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.
Highlights
Falls are the primary cause of injury among older adults, 65 years and older [1,2,3]
To demonstrate how the two-level Quick-STEADI algorithm was adapted from the Centers for Disease Control and Prevention (CDC) STEADI algorithm, we have provided a visual in which the components of the two-level QuickSTEADI algorithm are highlighted in yellow in the current CDC STEADI algorithm for fall risk screening (Figure 1)
When compared to the characteristics of the Bassett Healthcare Network (BHN) patient population (50.4% female; 90.5% white; 95% non-Hispanic; 11.6% completed less than high school, 35% high school graduates, and 53.4% completed at least some college), the study sample participants were fairly representative of the BHN patient population, with only minor differences as slightly more individuals were female, white, non-Hispanic and more educated in the study sample
Summary
Falls are the primary cause of injury among older adults, 65 years and older [1,2,3]. The Centers for Disease Control and Prevention (CDC)’s Stopping Elderly Accidents, Deaths and Injuries (STEADI) Tool Kit is intended to help health care providers integrate fall prevention into their practices and target these modifiable risk factors [10, 11]. The combination of the STEADI Tool Kit and a plan for fall prevention may reduce fall-related hospitalizations and associated costs [12, 13]. Falls account for the highest proportion of preventable injury among older adults. 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 plotted receiver operating characteristic (ROC) curves and measured the area under the curve (AUC) to determine the predictive ability of the Quick-STEADI algorithm.
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