Diagnostic Routes and Health Disparities in Advanced Colorectal Cancer: Evidence from a Nationwide Study in China

Diagnostic Routes and Health Disparities in Advanced Colorectal Cancer: Evidence from a Nationwide Study in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic Routes and Health Disparities in Advanced Colorectal Cancer: Evidence from a Nationwide Study in China Kexin Yi, Yin Liu, Huifang Xu, Hong Wang, Chenxi Feng, Hongwei Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7290862/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background The diagnostic route is an important determinant in advanced colorectal cancer, yet its impact remains understudied in China. Methods In this nationwide cross-sectional study (2020–2021), we enrolled 4,589 patients with advanced colorectal cancer in China. Diagnostic routes included symptomatic presentation, proactive health-seeking, and comorbidity presentation. Multivariable regression models were used to evaluate their determinants and associations with two outcomes: receipt of biomarker testing and post-treatment HRQOL, adjusted for baseline scores and covariates. Results The majority of patients (87.0%) were diagnosed via the symptomatic pathway, with the remainder following Proactive Health-Seeking (6.0%) or comorbidity (6.0%) pathways. Higher educational attainment was a significant predictor of presentation through a non-symptomatic pathway relative to the symptomatic pathway. Analysis of post-diagnosis outcomes revealed divergent associations for these non-symptomatic routes. The Comorbid pathway was independently associated with a higher likelihood of receiving biomarker testing (Odds Ratio [OR], 1.40; 95% CI, 1.05–1.88). A similar positive, though not statistically significant, trend was observed for the Proactive Health-Seeking pathway (OR, 1.33; 95% CI, 0.99–1.80). Conversely, both the Proactive Health-Seeking (β = -2.28; 95% CI, -3.94 to -0.63) and the Comorbid (β = -2.86; 95% CI, -4.49 to -1.23) pathways were significantly associated with a greater decline in the post-treatment physical functioning domain of HRQOL. Conclusions The predominance of symptomatic diagnoses highlights the urgent need for earlier detection. Although non-symptomatic routes improved access to biomarker testing, they were also associated with greater short-term physical decline, underscoring the need to integrate psychosocial support into early diagnostic pathways. Colorectal Cancer Diagnostic Route Biomarker Testing Health-Related Quality of Life Figures Figure 1 Introduction Colorectal cancer (CRC) represents a major global public health challenge and is one of the leading causes of cancer-related mortality worldwide [ 1 , 2 ]. This burden is particularly pronounced in China, where incidence and mortality rates have been steadily increasing [ 3 ]. While advancements in multidisciplinary treatment have improved outcomes, patients diagnosed with advanced-stage disease continue to face a poor prognosis and a substantial disease burden [ 4 ]. Therefore, a deeper understanding of the factors shaping the care journey for this vulnerable population is essential. The sequence of events leading to a cancer diagnosis, known as the diagnostic pathway, is increasingly recognized as a critical determinant of patient outcomes [ 5 – 7 ]. Large-scale studies have consistently shown that emergency presentations are associated with more advanced disease and worse survival compared to non-emergency routes [ 5 , 8 , 9 ]. Factors such as older age and socioeconomic status have also been linked to these unfavorable pathways [ 7 ]. However, significant knowledge gaps remain. First, most of this evidence originates from Western countries, and large-scale data characterizing diagnostic pathways within the unique Chinese healthcare context are scarce. Furthermore, the consequences of these pathways on patient-centered outcomes are largely unexplored. Conceptually, the diagnostic pathway can serve as a proxy for the quality and coordination of a patient’s initial healthcare interactions; thus, more organized pathways may be associated with more systematic, guideline-concordant care, such as biomarker testing [ 10 ]. Moreover, each pathway represents a distinct psycho-physical experience at the onset of the cancer journey, which may have a profound impact on a patient's short-term health-related quality of life (HRQOL) during treatment [ 11 , 12 ]. To address these gaps, this study utilized data from a nationwide cohort of patients with advanced CRC in China. We aimed to characterize the key sociodemographic and cognitive factors associated with the diagnostic route: symptomatic presentation, proactive health seeking, and comorbidity presentation. In addition, we assessed how the diagnostic route is independently associated with two crucial patient-centered outcomes: access to precision oncology, as measured by the receipt of biomarker testing, and psychophysical experience of treatment, as captured by post-treatment HRQOL. Methods Study Design and Participants This analysis utilized data from a nationwide, multicenter, cross-sectional study conducted across seven major geographic regions of China between March 2020 and March 2021 [ 13 , 14 ]. A multi-stage stratified sampling method was employed to recruit a nationally representative cohort of patients with newly diagnosed CRC. The sampling process was stratified by geographic region and city level using detailed methodology reported in prior publications [ 13 ]. A total of 19 hospitals were selected. The detailed information on enrollment data for 19 hospitals from 7 regions is presented in Fig. 1 and Table S1 . The study protocol was approved by the relevant institutional review board and all participants provided written informed consent. Data were collected using standardized questionnaires administered by trained research staff under stringent quality control protocols. Measures 1. Sociodemographic and Clinical Characteristics Patient-reported demographic information was collected, including age at diagnosis, sex, geographic region, occupation, marital status, highest educational level attained, annual household income, and primary type of health insurance. Clinical data were extracted from the medical records and included cancer site (colon, rectal, or both), clinical TNM stage at diagnosis, and metastatic status at diagnosis. 2. Diagnostic Route The primary explanatory variable for this study was the diagnostic route of the patient. This was determined from the self-reported answers to the survey question, "What was the reason for your first medical visit that led to the cancer diagnosis?". Based on their responses, patients were categorized into one of three mutually exclusive groups: (1) Symptomatic Presentation: Patients who sought medical care after self-discovering symptoms suggestive of CRC (e.g., rectal bleeding, severe abdominal pain, or changes in bowel habits). (2) Proactive Health-Seeking Pathway: Patients whose diagnosis was initiated by an abnormal finding during a routine health check-up or opportunistic screening in the absence of overt symptoms. This route encompasses not only cases detected via direct screening colonoscopy but also those identified through abnormal results from other nonspecific examinations (e.g., abdominal imaging or tumor marker tests) conducted as part of a general health assessment. (3) Comorbid Presentation: Patients whose cancer was discovered incidentally during a consultation or medical workup for other pre-existing health conditions. Patient Knowledge Regarding CRC Prediagnosis patient knowledge was assessed across three key domains: (1) high-risk factors for CRC, (2) CRC screening procedures, and (3) available CRC treatment options. This assessment was conducted using a semi-structured questionnaire (SSQ) specifically developed for this study by the research team based on established Chinese clinical guidelines [ 15 , 16 ]. The SSQ presented participants with three distinct multiple-choice questions, each retrospectively probing their awareness before diagnosis: "Before you were diagnosed with CRC, which of the following did you consider to be high-risk factors for CRC?" (11 items) "Before you were diagnosed with CRC, which of the following did you consider to be procedures for CRC screening?" (6 items) "Before you were diagnosed with CRC, which of the following colorectal cancer treatments did you know about?" (7 items) For statistical analysis, the response to each of the three questions was operationalized as a distinct binary variable to represent a patient's awareness in each domain. A patient was coded as '1' (aware) if they selected at least one correct item from the list of choices for a given question. Conversely, a patient was coded '0' (Unaware) if their response was 'I did not know. ’ Further detailed information regarding the SSQ instrument and its development was reported in a previous study [ 13 ]. 4. Screening, Treatment, and Economic Burden Patient-reported data on healthcare experiences were gathered using a separate, semi-structured questionnaire (SSQ). This instrument first assessed the patient’s screening history for CRC, including a binary variable for whether they had ever been screened. For individuals who had not undergone colonoscopy, the SSQ was further probed for self-reported barriers. The predefined list of potential barriers included lack of awareness, insufficient time for the procedure, fear that colonoscopy is painful, unaffordable cost, long waiting times for an appointment, and lack of insurance coverage. In relation to their current CRC diagnosis and treatment, the questionnaire also collected patient-reported information on two key aspects: (1) the utilization of biomarker testing and (2) the specific treatment modalities they had received. To assess the financial impact of the disease, data on medical expenditure were collated. This information was primarily extracted from hospital medical records where accessible; otherwise, it was supplemented by patient self-reports to ensure completeness. The key economic variables collected for this study were patients' total out-of-pocket costs incurred for CRC diagnosis and treatment as well as the overall reimbursement rate applied to their total medical expenses. 5. Health-related quality of life Health-related quality of life (HRQOL) was assessed as a primary patient-reported outcome at two time points: baseline (T1) upon hospital admission prior to initial CRC treatment, and follow-up (T2) on the day before discharge after the initial treatment course. The measurement was performed using a study-specific composite instrument, the FACT-C-plus-QLQ-C9, developed for this study based on expert consensus. This instrument integrates all 36 items from the validated traditional Chinese version of the Functional Assessment of Cancer Therapy-Colorectal (FACT-C, V.4) with nine selected items from the European Organization for Research and Treatment of Cancer QLQ-C30 (V.3) (see Supplementary Table S2 for details on the selected items) [ 13 , 17 – 20 ]. The Chinese versions of the FACT-C and the EORTC QLQ-C30 have been validated in previous studies [ 17 – 19 ]. The psychometric reliability of this composite scale was robust in our study cohort (Cronbach’s α = 0.80). For statistical analysis, all raw scores from the instrument, including the overall score and scores for each individual subscale (e.g., physical, emotional, and cognitive functioning), were linearly transformed to a standardized scale ranging from 0 to 100. Across all scales, a higher score consistently represents better HRQOL or functional status. Both baseline (T1) and post-treatment (T2) scores were used in the multivariable regression models, with T1 scores serving as a key covariate to adjust for baseline differences. Statistical Analysis Patient sociodemographic, clinical, and baseline characteristics (including pre-diagnosis knowledge and pre-treatment HRQOL scores) were summarized using descriptive statistics. Continuous variables were presented as means with standard deviations (SD) or medians with interquartile ranges (IQR) based on their distribution. Categorical variables are presented as frequencies and percentages (%). To compare these characteristics across the three diagnostic route groups (Symptomatic Presentation, Proactive Health-Seeking, and Comorbid Presentation), one-way analysis of variance or the Kruskal-Wallis test was used for continuous variables, and the Chi-square test or Fisher's exact test was used for categorical variables, as appropriate. To investigate the factors that determine a patient's diagnostic route, a multivariate multinomial logistic regression model was developed. The three-category diagnostic route served as the dependent variable, with the Symptomatic Presentation pathway set as the reference category. The model included key sociodemographic (e.g., age, sex, income, and education), clinical (e.g., cancer site), and patient awareness characteristics as independent variables. Results from this model are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). To assess the impact of the diagnostic route on HRQOL, a multivariate linear regression model was constructed. With the post-treatment HRQOL score (T2) as the dependent variable, this analysis examined diagnostic route as the primary independent variable. The model was robustly adjusted for the baseline HRQOL score (T1), in addition to a comprehensive set of covariates, including sociodemographic (age, sex, income), clinical (TNM stage, cancer site), and treatment-related factors (receipt of surgery, radiotherapy, and chemotherapy). This analysis was repeated for all relevant HRQOL subscales, with the results presented as beta coefficients (β) and their 95% CIs. All statistical analyses were conducted using the R Software (version 4.2.0, R Foundation for Statistical Computing). A two-sided P value less than .05 was considered statistically significant for all analyses. Results A total of 4,589 patients with advanced colorectal cancer were included in the final analysis. The majority of patients (n = 4015; 87.5%) were diagnosed via the symptomatic pathway, with the remainder diagnosed through Proactive Health-Seeking (n = 269; 5.9%) or Comorbid (n = 279; 6.1%) pathways. The detailed sociodemographic and clinical characteristics of the cohort stratified by the three diagnostic routes are presented in Table 1 . Table 1 Sociodemographic and Clinical Characteristics of Patients with Advanced Colorectal Cancer, Stratified by Diagnostic route Characteristics Overall ( N = 4589) Symptomatic Presentation ( N = 4015) Proactive Health-Seeking ( N = 269) Comorbid Presentation ( N = 279) P value Age group at diagnosis, years < 50 991 (21.7) 882 (22.0) 51 (19.2) 56 (20.1) 0.218 50–64 2180 (47.7) 1884 (47.1) 144 (54.4) 136 (48.9) ≥ 65 1401 (30.6) 1237 (30.9) 70 (26.4) 86 (31.0) Sex Male 2730 (59.5) 2380 (59.3) 179 (66.5) 156 (55.9) 0.029 Female 1859 (40.5) 1635 (40.7) 90 (33.5) 123 (44.1) Education level Primary school or below 1330 (29.0) 1218 (30.4) 42 (15.6) 62 (22.2) < 0.001 Middle school 1478 (32.2) 1295 (32.3) 89 (33.1) 87 (31.2) High school/specialized secondary schools 1044 (22.8) 905 (22.5) 64 (23.8) 67 (24.0) University/specialty or above 734 (16.0) 594 (14.8) 74 (27.5) 63 (22.6) Cancer site Colon 2063 (45.5) 1722 (43.4) 155 (58.7) 173 (62.9) < 0.001 Rectum 2470 (54.5) 2247 (56.6) 109 (41.3) 102 (37.1) Clinical stage at initial diagnosis Ⅰ 112 (2.5) 95 (2.5) 9 (3.4) 7 (2.6) < 0.001 Ⅱ 775 (17.6) 669 (17.4) 54 (20.7) 49 (18.0) Ⅲ 1970 (44.7) 1782 (46.3) 89 (34.1) 89 (32.7) Ⅳ 1550 (35.2) 1303 (33.8) 109 (41.8) 127 (46.7) Region Eastern 1319 (28.7) 1117 (27.8) 95 (35.3) 98 (35.1) 0.001 Northern 565 (12.3) 488 (12.2) 36 (13.4) 41 (14.7) Southern 672 (14.6) 572 (14.2) 41 (15.2) 47 (16.9) Central 690 (15.0) 634 (15.8) 35 (13.0) 20 (7.2) Northeast 364 (7.9) 315 (7.8) 23 (8.6) 22 (7.9) Southwest 652 (14.2) 590 (14.7) 27 (10.0) 35 (12.5) Northwest 327 (7.1) 299 (7.5) 12 (4.5) 16 (5.7) Metastasis at initial diagnosis No metastasis 2854 (62.5) 2551 (63.9) 153 (57.1) 135 (48.7) < 0.001 With liver or lung metastasis only 820 (18.0) 680 (17.0) 69 (25.7) 67 (24.2) With both liver and lung metastases other sites or multiple metastases throughout the body 889 (19.5) 761 (19.1) 46 (17.2) 75 (27.1) Occupation Government and public sector personnel 654 (14.3) 553 (13.8) 55 (20.5) 43 (15.4) 0.006 Service workers, migrant workers, and individuals 1733 (37.8) 1543 (38.4) 94 (34.9) 88 (31.6) Unemployment, layoffs, etc. 1936 (42.2) 1678 (41.8) 112 (41.6) 132 (47.3) Unknow 266 (5.8) 241 (6.0) 8 (3.0) 16 (5.7) Annual household income, Chinese Yuan None 763 (16.7) 690 (17.2) 28 (10.4) 43 (15.5) 0.001 < 50,000 1861 (40.7) 1645 (41.1) 111 (41.3) 96 (34.5) 50,000-100,000 1293 (28.3) 1120 (28.0) 77 (28.6) 90 (32.4) 100,000-200,000 523 (11.4) 440 (11.0) 36 (13.4) 41 (14.7) ≥ 200,000 133 (2.9) 107 (2.7) 17 (6.3) 8 (2.9) Health insurance Urban basic medical insurance 1926 (42.0) 1622 (40.4) 154 (57.3) 144 (51.6) < 0.001 Urban basic medical insurance 985 (21.5) 880 (21.9) 45 (16.7) 51 (18.3) New rural cooperative medical scheme 1558 (34.0) 1412 (35.2) 63 (23.4) 75 (26.9) Other 120 (2.6) 101 (2.5) 7 (2.6) 9 (3.2) Undergoing the colonoscopy before the initial diagnosis Yes 121 (2.6) 88 (2.2) 23 (8.6) 10 (3.6) < 0.001 No 4465 (97.4) 3925 (97.8) 245 (91.4) 269 (96.4) Barriers to undergoing colonoscopy † Lack of awareness 3883 (84.0) 3429 (87.4) 208 (85.3) 224 (83.3) 0.103 No time for a colonoscopy 369 (8.3) 302 (7.7) 42 (17.2) 22 (8.2) < 0.001 Perception that the colonoscopy is painful 716 (16.0) 611 (15.6) 61 (25.0) 37 (13.8) < 0.001 High cost of the colonoscopy 172 (3.9) 154 (3.9) 9 (3.7) 7 (2.6) 0.546 Long wait times for a colonoscopy appointment 174 (3.9) 143 (3.6) 13 (5.3) 15 (5.6) 0.132 Insurance doesn't cover it 97 (2.2) 85 (2.2) 7 (2.9) 5 (1.9) 0.714 Other 191 (4.3) 163 (4.2) 8 (3.3) 19 (7.1) 0.054 Awareness of CRC risk factors Yes 1597 (34.9) 1346 (33.6) 129 (48.1) 113 (40.5) < 0.001 No 2983 (65.1) 2661 (66.4) 139 (51.9) 166 (59.5) Awareness of CRC screening Yes 691 (15.1) 568 (14.2) 75 (28.3) 45 (16.2) < 0.001 No 3874 (84.9) 3428 (85.78) 190 (71.7) 233 (83.8) Awareness of CRC treatment Yes 2027 (44.2) 1733 (43.2) 142 (52.8) 137 (49.1) 0.002 No 2559 (55.8) 2280 (56.8) 127 (47.2) 142 (50.9) Undergoing biomarker testing, including RAS , BRAF , and MSI Yes 1982 (47.1) 1686 (45.8) 137 (56.1) 147 (56.8) < 0.001 No 2223 (52.9) 1993 (54.2) 107 (43.9) 112 (43.2) Barriers to undergoing biomarker testing Targeted therapy is not accepted (other treatment options are considered to be sufficient) 360 (16.2) 315 (15.8) 32 (29.6) 9 (8.0) 0.009 The test is too expensive and not reimbursable 529 (23.8) 486 (24.4) 16 (14.8) 26 (23.2) Anxious to receive treatment and unwilling to wait for genetic test results 126 (5.7) 112 (5.6) 7 (6.5) 6 (5.4) Plan to blind-eat targeted drugs 32 (1.4) 28 (1.4) 2 (1.9) 2 (1.8) Unknown 953 (42.9) 852 (42.8) 42 (38.9) 56 (50.0) Others 223 (10.0) 199 (1.0) 9 (8.3) 13 (11.6) Surgery Yes 3838 (83.8) 3388 (84.5) 216 (80.6) 213 (76.6) 0.001 No 742 (16.2) 620 (15.5) 52 (19.4) 65 (23.4) Endoscopic treatment Yes 142 (3.1) 118 (2.9) 14 (5.2) 10 (3.6) 0.103 No 4438 (96.9) 3890 (97.1) 254 (94.8) 268 (96.4) Radiotherapy Yes 1005 (21.9) 908 (22.7) 62 (23.1) 32 (11.5) < 0.001 No 3575 (78.1) 3100 (77.3) 206 (76.9) 246 (88.5) Chemotherapy Yes 3959 (86.4) 3463 (86.4) 237 (88.4) 235 (84.5) 0.413 No 621 (13.6) 545 (13.6) 31 (11.6) 43 (15.5) Targeted therapy Yes 1317 (28.8) 1117 (27.9) 85 (31.7) 103 (37.1) 0.002 No 3263 (71.2) 2891 (72.1) 183 (68.3) 175 (62.9) Out-of-pocket costs, Chinese Yuan < 50,000 1149 (25.1) 1000 (25.0) 71 (26.4) 74 (26.6) 0.962 50,000-100,000 1867 (40.8) 1641 (41.0) 107 (39.8) 113 (40.7) 100,000-200,000 1043 (22.8) 917 (22.9) 61 (22.7) 57 (20.5) ≥ 200,000 518 (11.3) 448 (11.1) 30 (11.1) 34 (12.2) Medical expenditure reimbursement ratio (%) 0.59 ± 0.18 0.582 ± 0.18 0.612 ± 0.20 0.610 ± 0.18 0.003 Overall HRQOL * 65.39 ± 10.30 65.42 ± 10.21 65.04 ± 10.78 65.73 ± 11.11 0.742 FACT-C ¶ Physical well-being 78.80 ± 14.62 78.99 ± 14.43 77.91 ± 15.74 77.09 ± 16.29 0.067 Social/Family well-being 82.10 ± 20.30 82.05 ± 19.99 81.58 ± 22.05 83.03 ± 23.31 0.681 Emotional well-being 74.42 ± 21.62 74.44 ± 21.53 74.09 ± 21.42 75.88 ± 22.75 0.532 Functional well-being 52.46 ± 24.82 52.27 ± 24.51 52.96 ± 27.74 54.46 ± 26.33 0.344 Colorectal cancer subscale 63.50 ± 16.26 63.43 ± 16.08 62.96 ± 17.48 65.01 ± 17.60 0.253 EORTC QLQ-C30 Functional scales and/or items ¶ Physical 85.94 ± 25.53 86.08 ± 25.19 83.74 ± 27.79 86.24 ± 28.21 0.339 Cognitive 77.62 ± 24.92 77.85 ± 24.61 78.44 ± 24.33 74.46 ± 28.99 0.078 Emotional 78.03 ± 22.44 78.24 ± 22.24 77.37 ± 22.41 76.61 ± 24.86 0.436 Social 65.63 ± 27.55 65.54 ± 27.32 67.66 ± 26.42 66.35 ± 30.82 0.438 Symptom items § Fatigue 22.23 ± 26.18 21.90 ± 26.04 26.30 ± 26.68 23.12 ± 27.54 0.024 Sleep disturbance 28.77 ± 29.10 28.63 ± 28.94 28.44 ± 28.24 31.45 ± 31.86 0.286 Financial impacts 39.50 ± 30.97 39.73 ± 30.73 33.27 ± 29.04 41.13 ± 34.77 0.003 Values are presented as mean (standard deviation) for continuous variables or n (%) for categorical variables. † Numbers total more than 100% because some patients underwent several therapies simultaneously. * Higher scores indicate better quality of life. ¶ Higher scores indicate higher functioning levels. § Higher scores indicate a greater degree of symptoms. Abbreviations: CRC, colorectal cancer; MSI, microsatellite instability. Significant differences among the groups were observed for most characteristics. Notably, a clear socioeconomic gradient is observed. Compared to patients in the Symptomatic Presentation group, those in the Proactive Health-Seeking pathway were significantly more likely to have a higher education level (e.g., 27.5% with university-level education vs. 14.8%, P < .001) and a higher annual household income (e.g., 6.3% in the ≥ 200,000 Yuan bracket vs. 2.7%, P = .001). Pre-diagnosis awareness and subsequent healthcare utilization also differed significantly among the groups. Patients in the Proactive Health-Seeking pathway demonstrated a markedly higher level of awareness regarding CRC screening than those in the symptomatic pathway (28.3% vs. 14.2%, respectively; P < .001). This pattern of proactive engagement appeared to extend into the post-diagnosis phase; the unadjusted rates of receiving biomarker testing were substantially higher in both the Proactive Health-Seeking (56.1%) and comorbidity (56.8%) pathways compared to the symptomatic pathway (45.8%; P < .001). From a clinical standpoint, the distribution of TNM stage at diagnosis differed significantly across routes ( P < .001), as detailed in Table 1 . The Proactive Health-Seeking pathway was associated with the highest proportion of early stage (Stage I-II) disease (24.1%), compared to 19.9% in the symptomatic group and 20.6% in the comorbidity group. However, it is noteworthy that a substantial proportion of patients in the Proactive Health-Seeking pathway were still diagnosed at Stage IV (41.8%), a rate numerically higher than that in the symptomatic group (33.8%), highlighting the challenge of detecting advanced asymptomatic CRC. Furthermore, the distribution of cancer sites varied, with colon cancer being more prevalent in non-symptomatic pathways. Despite these numerous differences in sociodemographic and clinical profiles, the overall HRQOL scores assessed after treatment did not differ significantly among the three groups ( P = .742). The results of the multivariable multinomial logistic regression, identifying the determinants of diagnostic routes, are presented in Table 2 . In this analysis, the Symptomatic Presentation pathway was used as the reference category for all the comparisons. Compared with the symptomatic pathway, a higher education level and greater pre-diagnosis awareness of CRC screening were significantly associated with a higher likelihood of being diagnosed via the Proactive Health-Seeking pathway. Household income showed a similar positive trend. For the Comorbid pathway, higher education level was also a significant independent predictor. In contrast to the proactive health seeking pathway, income was not significantly associated with this route. The detailed results for all variables are shown in Table 2 . Table 2 Multivariable Analysis of Determinants of Diagnostic Route in Patients with Advanced Colorectal Cancer Variables Proactive Health-Seeking vs. Symptomatic Incidental vs. Symptomatic OR (95%CI) P value OR (95%CI) P value Age group at diagnosis, years (Ref: <50) 50–64 1.51 (1.07, 2.14) 0.019 1.34 (0.95, 1.89) 0.090 ≥ 65 1.35 (0.90, 2.00) 0.144 1.46 (1.00, 2.13) 0.048 Sex (Ref = Female) Male 1.22 (0.92, 1.61) 0.160 0.77 (0.60, 1.00) 0.048 Education level (Ref: Primary school or below) Middle school 1.92 (1.28, 2.88) 0.002 1.40 (0.97, 2.02) 0.069 High school/specialized secondary schools 1.82 (1.17, 2.84) 0.008 1.58 (1.06, 2.36) 0.023 University/specialty or above 3.41 (2.11, 5.50) < 0.001 2.50 (1.60, 3.91) < 0.001 Annual household income, Chinese Yuan (Ref: None) < 50,000 1.25 (0.80, 1.95) 0.334 0.85 (0.57, 1.25) 0.401 50,000-100,000 1.01 (0.62, 1.64) 0.967 0.86 (0.57, 1.31) 0.493 100,000-200,000 1.08 (0.61, 1.91) 0.803 0.90 (0.54, 1.50) 0.685 ≥ 200,000 2.01 (0.99, 4.08) 0.055 0.68 (0.29, 1.56) 0.359 Region (Ref: Eastern) Northern 0.60 (0.39, 0.92) 0.019 0.80 (0.54, 1.19) 0.278 Southern 0.65 (0.43, 0.99) 0.045 0.80 (0.54, 1.17) 0.249 Central 0.73 (0.48, 1.11) 0.139 0.35 (0.21, 0.59) < 0.001 Northeast 0.65 (0.40, 1.05) 0.080 0.67 (0.41, 1.10) 0.114 Southwest 0.55 (0.34, 0.86) 0.010 0.69 (0.46, 1.04) 0.073 Northwest 0.46 (0.25, 0.86) 0.016 0.55 (0.32, 0.96) 0.035 Cancer site (Ref: Colon) 0.54 (0.42, 0.70) < 0.001 0.45 (0.35, 0.58) < 0.001 Rectum Awareness of CRC risk factors (Ref: No) Yes 1.31 (0.93, 1.84) 0.127 1.23 (0.90, 1.68) 0.194 Awareness of CRC screening (Ref: No) Yes 1.71 (1.18, 2.47) 0.005 0.80 (0.54, 1.19) 0.275 Awareness of CRC treatment (Ref: No) Yes 0.87 (0.63, 1.20) 0.40 1.06 (0.78, 1.42) 0.721 The model presented here was a multivariate multinomial logistic regression. The OR for presentation via the Proactive Health-Seeking and Comorbid pathways are estimated, with the Symptomatic Presentation pathway serving as the reference category for the dependent variable (Diagnostic Route). The model was adjusted for all variables listed in table. Abbreviations: CI, confidence interval. Multivariable analysis revealed that both diagnostic route and socioeconomic status were significant determinants of biomarker testing (Table 3 ). After full adjustment for all covariates, the Comorbid pathway was a robust independent predictor, with these patients having a significantly higher likelihood of undergoing testing compared to those in the symptomatic pathway (Odds Ratio 1.40; 95% CI, 1.05–1.88; P = .022). Interestingly, while the Proactive Health-Seeking pathway also showed a positive association, this effect was substantially attenuated and no longer statistically significant after adjusting for socioeconomic factors, particularly education level (OR, 1.33; 95% CI, 0.99–1.80; P = .061). Table 3 Association of Diagnostic Route with Receipt of Biomarker Testing Characteristics Model 1 Model 2 Model 3 OR (95%CI) P value OR (95%CI) P value OR (95%CI) P value Diagnostic Route (Ref: Symptomatic) Proactive Health-Seeking 1.49 (1.11, 2.01) 0.008 1.43 (1.06, 1.94) 0.018 1.33 (0.99, 1.80) 0.061 Comorbid 1.46 (1.10, 1.95) 0.009 1.47 (1.10, 1.97) 0.009 1.40 (1.05, 1.88) 0.022 Annual household income, Chinese Yuan (Ref: None) < 50,000 1.11 (0.90, 1.37) 0.311 1.05 (0.85, 1.30) 0.67 50,000-100,000 1.21 (0.97, 1.51) 0.092 1.01 (0.80, 1.28) 0.928 100,000-200,000 1.79 (1.36, 2.37) < 0.001 1.35 (1.00, 1.82) 0.051 ≥ 200,000 3.68 (2.27, 6.10) < 0.001 2.74 (1.66, 4.64) < 0.001 Education level (Ref: Primary school or below) Middle school 1.16 (0.96, 1.40) 0.138 High school/specialized secondary schools 1.47 (1.19, 1.81) < 0.001 University/specialty or above 1.92 (1.49, 2.48) < 0.001 Model 1: Adjusted for age, sex, cancer site, TNM stage, and region. Model 2: Adjusted for Model 1 variables + annual household income. Model 3: Adjusted for Model 2 variables + education level. The multivariate analysis results for the association between the diagnostic route and post-treatment HRQOL, adjusted for baseline QOL and other covariates, are presented in Table 4 . The findings revealed distinct patterns of impact for each nonsymptomatic pathway. The Proactive Health-Seeking pathway was associated with a significantly lower overall HRQOL score (β = -1.58; 95% CI, -2.62 to -0.54; P = .003) compared to the Symptomatic pathway. The subscale analysis indicated that this negative impact was primarily concentrated in the physical and emotional domains. Additionally, the patients in this group reported significantly higher levels of fatigue (β = 3.60; 95% CI, 0.66 to 6.54; P = .016). The Comorbid pathway was not significantly associated with the overall HRQOL score, but it demonstrated a unique and broader pattern of negative effects on specific subscales. This route was significantly associated with worse physical, emotional, and cognitive functioning outcomes. Furthermore, these patients reported significantly more severe sleep disturbances and a greater financial impact. Table 4 Association of Diagnostic Route with Post-treatment Health-Related Quality of Life Outcomes Number of items Proactive Health-Seeking vs. Symptomatic Incidental vs. Symptomatic Beta (95%CI) P value Beta (95%CI) P value Overall HRQOL * -1.58 (-2.62, -0.54) 0.003 -0.85 (-1.87, 0.17) 0.103 FACT-C ¶ Physical well-being 10 -2.28 (-3.94, -0.63) 0.007 -2.86 (-4.49, -1.23) 0.001 Social/Family well-being 7 0.81 (-0.26, 1.89) 0.138 -0.40 (-1.45, 0.66) 0.463 Emotional well-being 5 -2.53 (-4.82, -0.25) 0.030 -0.39 (-2.63, 1.85) 0.733 Functional well-being 7 -0.12 (-2.45, 2.21) 0.917 -0.30 (-2.59, 2.00) 0.801 Colorectal cancer subscale 7 -1.87 (-3.59, -0.14) 0.034 -0.21 (-1.9, 1.48) 0.810 EORTC QLQ-C30 Functional scales and/or items ¶ Physical 1 -3.4 (-5.83, -0.98) 0.006 0.35 (-2.04, 2.74) 0.774 Cognitive 1 -1.5 (-4.04, 1.04) 0.248 -4.40 (-6.89, -1.90) 0.001 Emotional 2 -2.52 (-4.96, -0.08) 0.043 -2.50 (-4.90, -0.11) 0.040 Social 2 -2.13 (-5.11, 0.84) 0.160 -1.02 (-3.94, 1.89) 0.491 Symptom items § Fatigue 1 3.60 (0.66, 6.54) 0.016 1.14 (-1.75, 4.02) 0.440 Sleep disturbance 1 0.82 (-1.94, 3.57) 0.562 4.12 (1.41, 6.83) 0.003 Financial impacts 1 -0.38 (-3.53, 2.78) 0.815 4.01 (0.91, 7.10) 0.011 Results were obtained from a multivariable linear regression model examining the association between the primary independent variable (diagnostic route) and dependent variable (Post-treatment HRQOL score). The model was adjusted for pretreatment HRQOL score, age at diagnosis, sex, income, region, cancer site, TNM stage, and receipt of surgery, radiotherapy, and chemotherapy. * Higher scores indicate better quality of life. ¶ Higher scores indicate higher functioning levels. § Higher scores indicate a greater degree of symptoms. Discussion This nationwide study of Chinese patients with advanced colorectal cancer characterized the determinants and consequences of three distinct diagnostic routes: symptomatic presentation, proactive health seeking, and comorbidity presentation. Our findings demonstrated that a patient's diagnostic route is significantly associated with socioeconomic status and pre-diagnosis awareness. Furthermore, non-symptomatic pathways (Proactive Health-Seeking and Comorbid) were associated with a higher likelihood of receiving guideline-recommended biomarker testing than the symptomatic pathway. However, these same routes were concurrently linked to a greater decline in post-treatment physical and emotional QoL. Taken together, these findings underscore the importance of integrating targeted psychosocial support into the diagnostic process, particularly in patients diagnosed without preceding symptoms. Our finding that higher socioeconomic status is a key determinant of presentation through non-symptomatic diagnostic routes is consistent with evidence from Western populations [ 7 , 21 ]. Our analysis further revealed that educational attainment is a more robust predictor than household income, suggesting that the underlying mechanism is driven more by health literacy than by direct financial capacity [ 22 ]. This interpretation is strengthened by our finding that a specific awareness of actionable screening procedures, a key component of functional health literacy, was significantly associated with the proactive pathway, whereas general risk awareness was not [ 23 ]. The central finding regarding the consequences is the divergent impact of the Proactive Health-Seeking pathway on clinical care and patient experience. This route was associated with a higher likelihood of receiving guideline-recommended biomarker testing, a finding that aligns with health services research, suggesting that more engaged patients often receive guideline-concordant care [ 24 ]. However, this clinical process advantage did not translate into better short-term patient-reported outcomes. Paradoxically, the Proactive Health-Seeking pathway was independently associated with a greater decline in post-treatment HRQOL, an effect primarily concentrated on the physical and emotional domains. This highlights a critical distinction between long-term clinical benefits and immediate patient experience. While the majority of research on diagnostic routes has appropriately focused on improved survival rates as the primary benefit of early or non-symptomatic detection [ 25 , 26 ], the acute psycho-physical impact of the diagnostic event itself represents a crucial but under-researched dimension. We propose that the acute deterioration in well-being that we observed is best explained by the "psychological shock" hypothesis. A substantial body of literature has established that an unexpected diagnosis of cancer can be a traumatic event that induces significant psychological distress [ 11 ]. Our study, by focusing on the immediate post-diagnosis period, captures a critical window of vulnerability that may be masked in studies with longer-term follow-up, suggesting that an optimal clinical pathway does not automatically equate to optimal patient experience. Our subscale analysis further distinguished the specific challenges faced by the patients in the Comorbid pathway. While this group, similar to the Proactive Health-Seeking group, also had greater access to biomarker testing, post-treatment HRQOL deterioration showed a different and broader pattern. It is uniquely characterized by significant declines in cognitive functioning, increased sleep disturbance, and greater financial impacts, in addition to decrements in physical and emotional well-being. This distinct profile of adverse outcomes likely reflects the "double burden" of managing a new cancer diagnosis, in addition to pre-existing chronic conditions. The presence of multiple comorbidities is a well-established factor that complicates cancer treatment and is associated with poor overall outcomes [ 27 ]. The significant cognitive decline we observed may be an exacerbation of baseline vulnerabilities by systemic cancer therapies, a phenomenon of increasing concern for patients with multi-morbidity [ 28 ]. Similarly, sleep disturbance and financial distress are highly prevalent in populations with chronic illness, and our findings suggest that the addition of a cancer diagnosis can amplify these pre-existing problems to a critical level [ 29 , 30 ]. Therefore, our results identified a particularly vulnerable patient subgroup that requires a comprehensive, multidisciplinary management approach that extends beyond standard oncologic care to address their complex medical and socioeconomic needs. The findings of this study have significant implications for both clinical practice and public health strategies, moving beyond traditional prognostic factors to highlight the profound impact of diagnostic experience on a patient's subsequent journey. Our results challenge the notion of treating patients with advanced CRC as a homogeneous group. Instead, they underscore the need for a more stratified and personalized approach to supportive care tailored to the unique clinical and psychosocial vulnerabilities associated with a patient's specific pathway to diagnosis. The strengths of this study include its large, nationwide multicenter sample, specific focus on the high-need advanced-stage patient population, and comprehensive dataset encompassing patient-reported outcomes, pre-diagnosis cognition, and objective clinical actions. However, the limitations of this study must be acknowledged. First, the cross-sectional design of the determinant analysis precludes definitive causal inferences. Second, prediagnosis knowledge and behaviors were based on patient recall and may be subject to bias. Finally, our HRQOL assessment was limited to the short-term perioperative period, and the long-term impact of the diagnostic routes remains to be explored. Conclusion This nationwide study of patients with advanced colorectal cancer in China demonstrates that the diagnostic route is a critical determinant of both the quality of treatment received and the patient's subjective experience. Our findings show that, while non-symptomatic pathways are associated with better access to optimal clinical care, they may simultaneously induce significant short-term psychophysical distress. This underscores the necessity of integrating patient-centered psychosocial support from the initial stages of cancer treatment, especially for patients diagnosed without preceding symptoms. Declarations Acknowledgements We are grateful to all participating centers and their dedicated staff for their invaluable contributions to data collection for this nationwide study. We would like to extend our sincere thanks to the following institutions (not in any particular order): Sichuan Cancer Hospital, Peking University Cancer Hospital and Institute, The First Affiliated Hospital of Baotou Medical College, Zhejiang Cancer Hospital, Cancer Hospital of China Medical University, Jining Medical University, Xinxiang Central Hospital, Dalian Medical University, Wuzhou Red Cross Hospital, The First Affiliated Hospital of Guangxi Medical University, The First Affiliated Hospital of Jinan University, Sun Yat-sen University Cancer Center, Affiliated Tumor Hospital of Xinjiang Medical University, Chongqing University Cancer Hospital, Chongqing Medical University, Gansu Provincial Cancer Hospital, and Chengdu Medical College. Author contributions Kexin Yi: Conceptualization, writing—original draft, software. Yin Liu: Methodology, Software, Validation, Writing, Review, and Editing. Huifang Xu: Writing—original draft, data curation. Hong Wang: Conceptualization, Methodology, Formal Analysis. Chenxi Feng: Conceptualization, methodology, validation, and investigation. Hongwei Liu: Project administration, conceptualization, writing, review, and editing. Shaokai Zhang: Funding acquisition, writing—original draft, visualization, project administration, writing—review and editing, resources. Funding This work was funded by the Beijing LoveBook Cancer Foundation and Merck Serono Co., Ltd. Data availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Henan Cancer Hospital (No.2019273) and the Ethics Committee of all other participating hospitals. Informed consent was obtained from all the participants. All the procedures were performed in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. 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Burton A, Balachandrakumar VK, Driver RJ, Tataru D, Paley L, Marshall A, Alexander G, Rowe IA, Palmer DH, Cross TJS: Regional variations in hepatocellular carcinoma incidence, routes to diagnosis, treatment and survival in England . Br J Cancer 2022, 126 (5):804-814. Pennisi F, Buzzoni C, Russo AG, Gervasi F, Braga M, Renzi C: Comorbidities, Socioeconomic Status, and Colorectal Cancer Diagnostic Route . JAMA Network Open 2025, 8 (5):e258867-e258867. Danckert B, Falborg AZ, Christensen NL, Frederiksen H, Lyratzopoulos G, McPhail S, Ryg J, Vedsted P, Thomsen LA, Jensen H: Routes to diagnosis and the association with the prognosis in patients with cancer – A nationwide register-based cohort study in Denmark . Cancer Epidemiology 2021, 74 :101983. Burton A, Wilburn J, Driver RJ, Wallace D, McPhail S, Cross TJS, Rowe IA, Marshall A: Routes to diagnosis for hepatocellular carcinoma patients: predictors and associations with treatment and mortality . Br J Cancer 2024, 130 (10):1697-1708. Kang S, McLeod SL, Walsh C, Grewal K: Patient outcomes associated with cancer diagnosis through the emergency department: A systematic review . Academic Emergency Medicine 2023, 30 (9):955-962. Cordova MJ, Riba MB, Spiegel D: Post-traumatic stress disorder and cancer . Lancet Psychiatry 2017, 4 (4):330-338. McPhail S, Elliss-Brookes L, Shelton J, Ives A, Greenslade M, Vernon S, Morris E, Richards M: Emergency presentation of cancer and short-term mortality . British journal of cancer 2013, 109 (8):2027-2034. Liu Y, Xu HF, Zhang X, Yu YQ, Zhao YQ, Zhang SK, Qiao YL: Disease knowledge, medical experience, health-related quality of life and health-care costs among patients with advanced colorectal cancer in China: protocol for a nationwide multicentre survey . BMJ Open 2022, 12 (3):e054403. Liu H, Xu H, Liu Y, Zhao Y, Zhang X, Yu Y, Du L, Liu Y, Wang W, Cao H et al : Comparative characteristics of early-onset vs. late-onset advanced colorectal cancer: a nationwide study in China . BMC Cancer 2024, 24 (1):503. [Chinese Protocol of Diagnosis and Treatment of Colorectal Cancer] . Zhonghua Wai Ke Za Zhi 2018, 56 (4):241-258. [China guideline for the screening, early detection and early treatment of colorectal cancer (2020, Beijing)] . Zhonghua Zhong Liu Za Zhi 2021, 43 (1):16-38. Zhao H, Kanda K: Testing psychometric properties of the standard Chinese version of the European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire 30 (EORTC QLQ-C30) . J Epidemiol 2004, 14 (6):193-203. Cheng JX, Liu BL, Zhang X, Zhang YQ, Lin W, Wang R, Zhang YQ, Zhang HY, Xie L, Huo JL: The validation of the standard Chinese version of the European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire 30 (EORTC QLQ-C30) in pre-operative patients with brain tumor in China . BMC Med Res Methodol 2011, 11 :56. Wong CK, Lam CL, Law WL, Poon JT, Chan P, Kwong DL, Tsang J: Validity and reliability study on traditional Chinese FACT-C in Chinese patients with colorectal neoplasm . J Eval Clin Pract 2012, 18 (6):1186-1195. Cella DF, Tulsky DS, Gray G, Sarafian B, Linn E, Bonomi A, Silberman M, Yellen SB, Winicour P, Brannon J et al : The Functional Assessment of Cancer Therapy scale: development and validation of the general measure . J Clin Oncol 1993, 11 (3):570-579. Li S, He Y, Liu J, Chen K, Yang Y, Tao K, Yang J, Luo K, Ma X: An umbrella review of socioeconomic status and cancer . Nat Commun 2024, 15 (1):9993. Nutbeam D, Lloyd JE: Understanding and Responding to Health Literacy as a Social Determinant of Health . Annu Rev Public Health 2021, 42 :159-173. Papadakos JK, Hasan SM, Barnsley J, Berta W, Fazelzad R, Papadakos CJ, Giuliani ME, Howell D: Health literacy and cancer self-management behaviors: A scoping review . Cancer 2018, 124 (21):4202-4210. Hibbard JH, Greene J: What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs . Health Aff (Millwood) 2013, 32 (2):207-214. Neal RD, Tharmanathan P, France B, Din NU, Cotton S, Fallon-Ferguson J, Hamilton W, Hendry A, Hendry M, Lewis R et al : Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review . Br J Cancer 2015, 112 Suppl 1 (Suppl 1):S92-107. Lyratzopoulos G, Wardle J, Rubin G: Rethinking diagnostic delay in cancer: how difficult is the diagnosis? Bmj 2014, 349 :g7400. Sarfati D, Koczwara B, Jackson C: The impact of comorbidity on cancer and its treatment . CA Cancer J Clin 2016, 66 (4):337-350. Lange M, Lefevre Arbogast S, Hardy-Léger I, Rigal O, Le Fel J, Pistilli B, Petrucci J, Lévy C, Capel A, Coutant C et al : Cognitive change in breast cancer patients up to 2 years after diagnosis . JNCI: Journal of the National Cancer Institute 2022, 115 (3):322-331. Savard J, Morin CM: Insomnia in the context of cancer: a review of a neglected problem . J Clin Oncol 2001, 19 (3):895-908. Yabroff KR, Zhao J, Zheng Z, Rai A, Han X: Medical Financial Hardship among Cancer Survivors in the United States: What Do We Know? What Do We Need to Know? Cancer Epidemiology, Biomarkers & Prevention 2018, 27 (12):1389-1397. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7290862","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507530273,"identity":"ed23a1a4-514f-4143-b26e-4b16d0dd09a4","order_by":0,"name":"Kexin Yi","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kexin","middleName":"","lastName":"Yi","suffix":""},{"id":507530274,"identity":"0f7dcca6-843b-4c5e-9f7d-1ecadd0860e7","order_by":1,"name":"Yin Liu","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Liu","suffix":""},{"id":507530275,"identity":"43f6b272-4171-40ff-bc83-503073d83185","order_by":2,"name":"Huifang Xu","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Xu","suffix":""},{"id":507530276,"identity":"ffc42034-71e8-4996-93ac-bb2551292383","order_by":3,"name":"Hong Wang","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Wang","suffix":""},{"id":507530277,"identity":"836e9ccf-e8a0-4433-a25d-db6667aa36ad","order_by":4,"name":"Chenxi Feng","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Feng","suffix":""},{"id":507530278,"identity":"79778dbe-6de4-4a6c-964d-6db1dad0bb81","order_by":5,"name":"Hongwei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3PvwqCQBzA8Z8cnMupqyLUKyhCLT3MSXPQ5NQfQbjVVd/ipmjLOMglmh2LnkBcakuwaNMbg+4LBz9+3AfuAFSqH8xEgLsJoaIbigGCvwRTSQIfAsSTJDqZXOv9Okx10tRPBiOzolqz7H0Ymfr5uQzzxOCewyBwKorcrJ9MXIOdFlwYnPoMQl7RdilDDoJci5DBVpasFhwRLT4yoN4wwZGTs2KbCRxo8cVuP3ZL3D5iWWJn12wTWKm4149oNjbL+bHpI+/EZ7Dbo8XDAGAjc0mlUqn+tRfwaUPgb8UF9gAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Liu","suffix":""},{"id":507530279,"identity":"9c5a629a-8dc8-4ed8-af60-aff6fe56f639","order_by":6,"name":"Shaokai Zhang","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaokai","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-08-04 11:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7290862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7290862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90880994,"identity":"0da88ae0-7922-4081-b510-c30a7c1948b1","added_by":"auto","created_at":"2025-09-09 09:39:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86286,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the 19 hospitals and geographical regions in China.\u003c/p\u003e\n\u003cp\u003eNote: The corresponding hospitals for each number are detailed in Table S1.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7290862/v1/4510c1ed2f1b76377cb0eee3.png"},{"id":90881650,"identity":"9afc1ec3-3ce8-4524-900e-1e9887d08f35","added_by":"auto","created_at":"2025-09-09 09:47:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4041416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7290862/v1/3beb0a40-6d7d-4a84-b3a9-b9b5e223c0ea.pdf"},{"id":90880988,"identity":"9c3191a7-a0c1-453a-9871-539a1e96438d","added_by":"auto","created_at":"2025-09-09 09:39:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25121,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmeterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7290862/v1/6121944b9da88cb6e2c0ec3d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Routes and Health Disparities in Advanced Colorectal Cancer: Evidence from a Nationwide Study in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) represents a major global public health challenge and is one of the leading causes of cancer-related mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This burden is particularly pronounced in China, where incidence and mortality rates have been steadily increasing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While advancements in multidisciplinary treatment have improved outcomes, patients diagnosed with advanced-stage disease continue to face a poor prognosis and a substantial disease burden [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, a deeper understanding of the factors shaping the care journey for this vulnerable population is essential.\u003c/p\u003e\u003cp\u003eThe sequence of events leading to a cancer diagnosis, known as the diagnostic pathway, is increasingly recognized as a critical determinant of patient outcomes [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Large-scale studies have consistently shown that emergency presentations are associated with more advanced disease and worse survival compared to non-emergency routes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Factors such as older age and socioeconomic status have also been linked to these unfavorable pathways [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, significant knowledge gaps remain. First, most of this evidence originates from Western countries, and large-scale data characterizing diagnostic pathways within the unique Chinese healthcare context are scarce. Furthermore, the consequences of these pathways on patient-centered outcomes are largely unexplored. Conceptually, the diagnostic pathway can serve as a proxy for the quality and coordination of a patient’s initial healthcare interactions; thus, more organized pathways may be associated with more systematic, guideline-concordant care, such as biomarker testing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, each pathway represents a distinct psycho-physical experience at the onset of the cancer journey, which may have a profound impact on a patient's short-term health-related quality of life (HRQOL) during treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address these gaps, this study utilized data from a nationwide cohort of patients with advanced CRC in China. We aimed to characterize the key sociodemographic and cognitive factors associated with the diagnostic route: symptomatic presentation, proactive health seeking, and comorbidity presentation. In addition, we assessed how the diagnostic route is independently associated with two crucial patient-centered outcomes: access to precision oncology, as measured by the receipt of biomarker testing, and psychophysical experience of treatment, as captured by post-treatment HRQOL.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis analysis utilized data from a nationwide, multicenter, cross-sectional study conducted across seven major geographic regions of China between March 2020 and March 2021 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A multi-stage stratified sampling method was employed to recruit a nationally representative cohort of patients with newly diagnosed CRC. The sampling process was stratified by geographic region and city level using detailed methodology reported in prior publications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A total of 19 hospitals were selected. The detailed information on enrollment data for 19 hospitals from 7 regions is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The study protocol was approved by the relevant institutional review board and all participants provided written informed consent. Data were collected using standardized questionnaires administered by trained research staff under stringent quality control protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1. Sociodemographic and Clinical Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatient-reported demographic information was collected, including age at diagnosis, sex, geographic region, occupation, marital status, highest educational level attained, annual household income, and primary type of health insurance. Clinical data were extracted from the medical records and included cancer site (colon, rectal, or both), clinical TNM stage at diagnosis, and metastatic status at diagnosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Diagnostic Route\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary explanatory variable for this study was the diagnostic route of the patient. This was determined from the self-reported answers to the survey question, \"What was the reason for your first medical visit that led to the cancer diagnosis?\". Based on their responses, patients were categorized into one of three mutually exclusive groups: (1) Symptomatic Presentation: Patients who sought medical care after self-discovering symptoms suggestive of CRC (e.g., rectal bleeding, severe abdominal pain, or changes in bowel habits). (2) Proactive Health-Seeking Pathway: Patients whose diagnosis was initiated by an abnormal finding during a routine health check-up or opportunistic screening in the absence of overt symptoms. This route encompasses not only cases detected via direct screening colonoscopy but also those identified through abnormal results from other nonspecific examinations (e.g., abdominal imaging or tumor marker tests) conducted as part of a general health assessment. (3) Comorbid Presentation: Patients whose cancer was discovered incidentally during a consultation or medical workup for other pre-existing health conditions.\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePatient Knowledge Regarding CRC\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003ePrediagnosis patient knowledge was assessed across three key domains: (1) high-risk factors for CRC, (2) CRC screening procedures, and (3) available CRC treatment options. This assessment was conducted using a semi-structured questionnaire (SSQ) specifically developed for this study by the research team based on established Chinese clinical guidelines [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The SSQ presented participants with three distinct multiple-choice questions, each retrospectively probing their awareness before diagnosis:\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003e\"Before you were diagnosed with CRC, which of the following did you consider to be high-risk factors for CRC?\"\u003c/em\u003e (11 items)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003e\"Before you were diagnosed with CRC, which of the following did you consider to be procedures for CRC screening?\"\u003c/em\u003e (6 items)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003e\"Before you were diagnosed with CRC, which of the following colorectal cancer treatments did you know about?\"\u003c/em\u003e (7 items)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003eFor statistical analysis, the response to each of the three questions was operationalized as a distinct binary variable to represent a patient's awareness in each domain. A patient was coded as '1' (aware) if they selected at least one correct item from the list of choices for a given question. Conversely, a patient was coded '0' (Unaware) if their response was 'I did not know. ’ Further detailed information regarding the SSQ instrument and its development was reported in a previous study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003e4. Screening, Treatment, and Economic Burden\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatient-reported data on healthcare experiences were gathered using a separate, semi-structured questionnaire (SSQ). This instrument first assessed the patient’s screening history for CRC, including a binary variable for whether they had ever been screened. For individuals who had not undergone colonoscopy, the SSQ was further probed for self-reported barriers. The predefined list of potential barriers included lack of awareness, insufficient time for the procedure, fear that colonoscopy is painful, unaffordable cost, long waiting times for an appointment, and lack of insurance coverage. In relation to their current CRC diagnosis and treatment, the questionnaire also collected patient-reported information on two key aspects: (1) the utilization of biomarker testing and (2) the specific treatment modalities they had received.\u003c/p\u003e\u003cp\u003eTo assess the financial impact of the disease, data on medical expenditure were collated. This information was primarily extracted from hospital medical records where accessible; otherwise, it was supplemented by patient self-reports to ensure completeness. The key economic variables collected for this study were patients' total out-of-pocket costs incurred for CRC diagnosis and treatment as well as the overall reimbursement rate applied to their total medical expenses.\u003c/p\u003e\u003cp\u003e\u003cb\u003e5. Health-related quality of life\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHealth-related quality of life (HRQOL) was assessed as a primary patient-reported outcome at two time points: baseline (T1) upon hospital admission prior to initial CRC treatment, and follow-up (T2) on the day before discharge after the initial treatment course. The measurement was performed using a study-specific composite instrument, the FACT-C-plus-QLQ-C9, developed for this study based on expert consensus. This instrument integrates all 36 items from the validated traditional Chinese version of the Functional Assessment of Cancer Therapy-Colorectal (FACT-C, V.4) with nine selected items from the European Organization for Research and Treatment of Cancer QLQ-C30 (V.3) (see Supplementary Table S2 for details on the selected items) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Chinese versions of the FACT-C and the EORTC QLQ-C30 have been validated in previous studies [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The psychometric reliability of this composite scale was robust in our study cohort (Cronbach’s α = 0.80).\u003c/p\u003e\u003cp\u003eFor statistical analysis, all raw scores from the instrument, including the overall score and scores for each individual subscale (e.g., physical, emotional, and cognitive functioning), were linearly transformed to a standardized scale ranging from 0 to 100. Across all scales, a higher score consistently represents better HRQOL or functional status. Both baseline (T1) and post-treatment (T2) scores were used in the multivariable regression models, with T1 scores serving as a key covariate to adjust for baseline differences.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003ePatient sociodemographic, clinical, and baseline characteristics (including pre-diagnosis knowledge and pre-treatment HRQOL scores) were summarized using descriptive statistics. Continuous variables were presented as means with standard deviations (SD) or medians with interquartile ranges (IQR) based on their distribution. Categorical variables are presented as frequencies and percentages (%). To compare these characteristics across the three diagnostic route groups (Symptomatic Presentation, Proactive Health-Seeking, and Comorbid Presentation), one-way analysis of variance or the Kruskal-Wallis test was used for continuous variables, and the Chi-square test or Fisher's exact test was used for categorical variables, as appropriate.\u003c/p\u003e\u003cp\u003eTo investigate the factors that determine a patient's diagnostic route, a multivariate multinomial logistic regression model was developed. The three-category diagnostic route served as the dependent variable, with the Symptomatic Presentation pathway set as the reference category. The model included key sociodemographic (e.g., age, sex, income, and education), clinical (e.g., cancer site), and patient awareness characteristics as independent variables. Results from this model are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eTo assess the impact of the diagnostic route on HRQOL, a multivariate linear regression model was constructed. With the post-treatment HRQOL score (T2) as the dependent variable, this analysis examined diagnostic route as the primary independent variable. The model was robustly adjusted for the baseline HRQOL score (T1), in addition to a comprehensive set of covariates, including sociodemographic (age, sex, income), clinical (TNM stage, cancer site), and treatment-related factors (receipt of surgery, radiotherapy, and chemotherapy). This analysis was repeated for all relevant HRQOL subscales, with the results presented as beta coefficients (β) and their 95% CIs.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using the R Software (version 4.2.0, R Foundation for Statistical Computing). A two-sided \u003cem\u003eP\u003c/em\u003e value less than .05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 4,589 patients with advanced colorectal cancer were included in the final analysis. The majority of patients (n\u0026thinsp;=\u0026thinsp;4015; 87.5%) were diagnosed via the symptomatic pathway, with the remainder diagnosed through Proactive Health-Seeking (n\u0026thinsp;=\u0026thinsp;269; 5.9%) or Comorbid (n\u0026thinsp;=\u0026thinsp;279; 6.1%) pathways. The detailed sociodemographic and clinical characteristics of the cohort stratified by the three diagnostic routes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic and Clinical Characteristics of Patients with Advanced Colorectal Cancer, Stratified by Diagnostic route\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4589)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSymptomatic Presentation (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4015)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProactive Health-Seeking (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;269)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eComorbid Presentation (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;279)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group at diagnosis, years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e991 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e882 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2180 (47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1884 (47.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e144 (54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e136 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1401 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1237 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86 (31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2730 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2380 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e179 (66.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e156 (55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1859 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1635 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90 (33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e123 (44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1330 (29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1218 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1478 (32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1295 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89 (33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school/specialized secondary schools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1044 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e905 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67 (24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity/specialty or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e734 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e594 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer site\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2063 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1722 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e155 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173 (62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2470 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2247 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e102 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical stage at initial diagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e112 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e775 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e669 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1970 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1782 (46.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅣ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1550 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1303 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109 (41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e127 (46.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1319 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1117 (27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e565 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e488 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e672 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e572 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e690 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e634 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNortheast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e364 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e315 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e652 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthwest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e327 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e299 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetastasis at initial diagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2854 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2551 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e153 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e135 (48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWith liver or lung metastasis only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e820 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e680 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWith both liver and lung metastases other sites or multiple metastases throughout the body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e889 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e761 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment and public sector personnel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e654 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e553 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eService workers, migrant workers, and individuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1733 (37.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1543 (38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployment, layoffs, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1936 (42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1678 (41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e112 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e132 (47.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e266 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e241 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual household income, Chinese Yuan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e763 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e690 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1861 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1645 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50,000-100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1293 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1120 (28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e90 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100,000-200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e523 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e440 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e133 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth insurance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban basic medical insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1926 (42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1622 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154 (57.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e144 (51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban basic medical insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e985 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e880 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew rural cooperative medical scheme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1558 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1412 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUndergoing the colonoscopy before the initial diagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4465 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3925 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e245 (91.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e269 (96.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBarriers to undergoing colonoscopy\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of awareness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3883 (84.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3429 (87.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e208 (85.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e224 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo time for a colonoscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e369 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e302 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerception that the colonoscopy is painful\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e716 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e611 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh cost of the colonoscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e172 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong wait times for a colonoscopy appointment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance doesn't cover it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e163 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of CRC risk factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1597 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1346 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e129 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e113 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2983 (65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2661 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e166 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of CRC screening\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e691 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e568 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3874 (84.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3428 (85.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e190 (71.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e233 (83.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of CRC treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2027 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1733 (43.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e142 (52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e137 (49.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2559 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2280 (56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e127 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e142 (50.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUndergoing biomarker testing, including\u003c/b\u003e \u003cb\u003eRAS\u003c/b\u003e, \u003cb\u003eBRAF\u003c/b\u003e, \u003cb\u003eand MSI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1982 (47.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1686 (45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e147 (56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2223 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1993 (54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e112 (43.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBarriers to undergoing biomarker testing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTargeted therapy is not accepted (other treatment options are considered to be sufficient)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e360 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e315 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe test is too expensive and not reimbursable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e529 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e486 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxious to receive treatment and unwilling to wait for genetic test results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e126 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlan to blind-eat targeted drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e953 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e852 (42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e223 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3838 (83.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3388 (84.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e216 (80.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e213 (76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e742 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEndoscopic treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4438 (96.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3890 (97.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e254 (94.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e268 (96.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1005 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e908 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3575 (78.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3100 (77.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e206 (76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e246 (88.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3959 (86.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3463 (86.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e237 (88.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e235 (84.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e621 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e545 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTargeted therapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1317 (28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1117 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85 (31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3263 (71.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2891 (72.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e183 (68.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e175 (62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOut-of-pocket costs, Chinese Yuan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1149 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50,000-100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1867 (40.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1641 (41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e113 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100,000-200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1043 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e917 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e518 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e448 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical expenditure reimbursement ratio (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.582\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.612\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.610\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall HRQOL\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.42\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFACT-C\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026para;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.80\u0026thinsp;\u0026plusmn;\u0026thinsp;14.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.99\u0026thinsp;\u0026plusmn;\u0026thinsp;14.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.91\u0026thinsp;\u0026plusmn;\u0026thinsp;15.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.09\u0026thinsp;\u0026plusmn;\u0026thinsp;16.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial/Family well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.10\u0026thinsp;\u0026plusmn;\u0026thinsp;20.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.05\u0026thinsp;\u0026plusmn;\u0026thinsp;19.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.58\u0026thinsp;\u0026plusmn;\u0026thinsp;22.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83.03\u0026thinsp;\u0026plusmn;\u0026thinsp;23.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.42\u0026thinsp;\u0026plusmn;\u0026thinsp;21.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74.44\u0026thinsp;\u0026plusmn;\u0026thinsp;21.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.09\u0026thinsp;\u0026plusmn;\u0026thinsp;21.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.88\u0026thinsp;\u0026plusmn;\u0026thinsp;22.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.46\u0026thinsp;\u0026plusmn;\u0026thinsp;24.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.27\u0026thinsp;\u0026plusmn;\u0026thinsp;24.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.96\u0026thinsp;\u0026plusmn;\u0026thinsp;27.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.46\u0026thinsp;\u0026plusmn;\u0026thinsp;26.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorectal cancer subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.50\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.43\u0026thinsp;\u0026plusmn;\u0026thinsp;16.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.96\u0026thinsp;\u0026plusmn;\u0026thinsp;17.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.01\u0026thinsp;\u0026plusmn;\u0026thinsp;17.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEORTC QLQ-C30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional scales and/or items\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.94\u0026thinsp;\u0026plusmn;\u0026thinsp;25.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.08\u0026thinsp;\u0026plusmn;\u0026thinsp;25.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.74\u0026thinsp;\u0026plusmn;\u0026thinsp;27.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.24\u0026thinsp;\u0026plusmn;\u0026thinsp;28.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.62\u0026thinsp;\u0026plusmn;\u0026thinsp;24.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.85\u0026thinsp;\u0026plusmn;\u0026thinsp;24.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.44\u0026thinsp;\u0026plusmn;\u0026thinsp;24.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.46\u0026thinsp;\u0026plusmn;\u0026thinsp;28.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.03\u0026thinsp;\u0026plusmn;\u0026thinsp;22.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.24\u0026thinsp;\u0026plusmn;\u0026thinsp;22.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.37\u0026thinsp;\u0026plusmn;\u0026thinsp;22.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e76.61\u0026thinsp;\u0026plusmn;\u0026thinsp;24.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.63\u0026thinsp;\u0026plusmn;\u0026thinsp;27.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.54\u0026thinsp;\u0026plusmn;\u0026thinsp;27.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.66\u0026thinsp;\u0026plusmn;\u0026thinsp;26.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66.35\u0026thinsp;\u0026plusmn;\u0026thinsp;30.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom items\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.23\u0026thinsp;\u0026plusmn;\u0026thinsp;26.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.90\u0026thinsp;\u0026plusmn;\u0026thinsp;26.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.30\u0026thinsp;\u0026plusmn;\u0026thinsp;26.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;27.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep disturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.77\u0026thinsp;\u0026plusmn;\u0026thinsp;29.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.63\u0026thinsp;\u0026plusmn;\u0026thinsp;28.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.44\u0026thinsp;\u0026plusmn;\u0026thinsp;28.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.45\u0026thinsp;\u0026plusmn;\u0026thinsp;31.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial impacts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.50\u0026thinsp;\u0026plusmn;\u0026thinsp;30.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.73\u0026thinsp;\u0026plusmn;\u0026thinsp;30.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.27\u0026thinsp;\u0026plusmn;\u0026thinsp;29.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.13\u0026thinsp;\u0026plusmn;\u0026thinsp;34.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are presented as mean (standard deviation) for continuous variables or n (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003eNumbers total more than 100% because some patients underwent several therapies simultaneously.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e Higher scores indicate better quality of life.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026para;\u003c/sup\u003e Higher scores indicate higher functioning levels.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e Higher scores indicate a greater degree of symptoms.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CRC, colorectal cancer; MSI, microsatellite instability.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSignificant differences among the groups were observed for most characteristics. Notably, a clear socioeconomic gradient is observed. Compared to patients in the Symptomatic Presentation group, those in the Proactive Health-Seeking pathway were significantly more likely to have a higher education level (e.g., 27.5% with university-level education vs. 14.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a higher annual household income (e.g., 6.3% in the \u0026ge;\u0026thinsp;200,000 Yuan bracket vs. 2.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003ePre-diagnosis awareness and subsequent healthcare utilization also differed significantly among the groups. Patients in the Proactive Health-Seeking pathway demonstrated a markedly higher level of awareness regarding CRC screening than those in the symptomatic pathway (28.3% vs. 14.2%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This pattern of proactive engagement appeared to extend into the post-diagnosis phase; the unadjusted rates of receiving biomarker testing were substantially higher in both the Proactive Health-Seeking (56.1%) and comorbidity (56.8%) pathways compared to the symptomatic pathway (45.8%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eFrom a clinical standpoint, the distribution of TNM stage at diagnosis differed significantly across routes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Proactive Health-Seeking pathway was associated with the highest proportion of early stage (Stage I-II) disease (24.1%), compared to 19.9% in the symptomatic group and 20.6% in the comorbidity group. However, it is noteworthy that a substantial proportion of patients in the Proactive Health-Seeking pathway were still diagnosed at Stage IV (41.8%), a rate numerically higher than that in the symptomatic group (33.8%), highlighting the challenge of detecting advanced asymptomatic CRC. Furthermore, the distribution of cancer sites varied, with colon cancer being more prevalent in non-symptomatic pathways. Despite these numerous differences in sociodemographic and clinical profiles, the overall HRQOL scores assessed after treatment did not differ significantly among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.742).\u003c/p\u003e\u003cp\u003eThe results of the multivariable multinomial logistic regression, identifying the determinants of diagnostic routes, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In this analysis, the Symptomatic Presentation pathway was used as the reference category for all the comparisons. Compared with the symptomatic pathway, a higher education level and greater pre-diagnosis awareness of CRC screening were significantly associated with a higher likelihood of being diagnosed via the Proactive Health-Seeking pathway. Household income showed a similar positive trend. For the Comorbid pathway, higher education level was also a significant independent predictor. In contrast to the proactive health seeking pathway, income was not significantly associated with this route. The detailed results for all variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable Analysis of Determinants of Diagnostic Route in Patients with Advanced Colorectal Cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eProactive Health-Seeking vs. Symptomatic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eIncidental vs. Symptomatic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group at diagnosis, years (Ref: \u0026lt;50)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51 (1.07, 2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.34 (0.95, 1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35 (0.90, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.46 (1.00, 2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (Ref\u0026thinsp;=\u0026thinsp;Female)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.22 (0.92, 1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77 (0.60, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level (Ref: Primary school or below)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.92 (1.28, 2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.40 (0.97, 2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school/specialized secondary schools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.82 (1.17, 2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.58 (1.06, 2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity/specialty or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.41 (2.11, 5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.50 (1.60, 3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual household income, Chinese Yuan (Ref: None)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25 (0.80, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85 (0.57, 1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50,000-100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.62, 1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86 (0.57, 1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100,000-200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08 (0.61, 1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90 (0.54, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.01 (0.99, 4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.68 (0.29, 1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion (Ref: Eastern)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.60 (0.39, 0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80 (0.54, 1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 (0.43, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80 (0.54, 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73 (0.48, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.35 (0.21, 0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNortheast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 (0.40, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.67 (0.41, 1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55 (0.34, 0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.69 (0.46, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthwest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46 (0.25, 0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55 (0.32, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer site (Ref: Colon)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54 (0.42, 0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45 (0.35, 0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of CRC risk factors (Ref: No)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.31 (0.93, 1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.23 (0.90, 1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of CRC screening (Ref: No)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.71 (1.18, 2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80 (0.54, 1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of CRC treatment (Ref: No)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87 (0.63, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.06 (0.78, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe model presented here was a multivariate multinomial logistic regression. The OR for presentation via the Proactive Health-Seeking and Comorbid pathways are estimated, with the Symptomatic Presentation pathway serving as the reference category for the dependent variable (Diagnostic Route). The model was adjusted for all variables listed in table.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultivariable analysis revealed that both diagnostic route and socioeconomic status were significant determinants of biomarker testing (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After full adjustment for all covariates, the Comorbid pathway was a robust independent predictor, with these patients having a significantly higher likelihood of undergoing testing compared to those in the symptomatic pathway (Odds Ratio 1.40; 95% CI, 1.05\u0026ndash;1.88; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022). Interestingly, while the Proactive Health-Seeking pathway also showed a positive association, this effect was substantially attenuated and no longer statistically significant after adjusting for socioeconomic factors, particularly education level (OR, 1.33; 95% CI, 0.99\u0026ndash;1.80; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.061).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of Diagnostic Route with Receipt of Biomarker Testing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnostic Route (Ref: Symptomatic)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProactive Health-Seeking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.49 (1.11, 2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.43 (1.06, 1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.33 (0.99, 1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.46 (1.10, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.47 (1.10, 1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.40 (1.05, 1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual household income, Chinese Yuan (Ref: None)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11 (0.90, 1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.05 (0.85, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50,000-100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.21 (0.97, 1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.01 (0.80, 1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100,000-200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.79 (1.36, 2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.35 (1.00, 1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.68 (2.27, 6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.74 (1.66, 4.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level (Ref: Primary school or below)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.16 (0.96, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school/specialized secondary schools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.47 (1.19, 1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity/specialty or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.92 (1.49, 2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: Adjusted for age, sex, cancer site, TNM stage, and region.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: Adjusted for Model 1 variables\u0026thinsp;+\u0026thinsp;annual household income.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3: Adjusted for Model 2 variables\u0026thinsp;+\u0026thinsp;education level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe multivariate analysis results for the association between the diagnostic route and post-treatment HRQOL, adjusted for baseline QOL and other covariates, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The findings revealed distinct patterns of impact for each nonsymptomatic pathway. The Proactive Health-Seeking pathway was associated with a significantly lower overall HRQOL score (β = -1.58; 95% CI, -2.62 to -0.54; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) compared to the Symptomatic pathway. The subscale analysis indicated that this negative impact was primarily concentrated in the physical and emotional domains. Additionally, the patients in this group reported significantly higher levels of fatigue (β\u0026thinsp;=\u0026thinsp;3.60; 95% CI, 0.66 to 6.54; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.016). The Comorbid pathway was not significantly associated with the overall HRQOL score, but it demonstrated a unique and broader pattern of negative effects on specific subscales. This route was significantly associated with worse physical, emotional, and cognitive functioning outcomes. Furthermore, these patients reported significantly more severe sleep disturbances and a greater financial impact.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of Diagnostic Route with Post-treatment Health-Related Quality of Life\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eProactive Health-Seeking vs. Symptomatic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eIncidental vs. Symptomatic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeta (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBeta (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall HRQOL\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.58 (-2.62, -0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.85 (-1.87, 0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFACT-C\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026para;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.28 (-3.94, -0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.86 (-4.49, -1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial/Family well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81 (-0.26, 1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.40 (-1.45, 0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.53 (-4.82, -0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.39 (-2.63, 1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.12 (-2.45, 2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.30 (-2.59, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorectal cancer subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.87 (-3.59, -0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.21 (-1.9, 1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEORTC QLQ-C30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional scales and/or items\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.4 (-5.83, -0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.35 (-2.04, 2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.5 (-4.04, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.40 (-6.89, -1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.52 (-4.96, -0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.50 (-4.90, -0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.13 (-5.11, 0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.02 (-3.94, 1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom items\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.60 (0.66, 6.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.14 (-1.75, 4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep disturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82 (-1.94, 3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.12 (1.41, 6.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial impacts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.38 (-3.53, 2.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.01 (0.91, 7.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eResults were obtained from a multivariable linear regression model examining the association between the primary independent variable (diagnostic route) and dependent variable (Post-treatment HRQOL score). The model was adjusted for pretreatment HRQOL score, age at diagnosis, sex, income, region, cancer site, TNM stage, and receipt of surgery, radiotherapy, and chemotherapy.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e Higher scores indicate better quality of life.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u0026para;\u003c/sup\u003e Higher scores indicate higher functioning levels.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e Higher scores indicate a greater degree of symptoms.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis nationwide study of Chinese patients with advanced colorectal cancer characterized the determinants and consequences of three distinct diagnostic routes: symptomatic presentation, proactive health seeking, and comorbidity presentation. Our findings demonstrated that a patient's diagnostic route is significantly associated with socioeconomic status and pre-diagnosis awareness. Furthermore, non-symptomatic pathways (Proactive Health-Seeking and Comorbid) were associated with a higher likelihood of receiving guideline-recommended biomarker testing than the symptomatic pathway. However, these same routes were concurrently linked to a greater decline in post-treatment physical and emotional QoL. Taken together, these findings underscore the importance of integrating targeted psychosocial support into the diagnostic process, particularly in patients diagnosed without preceding symptoms.\u003c/p\u003e\u003cp\u003eOur finding that higher socioeconomic status is a key determinant of presentation through non-symptomatic diagnostic routes is consistent with evidence from Western populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our analysis further revealed that educational attainment is a more robust predictor than household income, suggesting that the underlying mechanism is driven more by health literacy than by direct financial capacity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This interpretation is strengthened by our finding that a specific awareness of actionable screening procedures, a key component of functional health literacy, was significantly associated with the proactive pathway, whereas general risk awareness was not [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe central finding regarding the consequences is the divergent impact of the Proactive Health-Seeking pathway on clinical care and patient experience. This route was associated with a higher likelihood of receiving guideline-recommended biomarker testing, a finding that aligns with health services research, suggesting that more engaged patients often receive guideline-concordant care [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, this clinical process advantage did not translate into better short-term patient-reported outcomes. Paradoxically, the Proactive Health-Seeking pathway was independently associated with a greater decline in post-treatment HRQOL, an effect primarily concentrated on the physical and emotional domains. This highlights a critical distinction between long-term clinical benefits and immediate patient experience. While the majority of research on diagnostic routes has appropriately focused on improved survival rates as the primary benefit of early or non-symptomatic detection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the acute psycho-physical impact of the diagnostic event itself represents a crucial but under-researched dimension. We propose that the acute deterioration in well-being that we observed is best explained by the \"psychological shock\" hypothesis. A substantial body of literature has established that an unexpected diagnosis of cancer can be a traumatic event that induces significant psychological distress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our study, by focusing on the immediate post-diagnosis period, captures a critical window of vulnerability that may be masked in studies with longer-term follow-up, suggesting that an optimal clinical pathway does not automatically equate to optimal patient experience.\u003c/p\u003e\u003cp\u003eOur subscale analysis further distinguished the specific challenges faced by the patients in the Comorbid pathway. While this group, similar to the Proactive Health-Seeking group, also had greater access to biomarker testing, post-treatment HRQOL deterioration showed a different and broader pattern. It is uniquely characterized by significant declines in cognitive functioning, increased sleep disturbance, and greater financial impacts, in addition to decrements in physical and emotional well-being. This distinct profile of adverse outcomes likely reflects the \"double burden\" of managing a new cancer diagnosis, in addition to pre-existing chronic conditions. The presence of multiple comorbidities is a well-established factor that complicates cancer treatment and is associated with poor overall outcomes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The significant cognitive decline we observed may be an exacerbation of baseline vulnerabilities by systemic cancer therapies, a phenomenon of increasing concern for patients with multi-morbidity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similarly, sleep disturbance and financial distress are highly prevalent in populations with chronic illness, and our findings suggest that the addition of a cancer diagnosis can amplify these pre-existing problems to a critical level [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, our results identified a particularly vulnerable patient subgroup that requires a comprehensive, multidisciplinary management approach that extends beyond standard oncologic care to address their complex medical and socioeconomic needs.\u003c/p\u003e\u003cp\u003eThe findings of this study have significant implications for both clinical practice and public health strategies, moving beyond traditional prognostic factors to highlight the profound impact of diagnostic experience on a patient's subsequent journey. Our results challenge the notion of treating patients with advanced CRC as a homogeneous group. Instead, they underscore the need for a more stratified and personalized approach to supportive care tailored to the unique clinical and psychosocial vulnerabilities associated with a patient's specific pathway to diagnosis.\u003c/p\u003e\u003cp\u003eThe strengths of this study include its large, nationwide multicenter sample, specific focus on the high-need advanced-stage patient population, and comprehensive dataset encompassing patient-reported outcomes, pre-diagnosis cognition, and objective clinical actions. However, the limitations of this study must be acknowledged. First, the cross-sectional design of the determinant analysis precludes definitive causal inferences. Second, prediagnosis knowledge and behaviors were based on patient recall and may be subject to bias. Finally, our HRQOL assessment was limited to the short-term perioperative period, and the long-term impact of the diagnostic routes remains to be explored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis nationwide study of patients with advanced colorectal cancer in China demonstrates that the diagnostic route is a critical determinant of both the quality of treatment received and the patient's subjective experience. Our findings show that, while non-symptomatic pathways are associated with better access to optimal clinical care, they may simultaneously induce significant short-term psychophysical distress. This underscores the necessity of integrating patient-centered psychosocial support from the initial stages of cancer treatment, especially for patients diagnosed without preceding symptoms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all participating centers and their dedicated staff for their invaluable contributions to data collection for this nationwide study. We would like to extend our sincere thanks to the following institutions (not in any particular order): Sichuan Cancer Hospital, Peking University Cancer Hospital and Institute, The First Affiliated Hospital of Baotou Medical College, Zhejiang Cancer Hospital, Cancer Hospital of China Medical University, Jining Medical University, Xinxiang Central Hospital, Dalian Medical University, Wuzhou Red Cross Hospital, The First Affiliated Hospital of Guangxi Medical University, The First Affiliated Hospital of Jinan University, Sun Yat-sen University Cancer Center, Affiliated Tumor Hospital of Xinjiang Medical University, Chongqing University Cancer Hospital, Chongqing Medical University, Gansu Provincial Cancer Hospital, and Chengdu Medical College.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKexin Yi: Conceptualization, writing—original draft, software. Yin Liu: Methodology, Software, Validation, Writing, Review, and Editing. Huifang Xu: Writing—original draft, data curation. Hong Wang: Conceptualization, Methodology, Formal Analysis. Chenxi Feng: Conceptualization, methodology, validation, and investigation. Hongwei Liu: Project administration, conceptualization, writing, review, and editing. Shaokai Zhang: Funding acquisition, writing—original draft, visualization, project administration, writing—review and editing, resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Beijing LoveBook Cancer Foundation and Merck Serono Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of Henan Cancer Hospital (No.2019273) and the Ethics Committee of all other participating hospitals. Informed consent was obtained from all the participants. All the procedures were performed in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: \u003cstrong\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2024, \u003cstrong\u003e74\u003c/strong\u003e(3):229-263.\u003c/li\u003e\n\u003cli\u003eXi Y, Xu P: \u003cstrong\u003eGlobal colorectal cancer burden in 2020 and projections to 2040\u003c/strong\u003e. \u003cem\u003eTransl Oncol 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What Do We Need to Know?\u003c/strong\u003e \u003cem\u003eCancer Epidemiology, Biomarkers \u0026amp; Prevention \u003c/em\u003e2018, \u003cstrong\u003e27\u003c/strong\u003e(12):1389-1397.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal Cancer, Diagnostic Route, Biomarker Testing, Health-Related Quality of Life","lastPublishedDoi":"10.21203/rs.3.rs-7290862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7290862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic route is an important determinant in advanced colorectal cancer, yet its impact remains understudied in China.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this nationwide cross-sectional study (2020\u0026ndash;2021), we enrolled 4,589 patients with advanced colorectal cancer in China. Diagnostic routes included symptomatic presentation, proactive health-seeking, and comorbidity presentation. Multivariable regression models were used to evaluate their determinants and associations with two outcomes: receipt of biomarker testing and post-treatment HRQOL, adjusted for baseline scores and covariates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe majority of patients (87.0%) were diagnosed via the symptomatic pathway, with the remainder following Proactive Health-Seeking (6.0%) or comorbidity (6.0%) pathways. Higher educational attainment was a significant predictor of presentation through a non-symptomatic pathway relative to the symptomatic pathway. Analysis of post-diagnosis outcomes revealed divergent associations for these non-symptomatic routes. The Comorbid pathway was independently associated with a higher likelihood of receiving biomarker testing (Odds Ratio [OR], 1.40; 95% CI, 1.05\u0026ndash;1.88). A similar positive, though not statistically significant, trend was observed for the Proactive Health-Seeking pathway (OR, 1.33; 95% CI, 0.99\u0026ndash;1.80). Conversely, both the Proactive Health-Seeking (β = -2.28; 95% CI, -3.94 to -0.63) and the Comorbid (β = -2.86; 95% CI, -4.49 to -1.23) pathways were significantly associated with a greater decline in the post-treatment physical functioning domain of HRQOL.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe predominance of symptomatic diagnoses highlights the urgent need for earlier detection. Although non-symptomatic routes improved access to biomarker testing, they were also associated with greater short-term physical decline, underscoring the need to integrate psychosocial support into early diagnostic pathways.\u003c/p\u003e","manuscriptTitle":"Diagnostic Routes and Health Disparities in Advanced Colorectal Cancer: Evidence from a Nationwide Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:39:13","doi":"10.21203/rs.3.rs-7290862/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-22T06:30:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339747558832494847965566999273007406588","date":"2025-09-11T19:45:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259946417375327498180358805931782585828","date":"2025-09-08T06:28:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T14:07:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-07T08:33:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-05T04:40:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T04:40:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-08-04T11:41:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bf35235-3612-49a5-ad8a-bfdb6f3f0f0e","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T09:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 09:39:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7290862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7290862","identity":"rs-7290862","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}