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.     

军民融合协同创新驱动产业园区高质量发展研究

军民融合产业园区高质量发展对于推进军民深度融合、在全国形成可复制可推广经验具有重要意义。军民融合协同创新通过促进新知识涌现、推进新产业集聚发展，最终汇聚经济发展新动能，驱动园区高质量发展。在提出军民融合协同创新驱动园区高质量发展的机理与组织框架基础上，将发展模式归纳为以分享经济为背景的资源共享模式、以新型研发机构为核心的第三方模式、以产业联盟为核心的产业链拓展模式和以交易平台为核心的“PPP”模式，最后提出相关对策建议。     

管理层权力制衡强度、债务约束与企业创新投资

企业创新是国家经济可持续增长的关键，受到管理层意愿的影响，因而需要对内部经营者的权力进行制衡。以2010-2018年我国A股上市公司为研究样本，实证检验管理层权力制衡强度对企业创新投资的影响，以及不同债务约束情境下高商业信用配置、高负债水平的调节效应，此外，还考察了产权性质的差异化影响。研究表明，管理层权力制衡强度越大，企业创新投资水平越高；高商业信用强化了该促进作用，而高负债水平弱化了该促进作用。进一步研究发现，管理层权力制衡强度与企业创新投资的关系在民企中更显著；国企能够更好地获得和运用商业信用，使其高商业信用对该关系的强化效应更显著；民企具有更强的债务约束，其高负债水平对该关系的弱化效应更明显。     

我们为什么选择了社会主义市场经济

我们选择市场经济，是因为迄今为止的历史证明市场经济是可以富其国裕其民的。然而，我们所要的富裕，不是少数人的富裕，而是人民群众的共同富裕。不仅要富裕，而且必须是共同富裕的市场经济。因此，我们必须对市场经济进行改造，创造出我们自己的市场经济，这就是有中国特色的社会主义市场经济。该文遵循实事求是和历史唯物主义的原则，从市场经济旨在提高效率、社会主义则是坚持党的领导和社会公正、路径依赖和历史沿革体现了市场经济的中国特色等四个方面论证了中国共产党领导全国人民选择社会主义市场经济是一个具有历史必然性的伟大选择。这样的选择使得中国经济发展和人民富裕。     

乡村旅游核心利益相关者协调发展评价研究——以青海省海东市互助土族自治县小庄村为例

文章基于利益相关者理论的视角，选取青海省海东市互助县小庄村为案例地，通过田野调查和文献梳理分析出核心利益相关者的利益诉求和矛盾冲突，并构建乡村旅游核心利益相关者协调发展评价体系，采用EWM-Fuzzy综合评价模型和灰色关联模型对地方政府、旅游企业、社区居民及外来游客四类核心利益相关者利益诉求的紧急程度、利益关系协调发展状态及整体协调发展关系进行测评分析。研究表明，小庄村四类核心利益相关者对不同利益诉求的整体感知较好，不同利益诉求的紧急程度和协调发展状态均存在如下较大差异:四类核心利益相关者的利益诉求主要处于中度协调和轻度协调两种发展状态，优质协调发展状态的利益诉求表现相对较少，且存在濒临失调发展状态的利益诉求表现；小庄村旅游核心利益相关者相互间的协调发展关系呈现出多元化的发展状态，地方政府、旅游企业同其他利益相关者的利益关系协调发展的影响大小顺序完全一致，地方政府、社区居民同其他利益相关者的利益关系协调发展的影响大小顺序相对应，而外来游客、社区居民同其他利益相关者的利益关系协调发展的影响大小顺序基本相反；小庄村旅游核心利益相关者协调发展路径组合未达到纳什均衡状态，其协调发展模式为非对称互惠协调发展模式。上述验证结果与小庄村旅游发展的实际情况基本吻合，证明了评价体系的科学性。     

MODELING SOVEREIGN RISKS: FROM A HYBRID MODEL TO THE GENERALIZED DENSITY APPROACH

Motivated by the European sovereign debt crisis, we propose a hybrid sovereign default model that combines an accessible part taking into account the evolution of the sovereign solvency and the impact of critical political events, and a totally inaccessible part for the idiosyncratic credit risk. We obtain closed‐form formulas for the probability that the default occurs at critical political dates in a Markovian setting. Moreover, we introduce a generalized density framework for the hybrid default time and deduce the compensator process of default. Finally, we apply the hybrid model and the generalized density to the valuation of sovereign bonds and explain the significant jumps in long‐term government bond yields during the sovereign crisis.     

Evaluating museum visitor experiences based on user-generated travel photos

ABSTRACT

This research introduces online travel photos published on social media platforms as a complementary data resource to examine the behavior and experience of museum visitors. The practical value of online travel photos is demonstrated through a case study of popular Hong Kong museums, particularly by using the photo content and metadata available from the Flickr platform. The proposed approach is a generic method for understanding museum visitor behavior and preferences, and supports museum practitioners in developing improved products for visitors. The case study findings are particularly beneficial for tourism managers, especially those in Hong Kong, in promoting and attracting tourists to visit local museums.     

稻鳅共生种养模式试验研究

[目的]稻鳅共生是典型的稻田综合利用模式,符合生态农业发展方向。通过对稻鳅共生生态系统的生理生态学机制开展试验研究,为进一步推广稻鳅共生种养模式提供科学依据。[方法]采用田间试验方法,以水稻单作为对照,研究了不同泥鳅养殖密度下的稻鳅共生对水稻农艺性状、土壤理化性质、水稻产量构成的影响,并对稻鳅共生种养模式进行了经济效益分析。[结果]与对照处理相比,水稻长势在株高、有效分蘖率、根长等方面有一定提高。养殖田水稻株高增高了3%,有效分蘖率提高了8%~11%,根长提高了8.8%~31.3%。在土壤理化性质方面,与对照相比,土壤容重降低了7.1%~21.2%,孔隙度增加了4.1%~14.7%。实验前后养殖田内土壤有机质增加了3.5%~26.5%,对照田降低了2.5%~5.8%。土壤肥力(氮、磷、钾)减少,但减少幅度小于对照处理。稻鳅共生种养模式下水稻产量提高了5%~25%,同时稻田增收泥鳅1 725~3 375kg/hm2,净收入为1.836 0万~2.307 0万元/hm2,经济效益提高了3.65~4.84倍。稻鳅共生种养模式中泥鳅的养殖密度为30万尾/hm2时稻田的生态效益和经济效益最佳。[结论]稻鳅共生有效改善了土壤理化性质,促进了水稻的生长,提高了稻田产量和产值。     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.