Sex differences in the development of mild cognitive impairment and probable Alzheimer’s disease as predicted by hippocampal volume or white matter hyperintensities

This study examined biological sex differences in the development of mild cognitive impairment (MCI) and probable Alzheimer’s disease (AD) development as predicted by changes in the hippocampus or white matter hyperintensities. A secondary data analysis of the National Alzheimer’s Coordinating Center Uniform Data Set was conducted. We selected samples of participants with normal cognition at baseline who progressed to MCI (n = 483) and those who progressed to probable AD (n = 211) to determine if hippocampal volume or white matter hyperintensities (WMH) at baseline predicted progression to probable AD or MCI and whether the rate of progression differed between men and women. The survival analyses indicated that changes in hippocampal volumes affected the progression to probable AD (HR = 0.535, 95% CI [0.300–0.953]) only among women. White men had an increased rate of progression to AD (HR = 4.396, CI [1.012–19.08]; HR = 4.665, 95% CI [1.072–20.29]) compared to men in other race and ethnic groups. Among women, increases in hippocampal volume ratio led to decreased rates of progressing to MCI (HR = 0.386, 95% CI [0.166–0.901]). Increased WMH among men led to faster progression to MCI (HR = 1.048. 95% CI [1.011–1.086]). Women and men who were older at baseline were more likely to progress to MCI. In addition, results from longitudinal analyses showed that women with a higher CDR global score, older age at baseline, or more disinhibition symptoms experienced higher odds of MCI development. Changes in hippocampal volumes affect the progression to or odds of probable AD (and MCI) more so among women than men, while changes in WMH affected the progression to MCI only among men.     

Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

Medicare and Amyloid PET Imaging: The Battle Over Evidence

We examine a recent dispute regarding the Centers for Medicare and Medicaid Services’ (CMS) refusal to unconditionally pay for amyloid positron emission tomography (PET) imaging for Medicare beneficiaries being assessed for Alzheimer’s disease. CMS will only pay for amyloid PET imaging when patients are enrolled in clinical trials that meet certain criteria. The dispute reflects CMS’s willingness in certain circumstances to require effectiveness evidence that differs from the Food and Drug Administration’s standard for pre-market approval of a medical intervention and reveals how stakeholders with differing perspectives about evidentiary standards have played a role in attempting to shape the Medicare program’s coverage policies.     

Managing the Risk of Aggressive Dog Behavior: Investigating the Influence of Owner Threat and Efficacy Perceptions

Aggressive behavior in pet dogs is a serious problem for dog owners across the globe, with bite injuries representing a serious risk to both people and other dogs. The effective management of aggressive behavior in dogs represents a challenging and controversial issue. Although positive reinforcement training methods are now considered to be the most effective and humane technique to manage the risk of aggression, punishment‐based methods continue to be used. Unfortunately, there has been little scientific study into the various factors influencing whether dog owners choose to use positive reinforcement techniques to manage aggression in their dogs. As such, current understanding of how best to encourage and support dog owners to use these methods remains extremely limited. This article uses a survey methodology based on protection motivation theory (PMT) to investigate the factors that influence owner use of positive reinforcement methods to manage aggressive behavior, in an attempt to understand potential barriers and drivers of use. In addition, the article provides an initial exploration of the potential role of wider psychological factors, including owner emotional state, social influence, and cognitive bias. Findings show that the perceived efficacy of positive reinforcement methods and the perceived ability of owners to effectively implement the technique are both key factors predicting future intentions and current reported use. Future interventions should focus on enhancing owner confidence in the effective use of positive reinforcement techniques across multiple scenarios, as well as helping owners manage their own emotional responses when they encounter challenging situations and setbacks.     

Affective and Cognitive Factors Influencing Sensitivity to Probabilistic Information

In study 1 different groups of female students were randomly assigned to one of four probabilistic information formats. Five different levels of probability of a genetic disease in an unborn child were presented to participants (within‐subject factor). After the presentation of the probability level, participants were requested to indicate the acceptable level of pain they would tolerate to avoid the disease (in their unborn child), their subjective evaluation of the disease risk, and their subjective evaluation of being worried by this risk. The results of study 1 confirmed the hypothesis that an experience‐based probability format decreases the subjective sense of worry about the disease, thus, presumably, weakening the tendency to overrate the probability of rare events. Study 2 showed that for the emotionally laden stimuli, the experience‐based probability format resulted in higher sensitivity to probability variations than other formats of probabilistic information. These advantages of the experience‐based probability format are interpreted in terms of two systems of information processing: the rational deliberative versus the affective experiential and the principle of stimulus‐response compatibility.     

健康动物肠菌群的毒性试验

1993年,作者从健康动物猪、鸡、兔、鱼等动物肠道中分离、鉴定出22株正常菌群,先后经过小鼠增重育肥试验、生物拮抗试验和肠菌群微囊化及保质观察的研究,筛选出12株优良菌株进行本次试验,试验结果表明．12株菌以不同菌量饲喂小鼠、雏鸡都显示出良好的安全性,未发现任何毒副作用。     

疫情之下数字经济发展对经济的影响研究

2020年,我国第一季度GDP同比下降6.8%,消费、投资两大引擎均呈现显著的负增长,新冠肺炎疫情对国民经济和居民生活造成了巨大的冲击。文章通过对“非典”时期与新冠肺炎疫情时期我国经济发展状况的比较分析,研究数字经济发展对经济的影响。结果表明,现阶段我国产业结构和互联网发展程度相比“非典”时期有显著变化,在线医疗、在线教育、电商送货、在线办公等数字经济产业是我国应对疫情不利影响、实现产业转型升级并稳定就业的新引擎。加快推动数字经济和各行业的深度融合,是我国数字经济发展的重点方向。     

Testing for qualitative interaction of multiple sources of informative dropout in longitudinal data

Longitudinal studies suffer from patient dropout. The dropout process may be informative if there exists an association between dropout patterns and the rate of change in the response over time. Multiple patterns are plausible in that different causes of dropout might contribute to different patterns. These multiple patterns can be dichotomized into two groups: quantitative and qualitative interaction. Quantitative interaction indicates that each of the multiple sources is biasing the estimate of the rate of change in the same direction, although with differing magnitudes. Alternatively, qualitative interaction results in the multiple sources biasing the estimate of the rate of change in opposing directions. Qualitative interaction is of special concern, since it is less likely to be detected by conventional methods and can lead to highly misleading slope estimates. We explore a test for qualitative interaction based on simultaneous confidence intervals. The test accommodates the realistic situation where reasons for dropout are not fully understood, or even entirely unknown. It allows for an additional level of clustering among participating subjects. We apply these methods to a study exploring tumor growth rates in mice as well as a longitudinal study exploring rates of change in cognitive functioning for Alzheimer's patients.     

Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak

Abstract. A stochastic epidemic model is defined in which each individual belongs to a household, a secondary grouping (typically school or workplace) and also the community as a whole. Moreover, infectious contacts take place in these three settings according to potentially different rates. For this model, we consider how different kinds of data can be used to estimate the infection rate parameters with a view to understanding what can and cannot be inferred. Among other things we find that temporal data can be of considerable inferential benefit compared with final size data, that the degree of heterogeneity in the data can have a considerable effect on inference for non‐household transmission, and that inferences can be materially different from those obtained from a model with only two levels of mixing. We illustrate our findings by analysing a highly detailed dataset concerning a measles outbreak in Hagelloch, Germany.     

A case study in non-centering for data augmentation: Stochastic epidemics

In this paper, we introduce non-centered and partially non-centered MCMC algorithms for stochastic epidemic models. Centered algorithms previously considered in the literature perform adequately well for small data sets. However, due to the high dependence inherent in the models between the missing data and the parameters, the performance of the centered algorithms gets appreciably worse when larger data sets are considered. Therefore non-centered and partially non-centered algorithms are introduced and are shown to out perform the existing centered algorithms.