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. 2025 Apr 10;12(4):e70222. doi: 10.1002/nop2.70222

Patterns of Psychological Distress of Chinese Elderly Cancer Patients: A Latent Profile Analysis

Danyu Li 1, Zhuting Zheng 1, Qingmei Huang 1, Wen Zhang 1, Pengwei Hu 2, Changrong Yuan 1, Fulei Wu 1,✉
PMCID: PMC11985321  PMID: 40211097

ABSTRACT

Aim

To identify the group patterns of psychological distress and its predictors among Chinese elderly cancer patients. To examine the effect of profile on patients' Quality of Life (QOL).

Design

This was a cross‐sectional study conducted in accordance with the STROBE guidelines.

Methods

This study included 357 patients with cancer aged ≥ 60 years. Latent Profile Analysis (LPA) was used to analyse psychological distress collected using the Patient‐reported Outcome Measurement Information System (PROMIS) Anxiety Short Form 8a and Depression Short Form 8a. Univariate analysis and univariate multinormal logistic regression were used to examine the correlates of latent membership. Kruskal–Wallis H test was used to assess profile differences in self‐reported QOL.

Results

The best fit was a three‐profile solution: low psychological distress (34.7%), moderate psychological distress (35.6%) and high psychological distress (29.7%). Patients in the moderate psychological profile were more likely to be female, have religious relief, not be in marriage, have financial burden, and be under treatment; they were less likely to be diagnosed with gynaecological cancer and breast cancer. Patients in the high psychological profile were more likely to be older, female, have a high school or above educational background, have religious relief, live in the country, be retired, have no knowledge about the disease, be unaware of tumour metastasis, have no tumour metastasis, be under treatment, and have complications; they were less likely to be diagnosed with colorectal cancer and gynaecological cancer. Different psychological distress profiles were associated with QOL, with the low distress group reporting significantly higher QOL.

Patient or Public Contribution

Chinese elderly cancer patients who met the inclusion criteria completed the questionnaires.

Keywords: anxiety, depression, latent profile analysis, older cancer adults, psychological distress, quality of life

1. Introduction

Cancer is the most threatening public health concern, with its incidence and mortality rates remaining strikingly linked with advanced age (White et al. 2019). World Health Organisation (WHO) statistics show that people over the age of 60 years account for approximately 64% of all cancer cases in the general population. This proportion has increased to more than 70% of cancer deaths worldwide (Ju et al. 2023). As for China, it is projected that 2.79 million cases and 1.94 million deaths will occur among older adults, accounting for 55.8% and 68.2% of all cases and deaths, respectively, by 2022 (Ju et al. 2023). This large number of older cancer adults imposes a considerable financial burden on the healthcare system, which is an inevitable problem for the world.

Cancer adults suffer from a wide variety of symptoms, including fatigue, appetite loss and pain (Koo et al. 2020). Long‐term treatments such as chemotherapy, radiotherapy and surgery involve patients under unbearable stress (Sedighi et al. 2019). Overall, this can lead to a high level of psychological stress and an increased risk of developing psychiatric disorders (Alagizy et al. 2020). Studies reveal that the prevalence of anxiety and depressive disorders among patients with cancer is up to double that of the general population (Riedl and Schüßler 2022).

Although psychosocial variables such as anxiety and depression are known to have a huge influence on the increased systemic inflammation and mortality risk of cancer survivors (Won and Kim 2020), most studies on these two psychological symptoms in older cancer patients have focused on describing the general population (Cohen 2014). Moreover, many studies detecting the risk factors and associations of psychological symptoms in older cancer patients involve participant groups' average responses, thereby ignoring the potential heterogeneity of influencing factors across different individual responses (Amirmohamadi et al. 2017; Heidarzadeh et al. 2016; Nam et al. 2016). Although there are various person‐centered approaches (e.g., hierarchical clustering and K‐means), Latent Profile Analysis (LPA) is a model‐based method that offers more statistical rigour and provides rich diagnostic information (Schreiber and Pekarik 2014). To date, LPA has been widely applied in the physical and psychological health domains of cancer patient populations (Fox et al. 2020; Wang et al. 2024). With a focus on implementing more targeted strategies for improving psychological distress, LPA is a more suitable approach because it posits that any observed variables are indicators of an unobserved latent variable and then tries to explain this relationship using a small number of subgroups or classes (Huang et al. 2022). As LPA has aided researchers in conducting in‐depth analyses of subgroup characteristics, identifying distinct psychological distress classes using this technique will help inform the development of effective methods for identifying and treating subgroups with varying profiles.

2. Background

Anxiety and depression are always present in cancer populations (Alagizy et al. 2020); therefore, it has been recommended to assess the co‐existence of the two psychological symptoms clinically. Derived from Barlow's concepts, anxiety is a future‐oriented mood state associated with preparation for possible upcoming negative events (Barlow 2004). The symptoms of anxiety include worry, avoidance and muscle tension (Lang 1968). In cancer patients, many factors such as a decrease in income (Alacacioglu et al. 2013), changes in body image (Fingeret et al. 2014) and unpleasant treatment (Martin et al. 2014) cause anxiety, which may be further negatively associated with future recovery and QOL (Balhareth et al. 2019). Depression, normally detected as feeling sad and social isolation, is a more critical problem than anxiety in cancer patients (Hong and Tian 2014). Factors influencing depression in patients with cancer include disease awareness and reduced functioning (Riedl and Schüßler 2022). Depression leads to worse treatment adherence and poor prognosis (Arrieta et al. 2013).

The above‐mentioned literature was based on average statistics and did not explore the status of potential subgroups and each influencing factor, which poses barriers to designing tailored interventions. As LPA has grown in popularity in recent years, it may be possible to design focused and cost‐effective therapeutic techniques by discriminating between patient groups (Mangoni and Woodman 2019). Specifically, LPA can be used to identify distinct subgroups of psychological symptoms in Chinese elderly cancer patients. Saracino et al. (2020) used LPA to classify depressive symptoms in older cancer patients. These findings could help improve psychological outcomes in this population. However, no existing studies have used LPA to analyse anxiety and depression simultaneously, thus facilitating personalised interventions to improve the two most prevalent psychological symptoms.

In the present study, we first used LPA to identify a priori unknown latent profiles of psychological distress in Chinese elderly cancer patients based on anxiety and depression measures. Second, once the latent profiles were identified, univariate analysis and univariate multinomial logistic model were used to examine the correlates of sociodemographic and clinical characteristics with membership. In particular, we sought to identify factors that may be correlated with moderate and high psychological distress, to facilitate early detection, precise localisation and tailored interventions to optimise the health of vulnerable older cancer adults. The difference in QOL across profiles was demonstrated using the Kruskal–Wallis H test. The findings of this study will help study disparities in the psychological distress of older cancer adults and provide targeted interventions to patients in different symptom subgroups.

3. The Study

3.1. Study Design and Sample

This study was conducted in accordance with the STROBE guidelines for the reporting of observational studies. The participants were recruited from the cancer clinics of two tertiary teaching hospitals in Shanghai, China.

From December 2020 to October 2021, trained researchers analysed the patient's electronic health records to find the available participants and then acquired informed consent by adequately explaining the goal and information of the study. For data collection, the researchers conducted face‐to‐face interviews using a predefined questionnaire with patients willing to participate.

The inclusion criteria were as follows: (1) having a cancer diagnosis; (2) being ≥ 60 years old; (3) being aware of their cancer diagnosis; (4) having a basic understanding and writing of the Chinese language. Exclusion criteria were as follows: (1) in extremely critical or life‐threatening situations.

This study was approved by the Institutional Review Boards of Fudan University Cancer Hospital (No 1810192‐22) and Fudan University Zhongshan Hospital (No 2020‐076R).

3.2. Instrument

3.2.1. Sociodemographic and Clinical Information

Sociodemographic information was collected using the following indices: age, sex, education, religion, marital status, number of children, main caregiver, working status and self‐reported financial burden. Participants' clinical information included cancer type, knowledge of cancer, tumour metastasis, current treatment stage, comorbidities and complications.

3.2.2. PROMIS‐Emotional Distress‐Anxiety‐Short Form 8a

The PROMIS Emotional Distress‐Anxiety‐Short Form 8a was also used to assess the participants' anxiety. This scale consists of 8 items related to fear (fearfulness and panic), anxious misery (worry and dread) and hyperarousal (tension, nervousness and restlessness). Participants had to rate how often they experienced anxiety (e.g., ‘I felt fearful’) as occurring over the past 7 days on a 5‐point Likert response format (1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Always). Items were then summed, and raw scores were converted into T‐scores (mean T‐score = 50, SD = 10). The T‐score ranges from 37.1 to 83.1 and higher scores signifying a higher level of anxiety. In cancer patients, anxiety is classified as normal (< 55), mild (55–64), moderate (65–74) and severe (≥ 75) (Cella et al. 2014). The validity and reliability of the original version of this measure have been demonstrated in patients with knee osteoarthritis (Driban et al. 2015), multiple sclerosis (Marrie et al. 2018) and lumbar degenerative disease (Purvis et al. 2018). The Chinese version of this short form has been translated, cross‐cultural adapted and psychometrically validated among Chinses cancer patients, with a Cronbach's α was 0.95 (Cai et al. 2022).

3.2.3. The PROMIS Emotional Distress‐Depression‐Short Form 8a

The PROMIS Emotional Distress‐Depression‐Short Form 8a was also used to assess depression in the participants. This scale consists of eight items on negative mood (sadness and guilt), views of self (self‐criticism and worthlessness) and social cognition (loneliness and interpersonal alienation), as well as decreased positive affect and engagement (loss of interest, meaning and purpose). Participants had to rate how often they experienced depression (e.g., ‘I felt worthless’) as occurring over the past 7 days on a 5‐point Likert response format (1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Always). Items were then summed, and raw scores were converted into T‐scores (mean T‐score = 50, SD = 10). The T‐score ranges from 38.2 to 81.3 and higher scores signify a higher level of depression. In cancer patients, depression is classified as normal (< 55), mild (55–64), moderate (65–74) and severe (≥ 75) (Cella et al. 2014). The validity and reliability of the original version of this measure have been demonstrated in community‐dwelling older adults (Levin et al. 2015) and patients with multiple sclerosis (Marrie et al. 2018). The Chinese version of this short form has been translated, cross‐cultural adapted and psychometrically validated among Chinses cancer patients, with a Cronbach's α was 0.91 (Cai et al. 2022).

3.2.4. European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ‐C30)

The EORTC QLQ‐C30 was used to assess QOL. This scale consists of 30 questions classified into five functional scales (15 questions), nine symptom scales (13 questions) and global health status (two questions) (Kaasa et al. 1995). Responses were rated on a 4‐point Likert scale (1 = not at all, 2 = a little, 3 = quite a bit and 4 = very much) and graded according to the EORTC QLQ‐C30's scoring manual (Fayers et al. 1995). Higher scores on the functional scales and in the global health status section, and lower scores on the symptom section signified better QOL. The Cronbach's α of the Chinese version of EORTC QLQ‐C30 was 0.7 (Wan et al. 2008). In this study, we administered only four items from the emotional function domain (item 21st to item 24th).

3.3. Data Analysis

Descriptive statistical analysis was conducted using SPSS (version 26.0; IBM, Armonk, NY, USA) to explore the sociodemographic and clinical characteristics of the participants and their anxiety and depression levels. Then, using Mplus version 8.3 (Muthen & Muthen, Los Angeles, CA, USA), LPA was performed to classify the latent profiles of participants' anxiety and depression. The optimal number of latent profiles was identified by fitting a series of LPA models with increasing profile numbers and comparing each model to the previous one. Model selection criteria included Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample‐size adjusted BIC (aBIC), entropy, bootstrap likelihood ratio test (BLRT) and Lo–Mendell–Rubin likelihood ratio test (LMR‐LR). A lower value for AIC, BIC, or aBIC indicated a better model fit (McLachlan et al. 2019), while a p value below 0.05 for BLRT and LMR‐LR indicated a significant improvement in model fit (Nylund et al. 2007).

Once the optimal number of latent profiles was identified, the patients were classified into latent profile groups based on the most likely latent class membership. A series of univariate analyses of variance (e.g., Kruskal–Wallis H test and chi‐squared test) were conducted to examine between‐class differences in sociodemographic and clinical characteristics. Variables that were significant in univariate analysis (p ≤ 0.1, two‐sided probability) were entered into the regression model. Univariate multinomial logistic regression analysis was used to examine the odds of being in one latent profile. p < 0.05 was set as the threshold for the inclusion of a correlative variable.

The Kruskal–Wallis H test was also performed to demonstrate significant QOL differences between the identified psychological distress subgroups.

4. Results

4.1. Subjects

Table 1 presents the general characteristics of the participants. The mean age of participants was 68.18 years (SD = 6.26). Approximately 48.2% of the participants were men, and 70.3% had a middle school or lower educational qualification. Approximately 46.5% of the participants followed no religion. Most were married, and the remainder were divorced, widowed, or separated. The mean number of children was 1.81 (SD = 1.24), and two‐thirds had children as their primary caregivers. Only 26.3% of the participants lived in the country. Regarding working status, 24.6% performed full‐time work, 45.7% were retired and 29.7% had no jobs. Only 12.3% reported no financial burden, whereas 87.7% reported a financial burden. Most participants (63.9%) reported having no knowledge about cancer.

TABLE 1.

Sociodemographic and clinical characteristics of the three psychological distress profiles (mean ± SD)/(N, %).

Variables Total (N = 357) Profile 1: low psychological distress profile (N = 124, 34.7%) Profile 2: moderate psychological distress profile (N = 127, 35.6%) Profile 3: high psychological distress profile (N = 106, 29.7%) p
Age 68.18 ± 6.26 67.52 ± 5.19 66.61 ± 5.25 70.85 ± 7.59 < 0.001
Sex
Male 172 (48.2%) 98 (79.0%) 52 (40.9%) 22 (20.8%) < 0.001
Female 185 (51.8%) 26 (21.0%) 75 (59.1%) 84 (79.2%)
Education
Middle school or below 271 (70.3%) 84 (67.7%) 74 (58.3%) 54 (50.9%) 0.034
High School or above 106 (29.7%) 40 (32.3%) 53 (41.7%) 52 (49.1%)
Religion
No 166 (46.5%) 90 (72.6%) 72 (56.7%) 62 (58.5%) 0.019
Yes a 191 (53.5%) 34 (27.4%) 55 (43.3%) 44 (41.5%)
Marital Status
Not in the marriage b 106 (29.7%) 4 (3.2%) 18 (14.2%) 0 (0.0%) < 0.001
In marriage 251 (70.3%) 120 (96.8%) 109 (85.8%) 106 (100%)
Number of children 1.81 ± 1.24 1.87 ± 1.09 1.68 ± 1.45 1.91 ± 1.13 0.003
Primary caregiver
Spouse 119 (33.3%) 43 (34.7%) 44 (34.6%) 43 (40.6%) 0.715
Children 238 (66.7%) 81 (65.3%) 83 (65.4%) 74 (69.8%)
Residency area
Country 94 (26.3%) 28 (22.6%) 22 (17.3%) 44 (41.5%) < 0.001
City 263 (73.7%) 96 (77.4%) 105 (82.7%) 62 (58.5%)
Working status
Full‐time working 88 (24.6%) 50 (40.3%) 30 (23.6%) 8 (7.5%) < 0.001
Retired 163 (45.7%) 38 (30.6%) 61 (48.0%) 64 (60.4%)
Having no job 106 (29.7%) 36 (29.0%) 36 (28.3%) 34 (32.1%)
Financial burden c
No 44 (12.3%) 10 (8.1%) 26 (20.5%) 8 (7.5%) 0.002
Yes 313 (87.7%) 114 (91.9%) 101 (79.5%) 98 (92.5%)
Knowledge about disease
No 228 (63.9%) 64 (51.6%) 80 (63.0%) 84 (79.2%) < 0.001
Yes 129 (36.1%) 60 (48.4%) 47 (37.0%) 22 (20.8%)
Cancer type
Liver cancer 113 (31.7%) 58 (46.8%) 35 (27.6%) 20 (18.9%) < 0.001
Colorectal cancer 62 (17.4%) 16 (12.9%) 20 (12.7%) 26 (24.5%)
Gastric cancer 49 (13.7%) 31 (25.0%) 10 (7.9%) 8 (7.5%)
Gynaecological cancer 108 (30.3%) 16 (12.9%) 42 (33.1%) 50 (47.2%)
Breast cancer 25 (7.0%) 3 (2.4%) 20 (15.7%) 2 (1.9%)
Tumour metastasis
No 122 (34.2%) 58 (46.8%) 48 (37.8%) 16 (15.1%) < 0.001
Yes 114 (31.9%) 26 (20.5%) 30 (23.6%) 58 (54.7%)
Unknown 121 (33.9%) 40 (31.5%) 49 (38.6%) 32 (30.2%)
Treatment stage
No treatment 212 (59.4%) 88 (71.0%) 70 (55.1%) 54 (50.9%) 0.004
Under treatment 145 (40.6%) 36 (29.0%) 57 (44.9%) 52 (49.1%)
Other chronic diseases
No 78 (21.8%) 30 (24.2%) 22 (17.3%) 26 (24.5%) 0.306
Yes 279 (78.2%) 94 (75.8%) 105 (82.7%) 80 (75.5%)
Complication
No 271 (75.9%) 100 (80.6%) 109 (85.8%) 62 (58.5%) < 0.001
Yes d 86 (24.1%) 24 (19.4%) 18 (14.2%) 44 (41.5%)
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Note: Bold, significant at the 0.1 level.

a

Buddhism or Christianity or Islam.

b

Divorced or widowed or separated.

c

Self‐reported being under financial burden.

d

Fever, Decreased white blood cells/neutrophils, decreased platelets, decreased haemoglobin, pneumonia, pleural effusion, pericardial effusion, other.

The clinical characteristics of the participants are presented in Table 1. Regarding cancer type, 31.7% had liver cancer, 30.3% had gynaecological cancer, and 17.4%, 13.7% and 7.0% had colorectal, gastric and breast cancers, respectively. Approximately one‐third of the patients had tumour metastasis, one‐third had no tumour metastasis, and the rest had no definite diagnosis of metastasis. Of these patients, 40.6% underwent anticancer treatment. Most of the participants (78.2%) had chronic diseases other than cancer. Only 24.1% of participants experienced complications from cancer treatment.

4.2. Psychological Distress

Table 2 presents the scores for each item and T‐scores for the entire measure. Regarding anxiety, the mean score of each item ranged from 2.63 to 3.75, and the mean T‐score was 64.28, indicating a mild level of anxiety among the included participants. Regarding depression, the mean score of each item ranged from 1.88 to 3.94, and the mean score of the T‐score is 62.38, indicating a mild level of depression as well.

TABLE 2.

The results of psychological distress (N = 357).

Anxiety Mean ± SD Depression Mean ± SD
T‐score 64.28 ± 5.64 T‐score 62.38 ± 4.31
Item1 3.13 ± 0.87 Item1 2.58 ± 0.81
Item2 2.76 ± 0.75 Item2 3.94 ± 0.88
Item3 2.64 ± 0.78 Item3 3.69 ± 0.86
Item4 3.73 ± 0.83 Item4 1.88 ± 0.56
Item5 3.85 ± 0.75 Item5 2.35 ± 0.71
Item6 2.63 ± 0.81 Item6 3.67 ± 0.91
Item7 3.75 ± 0.80 Item7 3.32 ± 0.81
Item8 3.19 ± 0.83 Item8 2.81 ± 0.62
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4.3. Latent Profile Analysis of Chinese Elderly Cancer Patients on Psychological Distress

Using the Anxiety Short Form 8a and Depression Short Form 8a, and based on their item scores, LPA was used to classify participating Chinese elderly cancer patients to determine the ideal number of latent profile classes.

Table 3 presents the model fit indices for each latent class group. The AIC, BIC, aBIC, entropy and p‐values of both the LMRT and BLRT were used to determine the best latent class model. It is known that the smaller the values of AIC, BIC and aBIC, the better the fit of the model. Additionally, the BLRT and LMRT tests are also mainly considered for determining the number of profile classes in LPA. In the current study, the values for AIC and aBIC in the four‐class model were the lowest among the fitted models. However, the p‐value of the LMR was not significant in the four‐class model, suggesting that the three‐class model fit better than the four‐class model. Furthermore, the relative size of each class in the three‐class model was better than that of the four‐class model, which did not meet the minimum size requirement and should not have fewer than 50 cases (Muthén and Muthén 2000). In addition, the three‐class group had good explanatory power, with an entropy index higher than 0.80 (Clark 2010). Therefore, the three‐class model was selected for this study.

TABLE 3.

Model fit indices for latent class groups about psychological distress (N = 357).

Model AIC BIC aBIC Entropy BLRT LMR‐LR Relative class size
1 13407.676 13531.764 13430.245
2 10713.355 10903.364 10747.913 0.965 ** ** 0.47/0.53
3 10017.099 10273.030 10063.647 0.971 ** ** 0.35/0.36/0.30
4 9518.430 9840.282 9576.968 0.986 ** 0.12 0.06/0.42/0.38/0.15
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Note: Best‐fitting model in bold.

Abbreviations: aBIC, sample‐size adjusted BIC; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; BLRT, bootstrapped likelihood ratio test; LMR‐LR, Lo–Mendell–Rubin likelihood ratio test.

**

p < 0.01.

As illustrated in Figure 1, Profiles 1 and 3 had the lowest and highest psychological symptom scores across the anxiety and depression measure, and were thus defined as ‘low psychological distress’ and ‘high psychological distress’, respectively. Profile 2, in which the scores were intermediate across the three subscales, was labelled as ‘moderate psychological distress’. For the low, moderate and high psychological distress profiles, the total mean anxiety scores were 2.52, 3.24 and 3.98, respectively. The total mean depression scores were 2.41, 3.08 and 3.69 for the low, moderate and high psychological distress profiles, respectively. The group sizes were 124 (34.7%), 127 (35.6%) and 106 (29.7%) for the low, moderate and high psychological distress profiles, respectively (Table 3 and Figure 1).

FIGURE 1.

FIGURE 1

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Distribution of the mean of anxiety and depression items by profiles.

4.4. Examination of Correlates of the Psychological Distress Profiles

Once we identified the latent class membership of the patients via LPA, we performed a univariate analysis to explore the 16 potential risk factors for moderate and high psychological distress. Univariate analysis showed that 14 factors, including age, sex, education, religion, marital status, number of children, residency area, working status, financial burden, knowledge about the disease, cancer type, tumour metastasis, treatment stage and complications were significantly associated with psychological distress profiles. The differences in sociodemographic and clinical characteristics among the three subgroups are summarised in Table 1. Further, as shown in Table 4 of the univariate multinomial regression analysis, 13 predictors were significantly correlated with being in moderate or high psychological distress (as compared to low psychological distress).

TABLE 4.

Univariate multinomial logistic regression analysis of psychological distress profile.

Variables Profile 1: low psychological distress profile (refer) a versus
Profile 2: moderate psychological distress profile Profile 3: high psychological distress profile
B OR 95% CI for OR p B OR 95% CI for OR p
Age (one year increase) −0.029 0.971 0.929–1.016 0.201 0.082 1.086 1.041–1.133 < 0.001
Number of children (one unit increase) −0.134 0.875 0.711–1.076 0.206 0.021 1.021 0.835–1.250 0.838
Sex
Female 1.693 5.436 3.110–9.504 < 0.001 2.667 14.392 7.602–27.244 < 0.001
Male (refer)
Education
High School or above 0.408 1.504 0.898–2.520 0.121 0.704 2.022 1.184–3.455 0.010
Middle school or below (refer)
Religion
Yes 0.704 2.022 1.192–3.429 0.009 0.631 1.879 1.081–3.263 0.025
No (refer)
Marital status
Not in the marriage 1.600 4.954 1.626–15.093 0.005 −19.923 0.000 0.000 —
In marriage (refer)
Residency area
Country −0.331 0.718 0.385–1.340 0.298 0.889 2.433 1.374–4.308 0.002
City (refer)
Working status
Full‐time working −0.473 0.623 0.337–1.152 0.131 −0.578 0.561 0.303–1.039 0.066
Retired 0.511 1.667 0.873–3.182 0.122 1.775 5.903 2.445–14.250 < 0.001
Having no job (refer)
Financial burden
No 1.077 2.935 1.349–6.382 0.007 −0.072 0.931 0.352–2.450 0.884
Yes (refer)
Knowledge about disease
No 0.467 1.596 0.964–2.641 0.069 1.275 3.580 1.991–6.437 < 0.001
Yes (refer)
Cancer type
Colorectal cancer −0.728 0.483 0.221–1.053 0.067 −1.550 0.212 0.095–0.474 < 0.001
Gastric cancer 0.626 1.871 0.818–4.278 0.138 0.290 1.336 0.528–3.382 0.541
Gynaecological cancer −1.470 0.230 0.113–0.469 < 0.001 −2.204 0.110 0.052–0.236 < 0.001
Breast cancer −2.402 0.091 0.025–0.327 < 0.001 −0.659 0.517 0.081–3.323 0.487
Liver cancer (refer)
Tumour metastasis
Unknown −0.060 0.942 0.482–1.843 0.861 1.025 2.788 1.447–5.372 0.002
No 0.332 1.394 0.728–2.670 0.316 2.090 8.087 3.931–16.635 < 0.001
Yes (refer)
Treatment stage
Under treatment 0.688 1.990 1.181–3.355 0.010 0.856 2.354 1.367–4.053 0.002
No treatment (refer)
Complication
No (refer)
Yes −0.374 0.688 0.353–1.343 0.273 1.084 2.957 1.640–5.333 < 0.001
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Note: Bold, significant at the 0.05 level.

Abbreviations: CI, confidence interval; OR, odds radio.

a

The reference category is subgroup patients in the low psychological distress profile.

Patients in the moderate psychological profile were more likely to be female (OR = 5.436, 95% CI = 3.110–9.504, p < 0.001), have religious relief (OR = 2.022, 95% CI = 1.192–3.429, p = 0.009), not be in the marriage (OR = 4.954, 95% CI = 1.626–15.093, p = 0.005), have financial burden (OR = 2.935, 95% CI =1.349–6.382, p = 0.007), and be under treatment (OR = 1.990, 95% CI = 1.181–3.355, p = 0.010); they were less likely to have the diagnosis of gynaecological cancer (OR = 0.230, 95% CI = 0.113–0.469, p < 0.001) and breast cancer (OR = 0.091, 95% CI = 0.025–0.327, p < 0.001).

Patients in the high psychological profile were more likely to be older (OR = 1.086, 95% CI = 1.041–1.133, p < 0.001), female (OR = 14.392, 95% CI = 7.602–27.244, p < 0.001), have a high school or above education background (OR = 2.022, 95% CI = 1.184–3.455, p = 0.010), have religious relief (OR = 1.879, 95% CI = 1.081–3.263, p = 0.025), live in country (OR = 2.433, 95% CI = 1.374–4.308, p = 0.002), be retired (OR = 5.903, 95% CI = 2.445–14.250, p < 0.001), have no knowledge about disease (OR = 3.580, 95% CI = 1.991–6.437, p < 0.001), be unknown about tumour metastasis (OR = 2.788, 95% CI = 1.447–5.372, p = 0.002), have no tumour metastasis (OR = 8.087, 95% CI = 3.931–16.635, p < 0.001), be under treatment (OR = 2.354, 95% CI = 1.367–4.053, p = 0.002), and have complication treatment (OR = 2.957, 95% CI = 1.640–5.333, p < 0.001); and were less likely to be diagnosed with colorectal cancer (OR = 0.212, 95% CI = 0.095–0.474, p < 0.001) and gynaecological cancer (OR = 0.110, 95% CI = 0.052–0.236, p < 0.001).

4.5. Differences in QOL Among the Three Latent Profiles

The outcome of psychological distress was interpreted according to the quality of life. According to the Kruskal–Wallis H test of QOL emotional function score differences shown in Table 5, there was a significant difference among the three groups. We further compare the differences among each profile, and the results indicated the existence of significant differences between each pair of comparisons (Table 5) and the patients with the low psychological distress profile had significantly higher QOL emotional function scores, indicating better QOL compared with the moderate and high psychological distress subgroups.

TABLE 5.

Differences in QOL emotional function domain scores between the subgroups.

QOL Profile 1: low psychological distress profile (N = 124, 34.7%) Profile 2: moderate psychological distress profile (N = 127, 35.6%) Profile 3: high psychological distress profile (N = 106, 29.7%) Statistics a
69.46 ± 10.40 48.62 ± 6.04 34.79 ± 5.10 P < 0.001; 1 > 2 > 3
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a

Statistics by Kruskal–Wallis H test and post hoc analyses.

5. Discussion

This study aimed to classify Chinese elderly cancer patients based on their psychological distress and examine the factors associated with their class membership. In the current study, anxiety and depression symptoms in the participants were found to be at a mild level according to the PROMIS cutoff points among cancer patients. The distress caused by these two psychological symptoms was classified into three subgroups: ‘low psychological distress’, ‘moderate psychological distress’ and ‘high psychological distress’. The psychological distress of Chinese elderly cancer patients was similar to that of aged breast cancer patients in a study by Cai et al. (2023), which utilised the same PROMIS instruments, and the results indicated that only two profiles, but still showed the level of anxiety and depression, always kept pace with each other.

Our findings verified that age, sex, education, religion, marital status, residency area, current working status, financial burden, knowledge about the disease, cancer type, tumour metastasis, treatment stage and complication have an association with the identified psychological distress subgroups. Furthermore, only three variables constantly associated with the odds of being in moderate and high psychological distress profiles compared to low distress profiles are sex (being female), religion (having religious belief) and treatment stage (being under treatment).

Our findings align with the fact that women are twice as likely as men to experience depression (Johnson and Whisman 2013) and also reinforce existing literature suggesting that being female is consistently associated with higher levels of psychological distress among cancer patients (Linden et al. 2012). Previous studies have suggested that women have a higher tendency to ruminate and worry, which are considered central factors in anxiety and depression (Johnson and Whisman 2013). We hope that our results will help to guide further tailored intervention considering the gender difference. Specifically, interventions for female cancer patients could benefit from offering targeted support (e.g., mindfulness‐based and cognitive behaviour intervention) for effectively managing rumination and worry (Querstret and Cropley 2013). Additionally, routine psychoeducation for cancer patients could incorporate gender‐specific elements for female patients to improve mental health outcomes.

It's unusual for research to find that having religious beliefs is significantly associated with higher levels of psychological distress among cancer patients. Typically, religious beliefs are associated with lower levels of distress due to the comfort, support and coping mechanisms they provide (Canada et al. 2008). However, there could be specific circumstances or cultural contexts where religious beliefs might exacerbate distress. For example, if an individual's religious beliefs include strict doctrines or beliefs about illness and suffering that induce guilt or fear, they could contribute to higher distress levels. Additionally, conflicts between an individual's religious beliefs and their experience of illness or treatment might also lead to distress.

Being under active cancer treatment emerged as a key predictor of psychological distress in our study. Cancer treatment is often accompanied by physical discomfort, emotional upheaval and functional impairments, contributing to heightened distress levels among patients undergoing therapy (Andrykowski et al. 2008). Therefore, comprehensive supportive care interventions targeting patients undergoing active treatment are essential. These interventions should encompass psychoeducation, symptom management, psychosocial support and care coordination to alleviate distress and optimise treatment outcomes.

Our current study demonstrated the psychological distress profile differences in self‐reported QOL. A previous study conducted among cancer patients with metastatic spinal disease showed a significant and mutual correlation among QOL, anxiety and depression (Liu et al. 2022). Thus, healthcare professionals must actively confront and manage any unfavourable feelings that arise as part of the required interventions and could further improve patients' QOL (Gao et al. 2023; Park et al. 2022; Zheng et al. 2020).

However, stressing the need for routine collection of self‐reported mental health data in clinical or home settings would be more crucial before assisting older cancer patients in developing coping techniques for psychological symptoms. Additionally, healthcare personnel should inform patients as soon as their diagnosis is verified and make an effort to pay close attention to them to assist them in better understanding the severity and prognosis of their disease.

5.1. Limitations of the Work

Our study has several limitations. First, it is difficult to generalise our findings to a wider target population because the sampled participants included only elderly patients with cancer who visited two tertiary general hospitals in Shanghai, China. Thus, more studies with larger samples collected from different regions of China are needed to further classify elderly patients with cancer based on psychological distress. Second, only four items related to the emotional function of the EORTC QLQ‐C30 were answered by the participants, meaning that the difference between other domains and the holistic status of QOL among the three subgroups was unclear. Third, although cancer stage (early or late), self‐reported social support level, frequency of cancer recurrence, treatment history and tumour size are known factors associated with the psychological health of patients with cancer, these variables were not included in this study. Further studies on the relationship between these factors are required because in the current study, recurrence was substantially linked with subgroups of psychological distress among cancer survivors. Finally, the cross‐sectional design of the present study prevented the identification of causal correlations between variables from being identified. Therefore, additional longitudinal studies that can identify the causes of the interactions among the research variables are required.

6. Conclusions

This study found that about 34.7% of Chinese elderly cancer patients belong to the ‘low psychological distress’ group, 35.6% belong to the ‘moderate psychological distress’, and ‘29.7%’ belong to the high psychological distress. Female, have religious belief, and under cancer treatment were significantly associated with moderate and higher psychological distress. Therefore, it is necessary to develop intervention programs that aim to improve the psychological symptoms of older cancer patients, particularly those with characteristics similar to those of the moderate and high groups identified in this study.

7. Relevance to Clinical Practice

During the treatment of patients with cancer, nurses play a crucial role in outcome monitoring, mental health counselling, health education and self‐care guidance. The most vulnerable patients receive priority care when medical resources are scarce. This study provides a hint for nurses to identify vulnerable patients between the various psychological distress groups and offer more individualised care.

Author Contributions

Danyu Li: acquisition of data; analysis and interpretation of data; drafting of the manuscript; Zhuting Zheng: critical revision of the paper for important intellectual content; Qingmei Huang: concept and design; administrative, technical or logistic support. Wen Zhang: concept and design; critical revision of paper for important intellectual content; administrative, technical or logistic support. Pengwei Hu: statistical analysis. Changrong Yuan: concept and design; obtaining funding; supervision. Fulei Wu: concept and design; obtaining funding; supervision.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding: This worked was supported by the National Natural Science Foundation of China (No. 72104055).

Data Availability Statement

Research data are not shared.

References

  1. Alacacioglu, A. , Tarhan O., Alacacioglu I., Dirican A., and Yilmaz U.. 2013. “Depression and Anxiety in Cancer Patients and Their Relatives.” Journal of B.U.ON 18, no. 3: 767–774. [PubMed] [Google Scholar]
  2. Alagizy, H. , Soltan M., Soliman S., Hegazy N., and Gohar S.. 2020. “Anxiety, Depression and Perceived Stress Among Breast Cancer Patients: Single Institute Experience.” Middle East Current Psychiatry 27, no. 1: 1–10. 10.1186/s43045-020-00036-x. [DOI] [Google Scholar]
  3. Amirmohamadi, M. , Borhani F., Kiani M., Almasi‐Hashiani A., and Naghavi B.. 2017. “The Correlation Between Spiritual Wellbeing and Depression in Elderly Patients With Cancer in Iran.” Middle East Journal of Family Medicine 7, no. 10: 9. 10.5742/MEWFM.2017.93152. [DOI] [Google Scholar]
  4. Andrykowski, M. A. , Lykins E., and Floyd A.. 2008. “Psychological Health in Cancer Survivors.” Seminars in Oncology Nursing 24, no. 3: 193–201. 10.1016/j.soncn.2008.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Arrieta, O. , Angulo L. P., Núñez‐Valencia C., et al. 2013. “Association of Depression and Anxiety on Quality of Life, Treatment Adherence, and Prognosis in Patients With Advanced Non‐Small Cell Lung Cancer.” Annals of Surgical Oncology 20, no. 6: 1941–1948. 10.1245/s10434-012-2793-5. [DOI] [PubMed] [Google Scholar]
  6. Balhareth, A. , Aldossary M. Y., and McNamara D.. 2019. “Impact of Physical Activity and Diet on Colorectal Cancer Survivors' Quality of Life: A Systematic Review.” World Journal of Surgical Oncology 17: 1–12. 10.1186/s12957-019-1697-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Barlow, D. H. 2004. Anxiety and Its Disorders: The Nature and Treatment of Anxiety and Panic. Guilford Press. [Google Scholar]
  8. Cai, T. , Wu F., Huang Q., et al. 2022. “Validity and Reliability of the Chinese Version of the Patient‐Reported Outcomes Measurement Information System Adult Profile‐57 (PROMIS‐57).” Health and Quality of Life Outcomes 20, no. 1: 95. 10.1186/s12955-022-01997-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cai, T. , Zhou T., Chen J., Huang Q., Yuan C., and Wu F.. 2023. “Identification of Age Differences in Cancer‐Related Symptoms in Women Undergoing Chemotherapy for Breast Cancer in China.” BMC Women's Health 23, no. 1: 100. 10.1186/s12905-023-02256-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Canada, A. L. , Murphy P. E., Fitchett G., Peterman A. H., and Schover L. R.. 2008. “A 3‐Factor Model for the FACIT‐Sp.” Psychooncology 17, no. 9: 908–916. 10.1002/pon.1307. [DOI] [PubMed] [Google Scholar]
  11. Cella, D. , Choi S., Garcia S., et al. 2014. “Setting Standards for Severity of Common Symptoms in Oncology Using the PROMIS Item Banks and Expert Judgment.” Quality of Life Research 23, no. 10: 2651–2661. 10.1007/s11136-014-0732-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Clark, S. L. 2010. Mixture* Modeling With Behavioral Data. University of California. [Google Scholar]
  13. Cohen, M. 2014. “Depression, Anxiety, and Somatic Symptoms in Older Cancer Patients: A Comparison Across Age Groups.” Psycho‐Oncology 23, no. 2: 151–157. 10.1002/pon.3383. [DOI] [PubMed] [Google Scholar]
  14. Driban, J. B. , Morgan N., Price L. L., Cook K. F., and Wang C.. 2015. “Patient‐Reported Outcomes Measurement Information System (PROMIS) Instruments Among Individuals With Symptomatic Knee Osteoarthritis: A Cross‐Sectional Study of Floor/Ceiling Effects and Construct Validity.” BMC Musculoskeletal Disorders 16: 1–9. 10.1186/s12891-015-0715-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fayers, P. , Aaronson N. K., Bjordal K., and Sullivan M.. 1995. EORTC QLQ–C30 Scoring Manual. European Organisation for Research and Treatment of Cancer. [Google Scholar]
  16. Fingeret, M. C. , Teo I., and Epner D. E.. 2014. “Managing Body Image Difficulties of Adult Cancer Patients: Lessons Fro m Available Research.” Cancer 120, no. 5: 633–641. 10.1002/cncr.28469. [DOI] [PubMed] [Google Scholar]
  17. Fox, R. S. , Ancoli‐Israel S., Roesch S. C., et al. 2020. “Sleep Disturbance and Cancer‐Related Fatigue Symptom Cluster in Breast Cancer Patients Undergoing Chemotherapy.” Support Care Cancer 28, no. 2: 845–855. 10.1007/s00520-019-04834-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gao, L. , Cao Y., Cao X., et al. 2023. “Machine Learning‐Based Algorithms to Predict Severe Psychological Distress Among Cancer Patients With Spinal Metastatic Disease.” Spine Journal 23, no. 9: 1255–1269. 10.1016/j.spinee.2023.05.009. [DOI] [PubMed] [Google Scholar]
  19. Heidarzadeh, M. , Dadkhah B., and Gholchin M.. 2016. “Post‐Traumatic Growth, Hope, and Depression in Elderly Cancer Patients.” International Journal of Medical Research & Health Sciences 5, no. 9: 455–461. [Google Scholar]
  20. Hong, J. S. , and Tian J.. 2014. “Prevalence of Anxiety and Depression and Their Risk Factors in Chinese Cancer Patients.” Supportive Care in Cancer 22, no. 2: 453–459. 10.1007/s00520-013-1997-y. [DOI] [PubMed] [Google Scholar]
  21. Huang, Q. , Wu F., Zhang W., Stinson J., Yang Y., and Yuan C.. 2022. “Risk Factors for Low Self‐Care Self‐Efficacy in Cancer Survivors: Application of Latent Profile Analysis.” Nursing Open 9, no. 3: 1805–1814. 10.1002/nop2.926. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Johnson, D. P. , and Whisman M. A.. 2013. “Gender Differences in Rumination: A Meta‐Analysis.” Personality and Individual Differences 55, no. 4: 367–374. 10.1016/j.paid.2013.03.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ju, W. , Zheng R., Zhang S., et al. 2023. “Cancer Statistics in Chinese Older People, 2022: Current Burden, Time Trends, and Comparisons With the US, Japan, and the Republic of Korea.” Science China Life Sciences 66, no. 5: 1079–1091. 10.1007/s11427-022-2218-x. [DOI] [PubMed] [Google Scholar]
  24. Kaasa, S. , Bjordal K., Aaronson N., et al. 1995. “The EORTC Core Quality of Life Questionnaire (QLQ‐C30): Validity and r Eliability When Analysed With Patients Treated With Palliative Radioth Erapy.” European Journal of Cancer 31A, no. 13–14: 2260–2263. 10.1016/0959-8049(95)00296-0. [DOI] [PubMed] [Google Scholar]
  25. Koo, M. M. , Swann R., McPhail S., et al. 2020. “Presenting Symptoms of Cancer and Stage at Diagnosis: Evidence From a Cross‐Sectional, Population‐Based Study.” Lancet Oncology 21, no. 1: 73–79. 10.1016/S1470-2045(19)30595-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lang, P. J. 1968. “Fear Reduction and Fear Behavior: Problems in Treating a Construct. Research in Psychotherapy Conference, 3rd, May–Jun, 1966, Chicago, IL, US.”
  27. Levin, J. B. , Aebi M. E., Smyth K. A., et al. 2015. “Comparing Patient‐Reported Outcomes Measure Information System Depression Scale With Legacy Depression Measures in a Community Sample of Older Adults With Varying Levels of Cognitive Functioning.” American Journal of Geriatric Psychiatry 23, no. 11: 1134–1143. 10.1016/j.jagp.2015.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Linden, W. , Vodermaier A., MacKenzie R., and Greig D.. 2012. “Anxiety and Depression After Cancer Diagnosis: Prevalence Rates by Cancer Type, Gender, and Age.” Journal of Affective Disorders 141, no. 2: 343–351. 10.1016/j.jad.2012.03.025. [DOI] [PubMed] [Google Scholar]
  29. Liu, Y. , Cao X., Zhao X., Shi X., Lei M., and Qin H.. 2022. “Quality of Life and Mental Health Status Among Cancer Patients With Metastatic Spinal Disease.” Frontiers in Public Health 10: 916004. 10.3389/fpubh.2022.916004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Mangoni, A. A. , and Woodman R. J.. 2019. “The Potential Value of Person‐Centred Statistical Methods in Ageing Research.” Age and Ageing 48, no. 6: 783–784. 10.1093/ageing/afz140. [DOI] [PubMed] [Google Scholar]
  31. Marrie, R. A. , Zhang L., Lix L. M., et al. 2018. “The Validity and Reliability of Screening Measures for Depression and Anxiety Disorders in Multiple Sclerosis.” Multiple Sclerosis and Related Disorders 20: 9–15. 10.1016/j.msard.2017.12.007. [DOI] [PubMed] [Google Scholar]
  32. Martin, M. Y. , Fouad M. N., Oster R. A., et al. 2014. “What Do Cancer Patients Worry About When Making Decisions About Treatment? Variation Across Racial/Ethnic Groups.” Supportive Care in Cancer 22: 233–244. 10.1007/s00520-013-1958-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. McLachlan, G. J. , Lee S. X., and Rathnayake S. I.. 2019. “Finite Mixture Models.” Annual Review of Statistics and Its Application 6, no. 1: 355–378. 10.1146/annurev-statistics-031017-100325. [DOI] [Google Scholar]
  34. Muthén, B. , and Muthén L. K.. 2000. “Integrating Person‐Centered and Variable‐Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes.” Alcoholism: Clinical and Experimental Research 24, no. 6: 882–891. [PubMed] [Google Scholar]
  35. Nam, I. , Yoon H., Kim Y., Lim Y.‐O., Lee H., and Choi K.. 2016. “The Mediating Role of Optimism and Pessimism on the Relationship Between Spirituality and Depression Among Elderly Cancer Patients.” Korean Journal of Health Promotion 16, no. 2: 127–133. 10.15384/kjhp.2016.16.2.127. [DOI] [Google Scholar]
  36. Nylund, K. L. , Asparouhov T., and Muthén B. O.. 2007. “Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study.” Structural Equation Modeling: A Multidisciplinary Journal 14, no. 4: 535–569. 10.1080/10705510701575396. [DOI] [Google Scholar]
  37. Park, J. , Choi Y. D., Lee K., et al. 2022. “Quality of Life Patterns and Its Association With Predictors Among Non‐Muscle Invasive Bladder Cancer Survivors: A Latent Profile Analysis.” Asia‐Pacific Journal of Oncology Nursing 9, no. 6: 100063. 10.1016/j.apjon.2022.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Purvis, T. E. , Neuman B. J., Riley L. H. III, and Skolasky R. L.. 2018. “Discriminant Ability, Concurrent Validity, and Responsiveness of PROMIS Health Domains Among Patients With Lumbar Degenerative Disease Undergoing Decompression With or Without Arthrodesis.” Spine 43, no. 21: 1512–1520. 10.1097/BRS.0000000000002661. [DOI] [PubMed] [Google Scholar]
  39. Querstret, D. , and Cropley M.. 2013. “Assessing Treatments Used to Reduce Rumination and/or Worry: A Systematic Review.” Clinical Psychology Review 33, no. 8: 996–1009. 10.1016/j.cpr.2013.08.004. [DOI] [PubMed] [Google Scholar]
  40. Riedl, D. , and Schüßler G.. 2022. “Factors Associated With and Risk Factors for Depression in Cancer Patients—A Systematic Literature Review.” Translational Oncology 16: 101328. 10.1016/j.tranon.2021.101328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Saracino, R. M. , Cham H., Rosenfeld B., and Nelson C.. 2020. “Latent Profile Analyses of Depressive Symptoms in Younger and Older Oncology Patients.” Assessment 27, no. 7: 1383–1398. 10.1177/1073191118784653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Schreiber, J. B. , and Pekarik A. J.. 2014. “Using Latent Class Analysis Versus K‐Means or Hierarchical Clustering to Understand Museum Visitors.” Curator: The Museum Journal 57, no. 1: 45–59. [Google Scholar]
  43. Sedighi, M. , Zahedi Bialvaei A., Hamblin M. R., et al. 2019. “Therapeutic Bacteria to Combat Cancer; Current Advances, Challenges, and Opportunities.” Cancer Medicine 8, no. 6: 3167–3181. 10.1002/cam4.2148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wan, C. , Meng Q., Yang Z., et al. 2008. “Validation of the Simplified Chinese Version of EORTC QLQ‐C30 From the Measurements of Five Types of Inpatients With Cancer.” Annals of Oncology 19, no. 12: 2053–2060. 10.1093/annonc/mdn417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wang, X. , Li X., Zhang Z., et al. 2024. “A Latent Profile Analysis of Psychological Resilience in Gastric Cancer Survivors: A Cross‐Sectional Study.” European Journal of Oncology Nursing 70: 102612. 10.1016/j.ejon.2024.102612. [DOI] [PubMed] [Google Scholar]
  46. White, M. C. , Holman D. M., Goodman R. A., and Richardson L. C.. 2019. Cancer Risk Among Older Adults: Time for Cancer Prevention to Go Silver. Vol. 59, S1–S6. Oxford University Press US. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Won, E. , and Kim Y.‐K.. 2020. “Neuroinflammation‐Associated Alterations of the Brain as Potential Neural Biomarkers in Anxiety Disorders.” International Journal of Molecular Sciences 21, no. 18: 6546. 10.3390/ijms21186546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zheng, W. , Lei M., Liu Y., Lu X., Yu D., and Zhang X.. 2020. “An Algorithm to Stratify the Risk of Postoperative Emotional Distress in Cancer Patients With Advanced Metastatic Spinal Disease.” Psychology Research and Behavior Management 13: 721–731. 10.2147/prbm.S261613. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Data Availability Statement

Research data are not shared.

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