Prediction of disability levels and research on nursing economic costs for elderly people in China - Shiyan Lu; Yongxiu Kuang (PLoS ONE, 2025)
This study aims to solve the problem of the shortage of professional nursing staff and the inability of the quality of nursing services to meet the needs of the elderly. Therefore, a longitudinal research type was adopted for long-term tracking and observation, with a time span from 2017 to 2020. The disability status of the elderly in China was analyzed, and appropriate sample data were selected to construct a comprehensive disability level assessment system. Then, a prediction method for disability scale and level based on the queue element method was proposed. Finally, based on the prediction results, a pension cost optimization strategy was designed, and the current pension methods and economic costs of the elderly were discussed, aiming to providing new ways to solve the deep aging dilemma of Chinese society and families. The results showed that the disability scale prediction method calculated that the growth rate of elderly people from 2025 to 2035 would exceed 65%. Moreover, the prediction error accuracy of elderly people nationwide in 2025 was only −0.03%, and the growth rate of elderly people has reached 69.07%. Compared with mainstream random forests, artificial neural networks, and long short-term memory networks, the research method showed excellent prediction performance, with average absolute error, mean square error, average prediction error, and running time of 0.617576, 0.000053, 0.005007, and 10.24 ms, respectively. The economic cost of nursing for severe disabilities in institutions was nearly 3 times that of mild disabilities and 3.8 times that of home care. The above information shows that the research method can accurately assess the disability level and care needs of the elderly and propose targeted improvement strategies. This method can strengthen the establishment of the nursing service system for disabled elderly people, and build a full chain nursing system including hospitals, elderly care institutions, families, and communities. The study provides strong support for the comprehensive implementation of the long-term care insurance system.