The Long-term Coercive Effect of State Community Benefit Laws on Hospital Community Health Orientation

This study is an examination of the long-term coercive effect of state community benefit laws (CB Laws) on the provision of community health activities in U.S. acute care hospitals. The sample included all the not-for-profit and investor owned acute care hospitals for which 1994 and 2006 AHA Annual Survey data were available. A panel design was used to longitudinally examine the effect that state CB Laws had on hospital community health orientation activities and the provision of health promotion services, after controlling for the influence of other organizational and environmental variables that might affect these activities and services. The authors found that both CB Law state and non CB Law state hospitals increased their number of orientation activities and promotion services from 1994 to 2006. However, there was no significant difference in the gains in these activities and services between these two groups of hospitals. These results suggest that other environmental and organizational factors may mediate the effect of the state CB Laws over time

Efficient First Order Methods for Linear Composite Regularizers

A wide class of regularization problems in machine learning and statistics employ a regularization term which is obtained by composing a simple convex function \omega with a linear transformation. This setting includes Group Lasso methods, the Fused Lasso and other total variation methods, multi-task learning methods and many more. In this paper, we present a general approach for computing the proximity operator of this class of regularizers, under the assumption that the proximity operator of the function \omega is known in advance. Our approach builds on a recent line of research on optimal first order optimization methods and uses fixed point iterations for numerically computing the proximity operator. It is more general than current approaches and, as we show with numerical simulations, computationally more efficient than available first order methods which do not achieve the optimal rate. In particular, our method outperforms state of the art O(1/T) methods for overlapping Group Lasso and matches optimal O(1/T^2) methods for the Fused Lasso and tree structured Group Lasso.Comment: 19 pages, 8 figure