Summary
This study aimed to assess health utility values derived from the quality of life utility-core 10 dimensions (QLU-C10D) among 827 Chinese patients with six common cancers and to identify key factors influencing these values. Differences in QLU-C10D utility values across sociodemographic, behavioral, and clinical subgroups were analyzed using the Kruskal-Wallis H test, and key determinants were identified using median regression models. Gastric cancer patients had the highest median utility (0.802), and breast cancer patients had the lowest (0.671). Physical functioning and pain mainly influenced utility, especially in gastric and lung cancer. Economic pressure was most strongly associated with esophageal cancer, while disease stage was most strongly associated with breast cancer. This study assessed utility values in Chinese patients using the Chinese value set, offering key data for cost-utility analyses (CUA).
Subject areas: public health, patient social context, cancer
Graphical abstract
Highlights
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Utility values in patients with six cancers were assessed using the Chinese value set
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Disease-related health changes were more accurately been captured
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Key factors influencing health utility were identified
Public health; patient social context; cancer
Introduction
Global cancer statistics for 2022 indicate that there were around 20 million new cancer diagnoses and approximately 9.7 million cancer-related deaths worldwide, with China accounting for the highest proportion of both new cases and deaths.1 Among males, the primary causes of cancer mortality include lung, liver, gastric, colorectal, and esophageal cancers. Conversely, for females, the deadliest types of cancer are lung, colorectal, liver, gastric, and breast cancers.2 The high mortality rates associated with these malignancies negatively impact patient survival rates and overall quality of life, including reduced workforce participation3 and increased pressure on healthcare systems. The economic burden of cancer treatment, estimated at $220 billion per year,4 poses a considerable challenge for the efficient allocation of healthcare resources. In healthcare systems with limited resources, policymakers must implement evidence-based strategies to optimize resource allocation and improve health outcomes.
Cost-utility analysis (CUA) is a methodology utilized to assess the cost-effectiveness of various therapeutic interventions or healthcare strategies.5 Multi-attribute utility instruments (MAUIs) are the preferred tools for quantifying utility, valued for their simplicity and effectiveness. Recognized generic MAUIs, such as the EQ-5D6 and SF-6D,7 are commonly employed. However, numerous studies point out the limitations in sensitivity and applicability of these instruments among cancer patients.8,9,10 To address these shortcomings, in 2015, the quality of life utility-core 10 dimensions (QLU-C10D) was created from the established European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30).11 The QLU-C10D integrates cancer-specific dimensions to capture the distinctive symptomatology experienced by cancer patients, thereby providing a more precise evaluation of utility values within this population.12,13 Consequently, the QLU-C10D serves as a crucial tool for enhancing the accuracy of utility value measurement in cancer-related economic evaluations.14,15
The national and cultural contexts of cancer patients play a crucial role in shaping their preferences, prompting countries to develop value sets that reflect their unique preference.16 An accurate assessment of utility values in cancer patients requires a value set based on the preferences of the national population. Currently, QLU-C10D value sets have been established in several countries, including Australia, Germany, Canada, the United Kingdom, Austria, Italy, Poland, Spain, the Netherlands, France, the United States, Denmark, Japan, Norway, Sweden, and China.17,18,19,20,21,22,23,24,25,26,27,28,29 However, no study has yet applied it to assess utility values among the Chinese population.
The impact of cancer sites on utility values is complex and varied, underscoring the need for utility value assessments for each cancer type to support accurate clinical decision-making.30 In China, there exists a pressing requirement for CUA regarding interventions aimed at the six common cancers, which entails the collection of cancer-specific utility values as a crucial preliminary action. Consequently, conducting research dedicated to utility values for different cancer sites is vital for improving the efficacy and equity of healthcare resource allocation.
This study assessed health utility in Chinese patients with six common cancers (lung, gastric, liver, esophageal, colorectal, and breast) using the QLU-C10D and examined the influence of sociodemographic, behavioral, and clinical characteristics on utility across cancer types. The findings provide essential data for utility-based CUA and offer insights for decision-makers to identify key factors associating health utility, thereby supporting the optimization of healthcare resource allocation.
Results
Study design and population
This study determined sample quotas for each cancer type based on the age-standardized incidence rates (ASIRs) of six common cancers in China and employed a quota sampling approach to ensure that the sample composition closely approximated the national cancer epidemiological distribution.2 In addition, following established methodological recommendations, a minimum sample size of 50–100 cases per cancer type was adopted to ensure the robustness of subgroup analyses and multivariable modeling, thereby improving the stability and statistical efficiency of the estimates.
This study was conducted in Harbin, a major regional center in Northeast China with access to a diverse population of cancer patients, ensuring the feasibility of on-site data collection. Multiple hospitals with relevant diagnostic and treatment capacities and stable patient sources were selected to cover different cancer types and reduce single-institution bias. Both inpatients and outpatients were included to capture variation in disease severity. Previous valuation studies of the Chinese QLU-C10D value set suggest that regional differences have a limited impact on health utility preferences; therefore, under a quota framework based on national cancer incidence, the sample is considered to have a certain degree of comparability. Nevertheless, as the data were collected from a single region, the generalizability of the findings should be interpreted with caution.
The screening process is illustrated in Figure 1. A total of 887 patients were initially recruited according to cancer-type quotas. After removing 37 duplicate respondents, 850 patients were assessed for eligibility. Subsequently, 23 patients were excluded due to being underage, missing key variables, or failing the consistency check, resulting in a final analytical sample of 827 patients. This sample comprised 270 patients with lung cancer, 96 with gastric cancer, 88 with liver cancer, 71 with esophageal cancer, 142 with colorectal cancer, and 160 with breast cancer. As shown in Table 1, the Mann-Whitney U test showed that p > 0.05, suggesting that excluding these data did not significantly affect the results.
Figure 1.
Flowchart of the study selection process, related to the part of study design and population
Table 1.
Sensitivity analysis results, related to study design and population
| Group | Total samples | Lung cancer | Gastric cancer | Liver cancer | Esophageal cancer | Colorectal cancer | Breast cancer | |
|---|---|---|---|---|---|---|---|---|
| N | original | 827 | 270 | 96 | 88 | 71 | 142 | 160 |
| full | 874 | 270 | 99 | 100 | 96 | 144 | 167 | |
| difference | 47 | 0 | 3 | 12 | 20 | 5 | 7 | |
| Min | original | 0.083 | 0.083 | 0.155 | 0.117 | 0.083 | 0.124 | 0.095 |
| full | 0.083 | 0.083 | 0.155 | 0.117 | 0.083 | 0.124 | 0.095 | |
| difference | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| Median | original | 0.713 | 0.709 | 0.802 | 0.720 | 0.709 | 0.737 | 0.671 |
| full | 0.720 | 0.709 | 0.805 | 0.753 | 0.686 | 0.750 | 0.676 | |
| difference | 0.007 | 0.000 | 0.003 | 0.033 | 0.023 | 0.013 | 0.005 | |
| Max | original | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| full | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |
| difference | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| Mean | original | 0.704 | 0.708 | 0.748 | 0.703 | 0.718 | 0.697 | 0.673 |
| full | 0.708 | 0.708 | 0.755 | 0.725 | 0.697 | 0.706 | 0.678 | |
| difference | 0.004 | 0.000 | 0.007 | 0.022 | 0.023 | 0.009 | 0.005 | |
| Mann-Whitney U test |
Z | −0.396 | 0.000 | −0.270 | −0.715 | −0.480 | −0.358 | −0.254 |
| P | 0.692 | 1.000 | 0.787 | 0.475 | 0.631 | 0.720 | 0.799 | |
Table 2 reports the operational definitions and classification criteria for all variables across three categories: sociodemographic, behavioral, and clinical. Table 3 presents a summary of the characteristics of patients with the six distinct types of cancers according to these three categories. Sociodemographically, the male population constituted approximately 50% of the sample, with the exception of breast cancer patients, who consisted exclusively of females. The age group of 50-59 years exhibited the highest patient prevalence, accounting for 32.39%. Furthermore, roughly 60% of the participants experienced a moderate or severe economic pressure. In terms of behavioral characteristics, excluding those with breast cancer, around 20% of the patients disclosed a history of tobacco or alcohol use. Regarding cancer staging, approximately 60% of the cohort were classified in stages I or II, with the highest staging observed in esophageal cancer patients at 78.87%.
Table 2.
Definitions and classification of study variables
| Domain | Variable | Definition | Measurement method | Categorization |
|---|---|---|---|---|
| Sociodemographic | gender | biological sex of the respondent | self-reported | male/female |
| Sociodemographic | age | chronological age at the time of survey | self-reported | 18–29/30–39/40–49/50–59/≥60 |
| Sociodemographic | residence | place of usual residence | self-reported | urban/rural |
| Sociodemographic | education level | highest educational attainment | self-reported | high school or below/undergraduate/postgraduate |
| Sociodemographic | occupation status | current employment status | self-reported | employed/unemployed/retired/others |
| Sociodemographic | marital status | current marital status | self-reported | unmarried/married/divorced/widowed |
| Sociodemographic | economic pressure | self-reported perceived financial burden due to illness or living expenses | self-reported | none/mild/moderate/severe |
| Behavioral | smoking status | current smoking behavior | self-reported | yes/no |
| Behavioral | drinking status | current alcohol consumption | self-reported | yes/no |
| Behavioral | health examination status | having undergone health examination in the past year | self-reported | yes/no |
| Clinical | self-paying status | whether medical expenses are partially or fully paid out-of-pocket | self-reported | yes/no |
| Clinical | surgery status | history of cancer-related surgery | medical record/self-reported | yes/no |
| Clinical | radiotherapy/chemotherapy status | history of receiving radiotherapy or chemotherapy | medical record/self-reported | yes/no |
| Clinical | endocrine therapy status | history of receiving endocrine therapy | medical record/self-reported | yes/no |
| Clinical | targeted therapy status | history of receiving targeted therapy | medical record/self-reported | yes/no |
| Clinical | disease classification | clinical tumor stage at diagnosis | medical record | I/II/III/IV |
Related to the part of study design and population.
Table 3.
Sociodemographic, behavioral, and clinical characteristics of the overall sample and cancer subgroups
| Characteristics | Total samples (n = 827) | Lung cancer (n = 270) | Gastric cancer (n = 96) | Liver cancer (n = 88) | Esophageal cancer (n = 71) | Colorectal cancer (n = 142) | Breast cancer (n = 160) |
|---|---|---|---|---|---|---|---|
| Gender | |||||||
| Male | 348 (42.08%) | 135 (50.00%) | 53 (55.21%) | 48 (54.55%) | 35 (49.30%) | 77 (54.23%) | 0 (0.00%) |
| Female | 479 (57.92%) | 135 (50.00%) | 43 (44.79%) | 40 (45.45%) | 36 (50.70%) | 65 (45.77%) | 160 (100.00%) |
| Age | |||||||
| 18∼29 | 22 (2.66%) | 6 (2.22%) | 7 (7.29%) | 1 (1.14%) | 3 (4.23%) | 3 (2.11%) | 2 (1.25%) |
| 30-39 | 72 (8.71%) | 27 (10.00%) | 11 (11.46%) | 3 (3.41%) | 7 (9.86%) | 9 (6.34%) | 15 (9.38%) |
| 40-49 | 183 (22.13%) | 59 (21.85%) | 10 (10.42%) | 17 (19.32%) | 13 (18.31%) | 31 (21.83%) | 53 (33.13%) |
| 50-59 | 297 (35.91%) | 94 (34.81%) | 38 (39.58%) | 34 (38.64%) | 23 (32.39%) | 48 (33.80%) | 60 (37.50%) |
| ≥60 | 253 (30.59%) | 84 (31.11%) | 30 (31.25%) | 33 (37.50%) | 25 (35.21%) | 51 (35.92%) | 30 (18.75%) |
| Residence | |||||||
| Urban | 453 (54.78%) | 135 (50.00%) | 46 (47.92%) | 43 (48.86%) | 42 (59.15%) | 94 (66.20%) | 93 (58.13%) |
| Rural | 374 (45.22%) | 135 (50.00%) | 50 (52.08%) | 45 (51.14%) | 29 (40.85%) | 48 (33.80%) | 67 (41.88%) |
| Education level | |||||||
| High school or below | 647 (78.23%) | 226 (83.70%) | 74 (77.08%) | 69 (78.41%) | 58 (81.69%) | 88 (61.97%) | 132 (82.50%) |
| Undergraduate | 169 (20.44%) | 42 (15.56%) | 21 (21.88%) | 18 (20.45%) | 13 (18.31%) | 48 (33.80%) | 27 (16.88%) |
| Postgraduate | 11 (1.33%) | 2 (0.74%) | 1 (1.04%) | 1 (1.14%) | 0 (0.00%) | 6 (4.23%) | 1 (0.63%) |
| Occupation status | |||||||
| Employed | 523 (63.24%) | 178 (65.93%) | 62 (64.58%) | 48 (54.55%) | 43 (60.56%) | 98 (69.01%) | 94 (58.75%) |
| Unemployed | 89 (10.76%) | 27 (10.00%) | 9 (9.38%) | 10 (11.36%) | 12 (16.90%) | 9 (6.34%) | 22 (13.75%) |
| Retired | 197 (23.82%) | 59 (21.85%) | 20 (20.83%) | 27 (30.68%) | 16 (22.54%) | 33 (23.24%) | 42 (26.25%) |
| Others | 18 (2.18%) | 6 (2.22%) | 5 (5.21%) | 3 (3.41%) | 0 (0.00%) | 2 (1.41%) | 2 (1.25%) |
| Marital status | |||||||
| Unmarried | 35 (4.23%) | 11 (4.07%) | 6 (6.25%) | 5 (5.68%) | 5 (7.04%) | 4 (2.82%) | 4 (2.50%) |
| Married | 692 (83.68%) | 234 (86.67%) | 81 (84.38%) | 71 (80.68%) | 55 (77.46%) | 108 (76.06%) | 143 (89.38%) |
| Divorced | 46 (5.56%) | 11 (4.07%) | 5 (5.21%) | 6 (6.82%) | 6 (8.45%) | 11 (7.75%) | 7 (4.38%) |
| Widowed | 54 (6.53%) | 14 (5.19%) | 4 (4.17%) | 6 (6.82%) | 5 (7.04%) | 19 (13.38%) | 6 (3.75%) |
| Economic pressure | |||||||
| None | 120 (14.51%) | 27 (10.00%) | 22 (22.92%) | 13 (14.77%) | 16 (22.54%) | 31 (21.83%) | 11 (6.88%) |
| Mild | 170 (20.56%) | 70 (25.93%) | 12 (12.50%) | 16 (18.18%) | 11 (15.49%) | 29 (20.42%) | 32 (20.00%) |
| Moderate | 229 (27.69%) | 70 (25.93%) | 29 (30.21%) | 23 (26.14%) | 17 (23.94%) | 40 (28.17%) | 50 (31.25%) |
| Severe | 308 (37.24%) | 103 (38.15%) | 33 (34.38%) | 36 (40.91%) | 27 (38.03%) | 42 (29.58%) | 67 (41.88%) |
| Self-paying status | |||||||
| Yes | 40 (4.84%) | 14 (5.19%) | 2 (2.08%) | 6 (6.82%) | 5 (7.04%) | 7 (4.93%) | 6 (3.75%) |
| No | 787 (95.16%) | 256 (94.81%) | 94 (97.92%) | 82 (93.18%) | 66 (92.96%) | 135 (95.07%) | 154 (96.25%) |
| Smoking status | |||||||
| Yes | 182 (22.01%) | 60 (22.22%) | 24 (25.00%) | 20 (22.73%) | 21 (29.68%) | 46 (32.39%) | 11 (6.88%) |
| No | 645 (77.99%) | 210 (77.78%) | 72 (75.00%) | 68 (77.27%) | 50 (70.42%) | 96 (67.61%) | 149 (93.13%) |
| Drinking status | |||||||
| Yes | 153 (18.50%) | 43 (15.93%) | 26 (27.08%) | 17 (19.32%) | 16 (22.54%) | 40 (28.17%) | 11 (6.88%) |
| No | 674 (81.50%) | 227 (84.07%) | 70 (72.92%) | 71 (80.68%) | 55 (77.46%) | 102 (71.83%) | 149 (93.13%) |
| Health examination status | |||||||
| Yes | 584 (70.62%) | 191 (70.74%) | 71 (73.96%) | 65 (73.86%) | 44 (61.97%) | 96 (67.61%) | 117 (73.13%) |
| No | 243 (29.38%) | 79 (29.26%) | 25 (26.04%) | 23 (26.14%) | 27 (38.03%) | 46 (32.39%) | 43 (26.88%) |
| Surgery status | |||||||
| Yes | 406 (49.09%) | 125 (46.30%) | 48 (50.00%) | 27 (30.68%) | 26 (36.62%) | 89 (62.68%) | 91 (56.88%) |
| No | 421 (50.91%) | 145 (53.70%) | 48 (50.00%) | 61 (69.32%) | 45 (63.38%) | 53 (37.32%) | 69 (43.13%) |
| Radiotherapy/chemotherapy status | |||||||
| Yes | 499 (60.34%) | 169 (62.59%) | 51 (53.13%) | 47 (53.41%) | 54 (76.06%) | 68 (47.89%) | 110 (68.75%) |
| No | 328 (39.66%) | 101 (37.41%) | 45 (46.88%) | 41 (46.59%) | 17 (23.94%) | 74 (52.11%) | 50 (31.25%) |
| Endocrine therapy status | |||||||
| Yes | 88 (10.64%) | 26 (9.63%) | 6 (6.25%) | 2 (2.27%) | 11 (15.49%) | 4 (2.82%) | 39 (24.38%) |
| No | 739 (89.36%) | 244 (90.37%) | 90 (93.75%) | 86 (97.73%) | 60 (84.51%) | 138 (97.18%) | 121 (75.63%) |
| Targeted therapy status | |||||||
| Yes | 142 (17.17%) | 51 (18,89%) | 12 (12.50%) | 25 (28.41%) | 4 (5.63%) | 17 (11.97%) | 33 (20.63%) |
| No | 685 (82.83%) | 219 (81.11%) | 84 (87.50%) | 63 (71.59%) | 67 (94.37%) | 125 (88.03%) | 127 (79.38%) |
| Disease classification | |||||||
| I | 288 (34.82%) | 100 (37.04%) | 29 (30.21%) | 26 (29.55%) | 19 (26.76%) | 65 (45.77%) | 49 (30.63%) |
| II | 270 (32.65%) | 89 (32.96%) | 28 (29.17%) | 29 (32.95%) | 37 (52.11%) | 22 (15.49%) | 65 (40.63%) |
| III | 180 (21.77%) | 50 (18.52%) | 30 (31.25%) | 21 (23.86%) | 9 (12.68%) | 37 (26.06%) | 33 (20.63%) |
| IV | 89 (10.76%) | 31 (11.48%) | 9 (9.38%) | 12 (13.64%) | 6 (8.45%) | 18 (12.68%) | 13 (8.13%) |
Related to the part of study design and population.
QLU-C10D-derived utility values
The utility values for the total sample ranged from 0.083 to 1, with a mean of 0.704 (SD 0.225) and a median of 0.713. Figure 2 depicts the distribution of QLU-C10D utility values across different types of cancer. The mean utility values were listed as 0.708 (0.227) for lung cancer patients, 0.748 (0.232) for gastric cancer, 0.703 (0.223) for liver cancer patients, 0.718 (0.220) for esophageal cancer patients, 0.697 (0.236) for colorectal cancer patients, and 0.673 (0.207) for breast cancer patients. Recorded median utility values for these groups were 0.709, 0.802, 0.720, 0.709, 0.737, and 0.671, respectively, indicating that patients with gastric cancer presented the highest utility values, whereas those with breast cancer reported the lowest.
Figure 2.
Distribution of QLU-C10D utility values in the overall sample and cancer subgroups, related to the part of QLU-C10D-derived utility values
Distributions of responses to QLU-C10D descriptive systems
Figure 3 presents the distribution of QLU-C10D domain responses in the overall sample and by cancer type. The QLU-C10D comprises 10 dimensions: physical functioning (PF), role functioning (RF), emotional functioning (EF), social functioning (SF), pain (PA), fatigue (FA), nausea (NA), sleep (SL), appetite (AP), and bowel problems (BO). Across all cancer types, the highest proportion of reported problems was observed in PF (60.4%–85.0%) and FA (63.5%–85.6%), whereas the most severe problems occurred in PF (9.4%–14.1%) and SF (5.6%–14.6%). Among the six cancer types, breast cancer patients reported the greatest burden, particularly in FA (85.6%) and PF (85.0%), while gastric cancer patients reported the fewest problems, especially in AP (52.1%) and PA (43.7%). Notably, even patients with non-digestive system cancers experienced issues in AP, NA, and BO, with the highest prevalence seen in bowel problems (61.3%–75.0%).
Figure 3.
Distribution of responses across QLU-C10D dimensions in the overall sample and cancer subgroups
(A) Total.
(B) Lung cancer.
(C) Gastric cancer.
(D) Liver cancer.
(E) Esophageal cancer.
(F) Colorectal cancer.
(G) Breast cancer.
Health utility values across sociodemographic, behavioral, and clinical subgroups
Figures 4 and 5 show the utility values by sociodemographic, behavioral, and clinical characteristics in the overall population and across cancer types. Among sociodemographic characteristics, older patients reported lower utility values, and patients experiencing greater economic pressure had significantly lower values (p < 0.001). Behavioral characteristics were associated with utility: smoking reduced utility in patients with lung, liver, and esophageal cancers (p < 0.01), while alcohol consumption lowered utility in gastric (p < 0.01) and liver (p < 0.001) cancer patients. Participation in physical examinations decreased utility in esophageal cancer patients (p < 0.05) but increased utility in colorectal cancer patients (p < 0.001). For clinical characteristics, early-stage patients reported significantly higher utility than late-stage patients across both the overall sample and individual cancer types (p < 0.05).
Figure 4.
Utility values by sociodemographic subgroups
(A) Gender.
(B) Age group.
(C) Residence.
(D) Education level.
(E) Occupation status.
(F) Marital status.
(G) Economic pressure.
(H) Self-paying status.
Figure 5.
Utility values by behavioral and clinical subgroups
(I) Smoking status.
(J) Drinking status.
(K) Health examination status.
(L) Cancer stage.
(M) Surgery status.
(N) Radiotherapy/chemotherapy status.
(O) Endocrine therapy status.
(P) Targeted therapy status.
Sociodemographic, behavioral, and clinical factors associated with utility values
Figure 6 illustrates the association of various characteristics on patient utility values based on median regression analysis. Economic pressure and disease stage exerted the strongest effects on utility, whereas gender and age had comparatively smaller effects. Specifically, economic pressure had the greatest association on esophageal cancer patients (β = −0.257 to −0.144) and the least on breast cancer patients (β = −0.157 to −0.117). Disease stage showed the strongest effect in breast cancer patients (β = −0.050 to −0.332) and the weakest in gastric cancer patients (β = −0.032 to 0.010). Residence, education level, occupation status, and marital status were not significantly associated with utility values across cancer types. The effects of health examination status and treatment status varied by cancer type.
Figure 6.
Quantile regression coefficients of sociodemographic, behavioral, and clinical factors, related to the part of sociodemographic, behavioral, and clinical factors associated with utility values
Discussion
This study is pioneering in utilizing the QLU-C10D with the Chinese value set to quantify health utility in patients with six common cancers, providing crucial data to inform CUA and optimize health policy. These findings provide robust evidence to guide the design of targeted clinical interventions and the efficient allocation of healthcare resources. From a policy perspective, the choice of health utility instrument may influence the results of cost-utility analysis. Compared with generic instruments such as the EQ-5D, the QLU-C10D, as a cancer-specific measure, may be more sensitive to disease-related changes, potentially leading to different utility values estimates. These differences may further affect incremental cost- effectiveness ratio (ICER) and consequently influence reimbursement and resource allocation decisions.31,32
QLU-C10D descriptive statistics
In this study, patients with gastric cancer had the highest mean utility value, whereas those with breast cancer had the lowest, consistent with findings from a Chinese study using the SF-6Dv2.33 The lower utility among breast cancer patients may be attributed to psychological distress arising from changes in body image following treatment, highlighting the need for targeted mental health support in this population.34,35 Compared with studies in the Netherlands, colorectal cancer patients in our sample had lower utility values, with a median of 0.737.36 Otherwise, since the breast cancer subgroup consisted exclusively of female patients, the results may also be influenced by sex-related factors.
Distributions of responses to QLU-C10D descriptive systems
Patients across all six cancer types reported the most problems in PF, likely due to reduced strength, limited activity, and treatment-related side effects. In addition to reporting functional limitations, patients also frequently reported problems across symptom domains. These findings underscore the importance of cancer-specific instruments in evaluating patient health status, highlighting their greater discriminative ability compared with generic instruments. Consistent with previous studies, cancer-specific instruments are better able to distinguish differences in patients’ utility values.8,9,10
Sociodemographic factors associated with health utility values
Although previous studies suggest that utility values decline with age,37 this study found no strict linear relationship. Some studies have reported a “U-shaped” or “inverted U-shaped” relationship between age and well-being across different countries, suggesting that this association is shaped by multiple health and social determinants.38,39 Economic pressure was associated with esophageal cancer patients most and breast cancer patients least, likely because breast cancer patients’ work was less associated with disease-related constraints, whereas esophageal cancer patients often face employment difficulties, increasing their economic pressure.40
Behavioral factors associated with health utility values
Alcohol consumption has been shown to significantly reduce the utility values of patients with digestive system cancers, consistent with previous research.41 Chronic alcohol consumption not only elevates the risk of gastrointestinal cancers but also worsens treatment-related side effects, thereby further reducing patients’ health utility.42 Additionally, in this study, lower utility values among liver and esophageal cancer patients may reflect the psychological burden of examinations and concerns about disease progression.
Clinical factors associated with health utility values
Regression analysis confirmed a significant negative association between disease stage and utility values, highlighting disease stage as a key determinant of health utility and consistent with previous Chinese studies. Disease stage had the greatest association with breast cancer patients and the least with gastric cancer patients, likely because breast cancer is generally associated with a lower overall symptom burden and more favorable prognosis compared with other cancer types.43
Treatment effects varied across cancer types, suggesting the need for personalized interventions to optimize patient health utility. For instance, surgery was positively associated with health outcomes among patients with lung and esophageal cancer, whereas its association was either non-significant or negative for other cancer types. Related studies have noted that post-operative trends typically show an initial decline in utility values followed by a subsequent increase, which may reflect the delayed effects of treatment.44
This study applies the Chinese QLU-C10D value set to accurately determine utility values for patients afflicted with six common cancers in China, creating a foundational dataset to support CUA of cancer interventions. It offers valuable insights for devising more tailored interventions for cancer patients with diverse sociodemographic, behavioral, and clinical characteristics. Considering the research limitations, future investigations should refine patient subgroup evaluations and investigate variations in utility values across differing cohorts. Such efforts will aid in the development of personalized intervention strategies, thereby enhancing resource allocation.
Limitations of the study
This study is subject to several limitations, which are as follows. First, the sample was derived predominantly from Heilongjiang Province, which may constrain the generalizability of the findings, as regional populations may differ significantly in genetics, environment, lifestyle, and access to medical resources. Second, the hospital-centric nature of the sample could introduce selection bias, limiting the extent to which the findings represent the overall population of cancer patients. Caution is warranted in interpreting these results, necessitating further studies conducted across diverse settings to validate and expand upon these findings. Third, information on time since diagnosis was not available. Therefore, potential trajectory effects related to disease progression could not be accounted for, which may influence subgroup comparisons and should be interpreted with caution. In addition, comorbidity data and detailed treatment information, particularly for newer modalities such as immunotherapy, were not systematically collected. This may limit a comprehensive assessment of patients’ health status and treatment heterogeneity and could introduce bias in subgroup analyses. Fourth, in gender-related analyses, potential residual confounding may remain for certain cancer types, which could affect the interpretation of the estimated results. Finally, although we conducted a systematic analysis of the main variables, unobserved factors, and sample size limitations may still have influenced the findings. Future studies with more comprehensive data and larger sample sizes are warranted to further validate our results.
Resource availability
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Weidong Huang (huangweidong@hrbmu.edu.cn).
Materials availability
This study did not generate new unique reagents.
Data and code availability
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Data: The raw survey data containing individual patient responses cannot be publicly disclosed due to ethical and privacy restrictions, as they include potentially identifiable and sensitive health information.
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Code: The code has been placed in the supplementary file.
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Other items: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Acknowledgments
The authors sincerely appreciate the cancer patients involved in the study, as well as the invaluable efforts of the interviewers in the data collection process. The authors also gratefully acknowledge the financial support from the National Natural Science Foundation of China (grant no. 72274045 and 71974048).
Author contributions
Investigation, resources, data curation, writing – original draft, writing – review and editing, J.S.; investigation, resources, data curation, writing – original draft, writing – review and editing, L.W.; investigation, resources, data curation, writing – original draft, writing – review and editing, Y.C.; conceptualization, methodology, software, validation, formal analysis, L.X.; conceptualization, methodology, software, validation, formal analysis, L.L.; conceptualization, methodology, software, validation, formal analysis, H.L.; conceptualization, methodology, software, validation, formal analysis, A.S.; visualization, supervision, H.Y.; project administration, funding acquisition, W.H. The final version of the manuscript was reviewed and approved by all authors. W.H. is the leading corresponding author.
Declaration of interests
The authors declare no competing interests.
STAR★Methods
Key resources table
| REAGENT or RESOURCE | SOURCE | IDENTIFIER |
|---|---|---|
| Instrument and algorithms | ||
| QLU-C30 | EORTC Quality of Life Group | https://qol.eortc.org/questionnaires/ |
| QLU-C10D | EORTC Quality of Life Group | https://qol.eortc.org/eortc-qlu-c10d/ |
| QLU-C10D scoring algorithm using the Chinese value set | Cao et al.28 | https://link.springer.com/article/10.1007/s11136-024-03776-z |
Experimental model and study participant details
Data collection
The data for this study were obtained through a cross-sectional survey conducted between August 2023 and December 2023, involving cancer patients from six hospitals in Harbin: The First Affiliated Hospital of Harbin Medical University, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University Cancer Hospital, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang Provincial Hospital, and Harbin First Hospital. The inclusion criteria were as follows: (1) patients diagnosed with one of six cancer types (lung, gastric, liver, esophageal, colorectal, or breast cancer); (2) aged 18 years or older; and (3) without cognitive burden. The study was conducted with full respect for patient autonomy, ensuring that participation was voluntary and initiated only after obtaining informed consent. Data were collected through face-to-face interviews conducted by professionally trained interviewers. The questionnaire comprised three main sections: sociodemographic, behavioral, and clinical characteristics, as well as the QLQ-C30 and QLU-C10D instruments. Operational definitions and classification criteria for all variables are presented in Table 2. The QLQ-C30 and the QLU-C10D health state classification system are presented in Supplementary Materials, showing how the 10 QLU-C10D dimensions are derived from the 13 component items of the QLQ-C30, as well as how the duration attribute is incorporated in the discrete choice experiment (DCE) valuation survey.
Ethics statement
This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Harbin Medical University Ethics Committee (HMUIRB2023005) on July 6, 2023, prior to the initiation of data collection. All participants were fully informed about the study objectives and procedures, and written informed consent was obtained from all participants prior to their participation.
Method details
Development and model estimation of the QLU-C10D scoring system
The QLU-C10D health utility scoring system was developed based on random utility theory, following international valuation guidelines for multi-attribute utility instruments, using a DCE approach. The instrument comprises 10 health-related quality of life (HRQoL) dimensions, each with four levels. A duration attribute (1, 2, 5, and 10 years) was incorporated to enable anchoring onto the quality-adjusted life year (QALY) scale. Each choice task consisted of two hypothetical scenarios defined by a combination of health state and survival duration, and respondents were asked to indicate their preferred option. An efficient experimental design (D-efficient) was used to generate the choice sets. To reduce cognitive burden, a partial-profile design combined with a balanced incomplete block design was applied, allowing a maximum of four dimensions to vary between alternatives within each choice set. The full set of choice tasks was blocked and randomly assigned, with each respondent completing approximately 16 tasks. Data were collected from a general population sample in China through an online cross-sectional survey using quota sampling to ensure representativeness. Utility parameters were estimated using a conditional logit model. Anchoring was achieved through interaction terms between health state levels and duration, and utility decrements for each level were derived as the ratio of health state coefficients to the duration coefficient (β/α). Monotonicity was assessed during estimation, and constraints were applied where necessary. Model performance was evaluated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Individual utility scores were calculated by summing the utility decrements associated with each dimension level and subtracting them from full health. In this study, utility values were derived using the published Chinese value set for the QLU-C10D.
QLU-C10D and applicability in Chinese populations
The QLU-C10D, developed by the Multi-Attribute Utility Consortium for Cancer (MAUCaC) in collaboration with the EORTC.11 Among the 13 items of the QLQ-C30, the QLU-C10D encompasses 10 dimensions: physical functioning (PF), role functioning (RF), emotional functioning (EF), social functioning (SF), pain (PA), fatigue (FA), nausea (NA), sleep (SL), appetite (AP), and bowel problems (BO). All dimensions are measured on a four-level ordinal scale, with level 1 corresponding to “not at all,” level 2 to “a little,” level 3 to “quite a bit,” and level 4 to “very much.” The EORTC QLQ-C30 and the QLU-C10D, widely used instruments for assessing HRQoL in cancer patients, have undergone translation and systematic psychometric validation in Chinese populations. Previous studies have demonstrated that these instruments exhibit good validity, and responsiveness among Chinese cancer patients, indicating their ability to effectively capture HRQoL.45,46,47 Therefore, the use of these instruments in the present study is methodologically justified and appropriate for the study population. Similar to other countries, China has established a value set that reflects the priorities of its populace utilizing the Discrete Choice Experiment (DCE) methodology. In this study, health utility values for Chinese cancer patients were assessed using a local population-specific value set (Supplementary material).
Quantification and statistical analysis
Descriptive statistics were utilized to characterize the sociodemographic, behavioral, and clinical characteristics of the sample population. The QLU-C10D utility values exhibited an abnormal distribution, as indicated by the Kolmogorov-Smirnov test (p < 0.05). Therefore, boxplot was presented the mean, the minimum, first quartile (Q25), median (Q50), third quartile (Q75), and maximum values of the QLU-C10D. The distribution of responses across the QLU-C10D dimensions in the overall sample and cancer subgroups was presented by reporting the proportion of each level within each dimension.
QLU-C10D utility values were subsequently compared across subgroups defined by sociodemographic, behavioral, and clinical characteristics, with group differences evaluated using the Kruskal-Wallis H test and Dunn’s post hoc test applied when significant differences were detected (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001). To further explore the determinants of utility, a median regression model (τ = 0.5) was performed with QLU-C10D utility as the dependent variable and all relevant factors as independent variables. All models reported robust standard errors. Multicollinearity was assessed using variance inflation factors (VIF), and no evidence of severe multicollinearity was found (all VIF values were below 10). Given the absence of a clear hierarchical data structure or within-group correlation, clustering adjustments were not applied in the analysis.
Data processing was performed using Stata 15 and SPSS 27.0.
Footnotes
Supplemental information can be found online at https://doi.org/10.1016/j.isci.2026.116334.
Contributor Information
Hongjuan Yu, Email: yuhongjuan2008@163.com.
Weidong Huang, Email: huangweidong@hrbmu.edu.cn.
Supplemental information
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
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Data: The raw survey data containing individual patient responses cannot be publicly disclosed due to ethical and privacy restrictions, as they include potentially identifiable and sensitive health information.
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Code: The code has been placed in the supplementary file.
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Other items: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.