Comparisons of Visceral Adiposity Index, Body Shape Index, Body Mass Index and Waist Circumference and Their Associations with Diabetes Mellitus in Adults.

The associations between visceral adiposity index (VAI), body shape index and diabetes in adults were inconsistent. We assessed the predictive capacity of VAI and body shape index for diabetes by comparing them with body mass index (BMI) and waist circumference (WC). We used the data of 5838 Chinese men and women aged ≥18 years from the 2009 China Health and Nutrition Survey. Multivariate logistic regression analysis was performed to examine the independent associations between Chinese VAI (CVAI) or body shape index and diabetes. The predictive power of the two indices was assessed using the receiver-operating characteristic (ROC) curve analysis, and compared with those of BMI and WC. Both CVAI and body shape index were positively associated with diabetes. The odds ratios for diabetes were 4.9 (2.9-8.1) and 1.8 (1.2-2.8) in men, and 14.2 (5.3-38.2) and 2.0 (1.3-3.1) in women for the highest quartile of CVAI and body shape index, respectively. The area under the ROC (AUC) and Youden index for CVAI was the highest among all four obesity indicators, whereas BMI and WC are better indicators for diabetes screening. Higher CVAI and body shape index scores are independently associated with diabetes risk. CVAI has a higher overall diabetes diagnostic ability than BMI, WC and body shape index in Chinese adults. BMI and WC, however, are more appealing as screening indicators considering their easy use

Rebalanced Zero-shot Learning

Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalanced semantic predictions, i.e. these models could perform precisely for some semantics, but may not for others. To address the drawback, we aim to introduce an imbalanced learning framework into ZSL. However, we find that imbalanced ZSL has two unique challenges: (1) Its imbalanced predictions are highly correlated with the value of semantic labels rather than the number of samples as typically considered in the traditional imbalanced learning; (2) Different semantics follow quite different error distributions between classes. To mitigate these issues, we first formalize ZSL as an imbalanced regression problem which offers empirical evidences to interpret how semantic labels lead to imbalanced semantic predictions. We then propose a re-weighted loss termed Re-balanced Mean-Squared Error (ReMSE), which tracks the mean and variance of error distributions, thus ensuring rebalanced learning across classes. As a major contribution, we conduct a series of analyses showing that ReMSE is theoretically well established. Extensive experiments demonstrate that the proposed method effectively alleviates the imbalance in semantic prediction and outperforms many state-of-the-art ZSL methods. Our code is available at https://github.com/FouriYe/ReZSL-TIP23.Comment: Accepted to IEEE Transactions on Image Processing (TIP) 202

Prevalence of Type 2 Diabetes and Its Association with Added Sugar Intake in Citizens and Refugees Aged 40 or Older in the Gaza Strip, Palestine.

Little is known about the prevalence and risk factors of diabetes among Gaza Palestinians, 64% of whom are refugees with exceeded sugar intake. We aimed to estimate the prevalence of type 2 diabetes (T2D) and its association with added sugar intake among residents, with regular visits to primary healthcare centers (PHCs) across Gaza. From October to December of 2019, a cross-sectional survey was conducted among 1000 citizens and refugees in nine PHCs selected from the five governorates of the Gaza Strip. Information on dietary intake, medical history, and other risk factors was collected by trained health workers, using structured questionnaires. Anthropometry and biochemical data were extracted from the PHC medical record system. Overall, the prevalence of diagnosed T2D and undiagnosed T2D were 45.2% and 16.8%, respectively, in adults aged 42 to 74 years, with the differences among citizens and refugees (diagnosed: 46.2% vs. 43.8%; undiagnosed: 15.7% vs. 18.2%). The uncontrolled glycaemic rate was 41.9% and 36.8% for diagnosed patients in citizens and refugees, respectively. Among those without a clinical diagnosis of T2D, after multivariable adjustment, daily added sugar intake was positively associated with fasting glucose and the risk of undiagnosed T2D (odds ratio, 95% CI, highest vs. lowest intake, was 2.71 (1.12-6.54) ( < 0.001). In stratified analysis, the associations between added sugar intake and the risk of undiagnosed T2D tend to be stronger among refugees or those with higher body mass index. Among Palestinian adults, both citizens and refugees are affected by T2D. Added sugar intake is associated with the risk of undiagnosed T2D

Temporal, geographical and demographic trends of stroke prevalence in China: a systematic review and meta-analysis.

China has made large efforts to reduce stroke prevalence. We aimed to systematically examine the prevalence of stroke in China over the past two decades. Databases, including China National Knowledge Infrastructure, Wanfang, VIP, and PubMed, were systematically searched for studies published in English or Chinese that reported stroke prevalence in China during 2000-2017. Meta-analysis was conducted to estimate the pooled stroke prevalence and the variations in stroke prevalence subgroups stratified by age, gender, time period, and region. In total, 96 papers met the inclusion criteria. Meta-analysis showed that the overall estimated national prevalence was 5.1% (5.0-5.3%) with large variations across regions: 3.1% (2.5-3.6%) in south China, 3.4% (3.0-3.8%) in southwest China, 3.6% (3.3-3.8%) in east China, 5.0% (4.7-5.4%) in central China, 5.8% (4.6-7.1%) in northwest China, 6.0% (5.0-7.0%) in northeast China, and 8.0% (7.4-8.5%) in north China. Men had a higher prevalence than women [7.3% (6.9-7.7%) . 5.6% (5.2-6.0%)]. Stroke prevalence increased with age, was 1.2% (1.0-1.3%), 2.9% (2.6-3.2%), 5.9% (5.2-6.5%), and 8.7% (8.0-9.5%) in the 40-49, 50-59, 60-69, and ≥70 years old groups, respectively. Men, people being older, or living in northern China had higher stroke prevalence. More vigorous efforts are needed in China to prevent stroke.Funding: The study was supported in part by research grants from the China Medical Board (Grant No. 16-262), and the National Key Research and Development Program of China (Grant Number: 2017YFC0907200 & 2017YFC0907201), the National Natural Science Foundation of China (Grant Number: NSFC 81703220)

Association between war-related traumatic events and blood pressure trajectory: a population-based study among the mid-aged and older Palestinian adults living in Gaza

Background: Little is known regarding health status in an environment characterized by instability and ongoing war risks. This study investigated hypertension disease burden and associations of war-related traumatic events with blood pressure (BP) trajectory over time amongst mid-aged and older Palestinian adults in Gaza Strip. Methods: From nine primary healthcare centers, medical records between 2013 and 2019 were collected for 1,000 mid-aged and older Palestinian adults living in Gaza. Multinomial logistic regression analysis examined associations between war-related traumatic events and BP trajectories derived using latent class trajectory analysis (LCTA). Results: The prevalence of self-reported injury (of participants or their family members), death of a family member, and violence due to house bombing was 51.4%, 54.1%, and 66.5%, respectively. In total, 22.4% and 21.4% of participants had constant-very-high (CVH) systolic BP (SBP) (>160 mmHg) and diastolic BP (DBP) (>95 mmHg), and normal-stable SBP and DBP was found only 54.9% and 52.6%, respectively. Injury (participants or family members), death of a family member, and violence due to house bombing during wars were associated with CVH SBP with odds ratios [95 CI, OR = 1.79 (1.28–2.48), 1.90 (1.36–2.65), and 1.44 (1.01–2.05)], respectively. The corresponding figures were [95 CI, OR = 1.92 (1.36–2.71), 1.90 (1.35–2.68), and 1.62 (1.13–2.38)] for CVH DBP. Living in debt was positively associated with CVH SBP, [95 CI, OR = 2.49 (1.73–3.60)] and CVH DBP, [95 CI, OR = 2.37 (1.63–3.45)]. Conclusion: The disease burden related to war-related traumatic events is high and positively related to adverse BP trajectory among the mid-aged and older Palestinians living in Gaza. Intervention programs are needed to manage and prevent chronic diseases in this vulnerable population.This research was partly funded by the US-based China Medical Board (CMB, Grant Number 16-262); National Natural Science Foundation of China, Grant Numbers 82173504, 82011530197; and the Chinese National Key Research and Development Program (Grant Numbers 2017YFC0907200 and 2017YFC0907201)

Income-related health inequality among Chinese adults during the COVID-19 pandemic: evidence based on an online survey

Background: Partial- or full-lockdowns, among other interventions during the COVID-19 pandemic, may disproportionally affect people (their behaviors and health outcomes) with lower socioeconomic status (SES). This study examines income-related health inequalities and their main contributors in China during the pandemic. Methods: The 2020 China COVID-19 Survey is an anonymous 74-item survey administered via social media in China. A national sample of 10,545 adults in all 31 provinces, municipalities, and autonomous regions in mainland China provided comprehensive data on sociodemographic characteristics, awareness and attitudes towards COVID19, lifestyle factors, and health outcomes during the lockdown. Of them, 8448 subjects provided data for this analysis. Concentration Index (CI) and Corrected CI (CCI) were used to measure income-related inequalities in mental health and self-reported health (SRH), respectively. Wagstaff-type decomposition analysis was used to identify contributors to health inequalities. Results: Most participants reported their health status as “very good” (39.0%) or “excellent” (42.3%). CCI of SRH and mental health were − 0.09 (p < 0.01) and 0.04 (p < 0.01), respectively, indicating pro-poor inequality in ill SRH and pro-rich inequality in ill mental health. Income was the leading contributor to inequalities in SRH and mental health, accounting for 62.7% (p < 0.01) and 39.0% (p < 0.05) of income-related inequalities, respectively. The COVID-19 related variables, including self-reported family-member COVID-19 infection, job loss, experiences of food and medication shortage, engagement in physical activity, and five different-level pandemic regions of residence, explained substantial inequalities in ill SRH and ill mental health, accounting for 29.7% (p < 0.01) and 20.6% (p < 0.01), respectively. Self-reported family member COVID-19 infection, experiencing food and medication shortage, and engagement in physical activity explain 9.4% (p < 0.01), 2.6% (the summed contributions of experiencing food shortage (0.9%) and medication shortage (1.7%), p < 0.01), and 17.6% (p < 0.01) inequality in SRH, respectively (8.9% (p < 0.01), 24.1% (p < 0.01), and 15.1% (p < 0.01) for mental health).Conclusions: Per capita household income last year, experiences of food and medication shortage, self-reported family member COVID-19 infection, and physical activity are important contributors to health inequalities, especially mental health in China during the COVID-19 pandemic. Intervention programs should be implemented to support vulnerable groups

Research capacity of global health institutions in China: a gap analysis focusing on their collaboration with other low-income and middle-income countries.

INTRODUCTION: This paper presented qualitative and quantitative data collected on the research capacity of global health institutions in China and aimed to provide a landscaping review of the development of global health as a new discipline in the largest emerging economy of the world. METHODS: Mixed methods were used and they included a bibliometric analysis, a standardised survey and indepth interviews with top officials of 11 selected global health research and educational institutions in mainland China. RESULTS: The bibliometric analysis revealed that each institution had its own focus areas, some with a balanced focus among chronic illness, infectious disease and health systems, while others only focused on one of these areas. Interviews of key staff from each institution showed common themes: recognition that the current research capacity in global health is relatively weak, optimism towards the future, as well as an emphasis on mutual beneficial networking with other countries. Specific obstacles raised and the solutions applied by each institution were listed and discussed. CONCLUSION: Global health institutions in China are going through a transition from learning and following established protocols to taking a more leading role in setting up China's own footprint in this area. Gaps still remain, both in comparison with international institutions, as well as between the leading Chinese institutions and those that have just started. More investment needs to be made, from both public and private domains, to improve the overall capacity as well as the mutual learning and communication within the academic community in China

Consensus-based multidimensional due diligence of fintech-enhanced green energy investment projects

The purpose of this study is to provide a hybrid group decision-making approach to evaluate fintech-based financial alternatives for green energy investment projects. First, the multidimensional factors of due diligence for fintech-based financing alternatives of green energy investment projects are identified. In this regard, the balanced scorecard perspectives are considered. Next, consensus-based group decision-making analysis is performed. Second, impact-relation directions for fintech-based financing alternatives of green energy investment projects are defined. For this purpose, the spherical fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is applied. The novelty of this study is its proposal of a new outlook to due diligence of fintech-project financing for renewable energy investments by using the group and integrated decision-making approaches with spherical fuzzy DEMATEL. The findings indicate that customer expectations are the most essential factor for the revenue sharing and rewarding models. Additionally, this study identified that organizational competency plays the most important role with respect to the peer-to-business debt model. In contrast, the conclusion was reached that financial returns have the greatest importance for the equity sharing model.Philosophy and Social Science Planning Project of Guangdong Province ; Basic and Applied Basic Project of Guangzhou City ; Philosophy and Social Science Planning Project of Guangzhou City ; National Natural Science Foundation of China (NSFC

Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks

Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems in graph neural networks, and aims to predict the label of a graph with the help of training graph neural networks over graph-structural datasets. The graph pooling scheme is an important part of graph neural networks for the graph classification objective. Previous works typically focus on using the graph pooling scheme in a linear manner. In this paper, we propose the real quadratic-form-based graph pooling framework for graph neural networks in graph classification. The quadratic form can capture a pairwise relationship, which brings a stronger expressive power than existing linear forms. Experiments on benchmarks verify the effectiveness of the proposed graph pooling scheme based on the quadratic form in graph classification tasks