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.
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.
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.
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.
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.
- 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.
- 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.