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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%). [ABSTRACT FROM AUTHOR]

Background: Studies have shown a close association between home and community-based healthcare services (HCBHS) utilization and depressive symptoms in older adults. However, no studies have explored the underlying mechanism of this relationship in rural China. This study was designed to evaluate the roles of instrumental activities of daily living (IADL) and marital status in the association between HCBHS utilization and depressive symptoms in Chinese rural older adults. Methods: Data were obtained from the 2018 China Health and Retirement Longitudinal Study, and 5,981 rural respondents (≥ 60 years old) were included. Depression scores were calculated using the ten-item Center for Epidemiological Studies Depression Scale. Moderated mediation analysis was carried out applying Hayes' PROCESS macro (Model 7). Results: HCBHS utilization had a direct and negative effect on depressive symptoms. Furthermore, marital status moderated the association between HCBHS utilization and IADL, which belonged to the indirect influence of the first half on the association between HCBHS utilization and depressive symptoms. HCBHS utilization was associated with IADL in single but not in married respondents. Conclusion: The results demonstrated that marital status moderated the indirect relationship between HCBHS utilization and depressive symptoms, with HCBHS utilization being negatively associated with IADL among single but not married respondents. The government should focus on rural older adults, especially those who are single and have poor IADL function, and improve the provision of HCBHS to alleviate depressive symptoms. [ABSTRACT FROM AUTHOR]

Home and community-based physical activity (HCBPA) has been extensively utilized among older adults. Nevertheless, the varying types of HCBPA, including different duration, intensity, and frequency, have sparked controversy regarding their impact on the quality of life in older adults. This study aims to explore the effects of HCBPA on QoL in older adults. We conducted a systematic review and retrieved studies published from January 2000 to April 2023 from multiple databases (PubMed, Embase, Cochrane, and the Web of Science Library). Seventeen articles met the inclusion criteria for this study. Long-term HCBPA interventions may have a more pronounced positive impact on older adults' quality of life than short-term ones, with the intervention's intensity and frequency playing a key role in its effectiveness. The results of the meta-analyses showed significant differences in PCS but not in MCS, both with low certainty of evidence. Policymakers should prioritize the importance of promoting HCBPA interventions with appropriate duration, intensity, and frequency to create a more age-inclusive society., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Mild cognitive impairment (MCI) is a transitory yet reversible stage of dementia. Systematic, scientific and population-wide early screening system for MCI is lacking. This study aimed to construct prediction models using longitudinal data to identify potential MCI patients and explore its critical features among Chinese older adults., Methods: A total of 2,128 participants were selected from wave 5-8 of Chinese Longitudinal Healthy Longevity Study. Cognitive function was measured using the Chinese version of Mini-Mental State Examination. Long- short-term memory (LSTM) and three machine learning techniques, including 8 sociodemographic features and 12 health behavior and health status features, were used to predict individual risk of MCI in the next year. Performances of prediction models were evaluated through receiver operating curve and decision curve analysis. The importance of predictors in prediction models were explored using Shapley Additive explanation (SHAP) model., Results: The area under the curve values of three models were around 0.90 and decision curve analysis indicated that the net benefit of XGboost and Random Forest were approximate when threshold is lower than 0.8. SHAP models showed that age, education, respiratory disease, gastrointestinal ulcer and self-rated health are the five most important predictors of MCI., Conclusion: This screening method of MCI, combining LSTM and machine learning, successfully predicted the risk of MCI using longitudinal datasets, and enables health care providers to implement early intervention to delay the process from MCI to dementia, reducing the incidence and treatment cost of dementia ultimately., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Huang, Huang, Yang, Jin, Xu, Fu, Zhu, Zhang and Chen.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Huang, Xu, Yang, Pan, Zhan, Chen, Zhang and Chen.)

Background: Health literacy (HL) is considered a crucial determinant of disease prevention and control. However, the role of HL in the Coronavirus Disease 2019 (COVID-19) pandemic has not been studied using provincial representative data among Chinese residents. This study aimed to assess the association between HL and COVID-19 awareness among Zhejiang residents based on the 2020 China Health Literacy Survey (CHLS)., Methods: The study was conducted among 5,596 residents aged 15-69 in Zhejiang using multistage, stratified, and probability proportional to size sampling. COVID-19 awareness and HL were assessed using the "Chinese Citizen Health Literacy Questionnaire (2020)" in Zhejiang. The covariates were divided into predisposing factors, enabling factors, and need factors according to Anderson's model. Data were analyzed by the chi-square test and logistic regression., Results: The study showed that HL and COVID-19 awareness levels of residents were 24.84% and 8.06%, respectively, in Zhejiang in 2020. After adjusting for covariates, residents with adequate HL were more likely to have better COVID-19 awareness (odds ratio [OR] = 5.22, 95% CI = 4.13-6.59, p < 0.001). Three dimensions of HL (knowledge and attitudes, behavior and lifestyle, and health-related skills) were associated with COVID-19 awareness. Additionally, COVID-19 awareness was associated with age, occupation, family size, annual household income, and chronic conditions., Conclusion: COVID-19 awareness is significantly associated with HL, suggesting that promoting HL is an important component of health education, disease prevention, and health promotion in response to the COVID-19 pandemic and even possible public health emergencies in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Xu, Chen, Xu, Ge, Yao and Zhang.)