Aims: In light of the escalating global incidence of Parkinson’s disease and the dearth of therapeutic interventions that can alter the disease’s course, there exists an urgent necessity to comprehensively elucidate and quantify the disease’s global burden.Methods: This study analyzed the incidence, prevalence, and disability-adjusted life years (DALYs) of Parkinson’s disease at global, regional, and national levels based on the Global Burden of Disease Study 2021. Bayesian age-period cohort (BAPC) analysis was used to predict the burden in Parkinson’s disease from 2022 to 2035.Results: In 2021, 11.77 million people worldwide had Parkinson’s disease. Age-standardized rates of incidence, prevalence, and DALYs increased to 15.63/100,000, 138.63/100,000, and 89.59/100,000. The burden of Parkinson’s disease were higher in males than in females, and showed an increase and then a slight decrease with age. The disease burden was highest in East Asia. BAPC projection showed an increase in all metrics by 2035 except for a slight decrease in the age-standardized DALYs rates.Conclusion: The global burden of Parkinson’s disease has risen over the past 32 years, and there is a need to focus on key populations, as well as to improve health policies to prevent and treat Parkinson’s disease.

Objectives: This study aimed to compare the prevalence of oral frailty among community-dwelling older people in Nanjing, China with the usage of different measurements, and to investigate the potential risk factors of oral frailty. Design: Cross-sectional study. Setting and participants: A total of 338 community-dwelling older people in Nanjing, China were recruited. Methods: Oral frailty was measured based on the Oral Frailty Index-8 (OFI-8) scale and other measurement methods including the number of natural teeth (TN), repetitive saliva-swallowing test (RSST), and oral diadochokinesis (ODK). The chi-square test and the binary logistic regression analysis were performed to identify potential risk factors for oral frailty. Results: There were 310 participants included in the analysis. Prevalence of oral frailty by using the OFI-8, OFI-8 + TN, OFI-8 + ODK, OFI-8 + TN + ODK and RSST measurement methods were 69.0%, 27.4%, 51.9%, 21.0% and 2.9%, respectively. Passive smoking (OR = 2.04; 95%CI 1.03–4.03), being widowed/unmarried (OR1 = 2.53; 95%CI 1.25–5.10; OR2 = 2.94; 95%CI 1.12–7.77), pre-frailty (OR = 1.76; 95%CI 1.03–3.01), frailty (OR = 3.01; 95%CI 1.39–6.54), and aged 80 years and above (OR = 3.99; 95%CI 1.35–11.81) were found to be risk factors of oral frailty by the usage of the four kinds of measurement methods. Conclusions and implications: The definition and diagnostic criteria of oral frailty are strongly needed to be unified in future research. Only subjective assessment is not enough for assessing oral frailty. Among objective indicators, RSST is not suitable as a screening method for oral frailty. In addition, objective indicators including TN and ODK should be valued for early screening and preventive interventions. The risk factors of oral frailty include physical frailty, passive smoking, and being widowed.

Water pollution has been hindering the world’s sustainable development. The accurate inversion of water quality parameters in sewage with visible-near infrared spectroscopy can improve the effectiveness and rational utilization and management of water resources. However, the accuracy of spectral models of water quality parameters is usually prone to noise information and high dimensionality of spectral data. This study aimed to enhance the model accuracy through optimizing the spectral models based on the sensitive spectral intervals of different water quality parameters. To this end, six kinds of sewage water taken from a biological sewage treatment plant went through laboratory physical and chemical tests. In total, 87 samples of sewage water were obtained by adding different amount of pure water to them. The raw reflectance (R_raw) of the samples were collected with analytical spectral devices. The R_raw-SNV were obtained from the R_raw processed with the standard normal variable. Then, the sensitive spectral intervals of each of the six water quality parameters, namely, chemical oxygen demand (COD), biological oxygen demand (BOD), NH₃-N, the total dissolved substances (TDS), total hardness (TH) and total alkalinity (TA), were selected using three different methods: gray correlation (GC), variable importance in projection (VIP) and set pair analysis (SPA). Finally, the performance of both extreme learning machine (ELM) and partial least squares regression (PLSR) was investigated based on the sensitive spectral intervals. The results demonstrated that the model accuracy based on the sensitive spectral ranges screened through different methods appeared different. The GC method had better performance in reducing the redundancy and the VIP method was better in information preservation. The SPA method could make the optimal trade-offs between information preservation and redundancy reduction and it could retain maximal spectral band intervals with good response to the inversion parameters. The accuracy of the models based on varied sensitive spectral ranges selected by the three analysis methods was different: the GC was the highest, the SPA came next and the VIP was the lowest. On the whole, PLSR and ELM both achieved satisfying model accuracy, but the prediction accuracy of the latter was higher than the former. Great differences existed among the optimal inversion accuracy of different water quality parameters: COD, BOD and TN were very high; TA relatively high; and TDS and TH relatively low. These findings can provide a new way to optimize the spectral model of wastewater biochemical parameters and thus improve its prediction precision.

Background: Non-pharmacological interventions have a myriad of available intervention options and contain multiple components. Whether specific components of non-pharmacological interventions or combinations are superior to others remains unclear. The main aim of this study is to compare the effects of different combinations of non-pharmacological interventions and their specific components on health-related outcomes in adults with subjective cognitive decline. Methods: PubMed, Embase, Cochrane, CINAHL, PsycINFO, CENTRAL, Web of Science, and China’s two largest databases, CNKI and Wanfang, were searched from inception to 22nd, January 2023. Randomized controlled trials using non-pharmacological interventions and reporting health outcomes in adults with subjective cognitive decline were included. Two independent reviewers screened studies, extracted data, and assessed risk of bias. Component network meta-analysis was conducted employing an additive component model for network meta-analysis. This study followed the PRISMA reporting guideline and the PRISMA checklist is presented in Additional file 2. Results: A total of 39 trials with 2959 patients were included (range of mean ages, 58.79–77.41 years). Resistance exercise might be the optimal intervention for reducing memory complaints in adults with subjective cognitive decline; the surface under the cumulative ranking p score was 0.888, followed by balance exercise (p = 0.859), aerobic exercise (p = 0.832), and cognitive interventions (p = 0.618). Music therapy, cognitive training, transcranial direct current stimulation, mindfulness therapy, and balance exercises might be the most effective intervention components for improving global cognitive function (iSMD, 0.83; 95% CI, 0.36 to 1.29), language (iSMD, 0.31; 95% CI, 0.24 to 0.38), ability to perform activities of daily living (iSMD, 0.55; 95% CI, 0.21 to 0.89), physical health (iSMD, 3.29; 95% CI, 2.57 to 4.00), and anxiety relief (iSMD, 0.71; 95% CI, 0.26 to 1.16), respectively. Conclusions: The form of physical activity performed appears to be more beneficial than cognitive interventions in reducing subjective memory complaints for adults with subjective cognitive decline, and this difference was reflected in resistance, aerobic, and balance exercises. Randomized clinical trials with high-quality and large-scale are warranted to validate the findings. Trial registration: PROSPERO registry number. CRD42022355363.

Background: The increasing prevalence of multimorbidity has created a serious global public health problem in aging populations. Certain multimorbidity patterns across different age ranges and their association with health status remain unclear. The main aim of this study is to identify multimorbidity patterns discrepancies and associated health status between younger-old and oldest-old. Methods: The Ethics Committee of Nanjing Medical University approved the study protocol (No.2019–473). Convenience sampling method was used to recruit older adults aged ≥ 60 years with multimorbidity from July to December 2021 from 38 Landsea long-term care facilities in China. The multimorbidity patterns were analyzed using network analysis and two-step cluster analysis. One-Way ANOVA was utilized to explore their association with health status including body function, activity of daily living, and social participation. A Sankey diagram visualized the flow of health status within different multimorbidity patterns. This study is reported following the STROBE guidelines. Results: A total of 214 younger-old (60–84 years) and 173 oldest-old (≥ 85 years) were included. Leading coexisting diseases were cardiovascular disease (CD), metabolic and endocrine disease (MED), neurological disease (ND), and orthopedic disease (OD). Cluster 1 (53, 24.8%) of CD-ND (50, 94.3%; 31, 58.8%), cluster 2 (39, 18.2%) of MED-ND-CD (39, 100%; 39, 100%; 37, 94.9%), cluster 3 (37, 17.3%) of OD-CD-MED-ND (37, 100%; 33, 89.2%; 27, 73.0%; 16, 43.2%), and cluster 4 (34, 15.9%) of CD-MED (34, 100%; 34, 100%) were identified in the younger-old. In the oldest-old, the primary multimorbidity patterns were: cluster 1 (33, 19.1%) of CD-respiratory disease-digestive disease-urogenital disease (CD-RD-DSD-UD) (32, 97.0%; 9, 27.3%; 8, 24.2%; 7, 21.2%), cluster 2 (42, 24.3%) of ND-CD-MED (42, 100%; 35, 83.3%; 14, 33.3%), cluster 3 (28, 16.2%) of OD-CD-MED (28, 100%; 25, 89.3%; 18, 64.3%), and cluster 4 (35, 20.2%) of CD-MED (35, 100%; 35, 100%). Younger-old with CD-ND or MED-ND-CD, and oldest-old with ND-CD-MED have worse health status compared with other multimorbidity patterns (e.g., CD-MED and OD-CD-MED). Conclusion: Discrepancies in common patterns of multimorbidity across age groups suggest that caregivers in long-term care facilities should consider changes in multimorbidity patterns with ageing when developing prevention plans for individualized management. Neurological disease concurrent with other diseases was the major determinant of health status, especially for the oldest-old. Interventions targeting multimorbidity need to be focused, yet generic. It is essential to assess complex needs and health outcomes that arise from different multimorbidity patterns and manage them through an interdisciplinary approach and consider their priorities to gain high-quality primary care for older adults living in long-term care facilities.

As the country with the largest aging population, China faces an enormous challenge with its elderly support and care. One of the proposed solutions is the development of volunteerism for elderly care. The Senior Care Volunteers Training Program (SCVTP) was initiated by the Red Cross Society of China with the purpose of training volunteers to care for community seniors. As one of the four pilot provinces, Jiangsu Province launched the program since 2017. The present study was conducted to investigate the dropout rate of trained volunteer group leaders, the characteristics of the retained trained volunteer group leaders and the activities that their groups conducted. Additionally, the exploration of the factors influencing the SCVTP’s performance was listed as another aim. A cross-sectional study was designed. The study used purposive sampling to select participants who meet the criteria from all the trained volunteer group leaders (n = 623). Demographic questionnaire, volunteer role identity (VRI) scale, attitude toward helping others (AHO) scale, team climate and atmosphere (TCA) scale, and volunteer program performance evaluation (VPPE) questionnaire were used to collect the data online. Descriptive statistics were used to determine the dropout rate and general characteristics of the retained volunteers and the activities. A multiple linear regression equation was developed to study the factors that influence program performance. In total, 307 questionnaires were valid in the study. About 67.9%, 53.7%, and 30.0% of the trained volunteer group leaders dropped out of the program in the year of 2017, 2018, and 2019, respectively. The retained trained volunteer group leaders were more likely to be females (84.7%), those in excellent health (75.2%) and with a bachelor’s degree or above (87.6%). Less attention has been paid to frailty care (n = 76) than other volunteer caring activities (e.g., safe care: n = 277, diet care: n = 250, drug management care: n = 226). VRI (β = 0.118, p = 0.017), AHO (β = 0.134, p = 0.021), TCA (β = 0.459, p<0.001), and financial sustainability (β = 0.179, p<0.001) affected the SCVTP’s performance significantly (adjusted R² = 0.356). High rate of trained volunteer group leaders’ dropout should be brought to the policymaker’s attention. The characteristics of the retained trained volunteer group leaders provide a useful reference for the recruitment of trainees in the future. Frailty care may need more training by the volunteer service provider. In order to enhance program performance, a better team climate and atmosphere, financial sustainability, and volunteers with appropriate attitude and role identity are also necessary for the volunteer program.