Interpersonal Trust and Quality-of-Life: A Cross-Sectional Study in Japan

BACKGROUND: There is growing interest in psychosocial factors with positive attitudes, such as interpersonal trust, as determinants for Quality-of-life (QOL) or subjective well-being. Despite their longevity, Japanese people report a relatively poor subjective well-being, as well as lower interpersonal trust. Our aim in this study was to evaluate the possible association between interpersonal trust and QOL among Japanese people. METHODOLOGY AND PRINCIPAL FINDINGS: Based on the cross-sectional data for Japanese adults (2008), we analyzed the relationship between interpersonal trust and each of four domains of the WHOQOL-BREF. Interpersonal trust was assessed using three scales for trust in people, in human fairness and in human nature. In a total of 1000 participants (mean age: 45 years; 49% women), greater trust was recognized among women (vs. men), those aged 60-69 (vs. 20-29), or the high-income group (vs. low-income). Each of three trust scales was positively correlated with all domains of QOL. Multiple linear-regression models were constructed for each of QOL and the principal component score of the trust scales, adjusted for age, gender, area size of residence, income, education, and occupation. For all QOL domains, interpersonal trust was significantly and positively associated with better QOL with p<0.001 for all four domains including physical, psychological, social, and environmental QOL. Other factors associated with QOL included gender, age class, area size of residence, and income. Education and occupation were not associated with QOL. CONCLUSIONS AND SIGNIFICANCE: Greater interpersonal trust is strongly associated with a better QOL among Japanese adults. If a causal relationship is demonstrated in a controlled interventional study, social and political measures should be advocated to increase interpersonal trust for achieving better QOL

Filarial nematodes belonging to the superorders Diplotriaenoidea and Aproctoidea from wild and captive birds in Japan

Eight species of filarial nematodes of the superorders Diplotriaenoidea and Aproctoidea were collected from the lung, air sac, abdominal cavity, and subdermal layer of the neck of wild and captive birds in Japan. The species of the filarial nematodes were identified as Diplotriaena bargusinica, D. henryi, Serratospiculum kwangsiensis, S. tendo, Hamatospiculum accipitris, H. cylindricum, H. quadridens, and Lissonema noctuae based on morphometry and pathogenicity. D. henryi from Poecile varius, H. accipitris from Accipiter gentilis, H. cylindricum from Lanius bucephalus and H. quadridens from Otus flammeolus represent the first host records worldwide. Moreover, D. henryi, S. kwangsiensis, H. cylindricum, and L. noctuae were the first geographical records from Japan

Factorial orthogonality in the presence of covariates

The present paper obtains necessary and sufficient conditions for factorial orthogonality in the presence of covariates. In particular, when interactions are absent, combinatorial characterizations of the conditions, as natural generalizations of the well-known equal and proportional frequency criteria, have been derived

General definition and decomposition of projectors and some applications to statistical problems

A general definition of a set of projectors for decomposing a vector as the sum of vectors belonging to disjoint subspaces not necessarily spanning the whole space is given. Such projectors are defined only over the union of the disjoint subspaces. But their extension to the whole space is of some interest in statistical problems. Explicit expressions are obtained for projectors and their extensions in terms of matrices spanning the subspaces and g-inverses. Decomposition of a projector as the sum of projectors on subspaces is obtained and applied to problems arising in correlation analysis, analysis of variance and estimation of parameters in the Gauss-Markoff model

Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition

Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given becaus