Aims: This study aimed to establish the screening performance and optimal cut-off points for the Japanese version of Kessler (K)6, K10 and the Depression and Suicide Screen (DSS).Methods: A self-report questionnaire including K6, K10 and DSS, as well as the Center for Epidemiologic Studies -Depression Scale (CES-D), was administered to a random sample of community residents in Japan (non-cases, n = 147) and psychiatric outpatients diagnosed with mood or anxiety disorders according to DSM-IV (cases, n = 17). A receiver-operator characteristics (ROC) curve was drawn to estimate the area under the curve (AUC), the sensitivity, and specificity with the optimal cut-off points for K6, K10, and DSS, which were then compared with those of CES-D. The community sample was also asked to rate each measure on a scale from 'very easy' to 'very hard' to use.Results: K6 and K10 showed a high AUC (0.93-0.94), which was comparable to that of CES-D (0.95), but DSS showed a significantly smaller AUC (0.89) than CES-D (P < 0.05). The optimal cut-off points were estimated as 4/5 for K6, 9/10 for K10, and 1/2 for DSS. The sensitivity of these three scales was similar, but the specificity was lower for DSS than for the other two. K6, K10 and DSS were rated as being 'very easy' or 'easy to use' significantly more than CES-D (P < 0.01). Conclusion:The screening performance of the Japanese versions of K6 and K10 was comparable with that of CES-D, and better than that of DDS. K6/K10, particularly K6, might have an advantage, even over the CES-D, because of its similar screening performance and better acceptability.

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Humans prefer relatively equal distributions of resources, yet societies have varying degrees of economic inequality. To investigate some of the possible determinants and consequences of inequality, here we perform experiments involving a networked public goods game in which subjects interact and gain or lose wealth. Subjects (n = 1,462) were randomly assigned to have higher or lower initial endowments, and were embedded within social networks with three levels of economic inequality (Gini coefficient = 0.0, 0.2, and 0.4). In addition, we manipulated the visibility of the wealth of network neighbours. We show that wealth visibility facilitates the downstream consequences of initial inequality-in initially more unequal situations, wealth visibility leads to greater inequality than when wealth is invisible. This result reflects a heterogeneous response to visibility in richer versus poorer subjects. We also find that making wealth visible has adverse welfare consequences, yielding lower levels of overall cooperation, inter-connectedness, and wealth. High initial levels of economic inequality alone, however, have relatively few deleterious welfare effects.

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Molecular pathology diagnostics to subclassify diseases based on pathogenesis are increasingly common in clinical translational medicine. Molecular pathological epidemiology (MPE) is an integrative transdisciplinary science based on the unique disease principle and the disease continuum theory. While it has been most commonly applied to research on breast, lung, and colorectal cancers, MPE can investigate etiologic heterogeneity in non-neoplastic diseases such as cardiovascular diseases, obesity, diabetes mellitus, drug toxicity, and immunity-related and infectious diseases. This science can enhance causal inference by linking putative etiologic factors to specific molecular biomarkers as outcomes. Technological advances increasingly enable analyses of various -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, microbiome, immunomics, interactomics, etc. Challenges in MPE include sample size limitations (depending on availability of biospecimens or biomedical / radiological imaging), need for rigorous validation of molecular assays and study findings, and paucities of interdisciplinary experts, education programs, international forums, and standardized guidelines. To address these challenges, there are ongoing efforts such as multidisciplinary consortium pooling projects, the International Molecular Pathological Epidemiology (MPE) Meeting Series, and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)-MPE guideline project. Efforts should be made to build biorepository and biobank networks, and worldwide population-based MPE databases. These activities match with the purposes of the Big Data to Knowledge (BD2K), Genetic Associations and Mechanisms in Oncology (GAME-ON), and Precision Medicine Initiatives of the United States National Institute of Health. Given advances in biotechnology, bioinformatics, and computational / systems biology, there are wide open opportunities in MPE to contribute to public health.

Summary The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations, and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial, and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors, and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference, and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology, and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors, and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging, and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science.

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible−exposed−infectious−recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).

BackgroundStudies have reported that the predictive ability of self-rated health (SRH) for mortality varies by sex/gender and socioeconomic group. The purpose of this study is to evaluate this relationship in Japan and explore the potential reasons for differences between the groups.Methodology/Principal FindingsThe analyses in the study were based on the Aichi Gerontological Evaluation Study's (AGES) 2003 Cohort Study in Chita Peninsula, Japan, which followed the four-year survival status of 14,668 community-dwelling people who were at least 65 years old at the start of the study. We first examined sex/gender and education-level differences in association with fair/poor SRH. We then estimated the sex/gender- and education-specific hazard ratios (HRs) of mortality associated with lower SRH using Cox models. Control variables, including health behaviors (smoking and drinking), symptoms of depression, and chronic co-morbid conditions, were added to sequential regression models. The results showed men and women reported a similar prevalence of lower SRH. However, lower SRH was a stronger predictor of mortality in men (HR = 2.44 [95% confidence interval (CI): 2.14–2.80]) than in women (HR = 1.88 [95% CI: 1.44–2.47]; p for sex/gender interaction = 0.018). The sex/gender difference in the predictive ability of SRH was progressively attenuated with the additional introduction of other co-morbid conditions. The predictive ability among individuals with high school education (HR = 2.39 [95% CI: 1.74–3.30]) was similar to that among individuals with less than a high school education (HR = 2.14 [95% CI: 1.83–2.50]; p for education interaction = 0.549).ConclusionsThe sex/gender difference in the predictive ability of SRH for mortality among this elderly Japanese population may be explained by male/female differences in what goes into an individual's assessment of their SRH, with males apparently weighting depressive symptoms more than females.