ArticleBMC medicine2022
Predictive validity of the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program fall risk screening algorithms among community-dwelling Thai elderly.
Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Psychometric properties, acceptability, and feasibility of the Stability, Agility, Strength (SAS) Mat for balance assessment with potential for training.BMC geriatrics · 2026Article
- Development and validation of an Online Comprehensive Geriatric Screening (O-CGS) Tool.BMC public health · 2026Article
- Comparison and validation of machine learning models to predict 5-year fall risk among community-dwelling older adults in China.BMC geriatrics · 2026Article
- An explainable machine learning model predicts 30-day readmission after vertebral augmentation.iScience · 2026Article
- Exploring the Current Tools for Fall Risk Assessment in Older Adults Across Southeast Asia and the Need for Culturally Adapted Models: A Scoping Review.Therapeutics and clinical risk management · 2026Review
- Multifactorial Risk Assessment of Falls in Thai Community-Dwelling Older Adults: Findings from a Geriatric Cohort Study.Geriatrics (Basel, Switzerland) · 2025Article
- Evaluation of content validity and feasibility of the World Falls Guidelines' three key questions to identify falls among older adult users of home care services in Norway.BMC health services research · 2025Article
- Risk and Preventive Measures Among Older Adults in Nursing Homes in Saudi Arabia: An Exploratory Study on Falls.Healthcare (Basel, Switzerland) · 2025Article
- Validity of the German version of the Stay Independent Questionnaire applied by telephone interview: A diagnostic accuracy study.PloS one · 2025Article
- Article
- Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach.Journal of neuroengineering and rehabilitation · 2024Article
- Value of the short physical performance battery (SPPB) in predicting fall and fall-induced injury among old Chinese adults.BMC geriatrics · 2023Article
- Predictive validity of the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program fall risk screening algorithms among community-dwelling Thai elderly.BMC medicine · 2022Article
- The Predictive Validity and Clinical Application of Stopping Elderly Accidents, Deaths & Injuries (STEADI) for Fall Risk Screening.Advances in geriatric medicine and research · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundFall risk screening using multiple methods was strongly advised as the initial step for preventing fall. Currently, there is only one such tool which was proposed by the U.S. Centers for Disease Control and Prevention (CDC) for use in its Stopping Elderly Accidents, Death & Injuries (STEADI) program. Its predictive validity outside the US context, however, has never been investigated. The purpose of this study was to determine the predictive validity (area under the receiver operating characteristic curve: AUC), sensitivity, and specificity of the two-step sequential fall-risk screening algorithm of the STEADI program for Thai elderly in the community.
methodsA 1-year prospective cohort study was conducted during October 2018-December 2019. Study population consisted of 480 individuals aged 65 years or older living in Nakhon Ratchasima Province, Thailand. The fall risk screening algorithm composed of two serial steps. Step 1 is a screening by the clinician's 3 key questions or the Thai Stay Independent brochure (Thai-SIB) 12 questions. Step 2 is a screening by 3 physical fitness testing tools including Time Up and Go test (TUG), 30-s Chair Stand, and 4-stage balance test. Participants were then followed for their fall incidents. Statistical analyses were conducted by using Cox proportional hazard model. The AUC, sensitivity, specificity, and other relevant predictive validity indices were then estimated.
resultsThe average age of the participants was 73.3 ± 6.51 years (range 65-95 years), and 52.5% of them were female. The screening based on the clinician's 3 key questions in Step 1 had a high AUC (0.845), with the sensitivity and specificity of 93.9% (95% CI 88.8, 92.7) and 75.0% (95% CI 70.0, 79.6), respectively. Appropriate risk categorization however differed slightly from the original STEADI program.
conclusionsWith some modification, the fall risk screening algorithm based on the STEADI program was applicable in Thai context.
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