Seasonal and weather-related behavioral effects among urban Aboriginal, urban non-Aboriginal, and remote Aboriginal participants in Canada

The influence of seasonal change and weather on mood, social activity, weight, food consumption, and sleep length was compared across urban Aboriginals (n = 43), urban non-Aboriginals (n = 49), and remote Aboriginals (n = 39) in Ontario, Canada. Such research is important since climate change may differentially shape the well-being of social groups. Behavioral items—including mood, social activity, weight, sleep, and food consumption—were measured using the Seasonal Pattern Assessment Questionnaire, and associations between these items and meteorological data were examined with bivariate and multivariate approaches. Weather variables had consistent, significant associations with behavior except within the remote Aboriginal group despite living in a more extreme climate. Lifestyle and adaptation may contribute to an increased weather tolerance among remote Aboriginal people, intriguing findings as cultures grapple with the implications of future climate change.     

Health expectancies in Japan: gender differences and policy implications for women

Based on prospective observation of elderly people in the community in Japan, we compared the time-course of development and progression of physical disability between women and men. Men experienced disability at a younger age and at a faster rate than did women. The duration of time spent with disability in women was twice as long as in men. Consequently, women consume about two-thirds of the total resources of formal caregiving services in Japan. Women in Japan are increasingly educated, postponing marriage to higher ages, and less likely to care for parents in the home. Given these changes in family structure and social norms, the capacity for informal family caregiving has decreased dramatically. A recently enacted national long-term care insurance system may further change the picture of caregiving.     

A simple evolutionary model of dormancy and dispersal in heterogeneous patches with special reference to phytophagous lady beetles: I. Stable environments

Summary A simple evolutionary model of dormancy and dispersal is presented with special reference to phytophagous lady beetles. In order to investigate spatially heterogeneous environments, we assume the simplest patch structure, that is, there are only two patches, main and sub. Environments are also assumed to be temporally constant. The main patch is superior to the sub patch, but density effect at the main patch is higher than at the sub patch. Optimal dormancy and dispersal are obtained at the same time by the method of evolutionarily stable strategy (ESS). In the univoltine life cycle, dormancy strategy vanishes because dormant individuals do not reproduce at all but suffer from a certain mortality rate during winter hibernation. In the bivoltine life cycle, the dormancy and dispersal rates constitute a trade-off: the rates change together with a negative correlation when the mortality rate during dispersal or during winter hibernation changes. When suitability of the main patch gradually deteriorates, the optimal strategy changes as follows: neither dormancy nor dispersal is adopted at the most suitable condition, the dispersal rate is increased without dormancy in the intermediate condition, and then the dormancy rate is increased with a constant dispersal rate. We discuss the field observation data of lady beetles in the light of results of our model.     

Fetal movement frequency and the effect of associated perinatal factors: Multicenter study

Background

Decreased fetal movements are associated with adverse perinatal outcomes, including stillbirth. Delayed maternal visits to a health care provider after perceiving decreased fetal movements are frequently observed in stillbirths. Informing pregnant women of the normal range of fetal movement frequency is essential in their earlier visits in order to prevent stillbirth.

On mantel-haenszel type estimators in simple nested case-control studies

We are concerned with nested case-control studies in this article. For proportional hazards model, a class of over-all estimators of hazard ratios is presented when simple samples are drawn from risk sets. These estimators have the form of the Mantel-Haenszel estimator of odds ratio, and are consistent not only for large strata, but also for sparse data. Consistent estimators of the variances of the proposed hazard ratio estimators are also developed. An example is given to illustrate the proposed estimators.     

Estimating population characteristics by incorporating prior values in stratified random sampling/ranked set sampling

In this paper, we discuss the estimation of population characteristics using stratified random sampling in an infinite population framework, including ranked set sampling as a special case. The use of prior values is considered and the underlying distribution is assumed to be unknown. The estimator considered in each stratum is the weighted mean of the U-statistic and prior value. The optimum weight is obtained by minimizing the mean squared error of the estimator of the population characteristics, but it contains unknown parameters and those parameters are replaced with their estimates. Simulation results show the gains in efficiency of the proposed estimator, yielding gains of at least 1.2 times larger than the usual unbiased estimator under certain condition specified in the text. Guidelines for the usage of the proposed estimator are shown and an application to a real data set is provided.     

On Testing Sample Selection Bias Under the Multicollinearity Problem

This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993) is severe, (i) the t-test based on the Heckman-Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996) that the standard regression-based t-test (Heckman, 1979) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982), are robust to nonnormality but have very little power.     

Practical approaches for design and analysis of clinical trials of infertility treatments: crossover designs and the Mantel–Haenszel method are recommended

Crossover designs have some advantages over standard clinical trial designs and they are often used in trials evaluating the efficacy of treatments for infertility. However, clinical trials of infertility treatments violate a fundamental condition of crossover designs, because women who become pregnant in the first treatment period are not treated in the second period. In previous research, to deal with this problem, some new designs, such as re‐randomization designs, and analysis methods including the logistic mixture model and the beta‐binomial mixture model were proposed. Although the performance of these designs and methods has previously been evaluated in large‐scale clinical trials with sample sizes of more than 1000 per group, the actual sample sizes of infertility treatment trials are usually around 100 per group. The most appropriate design and analysis for these moderate‐scale clinical trials are currently unclear. In this study, we conducted simulation studies to determine the appropriate design and analysis method of moderate‐scale clinical trials for irreversible endpoints by evaluating the statistical power and bias in the treatment effect estimates. The Mantel–Haenszel method had similar power and bias to the logistic mixture model. The crossover designs had the highest power and the smallest bias. We recommend using a combination of the crossover design and the Mantel–Haenszel method for two‐period, two‐treatment clinical trials with irreversible endpoints. Copyright © 2015 John Wiley & Sons, Ltd.     

DETERMINING OUTPUT AND INFLATION VARIABILITY: ARE THE PHILLIPS CURVE AND THE MONETARY POLICY REACTION FUNCTION RESPONSIBLE?

This study analyzes the policy parameters in a Taylor monetary policy reaction function and a Phillips curve equation to determine the variability of inflation and output. The theoretical and empirical investigations yield two key results. First, countries with large parameters in the monetary policy reaction function have low and stable inflation. Second, countries with flatter Phillips curves (i.e., those with a higher degree of price stickiness) have larger output variability. This article also examines the determinants of inflation and output variability as well as determinants of the slope of the Phillips curve.(JEL E32 , E52 )