Distinct Stress Profiles Among Oncology Patients Undergoing Chemotherapy

Journal Pre-proof DISTINCT STRESS PROFILES AMONG ONCOLOGY PATIENTS UNDERGOING CHEMOTHERAPY Dale J. Langford, PhD, Bruce Cooper, PhD, Steven Paul, PhD, Janice Humphreys, RN, PhD, Marilyn J. Hammer, RN, PhD, Jon Levine, MD, PhD, Yvette P. Conley, PhD, Fay Wright, RN, PhD, Laura B. Dunn, MD, Christine Miaskowski, RN, PhD PII:

S0885-3924(19)30642-6

DOI:

https://doi.org/10.1016/j.jpainsymman.2019.10.025

Reference:

JPS 10286

To appear in:

Journal of Pain and Symptom Management

Received Date: 23 September 2019 Revised Date:

29 October 2019

Accepted Date: 30 October 2019

Please cite this article as: Langford DJ, Cooper B, Paul S, Humphreys J, Hammer MJ, Levine J, Conley YP, Wright F, Dunn LB, Miaskowski C, DISTINCT STRESS PROFILES AMONG ONCOLOGY PATIENTS UNDERGOING CHEMOTHERAPY, Journal of Pain and Symptom Management (2019), doi: https://doi.org/10.1016/j.jpainsymman.2019.10.025. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine

DISTINCT STRESS PROFILES AMONG ONCOLOGY PATIENTS UNDERGOING CHEMOTHERAPY Dale J. Langford, PhDa Bruce Cooper, PhDb Steven Paul, PhDb Janice Humphreys, RN, PhDc Marilyn J Hammer, RN, PhDd Jon Levine, MD, PhDb,e Yvette P. Conley, PhDf Fay Wright, RN, PhDg Laura B. Dunn, MDh,* Christine Miaskowski, RN, PhDb,* a

Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA

b

School of Nursing, University of California, San Francisco, San Francisco, CA, USA

c

School of Nursing, Duke University, Durham, NC, USA

d

Dana Farber Cancer Institute, Boston, MA, USA

e

School of Dentistry, University of California, San Francisco, San Francisco, CA, USA

f

School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA

g

Rory Meyers College of Nursing, New York University, New York, NY, USA

h

Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA

*Drs. Dunn and Miaskowski should be considered joint senior authors Address correspondence to: Laura B. Dunn, M.D. Department of Psychiatry and Behavioral Sciences Stanford University 401 Quarry Road Stanford, CA 94305 Tables (4); Figures (0); References (65); Word count = 3,356 ABSTRACT

1

Context: Cancer and its treatment are inherently stressful and stress impacts important patient outcomes. Patients vary considerably in their response to stress. Understanding this variability requires a patient-centered multidimensional approach. Objectives: The objectives of this study were to identify and characterize patient subgroups with distinct multidimensional stress profiles (stress appraisal, exposure, and adaptation) during cancer treatment. Methods: Among 957 patients undergoing chemotherapy for breast, gastrointestinal, gynecological, or lung cancer, latent profile analysis was performed to identify patient subgroups using concurrent evaluations of global (Perceived Stress Scale) and cancer-specific (Impact of Events Scale-Revised) stress, lifetime stress exposure (Life Stressor Checklist-Revised), and resilience (Connor-Davidson Resilience Scale-10). Results: Three latent classes were identified: “Normative” (54.3%; intermediate global stress and resilience, lower cancer-related stress, lowest life stress); “Stressed” (39.9%; highest global and cancer-specific stress scores, lowest resilience, most life stress); and “Resilient” (5.7%; lowest global stress, cancer-specific stress comparable to Normative class, highest resilience, intermediate life stress). Characteristics that distinguished the Stressed from the Normative class included: younger age, female gender, lower socioeconomic status, unmarried/partnered, living alone, poorer functional status, and higher comorbidity burden. Compared to Stressed patients, Resilient patients were more likely to be partnered, not live alone, and had a higher functional status. No demographic or clinical characteristics differentiated Normative from Resilient patients. Exposure to specific life stressors differed significantly among the classes. Conclusion: A subset of patients warrants intensive psychosocial intervention to reduce stress and improve adaptation to cancer. Intervention efforts may be informed by further study of Resilient patients. Key Message: This article uses a patient-centered approach to identify patient subgroups with distinct stress profiles. Normative, Stressed, and Resilient profiles were identified based on concurrent 2

evaluation of global and cancer-specific stress appraisal, stress exposure, and resilience and characterized with respect to demographic and clinical characteristics and specific types of life stressors. Key Words: cancer; chemotherapy; stress; resilience; latent profile analysis Running Title: Stress profiles in chemotherapy patients

Disclosures and Acknowledgements: The authors have no conflicts of interest to disclose. Funding: Funded by a grant from the National Cancer Institute (NCI CA134900). Dr. Miaskowski is funded by grants from the NCI (CA168960) and the American Cancer Society.

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INTRODUCTION Oncology patients navigate numerous stressors, including unrelieved symptoms, uncertainty, role changes,(1) functional limitations,(2), and financial challenges.(3) Patients vary greatly in their response to these stressors, influenced by a range of biopsychosocial factors.(4) Understanding this variability requires a multidimensional conceptualization and assessment of stress that is currently lacking.(5) The literature on stress in oncology patients encompasses a broad range of conceptual and empirical work.(6-8) Operational definitions of stress are inconsistent. Some studies focus on global stress appraisal (i.e., how individuals evaluate stressful situations in general),(7) while others focus on disease-specific stress appraisal (i.e., how individuals evaluate stressors due to cancer and its treatment).(4, 9) Other studies evaluate the number or nature of the stressors themselves (i.e., stress exposure), such as cumulative life stress(8) or childhood adversity.(10) Still others focus on “resilience”,(11) considered an adaptive ability to cope with stress.(11, 12) Further, there remains confusion between stress and psychological distress, the latter often referring to symptoms of anxiety and/or depression as a result of a stressor.(13) Variability in how stress is operationalized is compounded by measuring only a single stress construct in a study. Only five studies were identified that used multiple measures of stress in oncology patients.(14-18) Two of these studies evaluated relationships among perceived stress, cancer-specific stress, and recent life stressors in women following breast cancer surgery.(15, 17) In both studies, stress accounted for a significant amount of the variance in depressive symptoms(15) and quality of life.(17) Three studies evaluated the mediating effects of coping, stress appraisal, and/or resilience on the relationships between stressful life events and cancer-related stress,(16) anxiety and depression,(14) and quality of life.(18) In these studies, disengagement coping (16) and perceived stress (14, 18) mediated the relationships between life stress and the outcome studied. Collectively, these studies support the multidimensional nature of stress and its negative impact on patient outcomes. However, none of these studies concurrently evaluated perceived global and cancer-related 4

stress, lifetime stress exposure, and resilience. In addition, four of these studies were relatively small, enrolled only women, or focused on common and/or recent stressful life events,(14, 15, 17, 18) thereby overlooking the potential impact of cumulative life stress or childhood adversity. Moreover, these studies used variable-centered approaches (i.e., regression, structural models), which assume that the relationships between variables holds for all patients in the sample. To better evaluate inter-individual variability and account for the multidimensional nature of stress, a person-centered approach is needed. Latent profile analysis (LPA) is a person-centered approach useful for identifying subgroups of patients based on concurrent evaluation of multiple dimensions of a construct. LPA allows comprehensive subgroup characterization, which can be used to identify risk factors associated with subgroup membership and potential targets for interventions. Given the multidimensional nature of stress, numerous cancer-related stressors, the deleterious impact of stress on patient outcomes, and the limited extant literature that examines stress in oncology patients from a multidimensional perspective, characterizing patients with distinct multidimensional stress profiles is warranted. Therefore, in a sample of patients undergoing chemotherapy (CTX) for common cancers (n=957), the purposes of this study were to: (1) identify subgroups of patients, using LPA, with distinct stress profiles based on concurrent evaluation of patient-reported global stress appraisal, cancer-related stress appraisal, cumulative life stress exposure, and resilience; (2) evaluate for differences among the subgroups in demographic and clinical characteristics; and (3) evaluate for differences in the occurrence and impact of specific stressful life events among the subgroups. METHODS Patients, Settings, and Procedures This analysis used data from a descriptive, longitudinal study that evaluated the symptom experience of oncology outpatients receiving CTX.(19) Eligibility criteria included: ≥18 years of age; diagnosis of breast, gastrointestinal, gynecological, or lung cancer; received CTX within the preceding four weeks and scheduled to receive two additional cycles; and able to read, write, and understand 5

English. Recruitment occurred at two Comprehensive Cancer Centers, one Veteran’s Affairs hospital, and four community-based oncology clinics. Written informed consent was obtained from all patients. Study procedures were approved by the Committee on Human Research at the University of California, San Francisco and by each study site. Of the 2234 patients approached, 1343 consented to participate (60.1%). Patients who completed all of the stress measures (N=957) were included in this analysis. Instruments Demographic and Clinical Characteristics Patients completed a demographic questionnaire, the Karnofsky Performance Status (KPS) scale,(20) and the Self-Administered Comorbidity Questionnaire (SCQ).(21) Medical records were reviewed for disease and treatment information. Stress and Resilience Measures Perceived Stress Scale (PSS)(22) - The 14-item PSS was used to assess global stress. Patients indicated the degree to which they perceived life circumstances as stressful over the previous week. Items were rated from 0 (never) to 4 (very often). Higher sum scores indicate greater perceived stress. The PSS has well established validity and reliability.(22) In this sample, its Cronbach’s alpha was 0.89. Impact of Event Scale-Revised (IES-R)(23) - The 22-item IES-R was used to assess cancerrelated stress. Patients rated how distressing/bothersome each item was during the past week “with respect to cancer and its treatment.” Items were rated from 0 (not at all) to 4 (extremely). Sum scores ≥ 24 indicate clinically meaningful post-traumatic symptomatology, scores ≥ 33(24) indicate probable PTSD. The IES-R has well established validity and reliability.(24, 25) In this sample, its Cronbach’s alpha was 0.92. Life Stressor Checklist-Revised (LSC-R)(26) - The 30-item LSC-R was used to evaluate lifetime exposure to stressful life events. Patients indicated the occurrence of 30 stressors and rated the effect of each stressor on their past year of life, from 1 (not at all) to 5 (extremely). A higher number (i.e., count) of stressful life events and higher mean effect scores indicate greater stress exposure and 6

impact, respectively. LSC-R demonstrates adequate test–retest reliability and criterion-related validity among diverse populations (27, 28). Connor-Davidson Resilience Scale (CD-RISC-10) (29) - The 10-item CD-RISC-10 was used to measure resilience. Items were rated from 0 (not true at all) to 4 (true nearly all the time). Higher sum scores indicate greater resilience. CD-RISC-10 has adequate validity and reliability in diverse populations (12, 29). Cronbach’s alpha was 0.90. Data Analysis Latent profile analysis (LPA) was used to identify subgroups (i.e., latent classes) of patients with distinct stress profiles, using patients’ scores on the stress (PSS, IES-R, LSC-R) and resilience (CDRISC-10) measures. LPA was conducted using MPlusTM Version 7.4.(30) Estimation was conducted using robust maximum likelihood and the expectation maximization algorithm. Statistical fit indices were used to determine the number of classes that best captured variability, while maintaining conceptual clarity.(31, 32) Descriptive statistics and frequency distributions were calculated for demographic and clinical characteristics, stress and resilience scores, using SPSS version 23.(33) Analyses of variance, Kruskal-Wallis, or Chi-square analyses were used to evaluate for differences in stress and resilience scores and demographic and clinical characteristics among the classes. P-values of <0.05 were considered statistically significant. The Bonferroni correction was used for post hoc contrasts. RESULTS LPA The LPA identified three latent classes, based on stress and resilience measures. Table 1 displays the fit indices for the 1- through 4-class solutions. The 3-class solution was selected based on its lower BIC, higher entropy, and statistically significant VLMR, indicating the best fit. Of the entire sample, 382 patients (39.9%) were classified as “Stressed”, 520 patients (54.3%) as “Normative”, and 55 patients (5.7%) as “Resilient”. To name the classes (Table 2), mean stress and resilience scores were compared among the classes and to established cut-off scores(24) and national 7

normative data.(29, 34). Latent classes differed significantly on all of the stress and resilience scores (all p<0.001). Post hoc contrasts revealed consistent patterns for global stress (Stressed > Normative > Resilient) and resilience (Stressed < Normative < Resilient). For occurrence of life stressors, post hoc contrasts revealed the following pattern: Stressed > Resilient > Normative. For cancer-related stress, Stressed patients reported higher scores than Normative and Resilient patients (i.e., Stressed > Normative and Resilient). Differences in Demographic and Clinical Characteristics As shown in Table 3, compared to Normative patients, Stressed patients were younger, more likely to be female, had fewer years of education, lower annual household incomes, and were less likely to be employed. Compared to Normative and Resilient classes, Stressed patients were more likely to be single and live alone. For clinical characteristics, functional status, comorbidities, as well as self-reported depression and back pain differed among the latent classes. Compared to Normative patients, Stressed patients reported a higher comorbidity burden and were more likely to report back pain. Compared to Normative and Resilient patients, Stressed patients had lower functional status scores and were more likely to report depression. Demographic and clinical characteristics did not differ between Normative and Resilient patients. Moreover, no disease or treatment characteristics differed among the classes. Differences in Occurrence and Impact of Stressful Life Events Significant differences in occurrence of life stressors were found for 27 out of 30 events (90%) (Table 4). For these stressors, a higher proportion of Stressed patients endorsed each stressor compared to Normative patients. Compared to Resilient patients, a higher proportion of Stressed patients reported emotional abuse (16.0% vs 37.1%) and serious money problems (14.0% vs 33.0%). Compared to Normative patients, a higher proportion of Resilient patients reported forced sexual touching (1.7% vs 8.3%) and forced sex (1.0% vs 8.3%) after age 16. The impact of the various stressors on patients’ past year of life differed among the classes for the following stressors: serious money problems, separation/divorce, parents’ separation/divorce, being 8

in a serious disaster, abortion or miscarriage, and death (sudden and not sudden) of someone close (all p<0.02). For these stressors, Stressed patients reported a higher effect than Normative patients. In addition, Normative patients reported a higher effect of being separated or divorced than Resilient patients. DISCUSSION This study is the first to use LPA to identify subgroups of patients with distinct multidimensional stress profiles, derived from concurrent evaluation of global and cancer-specific stress appraisal, lifetime stress exposure, and resilience. This person-centered approach considers the complexity and inter-individual variability in stress responses and resilience among oncology patients receiving CTX. This approach is aligned with the National Academy of Medicine’s (formerly named Institute of Medicine) recommendation for comprehensive and holistic assessment and care of individuals with cancer.(35) Moreover, the use of LPA addresses the need for an evaluation of stress that incorporates stress appraisal, exposure, and adaptation.(5) Using LPA, three subgroups of patients with distinct stress profiles were identified. These subgroups were named “Stressed,” “Normative,” and “Resilient” based on differences in scores among the classes, as well as a comparison of these scores with established cut-off scores(36) and national normative data.(29, 34) While 54.3% of the patients were classified in the Normative class, a substantial proportion of patients (39.9%) were classified in the Stressed class, which was characterized by higher levels of perceived and cancer-specific stress, a higher number of stressful life events, and lower resilience compared to the other classes. Conversely, a small proportion of patients (5.7%) were classified as having a Resilient profile, characterized by the lowest global stress and highest resilience scores, comparable cancer-specific stress to the Normative class, and intermediate exposure to stressful life events. While Normative and Resilient patients’ cancer-specific stress scores fell below the cut-off score for probable PTSD,(24) 31.7% of the patients in the Stressed subgroup exceeded this cut-off and, on average, scored in the range of partial PTSD.(37) Differences in resilience scores among the latent 9

classes exceeded the minimal clinically important difference (2.7 on CD-RISC-10).(29) The average resilience score for the Normative class (32.2 ± 4.2) corresponded to US normative data (31.8 ± 5.4). However, average scores for the Stressed (25.7 ± 6.4) and Resilient (39.7 ± 0.5) patients were below and above normative data, respectively.(29) Demographic Characteristics Associated with Stress Profiles Consistent with previous reports,(38-41) risk factors for being in the Stressed class included: female gender, younger age, lower education, lower income, being unemployed, living alone, and being unpartnered/unmarried. These characteristics match those of women with a history of interpersonal violence and co-occurring substance abuse and mental health disorders.(41) Also, female gender and younger age were independently associated with elevated distress in patients with heterogeneous cancer diagnoses.(38) Similarly, younger women reported greater distress than older female and younger or older male oncology patients.(40) Gender and age differences among our patient subgroups may reflect underlying biological differences in stress responses(39, 42) or differential appraisal of stressors based on gender socialization.(42) Lower socioeconomic status (SES) is an established risk factor for increased stress in oncology patients and may be related to inadequate resources to deal with the stressors of cancer, including treatment-related financial challenges.(43, 44) Moreover, it is possible that patients with a lower SES live in environments where stress exposure is more common.(45) Of note, existing evidence suggests that a lower SES is associated with higher basal levels of stress hormones.(46) The finding that a higher proportion of Stressed patients lived alone and/or did not have a partner may reflect a lack of instrumental and/or emotional social support, factors known to buffer stress.(47-49) Clinical Characteristics Associated with Stress Profiles Consistent with previous research,(50-52) disease and treatment characteristics were not associated with membership in the Stressed class. The only clinical characteristics that distinguished the Stressed from the Normative and Resilient patients were a lower functional status and a higher comorbidity burden, including depression and back pain. Cohen’s d effect sizes indicate that observed 10

differences in functional status were moderately meaningful (d=-0.54 and -0.51 for Stressed vs. Normative and Resilient, respectively). The co-occurrence of stress, depression, and pain is well documented and may reflect shared underlying etiologies.(53) Further, this finding corroborates the negative association observed between functional status and psychological(2) and biological(54) stress measures. Exposure to and Impact of Stressful Life Events Among the Latent Classes Stressed patients reported an average of eight stressful life events. This LSC-R score is comparable to community-based Colombian (7.2 ± 3.8)(55) and Mexican (9.5 ± 4.2)(56) women and patients with prescription opioid addiction problems (7.7 ± 0.6),(27) but lower than women who experienced intimate partner violence (14 ± 5.7)(57) and incarcerated men (11, no SD reported).(58) Interestingly, and consistent with prior literature on resilience,(59, 60) Resilient patients reported an intermediate number of stressful life events (two more than Normative). It has been theorized that moderate exposure to life stressors may elicit a stress inoculation or buffering effect that protects or prepares individuals to perceive subsequent life stressors as less threatening.(59, 60) In addition to an absolute number, the nature of stressful life events can impact the stress response.(61) Uncontrollable or unpredictable stressors may contribute to feelings of helplessness,(61) while “milder” stressors that one can overcome or control may improve coping self-efficacy and resiliency.(61) Strikingly, the occurrence of every interpersonal stressor differed significantly among the subgroups. Common interpersonal stressors, occurring in ≥ 25% of Stressed patients were: witnessing family violence in childhood (38.1%), emotional abuse (37.1%), sexual harassment (27.1%), and physical abuse (25.0%). Importantly, interpersonal violence (IPV) is a particularly impactful stressor associated with long-term physical and psychological outcomes.(57) According to a Centers for Disease Control report, > 27% of women and 11% of men experience some form of IPV in their lifetime. (62) Overall, a high proportion of Stressed patients reported IPV events, placing them at increased risk for deleterious physical and psychological outcomes. While most of the differences in the occurrence of life stressors were observed between patients 11

in the Stressed and Normative classes, it is nevertheless important to note those stressors that differentiated the Resilient class. Sexual abuse after age 16 was more common among Resilient patients compared to Normative patients. It is possible that with adequate internal and external resources, Resilient patients were able to effectively cope and even thrive following these stressors. Further, trauma-related symptoms may be less complex or severe for patients who experienced sexual abuse in adulthood versus childhood.(63) Compared to the Resilient class, a higher proportion of Stressed patients reported serious money problems and emotional abuse. Serious money problems are in line with lower SES that characterized the Stressed class. In addition, the experience of emotional abuse may have an impact on subsequent relationships, and thus may be related to the finding that patients in the Stressed class were more likely to be unpartnered and to live alone. These findings suggest that Resilient patients may have more resources (both financial and social support) to cope more effectively with subsequent stressors, including cancer and its treatment. Given the importance of instrumental and emotional support during the cancer experience, these findings warrant further exploration. Interestingly, while the occurrence of most of the stressors differed among the classes, the impact of the stressors on recent life was largely similar across the classes. No significant differences were found with respect to the effect of interpersonal stressors on the past year of life. Most stressors that differed in impact among the classes were related to interpersonal loss/grief (e.g., death, divorce). Importantly, predictors of complicated grief include situational factors (e.g., financial instability) and interpersonal factors (e.g., lack of social support).(64) These factors characterized Stressed patients and may have contributed to complicated grief that amplified stress appraisal and disrupted stress adaptation. Across the classes, and as reported by our team in a prior report focused on the relationship between life stressors and cancer-related distress,(16) the life stressor with the strongest impact was a patient-specified event (e.g., illness in family, loss of parent, war/combat). Given that these events were specified by the patients,(16) this finding is not surprising, but underscores the notion that trauma can 12

be personal and individualized. Limitations Limitations of the study warrant consideration. First, while this sample was heterogeneous with respect to cancer diagnoses and types of CTX, it was fairly homogeneous in terms of gender, education, and ethnicity. Moreover, patients were highly educated (~16 years) and had relatively high levels of functioning (i.e., mean KPS score of 80). Therefore, this sample may not be representative of the entire population of oncology patients. In addition, the primary reason for refusal of study participation was being overwhelmed by cancer and its treatment. Therefore, this study likely underestimates the proportion of oncology patients who are “Stressed.” In addition, because the sample size of the Resilient class was small (n=55), other differentiating characteristics and stressors may have been missed or were not measured in this study. A deeper understanding of the Resilient class, including possible underlying physiological or genetic differences, may be a valuable approach to identify novel targets for intervention, as well as appropriate allocation of resources/intervention efforts. Finally, the cross-sectional nature of this analysis prevented an evaluation of the dynamic nature of stress and resilience over time. Conclusions Despite these limitations, this study confirms the large amount of inter-individual variability in oncology patients’ exposure and responses to a broad range of stressors. Moreover, while cancer and its treatment are inherently stressful, findings from this study are congruent with previous reports that individuals vary widely in their response to stressors.(60, 65) Of note, Resilient patients, who reported intermediate stress exposure, appear to exhibit successful adaptation, consistent with the concept of stress inoculation.(59) A deeper understanding of protective factors that foster resilience may guide the design of future intervention studies. Given that the Resilient class reported the lowest levels of perceived stress, despite undergoing CTX, an examination of the mechanisms underlying this

13

subgroup’s stress appraisal would be valuable. The Resilient and Normative classes both appeared relatively well-adapted in terms of their stress appraisal and resilience. However, the latent class analytic approach identified a relatively large subgroup of patients (i.e., Stressed class) who warrant comprehensive evaluation and appropriate interventions to reduce stress, enhance stress management, and increase support during CTX. Importantly, this study provides the foundation for future investigation of differences among the latent classes in important patient-reported outcomes, including physical (e.g., sleep, fatigue, pain) and psychological (e.g., depression, anxiety) symptoms, psychosocial adjustment, and quality of life. Moreover, the extant literature supports a salient role for coping in mediating the relationships between stress and various psychological outcomes, including cancer-related distress.(16) Follow-up studies will evaluate coping as a mediator between the identified multidimensional stress profiles and different psychological and physical symptoms. Finally, blood samples were collected from all patients who participated in this study, affording the opportunity to investigate molecular mechanisms (e.g., genetic polymorphisms, gene expression profiles) underlying these distinct stress profiles.

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21

Table 1. Latent Profile Solutions and Fit Indices for One Through Four Class Solutions Model

LL

AIC

BIC

Entropy

VLMR

1 Class

-3607.97

7243.93

7312.03

n/a

n/a

2 Class

-3446.31

6950.63

7091.68

.56

323.31

****

a

-3390.00

6868.01

7082.01

.73

112.62

****

4 Class

-3349.51

6817.02

7103.98

.65

80.99

3 Class

ns

**** p < .0001 a

The three class solution was selected because the BIC for that solution was lower than the BIC for both the 2-

and 4-class solutions. In addition, the VLMR was significant for the 3-class solution, indicating that three classes fit the data better than two classes. The VLMR was not significant for the 4-class solution, indicating that too many classes had been extracted.

Abbreviations: AIC = Akaike’s Information Criterion; BIC = Bayesian Information Criterion; LL = log-likelihood; n/a = not applicable, ns = not significant; VLMR = Vuong-Lo-Mendell-Rubin likelihood ratio test for the K vs. K-1 model.

22

Table 2. Differences Among the Latent Classes in the Stress Profile Measures Stressed (1) 39.9% (n = 382) Mean (SD)

Normative (2) 54.3% (n = 520) Mean (SD)

Resilient (3) 5.7% (n = 55) Mean (SD)

Statistics

Perceived Stress Scale (range 0-56)

25.4 (6.7)

14.2 (5.0)

8.5 (4.5)

F=502.26, p<.001 1>2>3

Impact of Event Scale-Revised (range 0 – 88)

27.9 (13.8)

12.0 (7.0)

15.4 (12.1)

F=244.85, p<.001 1>2 and 3

Life Stressor Checklist-Revised (range 0-30)

8.0 (4.7)

4.6 (2.4)

6.2 (4.2)

F=92.34, p<.001 1>3>2

Connor Davidson Resilience (range 0 - 40)

25.7 (6.4)

32.2 (4.2)

39.7 (0.5)

F=276.40, p<.001 1<2<3

Characteristic

Abbreviation: SD = standard deviation

Table 3. Differences in Demographic and Clinical Characteristics Among the Latent Classes Characteristic

Stressed (1)

Normative (2)

Resilient (3)

39.9% (n = 382)

54.3% (n = 520)

5.7% (n = 55)

Mean (SD)

Mean (SD)

Mean (SD)

56.2 (12.0)

58.2 (11.9)

59.7 (9.9)

Statistics

F=4.24, p=0.015 Age (years)

1<2 F=3.56, p=0.029 Education (years)

15.9 (2.9)

16.4 (3.0)

16.3 (3.3) 1<2

Body mass index (kg/m2)

26.5 (5.8)

26.0 (5.4)

27.1 (6.5)

Karnofsky Performance Status score

76.6 (12.4)

83.0 (11.5)

82.9 (12.0)

F=1.43, p=0.241 F=31.42, p<0.001 1<2 and 3

Self-administered Comorbidity Questionnaire

F=15.98, p<0.001 6.2 (3.5)

5.0 (2.8)

5.2 (3.1)

score

1>2

Time since diagnosis (years)

2.3 (4.6)

2.1 (3.9)

1.6 (3.0)

0.45

0.42

0.36

1.8 (1.5)

1.7 (1.5)

1.4 (1.4)

F=1.33, p=0.266

1.3 (1.3)

1.2 (1.2)

1.4 (1.4)

F=1.13, p=0.324

Hemoglobin

11.5 (1.4)

11.6 (1.4)

11.7 (1.3)

F=0.80, p=0.448

Hematocrit

34.5 (4.1)

34.8 (4.2)

35.2 (3.7)

F=1.10, p=0.334

KW, p=0.222 Time since diagnosis (years, median) Number of prior cancer treatments Number of metastatic sites including lymph node involvement

a

24

% (n)

% (n)

% (n) Χ2=15.03, p<0.001

Gender (% female)

85.1 (325)

74.4 (387)

78.2 (43) 1>2

Self-reported ethnicity White

69.1 (260)

73.0 (375)

68.5 (37)

Asian or Pacific Islander

12.4 (46)

12.5 (64)

13.0 (7)

Black

6.6 (25)

7.0 (36)

11.1 (6)

Hispanic, Mixed, or Other

12.0 (45)

7.6 (39)

7.4 (4)

2

Χ =6.56, p=0.364

Χ2=29.30, p< 0.001 Married or partnered (% yes)

55.8 (211)

70.5 (363)

83.6 (46) 1<2 and 3 2

Χ =11.34, p=0.003 Lives alone (% yes)

26.5 (100)

18.9 (97)

10.9 (6) 1>2 and 3 2

Χ =19.55, p<0.001 Currently employed (% yes)

26.8 (102)

40.8 (210)

29.1 (16) 1<2

Annual household income Less than $30,000

28.0 (97)

10.9 (50)

13.5 (7)

$30,000 to $70,000

23.4 (81)

20.7 (95)

13.5 (7)

KW, p<0.001

$70,000 to $100,000

16.5 (57)

17.9 (82)

15.4 (8)

1<2 and 3

Greater than $100,000

32.1 (111)

50.4 (231)

57.7 (30)

23.0 (86)

21.8 (111)

15.1 (8)

Child care responsibilities (% yes)

2

Χ =1.70, p=0.427

25

Elder care responsibilities (% yes)

2

9.4 (32)

6.7 (32)

7.8 (4)

Χ =1.94, p=0.379

66.0 (247)

72.0 (373)

74.5 (41)

Χ2=4.29, p=0.117

5.8 (22)

4.4 (23)

7.3 (4)

Χ =1.37, p=0.505

High blood pressure

31.2 (119)

29.8 (155)

38.2 (21)

Χ =1.67, p=0.434

Lung disease

10.5 (40)

12.9 (67)

12.7 (7)

Χ =1.26, p=0.533

Diabetes

9.9 (38)

7.3 (38)

12.7 (7)

Χ =3.15, p=0.207

Ulcer or stomach disease

6.5 (25)

3.7 (19)

7.3 (4)

Χ =4.49, p=0.106

Kidney disease

2.4 (9)

0.6 (3)

3.6 (2)

Χ =6.75, p=0.034

Exercise on a regular basis (% yes) Specific comorbid conditions Heart disease

2

2

2

2

2

2

No significant pairwise contrasts 2

Liver disease

5.8 (22)

7.7 (40)

1.8 (1)

Χ =3.49, p=0.174

Anemia or blood disease

14.9 (57)

11.7 (61)

7.3 (4)

Χ =3.59, p=0.166

Depression

33.8 (129)

10.0 (52)

9.1 (5)

Χ =83.45, p< 0.001

2

2

1>2 and 3 Osteoarthritis

13.6 (52)

11.0 (57)

14.5 (8)

Back pain

33.0 (126)

20.0 (104)

23.6 (13)

2

Χ =1.74, p=0.420 2

Χ =19.69, p<0.001 1>2

Rheumatoid arthritis

3.4 (13)

3.1 (16)

7.3 (4)

Χ2=2.63, p=0.268

Cancer diagnosis

26

Breast cancer

41.4 (158)

39.2 (204)

41.8 (23)

Gastrointestinal cancer

28.8 (110)

31.0 (161)

30.9 (17)

Gynecological cancer

20.2 (77)

15.8 (82)

20.0 (11)

Lung cancer

9.7 (37)

14.0 (73)

7.3 (4)

No prior treatment

18.3 (69)

23.2 (118)

26.9 (14)

Only surgery, CTX, or RT

44.7 (169)

42.8 (218)

42.3 (22)

Surgery and CTX, or surgery and RT, or

21.2 (80)

21.4 (109)

21.2 (11)

15.9 (60)

12.6 (64)

9.6 (5)

14 day cycle

37.4 (142)

43.3 (225)

48.1 (26)

21 day cycle

55.0 (209)

50.6 (263)

38.9 (21)

28 day cycle

7.6 (29)

6.2 (32)

13.0 (7)

2

X =7.75, p=0.257

Prior cancer treatment

2

X =5.85, p=0.441

CTX and RT Surgery and CTX and RT Cycle length

a

2

X =8.58, p=0.073

Total number of metastatic sites evaluated was 9. 2

Abbreviations: CTX = chemotherapy, kg = kilograms, KW = Kruskal Wallis, m = meters squared, RT = radiation therapy, SD = standard deviation

27

Table 4. Differences Among the Classes in Frequency and Effect of Stressful Life Events on Patients’ Past Year of Life

a

Mean (SD) Effect of Stressor on Life Over the Past Year % (N) of Patients Exposed to Stressor

Stressful Life Event

(range: 1 ‘not at all’ – 5 ‘extreme’)

Stressed

Normative

Resilient

Stressed

Normative

Resilient

(1)

(2)

(3)

(1)

(2)

(3)

39.9%

54.3%

5.7%

39.9%

54.3%

5.7%

(n = 382)

(n = 520)

(n = 55)

(n = 382)

(n = 520)

(n = 55)

2.0 (1.2)

1.7 (1.0)

1.9 (1.7)

F=2.17, p=0.117

2.7 (1.3)

2.2 (1.4)

2.8 (1.4)

F=2.93, p=0.056

1.7 (1.0)

1.5 (1.0)

1.3 (0.8)

F=0.40, p=0.675

2.1 (1.3)

1.9 (1.3)

1.3 (0.5)

F=1.52, p=0.222

2.0 (1.3)

1.7 (1.1)

1.3 (0.5)

F=1.48, p=0.232

2.3 (1.5)

1.6 (1.1)

2.5 (1.7)

Statistics

Statistics

Interpersonal Violence, Abuse, and Neglect Stressors X2=60.43, p<0.001 Family violence in childhood

38.1 (133)

14.8 (72)

20.8 (10) 1>2 2

X =80.24, p<0.001 Emotional abuse

37.1 (130)

11.4 (56)

16.0 (8) 1>2 and 3 2

X =37.82, p<0.001 Sexual harassment

27.1 (95)

11.0 (53)

12.5 (6) 1>2 2

X =51.29, p<0.001 Physical abuse <16 years

25.0 (88)

7.2 (35)

16.7 (8) 1>2 2

X =51.47, p<0.001 Physical abuse ≥16 years

22.3 (78)

5.6 (27)

14.6 (7) 1>2 2

X =30.67, p<0.001 Forced to touch <16 years

19.5 (69)

7.0 (34)

8.3 (4) 1>2

F=3.16, p=0.047 No significant pairwise

28

contrasts X2=43.25, p<0.001 Forced sex ≥16 years

13.0 (46)

1.7 (8)

8.3 (4)

1.9 (1.3)

1.4 (0.7)

1.5 (0.7)

F=1.59, p=0.214

2.0 (1.2)

1.4 (0.9)

1.5 (0.6)

F=0.84, p=0.439

2.8 (1.3)

2.4 (1.7)

2.5 (0.7)

F=0.25, p=0.782

2.3 (1.4)

1.4 (0.7)

1.5 (0.7)

F=1.93, p=0.160

2.5 (1.4)

1.9 (1.2)

2.4 (1.4)

1 and 3>2 2

X =53.96, p<0.001 Forced to touch ≥16 years

13.6 (48)

1.0 (5)

8.3 (4) 1 and 3>2 2

X =36.05, p<0.001 Physical neglect

10.0 (35)

1.0 (5)

6.0 (3) 1>2 2

X =23.51, p<0.001 Forced sex <16 years

8.8 (31)

1.7 (8)

4.2 (2) 1>2 Other Stressors 2

Death of someone close

X =10.58, p=0.005 81.6 (283)

74.0 (355)

89.4 (42)

(not sudden)

F=19.19, p<0.001

1>2

1>2

2

Death of someone close

X =8.75, p=0.013 54.5 (189)

45.1 (219)

58.3 (28)

F=8.73, p<0.001 2.5 (1.5)

(sudden)

1.9 (1.2)

1.9 (1.2)

1>2

1>2

2

X =7.14, p=0.028 F=6.64, p=0.002 Abortion or miscarriage

49.0 (144)

40.1 (149)

55.3 (21)

No significant

1.8 (1.2)

1.4 (0.9)

1.3 (0.6) 1>2

pairwise contrasts 2

X =6.75, p=0.034 Been in serious disaster

46.2 (164)

37.5 (182)

36.7 (18)

F=8.02, p<0.001 1.5 (0.9)

1.2 (0.6)

1.3 (0.7)

1>2 Seen serious accident

38.7 (138)

28.3 (137)

44.9 (22)

2

X =13.00, p=0.002

1>2 1.6 (0.9)

1.4 (0.8)

1.2 (0.5)

F= 2.93, p=0.055

29

1>2 X2=7.53, p=0.023 Separated/divorced (self)

40.9 (144)

31.8 (156)

34.0 (17)

F=7.52, p=0.001 2.4 (1.4)

1.8 (1.3)

1.5 (1.0)

1>2

1 > 2 and 3

2

X =20.95, p<0.001 Been robbed/mugged

34.7 (121)

20.8 (101)

33.3 (16)

1.8 (1.2)

1.5 (1.0)

1.6 (1.1)

F=2.62, p=0.075

2.8 (1.5)

2.3 (1.4)

2.4 (1.6)

F=2.64, p=0.074

1>2 2

Cared for someone with

X =20.76, p<0.001 30.9 (107)

18.0 (87)

33.3 (16)

mental/physical illness

1>2 F=3.41, p=0.035

2

X =19.44, p<0.001 Had serious accident

31.2 (110)

18.0 (87)

25.0 (12)

1.7 (1.1)

1.4 (0.9)

1.3 (0.5)

No significant pairwise

1>2 contrasts 2

X =33.64, p<0.001 Seen robbery/mugging

30.7 (109)

14.3 (70)

27.1 (13)

1.7 (1.1)

1.3 (0.7)

1.7 (1.5)

2.0 (1.3)

1.6 (0.9)

1.5 (0.7)

F=3.00, p=0.052

1>2 2

X =16.35, p<0.001 Separated/divorced (parents)

27.9 (99)

16.4 (80)

22.0 (11)

F=4.04, p=0.019

1>2

1>2

X2=22.47, p<0.001 Jail (family)

28.4 (100)

15.0 (73)

22.0 (11)

2.0 (1.5)

1.7 (1.1)

1.9 (1.4)

F=1.68, p=0.190

2.6 (1.3)

2.3 (1.4)

2.2 (1.3)

F=1.18, p=0.309

3.0 (1.7)

2.2 (1.5)

3.0 (1.9)

1>2 2

X =30.75, p<0.001 Illness (not cancer)

28.2 (100)

13.0 (64)

18.0 (9) 1>2 2

X =62.87, p<0.001 Serious money problems

33.0 (116)

11.0 (54)

14.0 (7) 1>2 and 3

F=4.40, p=0.014 1>2

30

2

X =44.39, p<0.001 Stressor for someone close

26.1 (85)

8.6 (41)

14.6 (7)

2.5 (1.4)

2.2 (1.4)

3.1 (1.8)

F=1.51, p=0.225

3.1 (1.5)

3.2 (1.6)

4.0 (1.4)

F=0.31, p=0.735

1.9 (1.4)

1.5 (0.9)

2.0 (1.4)

F=1.00, p=0.377

2.9 (1.5)

3.3 (1.4)

--

F=0.52, p=0.479

3.2 (1.6)

1.8 (1.5)

1.0 (-)

F=1.85, p=0.193

2.6 (1.7)

1.8 (1.0)

--

F=1.56, p=0.227

1>2 2

X =13.15, p=0.001 Patient-specified event

17.9 (59)

9.8 (46)

6.4 (3) 1>2 2

X =12.07, p=0.002 Jail (self)

10.2 (36)

4.3 (21)

4.0 (2) 1>2

Care for child with handicap

4.7 (16)

3.6 (17)

0.0 (0)

2

X =2.67, p=0.263 2

X =8.82, p=0.012 Separated from child

3.8 (13)

0.8 (4)

2.1 (1) 1>2

Foster care

3.4 (12)

2.0 (10)

0.0 (0)

2

X =2.93, p=0.232

a

Grouped by type (interpersonal violence, abuse, neglect vs. other stressful life events) and in descending order of average frequency across latent classes.

Abbreviations: pw = pairwise; SD = standard deviation; sig. = significant

31