Association between tea drinking and disability levels in older Chinese adults: a longitudinal analysis - Ma, Yinghui; Zhu, Yuying; Hong, Dandan; Zhao, Haiyue; Li, Lei (Frontiers in Nutrition, 2023)
Objective As the global population ages, disability among the elderly presents unprecedented challenges for healthcare systems. However, limited research has examined whether dietary interventions like tea consumption may alleviate and prevent disability in older adults. As an important dietary therapy, the health benefits of tea drinking have gained recognition across research disciplines. Therefore, this study aimed to investigate the association between tea drinking habits and disability levels in the elderly Chinese population. Methods Leveraging data from the 2008 to 2018 waves of the Chinese Longitudinal Healthy Longevity Survey, we disaggregated tea drinking frequency and activities of daily living (ADL) measures and deployed fixed-effect ordered logit models to examine the tea-disability association for the first time. We statistically adjusted for potential confounders and conducted stratified analyses to assess heterogeneity across subpopulations. Results Multivariable fixed-effect ordered logistic regression suggested tea drinking has protective effects against ADL disability. However, only daily tea drinking was associated with lower risks of basic activities of daily living (BADL) disability [odds ratio (OR) = 0.61; 95% confidence interval (CI), 0.41–0.92] and lower levels of instrumental activities of daily living (IADL) disability (OR = 0.78; 95% CI, 0.64–0.95). Stratified analyses indicated heterogeneous effects across age and income groups. Daily tea drinking protected against BADL (OR = 0.26 and OR = 0.28) and IADL disability (OR = 0.48 and OR = 0.45) for adults over 83 years old and high-income households, respectively. Conclusion We found that drinking tea almost daily was protective against disability in elderly people, warranting further research into optimal dosages. Future studies should utilize more rigorous causal inference methods and control for confounders.