Skip to main content
NCBI home page
Nursing Open logoLink to Nursing Open
. 2026 Sep 30;13(10):e70873. doi: 10.1002/nop2.70873

Prevalence and Factors Associated With Financial Toxicity in Cervical Cancer Patients: A Cross‐Sectional Study

Dongliu Li 1,2, Menghua Wen 1, Youfu Zhao 1, Tingyu Nie 1, Miaomiao Li 1, Li Yuan 1, Meiming Wang 1, Xiaoyi Zhu 3, Ying Li 1, Qian Zhang 1, Chunying He 4,✉
PMCID: PMC13625818  PMID: 42813918

ABSTRACT

Background

Financial toxicity threatens the survival and prognosis of cervical cancer patients, yet systematic exploration of its prevalence and full spectrum of protective and risk factors remains limited.

Aims

To investigate the prevalence and factors associated with financial toxicity among patients with cervical cancer.

Design

A cross‐sectional quantitative study.

Methods

We surveyed 351 cervical cancer patients from November 2023 to November 2024 using a general information questionnaire and the patient self‐reported Comprehensive Score for Financial Toxicity. Binary logistic regression identified associated factors.

Results

The 351 participants ranged in age from 25 to 88 years (57.70 ± 11.47 years). The prevalence of financial toxicity was 66.4%. Multivariable analysis demonstrated that each 1‐year increase in age, having commercial insurance and fewer hospitalizations (< 3 times) may be associated with a reduced risk of financial toxicity, whereas lower disposable household savings (< 100,000 CNY), having urban–rural resident basic medical insurance and receiving radiotherapy may be associated with an increased risk.

Conclusions

This study identified factors associated with financial toxicity in cervical cancer patients, with factors associated including age, commercial insurance, basic medical insurance type, disposable household savings, number of hospitalizations and radiotherapy. These findings may assist healthcare providers in identifying patients at higher risk of financial toxicity and inform the development of individualized care plans.

Implications for the Profession and/or Patient Care

These findings may assist healthcare providers to identify patients who may be at higher risk of financial toxicity early, which may support treatment adherence and health‐related quality of life.

Impact

This study examined financial toxicity prevalence and associated factors among cervical cancer patients, identified key modifiable and non‐modifiable factors for clinical screening, and may inform nursing practice and healthcare policy adjustments.

Reporting Method

This study followed the STROBE guidelines for cross‐sectional studies.

Patient or Public Contribution

No patient or public contribution.

Keywords: cancer, cervical cancer, cross‐sectional study, financial toxicity, risk factors

1. Introduction

Cervical cancer is one of the most common malignant tumours affecting women worldwide, with 604,000 new cases and 342,000 deaths annually globally, posing a severe threat to women's health and a major challenge to public health (Sung et al. 2021). According to data from the National Cancer Center of China, there are 119,300 new cases and 37,200 deaths of cervical cancer in China (Rongshu et al. 2023). Advances in cancer treatment technologies and diversified therapeutic approaches have improved patient survival rates but also led to a continuous increase in treatment costs (Peng et al. 2022). Up to 42.4% of patients exhaust all their savings within 2 years after treatment (Gilligan et al. 2018). The treatment cost for Chinese cervical cancer patients within 1 year of diagnosis ranges from 58,000 CNY (8062 USD) to 165,000 CNY (22,935 USD), with the annual average cost being approximately four times the household disposable income, imposing a heavy financial burden on patients and their families (Xi et al. 2023).

Financial toxicity refers to the negative impacts caused by financial hardship during patient treatment, including both objective financial burden (e.g., out‐of‐pocket medical expenses) and subjective financial stress (e.g., psychological anxiety) (Zafar and Abernethy 2013). This phenomenon may lead to significant changes in lifestyle, reduced quality of life, increased psychological distress and compromised treatment efficacy (Zafar and Abernethy 2013). In recent years, researchers have conducted a series of studies on financial toxicity in cancer patients. In the field of gynaecologic oncology, a systematic review of 10 studies reported an overall prevalence of financial toxicity of 43% (95% CI: 36%–49%) among patients with gynaecologic malignancies (Li 2025). A large nationwide Chinese study using COST and a multilevel conceptual framework further confirmed that financial toxicity is prevalent among cancer patients in China, with factors such as age, household income and insurance type playing significant roles (Xu, So, Choi, et al. 2024). However, research specifically on cervical cancer patients remains in its early stages. A survey of 165 cervical cancer patients undergoing chemotherapy found that 86.7% experienced financial toxicity (Yang et al. 2024). Another study reported that 67% of cervical cancer patients reported high levels of financial toxicity, with a higher prevalence than other gynaecologic malignancies, suggesting that cervical cancer patients may face more severe financial distress (Esselen et al. 2021).

Notably, these patients are 5.9 times more likely to delay or refuse treatment than patients with other malignant tumours. In addition, patients with weak financial capacity are 7.3 times more likely to develop severe financial toxicity and 4 times more likely to refuse medication than ordinary patients (Esselen et al. 2021; Rongshu et al. 2023). A longitudinal survey found that more than 50% of patients exhibit psychological reactions related to financial hardship within the first 8 weeks of initiating systematic treatment (Liang et al. 2020). Another study demonstrated that communication about treatment costs and provision of more cost‐effective treatment regimens by medical staff within 12 months after diagnosis can alleviate financial burden (Kazzi et al. 2022). Therefore, early screening and assessment of financial toxicity in cervical cancer patients can provide direction for subsequent personalized interventions and risk prevention. This requires healthcare providers to first understand its prevalence and influencing factors.

In terms of influencing factors, existing studies have conducted preliminary explorations. A survey of 109 Japanese patients with gynaecologic malignancies reported that age, household savings and disease duration were associated with financial toxicity (Kajimoto et al. 2023). Another study of 121 American gynaecologic cancer patients found that age, annual income and insurance type were the main influencing factors (Liang et al. 2020). A survey of 50 patients with various gynaecologic malignancies showed that age, tumour type and income were highly correlated with financial toxicity (Zeybek et al. 2021). A Chinese study of 165 cervical cancer patients undergoing chemotherapy further identified infusion route, employment status, social support and medical insurance type as independent influencing factors (Yang et al. 2024). However, these studies generally have small sample sizes and tend to focus on demographic or single clinical characteristics, limiting the generalizability of their findings. In addition, differences in geographic regions, healthcare systems and insurance policies across studies have led to inconsistent conclusions.

Although researchers have indicated that patients with gynaecologic malignancies face a higher risk of financial burden, studies on financial toxicity specifically in cervical cancer patients remain limited. Therefore, this study took cervical cancer patients as the research object, and based on a multidimensional analytical framework combining literature evidence, clinical practice and statistical analysis, systematically explored the characteristics and influencing factors of financial toxicity from three levels: individual resources, healthcare system and treatment burden. The aim was to identify susceptible groups with ‘high financial toxicity’ and provided an evidence‐based basis for healthcare providers to develop early warning indicators and personalized cost communication and comprehensive measures, thereby reducing financial toxicity and its potential adverse consequences in patients.

2. Aims

The aim of this study was to investigate the prevalence of financial toxicity among cervical cancer patients and identify its associated factors, so as to provide preliminary evidence‐based references for early identification of high‐risk patients and development of targeted supportive nursing interventions.

3. Methods

3.1. Study Design and Setting

This was a cross‐sectional study. Participants were cervical cancer patients who met the inclusion and exclusion criteria and were treated at a tertiary hospital in Yan'an University Affiliated Hospital from November 2023 to November 2024. The inclusion criteria were as follows: (1) Patients with primary cervical cancer confirmed by histopathological examination, with clinical Stages I–IV according to FIGO 2018 staging; (2) age ≥ 18 years; (3) conscious and capable of basic communication and form completion and (4) voluntary participation in the study and signed informed consent. The exclusion criteria included: (1) Patients with severe mental illness or severe complications and (2) newly diagnosed patients who did not receive any anti‐cancer treatment (surgery, radiotherapy or chemotherapy). The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

A total of 18 variables were included in the investigation. According to Kendall's rule of thumb, the sample size should be 5–10 times the number of variables. Considering 20% invalid data, the required sample size for this study was 113–225. Finally, 351 patients were enrolled.

3.2. Data Collection

Investigators conducted questionnaire surveys on eligible patients. The questionnaires were uniformly distributed by the researcher and four uniformly trained postgraduate nursing students. Before distribution, all participants were fully informed of the purpose, content, significance, estimated time consumption and privacy protection measures of the survey. All participants signed written informed consent. The questionnaires were completed through face‐to‐face questioning one by one, with standard terminology used to answer patients' questions. No induction was imposed on patients during completion to ensure the authenticity and reliability of the questionnaires. Completed questionnaires were collected and checked for completeness by investigators.

3.3. Research Tools

3.3.1. General Information Questionnaire

This study adopted a ‘theory–clinical–empirical’ three‐dimensional integration strategy to screen potential influencing factors. In the theoretical dimension, based on the financial toxicity framework proposed by Carrera et al. (2018), candidate variables were categorized into three layers: individual resources (including age, marital status, education level, employment status, monthly household per capita income, disposable household savings and commercial insurance), healthcare system (including basic medical insurance type and one‐way transportation cost) and treatment burden (including disease stage, disease duration, number of hospitalizations, surgery history, chemotherapy history, radiotherapy history and comorbid chronic diseases), to systematically capture the potential pathways of financial toxicity. In the clinical dimension, considering the actual context of cervical cancer treatment, two variables‐number of hospitalizations and radiotherapy‐were further specified. In the empirical dimension, univariate analysis adopted a liberal inclusion threshold (p < 0.20) to reduce type II error and avoid omitting potentially important factors (Greenland 1989), achieving a balance between statistical rigour and clinical utility. Through literature review and theoretical considerations (Li 2025), 18 factors were included. All medical costs in this study were calculated in Chinese Yuan (CNY) and converted to US Dollars (USD) at the average exchange rate during the study period (1 USD = 7.194 CNY) for international comparison.

3.3.2. Comprehensive Score for Financial Toxicity‐Patient Reported Outcome Measure (COST‐PROM)

The COST‐PROM used in this study was developed by de Souza et al. (2014) and is the first self‐reported tool for measuring financial hardship in cancer patients. Chinese scholars Yu et al. (2017) translated the COST‐PROM into a Chinese version, which includes three dimensions (active financial expenditure, passive financial resources and psychosocial responses) with 11 items. A 5‐point Likert scale was adopted, with scores ranging from 0 to 4 for each item. Items 1, 6, 7 and 11 were scored positively, and the remaining items were scored reversely. The total score ranges from 0 to 44; a lower score indicates more severe financial toxicity (Ao 2022). A binary cutoff is widely used internationally for rapid screening of clinically high‐risk populations: total score ≥ 26 indicates no financial toxicity and < 26 indicates the presence of financial toxicity (de Souza et al. 2014, 2016). Although the original scale provides a four‐level severity classification, this study adopted the binary approach as the primary outcome measure to enhance clinical applicability and comparability with international studies. The scale had a content validity index of 0.82, with item content validity indices ranging from 0.83 to 1.00. Reliability and validity analysis showed a Cronbach's α coefficient of 0.889 (good internal consistency) and a Pearson correlation coefficient of 0.77–0.98 for test–retest results (all p < 0.05). In our sample, the COST‐PROM demonstrated good internal consistency, with a Cronbach's α coefficient of 0.9335. The tool is widely used in financial toxicity assessment with good applicability.

3.4. Statistical Analysis

Descriptive statistics were performed using SPSS 26.0 software. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequency (percentage). For univariate analysis, independent‐samples t‐test was used for normally distributed continuous variables, Mann–Whitney U test for non‐normally distributed variables and χ 2 test or Fisher's exact test for categorical variables, with a significance threshold of p < 0.20.

To construct the multivariable model, candidate factors with p < 0.2 were included in binary logistic regression (backward conditional method), and odds ratios (OR) and 95% confidence intervals (CI) were output with a two‐sided significance level of α = 0.05 (Greenland 1989). Age was included in the regression model as it met the prespecified p < 0.20 inclusion threshold (p = 0.118). For multicollinearity diagnosis, variance inflation factor (VIF) and tolerance of each predictive variable were calculated, supplemented by condition index and eigenvalue matrix to analyse potential multicollinearity. In this study, all VIF < 2.5, tolerance > 0.40 and CI < 10, indicating no significant collinearity between independent variables and stable and reliable model parameter estimation. Model fit was assessed using the Hosmer–Lemeshow test (χ 2 = 9.458, p = 0.305), indicating acceptable calibration.

3.5. Ethical Considerations

This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and approved by the Institutional Review Board of Yan'an University Affiliated Hospital (Approval No. YA‐L20240006; Approval Date: 24 November 2023). Written informed consent was obtained from all participants. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The authors declare no conflicts of interest.

4. Results

4.1. Demographic Characteristics

During the study period, 420 eligible patients were screened, 372 participated in the survey and 351 were finally enrolled, with an actual response rate of 88.6% and a questionnaire effective rate of 94.4%. The patients' age ranged from 25 to 88 years (57.70 ± 11.47 years). Other demographic data are shown in Table 1.

TABLE 1.

Characteristics of patients with cervical cancer (n = 351).

Variables Category N (%)
Place of residence Urban 214 (61.0)
Rural 137 (39.0)
Marital status Married 304 (86.6)
Single/divorced/widowed 47 (13.4)
Education level Illiterate 121 (34.5)
Primary school 105 (29.9)
Middle school 81 (23.1)
High school or above 44 (12.5)
Basic medical insurance a BMIURR 294 (83.8)
BMIUE 57 (16.2)
Employment status Employed 97 (27.6)
Unemployed 201 (57.3)
Retired 53 (15.1)
Profession Freelance 66 (18.8)
Peasant 201 (57.3)
Staff 84 (23.9)
Commercial insurance Yes 56 (16.0)
No 295 (84.0)
Transportation fee (yuan, RMB) b ≤ 50 272 (77.5)
> 50 79 (22.5)
Personal monthly income (yuan, RMB) b < 2000 92 (26.2)
2000–5000 182 (51.9)
> 5000 77 (21.9)
Disposable household savings (yuan, RMB) b < 100,000 124 (35.3)
100,000–200,000 156 (44.4)
> 200,000 71 (20.2)
Tumour stage I–II 182 (51.9)
III 124 (35.3)
IV 45 (12.8)
Duration of illness (years) < 1 240 (68.4)
1–5 69 (19.7)
> 5 42 (12.0)
Number of hospitalizations (times) 1–2 136 (38.7)
3–5 98 (27.9)
> 5 117 (33.3)
Surgery history No 163 (46.4)
Yes 188 (53.6)
Chemotherapy history No 84 (23.9)
Yes 267 (76.1)
Radiotherapy history No 107 (30.5)
Yes 244 (69.5)
Combined chronic diseases No 246 (70.1)
Yes 105 (29.9)
Open in a new tab

Note: N, sample size.

a

BMIUE, the Basic Medical Insurance for Urban Employees; BMIURR, the Basic Medical Insurance for Urban and Rural Residents.

b

Yuan, RMB, Chinese Renminbi, approximately 0.139 US dollars during the survey period.

4.2. Prevalence of Financial Toxicity

Patients were scored according to the COST‐PROM criteria. The mean COST‐PROM score was 20.66 ± 8.59 (range: 0–44). Financial toxicity was defined as a COST‐PROM score < 26 and no financial toxicity as a score ≥ 26. The results showed that the prevalence of financial toxicity in cervical cancer patients was 66.4% (n = 233). Using the four‐level severity classification, 33.6% (n = 118) had no financial toxicity, 45.6% (n = 160) had mild financial toxicity, 20.5% (n = 72) had moderate financial toxicity and 0.3% (n = 1) had severe financial toxicity.

4.3. Univariate Analysis

Univariate analysis (Table 2) showed statistically significant differences in financial toxicity across 10 variables: place of residence, education level, basic medical insurance type, current employment status, profession, commercial insurance, personal monthly income, disposable household savings, number of hospitalizations and radiotherapy history (all p < 0.05). To avoid missing key variables (as univariate analysis may underestimate or mask the true effect of some variables), age was also included as an independent variable in regression analysis (p = 0.118).

TABLE 2.

Univariate analysis of factors associated with financial toxicity in patients with cervical cancer (n = 351).

Variables No FT (n = 118) FT (n = 233) t/χ 2 p
Age (year) 59.04 ± 11.59 57.02 ± 11.38 1.565 b 0.118
Place of residence 15.608 a 0.001
Urban 89 (75.4) 125 (53.6)
Rural 29 (24.6) 108 (46.4)
Marital status 0.004 a 0.947
Married 102 (86.4) 202 (86.7)
Single/divorced/widowed 16 (13.6) 31 (13.3)
Education level 22.300 a 0.001
Illiterate 36 (30.5) 85 (36.5)
Primary school 26 (22.0) 79 (33.9)
Middle school 28 (23.7) 53 (22.7)
High school or above 28 (23.7) 16 (6.9)
Basic medical insurance c 33.305 a 0.001
BMIURR 80 (67.8) 214 (91.8)
BMIUE 38 (32.2) 19 (8.2)
Employment status 15.159 a 0.001
Employed 31 (26.3) 66 (28.3)
Unemployed 57 (48.3) 144 (61.8)
Retired 30 (25.4) 23 (9.9)
Profession 15.825 a 0.001
Freelance 21 (17.8) 45 (19.3)
Peasant 54 (45.4) 147 (63.1)
Staff 43 (36.4) 41 (17.6)
Commercial insurance 37 (31.4) 19 (8.2) 31.446 a 0.001
Yes 81 (68.6) 214 (91.8)
No
Transportation fee (yuan, RMB) c 1.444 a 0.229
≤ 50 87 (73.7) 185 (79.4)
31 (26.3) 48 (20.6)
Personal monthly income (yuan, RMB) c 39.318 a 0.001
13 (11.0) 79 (33.9)
2000–5000 59 (50.0) 123 (52.8)
46 (39.0) 31 (13.3)
Disposable household savings (yuan, RMB) c 64.815 a 0.001
100,000 14 (11.9) 110 (47.2)
100,000–200,000 56 (47.5) 100 (42.9)
48 (40.7) 23 (9.9)
Tumour stage 0.094 a 0.954
I–II 61 (51.7) 121 (51.9)
III 41 (34.7) 83 (35.6)
IV 16 (13.6) 29 (12.4)
Duration of illness (years) 0.652 a 0.722
84 (71.2) 156 (67)
1–5 21 (17.8) 48 (20.6)
13 (11.0) 29 (12.4)
Number of hospitalizations (times) 8.740 a 0.013
1–2 53 (44.9) 83 (35.6)
3–5 38 (32.2) 60 (25.8)
> 5 27 (22.9) 90 (38.6)
Surgery history 0.074 a 0.785
No 56 (47.5) 107 (45.9)
Yes 62 (52.5) 126 (54.1)
Chemotherapy history 0.534 a 0.465
No 31 (26.3) 53 (22.7)
Yes 87 (73.7) 180 (77.3)
Radiotherapy history 6.059 a 0.014
No 46 (39.0) 61 (26.2)
Yes 72 (61.0) 172 (73.8)
Combined chronic diseases 0.834 a 0.361
No 79 (66.9) 167 (71.7)
Yes 39 (33.1) 66 (28.3)
Open in a new tab

Abbreviation: FT, financial toxicity.

a

Chi‐square test.

b

T‐test.

c

BMIUE, the Basic Medical Insurance for Urban Employees; BMIURR, the Basic Medical Insurance for Urban and Rural Residents. Yuan, RMB, approximately 0.139 US dollars during the survey period.

4.4. Logistic Regression Analysis

The results of binary logistic regression analysis using the backward conditional method (Table 3) demonstrated that older age was associated with a lower risk of financial toxicity (OR = 0.956, 95% CI: 0.931–0.982). Compared with patients without commercial insurance, those with insurance had a lower risk of financial toxicity (OR = 0.204, 95% CI: 0.095–0.438). Patients with fewer hospitalizations (< 3 times and 3–5 times) had a lower risk of financial toxicity compared with those with > 5 hospitalizations (OR = 0.479, 95% CI: 0.230–0.997 and OR = 0.439, 95% CI: 0.208–0.924, respectively). With no radiotherapy as the reference group, patients who received radiotherapy had an increased likelihood of financial toxicity (OR = 3.652, 95% CI: 1.833–7.276). Compared with patients with disposable household savings > 200,000 CNY (27,800 USD), those with 100,000–200,000 CNY (13,900–27,800 USD) (OR = 2.477, 95% CI: 1.238–4.959) and < 100,000 CNY (13,900 USD) (OR = 11.054, 95% CI: 4.672–26.154) had an increased risk of financial toxicity. Compared with those covered by employee basic medical insurance, patients covered by urban–rural resident basic medical insurance had an increased risk of financial toxicity (OR = 5.544, 95% CI: 2.502–12.285).

TABLE 3.

Binary logistic regression analysis of factors associated with financial toxicity in patients with cervical cancer (n = 351).

Variable β SE Wald χ 2 p Exp (B) 95% CI
Age (year) −0.045 0.014 10.624 0.001 0.956 0.931–0.982
Commercial insurance (ref. = no) −1.591 0.391 16.563 < 0.001 0.204 0.095–0.438
Number of hospitalizations (ref. ≥ 5) — — 5.523 0.063 — —
< 3 −0.735 0.374 3.873 0.049 0.479 0.230–0.997
3–5 −0.824 0.380 4.704 0.030 0.439 0.208–0.924
Radiotherapy history (ref. = no) 1.295 0.352 13.568 < 0.001 3.652 1.833–7.276
Disposable household savings (ref. ≥ 200,000 yuan, RMB) a — — 30.719 < 0.001 — —
< 100,000 yuan 2.403 0.439 29.901 < 0.001 11.054 4.672–26.154
100,000–200,000 yuan 0.907 0.354 6.564 0.010 2.477 1.238–4.959
BMIURR (ref. = BMIUE) a 1.713 0.406 17.800 < 0.001 5.544 2.502–12.285
Constant 2.028 0.893 5.163 0.023 7.600 —
Open in a new tab

Note: ref. = reference group.

a

BMIUE, the Basic Medical Insurance for Urban Employees; BMIURR, the Basic Medical Insurance for Urban and Rural Residents. Yuan, RMB, Chinese Renminbi, approximately 0.139 US dollars during the survey period.

5. Discussion

This cross‐sectional study revealed that the prevalence of financial toxicity in cervical cancer patients was as high as 66.4%, and systematically identified that age, commercial insurance, basic medical insurance type, number of hospitalizations, disposable household savings and radiotherapy are factors associated with financial toxicity. These findings provide empirical evidence for identifying patients at higher risk of financial toxicity and indicate directions for subsequent targeted interventions.

This study found a 66.4% prevalence of financial toxicity in cervical cancer patients. A Chinese study (Yang et al. 2024) investigating 165 cervical cancer patients undergoing chemotherapy reported an 86.7% prevalence of financial toxicity. Another study (Peng et al. 2024) on financial toxicity in cervical cancer survivors showed a median COST‐PROM score of 20.00 (14.00, 24.00) and a 76.5% prevalence of financial toxicity. The prevalence in our study was lower than in these two studies. Possible reasons include: first, our study included patients across all treatment stages, whereas the Yang et al. (2024) study focused only on chemotherapy patients, who receive more intensive treatment with higher associated costs; second, the region where our study was conducted may have broader medical insurance coverage, better policy implementation and more comprehensive supplementary insurance; third, financial toxicity has dynamic characteristics and patients at different treatment nodes may perceive financial pressure differently (Aviki et al. 2022). Despite the different studies reporting varying prevalence rates of financial toxicity in cervical cancer patients, all confirm a high level of financial toxicity. This suggests that medical staff should incorporate financial toxicity screening into initial diagnosis and follow‐up, and develop personalized management plans based on patients' financial burden to effectively prevent or mitigate the adverse consequences of financial toxicity.

This study found that younger patients had a higher risk of financial toxicity, consistent with the findings of Liang et al. (2020). The possible reason is that the ‘421’ (4 elderly, 1 couple, 1 child) or ‘422’ (4 elderly, 1 couple, 2 children) family structure has become mainstream, increasing the support burden of children, weakening traditional care capacity, inadequate social security coverage and low personal savings, further aggravating household out‐of‐pocket pressure (Hu and Hongxi 2017). Notably, the incidence of cervical cancer has shown a gradual trend of younger age onset, and its peak incidence coincides with women's prime working years and reproductive period (Xiao et al. 2025). Disease progression may lead to reduced working capacity or income interruption, with an opportunity cost of income loss far higher than that of retired elderly patients (Jewett et al. 2024; Wang, Sun, et al. 2024). Meanwhile, younger patients have relatively limited savings accumulation, making their financial cushioning capacity weaker when facing high treatment costs. In contrast, older retired patients have accumulated certain savings, enjoy stable pensions and more comprehensive medical insurance coverage (Siyao 2019), and may avoid some costly radiotherapy and chemotherapy due to earlier disease stage and more surgical opportunities. In addition, for middle‐aged patients aged 45–59 years who bear the dual burden of supporting both the elderly and children, financial toxicity is equally severe (Qiu et al. 2023). Therefore, medical staff should prioritize financial toxicity screening for young patients, actively communicate treatment costs, provide personalized treatment plans, standardize treatment behaviours and avoid higher subsequent treatment costs.

The results showed that more hospitalizations (Zhou et al. 2023) and limited disposable household savings (Honda et al. 2019) were associated with a higher risk of financial toxicity. Notably, patients with disposable household savings < 100,000 CNY had an 11‐fold higher risk of financial toxicity than those with savings > 200,000 CNY (OR = 11.054), suggesting that household savings serve as a critical buffer against financial toxicity. Patients with frequent hospitalizations and limited disposable savings may face cumulative indirect costs (e.g., bed fees, examination fees, round‐trip transportation costs and lost work wages) due to the complexity of cervical cancer treatment regimens, weakening their ability to cope with sudden financial shocks (Dugan et al. 2025; Gao et al. 2020). This financial pressure not only aggravates anxiety during treatment but may also force patients to abandon treatment to reduce financial burden, ultimately facing a higher risk of death (Wang, Xin‐yu, et al. 2024). Combined with China's current medical insurance system, the government has set a reimbursement deductible line in medical insurance policies. For cervical cancer patients requiring continuous treatment, multiple hospitalizations mean repeated out‐of‐pocket expenses below the deductible line. The increased medical costs caused by such policy differences impose greater financial pressure on patients' families (Shi and Li 2022). Therefore, the government should further optimize the medical insurance reimbursement deductible line, improve the critical illness insurance, critical illness assistance and supplementary medical insurance systems, and provide counselling and guidance for cancer patients to alleviate financial toxicity and psychological burden (Shi and Li 2022). Medical institutions can provide telemedicine services (telephone and video consultations) when conditions permit to reduce indirect medical costs (Lustbader et al. 2017).

This study showed that patients receiving radiotherapy had a higher risk of financial toxicity by approximately 3.6‐fold, consistent with the conclusion of Blanco et al. (2021) that the medical cost of patients receiving postoperative radiotherapy within 1 year after diagnosis was more than twice that of those not receiving radiotherapy. In addition, patients receiving concurrent chemoradiotherapy face further increased financial burden (Dai et al. 2024), possibly due to the high cost of single radiotherapy sessions. Although chemotherapy has a lower single‐session cost, long cycles and frequent sessions continuously increase treatment burden and psychological pressure (Dong et al. 2022; Setsoafia et al. 2025). Notably, a longitudinal study on financial toxicity among radiotherapy patients at a German tertiary radiation centre demonstrated that patients with pelvic malignancies (including cervical, uterine, endometrial, renal pelvic, bladder and renal cell cancers) had a median COST score of 19, compared with 33 for prostate cancer patients, with a statistically significant difference (Kruskal–Wallis test, p = 0.01) (Kreuser et al. 2025). A study from the Wellington Blood and Cancer Centre in New Zealand also confirmed that cervical cancer patients receiving radical radiotherapy had significantly higher levels of financial toxicity than other cancer patients (e.g., breast cancer) at the same centre (COST‐FACIT score: 20.6 vs. 29.7, p = 0.02), with median out‐of‐pocket costs and income loss reaching 1814 New Zealand Dollar (NZD) compared with only 264 NZD for other patients (p = 0.006) (Franklin et al. 2026). This may be related to the lengthy radiotherapy course for cervical cancer, the need for repeated hospital admissions to complete treatment and cross‐regional treatment seeking due to the scarcity of radiotherapy equipment. Meanwhile, most chemotherapy patients have advanced disease or multiple comorbidities (Dai et al. 2024). In terms of the selection of novel therapeutic technologies and medications, the uneven distribution of medical resources in China often forces patients to travel across regions to higher‐level radiotherapy centres, thereby increasing indirect costs such as transportation and accommodation and further exacerbating financial toxicity (Silverwood et al. 2024). However, since this study only enrolled a single cancer type and analysed only one treatment modality, the differences in financial toxicity across cancer types among patients receiving radiotherapy require further validation through multi‐centre, multi‐cancer studies.

The results showed that patients with urban–rural resident basic medical insurance had a higher prevalence of financial toxicity than those with employee basic medical insurance, and lack of commercial insurance was another factor associated with financial toxicity in cervical cancer patients (Ezeife et al. 2019). The two factors interact to produce a superimposed effect on financial toxicity. The impact of basic medical insurance on financial toxicity may be directly related to the reimbursement rate difference between the two medical insurance systems (Liu et al. 2023). Another study (Ezeife et al. 2019) found that patients without commercial insurance had a 3.7‐fold higher risk of financial toxicity than those with commercial insurance. This may be because most Chinese residents purchase basic medical insurance as the main reimbursement channel for disease costs, reflecting their fragile financial resilience to disease shocks and a higher likelihood of choosing cost‐reducing treatment plans or even abandoning treatment due to financial pressure (Esselen et al. 2021).

Although China has established a multi‐level medical insurance system including critical illness insurance and commercial insurance, the National Healthcare Security Administration has implemented Diagnosis‐Intervention Packet (DIP) and Diagnosis Related Groups (DRG) reforms in hospitals to reduce patients' treatment burden (Xiao et al. 2026). However, the coverage of supplementary medical insurance remains low, especially as rural residents with low education levels have low awareness of supplementary medical insurance and cannot implement relevant policies due to cognitive limitations and complex terms, further aggravating patients' financial burden (Qiu et al. 2023). Therefore, the government should optimize the medical insurance reimbursement deductible line, adjust medical resource allocation, narrow regional reimbursement differences, strengthen publicity of supplementary medical insurance, rationally plan the development of commercial medical insurance and reduce the prevalence of financial toxicity (Shi and Li 2022). At the clinical level, medical staff should promote the implementation of medical insurance policies and insurance systems, take the initiative to communicate with patients and remind them to effectively cope with financial burden through proper planning of medical insurance and commercial insurance.

Notably, a recent multi‐centre study conducted among 1208 Chinese cancer patients proposed a COST cutoff of 18.5 for identifying cost‐related treatment nonadherence and impaired health‐related quality of life (Xu, So, and Choi 2024). This cutoff, with a sensitivity of 76.5% and specificity of 71.4%, represents a more stringent threshold than the conventional 26‐point cutoff. The 26‐point cutoff is widely used internationally to distinguish the presence versus absence of financial toxicity, whereas the 18.5 cutoff may better capture patients at risk of clinically meaningful consequences such as treatment nonadherence. In our sample, applying this more stringent threshold would classify 43.9% (n = 154) of patients as having clinically severe financial toxicity warranting targeted intervention, compared with 66.4% using the conventional 26‐point cutoff. This discrepancy suggests that the 18.5 cutoff may better identify patients with imminent clinical consequences, though the optimal threshold specific to cervical cancer populations requires further validation.

6. Limitations

This study has several limitations. First, the generalizability of the findings is limited as it is based on data from a single medical centre. Multi‐centre studies are recommended to further explore the influencing factors of financial toxicity in cervical cancer and other cancer patients. Second, variables such as social support, quality of life and self‐efficacy that affect financial toxicity in cancer patients were not included, in part because this study focused on objectively measurable demographic and clinical indicators for rapid clinical screening; nonetheless, these psychosocial factors may also have important influences on financial toxicity. Future studies should explore the impact of these factors to comprehensively analyse and understand financial toxicity in cancer populations. Third, financial toxicity assessment relies on patient self‐report, which may introduce self‐report bias. Future studies should include more objective economic indicators to improve the accuracy and reliability of financial toxicity assessment. Fourth, as a cross‐sectional study, this study only reveals the prevalence of financial toxicity in cervical cancer patients at a single time point and cannot reflect its dynamic changes with disease progression and time. Future studies can analyse the key influencing factors of financial toxicity from a dynamic trajectory perspective with multiple time points, clarify the variation pattern of financial toxicity at different disease stages and time points and adopt more effective interventions to reduce financial toxicity in cancer patients.

7. Implications for Nursing Practice

Our study identified factors associated with financial toxicity among cervical cancer patients, which may assist healthcare providers in identifying patients at higher risk of financial toxicity and inform the development of targeted supportive interventions. Early recognition of these risk‐associated factors may support treatment adherence and health‐related quality of life, though this requires further validation in future studies.

8. Conclusion

This study found six factors associated with financial toxicity in cervical cancer patients: age, commercial insurance, basic medical insurance type, number of hospitalizations, disposable household savings and radiotherapy. These findings provide empirical evidence that may assist healthcare providers in identifying patients at higher risk of financial toxicity in clinical practice. Given the cross‐sectional design, the identified associations do not imply causality. Future longitudinal studies are warranted to confirm these associations and to evaluate whether interventions targeting these factors can reduce financial toxicity and improve patient outcomes.

Author Contributions

Dongliu Li: writing – review and editing, writing – original draft, project administration, methodology, investigation, formal analysis, data curation, conceptualization, resources. Menghua Wen: conceptualization, investigation, writing – original draft, methodology, writing – review and editing, formal analysis, project administration, data curation, resources. Youfu Zhao: conceptualization, investigation, writing – original draft, methodology, writing – review and editing, formal analysis, project administration, data curation, resources. Tingyu Nie: formal analysis, writing – review and editing, investigation, resources. Miaomiao Li: writing – review and editing, resources, investigation. Li Yuan: writing – review and editing, resources, investigation. Meiming Wang: writing – review and editing, resources. Xiaoyi Zhu: writing – review and editing, resources. Ying Li: resources, investigation. Qian Zhang: resources, investigation. Chunying He: supervision, funding acquisition.

Funding

This work was supported by research funds from Research project of Yan'an Science and Technology Development (2023‐SFGG‐055), Yan'an Science and Technology Development (2022) (41), Science and Technology Development Research Project of Affiliated Hospital of Yan'an University (2022PT‐26) and Natural Science Foundation of Shaanxi Province (2025JC‐YBMS‐835).

Ethics Statement

The study protocol, approved by the Human Ethics Committee of the Affiliated Hospital of Yan'an University and conducted according to the Declaration of Helsinki, obtained informed consent from all participants (YA‐L20240006).

Consent

Written informed consent for publication was obtained from all participants.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgements

We express our heartfelt gratitude to all the cervical cancer patients who participated in this research. We also thank the medical staff of the Affiliated Hospital of Yan'an University for their assistance in data collection. Special thanks go to our supervisor, Ms. Chunying He, and the members of the research team, who provided valuable guidance and support throughout the entire project. Declaration of Generative AI and AI‐assisted technologies in the writing process: During the process of conducting this research, the authors employed AI to enhance the language. After using this tool, the authors will review and edit the content as needed and be fully responsible for the content of the publication.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Ao, D. 2022. Correlation Between Financial Toxicity, Social Support and Quality of Life in Cancer Patients Undergoing Chemotherapy. Shihezi University. [Google Scholar]
  2. Aviki, E. M. , Manning‐Geist B. L., Sokolowski S. S., et al. 2022. “Risk Factors for Financial Toxicity in Patients With Gynecologic Cancer.” American Journal of Obstetrics and Gynecology 226, no. 6: 817.e811–817.e819. 10.1016/j.ajog.2021.12.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Blanco, M. , Chen L., Melamed A., et al. 2021. “Cost of Care for the Initial Management of Cervical Cancer in Women With Commercial Insurance.” American Journal of Obstetrics and Gynecology 224, no. 3: 286.e281–286.e211. 10.1016/j.ajog.2020.08.039. [DOI] [PubMed] [Google Scholar]
  4. Carrera, P. M. , Kantarjian H. M., and Blinder V. S.. 2018. “The Financial Burden and Distress of Patients With Cancer: Understanding and Stepping‐Up Action on the Financial Toxicity of Cancer Treatment.” CA: A Cancer Journal for Clinicians 68, no. 2: 153–165. 10.3322/caac.21443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Dai, Y. , Yefeng Y., Yujian S., Yue Z., Yi X., and Yufan P.. 2024. “Incidence and Influencing Factors of Financial Toxicity in Cancer Patients: A Meta‐Analysis.” Journal of Modern Medicine and Health 40, no. 24: 4272–4279. 10.3969/j.issn.1009-5519.2024.24.029. [DOI] [Google Scholar]
  6. de Souza, J. A. , Yap B. J., Hlubocky F. J., et al. 2014. “The Development of a Financial Toxicity Patient‐Reported Outcome in Cancer: The COST Measure.” Cancer 120, no. 20: 3245–3253. 10.1002/cncr.28814. [DOI] [PubMed] [Google Scholar]
  7. de Souza, J. A. , Yap B. J., Wroblewski K., et al. 2016. “Measuring Financial Toxicity as a Clinically Relevant Patient‐Reported Outcome: The Validation of the COmprehensive Score for Financial Toxicity (COST).” Cancer 123, no. 3: 476–484. 10.1002/cncr.30369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dong, A. , Ling J., Jing W., et al. 2022. “Analysis on Level of Financial Toxicity and Its Influencing Factors in Cancer Patients Undergoing Chemotherapy.” Shanghai Nursing 22, no. 5: 27–31. 10.3969/j.issn.1009-8399.2022.05.006. [DOI] [Google Scholar]
  9. Dugan, M. M. , Pruett R., Romero D., Parreco J. P., Edwards C. M., and Buicko J. L.. 2025. “Impact of Financial Toxicity in Surgical Oncology Patients on Postoperative Hospital Readmission.” American Surgeon 91, no. 12: 2079–2083. 10.1177/00031348251346531. [DOI] [PubMed] [Google Scholar]
  10. Esselen, K. M. , Stack‐Dunnbier H., Gompers A., and Hacker M. R.. 2021. “Crowdsourcing to Measure Financial Toxicity in Gynecologic Oncology.” Gynecologic Oncology 161, no. 2: 595–600. 10.1016/j.ygyno.2021.01.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ezeife, D. A. , Morganstein B. J., Lau S., et al. 2019. “Financial Burden Among Patients With Lung Cancer in a Publically Funded Health Care System.” Clinical Lung Cancer 20, no. 4: 231–236. 10.1016/j.cllc.2018.12.010. [DOI] [PubMed] [Google Scholar]
  12. Franklin, G. , Larsen P., Henry C., and Johnson C.. 2026. “Financial Toxicity in Cervical Cancer Patients Undergoing Radical Treatment With External Beam Radiation and Brachytherapy at a Tertiary New Zealand Radiation Oncology Centre.” Journal of Medical Imaging and Radiation Oncology: 1–8. 10.1111/1754-9485.70112. [DOI] [PubMed] [Google Scholar]
  13. Gao, L. , Wu Q., Jia M., et al. 2020. “The Economic Burden of Cervical Cancer From Diagnosis to One Year After Final Discharge in Henan Province, China: A Retrospective Case Series Study.” PLoS One 15, no. 5: e0232129. 10.1371/journal.pone.0232129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gilligan, A. M. , Alberts D. S., Roe D. J., et al. 2018. “Death or Debt? National Estimates of Financial Toxicity in Persons With Newly‐Diagnosed Cancer.” American Journal of Medicine 131: 1187–1199.e5. 10.1016/j.amjmed.2018.05.020. [DOI] [PubMed] [Google Scholar]
  15. Honda, K. , Gyawali B., Ando M., et al. 2019. “Prospective Survey of Financial Toxicity Measured by the Comprehensive Score for Financial Toxicity in Japanese Patients With Cancer.” Journal of Global Oncology 5: 1–8. 10.1200/jgo.19.00003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hu, H. , and Hongxi Y.. 2017. “The Construction of the Integrated Welfare Facilities for Elderly and Children Based on the “421” Family Structure in the Community.” Journal of Human Settlements in West China 2017, no. 3: 35–41. 10.13791/j.cnki.hsfwest.20170306. [DOI] [Google Scholar]
  17. Jewett, P. I. , Purani H., Vogel R. I., Parsons H. M., Borrero M., and Blaes A.. 2024. “Comparisons of Financial Hardship in Cancer Care by Family Structure and Among Those With and Without Minor Children Using Nationally Representative Data.” Cancer Medicine 13, no. 6: e7088. 10.1002/cam4.7088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kajimoto, Y. , Honda K., Suzuki S., et al. 2023. “Association Between Financial Toxicity and Health‐Related Quality of Life of Patients With Gynecologic Cancer.” International Journal of Clinical Oncology 28, no. 3: 454–467. 10.1007/s10147-023-02294-1. [DOI] [PubMed] [Google Scholar]
  19. Kazzi, B. , Chino F., Kazzi B., et al. 2022. “Shared Burden: The Association Between Cancer Diagnosis, Financial Toxicity, and Healthcare Cost‐Related Coping Mechanisms by Family Members of Non‐Elderly Patients in the USA.” Supportive Care in Cancer 30, no. 11: 8905–8917. 10.1007/s00520-022-07234-9. [DOI] [PubMed] [Google Scholar]
  20. Kreuser, A. L. , Ziegler S., Bendrich S., et al. 2025. “Longitudinal Development and Clinical Predictors of Financial Toxicity Among Radiation Oncology Patients: Final Results of the SOCOFIN Study.” Strahlentherapie und Onkologie 202, no. 3: 330–348. 10.1007/s00066-025-02479-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li, D. 2025. Construction of Risk Prediction Model for Financial Toxicity Inpatients With Cervical Cancer. Yan'an University. [Google Scholar]
  22. Liang, M. I. , Pisu M., Summerlin S. S., et al. 2020. “Extensive Financial Hardship Among Gynecologic Cancer Patients Starting a New Line of Therapy.” Gynecologic Oncology 156, no. 2: 271–277. 10.1016/j.ygyno.2019.11.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Liu, L. , Mingzhu S., Jinxin Z., et al. 2023. “The Prevalence of Financial Toxicity Based on Comprehensive Scores for Financial Toxicity in Chinese Cancer Patients: A Meta‐Analysis.” Chinese Journal of Evidence‐Based Medicine 23, no. 11: 1269–1274. 10.7507/1672-2531.202306083. [DOI] [Google Scholar]
  24. Lustbader, D. , Mudra M., Romano C., et al. 2017. “The Impact of a Home‐Based Palliative Care Program in an Accountable Care Organization.” Journal of Palliative Medicine 20, no. 1: 23–28. 10.1089/jpm.2016.0265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mickey, R. M. , and Greenland S.. 1989. “The Impact of Confounder Selection Criteria on Effect Estimation.” American Journal of Epidemiology 129, no. 1: 125–137. 10.1093/oxfordjournals.aje.a115101. [DOI] [PubMed] [Google Scholar]
  26. Peng, C. , Xie Y., Zhang X., et al. 2022. “Research Progress of Maintenance Therapy for Cervical Cancer.” Journal of International Oncology 49, no. 07: 430–435. 10.3760/cma.j.cn371439-20220602-00082. [DOI] [Google Scholar]
  27. Peng, X. , Yefen H., Xiaomin L., et al. 2024. “Current Status of Economic Toxicity of Cervical Cancer Survivors and Its Influencing Factors.” Guangxi Medical Journal 46, no. 12: 1869–1876. 10.11675/j.issn.0253-4304.2024.12.10. [DOI] [Google Scholar]
  28. Qiu, Z. , Yao L., and Jiang J.. 2023. “Financial Toxicity Assessment and Associated Factors Analysis of Patients With Cancer in China.” Supportive Care in Cancer 31, no. 5: 264. 10.1007/s00520-023-07714-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Rongshu, Z. , Zhang S., Sun K., et al. 2023. “Cancer Statistics in China, 2016.” Chinese Journal of Oncology 45, no. 3. 10.3760/cma.j.cn112152-20220922-00647. [DOI] [PubMed] [Google Scholar]
  30. Setsoafia, E. A. , Sarfo‐Annan G., Daniels J., Addae J., and Abraham S. A.. 2025. “Experiences of Patients With Cervical Cancer Undergoing Concurrent Chemoradiation Therapy at the Korle‐Bu Teaching Hospital in Ghana: An Explorative Qualitative Study.” BMC Cancer 25, no. 1: 1547. 10.1186/s12885-025-14824-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Shi, P. , and Li X.. 2022. “Analysis and Research on Hospitalization Expenses and Compensation Ratios of 5 Types of Cancer Patients in a City of Jiangsu Province Under Different Medical Insurance Systems.” Medicine and Society 35, no. 6: 106–110. 10.13723/j.yxysh.2022.06.020. [DOI] [Google Scholar]
  32. Silverwood, S. M. , Waeldner K., Demeulenaere S. K., et al. 2024. “The Relationship Between Travel Distance for Treatment and Outcomes in Patients Undergoing Radiation Therapy: A Systematic Review.” Advances in Radiation Oncology 9, no. 12: 101652. 10.1016/j.adro.2024.101652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Siyao, G. 2019. The Problems and Countermeasures of Urban Retired Workers Medical Insurance System. Tianjin University of Finance and Economics. [Google Scholar]
  34. Sung, H. , Ferlay J., Siegel R. L., et al. 2021. “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.” CA: A Cancer Journal for Clinicians 71, no. 3: 209–249. 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
  35. Wang, K. , Xin‐yu H., Rui‐lian L., et al. 2024. “Analysis of the Economic Toxicity Profile and Influencing Factors in Lung Cancer Patients in Luzhou City.” Modern Preventive Medicine 51, no. 21: 4008–4014. 10.20043/j.cnki.MPM.202406380. [DOI] [Google Scholar]
  36. Wang, L. , Sun R., Tian L., and Xu R. H.. 2024. “Influence of Age on Financial Toxicity in Cancer Patients.” Asia‐Pacific Journal of Oncology Nursing 11, no. 9: 100552. 10.1016/j.apjon.2024.100552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Xi, C. , Shen J. J., Burston B., Upadhyay S., and Zhou S.. 2023. “Epidemiological/Disease and Economic Burdens of Cervical Cancer in 2010–2014: Are Younger Women at Risk?” Healthcare 11, no. 1: 144. 10.3390/healthcare11010144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Xiao, C. , Deng J., Ren F., and Zhang C.. 2025. “Cervical Cancer Burden Among Females Under 40 Years in China, Japan, and South Korea, 1990–2021: A Systematic Analysis for the Global Burden of Disease Study 2021.” Frontiers in Public Health 13: 1695004. 10.3389/fpubh.2025.1695004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Xiao, H. , Honghui W., and Yang Y.. 2026. “Cultural Adaptation Strategy of Tertiary Public Hospitals Under the DRG/DIP Payment Method Reform.” Chinese Hospital Management 46, no. 04: 23–26. [Google Scholar]
  40. Xu, B. , So W. K. W., and Choi K. C.. 2024. “Determination of a Cut‐Off COmprehensive Score for Financial Toxicity (COST) for Identifying COST‐Related Treatment Nonadherence and Impaired Health‐Related Quality of Life Among Chinese Patients With Cancer.” Supportive Care in Cancer 32, no. 2: 136. 10.1007/s00520-024-08320-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Xu, B. , So W. K. W., Choi K. C., et al. 2024. “Financial Toxicity and Its Risk Factors Among Patients With Cancer in China: A Nationwide Multisite Study.” Asia‐Pacific Journal of Oncology Nursing 11, no. 5: 100443. 10.1016/j.apjon.2024.100443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Yang, L. , Guimei Z., Bendi M., et al. 2024. “Analysis of Economic Toxicity Status and Influencing Factors in Patients With Cervical Cancer Chemotherapy.” Psychologies Magazine 19, no. 11: 43–45+48. 10.19738/j.cnki.psy.2024.11.010. [DOI] [Google Scholar]
  43. Yu, H. , Bi X., and YunYong L.. 2017. “Reliability and Validity of the Chinese Version on Comprehensive Scores for Financial Toxicity Based on the Patient‐Reported Outcome Measures.” Chinese Journal of Epidemiology 38, no. 8. 10.3760/cma.j.issn.0254-6450.2017.08.024. [DOI] [PubMed] [Google Scholar]
  44. Zafar, S. Y. , and Abernethy A. P.. 2013. “Financial Toxicity, Part I: A New Name for a Growing Problem.” Oncology (Williston Park, N.Y.) 27, no. 2: 80–81, 149. [PMC free article] [PubMed] [Google Scholar]
  45. Zeybek, B. , Webster E., Pogosian N., et al. 2021. “Financial Toxicity in Patients With Gynecologic Malignancies: A Cross Sectional Study.” Journal of Gynecologic Oncology 32, no. 6: e87. 10.3802/jgo.2021.32.e87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhou, Y. , Li Y., Yanxin Z., et al. 2023. “Analysis of Financial Toxicity Among Nasopharyngeal Carcinoma Survivors and Its Influencing Factors.” Military Nursing 40, no. 1: 15–18. 10.3969/j.issn.2097-1826.2023.01.004. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Nursing Open are provided here courtesy of Wiley

RESOURCES