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This study developed a fall risk index using multidimensional data from the Canadian Longitudinal Study on Aging by selecting 36 potential risk factors from systematic reviews and guidelines. Using baseline data from 12,646 adults (≥65) and injurious falls from follow-up 1 (8.07%), stepwise multivariable logistic regression identified 14 predictors. Model performance was modest (AUC=0.63), offering better discrimination for ruling out low-risk individuals and limited utility for confirming high-risk cases, supporting screening use to focus prevention.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-of-a-fall-prediction-risk-using-multidimensional-data-from-the-canadian-longitudinal-study-on-aging/427492/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-of-a-fall-prediction-risk-using-multidimensional-data-from-the-canadian-longitudinal-study-on-aging/427492.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-14","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the study’s main objective?","Question",{"text":112,"@type":113},"To develop a fall risk index using multidimensional data from the Canadian Longitudinal Study on Aging.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How many risk factors and predictors were identified?",{"text":117,"@type":113},"The study selected 36 potential fall risk factors, and the final stepwise logistic regression model retained 14 predictors associated with injurious falls.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the model’s key performance results?",{"text":121,"@type":113},"Performance was modest (AUC=0.63) with sensitivity 75.50%, specificity 46.77%, positive predictive value 10.07%, and negative predictive value 96.03%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427492,1791156579,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":24},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","772 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.2624  \nDEVELOPMENT OF A FALL PREDICTION RISK USING MULTIDIMENSIONAL DATA FROM THE CANADIAN LONGITUDINAL STUDY ON AGING  \nPaulo Roberto Carvalho do Nascimento, Marla Beauchamp, Jinhui Ma, Luciana Macedo, and Lauren Griffith, McMaster University, Hamilton, Ontario, Canada  \nFalls rank first in injury prevention priorities in Canada. Approximately 40% of falls among community-dwelling older adults could be prevented with proper strategies. This study aimed to develop a fall risk index using multidimensional data from the Canadian Longitudinal Study on Aging. We selected  \nInnovation in Aging, 2025, Vol. 9, No. S2 773  \n36 potential fall risk factors from systematic reviews and fall guidelines. Predictor variables were extracted from baseline data of older adults (≥ 65 years, n = 12,646), while incident injurious falls were retrieved from follow-up 1 (FUP 1) . At FUP1, 8.07% of the participants had an injurious fall. A stepwise multivariable logistic regression model identified 14 predictors associated with injurious falls. Significant (p \u003C 0.05) predictors including age, previous falls, previous injurious falls, vision impairment, pain, home dissatisfaction, comorbidities, grip strength, and use of antidepressants were used to create the final model. The dataset was split into training (70%) and test (30%) datasets for model fitting and predict the probabilities, respectively. The model’s performance was modest (AUC = 0.63), with a sensitivity of 75.50%, specificity (46.77%), positive predictive value (10.07%), and negative predictive value (96.03%) . Although these results limit the model’s reliability in confirming high-risk cases, it suggests that the model is more effective at ruling out individuals at low risk of injurious falls. Therefore, while the model may have limited utility for identifying those at high risk, it could be valuable as a screening tool to exclude low-risk individuals and focus preventive strategies on those more likely to experience falls.","cbCaicThSC9J2BdQ","https://ap.wps.com/l/cbCaicThSC9J2BdQ","pdf",1361754,"English","# Abstract\n## Study aim and data source\n## Methods and predictors\n## Model training and performance\n## Interpretation and screening implications","[{\"question\":\"What was the study’s main objective?\",\"answer\":\"To develop a fall risk index using multidimensional data from the Canadian Longitudinal Study on Aging.\"},{\"question\":\"How many risk factors and predictors were identified?\",\"answer\":\"The study selected 36 potential fall risk factors, and the final stepwise logistic regression model retained 14 predictors associated with injurious falls.\"},{\"question\":\"What were the model’s key performance results?\",\"answer\":\"Performance was modest (AUC=0.63) with sensitivity 75.50%, specificity 46.77%, positive predictive value 10.07%, and negative predictive value 96.03%.\"}]","Development of a Fall Prediction Risk Using Multidimensional Data from the Canadian Longitudinal Study on Aging | PDF",1790644011]