METHOD: A systematic search was conducted in four electronic databases. Studies reporting data between 2010 and 2023 on the geographical incidences of hip fractures in individuals aged ≥50 were included. Exclusion criteria were studies reporting solely on high-trauma, atypical, or periprosthetic fractures. We calculated the crude incidence, age- and sex-standardised incidence, and the female-to-male ratio. The systematic review was registered with PROSPERO (CRD42020162518).
RESULTS: Thirty-eight studies were included across nine countries/regions (out of 41 countries/regions). The crude hip fracture incidence ranged from 89 to 341 per 100,000 people aged ≥50, with the highest observed in Australia, Taiwan, and Japan. Age- and sex-standardised rates ranged between 90 and 318 per 100,000 population and were highest in Korea and Japan. Temporal decreases in standardised rates were observed in Korea, China, and Japan. The female-to-male ratio was highest in Japan and lowest in China.
CONCLUSION: Fragility hip fracture incidence varied substantially within the Asia-Pacific region. This observation may reflect actual incidence differences or stem from varying research methods and healthcare recording systems. Future research should use consistent measurement approaches to enhance international comparisons and service planning.
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.