Can long-term care insurance reduce catastrophic health and long-term care expenditures among older adults? A quasi-experimental study in China

Older adults with cognitive and/or physical limitations often face excessive health and long-term care (LTC) costs, which significantly affect their living standards. However, current measures of catastrophic health expenditure primarily focus on healthcare costs, overlooking the substantial burden of LTC costs. Our study is among the first to introduce the concept of catastrophic health and long-term care expenditures (CHLTCE) to comprehensively capture the economic burden of health and LTC costs associated with functional limitations. Drawing data from 2011, 2013, 2015, and 2018 waves of China Health and Retirement Longitudinal Study, we employed a staggered difference-in-differences approach to assess the impact of long-term care insurance (LTCI) on alleviating CHLTCE. We found that after accounting for LTC costs, both total health and LTC expenditures and CHLTCE risk significantly increased compared to when only healthcare costs were considered. Furthermore, LTCI coverage significantly reduced CHLTCE risks among older adults with cognitive and/or physical limitations. This effect was more pronounced among those with severe cognitive impairments and those with physical limitations and chronic diseases. In addition, its effectiveness in reducing CHLTCE risks for individuals with complex care needs was observed only at the high threshold, but not at lower thresholds. Our findings offer empirical contributions by incorporating both health and LTC costs into catastrophic expenditures analysis. We recommend that policymakers refine LTCI eligibility criteria and implement tiered coverage options based on various levels and dimensions of cognitive and physical function—beyond activities of daily living—to offer targeted protection for individuals with complex care needs.</p

Social support, cognition, and mental health among older people in China:A longitudinal life course study

Research suggests that stronger social support systems across life stages facilitate continuous learning and emotional well-being, contributing to better cognition and mental health. However, evidence on the cumulative effect of social support over the life course remains scarce, and even less is known about the impact of broader neighborhood-level social support on individuals’ cognition—a crucial dimension in many countries, where neighborhoods serve as key support units for individuals. Using data from 2014, 2015, 2018, and 2020 waves of the China Health and Retirement Longitudinal Study, we employed mediation analysis to explore how individual and neighborhood social support accumulated across life stages are associated with cognition and depression at older ages. Our findings indicate that receiving individual support in youth was associated with a 0.5-point improvement in later-life cognitive scores, although no direct association was observed with depression. In addition, a one-point increase in the neighborhood support score was associated with a 1.2-point improvement in cognitive scores and 0.5 fewer depressive symptoms at older ages. These associations persist among individuals who were socioeconomically disadvantaged in their youth. Furthermore, both individual and neighborhood support in youth exert indirect effects on later-life cognition and depression by helping individuals maintain social support as they age. Our findings contribute theoretical insights into the importance of social support across life stages and highlight the stronger role of neighborhood support in shaping cognition and depression. Strengthening neighborhood social support and fostering lifelong supportive environments may help mitigate disadvantages in earlier life stages and reduce health disparities

Flipping the classoom in teaching Chinese as a foreign language

Through an in-depth analysis of quantitative and qualitative data, this article offers a case study of the advantages and challenges in the application of the flipped learning approach in the instruction of Chinese as a foreign language at the beginning level. Data were collected from two first-year Chinese classes (one in traditional and the other in flipped format) to investigate whether there were statistically significant differences in learning outcomes and students’ levels of satisfaction between the two classes. Final exam and oral test scores showed that students in the flipped class performed better in speaking, since more time was devoted to meaningful interactions in class. As indicated in the results of the end-of-the-semester questionnaire, these students also gave higher average ratings on three aspects of their learning experience: level of required self-directedness, amount of practice in class, and stimulation of interest in the subject

TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by flow-based motion estimations, 2) blending the warped pixels to form a full frame through deep neural synthesis networks. However, due to the inconsistent warping from the two consecutive frames, the warped features for new frames are usually not aligned, which leads to distorted and blurred frames, especially when large and complex motions occur. To solve this issue, in this paper we propose a novel Trajectory-aware Transformer for Video Frame Interpolation (TTVFI). In particular, we formulate the warped features with inconsistent motions as query tokens, and formulate relevant regions in a motion trajectory from two original consecutive frames into keys and values. Self-attention is learned on relevant tokens along the trajectory to blend the pristine features into intermediate frames through end-to-end training. Experimental results demonstrate that our method outperforms other state-of-the-art methods in four widely-used VFI benchmarks. Both code and pre-trained models will be released soon

On the distance-edge-monitoring numbers of graphs

Foucaud et al. [Discrete Appl. Math. 319 (2022), 424-438] recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. For a set $M$ of vertices and an edge $e$ of a graph $G$, let $P(M, e)$ be the set of pairs $(x, y)$ with a vertex $x$ of $M$ and a vertex $y$ of $V(G)$ such that $d_G(x, y)\neq d_{G-e}(x, y)$. For a vertex $x$, let $EM(x)$ be the set of edges $e$ such that there exists a vertex $v$ in $G$ with $(x, v) \in P(\{x\}, e)$. A set $M$ of vertices of a graph $G$ is distance-edge-monitoring set if every edge $e$ of $G$ is monitored by some vertex of $M$, that is, the set $P(M, e)$ is nonempty. The distance-edge-monitoring number of a graph $G$, denoted by $dem(G)$, is defined as the smallest size of distance-edge-monitoring sets of $G$. The vertices of $M$ represent distance probes in a network modeled by $G$; when the edge $e$ fails, the distance from $x$ to $y$ increases, and thus we are able to detect the failure. It turns out that not only we can detect it, but we can even correctly locate the failing edge. In this paper, we continue the study of \emph{distance-edge-monitoring sets}. In particular, we give upper and lower bounds of $P(M,e)$, $EM(x)$, $dem(G)$, respectively, and extremal graphs attaining the bounds are characterized. We also characterize the graphs with $dem(G)=3$

Slipping Through the Gap: Exploring the Influence of Social Health Insurance on Access to Healthcare for Older Migrant Workers

Objectives: Older migrant workers (OMWs) frequently confront barriers to accessing care, as their Social Health Insurance (SHI) coverage may not extend beyond their hometown. This study seeks to investigate whether Chinese OMWs can still derive benefits from SHI in accessing healthcare services, even when their SHI is not registered in the same location as their current residence.Methods: This study used data from 2015 China Migrants Dynamic Survey and focused on OMWs aged 60 years and older (N = 3,050). Logistic regression models were employed to investigate the factors influencing healthcare use.Results: Having SHI registered in current place of residence and interprovincial migration were significantly associated with increased likelihoods of doctor visits among OMWs. However, inpatient services use did not appear to be associated with the SHI registration place and migration range.Conclusion: Chinese OMWs derive fewer benefits from SHI in accessing healthcare services when their SHI is not registered in current residence. Governments in Low- and Middle-Income Countries should consider implementing targeted policies to provide adequate protection for OMWs and expand the coverage of direct reimbursement for cross-province healthcare services

Adonis: Practical and Efficient Control Flow Recovery through OS-Level Traces

Control flow recovery is critical to promise the software quality, especially for large-scale software in production environment. However, the efficiency of most current control flow recovery techniques is compromised due to their runtime overheads along with deployment and development costs. To tackle this problem, we propose a novel solution, Adonis, which harnesses OS-level traces, such as dynamic library calls and system call traces, to efficiently and safely recover control flows in practice. Adonis operates in two steps: it first identifies the call-sites of trace entries, then it executes a pair-wise symbolic execution to recover valid execution paths. This technique has several advantages. First, Adonis does not require the insertion of any probes into existing applications, thereby minimizing runtime cost. Second, given that OS-level traces are hardware-independent, Adonis can be implemented across various hardware configurations without the need for hardware-specific engineering efforts, thus reducing deployment cost. Third, as Adonis is fully automated and does not depend on manually created logs, it circumvents additional development cost. We conducted an evaluation of Adonis on representative desktop applications and real-world IoT applications. Adonis can faithfully recover the control flow with 86.8% recall and 81.7% precision. Compared to the state-of-the-art log-based approach, Adonis can not only cover all the execution paths recovered, but also recover 74.9% of statements that cannot be covered. In addition, the runtime cost of Adonis is 18.3× lower than the instrument-based approach; the analysis time and storage cost (indicative of the deployment cost) of Adonis is 50× smaller and 443× smaller than the hardware-based approach, respectively. To facilitate future replication and extension of this work, we have made the code and data publicly available

Angelica sinensis polysaccharide promotes apoptosis by inhibiting JAK/STAT pathway in breast cancer cells

Purpose: To determine whether Angelica polysaccharide (APS) induced apoptosis via regulation of the Janus kinase (JAK)/signal transducers and activators of transcription (STAT) pathway in breast cancer cells. Methods: Human MCF-7 cells were treated with APS. Cell proliferation, apoptosis, expression of apoptotic proteins, and the phosphorylation level of Janus kinase (JAK) and STAT were measured, respectively. For further analysis, MCF-7 cells were transfected with a JAK2 overexpression plasmid or treated with a classical JAK inhibitor, ruxolitinib. Results: Treatment with APS dose-dependently reduced cell proliferation, induced apoptosis, and downregulated the levels of phosphorylated JAK and STAT in MCF-7 cells. JAK inhibitor, ruxolitinib, blocked JAK/STAT pathway and induced cell apoptosis in MCF-7 cells. Besides, JAK2 overexpression reversed the effects of APS on cell viability and apoptosis. Conclusion: The results indicate that polysaccharide isolated from Angelica sinensis promotes apoptosis by inhibiting JAK/STAT pathway in breast cancer cells. Thus, APS may be useful as a potential therapeutic agent for breast cancer