Bayesian network analysis of the effect of living environment on cognitive impairment in empty-nest elderly people

Shiji Zhang^1,2Yanzhen Tian^1*Nina Feng^1,2Jinxiu Li²Tao Zhang2²Libang Deng²Jianjun Fu³

• ¹Zhuzhou Central Hospital, Zhuzhou, China
• ²Jishou University School of Medicine, Jishou, China
• ³Wuhan Sixth Hospital, Wuhan, China

Objective: Based on the Bayesian network, this study investigates the impact pathways of multidimensional factors related to the living environment—specifically housing factors, exposure to daily chemical agents, daily fuel use, air quality, and drinking water sources—on cognitive impairment in empty-nest elderly individuals. The aim is to identify key direct and indirect predictors and provide a foundation for targeted environmental interventions. Methods: The study utilized data from China's 2018 Comprehensive Longitudinal Health Survey (CLHLS) to track health-affecting factors, including a sample of 5,961 empty-nest elderly individuals. Potential predictive variables were initially screened through univariate analysis, followed by further screening of significant variables using binary logistic regression. We constructed the Bayesian Network structure with R's bnlearn package and made probability predictions using Netica. Results: The incidence of cognitive impairment among the empty-nest elderly individuals is 18.7%. Results from a binary logistic regression analysis suggest that several factors are associated with an increased risk of cognitive impairment in this population. These factors encompass living in rural areas, exposure to daily chemical agents, lack of access to piped natural gas, use of kerosene and coal, insufficient kitchen ventilation, presence of a musty smell in the living space, smoking, failure to open windows during winter, and consumption of untreated water. Furthermore, the results from a Bayesian network model indicate that smoking, the absence of piped natural gas, musty odors in the room, and exposure to daily chemical agents are directly related to cognitive impairment. In contrast, living in rural areas, drinking untreated water, using coal, not opening indoor windows during winter, inadequate kitchen ventilation, a lack of air purification devices, and reliance on kerosene are indirectly associated with cognitive impairment. Notably, elderly individuals at the highest risk of cognitive impairment (41.5%) are those who smoke, experience musty odors in their residences, are exposed to daily chemicals, and lack access to piped gas. Conclusion: Factors related to the living environment can influence the cognitive functions of empty-nest elderly individuals through multiple pathways. Therefore, strategies for preventing cognitive impairment should adopt a multifactorial and integrated approach, incorporating both community and home-based interventions.