Who Are the Fallers and Why Do They Fall? A Comprehensive Study of Falls in Older Adults in a Chronic Care Facility

1. WHO ARE THE FALLERS AND WHY
DO THEY FALL?
A COMPREHENSIVE STUDY OF FALLS IN OLDER
ADULTS IN A CHRONIC CARE FACILITY
PAMELA JARRETT, MD, FRCPC, FACP
ASSOCIATE PROFESSOR, INTERNAL MEDICINE DALHOUSIE AND MEMORIAL UNIVERSITY;
GERIATRICIAN HORIZON HEALTH NETWORK, SAINT JOHN, NB.
CHRIS A. MCGIBBON, PHD
PROFESSOR, FACULTY OF KINESIOLOGY AND INSTITUTE OF BIOMEDICAL ENGINEERING,
UNIVERSITY OF NEW BRUNSWICK, FREDERICTON, NB

3. WHAT WAS THE ISSUE?
• Falls in hospitalized seniors on specialized inpatient geriatric units
• Falls reports about these falls but limited unique patient information
• Many publications on falls but often limited to results from incident reports
How are falls related both to the care environment, patient
related characteristics and how are they related?

4. WHAT WERE THE DATA SETS
• Clinically based data set (all discharges from these units with diagnostic and
functional information using valid measures)
• Developed over 10 years ago by consensus from the interdisciplinary team for the
Health and Aging Program
• Research, Quality Improvement Activities, Monitoring
• Maintained by Health and Aging Program in Access
• Falls Data Set
• Created as part of Falls Prevention Strategy
• Information reported in incident reports collected electronically

5. COLLABORATIVE RESEARCH OPPORTUNITY
• Clinically important questions to answer to help inform future direction for “Falls
Prevention”.
• Needed a “Deeper Dive” to understand what mattered for our patients regarding
falls prevention.
• Common interest among clinically based researchers and academically based
researchers led to combination of skill sets that facilitated the research

6. KEY FINDINGS
• Fall rates were driven by
repeat fallers
• All falls
• 8.48 falls/1000obd
• First falls
• 2.72 falls/1000obd
• Focus should be on
preventing repeat falls 2.72
8.48

7. KEY FINDINGS
• Common fall risk assessment tools
DO NOT predict falls in a geriatric
hospital setting:
• Morse Fall Scale
• Timed Up and Go
• Berg Balance Scale
• Clinical Frailty Scale
• Best predictor (OR 2.5 p<.001) but
still less than ideal (44.5% true
positive rate)
• Better fall risk assessment tools are
needed
Clinical Frailty Scale
Morse Fall Scale
No fall 1 Fall 2 Falls >2 Falls No fall 1 Fall 2 Falls >2 Falls

8. KEY FINDINGS
• Patient fall reports added contextual information for understanding who falls and
why.
• Patients with mobility aids were more likely to fall in their room/bathroom (p < .001)
and during non-ambulatory activity (p < .001)
• Patients who fell outside of their room had less impaired balance (p = .009) but more
impaired cognition (p < .001).
• Injury rates were higher in those with a mobility aid (p = .020), mostly occurring in
patients’ rooms.
• Real-time tracking of fall contextual data may help better predict who will fall and
under what circumstances it is likely to happen.

9. WHAT COULD WE DO NOW?
• Stop doing Morse Fall Scale in this setting – (other types of hospital beds too?)
• Use the Clinical Frailty Scale score to determine risk? Evaluate this!
• Target repeat fallers so that interventions are patient specific and meaningful –
Evaluate this!

10. WHERE TO GO FROM HERE?
Strong collaboration between researchers and expansion of work group to include MSSU and NB-
IRDT
1. Further research with more details around patient specific characteristics, chart reviews
• Quantitative and Qualitative
2. Additional Researchers added to this work – Dr. Sandra Magalhaes – work in progress to connect to NB IRDT
data
3. Predictive Modeling to develop a clinically meaningful falls prediction tool
4. Testing of “real time” meaningful data that could be incorporated into daily clinical care
5. Endless Opportunities!!!

11. QUESTIONS?