# FALLS AMONG MIDDLE-AGED VETERANS: STEPS TOWARDS PREVENTION

> **NIH VA IK3** · VA CONNECTICUT HEALTHCARE SYSTEM · 2020 · —

## Abstract

Background. 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.
Objectives. The proposed project will explore community falls among middle-aged Veterans by characterizing
prevalence incidence, sequelae, and risk factors for medically significant community falls among middle-aged
Veterans (SA1). We will then develop a risk prediction tool to calculate the one year probability of a community
fall (SA2). Long-term, we will develop a tool that will provide useful information to clinicians (RNs, APRNs,
MDs, PAs) regarding falls risk and that will be easy to use. To this end, we will explore barriers and facilitators
that clinicians experience when using clinical decision support tools, highlighting input from RNs and APRNs in
the context of a multidisciplinary team (SA3).
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. We will use data obtained from the electronic health record (EHR) of Veterans ages 45-65 in the VA
Birth Cohort. 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, rates of related injury, hospitalization and death,
and the prevalence of related risk factors 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 will then develop a prediction tool for community falls in middle-aged Veterans. We will apply Bayesian
Model Averaging which will identify a small group of risk factor models within a given range of the minimal
...

## Key facts

- **NIH application ID:** 9889082
- **Project number:** 5IK3HX002269-02
- **Recipient organization:** VA CONNECTICUT HEALTHCARE SYSTEM
- **Principal Investigator:** Julie A Womack
- **Activity code:** IK3 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2018-04-01 → 2020-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9889082

## Citation

> US National Institutes of Health, RePORTER application 9889082, FALLS AMONG MIDDLE-AGED VETERANS: STEPS TOWARDS PREVENTION (5IK3HX002269-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9889082. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*