Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample

Lohman, Matthew C.; Crow, Rebecca S.; DiMilia, Peter R.; Nicklett, Emily J.; Bruce, Martha L.; Batsis, John A.

doi:10.1136/jech-2017-209769

Cited by 46 publications

(49 citation statements)

References 38 publications

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“…A major strength of this study was the inclusion of several falls‐related questions and a combination of self‐reported and performance‐based measures of physical functioning, enabling use of the CDC STEADI algorithm in a nationally representative sample of older adults. There are some differences between the physical function measures used in NHATS and those recommended in the CDC STEADI toolkit that might result in misclassification of fall‐risk, but the validity of using the NHATS physical performance measures to adapt the STEADI algorithm and predict future falls was previously demonstrated …”

Section: Discussionmentioning

confidence: 99%

“…There are some differences between the physical function measures used in NHATS and those recommended in the CDC STEADI toolkit that might result in misclassification of fall-risk, but the validity of using the NHATS physical performance measures to adapt the STEADI algorithm and predict future falls was previously demonstrated. 23 Deaths attributable to falls in the United States increased by 31% between 2007 and 2016, and fall-related mortality increased the most in the oldest old. 24 Improvements in screening and referrals to evidence-based programs, including community-based fall prevention programs, exercise, and rehabilitation, are a critical component of reducing the alarming upward trend in fallrelated mortality, 25,26 The Medicare-sponsored annual wellness visit is a primary opportunity to conduct a fallrisk screening.…”

Section: Discussionmentioning

confidence: 99%

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Rehabilitation Services Use of Older Adults According to Fall‐Risk Screening Guidelines

Gell

Patel

2018

J American Geriatrics Society

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OBJECTIVE: To characterize rehabilitation services use among older adults according to fall-risk classification based on screening guidelines. DESIGN: Cross-sectional analysis of the 2015 National Health and Aging Trends Study SETTING: Study participants’ homes. PARTICIPANTS: National sample of 7,440 community-dwelling Medicare beneficiaries. MEASUREMENTS: In-person interviews and functional assessments (gait speed, balance and strength testing). Based on CDC’s STEADI criteria, participants were classified into low, moderate, and high fall-risk. RESULTS: Among older adults, 23.3% of those classified as moderate fall-risk (n=2602) and 40.6% of those at high fall-risk (n=940), reported rehabilitation services use in the past year. Rehabilitation services use was associated with older age, higher education, non-Hispanic white race/ethnicity, and osteoarthritis in those with high fall-risk. Among older adults who reported rehabilitation services in the past year (n=1,505), treatment to address falls was reported by 2.8%, 12.6%, and 34.7% of those classified with low, moderate, and high fall-risk, respectively (p<0.001). Older adults with high fall-risk who did not receive rehabilitation services had significantly better self-reported physical capacity (p=0.02) but comparable physical performance (gait speed, grip strength, tandem stand, five time sit to stand; all p’s >0.05) relative to those who received rehabilitation. CONCLUSION: Older adults at high risk for falls were significantly more likely to report rehabilitation services use compared to those with low and moderate risk of falling. However, the findings also indicate that there is low adherence to national clinical recommendations for rehabilitation services use in older adults vulnerable to falls-related injury. Among the high fall-risk group, those who did not receive rehabilitation services had similarly low physical function as compared with those who received rehabilitation, indicating potential unmet need to address physical impairments related to fall-risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Rehabilitation Services Use of Older Adults According to Fall‐Risk Screening Guidelines

Gell

Patel

2018

J American Geriatrics Society

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“…risk and to coordinate clinical and community-based fall prevention initiatives are warrant (Lohman et al, 2017). Johnston et al (2019) recently demonstrated that the implementation of STEADI fall risk screening and prevention strategies among older adults in primary care settings could reduce fallrelated hospitalizations and lower the associated health care costs (Johnston et al, 2019) .…”

Section: Discussionmentioning

confidence: 99%

“…For individuals aged 65-74, the prevalence of falls was 67.1%, and for those older than 75, it was 78.2% (Health Research and Development Agency, 2013). The Fall Risk Instruments that widely use, are include Stopping Elderly Accidents, Deaths, & Injuries (STEADI) Initiative from the Centers for Disease Control and Prevention (CDC) and The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) from the Johns Hopkins University (Poe et al, 2007;Karakiewicz, 2015;Lohman et al, 2017;Hojati, risk instruments in some countries are develop by by JHFRAT (Thiamwong et al, 2008). However, a few researchers used it to predict fall risk for the Indonesia elderly.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Fall Risk Assessment Tools for Older Indonesian Adults in the Elderly Home and the Community

Susilowati

Nugraha

Sabarinah

et al. 2019

IJOSH

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Introduction: One of the causes of disability among elderly is falling. The ability to predict the risk of falls among this group is important so that the appropriate treatment can be provided to reduce the risk. The objective of this study was to compare the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) Initiative from the Centers for Disease Control and Prevention (CDC) and The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) from the Johns Hopkins University. Methods: This study used the STEADI tool, JHFRAT, Activities-Specific Balance Confidence Scale (ABC), and The Geriatric Depression Scale (GDS). The study areas were in community and elderly home in both public and private sectors and the samples were 427 after cleaning. Results: The results for the STEADI and JHFRAT tools were similar where the respondents at highest risk of falling among women (STEADI: 49%; JHFRAT: 3.4%), in Bandung area (63.5%; 5.4%), in private homes (63.3%; 4.4%), non-schools (54.6%; 6.2%), aged 80 or older (64.8%; 6.7%) and not working (48.9%;3.3%). The regression analysis indicated that there was a significant relationship between the risk factors for falls in the elderly determined by the JHFRAT and STEADI tools: namely, region, type of home, age, disease history, total GDS and ABC averages. Conclusion: Despite the similarity in the risk factors obtained through these assessments, there was a significant difference between the results for the STEADI tool and the JHFRAT. The test strength was 43%. However, STEADI is more sensitive to detect fall risk smong elderly than JHFRATKeywords: Activities-Specific Balance Confidence scale, elderly, fall risk,The Johns Hopkins Fall Risk Assessment Tool, the Stopping Elderly Accidents, Deaths, & Injuries

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“…This situation can influence a different health condition than of those who are not enrolled in a FHS, both fall prevention initiatives. (21) Therefore, the nurse must be prepared to assist in the prevention of falls, to use fall screening tools and to orient the patient to maintain a safe environment and correctly take their medications.…”

Section: Discussionmentioning

confidence: 99%

Fragilidade em idosos da zona rural: proposta de algoritmo de cuidados

et al. 2017

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Objective: To present a care algorithm for Frailty Syndrome in older adults. Methods: A quantitative, descriptive, cross-sectional study with 820 people aged 60 years or more living in the rural area of the city of Pelotas in the state of Rio Grande do Sul, Brazil. Data was collected from July to October 2014, using a semi-structured form. To assess frailty a self-reported instrument was used to obtain information from the older adult or their assistant/substitution respondent. Multiple Poisson Regression was used to analyze the associated factors. Through the identification of the associated factors, the care algorithm for the Frailty Syndrome was constructed. Results: 43.4% of the older adults presented frailty. The factors associated with the frailty syndrome were low education level (PR:1.45; p<0.001); obesity (PR: 1.89, p=0.001), no physical activity practice (PR: 1.93, p<0.001); presence of cognitive deficit (PR: 2.07; p=0.002); bad health self-perception (PR: 8.21; p<0.001); fall in the last 12 months (PR: 1.70; p<0.001) and presence of disease and morbidity (PR: 2.22, p<0.014). These factors led to the elaboration of the care algorithm, which was structured in care for the non-frail, pre-frail and frail older adults. Conclusion: The importance of health professionals, especially nurses, in the identification of the profile and lifestyle of the older adults is highlighted. They are expected to intervene in the problems associated with frailty, using the frailty care algorithm as a guide to delay and prevent the progression of the syndrome. ResumoObjetivo: Apresentar algoritmo de cuidados para Síndrome de Fragilidade no Idoso. Métodos: Estudo de abordagem quantitativa, descritiva, com delineamento de corte transversal, com 820 pessoas com idade igual ou superior a 60 anos, residentes na zona rural do Município de Pelotas/Rio Grande do Sul, Brasil. A coleta de dados ocorreu no período de julho a outubro de 2014, com uso de um formulário semiestruturado. Para avaliar a fragilidade utilizou-se o instrumento autorreferido obtido por meio de informações relatadas pelo idoso ou por seu respondente substituto/auxiliar. Utilizou-se Regressão de Poisson Múltipla para a análise dos fatores associados. Mediante a identificação dos fatores associados, elaborou-se o Algoritmo de cuidados para a Síndrome de Fragilidade. Resultados: Constatou-se que 43,4% dos idosos apresentaram fragilidade. Os fatores associados à síndrome da fragilidade foram baixa escolaridade (RP: 1,45; p<0,001); obesidade (RP: 1,89; p=0,001), não realização de atividade física (RP: 1,93; p<0,001); apresentar déficit cognitivo (RP: 2,07; p=0,002); autopercepção de saúde ruim (RP: 8,21; p<0,001), queda nos últimos 12 meses (RP: 1,70; p<0,001) e presença de doença morbidade (RP: 2,22; p<0,014). Esses fatores direcionaram a elaboração do algoritmo de cuidados, que foi estruturado em cuidados para idosos não frágeis e pré-frágeis/frágeis. Conclusão: Percebe-se a importância dos profissionais de saúde, em destaque os enfermeiros, na identificação ...

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Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample

Cited by 46 publications

References 38 publications

Rehabilitation Services Use of Older Adults According to Fall‐Risk Screening Guidelines

Rehabilitation Services Use of Older Adults According to Fall‐Risk Screening Guidelines

Comparison of Fall Risk Assessment Tools for Older Indonesian Adults in the Elderly Home and the Community

Fragilidade em idosos da zona rural: proposta de algoritmo de cuidados

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