Effectiveness of a hospital-initiated smoking cessation programme: 2-year health and healthcare outcomes

Background: Tobacco-related illnesses are leading causes of death and healthcare use. Our objective was to determine whether implementation of a hospitalinitiated smoking cessation intervention would reduce mortality and downstream healthcare usage. Methods: A 2-group effectiveness study was completed comparing patients who received the ‘Ottawa Model’ for Smoking Cessation intervention (n=726) to usual care controls (n=641). Participants were current smokers, >17 years old, and recruited during admission to 1 of 14 participating hospitals in Ontario, Canada. Baseline data were linked to healthcare administrative data. Competing-risks regression analysis was used to compare outcomes between groups. Results: The intervention group experienced significantly lower rates of all-cause readmissions, smoking-related readmissions, and all-cause emergency department (ED) visits at all time points. The largest absolute risk reductions (ARR) were observed for allcause readmissions at 30 days (13.3% vs 7.1%; ARR, 6.1% (2.9% to 9.3%); p<0.001), 1 year (38.4% vs 26.7%; ARR, 11.7% (6.7% to 16.6%); p<0.001), and 2 years (45.2% vs 33.6%; ARR, 11.6% (6.5% to 16.8%); p<0.001). The greatest reduction in risk of allcause ED visits was at 30 days (20.9% vs 16.4%; ARR, 4.5% (0.4% to 8.7%); p=0.03). Reduction in mortality was not evident at 30 days, but significant reductions were observed by year 1 (11.4% vs 5.4%; ARR 6.0% (3.1% to 9.0%); p<0.001) and year 2 (15.1% vs 7.9%; ARR, 7.3% (3.9% to 10.7%); p<0.001). Conclusions: Considering the relatively low cost, greater adoption of hospital-initiated tobacco cessation interventions should be considered to improve patient outcomes and decrease subsequent healthcare usage

An Efficient Molecular Dynamics Scheme for the Calculation of Dopant Profiles due to Ion Implantation

We present a highly efficient molecular dynamics scheme for calculating the concentration depth profile of dopants in ion irradiated materials. The scheme incorporates several methods for reducing the computational overhead, plus a rare event algorithm that allows statistically reliable results to be obtained over a range of several orders of magnitude in the dopant concentration. We give examples of using this scheme for calculating concentration profiles of dopants in crystalline silicon. Here we can predict the experimental profile over five orders of magnitude for both channeling and non-channeling implants at energies up to 100s of keV. The scheme has advantages over binary collision approximation (BCA) simulations, in that it does not rely on a large set of empirically fitted parameters. Although our scheme has a greater computational overhead than the BCA, it is far superior in the low ion energy regime, where the BCA scheme becomes invalid.Comment: 17 pages, 21 figures, 2 tables. See: http://bifrost.lanl.gov/~reed

Front-end process modeling in silicon

Front-end processing mostly deals with technologies associated to junction formation in semiconductor devices. Ion implantation and thermal anneal models are key to predict active dopant placement and activation. We review the main models involved in process simulation, including ion implantation, evolution of point and extended defects, amorphization and regrowth mechanisms, and dopant-defect interactions. Hierarchical simulation schemes, going from fundamental calculations to simplified models, are emphasized in this Colloquium. Although continuum modeling is the mainstream in the semiconductor industry, atomistic techniques are starting to play an important role in process simulation for devices with nanometer size features. We illustrate in some examples the use of atomistic modeling techniques to gain insight and provide clues for process optimization