Centers for Disease Control and Prevention (CDC) for use in its Stopping Elderly Accidents, Death & Injuries (STEADI) program. Ĭurrently, there is only one multi-tool fall risk screening algorithm based on sequential test, which was proposed by the U.S. There are only recommendations mentioning that since there is no single tool showing sufficiently high predictive validity, multiple tools should be used in combination without specific detail on the suggested combined procedure. While a number of fall risk screening tools do exist currently, no information has clearly identified which tools are best. Therefore, a screening tool for fall risk is the first key and should be sensitive and specific in predicting fall risk as well as having the ability to identify the cause or risk factor(s) of fall. If the program is managed properly, it can reduce the rate of falls by 24%. Ī fall prevention program comprising screening for individual’s risk factors together with risk factor management is the most effective way to prevent accidental falls. The direct medical costs for falls total nearly $30 billion annually. Non-fatal falls resulted in minor to very severe injuries, with some of the fallers having disability and premature death. In Thailand and worldwide, falls are the second leading cause of injury death after road traffic accidents. Falls account for 40% of all injurious deaths. An estimated 646,000 elderly people around the world die from falls each year. With some modification, the fall risk screening algorithm based on the STEADI program was applicable in Thai context.įalling is a major threat to the elderly’s quality of life, often causing a decline in self-care ability and social activities. Appropriate risk categorization however differed slightly from the original STEADI program. 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. The average age of the participants was 73.3 ± 6.51 years (range 65–95 years), and 52.5% of them were female. The AUC, sensitivity, specificity, and other relevant predictive validity indices were then estimated. Statistical analyses were conducted by using Cox proportional hazard model. Participants were then followed for their fall incidents. 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. Step 1 is a screening by the clinician’s 3 key questions or the Thai Stay Independent brochure (Thai-SIB) 12 questions. The fall risk screening algorithm composed of two serial steps. Study population consisted of 480 individuals aged 65 years or older living in Nakhon Ratchasima Province, Thailand. MethodsĪ 1-year prospective cohort study was conducted during October 2018–December 2019. 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. Its predictive validity outside the US context, however, has never been investigated. Currently, there is only one such tool which was proposed by the U.S. Fall risk screening using multiple methods was strongly advised as the initial step for preventing fall.
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