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

  • Stevanus Pangestu
    Correspondence
    Correspondence: Stevanus Pangestu, MBA, Department of Health Policy, Corvinus University of Budapest, Fővám tér 8, Budapest 1093, Hungary.
    Affiliations
    Department of Health Policy, Corvinus University of Budapest, Budapest, Hungary

    Doctoral School of Business and Management, Corvinus University of Budapest, Budapest, Hungary

    Faculty of Economics and Business, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia
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  • Fanni Rencz
    Affiliations
    Department of Health Policy, Corvinus University of Budapest, Budapest, Hungary
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Open AccessPublished:September 02, 2022DOI:https://doi.org/10.1016/j.jval.2022.07.017

      Highlights

      • Health-related quality of life (HRQOL) is a key outcome in oncology. Financial toxicity is recognized as an important adverse effect of cancer care that may decrease patients’ HRQOL. This is the first systematic review and meta-analysis on the association of HRQOL and financial toxicity as measured by the Comprehensive Score for Financial Toxicity, the most widely used and validated cancer-specific measure of financial toxicity.
      • Overall, 31 studies were included with a total sample of 13 481 patients and survivors with more than 25 cancer types from 9 countries. Ten different HRQOL domains were identified to be related to financial toxicity. A moderate correlation was demonstrated between financial toxicity and overall HRQOL through random-effects meta-analysis. Nine studies reported financial toxicity as a significant predictor of HRQOL using multivariate regression models after controlling for sociodemographic characteristics.
      • Our findings contribute to the understanding of the burden patients with cancer experience and substantiate financial toxicity as a relevant outcome of cancer care that decreases HRQOL. Reducing financial toxicity may contribute to the improvement of HRQOL in patients with cancer and survivors.

      Abstract

      Objectives

      Financial toxicity is recognized as an important adverse effect of cancer treatment that may decrease patients’ health-related quality of life (HRQOL). We aim to perform a systematic review and meta-analysis on studies investigating the association of HRQOL and financial toxicity measured with the Comprehensive Score for Financial Toxicity in patients with cancer and survivors.

      Methods

      A systematic literature search was completed in PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature, and PsycInfo (last update April 2022). Methodological quality of included studies was assessed using the Appraisal Tool for Cross-Sectional Studies and the Critical Appraisal Skills Program Cohort Study Checklist. Where possible, study outcomes were pooled by random-effects meta-analysis.

      Results

      Thirty-one studies were included with a combined sample of 13 481 patients and survivors with more than 25 cancer types from 9 countries. Nineteen different validated HRQOL instruments were used in these studies, with the Functional Assessment of Cancer Therapy – General (n = 9), the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (n = 5), and EQ-5D (n = 5) being the most common. All but one included studies reported that higher financial toxicity was significantly associated with worse HRQOL. Ten HRQOL domains were correlated with financial toxicity, including physical health (r = 0.34-0.66), social health (r = 0.16-0.55), mental health (r = 0.21-0.54), and daily functioning (r = 0.23-0.52). The meta-analysis indicated a moderate correlation between financial toxicity and overall HRQOL as measured by the Functional Assessment of Cancer Therapy instruments (r = 0.49, 95% confidence interval 0.44-0.54).

      Conclusions

      This is the first systematic review and meta-analysis to summarize the literature on the association of financial toxicity and HRQOL in patients with cancer and survivors. Our findings substantiate financial toxicity as a relevant outcome of cancer care that is associated with a decline of HRQOL.

      Keywords

      Introduction

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      Recently, a large number of studies described the association between higher financial toxicity and worse HRQOL in patients with cancer and survivors, using both qualitative and quantitative methods.
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      This signifies financial toxicity as a potentially important predictor of HRQOL. So far, a systematic review has summarized the factors associated with financial toxicity in patients with cancer,
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      Financial toxicity among patients with prostate, bladder, and kidney cancer: a systematic review and call to action.
      but this was limited to urological malignancies and did not specifically focus on subjective financial toxicity and HRQOL outcomes.
      Therefore, we aim to perform a systematic review and meta-analysis on studies investigating the association of subjective financial toxicity and HRQOL in patients with cancer and survivors. Previous systematic reviews have highlighted the lack of uniformity and the frequent use of nonvalidated instruments in the measurement of subjective financial toxicity.
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      • et al.
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      • et al.
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      Methods

      Search Strategy

      This systematic review and meta-analysis were performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
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      Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.
      Four electronic databases (PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature, and PsycInfo) were searched and updated in April 2022. The protocol of this systematic review and meta-analysis was registered in the International Prospective Register of Systematic Reviews under number CRD42022302272.
      The search strategy was compiled based on keywords related to cancer, financial toxicity, and HRQOL (Appendix File 1 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2022.07.017). For cancer, we used a PubMed cancer filter developed by the National Library of Medicine and the National Cancer Institute.
      Search strategy used to create the PubMed cancer filter. National Library of Medicine.
      This filter combines a set of Medical Subject Headings terms for neoplasms and cancer-related journal titles and text words. For HRQOL, the search terms were compiled based on a filter to identify HRQOL studies and a list of instrument names from a systematic literature review about HRQOL instruments used in cancer.
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      Measuring health-related quality of life in patients with advanced cancer: a systematic review of self-administered measurement instruments.
      Financial toxicity search terms were adopted from a previous systematic review on financial toxicity in patients with urologic cancer,
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      • Psutka S.P.
      • Burg M.L.
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      Financial toxicity among patients with prostate, bladder, and kidney cancer: a systematic review and call to action.
      combined with several other terms that we had identified during preliminary literature search. Google Scholar was also used for citation tracking and manual hand-searching of literature.

      Study Inclusion and Exclusion Criteria

      Studies were included if they (1) were published in English, (2) were published as original research articles, (3) had any study design that involved primary data collection, (4) involved patients or survivors with any type of cancer aged at least 18 years who had undergone treatment for cancer, (5) measured financial toxicity using COST (any version), and (6) measured HRQOL using any standardized and validated instrument (ie, instruments consisting of a standard set of questions with a scoring system and adhering to quality criteria for measurement properties of health status measures).
      Studies were excluded if they (1) were not published in English, (2) were published as reviews, editorials, or conference publications, (3) did not include primary data collection, (4) involved pediatric patients with cancer or diseases other than cancer, (5) did not measure financial toxicity using COST, (6) did not involve HRQOL outcomes that were measured using a standardized and validated instrument, and (7) did not analyze the association between financial toxicity and HRQOL. Pediatric oncology patients were excluded because COST was developed to be responded by patients with cancer aged 18 years and older and our review aimed to focus on patients’ perception and experience of financial toxicity and HRQOL, that is, not proxy or observer reported.
      The inclusion of studies was performed independently by the 2 authors. The inclusion and exclusion criteria were applied to titles and abstracts to identify relevant studies. Full-text articles were also screened to assure the inclusion of eligible studies. Discrepancies between reviewers were solved through discussion until reaching a consensus.

      The COST

      COST is a patient-reported outcome measure for subjective financial toxicity in patients with any kind of cancer.
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      The instrument has a recall period of 7 days. The original version (V1) consists of 11 items, and the most recent second version (V2) has 12 items. The items relate to financial adequacy, psychosocial reaction, financial efficacy and satisfaction, and the impact of financial hardship on family, among others. Each item has the following 5 response options: “not at all” (= 0), “a little bit” (= 1), “somewhat” (= 2), “quite a bit” (= 3), and “very much” (= 4). Four items are scored in reverse (items 1, 6, 7, and 11). A total score is computed from the sum of items 1 through 11 for either version of the scale (excluding item 12 for V2 of the scale). The total score ranges from 0 to 44, where lower scores indicate worse financial toxicity.

      Data Extraction

      The following data were extracted from the included studies: title, author names, year of publication, country, sample size, sex ratio, study design, cancer type, treatment status, cancer stage, time since diagnosis, COST instrument version, COST language version, HRQOL instruments used, statistical analysis methods performed, and main findings. The main findings included the results of the statistical analysis (eg, correlation coefficients, beta coefficients, and P values) and the conclusion about the association between financial toxicity and HRQOL (eg, higher financial toxicity was associated with worse HRQOL). Data extraction was completed by S.P. and verified by F.R.

      Critical Appraisal of Included Studies

      Two critical appraisal tools were used to assess the methodological quality of the included studies. The Appraisal Tool for Cross-Sectional Studies (AXIS) and the Critical Appraisal Skills Program (CASP) Cohort Study Checklist were used for cross-sectional and cohort study designs, respectively.
      • Downes M.J.
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      • Williams H.C.
      • Dean R.S.
      Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS).
      ,
      CASP cohort study checklist. Critical Appraisal Skills Programme.
      All subparts of the studies (including introduction, methods, results, and discussion) were evaluated. The AXIS includes 20 items with “yes,” “no,” and “unclear” responses. The CASP Cohort Study checklist consists of 12 items with “yes,” “no,” and “can’t tell” responses. For the sake of consistency, critical appraisal responses on both appraisal tools that were initially “yes” and “no” were relabeled as “favorable” and “unfavorable” as 2 AXIS components were originally scored reversely. The responses “unclear” and “can’t tell” were reported under “unclear.” Percentage scores were computed by dividing the number of favorable responses with the total number of items of the respective appraisal.
      For both appraisal tools, a study was assessed to be (1) good quality if its score was equal to or exceeded 70% of the total, (2) fair quality if the score was between 60% and 69.9%, and (3) low quality if the score was below 60%.
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      S.P. performed the critical appraisal of the included studies and F.R. verified them. Discrepancies were resolved through discussion.

      Qualitative and Quantitative Syntheses

      Extracted HRQOL outcomes were categorized as total or overall HRQOL scores and domain scores (eg, mental or emotional, social, and physical health). Every HRQOL domain was extracted except financial difficulties because it was considered as a possible direct measure of financial toxicity.
      Meta-analysis was performed to pool good-quality studies using the same HRQOL instrument family, where at least 3 studies were available. Among the statistical methods used in the included studies, only bivariate correlations were reported in sufficient number of studies for meta-analysis. The meta-analysis was conducted on the correlation coefficients (Spearman’s or Pearson’s) between COST and HRQOL scores. The absolute value was taken for the correlation coefficients to correct for the directionality of the scales. Then, the coefficients were transformed into z values using Fisher’s transformation.
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      Next, we performed a random-effects meta-analysis using the transformed values. Finally, we converted back the Fisher’s z transformed correlations to r for the sake of presentation. Correlation coefficients were interpreted as follows: very weak (< 0.2), weak (0.20-0.39), moderate (0.40-0.59), strong (0.60-0.79), and very strong (≥ 0.8).
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      Analysing data and undertaking meta-analyses.
      When moderate heterogeneity was detected (I-square statistic between 30 and 60%),
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      Forest plots were generated to present the summary of individual and pooled correlation coefficients. Narrative synthesis was presented for results that were ineligible for meta-analysis due to substantial heterogeneity (I-squared statistic greater than 60%) or insufficient number of studies reporting the correlation between COST and HRQOL scores using the same instrument family. Publication bias was assessed using Egger’s regression, where a P value < .05 indicated possible publication bias.
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      The meta-analysis was performed in Jamovi statistical software version 1.6 (The jamovi project, Sydney, Australia, 2021).
      The Jamovi (version 1.6). The Jamovi Project.

      Results

      Study Selection

      A sum of 5962 records were identified from the systematic search of electronic databases (Fig. 1). After removal of duplicates and screening of titles and abstracts for eligible studies, 172 full-text articles were screened for inclusion. A total of 30 studies fulfilled all the inclusion criteria. Afterward, one additional study was added through manual hand-searching on Google Scholar. Thus, 31 studies were included in this systematic review.
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      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
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      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
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      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
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      Financial toxicity in gynecologic oncology.
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      Translation and validation of the traditional Chinese version of the comprehensive score for financial toxicity-functional assessment of chronic illness therapy (version 2).
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      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
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      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
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      A national cross-sectional survey of financial toxicity among bladder cancer patients.
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      Crowdsourcing to measure financial toxicity in gynecologic oncology.
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      The economic impact on Australian patients with neuroendocrine tumours.
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      Financial toxicities in patients receiving systemic therapy for brain tumors: a cross-sectional study.
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      Measuring financial distress and quality of life over time in patients with gynecologic cancer-making the case to screen early in the treatment course.
      • Mady L.J.
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      Understanding financial toxicity in head and neck cancer survivors.
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      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      • Mejri N.
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      Translation and validation of the Comprehensive Score of Financial Toxicity for cancer patients into Arabic.
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      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
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      Re-validation of the Comprehensive Score for Financial Toxicity (COST): assessing the scale’s utility in chronic disease populations.
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      An exploration of financial toxicity among low-income patients with cancer in Central Texas: a mixed methods analysis.
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      • Di Pede P.
      • et al.
      The validation of the Italian version of the Comprehensive Score for Financial Toxicity (COST).
      • Rosenzweig M.
      • West M.
      • Matthews J.
      • et al.
      Financial toxicity among women with metastatic breast cancer.
      • Shim S.
      • Kang D.
      • Kim N.
      • et al.
      Validation of Korean version of the Comprehensive Score for Financial Toxicity (COST) among breast cancer survivors.
      • Thom B.
      • Mamoor M.
      • Lavery J.A.
      • et al.
      The experience of financial toxicity among advanced melanoma patients treated with immunotherapy.
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      • Xu R.H.
      • Wang L.-L.
      • Zhou L.-M.
      • Wong E.L.-Y.
      • Wang D.
      Urban-rural differences in financial toxicity and its effect on cancer survivors’ health-related quality of life and emotional status: a latent class analysis.
      • Xu T.
      • Xu L.
      • Xi H.
      • et al.
      Assessment of financial toxicity among patients with advanced lung cancer in Western China.
      • Yu H.H.
      • Yu Z.F.
      • Li H.
      • Zhao H.
      • Sun J.M.
      • Liu Y.Y.
      The Comprehensive Score for Financial Toxicity in China: validation and responsiveness.
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      Figure thumbnail gr1
      Figure 1PRISMA flow diagram.
      CINAHL indicates Cumulative Index to Nursing and Allied Health Literature; COST, Comprehensive Score for Financial Toxicity; HRQOL, health-related quality of life; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

      Characteristics of Included Studies

      The complete overall study characteristics are presented in Table 1. The included studies were published between 2017 and 2022, with more than two-thirds of them published in 2021 or 2022 (n = 21, 68%). Twenty-nine of the study designs were cross-sectional (94%), and the remaining 2 were prospective cohort studies with a follow-up period of 6 months (6%). The studies were conducted in the United States (n = 18, 58%), China (n = 4, 13%), Australia (n = 3, 10%), India (n = 1, 3%), Italy (n = 1, 3%), Nigeria (n = 1, 3%), South Korea (n = 1, 3%), Tunisia (n = 1, 3%), and Turkey (n = 1, 3%). The most used languages for the survey instruments were English (n = 22, 71%) and Mandarin Chinese (n = 5, 16%). The total patient sample size of all included studies was 13 481. The sample size of individual studies ranged from 53 to 4297 with a median of 179. Among 17 studies (55%) that reported the mean age of respondents, the overall mean age was 57 years. There were 8 studies (26%) that included only female respondents and 1 study (3%) that included only male respondents. The remaining 22 studies (71%) included both sexes.
      Table 1Overall study characteristics.
      Author (year)CountryStudy designSample sizeMean age (± SD)Female sex ratioCancer typeCancer stageTime since diagnosisTreatment statusCOST versionCOST languageHRQOL instrument
      Akin-Odanye et al (2021)
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      NigeriaCS17371.57 ± 11.180ProstateNRNRActiveV2NRFACT-P
      Belcher et al (2021)
      • Belcher S.M.
      • Lee H.
      • Nguyen J.
      • et al.
      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
      United StatesCS7856.6 ± 12.256.4%Breast, gastrointestinal, lung, liver, prostate, melanoma, pancreatic, head and neck, gynecologic, kidney, and othersAdvanced35.5 months, medianActive, palliative careV1EnglishSF-36
      Benedict et al (2022)
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      United StatesCS27354.65 ± 12.08100%Breast, gynecologic (including ovarian, cervical, uterine/endometrial, vaginal, vulvar, peritoneal, and fallopian tube carcinoma)0-43.42 years, averageActive and completedV2EnglishFACT-G
      Bouberhan et al (2019)
      • Bouberhan S.
      • Shea M.
      • Kennedy A.
      • et al.
      Financial toxicity in gynecologic oncology.
      United StatesCS24056
      Indicates median.
      100%Gynecologic (ovarian, uterine, cervical, and vaginal)1-4, benign2 years, medianActive and in surveillanceV1English, Spanish, Mandarin Chinese, Portuguese, Haitian CreoleEQ VAS (EQ-5D descriptive system was not used)
      Chan et al (2021)
      • Chan D.N.S.
      • Choi K.C.
      • Ng M.S.N.
      • et al.
      Translation and validation of the traditional Chinese version of the comprehensive score for financial toxicity-functional assessment of chronic illness therapy (version 2).
      ChinaCS64059.9 ± 11.164.2%Breast, gynecologic, head and neck, gastrointestinal, genitourinary, lung, hematologic, brain, endocrine glands, bone and soft tissue, and others1-414 months, medianActive and completedV2Mandarin ChineseFACT-G
      Coroneos et al (2020)
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      United StatesCS53258 ± 12100%Breast0-3, undeterminedNRCompleted (postsurgery)V1EnglishBREAST-Q and SF-12

      (only 1 of 3 BREAST-Q domains used)
      de Souza et al (2017)
      • de Souza J.A.
      • Yap B.J.
      • Wroblewski K.
      • et al.
      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
      United StatesCS23358.42 ± 11.4758.4%American Joint Committee on Cancer Stage 4 solid tumor (including breast, gastrointestinal, head and neck, pancreatic, prostate, lung, and bladder)4< 1 year (39%); > 1 year (61%)ActiveV1EnglishFACT-G and EORTC QLQ-C30
      Durber et al (2021)
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      AustraliaCS25763
      Indicates median.
      54%Breast, lung, skin, gastrointestinal, gynecologic, central nervous system, urologic, head and neck, multiple cancers, sarcoma, and others1-4, not staged< 1 year (48%), > 1 year (52%)Active and withoutV1EnglishFACT-G
      Ehlers et al (2021)
      • Ehlers M.
      • Bjurlin M.
      • Gore J.
      • et al.
      A national cross-sectional survey of financial toxicity among bladder cancer patients.
      United StatesCS22668
      Indicates median.
      36%BladderNoninvasive, invasive, metastatic2 years (12%), 2-5 years (47%),> 5 years (40%)Active and in surveillanceV1EnglishEQ-5D-5L
      Esselen et al (2021)
      • Esselen K.M.
      • Stack-Dunnbier H.
      • Gompers A.
      • Hacker M.R.
      Crowdsourcing to measure financial toxicity in gynecologic oncology.
      United StatesCS33455
      Indicates median.
      100%Gynecologic (ovarian, uterine, and cervical)1-45 years, medianActive and in remissionV1EnglishEQ-5D-3L
      Gordon et al (2020)
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      AustraliaCS20458.7 ± 11.750%Neuroendocrine tumor (including gastrointestinal, pancreatic, liver, and lung)NR< 3 years (45%), > 3 years (55%)NRV1EnglishEQ-5D-5L
      Hazell et al (2020)
      • Hazell S.Z.
      • Fu W.
      • Hu C.
      • et al.
      Financial toxicity in lung cancer: an assessment of magnitude, perception, and impact on quality of life.
      United StatesCS13165
      Indicates median.
      47.3%Lung2-4NRActive and newly diagnosedV2EnglishFACT-L
      Kalra et al (2020)
      • Kalra D.
      • Menon N.
      • Singh G.K.
      • et al.
      Financial toxicities in patients receiving systemic therapy for brain tumors: a cross-sectional study.
      IndiaCS14738
      Indicates median.
      32.5%Brain1-4NRActiveV2EnglishFACT-Br
      Liang et al (2021)
      • Liang M.I.
      • Summerlin S.S.
      • Blanchard C.T.
      • et al.
      Measuring financial distress and quality of life over time in patients with gynecologic cancer-making the case to screen early in the treatment course.
      United StatesPC (6 months)12160100%Gynecologic (ovarian, uterine, cervical, vulvar, and vaginal)NRNRActive and completedV1EnglishFACT-G
      Mady et al (2019)
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      United StatesCS10464
      Indicates median.
      23.1%Head and neck (larynx, oral cavity, and oropharynx)1-4NRCompletedV1EnglishUWQOL
      McLean et al (2021)
      • McLean L.
      • Hong W.
      • McLachlan S.A.
      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      AustraliaCS5363.5
      Indicates median.
      55%Solid organ malignancyEarly and advancedNRActiveV1EnglishEORTC QLQ-C30
      Mejri et al (2021)
      • Mejri N.
      • Rachdi H.
      • Mnif A.
      • et al.
      Translation and validation of the Comprehensive Score of Financial Toxicity for cancer patients into Arabic.
      TunisiaCS17952 ± 12.370.9%Breast, gastrointestinal, and lung0-4NRActiveV1ArabicFACT-G
      Miller et al (2021)
      • Miller K.A.
      • Stal J.
      • Gallagher P.
      • et al.
      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
      United StatesCS19632.2± 4.540.1%Gastrointestinal (colon and rectal)1-4NRActive, completed, and in surveillanceV1EnglishFACT-C
      Pavela et al(2021)
      • Pavela G.
      • Fifolt M.
      • Tison S.
      • Allison M.
      • Burton B.S.
      • Ford E.W.
      Re-validation of the Comprehensive Score for Financial Toxicity (COST): assessing the scale’s utility in chronic disease populations.
      United StatesCS2755NR77.1%American Joint Committee on Cancer Stage 4 solid tumor4NRActiveV1EnglishPROMIS Global-10
      Petruzzi et al (2022)
      • Petruzzi L.J.
      • Prezio E.
      • Phillips F.
      • et al.
      An exploration of financial toxicity among low-income patients with cancer in Central Texas: a mixed methods analysis.
      United StatesCS11554.6 ± 11.666%Gastrointestinal, hematologic, breast, lung, and others1-4NRActiveNREnglishFACT-G, PROMIS CAT (anxiety, depression, fatigue, pain interference, and physical function)
      Phillips et al (2021)
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      United StatesCS115NR57%Gastrointestinal, hematologic, lung, and breast1-40-6 months (28%), 7-18 months (23%), 19-35 months (26%), > 36 months (23%)ActiveV1English, SpanishFACT-G
      Ripamonti et al (2020)
      • Ripamonti C.I.
      • Chiesi F.
      • Di Pede P.
      • et al.
      The validation of the Italian version of the Comprehensive Score for Financial Toxicity (COST).
      ItalyCS11861.46 ± 12.7NRBreast, lung, gastrointestinal, liver, endometrial, prostate, sarcoma, bladder, head and neck, lymphoma, leukemia, myeloma, and othersNRNRActive and completedV1ItalianEORTC QLQ-C30
      Rosenzweig et al (2019)
      • Rosenzweig M.
      • West M.
      • Matthews J.
      • et al.
      Financial toxicity among women with metastatic breast cancer.
      United StatesCS14558.1 ± 12.5100%Breast4NRActiveV1EnglishFACT-B
      Shim et al (2021)
      • Shim S.
      • Kang D.
      • Kim N.
      • et al.
      Validation of Korean version of the Comprehensive Score for Financial Toxicity (COST) among breast cancer survivors.
      South KoreaCS429750.4 ± 8.6100%Breast0-4NRCompletedV1KoreanEORTC QLQ-C30
      Thom et al (2021)
      • Thom B.
      • Mamoor M.
      • Lavery J.A.
      • et al.
      The experience of financial toxicity among advanced melanoma patients treated with immunotherapy.
      United StatesCS10663.0 ± 12.5443%Melanoma3-4NRActive and completedV1EnglishEORTC QLQ-C30
      Urek and Ugurluoglu (2022)
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      TurkeyCS31656
      Indicates median.
      42.1%Gastrointestinal, hematologic, breast, lung, musculoskeletal system, and others1-4, not staged< 15 months (49%), > 15 months (51%)ActiveV1TurkishFACT-G
      Ver Hoeve et al (2021)
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      United StatesCS10367.28 ± 10.1248%Breast, gastrointestinal, head and neck, lung, and prostate1-3NRCompletedV1EnglishPROMIS-29
      Xu et al (2022)
      • Xu R.H.
      • Wang L.-L.
      • Zhou L.-M.
      • Wong E.L.-Y.
      • Wang D.
      Urban-rural differences in financial toxicity and its effect on cancer survivors’ health-related quality of life and emotional status: a latent class analysis.
      ChinaCS590NR44.7%Liver, breast, kidney, gastrointestinal, thyroid, lung, esophageal, cervical, bladder, lymphomaNRNRActive, completedV2Mandarin ChineseEQ-5D-5L
      Xu et al (2022)
      • Xu T.
      • Xu L.
      • Xi H.
      • et al.
      Assessment of financial toxicity among patients with advanced lung cancer in Western China.
      ChinaCS15262.1
      Indicates median.
      46.7%Lung3-4NRNRV1Mandarin ChineseFACT-L
      Yu et al (2020)
      • Yu H.H.
      • Yu Z.F.
      • Li H.
      • Zhao H.
      • Sun J.M.
      • Liu Y.Y.
      The Comprehensive Score for Financial Toxicity in China: validation and responsiveness.
      ChinaPC (6 months)44057.0 ± 9.254.3%Lung, gastrointestinal, and breast1-40-2 monthsActive and completedV1Mandarin ChineseWHOQOL-BREF
      Yusuf et al (2022)
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      United StatesCS10855
      Indicates median.
      100%Breast0-47.89 months, meanCompletedV1EnglishFACT-G7
      COST indicates Comprehensive Score for Financial Toxicity; CS, cross-sectional study design; EORTC QLQ-C30, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30; EQ-5D-3L, 3-level EQ-5D version; EQ-5D-5L, 5-level EQ-5D version; EQ VAS, EuroQol Visual Analogue Scale; FACT, Functional Assessment of Cancer Therapy; FACT-B, FACT – Breast cancer; FACT-Br, FACT – Brain cancer; FACT-C, FACT – Colorectal cancer; FACT-G, FACT – General; FACT-G7, FACT-G – 7-Item Version; FACT-L, FACT – Lung cancer; FACT-P, FACT – Prostate cancer; HRQOL, health-related quality of life; NR, not reported; PC, prospective cohort study design; PROMIS, Patient-Reported Outcomes Measurement Information System; PROMIS CAT, PROMIS Computer Adaptive Tests; PROMIS Global-10, PROMIS 10-Item Global Health Survey; PROMIS-29, PROMIS 29-Item Profile Measure; SF-12, 12-Item Short Form Health Survey; SF-36, 36-Item Short Form Health Survey; UWQOL, University of Washington Quality of Life; V1, 11-item first version; V2, 12-item second version; WHOQOL-BREF, World Health Organization Quality of Life Brief Version.
      Indicates median.
      The investigated cancer types varied widely. The most studied types of cancer included breast (n = 17, 55%), lung (n = 15, 48%), gastrointestinal (n = 13, 42%), and gynecologic (n = 8, 26%). There were studies that recruited patients with different types of cancer (n = 21, 68%), whereas others considered solely one type of cancer (n = 10, 32%). Twenty-six of the studies (84%) recruited patients up to stage IV of cancer. Thirteen studies (42%) reported the time since cancer diagnosis that ranged from “between 0 and 2 months” to “more than 5 years.” Active or completed interventions that were reported in all the studies included chemotherapy (n = 22, 71%), surgery (n = 17, 55%), radiotherapy (n = 15, 48%), hormone therapy (n = 9, 29%), and immunotherapy (n = 8, 26%).
      Twenty-four studies measured financial toxicity using the 11-item first version of COST (77%), 6 studies used the 12-item second version (19%), and 1 study did not report the version. A total of 19 HRQOL instruments were identified from the included studies (Table 2). These instruments can be categorized into 3 types: generic (n = 9), cancer specific (n = 3), and condition specific (n = 7). EQ-5D was the most used generic HRQOL instrument (n = 5, 16%), with the 3-level version used in 1 study, the 5-level version used in 3 studies, and EuroQol Visual Analogue Scale without the descriptive system used in 1 study. The most used cancer-specific HRQOL instruments were Functional Assessment of Cancer Therapy – General (FACT-G) (n = 9, 29%) and EORTC QLQ-C30 (n = 5, 16%). The most used condition-specific HRQOL instrument was FACT – Lung cancer (n = 2, 6%), which was developed for patients with lung cancer.
      Table 2HRQOL instruments and usage in the included studies.
      Type of instrumentInstrument nameStudy (n)Cancer type
      Cancer types were grouped into larger categories.
      BrainBreastEndocrineGastrointestinalGynecologicHead and neckHematologicLiverLungMusculoskeletalProstateSkinUrologicOthers
      GenericEQ-5D-5L
      • Ehlers M.
      • Bjurlin M.
      • Gore J.
      • et al.
      A national cross-sectional survey of financial toxicity among bladder cancer patients.
      ,
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      ,
      • Xu R.H.
      • Wang L.-L.
      • Zhou L.-M.
      • Wong E.L.-Y.
      • Wang D.
      Urban-rural differences in financial toxicity and its effect on cancer survivors’ health-related quality of life and emotional status: a latent class analysis.
      3
      EQ-5D-3L
      • Esselen K.M.
      • Stack-Dunnbier H.
      • Gompers A.
      • Hacker M.R.
      Crowdsourcing to measure financial toxicity in gynecologic oncology.
      1
      EQ VAS
      • Bouberhan S.
      • Shea M.
      • Kennedy A.
      • et al.
      Financial toxicity in gynecologic oncology.
      (EQ-5D descriptive system not used)
      1
      PROMIS-29
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      1
      PROMIS CAT
      • Petruzzi L.J.
      • Prezio E.
      • Phillips F.
      • et al.
      An exploration of financial toxicity among low-income patients with cancer in Central Texas: a mixed methods analysis.
      1
      PROMIS Global-10
      Cancer type not reported.
      69
      1
      SF-12
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      1
      SF-36
      • Belcher S.M.
      • Lee H.
      • Nguyen J.
      • et al.
      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
      1
      WHOQOL-BREF
      • Yu H.H.
      • Yu Z.F.
      • Li H.
      • Zhao H.
      • Sun J.M.
      • Liu Y.Y.
      The Comprehensive Score for Financial Toxicity in China: validation and responsiveness.
      1
      Cancer-specificFACT-G
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Chan D.N.S.
      • Choi K.C.
      • Ng M.S.N.
      • et al.
      Translation and validation of the traditional Chinese version of the comprehensive score for financial toxicity-functional assessment of chronic illness therapy (version 2).
      ,
      • de Souza J.A.
      • Yap B.J.
      • Wroblewski K.
      • et al.
      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      ,
      • Liang M.I.
      • Summerlin S.S.
      • Blanchard C.T.
      • et al.
      Measuring financial distress and quality of life over time in patients with gynecologic cancer-making the case to screen early in the treatment course.
      ,
      • Mejri N.
      • Rachdi H.
      • Mnif A.
      • et al.
      Translation and validation of the Comprehensive Score of Financial Toxicity for cancer patients into Arabic.
      ,
      • Petruzzi L.J.
      • Prezio E.
      • Phillips F.
      • et al.
      An exploration of financial toxicity among low-income patients with cancer in Central Texas: a mixed methods analysis.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      9
      EORTC QLQ-C30
      • de Souza J.A.
      • Yap B.J.
      • Wroblewski K.
      • et al.
      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • McLean L.
      • Hong W.
      • McLachlan S.A.
      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      ,
      • Ripamonti C.I.
      • Chiesi F.
      • Di Pede P.
      • et al.
      The validation of the Italian version of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • Shim S.
      • Kang D.
      • Kim N.
      • et al.
      Validation of Korean version of the Comprehensive Score for Financial Toxicity (COST) among breast cancer survivors.
      ,
      • Thom B.
      • Mamoor M.
      • Lavery J.A.
      • et al.
      The experience of financial toxicity among advanced melanoma patients treated with immunotherapy.
      5
      FACT-G7
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      1
      Condition-specificFACT-L
      • Hazell S.Z.
      • Fu W.
      • Hu C.
      • et al.
      Financial toxicity in lung cancer: an assessment of magnitude, perception, and impact on quality of life.
      ,
      • Xu T.
      • Xu L.
      • Xi H.
      • et al.
      Assessment of financial toxicity among patients with advanced lung cancer in Western China.
      2
      FACT-B
      • Rosenzweig M.
      • West M.
      • Matthews J.
      • et al.
      Financial toxicity among women with metastatic breast cancer.
      1
      FACT-Br
      • Kalra D.
      • Menon N.
      • Singh G.K.
      • et al.
      Financial toxicities in patients receiving systemic therapy for brain tumors: a cross-sectional study.
      1
      FACT-C
      • Miller K.A.
      • Stal J.
      • Gallagher P.
      • et al.
      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
      1
      FACT-P
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      1
      BREAST-Q
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      1
      UWQOL
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      1
      EQ-5D-3L indicates 3-level EQ-5D version; EQ-5D-5L, 5-level EQ-5D version; EQ VAS, EuroQol Visual Analogue Scale; EORTC QLQ-C30, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30; FACT, Functional Assessment of Cancer Therapy; FACT-B, FACT – Breast cancer; FACT-Br, FACT – Brain cancer; FACT-C, FACT – Colorectal cancer; FACT-G, FACT – General; FACT-G7, FACT-G – 7-Item Version; FACT-L, FACT – Lung cancer; FACT-P, FACT – Prostate cancer; PROMIS, Patient-Reported Outcomes Measurement Information System; PROMIS-29, PROMIS 29-Item Profile Measure; PROMIS CAT, PROMIS Computer Adaptive Tests; PROMIS Global-10, PROMIS 10-Item Global Health Survey; SF-12, 12-Item Short Form Health Survey; SF-36, 36-Item Short Form Health Survey; UWQOL, University of Washington quality of life; WHOQOL-BREF, World Health Organization Quality of Life Brief Version.
      Cancer types were grouped into larger categories.
      Cancer type not reported.

      Methodological Quality of Included Studies

      The appraisal scores of each included study are presented in Figure 2. The appraisal scores of the cross-sectional studies using AXIS ranged from 14 (70%) to 19 (95%) of 20 (n = 29, M = 16.2 [81%], SD = 0.97 [4.9%]). The 2 prospective cohort studies that were rated using CASP received scores of 10 (83%) and 11 (92%) of 12, respectively. The 3 AXIS components in which most studies had unfavorable responses were (1) description of nonresponders (n = 26, 84%), (2) measures to address and categorize nonresponders (n = 25, 81%), and (3) justification of sample size (n = 22, 71%). Nevertheless, given that every study had an AXIS or CASP score of greater than or equal to 70%, it can be concluded that all included studies had generally good methodological quality.
      Figure thumbnail gr2
      Figure 2Methodological quality assessment of the included studies.
      AXIS indicates Appraisal tool for Cross-Sectional Studies; CASP, Critical Appraisal Skills Program Cohort Study Checklist.

      Qualitative Synthesis

      The main findings and statistical analysis techniques used in the included studies are summarized in Table 3. In assessing the association between financial toxicity and HRQOL, 16 studies (52%) performed univariate analysis, 4 studies (13%) performed multivariate analysis, and 11 (35%) performed both.
      Table 3Summary of findings of the included studies.
      Author (year)Main findingStatistical analysis
      Akin-Odanye et al (2021)
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-P correlation: r = 0.416 (P < .010)
      • -
        COST and FACT-P regression: B = 0.392, b = 0.181 (P < .050)
      • (1)
        Correlation (unspecified)
      • (2)
        Multivariate linear regression (hierarchical)
      Belcher et al (2021)
      • Belcher S.M.
      • Lee H.
      • Nguyen J.
      • et al.
      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
      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 = .005)
      • -
        COST and SF-36 general health correlation: r = 0.025 (P = .832)
      • -
        COST and SF-36 social functioning correlation: r = 0.183 (P = .119)
      • -
        COST and SF-36 role limitations (emotional) correlation: r = 0.276 (P = .017)
      • -
        COST and SF-36 energy/fatigue correlation: r = 0.014 (P = .236)
      • -
        COST and SF-36 emotional well-being correlation: r = 0.393 (P < .001)
      • -
        COST and SF-36 role limitations (physical) regression: B = 1.31, b = 0.38 (P < .010)
      • -
        COST and SF-36 pain regression: B = 1.03, b = 0.41 (P < .010)
      • -
        COST and SF-36 role limitations (emotional) regression: B = 0.94, b = 0.23 (P = .104)
      • -
        COST and SF-36 emotional well-being regression: B = 0.65, b = 0.27 (P < .050)
      • (1)
        Pearson’s correlation
      • (2)
        Multivariate linear regression (hierarchical)
      Benedict et al (2022)
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      Higher financial toxicity is associated with worse HRQOL

      COST and FACT-G regression: B = 0.88, b = 0.58 (P < .001)
      Multivariate linear regression (stepwise)
      Bouberhan et al (2019)
      • Bouberhan S.
      • Shea M.
      • Kennedy A.
      • et al.
      Financial toxicity in gynecologic oncology.
      Higher financial toxicity is associated with worse self-reported health

      COST and EQ VAS correlation: r = 0.47 (P < .001)
      Spearman’s correlation
      Chan et al (2021)
      • Chan D.N.S.
      • Choi K.C.
      • Ng M.S.N.
      • et al.
      Translation and validation of the traditional Chinese version of the comprehensive score for financial toxicity-functional assessment of chronic illness therapy (version 2).
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-G correlation: r = −0.46 (P < .001)
      • -
        COST and FACT-G physical well-being correlation: r = −0.34 (P < .001)
      • -
        COST and FACT-G social/family well-being correlation: r = −0.23 (P < .001)
      • -
        COST and FACT-G emotional well-being correlation: r = −0.42 (P < .001)
      • -
        COST and FACT-G functional well-being correlation: r = −0.39 (P < .001)
      Pearson’s correlation
      Coroneos et al (2020)
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      Higher financial toxicity is associated with worse HRQOL (1) PearsonEntire cohort
      • -
        COST and BREAST-Q psychosocial well-being correlation: r = 0.54 (P < .001)
      • -
        COST and SF-12 physical correlation: r = 0.41 (P < .001)
      • -
        COST and SF-12 mental: r = 0.52 (P < .001)
      • Reconstruction sub-cohort
      • -
        COST and BREAST-Q psychosocial well-being correlation: r = 0.49 (P < .001)
      • -
        COST and SF-12 physical correlation: r = 0.43 (P < .001)
      • -
        COST and SF-12 mental correlation: r = 0.48 (P < .001)
      (2) Change in HRQOL (95% CI) per unit of COST scoreEntire cohort
      • -
        BREAST-Q psychosocial well-being 0.99 (0.86-1.12)
      • -
        SF-12 physical 0.38 (0.31-0.46)
      • -
        SF-12 mental 0.49 (0.42-0.56)
      • Reconstruction sub-cohort
      • -
        BREAST-Q psychosocial well-being 0.90 (0.73-1.07)
      • -
        SF-12 physical 0.39 (0.30-0.48)
      • -
        SF-12 mental 0.45 (0.36-0.55)
      (3) Change in HRQOL (95% CI) per unit of COST scoreEntire cohort
      • -
        BREAST-Q psychosocial well-being 0.89 (0.76-1.03)
      • -
        SF-12 physical 0.32 (0.24-0.40)
      • -
        SF-12 mental 0.45 (0.38-0.52)
      • Reconstruction sub-cohort
      • -
        BREAST-Q psychosocial well-being 0.80 (0.63-0.97)
      • -
        SF-12 physical 0.32 (0.23-0.41)
      • -
        SF-12 mental 0.37 (0.27-0.46)
      • (1)
        Pearson’s correlation
      • (2)
        Bivariate linear regression
      • (3)
        Multivariate linear regression
      de Souza et al (2017)
      • de Souza J.A.
      • Yap B.J.
      • Wroblewski K.
      • et al.
      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-G correlation: r = 0.42 (P < .001)
      • -
        COST and EORTC QLQ-C30 correlation: r = 0.33 (P < .001)
      Pearson’s correlation
      Durber et al (2021)
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      Higher financial toxicity is associated wṇith worse HRQOL
      • -
        COST and FACT-G correlation: r = 0.53 (P < .001)
      • -
        COST and FACT-G physical well-being correlation: r = 0.40 (P < .001)
      • -
        COST and FACT-G emotional well-being correlation: r = 0.43 (P < .001)
      • -
        COST and FACT-G social well-being correlation: r = 0.18 (P < .001)
      • -
        COST and FACT-G functional well-being correlation: r = 0.35 (P < .001)
      • -
        FACT-G physical well-being and COST regression: b = 0.504, 95% CI 0.344-0.665 (P < .001)
      • -
        FACT-G emotional well-being and COST regression: b = 0.499, 95% CI 0.384-0.796 (P < .001)
      • -
        FACT-G functional well-being and COST regression: b = 0.442, 95% CI 0.264-0.62 (P < .001)
      • (1)
        Spearman’s correlation
      • (2)
        Multivariate linear regression
      Ehlers et al (2021)
      • Ehlers M.
      • Bjurlin M.
      • Gore J.
      • et al.
      A national cross-sectional survey of financial toxicity among bladder cancer patients.
      Higher financial toxicity is associated with worse HRQOL

      COST (M = 28.4) and EQ-5D-5L Wilcoxon rank-sum test (P < .001)
      Wilcoxon rank-sum test
      Esselen et al (2021)
      • Esselen K.M.
      • Stack-Dunnbier H.
      • Gompers A.
      • Hacker M.R.
      Crowdsourcing to measure financial toxicity in gynecologic oncology.
      Higher financial toxicity is associated with worse HRQOL

      COST and EQ-5D-3L correlation: r = 0.49 (P < .001).
      Spearman’s correlation
      Gordon et al (2020)
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      Higher financial toxicity is associated with worse HRQOL

      COST and EQ-5D-5L:

      Unadjusted scores (P < .001)
      • -
        With financial toxicity: M = 0.47, 95% CI, 0.67-0.75
      • -
        No financial toxicity: M = 0.71, 95% CI 0.41-0.54
      Adjusted scores (P = .01)
      • -
        With financial toxicity: M = 0.53, 95% CI, 0.45-0.61
      • -
        No financial toxicity: M = 0.69, 95% CI, 0.65-0.73
      • (1)
        Student’s t test
      • (2)
        Generalized linear model
      Hazell et al (2020)
      • Hazell S.Z.
      • Fu W.
      • Hu C.
      • et al.
      Financial toxicity in lung cancer: an assessment of magnitude, perception, and impact on quality of life.
      Higher financial toxicity is associated with worse HRQOL

      COST and FACT-L correlation: r = 0.41 (P < .001)
      Pearson’s correlation
      Kalra et al (2020)
      • Kalra D.
      • Menon N.
      • Singh G.K.
      • et al.
      Financial toxicities in patients receiving systemic therapy for brain tumors: a cross-sectional study.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-Br TOI correlation (P < .001)
      • -
        COST and FACT-G correlation (P < .001)
      • -
        COST and FACT-Br Total correlation (P < .001)
      • -
        COST and FACT-Br TOI regression: beta = 2.4
      • -
        COST and FACT-G regression: beta = 2.0
      • -
        COST and FACT-Br total regression: beta = 3.0
      • (1)
        Pearson’s correlation
      • (2)
        Bivariate linear regression
      Liang et al (2021)
      • Liang M.I.
      • Summerlin S.S.
      • Blanchard C.T.
      • et al.
      Measuring financial distress and quality of life over time in patients with gynecologic cancer-making the case to screen early in the treatment course.
      Higher financial toxicity is associated with worse HRQOL

      At baseline, 3 months, and 6 months:
      • -
        COST and FACT-G correlations: r = 0.63, r = 0.61, r = 0.60
      • -
        COST and FACT-G physical well-being correlation: r = 0.66, r = 0.62, r = 0.52
      • -
        COST and FACT-G social well-being correlation: r = 0.30, r = 0.33, r = 0.37
      • -
        COST and FACT-G emotional well-being correlation: r = 0.37, r = 0.54, r = 0.43
      • -
        COST and FACT-G functional well-being correlation: r = 0.42, r = 0.47, and r = 0.46
      Correlation (unspecified)
      Mady et al (2019)
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      Higher financial toxicity is associated with worse HRQOL

      COST and UWQOL regression: Roy’s greatest root value 0.08, F-value 3.61, beta = 0.47 (P = .030)
      Multivariate linear regression
      McLean et al (2021)
      • McLean L.
      • Hong W.
      • McLachlan S.A.
      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and EORTC QLQ-C30 correlation: r = 0.73
      • -
        COST and EORTC QLQ-C30 regression: b = −0.90, P = .004, 95% CI −1.51 to 0.30
      • (1)
        Pearson’s correlation
      • (2)
        Bivariate linear regression
      Mejri et al (2021)
      • Mejri N.
      • Rachdi H.
      • Mnif A.
      • et al.
      Translation and validation of the Comprehensive Score of Financial Toxicity for cancer patients into Arabic.
      Higher financial toxicity is associated with worse HRQOL

      COST and FACT-G correlation: r = 0.39 (P = .047)
      Pearson’s correlation
      Miller et al (2021)
      • Miller K.A.
      • Stal J.
      • Gallagher P.
      • et al.
      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
      Financial toxicity is not associated with HRQOL
      • -
        COST and FACT-C regression: b = 1.01 (P > .100)
      • -
        COST and FACT-C emotional well-being regression: b = 0.33 (P > .100)
      • -
        COST and FACT-C physical well-being regression: b = 0.32 (P > .100)
      • -
        COST and FACT-C social well-being regression: b = −0.03 (P > .100)
      • -
        COST and FACT-C functional well-being regression: b = 0.20 (P > .100)
      Bivariate linear regression
      Pavela et al (2021)
      • Pavela G.
      • Fifolt M.
      • Tison S.
      • Allison M.
      • Burton B.S.
      • Ford E.W.
      Re-validation of the Comprehensive Score for Financial Toxicity (COST): assessing the scale’s utility in chronic disease populations.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and PROMIS-10 physical global health correlation: r = 0.46 (P < .001)
      • -
        COST and PROMIS-10 mental global health correlation: r = 0.45 (P < .001)
      • -
        COST and PROMIS-10 physical global health regression: beta = 0.28 (P < .001)
      • -
        COST and PROMIS-10 mental global health regression: beta = 0.13 (P < .001)
      • (1)
        Pearson’s correlation
      • (2)
        Multivariate linear regression
      Petruzzi et al (2022)
      • Petruzzi L.J.
      • Prezio E.
      • Phillips F.
      • et al.
      An exploration of financial toxicity among low-income patients with cancer in Central Texas: a mixed methods analysis.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-G regression: b = 0.17 (P = .008)
      • -
        COST and PROMIS anxiety regression: b = −0.08 (P = .590)
      • -
        COST and PROMIS depression regression: b = 0.06 (P = .690)
      • -
        COST and PROMIS fatigue regression: b = −0.2 (P = .150)
      • -
        COST and PROMIS pain interference regression: b = −0.06 (P = .660)
      • -
        COST and PROMIS physical function regression: b = −0.02 (P = .900)
      Multivariate linear regression
      Phillips et al (2021)
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-G regression for raw score: b = 0.59 (P < .010)
      • -
        COST and FACT-G regression for US population standardized T-scores: b = 0.32 (P < .010)
      • -
        COST and FACT-G regression for adult patients with cancer standardized T-scores: b = 0.34 (P < .010)
      Multivariate linear regression
      Ripamonti et al (2020)
      • Ripamonti C.I.
      • Chiesi F.
      • Di Pede P.
      • et al.
      The validation of the Italian version of the Comprehensive Score for Financial Toxicity (COST).
      Higher financial toxicity is associated with worse HRQOL

      COST and EORTC QLQ-C30 correlation: r = −0.52 (P < .001)
      Pearson’s correlation
      Rosenzweig et al (2019)
      • Rosenzweig M.
      • West M.
      • Matthews J.
      • et al.
      Financial toxicity among women with metastatic breast cancer.
      Higher financial toxicity is associated with worse HRQOL

      COST and FACT-B correlation: r = 0.56 (P < .001)
      Pearson’s correlation
      Shim et al (2021)
      • Shim S.
      • Kang D.
      • Kim N.
      • et al.
      Validation of Korean version of the Comprehensive Score for Financial Toxicity (COST) among breast cancer survivors.
      Higher financial toxicity is associated with worse HRQOL:
      • -
        COST and EORTC QLQ-C30 global health status correlation: r = 0.36
      • -
        COST and EORTC QLQ-C30 physical functioning correlation: r = 0.30
      • -
        COST and EORTC QLQ-C30 role functioning correlation: r = 0.32
      • -
        COST and EORTC QLQ-C30 emotional functioning correlation: r = 0.37
      • -
        COST and EORTC QLQ-C30 cognitive functioning correlation: r = 0.30
      • -
        COST and EORTC QLQ-C30 social functioning correlation: r = 0.44
      • -
        COST and EORTC QLQ-C30 fatigue correlation: r = −0.30
      • -
        COST and EORTC QLQ-C30 nausea and vomiting correlation: r = −0.18
      • -
        COST and EORTC QLQ-C30 pain correlation: r = −0.26
      • -
        COST and EORTC QLQ-C30 dyspnea correlation: r = −0.21
      • -
        COST and EORTC QLQ-C30 sleep disorder correlation: r = −0.21
      • -
        COST and EORTC QLQ-C30 appetite loss correlation: r = −0.16
      • -
        COST and EORTC QLQ-C30 constipation correlation: r = −0.14
      • -
        COST and EORTC QLQ-C30 diarrhea correlation: r = −0.14
      P values not reported.
      Pearson’s correlation
      Thom et al (2021)
      • Thom B.
      • Mamoor M.
      • Lavery J.A.
      • et al.
      The experience of financial toxicity among advanced melanoma patients treated with immunotherapy.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and EORTC QLQ-C30 global health status correlation: r = 0.44 (P < .001)
      • -
        COST and EORTC QLQ-C30 social correlation: r = 0.55 (P < .001)
      • -
        COST and EORTC QLQ-C30 emotional correlation: r = 0.45 (P < .001)
      • -
        COST and EORTC QLQ-C30 physical correlation: r = 0.33 (P < .001)
      • -
        COST and EORTC QLQ-C30 role correlation: r = 0.52 (P < .001)
      • -
        COST and EORTC QLQ-C30 cognitive correlation: r = 0.22 (P < .001)
      Partial correlation, when controlling for age:
      • -
        COST and EORTC QLQ-C30 global health status correlation: r = 0.11 (P = .030)
      • -
        COST and EORTC QLQ-C30 financial difficulties correlation: r = 0.62 (P < .001)
      • -
        COST and EORTC QLQ-C30 social correlation: r = 0.34 (P < .001)
      • -
        COST and EORTC QLQ-C30 emotional correlation: r = 0.22 (P = .030)
      • -
        COST and EORTC QLQ-C30 role correlation: r = 0.26 (P < .010)
      • (1)
        Pearson’s correlation
      • (2)
        Partial Correlation
      Urek and Ugurluoglu (2022)
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-G physical well-being correlation r = 0.405 (P < .001)
      • -
        COST and FACT-G social/family well-being correlation r = 0.160 (P < .001)
      • -
        COST and FACT-G emotional well-being correlation r = 0.344 (P < .001)
      • -
        COST and FACT-G functional well-being correlation r = 0.226 (P < .001)
      • -
        COST and FACT-G regression: b = 0.389 (P < .001)
      • (1)
        Pearson’s correlation
      • (2)
        Multivariate linear regression
      Ver Hoeve et al (2021)
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and PROMIS-29 anxiety correlation: r = −0.34 (P = .001)
      • -
        COST and PROMIS-29 depression correlation: r = −0.21 (P = .031)
      • -
        COST and PROMIS-29 fatigue correlation: r = −0.41, (P = .000)
      • -
        COST and PROMIS-29 sleep correlation: r = −0.25 (P = .010)
      • -
        COST and PROMIS-29 pain correlation: r = −0.27 (P = .006)
      • -
        COST and PROMIS-29 physical functioning correlation: r = 0.31 (P = .001)
      • -
        COST and PROMIS-29 social functioning correlation: r = −0.31 (P = .002)
      • -
        COST and PROMIS-29 anxiety regression: B = −0.09, b = −0.28 (P = .012)
      • -
        COST and PROMIS-29 fatigue regression: B = −0.16, b = −0.16 (P = .001)
      • -
        COST and PROMIS-29 pain interference regression: B = −0.07, b = −0.07 (P = .206)
      • -
        COST and PROMIS-29 physical functioning regression: B = 0.11, b = 0.11 (P = .020)
      • -
        COST and PROMIS-29 social functioning regression: B = −0.17, b = −0.17 (P = .013)
      • (1)
        Pearson’s correlation
      • (2)
        Multivariate linear regression
      Xu et al (2022)
      • Xu R.H.
      • Wang L.-L.
      • Zhou L.-M.
      • Wong E.L.-Y.
      • Wang D.
      Urban-rural differences in financial toxicity and its effect on cancer survivors’ health-related quality of life and emotional status: a latent class analysis.
      Higher financial toxicity is associated with worse HRQOL(1) Wilcoxon rank-sum testCSS
      • -
        Mobility: no problem (M = 14.8), some problems (M = 11)
      • -
        Self-care: no problem (M = 14.9), some problems (M = 10.2)
      • -
        Usual activities: no problem (M = 15.6), some problems (M = 10.4)
      • -
        Pain/discomfort: no problem (M = 17.1), some problems (M = 17.4)
      (2) Latent class analysisDivided into 4 classes based on their health statuses measured using 4 physical dimensions of EQ-5D-5L (mobility, self-care, usual activities, and pain/discomfort) and 3 subscales of DASS-21 (depression, anxiety, and stress); Class 1: low physical and psychological (M = 11.9), Class 2: high physical and low psychological (M = 10.9), Class 3: low physical and high psychological (M = 8.1), Class 4: high physical and psychological (M = 16.9)

      -COST and the 4 latent classes (P < .001)
      • (1)
        Wilcoxon rank-sum test
      • (2)
        Latent class analysis
      Xu et al (2022)
      • Xu T.
      • Xu L.
      • Xi H.
      • et al.
      Assessment of financial toxicity among patients with advanced lung cancer in Western China.
      Higher financial toxicity is associated with worse HRQOL

      COST and FACT-L correlation: r = 0.44 (P < .001)
      Pearson’s correlation
      Yu et al (2020)
      • Yu H.H.
      • Yu Z.F.
      • Li H.
      • Zhao H.
      • Sun J.M.
      • Liu Y.Y.
      The Comprehensive Score for Financial Toxicity in China: validation and responsiveness.
      Higher financial toxicity is associated with worse HRQOL

      COST and WHOQOL-BREF correlation: r = 0.34 (P < .010)
      Correlation (unspecified)
      Yusuf et al (2022)
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      Higher financial toxicity is associated with worse HRQOL
      • -
        COST and FACT-G7 correlation: r = 0.617 (P < .001)
      • -
        COST and FACT-G7 bivariate regression: beta = 0.973 (P < .001)
      • -
        COST and FACT-G7 multivariate regression: beta = 0.874 (P < .001)
      • (1)
        Pearson’s correlation
      • (2)
        Bivariate linear regression
      • (3)
        Multivariate linear regression
      B indicates unstandardized coefficient; b, standardized coefficient; beta, not indicated whether standardized or not; CI, confidence interval; COST, Comprehensive Score for Financial Toxicity; DASS-21, Depression, Anxiety, and Stress Scale – 21 Items; EORTC QLQ-C30, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30; EQ-5D-3L, 3-level EQ-5D version; EQ-5D-5L, 5-level EQ-5D version; EQ VAS, EuroQol Visual Analogue Scale; FACT, Functional Assessment of Cancer Therapy; FACT-B, FACT – Breast cancer; FACT-Br, FACT – Brain cancer; FACT-C, FACT – Colorectal cancer; FACT-G, FACT – General; FACT-G7, FACT-G – 7-Item Version; FACT-L, FACT – Lung cancer; FACT-P, FACT – Prostate cancer; HRQOL, health-related quality of life, M, mean score of COST; PROMIS, Patient-Reported Outcomes Measurement Information System; PROMIS CAT, PROMIS Computer Adaptive Tests; PROMIS Global-10, PROMIS 10-Item Global Health Survey; PROMIS-29, PROMIS 29-Item Profile Measure; SF-12, 12-Item Short Form Health Survey; SF-36, 36-Item Short Form Health Survey; TOI, Trial Outcome Index; UWQOL, University of Washington quality of life; WHOQOL-BREF, World Health Organization Quality of Life Brief Version.

      Univariate analyses

      The univariate approaches used in the included studies consisted of correlation analysis (n = 23, 74%), bivariate linear regression (n = 5, 16%), Wilcoxon rank-sum test (n = 2, 6%), and Student’s t test (n = 1, 3%). For correlation analysis, 17 studies used Pearson’s correlation, 3 studies used Spearman’s correlation, and 3 did not specify the type. All but one studies using univariate analyses
      • Miller K.A.
      • Stal J.
      • Gallagher P.
      • et al.
      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
      reported a significant association between financial toxicity and HRQOL. Across the 4 studies that used bivariate linear regressions to predict HRQOL from COST scores, none used the same HRQOL instruments: BREAST-Q and 12-Item Short Form Health Survey,
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      FACT – Brain cancer,
      • Kalra D.
      • Menon N.
      • Singh G.K.
      • et al.
      Financial toxicities in patients receiving systemic therapy for brain tumors: a cross-sectional study.
      EORTC QLQ-C30,
      • McLean L.
      • Hong W.
      • McLachlan S.A.
      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      and FACT – Colorectal cancer (FACT-C).
      • Miller K.A.
      • Stal J.
      • Gallagher P.
      • et al.
      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
      One study estimated a different model by regressing COST scores on FACT-G – 7-Item Version.
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.

      Multivariate analyses

      Among multivariate approaches, multivariate linear regression (n = 12, 39%), generalized linear model (n = 1, 3%), partial correlation (n = 1, 3%), and latent class analysis (n = 1, 3%) were performed in the included studies. All these studies reported significantly better HRQOL in patients with cancer with lower financial toxicity. Nine studies reported financial toxicity as a significant predictor of HRQOL using multivariate regression models.
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      • Belcher S.M.
      • Lee H.
      • Nguyen J.
      • et al.
      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      ,
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      ,
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      ,
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      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),
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      0.47 points (University of Washington quality of life),
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      and 0.39 points (FACT – prostate cancer).
      • Sterne J.A.C.
      • Sutton A.J.
      • Ioannidis J.P.A.
      • et al.
      Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials.
      All these regression models were adjusted for sociodemographic factors. These included age,
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      • Belcher S.M.
      • Lee H.
      • Nguyen J.
      • et al.
      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      ,
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      ,
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      ,
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      ,
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      employment status,
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Coroneos C.J.
      • Lin Y.L.
      • Sidey-Gibbons C.
      • et al.
      Correlation between financial toxicity, quality of life, and patient satisfaction in an insured population of breast cancer surgical patients: a single-institution retrospective study.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      ,
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      education,
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      ,
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      ,
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      race,
      • Belcher S.M.
      • Lee H.
      • Nguyen J.
      • et al.
      Financial hardship and quality of life among patients with advanced cancer receiving outpatient palliative care: a pilot study.
      ,
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      sex,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      ,
      • Ver Hoeve E.S.
      • Ali-Akbarian L.
      • Price S.N.
      • Lothfi N.M.
      • Hamann H.A.
      Patient-reported financial toxicity, quality of life, and health behaviors in insured US cancer survivors.
      (loss of) income,
      • Mady L.J.
      • Lyu L.
      • Owoc M.S.
      • et al.
      Understanding financial toxicity in head and neck cancer survivors.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      and marital status.
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      Furthermore, in several studies, the regression models were controlled for clinical variables, such as comorbidities,
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      ,
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      cancer type,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      ,
      • Urek D.
      • Ugurluoglu O.
      Predictors of financial toxicity and its associations with health-related quality of life and treatment non-adherence in Turkish cancer patients.
      cancer stage,
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      and cancer recurrence.
      • Benedict C.
      • Fisher S.
      • Schapira L.
      • et al.
      Greater financial toxicity relates to greater distress and worse quality of life among breast and gynecologic cancer survivors.
      ,
      • Phillips F.
      • Prezio E.
      • Miljanic M.
      • et al.
      Patient reported outcomes affecting quality of life in socioeconomically disadvantaged cancer patients.
      Four other studies estimated multivariate regression models differently by using HRQOL to predict financial toxicity.
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      ,
      • Pavela G.
      • Fifolt M.
      • Tison S.
      • Allison M.
      • Burton B.S.
      • Ford E.W.
      Re-validation of the Comprehensive Score for Financial Toxicity (COST): assessing the scale’s utility in chronic disease populations.
      ,
      • Petruzzi L.J.
      • Prezio E.
      • Phillips F.
      • et al.
      An exploration of financial toxicity among low-income patients with cancer in Central Texas: a mixed methods analysis.
      ,
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      One study determined the association between COST and EORTC QLQ-C30 using partial correlation while controlling for patients’ age.
      • Thom B.
      • Mamoor M.
      • Lavery J.A.
      • et al.
      The experience of financial toxicity among advanced melanoma patients treated with immunotherapy.
      Another study used latent class analysis to compare COST scores of patients grouped into 4 latent classes based on EQ-5D responses.
      • Xu R.H.
      • Wang L.-L.
      • Zhou L.-M.
      • Wong E.L.-Y.
      • Wang D.
      Urban-rural differences in financial toxicity and its effect on cancer survivors’ health-related quality of life and emotional status: a latent class analysis.

      Qualitative synthesis of correlations

      Ten studies (32%) reported correlations between COST and any HRQOL domain scores (Appendix File 2 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2022.07.017). Ten HRQOL domains were included in these correlation analyses: physical health (r = 0.34-0.66), social health (r = 0.16-0.55), mental health (r = 0.21-0.54), daily functioning (r = 0.23-0.52), global health (r = 0.03-0.44), fatigue (r = 0.01-0.41), physical functioning (r = 0.06-0.33), pain (r = 0.26-0.32), cognitive functioning (r = 0.22-0.30), and sleep (r = 0.21-0.25).

      Quantitative Synthesis

      Meta-analysis was only possible for overall HRQOL scores. Ten studies involving 2139 patients measured the association between financial toxicity and overall HRQOL using the following cancer-specific and condition-specific instruments: FACT-G, FACT-G – 7-item version, FACT – Breast cancer, FACT – lung cancer, and FACT – prostate cancer. A random-effects meta-analysis was performed with moderate heterogeneity being present in the model (I-squared = 60%). The P value for Egger’s regression was .638, which indicated no publication bias. The pooled correlation coefficient was moderate (r = 0.49, 95% confidence interval 0.44-0.54) (Fig. 3).
      Figure thumbnail gr3
      Figure 3Meta-analysis on the correlations between financial toxicity (COST) and HRQOL scores.
      COST, indicates Comprehensive Score for Financial Toxicity; FACT-B, FACT - Breast cancer; FACT-G, FACT - General; FACT-G7, FACT-G 7-Item Version; FACT-L, FACT - Lung cancer; FACT-P, FACT - Prostate cancer; HRQOL, health-related quality of life.

      Discussion

      This is the first systematic review to summarize the published literature on the association of HRQOL and subjective financial toxicity using the COST measure in patients with cancer and survivors, as well as the first to conduct a meta-analysis on these outcomes. We included 31 studies in the qualitative synthesis and 10 studies in the meta-analysis. Overall, these studies involved more than 13 000 patients and survivors from 9 countries of 4 continents diagnosed of more than 25 types of cancer. All included studies had a generally good methodological quality and were published in the past 5 years, with more than two-thirds published in 2021 to 2022.
      The studies used 19 validated HRQOL instruments, of which the most common was the cancer-specific FACT-G used in 9 studies. All, but one, included studies reported that higher financial toxicity was significantly associated with worse HRQOL. We demonstrated a moderate correlation between financial toxicity and overall HRQOL through meta-analysis. We identified 10 HRQOL domains that were related to financial toxicity, namely mental health, daily functioning, social health, physical health, physical functioning, global health, pain, fatigue, cognitive functioning, and sleep. This aligns well with findings of previous studies that reported an association between financial toxicity and a range of clinical symptoms known to be related to the mental and physical domains of HRQOL, such as depression, anxiety, and pain severity.
      • Chan R.J.
      • Gordon L.G.
      • Tan C.J.
      • et al.
      Relationships between financial toxicity and symptom burden in cancer survivors: a systematic review.
      Furthermore, health utilities were estimated in 3 of 5 studies that measured HRQOL using EQ-5D instruments.
      • Ehlers M.
      • Bjurlin M.
      • Gore J.
      • et al.
      A national cross-sectional survey of financial toxicity among bladder cancer patients.
      • Esselen K.M.
      • Stack-Dunnbier H.
      • Gompers A.
      • Hacker M.R.
      Crowdsourcing to measure financial toxicity in gynecologic oncology.
      • Gordon L.G.
      • Elliott T.M.
      • Wakelin K.
      • et al.
      The economic impact on Australian patients with neuroendocrine tumours.
      Findings of these studies suggest that there is a significant association between financial toxicity and utility loss, and therefore, it may be possible that the mitigation of financial toxicity improves quality-adjusted life-year gains in patients with cancer and survivors.
      The linear regression models used in numerous studies indicated financial toxicity as a predictor of HRQOL. Given that both financial toxicity and HRQOL are influenced by sociodemographic factors, one may conclude that the association identified between the 2 constructs is attributable to these variables. Nevertheless, in our review, there were 9 studies reporting HRQOL to be significantly predicted by financial toxicity after adjusting for several sociodemographic characteristics. Future research is warranted to further explore for the potential effect of these individual characteristics.
      There are some distinctive findings from the included studies. First, 1 study in the United States failed to detect a significant association between financial toxicity and HRQOL in colorectal patients, using FACT-C.
      • Miller K.A.
      • Stal J.
      • Gallagher P.
      • et al.
      Time from diagnosis and correlates of health-related quality of life among young adult colorectal cancer survivors.
      Despite the results being insignificant, the association between COST and FACT-C was as expected indicating a decline in HRQOL with higher financial toxicity outcomes. Interestingly, this was the only study to focus on young adults with a mean age of 32 years (range = 20-42). Second, 4 studies regressed financial toxicity on HRQOL outcomes and not vice versa. Among them there were 2 studies that aimed to validate COST, that is, a test of construct validity in Australia or the United States.
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      ,
      • Pavela G.
      • Fifolt M.
      • Tison S.
      • Allison M.
      • Burton B.S.
      • Ford E.W.
      Re-validation of the Comprehensive Score for Financial Toxicity (COST): assessing the scale’s utility in chronic disease populations.
      Third, among the studies conducted in patients who completed treatment, 1 study in South Korea exclusively recruited recovered patients with breast cancer.
      • Shim S.
      • Kang D.
      • Kim N.
      • et al.
      Validation of Korean version of the Comprehensive Score for Financial Toxicity (COST) among breast cancer survivors.
      This indicates that COST may also be used outside its original target population and sheds light on possible further implications such as experiencing financial toxicity after remission.
      Our systematic review pointed out gaps in the existing literature. Most of the included studies were from the United States and the most common languages used for COST administration were English and Mandarin Chinese, whereas there were only 2 studies from Europe and 2 from Africa. More evidence is needed from other countries to better represent different populations. Additionally, only 2 longitudinal studies were identified. More longitudinal investigations are required to understand the dynamics of financial toxicity during the disease course and its impact on HRQOL. Interestingly, our included studies showed that the correlation strengths between financial toxicity and overall HRQOL were slightly stronger in studies that used English instruments (median = 0.53, range = 0.33-0.73)
      • de Souza J.A.
      • Yap B.J.
      • Wroblewski K.
      • et al.
      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      ,
      • Esselen K.M.
      • Stack-Dunnbier H.
      • Gompers A.
      • Hacker M.R.
      Crowdsourcing to measure financial toxicity in gynecologic oncology.
      ,
      • Hazell S.Z.
      • Fu W.
      • Hu C.
      • et al.
      Financial toxicity in lung cancer: an assessment of magnitude, perception, and impact on quality of life.
      ,
      • Liang M.I.
      • Summerlin S.S.
      • Blanchard C.T.
      • et al.
      Measuring financial distress and quality of life over time in patients with gynecologic cancer-making the case to screen early in the treatment course.
      ,
      • McLean L.
      • Hong W.
      • McLachlan S.A.
      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      ,
      • Rosenzweig M.
      • West M.
      • Matthews J.
      • et al.
      Financial toxicity among women with metastatic breast cancer.
      ,
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      than those in other languages, for example, Mandarin Chinese, Italian, and Arabic (median = 0.43, range = 0.34-0.52),
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      ,
      • Chan D.N.S.
      • Choi K.C.
      • Ng M.S.N.
      • et al.
      Translation and validation of the traditional Chinese version of the comprehensive score for financial toxicity-functional assessment of chronic illness therapy (version 2).
      ,
      • Mejri N.
      • Rachdi H.
      • Mnif A.
      • et al.
      Translation and validation of the Comprehensive Score of Financial Toxicity for cancer patients into Arabic.
      ,
      • Ripamonti C.I.
      • Chiesi F.
      • Di Pede P.
      • et al.
      The validation of the Italian version of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • Xu T.
      • Xu L.
      • Xi H.
      • et al.
      Assessment of financial toxicity among patients with advanced lung cancer in Western China.
      ,
      • Yu H.H.
      • Yu Z.F.
      • Li H.
      • Zhao H.
      • Sun J.M.
      • Liu Y.Y.
      The Comprehensive Score for Financial Toxicity in China: validation and responsiveness.
      and conducted in countries with universal health coverage (median = 0.49, range = 0.34-0.73)
      • Chan D.N.S.
      • Choi K.C.
      • Ng M.S.N.
      • et al.
      Translation and validation of the traditional Chinese version of the comprehensive score for financial toxicity-functional assessment of chronic illness therapy (version 2).
      ,
      • Durber K.
      • Halkett G.K.
      • McMullen M.
      • Nowak A.K.
      Measuring financial toxicity in Australian cancer patients - validation of the Comprehensive Score for Financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients.
      ,
      • McLean L.
      • Hong W.
      • McLachlan S.A.
      Financial toxicity in patients with cancer attending a public Australian tertiary hospital: a pilot study.
      ,
      • Ripamonti C.I.
      • Chiesi F.
      • Di Pede P.
      • et al.
      The validation of the Italian version of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • Xu T.
      • Xu L.
      • Xi H.
      • et al.
      Assessment of financial toxicity among patients with advanced lung cancer in Western China.
      ,
      • Yu H.H.
      • Yu Z.F.
      • Li H.
      • Zhao H.
      • Sun J.M.
      • Liu Y.Y.
      The Comprehensive Score for Financial Toxicity in China: validation and responsiveness.
      than those without (median = 0.42, range = 0.33-0.63).
      • Akin-Odanye E.O.
      • Ogo C.N.
      • Sulaiman F.A.
      • et al.
      Examining the influence of illness perception and financial toxicity on the quality of life of prostate cancer patients.
      ,
      • de Souza J.A.
      • Yap B.J.
      • Wroblewski K.
      • et al.
      Measuring financial toxicity as a clinically relevant patient-reported outcome: the validation of the Comprehensive Score for Financial Toxicity (COST).
      ,
      • Esselen K.M.
      • Stack-Dunnbier H.
      • Gompers A.
      • Hacker M.R.
      Crowdsourcing to measure financial toxicity in gynecologic oncology.
      ,
      • Hazell S.Z.
      • Fu W.
      • Hu C.
      • et al.
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      ,
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      • et al.
      Measuring financial distress and quality of life over time in patients with gynecologic cancer-making the case to screen early in the treatment course.
      ,
      • Mejri N.
      • Rachdi H.
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      ,
      • Rosenzweig M.
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      Financial toxicity among women with metastatic breast cancer.
      ,
      • Yusuf M.
      • Pan J.
      • Rai S.N.
      • Eldredge-Hindy H.
      Financial toxicity in women with breast cancer receiving radiation therapy: final results of a prospective observational study.
      Nevertheless, we had inadequate number of studies to further examine (eg, performing subgroup analysis) the potential impact of instrument language or universal health coverage on the association between financial toxicity and HRQOL.
      • Deeks J.J.
      • Higgins J.P.
      • Altman D.G.
      • et al.
      Analysing data and undertaking meta-analyses.
      Exploring the role of universal health coverage and mitigation strategies in alleviating financial toxicity and improving HRQOL may be an important future research direction. Some possible strategies include coverage for direct and indirect healthcare costs, patient assistance programs through industry or charity, and financial navigation programs.
      • Arastu A.
      • Hamilton A.
      • Chen E.Y.-s.
      Interventions to alleviate financial toxicity among patients with cancer: a systematic review.
      In addition, previous investigations suggest that income loss due to cancer may be explained by the decline of productivity or job loss,
      • Andersen I.
      • Kolodziejczyk C.
      • Thielen K.
      • Heinesen E.
      • Diderichsen F.
      The effect of breast cancer on personal income three years after diagnosis by cancer stage and education: a register-based cohort study among Danish females.
      • Zajacova A.
      • Dowd J.B.
      • Schoeni R.F.
      • Wallace R.B.
      Employment and income losses among cancer survivors: estimates from a national longitudinal survey of American families.
      • Kong Y.C.
      • Wong L.P.
      • Ng C.W.
      • et al.
      Understanding the financial needs following diagnosis of breast cancer in a setting with universal health coverage.
      • Blinder V.S.
      • Gany F.M.
      Impact of cancer on employment.
      which also occurs in countries with universal health coverage and may even lead to widening economic inequalities.
      • Bhoo-Pathy N.
      • Ng C.W.
      • Lim G.C.
      • et al.
      Financial toxicity after cancer in a setting with universal health coverage: a call for urgent action.
      ,
      • Alleaume C.
      • Bendiane M.K.
      • Peretti-Watel P.
      • Bouhnik A.D.
      Inequality in income change among cancer survivors five years after diagnosis: evidence from a French national survey.
      Considering that subjective financial toxicity is contingent upon its objective counterpart, improvements may also be made by compensating the negative income effects of cancer. One plausible way to achieve this is by developing income protection and employment reintegration programs for patients and survivors.
      This systematic review has a few limitations. Even though we provided evidence of moderate correlation between financial toxicity and HRQOL, the moderate heterogeneity calls for a more cautious interpretation of the meta-analysis results. The generalizability of our findings may be limited to the observed patient groups in the included studies. Some very likely sources of heterogeneity in our pooled model include a variety of individual (eg, age, ethnicity, and cancer type and stage) and country-related characteristics (eg, health insurance system and social support availability). Ideally, techniques such as meta-regression or subgroup analysis would be further conducted to precisely identify the cause of heterogeneity. Nevertheless, they were not feasible due to the low number of studies.
      • Deeks J.J.
      • Higgins J.P.
      • Altman D.G.
      • et al.
      Analysing data and undertaking meta-analyses.
      The covariates of interests (eg, income and ethnicity) for modeling were also unevenly distributed and insufficiently reported across the included studies. Future research may focus on the association between financial toxicity and HRQOL by considering cross-country differences, for example, health payment system and cultural specificity, while also accounting for sociodemographic variables and the use of different HRQOL measures.

      Conclusions

      We provided a summary of the increasing body of literature on financial toxicity and its association with HRQOL in patients with cancer and survivors. Several HRQOL domains, including physical, mental, and social health, were found to be related to financial toxicity. Through meta-analysis, we demonstrated financial toxicity to be moderately correlated with overall HRQOL. Our findings contribute to the understanding of the burden patients with cancer experience and confirm financial toxicity as a relevant adverse outcome of cancer care.

      Article and Author Information

      Author Contributions: Concept and design: Pangestu, Rencz
      Acquisition of data: Pangestu, Rencz
      Analysis and interpretation of data: Pangestu, Rencz
      Drafting the manuscript: Pangestu, Rencz
      Critical revision of paper for important intellectual content: Pangestu, Rencz
      Statistical analysis: Pangestu
      Provision of study materials: Pangestu
      Administrative, technical, or logistic support: Pangestu
      Supervision: Rencz
      Conflict of Interest Disclosures: The authors reported no conflicts of interest.
      Funding/Support: The authors received no financial support for this research.

      Acknowledgment

      The authors thank Michael Valentine Chandra and Bennett Marius Kumala for their research assistance and Stanislaus S. Uyanto, PhD, for his comments on statistical methods. The authors are also grateful to Christ Billy Aryanto for his help in literature search.

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