Analysis Of Influencing Factors And Prediction Of Falls Among Rural Older Adults In China Based On A Nomogram Model

Analysis Of Influencing Factors And Prediction Of Falls Among Rural Older Adults In China Based On A Nomogram Model | 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 Analysis Of Influencing Factors And Prediction Of Falls Among Rural Older Adults In China Based On A Nomogram Model Yan Wu, Shangci Cao, Pengcheng Wan, Yuran Liu, Yaodong Zhao, Yujie Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4805068/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Editorial Note 5 January, 2026. The title, author details, and abstract metadata for /v2 of this preprint have been updated to reflect the information in the manuscript file. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract Objective To explore the factors influencing falls among older adults in rural China and to construct a nomogram prediction model. This study aims to provide a scientific basis for identifying high-risk populations and implementing targeted fall prevention interventions. Methods The stratified multi-stage cluster random sampling method was employed, selecting one city each from the northern, central, and southern regions of Anhui Province. From each city, one county was randomly selected, and within these counties, a total of 18 villages were randomly chosen. Potential participants, identified through local household registration records and recruited via village committees, completed face-to-face interviews that incorporated a structured questionnaire, physical measurements, and environmental observations. A total of 1546 older adults were included. Inclusion criteria were: (1) aged ≥60 years; (2) local residents for at least six months;capability for effective communication.. Exclusion criteria included: (1) severe physical or mental conditions preventing participation; (2) being bedridden. A fall was explicitly defined as "an unexpected event where the participant comes to rest on the ground, floor, or lower level," excluding instances due to sudden paralysis, stroke, or violent collision. These participants were randomly divided into a training set (1208 individuals) and a validation set (338 individuals) in an 8:2 ratio. Univariate analysis was conducted using the Mann-Whitney U test and Kruskal-Wallis H test, and variables with a p-value < 0.05in the univariate analyses were included in the initial multivariate binary logistic regression model. Backward stepwise selection was then employed to identify independent predictors.. A nomogram model was subsequently developed based on these factors. Results From the univariate and multivariate analyses of the training set, five variables were identified: age, anxiety, frailty, living style, and frequency of coarse grain consumption. These variables were incorporated into the nomogram model, which exhibited an area under the ROC curve (AUC) of 0.722, indicating good discriminative ability. The calibration curve demonstrated high calibration accuracy. Internal validation of the nomogram model using the validation set yielded an AUC of 0.703, reflecting high discriminative ability, and the Hosmer-Lemeshow test result of P=0.08 indicated no significant deviation between predicted and observed probabilities, suggesting good calibration. Conclusion The constructed nomogram, incorporating age, anxiety, frailty, living style, and coarse grain consumption frequency, serves as a practical tool for predicting fall risk among rural older adults. It provides significant value for identifying high-risk populations and implementing targeted interventions. rural areas older adults falls influencing factors nomogram prediction model Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4805068","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569798662,"identity":"e00ebc35-ef1f-4250-8217-68e8925eeb45","order_by":0,"name":"Yan Wu","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wu","suffix":""},{"id":569798663,"identity":"f56afc7c-ce44-411d-9de4-8f88bb681e89","order_by":1,"name":"Shangci Cao","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shangci","middleName":"","lastName":"Cao","suffix":""},{"id":334394947,"identity":"51210c11-a5f2-42fd-b78c-e4a06edff645","order_by":2,"name":"Pengcheng Wan","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pengcheng","middleName":"","lastName":"Wan","suffix":""},{"id":569798664,"identity":"af165b00-d638-4b31-8c1b-85906e12f569","order_by":3,"name":"Yuran Liu","email":"","orcid":"","institution":"Anhui Medical University,","correspondingAuthor":false,"prefix":"","firstName":"Yuran","middleName":"","lastName":"Liu","suffix":""},{"id":334394936,"identity":"b2138128-163b-4dca-973e-b407ff8fe3ae","order_by":4,"name":"Yaodong Zhao","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaodong","middleName":"","lastName":"Zhao","suffix":""},{"id":334394941,"identity":"6dc8bfd2-326a-4f9b-95ea-c232f65469cc","order_by":5,"name":"Yujie Chen","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Chen","suffix":""},{"id":334394952,"identity":"d09cbad1-3942-4b20-a9fe-6c7dddfd6c87","order_by":7,"name":"Yi Li","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Li","suffix":""},{"id":334394954,"identity":"d1929c86-026c-42ba-abbd-163f735fae27","order_by":9,"name":"Hong Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDCCwwwMzCCan5mx+cEHAxs54rVItjcfM5xRkGZMWMsBqBaDM8cSpHk+HE4kqIPvOO/h1wUVd+wabuQYGNsYMCcwsB8+ugGfFsnDfGnWM848S26ckWPwOMeALY+BJy3tBj4tBod5zIx52w4nM0sAbckx4ClmkOAxI04LG1CLtIWBRGIDEVqMHwO12PHwAL3PYGBAWIsk0BZmnjOHEyTYgYHcY5BgzEbIL3znzxh/5qk4bG9/GBiVP/78l+NnP3wMrxYgYJMAEokNcC4B5SDA/AFI2BOhcBSMglEwCkYqAADwqUs51f2ATQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Health Service Management, School of Health Management, Anhui Medical University, Anhui, China","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-07-26 03:31:00","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4805068/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4805068/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"\u003cp\u003e5 January, 2026. 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This study aims to provide a scientific basis for identifying high-risk populations and implementing targeted fall prevention interventions.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eThe stratified multi-stage cluster random sampling method was employed, selecting one city each from the northern, central, and southern regions of Anhui Province. From each city, one county was randomly selected, and within these counties, a total of 18 villages were randomly chosen. Potential participants, identified through local household registration records and recruited via village committees, completed face-to-face interviews that incorporated a structured questionnaire, physical measurements, and environmental observations. A total of 1546 older adults were included. Inclusion criteria were: (1) aged ≥60 years; (2) local residents for at least six months;capability for effective communication.. Exclusion criteria included: (1) severe physical or mental conditions preventing participation; (2) being bedridden. A fall was explicitly defined as \"an unexpected event where the participant comes to rest on the ground, floor, or lower level,\" excluding instances due to sudden paralysis, stroke, or violent collision. These participants were randomly divided into a training set (1208 individuals) and a validation set (338 individuals) in an 8:2 ratio. Univariate analysis was conducted using the Mann-Whitney U test and Kruskal-Wallis H test, and variables with a p-value \u0026lt; 0.05in the univariate analyses were included in the initial multivariate binary logistic regression model. Backward stepwise selection was then employed to identify independent predictors.. A nomogram model was subsequently developed based on these factors.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eFrom the univariate and multivariate analyses of the training set, five variables were identified: age, anxiety, frailty, living style, and frequency of coarse grain consumption. These variables were incorporated into the nomogram model, which exhibited an area under the ROC curve (AUC) of 0.722, indicating good discriminative ability. The calibration curve demonstrated high calibration accuracy. Internal validation of the nomogram model using the validation set yielded an AUC of 0.703, reflecting high discriminative ability, and the Hosmer-Lemeshow test result of P=0.08 indicated no significant deviation between predicted and observed probabilities, suggesting good calibration.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eThe constructed nomogram, incorporating age, anxiety, frailty, living style, and coarse grain consumption frequency, serves as a practical tool for predicting fall risk among rural older adults. 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