Use of a fracture prevention screening algorithm predicts self-reported falls in postmenopausal women

ABSTRACT

Background and Purpose: The prevention of falls and fall-related fractures following menopause is an important health initiative. The Fracture Prevention Screening Algorithm (FPSA) uniquely uses fracture risk to prompt fall risk assessment to classify both fall and fracture risk in individuals. The purpose of this study was to determine whether use of the FPSA accurately predicted self-reported falls in post-menopausal women over one year. Methods: 142 postmenopausal women were recruited. Based on Fracture Risk Assessment Tool (FRAX®) scores, women with a ≥3% 10-year probability of hip fracture (high risk), or who self-identified as having balance problems or a fall history, underwent the Functional Gait Assessment (FGA) to estimate fall risk (high risk = ≤22/30). This allowed classification on the FPSA into one of four risk categories: low fall/low fracture risk; low fall/high fracture risk; high fall/low fracture risk; high fall/high fracture risk. Participants were contacted monthly for one year to determine fall and injury occurrence. Results: Fall/injury surveillance was conducted with 136 subjects over one year. Compared to women in the low fall/low fracture risk group, both high fall risk groups demonstrated significantly greater fall rates. Falls were 81–89% more likely in women with FGA scores of 22/30 or less. All injuries were rare events across all risk strata and did not differ between risk groups. Conclusion: These findings support the use of fracture risk as a trigger for fall screening to comprehensively classify risk in post-menopausal women as proposed by the FPSA.

Declaration of interest

The authors repost no conflict of interest.

Additional information

Funding

Drs. Downey and Perry received funding for this project from the Chatham University Research and Sabbatical Committee.