Background: The association between sarcopenia and mild cognitive impairment (MCI) among elderly adults in China remains unclear. The present study aimed to examine the association based on a nationally representative large‐scale survey.Methods: The study used two waves of data from China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018. All subjects met the inclusion criteria were classified based on Asia Working Group for Sarcopenia 2019 criteria. Aging‐associated cognitive decline is used to define MCI, and cognitive function is measured based on four dimensions: orientation, computation, memory, and drawing. OLS and logistic regression model were conducted to analyse the cross‐sectional association between sarcopenia and different cognitive functions. Logistic regression model was conducted to analyse the longitudinal association between sarcopenia and MCI.Results: Totally, 5715 participants aged over 60 years (43.8% women; mean age 67.3 ± 6.0 years) were enrolled in a cross‐sectional association study in 2015, and further 2982 elderly adults were followed up in 2018. During the period, sarcopenia and possible sarcopenia increased from 8.5% to 29.6%. Scores of cognitive and four dimensions (orientation, computation, memory, and drawing) exhibited a decreasing trend from non‐sarcopenia to sarcopenia (P < 0.001). In the fully adjusted OLS regression model, scores of four dimensions were lower in possible sarcopenia and sarcopenia groups when compared with the non‐sarcopenia group (P < 0.05) respectively. The incidence of MCI was 10.1%, 16.5%, and 24.2% for non‐sarcopenia, possible sarcopenia, and sarcopenia groups from 2015 to 2018, with a significantly statistical difference (P < 0.001). Logistic regression model revealed an odds ratio of 1.43 [95% confidence interval (CI): 1.06–1.91, P = 0.017] for the possible sarcopenia group and 1.72 (95% CI: 1.04–2.85, P = 0.035) for sarcopenia group when compared with the non‐sarcopenia group.Conclusions: Sarcopenia is associated with worse cognitive impairment, which provided new evidence for a strong association that warrants further research into mechanistic insights.

China’s population has rapidly aged over the recent decades of social and economic development as neurodegenerative disorders have proliferated, especially Alzheimer’s disease (AD) and related dementias (ADRD). AD’s incidence rate, morbidity, and mortality have steadily increased to make it presently the fifth leading cause of death among urban and rural residents in China and magnify the resulting financial burdens on individuals, families and society. The ‘Healthy China Action’ plan of 2019–2030 promotes the transition from disease treatment to health maintenance for this expanding population with ADRD. This report describes related epidemiological trends, evaluates the economic burden of the disease, outlines current clinical diagnosis and treatment status and delineates existing available public health resources. More specifically, it examines the public health impact of ADRD, including prevalence, mortality, costs, usage of care, and the overall effect on caregivers and society. In addition, this special report presents technical guidance and supports for the prevention and treatment of AD, provides expertise to guide relevant governmental healthcare policy development and suggests an information platform for international exchange and cooperation.

To investigate the longitudinal associations between loneliness, social isolation, depression and anxiety in the elderly. A longitudinal cohort study was conducted among 634 older adults from three districts of Shanghai. Data were collected at baseline and 6-month follow-up. Loneliness and social isolation were measured using the De Jong Gierveld Loneliness Scale and the Lubben Social Network Scale respectively. Depressive and anxiety symptoms were assessed using the subscales of Depression Anxiety Stress Scales. Logistic regression and negative binomial regression models were used to examine the associations. We found that moderate to severe loneliness at baseline predicted higher depression scores 6 months later (incidence rate ratio (IRR)=1.99, 95% confidence interval (CI) [1.12, 3.53], p=0.019), while higher depression scores at baseline predicted social isolation at follow-up (odds ratio (OR)=1.14, 95% CI [1.03, 1.27], p=0.012). We also observed that higher anxiety scores predicted lower risk of social isolation (OR=0.87, 95% CI [0.77, 0.98], p=0.021). Additionally, persistent loneliness at both timepoints was significantly associated with higher depression scores at follow-up, and persistent social isolation was associated with a greater likelihood of having moderate to severe loneliness and higher depression scores at follow-up. Loneliness was found to be a robust predictor of changes in depressive symptoms. Both persistent loneliness and social isolation were closely associated with depression. We should develop effective and feasible interventions for older adults who have depressive symptoms or who are at risk of long-term social relationship problems to avoid the vicious circle among depression, social isolation and loneliness.

With the sustained growth of the economy and significant changes in social demographics, the issue of elderly‐related diseases has increasingly drawn attention, particularly. Alzheimer's disease (AD), as a representative disease of neurodegenerative diseases, has become a major challenge, affecting the health and quality of life of the elderly population severely. In recent years, the incidence, prevalence and mortality rates of AD have increased in China, imposing substantial economic burdens on families, society and the entire healthcare system. To proactively address this challenge and respond to the national ‘Healthy China Action’ initiative, leading experts from authoritative institutions jointly authored the China Alzheimer Report 2025. Building on previous editions, this report updates epidemiological data on AD in China, thoroughly analyses the latest economic burdens of the disease and comprehensively evaluates the current status of AD diagnosis and treatment services, as well as the allocation of public health resources in our country. Its release reflects China's progress in AD research and prevention, underscores societal concern for elderly health and aims to provide scientific guidance and data support for AD prevention, diagnosis and treatment. It also facilitates academic exchanges and cooperation, enhancing public awareness and promoting active participation in elderly healthcare, towards achieving ‘healthy ageing’ in China.

Background: Functional disability and multimorbidity are common among older people. However, little is known about the relationship between functional disability and different multimorbidity combinations. We aimed to identify multimorbidity patterns and explore the associations between these patterns and functional disability. Methods: We investigated a multi-stage random sample of 1871 participants aged ≥60 years and covered by long-term care insurance in Shanghai, China. Multimorbidity was defined as the simultaneous presence of two or more chronic diseases in an individual. Participants completed scales to assess basic and instrumental activities of daily living (BADL and IADL, respectively). Multimorbidity patterns were identified via exploratory factor analysis. Binary logistic regression models were used to determine adjusted associations between functional disability and number and patterns of multimorbidity. Results: Multimorbidity was present in 74.3% of participants. The prevalence of BADL disability was 50.7% and that of IADL disability was 90.7%. There was a strong association between multimorbidity and disability. We identified three multimorbidity patterns: musculoskeletal, cardio-metabolic, and mental-degenerative diseases. The cardio-metabolic disease pattern was associated with both BADL (OR 1.28, 95%CI 1.16–1.41) and IADL (OR 1.41, 95%CI 1.19–1.68) disability. The mental-degenerative disease pattern was associated with BADL disability (OR 1.55, 95%CI 1.40–1.72). Conclusions: Multimorbidity and functional disability are highly prevalent among older people covered by long-term care insurance in Shanghai, and distinct multimorbidity patterns are differentially associated with functional disability. Appropriate long-term healthcare and prevention strategies for older people may help reduce multimorbidity, maintain functional ability, and improve health-related quality of life.

Objective: To compare an autoregressive integrated moving average (ARIMA) model with a model that combines ARIMA with the Elman recurrent neural network (ARIMA-ERNN) in predicting the incidence of pertussis in mainland China. Background: The incidence of pertussis has increased rapidly in mainland China since 2016, making the disease an increasing public health threat. There is a pressing need for models capable of accurately predicting the incidence of pertussis in order to guide prevention and control measures. We developed and compared two models for predicting pertussis incidence in mainland China. Methods: Data on the incidence of pertussis in mainland China from 2004 to 2019 were obtained from the official website of the Chinese Center for Disease Control and Prevention. An ARIMA model was established using SAS (ver. 9.4) software and an ARIMA-ERNN model was established using MATLAB (ver. R2019a) software. The performances of these models were compared. Results: From 2004 to 2019, there were 104,837 reported cases of pertussis in mainland China, with an increasing incidence over time. The incidence of pertussis showed obvious seasonal characteristics, with the peak lasting from March to September every year. Compared with the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ARIMA model, those of the ARIMA-ERNN model were 81.43%, 95.97% and 80.86% lower, respectively, in fitting performance. In terms of prediction performance, the MAE, MSE and MAPE were 37.75%, 56.88% and 43.75% lower, respectively. Conclusion: The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA model. This provides theoretical support for the prediction of infectious diseases and should be beneficial to public health decision making.