Abstract
Context:
Few studies have examined the association between financial strain and quality of life outcomes in breast cancer.
Objectives:
To examine the association between financial strain and key elements of physical and emotional quality of life among women with breast cancer.
Methods:
Across three geographically diverse samples (census regions: Northeast=13.2%, Midwest=26.8%, South=35.5%, West=17.4%; international=7.1%), 309 women with a history of breast cancer completed online surveys including measures of financial strain, depression, anxiety, symptom burden, and perceived health. The third sample (N=134) also reported financial toxicity that specifically documents financial strain due to medical care costs. Primary analyses assessed the association between financial strain and measures of emotional and physical quality of life. Sensitivity analyses examined associations using the measure of financial toxicity. All analyses controlled for key covariates.
Results:
Results showed that 37.5% of women experienced financial strain (Samples 1–3), varying from 12.1% among older, married, and college-educated women to 81.0% among women who were younger, unmarried and lacked a college education. Additionally, 26.1% reported treatment-specific financial toxicity (Sample 3). Financial strain was associated with more severe symptoms of depression (p<.001) and anxiety (p<.001) and worse physical symptom burden (p<.001) and perceived health (p<.001). Observed effects were sustained in sensitivity analyses using the financial toxicity measure.
Conclusions:
The present investigation illustrates the importance of financial strain in breast cancer. Healthcare systems are encouraged to expand interdisciplinary palliative and supportive care services that have the expertise necessary to help financially strained patients navigate the cancer care continuum.
Keywords: Breast Cancer, Financial Toxicity, Depression, Anxiety, Symptom burden, Perceived health
Background
Breast cancer has the highest incidence and mortality rates among women of all cancers worldwide, and many patients experience financial strain (i.e., lacking the financial resources necessary to meet basic needs) [1] that undermines cancer care outcomes. Estimates of financial strain in breast cancer range from 13% in studies in Austria [2] and Sweden [3] to 50% in one U.S. study [3, 4], with most estimates near the middle (U.S.: 20–44%, Lithuania: 40%) [5-7]. Sources of financial strain include costs associated with cancer care services (e.g., medication, co-pay, transportation), and reduced income due to missing work, loss of employment or retirement [8]. Financial strain is stressful and may reduce emotional quality of life [3, 6, 9, 10]. It is also associated with earlier mortality and reduced physical quality of life, partly because individuals may be unable to continue paying for expensive treatment regimens [4-6, 8, 10-15]. However, studies are needed examining financial strain as the primary independent variable of interest [4, 6], with a multi-item scale [4], and in combination with a comprehensive assessment of quality of life [5].
In the present study we examined the cross-sectional associations between a four-item measure of financial strain [1] and key elements of emotional and physical quality of life. To achieve greater power and examine the replicability of findings across samples, we combined data from three samples of women with breast cancer. For sensitivity analyses in the third sample, we also included a more targeted measure of ‘financial toxicity’ [8, 16] that focuses on financial strain specifically attributed to treatment costs. We hypothesized that financial strain and toxicity would be associated with worse quality of life.
Methods
Participants and Procedures
Three samples of women with breast cancer (Total N=309; Sample 1: N=55; Sample 2: N=120; Sample 3: N=134) participated in this research, conducted from 2011–17. Participants with a history of breast cancer were recruited using the NIH ResearchMatch database [17]. Our study website was open-access and was also made available on breast cancer websites and listservs, and via informal word of mouth. Participants completed a consent document prior to the survey, and procedures were identical except where noted. For example, in Sample 1 only, participants could enter into a lottery for $100 for participating. The inclusion criteria were having a current or past diagnosis of breast cancer, being female, and being at least 18 years of age. Patients unable to read English were excluded. Study procedures were conducted with all necessary approvals from ResearchMatch, NIH, and the relevant Institutional Review Boards at the James P. Wilmot Cancer Institute (ethical approval # RSRB00037941) and Tulane University (ethical approval #16–959396UE and #2017–723).
Assessments and Measures
Health history and demographic data.
Participants reported the recency of diagnosis (months), presence of metastases, and type of cancer treatments received. Demographic variables included age, education level, marital status, race, and geographic location.
Financial strain.
We used a four-item checklist [1] that asks participants to indicate whether their income is sufficient to allow them to afford: (1) food and housing, (2) clothing, medicine, home repairs, (3) going out for a meal and entertainment, and/or (4) a week-long vacation, health permitting. Participants were classified as financially strained if they indicated that they could not afford one or more of the four options. The measure has been used previously in a large sample of older adults in the U.S. [1].
Financial toxicity.
Financial toxicity was assessed, in Sample 3 only, using an item from the financial domain of the Patient Satisfaction Questionnaire rated from 1 (strongly disagree) to 5 (strongly agree): “I have to pay for more of my medical care than I can afford” [18]. Presence of financial toxicity was determined if participants rated the item a 4 or 5 (agree or strongly agree). Prior research has established the validity of this question in cancer [19].
Emotional quality of life.
Sample 1 participants completed the depression and anxiety subscales of the Depression Anxiety Stress Scale (DASS) [20]. The validity of the DASS is well-established, including in cancer samples [20, 21] and both the depression (α=.86) and anxiety (α=.69) scales were reliable in the present sample. Given growing interest [22] in the Patient- Reported Outcome Measurement Information System (PROMIS), participants in Samples 2 and 3 completed the PROMIS depression scale, and Sample 3 also completed the PROMIS anxiety scale [23]. The anxiety measure was omitted in Sample 2 to accommodate ancillary measures for other studies. The PROMIS depression and anxiety scales have shown evidence of reliability and validity [22, 23], and were reliable in the present samples (αs from .90 to .94).
Physical quality of life.
Participants in all three samples completed the SF-1 perceived health item [24] and the physical symptom subscale of the Functional Assessment of Cancer Therapy-General (FACT-G) [25]. Both have been used extensively in prior studies [26, 27] and the FACT-G was reliable here (α=. 87).
Analysis
First, we examined descriptive statistics for each variable. Chi-Square tests of independence were used to examine whether demographic or health history variables had significant zero-order associations with financial strain. In each sample, analyses of covariance (ANCOVAs) were used to examine whether financial strain (independent variable) was associated with each quality of life outcome (PROMIS Depression, SF-1, etc.), while controlling for of age, education level (presence/absence of Bachelor’s degree), marital status (married versus unmarried), presence/absence of metastases, and recency of diagnosis (≤1 year versus longer). We calculated the sample-size weighted average effect for these outcomes across all participants, which afforded greater power than the within-sample analyses. In sensitivity analyses involving the financial toxicity measure included in Sample 3, we used ANCOVA models comparable to our primary analyses to examine whether presence of financial toxicity (independent variable) was associated with each quality of life variable (dependent variable) while controlling for the covariates. Effect sizes were assessed using the standardized mean difference, Cohen’s d [28], which were computed by hand from covariate-adjusted means and standard deviations from the ANCOVA.
Results
Demographics
Participants were 309 female breast cancer patients who ranged in age from 27 to 86 (see Table 1). Most held at least a bachelor’s degree (72.6%), were married (59.0%), and were white (94.9%). They were geographically distributed across 40 U.S. states (census regions: Northeast=13.2%, Midwest=26.8%, South=35.5%, West=17.4%) and internationally (7.1%). The median time since initial diagnosis was 5 years, with 19.1% in the past year and 24.2% at least 10 years prior. Approximately 1 in 5 reported metastases. Their treatment regimens included surgery (73.5%), chemotherapy (60.3%), radiation (61.9%), biologic/targeted therapy (14.5%), or no treatments (3.8%).
Table 1.
Participant Characteristics (N=309)
| Characteristic | M (SD) | or n (%) |
|---|---|---|
| Age, years | 58.31 | (11.07) |
| Education, Bachelor’s degree or higher | 224 | (72.5%) |
| Metastases, present | 58 | (18.8%) |
| Recent Diagnosis, past year | 59 | (19.1%) |
| Relationship Status, married | 183 | (59.2%) |
| Financial Strain, present | 116 | (37.5%) |
| Financial Toxicity, presenta | 46 | (26.6%) |
Note. N=309.
Sample 3 data only (n=134).
Financial Strain and Toxicity
Table 1 shows that 37.5% of the total sample reported financial strain. Participants who were older (p=.011), married (p<.001), or had a Bachelor’s degree (p=.001) were less likely to experience financial strain (see Table 2). Based on these demographics, the prevalence of financial strain in the lowest-risk subgroup (married, college-educated, and ≥65) was 12.1%, whereas the prevalence in the highest-risk subgroup (unmarried, not college-educated, and <65) was 81.0%. Financial toxicity was present in 26.1% of Sample 3.
Table 2.
Point Prevalence of Financial Strain within Demographic and Health Subgroups
| Financial Strain Absent (N=193) |
Financial Strain Present (N=116) |
||||
|---|---|---|---|---|---|
| Characteristic | n | (%) | n | (%) | p |
| Age | |||||
| <65 | 127 | (58.0%) | 92 | (42.0%) | .011 |
| ≥65 | 66 | (73.3%) | 24 | (26.7%) | |
| Marital Status | |||||
| Unmarried | 61 | (48.4%) | 65 | (51.6%) | <.001 |
| Married | 132 | (72.2%) | 51 | (27.8%) | |
| Education | |||||
| Bachelor’s absent | 41 | (48.2%) | 44 | (51.8%) | .001 |
| Bachelor’s present | 152 | (67.9%) | 72 | (32.1%) | |
| Time since diagnosis | |||||
| ≤ 1 year | 28 | (52.8%) | 27 | (47.2%) | .112 |
| > 1 year | 165 | (64.5%) | 91 | (35.5%) | |
| Metastases | |||||
| Absent | 155 | (61.8%) | 96 | (38.2%) | .594 |
| Present | 38 | (65.5%) | 20 | (34.5%) | |
Note. N=309 across three samples. P-values were obtained from Chi-Square tests of independence to examine the zero-order association between each participant characteristic (dichotomous variable) and presence of financial strain (dichotomous variable).
Emotional Quality of Life
Financial strain was associated with worse emotional quality of life in all three samples (see Table 3), while accounting for the effects of age, education level, marital status, recency of diagnosis, and presence of metastases. In specific, financial strain was associated with greater depression (average d=0.70, p<.001) and anxiety (average d=0.51, p<.001) symptom severity (see Table 4). Older participants reported better emotional quality of life. They reported lower levels of depression in Samples 2 and 3 (ps≥.048) and lower anxiety in Samples 1 and 3 (ps≤.012). More recently diagnosed participants had increased anxiety in Sample 1 (p=.044), and those without a bachelor’s degree had increased depression and anxiety in Sample 3 (ps≤.023). Sensitivity analyses in Sample 3 were consistent with our primary analyses. Financial toxicity was associated with symptoms of depression (d=0.83, p<.001) and anxiety (d=0.81, p<.001) while controlling for the same covariates.
Table 3.
Financial Strain and Quality of Life in Breast Cancer
| Financial Strain Absent (n=193) |
Financial Strain Present (n=116) |
|||||
|---|---|---|---|---|---|---|
| Quality of Life Measures | M | (SD) | M | (SD) | Standardized Mean Difference (Cohen’s d) |
p |
| Sample 1 (N=55) | n=32 | n=23 | ||||
| Emotional | ||||||
| Depression Symptom Severity (DASS) | 2.81 | (2.83) | 5.35 | (4.03) | 0.77 | .008 |
| Anxiety Symptom Severity (DASS) | 2.19 | (2.75) | 3.09 | (2.56) | 0.32 | .253 |
| Physical | ||||||
| Perceived Health (SF-1) | 3.53 | (1.08) | 3.04 | (0.77) | 0.74 | .011 |
| Physical Symptom Burden (FACT-G) | 1.08 | (0.91) | 1.61 | (0.98) | 0.50 | .080 |
| Sample 2 (N = 120) | n=84 | n=36 | ||||
| Emotional | ||||||
| Depression Symptom Severity (PROMIS) | 2.18 | (2.81) | 5.00 | (3.93) | 0.85 | <.001 |
| Physical | ||||||
| Perceived Health (SF-1) | 3.54 | (0.87) | 2.97 | (0.97) | 0.50 | .021 |
| Physical Symptom Burden (FACT-G) | 0.55 | (0.65) | 1.21 | (0.94) | 0.80 | <.001 |
| Sample 3 (N = 134) | ||||||
| Emotional | n=77 | n=57 | ||||
| Depression Symptom Severity (PROMIS) | 1.62 | (2.80) | 3.91 | (3.84) | 0.53 | .004 |
| Anxiety Symptom Severity (PROMIS) | 2.57 | (2.82) | 5.49 | (4.48) | 0.59 | .002 |
| Physical | ||||||
| Perceived Health (SF-1) | 3.49 | (0.96) | 2.81 | (0.90) | 0.56 | .003 |
| Physical Symptom Burden (FACT-G) | 0.55 | (0.65) | 1.22 | (0.94) | 0.66 | <.001 |
Note. DASS = Depression Anxiety Stress Scale (summated score for depression and anxiety computed from 7 items rated from 0–3) [22]. SF-1 = first item from the Short Form Health Survey (rated from 1–5) [26]. FACT-G = Functional Assessment of Cancer Therapy - General (mean composite of 7 symptom items rated from 0–4) [27]. PROMIS = Patient Reported Outcome Measurement Information System (summated score for depression and anxiety computed from 4 items rated from 0–4) [25]. Financial strain in each sample was assessed using a four-item checklist [3]. Means and standard deviations are unadjusted for covariates. The standardized mean difference and significant test control for age, education level, presence/absence of metastases, and time since diagnosis.
Table 4.
Meta-Analysis of Effect Sizes for the Associations between Financial Strain and Indicators of Quality of Life in Three Samples of Patients with Breast Cancer
| Quality of Life Indicator | Measures | Studies | N | Standardized Mean Difference (Cohen’s d) |
p |
|---|---|---|---|---|---|
| Depression Symptom Severity | PROMIS, DASS | 1, 2, 3 | 309 | 0.70 | <.001 |
| Anxiety Symptom Severity | PROMIS, DASS | 1, 3 | 189 | 0.51 | <.001 |
| Perceived Health | SF-1 | 1, 2, 3 | 309 | 0.57 | <.001 |
| Physical Symptom Burden | FACT-G | 1, 2, 3 | 309 | 0.67 | <.001 |
Note. PROMIS = Patient Reported Outcomes Measurement Information System [25]. DASS = Depression Anxiety Stress Scale [22]. SF-1 = first item from the Short Form Health Survey [26]. FACT-G = Functional Assessment of Cancer Therapy - General [27]. Cohen’s d values reflect the sample-size weighted average values across samples. All analyses control for age, education level, presence/absence of metastases, and time since diagnosis.
Physical Quality of Life
Financial strain was associated with two indicators of physical quality of life (see Table 3) while controlling for the same covariates in three samples. Specifically, financial strain was associated with worse perceived health (average d=0.57, p<.001) and greater physical symptom burden (average d=0.60, p<.001; see Table 4). Presence of metastases was associated with greater symptom burden in Sample 2 (p=.002). In Sample 3, individuals with a bachelor’s degree reported better physical quality of life on both measures (ps≤.025) and older individuals had better perceived health (p=.030). In sensitivity analyses, Sample 3 participants who experienced financial toxicity also reported worse perceived health (d=0.61, p=.005) and worse symptom burden (d=0.46, p=.025). Older individuals and those with a bachelor’s degree reported better physical quality of life on both measures (ps≤.044).
Discussion
Nearly two in five (37.5%) women with breast cancer in this sample experienced financial strain. We found that financial strain is associated with worse emotional and physical quality of life. Financial strain was most common (81.0%) among unmarried, younger women who lacked college degrees. Findings from Sample 3 suggest that some of the financial strain experienced by women with breast cancer could be directly attributed to the cost of medical care. Findings emphasize the importance of socioeconomic inequalities in quality of life in breast cancer and have implications for the prioritization and allocation of services.
Whereas prior research has shown that financial strain is associated with single components of quality of life in breast cancer [2-4], we observed the detrimental effects of financial strain across multiple components of quality of life and several assessment instruments. Averaging across our samples, financial strain was associated with scoring more than 2/3 of a standard deviation higher on depression symptoms and about 1/2 of a standard deviation higher on anxiety symptoms. Women who were financially strained also experienced 2/3 of a standard deviation worse symptom burden and perceived their health to be more than 1/2 of a standard deviation poorer compared to women who were not financially strained. These are considered “medium” effects [28], comparable to the magnitude reported in recent meta-analyses for the impact of common mental health treatments on emotional quality of life [29, 30], and larger than the effect sizes reported for the impact of palliative cancer care interventions on physical quality of life [31, 32]. Thus, the impact of financial strain could be profound and the broader context of patient resources and financial strain are important to address in clinical care [33].
Study Limitations
Most participants were white, married, and college educated, and all could read English. Also, Sample 1 had a small sample size (N=55). However, to partly account for these issues, we reported several descriptive analyses for demographic subgroups (see Table 2), and pooled results from all three samples (see Table 4). Furthermore, most participants were from the U.S., and patients in other countries that have stronger social safety nets may experience less strain [2, 3]. Finally, this was a cross-sectional online survey that used short measures of financial strain and toxicity, and estimates may have been prone to sampling or self-report bias. To draw causal, generalizable inferences, future studies should use more representative samples, a comprehensive and validated measure of financial burden [34], and longitudinal designs. Nevertheless, the present investigation was unique in using multiple measures of financial strain and assessing multiple aspects of physical and emotional quality of life. Furthermore, analyses controlled for potential confounders and replicated several results across three independent samples. It was also encouraging to note that our findings on financial strain were consistent in sensitivity analyses in the third sample using a treatment-specific measure of financial toxicity, suggesting that reduced quality of life experienced by financially strained participants can at least be partly attributed to the direct costs of medical care burdening women with breast cancer.
Clinical Implications
Our findings have implications for interventions aimed at improving quality of life for financially strained patients with cancer. At the policy level, these findings suggest the importance of mitigating the financial burden of cancer, perhaps by expanding access to quality and affordable health insurance, improving family and medical leave policies, or otherwise strengthening the social safety net in the U.S. Although policy-level changes could have a broad impact, they are potentially the most controversial and difficult to achieve [16, 35]. Healthcare systems, especially those serving low-income populations, can take smaller steps such as providing free transportation, legal services, or financial counselors [36-38]. As well, they could expand programs designed to improve quality of life [16] through expanded access to multidisciplinary palliative care teams and supportive care services. [39, 40].
In conclusion, the present investigation showed that financially strained women with breast cancer experienced worse emotional and physical quality of life. Findings suggest the importance of interventions to reduce the financial burden of cancer and improve quality of life.
Disclosures and Acknowledgements
The authors have no conflicts of interest to disclose. This work was supported by the Leonard F. Salzman Research Award, the National Institute of Mental Health (T32MH018911), the National Institute of General Medical Sciences (U54GM104940), the National Cancer Institute (R01CA168387), the National Institute of Minority Health and Human Development (U54MD007595), and the Louisiana Board of Regents.
Footnotes
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References
- 1.Friedman B, Conwell Y, Delavan RL. Correlates of late-life major depression: A comparison of urban and rural primary care patients. The American Journal of Geriatric Psychiatry. 2007;15(1):28–41. [DOI] [PubMed] [Google Scholar]
- 2.Andritsch E, Dietmaier G, Hofmann G, Zloklikovits S, Samonigg H. Global quality of life and its potential predictors in breast cancer patients: an exploratory study. Supportive Care in Cancer. 2007;15(1):21–30. [DOI] [PubMed] [Google Scholar]
- 3.Alfonsson S, Olsson E, Hursti T, Lundh MH, Johansson B. Socio-demographic and clinical variables associated with psychological distress 1 and 3 years after breast cancer diagnosis. Supportive Care in Cancer. 2016;24(9):4017–4023. [DOI] [PubMed] [Google Scholar]
- 4.Meneses K, Azuero A, Hassey L, McNees P, Pisu M. Does economic burden influence quality of life in breast cancer survivors? Gynecologic Oncology. 2012;124(3):437–443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ivanauskiene R, Padaiga Ž, Šimoliūniene R, Smailytė G, Domeikienė A. Well-being of newly diagnosed women with breast cancer: which factors matter more? Supportive Care in Cancer. 2014;22(2):519–526. [DOI] [PubMed] [Google Scholar]
- 6.Kale HP, Carroll NV. Self-reported financial burden of cancer care and its effect on physical and mental health-related quality of life among US cancer survivors. Cancer. 2016;122:283e289. [DOI] [PubMed] [Google Scholar]
- 7.Irwin B, Kimmick G, Altomare I, et al. Patient experience and attitudes toward addressing the cost of breast cancer care. The Oncologist. 2014;19(11):1135–1140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Carrera PM, Kantarjian HM, Blinder VS. The financial burden and distress of patients with cancer: understanding and stepping-up action on the financial toxicity of cancer treatment. CA Cancer J Clini;2018;68:153e165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Reyes-Gibby CC, Anderson KO, Morrow PK, Shete S, Hassan S. Depressive symptoms and health-related quality of life in breast cancer survivors. Journal of Women’s Health. 2012;21(3):311–318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Delgado-Guay M, Ferrer J, Rieber AG, Rhondali W, Tayjasanant S, Ochoa J, et al. Financial distress and its associations with physical and emotional symptoms and quality of life among advanced cancer patients. The oncologist. 2015;20(9):1092–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ramsey SD, Bansal A, Fedorenko CR, Blough DK, Overstreet KA, Shankaran V, et al. Financial insolvency as a risk factor for early mortality among patients with cancer. Journal of Clinical Oncology. 2016;34(9):980–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Lee ES, Lee MK, Kim SH, et al. Health-related quality of life in survivors with breast cancer 1 year after diagnosis compared with the general population: a prospective cohort study. Annals of Surgery. 2011. ;253(1): 101–108. [DOI] [PubMed] [Google Scholar]
- 13.Høyer M, Johansson B, Nordin K, et al. Health-related quality of life among women with breast cancer-a population-based study. Acta Oncologica. 2011;50(7):1015–1026. [DOI] [PubMed] [Google Scholar]
- 14.Moon Z, Moss-Morris R, Hunter MS, Carlisle S, Hughes LD. Barriers and facilitators of adjuvant hormone therapy adherence and persistence in women with breast cancer: a systematic review. Patient preference and adherence. 2017;11:305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Farias AJ, Wu W-H, Du XL. Racial differences in long-term adjuvant endocrine therapy adherence and mortality among Medicaid-insured breast cancer patients in Texas: Findings from TCR-Medicaid linked data. BMC cancer. 2018;18(1):1214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Rotter J, Spencer JC, Wheeler SB. Financial Toxicity in Advanced and Metastatic Cancer: Overburdened and Underprepared. Journal of oncology practice. 2019:JOP. 18.00518. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Harris PA, Scott KW, Lebo L, et al. ResearchMatch: A national registry to recruit volunteers for clinical research. Academic Medicine: Journal of the Association of American Medical Colleges. 2012;87(1):66–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Marshall GN, Hays RD. The patient satisfaction questionnaire short-form (PSQ-18). Santa Monica, CA: RAND; 1994. [Google Scholar]
- 19.Chino F, Peppercorn J, Taylor DH, et al. Self-reported financial burden and satisfaction with care among patients with cancer. The Oncologist. 2014;19(4):414–420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lovibond PF, Lovibond SH. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour research and therapy. 1995;33(3):335–343. [DOI] [PubMed] [Google Scholar]
- 21.Fox RS, Lillis TA, Gerhart J, Hoerger M, Duberstein P. Multiple group confirmatory factor analysis of the DASS-21 Depression and Anxiety Scales: How do they perform in a cancer sample? Psychological Reports. 2018;121(3):548–565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Cella D, Stone AA. Health-Related quality of life measurement in oncology advances and opportunities. American Psychologist. 2015;70(2):175–185. [DOI] [PubMed] [Google Scholar]
- 23.Pilkonis PA, Choi SW, Reise SP, et al. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment. 2011;18(3):263–283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36): Conceptual framework and item selection. Medical care. 1992:30(6):473–483. [PubMed] [Google Scholar]
- 25.Cella DF, Tulsky DS, Gray G, et al. The Functional Assessment of Cancer Therapy scale: Development and validation of the general measure. Journal of Clinical Oncology. 1993; 11 (3): 570–579. [DOI] [PubMed] [Google Scholar]
- 26.Buffart LM, Kalter J, Sweegers MG, et al. Effects and moderators of exercise on quality of life and physical function in patients with cancer: An individual patient data meta-analysis of 34 RCTs. Cancer treatment reviews. 2017;52:91–104. [DOI] [PubMed] [Google Scholar]
- 27.Mavaddat N, Parker RA, Sanderson S, Mant J, Kinmonth AL. Relationship of self-rated health with fatal and non-fatal outcomes in cardiovascular disease: A systematic review and meta-analysis. PloS One. 2014;9(7]:el03509. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Cohen J Statistical power analysis for the behavioral sciences 2nd edn. Erlbaum Associates, Hillsdale; 1988. [Google Scholar]
- 29.De Vries YA, De Jonge P, van den Heuvel E, Turner EH, Roest AM. Influence of baseline severity on antidepressant efficacy for anxiety disorders: Meta-analysis and meta-regression. The British Journal of Psychiatry. 2016;208(6):515–521. [DOI] [PubMed] [Google Scholar]
- 30.Cuijpers P, Cristea IA, Karyotaki E, Reijnders M, Huibers MJ. How effective are cognitive behavior therapies for major depression and anxiety disorders? A meta - analytic update of the evidence. World Psychiatry. 2016;15(3]:245–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Hoerger M, Wayser GR, Schwing G, Suzuki A, Perry LM. Impact of Interdisciplinary Outpatient Specialty Palliative Care on Survival and Quality of Life in Adults With Advanced Cancer: A Meta-Analysis of Randomized Controlled Trials. Annals of Behavioral Medicine, in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Fulton JJ, LeBlanc TW, Cutson TM, Porter Starr KN, Kamal A, Ramos K, et al. Integrated outpatient palliative care for patients with advanced cancer: A systematic review and meta-analysis. Palliative medicine. 2019;33(2]: 123–134 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Hobfoll S, Tirone V, Holmgreen L, Gerhart J. Conservation of resources theory applied to major stress Stress: Concepts, Cognition, Emotion, and Behavior: Elsevier; 2016. p. 65–71. [Google Scholar]
- 34.de Souza JA, Yap BJ, Wroblewski K, Blinder V, Araújo FS, Hlubocky FJ, et al. Measuring financial toxicity as a clinically relevant patient-reported outcome: The validation of the Comprehensive Score for financial Toxicity (COST], Cancer. 2017;123(3]:476–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Frieden TR. The future of public health. New England Journal of Medicine. 2015;373(18):1748–1754. [DOI] [PubMed] [Google Scholar]
- 36.Sharp L, Carsin AE, Timmons A. Associations between cancer- related financial stress and strain and psychological well- being among individuals living with cancer. Psycho- Oncology. 2013;22(4):745–755. [DOI] [PubMed] [Google Scholar]
- 37.Gottlieb LM, Wing H, Adler NE. A systematic review of interventions on patients’ social and economic needs. American journal of preventive medicine. 2017;53(5):719–729. [DOI] [PubMed] [Google Scholar]
- 38.Berkowitz SA, Hulberg AC, Standish S, Reznor G, Atlas SJ. Addressing unmet basic resource needs as part of chronic cardiometabolic disease management. JAMA internal medicine. 2017;177(2):244–252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.(NCCN) NCCN. Distress Management 2018. [Available from: https://www.nccn.org/.
- 40.Ferrell BR, Temel JS, Temin S, Alesi ER, Balboni TA, Basch EM, et al. Integration of palliative care into standard oncology care: American Society of Clinical Oncology clinical practice guideline update. Journal of Clinical Oncology. 2017; 13(2): 119–121. [DOI] [PubMed] [Google Scholar]