Individual- and environmental-level factors may explain differential trajectories in lifespace mobility in older adults. The current study tested whether driving status was associated with lifespace, whether lifespace change varied by driving status, and whether residential context moderated the relationship between driving status and lifespace. Participants were older adults ages 65 to 94 (mean = 73.6 + 5.9) enrolled in the Advanced Cognitive Training for Independent and Vital Elderly Study (N = 2,792). Lifespace and driving status were assessed at baseline and first, second, third, and fifth annual follow-up visits. Residential population density was measured as the population density for participants' enrollment site counties. Two sites were categorized as low density ( 1,200 per square mile). Multilevel longitudinal models tested relationships between driving status, residential population density, and lifespace over five years. After controlling for potential confounders, results indicated that non-drivers had smaller mean lifespace than drivers across five years. Rates of lifespace declines did not differ between drivers and non-drivers. Non-drivers at baseline residing in low population density areas had smaller lifespace than non-drivers in high population density areas and all drivers regardless of population density. The findings suggest that residential context plays a role in older adults' travel behaviors and choices. Further research is needed to understand what residential characteristics support or hinder lifespace maintenance for older adult non-drivers, such as availability and usability of transportation and walkability.We consider the situation where there is a known regression model that can be used to predict an outcome, Y, from a set of predictor variables X. A new variable B is expected to enhance the prediction of Y. A dataset of size n containing Y, X and B is available, and the challenge is to build an improved model for Y|X,B that uses both the available individual level data and some summary information obtained from the known model for Y|X. We propose a synthetic data approach, which consists of creating m additional synthetic data observations, and then analyzing the combined dataset of size n+m to estimate the parameters of the Y|X, B model. This combined dataset of size n+m now has missing values of B form of the observations, and is analyzed using methods that can handle missing data (e.g. multiple imputation). We present simulation studies and illustrate the method using data from the Prostate Cancer Prevention Trial. Though the synthetic data method is applicable to a general regression context, to provide some justification, we show in two special cases that the asymptotic variance of the parameter estimates in the Y|X, B model are identical to those from an alternative constrained maximum likelihood estimation approach. This correspondence in special cases and the method's broad applicability makes it appealing for use across diverse scenarios.We combine nationally representative household and labor force survey data from 1992 to 2016 to provide a detailed description of rural labor market evolution and how it relates to the structural transformation of rural Vietnam, especially within the agricultural sector. Our study adds to the emerging literature on structural transformation in low-income countries using micro-level data and helps to answer several policy-related questions. We find limited employment creation potential of agriculture, especially for youth. Rural-urban real wage convergence has gone hand-in-hand with increased diversification of the rural economy into the non-farm sector nationwide and rapid advances in educational attainment in all sectors' and regions' workforce. Minimum wage laws seem to have played no significant role in increasing agricultural wages. This enhanced integration also manifests in steady attenuation of the longstanding inverse farm size-yield relationship. Farming has remained securely household-based and the family farmland distribution has remained largely unchanged. Small farm sizes have not obstructed mechanization nor the uptake of labor-saving pesticides, consistent with factor substitution induced by rising real wage rates. As rural households rely more heavily on the labor market, human capital accumulation (rather than land endowments) have become the key correlate of improvements in rural household well-being.This paper develops a new estimation procedure for ultrahigh dimensional sparse precision matrix, the inverse of covariance matrix.  https://www.selleckchem.com/products/CP-690550.html  Regularization methods have been proposed for sparse precision matrix estimation, but they may not perform well with ultrahigh dimensional data due to the spurious correlation. We propose a refitted cross validation (RCV) method for sparse precision matrix estimation based on its Cholesky decomposition, which does not require the Gaussian assumption. The proposed RCV procedure can be easily implemented with existing software for ultrahigh dimensional linear regression. We establish the consistency of the proposed RCV estimation and show that the rate of convergence of the RCV estimation without assuming banded structure is the same as that of those assuming the banded structure in Bickel and Levina (2008b). Monte Carlo studies were conducted to access the finite sample performance of the RCV estimation. Our numerical comparison shows that the RCV estimation outperforms the existing ones in various scenarios. We further apply the RCV estimation for an empirical analysis of asset allocation.
  Suicide is now the 2
  leading cause of death among adolescents and young adults. Social media's influence on youth suicidal risk or attenuation of risk is a novel and rapidly expanding topic of research that requires attention from a broad range of mental health services professionals. We aimed to provide an updated review of social media-related risk and protective factors to youth deliberate-self harm (DSH) to guide mental health services professionals in offering care and support to youth vulnerable to suicide.
 
  Studies on which primary research was conducted that evaluated young people's use of social media platforms related to DSH were systematically searched via Scopus and identified through expert recommendation and the Association for Computing Machinery's digital library of conference materials. The search focused on the timeframe June 2014 to September 2019, to offer an update since the time the most recent systematic reviews on this topic concluded their literatures searches. Quality was reviewed using the Mixed Methods Appraisal Tool (MMAT).