The effects of a financial navigation intervention on financial toxicity among patients with breast cancer in China: A randomized controlled trial.

Background

Breast cancer remains the most common malignancy among women worldwide, with over 2.3 million new cases in 2022 globally [ 1 ], posing a significant disease burden on patients, families and healthcare system. Compared with other solid tumors, the 5-year survival rate for breast cancer is higher and it requires prolonged comprehensive treatment, including surgery, chemotherapy, radiotherapy, targeted and immunotherapies, increasingly, endocrine therapy often extending for 5 to 10 years. These long treatment trajectories generate cumulative direct medical costs (e.g., treatment expenses, hospital and pharmacy bills, laboratory charges), substantial indirect costs (e.g., work disruption, caregiving needs, transportation, food, and lodging related to treatment), and significant out-of-pocket expenditures. Prior evidence showed that the estimates of lifetime per-patient costs of breast cancer ranged from $20,000 to $100,000 in the United States [ 2 ]. Especially for younger and midlife women with metastatic breast cancer, the total cost could top $200,000 [ 3 ]. According to Annual Report to the Nation on the Status of Cancer [ 4 ], the total out-of-pocket costs for breast cancer care was nearly $3.14 billion, and annual time costs were $1.11 billion in 2019, which were more than any other type of cancer. Based on analysis of comparable measures of financial toxicity, researchers found that the pooled rate of financial toxicity among patients with breast cancer was 78.8% in low- and middle-income countries and 35.3% in high-income countries. Therefore, interventions designed to offset the burden of direct medical and nonmedical costs are required to improve the financial health of vulnerable patients with breast cancer.

In China, the rising incidence and high out-of-pocket costs for breast cancer contribute to financial toxicity (FT) [ 5 , 6 ], affecting 64.4% of patients with breast cancer [ 7 ], far exceeding the 35.3% reported in high-income countries [ 8 ]. Drawing on recent comments on addressing FT in China [ 9 ], healthcare disparities often force patients to seek cross-regional care and strong family-oriented cultural norms often motivate relatives to support cancer treatment at all costs. Many families adopt a “total rest” approach that discourages patients from engaging in any work or household responsibilities. However, employed individuals typically receive higher insurance coverage, while non-working groups face lower reimbursement rates. Furthermore, limited cost communication hinders patients’ ability to manage FT effectively. These factors result in substantial additional expenses and reduced income, further exacerbating FT. FT has been linked to financial hardship, psychological distress, reduced treatment adherence and impaired quality of life [ [10] , [11] , [12] ]. Therefore, effective interventions are urgently needed to address FT and its multifaceted impacts on patients with breast cancer.

Financial navigation (FN) is an individual-and healthcare provider-level intervention that integrates financial assessment, education, decision support, and resource referral to help cancer patients manage treatment-related costs, enhance access to quality care, and alleviate FT [ 13 , 14 ]. Compared with less readily modifiable policy-level strategies, FN is recognized as a practical and cost-effective approach [ 15 ]. Despite its feasibility, acceptability, and promise in managing FT, existing FN studies are limited by the heterogeneity of intervention components, delivery modes, and frequency, as reported in several scoping reviews [ 16 , 17 ]. Current evidence on its effectiveness remains inconsistent due to small sample sizes, non-randomized designs, and short follow-up periods. Moreover, while FN interventions have been explored in some high-income countries [ [18] , [19] , [20] ], differences in socioeconomic, cultural, and healthcare systems between Western countries and China necessitate tailored interventions [ 9 , 21 ].

Based on social stress theory [ 22 ], stressors such as financial burden can negatively affect individual well-being while personal and social resources may mitigate these effects. FN intervention serves as a mediator, equipping patients with diverse coping resources and support to alleviate FT. In the previous study, FT was also expected to enhance cost-related health literacy (CRHL), improve shared decision-making (SDM), and reduce perceived stress [ 23 ]. Therefore, this study included CRHL, SDM and perceived stress as secondary outcomes, which were measured by validated social psychological scales. Examining these constructs alongside FT provides a more comprehensive understanding of the mechanisms through which financial navigation interventions may influence patient outcomes.

Few high-quality controlled trials have rigorously evaluated the effectiveness of FN on FT in diverse healthcare settings, particularly in low- and middle-income countries. Our pilot study of 26 participants tailored a structured FN intervention based on the best available evidence and the unique needs of Chinese patients, and preliminarily affirmed the feasibility and acceptability of FN, laying the foundation for a full-scale randomized controlled trial (RCT) [ 24 ]. To address these research gaps, this study aimed to evaluate the effects of the FN intervention on FT, cost-related health literacy (CRHL), shared decision-making (SDM) ability, and perceived stress among patients with breast cancer in China.

Methods

Study design

This study is a cluster randomized controlled trial, which is reported according to the Consolidated Standards of Reporting Trials (CONSORT) statement [ 25 ].

Study setting and participants

This study was conducted in a breast surgery ward at Fudan University Shanghai Cancer Center in China. Participants were recruited using consecutive sampling method from June to August 2024, with follow-up ending in November 2024. Participants were eligible if they: (a) were Chinese women aged 18 years or older; (b) had newly diagnosed with breast cancer and underwent surgery during this hospitalization; (c) were undergoing or scheduled to receive one or more adjuvant therapy (e.g., chemotherapy, radiotherapy, targeted therapy) due to the high risk of severe FT associated with these comprehensive treatments; (d) had an Eastern Cooperative Oncology Group Performance Status (ECOG-PS) of 0-2; and (e) provided informed consent. Exclusion criteria were as follows: (a) a diagnosis of ductal carcinoma in situ (DCIS), as their medical expenses were primarily covered by basic health insurance, leading to minimal FT; (b) severe psychiatric disorders; (c) cognitive impairments; or (d) difficulties in reading, writing, or communicating in Chinese.

The sample size was estimated using a two-sample mean calculation [ 26 ] based on the primary outcome of FT, measured by the Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy Version 2 (COST-FACIT-V2). Given the absence of prior RCTs evaluating the effectiveness of FN in mitigating FT, the estimation was informed by Sadigh et al.'s feasibility study [ 27 ]. We assumed baseline COST-FACIT scores of 10.0 ± 9.6 in both groups. Post-intervention, the scores were expected to be 16.9 ± 8.1 in the intervention group and 10.0 ± 9.6 in the control group. Using G∗Power 3.1 software, a sample size of 28 participants per group (n 1 = n 2 = 28) was required to detect a significant between-group difference in COST-FACIT scores, with 80% power ( β = 0.20) at a two-trialed 5% level of significance. Accounting for a 30% attrition rate, the final planned sample size was at least 80 participants, with 40 in each group.

Recruitment and randomization

The principal researcher (PI) screened medical records and communicated with patients to identify eligible participants and exclude those who did not meet the criteria. Then, the participants were distributed invitation leaflets containing a brief overview of the study. Interested patients could contact the PI, as provided on the leaflet. The PI then approached potential participants, explained study details (e.g., objectives, procedures, and potential benefits and risks), and obtained written informed consent before enrollment.

To minimize contamination among patients, a cluster randomization design was implemented, using four-person rooms in the breast surgery ward as the randomization units. Rooms were sequentially numbered from 1 to 12 according to bed number in ascending order. A research assistant (RA), independent of recruitment, intervention delivery, and outcome assessment, generated the randomization sequence via an online tool ( https://www.random.org/ ). Consequently, rooms 1, 4, 6, 9, 10 and 11 were allocated to the intervention group, while rooms 2, 3, 5, 7, 8 and 12 were assigned to the control group.

The allocation of rooms to the intervention and control groups was predefined at the ward level prior to patient enrollment and remained fixed throughout the study period. Importantly, patient bed assignment was performed routinely by administrative staff based on bed availability, without involvement in the study and without consideration of group allocation. Intervention materials were provided only to participants in the intervention group and were not made available in public ward areas. The financial navigation intervention was delivered by a designated nurse navigator who was not involved in routine care for control participants. Face-to-face sessions were conducted in a private or semi-private setting when feasible, while post-discharge consultations were delivered individually via telephone or WeChat.

Blinding of the participants and interveners was not feasible; however, outcome assessors remained blinded [ 28 ]. An independent researcher, who was not involved in the delivery of the intervention and the randomization process, conducted all outcome assessments. Participants were instructed at the beginning of each follow-up call not to disclose any details about the intervention they may have received. Standardized assessment scripts were used to guide all interviews, minimizing opportunities for the assessor to infer group allocation based on participants’ responses.

Intervention

Intervention group

The intervention group received a financial navigation intervention in addition to usual care. The details of the development and preliminary evaluation of the FN intervention were previously described [ 24 ]. The updated intervention included four components: (a) Needs Assessment Module: On the 1st day of admission, a nurse navigator initiated a brief introduction to FT and assessed patients' informational needs (5-10 min). (b) Cost-Related Health Education Module: On the 2nd-3rd day post-admission, structured face-to-face health education covered topics such as cost communication, medical-related expenses, health insurance, and household financial adjustments (8-10 min per session, 2-3 sessions in total). An education booklet, developed by the research team were available for participants to facilitate self-learning and reinforce understanding. Its accuracy, significance, and readability were reviewed by an expert panel. (c) Resource/Service Referral Module: From postoperative to 3 months post-discharge, participants received individualized financial assistance navigation and referrals via in-person or telephone consultations (5-10 min per session, as needed). Those with moderate to severe psychological distress, assessed by the Patient Health Questionnaire (PHQ-9) ≥10 29 or Generalized Anxiety Disorder (GAD-7) ≥10, 30 were referred to psychologists[ 29 , 30 ], while those experiencing significant adverse effects (e.g., wound infection, upper limb lymphedema, bone marrow suppression, cardiotoxicity, moderate to severe radiation dermatitis, or severe osteoporosis) were directed to physicians. (d) Personalized Counseling Module: From discharge to 3 months post-discharge, coping strategies were provided through monthly telephone or WeChat follow-ups (5-10 min per session). It mainly included providing information on symptom management and how to return normal life and work; assessing maladaptive behaviors due to cost concern; encouraging coping skill exchange for FT within WeChat-based peer support group; providing counseling for personalized cost-related issues. Tailored elements were incorporated based on individual assessments, allowing counselors to adapt strategies to each participant's financial situation, psychological needs, and personal goals. The supplementary material ( Table S1 ) provided more detailed information on the interventions.

The intervention was designed as a flexible, patient-centered program, with two core sessions delivered to all participants and an optional third session provided based on individual needs. Specifically, the additional session was offered when participants required further support (i.e., Resource/Service Referral Module and Personalized Counseling Module). Consultations could be initiated by participants (e.g., when they sought additional assistance) and the frequency of these consultations was not fixed. The FN intervention was delivered by a nurse navigator, an experienced breast cancer nurse with substantial knowledge of financial toxicity, ensuring consistency in intervention implementation. The program was primarily patient-centered, with patients serving as the main participants, while family members or caregivers were welcome to be involved at the patient's discretion.

Control group

Standard care, routine health education, and post-discharge follow-up were provided to the control group for attention purposes. Heath education covered key topics such as ward environment, preoperative preparation, wound care, rehabilitation exercise, nutrition, emotional support, follow-up schedules, and complications signs. These sessions were delivered by nurse or other healthcare providers during hospitalization and reinforced via telephone follow-ups after discharge.

Measurements

Sociodemographic and clinical information

A self-developed questionnaire was used to collect participants' sociodemographic and clinical characteristics, including age, residence, education level, marital status, family savings, cancer stage, treatment and so on. Sociodemographic information was obtained through questionnaire, while clinical data were collected from medical records.

Primary outcomes

Financial toxicity

FT was assessed across three domains: material hardship, psychological distress, and behavioral impact, as identified in several systematic reviews [ 12 , 31 ]. Material and behavioral domains of FT were assessed using six questions derived from the Medical Expenditure Panel Survey (MEPS) [ 32 ] and relevant literature [ 19 , 27 , 33 ]. There are two items examining debt and significant asset change due to costs (meeting any one of them is counted as “yes”); three items assessing delayed, foregone and changed treatments due to costs (meeting any one of them is counted as “yes”); and one item evaluating employment changes (reduction in working time or unemployment is counted as “yes”). Psychological domain was measured using the 12-item COST-FACIT-V2 scale, a validated patient-reported tool developed by de Souza et al., in 2014 [ 34 ]. Each item is rated on a 5-point Likert scale from 0 (not at all) to 4 (very much), with the total score calculated by summing the first 11 items, yielding a range of 0 to 44. Lower scores indicate greater FT. The Chinese version, translated by Chan et al., in 2021 [ 35 ], has showed good internal consistency (Cronbach's α = 0.86) and reliability (intraclass correlation coefficient = 0.71).

Secondary outcomes

Cost-related health literacy

Since no validated instrument is available for assessing CRHL in the Chinese population, a questionnaire was developed by the research team based on a concept analysis [ 36 ] and key intervention components. It primarily evaluates patients' awareness and confidence in managing FT, their ability to understand and communicate cost-related health information for informed decision-making, and their knowledge of accessing financial assistance. The questionnaire comprises 10 items rated on a 4-point Likert (1 = strongly disagree to 4 = strongly agree), with higher scores indicating better CRHL. Five experts from relevant disciplines reviewed and refined the questionnaire, achieving a content validity index of 0.80 to 1.00. The overall Cronbach's alpha coefficient of the scale was 0.805, indicating good internal consistency. The test-retest reliability over a two-week interval among 32 participants was 0.769, reflecting acceptable temporal stability.

Shared decision-making

Patient involvement in medical decision-making was assessed using the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), a validated instrument developed by Kriston et al. [ 37 ] and translated into Chinese by Luo BH et al. [ 38 ] The Chinese version has showed excellent internal consistency (Cronbach's α = 0.95). Each item is rated on a 6-point Likert scale (0 = completely disagree to 5 = completely agree), with total scores ranging from 0 to 45. Higher scores indicate a greater extent of SDM ability.

Perceived stress

The 14-item Perceived Stress Scale (PSS-14), developed by Cohen et al., [ 39 ] is widely used for assessing patients' perception of life as unpredictable, uncontrollable or overwhelming. The scale consists of seven negative and seven positive items, each rated on a 5-point Likert scale (0 = never to 4 = very often), with higher scores indicating greater perceived stress. The Chinese version, translated by Deng QL et al., [ 40 ] has demonstrated good reliability and validity (Cronbach's α = 0.89).

Data collection

The FN intervention was primarily conducted during hospitalization and within the 1st month post-discharge, with subsequent follow-ups focusing on ongoing monitoring and reinforcement. Based on our previous longitudinal study [ 41 ], FT typically worsens and peaks within 3 months post-surgery due to the intensive treatment phase. Thereafter, FT gradually declines and stabilizes. Thus, data collection was scheduled at baseline (T 0 ), one month post-baseline (T 1 ), and three months post-baseline (T 2 ). The PI conducted face-to-face baseline assessments after obtaining written informed consent, collecting sociodemographic and clinical data, along with COST-FACIT-V2, CRHL, SDM-Q-9 and PSS-14. A research assistant, blinded to group allocation and intervention implementation, conducted follow-up assessments (T 1 , T 2 ) via telephone, gathering data on FT (material, psychological and behavioral domains), CRHL, SDM-Q-9 and PSS-14.

Statistical analysis

Data analyses were conducted using IBM SPSS version 27.0 (IBM Corp., Armonk, NY, USA). An intention-to-treat (ITT) approach was adopted for analytical strategy, with missing data managed using the last observation carried forward (LOCF) method [ 42 ]. Sociodemographic and clinical characteristics as well as outcome variables at each time point, were summarized using descriptive statistics, including mean (standard deviation), frequency (percentage). Independent samples t -tests and chi-square tests were applied to compare the baseline characteristics between two groups and among post-intervention (T 1 , T 2 ) comparisons. When expected frequencies of events were low, Fisher's exact tests were adopted. To examine the effects of group, time, and interaction on COST-FACIT, CRHL, SDM-Q-9, and PSS-14, generalized estimating equations (GEEs) with a linear link function were employed, as all dependent variables were continuous [ 43 ]. Additionally, chi-square tests were used to compare the differences of the material and behavioral domains of FT between the intervention group and the control group at T 1 and T 2 , and the results were presented as odds ratios (OR) with 95% confidence intervals (CI). All statistical tests were two-sided with the level of significance set at 0.05.

Ethical considerations

This study received ethical approval from the Ethics Committee of Fudan University Shanghai Cancer Center (IRB No. 2312288-16) and was prospectively registered on ClinicalTrials.gov (ID: NCT06484140 ). Its implementation adhered to the Declaration of Helsinki. Written informed consent was obtained from all participants prior to enrollment, ensuring they were fully aware of their right to withdraw at any time without consequences.

Results

Recruitment and attrition

A total of 311 patients were screened for eligibility, with 121 excluded due to not meeting the inclusion criteria: 45 had non-cancer diagnoses, 48 had DCIS, 26 had recurrent breast cancer, and 2 were under 18 years old, yielding an eligibility rate of 61.1%. Additionally, 108 declined participation citing schedule conflicts (42 [38.9%]), lack of interest (35 [32.4%]), or unspecified reasons (31 [28.7%]). The target sample size was set at 40 participants per group; however, during the final stage of recruitment, one additional eligible participant was enrolled in each group due to ongoing admissions and the cluster-based (room-level) allocation process. Given the minimal difference and to preserve the integrity of cluster assignment, these participants were retained in the analysis. Ultimately, 82 eligible participants were included, with 41 in the intervention group and 41 in the control group, resulting in a consent rate of 43.2%.

Of these, 76 (92.7%) completed data collection at T 1 and 73 (89.0%) at T 2 . Dropout reasons included time constraints ( n = 5), loss of contact ( n = 3), and disease progression ( n = 1). The overall attrition rate was 11.0%. A flow diagram depicting recruitment, allocation, and attrition is provided in Fig. 1 . Fig. 1 Flow diagram of participants' recruitment, allocation, and attrition.

Intervention delivery

All interventions and follow-ups were completed by November, 2024. All intervention group participants underwent needs assessment and completed cost-related health education. The nurse navigator actively provided resource and service information, leading 10 participants (50.0%) to seek further details on financial assistance, 3 (15.0%) to request psychological referrals for moderate anxiety ( n = 1, GAD-7≥10), moderate depression ( n = 1, PHQ-9≥10), or severe anxiety ( n = 1, GAD-7≥15). Additionally, 7 (35.0%) were referred to physicians for wound infection ( n = 1), bone marrow suppression ( n = 2), moderate radiation dermatitis ( n = 1), insomnia ( n = 1), or moderate diarrhea ( n = 2). A total of 90 personalized counseling sessions were conducted, lasting approximately 247 min, delivered via face-to-face (18.9%), WeChat (36.7%) and telephone (44.4%) consultations. Discussion topics primarily covered medical insurance policies (38.9%), rehabilitation guidance (37.8%), commercial insurance (2.2%) and healthcare access procedures (21.1%).

Participants characteristics

As detailed in Table 1 , participants had a mean age of 47.66 ± 11.72 years. The majority were married (77 [93.9%]), had at least a senior high school education (58 [70.7%]), and were primarily cared by relatives (68 [82.9%]). Less than half (31 [41.3%]) had financially independent adult children. The majority (61 [74.4%]) reported family saving of less than 200,000 RMB. While, all participants had basic medical insurance, only 31 (37.8%) carried commercial insurance. For most (66 [86.8%]), basic medical insurance was the primary source of medical expenses. Table 1 The sociodemographic characteristics of participants ( N = 82). Table 1 Variables Mean ± SD/ n (%) All sample ( n = 82) Intervention Group ( n = 41) Control Group ( n = 41) Statistical Values P -value Age, y 47.66 ± 11.72 49.85 ± 12.08 45.46 ± 11.07 t = 1.716 0.090 Residence Shanghai 30 (36.6%) 19(46.3%) 11(26.8%) χ [ 2 ] = 3.364 0.067 Other provinces 52(63.4%) 22(53.7%) 30(73.2%) Education level Junior high school or below 24(29.3%) 11(26.8%) 13(31.7%) χ [ 2 ] = 2.290 0.318 Senior high school or Junior college education 13(15.8%) 9(22.0%) 4(9.8%) University/college or high 45(54.9%) 21(51.2%) 24(58.5%) Marital status Unmarried 5(6.1%) 2(4.9%) 3(7.3%) - a 1.000 Married 77(93.9%) 39(95.1%) 38(92.7%) Having children No 7(8.5%) 2(4.9%) 5(12.2%) - a 0.432 Yes 75(91.5%) 39(95.1%) 36(87.8%) Children's status ( n =75) all are dependents 27(36.0%) 13(33.3%) 14(38.9%) χ [ 2 ] = 0.267 0.875 some are adults but financially dependent 17(22.7%) 9(23.1%) 8(22.2%) all are adults and financially independent 31(41.3%) 17(43.6%) 14(38.9%) Caregivers Relatives 68(82.9%) 34(82.9%) 34(82.9%) 0.000 1.000 Others 14(17.1%) 7(17.1%) 7(17.1%) Having religion No 76(92.7%) 36(87.8%) 40(97.6%) - a 0.201 Yes 6(7.3%) 5(12.2%) 1(2.4%) Family savings (in 10,000 RMB) 0∼10 34(41.5%) 16(39.0%) 18(43.9%) 2.719 a 0.445 11∼20 27(32.9%) 15(36.6%) 12(29.3%) 21∼50 12(14.6%) 4(9.8%) 8(19.5%) >50 9(11.0%) 6(14.6%) 3(7.3%) Having commercial insurance No 51(62.2%) 25(61.0%) 26(63.4%) χ [ 2 ] = 0.052 0.820 Yes 31(37.8%) 16(39.0%) 15(36.6%) Major sources of medical expenses at 1-month post-discharge ( n =76) b Out-of-pocket 10(13.2%) 3(7.9%) 7(18.4%) χ [ 2 ] = 1.842 0.309 Basic medical insurance 66(86.8%) 35(92.1%) 31(81.6%) a Fisher exact test. b Six participants dropped out at the 1-month follow up.

As presented in Table 2 , most participants were diagnosed with invasive breast cancer (79 [96.3%]), with 82.9% ( n = 68) at stages II/III and 89.0% ( n = 73) had no other chronic diseases. All underwent surgical treatment, including breast-conserving surgery (32 [39.0%]), modified radical mastectomy (13 [15.9%]), tumor resection (22 [26.8%]), and reconstructive surgery (15 [18.3%]). At one month post-intervention, the predominant treatments included chemotherapy (36 [47.4%]), hormone therapy (28 [36.8%]), or radiotherapy (17 [22.4%]). By three months post-intervention, hormone therapy (33 [45.2%]) became the most common, followed by chemotherapy (24 [32.9%]) and radiotherapy (22 [30.1%]). No significant between-group differences were found in sociodemographic or clinical characteristics (see Table 1 , Table 2 ). Table 2 The clinical characteristics of participants ( N = 82). Table 2 Variables Mean ± SD/ n (%) All sample ( n = 82) Intervention Group ( n = 41) Control Group ( n = 41) χ [ 2 ] value P -value Type of breast cancer Invasive carcinoma of no special type 79(96.3%) 40(97.6%) 39(95.1%) - a 1.000 Invasive carcinoma of special types 3(3.7%) 1(2.4%) 2(4.9%) Breast cancer surgery Breast-conserving surgery 32(39.0%) 16(39.0%) 16(39.0%) 0.859 0.835 Modified radical mastectomy 13(15.9%) 7(17.1%) 6(14.6%) Tumor resection 22 (26.8%) 12(29.3%) 10(24.4%) Reconstructive surgery 15(18.3%) 6(14.6%) 9(22.0%) Stage of cancer Stage I 7(8.5%) 4(9.7%) 3(7.3%) 1.838 a 0.649 Stage II 45(54.9%) 22(53.7%) 23(56.1%) Stage III 23(28.0%) 13(31.7%) 10(24.4%) Stage IV 0(0.0%) 0(0.0%) 0(0.0%) Unclear 7(8.5%) 2(4.9%) 5(12.2%) Having other chronic diseases No 73(89.0%) 36(87.8%) 37(90.2%) - a 1.000 Yes 9(11.0%) 5(12.2%) 4(9.8%) Current treatment at 1-month post-discharge ( n =76) b Chemotherapy 36(47.4%) 22(57.9%) 14(36.8%) 3.378 0.066 Hormone therapy 28(36.8%) 14(36.8%) 14(36.8%) 0.000 1.000 Radiotherapy 17(22.4%) 6(15.8%) 11(28.9%) 1.894 0.169 Targeted therapy 13(17.1%) 10(26.3%) 3(7.9%) 4.547 0.065 Traditional Chinese medicine 1(1.3%) 1(2.6%) 0(0.0%) - a 1.000 None 5(6.6%) 2(5.3%) 3(7.9%) - a 1.000 Current treatment at 3-month post-discharge ( n =73) c Hormone therapy 33(45.2%) 16 (43.2%) 17(47.2%) 0.117 0.733 Chemotherapy 24(32.9%) 14 (37.8%) 10(27.8%) 0.837 0.360 Radiotherapy 22(30.1%) 9 (24.3%) 13(36.1%) 1.204 0.273 Targeted therapy 14(19.2%) 9 (24.3%) 5 (13.9%) 1.282 0.258 Traditional Chinese medicine 9 (12.3%) 5 (13.5%) 4 (11.1%) - a 1.000 Immunotherapy 2 (2.7%) 2 (5.4%) 0 (0.0%) - a 0.493 None 3 (4.1%) 1 (2.7%) 2 (5.6%) - a 0.615 a Fisher exact test. b Six participants dropped out at the 1-month follow up. This variable allowed multiple responses. c An additional three participants dropped out at the 3-month follow up. This variable allowed multiple responses.

Effects on COST-FACIT-V2

As shown in Table 3 , the GEE analysis of the FN intervention's effect on COST-FACIT-V2 revealed a significant time effect ( P = 0.010), indicating a statistical difference in COST-FACIT-V2 scores across time points between two groups. Additionally, a significant group × time interaction effect was observed ( χ 2 group×time = 18.998, P < 0.001). Table 3 Validity assessment of the GEE model for outcomes. Table 3 Outcomes Group effect Time effect Group × Time interaction Wald χ 2 P -value Wald χ 2 P -value Wald χ 2 P -value COST-FACIT-V2 1.700 0.192 9.229 0.010 18.998 <0.001 CRHL 4.522 0.033 73.539 <0.001 31.535 <0.001 SDM-Q-9 5.254 0.022 11.908 0.003 2.906 0.234 PSS-14 4.624 0.032 8.478 0.014 0.579 0.749

Table 4 presents the between-group comparison of COST-FACIT-V2 at each time point. At T 0 , the mean score of the intervention group was 2.512 lower than the control group, with no statistically significant difference ( P = 0.255). At T 1 , the intervention group's mean score was 4.756 higher than the control group, showing a statistically significant difference ( P = 0.020), indicating less severe subjective FT in the intervention group. At T 2 , the mean score in the intervention group remained higher by 4.659 compared to the control group, with a statistically significant difference ( P = 0.014). Table 4 Between-group comparison of outcomes across time points ( n = 82) a . Table 4 Outcomes Intervention group ( n = 41) Control group ( n = 41) Control group vs. Intervention group Mean difference (95% CI) P -value COST-FACIT-V2 T 0 19.90 ± 10.94 22.41 ± 9.21 2.512 (−1.811, − 6.836) 0.255 T 1 22.24 ± 8.60 16.85 ± 9.89 −4.756 (−8.758, −0.754) 0.020 T 2 24.44 ± 7.62 19.78 ± 9.64 −4.659 (−8.374, −0.943) 0.014 CRHL T 0 20.44 ± 5.85 23.41 ± 6.65 2.976 (0.296, 5.655) 0.030 T 1 29.05 ± 4.98 25.46 ± 5.33 −3.146 (−5.382, −0.911) 0.006 T 2 31.15 ± 4.36 25.59 ± 5.10 −5.561 (−7.590, −3.532) <0.001 SDM-Q-9 b T 0 37.10 ± 6.52 32.32 ± 10.22 −3.240 (−6.00, −0.470) 0.022 T 1 34.73 ± 6.88 31.22 ± 8.74 T 2 36.22 ± 7.17 34.32 ± 8.44 PSS-14 b T 0 25.12 ± 9.44 21.02 ± 7.49 −3.360 (−6.420, −0.30) 0.032 T 1 26.05 ± 7.29 24.02 ± 8.78 T 2 26.83 ± 7.87 23.80 ± 8.17 a Missing data were handled using the last observation carried forward (LOCF) method following the intention-to-treat (ITT) principle. b Because the group × time interaction effect was not statistically significant, only main effect analysis were conducted.

As shown in Table 5 , within-group comparison of COST-FACIT-V2 revealed that in the intervention group, the mean score at T 2 was 4.54 higher than that at T 0 , with a statistically significant difference ( P < 0.001). No significant differences were found between T 1 and T 0 ( P = 0.473) or T 2 and T 1 ( P = 0.183). In the control group, the mean score at T 1 was 5.24 lower than at T 0 ( P < 0.001), and the score at T 2 was 2.61 higher than at T 1 ( P = 0.032), both showing statistical significance. However, the difference between T 2 and T 0 was not statistically significant ( P = 0.075). Table 5 Pairwise comparison of outcomes across time points ( n = 82) a . Table 5 Outcomes T 1 vs. T 0 T 2 vs. T 0 T 2 vs. T 1 Mean difference (95% CI) P -value Mean difference (95% CI) P -value Mean difference (95% CI) P -value COST-FACIT-V2 Intervention group ( n = 41) 2.02 (−1.40 , 5.450) 0.473 4.54 (1.420 , 7.650) 0.001 2.51 (−0.70 , 5.720) 0.183 Control group ( n = 41) −5.24 (−8.550 , −1.940) <0.001 −2.63 (−5.450 , 0.180) 0.075 2.61 (0.160 , 5.060) 0.032 CRHL Intervention group ( n = 41) 8.39 (5.750 , 11.030) <0.001 10.71 (8.290 , 13.130) <0.001 2.32 (0.830 , 3.810) 0.001 Control group ( n = 41) 2.27 (0.010 , 4.530) 0.049 2.17 (−0.550 , 4.890) 0.167 −0.10 (−2.030 , 1.840) 1.000 SDM-Q-9 b −1.73 (−4.410 , 0.940) 0.364 0.56 (−1.720 , 2.840) 1.000 2.29 (0.70 , 3.890) 0.002 PSS-14 b 1.96 (0.040 , 3.880) 0.043 2.24 (0.330 , 4.160) 0.015 0.28 (−1.250 , 1.810) 1.000 a Missing data were handled using the last observation carried forward (LOCF) method following the intention-to-treat (ITT) principle. b Because the group × time interaction effect was not statistically significant, only main effect analysis were conducted.

Effects on CRHL

As summarized in Table 3 , the GEE analysis demonstrated significant group and time effects of the FN intervention on CRHL, with a notable group × time interaction effect ( χ 2 group×time = 31.535, P < 0.001).

Table 4 provides an overview of the CRHL differences between groups across time points. At T 0 , the mean CRHL in the intervention group was 2.976 lower than the control group, showing a statistically significant difference ( P = 0.030). At T 1 , the intervention group's mean CRHL was 3.146 higher than the control group ( P = 0.006). At T 2 , the intervention group outperformed the control group by 5.561 points, with a highly significant difference ( P < 0.001).

Within-group comparisons in Table 5 revealed significant improvements in CRHL over time in the intervention group ( P ≤ 0.001). Specifically, the mean CRHL at T 1 and at T 2 was 8.39 ( P < 0.001) and 10.71 ( P < 0.001) higher than at T 0 , respectively, and the mean score at T 2 was 2.32 higher than at T 1 ( P = 0.001). In the control group, the mean CRHL at T 1 was 2.27 higher than at T 0 ( P = 0.049), while no significant differences were observed in other within-group comparisons (T 2 vs. T 0 , T 2 vs. T 1 ).

Effects on SDM-Q-9

Table 3 presents that significant group and time effects of the FN intervention's effect on SDM-Q-9 were observed ( P < 0.05), while the group × time interaction effect was not statistically significant ( χ 2 group×time = 2.906, P = 0.234).

As shown in Table 4 , the mean SDM-Q-9 score in the intervention group was 3.240 higher than in the control group, with a statistically significant difference ( P = 0.022).

Table 5 shows that the mean SDM-Q-9 score at T 2 was 2.29 higher than at T 1 ( P = 0.002). However, no statistically significant differences were found between T 1 and T 0 ( P = 0.364) or between T 2 and the T 0 ( P = 1.000).

Effects on PSS-14

Table 3 demonstrates that the FN intervention had significant group and time effects on PSS-14 ( P < 0.05), while the group × time interaction effect was not statistically significant ( χ 2 group×time = 0.579, P = 0.749).

As summarized in Table 4 , the mean PSS-14 score in the intervention group was 3.360 higher than the control group, showing a statistically significant difference ( P = 0.032).

Table 5 reveals the mean PSS-14 score at T 1 and T 2 were 1.96 ( P = 0.043) and 2.24 ( P = 0.015) higher than those at T 0 , respectively. However, no statistically significant difference was observed between T 2 and at T 1 ( P = 1.000).

Effects on material and behavioral domains of FT

As presented in Table 6 , at T 1 , 21.1% participants in the intervention group and 23.7% in the control group reported experiencing debt or significant asset changes due to antineoplastic therapy, with no statistically significant difference between the groups ( OR = 1.161, 95%CI: 0.395 , 3.429). Regarding treatment adherence, no significant difference was observed between two groups ( OR = 1.000, 95%CI: 0.060, 16.594). In terms of employment changes, 71.1% of the intervention group and 78.9% of the control group reported reduced working time or unemployment following a breast cancer diagnosis, with no significant between-group difference ( OR = 1.528, 95%CI: 0.535 , 4.360). Table 6 Material and Behavioral Domains of FT Table 6 Time point Variables n (%) All Sample ( n =76) a Intervention Group ( n =38) Control Group ( n =38) OR (95% CI) P-value T 1 Having debt / significant asset change due to costs Yes 17 (22.4%) 8 (21.1%) 9 (23.7%) 1.161 (0.395 , 3.429) 1.000 No 59 (77.6%) 30 (78.9%) 29 (76.3%) Have delayed / foregone / changed treatments due to costs Yes 2 (2.6%) 1 (2.6%) 1 (2.6%) 1.000 (0.060 , 16.594) 1.000 No 74 (97.4%) 37 (97.4%) 37 (97.4%) Employment changes No changes 19 (25.0%) 11 (28.9%) 8 (21.1%) 1.528 (0.535 , 4.360) 0.597 Reduction in working time / unemployment 57 (75.0%) 27 (71.1%) 30 (78.9%) T 2 All Sample ( n =73) b Intervention Group ( n =37) Control Group ( n =36) OR (95% CI) P-value Having debt / significant asset change due to costs Yes 16 (21.9%) 6 (16.2%) 10 (27.8%) 1.987 (0.637 , 6.204) 0.269 No 57 (78.1%) 31 (83.8%) 26 (72.2%) Have delayed / foregone / changed treatments due to costs Yes 2 (2.7%) 1 (2.7%) 1 (2.8%) 1.029 (0.062 , 17.094) 1.000 No 71 (97.3%) 36 (97.3%) 35 (97.2%) Employment changes No changes 21 (28.8%) 12 (32.4%) 9 (25.0%) 1.440 (0.519 , 3.998) 0.607 Reduction in working time / unemployment 52 (71.2%) 25 (67.6%) 27 (75.0%) Abbreviations: OR , Odds ratio. a Six participants dropped out at the 1-month follow up. b An additional three participants dropped out at the 3-month follow up.

At T 2 , the proportion of participants reporting debt or significant asset changes due to antineoplastic therapy was lower in the intervention group compared to the control group (16.2% vs. 27.8%), though this difference was not statistically significant ( OR = 1.987, 95%CI: 0.637 , 6.204). Similarly, no significant difference in treatment adherence was noted between groups ( OR = 1.029, 95% CI: 0.062, 17.094). For employment changes, 67.6% of the intervention group and 75.0% of the control group reported reduced working hours or unemployment, with no statistically significant difference ( OR = 1.440, 95%CI: 0.519, 3.998).

Discussion

We developed a FN intervention comprising four components: needs assessment module, cost-related health education module, resource/service referral module, and personalized counseling module, aimed at alleviating FT. This study provides evidence that FN is an effective strategy for mitigating subjective FT and enhancing CRHL among patients with breast cancer. These findings underscore the value of incorporating FN into routine FT management.

Our findings showed FN intervention produced a distinct longitudinal improvement in subjective FT compared with usual care. This aligns with Wheeler et al., [ 20 ] who reported a 6.9 increase in the COST-FACIT score following FN intervention ( P < 0.05). Consistently, Shankaran et al. [ 44 ] reported a gradual reduction in financial anxiety in 33% of cancer patients following FN intervention. Similarly, Wheeler et al. [ 20 ] observed a 53% and 18% decrease in distress from financial concerns and lack of cost-related knowledge, respectively, after the intervention. However, our feasibility study did not observe significant group differences in FT due to limited statistical power [ 24 ]. Drawing from social stress theory [ 22 , 45 ], the FN intervention likely improve FT by equipping patients with cost-management skills, enhancing their financial control, and reducing financial stress. Personalized needs assessment and consultations meet patients’ emotional needs, fostering support and alleviating financial stress. Future research should consider extending the follow-up period to evaluate the long-term impact on FT.

No significant effects were observed in the material and behavioral domains, including debt accumulation, treatment delay, and employment changes. This may be attributed to the structural nature of these outcomes, which are less amenable to short-term modification and often require sustained changes in financial resources or healthcare systems. Financial navigation primarily aims to improve patients’ understanding, decision-making, and coping strategies, and its effects may manifest earlier in psychological domains rather than distal economic outcomes. In addition, the low baseline prevalence of certain events (e.g., treatment delay) limited the statistical power to detect differences, as reflected by the wide confidence intervals. These findings suggest that longer follow-up and larger sample sizes may be needed to capture potential downstream effects of the intervention. This observation is consistent with other FT-focused interventions. For example, Djulbegovic et al. [ 18 ] reported that a financial navigation program had no significant effect cost-coping behaviors ( P > 0.05) for patients with multiple myeloma within 4 months. Moreover, this study did not engage clinicians through this process. Pairing FN program with clinicians and system might reduce the burden of care costs on patients. Future studies should consider following patients for a full year to ensure adequate time to examine health care use and consider addressing the needs of the most vulnerable patients [ 46 ].

This study found a significant improvement in CRHL in the intervention group compared to the control group. The observed improvements in CRHL are likely attributable to several key features of the intervention. The provision of practical, actionable information (e.g., explanation of insurance concepts, cost-related terminology, and navigation strategies) may have reduced cognitive barriers and enabled patients to better interpret and use cost information. The accessible format and timing of the intervention may have facilitated knowledge retention and ongoing application, which could explain the continued improvement observed at T2. Additionally, by explicitly addressing cost-related concerns, the intervention may have increased patients' awareness of financial issues and prompted more active engagement with cost-related decision-making. This finding aligns with our feasibility study [ 24 ] and is consistent with Politi et al., [ 33 ] who reported that health insurance education significantly enhanced patients' understanding and confidence in using insurance. Biddell et al. [ 47 ] also highlighted the importance of effective cost communication and education in identifying financial needs and alleviating FT. Providing patients with practical and accessible information can strengthen their ability to manage costs and reduce FT. Medical institutions should integrate cost-related education into regular practice to support patients’ financial well-being and empower them to make informed decisions.

Our findings showed significant group and time effects on SDM ability, whereas the group × time interaction effect was not statistically significant. Specifically, patients in the intervention group demonstrated overall higher SDM-Q-9 scores than those in the control group. However, the absence of a significant interaction effect suggests that the FN intervention did not produce a distinct longitudinal improvement in SDM ability compared with usual care. SDM, initially proposed by Strull et al., in 1984 [ 48 ], refers to a collaborative process in which healthcare providers and patients jointly evaluate treatment options by considering potential benefits, risks, and economic implications, thereby making decisions aligned with patients' values and preferences. Previous studies have suggested that patient engagement and communication with healthcare providers are closely associated with SDM in clinical settings [ 49 ]. In the present study, the overall higher SDM scores observed in the intervention group may be related to the FN intervention facilitating discussions about treatment costs, available financial resources, and care planning between patients and healthcare professionals. Meanwhile, the observed improvement in SDM scores over time may also reflect patients’ gradual adaptation to the treatment process and increased familiarity with their disease and treatment options during follow-up [ 50 ]. Nevertheless, because the interaction effect was not statistically significant, the current findings provide limited evidence regarding the specific effectiveness of FN in enhancing SDM ability over time. Future studies with larger sample sizes and longer follow-up periods are warranted to further clarify the relationship between FN interventions and SDM among patients with breast cancer.

Similarly, the present study identified significant group and time effects on perceived stress, whereas the group × time interaction effect was not statistically significant. Specifically, patients in the intervention group reported overall higher PSS-14 scores than those in the control group, and perceived stress levels increased at one and three months post-discharge compared with baseline. FN intervention did not demonstrate a distinct longitudinal effect on reducing perceived stress relative to usual care. Patients with breast cancer often face multifaceted stressors, including physical and mental burden from cancer and treatment, disruptions in life and social roles, fear of recurrence, financial concerns, and uncertainty about the future [ [51] , [52] , [53] ]. Financial support alone may be insufficient to substantially reduce overall perceived stress. The increase in perceived stress over time observed in both groups may reflect the ongoing challenges encountered during the post-discharge and treatment recovery period. Additionally, stress management is inherently complex and typically requires multidimensional and sustained interventions [ 54 ]. Smith et al. [ 14 ] emphasized the importance of incorporating dynamic distress assessment into FN programs and recommended integrating targeted psychological support strategies, such as psychoeducation, coping skills training, cognitive behavioral therapy, problem-solving therapy, and acceptance and commitment therapy. Therefore, future FN programs should integrate structured stress management components delivered through a multidisciplinary team including nurses, psychologists, social workers, and financial counselors. Furthermore, incorporating routine distress screening and referral pathways could allow for early identification of patients at risk of psychological distress and ensure timely support.

FT is increasingly recognized as a global public health concern, affecting cancer patients not only in high-income settings but also in low- and middle-income countries due to escalating treatment costs and insurance gaps [ 55 ]. Our results suggested that financial navigation may serve as a scalable and adaptable strategy to reduce subjective FT and improve patients’ financial empowerment. In countries with fragmented insurance coverage or limited financial counseling resources, FN may help bridge systemic gaps by improving cost transparency, access to resources, and patient–provider communication, thereby contributing to global efforts toward health equity in cancer survivorship. From a long-term perspective, the improvements in subjective FT and CRHL highlight the potential durability of FN benefits. However, longer follow-up periods are needed to evaluate whether these improvements translate into reduced debt, improved treatment adherence, and better quality of life. Moreover, the feasibility of scaling FN interventions depends on workforce training, institutional support, and integration into existing oncology care pathways. Digital platforms, tele-navigation, and community-based partnerships may further enhance scalability and cost-effectiveness, particularly in resource-limited settings [ 20 , 55 ].

Limitations

This study has several limitations. First, the sample size estimation was based on an effect size derived from a previous pilot study, due to the lack of prior RCT evidence in this area. Because effect sizes from pilot studies are often imprecise and may over- or underestimate the true effect, the precision of our sample size calculation was limited. Therefore, the interpretation and application of the research results require caution. Second, given the limited sample size and short study duration, the long-term effects of intervention were not determined. Third, this study focused solely on negative coping behaviors related to FT, such as debt and treatment nonadherence. Future large-scale RCTs with extended follow-up periods should comprehensively assess FT, including positive coping behaviors like successful access to financial assistance. Finally, participants were recruited exclusively from a top-tier national cancer center, which may have restricted the diversity of the sample and limited the generalizability of findings. To enhance the applicability of the intervention across diverse populations, future multicenter RCTs should be conducted in varied socioeconomic settings, particularly in underserved or rural areas with limited healthcare resources.

Clinical implementations

The findings of this study suggest that FN can be a practical and beneficial addition to routine oncology care for patients with breast cancer, particularly in settings with substantial out-of-pocket costs such as China. In clinical practice, FN could be implemented as a structured service delivered by trained personnel (e.g., nurses, social workers, or financial counselors) at key time points along the care pathway, especially during hospitalization and early post-discharge. Core components may include individualized financial assessment, cost-related education, guidance on insurance and reimbursement policies, and support for treatment-related financial planning. Future implementation should consider local healthcare policies, insurance structures, and resource availability to maximize feasibility and sustainability. Overall, FN represents a promising strategy to support more equitable, patient-centered cancer care by addressing the financial burden associated with treatment while promoting informed decision-making.

Conclusion

This study suggests that a well-designed FN intervention may help alleviate subjective FT and enhance CRHL among patients with breast cancer. Future large-scale randomized controlled trials are warranted to confirm these findings, examine long-term effects, and evaluate the intervention across diverse cultural and healthcare contexts.

Fundings

This work was supported by Humanity and Social Science foundation of 10.13039/501100002338 Ministry of Education of China ( 25YJA84001 ); and Clinical Research Funding of 10.13039/100017950 Shanghai Municipal Health Commission ( 202340089 ).

CRediT authorship contribution statement

Feng Jing: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing. Xiaoyi Yuan: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing. Yimin Tang: Investigation. Yue Liu: Investigation. Yuxuan Huang: Investigation. Liqin Chen: Investigation, Resources. Lichen Tang: Investigation, Resources. Jiajia Qiu: Investigation, Resources. Weijie Xing: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Weijie Xing reports financial support was provided by Humanity and Social Science foundation of Ministry of Education of China and Clinical Research Funding of Shanghai Municipal Health Commission. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Supplementary data

The following is the Supplementary data to this article: Multimedia component 1

Supplementary data to this article can be found online at https://doi.org/10.1016/j.breast.2026.104824 .