Pilar Gargallo

Weight Gain and Anxiety Levels in Recent Ex-Smokers

OBJECTIVE To evaluate weight gain and its relation to anxiety in a group of smokers after 3 months of cessation treatment. PATIENTS AND METHODS The target population for this prospective, analytical, longitudinal study was smokers being treated in a specialist smoking cessation clinic who were still abstinent at the conclusion of a 3-month treatment program. The following variables were analyzed: age, sex, nicotine dependence (Fagerström test), daily cigarette consumption, number of pack-years, pharmacological treatment (nicotine replacement/bupropion), use of nicotine gum (yes/no), weight gain, body mass index, and degree of state and trait anxiety. Successful cessation was defined as self-reported abstinence confirmed by measurement of expired carbon monoxide (CO) level (< or = 10 ppm). Anxiety was evaluated using the State-Trait Anxiety Inventory. The state anxiety and weight variables were measured on 5 occasions: before smoking cessation, and at the end of week 1, month 1, month 2, and month 3 after cessation. Results for the quantitative variables were expressed as means (SD), and results for the qualitative variables were expressed as percentages and absolute frequencies. RESULTS The study population consisted of 122 individuals, 76 of whom were men (62%) and 46 of whom were women (38%). The mean age was 43.9 (9.9) years, and mean nicotine dependence according to the Fagerström scale was 6.2 (2.2) points. Average weight gain was 2.6 kg (3.6%), with no significant difference between the sexes. Weight gain in 25% of this population was greater than 4.2 kg, and maximum weight gain was 9.2 kg. Levels of state anxiety fell progressively as weight increased, although there was no evident relationship between the 2 variables. CONCLUSIONS Weight gain is moderate as smokers quit. Anxiety levels, which are greater in the first few weeks after cessation, do not explain weight variation, which is more related to the metabolic effects of nicotine rather than to psychological variables.

Automatic monitoring and intervention in multivariate dynamic linear models

An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in a matrix normal dynamic linear model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. The procedure is illustrated with two empirical examples taken from the literature.

Results of Smoking Cessation Therapy in a Specialist Unit

OBJECTIVE The aim of this study was to assess the results of smoking cessation therapy in a specialist unit by calculating the probability of continued abstinence at 6-month follow-up and analyzing differences according to the characteristics of the individuals. PATIENTS AND METHODS A prospective longitudinal study was undertaken in smokers who received multicomponent smoking-cessation therapy over a period of 3 months. Continued abstinence was assessed on the basis of self-report by participants and confirmed by measurement of exhaled carbon monoxide levels. Kaplan-Meier survival analysis was performed to assess the probability of continued abstinence. Log-rank tests were used to analyze differences in continued abstinence according to different qualitative variables. RESULTS The 1120 patients who participated in the study (56% men and 44% women) had a mean (SD) age of 44.1 (9.5) years. The mean score on the Fagerström test was 6.3 (2.1). Nicotine replacement therapy was provided in 70.8% of patients while 29.2% received bupropion. The probability of continued abstinence at 6 months was 62.2%. Individuals with a high dependence had a lower probability of continued abstinence at 6 months, as did those in whom treatment adherence was poor. No differences were observed in the probability of abstinence according to sex or type of pharmacological treatment. CONCLUSIONS Individuals with a high nicotine dependence can benefit from intensive smoking-cessation treatment in a specialist unit to achieve continued abstinence.

Systemic decision making in AHP: a Bayesian approach

Systemic decision making is a new approach for dealing with complex multiactor decision making problems in which the actors’ individual preferences on a fixed set of alternatives are incorporated in a holistic view in accordance with the “principle of tolerance”. The new approach integrates all the preferences, even if they are encapsulated in different individual theoretical models or approaches; the only requirement is that they must be expressed as some kind of probability distribution. In this paper, assuming the analytic hierarchy process (AHP) is the multicriteria technique employed to rank alternatives, the authors present a new methodology based on a Bayesian analysis for dealing with AHP systemic decision making in a local context (a single criterion). The approach integrates the individual visions of reality into a collective one by means of a tolerance distribution, which is defined as the weighted geometric mean of the individual preferences expressed as probability distributions. A mathematical justification of this distribution, a study of its statistical properties and a Monte Carlo method for drawing samples are also provided. The paper further presents a number of decisional tools for the evaluation of the acceptance of the tolerance distribution, the construction of tolerance paths that increase representativeness and the extraction of the relevant knowledge of the subjacent multiactor decisional process from a cognitive perspective. Finally, the proposed methodology is applied to the AHP-multiplicative model with lognormal errors and a case study related to a real-life experience in local participatory budgets for the Zaragoza City Council (Spain).

Predictors of outcome of a smoking cessation treatment by gender

OBJECTIVE To identify factors predictive of the outcome of a smoking cessation program by gender. METHODS A cross-sectional study of smokers starting treatment in a smoking cessation clinic from 2002 to 2007 was conducted. The variables consisted of data on sociodemographic factors, smoking habits, the social context of smoking and psychiatric comorbidity prior to or during the smoking cessation process. All patients received multicomponent treatment consisting of psychological and pharmacological interventions. Success was defined as self-reported continuous abstinence confirmed by cooximetry (CO ≤10 ppm). Logistic regression was used to analyze the factors predictive of success. RESULTS A total of 1302 persons (52.1% men and 47.9% women), with a mean age of 43.4 (10.2) years, were included. The mean number of cigarettes smoked per day was 25.3 (10.4) and the mean Fagerström test score was 6.2 (2.2) points. The success rate was 41.3% (538) with no differences by gender. Positive predictors were lower nicotine dependence and having a non-smoking partner in men and older age, smoking fewer cigarettes per day, having fewer smoking friends and not experiencing depression or anxiety during the treatment in women. CONCLUSIONS Men and women have similar tobacco abstinence outcomes although gender factors play a role in determining abstinence. The gender perspective should be incorporated in smoking prevention and cessation programs.

Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets

As a result of increased mobility patterns of workers, explaining labor flows and partitioning regions into local labor markets (LLMs) have become important economic issues. For the former, it is useful to understand jointly where individuals live and where they work. For the latter, such markets attempt to delineate regions with a high proportion of workers both living and working. To address these questions, we separate the problem into two stages. First, we introduce a stochastic modeling approach using a hierarchical spatial interaction specification at the individual level, incorporating individual-level covariates, origin (O) and destination (D) covariates, and spatial structure. We fit the model within a Bayesian framework. Such modeling enables posterior inference regarding the importance of these components as well as the O-D matrix of flows. Nested model comparison is available as well. For computational convenience, we start with a minimum market configuration (MMC) upon which our model is overlaid. At the second stage, after model fitting and inference, we turn to LLM creation. We introduce a utility with regard to the performance of an LLM partition and, with posterior samples, we can obtain the posterior distribution of the utility for any given LLM specification which we view as a partition of the MMC. We further provide an explicit algorithm to obtain good partitions according to this utility, employing these posterior distributions. However, the space of potential market partitions is huge and we discuss challenges regarding selection of the number of markets and comparison of partitions using this utility. Our approach is illustrated using a rich dataset for the region of Aragon in Spain. In particular, we analyze the full dataset and also a sample. Future data collection will arise as samples of the working population so assessing population level inference from the sample is useful.

Predictors of Success at 6-Month Follow up for Smokers Treated at a Smoking Cessation Clinic

OBJECTIVE To identify the predictors of successful outcome in a smoking cessation program at 6-month follow-up. MATERIAL AND METHODS Cross-sectional descriptive study of a sample of smokers who attended a smoking cessation clinic for combined medical and cognitive-behavioral group therapy. The independent variables assessed included age, sex, level of education, nicotine dependence (Fagerström test), prior attempts to quit smoking, medication prescribed, compliance with group therapy regimen, and success at one week and 3 months. Success was defined as self-reported abstinence, confirmed by CO-oximetry (carbon monoxide <10 ppm). Odds ratios (with 95% confidence intervals) were calculated for the categorical variables and a test of statistical significance of differences between means was performed for quantitative variables. Univariate logistic regression analysis was performed and significant variables were entered into a multivariate logistic regression model. RESULTS The study population comprised 248 individuals, 67.7% male and 32.3% female, with a mean (SD) age of 43.1 (10.5) years. The mean score on the Fagerström test was 6.3 (2.1) points and 84.7% of the individuals complied with the treatment regimen. Success rates were as follows: 77% at one week, 30.2% at 3 months, and 31.9% at 6 months. Three variables--success at 3 months, age, and nicotine dependence--were entered into the multivariate logistic regression model; the only variable predictive of successful smoking cessation at 6 months was success at 3 months. CONCLUSIONS Individuals who fully comply with treatment and abstain from smoking during the first weeks are more likely to be successful at 6 months.

Automatic selective intervention in dynamic linear models

In this paper we propose an algorithm to carry out the monitoring and retrospective intervention process in Dynamic Linear Models, both selectively and automatically. The algorithm is illustrated by analysing several series taken from the literature, in which the proposed procedure is shown to perform better than the scheme proposed by West & Harrison (1997, Chapter 11).

Analysis of housing price by means of STAR models with neighbourhood effects: a Bayesian approach

In this paper, we extend the Bayesian methodology introduced by Beamonte et al. (Stat Modelling 8:285–311, 2008) for the estimation and comparison of spatio-temporal autoregressive models (STAR) with neighbourhood effects, providing a more general treatment that uses larger and denser nets for the number of spatial and temporal influential neighbours and continuous distributions for their smoothing weights. This new treatment also reduces the computational time and the RAM necessities of the estimation algorithm in Beamonte et al. (Stat Modelling 8:285–311, 2008). The procedure is illustrated by an application to the Zaragoza (Spain) real estate market, improving the goodness of fit and the outsampling behaviour of the model thanks to a more flexible estimation of the neighbourhood parameters.

Bayesian inference in STAR models using neighbourhood effects

In this paper we propose a semi-parametric Bayesian analysis of a spatio-temporal autoregressive model (STAR) with neighbourhood effects similar to those of Pace et al. (1998, 2000). This approach allows us to make inferences about the parameters of the model and, more particularly, about the number of neighbours, without having to appeal to asymptotic results. In addition, the procedure used to obtain the out-sample predictions takes into account the uncertainty associated to the estimation of the model parameters in a more realistic way. The methodology is illustrated by means of an application to the real estate market in the city of Zaragoza (Spain).