Perceived illness uncertainty and its associated factors among oesophageal cancer patients: a cross-sectional study based on the health ecology model

Abstract

Objective

Based on the health ecology model, this study investigates the current status of illness uncertainty perception in oesophageal cancer patients scheduled for surgery and analyses its associated factors.

Design

A cross-sectional study.

Setting

The study was conducted in the Department of Thoracic Surgery at a tertiary hospital in Suzhou, China, from May 2024 to December 2025.

Participants

A total of 230 oesophageal cancer patients scheduled for surgery were recruited using convenience sampling. All participants were hospitalised in the Department of Thoracic Surgery during the study period and met the inclusion criteria for the research.

Primary and secondary outcome measures

The primary outcome was perceived illness uncertainty, measured using the Short Intolerance of Uncertainty Scale. Secondary outcomes included state anxiety (assessed by the State Anxiety Inventory), medical coping styles (assessed by the Medical Coping Modes Questionnaire) and perceived social support (assessed by the Perceived Social Support Scale). Sociodemographic and clinical characteristics were also collected using a self-designed general information questionnaire.

Results

A total of 230 patients were included. The total score for perceived illness uncertainty was 36.25±5.36. Multiple linear regression analysis showed that receipt of neoadjuvant therapy (β=−0.131, 95% CI −2.524 to −0.965, p<0.001), shorter daily sleep duration (β=−0.074, 95% CI −1.373 to −0.014, p=0.045), high state anxiety (β=0.428, 95% CI 0.222 to 0.357, p<0.001), avoidance coping tendency (β=0.123, 95% CI 0.104 to 0.442, p=0.002) and resignation coping tendency (β=0.120, 95% CI 0.061 to 0.589, p=0.016) and poor social support (β=−0.269, 95% CI −0.245 to −0.115, p<0.001) were the prominent factors associated with patients’ perceived illness uncertainty, collectively explaining 82.1% of the total variance.

Conclusions

Oesophageal cancer patients scheduled for surgery exhibit a high level of perceived illness uncertainty, which is related to multiple correlates across various levels, including personal characteristics, behavioural traits and interpersonal networks. Preoperative receipt of neoadjuvant therapy, shorter daily sleep duration, high anxiety state, avoidance coping tendency, resignation coping tendency and poor social support are identified as associated factors. Nursing staff should provide targeted interventions to reduce patients’ uncertainty and enhance their psychological adaptation, thereby facilitating postoperative recovery and improving quality of life.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This study is grounded in the health ecology model as its theoretical framework, providing a multidimensional analytical framework for exploring the related factors of illness uncertainty.

• Validated measurement instruments were used to assess uncertainty perception, state anxiety, coping styles and social support.

• Multiple linear regression analysis was employed to test the explanatory power of the theoretical model.

• As a single-centre study conducted in a tertiary hospital in Suzhou, the generalisability of its findings to patients in other regions or healthcare institutions may be limited.

• The cross-sectional study design precludes causal inference, and self-reported data may be subject to recall bias or social desirability bias.

Introduction

Oesophageal cancer is a highly prevalent malignant tumour of the digestive tract in China. According to the Global Cancer Statistics (Globocan) 2022 database,¹ there were 511 000 new cases and 445 000 deaths from oesophageal cancer globally in 2022. China accounted for 43.8% of the world’s new cases and 42.1% of oesophageal cancer-related deaths.² Surgery remains the cornerstone of curative treatment for oesophageal cancer.³ However, most patients are diagnosed at a locally advanced stage, where surgery alone often results in a low R0 resection rate and a high risk of postoperative recurrence and metastasis.⁴ In recent years, preoperative neoadjuvant therapy and postoperative adjuvant therapy, including chemotherapy, radiotherapy, immunotherapy or their combinations, have become the standard treatment for locally advanced oesophageal cancer. These approaches aim to downstage the disease, improve the success rate of radical surgery and enhance long-term survival.^{5 6} Nevertheless, while this intensified treatment model offers survival benefits, it also subjects patients to various stressors associated with high-intensity therapy. Preoperatively, patients often experience progressive dysphagia or other symptoms that affect eating and even daily life. During consultation, they face multiple uncertainties, such as whether surgery is feasible, the risks associated with surgery, the effects of neoadjuvant and adjuvant therapies and long-term outcomes. Studies have shown that cancer patients commonly experience a high level of perceived uncertainty when confronted with complex medical information and unknown outcomes.^{7 8}

Perceived illness uncertainty refers to a cognitive experience in which individuals are unable to accurately predict disease progression and outcomes due to insufficient information.⁹ Tolerance of uncertainty refers to individual differences in emotional and behavioural response tendencies when facing uncertain situations, events or illnesses.¹⁰ It determines the cognition and judgement that patients will make when confronted with disease-related information, and can be used to reflect the level of patients’ illness uncertainty perception.¹¹ The lower an individual’s tolerance for uncertainty, the higher their level of illness uncertainty perception, and the more they will averse uncertainty in the situation, leading to negative responses in cognition, emotion and behaviour.^{12 13} Sources of illness uncertainty in oesophageal cancer patients may include concerns about treatment sensitivity and side effects, anxiety regarding surgical feasibility, perioperative risks and long-term survival, as well as deeper worries about changes in family roles, social functioning and economic burdens. Higher levels of uncertainty can exacerbate anxiety and depressive symptoms, reduce treatment adherence and even impair clinical decision-making and rehabilitation outcomes.^14–16 Although a limited number of studies have begun to explore illness uncertainty in oesophageal cancer patients, most have focused on single-dimensional attributions and lack a multidimensional, systematic analysis of associated factors.^{17 18}

The health ecology model posits that health-related experiences and behaviours arise from multilevel interactions wherein individuals are nested within their environments, encompassing—from innermost to outermost levels—personal characteristics, behavioural characteristics, interpersonal networks, living and working conditions, and policy environment.^{19 20} This model has been applied to patients with chronic diseases,²¹ stroke,²² maintenance haemodialysis²³ and depression,²⁴ for purposes such as analysing associated factors of symptoms, reasons for delayed medical consultation and constructing health management programmes. However, it has not yet been used in studies of oesophageal cancer patients. For oesophageal cancer patients, their illness uncertainty perception and tolerance of uncertainty are dynamically constructed and regulated through continuous interactions within this multilevel environmental system, encompassing factors such as disease cognition, physical and psychological state, family support and peer influence, access to medical and nursing resources, and financial resources and related stressors. Therefore, based on the integrative theoretical framework of the health ecology model, this study comprehensively considers the interaction between individuals and their environments. It investigates the current status of illness uncertainty perception in oesophageal cancer patients across five levels and systematically analyses its associated factors, aiming to provide a reference for developing early intervention strategies to improve patient prognosis.

Material and methods

Study design and participants

This cross-sectional study included patients with oesophageal cancer who were hospitalised in the Department of Thoracic Surgery at a tertiary hospital in Suzhou from May 2024 to December 2025 and scheduled for surgery and were recruited as the study participants. The inclusion criteria were as follows: pathologically confirmed diagnosis of oesophageal cancer, admitted to the thoracic surgery ward for planned radical oesophagectomy, age ≥18 years, conscious and capable of basic communication and reading comprehension, provision of informed consent. The exclusion criteria were as follows: comorbid severe physical illness or psychiatric disorders, presence of other malignant tumours or metastases to the oesophagus from other malignancies, participation in other interventional clinical studies, discharged without undergoing surgery.

Questionnaire introduction

Perceived illness uncertainty

Perceived illness uncertainty was measured using the short-form Intolerance of Uncertainty Scale (IUS-12), developed by Carleton et al²⁵ and translated into Chinese by Wu et al.²⁶ This scale assesses individuals’ perceptions, interpretations and cognitive biases regarding uncertain situations or events. It consists of 12 items across three subscales: prospective anxiety (6 items), inhibitory anxiety (3 items) and prospective emotion (3 items). Items are rated on a 5-point Likert scale ranging from ‘strongly disagree’ (1 point) to ‘strongly agree’ (5 points), with total scores ranging from 12 to 60. Higher scores indicate greater intolerance of uncertainty. The Chinese version of the scale has demonstrated a Cronbach’s α of 0.79.²⁶

Anxiety

Anxiety was assessed using the State Anxiety Inventory (SAI), which is part of the State-Trait Anxiety Inventory developed by Spielberger²⁷ and translated into Chinese by Zheng et al.²⁸ The SAI can be used independently to evaluate fearful or unpleasant feelings and experiences in the present or recent period. It consists of 20 items across two dimensions: positive affect and negative affect. Items are rated on a 4-point Likert scale ranging from ‘not at all’ (1 point) to ‘very much so’ (4 points), with positively worded items reverse-scored. Total scores range from 20 to 80, with higher scores indicating higher levels of state anxiety. Scores ≤39 indicate mild anxiety, 40–59 moderate anxiety and ≥60 severe anxiety. The Chinese version has shown a Cronbach’s α of 0.92.²⁸

Coping styles

Coping styles were evaluated using the Medical Coping Modes Questionnaire, developed by Feifel et al²⁹ and adapted into Chinese by Shen and Jiang.³⁰ This questionnaire assesses patients’ coping strategies in response to health problems, comprising three dimensions: confrontation (8 items), avoidance (7 items) and acceptance-resignation (5 items), totalling 20 items. Responses are scored on a 4-point Likert scale, with eight items reverse-scored. Total scores range from 20 to 80, and subscale scores range from 8 to 32 (confrontation), 7 to 28 (avoidance) and 5 to 20 (resignation). Higher scores indicate a stronger tendency towards the respective coping style. The Chinese version has a Cronbach’s α of 0.706.³⁰

Social support

Social support was measured using the Perceived Social Support Scale, developed by Zimet et al³¹ and translated into Chinese by Jiang.³² This scale evaluates individuals’ perceived level of social support across three dimensions: family support (4 items), friend support (4 items) and other support (4 items), totalling 12 items. Items are rated on a 7-point Likert scale from ‘strongly disagree’ (1 point) to ‘strongly agree’ (7 points). Total scores range from 12 to 84, with higher scores indicating greater perceived social support. Scores of 12–36 indicate low support, 37–60 moderate support and 61–84 high support. The Chinese version has demonstrated a Cronbach’s α of 0.88.³²

Data collection

Based on a literature review, our research team included a total of 26 variables according to the five levels of the health ecology model. These included sociodemographic variables (eg, age, gender, education level, marital status, place of residence) and disease-related variables (eg, clinicopathological stage, neoadjuvant therapy, comorbid chronic conditions). These variables are presented in figure 1. In accordance with the sample size requirement for multiple linear regression analysis (5–10 times the number of independent variables) and accounting for a 20% rate of invalid responses, the estimated sample size ranged from 156 to 312 participants, with 230 patients ultimately enrolled. All researchers responsible for data collection had worked in the Department of Thoracic Surgery for 5 years or more and possessed strong communication skills. They screened oesophageal cancer surgery patients who met the inclusion and exclusion criteria through the thoracic surgery inpatient medical record system, and confirmed with the attending physicians and ward nurses that the patients were in stable condition and suitable for the survey. One day before surgery, the researchers introduced the study purpose, content and procedures to the patients face-to-face in the wards and conducted the survey after obtaining informed consent. To assess the feasibility of the survey process and estimate the questionnaire completion time, a presurvey was conducted prior to the formal investigation among patients of different genders, ages and educational levels who met the inclusion and exclusion criteria. Before completing the questionnaires, the researchers explained the purpose of the survey and relevant precautions to the patients and informed them that no substantial rewards would be given for participation. The survey was administered after the patients signed the informed consent forms. A total of 27 patients were enrolled in the presurvey, and the results showed that all questionnaires could be completed within 30 min, with no comprehension difficulties regarding the items identified. These data were included in the final analysis. To maintain the authenticity and validity of responses, all questionnaires were distributed and collected on-site by the research team. For patients unable to complete the questionnaire independently, items were read aloud by a researcher in a neutral manner, and responses were recorded based on the patients’ actual situations. Immediately after collection, questionnaires were checked for completeness, with any missing or incorrect entries clarified or corrected by the patients on the spot. To ensure data accuracy, all collected questionnaires were organised on the same day, and data entry and verification were performed independently by two researchers.

Figure 1. A health ecology model of factors associated with perceived illness uncertainty in oesophageal cancer patients. BMI, body mass index.

Statistical analysis

Statistical analysis was performed using SPSS software (V.27.0). Measurement data are expressed as mean±SD if normally distributed, or as median with IQR if non-normally distributed. Categorical data are presented as frequency and percentage. Group comparisons were conducted using T-tests and analysis of variance. Correlation analysis was performed with Pearson’s correlation. Multivariate analysis was carried out using multiple linear regression. P value <0.05 was considered statistically significant.

Patient and public involvement

Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

Results

Participant characteristics

Current status of participants and their perception of illness uncertainty

A total of 236 questionnaires were distributed in this study. Among them, six patients withdrew from surgery due to personal reasons, resulting in 230 valid questionnaires collected, yielding an effective response rate of 97.5%. The general demographic characteristics of the patients are presented in table 1. The total score of the IUS-12 was 36.25±5.36 points.

Table 1. Univariate analysis of general characteristics and illness uncertainty perception in oesophageal cancer patients (n=230).

Variables		n (%)	IUS-12 scores (x̄±s)	F/t	P value
Age (years)	<65	56	34.13±5.03	14.176*	<0.001
	65～74	123	35.96±5.43
	≥75	51	39.28±4.14
Gender	Male	187	36.11±5.34	−0.831†	0.407
	Female	43	36.86±5.44
Clinicopathological stage	Stage 0～I	65	34.15±5.03	37.82*	<0.001
	Stage II	97	35.07±5.29
	Stage III and above	68	39.93±3.71
Pathological type	Squamous carcinoma	195	36.36±5.42	1.001*	0.369
	Adenocarcinoma	24	34.88±5.26
	Other	11	37.18±4.14
Comorbidity	Yes	139	36.51±5.51	0.920†	0.359
	No	91	35.85±5.13
Family history of cancer	Yes	9	35.89±3.52	−0.304†	0.767
	No	221	36.26±5.42
Receipt of neoadjuvant therapy	Yes	46	38.45±4.28	3.506†	<0.001
	No	184	35.72±5.48
BMI (kg/m2)	<18.5	14	38.21±5.32	1.039*	0.356
	18.5～24.9	156	36.18±5.44
	≥25	60	35.97±5.14
Sleep duration (hours/day)	<6	94	39.80±4.01	52.698*	<0.001
	6～8	125	33.97±4.72
	＞8	11	31.82±5.06
Sedentary behaviour	Yes	164	36.89±5.20	2.913†	0.004
	No	66	34.65±5.45
Smoking history	Yes	117	35.74±5.35	−1.481†	0.140
	No	113	36.78±5.34
Alcohol consumption history	Yes	109	35.55±5.64	−1.873†	0.062
	No	121	36.88±5.03
Marital status	Married	224	36.17±5.38	−1.355†	0.177
	Divorced/widowed	6	39.17±3.37
Residence	Rural	150	35.97±5.17	−1.090†	0.277
	Urban	80	36.78±5.69
Education	Primary school and below	141	37.57±5.27	13.501*	<0.001
	Junior high school	70	34.60±4.74
	High school and above	19	32.53±4.94
Occupation	Employed	14	33.93±4.89	3.439*	0.034
	Retired	203	36.58±5.34
	Other	13	33.54±5.14
Monthly household income (yuan)	≤3000	44	38.02±4.67	3.271*	0.022
	3001～5000	136	36.24±5.43
	5001～8000	40	34.98±5.12
	＞8000	10	33.60±6.31
Health insurance	Yes	224	36.24±5.37	−0.117†	0.907
	No	6	36.50±5.54

^*F.

^†t.

IUS-12, the short-form Intolerance of Uncertainty Scale.

Results of univariate analysis

Univariate analysis revealed that age, clinical stage, receipt of neoadjuvant therapy, daily sleep duration, sedentary behaviour, educational level, occupation and monthly household income showed statistically significant differences in IUS-12 scores (all p<0.05), as shown in table 1.

Results of correlation analysis

The scores of the 230 oesophageal cancer patients in terms of state anxiety, coping styles and social support are presented in table 2. The results of the correlation analysis between illness uncertainty perception and anxiety, coping styles and social support are presented in table 3.

Table 2. Scores of state anxiety, coping styles and perceived social support in oesophageal cancer patients (n=230).

Scales	Number of items	Score range	Score (x̄±s）
SAI	20	20～80	48.84±7.92
MCMQ-C	8	8～32	19.50±5.60
MCMQ-A	7	7～28	15.69±2.40
MCMQ-R	5	5～20	9.30±1.98
PSSS	12	12～84	57.07±8.02

IUS-12, the short-form Intolerance of Uncertainty Scale; MCMQ-A, Medical Coping Modes Questionnaire-Avoidance; MCMQ-C, Medical Coping Modes Questionnaire-Confrontation; MCMQ-R, Medical Coping Modes Questionnaire-Resignation; PSSS, Perceived Social Support Scale; S-AI, State Anxiety Inventory.

Table 3. Correlation analysis of illness uncertainty perception with state anxiety, coping styles and perceived social support in oesophageal cancer patients (n=230).

Variables	IUS-12	SAI	MCMQ-C	MCMQ-A	MCMQ-R	PSSS
IUS-12	1
SAI	0.849**	1
MCMQ-C	−0.801**	−0.798**	1
MCMQ-A	0.588**	0.497**	−0.630**	1
MCMQ-R	0.695**	0.662**	−0.794**	0.378**	1
PSSS	−0.790**	−0.726**	0.771**	−0.529**	−0.671**	1

*p<0.05, **p<0.01, ***p<0.001.

IUS-12, the short-form Intolerance of Uncertainty Scale; MCMQ-A, Medical Coping Modes Questionnaire-Avoidance; MCMQ-C, Medical Coping Modes Questionnaire-Confrontation; MCMQ-R, Medical Coping Modes Questionnaire-Resignation; PSSS, Perceived Social Support Scale; S-AI, State Anxiety Inventory.

Multiple linear regression analysis

The IUS-12 score of oesophageal cancer patients was used as the dependent variable, and factors that showed statistical significance in the univariate analysis were included as independent variables in the multiple linear regression analysis. Continuous variables were entered as raw values, and categorical variables were coded as follows: age (years): <65 = 1, 65–74=2, ≥75 = 3; clinical stage: stage 0–I=1, stage II=2, stage III and above=3; receipt of neoadjuvant therapy: yes=1, no=2; daily sleep duration (hours/day): <6 = 1, 6–8=2, >8 = 3; sedentary behaviour: yes=1, no=2; educational level: primary school and below=1, junior high school=2, senior high school and above=3; occupation: employed=1, retired=2, others=3; monthly household income (RMB): ≤3000 = 1, 3001–5000 = 2, 5001–8000 = 3, >8000 = 4. The results of the multiple linear regression analysis indicated F=81.728, p<0.001. Receipt of neoadjuvant therapy, shorter daily sleep duration, higher state anxiety, avoidance coping tendency, resignation coping tendency and poor social support were identified as factors associated with illness uncertainty perception in oesophageal cancer patients (p<0.05), as shown in table 4.

Table 4. Multiple linear regression analysis of illness uncertainty perception in oesophageal cancer patients (n=230).

Variables	Unstandardised coefficient		Standardised coefficient	t	P value	95% CI
	B	SE	β
Constant	29.153	4.821	–	6.046	<0.001	19.650 to 38.656
Neoadjuvant therapy	−1.745	0.396	−0.131	−4.411	<0.001	−2.524 to −0.965
Sleep duration	−0.694	0.345	−0.074	−2.013	0.045	−1.373 to −0.014
SAI	0.290	0.034	0.428	8.449	<0.001	0.222 to 0.357
MCMQ-A	0.273	0.086	0.123	3.182	0.002	0.104 to 0.442
MCMQ-R	0.325	0.134	0.120	2.425	0.016	0.061 to 0.589
PSSS	−0.180	0.033	−0.269	−5.451	<0.001	−0.245 to −0.115

R2=0.831, adjusted R2=0.821; F=81.728, p<0.001.

MCMQ-A, Medical Coping Modes Questionnaire-Avoidance; MCMQ-R, Medical Coping Modes Questionnaire-Resignation; PSSS, Perceived Social Support Scale; S-AI, State Anxiety Inventory.

Discussion

This study found that the total score of illness uncertainty perception in oesophageal cancer patients scheduled for surgery was 36.25±5.36, which was higher than the levels reported in some studies on cancer patients,³³ and the difference may be related to different cancer types and regional variations. Sources of illness uncertainty perception included symptoms caused by the disease itself, complex treatment and care, lack of access to medical information and unpredictable disease progression. At the same time, patients had a low level of perceived control over their own disease and treatment, which further increased their illness uncertainty perception.³⁴ The results showed that receiving neoadjuvant therapy, shorter daily sleep duration, high anxiety levels, a tendency to use avoidant and resigned coping styles, and poor social support were associated factors of illness uncertainty perception in this population, and these factors together explained 82.1% of the total variance, indicating a reliable model fit. Based on the health ecology model, this study explored the multilevel related factors of the environment on individuals and the diversity and complexity of associated factors and identified correlates of illness uncertainty perception in oesophageal cancer patients from three levels: individual characteristics, behavioural features and interpersonal networks. This provides a scientific basis and practical direction for clinical identification of high-risk groups and the development of targeted intervention strategies.

The results of this study indicate that within the personal characteristics level of the health ecology model, whether patients receive neoadjuvant therapy is associated with illness uncertainty perception scores, which is consistent with the findings of Bencova et al.³⁵ Patients undergoing neoadjuvant therapy typically require longer durations of multimodal treatment, and the medical context filled with unknowns and variables directly heightens their perception of illness uncertainty. This finding suggests that clinical teams should regard the receipt of neoadjuvant therapy as a high-risk characteristic in patients during preoperative systematic assessments and integrate interventions in advance. For patients who need neoadjuvant therapy before surgery following initial diagnosis, the full-cycle optimised management can start from the very beginning of diagnosis.

At the behavioural characteristics level, coping style, anxiety state and daily sleep duration are associated with illness uncertainty perception in oesophageal cancer patients. Patients inclined to adopt negative coping strategies (such as avoidance and resignation) exhibited higher levels of illness uncertainty perception, which is consistent with the findings of Zhang et al.³⁶ When facing disease-related stress, patients may lose confidence and a sense of control over their own condition, leading them to prefer negative coping mechanisms.³⁷ Such patients are often unwilling to actively seek disease information or participate in treatment decision-making, exacerbating their sense of loss of control over prognosis and symptoms, thereby sustaining or even amplifying their uncertainty. The degree of anxiety was positively correlated with illness uncertainty perception, aligning with the results reported by Huntley et al.³⁸ From diagnosis to hospitalisation, through various tests and imaging examinations during hospitalisation, to surgical decisions and concerns about prognosis, patients experience role transitions and adaptation difficulties to varying degrees, contributing to anxiety.³⁹ An anxious state tends to amplify patients’ negative perceptions regarding disease prognosis, treatment effectiveness and recurrence risk, significantly enhancing their illness uncertainty perception.⁴⁰ Simultaneously, shorter sleep duration and insomnia can exacerbate cognitive fatigue and negative thinking in patients,⁴¹ impair attention control, increase the sense of loss of control after illness and significantly elevate illness uncertainty perception, which is consistent with the results reported by Tang et al.⁴² The intrinsic psychological and behavioural characteristics of patients interact with the healthcare environment. This level provides a clear direction for developing multitarget intervention strategies, emphasising the equal importance of psychological and behavioural interventions, while regulating patients’ emotions, it also enhances their stress-coping efficacy.

Within the interpersonal network level, social support was found to be associated with patients’ illness uncertainty perception. This finding once again strongly validates the fundamental assumption of the health ecology model, namely that an individual’s health cognition and behaviour are not formed in isolation but are associated with their social networks and informational environment. The level of social support showed a significant negative correlation with illness uncertainty perception, which aligns with conclusions from previous studies.^{43 44} In the specific stress context of surgery, adequate social support facilitates medical information exchange and illness narrative between patients and their family or friends, thereby alleviating patients’ sense of uncertainty. Strong core social relationships enhance patients’ coping resilience. Therefore, patients’ social support structure and information environment should be systematically assessed before surgery, with early identification and proactive intervention for high-risk patients to strengthen the protective factors within their interpersonal networks.

Finally, at the levels of living and working conditions and policy environment within the health ecology model, no statistically significant variables were identified in the final results of this study. Although univariate analysis revealed significant associations between illness uncertainty perception in oesophageal cancer patients and educational level, occupation and household per capita income (p<0.05), these associations lost statistical significance after adjusting for age, clinical stage, daily sleep duration, anxiety state and social support in the multivariate regression analysis. This finding suggests that illness uncertainty perception in oesophageal cancer patients results from the combined effects of multiple factors, and variables such as educational level, occupation and household per capita income should not be considered in isolation. The city in which the study hospital is located has a relatively high level of economic development, and consequently, the medical conditions and social environment are also comparatively favourable.⁴⁵ These factors provide patients with stable economic capacity and environmental support during disease onset and treatment, consequently reducing their illness uncertainty perception. In the dimension of policy environment, as of the end of 2024, the national basic medical insurance coverage rate has stabilised at 95%.⁴⁶ In this study, the medical insurance coverage rate among patients was 97.4%, essentially ensuring a financial safety net for medical expenses and mitigating policy-related disparities.

This study has several limitations. As a single-centre study conducted in a tertiary hospital in Suzhou, the generalisability of its findings to patients in other regions or healthcare settings is limited. The cross-sectional design only provides a snapshot of patients’ illness uncertainty perception at a specific time point, for example, 1 day prior to surgery, which precludes causal inference, and self-reported data may be subject to recall bias or social desirability bias. In addition, the convenience sampling method may also have certain limitations. In future studies, the sample size could be expanded to further validate the results of the relevant levels, and longitudinal studies could be conducted to explore the dynamic changes in illness uncertainty perception.

Conclusions

Oesophageal cancer patients scheduled for surgery have a notable level of illness uncertainty perception. From the perspective of the health ecology model, the main associated factors are preoperative neoadjuvant therapy, shorter daily sleep duration, high anxiety state, avoidance coping tendency, resignation coping tendency and poor social support. These factors fall into three levels: personal characteristics, behavioural traits and interpersonal networks. Healthcare providers should actively assess illness uncertainty perception in these patients and provide timely interventions. This can improve patients’ readiness for surgery and enhance their healthcare experience and recovery. Future studies can design and test nursing interventions based on this model.

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.