Prediction models of burst strength degradation for casing with considerations of both wear and corrosion

Casing wear and casing corrosion are serious problems affecting casing integrity failure in deep and ultra-deep wells.This paper aims to predict the casing burst strength with considerations of both wear and corrosion.Firstly,the crescent wear shape is simplified into three categories according to common mathematical models.Then,based on the mechano-electrochemical(M-E)interaction,the prediction model of corrosion depth is built with worn depth as the initial condition,and the prediction models of burst strength of the worn casing and corroded casing are obtained.Secondly,the accuracy of different prediction models is validated by numerical simulation,and the main influence factors on casing strength are obtained.At last,the theoretical models are applied to an ultra-deep well in Northwest China,and the dangerous well sections caused by wear and corrosion are predicted,and the corrosion rate threshold to ensure the safety of casing is obtained.The results show that the existence of wear defects results in a stress concentration and enhanced M-E interaction on corrosion depth growth.The accuracy of different mathematical models is different:the slot ring model is most accurate for predicting corrosion depth,and the eccentric model is most accurate for predicting the burst strength of corroded casing.The burst strength of the casing will be overestimated by more than one-third if the M-E interaction is neglected,so the coupling effect of wear and corrosion should be sufficiently considered in casing integrity evaluation.

Effects of Integrated Case Payment on Medical Expenditure and Readmission of Inpatients with Chronic Obstructive Pulmonary Disease： A Nonrandomized, Comparative Study in Xi County, China

In the past few decades, Chinese government attempted to reduce the economic burden of chronic diseases and lower family financial risk of patients by establishing a nationwide coverage of Social Health Insurance system. However, the payment mode of Social Health Insurance varies across Chinese healthcare settings, and the effectiveness of each mode differs. This study aimed to evaluate the effects of integrated case payment on medical expenditure and readmission of inpatients with chronic obstructive pulmonary disease （COPD）, a complex, multicomponent, chronic condition. A nonrandomized, comparative method was used in this study. Inpatients with COPD before （n=1569） and after the integrated case payment reform （n=4764） were selected from the inpatient information database of the New Cooperative Medical Scheme Agency of Xi County. The integrated case payment comprises the case payment （including price-cap case payment and fixed-reimbursement case payment） and clinical pathway （including clinical pathway A, clinical pathway B and clinical pathway C）. Effects of integrated case payment were evaluated with indicators of per capita total medical expense and readmission within 30 days. A multivariate linear regression and a binary logistic regression were used to conduct statistical analysis. The results showed that case payment, comprising price-cap case payment （β=2382.988, P〈0.001） and fixed-reimbursement case payment β=2613.564, P〈0.001）, and clinical pathway C （β=1996.467, P〈0.001） were risk factors of per capita total medical expenses. Clinical pathway A （β=-1443.409, P〈0.001） and clinical pathway B （β=-1583.791, P〈0.001） were protective factors. The interactive effects of case payment with hospital level （β=0.710, P〈0.001） lowered the readmission rate within 30 days. Meanwhile, clinical pathways A （β=18.949, P〈0.001）, B （β=-19.752, P〈0.001） and C （β=-1.882, P〈0.1） were associated with the rate increase. The findings revealed that integrated case payment ensured the quality of care for inpatients with COPD to some extent. However, this payment mode increased the per capita total medical expense. Further, policy-makers should set reasonable reimbursement standards of case payment, unify the type of case payment, and strengthen the supervision of the reform to enhance its function on medical cost control.

Optimal resource allocation with spatiotemporal transmission discovery for efective disease control

Background:The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world,especially in highly populated regions.The healthcare capacity(especially the testing resources,vaccination coverage,and hospital capacity)is becoming extremely insufcient as the demand will far exceed the supply.To address this time-critical issue,we need to answer a key question:How can we efectively infer the daily transmission risks in diferent districts using machine learning methods and thus lay out the corresponding resource prioritization strategies,so as to alleviate the impact of the Omicron outbreaks?Methods:We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant.We collect the publicly available data from the ofcial website of the Hong Kong Special Administrative Region(HKSAR)Government and the study period in this paper is from December 27,2021 to July 17,2022(including a period for future prediction).First,we construct the spatiotemporal transmission intensity matrices across diferent districts based on infection case records.With the constructed cross-district transmission matrices,we forecast the future risks of various locations daily by means of the Gaussian process.Finally,we develop a transmission-guided resource prioritization strategy that enables efective control of Omicron outbreaks under limited capacity.Results:We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong,China.The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks,making it difcult for the public health authority to foresee the outbreaks and plan the responses accordingly.With the guidance of the inferred transmission risks,the developed prioritization strategy enables the optimal testing resource allocation for integrative case management(including case detection,quarantine,and further treatment),i.e.,with the 300,000 testing capacity per day;it could reduce the infection peak by 87.1% compared with the population-based allocation strategy(case number reduces from 20,860 to 2689)and by 24.2% compared with the case-based strategy(case number reduces from 3547 to 2689),signifcantly alleviating the burden of the healthcare system.Conclusions:Computationally characterizing spatiotemporal transmission patterns allows for the efective risk mapping and resource prioritization;such adaptive strategies are of critical importance in achieving timely outbreak control under insufcient capacity.The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants.Moreover,the investigation conducted in Hong Kong,China provides useful suggestions on how to achieve efective disease control with insufcient capacity in other highly populated countries and regions.