• Sneha Kannoth, Thelma J Mielenz, David W Eby, Lisa J Molnar, Haomiao Jia, Guohua Li, and David Strogatz.
• Center for Injury Science and Prevention, Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.
• Am J Prev Med. 2021 Jul 1; 61 (1): 105114105-114.

IntroductionFall fatality rates among U.S. older adults increased 30% from 2007 to 2016. In response, the Centers for Disease Control and Prevention developed the Stopping Elderly Accidents, Deaths, and Injuries algorithm for fall risk screening, assessment, and intervention. The current Stopping Elderly Accidents, Deaths, and Injuries algorithm with 2 levels (at risk and not at risk) was adapted to an existing cohort of older adult drivers.MethodsA U.S. multisite prospective cohort (N=2,990) of drivers (aged 65-79 years), from 2015 to 2017, was used for these analyses completed in January 2020-October 2020. To measure the adapted Stopping Elderly Accidents, Deaths, and Injuries key questions for fall risk screening performance in predicting future falls, adjusted logistic regression determined the area of the receiver operating characteristic curve. An adjusted mixed logistic regression modeled the association between the adapted Stopping Elderly Accidents, Deaths, and Injuries key questions and future falls.ResultsThe adapted Stopping Elderly Accidents, Deaths, and Injuries key questions yielded an area under the curve of 0.65 in determining any fall over 2 years. The adjusted mixed logistic regression model suggests that those at risk for falls at baseline were associated with 2.37 times higher odds of any fall (95% CI=2.00, 2.80) and 3.60 times higher odds of multiple falls (95% CI=2.88, 4.51) over 2 years.ConclusionsThe adapted Stopping Elderly Accidents, Deaths, and Injuries key questions for fall risk screening yielded fair predictive ability for falls over 2 years and were strongly associated with future falls for older adult drivers. The adapted Stopping Elderly Accidents, Deaths, and Injuries key questions can be applied to existing data in nonclinical settings to strengthen fall screening and prevention at a population level.Copyright © 2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

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