The economic burden of depression among adults with rheumatoid arthritis in the United States

Aims: Depression is the most frequent comorbidity reported among patients with rheumatoid arthritis (RA). Comorbid depression negatively impacts RA patients’ health-related quality-of-life, physical function, mental function, mortality, and experience of pain and symptom severity. The objective of this study was to assess healthcare utilization, expenditures, and work productivity among patients with RA with or without depression.

Materials and methods: Data from adult patients who had at least two visits each related to RA and depression over a 1-year period were extracted from the Truven Health MarketScan research databases. Outcomes comprised healthcare resource utilization, work productivity loss, and direct healthcare costs comparing patients with RA with depression (n?=?3,478) vs patients with RA without depression (n?=?43,222).

Results: Patients with RA and depression had a significantly greater relative risk of hospitalization and number of all-cause and RA-related hospitalizations, utilization of emergency services, days spent in the hospital, physician visits, and RA-related surgeries compared with RA patients without depression. Patients with RA and depression had a higher risk of and experienced more events and days of short-term disability compared with patients without depression. The incremental adjusted annual all-cause and RA-related direct costs were $8,488 (95% CI = $6,793–$10,223) and $578 (95% CI = –$98–$1,243), respectively, when comparing patients with RA and depression vs RA only.

Limitations: The current analysis is subject to the known limitations of retrospective studies based on administrative claims data.

Conclusions: This study suggested increased healthcare utilization, work productivity loss, and economic burden among RA patients due to comorbid depression. These findings emphasize the importance of managing depression and including depression as a factor when devising treatment algorithms for patients with RA.     

Recognition versus disclosure of finance leases: Evidence from Japan

This study examines whether credit market participants—bond investors and credit rating agencies—treat recognized and disclosed finance leases differently when assessing firms’ credit risk in Japan. I use firms’ credit risk, measured by bond spreads and credit ratings, to investigate the relations between recognized versus disclosed finance lease obligations and firms’ credit risk following the adoption of Statement No. 13, Accounting Standard for Lease Transactions. For a sample of firms issuing new bonds, I find that, unlike recognized finance leases, disclosed finance leases are not associated with bond spreads. Moreover, the associations between recognized versus disclosed finance leases and bond spreads are substantially different. Conversely, recognized and disclosed finance leases are associated with credit ratings and are processed similarly when credit ratings are determined. Taken together, my results suggest that the sophistication of capital market participants influences their credit risk assessments of recognized versus disclosed finance leases.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

Ownership discrimination and private firms financing in China

This study examines the financing/funding of private firms in China. Our results show that private firms are significantly less funded through formal financing channels such as bank loans than state-owned firms, and hence have to resort to alternative financing such as trade credit. Consistent with the theoretical expectation and literature, there is a substitution effect between trade credit and bank loans for private firms, but this effect is much weaker compared to that of state-owned firms. Moreover, while the univariate comparisons indicate that private firms obtain more notes payable than state-owned firms, the multivariate regression analyses show that the relation between bank loan and notes payable is positive and indifferent between private and state-owned firms.     

严监管背景下农信社合规风险管理体系建设

2018年,银行业监管环境进一步趋严,不但强调对银行的监管,更提出了对监管人员职责的监管,这种严格的外部监管环境对农信社的合规风险管理带来了极大的挑战。农信社合规风险管理体系的建设和完善已势不容缓。论文提出了农信社合规风险管理体系建设要从战略定位、文化渗透、防线搭建、专业专职化、考核和追究六个维度入手,推进合规风险管理长效机制的建设。     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.     

工商管理类专业课程教学质量监控体系研究

课程教学质量是高等学校的生命线，学分制模式下，工商管理类专业课程因其本身固有的特性，课程教学质量的提升在较大程度上依托于一个完善的质量监控体系的有效运作，为此高校要采取措施，在内部构建起课程教学质量的监控体系来确保人才培养质量，从而提高办学水平。     

基于Elman神经网络的商业银行信贷风险识别及评估模型

建立了基于Elman神经网络的商业银行信贷风险识别及评估模型，并通过实例验证了模型的准确性和可靠性。研究过程及结果表明，基于Elman神经网络的商业银行信贷风险识别及评估模型能够很好地反映信贷过程中的非线性因素，准确地预测出完整的信贷风险评估指标和信用等级之间的映射关系，能快速评估和有效减低商业的信贷风险。一组实例结果显示该评估模型的准确率接近90％。