AUTHOR=Mielenz Thelma J. , Kannoth Sneha , Jia Haomiao , Pullyblank Kristin , Sorensen Julie , Estabrooks Paul , Stevens Judy A. , Strogatz David 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 8 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2020.00373

DOI=10.3389/fpubh.2020.00373

ISSN=2296-2565

ABSTRACT=Background and Objectives
Falls account for the highest proportion of preventable injury among older adults. In an effort to aid clinicians screen, assess, and intervene to decrease fall risk in older adults, the United States’ Centers for Disease Control and Prevention (CDC) developed the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) Tool Kit. We referred to our adaptation of the STEADI algorithm as ‘Quick-STEADI’ and compared the predictive abilities of the three-level (low, moderate, high risk) and two-level (at-risk, not at-risk) Quick-STEADI fall risk screening algorithms.

Research Design and Methods
The prospective cohort study sample consisted of 200 participants, 65 years and older, from the Bassett Healthcare Network in Cooperstown, New York. We followed the participants for approximately six 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 fall risk screening algorithms.

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). 

Discussion and Implications
The two-level Quick-STEADI may be suitable as an alternative fall risk screening algorithm. Qualitative assessments of the two-level Quick-STEADI algorithm also demonstrated feasibility in integrating a falls screening program in a healthcare network setting. Future research should continue to address the validation of the two-level Quick-STEADI algorithm. New directions may include assessing the implementation of the two-level Quick-STEADI algorithm in community health settings to determine if fall risk 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.