Parental education and children's online health information seeking: Beyond the digital divide debate

Research has shown that increasing numbers of teenagers are going online to find health information, but it is unclear whether there are disparities in the prevalence of online health seeking among young Internet users associated with social and economic conditions. Existing literature on Internet uses by adults indicates that low income, less educated, and minority individuals are less likely to be online health seekers. Based on the analysis of data from the Pew Internet and American Life Project for the US, this study finds that teens of low education parents are either as likely as or even more likely than teens of high education parents to seek online health information. Multiple regression analysis shows that the higher engagement in health seeking by teens of low education parents is related to a lower prevalence of parental Internet use, suggesting that some of these teens may be seeking online health information on behalf of their low education parents. Implications of these findings are discussed in relation to the issues of the digital divide and digital empowerment.     

新生儿出生信息样本分析

目的:了解和探讨新生儿出生信息的分布态势及规律。方法:对本院2006年1~6月间所有新生儿出生信息进行样本分析。结果:半年间分娩的新生儿共1075例,其中死亡19例,双胞胎15例,以1028例单胎活产新生儿的出生体重、胎龄及产妇年龄作为研究样本进行分析,统计数据与传统意义上的正常胎龄和新生儿体重基本相符,胎龄、体重均值均有所提高。结论:孕产妇的健康状况和新生儿的体质均有所改善,随胎龄的增加,新生儿出生体重亦随之增加,并且,目前新生儿的整体统计体重要大于现有的临床指标,因此,应对现有的临床指标进行适当调整,重新确定低出生体重儿和巨大儿的临床指标。     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

Enhancing laboratory report contents to improve outpatient management of test results

In today''s environment, providers are extremely time-constrained. Assembling relevant contextual data to make decisions on laboratory results can take a significant amount of time from the day. The Regenstrief Institute has created a system which leverages data within Indiana Health Information Exchange''s (IHIE''s) repository, the Indiana Network for Patient Care (INPC), to provide well-organized and contextual information on returning laboratory results to outpatient providers. The system described here uses data extracted from INPC to add historical test results, medication-dispensing events, visit information, and clinical reminders to traditional laboratory result reports. These “Enhanced Laboratory Reports” (ELRs) are seamlessly delivered to outpatient practices connected through IHIE via the DOCS4DOCS clinical messaging service. All practices, including those without electronic medical record systems, can receive ELRs. In this paper, the design and implementation issues in creating this system are discussed, and generally favorable preliminary results of attitudes by providers towards ELRs are reported.     

Fisher information matrix for nonlinear mixed effects multiple response models: Evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model

We focus on the Fisher information matrix used for design evaluation and optimization in nonlinear mixed effects multiple response models. We evaluate the appropriateness of its expression computed by linearization as proposed for a single response model. Using a pharmacokinetic–pharmacodynamic (PKPD) example, we first compare the computation of the Fisher information matrix with approximation to one derived from the observed matrix on a large simulation using the stochastic approximation expectation–maximization algorithm (SAEM). The expression of the Fisher information matrix for multiple responses is also evaluated by comparison with the empirical information obtained through a replicated simulation study using the first‐order linearization estimation methods implemented in the NONMEM software (first‐order (FO), first‐order conditional estimate (FOCE)) and the SAEM algorithm in the MONOLIX software. The predicted errors given by the approximated information matrix are close to those given by the information matrix obtained without linearization using SAEM and to the empirical ones obtained with FOCE and SAEM. The simulation study also illustrates the accuracy of both FOCE and SAEM estimation algorithms when jointly modelling multiple responses and the major limitations of the FO method. This study highlights the appropriateness of the approximated Fisher information matrix for multiple responses, which is implemented in PFIM 3.0, an extension of the R function PFIM dedicated to design evaluation and optimization. It also emphasizes the use of this computing tool for designing population multiple response studies, as for instance in PKPD studies or in PK studies including the modelling of the PK of a drug and its active metabolite. Copyright © 2009 John Wiley & Sons, Ltd.     

Working‐correlation‐structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well‐known criterion of QIC for selecting a working correlation structure, and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads us to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study. Copyright © 2008 John Wiley & Sons, Ltd.     

随机系统的最小熵控制

本文放弃传统的MSE准则,从信息论观点出发,发展了随机系统的一种最小熵(ME)控制方法,并从ME控制的等价性、控制限度、存在性、唯一性及必要条件等方面,研究了ME控制的合理性和实用性。ME方法是一种全局分析法,它不仅包含了MSE和MAP准则,且由于全文在整个推导中并未假定高斯性、线性和非时变等,因而原则上适用于诸如非高斯、非线性、时变等传统方法难以处理的复杂系统;不仅适用于工程系统,将其推广到决策、社会、经济等领域会更为自然和切合实际。     

Causality Between the Amplitude and Frequency of Cardiac Oscillations

Using an information-theoretic approach, causality between the systolic amplitude in blood pressure and the frequency of the heart beat was analyzed. Blood pressure and ECG were noninvasively recorded in young, healthy subjects. Three successive measurements were performed: during spontaneous breathing and during paced respiration—at frequencies both higher and lower than that of spontaneous respiration. We demonstrate that the amplitude and frequency of the cardiac rhythm are synchronized for most of the time. The synchronization is stronger during paced respiration at a frequency lower than that of spontaneous breathing. Episodes where the cardiac frequency was driven by the systolic pressure were also detected during slow, paced respiration.     

Establishing the Cost-Effectiveness of New Pharmaceuticals under Conditions of Uncertainty—When Is There Sufficient Evidence?

Decisions about which health-care interventions represent adequate value to collectively funded health-care systems are as widespread as they are unavoidable. In the case of new pharmaceuticals, many countries now require formal cost-effectiveness analysis to inform this decision-making process. This requires evidence on parameters associated with health-related utilities, treatment effects, resource use, and costs, for which data from available regulatory trials are invariably absent or highly uncertain. This uncertainty results from a number of factors including the predominance of intermediate end points in the clinical evidence-base and the limited period of follow-up of patients in clinical studies. Despite these imperfections in the evidence base, decisions about whether new pharmaceuticals are sufficiently cost-effective for reimbursement cannot be side-stepped. Data limitations do, however, require the use of rigorous analytical methods to support decision making. Probabilistic decision models and value of information analysis offer a means of structuring decision problems, synthesizing all available data, characterizing the uncertainty in the decision, quantifying the cost of uncertainty, and establishing the expected value of perfect information. This analytical framework is important because it addresses two fundamental questions about new pharmaceuticals. First, is the product expected to be cost-effective on the basis of existing evidence? Second, is additional research concerning the product itself cost-effective? In addressing these questions, the analytical framework can establish when sufficient evidence exists to sustain a claim for a new pharmaceutical to be cost-effective.