Long-Term Benefits of Smoking Cessation on Gastroesophageal Reflux Disease and Health-Related Quality of Life

ObjectiveSmoking is associated with gastroesophageal reflux disease (GERD). Varenicline, a nicotinic receptor partial agonist, is used to aid smoking cessation. The purpose of this study was to prospectively examine the long-term benefits of smoking cessation on GERD and health-related quality of life (HR-QOL).MethodsPatients treated with varenicline were asked to fill out a self-report questionnaire about their smoking habits, gastrointestinal symptoms, and HR-QOL before and 1 year after smoking cessation. The prevalence of GERD, frequency of symptoms, and HR-QOL scores were compared. We also investigated associations between clinical factors and newly-developed GERD.ResultsA total of 141 patients achieved smoking cessation (success group) and 50 did not (failure group) at 1 year after the treatment. The GERD improvement in the success group (43.9%) was significantly higher than that in the failure group (18.2%). The frequency of reflux symptoms significantly decreased only in the success group. There were no significant associations between newly developed GERD and clinical factors including increased body mass index and successful smoking cessation. HR-QOL significantly improved only in the success group.ConclusionsSmoking cessation improved both GERD and HR-QOL. Smoking cessation should be recommended for GERD patients.     

Point of care tobacco treatment sustains during COVID-19, a global pandemic

BackgroundTobacco cessation treatment for cancer patients is essential to providing comprehensive oncologic care. We have implemented a point of care tobacco treatment care model enabled by electronic health record (EHR) modifications in a comprehensive cancer center. Data are needed on the sustainability of both reach of treatment and effectiveness over time, including the COVID-19 pandemic.MethodsUsing EHR data from the pre-implementation (P: 5 months) and post-implementation periods (6 month-blocks, T1-T5 for a total of 30 months), we compared two primary outcomes: 1) reach of treatment among those smoking and 2) effectiveness assessed by smoking cessation among those smoking in the subsequent 6 month period. We analyzed the data using generalized estimation equation regression models.ResultsWith the point of care tobacco treatment care model, reach of treatment increased from pre to post T5 (3.2 % vs. 48.4 %, RR 15.50, 95 % CI 10.56–22.74, p < 0.0001). Reach of treatment in all post periods (T1-T5 including the COVID-19 pandemic time) remained significantly higher than the pre period. Effectiveness, defined by smoking cessation among those smoking, increased from pre to post T2 before the pandemic (12.4 % vs. 21.4 %, RR 1.57, 95 % CI 1.31–1.87, p < 0.0001). However, effectiveness, while higher in later post periods (T3, T4), was no longer significantly increased compared with the pre period.ConclusionA point of care EHR-enabled tobacco treatment care model demonstrates sustained reach up to 30 months following implementation, even during the COVID-19 pandemic and changes in healthcare prioritization. Effectiveness was sustained for 12 months, but did not sustain through the subsequent 12 months.     

Principal stratification with predictors of compliance for randomized trials with 2 active treatments

In behavioral medicine trials, such as smoking cessation trials, 2 or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. The principal stratification framework permits inference about causal effects among subpopulations characterized by potential compliance. However, in the absence of prior information, there are 2 significant limitations: (1) the causal effects cannot be point identified for some strata and (2) individuals in the subpopulations (strata) cannot be identified. We propose to use additional information-compliance-predictive covariates-to help identify the causal effects and to help describe characteristics of the subpopulations. The probability of membership in each principal stratum is modeled as a function of these covariates. The model is constructed using marginal compliance models (which are identified) and a sensitivity parameter that captures the association between the 2 marginal distributions. We illustrate our methods in both a simulation study and an analysis of data from a smoking cessation trial.     

Influence of trial duration on the bias of the estimated treatment effect in clinical trials when individual heterogeneity is ignored

In randomized clinical trials where the times to event of two treatment groups are compared under a proportional hazards assumption, it has been established that omitting prognostic factors from the model entails an underestimation of the hazards ratio. Heterogeneity due to unobserved covariates in cancer patient populations is a concern since genomic investigations have revealed molecular and clinical heterogeneity in these populations. In HIV prevention trials, heterogeneity is unavoidable and has been shown to decrease the treatment effect over time. This article assesses the influence of trial duration on the bias of the estimated hazards ratio resulting from omitting covariates from the Cox analysis. The true model is defined by including an unobserved random frailty term in the individual hazard that reflects the omitted covariate. Three frailty distributions are investigated: gamma, log‐normal, and binary, and the asymptotic bias of the hazards ratio estimator is calculated. We show that the attenuation of the treatment effect resulting from unobserved heterogeneity strongly increases with trial duration, especially for continuous frailties that are likely to reflect omitted covariates, as they are often encountered in practice. The possibility of interpreting the long‐term decrease in treatment effects as a bias induced by heterogeneity and trial duration is illustrated by a trial in oncology where adjuvant chemotherapy in stage 1B NSCLC was investigated.     

A comparison of the beta-geometric model with landmarking for dynamic prediction of time to pregnancy

We conducted a simulation study to compare two methods that have been recently used in clinical literature for the dynamic prediction of time to pregnancy. The first is landmarking, a semi-parametric method where predictions are updated as time progresses using the patient subset still at risk at that time point. The second is the beta-geometric model that updates predictions over time from a parametric model estimated on all data and is specific to applications with a discrete time to event outcome. The beta-geometric model introduces unobserved heterogeneity by modelling the chance of an event per discrete time unit according to a beta distribution. Due to selection of patients with lower chances as time progresses, the predicted probability of an event decreases over time. Both methods were recently used to develop models predicting the chance to conceive naturally. The advantages, disadvantages and accuracy of these two methods are unknown. We simulated time-to-pregnancy data according to different scenarios. We then compared the two methods by the following out-of-sample metrics: bias and root mean squared error in the average prediction, root mean squared error in individual predictions, Brier score and c statistic. We consider different scenarios including data-generating mechanisms for which the models are misspecified. We applied the two methods on a clinical dataset comprising 4999 couples. Finally, we discuss the pros and cons of the two methods based on our results and present recommendations for use of either of the methods in different settings and (effective) sample sizes.     

What Are the Major Determinants in the Success of Smoking Cessation: Results from the Health Examinees Study

Understanding mechanisms underlying smoking-related factors should be prioritized in establishing smoking prevention and cessation policy. The aim of this study was to identify factors significantly associated with smoking initiation and/or smoking cessation as well as the most important determinants of successful smoking cessation in a developed non-Western setting. Based on multiple logistic regression models, the odds ratios (ORs) for smoking initiation and cessation were estimated among males (N = 24,490) who had participated in the Health Examinees (HEXA) study. The Cox proportional hazards regression model was used to assess the association between selected predictors of smoking cessation and the likelihood of reaching this goal. Finally, Kaplan–Meier curves were constructed to illustrate the distribution of time from age at smoking initiation to age at smoking cessation. We found that the ORs for successfully quitting smoking increased with age, married status, educational achievement, having a non-manual job, drinking cessation and disease morbidity. Those exposed to secondhand smoking showed less likelihood of quitting smoking. A continual decrease in the ORs for successfully quitting smoking was observed according to increased smoking duration, smoking dose per day and lifetime tobacco exposure (p _trend <0.001). Among the selected predictors, lifetime tobacco exposure, educational attainment, alcohol drinking status and birth cohort were the major determinants in the success of smoking cessation. Our findings suggest that lifetime tobacco exposure, educational attainment, alcohol drinking status and birth cohort can determine success in smoking cessation. Public interventions promoting a smoke-free environment are needed to reinforce discouraging the initiation of, reducing, and quitting cigarette smoking.     

Mating Success in the Polyandrous Social Wasp Vespula maculifrons

The mating decisions made by social insect males and females profoundly affect the structure of colonies and populations. However, few studies have used experimental approaches to understand mating behavior and mate choice in social insect taxa. This study investigated mating success in the polyandrous social wasp Vespula maculifrons. Mating trials were designed to test predictions that characteristics of body size and colony‐of‐origin would affect mating success. We first investigated if size differences existed among individuals and found that males from different colonies differed significantly in the size of nine morphological traits. However, male trait size was not significantly associated with male mating success. In contrast, females from different colonies differed significantly in only six of the nine measured traits, and four of these traits were associated with successful mating behaviors. Specifically, the correlated traits of gaster length, third tergum length, antennal length, and total length were positively associated with female mating success. Thus, long females experience mating advantages over females that are short. We also found that males and females from one particular colony displayed significantly greater mating activity than individuals from other colonies. Thus, the colony from which individuals originate plays an important role in determining mating success. Finally, our experiments failed to detect any evidence of nestmate avoidance during the mating trials. Overall, our data suggest that social insect reproductives may experience differential mating success based on their phenotype or developmental environment.     

Pharmacogenetics of smoking cessation: role of nicotine target and metabolism genes

Many smokers attempt to quit smoking but few are successful in the long term. The heritability of nicotine addiction and smoking relapse have been documented, and research is focused on identifying specific genetic influences on the ability to quit smoking and response to specific medications. Research in genetically modified cell lines and mice has identified nicotine acetylcholine receptor subtypes that mediate the pharmacological and behavioral effects of nicotine sensitivity and withdrawal. Human genetic association studies have identified single nucleotide polymorphisms (SNPs) in genes encoding nicotine acetylcholine receptor subunits and nicotine metabolizing enzymes that influence smoking cessation phenotypes. There is initial promising evidence for a role in smoking cessation for SNPs in the β2 and α5/α3/β4 nAChR subunit genes; however, effects are small and not consistently replicated. There are reproducible and clinically significant associations of genotypic and phenotypic measures of CYP2A6 enzyme activity and nicotine metabolic rate with smoking cessation as well as response to nicotine replacement therapies and bupropion. Prospective clinical trials to identify associations of genetic variants and gene–gene interactions on smoking cessation are needed to generate the evidence base for both medication development and targeted therapy approaches based on genotype.     

Comparative effectiveness of post-discharge interventions for hospitalized smokers: study protocol for a randomized controlled trial

ABSTRACT: BACKGROUND: A hospital admission offers smokers an opportunity to quit. Smoking cessation counseling provided in the hospital is effective, but only if it continues for more than one month after discharge. Providing smoking cessation medication at discharge may add benefit to counseling. A major barrier to translating this research into clinical practice is sustaining treatment during the transition to outpatient care. An evidence-based, practical, cost-effective model that facilitates the continuation of tobacco treatment after discharge is needed. This paper describes the design of a comparative effectiveness trial testing a hospital-initiated intervention against standard care. Methods/design: A 2-arm randomized controlled trial compares the effectiveness of standard post-discharge care with a multi-component smoking cessation intervention provided for 3 months after discharge. Current smokers admitted to Massachusetts General Hospital who receive bedside smoking cessation counseling, intend to quit after discharge and are willing to consider smoking cessation medication are eligible. Study participants are recruited following the hospital counseling visit and randomly assigned to receive Standard Care or Extended Care after hospital discharge. Standard Care includes a recommendation for a smoking cessation medication and information about community resources. Extended Care includes up to 3 months of free FDA-approved smoking cessation medication and 5 proactive computerized telephone calls that use interactive voice response technology to provide tailored motivational messages, offer additional live telephone counseling calls from a smoking cessation counselor, and facilitate medication refills. Outcomes are assessed at 1, 3, and 6 months after hospital discharge. The primary outcomes are self-reported and validated 7-day point prevalence tobacco abstinence at 6 months. Other outcomes include short-term and sustained smoking cessation, post-discharge utilization of smoking cessation treatment, hospital readmissions and emergency room visits, and program cost per quit. DISCUSSION: This study tests a potentially disseminable smoking intervention model for hospitalized smokers. If effective and widely adopted, it could help to reduce population smoking rates and thereby reduce tobacco-related mortality, morbidity, and health care costs.     

Changes in cigarette smoking behavior among breast cancer and unaffected women – A prospective study in the MARIE cohort

BackgroundSmoking cessation after a cancer diagnosis can reduce adverse cancer treatment outcomes. Whether a breast cancer diagnosis, a cancer commonly seen as unrelated to smoking cigarettes, motivates changes in smoking behavior is not fully understood. We aimed to compare long-term changes at three follow-up times of cigarette smoking behavior in women with breast cancer and baseline age- and region-matched unaffected women.MethodsWe used longitudinal data from the population-based case-control study MARIE (Mamma Carcinoma Risk Factor Investigation). Women with breast cancer (N = 3813) and unaffected women (N = 7341) aged 50–74 years were recruited from 2002 to 2005. Analyses on changes in smoking were based on data from those who also completed follow-up 1 in 2009–2012, follow-up 2 in 2014–2016 and follow-up 3 in 2020. Multinomial logistic regression for changes (quitting, stable, or start smoking) adjusted for age, study region, education, comorbidities, living situation, and follow-up time, was applied to examine the associations between breast cancer status and changes in smoking behavior.ResultsWomen with breast cancer had significantly higher odds than unaffected women of quitting smoking (OR = 1.38, 95 % CI: 1.01–1.89) and lower odds of returning to smoking (OR = 0.29, 95 % CI: 0.09–0.94) at follow-up 1, but were more likely to start or return to smoking at follow-up 2 (OR = 2.11, 95 % CI 1.08–4.15). No significant group differences were found for changes in smoking behavior at follow-up 3.ConclusionOur findings indicate that short-term changes in smoking behavior can be attributed to a breast cancer diagnosis, but that over time the effect diminishes and changes in smoking no longer differ between breast cancer and breast cancer-free women. To support smoking cessation and to prevent relapse, guidelines to address smoking in cancer care, as well as comprehensive tobacco treatment services, are needed.     

Delivering health care on US$19 per capita

Background

Many placebo-controlled trials have demonstrated the efficacy of individual pharmacotherapies approved for smoking cessation. However, few direct or indirect comparisons of such interventions have been conducted. We performed a meta-analysis to compare the treatment effects of 7 approved pharmacologic interventions for smoking cessation.

Methods

We searched the US Centers for Disease Control and Prevention''s Tobacco Information and Prevention database as well as MEDLINE, EMBASE and the Cochrane Library for published reports of placebo-controlled, double-blind randomized controlled trials of pharmacotherapies for smoking cessation. We included studies that reported biochemically validated measures of abstinence at 6 and 12 months. We used a hierarchical Bayesian random-effects model to summarize the results for each intervention.

Results

We identified 70 published reports of 69 trials involving a total of 32 908 patients. Six of the 7 pharmacotherapies studied were found to be more efficacious than placebo: varenicline (odds ratio [OR] 2.41, 95% credible interval [CrI] 1.91–3.12), nicotine nasal spray (OR 2.37, 95% CrI 1.12–5.13), bupropion (OR 2.07, 95% CrI 1.73–2.55), transdermal nicotine (OR 2.07, 95% CrI 1.69–2.62), nicotine tablet (OR 2.06, 95% CrI 1.12–5.13) and nicotine gum (OR 1.71, 95% CrI 1.35–2.21). Similar results were obtained regardless of which measure of abstinence was used. Although the point estimate favoured nicotine inhaler over placebo (OR 2.17), these results were not conclusive because the credible interval included unity (95% CrI 0.95–5.43). When all 7 interventions were included in the same model, all were more efficacious than placebo. In our analysis of data from the varenicline trials that included bupropion control arms, we found that varenicline was superior to bupropion (OR 2.18, 95% CrI 1.09–4.08).

Interpretation

Varenicline, bupropion and the 5 nicotine replacement therapies were all more efficacious than placebo at promoting smoking abstinence at 6 and 12 months.Health Canada recently approved the use of varenicline as a pharmacotherapy for smoking cessation. Varenicline works by stimulating dopamine, which results in reduced cravings and withdrawal symptoms. The drug also blocks nicotine receptors, which prevents the dopamine release associated with nicotine consumption.¹ The drug has been examined in a few small randomized controlled trials.^2–5 Despite limited evidence concerning its use, varenicline is viewed by many clinicians and researchers as the most effective smoking cessation aid. Consequently, there is a need for a systematic assessment of the effectiveness of varenicline relative to placebo. Furthermore, there is a need to compare the efficacy of varenicline with that of existing pharmacotherapies, including sustained-release bupropion and approved nicotine replacement therapies.We undertook a meta-analysis of placebo-controlled randomized controlled trials of the efficacy of 7 pharmacotherapies approved for smoking cessation. We had 3 objectives: to summarize the efficacy of each pharmacotherapy; to undertake a direct comparison of varenicline and bupropion by analyzing trials that contained both varenicline and bupropion treatment arms; and to undertake an indirect comparison of all 7 pharmacotherapies using the results of the individual trials.     

Physiology-Based Pharmacokinetic Modeling—Promise for Pediatric Drug Development?

Physiologically based modeling has gained much interest from pharmaceutical industry. Prediction of pharmacokinetic properties based on physicochemical properties and in vitro data appears possible. Two applications are of high interest: the prediction of the pharmacokinetics before conducting first in man studies based on preclinical data, and the prediction of pharmacokinetics in children based on the pharmacokinetics in adults. To date, only a few investigations describing the prediction of the pharmacokinetics in children have been published. Some of these investigations showed data on the precision of these predictions by comparing the model with experimental pharmacokinetic data form clinical investigations in children. However, the method holds the promise of speeding up drug development in children, by rationalizing study planning and thus avoiding unnecessary clinical studies. This would ultimately save costs and more importantly, reduce the risks for children in clinical studies. Unfortunately, most of the work done in this field is not published, as investigations are conducted by the pharmaceutical industry during drug development. Therefore, it is difficult to assess the success rate of this approach. However, as practical experience is gained and knowledge on drug-metabolizing enzymes and drug transporters increases, the value of this approach will probably increase in the next few years.     

A methodological framework to optimize models predicting critical dates of xylem phenology based on dendrometer data

Dendrometer measurements are a frequently used alternative to the laborious and time consuming microcoring to investigate intra-annual growth dynamics of trees. However, since dendrometer data not only comprise cambial growth, but also hydrological fluctuations, both signals need to be disentangled to derive critical dates of xylem phenology from dendrometer data. For this purpose, various approaches can be found in the literature, however a systematic comparison of the different options is still missing. In this study we present a methodological framework to evaluate the accuracy of different mathematical fittings to derive tree-ring phenology from dendrometer data and apply this approach to a data set comprised of three conifer species where high-resolution band dendrometer measurements and microcore sampling have been done in parallel. Based on our study we provide evidence that the most common approaches to derive onset and cessation of xylem cell enlargement from dendrometer data, i.e. applying absolute and relative thresholds to deterministic models fitted to dendrometer data, caused systematic deviations from the reference of xylogenesis observations, but could be improved by small adaptations. The most precise and unbiased predictions of xylem growth phenology were obtained by fitting the three-parameter Gompertz model to the dendrometer data and applying a relative threshold of 3.5% of annual increment to the model predictions for growth onset and an absolute threshold of 4.5 µm day^-1 based on the first model derivative for growth cessation. Our framework enables an improved usage of dendrometer data for the prediction of the onset and cessation of xylem cell enlargement, which are important ecological indicators to quantify the effects environmental changes on forest growth and the terrestrial carbon cycle.     

Smoking cessation and weight gain: Evidence from China

Cigarette smoking has long been viewed as a means to control body weight. However, studies on the association between smoking cessation and weight gain have reported mixed findings and, notably, there is limited evidence among the Chinese population – the world’s largest smoker population. The extent to which smoking cessation is positively associated with body weight is of interest as excessive weight gain contributes to heart disease, diabetes, hypertension, musculoskeletal disorders, and some cancers. Additionally, concerns over weight gain may dissuade current smokers from quitting. Using data from the China Health and Nutrition Survey (CHNS), we examine the association between smoking cessation and body weight in China. To account for the nonrandom nature of smoking cessation, our research design relies on within-individual variation in smoking status to remove the influence of time-invariant unobserved differences across individuals that are correlated with both cessation and body weight. We find that smoking cessation is associated with a modest increase in weight (0.329 kg, 0.51 % off the mean) and no significant changes in the prevalence of overweight or obesity.     

An algorithm for protein secondary structure prediction based on class prediction

An algorithm has been developed to improve the success rate in the prediction of the secondary structure of proteins by taking into account the predicted class of the proteins. This method has been called the 'double prediction method' and consists of a first prediction of the secondary structure from a new algorithm which uses parameters of the type described by Chou and Fasman, and the prediction of the class of the proteins from their amino acid composition. These two independent predictions allow one to optimize the parameters calculated over the secondary structure database to provide the final prediction of secondary structure. This method has been tested on 59 proteins in the database (i.e. 10,322 residues) and yields 72% success in class prediction, 61.3% of residues correctly predicted for three states (helix, sheet and coil) and a good agreement between observed and predicted contents in secondary structure.     

The simulation and prediction of drought

Current capabilities for simulating and predicting drought and other rainfall anomalies are reviewed. Simulation of drought requires the insertion of suitable precursors into the model. The principal precursor is identified to be sea surface temperature anomalies, but other possibilities are discussed. Model global climatic models are able to simulate with commendable realism most features of observed drought, as illustrated by results from a number of such models. Importantly, the models also provide insight into the mechanisms responsible for the rainfall perturbations.Both statistical and deterministic drought prediction methods are considered. The former can be used to make useful predictions for limited timeframes, but require separate relations to be derived for each region considered. A variety of statistical methods have now been developed, and some are being used operationally with mixed success. Deterministic drought prediction methods are still in their infancy, but have many attractions being physically based. In addition their ability to make predictions for the normal range of climatic variables usually required for practical utility provides a considerable advantage over statistical methods. The major requirement for accurate rainfall predictions is a good prediction of the spatial and temporal variability of the precursor. Examples of drought predictions are provided to illustrate the potential of the method. The numerous research problems which still have to be resolved are noted.     

A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions

To solve the problem of muscle redundancy at the level of opposing muscle groups, an alternative method to inverse dynamics must be employed. Considering the advantages of existing alternatives, the present study was aimed to compute knee joint moments under dynamic conditions using electromyographic (EMG) signals combined with non-linear constrained optimization in a single routine. The associated mathematical problems accounted for muscle behavior in an attempt to obtain accurate predictions of the resultant moment as well as physiologically realistic estimates of agonist and antagonist moments. The experiment protocol comprised (1) isometric trials to determine the most effective EMG processing for the prediction of the resultant moment and (2) stepping-in-place trials for the calculation of joint moments from processed EMG under dynamic conditions. Quantitative comparisons of the model predictions with the output of a biological-based model, showed that the proposed method (1) produced the most accurate estimates of the resultant moment and (2) avoided possible inconsistencies by enforcing appropriate constraints. As a possible solution for solving the redundancy problem under dynamic conditions, the proposed optimization formulation also led to realistic predictions of agonist and antagonist moments.     

A community resource benchmarking predictions of peptide binding to MHC-I molecules

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.