Urban-Rural Disparities in the Association Between Home- and Community-Based Service Utilization and Levels of Disability Among Chinese Older Adults With Disabilities.

Home- and community-based services (HCBS) are optimal ways to deal with disability problems among older adults. This study aims to analyze urban-rural disparities in the relationship between HCBS utilization and levels of disability among Chinese older adults with disabilities, so as to meet the long-term care needs of them. In applying the Andersen Behavioral Model, bivariate analysis and multivariate regression models were employed using data from 843 older adults with disabilities from the 2018 China Longitudinal Aging Social Survey (CLASS). After adjusting covariates, disability levels among Chinese older adults with disabilities were significantly correlated with HCBS utilization in urban areas but not in rural areas. The urban-rural disparities may be due to the low utilization of HCBS in rural areas (only 11.2%) among older adults with disabilities compared with their urban counterparts (22.7%).

Demand prediction of medical services in home and community-based services for older adults in China using machine learning.

Background: Home and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services. Methods: This was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized 60% of older adults to develop the model, 20% of the samples to examine the performance of models, and the remaining 20% of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model. Results: Random Forest and XGboost models produced the best results, in which both models were over 80% at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education. Conclusion: Andersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging.