The COVID‐19 pandemic presented unparalleled challenges to prompt and adaptive responses from nations worldwide. This review examines China's multifaceted approach to the crisis, focusing on five key areas of response: infrastructure and system design, medical care and treatment, disease prevention and control, economic and social resilience, and China's engagement in global health. This review demonstrates the effectiveness of a top‐down command system at the national level, intersectoral coordination, a legal framework, and public social governance. This study also examines medical care and treatment strategies, highlighting the importance of rapid emergency response, evidence‐based treatment, and well‐planned vaccination rollout. Further discussion on disease prevention and control measures emphasizes the importance of adaptive measures, timely infection control, transmission interruption, population herd immunity, and technology applications. Socioeconomic impact was also assessed, detailing strategies for disease prevention, material supply, livelihood preservation, and social economy revival. Lastly, we examine China's contributions to the global health community, with a focus on knowledge‐sharing, information exchange, and multilateral assistance. While it is true that each nation's response must be tailored to its own context, there are universal lessons to be drawn from China's approach. These insights are pivotal for enhancing global health security, especially as the world navigates evolving health crises.
Open Access
Review
Issue
Open Access
Original Article
Issue
Frailty in older adults is linked to increased risks and lower quality of life. Pre‐frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre‐frailty risk assessment among community‐dwelling older adults.
The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre‐frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre‐frailty risk. A model was constructed using recursive feature elimination and a stacking‐CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set.
The study used data from 2508 community‐dwelling older adults (mean age, 67.24 years [range, 60–96]; 1215 [48.44%] females) to develop a pre‐frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre‐frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions.
An accurate and interpretable pre‐frailty risk assessment framework using state‐of‐the‐art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre‐frailty risk.
京公网安备11010802044758号