BACKGROUND/RATIONALE:
Among middle-aged individuals (45-65 years), falls that occur in the community (community falls) are a leading cause of non-fatal injuries treated in hospital emergency departments and are responsible annually for the loss of 422,000 disability-adjusted life-years (DALYs). Intrinsic risk factors (risk factors inherent to the individual) likely contribute significantly to falls risk in this age group, but a consistently effective approach to outpatient fall prevention has not been realized within the VA.

OBJECTIVE(S):
The proposed project will explore community falls among middle-aged Veterans by characterizing prevalence and incidence of medically significant community falls among middle-aged Veterans.
This project challenges the assumption held by most healthcare providers that community falls related to intrinsic risk factors are only a problem in older adults. We suggest that this is an important problem among middle-aged adults as well but that risk factors differ by age group, suggesting that interventions appropriate to older adults may not be effective among middle-aged. This project will provide the information necessary to develop falls prevention interventions for middle-aged Veterans. This project also uses an innovative approach to identify falls in the EHR: the use of machine learning to identify falls in radiology reports.

METHODS:
This project will investigate SA1 only at the request of HSR&D. We will use data obtained from the electronic health record (EHR) of Veterans ages 45-65 in the VA Birth Cohort. The sample size of a randomly selected subset (N= 325,999) ensures that we are powered to explore these outcomes. We have developed a machine learning algorithm that identifies community falls in radiology reports and will validate this algorithm in the VA Birth Cohort. We will develop a reference standard from a randomly selected subset of the radiology reports in this cohort that have been reviewed by a clinician and identified as addressing a fall or not. These results will be compared with those from the algorithm.
We will first calculate rates of occurrence of community falls among middle-aged Veterans. Descriptive statistics (means, medians, frequencies, and standard deviations) will be used to characterize the distribution of risk factors and outcomes among the study participants.

FINDINGS/RESULTS:
We anticipate that the machine learning algorithm will detect falls with a sensitivity >90%. We anticipate that falls risk factors identified in middle-aged Veterans will be different from those identified in older age groups, suggesting that falls prevention interventions will also differ.

IMPACT:
This project has the potential for significant impact on community fall prevention among middle-aged Veterans. Of key importance may be the role of alcohol and other substance use as fall risk factors.

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External Links for this Project

NIH Reporter

Grant Number: IK3HX002269-01A2
Link: https://reporter.nih.gov/project-details/9499802

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PUBLICATIONS:

Journal Articles

1. Womack JA, Murphy TE, Bathulapalli H, Smith A, Bates J, Jarad S, Redeker NS, Luther SL, Gill TM, Brandt CA, Justice AC. Serious Falls in Middle-Aged Veterans: Development and Validation of a Predictive Risk Model. Journal of the American Geriatrics Society. 2020 Dec 1; 68(12):2847-2854. [view]

Journal Other

1. Womack JA. Commentary for falls in community-dwelling older adults with heart failure: A retrospective cohort study. Heart & lung : the journal of critical care. 2020 May 1; 49(3):336. [view]

DRA: none
DRE: Prevention, Technology Development and Assessment
Keywords: Mobility Impairment
MeSH Terms: none