人口流动对健康的影响

在当今世界,人口流动的重要性日益突出,其对健康的影响也日益扩大。尽管涉猎人口流动与健康这个交叉领域的学者很多。但致力于考察前者对后者的影响的作品却为数不多。原因在于流行病学的视角局限了大家的视野。为了扭转这种局面,笔者强调指出,要考察人口流动对健康的影响,必须比较流动前后人群在健康状况方面的变化。以此为导向。在回顾文献的基础上。笔者初步回答了两个问题：流动对健康的影响体现在何处？这些影响得以实现的机制是什么？最后,笔者还为将来的研究提供了一些建议。     

医疗保险对农村居民家庭消费的影响分析--基于不同收入层级的跨地区比较

利用2009、2010农村固定观察点数据,分析了医疗保险对不同地区、不同收入层级的农村居民家庭医疗消费支出和非医疗消费支出的影响。实证结果表明：医疗保险对农村居民家庭的医疗类消费不存在显著影响,对非医疗类消费支出则存在显著的正向促进作用。分地区来看,医疗保险对东西部地区农村居民家庭医疗类消费的影响差异显著,对东中部地区农村居民家庭非医疗类消费支出的影响同样差异显著;分收入层级来看,医疗保险对家庭医疗类消费的影响在不同收入层级的农村居民家庭之间不存在显著差异,但对非医疗类消费支出的影响在不同收入层级之间差异显著。此外,商业医疗保险作为医疗保险体系的重要组成部分,对促进农村消费起到带动作用。最后,本文从加大政府补贴和完善保障机制两个方面提出政策建议以期有效降低农村居民家庭超常的预防性储蓄,促进农村居民家庭消费。     

Response of sensitive behaviors to frequent measurement

We study the influence of frequent survey measurement on behavior. Widespread access to the Internet has made important breakthroughs in frequent measurement possible—potentially revolutionizing social science measurement of processes that change quickly over time. One key concern about using such frequent measurement is that it may influence the behavior being studied. We investigate this possibility using both a population-based experiment with random assignment to participation in a weekly journal for twelve months (versus no journal) and a large-scale, population-based, journal-keeping study with weekly measurement for 30 months. Results reveal few of the measured behaviors are correlated with assignment to frequent measurement. Theoretical reasoning regarding the likely behavioral response to frequent measurement correctly predicts domains most vulnerable to this possibility. Overall, however, we found little evidence of behavioral response to frequent measurement.     

Enabling robust assessment of QTc prolongation in early phase clinical trials

Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a “thorough QTc” (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration‐QTc (conc‐QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study. In December 2015, ICH released an “E14 Q&As (R3)” document supporting the use of conc‐QTc modeling for regulatory decisions. In this article, we propose a simple improvement of two popular conc‐QTc assessment methods for typical first‐in‐human crossover‐like single ascending dose clinical pharmacology trials. The improvement is achieved, in part, by leveraging routinely encountered (and expected) intrasubject correlation patterns encountered in such trials. A real example involving a single ascending dose and corresponding TQT trial, along with results from a simulation study, illustrate the strong performance of the proposed method. The improved conc‐QTc assessment will further enable highly reliable go/no‐go decisions in early phase clinical development and deliver results that support subsequent TQT study waivers by regulators.     

Decomposition of the main effects and interaction term by using orthogonal polynomials in multiple non symmetrical correspondence analysis

The multiple non symmetric correspondence analysis (MNSCA) is a useful technique for analyzing a two-way contingency table. In more complex cases, the predictor variables are more than one. In this paper, the MNSCA, along with the decomposition of the Gray–Williams Tau index, in main effects and interaction term, is used to analyze a contingency table with two predictor categorical variables and an ordinal response variable. The Multiple-Tau index is a measure of association that contains both main effects and interaction term. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition, while the interaction effect represents the combined effect of predictor categorical variables on the ordinal response variable. Moreover, for ordinal scale variables, we propose a further decomposition in order to check the existence of power components by using Emerson's orthogonal polynomials.     

Substance‐dependent women becoming mothers: breaking the cycle of adverse childhood experiences

Parenting may be particularly challenging for substance‐dependent mothers who have grown up with parents who themselves had substance use disorders (SUDs). The aim of this study was to explore how substance‐dependent mothers describe their childhood experiences with substance‐abusing parents and the association between these earlier experiences and their own role as caregivers. Using purposeful sampling, mothers admitted for 1 year to a family ward at a substance abuse clinic were approached. Through in‐depth, qualitative interviews, nine substance‐dependent mothers described their lives in the form of present, past and future tense. The findings indicate that substance‐dependent women, who have experienced SUDs in their families of origin, face several major challenges when they become mothers. Some describe having lived their whole lives ‘on the edge of society'. This makes their rehabilitation process more complex. All mothers work to abstain from substances, process traumatic experiences and integrate their family into society. They need help to build supportive social networks and to establish a safe and predictable family environment for themselves and their children. The therapeutic implications of these findings will be discussed.     

Mortality among elder abuse victims in rural Malaysia: A two-year population-based descriptive study

Our study aims at describing mortality among reported elder abuse experiences in rural Malaysia. This is a population-based cohort study with a multistage cluster sampling method. Older adults in Kuala Pilah (n = 1,927) were interviewed from November 2013 to May 2014. Mortality was traced after 2 years using the National Registration Department database. Overall, 139 (7.2%) respondents died. Fifteen (9.6%) abuse victims died compared to 124 (7.0%) not abused. Mortality was highest with financial abuse (13%), followed by psychological abuse (10.8%). There was a dose-response relationship between mortality and clustering of abuse: 7%, 7.7%, and 14.0% for no abuse, one type, and two types or more, respectively. Among abuse victims, 40% of deaths had ill-defined causes, 33% were respiratory-related, and 27% had cardiovascular and metabolic origin. Results suggest a link between abuse and mortality. Death proportions varied according to abuse subtypes and gender.     

A Note on High-Dimensional Linear Regression With Interactions

The problem of interaction selection in high-dimensional data analysis has recently received much attention. This note aims to address and clarify several fundamental issues in interaction selection for linear regression models, especially when the input dimension p is much larger than the sample size n. We first discuss how to give a formal definition of “importance” for main and interaction effects. Then we focus on two-stage methods, which are computationally attractive for high-dimensional data analysis but thus far have been regarded as heuristic. We revisit the counterexample of Turlach and provide new insight to justify two-stage methods from the theoretical perspective. In the end, we suggest new strategies for interaction selection under the marginality principle and provide some simulation results.     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.