The mediating roles of depressive symptoms and social participation in the relationship between the effects of pain and cognitive function among Chinese older adults: A longitudinal study.

Decline in cognitive function poses a substantial burden on individuals, families, and society. However, the longitudinal potential mechanism underlying the link of pain and cognitive function remains unclear. Using data of 4247 participants aged 60 years and over from the China Health and Retirement Longitudinal Study in 2011, 2013, 2018, and 2020, we discussed the longitudinal predictive effect of pain on cognitive function and the mediating effects of depressive symptoms and social participation. The longitudinal mediation model analysis revealed that pain could not directly influence cognitive function, but it could indirectly predict cognitive function through the independent mediation effects of depressive symptoms and social participation. Moreover, the association between pain and cognitive function was serially mediated by depressive symptoms and social participation. Diversified interventions aimed at relieving pain and depressive symptoms, and increasing social participation in older adults would be beneficial for their cognitive function.

The association between dual sensory loss and healthcare expenditure: Mediating effect of depression.

BACKGROUND: Previous studies have suggested the dual sensory loss (DSL) is linked to depression, and that they are associated with higher healthcare expenditures, respectively. However, the association between DSL, depression and healthcare expenditures remains ambiguous. OBJECTIVES: The current study aims to examine the association between DSL, depression and healthcare expenditures as well as catastrophic health expenditures (CHE) among Chinese people aged 45 and above. METHODS: We first utilized the China Health and Retirement Longitudinal Survey (CHARLS) 2018 to obtain data from a total of 13,412 Chinese individuals aged 45 and above to conduct a cross-sectional study. DSL was defined as a combined variable of self-reported vision loss and hearing loss. Depression was measured using The Center for Epidemiologic Studies Depression Scale (CESD-10). The healthcare expenditures, including outpatient out-of-pocket cost and inpatient out-of-pocket cost, were obtained from the Harmonized CHARLS section. CHE were defined as out-of-pocket (OOP) health spending equal to or higher than 40 % of a household's capacity to pay. A Tobit linear regression with three models and a path analysis were conducted to estimate the association between DSL, depression and healthcare expenditures and CHE. Then we utilized 2011CHARLS and 2018CHARLS to present a longitudinal analysis. A path analysis was conducted to estimate the association between 2011DSL, 2018depression and 2018healthcare expenditures and CHE. RESULTS: Depression has a significant mediating effect between DSL and healthcare expenditures. (For outpatient OOP cost: a = 0.453, b = 23.559, c = 25.257, the proportion of mediating effect in total effect = 29.71 %; for inpatient OOP cost: a = 0.453, b = 13.606, c = 15.463, the proportion of mediating effect in total effect = 28.50 %; all P < 0.05). The mediating effect of depression also exists in the association between DSL and CHE (a = 0.453, b = 0.018, c = 0.043, the proportion of mediating effect in total effect = 15.90 %; P < 0.05). The mediation effect of depression on healthcare expenditures and CHE also exists in the longitudinal analysis using CHARLS 2011 and CHARLS 2018 (all P < 0.05). LIMITATIONS: The DSL status were based on self-report and we used 2018CHARLS to conduct the study, which may cause some bias. CONCLUSION: Significant mediating effect of depression exists between DSL and higher healthcare expenditures and CHE. The mental health of elder people with DSL should be focused on, and we should have an overall viewpoint on the topic of healthcare expenditures and CHE.

BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence.

The emerging prosperity of cryptocurrencies, such as Bitcoin, has come into the spotlight during the past few years. Cryptocurrency exchanges, which act as the gateway to this world, now play a dominant role in the circulation of Bitcoin. Thus, delving into the analysis of the transaction patterns of exchanges can shed light on the evolution and trends in the Bitcoin market, and participants can gain hints for identifying credible exchanges as well. Not only Bitcoin practitioners but also researchers in the financial domains are interested in the business intelligence behind the curtain. However, the task of multiple exchanges exploration and comparisons has been limited owing to the lack of efficient tools. Previous methods of visualizing Bitcoin data have mainly concentrated on tracking suspicious transaction logs, but it is cumbersome to analyze exchanges and their relationships with existing tools and methods. In this paper, we present BitExTract, an interactive visual analytics system, which, to the best of our knowledge, is the first attempt to explore the evolutionary transaction patterns of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange versus client. In particular, BitExTract summarizes the evolution of the Bitcoin market by observing the transactions between exchanges over time via a massive sequence view. A node-link diagram with ego-centered views depicts the trading network of exchanges and their temporal transaction distribution. Moreover, BitExTract embeds multiple parallel bars on a timeline to examine and compare the evolution patterns of transactions between different exchanges. Three case studies with novel insights demonstrate the effectiveness and usability of our system.

SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data.

Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.