Exploration of Relationships Between Symptoms,Work Characteristics,and Quality of Life in Young Adult Hematologic Cancer Survivors

Abstract

This study explores relationships between individual, microsystem (work) characteristics, and quality of life (QOL) among young adult (YA; ages 20–39 years at diagnosis) hematologic cancer survivors. Forty YAs who had completed cancer therapy within the past 5 years were recruited through social media and completed an online survey. Poorer QOL was associated with higher levels of depressive symptoms, fatigue, impaired cognitive function, and poorer work ability and financial health (all p < 0.05). A comprehensive understanding of work characteristics, including work ability, may lead to multilevel interventions improving QOL. Future research should include larger, more diverse samples of YA cancer survivors.

Introduction

For young adult cancer survivors (YACS; ages 20–39 years at diagnosis), the period of young adulthood encompasses a myriad of milestones and tasks, including the early career stage, where education and employment are essential to their lives and may contribute to their future financial health and quality of life (QOL).¹ YACS have personal characteristics shaping early career experiences, in relation to characteristics of their work and work environment.

Hematologic cancers are among the most common cancers in young adulthood.² YAs with leukemia or lymphoma are at higher risk for poorer QOL due to treatment regimen demands,³ including multiple chemotherapeutic agents, radiation therapy, and/or stem cell transplantation.² Although these treatments improve disease-free and overall survival,⁴ treatment with regimens including chemotherapy have been associated with an increased likelihood of person-reported physical and mental impairments interfering with work tasks and leading to employment disruption.⁵ Symptoms, such as cancer-related fatigue (CRF), potentially impact work ability,⁶ defined as one's perceived ability to work now and in the near future, with respect to work demands, physical health, and mental resources.⁷

With many work years ahead, YACS constitute an integral part of the workforce yet have unmet work-related needs. Although work has been shown to enhance overall QOL in YACS,⁸ work-related stress and financial stress (worry about money) can lead to poorer physical and psychological well-being.⁹ To date, limited research has addressed associations between treatment-related symptoms, financial distress, work, and QOL in YACS. Moreover, few studies describe work factors, including job control, demand, and workplace support among YACS. Thus, the study purpose was to explore relationships between individual-level characteristics (symptoms, psychological well-being, financial distress, and work ability), microsystem-level work-related characteristics (job control, job demand, workplace support) and QOL in a sample of young adult (YA) hematologic cancer survivors.

The study was guided by the Social Ecological Model (SEM)¹⁰ and Life Course Perspective (LCP)¹¹ to identify individual- and microsystem-level characteristics affecting QOL. The individual level represents YACs' personal characteristics, such as sociodemographic factors, symptoms, work ability (self-assessment of work), and financial distress. The microsystem level is closest to the YACS, represented in this study by characteristics of their work and work environment. The SEM posits that characteristics of the individual and microsystem levels influence each other bidirectionally.¹⁰ Thus, influences on work and QOL occur in workplace settings when individuals spend significant amounts of their time in these environments. Integration of LCP with the SEM framework highlights the continual interplay between life's social course and development of YACS.¹¹

Methods

This study received institutional review board approval (No. 2020-4281) and was conducted between April and August 2020. This analysis is part of a primary convergent mixed-methods study. Information about social media study recruitment, selection criteria, and the informed consent process have been previously reported in detail.¹² In brief, YACS were eligible for the study if they were within 5 years of a leukemia or lymphoma diagnosis between ages 20 and 39 years, and had completed cancer therapy.

Data collection

In response to social media posts through cancer support groups and organizations, YACS contacted the study team by email to express interest in the study. After electronic informed consent through DocuSign, and completion of study eligibility screener, we emailed an individualized link to the study survey using REDCap electronic data capture.¹³ The survey included investigator-developed items asking for demographic and clinical information, and well-established measures and item-sets.

Individual-level factors

We used Patient-Reported Outcome Measurement Information System (PROMIS) measures to assess anxiety and depressive symptoms (15 items),¹⁴ and cognitive function (4 items).¹⁵ Raw scores were converted to standardized t-scores, with higher scores indicating higher levels of anxiety and/or depressive symptoms, and better cognitive functioning. Clinically meaningful severity thresholds were used for reporting.¹⁶

We used the 11-item Comprehensive Score for Financial Toxicity (CoST)¹⁷ to measure financial distress. CoST items tap into financial demands, stress, and work-related issues. Items 1, 6, 7, and 11 were scored directly and remaining items were reverse-scored. Items scores were summed to estimate total scores; lower scores reflected worse financial distress.

We used the 10-item Work Ability Index (WAI) to measure work ability,⁷ with higher scores indicating better work ability. One item in this measure captures the “WAI global score,” that is, overall work ability, using a 10-point scale ranging from 0 (currently not able to work) to 10 (work ability as previous lifetime best).¹⁸

Microsystem-level (work-related) factors

We used the 27-item Job Content Questionnaire (JCQ)¹⁹ to measure work-related factors, including job control, psychological and physical job demands, workplace (supervisor and coworker) support, and job security. JCQ items are scored on a 4-point scale from 1 (strongly disagree or never) to 4 (strongly agree or often). Subscale scores were calculated according to the JCQ user guide; some subscales were reverse-scored (job security).¹⁹ Higher scores reflect greater job control, demands (psychological or physical), workplace support (supervisor or coworker), and job insecurity.

Quality of life

Finally, we assessed QOL with the 2-item global health status (GHS) within the 30-item European Organization for the Research Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30).²⁰ GHS items are scored on 7-point Likert scales, ranging from 1 (very poor) to 7 (excellent) perceived health and QOL. Raw scores were averaged and converted to t-scores; a higher t-score represents better QOL.

Statistical analysis

We analyzed data using STATA v.16. Scores for the measures were standardized. For example, PROMIS item sets raw scores were converted to standardized t-scores with a normative mean of 50, and standard deviation = 10, with higher scores indicating higher symptom levels or cognitive functioning.

We examined distributions of the data for normality, and calculated descriptive statistics to describe demographic individual- and microsystem-level characteristics of the sample. We analyzed relationships between individual-level (symptoms and work ability), microsystem-level (job control/demands and workplace support) factors, and QOL using appropriate correlations (Spearman's rho for non-normal distributions; Pearson's for continuous variables and normal distributions).

Sample size

In planning the study, we used a range of effect sizes from prior studies of social and physical functioning and QOL in YACS to determine necessary sample size for the analytic approach. The literature reports effect sizes for relationships between social/physical functioning and QOL ranging from r = 0.4 to 0.9.²¹ Applying Whitehead's²² stepped sample size rules of thumb, which identifies sample sizes for exploratory studies by effect size; given the range of expected effect sizes (0.4–0.9), a sample size of 40 was deemed sufficient to detect significant relationships. Sample size was also dictated by needing to have the same set of participants for both quantitative- and qualitative-arms of the primary mixed-methods analysis.

Results

Participants (N = 40) resided in 23 states in the United States. Most were female (65%), white (72.5%), and employed in professional work (37.5%). Mean age at diagnosis was 28 ± 5.3 years. Participants were 1.3 (range = 0.92–2.75) years from treatment completion, and 2.5 ± 1.3 years from cancer diagnosis (Table 1).

Table 1.

Basic Participant Demographics (N = 40)

Sociodemographic variable	Results
Age at diagnosis (years)	28 ± 5.3^a
Length of time since cancer diagnosis (years)	2.5 ± 1.3^a
Length of time since treatment completion (years)	1.3 (0.9–2.8)^b
Education (years)	16 (14–18)^b

	n (%)
Female	26 (65)
Race
White	29 (72.5)
Black or African American	4 (10)
Asian	2 (5)
Mixed race	5 (12.5)
Ethnicity
Non-Hispanic	35 (87.5)
Job type
Professional, technical	15 (37.5)
Health care worker	9 (22.5)
Education or research	7 (17.5)
Sales, service, or trade worker	6 (15.0)
Student	2 (5.0)
Currently not in workforce	1 (2.5)

Mean ± SD.

Median (interquartile range).

SD, standard deviation.

Descriptive statistics for individual- and microsystem-level factors are presented in Table 2. All scales demonstrated good to strong reliability in the study sample (α range = 0.65 to 0.94, Table 2). Average QOL score was 68.1 ± 14.48, which is slightly above the normative mean (m = 61) reference for adult cancer survivors younger than age 50 years.²³ Generally, this sample reported mild levels of anxiety (59.7 ± 8.57) and depressive (55.2 ± 7.88) symptoms. Most (52.5%) reported high perceived work ability, high job control (76.67 ± 12.04), and workplace support (by both supervisors (median = 12 [range = 11–15]) and by coworkers [13.13 ± 2.10]). The sample also reported high levels of psychological job demands (33.53 ± 7.31).

Table 2.

Individual- and Microsystem-Level Factors Among Participants

Overall (N = 40)
Variable [potential score range]	n	Mean ± SD	Observed range	Cronbach's alpha (α)
PROMIS
Depressive symptoms [0–100]	40	55.2 ± 7.9	37.1–70.6	0.93
Anxiety [0–100]	40	59.7 ± 8.6	42.8–79.5	0.93
Cognitive function [0–100]	40	42.7 ± 10.3	24.9–61.1	0.94
CRF [0–100]	40	39.7 ± 25.2	0–100	0.86
EORTC QLQ-C30 (QOL Global Score) [0–100]	40	68.1 ± 14.5	25–100	0.88
COST (financial distress) [0–44]	40	19.9 ± 10.3	0–36	0.86
JCQ subscales				0.82
Job control [24–96]	39	76.7 ± 12.0	48–96	0.83
Psychological demands [12–46]	38	33.5 ± 7.3	16–46	0.78
Physical job demands [1–4]	40	2.3 ± 1.1	1–4	(single-item)
Supervisor support [4–16]	39	12 (11–15)^a	5–16	0.94
Coworker support [4–16]	39	13.1 ± 2.1	9–16	0.74
Job insecurity [3–13]	38	5 (4–7)^a	3–13	0.65
WAI total score [6–42]	40	30.8 ± 6.3	14–42	0.83
	n	%
WAI single-item score “high” (≥8)	40	52.5%	—	—

Median (interquartile range).

CoST, Comprehensive Score for Financial Toxicity; CRF, cancer-related fatigue; EORTC, European Organization for the Research Treatment of Cancer; JCQ, Job Content Questionnaire; PROMIS, Patient-Reported Outcome Measurement Information System; QOL, quality of life; WAI, Work Ability Index.

Associations between individual- and microsystem-level factors are shown in Table 3. Significant associations were medium (>.3) to large (>.5) in magnitude.²⁴ Poorer QOL was associated with higher levels of depressive symptoms and CRF, and with poorer cognitive function, financial distress, and perceived work ability. Anxiety was significantly associated with depressive symptoms, but not with QOL. No work-related factors were associated with QOL in the study sample.

Table 3.

Associations Between Individual-Level, Microsystem-Level Variables, and Quality of Life

Variable	Anx	Dep	CRF	CF	FD	WA	JC	PsyD	PhyD	SS	CS	JI
Anx	1	—	—	—	—	—	—	—	—	—	—	—
Dep	0.55^a,^***	—	—	—	—	—	—	—	—	—	—	—
CRF	0.28^a	0.31^a,^*	—	—	—	—	—	—	—	—	—	—
CF	−0.31^a	−0.34^a,^*	−0.71^a,^***	—	—	—	—	—	—	—	—	—
FD	−0.13^a	−0.37^a,^*	−0.36^a,^*	0.33 ^a, ^*	—	—	—	—	—	—	—	—
WA	−0.18^a	−0.33^a,^*	−0.60^a,^***	0.59 ^a, ^***	0.57 ^a, ^***	—	—	—	—	—	—	—
JC	0.15^a	0.04^a	−0.14^a	−0.22^a	0.15^a	−0.11^a	—	—	—	—	—	—
PsyD	−0.00^a	0.16^a	0.09^a	−0.03^a	−0.01^a	−0.02^a	0.21^a	—	—	—	—	—
PhyD	0.09^a	0.03^a	0.03^a	−0.11^a	−0.27^a	−0.34^a,^*	0.09^a	0.20^a	—	—	—	—
SS	−0.36 ^b, ^*	−0.29^b	−0.00^b	−0.14^b	0.05^b	−0.07^b	0.29^b	−0.04^b	−0.18^b	—	—	—
CS	−0.41 ^a, ^*	−0.44^a,^**	−0.01^a	0.01^a	0.24^a	0.09^a	0.25^a	0.02^a	0.24^a	0.65^a,^***	—	—
JI	0.26^b	0.33^b,^*	0.13^b	−0.09^b	−0.49 ^b, ^***	−0.27^b	0.07^b	0.09^b	0.24^b	−0.32^b,^*	−0.41^b,^*	—
QOL	−0.27	−0.40^a,^*	−0.68^a,^*	0.39^a,^*	0.33^a,^*	0.54^a,^*	0.17^a	−0.03^a	0.04^a	0.20^b	0.09^b	−0.22^b

Bold values indicate statistical significance at either the p < 0.05, p < 0.01, or p < 0.001 levels.

p < 0.05; ^**p < 0.01; ^***p < 0.001.

Pearson's correlation.

Spearman's correlation.

Anx, anxiety; Dep, depressive symptoms; CF, cognitive function; FD, financial distress; WA, work ability; JC, job control; PsyD, psychological demands; PhyD, physical demands; SS, supervisor support; CS, coworker support; JI, job insecurity.

Discussion

This study described relationships between individual- and microsystem-level variables and QOL in a sample of working YA hematologic cancer survivors. This study, to our knowledge, is one of the first to look at specific work-related microsystem factors such as job control and demand in YACS. Scores for the job control and job insecurity subscales of the JCQ were lower than found in other studies with comparable chronic illness populations,²⁵ which suggests worse overall job autonomy and flexibility. Their work as professionals could explain the high levels of psychological job demands, reported by our sample.

This brief report has important implications for practice and future research on work-related factors among YACS. The impact of multiple work roles on anxiety, depressive symptoms, and QOL requires further study. Greater physical and psychological symptom burden, and poorer QOL as compared with age peers are common among YACS. In this study, QOL was comparable with results of prior research of post-treatment YACS,²⁶ and poorer than healthy age peers and population norms by sex.²⁷ Global QOL score for the sample (68.1 ± 14.48) was lower than the EORTC QLQ-C30 general population. Anxiety (59.7 ± 8.6) and depressive symptom (55.2 ± 7.9) levels were higher than for the PROMIS normative population, and consistent with prior studies of YACS, although across cancer types, after completion of cancer therapy.²⁸

Consistent with prior studies of YACS, work ability was positively associated with QOL.⁸ Additionally, significant relationships between depressive symptoms, CRF, and QOL found in this study are similar to prior research.⁹ A recent systematic review on CRF in YAs found a major gap in the literature addressing the influence of CRF on their lives.⁶ YACS can experience CRF even years after completing cancer therapy and report more burden from persistent CRF than older patients.⁹ Although not statistically significant (p-values not displayed in Table 1), associations between time since diagnosis, time since treatment completion, and QOL have implications for future research. Examination of trajectories of relationships between work characteristics and QOL from diagnosis through durable survivorship using multivariate analyses and covariates is a next step.

The association between financial distress and QOL is also consistent with past research.²⁹ Financial distress can also be accompanied by cognitively, emotionally, and behaviorally burdensome tasks related to navigating health insurance and managing medical expenses while also working to, among other things, fulfill existing obligations (educational loans) and paying for basic necessities (food and housing). More research is needed on how psychological burdens related to financial distress affects QOL.

Practice implications include increased sensitivity by oncology care team to the importance of work for YACS. This includes regular queries about work status and the work environment and help addressing possible reduced work ability. With permission from YACS, communication between oncology care team and the YACs' employer may advance understandings of how to identify and better respond to needs at work.

Study limitations include a homogeneous and small sample. Some participants held multiple work roles (schooling and working for pay); however, the study survey asked participants to respond to work-related measures based on their primary work type. Furthermore, we compensated participants for study participation, which may have influenced their decision to participate.

Finally, we collected data during the COVID-19 pandemic, which likely affected participants' anxiety and depressive symptoms and work-related factors. For example, workplace settings may have changed because of ability to work remotely during the pandemic. Study strengths include recruitment and retention of participants from 23 states amidst the pandemic, and integration of the model guiding the study, SEM¹⁰ and LCP.¹¹ The timing of the study necessitated remote methods, including social media recruitment that can be applied to extend the reach, accessibility, and acceptability of future studies of YACS.

Conclusions

In this sample of YACS, poorer QOL was associated with higher levels of depressive symptoms and CRF, and worse financial distress, work ability, and cognitive function. Future research should include larger more diverse samples to better understand the relationships between individual- and microsystem-level characteristics and QOL in YACS. Comprehensive understanding of work-related factors, including perceived work ability, can inform multilevel interventions that include components to improve QOL in YACS.

Footnotes

Acknowledgments

The authors thank the young adult cancer survivors who participated in this study.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

Research reported in this publication was supported by the National Institute of Occupational Safety and Health under Award No. T42-OH-008422.

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