The Relationship Between Alcohol Consumption and Frailty Among Older Adults in China: Results From the Chinese Longitudinal Healthy Longevity Survey.

INTRODUCTION: Alcohol consumption has an impact on the frailty, but current research in China lacks a detailed classification of alcohol use. This study aimed to explore the relationship between different drinking patterns and frailty in older adults. METHODOLOGY: The data came from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) study, which included older adults (aged ≧ 60). Their demographic data, drinking status, and frailty index were collected in CLHLS. Through logistic regression models to analyze the correlation between alcohol consumption and frailty. RESULTS: A total of 14,931 participants were included in the analysis. The prevalence of frailty was 29.1%, 35.2%, and 14.9% among risk-free, past risky, and now risky drinkers, respectively. After adjusting for covariates, past risky drinking was a risk factor for frailty (p = .003). DISCUSSION: High-risk alcohol consumption is positively correlated with frailty. Prevention and reduction of risky drinking in older adults may help protect them from developing frailty.

Evaluating the quality of home care in community health service centres: A machine learning approach.

AIMS: The aim of the study is to develop a model using a machine learning approach that can effectively identify the quality of home care in communities. DESIGN: A cross-sectional design. METHODS: In this study, we evaluated the quality of home care in 170 community health service centres between October 2022 and February 2023. The Home Care Service Quality Questionnaire was used to collect information on home care structure, process and outcome quality. Then, an intelligent and comprehensive evaluation model was developed using a convolutional neural network, and its performance was compared with random forest and logistic regression models through various performance indicators. RESULTS: The convolutional neural network model was built upon seven variables, which encompassed the qualification of home nursing staff, developing and practicing emergency plan to cope with different emergency rescues in home environment, being equipped with medication and supplies for first aid according to specific situations, assessing nutrition condition of home patients, allocation of the number of home nursing staff, cases of new pressure ulcers and patient satisfaction rate. Remarkably, the convolutional neural network model demonstrated superior performance, outperforming both the random forest and regression models. CONCLUSION: The successful development and application of the convolutional neural network model highlight its ability to leverage data from community health service centres for rapid and accurate grading of home care quality. This research points the way to home care quality improvement. IMPACT: The model proposed in this study, coupled with the aforementioned factors, is expected to enhance the accuracy and efficiency of a comprehensive evaluation of home care quality. It will also help managers to take purposeful measures to improve the quality of home care. REPORTING METHOD: The reporting of this study (Observational, cross-sectional study) conforms to the STROBE statement. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The application of this model has the potential to contribute to the advancement of high-quality home care, particularly in lower-middle-income communities.