Status of financial toxicity and its influence on quality of life in patients with gynecological malignancies in China
- Published
- Accepted
- Received
- Academic Editor
- Faiza Farhan
- Subject Areas
- Nursing, Oncology, Women’s Health
- Keywords
- Gynecologic malignancy, Financial toxicity, Quality of life, Cross-sectional study, Correlation study
- Copyright
- © 2026 Zhang et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits using, remixing, and building upon the work non-commercially, as long as it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
- Cite this article
- 2026. Status of financial toxicity and its influence on quality of life in patients with gynecological malignancies in China. PeerJ 14:e20560 https://doi.org/10.7717/peerj.20560
Abstract
Background
Gynecological malignancies impose a substantial health and economic burden in China. The high cost of treatment often results in considerable financial toxicity, placing economic strain on patients and their families. These challenges can reduce treatment adherence, heighten emotional distress, and impair quality of life. This study aims to assess the degree of financial toxicity among patients with gynecological cancers, evaluate its impact on quality of life, and provide evidence to support the development of targeted clinical interventions to mitigate its adverse effects.
Methods
A convenience sampling approach was employed to recruit 281 patients with gynecological malignancies from two hospitals in Nanjing. A cross-sectional survey was conducted between November 2022 and December 2024. Data were collected using general information questionnaires, the financial toxicity comprehensive scale, and the cancer patient quality-of-life assessment scale.
Results
Data validity was confirmed using Harman’s single-factor test (first factor explanatory rate was 30.44%). The mean ± SD total financial toxicity score of the patients was 20.80 ± 7.32 points, with 73% (205/281) experiencing significant financial burdens. The financial resource dimension had the lowest mean score 3.40 ± 1.37 points. Multivariate linear regression analysis indicated that the age of children, family per capita monthly income, treatment costs in the past three months, and marital status were the core influencing factors (adjusted R2 = 0.310). The mean ± SD total quality of life score of the patients was 65.79 ± 11.39 points, and the total financial toxicity score was significantly positively correlated with quality of life (r = 0.553, P < 0.01). Participants had a mean age of 52.27 ± 10.78 years; cervical cancer accounted for the highest proportion (49.5%); 29.2% were unemployed or had resigned due to illness, and 96.8% reported medical expenses exceeding CNY 5,000 (roughly $701.50) in the past three months.
Conclusion
The majority of studied patients experienced financial toxicity, with financial status, family dynamics, and widowhood being key influencing factors.
Introduction
Globally, gynecological malignancies, including cervical, ovarian, and endometrial cancers, constitute a significant public health challenge (Silva, 2024). According to Global Cancer Observatory (GLOBOCAN) statistics (Sung et al., 2021), the number of patients with cervical cancer, ovarian cancer, and endometrial cancer exceeded 1.33 million worldwide in 2020. In China, the number of new cases is approximately 170,000. Gynecological malignancies account for 12.5% of the new malignant tumors in women and 11.2% of the total number of female deaths (Sung et al., 2021). While advancements in medical science have improved survival rates and therapeutic efficacy for gynecological cancers, they are associated with significant increases in financial costs, which encompass a wide range of expenditures, including medications, surgeries, radiation therapy, chemotherapy, and post-treatment care.
Studies (Kale & Carroll, 2016; Zafar & Abernethy, 2013) have reported that income reductions and the treatment-related financial expenses of patients with cancer directly or indirectly affect their quality of life (QoL) and mental health. This issue is especially impactful, as the burden transcends physical afflictions to encompass a formidable economic challenge labeled “financial toxicity.” It encapsulates the extensive financial strain and psychological distress imposed on patients and their families by the exorbitant costs associated with cancer care (Abrams et al., 2021). The repercussions extend beyond immediate monetary expenses, often precipitating financial hardship and, in severe cases, impoverishment, which can critically influence subsequent treatment choices and daily living arrangements (Banegas et al., 2016). A study on patients with breast cancer reported that the average monthly income lost and out-of-pocket (OOP) cost were $1,455 (Jagsi et al., 2014). Another study reported that approximately 12% of patients with breast cancer had medical debt four years after diagnosis (Ramsey et al., 2013).
Financial toxicity infiltrates the psycho-emotional domain, exacerbating psychological distress, fostering conditions like depression and anxiety, and eroding overall life satisfaction and happiness, thereby exacting a profound toll on the patient’s QoL (Ver Hoeve et al., 2021). A systematic review has revealed that increasing levels of financial toxicity are associated with worse QoL, particularly in terms of mental functioning (Jiang et al., 2023). At the same time, several scholars (De Felice et al., 2022) have highlighted that women and girls face pronounced disparities in access to health information and services compared to men and boys. Factors such as restricted mobility, limited decision-making power, lower literacy, and discrimination from communities and healthcare providers contribute to these inequities, which are further linked to lower reported QoL. In recent years, research interest in financial toxicity and its impact on QoL among patients with gynecological cancer has grown. A broad consensus has emerged from these studies, confirming the substantial adverse impact of financial toxicity on patients’ QoL (Ogamba et al., 2025; Kajimoto et al., 2023).
However, the research landscape surrounding financial toxicity among patients with gynecological cancer in China remains underexplored. Evidence has emerged that families of patients with cancer in China often experience severe financial burdens, which are associated with a range of socioeconomic, demographic, and clinical factors (Xu et al., 2022a; Xu et al., 2022b). Understanding these relationships is further compounded by the necessity to recognize different experiences across diverse geographic regions, economic strata, and cultural backgrounds, where varying levels of economic pressure and coping strategies may yield distinct outcomes. Therefore, this study aims to investigate the current status of financial toxicity, identify its contributing factors, and examine its association with QoL among patients with gynecological malignancies in China.
Materials & Methods
Study design and participants
This cross-sectional pilot study recruited 281 patients with cervical, ovarian, or endometrial cancer who were treated at one of two tertiary-level hospitals in Nanjing between November 2022 and December 2024. The inclusion criteria for this study were (1) patients diagnosed with cervical, ovarian, or endometrial cancer based on histopathological examination; (2) individuals aged 18 years or older; (3) those who had initiated treatment, including surgery, chemotherapy, or radiotherapy; (4) participants capable of completing the questionnaire independently or with assistance from the researcher; and (5) individuals who voluntarily provided informed consent to participate in the study. The exclusion criteria included patients with comorbid psychiatric disorders or cognitive impairments, as well as those receiving protective medical treatments or those unable to communicate effectively due to hearing or speech disorders. The sample size was determined using G*Power software (ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Germany; http://www.gpower.hhu.de/) (Kang, 2021). The power analysis was based on the recommendations for multiple linear regression (Faul et al., 2009; Kang, 2021). The parameters were set as follows: a medium effect size (f2 = 0.15), a significance level (α) of 0.05, and a test power (1-β) of 0.90. Considering 29 independent variables in this study, the required sample size was calculated to be at least 223 cases. The final sample size for this study was set at 257 after accounting for potential unqualified samples and expanding by 15%.
Demographic and clinical data
Demographic and clinical data on the enrolled participants were collected by reviewing the medical records and interviewing the patients, including demographic information, such as age, nationality, place of residence, marital status, occupation, the impact of illness on work, educational status, number of children, children’s age, family member monthly income, medical insurance, commercial insurance, receive subsidy, endowment insurance, caregiver, medical treatment in different locations, how long it takes to get to the hospital and treatment costs in the last three months. Information related to disease characteristics, such as disease diagnosis, pathological stage, course of disease, metastasis or not, relapse or not, complicated chronic disease, mode of operation, chemotherapy, radiotherapy, biological immunotherapy, molecular targeted therapy, traditional Chinese medicine (TCM) therapy, supportive treatment, hospitalizations, and complication.
Assessment of financial toxicity
The Comprehensive Score for Financial Toxicity (COST) scale evaluated the level of financial toxicity experienced by patients. This scale was developed by Jonas scholars in the United States in 2014 (De Souza et al., 2014) and has since been widely utilized to assess financial toxicity among patients with cancer both domestically and internationally (Bouberhan et al., 2019). The instrument comprised a total of 11 items encompassing three dimensions: financial expenditure (item 2), financial resources (itmes 1, 7), and financial effect (items 3, 4, 5, 6, 8, 9, 10, 11) (De Souza et al., 2014; De Souza et al., 2017). It demonstrated high internal consistency, with a Cronbach’s α coefficient of 0.9. Each item was rated on a five-point Likert scale as “not at all (0),” “a little (1),” “some (2),” “quite a lot (3),” and “very much (4).” The total score (range: 0-44) was calculated by summing the responses, with items 1, 2, 6, 7, and 11 scored positively and items 3, 4, 5, 8, 9, and 10 reverse-scored. The financial expenditure dimension was scored on a scale of 0 to 4, the financial resources dimension on a scale of 0 to 8, and the financial effect dimension on a scale of 0 to 32. Interpretation was such that lower scores reflected greater financial toxicity. The established cut-off scores were: ≥26 for no/mild, 14-25 for moderate, and ≤13 for severe financial toxicity. In 2017, Yu, Bi & Liu (2017) adapted this scale for use in China. Their findings indicated that the Chinese version of the COST scale had good reliability and structural validity, with a Cronbach’s α coefficient of 0.889, making it suitable for application to the Chinese cancer population. The Cronbach’s α coefficient for this scale was 0.892 in this study.
Assessment of QoL
The Functional Assessment of Cancer Therapy-Generic Scale (FACT-G) was developed by the Center for Outcome Research and Education at Northwestern University (Cella et al., 1993). FACT-G was a widely utilized instrument designed to assess the overall QoL of patients with cancer. The fourth edition of FACT-G comprised 27 items, categorized into four dimensions: physical well-being (seven items), social/family well-being (seven items), emotional well-being (six items), and functional well-being (seven items). Each item was rated on a five-point Likert scale ranging from 0 to 4, corresponding to “not at all,” “somewhat,” “moderately,” “quite a bit,” and “very much.” The total score for each dimension contributed to the overall score, with higher scores indicating a better QoL. The Chinese version of FACT-G (Wan et al., 2006) demonstrated effective applicability among patients with cancer in China and has been extensively employed in research. In this study, Cronbach’s α coefficient for this scale was 0.868.
Data collection
After obtaining approval from the Institutional Ethical Review Board for Medical Research of Women’s Hospital of Nanjing Medical University, data were collected by the researchers (Lei Zhang, Sijing Chen, and Jingjing Zhang) using a structured questionnaire. A convenience sampling approach was utilized to recruit study participants in strict accordance with the predefined inclusion and exclusion criteria. The purpose and significance of the study were clearly explained to all potential participants prior to distributing the questionnaires. Written informed consent was obtained from each participant before proceeding. Questionnaires were then distributed on-site, accompanied by detailed instructions for uniform completion. The researcher administered the questionnaire orally to participants with limited literacy or those unable to complete the questionnaire independently due to health conditions, reading each item aloud and recording responses verbatim based on their selections. All completed questionnaires were collected and immediately verified for completeness and accuracy.
Statistical analysis
Data entry and sorting were performed using Excel 2019 software, while statistical analyses were conducted using SPSS 26.0 software (IBM Corp., Armonk, NY, USA). Statistical significance was assessed based on two-tailed P-values, with P < 0.05 considered significant. For the analysis of general demographic and disease-related characteristics, financial toxicity, and QoL, continuous variables with a normal distribution and equal variances were expressed as mean ± standard deviation (SD), SD those that deviate from normality were reported as median and interquartile range. Categorical variables were presented as numbers and percentages. In univariate analyses, independent-samples t-tests and one-way analysis of variance (ANOVA) were used for binary and multi-categorical variables, respectively. Variables showing significant associations (P < 0.05) in univariate analyses, or considered clinically relevant based on the literature, were included in multivariable models. Multiple linear regression analysis was used to identify factors associated with the continuous COST score. The relationship between continuous financial toxicity score and QoL score was assessed using Pearson’s correlation when both variables were normally distributed. Otherwise, Spearman’s rank correlation was used. In this study, data were collected using self-reported assessments. Harman’s single-factor test (Kock, 2022) was employed for exploratory factor analysis to assess the objectivity of the data.
Results
Common method bias analysis
Harman’s single-factor test indicated that the variance explained by the first factor was 30.437%, which is below the critical threshold of 40%. This finding suggests that no significant common method bias was present in the study.
Social demographics
Questionnaires were initially distributed to 290 eligible patients with gynecological cancer. After excluding seven patients who declined participation and three questionnaires with patterned responses, 281 valid responses were included in the analysis, resulting in a high valid response rate of 96.9%. These included 281 patients diagnosed with gynecological malignancies, aged between 21 and 75 years, with a mean age of 52.27 (SD = 10.78) years. The largest proportion of patients (52.7%) was between 45 and 60 years old. Among the respondents, 139 had cervical cancer (49.5%), 65 had endometrial cancer (23.1%), and 77 had ovarian cancer (27.4%). The mean number of children was 1.38 (SD = 0.723). Of them, 54.4% had a family member income of less than CNY 5,000 (roughly $701.50) per month, and 96.8% of the patients spent more than CNY 5,000 (roughly $701.50) on medical expenses in the last three months (Tables 1 and 2).
| Variables | Groups | Total (n) | % |
|---|---|---|---|
| Age (years) | 18–30 | 8 | 2.8 |
| 31–44 | 66 | 23.5 | |
| 45–60 | 148 | 52.7 | |
| >60 | 59 | 21.0 | |
| Nationality | Han people | 278 | 98.9 |
| Ethnic minorities | 3 | 1.1 | |
| Place of residence | City | 145 | 51.6 |
| County/town | 41 | 14.6 | |
| Rural/suburban | 95 | 33.8 | |
| Marital status | Married | 270 | 96.1 |
| Single | 3 | 1.1 | |
| Divorced | 6 | 2.1 | |
| Widowed | 2 | 0.7 | |
| Occupation | Farmers/Fishermen | 21 | 7.5 |
| Worker/Waiter | 41 | 14.6 | |
| Individual household | 45 | 16.0 | |
| Professional Technical/administrative Personnel | 56 | 19.9 | |
| Retire | 85 | 30.2 | |
| Wait for employment | 17 | 6.0 | |
| Dimission | 16 | 5.7 | |
| The impact of illness on work | Have no effect | 196 | 69.8 |
| Early retirement | 3 | 1.1 | |
| Unemployment/resignation | 82 | 29.2 | |
| Educational Status | Never went to school | 28 | 10.0 |
| Primary school | 49 | 17.4 | |
| Junior high school | 98 | 34.9 | |
| High school/technical secondary school | 50 | 17.8 | |
| Junior college | 27 | 9.6 | |
| Bachelor degree or above | 29 | 10.3 | |
| Number of children | None | 13 | 4.6 |
| 1 | 167 | 59.4 | |
| 2 | 87 | 31.0 | |
| 3 | 9 | 3.2 | |
| 4 | 4 | 1.4 | |
| 5 | 1 | 0.4 | |
| Children’s age (years) | 0–6 | 15 | 5.3 |
| 7–17 | 38 | 13.5 | |
| 18–24 | 46 | 16.4 | |
| >24 | 169 | 60.1 | |
| Monthly per capita family income | ≤CNY 1,000 | 9 | 3.2 |
| CNY 1,001–2,999 | 41 | 14.6 | |
| CNY 3,000–4,999 | 103 | 36.7 | |
| ≥CNY 5,000 | 128 | 45.6 | |
| Medical insurance | Self-financing | 11 | 3.9 |
| Medical insurance for urban workers | 111 | 39.5 | |
| Medical insurance for urban and rural residents | 159 | 56.6 | |
| Commercial insurance | No | 278 | 98.9 |
| Yes | 3 | 1.1 | |
| Receive subsidy | No | 265 | 94.3 |
| Yes | 16 | 5.7 | |
| Endowment insurance | No | 53 | 18.9 |
| Yes | 228 | 81.1 | |
| Caregiver | None | 2 | 0.7 |
| Hubby | 208 | 74.0 | |
| Sons and daughters | 48 | 17.1 | |
| Parent | 8 | 2.8 | |
| Relatives and friends | 12 | 4.3 | |
| Others | 3 | 1.1 | |
| Medical treatment in different locations | No | 182 | 64.8 |
| Yes | 99 | 35.2 | |
| How long it takes to get to the hospital | <2 h | 25 | 8.9 |
| 2–5 h | 70 | 24.9 | |
| >5 h | 5 | 1.8 | |
| Treatment costs in the last 3 months | CNY 1,001–2,999 | 1 | 0.4 |
| CNY 3,000–4,999 | 8 | 2.8 | |
| ≥CNY 5,000 | 272 | 96.8 |
Notes:
CNY 1,000 (roughly $140.30); CNY 1,001–2,999 (roughly $140.44–$420.76); CNY 3,000–4,999 (roughly $420.90–701.36); CNY 5,000 (roughly $701.50).
| Variables | Groups | Total (n) | % |
|---|---|---|---|
| Disease diagnosis | Cervical cancer | 139 | 49.5 |
| Endometrial cancer | 65 | 23.1 | |
| Ovarian cancer | 77 | 27.4 | |
| Pathological stage | Stage I | 114 | 40.6 |
| Stage II | 74 | 26.3 | |
| Stage III | 83 | 29.5 | |
| Stage IV | 10 | 3.6 | |
| Course of disease | <3 months | 212 | 75.4 |
| 3–6 months | 40 | 14.2 | |
| 6–12 months | 13 | 4.6 | |
| >12 months | 16 | 5.7 | |
| Metastasis or not | No | 232 | 82.6 |
| Yes | 49 | 17.4 | |
| Relapse or not | No | 262 | 93.2 |
| Yes | 19 | 6.8 | |
| Complicated chronic disease | None | 195 | 69.4 |
| 1 | 74 | 26.3 | |
| 2 | 12 | 4.3 | |
| Mode of operation | No | 8 | 2.8 |
| Laparotomy | 136 | 48.4 | |
| Laparoscopic surgery | 137 | 48.8 | |
| Chemotherapy | No | 122 | 43.4 |
| Yes | 159 | 56.6 | |
| Radiotherapy | No | 245 | 87.2 |
| Yes | 36 | 12.8 | |
| Biological immunotherapy | No | 278 | 98.9 |
| Yes | 3 | 1.1 | |
| Molecular targeted therapy | No | 274 | 97.5 |
| Yes | 7 | 2.5 | |
| Traditional Chinese medicine (TCM) therapy | No | 278 | 98.9 |
| Yes | 3 | 1.1 | |
| Supportive treatment | No | 279 | 99.3 |
| Yes | 2 | 0.7 | |
| Hospitalizations | 1–2 | 138 | 49.1 |
| 3–5 | 83 | 29.5 | |
| 6-10 | 37 | 13.2 | |
| ≥10 | 23 | 8.2 | |
| Complication | No | 141 | 50.2 |
| Yes | 140 | 49.8 |
Comparison of financial toxicity
In this study, the average COST score for patients with gynecological malignancies ranged from 0 to 40 points, with a mean total score of 20.80 (SD = 7.32) points. The average score for the financial expenditure dimension was 2.07 (SD = 0.96) points, while the average score for the financial resources dimension was 3.40 (SD = 1.37) points. The average score for the financial effect dimension was 15.33 (SD = 5.81) points. Among the 281 patients diagnosed with gynecological malignancies, 205 had a COST score of <26, indicating financial toxicity (Table 3).
| COST Score | Total (n) | % |
|---|---|---|
| ≥26 | 76 | 27.0 |
| 14–25 | 163 | 58.0 |
| 1–13 | 41 | 14.6 |
| 0 | 1 | 0.4 |
Single-factor analysis of financial toxicity in patients with gynecological malignancies
The study results indicate that various factors, including age, place of residence, marital status, occupation, the impact of disease on work, children’s ages, monthly per capita family income, medical insurance, commercial insurance, caregiver, access to medical treatment in different locations, costs incurred over the last 3 months of treatment, disease diagnosis and pathological stage, disease course (including metastasis and recurrence), surgical methods employed (such as chemotherapy and molecular targeted therapy), as well as whether or not any treatment was received, significantly influenced the financial toxicity scores of the patients with gynecological malignancies (P < 0.05) (Table 4).
| Variables | Groups | Total (n) | COST score | t/F | P |
|---|---|---|---|---|---|
| Age (years) | 18∼30 | 8 | 11.88 ± 6.38 | 9.227 | < 0.001 |
| 31∼44 | 66 | 19.42 ± 6.55 | |||
| 45∼60 | 148 | 20.64 ± 7.51 | |||
| >60 | 59 | 23.98 ± 6.26 | |||
| Nationality | Han people | 278 | 20.73 ± 7.29 | −1.557 | 0.121 |
| Ethnic minorities | 3 | 27.33 ± 9.07 | |||
| Wohnort | City | 145 | 22.83 ± 6.75 | 14.755 | < 0.001 |
| County/town | 41 | 20.51 ± 6.95 | |||
| Rural/suburban | 95 | 17.83 ± 7.35 | |||
| Marriage | Married | 270 | 21.01 ± 7.17 | 4.744 | 0.003 |
| Single | 3 | 6.67 ± 3.06 | |||
| Divorced | 6 | 21.00 ± 8.85 | |||
| Widowed | 2 | 13.00 ± 7.07 | |||
| Occupation | Farmers/Fishermen | 21 | 17.86 ± 9.15 | 6.987 | < 0.001 |
| Worker/Waiter | 41 | 18.34 ± 7.46 | |||
| Individual household | 45 | 20.82 ± 5.39 | |||
| Professional technical/administrative Personnel | 56 | 22.05 ± 6.65 | |||
| Retire | 85 | 23.66 ± 6.47 | |||
| Wait for employment | 17 | 17.88 ± 7.96 | |||
| Dimission | 16 | 14.50 ± 7.67 | |||
| The impact of illness on work | Have no effect | 196 | 22.36 ± 6.83 | 16.223 | < 0.001 |
| Early retirement | 3 | 18.67 ± 14.04 | |||
| Unemployment/resignation | 82 | 17.17 ± 6.98 | |||
| Educational status | Never went to school | 28 | 18.93 ± 6.59 | 1.730 | 0.128 |
| Primary school | 49 | 18.92 ± 8.95 | |||
| Junior high school | 98 | 21.00 ± 6.77 | |||
| High school/technical secondary school | 50 | 21.54 ± 6.33 | |||
| Junior college | 27 | 22.89 ± 8.43 | |||
| Bachelor degree or above | 29 | 21.93 ± 6.77 | |||
| Number of children | None | 13 | 19.15 ± 10.34 | 1.211 | 0.304 |
| 1 | 167 | 21.14 ± 7.21 | |||
| 2 | 87 | 19.84 ± 7.25 | |||
| 3 | 9 | 23.67 ± 4.64 | |||
| 4 | 4 | 24.75 ± 5.12 | |||
| 5 | 1 | 28.00 | |||
| Age of children (years) | 0 - 6 | 15 | 16.07 ± 6.54 | 4.106 | 0.007 |
| 7–17 | 38 | 19.08 ± 6.55 | |||
| 18–24 | 46 | 20.65 ± 7.52 | |||
| >24 | 169 | 21.78 ± 7.05 | |||
| Monthly per capita family income | ≤CNY 1,000 | 9 | 14.67 ± 7.65 | 15.197 | < 0.001 |
| CNY 1,001–2,999 | 41 | 15.46 ± 7.22 | |||
| CNY 3,000–4,999 | 103 | 20.75 ± 6.56 | |||
| ≥CNY 5,000 | 128 | 22.99 ± 6.84 | |||
| Medical insurance | Self-financing | 11 | 16.73 ± 10.05 | 11.269 | < 0.001 |
| Medical insurance for urban workers | 111 | 23.20 ± 6.52 | |||
| Medical insurance for urban and rural residents | 159 | 19.42 ± 7.20 | |||
| Commercial insurance | No | 278 | 20.77 ± 7.35 | −0.839 | 0.402 |
| Yes | 3 | 24.33 ± 2.31 | |||
| Receive subsidy | No | 265 | 20.91 ± 7.30 | 0.944 | 0.346 |
| Yes | 16 | 19.13 ± 7.67 | |||
| Endowment insurance | No | 53 | 16.72 ± 8.47 | −4.677 | < 0.001 |
| Yes | 228 | 21.75 ± 6.70 | |||
| Caregiver | None | 2 | 14.50 ± 0.71 | 2.522 | 0.030 |
| Hubby | 208 | 20.45 ± 6.76 | |||
| Sons and daughters | 48 | 23.40 ± 8.48 | |||
| Parent | 8 | 16.00 ± 8.86 | |||
| Relatives and friends | 12 | 20.00 ± 7.32 | |||
| Others | 3 | 24.33 ± 13.65 | |||
| Medical treatment in different places | No | 182 | 21.79 ± 7.24 | 3.111 | 0.002 |
| Yes | 99 | 18.99 ± 7.17 | |||
| How long it takes to get to the hospital | <2 h | 25 | 19.28 ± 7.16 | 0.372 | 0.691 |
| 2–5 h | 70 | 19.00 ± 6.81 | |||
| >5 h | 5 | 21.80 ± 9.52 | |||
| Treatment costs in the last 3 months | CNY 1,001–2,999 | 1 | 14.00 | 3.574 | 0.029 |
| CNY 3,000–4,999 | 8 | 14.50 ± 8.30 | |||
| ≥CNY 5,000 | 272 | 21.01 ± 7.23 | |||
| Disease diagnosis | Cervical cancer | 139 | 21.33 ± 6.89 | 4.588 | 0.011 |
| Endometrial cancer | 65 | 22.12 ± 7.02 | |||
| Ovarian cancer | 77 | 18.74 ± 7.97 | |||
| Pathological stage | Stage I | 114 | 21.75 ± 6.81 | 9.278 | < 0.001 |
| Stage II | 74 | 23.12 ± 6.81 | |||
| Stage III | 83 | 18.02 ± 7.40 | |||
| Stage IV | 10 | 16.00 ± 7.70 | |||
| Course of disease | <3 months | 212 | 21.27 ± 7.22 | 3.132 | 0.026 |
| 3–6 months | 40 | 21.10 ± 6.42 | |||
| 6–12 months | 13 | 18.00 ± 7.86 | |||
| >12 months | 16 | 16.19 ± 8.88 | |||
| Metastasis or not | No | 232 | 21.74 ± 6.73 | 4.199 | < 0.001 |
| Yes | 49 | 16.37 ± 8.41 | |||
| Relapse or not | No | 262 | 21.16 ± 7.09 | 3.106 | 0.002 |
| Yes | 19 | 15.84 ± 8.80 | |||
| Complicated chronic disease | None | 195 | 20.19 ± 7.49 | 2.302 | 0.102 |
| 1 | 74 | 22.28 ± 6.93 | |||
| 2 | 12 | 21.67 ± 5.93 | |||
| Mode of operation | No | 8 | 14.75 ± 10.11 | 6.636 | 0.002 |
| Laparotomy | 136 | 19.79 ± 7.50 | |||
| Laparoscopic surgery | 137 | 22.16 ± 6.65 | |||
| Chemotherapy | No | 122 | 22.98 ± 6.74 | 4.501 | < 0.001 |
| Yes | 159 | 19.14 ± 7.33 | |||
| Radiotherapy | No | 245 | 20.94 ± 7.26 | 0.803 | 0.423 |
| Yes | 36 | 19.89 ± 7.78 | |||
| Biological immunotherapy | No | 278 | 20.82 ± 7.24 | 0.349 | 0.727 |
| Yes | 3 | 19.33 ± 15.50 | |||
| Molecular targeted therapy | No | 274 | 20.95 ± 7.30 | 2.137 | 0.033 |
| Yes | 7 | 15.00 ± 6.06 | |||
| TCM (traditional Chinese medicine) therapy | No | 278 | 20.76 ± 7.31 | −1.077 | 0.282 |
| Yes | 3 | 25.33 ± 8.51 | |||
| Supportive treatment | No | 279 | 20.84 ± 7.34 | 0.931 | 0.353 |
| Yes | 2 | 16.00 ± 2.83 | |||
| Hospitalizations | 1-2 | 138 | 22.36 ± 7.33 | 7.313 | < 0.001 |
| 3–5 | 83 | 20.13 ± 6.65 | |||
| 6–10 | 37 | 19.97 ± 6.35 | |||
| ≥10 | 23 | 15.26 ± 8.18 | |||
| Complication | No | 141 | 21.09 ± 7.36 | 0.645 | 0.520 |
| Yes | 140 | 20.52 ± 7.31 |
Notes:
CNY 1,000 (roughly $140.30); CNY 1,001–2,999 (roughly $140.44-$420.76); CNY 3,000–4,999 (roughly $420.90–701.36); CNY 5,000 (roughly $701.50).
Multivariate linear regression analysis of influencing factors of financial toxicity in patients with gynecological malignancies
In this study, the total financial toxicity score of the patients was designated as the dependent variable. Statistically significant variables, including age, place of residence, marital status, occupation, impact of disease on work, children’s ages, monthly per capita family income, medical insurance coverage, pension insurance status, the presence of caregivers, whether patients sought medical treatment in different locations, treatment costs incurred over the last 3 months, disease diagnosis and pathological stage, illness course duration, the presence or absence of metastasis and recurrence, the surgical modality employed and receipt of chemotherapy were included in the univariate analysis. Whether the patients received molecular targeted therapy and the number of hospitalizations were additionally examined as independent variables. Multiple linear regression analysis revealed that children’s age, monthly per capita family income, treatment costs over the past 3 months, and marital status significantly influenced financial toxicity among the patients (P < 0.05) (Table 5). These factors accounted for 31.0% of the variation in financial toxicity observed in this patient population (adjusted R2 = 0.310).
| Independent variable | Regression coefficient | Standard error | Normalized regression coefficient | t | 95% confidence intervals | P |
|---|---|---|---|---|---|---|
| (Constant) | −8.913 | 10.041 | −.888 | −28.696, 10.871 | 0.376 | |
| Children’s age (years) | 1.982 | 0.678 | 0.256 | 2.923 | 0.646, 3.319 | 0.004 |
| Monthly per capita family income | 2.217 | 0.613 | 0.251 | 3.613 | 1.008, 3.425 | 0.001 |
| Treatment costs in the last 3 months | 4.401 | 2.157 | 0.123 | 2.040 | 0.151, 8.651 | 0.042 |
| Marital status | −11.421 | 4.947 | −0.138 | −2.309 | −21.167, −1.675 | 0.022 |
Notes:
R2 = 0.403, adjusted R2 = 0.310, F = 4.338, P < 0.001.
Correlation analysis of financial toxicity level and QoL in patients with gynecological malignant tumors
The analysis showed that the total QoL scores of patients with gynecological malignant tumors were 65.79 (SD = 11.39) points, the physiological status dimension score was 17.52 (SD = 4.04) points, the social and family status score was 21.75 (SD = 3.78) points, and the emotional status score was 14.20 (SD = 4.06) points. Functional status scores were 12.32 (SD = 4.54) points. Pearson’s correlation analysis showed that QoL was positively correlated with total financial toxicity (r = 0.553, P < 0.01) (Table 6).
| Financial toxicity score | Financial expenditure | Financial resources | Financial effect | Quality of life score | Physiological condition | Social and family status | Emotional status | Functional status | |
|---|---|---|---|---|---|---|---|---|---|
| Financial toxicity score | 1 | ||||||||
| Financial expenditure | 0.714** | 1 | |||||||
| Financial resources | 0.675** | 0.346** | 1 | ||||||
| Financial effect | 0.983** | 0.652** | 0.557** | 1 | |||||
| Quality of life score | 0.553** | 0.398** | 0.318** | 0.556** | 1 | ||||
| Physiological condition | 0.504** | 0.331** | 0.196** | 0.535** | 0.758** | 1 | |||
| Social and family status | 0.202** | 0.151* | 0.443** | 0.125* | 0.485** | 0.104 | 1 | ||
| Emotional status | 0.412** | 0.340** | −0.012 | 0.466** | 0.694** | 0.501** | −0.005 | 1 | |
| Functional status | 0.401** | 0.274** | 0.265** | 0.398** | 0.810** | 0.476** | 0.295** | 0.404** | 1 |
Discussion
Status of financial toxicity in patients with gynecological malignancies
The study results demonstrated that the financial toxicity score among patients with gynecologic malignant tumors was 20.80 (SD = 7.32) points, and approximately 73% of these patients experienced financial toxicity. A study conducted by Bouberhan et al. (2019) revealed that 31.6% of patients with gynecological malignancies experienced financial toxicity, while Liang et al. (2020) reported that approximately 53.7% of patients with gynecological cancer faced high levels of financial toxicity. The present study findings are relatively higher, indicating that patients with gynecological malignancies in China may be more vulnerable to financial toxicity, which may be related to sociodemographic characteristics. This prevalence is also slightly higher than the 64.4% reported among patients with breast cancer in a study by Yuan et al. (2022). Approximately 33.8% of participants in this study were from rural areas. Previous research has demonstrated disparities in medical spending in China, with rural patients’ healthcare expenditures being roughly half that of urban patients, while facing significantly higher indirect costs, including travel, income loss, and limited family support (Leng et al., 2019). Rural patients with cancer demonstrated a higher prevalence of financial toxicity compared to their urban counterparts (Xu et al., 2022b). The survey site of Yuan and other scholars was the Xuhui District of Shanghai, which has a more developed economy, higher per capita income, and stronger resistance to the financial toxicity of patients (Yuan et al., 2022). The mean age of the study participants was 63.40 (SD =10.33) years, which is significantly higher than that of another study reporting a mean age of 52.27 (SD = 10.78) years. Older patients may have accumulated greater savings, thereby enhancing their capacity to mitigate financial toxicity. Furthermore, this phenomenon might also be linked to the type of disease. Previous studies have reported that the hospitalization cost for breast cancer is significantly lower than that for ovarian cancer (Esselen et al., 2021b), which could explain the observed differences in outcomes. The treatment of malignant gynecological tumors is a long and repetitive process. As the disease progresses, treatment costs continue to accumulate, leading to financial toxicity among patients. This highlights the importance of conducting early-stage financial toxicity screening for patients with gynecological malignancies by medical staff. Discussions regarding treatment costs should be initiated with high-risk groups, and medical insurance-related knowledge should be disseminated. Additionally, assisting patients in building psychological expectations and understanding reimbursement ratios can help reduce adverse coping behaviors, thereby improving treatment compliance and enhancing the patient’s QoL.
Factors influencing financial toxicity in patients with gynecological malignant tumors
The results suggest that the children’s age, the monthly per capita family income, the cost of treatment in the last 3 months, and widowhood are factors influencing financial toxicity in patients with gynecological malignancies.
Children’s age
This study found a statistically significant difference in financial toxicity among patients with malignant gynecological tumors based on their children’s ages. Specifically, patients with older children exhibited lower levels of financial toxicity, whereas those with younger children experienced higher financial toxicity. Adult and economically independent children can share the medical expenses of patients with gynecological malignancies, thereby alleviating economic burdens. Moreover, the daily care provided by these children can positively influence the physical and mental health of patients, jointly mitigating financial toxicity from two perspectives. Conversely, younger children require more time, energy, and financial investment from the patient. In the context of high treatment costs, patients with younger children must also bear the additional economic pressure of parenting, leading to a heavier overall financial burden and increased susceptibility to the adverse effects of financial toxicity and parenting concerns (Jewett et al., 2024). These findings indicate that nursing staff should fully leverage the intergenerational support role of children and develop family-centered intervention strategies.
Monthly per capita family income
This study revealed that as per capita family income increases, the financial toxicity experienced by patients with gynecological malignancies decreases. These findings align with previous study findings (Esselen et al., 2021a; Qiu, Yao & Jiang, 2023; Zeybek et al., 2021), which highlight that low-income patients are more vulnerable to financial toxicity. Research has shown that low-income patients exhibit reduced compliance in early screening, timely diagnosis and treatment, and continuity of care (Nnaji et al., 2022). Thus, they are more likely to become trapped in a cycle characterized by “disease-increased expenditure-adverse coping behaviors-deterioration of health outcomes-decreased income-financial toxicity” (Carrera, Kantarjian & Blinder, 2018). Therefore, nursing staff should provide tailored suggestions based on the economic conditions of individual patients. Furthermore, efforts should focus on enhancing health education for low-income patients diagnosed with gynecological malignancies. Providing information on treatment costs and available economic resources can encourage these patients to actively participate in their treatment plans, potentially reducing complications and alleviating the impact of financial toxicity.
Treatment costs in the last 3 months
Statistically significant differences in financial toxicity were observed in the last 3 months of treatment for patients with gynecological malignancies. The results suggest that higher treatment costs during this period are associated with increased financial toxicity scores in these patients, whereas lower costs correspond to reduced financial toxicity. This finding contrasts with the research reported by previous studies (Chatterjee et al., 2017; Jordan et al., 2020). One explanation for the observed discrepancy could be attributed to variations in treatment cost structures between the current study and prior studies. In this study, treatment costs over the last 3 months were classified into three categories. Notably, 96.8% of patients reported treatment costs exceeding CNY 5,000 (roughly $701.50) during this period. Only one patient incurred expenses within the range of CNY 1,001 (roughly $140.44) to CNY 2,999 (roughly $420.76), while eight patients fell within the range of CNY 3,000 (roughly $420.90) to CNY 4,999 (roughly $701.36) for their final 3 months of treatment. Nevertheless, the financial burden associated with the clinical management of gynecological malignancies remains relatively significant, often measured in units of thousand. This discrepancy may introduce bias into the results, which should be further validated in subsequent studies.
Widow status
The findings of this study revealed that widowed patients with gynecologic malignancies experience greater financial toxicity compared to their married counterparts. This observation is consistent with the results reported by Benedict et al. (2022), who found that single patients with breast cancer and gynecologic malignancies also encounter higher financial toxicity than married patients. Prior research (Lloyd-Sherlock, Corso & Minicuci, 2015) has shown that widowed women often have lower socioeconomic status and are at higher risk of poverty than married patients, particularly in developing countries. For these individuals, limited financial resources make it difficult to effectively manage emergencies, such as being diagnosed with a gynecologic malignancy. Moreover, they frequently lack the emotional and practical support typically provided by spouses, which increases their vulnerability to depression and economic strain, thereby contributing to a reduced QoL (Liang et al., 2020; Marano & Mazza, 2024). However, it should be noted that only two patients with gynecologic malignancies in this study reported spousal loss. Therefore, further investigations are necessary to validate these findings.
Correlation between financial toxicity and QoL in patients with gynecological malignancies
The correlation analysis results of this study revealed a significant positive relationship between financial toxicity scores and QoL among patients with gynecological malignancies (r = 0.553, P < 0.01). Specifically, higher levels of financial toxicity were associated with a lower QoL for these patients. Studies have demonstrated that 33% to 83% of patients with gynecological malignancies (e.g., ovarian cancer and endometrial cancer) experience financial toxicity, and 58% of these patients bear a substantial financial burden, which is directly associated with a decline in their QoL (Bouberhan et al., 2019; Kajimoto et al., 2022; Zeybek et al., 2021). Of these patients, 66% experience depression or anxiety due to financial stress, and the QoL scores of patients with severe financial toxicity are significantly lower than those with no/mild financial burden (Smith, Nicolla & Zafar, 2014). Patients may decrease their leisure activities, cut basic expenses, and even use savings or borrow money, exacerbating the family’s financial difficulties (Zafar et al., 2013). The impact of financial toxicity on a patient’s QoL is complex and multidimensional, varying according to disease type, modes of financial burden, and socioeconomic background (De La Cruz & Delgado-Guay, 2021; Delgado-Guay et al., 2015; Semin et al., 2020). Patients with higher financial toxicity are more likely to delay medical treatment and forgo treatment than patients with lower financial toxicity. For example, patients with severe financial hardship are at a five-fold increased risk of drug non-adherence and are more likely to discontinue treatment due to cost issues. This non-adherence further worsens symptoms and reduces survival (De La Cruz & Delgado-Guay, 2021; Nogueira et al., 2020; Zeybek et al., 2021). The association between financial toxicity and a decline in QoL is more significant among low-income patients than high-income patients, and existing assessment tools (such as the COST scale) may not fully capture their financial distress (Petruzzi et al., 2023). This study identified a bidirectional reinforcing relationship between financial stress and mental health. Quantitative analyses indicate that 29% of patients experiencing moderate to severe financial toxicity also present with depressive symptoms, and 36% have anxiety disorders (Chen, Chen & Xiao, 2022; Zhao et al., 2024). This psychological distress, compounded by physical symptoms such as fatigue and pain, establishes a cycle that contributes to decreases in social functioning scores (Delgado-Guay et al., 2015).
Conclusion
The financial burden associated with gynecological malignancies in China is substantial, with 73% of patients experiencing moderate or higher levels of financial toxicity. This finding underscores the gaps within the current prevention and control system for disease-related economic risks. Financial toxicity exhibits multidimensional sociodemographic characteristics, with vulnerabilities in family structures, low-income levels, and high short-term treatment costs identified as core risk factors. These factors indicate that financial toxicity fundamentally stems from a combination of inadequate family economic resilience and the financial strain imposed by medical expenses. Furthermore, this study demonstrates that a higher financial toxicity among patients with gynecological malignancies was significantly associated with a poorer quality of life. The novelty of this study lies in addressing research gaps related to financial toxicity within the field of gynecological oncology in China, establishing a localized evaluation framework, and providing an empirical foundation for developing stratified intervention strategies. The primary limitation of this study is the use of a convenience sampling method from a single institution, which may introduce selection bias and limit the generalizability of the findings. Future studies utilizing random or consecutive sampling are warranted to validate and extend these results. At the same time, due to inherent limitations of a cross-sectional design, caution is warranted when generalizing these findings. Future research should focus on creating longitudinal cohorts and incorporating mediating variables, such as medical payment methods and social support networks, to enable a more in-depth analysis of the dynamic evolution of financial toxicity and how it influences the QoL.