Dataset Information

Evaluating a Two-Level vs. Three-Level Fall Risk Screening Algorithm for Predicting Falls Among Older Adults.

ABSTRACT: Background and Objectives: Falls account for the highest proportion of preventable injury among older adults. Thus, the United States' Centers for Disease Control and Prevention (CDC) developed the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) algorithm to screen for fall risk. We referred to our STEADI algorithm adaptation as "Quick-STEADI" and compared the predictive abilities of the three-level (low, moderate, and high risk) and two-level (at-risk and not at-risk) Quick-STEADI algorithms. We additionally assessed the qualitative implementation of the Quick-STEADI algorithm in clinical settings. Research Design and Methods: We followed a prospective cohort (N = 200) of adults (65+ years) in the Bassett Healthcare Network (Cooperstown, NY) for 6 months in 2019. We conducted a generalized linear mixed model, adjusting for sociodemographic variables, to determine how baseline fall risk predicted subsequent daily falls. We plotted receiver operating characteristic (ROC) curves and measured the area under the curve (AUC) to determine the predictive ability of the Quick-STEADI algorithm. We identified a participant sample (N = 8) to gauge the experience of the screening process and a screener sample (N = 3) to evaluate the screening implementation. Results: For the three-level Quick-STEADI algorithm, participants at low and moderate risk for falls had a reduced likelihood of daily falls compared to those at high risk (-1.09, p = 0.04; -0.99, p = 0.04). For the two-level Quick-STEADI algorithm, participants not at risk for falls were not associated with a reduced likelihood of daily falls compared to those at risk (-0.89, p = 0.13). The discriminatory ability of the three-level and two-level Quick-STEADI algorithm demonstrated similar predictability of daily falls, based on AUC (0.653; 0.6570). Furthermore, participants and screeners found the Quick-STEADI algorithm to be efficient and viable. Discussion and Implications: The Quick-STEADI is a suitable, alternative fall risk screening algorithm. Qualitative assessments of the Quick-STEADI algorithm demonstrated feasibility in integrating a falls screening program in a clinical setting. Future research should address the validation and the implementation of the Quick-STEADI algorithm in community health settings to determine if falls screening and prevention can be streamlined in these settings. This may increase engagement in fall prevention programs and decrease overall fall risk among older adults.

SUBMITTER: Mielenz TJ

PROVIDER: S-EPMC7438745 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Evaluating a Two-Level vs. Three-Level Fall Risk Screening Algorithm for Predicting Falls Among Older Adults.

Mielenz Thelma J TJ Kannoth Sneha S Jia Haomiao H Pullyblank Kristin K Sorensen Julie J Estabrooks Paul P Stevens Judy A JA Strogatz David D

Frontiers in public health 20200813

<b>Background and Objectives:</b> Falls account for the highest proportion of preventable injury among older adults. Thus, the United States' Centers for Disease Control and Prevention (CDC) developed the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) algorithm to screen for fall risk. We referred to our STEADI algorithm adaptation as "Quick-STEADI" and compared the predictive abilities of the three-level (low, moderate, and high risk) and two-level (at-risk and not at-risk) Quick-STE ...[more]

PMID: 32903603

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Project description:Background and objectivesMedication exposure is a potential risk factor for falls and subsequent death and functional decline among older adults. However, controversy remains on the best way to assess medication exposure and which approach best predicts falls. The objective of the current study was to examine the association between different measures of medication exposure and falls risk among community-dwelling older adults.Research design and methodsThis retrospective cohort study was conducted using Falls Free PA program data and a linked prescription claims data from Pennsylvania's Pharmaceutical Assistance Contract for the Elderly program. Participants were community-dwelling older adults living in Pennsylvania, United States. Three measures of medication exposure were assessed: (a) total number of regular medications (polypharmacy); (b) counts of potentially inappropriate medications derived from current prescription guidance tools (Fall Risk-Increasing Drugs [FRIDs], Beers Criteria); and (c) medication burden indices based on pharmacologic mechanisms (Anticholinergic Cognitive Burden, Drug Burden Index) all derived from claims data. The associations between the different medication risk measures and self-reported falls incidence were examined with univariate and multivariable negative binomial regression models to estimate incidence rate ratios (IRRs).ResultsOverall 343 older adults were included and there were 236 months with falls during 2,316 activity-adjusted person-months (10.2 falls per 100 activity-adjusted person-months). Of the 6 measures of medication risk assessed in multivariate models, only the use of 2 or more FRIDs (adjusted IRR 1.67 [95% CI: 1.04-2.68]) independently predicted falls risk. Among the 13 FRID drug classes, the only FRID class associated with an increased fall risk was antidepressants.Discussion and implicationsThe presence of multiple FRIDs in a prescription is an independent risk factor for falls, even in older adults with few medications. Further investigation is required to examine whether deprescribing focused on FRIDs effectively prevents falls among this population.

Project description:OBJECTIVES:To determine the prevalence of falls, fear of falling (FoF), and activity limitation due to FoF in a nationally representative study of older adults with self-reported vision impairment (VI). DESIGN:Cross-sectional analysis of panel survey data. SETTING:National Health and Aging Trends Study, a nationally representative survey administered annually from 2011 to 2016 to U.S. Medicare beneficiaries aged 65 and older. PARTICIPANTS:Respondents (N=11,558) who contributed 36,229 participant observations. MEASUREMENTS:We performed logistic regression to calculate the unadjusted and adjusted prevalence of self-reported history of more than 1 fall in the past year, any fall in the past month, FoF, and activity limitation due to FoF in participants with and without self-reported VI. RESULTS:The weighted proportion of participants reporting VI was 8.6% (95% confidence interval (CI)=8.0-9.2%). The unadjusted prevalence of more than 1 fall in the past year was 27.6% (95% CI=25.5-29.7%) in participants with self-reported VI and 13.2% (95% CI=12.7-13.7%) in those without self-reported VI. In respondents with self-reported VI, the prevalence of FoF was 48.3% (95% CI=46.1-50.6%) and of FoF limiting activity was 50.8% (95%CI 47.3-54.2%), and in those without self-reported VI, the prevalence of FoF was 26.7% (95% CI=25.9-27.5%) and of FoF limiting activity was 33.9% (95% CI=32.4-35.4%). The prevalence of all fall and fall-related outcomes remained significantly higher among those with self-reported VI after adjusting for sociodemographics and potential confounders. CONCLUSION:The prevalence of falls, FoF, and activity limitation due to FoF is high in older adults with self-reported VI. This is the first study to provide nationally representative data on the prevalence of fall-related outcomes in older Americans with self-reported VI. These findings demonstrate the need to treat avoidable VI and to develop interventions to prevent falls and fall-related outcomes in this population. J Am Geriatr Soc 67:239-245, 2019.

Project description:Limited attention has been paid in the literature to multiple component fall prevention interventions that comprise two or more fixed combinations of fall prevention interventions that are not individually tailored following a risk assessment. The study objective was to determine the effect of multiple component interventions on fall rates, number of fallers and fall-related injuries among older people and to establish effect sizes of particular intervention combinations.Medline, EMBASE, CINAHL, PsychInfo, Cochrane, AMED, UK Clinical Research Network Study Portfolio, Current Controlled Trials register and Australian and New Zealand Clinical Trials register were systematically searched to August 2013 for randomised controlled trials targeting those aged 60 years and older with any medical condition or in any setting that compared multiple component interventions with no intervention, placebo or usual clinical care on the outcomes reported falls, number that fall or fall-related injuries. Included studies were appraised using the Cochrane risk of bias tool. Estimates of fall rate ratio and risk ratio were pooled across studies using random effects meta-analysis. Data synthesis took place in 2013.Eighteen papers reporting 17 trials were included (5034 participants). There was a reduction in the number of people that fell (pooled risk ratio = 0.85, 95% confidence interval (95% CI) 0.80 to 0.91) and the fall rate (pooled rate ratio = 0.80, 95% CI 0.72 to 0.89) in favour of multiple component interventions when compared with controls. There was a small amount of statistical heterogeneity (I(2) =20%) across studies for fall rate and no heterogeneity across studies examining number of people that fell.This systematic review and meta-analysis of randomised controlled trials found evidence that multiple component interventions that are not tailored to individually assessed risk factors are effective at reducing both the number of people that fall and the fall rate. This approach should be considered as a service delivery option.

Project description:ObjectiveTo systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteriaProspective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.Information sourceMEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.Data extraction and risk of biasTwo authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.ResultsAfter screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models' The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.ConclusionsAn abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.Prospero registration numberCRD42019124021.

Dataset Information

Evaluating a Two-Level vs. Three-Level Fall Risk Screening Algorithm for Predicting Falls Among Older Adults.

Publications

Evaluating a Two-Level vs. Three-Level Fall Risk Screening Algorithm for Predicting Falls Among Older Adults.

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