Affiliations 

  • 1 Department of Medicine, Faculty of Medicine, University of Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
  • 2 Department of Medicine, Faculty of Medicine, University of Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia; Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway 47500 Petaling Jaya, Selangor, Malaysia
  • 3 Centre for Healthy Ageing & Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia 50300 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
  • 4 Faculty of Pharmacy, Universiti Teknologi MARA(UiTM) Cawangan Selangor, Kampus Puncak Alam 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
  • 5 Department of Medicine, Faculty of Medicine, University of Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia. Electronic address: hmkhor@um.edu.my
Arch Gerontol Geriatr, 2024 Oct;125:105523.
PMID: 38878671 DOI: 10.1016/j.archger.2024.105523

Abstract

AIM: The World Falls Guidelines (WFG) Task Force published a falls risk stratification algorithm in 2022. However, its adaptability is uncertain in low- and middle-income settings such as Malaysia due to different risk factors and limited resources. We evaluated the effectiveness of the WFG risk stratification algorithm in predicting falls among community-dwelling older adults in Malaysia.

METHODS: Data from the Malaysian Elders Longitudinal Research subset of the Transforming Cognitive Frailty into Later-Life Self-Sufficiency cohort study was utilized. From 2013-2015, participants aged ≥55 years were selected from the electoral rolls of three parliamentary constituencies in Klang Valley. Risk categorisation was performed using baseline data. Falls prediction values were determined using follow-up data from wave 2 (2015-2016), wave 3 (2019) and wave 4 (2020-2022).

RESULTS: Of 1,548 individuals recruited, 737 were interviewed at wave 2, 858 at wave 3, and 742 at wave 4. Falls were reported by 13.4 %, 29.8 % and 42.9 % of the low-, intermediate- and high-risk groups at wave 2, 19.4 %, 25.5 % and 32.8 % at wave 3, and 25.8 %, 27.7 % and 27.0 % at wave 4, respectively. At wave 2, the algorithm generated a sensitivity of 51.3 % (95 %CI, 43.1-59.2) and specificity of 80.1 % (95 %CI, 76.6-83.2). At wave 3, sensitivity was 29.4 % (95 %CI, 23.1-36.6) and specificity was 81.6 % (95 %CI, 78.5-84.5). At wave 4, sensitivity was 26.0 % (95 %CI, 20.2-32.8) and specificity was 78.4 % (95 %CI, 74.7-81.8).

CONCLUSION: The algorithm has high specificity and low sensitivity in predicting falls, with decreasing sensitivity over time. Therefore, regular reassessments should be made to identify individuals at risk of falling.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.