Zhai Xiangyu, Yu Doris S F
School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Sau Po Centre on Aging, The University of Hong Kong, Hong Kong, China.
Ann Med. 2025 Dec;57(1):2534085. doi: 10.1080/07853890.2025.2534085. Epub 2025 Jul 25.
Individual-level socioeconomic characteristics are known to predict sarcopenia, but little is known about how the neighborhood context shapes this debilitating problem among older adults. This study examined the association between neighborhood socioeconomic inequality and sarcopenia.
Data from three impact evaluation studies aimed at promoting healthy aging by using the WHO Integrated Care for Older People Model, tailored exercise training, and social prescribing strategies. A total of 1650 older adults (aged 60 or above) were included in this secondary data analysis. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019 algorithm. A neighborhood socioeconomic status (nSES) index was constructed using a weighted combination of seven validated neighborhood-level socioeconomic indicators derived from government data across 292 Tertiary Planning Units in Hong Kong. The index was categorized into distribution-based tertiles as high (T1), moderate (T2), and low (T3). Logistic regression analysis identified the association between nSES and sarcopenia, adjusting for age, gender, body mass index, and individual-level socioeconomic characteristics.
Among the participants, 11.03% ( = 182) were diagnosed with sarcopenia. Compared with the high nSES tertiles (T1), the lower ones showed higher odds of sarcopenia after adjustment for age, sex, body mass index, and individual-level socioeconomic characteristics (T2: adjusted OR [aOR] = 1.49 [95% CI, 0.85-2.64], T3: aOR = 1.68 [95% CI, 1.02-2.74]).
This is the first study to identify the negative relationship between comprehensive nSES and the prevalence of sarcopenia. The findings highlight the need to develop socio-environmental sensitive risk stratification and preventive care to manage later-age sarcopenia.
个体层面的社会经济特征已知可预测肌肉减少症，但对于邻里环境如何影响老年人这一衰弱问题却知之甚少。本研究探讨了邻里社会经济不平等与肌肉减少症之间的关联。
数据来自三项影响评估研究，这些研究旨在通过采用世界卫生组织老年人综合照护模式、量身定制的运动训练和社会处方策略来促进健康老龄化。在本次二次数据分析中，共纳入了1650名60岁及以上的老年人。根据亚洲肌肉减少症工作组2019年的算法诊断肌肉减少症。利用从香港292个三级规划单元的政府数据中得出的七个经过验证的邻里层面社会经济指标的加权组合，构建了一个邻里社会经济地位（nSES）指数。该指数被分为基于分布的三分位数，即高（T1）、中（T2）和低（T3）。逻辑回归分析确定了nSES与肌肉减少症之间的关联，并对年龄、性别、体重指数和个体层面的社会经济特征进行了调整。
在参与者中，11.03%（n = 182）被诊断为患有肌肉减少症。与高nSES三分位数（T1）相比，在对年龄、性别、体重指数和个体层面的社会经济特征进行调整后，较低的三分位数显示出患肌肉减少症的几率更高（T2：调整后的比值比[aOR] = 1.49 [95%置信区间，0.85 - 2.64]，T3：aOR = 1.68 [95%置信区间，1.
