Financial Hardship and Quality of Life Among Patients With Advanced Cancer Receiving Outpatient Palliative Care

Abstract

BackgroundPatients with advanced cancer are increasingly experiencing financial hardship (FH) and associated negative health outcomes.ObjectiveThe aims of this study were to describe FH and explore its relationship to quality of life (QOL) in patients with advanced cancer receiving outpatient palliative care (PC).MethodsValidated questionnaires assessed FH, QOL dimensions, symptom burden, and sociodemographic and clinical characteristics. Descriptive statistics characterized the sample and described FH. Pearson correlation and linear regression assessed relationships between FH and QOL.ResultsThe average participant (n = 78) age was 56.6 (SD, 12.2) years. Most were female (56.4%), White (50%) or Black (46.2%), and had a range of education, partner statuses, and cancer diagnoses. Median time since cancer diagnosis was 35.5 months (interquartile range, 9-57.3 months). Highest mean symptom burden scores were for pain (2.5 [SD, 1.0]) and fatigue (2.0 [SD, 1.1]), on a 0- to 3-point scale (higher score representing worse symptom burden). The median COST (COmphrehensive Score for financial Toxicity) score was 15.0 (interquartile range, 9.0-23.0). Most (70%) had some (n = 43) or extreme (n = 9) difficulty paying for basic needs. Greater than 28% (n = 21) incurred cancer-related debt. Multivariate models indicated that FH negatively affected role limitations due to physical health (P = .008), pain (P = .003), and emotional well-being (P = .017) QOL dimensions.ConclusionsFinancial hardship, QOL, and symptom burden scores demonstrate need for continued support for and research among patients with advanced cancer. Data support links between FH and important QOL dimensions. Larger, longitudinal studies are needed to understand how FH affects QOL in patients with advanced cancer.Implications for PracticeProactive financial assessment and interventions are needed to support patients with advanced cancer experiencing the cumulative effects of cancer and its treatment.

Comprehensive Score for Financial Toxicity and Health-Related Quality of Life in Patients With Cancer and Survivors: A Systematic Review and Meta-Analysis

“…Some possible strategies include coverage for direct and indirect healthcare costs, patient assistance programs through industry or charity, and financial navigation programs. 82 In addition, previous investigations suggest that income loss due to cancer may be explained by the decline of productivity or job loss, [83][84][85][86] which also occurs in countries with universal health coverage and may even lead to widening economic EQ-5D-3L 60 1 U EQ VAS 54 (EQ-5D descriptive system not used) 1 U PROMIS-29 77 1 U U PROMIS CAT 70 1 U U PROMIS Global-10 †69 1 SF-12 56 1 U SF-36 52 1 inequalities. 87,88 Considering that subjective financial toxicity is contingent upon its objective counterpart, improvements may also be made by compensating the negative income effects of cancer.…”

“…Belcher et al (2021) 52 Higher financial toxicity is associated with worse HRQOL -COST and SF-36 physical functioning correlation: r = 0.062 (P = .599) -COST and SF-36 role limitations (physical) correlation: r = 0.282 (P = .015) -COST and SF-36 pain correlation: r = 0.320 (P = . 60 Higher financial toxicity is associated with worse HRQOL COST and EQ-5D-3L correlation: r = 0.49 (P , .001).…”

“…Nine studies reported financial toxicity as a significant predictor of HRQOL using multivariate regression models. [51][52][53]56,61,65,71,76,77 For instance, every 1-point increase in COST score, which indicated less financial toxicity, improved HRQOL scores measured with different instruments by 0.59 points (FACT-G), 71 0.47 points (University of Washington quality of life), 65 and 0.39 points (FACTprostate cancer). 49 All these regression models were adjusted for sociodemographic factors.…”

“…49 All these regression models were adjusted for sociodemographic factors. These included age, [51][52][53]56,58,61,65,71,76,77 employment status, 53,56,76,77 education, 51,53,76,77 race, 52,53,71 sex, 71,77 (loss of) income, 65,71 and marital status. 51,76 Furthermore, in several studies, the regression models were controlled for clinical variables, such as comorbidities, 51,61 cancer type, 71,76 cancer stage, 53,71 and cancer recurrence.…”