Prospective Evaluation of High-risk Assessment Strategy for Esophageal Cancer: a community-based cancer screening cohort in rural China

Prospective Evaluation of High-risk Assessment Strategy for Esophageal Cancer: a community-based cancer screening cohort in rural 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 Prospective Evaluation of High-risk Assessment Strategy for Esophageal Cancer: a community-based cancer screening cohort in rural China Hang Yu, Hengmin Ma, Xinyi Wang, Zunlong Fang, Boyu Liu, Wenxuan Yan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8479049/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 Objective To evaluate a questionnaire-based strategy for risk stratification in population-based esophageal cancer(EC) screening in China. Methods 120,488 residents from four cancer screening centers in Shandong Province were enrolled in a prospective cohort between 2013 and 2018. Participants were followed prospectively for the incidence EC, gastric cancer(GC), and upper gastrointestinal cancers(UGI). Cumulative incidence was estimated using the Kaplan–Meier method. Associations between high-risk classification and cancer incidence were evaluated using Cox proportional hazards models. Results were expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Propensity score matching (PSM) was performed to minimize potential confounding. Results During a median follow-up of 8.32 years (IQR, 6.76–9.93), 593 EC, 374 GC and 967 UGI cases were identified, corresponding to incidence densities of 58.91, 37.16, and 96.07 per 100,000 person-years, respectively. High-risk participants had significantly higher cumulative incidences of all outcomes compared with non–high-risk participants (log-rank test, P < 0.05). High-risk classification remained significantly associated with increased risks of EC (HR, 1.74;95%CI, 1.47–2.06), GC (HR, 1.29;95%CI, 1.05–1.58), and UGI (HR, 1.55;95%CI, 1.36–1.76). Conclusions The questionnaire-based high-risk assessment strategy effectively identifies asymptomatic individuals at elevated risk of EC, GC, and UGI who may benefit from prioritized endoscopic examination. This strategy is an efficient, scalable tool for primary risk stratification in population-based screening programs. Esophageal cancer Population-based High-risk assessment Prospective cohort Risk stratification Figures Figure 1 Figure 2 Introduction Esophageal cancer (EC) is a common malignancy and a significant global health burden [ 1 ] . In 2022, an estimated 510,566 new cases of EC were diagnosed, ranking 11th among all cancers worldwide, [ 2 ] with nearly 75% of cases and deaths occurring in Asia. [ 3 ] EC typically presents insidiously, and the majority of patients are diagnosed at advanced stages. The 5-year survival rate remains below 30%, [ 4 ] imposing a substantial burden on health care and economic systems. [ 5 ] In China, multiple large-scale, population-based screening programs have been implemented in high-risk regions (such as Linzhou, Cixian, and Feicheng), achieving substantial success and providing extensive experience in EC screening. [ 6 – 8 ] Supported by strong governmental initiatives, the incidence of EC in China has steadily declined [ 6 ] . Nevertheless, China still bears one of the world’s heaviest burdens of EC, with population growth and aging imposing substantial challenges on the public health system in achieving effective prevention and control [ 9 , 10 ] . Substantial evidence indicates that EC is both preventable and treatable. Lugol chromoendoscopy with targeted biopsy is internationally regarded as the gold standard, [ 11 , 12 ] and screening with early detection followed by timely treatment remains the most effective strategy to reduce the EC burden in China [ 13 – 15 ] . However, the procedure is invasive, resource and intensive requires substantial technical expertise. Given China’s vast population and the marked geographic heterogeneity in EC prevalence [ 16 ] . Incorporating risk-based stratification to identify high-risk groups for targeted endoscopic screening can substantially improve efficiency and reduce unnecessary procedures in population-based EC screening and early detection programs. Currently, no internationally accepted definition exists. China pioneered organized, population-based screening through three major national programs launched in 2005, 2007, and 2012 (“Rural Cancer Early Detection and Treatment Program”, “Huaihe River Basin Early Detection and Treatment Program”, and “Urban Cancer Early Detection and Treatment Program”). These initiatives markedly advanced regional efforts in the early detection and treatment of EC. However, they applied inconsistent criteria, relying primarily on limited epidemiologic indicators to qualitatively define high-risk groups. These limitations highlight the urgent need for a rigorous, standardized, and broadly applicable risk assessment tool to improve screening efficiency and optimize resource use in China. In response, the National Cancer Center (NCC) led the development of a high-risk assessment tool, which was subsequently validated in multicenter practice. Several studies have sought to improve high-risk identification by developing risk prediction models incorporating epidemiologic characteristics, lifestyle factors, and family history [ 17 – 20 ] . However, these models remain limited by instability, insufficient external validation, and poor generalizability, with no standardized, broadly applicable tool currently available. China’s vast population and marked regional heterogeneity make it challenging for any single screening model to balance efficiency and cost effectively. Identifying high-risk populations with limited resources, while improving screening coverage and effectiveness, is critical to the success of screening programs and a key scientific challenge. Based on the Huaihe River Basin Early Detection and Treatment Project, we evaluated the effectiveness of the high-risk assessment strategy for EC screening across four centers that implemented screening between 2012 and 2018. Methods Study design and study population This population-based, multicenter prospective cohort study, conducted under the Shandong Huaihe River Basin Early Detection and Treatment Project, aimed to identify high-risk individuals and lower EC incidence. Participants were enrolled from four screening centers (Wenshang, Tengzhou, Liangshan, and Mudan) between 2013 and 2018. Eligibility criteria were as follows: 1) local residents aged 40–69 years at baseline; 2) completion of the baseline health questionnaire with complete information; 3) no prior history of malignancy; 4) voluntary participation with signed informed consent. Data collection and definition of the high-risk population Participants were enrolled at the selected research centers through cluster sampling. Following informed consent, all eligible residents completed face-to-face questionnaires and high-risk assessments conducted by trained project staff. The survey collected data on birth date, sex, household income, sources of drinking water, smoking and alcohol consumption, family history of cancer, dietary habits, and medication use. Baseline physical assessments included measurements of height, weight, pulse, and blood pressure, which were used to calculate body mass index (BMI). The NCC developed the high-risk assessment tool, selecting all candidate variables based on systematic review and expert consensus. The assessment tool includes: 1) smoking; 2) alcohol consumption; 3) intake of pickled or salted foods; 4) consumption of very hot foods; 5) family history of upper gastrointestinal cancer; 6) current clinical symptoms (dysphagia, odynophagia, chest, back or neck pain, or progressive weight loss). Detailed information on candidate variables is provided in Supplementary Table 1. Individuals scoring ≥ 2 were classified as high-risk and referred for endoscopy. Management of study participants, follow-up and outcomes Participants were followed until December 31, 2024, using both active and passive approaches: project staff performed phone calls or home visits, and data were matched with the local cancer registry to ensure completeness and accuracy. All cancers were classified by site using the International Classification of Diseases, 10th edition (ICD-10), and by histology according to the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3). The primary outcome was incidence of EC (ICD-10: C15), with secondary outcomes including GC (ICD-10: C16) and other UGI; cardia and non-cardia gastric cancers were not differentiated. Statistical analysis Continuous variables are presented as mean ± standard deviations (SDs) or median and interquartile ranges (IQRs), and categorical variables are reported as counts and percentages. Standardized mean differences (SMD) were used to assess baseline group differences. Cumulative incidence was estimated by the Kaplan–Meier method. Associations between high-risk status and cancer incidence were evaluated using Cox regression models. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). Propensity score matching (PSM) was applied to reduce potential confounding. Cox regression models. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariable Cox regression was adjusted for age, sex, education, household income, and BMI, with stratification by sex. Baseline differences were addressed using 1:1 propensity score matching (PSM), and cumulative incidence was then compared between the matched groups. Four sensitivity analyses were performed with univariable and multivariable Cox models to evaluate the association between high-risk status and incidence of cancers: 1) participants with prior endoscopy were excluded; 2) individuals lost to follow-up were excluded; 3) defining the outcome endpoint at 5 years of follow-up; 4) defining the outcome endpoint at 10 years of follow-up. All analyses were conducted with R software (version 4.2.2). All tests were two-sided, with P < 0.05 considered significant. Result Baseline After excluding residents outside the age range or with a history of cancer, 120,488 participants were included from the 122,514 individuals who completed the baseline questionnaire. Participant inclusion and exclusion are presented in Figure 1. The baseline characteristics of high-risk and non–high-risk groups based on the risk assessment results are shown in Table 1. High-risk participants had a mean age of 54.81 years (SD 7.75); male were more frequently classified as high-risk, and the proportion increased with age. Furthermore, participants with higher education, income level ≥20,000 RMB, and BMI ≥22 kg/m² were more likely to be classified as high-risk. Propensity score matching achieved standardized mean differences (SMDs) <0.10 for all variables, confirming satisfactory baseline balance between the high-risk and non–high-risk groups. Cumulative Incidence By December 31, 2024, the 120,488 participants contributed 1,006,546.48 person-years of follow-up, with a median follow-up of 8.32 years (IQR, 6.76–9.93). During follow-up, 378 EC were newly diagnosed in the high-risk group (79.92/10⁵ person-years) compared with 215 cases in the non-high-risk group (40.29/10⁵). For GC, 210 and 164 new cases occurred in the high-risk and non-high-risk groups, with incidence rates of 44.40/10⁵ and 30.74/10⁵ person-years, respectively. Incident upper gastrointestinal cancers were observed in 588 high-risk participants (124.32/10⁵ person-years) and 379 non-high-risk participants (71.03/10⁵), as detailed in the supplementary table2. (Table2) Figure 2 shows that participants classified as high-risk by the questionnaire had higher incidences of EC, GC and UGI compared with the non-high-risk group (log-rank: P <0.01). In multivariable Cox regression, high-risk status was independently associated with the incidence of EC, GC and UGI, with HRs of 1.74 (95% CI: 1.47–2.06), 1.29 (95% CI: 1.05–1.58), and 1.55 (95% CI: 1.36–1.76), respectively, compared with non-high-risk participants (Table3). These associations were consistent in both male and female. Sensitivity analyses consistently showed the positive association between high-risk status and the incidence of all cancers. In certain subgroups, the association with GC didn’t reach statistical significance and only became evident during long-term follow-up. Characteristic Before propensity score matching After propensity score matching Non-high-risk group (n, %) High-risk group (n, %) SMD Non-high-risk group (n, %) High-risk group (n, %) SMD Total 64932(53.89) 55555(46.11) 55555(50.00) 55555(50.00) Sex 0.03 0.07 Male 29351(52.97) 26058(47.03) 24131(48.08) 26058(51.92) Female 35581(54.67) 29497(45.33) 31424(51.58) 29497(48.42) Age,yr 0.13 0.02 Mean (SD) 53.77 (7.92) 54.81 (7.75) 54.62 (8.02) 54.81 (7.75) 40-49 22912(58.54) 16226(41.46) 17203(51.46) 16226(48.54) 50-59 23587(52.45) 21386(47.55) 19919(48.22) 21386(51.78) 60-69 18433(50.67) 17943(49.33) 18433(50.67) 17943(49.33) Education level 0.19 0.09 Primary school and below 43703(57.58) 32193(42.42) 34326(51.60) 32193(48.40) Secondary school and above 21229(47.61) 23362(52.39) 21229(47.61) 23362(52.39) Income level 0.01 0.01 <20,000 RMB 14516(53.30) 12716(46.70) 12530(49.63) 12716(50.37) ≥20,000 RMB 50416(54.06) 42839(45.94) 43025(50.11) 42839(49.89) BMI, kg/m2 0.03 0.02 <22 11778(55.26) 9536(44.74) 9877(50.88) 9536(49.12) ≥22 53154(53.60) 46019(46.40) 45678(49.81) 46019(50.19) SD = stand deviation; yr = year; RMB = Renminbi; BMI = body mass index. Table 1. Baseline demographic characteristics of non-high-risk and high-risk groups Initial assessment results Esophageal cancer Gastric cancer Upper GI cancer Follow-up (person-years) No. of cases Rate per 100,000 pyrs No. of cases Rate per 100,000 pyrs No. of cases Rate per 100,000 pyrs Total 1006546.48 593 58.91 374 37.16 967 96.07 Non-high-risk group 533576.37 215 40.29 164 30.74 379 71.03 High-risk group 472970.11 378 79.92 210 44.40 588 124.32 pyrs = person-years Tab 2. Incidence rates of esophageal cancer, Gastric cancer and upper GI cancer in the cohort population Stratified variables Esophageal cancer Gastric cancer UGI cancer Person-years (cases) Crude HR (95% CI) Adjusted HR (95% CI) Person-years (cases) Crude HR (95% CI) Adjusted HR (95% CI) Person-years (cases) Crude HR (95% CI) Adjusted HR (95% CI) ALL Non-high-risk group 533576.37 (215) Ref. Ref. 533576.37 (164) Ref. Ref. 533576.37 (379) Ref. Ref. High-risk group 472970.11 (378) 2.01 (1.70-2.37) 1.74 (1.47-2.06) 472970.11 (210) 1.46 (1.19-1.79) 1.29 (1.05-1.58) 472970.11 (588) 1.77 (1.55-2.01) 1.55 (1.36-1.76) Men Non-high-risk group 239179.55 (136) Ref. Ref. 239179.55 (111) Ref. Ref. 239179.55 (247) Ref. Ref. High-risk group 223086.57 (264) 2.11 (1.71-2.59) 1.82 (1.48-2.25) 223086.57 (155) 1.51 (1.18-1.92) 1.35 (1.05-1.72) 223086.57 (419) 1.84 (1.57-2.15) 1.61 (1.37-1.89) Women Non-high-risk group 294396.82 (79) Ref. Ref. 294396.82 (53) Ref. Ref. 294396.82 (132) Ref. Ref. High-risk group 249883.54 (114) 1.72 (1.29-2.30) 1.61 (1.21-2.14) 249883.54 (55) 1.23 (0.84-1.80) 1.16 (0.80-1.71) 249883.54 (169) 1.53 (1.22-1.92) 1.43 (1.14-1.80) CI = confidence interval; HR = hazard ratio. Table 3. Esophageal, gastric, and upper GI Cancers incidence risk Discussion Based on a large prospective screening cohort in the Huaihe River Basin, this study systematically evaluated the effectiveness of a questionnaire-based high-risk tool for predicting EC risk during long-term follow-up and examined its applicability to GC and UGI. The tool effectively stratified risk, as high-risk individuals had consistently higher incidences of cancers These associations remained after PSM, underscoring the robustness and reliability of the tool over long-term follow-up. Of the 120,488 participants, high-risk individuals were predominantly men, and the likelihood of high-risk classification increased with age. Participants with higher education, higher income, and higher BMI were more likely to be classified as high-risk. Ding et al. emphasized that the substantial burden of EC in China is strongly associated with smoking and alcohol consumption, dietary patterns, and socioeconomic factors [ 21 ] . In line with our findings, these observations underscore the need for risk assessment tools to integrate socioeconomic factors and be tested across heterogeneous populations as living standards continue to rise. The study showed a substantially increased risk of EC in high-risk individuals, and this association remained significant after adjustment for confounders and propensity score matching. The incidence density of EC was 79.92 per 100,000 person-years in high-risk individuals compared with 40.29 per 100,000 person-years in non–high-risk individuals. Multivariable analysis demonstrated that high-risk individuals had a significantly elevated risk of EC, with a hazard ratio of 1.74 (95% CI:1.47–2.06). Initial screening results suggested that high-risk individuals had increased risks of GC (HR = 1.29; 95% CI:1.05–1.58) and upper gastrointestinal cancer (HR = 1.55; 95% CI:1.36–1.76). In rural community–based screening in China, Chen et al. reported that questionnaire-based high-risk stratification significantly improved the diagnostic yield of endoscopy, supporting its feasibility as a preliminary screening approach [ 22 ] . Although the overall incidence was lower than in previous studies, the risk assessment tool remained robust and predictive value for GC and UGI. Several risk factors assessed in this study, such as smoking and alcohol use, consumption of very hot foods, and family history of cancer, have been widely validated in predictive models and shown to be independently associated with EC risk. Han et al. developed a model based on a rural Chinese cohort showing that age, sex, BMI, alcohol consumption, and fruit intake effectively identify individuals at risk for esophageal squamous cell carcinoma (ESCC) [ 23 ] . Jiang et al. systematically reviewed and externally validated predictive models across multiple high-risk regions, showing that despite differences in model construction, most accurately identified high-risk individuals in high-incidence areas [ 24 ] . Taken together with previous studies, our results confirm that risk stratification remains robust in population screening, and that high-risk assessment can function as an efficient prescreening method to identify at-risk populations. The prospective cohort study based on a high-risk assessment strategy followed high-risk and non-high-risk individuals for a median of 8.32 years (IQR: 6.76–9.93). Our findings show that the questionnaire-based tool reliably identifies individuals at high risk for EC. Developed by the NCC of China following established guidelines, the program was implemented with active and passive follow-up to ensure complete and reliable data collection. This study also has several limitations. The project protocol excluded endoscopic screening for non-high-risk participants, limiting early cancer detection and preventing assessment of individual variables’ sensitivity and specificity. Future work will integrate RSES scoring with the current approach to develop a more robust esophageal cancer prediction model and comprehensively evaluate all questionnaire items to improve risk stratification. Declarations Acknowledgments We thank all staff members who contributed to this project, particularly those who conducted the baseline survey and measurements. We are also grateful to the Shandong Cancer Center for their valuable guidance and rigorous quality control, which ensured the authenticity and reliability of the data. Author ’ contributions Hang Yu, Conceptualization, Data curation, Investigation, Formal analysis; Hengmin Ma, Writing-review and editing; Xinyi Wang, Data curation, Formal analysis, Investigation; Zunlong Fang, Investigation, Methodology; Boyu Liu, Investigation, Methodology, Software; Wenxuan Yan, Investigation, Methodology, Software; Nan Zhang, Writing-review and editing, Supervision. Fundings The National Natural Science Foundation of China (72574131); Collaborative Academic Innovation Project of Shandong Cancer Hospital (TS001). Data Availability Statement The data underlying this article will be shared on reasonable request to the corresponding author. Ethics approval and consent to participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Committee of Institutional Review Board of the Cancer Institute/Hospital, Chinese Academy of Medical Sciences. 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Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Editor invited by journal 06 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8479049","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580193569,"identity":"41916a8c-551a-4132-b14f-d6c65037a698","order_by":0,"name":"Hang Yu","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Yu","suffix":""},{"id":580193570,"identity":"7bd85973-104f-44d4-b600-cdfbc8b62f7c","order_by":1,"name":"Hengmin Ma","email":"","orcid":"","institution":"Shandong Tumor Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hengmin","middleName":"","lastName":"Ma","suffix":""},{"id":580193571,"identity":"0c9b4675-67a1-48aa-9fe1-5373eb2e9337","order_by":2,"name":"Xinyi Wang","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Wang","suffix":""},{"id":580193572,"identity":"7392a7bf-c329-479a-9cd1-c65018c90a37","order_by":3,"name":"Zunlong Fang","email":"","orcid":"","institution":"Shandong Tumor Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zunlong","middleName":"","lastName":"Fang","suffix":""},{"id":580193573,"identity":"9e0bb8a4-dfe2-4f00-bc65-be1aa947ae76","order_by":4,"name":"Boyu Liu","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Boyu","middleName":"","lastName":"Liu","suffix":""},{"id":580193574,"identity":"26621308-9db2-48ee-8e2a-135c777aa9d1","order_by":5,"name":"Wenxuan Yan","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenxuan","middleName":"","lastName":"Yan","suffix":""},{"id":580193575,"identity":"aa690b9a-731d-4af3-9561-48d2082ed148","order_by":6,"name":"Nan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PoQvCQBTH8SeDXTlZncW/4cmBIoj7V24cLA0RrIaJMIuY/TMEwTx5qEWxLhhMJoMgmETlBOs2m+B9wy+9T3gAJtOPZulxoBQBYKtanFQiTbqBKE4w0XshP/e8wXZ07fYP7Xmq4iOgJYHRapZFmuOOFNP1SS1Sf4SAdgd4EKRZBJMQFbdJ1bfL2AXkPXB5PZvsz0j8QUqM38T1o1yShrVhOaY2soEmWISchVWekHQ3gxFKlMLO/WUfiiu/kecM2el4uT+rDqN1JvnkRwA2SNBbMO+bY5PJZPqzXgCBSfKkHxppAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Tumor Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-12-30 08:23:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8479049/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8479049/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101297922,"identity":"bb8ee71b-b6a7-40d6-b5a0-baa0a383d2b2","added_by":"auto","created_at":"2026-01-28 09:29:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76821,"visible":true,"origin":"","legend":"\u003cp\u003eDefinition of the study population.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8479049/v1/476bc43df28e5b8d30fec42e.jpg"},{"id":101273361,"identity":"8568a8ab-b9d0-4e8a-a52e-7ea00751b468","added_by":"auto","created_at":"2026-01-28 03:04:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1552147,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the cumulative incidence of EC, GC and UGI based on high-risk assessment results at baseline. (A)Cumulative incidence of EC among all people before match stratification. (B)Cumulative incidence of EC among all people after match stratification. (C)Cumulative incidence of GC among all people before match stratification. (D)Cumulative incidence of GC among all people after match stratification. (E)Cumulative incidence of UGI among all people before match stratification. (F)Cumulative incidence of UGI among all people after match stratification.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8479049/v1/598f8d4cabefbbfc5ec22a6d.jpg"},{"id":101299278,"identity":"32d771bd-f0db-4eb3-b159-9405d97ecbd9","added_by":"auto","created_at":"2026-01-28 09:41:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2330400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8479049/v1/95fe0237-096d-4c40-a83d-adbce01ba66c.pdf"},{"id":101273360,"identity":"4f1bc0d3-ba4d-40af-a7ad-36d6f12a80ef","added_by":"auto","created_at":"2026-01-28 03:04:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":135327,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8479049/v1/111d11e807f964bb12cfd0f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prospective Evaluation of High-risk Assessment Strategy for Esophageal Cancer: a community-based cancer screening cohort in rural China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal cancer (EC) is a common malignancy and a significant global health burden \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In 2022, an estimated 510,566 new cases of EC were diagnosed, ranking 11th among all cancers worldwide,\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e with nearly 75% of cases and deaths occurring in Asia.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e EC typically presents insidiously, and the majority of patients are diagnosed at advanced stages. The 5-year survival rate remains below 30%,\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e imposing a substantial burden on health care and economic systems.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn China, multiple large-scale, population-based screening programs have been implemented in high-risk regions (such as Linzhou, Cixian, and Feicheng), achieving substantial success and providing extensive experience in EC screening.\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e Supported by strong governmental initiatives, the incidence of EC in China has steadily declined\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, China still bears one of the world\u0026rsquo;s heaviest burdens of EC, with population growth and aging imposing substantial challenges on the public health system in achieving effective prevention and control\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Substantial evidence indicates that EC is both preventable and treatable. Lugol chromoendoscopy with targeted biopsy is internationally regarded as the gold standard,\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e and screening with early detection followed by timely treatment remains the most effective strategy to reduce the EC burden in China\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, the procedure is invasive, resource and intensive requires substantial technical expertise. Given China\u0026rsquo;s vast population and the marked geographic heterogeneity in EC prevalence\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Incorporating risk-based stratification to identify high-risk groups for targeted endoscopic screening can substantially improve efficiency and reduce unnecessary procedures in population-based EC screening and early detection programs.\u003c/p\u003e \u003cp\u003eCurrently, no internationally accepted definition exists. China pioneered organized, population-based screening through three major national programs launched in 2005, 2007, and 2012 (\u0026ldquo;Rural Cancer Early Detection and Treatment Program\u0026rdquo;, \u0026ldquo;Huaihe River Basin Early Detection and Treatment Program\u0026rdquo;, and \u0026ldquo;Urban Cancer Early Detection and Treatment Program\u0026rdquo;). These initiatives markedly advanced regional efforts in the early detection and treatment of EC. However, they applied inconsistent criteria, relying primarily on limited epidemiologic indicators to qualitatively define high-risk groups. These limitations highlight the urgent need for a rigorous, standardized, and broadly applicable risk assessment tool to improve screening efficiency and optimize resource use in China. In response, the National Cancer Center (NCC) led the development of a high-risk assessment tool, which was subsequently validated in multicenter practice.\u003c/p\u003e \u003cp\u003eSeveral studies have sought to improve high-risk identification by developing risk prediction models incorporating epidemiologic characteristics, lifestyle factors, and family history\u003csup\u003e[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, these models remain limited by instability, insufficient external validation, and poor generalizability, with no standardized, broadly applicable tool currently available. China\u0026rsquo;s vast population and marked regional heterogeneity make it challenging for any single screening model to balance efficiency and cost effectively. Identifying high-risk populations with limited resources, while improving screening coverage and effectiveness, is critical to the success of screening programs and a key scientific challenge.\u003c/p\u003e \u003cp\u003eBased on the Huaihe River Basin Early Detection and Treatment Project, we evaluated the effectiveness of the high-risk assessment strategy for EC screening across four centers that implemented screening between 2012 and 2018.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study population\u003c/h2\u003e \u003cp\u003eThis population-based, multicenter prospective cohort study, conducted under the Shandong Huaihe River Basin Early Detection and Treatment Project, aimed to identify high-risk individuals and lower EC incidence. Participants were enrolled from four screening centers (Wenshang, Tengzhou, Liangshan, and Mudan) between 2013 and 2018.\u003c/p\u003e \u003cp\u003eEligibility criteria were as follows: 1) local residents aged 40\u0026ndash;69 years at baseline; 2) completion of the baseline health questionnaire with complete information; 3) no prior history of malignancy; 4) voluntary participation with signed informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection and definition of the high-risk population\u003c/h3\u003e\n\u003cp\u003e Participants were enrolled at the selected research centers through cluster sampling. Following informed consent, all eligible residents completed face-to-face questionnaires and high-risk assessments conducted by trained project staff. The survey collected data on birth date, sex, household income, sources of drinking water, smoking and alcohol consumption, family history of cancer, dietary habits, and medication use. Baseline physical assessments included measurements of height, weight, pulse, and blood pressure, which were used to calculate body mass index (BMI).\u003c/p\u003e \u003cp\u003e The NCC developed the high-risk assessment tool, selecting all candidate variables based on systematic review and expert consensus. The assessment tool includes: 1) smoking; 2) alcohol consumption; 3) intake of pickled or salted foods; 4) consumption of very hot foods; 5) family history of upper gastrointestinal cancer; 6) current clinical symptoms (dysphagia, odynophagia, chest, back or neck pain, or progressive weight loss). Detailed information on candidate variables is provided in Supplementary Table\u0026nbsp;1. Individuals scoring\u0026thinsp;\u0026ge;\u0026thinsp;2 were classified as high-risk and referred for endoscopy.\u003c/p\u003e\n\u003ch3\u003eManagement of study participants, follow-up and outcomes\u003c/h3\u003e\n\u003cp\u003eParticipants were followed until December 31, 2024, using both active and passive approaches: project staff performed phone calls or home visits, and data were matched with the local cancer registry to ensure completeness and accuracy. All cancers were classified by site using the International Classification of Diseases, 10th edition (ICD-10), and by histology according to the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3).\u003c/p\u003e \u003cp\u003eThe primary outcome was incidence of EC (ICD-10: C15), with secondary outcomes including GC (ICD-10: C16) and other UGI; cardia and non-cardia gastric cancers were not differentiated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) or median and interquartile ranges (IQRs), and categorical variables are reported as counts and percentages. Standardized mean differences (SMD) were used to assess baseline group differences. Cumulative incidence was estimated by the Kaplan\u0026ndash;Meier method. Associations between high-risk status and cancer incidence were evaluated using Cox regression models. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). Propensity score matching (PSM) was applied to reduce potential confounding. Cox regression models. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariable Cox regression was adjusted for age, sex, education, household income, and BMI, with stratification by sex. Baseline differences were addressed using 1:1 propensity score matching (PSM), and cumulative incidence was then compared between the matched groups.\u003c/p\u003e \u003cp\u003eFour sensitivity analyses were performed with univariable and multivariable Cox models to evaluate the association between high-risk status and incidence of cancers: 1) participants with prior endoscopy were excluded; 2) individuals lost to follow-up were excluded; 3) defining the outcome endpoint at 5 years of follow-up; 4) defining the outcome endpoint at 10 years of follow-up.\u003c/p\u003e \u003cp\u003eAll analyses were conducted with R software (version 4.2.2). All tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter excluding residents outside the age range or with a history of cancer, 120,488 participants were included from the 122,514 individuals who completed the baseline questionnaire.\u0026nbsp;Participant inclusion and exclusion are presented in Figure 1.\u0026nbsp;The baseline characteristics of high-risk and non\u0026ndash;high-risk groups based on the risk assessment results are shown in Table 1.\u0026nbsp;High-risk participants had a mean age of 54.81 years (SD 7.75); male were more frequently classified as high-risk, and the proportion increased with age.\u0026nbsp;Furthermore, participants with higher education, income level \u0026ge;20,000 RMB, and BMI \u0026ge;22 kg/m\u0026sup2; were more likely to be classified as high-risk.\u0026nbsp;Propensity score matching achieved standardized mean differences (SMDs) \u0026lt;0.10 for all variables, confirming satisfactory baseline balance between the high-risk and non\u0026ndash;high-risk groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative Incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy December 31, 2024, the 120,488 participants contributed 1,006,546.48 person-years of follow-up, with a median follow-up of 8.32 years (IQR, 6.76\u0026ndash;9.93).\u0026nbsp;During follow-up, 378 EC were newly diagnosed in the high-risk group (79.92/10⁵ person-years) compared with 215 cases in the non-high-risk group (40.29/10⁵). For GC, 210 and 164 new cases occurred in the high-risk and non-high-risk groups, with incidence rates of 44.40/10⁵ and 30.74/10⁵ person-years, respectively. Incident upper gastrointestinal cancers were observed in 588 high-risk participants (124.32/10⁵ person-years) and 379 non-high-risk participants (71.03/10⁵), as detailed in the supplementary table2. (Table2)\u003c/p\u003e\n\u003cp\u003eFigure 2 shows that participants classified as high-risk by the questionnaire had higher incidences of EC, GC and UGI compared with the non-high-risk group (log-rank: \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003eIn multivariable Cox regression, high-risk status was independently associated with the incidence of EC, GC and UGI, with HRs of 1.74 (95% CI: 1.47\u0026ndash;2.06), 1.29 (95% CI: 1.05\u0026ndash;1.58), and 1.55 (95% CI: 1.36\u0026ndash;1.76), respectively, compared with non-high-risk participants (Table3).\u0026nbsp;These associations were consistent in both male and female.\u0026nbsp;Sensitivity analyses consistently showed the positive association between high-risk status and the incidence of all cancers.\u0026nbsp;In certain subgroups, the association with GC didn\u0026rsquo;t reach statistical significance and only became evident during long-term follow-up.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eBefore propensity score matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eAfter propensity score matching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNon-high-risk group (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHigh-risk group (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNon-high-risk group (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHigh-risk group (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e64932(53.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e55555(46.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e55555(50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e55555(50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e29351(52.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26058(47.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e24131(48.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26058(51.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e35581(54.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29497(45.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e31424(51.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29497(48.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAge,yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e53.77 (7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e54.81 (7.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e54.62 (8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e54.81 (7.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e22912(58.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16226(41.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e17203(51.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16226(48.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e23587(52.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21386(47.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e19919(48.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21386(51.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18433(50.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17943(49.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18433(50.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17943(49.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003ePrimary school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e43703(57.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32193(42.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e34326(51.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32193(48.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSecondary school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e21229(47.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e23362(52.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e21229(47.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e23362(52.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eIncome level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;20,000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e14516(53.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12716(46.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e12530(49.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12716(50.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;20,000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e50416(54.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42839(45.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e43025(50.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42839(49.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBMI, kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e11778(55.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9536(44.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e9877(50.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9536(49.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026ge;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e53154(53.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e46019(46.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e45678(49.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e46019(50.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD = stand deviation; yr = year; RMB = Renminbi; BMI = body mass index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline demographic characteristics of non-high-risk and high-risk groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003eInitial assessment results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eUpper GI cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003e(person-years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003ecases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eRate per 100,000 pyrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003ecases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eRate per 100,000 pyrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003ecases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eRate per 100,000 pyrs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1006546.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e58.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e37.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e96.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNon-high-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e533576.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e40.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e30.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e71.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHigh-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e472970.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e79.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e44.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e124.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003epyrs = person-years\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab 2.\u0026nbsp;\u003c/strong\u003eIncidence rates of esophageal cancer, Gastric cancer and upper GI cancer in the cohort population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eStratified variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUGI cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerson-years (cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrude HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerson-years (cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrude HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerson-years (cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrude HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-high-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e533576.37\u003c/p\u003e\n \u003cp\u003e(215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e533576.37\u003c/p\u003e\n \u003cp\u003e(164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e533576.37\u003c/p\u003e\n \u003cp\u003e(379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e472970.11\u003c/p\u003e\n \u003cp\u003e(378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003cp\u003e(1.70-2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003cp\u003e(1.47-2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e472970.11\u003c/p\u003e\n \u003cp\u003e(210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e(1.19-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(1.05-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e472970.11\u003c/p\u003e\n \u003cp\u003e(588)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e(1.55-2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003cp\u003e(1.36-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-high-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e239179.55\u003c/p\u003e\n \u003cp\u003e(136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e239179.55\u003c/p\u003e\n \u003cp\u003e(111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e239179.55\u003c/p\u003e\n \u003cp\u003e(247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e223086.57\u003c/p\u003e\n \u003cp\u003e(264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003cp\u003e(1.71-2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003cp\u003e(1.48-2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e223086.57\u003c/p\u003e\n \u003cp\u003e(155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(1.18-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003cp\u003e(1.05-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e223086.57\u003c/p\u003e\n \u003cp\u003e(419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e(1.57-2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(1.37-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-high-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e294396.82\u003c/p\u003e\n \u003cp\u003e(79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e294396.82\u003c/p\u003e\n \u003cp\u003e(53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e294396.82\u003c/p\u003e\n \u003cp\u003e(132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e249883.54\u003c/p\u003e\n \u003cp\u003e(114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003cp\u003e(1.29-2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(1.21-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e249883.54\u003c/p\u003e\n \u003cp\u003e(55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(0.84-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(0.80-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e249883.54\u003c/p\u003e\n \u003cp\u003e(169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003cp\u003e(1.22-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003cp\u003e(1.14-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI = confidence interval; HR = hazard ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eEsophageal, gastric, and upper GI Cancers incidence risk\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a large prospective screening cohort in the Huaihe River Basin, this study systematically evaluated the effectiveness of a questionnaire-based high-risk tool for predicting EC risk during long-term follow-up and examined its applicability to GC and UGI. The tool effectively stratified risk, as high-risk individuals had consistently higher incidences of cancers These associations remained after PSM, underscoring the robustness and reliability of the tool over long-term follow-up.\u003c/p\u003e \u003cp\u003eOf the 120,488 participants, high-risk individuals were predominantly men, and the likelihood of high-risk classification increased with age. Participants with higher education, higher income, and higher BMI were more likely to be classified as high-risk. Ding et al. emphasized that the substantial burden of EC in China is strongly associated with smoking and alcohol consumption, dietary patterns, and socioeconomic factors\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In line with our findings, these observations underscore the need for risk assessment tools to integrate socioeconomic factors and be tested across heterogeneous populations as living standards continue to rise.\u003c/p\u003e \u003cp\u003eThe study showed a substantially increased risk of EC in high-risk individuals, and this association remained significant after adjustment for confounders and propensity score matching. The incidence density of EC was 79.92 per 100,000 person-years in high-risk individuals compared with 40.29 per 100,000 person-years in non\u0026ndash;high-risk individuals. Multivariable analysis demonstrated that high-risk individuals had a significantly elevated risk of EC, with a hazard ratio of 1.74 (95% CI:1.47\u0026ndash;2.06). Initial screening results suggested that high-risk individuals had increased risks of GC (HR\u0026thinsp;=\u0026thinsp;1.29; 95% CI:1.05\u0026ndash;1.58) and upper gastrointestinal cancer (HR\u0026thinsp;=\u0026thinsp;1.55; 95% CI:1.36\u0026ndash;1.76). In rural community\u0026ndash;based screening in China, Chen et al. reported that questionnaire-based high-risk stratification significantly improved the diagnostic yield of endoscopy, supporting its feasibility as a preliminary screening approach\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Although the overall incidence was lower than in previous studies, the risk assessment tool remained robust and predictive value for GC and UGI.\u003c/p\u003e \u003cp\u003eSeveral risk factors assessed in this study, such as smoking and alcohol use, consumption of very hot foods, and family history of cancer, have been widely validated in predictive models and shown to be independently associated with EC risk. Han et al. developed a model based on a rural Chinese cohort showing that age, sex, BMI, alcohol consumption, and fruit intake effectively identify individuals at risk for esophageal squamous cell carcinoma (ESCC)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Jiang et al. systematically reviewed and externally validated predictive models across multiple high-risk regions, showing that despite differences in model construction, most accurately identified high-risk individuals in high-incidence areas\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Taken together with previous studies, our results confirm that risk stratification remains robust in population screening, and that high-risk assessment can function as an efficient prescreening method to identify at-risk populations.\u003c/p\u003e \u003cp\u003eThe prospective cohort study based on a high-risk assessment strategy followed high-risk and non-high-risk individuals for a median of 8.32 years (IQR: 6.76\u0026ndash;9.93). Our findings show that the questionnaire-based tool reliably identifies individuals at high risk for EC. Developed by the NCC of China following established guidelines, the program was implemented with active and passive follow-up to ensure complete and reliable data collection.\u003c/p\u003e \u003cp\u003eThis study also has several limitations. The project protocol excluded endoscopic screening for non-high-risk participants, limiting early cancer detection and preventing assessment of individual variables\u0026rsquo; sensitivity and specificity. Future work will integrate RSES scoring with the current approach to develop a more robust esophageal cancer prediction model and comprehensively evaluate all questionnaire items to improve risk stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all staff members who contributed to this project, particularly those who conducted the baseline survey and measurements. We are also grateful to the Shandong Cancer Center for their valuable guidance and rigorous quality control, which ensured the authenticity and reliability of the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHang Yu, Conceptualization, Data curation, Investigation, Formal analysis;\u003c/p\u003e\n\u003cp\u003eHengmin Ma, Writing-review and editing; Xinyi Wang, Data curation, Formal analysis, Investigation; Zunlong Fang, Investigation, Methodology; Boyu Liu, Investigation, Methodology, Software; Wenxuan Yan, Investigation, Methodology, Software; Nan Zhang, Writing-review and editing, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Natural Science Foundation of China (72574131);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollaborative Academic Innovation Project of Shandong Cancer Hospital (TS001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Committee of\u0026nbsp;Institutional Review Board of the Cancer Institute/Hospital, Chinese Academy of Medical Sciences. All participants voluntarily participated in the study and provided written informed consent prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmyth EC, Lagergren J, Fitzgerald RC, et al. Oesophageal cancer[J]. Nat Reviews Disease Primers. 2017;3:17048.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. Cancer J Clin. 2024;74(3):229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeng Y, Xia C, Cao M, et al. Esophageal cancer global burden profiles, trends, and contributors[J]. Cancer Biology Med. 2024;21(8):656\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng H, Chen W, Zheng R, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries[J]. Lancet Global Health. 2018;6(5):e555\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou B, Bie F, Zang R, et al. Global burden and temporal trends in incidence and mortality of oesophageal cancer[J]. J Adv Res. 2023;50:135\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet. 2019;394(10204):1145\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Z, Liu Z, Liu M, et al. Efficacy of endoscopic screening for esophageal cancer in China (ESECC): design and preliminary results of a population-based randomised controlled trial[J]. Gut. 2019;68(2):198\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Z, Ke Y. Precision screening for esophageal squamous cell carcinoma in China[J]. Chin J Cancer Res = Chung-Kuo Yen Cheng Yen Chiu. 2020;32(6):673\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolary E, Abou-Zeid N, Calvo F. Ageing and cancer: a research gap to fill[J]. Mol Oncol. 2022;16(18):3220\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang AY, Skirbekk VF, Tyrovolas S, et al. Measuring population ageing: an analysis of the Global Burden of Disease Study 2017[J]. The Lancet. Public Health. 2019;4(3):e159\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawsey SM, Fleischer DE, Wang GQ, et al. Mucosal iodine staining improves endoscopic visualization of squamous dysplasia and squamous cell carcinoma of the esophagus in Linxian, China[J]. Cancer. 1998;83(2):220\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMannath J, Ragunath K. Role of endoscopy in early oesophageal cancer[J]. Nat Rev Gastroenterol Hepatol. 2016;13(12):720\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen R, Liu Y, Song G, et al. Effectiveness of one-time endoscopic screening programme in prevention of upper gastrointestinal cancer in China: a multicentre population-based cohort study[J]. Gut. 2021;70(2):251\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N, Li Y, Chang X, et al. 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Cancer Screen Prev. 2024;3(2):106\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Li H, Zheng R, et al. An initial screening strategy based on epidemiologic information in esophageal cancer screening: a prospective evaluation in a community-based cancer screening cohort in rural China[J]. Gastrointest Endosc. 2021;93(1):110\u0026ndash;e1182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan J, Wang L, Zhang H et al. Development and Validation of an Esophageal Squamous Cell Carcinoma Risk Prediction Model for Rural Chinese: Multicenter Cohort Study[J]. Front Oncol, 2021, 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang H, Chen R, Li Y, et al. Performance of Prediction Models for Esophageal Squamous Cell Carcinoma in General Population: A Systematic Review and External Validation Study[J]. Am J Gastroenterol. 2024;119(5):814\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\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":"Esophageal cancer, Population-based, High-risk assessment, Prospective cohort, Risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-8479049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8479049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo evaluate a questionnaire-based strategy for risk stratification in population-based esophageal cancer(EC) screening in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e120,488 residents from four cancer screening centers in Shandong Province were enrolled in a prospective cohort between 2013 and 2018. Participants were followed prospectively for the incidence EC, gastric cancer(GC), and upper gastrointestinal cancers(UGI). Cumulative incidence was estimated using the Kaplan\u0026ndash;Meier method. Associations between high-risk classification and cancer incidence were evaluated using Cox proportional hazards models. Results were expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Propensity score matching (PSM) was performed to minimize potential confounding.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 8.32 years (IQR, 6.76\u0026ndash;9.93), 593 EC, 374 GC and 967 UGI cases were identified, corresponding to incidence densities of 58.91, 37.16, and 96.07 per 100,000 person-years, respectively. High-risk participants had significantly higher cumulative incidences of all outcomes compared with non\u0026ndash;high-risk participants (log-rank test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). High-risk classification remained significantly associated with increased risks of EC (HR, 1.74;95%CI, 1.47\u0026ndash;2.06), GC (HR, 1.29;95%CI, 1.05\u0026ndash;1.58), and UGI (HR, 1.55;95%CI, 1.36\u0026ndash;1.76).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe questionnaire-based high-risk assessment strategy effectively identifies asymptomatic individuals at elevated risk of EC, GC, and UGI who may benefit from prioritized endoscopic examination. This strategy is an efficient, scalable tool for primary risk stratification in population-based screening programs.\u003c/p\u003e","manuscriptTitle":"Prospective Evaluation of High-risk Assessment Strategy for Esophageal Cancer: a community-based cancer screening cohort in rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 03:04:42","doi":"10.21203/rs.3.rs-8479049/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-10T00:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50959837986332754569598143736401293098","date":"2026-01-29T17:32:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157787895852405867524688974684353869028","date":"2026-01-29T12:29:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T10:11:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-15T03:48:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T06:59:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T02:29:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-06T02:22:33+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":"a6ff5382-985f-4a7b-b001-7d31dcf6c563","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T03:04:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 03:04:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8479049","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8479049","identity":"rs-8479049","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}