Determinants of patient delay and its association with quality of life among young women with breast cancer in China: A cross-sectional study

Abstract

Objective

The incidence of breast cancer in young women (< 40 years old) has been increasing in recent years. Patient delay is significant to disease prognosis, thus early diagnosis and prompt effective therapy represent the key elements in controlling breast cancer. This study aimed to identify determinants of patient delay and assess the association of patient delay with quality of life (QoL).

Methods

A cross-sectional study was conducted among 286 young patients with breast cancer from October, 2022 to March, 2023 at four tertiary hospitals in Hunan province, China with the guidance of Andersen Behavioral Model of Health Services Utilization. Determinants of patient delay were identified using binary logistic regression analysis. The association between patient delay and QoL was analyzed using linear regression.

Results

A total of 106 (37.06%) young patients with breast cancer had experienced patient delay, indicating that more than one in three women faced this critical barrier to timely care. Patients who lived in countryside (odd ratio [OR] 3.676, 95% confidence interval [CI] 1.468–9.206), had an initial outpatient consultation to other department (OR 3.910, 95% CI 1.439–10.624), waited > 7-day for initial outpatient consultation (OR 11.350, 95% CI 3.450–37.345) were more likely to experience patient delay. Detecting disease at routine physical examination (OR 0.208, 95% CI 0.045–0.967), having higher critical health literacy (OR 0.676, 95% CI 0.506–0.904), and having more friend support (OR 0.921, 95% CI 0.855–0.993), patients were less likely to experience patient delay. Patient delay was negatively correlated with QoL including overall FACT-B (t = −2.443, P = 0.015), physical well-being (t = −3.526, P < 0.001), functional well-being (t = −2.077, P = 0.039), and additional concerns (t = −1.974, P = 0.049).

Conclusions

Waiting time for initial outpatient consultation > 7-day was the strongest determinant of patient delay among young women with breast cancer. Other significant factors included rural residence and initial consultation in non-specialist departments, while higher health literacy, friend support, and detection via physical examination were protective. Patient delay was significantly associated with poorer QoL, particularly in the domains of physical well-being, functional well-being, and additional concerns. These findings underscore the need for interventions targeting health care access to reduce patient delay and improve patient health outcomes.

Introduction

Breast cancer is the most common cancer in women and severely threatens women's health, with 2.3 million new patients with breast cancer diagnosed worldwide in 2022.¹ China accounts for over 15% of the global breast cancer burden, with 357,000 cases and 75,000 deaths annually.^2,3 Notably, the incidence of breast cancer in women aged < 40 years old has risen substantially in recent years. The fifth international consensus guidelines for breast cancer in young women (BCY5), jointly issued by the European School of Oncology (ESO) and the European Society of Medical Oncology (ESMO), define this demographic as women diagnosed before age 40.⁴ Recent epidemiological data indicate that Chinese women < 40 years constitute 18.7% of breast cancer cases, significantly exceeding the proportion observed in the United States (5.6%).⁵

Age constitutes an independent prognostic factor in breast cancer survival. Younger patients with breast cancer have a higher risk of recurrence and death and a worse clinical prognosis than middle-aged and older patients.⁶ In addition to the challenges posed by the disease and its treatment, young patients with breast cancer often face a range of psychosocial burdens that indirectly affect their quality of life (QoL). Women under 40 years of age, who are at a critical developmental stage and often juggle multiple social roles, are particularly susceptible to psychological issues such as anxiety, depression, and fear following diagnosis and during treatment.⁷ In 2000, the World Health Organization (WHO) proposed that approximately one-third of cancers could be cured entirely by early diagnosis.⁸ Reducing the delay in patient consultation is essential for achieving “early detection, diagnosis, and treatment”.⁸ Patient delay was defined by an interval of more than three months between the discovery of symptoms and the first health care consultation.⁹ Patient delay for patients with breast cancer correlates with advanced tumor progression, including increased tumor diameter, higher distant metastasis incidence, and advanced TNM staging.¹⁰ Critically, long-term patient delay exacerbates disease progression, a concern particularly relevant for young patients with breast cancer, as evidence indicates that diagnostic delay is significantly associated with more advanced disease and worsened survival outcomes in this population.¹¹ Therefore, it is highly necessary to conduct research on patient delay among young patients with breast cancer in China.

Since its initial development by Professor Ronald M. Andersen in 1968, the Andersen Behavioral Model of Health Services Utilization (BMHSU) has undergone five major revisions through expansions in measurable indicators, structural adjustments, enriched pathway relationships, and shifts in analytical approaches. These refinements have strengthened the model's theoretical completeness and empirical feasibility, leading to its broad international academic recognition. Widely applied in health services research, BMHSU serves as a key framework for analyzing a variety of health utilization behaviors, including care-seeking, medical expenditures, disease screening, and medication use. It has become the leading model for explaining and predicting health-related behavior in health services studies.¹² This model has three key elements: predisposing, enabling, and need factors. These elements can either expedite or hinder individuals' utilization of services and health behaviors can influence health outcomes. Predisposing factors represent the likelihood that an individual will need health care services, like older age, rural residence, low level of education, and lack of awareness, are accountable for patient delay in patients with breast cancer.13, 14, 15, 16 Enabling factors refer to an individual's ability to access health care services and the availability of health care services. Some enabling factors like low income, low level of social support, and excessive waiting time for outpatient appointments are influencing factors in increasing patient delay.17, 18, 19 Need factors, such as initial symptoms, refer to an individual's perceived need for health care services.

A recent integrative literature review investigating patient delay among Chinese patients with breast cancer identified the affecting factors, including symptom appraisal, Chinese cultural factors, knowledge of breast cancer symptoms and screening, health history, personality, social and health care factors, and background factors.²⁰ In summary, patient delay in patients with breast cancer is influenced by multiple factors and represent a complex behavioral phenomenon. While most existing studies on this topic have focused on the general patients with breast cancer, there is a notable lack of research specifically addressing younger patients. The factors influencing patient delay among young patients with breast cancer in China remain inadequately understood. Importantly, isolated investigations have examined discrete influencing factors, no extant study has systematically integrated determinants within BMHSU to analyze factors that may be associated with patient delay of breast cancer in young women. Thus, investigating determinants of patient delay in this population with the guidance of the BMHSU is essential for achieving early detection and timely intervention.

The BMHSU hypothesizes the relationship between health behaviors and health outcomes: health behaviors influence health outcomes. Existing empirical studies have also demonstrated the direct adverse consequences of patient delay on tumors, such as larger tumor diameter, increased TNM staging, and high rate of lymph node metastasis.^9,21 These adverse consequences may subsequently exert a negative impact on patients' QoL. Therefore, it is also reasonable to hypothesize that patient delay may influence the QoL in young patients with breast cancer.

Overall, this study aims to (1) investigate the patient delay rate, (2) identify the determinants of patient delay, and (3) examine the association between patient delay and health outcome (QoL) in young patients with breast cancer (< 40 years old) in China. We used the BMHSU, which provides a theoretical framework to understand how predisposing factors (age, ethnicity, et al.), enabling factors (household income, medical insurance, et al.) and need factors (initial symptoms) influenced health behavior (patient delay) and how health behavior (patient delay) subsequently influence the health outcome (QoL) among young patients with breast cancer in China (Fig. 1). Findings will provide critical insights for developing targeted interventions to optimize health care utilization behaviors, directly advancing “the WHO Global Breast Cancer Initiative (GBCI)”.²²

Fig. 1.

Theoretical framework.

Methods

This article was written according to The Strengthening the Reporting of Observational Studies in Epidemiology 2007 (STROBE 2007).²³ The STROBE checklist is available in Appendix A.

Design and setting

We conducted a cross-sectional study of patient delay in young patients with breast cancer in Hunan Province, China. The crude incidence rate of breast cancer in Hunan Province in 2021 was 38.02/100,000, occupying the top of the list of female cancer incidence rates, demonstrating a distinct trend toward younger-onset cases.²⁴ The eligible participants were recruited from breast surgery departments of three comprehensive hospitals and one cancer hospital between October 2022 and March 2023. The participating hospitals are large, tertiary A-grade public hospitals, which in the Chinese health care system function as regional diagnostic and treatment centers. Patients from a wide geographical area, including rural and urban settings, are often referred to these institutions for specialized care, especially for complex conditions like cancer. The recruitment of research objects from the hospital can make the samples sufficiently representative.

Participants

Researchers screened potential participants by reviewing electronic medical records of young patients with breast cancer in the inpatient wards. A research nurse then approached them, explained the study's purpose and procedures, and invited them to participate. Eligible participants received standardized verbal and written disclosures detailing study objectives, potential benefits/risks, and voluntary participation principles. Following written informed consent acquisition, paper questionnaires were distributed.

The criteria for inclusion were as follows: (a) Age < 40 years at diagnosis, (b) diagnosis of breast cancer confirmed by clinical and pathological examination, and (c) Mandarin literacy with intact communication capacity. The exclusion criteria were as follows: Individuals with severe physical or mental illness and inability to cooperate with the investigation.

The required sample size was determined by using the single-population proportion formula based on the following assumptions. About 20% of patient delay data were taken from the results of previous studies,25, 26, 27 with 95% confidence interval (CI) and a margin of error of 5%. The minimum sample size was calculated based on the following formula:

$$N=\frac{{\left(Z_{\raisebox{1ex}{$\alpha $}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right)}^{2}P\left(1-P\right)}{{\sigma }^2}$$

where N is the sample size, Z_{α /2} is the normal distribution value at 95% CI (Z_{α /2} = 1.96), P is the proportion of patient delay (20%), and σ is the margin of error (5%). Therefore, after adding a 10% non-response rate, the final sample size was 269.

Variables and measures

The key variable was the rate of patient delay among young patients with breast cancer. We designed a patient delay questionnaire that included the time of initial symptom onset and the time of initial outpatient consultation. A time interval exceeding three months between these two time points was defined as patient delay. Independent variables were selected and grouped based on two reasons: first on conclusions from previous studies that reported factors associated with patient delay in women with breast cancer;^14,25,28 second on assumptions in the BMHSU.

Predisposing factors included sociodemographic characteristics (age, ethnicity, religion, marital status, educational status), family history of breast disease, health literacy, and health beliefs related to breast self-examination (BSE) and breast cancer screening. We used the Health Literacy Scale (AAHLS) to measure health literacy.²⁹ The Cronbach's alpha value of this scale was 0.81.

Enabling factors included average monthly household income, medical insurance, the nearest health care facility, primary care department, waiting time for the initial outpatient consultation, and social support. We used the Perceived Social Support Scale (PSSS) to measure social support level.³⁰ The Cronbach's alpha value and the cumulative variance contribution rate of this scale were 0.84 and 70.2%, respectively.

Need factors were operationalized as ways of detecting the disease and initial symptoms.

The health outcome was operationalized as QoL. We used the Functional Assessment of Cancer Therapy-Breast (FACT-B) to measure QoL. The 36-item FACT-B comprised five subscales assessing: physical well-being, social/family well-being, emotional well-being, functional well-being, and additional concerns.³¹ The split-half reliability of this scale was 0.91. See Table 1 below for details.

Table 1.

Variables and measurements.

Factors	Variables	Measurements	Notes
Predisposing factors	Age at diagnosis	Self-designed questionnaire
	Family history of breast cancer
	Family history of benign breast disease
	Ethnicity
	Religion
	Education status
	Marital status
	Number of children
	Habitual residence
	Occupation
	Health belief		4 closed questions:
			Whether know how to properly perform BSE prior to diagnosis
			Whether perform regular BSE prior to diagnosis
			Whether know the breast cancer screening policy in Hunan province prior to diagnosis
			Whether perform regular breast cancer screening before contracting prior to diagnosis
	Health literacy	AAHLS
Enabling factors	Average monthly household income (CNY)	Self-designed questionnaire
	Medical insurance
	Nearest health care facility
	Distance to nearest health care facility (km)
	Serviceable time of nearest health care facility
	Health care facility for initial outpatient consultation
	Department for initial outpatient consultation
	Waiting time for initial outpatient consultation
	Social support	PSSS
Need factors	Pathway of symptom detection	Self-designed questionnaire
	Initial symptom
Health behavior	Patient delay
Health outcome	QoL	FACT-B

AAHLS, All Aspects of Health Literacy Scale; BSE, breast self-examination; FACT-B, the Functional Assessment of Cancer Therapy-Breast; PSSS, the Perceived Social Support Scale; QoL, quality of life.

The self-designed questionnaire was developed to collect factual data on sociodemographic and patient delay. Items were generated based on the BMHSU and literature review. Content validity was assessed by an expert panel comprising five specialists (two oncology nurse specialists, two epidemiologists, and one public health researcher). The scale-level content validity index was 0.92, and all items had an item-content validity index greater than 0.78. The questionnaire was pilot-tested with a convenience sample of 20 young patients with breast cancer to ensure clarity, comprehensibility, and appropriateness of the questions. Minor modifications to the wording were made based on the pilot feedback.

Data collection

Data collection was conducted through on-site structured questionnaire surveys. All investigators involved in data collection received standardized training prior to the study. This training emphasized the importance of maintaining neutrality and strictly avoiding any leading questions, suggestive tones, or non-verbal cues that could influence participants' answers. While the investigators were not blinded to the overall study hypothesis (as they were part of the research team), they were blinded to any specific subgroup analyses or secondary hypotheses to prevent conscious or unconscious bias during data collection. Participants anonymously completed the questionnaires while systematically trained investigators provided real-time clarification for queries arising during the process. To ensure the accuracy of self-reported timelines and mitigate potential recall bias, all key dates (including the date of first symptom onset, first medical consultation, and diagnosis) were cross-verified with hospital electronic medical records where possible. This verification was performed independently by two research team members. Any discrepancies exceeding 14 days were reconciled by reviewing the original clinical notes. The completion time ranged from 15 to 20 minutes. Upon completion, investigators performed on-site verification; any omitted or ambiguous items were addressed through investigators inquiry and supplemental recording. Questionnaires were retained only after thorough validation. All procedures occurred within private settings. Each participant received a gift (approximately 1.5 USD) as compensation upon survey conclusion.

Statistical analysis

The data were analyzed using SPSS software version 26.0. Descriptive statistics summarized continuous variables using measures of central tendency (mean) and dispersion (standard deviation [SD]), and categorical variables using frequencies and percentages. The Mann–Whitney U test and χ² test as univariate analysis were used to compare the two samples' differences in continuous or categorical variables. Variables significantly associated with patient delay (P < 0.05) in univariate analyses were included in binary logistic regression models. After the multicollinearity test, binary logistic analysis models using a forward stepwise method identified the independent determinants of patient delay. The univariate linear regression analyzed the association between patient delay and QoL. The odds ratios (OR) and 95% CI were calculated. Statistical significance was set at two-sided P < 0.05.

Results

Characteristics of the study population

In this study, 319 young patients with breast cancer met the inclusion criteria, excluding 27 patients who refused to be surveyed after informed consent, 292 questionnaires were distributed, and the final valid questionnaires recovered were 286, with a response rate of 97.9%. A flowchart of the recruitment process is provided in Fig. 2.

Fig. 2.

Flow diagram of recruitment and participation.

The predisposing, enabling, and need factors of the study population (n = 286) are presented in Table 2, Table 3, Table 4. 95.5% of participants were aged ≥ 30 years. The majority of patients had no family history of breast cancer and benign breast disease, 90.9% and 92.0%, respectively. 70.3% of patients lived in cities. The proportion of patients with an average monthly household income of 3000–5000 RMB was 30.1%, and those with ≤ 1000 RMB were the smallest, accounting for only 9.4%. The nearest health care facilities were mainly community/township health centers and tertiary A-grade public hospitals, accounting for 33.9% and 25.2%. 89.5% of the patients detected their symptoms on their own, while only 2.4% were detected via free breast cancer screening. Young patients with breast cancer had more initial symptoms, 69.8% of patients had breast lumps.

Table 2.

Predisposing factors among young patients with breast cancer (N = 286).

Variables	Categories	Mean or frequency	SD or %
Age at diagnosis, n (%), years	≥ 18, < 25	2	0.7
	≥ 25, < 30	9	3.1
	≥ 30, < 35	85	29.7
	≥ 35, < 40	190	66.4
Family history of breast cancer, n (%)	No	260	90.9
	Yes	26	9.1
Family history of benign breast disease, n (%)	No	263	92.0
	Yes	23	8.0
Ethnicity, n (%)	Han	261	91.3
	Ethnic minorities	25	8.7
Religion, n (%)	No religion	263	92.0
	Yes	23	8.0
Education status, n (%)	Primary school or below	6	2.1
	Junior high school	65	22.7
	Technical secondary school/ higher vocational colleges	46	16.1
	Senior high school	60	21.0
	Junior college	45	15.7
	Undergraduate college	59	20.6
	Postgraduate and above	5	1.7
Marital status, n (%)	Spinsterhood	22	7.7
	Married	248	86.7
	Divorced	14	4.9
	Widowed	2	0.7
Number of children, n (%)	0	24	8.4
	1	103	36.0
	≥ 2	159	55.6
Habitual residence, n (%)	City	201	70.3
	Countryside	85	29.7
Occupation, n (%)	Unemployed	75	26.2
	Farmer	38	13.3
	Merchant	44	15.4
	Enterprise staff	84	29.4
	Civil servant	43	15.0
	Student	2	0.7
Whether know how to properly perform BSE prior to diagnosis, n (%)	Yes	111	38.8
	No	175	61.2
Whether perform regular BSE prior to diagnosis, n (%)	Yes	101	35.3
	No	185	64.7
Whether know the breast cancer screening policy in Hunan province prior to diagnosis, n (%)	Yes	176	61.5
	No	110	38.5
Whether perform regular breast cancer screening before contracting prior to diagnosis, n (%)	Yes	91	31.8
	No	195	68.2
AAHLS, mean (SD)	Total score	25.40	3.06
	Functional health literacy	8.30	1.57
	Communicative health literacy	8.13	1.23
	Critical health literacy	8.97	1.59

AAHLS, All Aspects of Health Literacy Scale; BSE, breast self-examination; SD, standard deviation.

Table 3.

Enabling factors among young patients with breast cancer (N = 286).

Variables	Categories	Mean or frequency	SD or %
Average monthly household income (RMB), n (%)	≤ 1000	27	9.4
	> 1000, ≤ 3000	67	23.4
	> 3000, ≤ 5000	86	30.1
	> 5000, ≤ 10,000	69	24.1
	> 10,000	37	12.9
Medical insurance, n (%)	Basic medical insurance system for urban residents	144	41.3
	New-type Rural cooperative Medical Insurance	135	38.7
	Commercial insurance	66	18.9
	No insurance	4	1.1
Nearest health care facility, n (%)	Tertiary A-grade public hospital	72	25.2
	Non- Tertiary A-grade public hospital	54	18.9
	Maternal and child health hospital	28	9.8
	Hospital of traditional Chinese medicine	8	2.8
	Community/Twon health care center	97	33.9
	Private hospital	27	9.4
Distance to nearest health care facility (km), n (%)	< 1	69	24.1
	≥ 1, < 5	156	54.5
	≥ 5, < 10	36	12.6
	≥ 10	25	8.7
Serviceable time of nearest health care facility, n (%)	24 hours	192	67.1
	Daytime Monday to Sunday	74	25.9
	All day Monday to Friday	10	3.5
	Daytime Monday to Friday	5	1.7
	Other	5	1.7
Health care facility for initial outpatient consultation, n (%)	Tertiary A-grade public hospital	137	47.9
	Non-Tertiary A-grade public hospital	65	22.7
	Maternal and child health hospital	50	17.5
	Hospital of traditional Chinese medicine	9	3.1
	Community/Twon health care center	10	3.5
	Private hospital	15	5.2
Department for initial outpatient consultation, n (%)	Breast specialty	198	69.2
	General practice medicine department	45	15.7
	Gynecology department	13	4.5
	Other	30	10.5
Waiting time for initial outpatient consultation, n (%)	> 7-day	259	90.6
	≤ 7-day	27	9.4
PSSS, mean (SD)	Total score	67.25	9.80
	Family support	23.77	3.47
	Friend support	21.29	4.25
	Support from others	22.19	3.77

PSSS, Perceived Social Support Scale; SD, standard deviation.

Table 4.

Need factors among young patients with breast cancer (N = 286).

Variables	Categories	Frequency	%
Pathway of symptom detection	Free breast cancer screenings	7	2.4
	Routine physical examination	23	8.0
	Self-detection of symptoms	256	89.5
Initial symptom	Breast lump	222	69.8
	Breast pain	45	14.2
	Nipple erosion	12	3.8
	Premenstrual breast discomfort	9	2.8
	Axillary lump	5	1.6
	Edema or dimpling of the areola skin	5	1.6
	Sunken nipples	4	1.3
	Itchy breast skin	4	1.3
	Breastfeeding blockage	2	0.6
	Arm pain	2	0.6
	Fatigue, loss of appetite, fever	2	0.6
	Shoulder pain	1	0.3
	Sternal pain	1	0.3
	Backache	1	0.3
	Buttock pain	1	0.3
	Axillary pain	1	0.3
	Breast odor	1	0.3

Patient delay and its determinants among young patients with breast cancer

Among the 286 respondents, 106 (37.06%) experienced patient delay. The delay duration ranged from 91 to 2160 days, with a mean (SD) of 287.51 (333.017) days.

The univariate analysis identified 13 candidate variables for potential association with patient delay in young patients with breast cancer. To evaluate the presence of multicollinearity among the covariates included in the multivariable logistic regression model, the Variance Inflation Factor (VIF) was calculated for each variable (Table 5). The VIF values ranged from 1.030 to 2.260, which were considerably below the commonly accepted thresholds of 5 or 10. These findings confirmed the absence of significant multicollinearity, ensuring the reliability of the regression estimates.

Table 5.

Variance inflation factor results.

Variable	VIF Value
Educational status	1.756
Habitual residence	1.488
Occupation	1.653
Department for initial outpatient consultation	1.048
Waiting time for initial outpatient consultation	1.030
Average monthly household income	1.294
Whether know how to properly perform BSE prior to diagnosis	1.427
Whether perform regular BSE prior to diagnosis	1.446
Whether perform regular breast cancer screening before contracting prior to diagnosis	1.188
Critical health literacy	2.260
Total score of AAHLS	2.248
Friend support	1.128
Pathway of symptom detection	1.130

AAHLS, All Aspects of Health Literacy Scale; BSE, breast self-examination.

After multivariable adjustment, the binary logistic regression analysis showed that patients who lived in rural areas (OR = 3.676, 95% CI: 1.468, 9.206), had an initial outpatient consultation to other departments (OR = 3.910, 95% CI: 1.439, 10.624), waited more than 7-day for initial outpatient consultation (OR = 11.350, 95% CI: 3.450, 37.345) of patients were more likely to experience patient delay; to detect disease at routine physical examination (OR = 0.208, 95% CI: 0.045, 0.967), to have higher critical health literacy (OR = 0.676, 95% CI: 0.506, 0.904), and to have higher level of friend support (OR = 0.921, 95% CI: 0.855, 0.993) patients were less likely to experience patient delay (Table 6). It should be noted that several variables, including “Wait time for initial outpatient consultation” and the “Other” category, were associated with wide confidence intervals. This reflected the limited precision of these estimates, likely attributable to unbalanced group distributions. Therefore, the ORs for these factors should be interpreted with caution.

Table 6.

Determinants of patient delay among young patients with breast cancer.

Variables	OR	95% CI	P-value
Habitual residence
Countryside	3.676	1.468–9.206	0.005
City	Ref.	Ref.
Department for initial outpatient consultation
General practice medicine	1.932	0.805–4.636	0.140
Gynecology department	2.845	0.774–10.447	0.115
Other	3.910	1.439–10.624	0.008
Breast specialty	Ref.	Ref.
Waitingtime for initial outpatient consultation
> 7-day	11.350	3.450–37.345	< 0.001
≤ 7-day	Ref.	Ref.
Pathway of symptom detection
Routine physical examination	0.208	0.045–0.967	0.045
Free breast cancer screening	0.131	0.010–1.686	0.119
Self-detection of symptoms	Ref.	Ref.
Critical health literacy	0.676	0.506–0.904	0.008
Friend support	0.921	0.855–0.993	0.033

The association between patient delay and QoL

The categorical variable (whether patient delay occurred) was converted into a dummy variable, with the non-patient delay group set as the reference. The results showed that, compared to the non-patient delay group, the patient delay group showed statistically significant differences in the overall FACT-B (t = −2.443, P = 0.015) and the domains of physical well-being (t = −3.526, P < 0.001), functional well-being (t = −2.077, P = 0.039), and additional concerns (t = −1.974, P = 0.049) among Chinese young patients with breast cancer . In contrast, no statistically significant differences were observed between two groups in the social/family well-being and emotional well-being domains (P > 0.05). For details, see Table 7.

Table 7.

Univariate linear regression analysis results of the association between patient delay and QoL.

Domains	Variables	B	SE	ß	t	P-value
Overall FACT-Ba	(Constant term)	98.994	1.244		79.552	< 0.001
	Patient delay group	−4.994	2.04	−0.143	−2.443	0.015
Physical well-beingb	(Constant term)	22.194	0.325		68.257	< 0.001
	Patient delay group	−1.883	0.534	−0.205	−3.526	< 0.001
Social/family well-beingc	(Constant term)	19.117	0.410		46.622	< 0.001
	Patient delay group	0.044	0.674	0.004	0.065	0.948
Emotional well-beingd	(Constant term)	17.222	0.2698		57.856	< 0.001
	Patient delay group	−0.590	0.489	−0.071	−1.207	0.228
Functional well-beinge	(Constant term)	15.194	0.419		36.246	< 0.001
	Patient delay group	−1.430	0.689	−0.122	−2.077	0.039
Additional concernsf	(Constant term)	25.267	0.350		72.207	< 0.001
	Patient delay group	−1.135	0.575	−0.116	−1.974	0.049

F_a = 5.970, R_a² = 0.021, Adjust R_a² = 0.017.

F_b = 12.431, R_b² = 0.042, Adjust R_b² = 0.039.

F_c = 0.004, R_c² = 0.010, Adjust R_c² = 0.004.

F_d = 1.457, R_d² = 0.005, Adjust R_d² = 0.002.

F_e = 4.315, R_e² = 0.015, Adjust R_e² = 0.011.

F_f = 3.897, R_f² = 0.014, Adjust R_f² = 0.010.

Discussion

Patient delay in young patients with breast cancer

This study found a 37.06% patient delay rate among respondents, substantially higher than rates in developed countries including the United States (12%),²⁵ Germany (18%),²⁶ and the United Kingdom (20–30%).³² Compared to other developing countries, this rate was higher than in Mexico (20%),²⁷ slightly higher than in Iran (34%),¹⁴ lower than in Vietnam (49%),²⁸ and significantly lower than in Morocco (70.1%).³³ The delay duration ranged from 1 to 2160 days with a median of 30 days. This median delay was shorter than the six-month delay reported in Morocco³³ and the 5.5-month delay in Vietnam,²⁸ but longer than the 10-day delay in Mexico²⁷ and 16-day delay in Germany.²⁶ While direct statistical comparison across studies is limited by methodological heterogeneity, these trends suggest an intermediate-to-high rate of patient delay among young patients with breast cancer in China, significantly exceeding rates in developed countries while remaining below those in certain developing countries. This distribution pattern indicates no simple linear correlation between economic development levels and patient delay rates.

The observed international variations in patient delay rates likely stem from a complex interplay of factors, including economic development, national health policies, sociocultural norms, and differences in study methodologies. Notably, Mexico—another developing country—demonstrates substantially lower patient delay rate than China. This advantage may be associated with its System of Social Protection for Health and the Seguro Popular, which predominantly covers impoverished populations while bolstering investments in primary health infrastructure.³⁴ Our data reveal 20% of young patients relied on commercial insurance or out-of-pocket payments, with over 20% commuting > 5 km to access health care facilities—highlighting deficits in health care accessibility in China.

Patient delay may be partly explained by a prevalent underestimation of risk among young women. Existing studies found that a considerable number of patients regarded their breast symptoms as minor issues and did not take them seriously.^33,35 This low perceived threat can be attributed to two factors: first, public health messaging often associates breast cancer with older ages, leading young women to underestimate their susceptibility; second, the absence of pain or functional impairment in early-stage disease fosters complacency. Consequently, help-seeking is postponed until symptoms escalate, consistent with the established notion that care-seeking likelihood increases with symptom severity.³⁶

Furthermore, time constraints are also a common reason for patient delay among young women with breast cancer. Rayne et al. found that a small proportion of patients delayed seeking medical care due to time constraints, including childcare and work commitments, which prevented them from making time for hospital visits.³⁷

Young women are in a critical stage of personal development, needing to balance family and work responsibilities, resulting in uneven time allocation. As noted by Agbeko et al., an individual's decision-making process regarding health care is an iterative process of prioritizing, with the ultimate goal of maintaining important family and social functions.³⁸ Influenced by Chinese traditional gender roles, women tend to habitually prioritize family matters over their own health. Breast cancer remains a stigmatized disease associated with a loss of femininity, and the associated seriousness and social consequences are a source of significant anxiety.³⁹ These findings collectively highlight that interventions must address not only knowledge gaps but also the profound sociocultural and gendered contexts that shape health-seeking behavior.

Determinants of patient delay among young patients with breast cancer

Our study focused on patients with breast cancer under the age of 40 and, guided by the BMHSU, identified factors influencing patient delay. Our findings revealed that these determinants were multifaceted, encompassing predisposing factors (habitual residence, health literacy), enabling factors (department for initial outpatient consultation, waiting time for initial outpatient consultation, friend support), and need factors (pathway of symptom detection).

First, the enabling factor, namely, waiting time for initial outpatient consultation > 7-day, was the strongest determinants of patient delay among young patients with breast cancer, persisting after adjustment for predisposing and need variables. Young women with experiencing > 7-day of waiting time for initial outpatient consultation had significantly higher likelihood of experiencing patient delay than those with ≤ 7-day waits. A Hong Kong study also showed that treatment in public hospitals predicted prolonged patient intervals, because people faced long waits for their first clinic consultation in public hospitals for their suspicious breast symptoms.⁴⁰ Statistical analyses revealed that nearly half of patients initially sought care at Tertiary A-grade public hospitals. This study was conducted in such hospitals within Hunan Province, which exhibit strong service capacity and high patient volumes. Similar findings were reported in Saudi Arabia, where appointment difficulties constituted the most common barrier to timely breast cancer care.⁴¹ It is necessary to improve primary health care institutions service by reducing waiting time through system-level improvements such as mobile screening devices and simplified referral pathways.

Second, another enabling factor, namely the department for initial outpatient consultation, was a significant correlate of patient delay in young patients with breast cancer. Compared to patients whose first department was a breast specialty, young patients with breast cancer who visited other departments (e.g. traditional Chinese medicine department, orthopedics department, pain management department) firstly were more like to experience patient delay. The department for initial outpatient consultation is closely related to patients' initial symptoms. Chinese medicine is an important alternative therapy for breast diseases,⁴² some patients were more willing to go to the Chinese medicine department when they experienced mild symptoms. Additionally, for patients whose initial symptoms manifest as pain in various parts of the body (such as the shoulders, arms, sternum, etc.), the orthopedics department and pain management department were often their first outpatient departments. However, other non-breast specialists may lack disease-specific diagnostic expertise, delaying referrals s to breast specialties.⁴³

We also found that rural residents were more likely to experience patient delay than urban residents. While consistent with prior studies, our research further validated this health inequality's cross-geocultural generalizability by including ethnic-minority-inhabited areas in Hunan Province. Influenced by Taoism and other traditional religious cultures, young women in rural areas often pursue traditional Chinese herbal therapies or superstitious healing activities. Such behaviors may further exacerbate the patient delay.⁴⁴

In addition, critical health literacy was an essential factor in reducing patient delay. According to Nutbeam's model of health literacy,⁴⁵ critical health literacy is the highest level of cognitive skills that focuses on the critical analysis of information and is more vital in regulating health status than functional health literacy and interactive health literacy. This study's respondents (women < 40 years) generally possessed higher educational attainment than older cohorts. This group exhibited enhanced critical appraisal of health information, explaining why health literacy played a more significant role in patient delay among young patients.

Regarding social support, family support facilitating effect on patient delay has been reported more frequently in previous studies.^16,46,47 However, we found that friend support was an influencing factor in patient delay in young patients with breast cancer. Several factors may account for the discrepancies between the results of this study and previous research. Young patients generally place a high premium on personal privacy and emotional communication. Friends, as an intimate part of their social circle, may be more attuned to their psychological experiences compared to family members. This emotional closeness endows friends' opinions and suggestions with significant influence over patients' decisions regarding medical consultation. Additionally, given the private nature of breast-related symptoms, young patients are more likely to confide in female friends, further highlighting the pivotal role of peer support in their healthcare-seeking behaviors. These findings align with Tsuchiya et al. 's demonstration that young patients with cancer derive greater satisfaction from friend support and require more emotional help from close or female friends than from family.⁴⁸

An innovative finding of this study was that young patients with breast cancer whose disease was detected through routine physical examinations were less likely to experience patient delay than those who noticed symptoms on their own. Self-discovered symptoms are influenced by individual cognitive level, and some young patients misinterpret symptoms and overlook initial symptoms of breast cancer, thus delaying consultation.³⁸ In the future, community-based empowerment strategies should integrate health literacy education with peer support networks. Prioritizing routine screening participation and health information dissemination via digital platforms (e.g. 3D breast anatomy visualization animations, AR-guided breast self-examination training modules) could amplify intervention effectiveness.

Association between patient delay and QoL

Notably, this study demonstrated that patient delay was associated with a poor QoL, which was significantly reflected in physical, functional well-being and additional concerns. Among these domains, physical well-being was most substantially impacted by patient delays. This finding supports the hypothesis of BMHSU. Previous studies have widely discussed the impact of patient delay on the progression of breast cancer.²¹ Young women inherently experience more rapid breast tumor progression,⁴⁹ and when combined with treatment-seeking delays, they are more likely to develop adverse prognostic outcomes such as increased tumor size and lymph node metastasis. Patients who present a poorer breast cancer prognosis often require more aggressive treatment (e.g., axillary-lymph-node dissection, modified radical mastectomy). Consequently, young patients with breast cancer experiencing patient delay are more likely to encounter more severe physiological problems after surgery, such as decreased physical activity, reduced muscle strength, and lymphedema.⁵⁰ Furthermore, women under 40 occupy critical life- development stages with social responsibilities. Post-treatment functional decline can be particularly sharp in this group, exacerbating their psychological disparity and resulting in a lower self-reported functional status. Compared to older patients with breast cancer, young patients pay more attention to their self-image and sexual relationships. Side effects such as breast loss, hair loss, and weight gain caused by treatment are also a heavy blow to young patients. Additionally, patient delay can lead to subsequent diagnostic and treatment delays. The anxiety stemming from prolonged waiting for diagnosis and treatment may further compromise QoL of young patients with breast cancer.⁵¹

Personalized rehabilitation targeting limb function, alongside dedicated fertility counseling and assisted reproductive technology support, should be integral components of care for young patients with breast cancer, addressing their specific functional and survivorship needs to promote successful reintegration into personal and social life. Additionally, enhanced psychological interventions in young patients with breast cancer are needed to address self-image anxiety, social barriers, fertility concerns, and other issues triggered by the disease and its treatment. Concurrently, fostering emotional support networks (family/friends) and organizing peer-led mutual-aid groups can strengthen psychological resilience and life confidence through experience sharing.

Future care should integrate symptom monitoring guided by patient-reported outcomes (PROs) to enable timely and personalized rehabilitation support, ultimately improving treatment experience and QoL in young patients with breast cancer. Subsequent research may also incorporate the minimal important difference (MID) to enhance the interpretability and clinical relevance of PROs assessments.

Implications for nursing practice and research

The findings of this study highlight several critical avenues for intervention to reduce patient delay among young patients with breast cancer in China. To translate these insights into actionable strategies, we propose the following recommendations: First and foremost, there is an urgent need to develop and disseminate public awareness campaigns specifically designed for young women. These campaigns, delivered through social media and community platforms, should focus on demystifying breast cancer, educating about self-examination techniques for early detection, and emphatically challenging the stigma associated with the disease. Currently, breast health education should be systematically integrated into routine primary care and gynecological services. Health care providers, particularly general practitioners, should be trained to proactively initiate conversations about breast awareness with young patients, thereby normalizing the topic and facilitating early help-seeking. From a policy perspective, policymakers should work to lower financial and geographical barriers to care. This can be achieved by expanding the coverage of public health insurance for diagnostic procedures and strengthening the capacity of primary health care facilities in rural and underserved areas to provide initial evaluation and referral services. Finally, health care professionals must adopt culturally sensitive communication strategies. This involves using empathetic language, ensuring patient privacy, and acknowledging cultural concerns around modesty to build trust and encourage young patients to report symptoms without fear or shame.

Limitations

This study has several limitations that should be considered when interpreting the findings. First, its cross-sectional design precludes the establishment of causal relationships between the identified factors and patient delay. Second, as a hospital-based study, the sample may not be fully representative of the broader population of young patients with breast cancer in China, potentially limiting generalizability. Third, despite employing strategies to aid recall, the reliance on self-reported data for key dates and experiences remains susceptible to recall bias. Fourth, the use of a self-developed questionnaire, while necessary due to the lack of a pre-existing validated instrument for our specific context, may introduce measurement bias. Fifth, although modest and approved by the ethics committee, the provision of incentives might have influenced participation and response behavior. Sixth, the application of stepwise regression for variable selection, though useful for exploratory analysis, was data-driven and increases the risk of model overfitting and identifying spurious associations; future studies should aim for theory-driven model building with a priori variable selection. Finally, the lack of control for potential confounders such as detailed socioeconomic status, patient resilience or the stigma associated with the disease, and specific health insurance coverage may have led to residual confounding in the observed associations.

Conclusions

This study provides a critical examination of patient delay and its association with QoL, with a specific focus on young patients with breast cancer in China under 40—a demographic frequently overlooked in existing literature. We identified a high rate of patient delay (37.06%), which was shaped by a complex interplay of predisposing, enabling, and need factors, and was significantly associated with poorer QoL. To translate these findings into action, we emphasize the necessity of integrating routine QoL assessments into standard oncology care, developing targeted education programs to empower young women and reduce stigma, and implementing policy reforms that enhance health care accessibility. Ultimately, this study underscores that addressing patient delay in young women is not merely a clinical imperative but a multifaceted public health priority, essential for improving survival and well-being in this vulnerable population.

CRediT authorship contribution statement

Yueyang Peng: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing- Original draft preparation. Jun Ma: Methodology, Conceptualization, Writing – Reviewing and Editing. Li Liu: Writing – Reviewing and Editing. Jinnan Xiao: Writing – Reviewing and Editing. Can Gu: Supervision, Validation, Resources, Project administration, Review and editing. All authors have read and approved the final manuscript.

Ethics statement

The study was approved by the Research Ethics Review Committee of Xiangya School of Nursing (Approval No. E2022112) and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All participants provided written informed consent.

Data availability statement

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

Declaration of Generative AI and AI-assisted technologies in the writing process

No AI tools/services were used during the preparation of this work.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 82272924) and the Hunan Women's Federation (Grant No. 24YB01). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Declaration of competing interest

The authors declare no conflict of interest. The corresponding author, Prof. Can Gu, is an editorial board member of Asia-Pacific Journal of Oncology Nursing. The article was subject to the journal's standard procedures, with peer review handled independently of Prof. Gu and their research groups.

Appendix A. Supplementary data

The following is the Supplementary data to this article: