoms. This hypothesis is supported by findings from a meta-analysis that noted that oncology patients with a strong social support network were less likely to report depressive symptoms (Wen et al., 2019). However, clinicians need to be mindful that patients with established support networks warrant ongoing evaluation because patients with depressive symptoms are at increased risk for declining social support over the trajectory of their cancer experience (Chang et al., 2019).

Compared to the None class, patients in the Subsyndromal class were more likely to be unemployed and patients in the other three classes reported a lower annual household income. This finding is important because oncology patients with lower incomes, who screen positive for depression, are less likely to receive referrals to psychology or psychiatry services (Hallet et al., 2020). In addition, patients in the Subsyndromal class were more likely to have elder care responsibilities. While not evaluated in oncology patients, in a study of family caregivers (Butler et al., 2005), elder care responsibilities increased the risk for depressive symptoms.

Consistent with previous reports (Bergerot et al., 2017; Duc et al., 2017), most of the cancer-specific characteristics (i.e., diagnosis, prior treatments, metastatic sites, type of regimen, chemotherapy cycle length) were not associated with latent class membership. However, the two clinical characteristics that were unique to the Moderate class (i.e., higher MAX2 score, receipt of an antiemetic regimen that included a NK-1 receptor antagonist and two other antiemetics) suggest that patients who were receiving a more toxic and/or emetogenic chemotherapy regimen may be at increased risk for depressive symptoms. Additional research is warranted that examines the relationship between chemotherapy regimens and psychological symptom trajectories.

Of note, compared to the None class, the four clinical characteristics that were common to the Subsyndromal, Moderate, and High classes (i.e., lower functional status, higher comorbidity burden, self-reported diagnosis of depression or back pain) highlight the positive relationships between a higher comorbidity burden and more severe depressive symptom profiles. As noted in one review (Menear et al., 2015), compared to the general population, depressive symptoms were two to three times more common in patients with multiple co-morbidities. In addition, in a large cohort study of patients with breast cancer (Yang et al., 2017), similar associations were found.

In terms of back pain, while not evaluated in oncology patients, in a longitudinal study of twins (Fernandez et al., 2017), chronic back pain was associated with an increased risk for the development of depressive symptoms. In addition, as noted in two reviews of patients with breast cancer (Caplette-Gingras and Savard, 2008; Reich et al., 2008), individuals with a previous history of depression were more likely to develop depressive symptoms during cancer. These findings warrant careful consideration because in a study that compared patients who developed depressive symptoms and/or functional limitations after a cancer diagnosis to those who had a history of depressive symptoms and/or functional limitations at the time of diagnosis (Stommel et al., 2002), individuals with a history of either or both had a 2.6 times greater hazard of dying in the 19 months following the diagnosis. Therefore, comprehensive assessment and optimal management of comorbid conditions are essential components of oncology care that may have a significant impact on depressive symptoms as well as other important patient outcomes.

Stress and resilience characteristics associated with higher depressive symptom profiles

As noted in one review (Smith, 2015), while both acute and chronic stress can contribute to depressive symptoms in oncology patients, a need exists to evaluate multiple types of stress concurrently. In terms of global stress, our findings are consistent with previous studies of women with breast (Li et al., 2021a; Li et al., 2021b) and ovarian (Liu et al., 2017) cancer that found significant positive associations with depressive symptoms and perceived stress. In terms of cancer-specific stress, in our study, patients in the Subsyndromal and Moderate classes (i.e., 47.4% of the total sample) had IES-R total scores indicating post-traumatic symptomatology and those in the High class had IES-R scores indicative of probable PTSD. Our findings are consistent with a previous study of women with breast cancer (Kang et al., 2012), that found that IES scores were positively correlated with depressive symptoms.

In terms of cumulative life stress, our samples’ LSC-R scores were similar to Americans with concurrent depression and substance-use disorders (Mahoney et al., 2015). In addition, our findings are consistent with a study of women with heterogeneous cancer types (Seib et al., 2018), that found that both recent and lifetime stressors were positively associated with depressive symptoms. As noted by the authors in this study (Seib et al., 2018), the “stress sensitization hypothesis” supports these findings because previous stressors may increase a patient’s susceptibility to the deleterious psychological impacts of the cancer experience. However, this association warrants additional investigation because in a study of women with breast cancer (Kazlauskiene and Bulotiene, 2020), patients with a history of traumatic events were less likely to report PTSD symptoms during cancer treatment. The authors hypothesized that patients with a history of stress may have developed more adaptive ways to cope with their cancer. Taken together, these findings suggest that multiple types of stress (i.e., global, cancer-specific, cumulative life stress) warrant assessment in oncology patients undergoing chemotherapy because unrelieved stress can prevent adoption and/or continuation of healthy behaviors (Stults-Kolehmainen and Sinha, 2014). Equally important, the use of stress reduction interventions can increase oncology patients’ quality of life and prolong their survival (Mravec et al., 2020b).

In terms of resilience, patients in the Subsyndromal, Moderate, and High classes had resilience scores below the normative mean score for adults in the United States (Campbell-Sills et al., 2009). As noted in a recent review (Tamura et al., 2021), resilience levels of oncology patients decrease as the number and/or severity of symptoms increase which may explain the findings from our study. More specifically, in a study of patients with breast cancer (Ristevska-Dimitrovska et al., 2015), a significant negative correlation was found between depression and resilience. The authors hypothesized that resilience may be protective against depressive symptoms. In contrast, in a study of patients with prostate cancer (Sharpley et al., 2018), in those individuals who reported chronic stress, this inverse relationship between depressive symptoms and resilience was absent. Given these conflicting findings, clinicians need to assess for depressive symptoms and the impact of stress and resiliency on individual patients as they plan personalized interventions (Sharpley et al., 2018).

Multiple co-occurring symptoms associated with higher depressive symptom profiles

While oncology patients are known to experience multiple symptoms (Cooley and Siefert, 2016), our findings support previous research that found positive associations between higher levels of depressive symptoms and other common symptoms (Doong et al., 2015; Grotmol et al., 2019; Mercadante et al., 2019). For example, in a large study of patients with heterogeneous types of advanced cancer (Grotmol et al., 2019), after controlling for disease, treatment status, and prognosis, depressive symptoms were associated with a higher number of co-occurring symptoms. In addition, depressive symptoms, pain, fatigue, and sleep disturbances are known to cluster together in oncology patients (Doong et al., 2015). Furthermore, the co-occurrence of depressive symptoms and anxiety is associated with an increase in the severity of other co-occurring symptoms (Mercadante et al., 2019). For clinicians, diagnosis and management of depressive symptoms in the context of multiple co-occurring symptoms may present challenges because many of the symptoms of cancer and/or cancer treatments (e.g., fatigue, insomnia) are part of the diagnostic criteria for depression (Akechi et al., 2003).

Of note, for over 50% of our patients, the management of the most common symptoms associated with cancer treatment appears to be inadequate. The presence of psychological symptoms may make it more difficult to achieve adequate symptom control (Mercadante et al., 2019). Clinicians should remain vigilant in assessing multiple symptoms and aim to identify the root cause(s) of the various symptoms in order to prescribe targeted interventions. Furthermore, additional research is warranted to understand the underlying mechanism(s) for these common and co-occurring symptoms.

Limitations

Some limitations warrant consideration. First, stress and resilience measures were evaluated at only one timepoint. Future studies should evaluate for changes in depressive symptoms, as well as stress and resilience over time. Second, the sample was relatively homogenous in terms gender, ethnicity, education, and income which may limit the generalizability of our findings. Third, information on medications used to treat depression was not obtained and may have assisted with the interpretation of our findings. Lastly, the major reason for refusal to participate was being overwhelmed with cancer treatment which suggests a possible underestimation of depression in this sample.

Conclusion

In patients receiving chemotherapy, inter-individual variability in depressive symptoms was associated with multiple types of stress, as well as with the increased severity of multiple co-occurring symptoms. In oncology patients, depressive symptoms are often not diagnosed (Grotmol et al., 2019). If left untreated, this symptom can have significant deleterious effects (Li et al., 2017; Reiche et al., 2004). Therefore, the risk factors associated with worse depressive symptom profiles can assist clinicians to identify high risk patients and initiate more timely supportive care interventions.