Evidence map›Paper›PMID 41904453›Full record

ArticleBMC public health2026

The interplay of risk factors for future falls and fall-related injuries among community-dwelling older adults: a Bayesian network analysis.

Yan Cai, Wei Zhu, Chenshu Wu, Yan Jiang

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Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yan CaiCenter of Gerontology and Geriatrics, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, Sichuan, China.
Wei ZhuDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Chenshu WuDepartment of Computer Science, The University of Hong Kong, Hong Kong, China.
Yan JiangDepartment of Nursing, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu, Sichuan, China. hxhljy2018@163.com.

Funding

National Key Research and Development Program of China 2023YFC3605900
6 · The paper itself

Abstract

backgroundFalls are a leading global cause of injury and mortality in older adults, necessitating understanding risk factor interplay for prevention. This study aims to investigate complex interactions among risk factors associated with future falls and fall-related injuries in older adults.

methodsA prospective cohort study used China Health and Retirement Longitudinal Study (2015 and 2018) data from 8,316 community-dwelling older adults. Falls and fall-related injuries were self-reported. The least absolute shrinkage and selection operator (LASSO) regression was employed for variable selection. A Bayesian network analysis utilizing the Max-Min Hill Climbing algorithm and Bayesian estimation method was subsequently implemented to identify interactions among risk factors related to falls and fall-related injuries.

resultsFall incidence was 23.29%, and the incidence of severe injuries after falls was 10%. According to variables identified by LASSO regression, two Bayesian networks were constructed for falls (21 nodes and 48 directed edges) and fall-related injuries (20 nodes and 48 directed edges). Results revealed complex associations among the risk factors for falls and fall-related injuries, highlighting direct factors such as fall history, hip fracture history and balance, as well as the indirect effects of factors such as age, vision and cognitive function. Some specific combinations of direct risk factors contributing to a high probability of falls and fall-related injuries were also identified.

conclusionFalls and related injuries are frequent in community-dwelling older adults, associated with by multiple interconnected factors. Bayesian network analysis helps uncover the relationships between these variables and quantify the associations of major factor. Clinical practice may benefit from prioritizing multifactorial exercise programs targeting grip strength, lower limb strength, and balance. Consideration should be given to fall risk assessments and tailored interventions for older adults with prior falls, hip fracture history, or those using walking aids to effectively reduce falls and related injuries.

Indexed as

Accidental FallsIndependent LivingWounds and InjuriesAgedAged, 80 and overAging in PlaceBayes TheoremChinaFemaleHumansIncidenceLongitudinal StudiesMaleProspective StudiesRisk FactorsBayesian networkFallsInjuryOlder adultsRisk factors

Identifiers

PMID41904453
PMCPMC13151155

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.