Diagnostic sequence of cocaine use disorder in relation to other mental health conditions among college students

Abstract

Objective: Cocaine use is increasing. Comorbidities and diagnostic sequencing are needed among college students to inform treatment of cocaine use disorder (CUD). Method: Using electronic medical records from the psychiatric clinic at the student health care center of a large, public university from 2005 to 2015, patients diagnosed with CUD were identified. Their top mental health conditions were identified and assessed to see whether the first diagnosis of these conditions was made (1) before, (2) at the same time as, or (3) after the first diagnosis of CUD. Results: Among the 50 CUD patients, their most common mental health comorbidities were alcohol use disorder, anxiety, depression, and cannabis use disorder. Anxiety and depression were likely to be diagnosed before CUD; alcohol and cannabis use disorders were likely to be diagnosed concurrently with CUD. Conclusion: Diagnostic sequencing can be used to inform screening, workup, and treatment for patients with CUD.     

Child labour measurement: Whom should we ask?

Funding decisions to support vulnerable children are tied to child labour statistics, hence the importance of an accurate measurement. The author analyses whether the type of respondent plays an important role in explaining variations in child labour statistics. Using data from two sites in Tanzania, the analysis shows that “whom we ask” matters considerably when estimating the prevalence of child labour. The results suggest that prevalence increases by approximately 35 to 65 per cent when using child self‐reports rather than proxy reports. This bias affects 14 to 31 per cent of the sample, depending on the indicator. Discrepancies decrease as the child ages and increase if proxy attitudes demonstrate opposition to child labour.     

Analysis of social innovation on social networking services

ABSTRACT

Social network services are allowing more social connectivity and making possible sharing information and knowledge of a different nature. This article analyses if social workers related to active social policies from Malaga province (Spain) are using social network services to share information and knowledge and if there is a mirror between online and offline relationships. Since it is an experimental model, through virtual ethnography and through social network analysis methodology, we observed the presence, connectivity and analysed the structure of relationships that keep 235 professionals from 52 organizations in Facebook®. Moreover, the model uses the statistical technique of modularity for detecting online communities, which are compared with distribution of professionals in their organizations. Results show how social network services applied to social intervention are massively and frequently used by professionals. On the other hand, the detected communities reflect existence of analogy between online and offline relationships. The online structure shows a high degree of cohesion and how certain professionals have higher capacity of influence and diffusion depending on position in the online structure. It argues about the opportunity to incorporate social network services towards social intervention as a manner of social innovation to improve cooperation and diffusion of information and knowledge.     

大数据战略视野下我国信息网络安全立法分析

大数据战略视野下,很多国家纷纷制定、完善和执行着自己的信息网络安全法律,并已形成世界潮流.从国内外的大数据战略与信息网络安全立法分析来看,我国既有成绩,也有不足.对于我国信息网络安全的法律保障,目前需要完善的主要内容有:进一步明确国家信息网络安全立法的目的与宗旨;基本法层面,尽快构建完备的信息网络安全法律保障有机体系,重点包括政府信息公开法与个人信息保护法等;实施层面,加强与完善具体领域的单行法与配套法建设,增强法律规定的可操作性;完善法律框架之外的协作保障体系.     

Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science

ABSTRACT

Scientific research of all kinds should be guided by statistical thinking: in the design and conduct of the study, in the disciplined exploration and enlightened display of the data, and to avoid statistical pitfalls in the interpretation of the results. However, formal, probability-based statistical inference should play no role in most scientific research, which is inherently exploratory, requiring flexible methods of analysis that inherently risk overfitting. The nature of exploratory work is that data are used to help guide model choice, and under these circumstances, uncertainty cannot be precisely quantified, because of the inevitable model selection bias that results. To be valid, statistical inference should be restricted to situations where the study design and analysis plan are specified prior to data collection. Exploratory data analysis provides the flexibility needed for most other situations, including statistical methods that are regularized, robust, or nonparametric. Of course, no individual statistical analysis should be considered sufficient to establish scientific validity: research requires many sets of data along many lines of evidence, with a watchfulness for systematic error. Replicating and predicting findings in new data and new settings is a stronger way of validating claims than blessing results from an isolated study with statistical inferences.     

Semiparametric regression of clustered current status data

This paper discusses regression analysis of clustered current status data under semiparametric additive hazards models. In particular, we consider the situation when cluster sizes can be informative about correlated failure times from the same cluster. To address the problem, we present estimating equation-based estimation procedures and establish asymptotic properties of the resulting estimates. Finite sample performance of the proposed method is assessed through an extensive simulation study, which indicates the procedure works well. The method is applied to a motivating data set from a lung tumorigenicity study.     

Small‐sphere distributions for directional data with application to medical imaging

We propose novel parametric concentric multi‐unimodal small‐subsphere families of densities for p ? 1 ≥ 2‐dimensional spherical data. Their parameters describe a common axis for K small hypersubspheres, an array of K directional modes, one mode for each subsphere, and K pairs of concentrations parameters, each pair governing horizontal (within the subsphere) and vertical (orthogonal to the subsphere) concentrations. We introduce two kinds of distributions. In its one‐subsphere version, the first kind coincides with a special case of the Fisher–Bingham distribution, and the second kind is a novel adaption that models independent horizontal and vertical variations. In its multisubsphere version, the second kind allows for a correlation of horizontal variation over different subspheres. In medical imaging, the situation of p ? 1 = 2 occurs precisely in modeling the variation of a skeletally represented organ shape due to rotation, twisting, and bending. For both kinds, we provide new computationally feasible algorithms for simulation and estimation and propose several tests. To the best knowledge of the authors, our proposed models are the first to treat the variation of directional data along several concentric small hypersubspheres, concentrated near modes on each subsphere, let alone horizontal dependence. Using several simulations, we show that our methods are more powerful than a recent nonparametric method and ad hoc methods. Using data from medical imaging, we demonstrate the advantage of our method and infer on the dominating axis of rotation of the human knee joint at different walking phases.     

What influences the size of child maintenance payments? A register‐based study of maintenance payments in Finland

The aim of this study was to explore how particular economic and demographic factors contribute to the level of the child maintenance payment (CMP) paid by the non‐resident parent. For this study, we used 5‐year longitudinal panel data from years 2009 to 2013 consisting of over 80,000 non‐resident parents from the Finnish Tax Administration and The Finnish Population Register Centre. Results from regression models indicate that the single biggest factor affecting the size of CMPs is the number of dependent children. We found that the non‐resident parent's higher income is associated with higher CMPs and that non‐resident fathers pay on average larger CMPs than non‐resident mothers, even after accounting for differences in income. Unexpectedly, we found that the age of the dependent child did not predict changes in CMPs. This suggests that once formal CMP‐contracts are determined between the parents, they are seldom changed. Our results suggest that some degree of mandatory periodic review for maintenance contracts is worth considering.     

《民族学刊》2014~2018年影响因子比较分析——基于《中国学术期刊影响因子年报（人文社会科学）》数据

《民族学刊》于2010年6月创刊，相继入选“复印报刊资料重要转载来源期刊”“北大核心期刊”“人文社科核心期刊”和“CSSCI来源期刊”，表明了《民族学刊》在学界和期刊界的学术影响，其期刊的影响因子值得研究和探讨。本文基于《中国学术期刊影响因子年报（人文社会科学）》公布的影响因子、各类计量指标和人文社科类影响因子、被引频次及可被引文献量等数据，对《民族学刊》的相应数据与C95民族学类期刊在期刊影响力指数、影响因子均值及量效指数均值、各类计量指标均值和人文社科类影响因子、被引频次及可被引文献量均值等方面进行统计对比，分析《民族学刊》2014~2018年五年间的影响因子数据在民族学类期刊位置及其学术影响。     

Probabilistic frontier regression models for binary type output data

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.