E-cigarette use among women of reproductive age: Impulsivity, cigarette smoking status, and other risk factors.

INTRODUCTION: The study aim was to examine impulsivity and other risk factors for e-cigarette use among women of reproductive age comparing current daily cigarette smokers to never cigarette smokers. Women of reproductive age are of special interest because of the additional risk that tobacco and nicotine use represents should they become pregnant. METHOD: Survey data were collected anonymously online using Amazon Mechanical Turk in 2014. Participants were 800 women ages 24-44years from the US. Half (n=400) reported current, daily smoking and half (n=400) reported smokingsociodemographics, tobacco/nicotine use, and impulsivity (i.e., delay discounting & Barratt Impulsiveness Scale). Predictors of smoking and e-cigarette use were examined using logistic regression. RESULTS: Daily cigarette smoking was associated with greater impulsivity, lower education, past illegal drug use, and White race/ethnicity. E-cigarette use in the overall sample was associated with being a cigarette smoker and greater education. E-cigarette use among current smokers was associated with increased nicotine dependence and quitting smoking; among never smokers it was associated with greater impulsivity and illegal drug use. E-cigarette use was associated with hookah use, and for never smokers only with use of cigars and other nicotine products. CONCLUSIONS: E-cigarette use among women of reproductive age varies by smoking status, with use among current smokers reflecting attempts to quit smoking whereas among non-smokers use may be a marker of a more impulsive repertoire that includes greater use of alternative tobacco products and illegal drugs

Applying Behavioral Economics to the Challenge of Reducing Cocaine Abuse

This paper focuses on potential contributions of behavioral economics to reducing cocaine abuse. More specifically, this paper underscores the fundamental role of reinforcement in the genesis and maintenance of cocaine use and explores how reinforcement and consumer-demand theory might be translated into effective strategies for reducing cocaine use. A broad range of relevant research findings are discussed, including preclinical studies conducted with laboratory animals, laboratory and treatment-outcome studies conducted with cocaine abusers, and large epidemiological studies conducted with national samples of the U.S. population.

Delay Discounting is Associated with Treatment Response among Cocaine-Dependent Outpatients

Rationale—Delay discounting (DD) describes the rate at which reinforcers lose value as the temporal delay to their receipt increases. Steeper discounting has been positively associated with vulnerability to substance use disorders, including cocaine use disorders. Objectives—In the present study, we examined whether DD of hypothetical monetary reinforcers is associated with the duration of cocaine abstinence achieved among cocainedependent outpatients. Methods—Participants were 36 adults who were participating in a randomized controlled trial examining the efficacy of voucher-based contingency management (CM) using low-magnitude (N = 18) or high-magnitude (N = 18) voucher monetary values. Results—DD was associated with the number of continuous weeks of cocaine abstinence achieved, even after adjusting for treatment condition during the initial 12-week (t(33) = 2.48, p = .045) and entire recommended 24-week of treatment (t(33) = 2.40, p = .022). Participants who exhibited steeper discounting functions achieved shorter periods of abstinence in the Lowmagnitude voucher condition (12-week: t(16) = 2.48, p = .025; 24-week: t(16) = 2.68, p = .017), but not in the High-magnitude voucher condition (12-week: t(16) = 0.51, p = .618; 24-week: t(16) = 1.08, p = .298), although the interaction between DD and treatment condition was not significant (12-week: t(32) = −1.12, p = .271; 24-week: t(32) = −0.37, p = .712). Conclusions—These results provide further evidence on associations between DD and treatment response and extend those observations to a new clinical population (i.e., cocainedependent outpatients), while also suggesting that a more intensive intervention like the Highmagnitude CM condition may diminish this negative relationship between DD and treatment response

Fast Evaluation of Feynman Diagrams

We develop a new representation for the integrals associated with Feynman diagrams. This leads directly to a novel method for the numerical evaluation of these integrals, which avoids the use of Monte Carlo techniques. Our approach is based on based on the theory of generalized sinc ($\sin(x)/x$) functions, from which we derive an approximation to the propagator that is expressed as an infinite sum. When the propagators in the Feynman integrals are replaced with the approximate form all integrals over internal momenta and vertices are converted into Gaussians, which can be evaluated analytically. Performing the Gaussians yields a multi-dimensional infinite sum which approximates the corresponding Feynman integral. The difference between the exact result and this approximation is set by an adjustable parameter, and can be made arbitrarily small. We discuss the extraction of regularization independent quantities and demonstrate, both in theory and practice, that these sums can be evaluated quickly, even for third or fourth order diagrams. Lastly, we survey strategies for numerically evaluating the multi-dimensional sums. We illustrate the method with specific examples, including the the second order sunset diagram from quartic scalar field theory, and several higher-order diagrams. In this initial paper we focus upon scalar field theories in Euclidean spacetime, but expect that this approach can be generalized to fields with spin.Comment: uses feynmp macros; v2 contains improved description of renormalization, plus other minor change

Behavioral Economic Measurement of Cigarette Demand: A Descriptive Review of Published Approaches to the Cigarette Purchase Task

The cigarette purchase task (CPT) is a behavioral economic method for assessing demand for cigarettes. Growing interest in behavioral correlates of tobacco use in clinical and general populations as well as empirical efforts to inform policy has seen an increase in published articles employing the CPT. Accordingly, an examination of the published methods and procedures for obtaining these behavioral economic metrics is timely. The purpose of this investigation was to provide a review of published approaches to using the CPT. We searched specific Boolean operators ([“behavioral economic” AND “purchase task”] OR [“demand” AND “cigarette”]) and identified 49 empirical articles published through the year 2018 that reported administering a CPT. Articles were coded for participant characteristics (e.g., sample size, population type, age), CPT task structure (e.g., price framing, number and sequence of prices; vignettes, contextual factors), and data analytic approach (e.g., method of generating indices of cigarette demand). Results of this review indicate no standard approach to administering the CPT and underscore the need for replicability of these behavioral economic measures for the purpose of guiding clinical and policy decisions

Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds

The BBS is undertaken by the British Trust for Ornithology (BTO) and jointly funded by the BTO, the Joint Nature Conservation Committee and the Royal Society for the Protection of Birds.1. Modelling spatio-temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate whose effect is expressed through a low-order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates. 2. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space-time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK’s Breeding Bird Survey, 1994-2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships. 3. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space-time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space. 4. Our methods estimate the effect of temporal changes in weather, while accounting for spatial effects of long-term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large-scale data sets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in analyses of large-scale data sets.PostprintPeer reviewe