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More reports of relapse curves of self-quitters are needed. Smoking cessation interventions should focus on the first week of abstinence. Interventions that produce abstinence rates of 5-10% may be effective. Cessation studies should report relapse curves.

This article updates a 1990 review of the effects of tobacco abstinence by reviewing (a) which symptoms are valid indicators of tobacco abstinence and (b) the time course of tobacco abstinence symptoms. The author searched several databases to locate more than 3,500 citations on tobacco abstinence effects between 1990 and 2004; 120 of these were used in this review. Data collection and interpretation were based solely on the author's subjective judgments. For brevity, the review does not evaluate craving, hunger, performance, and several other possible outcomes as withdrawal symptoms. Anger, anxiety, depression, difficulty concentrating, impatience, insomnia, and restlessness are valid withdrawal symptoms that peak within the first week and last 2-4 weeks. Constipation, cough, dizziness, increased dreaming, and mouth ulcers may be abstinence effects. Drowsiness, fatigue, and several physical symptoms are not abstinence effects. In conclusion, no major changes are suggested for DSM-IV criteria for tobacco/nicotine withdrawal, but some deletions are suggested for ICD-10 criteria. Future studies need to investigate several possible new symptoms of withdrawal and to define more clearly the time course of symptoms.

Non-Gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology.When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model offers a very popular and flexible approach to modelling such data, but this model suffers from two major shortcomings: variance inflation due to spatial confounding and high dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging. We propose a new parameterization of the spatial generalized linear mixed model that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count and Gaussian spatial data sets, and to a large infant mortality data set.

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