The Impacts of Social Security Expenditure on Rural Residents' Medical Consumption in Hubei Province

This paper analyzes the effect ofSocial Security Expenditure issued by Hubei Province on health services for the peasantry, and provides the evidences towards the integration process regarding medical insurances of the urban and the countryside.Based on the related economical data collected from countrysides area of Hubei province from 2003-2019, this paper utilizes the outcome of Cointegration and Granger Casual Relation Test to prove the relation from social security and employment fiscal expenditures and rural households’ annual per capita net income.Then the research adds the control variables to perform the examination on robustness. The study shows that the social security expenditure has a significant impact on rural residents' medical consumption.Considering that medical insurance is an important part of social security,we should ensure the steadiness of the further improvement achieved by system of health insurance,increase government financial investment and strengthen the construction of rural basic health and medical facilities.Hubei Province should improve the level of rural medical and health security, strengthen management, improve efficiency and improve mechanisms, and deepen the reform of the medical and health system.Therefore, the economical burden of rural residents brought by seeking medical treatment will be reduced in order to narrow the difference between the urban and the countryside which will further promote the equality in medical system

Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling

Fairness or equity in machine learning is profoundly important for societal well-being, but limited public datasets hinder its progress, especially in the area of medicine. It is undeniable that fairness in medicine is one of the most important areas for fairness learning's applications. Currently, no large-scale public medical datasets with 3D imaging data for fairness learning are available, while 3D imaging data in modern clinics are standard tests for disease diagnosis. In addition, existing medical fairness datasets are actually repurposed datasets, and therefore they typically have limited demographic identity attributes with at most three identity attributes of age, gender, and race for fairness modeling. To address this gap, we introduce our Eye Fairness dataset with 30,000 subjects (Harvard-EF) covering three major eye diseases including age-related macular degeneration, diabetic retinopathy, and glaucoma affecting 380 million patients globally. Our Harvard-EF dataset includes both 2D fundus photos and 3D optical coherence tomography scans with six demographic identity attributes including age, gender, race, ethnicity, preferred language, and marital status. We also propose a fair identity scaling (FIS) approach combining group and individual scaling together to improve model fairness. Our FIS approach is compared with various state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our Harvard-EF dataset for fairness learning. To facilitate fairness comparisons between different models, we propose performance-scaled disparity measures, which can be used to compare model fairness accounting for overall performance levels. The dataset and code are publicly accessible via https://ophai.hms.harvard.edu/datasets/harvard-ef30k

Controlling Factors of Microplastic Riverine Flux and Implications for Reliable Monitoring Strategy

Embargo until December 12, 2022A significant proportion of marine plastic debris and microplastics is assumed to be derived from river systems. In order to effectively manage plastic contamination of the marine environment, an accurate quantification of riverine flux of land-based plastics and microplastics is imperative. Rivers not only represent pathways to the ocean, but are also complex ecosystems that support many life processes and ecosystem services. Yet riverine microplastics research is still in its infancy, and many uncertainties still remain. Major barriers exist in two aspects. First, nonharmonized sampling methodologies make it problematic for compiling data across studies to better estimate riverine fluxes of microplastics globally; Second, the significant spatiotemporal variation of microplastics in rivers which was affected by the river characteristics, MPs properties, etc. also have important influence on the estimation of riverine MPs fluxes. In this study, we made a comprehensive review from the above two aspects based on published peer-reviewed studies and provide recommendations and suggestions for a reliable monitoring strategy of riverine MPs, which is beneficial to the further establish sampling methods for rivers in different geographical locations. Besides, methods for achieving a high level of comparability across studies in different geographical contexts are highlighted. Riverine microplastic flux monitoring is another important part of this manuscript. The influential factors and calculation methods of microplastic flux in rivers are also discussed in this paper.acceptedVersio

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}

A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods

Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results

Background: To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM). Methods: A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results. Results: Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95–99.97%) in Hong Kong and 100% (95%CI, 100–100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97–99.98%) in Hong Kong and 100% (95%CI, 100–100%) in Shanghai], specificity [99.87% (95%CI, 99.82–99.90%) in Hong Kong and 100% (95%CI, 99.92–100%) in Shanghai], PPV [99.98% (95%CI, 99.97–99.99%) in Hong Kong and 100% (95%CI, 99.99–100%) in Shanghai] and NPV [99.85% (95%CI, 99.80–99.88%) in Hong Kong and 100% (95%CI, 99.90–100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai. Conclusions: The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM

The prognostic value of CZT SPECT myocardial blood flow (MBF) quantification in patients with ischemia and no obstructive coronary artery disease (INOCA): a pilot study.

BACKGROUND Despite the demonstrated adverse outcome, it is difficult to early identify the risks for patients with ischemia and no obstructive coronary artery disease (INOCA). We aimed to explore the prognostic potential of CZT SPECT in INOCA patients. METHODS The study population consisted of a retrospective cohort of 118 INOCA patients, all of whom underwent CZT SPECT imaging and invasive coronary angiography (ICA). Dynamic data were reconstructed, and MBF was quantified using net retention model. Major adverse cardiovascular events (MACEs) were defined as cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, heart failure, late coronary revascularization, or hospitalization for unstable angina. RESULTS During a median follow-up of 15 months (interquartile range (IQR) 11-20), 19 (16.1%) MACEs occurred; both stress myocardial blood flow (sMBF) ([Formula: see text]) and coronary flow reserve (CFR) ([Formula: see text]) were significantly lower in the MACE group. Optimal thresholds of sMBF<3.16 and CFR<2.52 were extracted from the ROC curves, and both impaired sMBF (HR: 15.08; 95% CI 2.95-77.07; [Formula: see text]) and CFR (HR: 6.51; 95% CI 1.43-29.65; [Formula: see text]) were identified as prognostic factors for MACEs. Only sMBF<3.16 (HR: 11.20; 95% CI 2.04-61.41; [Formula: see text]) remained a robust predictor when sMBF and CFR were integrated considered. Compared with CFR, sMBF provides better prognostic model discrimination and reclassification ability (C-index improvement = 0.06, [Formula: see text]; net reclassification improvement (NRI) = 0.19; integrated discrimination improvement (IDI) = 0.10). CONCLUSION The preliminary results demonstrated that quantitative analysis on CZT SPECT provides prognostic value for INOCA patients, which may allow the stratification for early prevention and intervention

FT4/FT3 ratio: A novel biomarker predicts coronary microvascular dysfunction (CMD) in euthyroid INOCA patients.

Background Ischemia and no obstructive coronary artery disease (INOCA) patients who presented coronary microvascular dysfunction (CMD) demonstrate a poor prognosis, yet the risk factors for CMD remain unclear. Subtle changes in thyroid hormone levels within the normal range, especially the free thyroxine (FT4)/free triiodothyronine (FT3) ratio, have been shown to regulate the cardiovascular system. This prospective study investigated the correlation between FT4/FT3 ratio and CMD in euthyroid patients with INOCA. Methods This prospective study (www.chictr.org.cn/, ChiCTR2000037112) recruited patients with myocardial ischemia symptoms who underwent both coronary angiography (CAG) and myocardial perfusion imaging (MPI) with dynamic single-photon emission computed tomography (D-SPECT). INOCA was defined as coronary stenosis< 50% and CMD was defined as coronary flow reserve (CFR)<2.5. All patients were excluded from abnormal thyroid function and thyroid disease history. Results Among 71 INOCA patients (15 [21.1%] CMD), FT4 and FT4/FT3 ratio in CMD group were significantly higher and both showed significantly moderate correlation with CFR (r=-0.25, p=0.03; r=-0.34, p=0.003, respectively). The ROC curve revealed that FT4/FT3 ratio had the highest efficacy for predicting CMD with an optimized cutoff value>3.39 (AUC 0.78, p<0.001, sensitivity, 80.0%; specificity, 71.4%). Multivariate logistic regression showed that FT4/FT3 ratio was an independent predictor of CMD (OR 7.62, 95% CI 1.12-51.89, p=0.038, P for trend=0.006). Conclusion In euthyroid INOCA patients, increased FT4/FT3 ratio levels are associated with the occurrence of CMD, presenting a novel biomarker for improving the risk stratification