School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.
The State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China.
Front Public Health. 2023 Mar 16;11:1142794. doi: 10.3389/fpubh.2023.1142794. eCollection 2023.
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.
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.
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.
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.
家庭和社区为基础的服务被认为是中国老年人适当且关键的护理方式。然而，通过机器学习技术和全国代表性数据来研究家庭和社区为基础的服务中的医疗服务需求的研究尚未开展。本研究旨在解决家庭和社区为基础的服务中缺乏完整和统一的需求评估系统的问题。
这是一项基于 2018 年中国纵向健康长寿调查的横断面研究，共纳入了 15312 名老年人。使用五种机器学习方法（逻辑回归、逻辑回归的 LASSO 正则化、支持向量机、随机森林和极端梯度提升（XGBoost））构建了预测需求的模型，并基于 Andersen 的健康服务使用行为模型。方法使用 60%的老年人来开发模型，20%的样本来检验模型的性能，其余 20%的病例来评估模型的稳健性。为了研究家庭和社区为基础的服务中的医疗服务需求，个体特征（倾向因素、促成因素、需求因素和行为因素）构成了四个组合，以确定最佳模型。
随机森林和 XGBoost 模型产生了最佳结果，特异性均超过 80%，在验证集产生了稳健的结果。Andersen 的行为模型允许结合优势比并估计随机森林和 XGBoost 模型中每个变量的贡献。影响老年人需要家庭和社区为基础的医疗服务的三个最重要特征是自我评估的健康状况、锻炼和教育。
Andersen 的行为模型结合机器学习技术成功构建了一个具有合理预测因子的模型，以预测可能对家庭和社区为基础的医疗服务有更高需求的老年人。此外，该模型捕捉到了他们的关键特征。这种预测需求的方法对于社区和管理人员安排有限的初级医疗资源以促进健康老龄化可能具有重要价值。
