Disease Prevalence and Health Determinants in Nevada

In this report, we use recent data to describe the health of Nevada and update the previous report (Monnat, 2012) on health outcomes and health determinants in the Silver State. Data for this report are mainly obtained from the County Health Rankings, America’s Health Rankings, and the 2016 Center for Disease Control and Prevention’s Division of Nutrition, Physical Activity, and Obesity report. In line with the previous report by Monnat (2012), we reference Nevada’s disease prevalence in relation to the U.S. average and other states, as well as disease distribution among counties in Nevada. We also compaire health indicators in the U.S. with those in other countries, discuss the health determinants and examine their impact on Nevadans’ health. The chapter concludes with the recommendations on strategies to improve health of Nevada residents. Additional resources include the national, state and community public health organizations (Appendix 1) and examples of public health related legislative actions in Nevada (Appendix 2). In the last several decades, there has been growing interest in how modifiable risk factors contribute to disease and mortality rates. Studies point to five key domains as the principal determinants of health: (a) genetic predisposition, (b) social circumstances, (c) environmental conditions, (d) health behaviors, and (e) medical care. It is estimated that as much as 60% of disease risk is attributable to modifiable factors, such as health behaviors, social circumstances, and environmental conditions (McGinnis, Williams-Ruso, & Knickman, 2002). In recent years, health behaviors (e.g. tobacco use, poor diet, and physical inactivity) and social determinants (e.g. poverty, access to health care) have been singled out as contributing to health inequalities (Lewis & Burb-Sharps, 2010; Marmot, 2005). The effect of behavioral and social determinants of health outcomes is evident throughout the U.S and Nevada

The Relationship of Education and Acculturation with Vigorous Intensity Leisure Time Physical Activity by Gender in Latinos

Objectives: Latinos have poorer health outcomes among certain conditions (e.g. diabetes, obesity, mental health) compared to non-Latino Whites in the U.S., in part due to difference in the amount of physical activity, which are heavily influenced by sociocultural factors such as educational attainment and acculturation. Vigorous-intensity leisure time physical activity (VLTPA) may provide health benefits with a shorter amount of time than moderate-to-light physical activity. However, VLTPA has been significantly understudied compared to LTPA in general. The purpose of this study is to examine the associations between educational attainment, acculturation, and VLTPA by gender among Latino adults in the U.S. Design: Nationally representative samples of Latino adults aged 25 years and older (n = 4393) from the 2010 National Health Interview Survey were analyzed. VLTPA was measured as the number of hours per week of VLTPA consisting of heavy sweating or large increases in breathing and heart rate. Acculturation was measured as the degree to which the English language versus the Spanish language was spoken most often. The zero-inflated Poisson regression model was constructed using the full information maximum likelihood estimation and controlling for a series of sociodemographic characteristics and relevant health behaviors. Results: Educational attainment was positively associated with VLTPA among Latino adults [exp(b) = 1.09, p \u3c 0.05)]. Similarly, greater acculturation was associated with greater hours/week of VLTPA [exp(b) = 1.10, p \u3c 0.05)]. Lastly, the effect of educational attainment on VLTPA significantly varied by gender. Conclusions: Education had a positive association and acculturation had negative association with the hours/week of VLTPA among Latinos. Also, the association between education and VLTPA was significantly stronger among women than men. These findings inform culturally and socially sensitive approaches to improve the health of Latinos, in hopes to address health disparities by race/ethnicity the U.S

Effect of Hund's rule coupling on SU(4) spin-orbital system

We investigate the ground-state property of a one-dimensional two-orbital Hubbard model at quarter filling by numerical techniques such as the density-matrix renormalization group method and the exact diagonalization. When the Hund's rule coupling $J$ is zero, the model is SU(4) symmetric. In fact, both spin and orbital correlations have a peak at $q=\pi/2$, indicating an SU(4) singlet state with a four-site periodicity. On the other hand, with increasing $J$, it is found that the peak position of the orbital correlation changes to $q=\pi$, while that of the spin correlation remains at $q=\pi/2$. We briefly discuss how the SU(4) symmetry is broken by $J$.Comment: 2 pages, 2 figures, Proceedings of ICM2006 (August 20-25, 2006, Kyoto

One-Shot Machine Unlearning with Mnemonic Code

Deep learning has achieved significant improvements in accuracy and has been applied to various fields. With the spread of deep learning, a new problem has also emerged; deep learning models can sometimes have undesirable information from an ethical standpoint. This problem must be resolved if deep learning is to make sensitive decisions such as hiring and prison sentencing. Machine unlearning (MU) is the research area that responds to such demands. MU aims at forgetting about undesirable training data from a trained deep learning model. A naive MU approach is to re-train the whole model with the training data from which the undesirable data has been removed. However, re-training the whole model can take a huge amount of time and consumes significant computer resources. To make MU even more practical, a simple-yet-effective MU method is required. In this paper, we propose a one-shot MU method, which does not need additional training. To design one-shot MU, we add noise to the model parameters that are sensitive to undesirable information. In our proposed method, we use the Fisher information matrix (FIM) to estimate the sensitive model parameters. Training data were usually used to evaluate the FIM in existing methods. In contrast, we avoid the need to retain the training data for calculating the FIM by using class-specific synthetic signals called mnemonic code. Extensive experiments using artificial and natural datasets demonstrate that our method outperforms the existing methods.Comment: 14 pages, welcome coment