Markov model of smoking cessation

An Introduction and Practical Guide to Strategies for Analyzing Longitudinal Data in Clinical Trials of Smoking Cessation Treatment: Beyond Dichotomous Point-Prevalence Outcomes

Conceptualizing tobacco dependence as a chronic relapsing condition suggests the need to use analytic strategies that reflect that premise. However, clinical trials for smoking cessation typically define the primary endpoint as a measure of abstinence at a single timepoint distal to the intervention, typically 3-12 months. This reinforces the concept of tobacco outcomes as a dichotomous state-one is, or is not, abstinent. Fortunately, there are several approaches available to handle longitudinal data that reflect the relapsing and remitting nature of tobacco use during treatment studies. In this paper, sponsored by the Society for Research on Nicotine and Tobacco's Treatment Research Network, we present an introductory overview of these techniques and their application in smoking cessation clinical trials. Topics discussed include models to examine abstinence outcomes (eg, trajectory models of abstinence, models for transitions in smoking behavior, models for time to event), models that examine reductions in tobacco use, and models to examine joint outcomes (eg, examining changes in the use of more than one tobacco product). Finally, we discuss three additional relevant topics (ie, heterogeneity of effects, handling missing data, and power and sample size) and provide summary information about the type of model that can be used based on the type of data collected and the focus of the study. We encourage investigators to familiarize themselves with these techniques and use them in the analysis of data from clinical trials of smoking cessation treatment. Implications Clinical trials of tobacco dependence treatment typically measure abstinence 3-12 months after participant enrollment. However, because smoking is a chronic relapsing condition, these measures of intervention success may not accurately reflect the common trajectories of tobacco abstinence and relapse. Several analytical techniques facilitate this type of outcome modeling. This paper is meant to be an introduction to these concepts and techniques to the global nicotine and tobacco research community including which techniques can be used for different research questions with visual summaries of which types of models can be used for different types of data and research questions.

Improving Prediction of Tobacco Use Over Time: Findings from Waves 1-4 of the Population Assessment of Tobacco and Health Study

INTRODUCTION

First-order Markov models assume future tobacco use behavior is dependent on current tobacco use and are often used to characterize patterns of tobacco use over time. Higher-order Markov models that assume future behavior is dependent on current and prior tobacco use may better estimate patterns of tobacco use.

AIMS AND METHODS

This study compared Markov models of different orders to examine whether incorporating information about tobacco use history improves model estimation of tobacco use and estimated tobacco use transition probabilities. We used data from four waves of the Population Assessment of Tobacco and Health Study. In each Wave, a participant was categorized into one of the following tobacco use states: never smoker, former smoker, menthol cigarette smoker, non-menthol cigarette smoker, or e-cigarette/dual user. We compared first-, second-, and third-order Markov models using multinomial logistic regression and estimated transition probabilities between tobacco use states.

`RESULTS

The third-order model was the best fit for the data. The percentage of former smokers, menthol cigarette smokers, non-menthol cigarette smokers, and e-cigarette/dual users in Wave 3 that remained in the same tobacco use state in Wave 4 ranged from 63.4% to 97.2%, 29.2% to 89.8%, 34.8% to 89.7%, and 20.5% to 80.0%, respectively, dependent on tobacco use history. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit.

CONCLUSIONS

Transition probabilities between tobacco use states varied widely depending on tobacco use history. Higher-order Markov models improve estimation of tobacco use over time and can inform understanding of trajectories of tobacco use behavior.

IMPLICATIONS

Findings from this study suggest that transition probabilities between tobacco use states vary widely depending on tobacco use history. Tobacco product users (cigarette or e-cigarette/dual users) who were in the same tobacco use state in the prior two years were least likely to quit. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit. Quitting smoking for at least two years is an important milestone in the process of cessation.

Access to effective smoking cessation medications in patients with medicare, medicaid and private insurance

Objectives

Compare financial barriers to the most effective smoking cessation medications - varenicline and combination nicotine replacement therapy (CNRT) across major insurance categories and determine whether these financial barriers impact smoking cessation outcomes.

Study design

Longitudinal retrospective observational cohort study.

Methods

Patients seen at Duke Smoking Cessation Program 05/2016 through 07/2021 were studied. Those prescribed varenicline or CNRT were determined to have financial barriers to access if they could not purchase the medication using insurance or their own funds. Outcomes were compared between Medicare, Medicaid, and private insurers. Abstinence was defined as self-reported 7-day smoking abstinence.

Results

Patients with Medicare were 5.08 times more likely to face a financial barrier to highly effective smoking cessation medications compared to patients with private insurance (p<0.00001) and 2.81 times more likely compared to Medicaid (p<0.00001). Patients able to access these highly effective medications achieved a smoking abstinence rate that was 1.58 times higher than those who could not (p = 0.01).

Conclusions

Findings suggest Medicare coverage of the most effective smoking cessation medications is considerably worse than Medicaid or private insurance; inability to access these medications may lead to lower rates of smoking abstinence.

Cohort profile for the Loma Linda University Health BREATHE programme: a model to study continuously incentivised employee smoking cessation

PURPOSE

The purpose of the Loma Linda University Health (LLUH) BREATHE cohort is to test the efficacy of a novel method of continuously incentivising participation in workplace smoking cessation on participation, long-term abstinence, health outcomes, healthcare costs and healthcare utilisation.

PARTICIPANTS

In 2014, LLUH-a US academic medical centre and university-incentivised participation in a workplace smoking cessation programme (LLUH BREATHE) by lowering health plan costs. Specifically, LLUH introduced a Wholeness Health Plan (WHP) option that, for the smokers, continuously incentivises participation in nicotine screening and the LLUH BREATHE smoking cessation programme by offering an 'opt-in wellness discount' that consisted of 50%-53% lower out of pocket health plan costs (ie, monthly employee premiums, copayments). This novel 'continuously incentivised' model lowers annual health plan costs for smokers who, on an annual basis, attempt or maintain cessation from tobacco use. The annual WHP cost savings for smokers far exceed the value of short-term incentives that have been tested in workplace cessation trials to date. This ongoing health plan option offered to over 16 000 employees has created an open, dynamic LLUH BREATHE cohort of current and former smokers (n=1092).

FINDINGS TO DATE

Our profile of the LLUH BREATHE cohort indicates that after 5 years of follow-up in a prospective cohort study (2014-2019), continuously incentivised smoking cessation produced a 74% participation (95% CI (71% to 77%)) in employer-sponsored smoking cessation attempts that were occurring less than a year after the incentive was offered. The cohort can be purposed to examine the effect of continuously incentivised cessation on cessation outcomes, health plan utilisation/costs, use of electronic nicotine delivery systems, and COVID-19 outcomes.

A Longitudinal Examination of Behavioral Transitions among Young Adult Menthol and Non-Menthol Cigarette Smokers Using a Three-State Markov Model

INTRODUCTION

Young adult cigarette smoking behaviors are complex and dynamic. Emerging research suggests a growing rate of switching from non-menthol to menthol cigarettes. Transitions across cigarette smoking states are not well understood. This research longitudinally explores transitions in cigarette smoking behaviors among 18-29 year olds.

METHODS

We applied a Markov model to data collected biannually for 1542 initially 18-29 year old young adults (mean age: 20.9 years; SD = 2.6) in Texas, who provided 7021 total observations from Fall 2014 to Spring 2017. All participants were past 30 day menthol or non-menthol cigarette smokers at first observation. We examined transitions across three states of cigarette smoking (menthol, non-menthol, and nonsmoking) and compared predictors of each transition, during young adulthood.

RESULTS

Descriptively, 22.2% of menthol and 14.3% of non-menthol smokers switched products while 25.6% of menthol and 26.0% of non-menthol smokers quit smoking. Among quitters, 20.0% relapsed via menthol and 28.2% relapsed via non-menthol cigarettes. Results from Markov model indicated that Hispanic/Latinos (Hazard Ratio [HR]: 3.69) and Asians (HR: 2.85) were significantly more likely to switch from non-menthol to menthol cigarettes, relative to non-Hispanic whites. Among recent quitters, the use of non-cigarette products was associated with increased risk of relapse via menthol (HR: 1.54) and non-menthol (HR: 1.85) cigarettes.

CONCLUSION

A substantial proportion of young adult cigarette smokers transitioned across cigarette smoking states over the course of 2.5 years. Other tobacco use and nicotine dependence were impediments to becoming and remaining a non-smoker. Hispanic/Latinos and Asians, relative to non-Hispanic whites, had greater odds of transitioning from non-menthol smoking to both non-smoking and to menthol smoking. Findings suggest racial/ethnic differences in cigarette smoking transitions during young adulthood.

IMPLICATIONS

This paper examined multidirectional transitions across cigarette smoking, including switching between menthol and non-menthol cigarettes, among young adults. Results indicate that Hispanic/Latino and Asian young adults are at increased risk of transition to menthol cigarette smoking compared with non-Hispanic white young adults. Findings highlight need for further study of Hispanic/Latino and Asian young adult smoking behaviors.

Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness

Background

Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this paper is to explain how agent-based modeling could fulfill this role.

Methods

We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics.

Results

For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland.

Conclusion

In this paper we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components.

Efficacy of Smartphone Applications for Smoking Cessation: A Randomized Clinical Trial

IMPORTANCE

Smoking is a leading cause of premature death globally. Smartphone applications for smoking cessation are ubiquitous and address barriers to accessing traditional treatments, yet there is limited evidence for their efficacy.

OBJECTIVE

To determine the efficacy of a smartphone application for smoking cessation based on acceptance and commitment therapy (ACT) vs a National Cancer Institute smoking cessation application based on US clinical practice guidelines (USCPG).

DESIGN, SETTING, AND PARTICIPANTS

A 2-group, stratified, double-blind, individually randomized clinical trial was conducted from May 27, 2017, to September 28, 2018, among 2415 adult cigarette smokers (n = 1214 for the ACT-based smoking cessation application group and n = 1201 for the USCPG-based smoking cessation application group) with 3-, 6-, and 12-month postrandomization follow-up. The study was prespecified in the trial protocol. Follow-up data collection started on August 26, 2017, and ended at the last randomized participant's 12-month follow-up survey on December 23, 2019. Data were analyzed from February 25 to April 3, 2020. The primary analysis was performed on a complete-case basis, with intent-to-treat missing as smoking and multiple imputation sensitivity analyses.

INTERVENTIONS

iCanQuit, an ACT-based smoking cessation application, which taught acceptance of smoking triggers, and the National Cancer Institute QuitGuide, a USCPG-based smoking cessation application, which taught avoidance of smoking triggers.

MAIN OUTCOMES AND MEASURES

The primary outcome was self-reported 30-day point prevalence abstinence (PPA) at 12 months after randomization. Secondary outcomes were 7-day PPA at 12 months after randomization, prolonged abstinence, 30-day and 7-day PPA at 3 and 6 months after randomization, missing data imputed with multiple imputation or coded as smoking, and cessation of all tobacco products (including e-cigarettes) at 12 months after randomization.

RESULTS

Participants were 2415 adult cigarette smokers (1700 women [70.4%]; 1666 White individuals [69.0%] and 868 racial/ethnic minorities [35.9%]; mean [SD] age at enrollment, 38.2 [10.9] years) from all 50 US states. The 3-month follow-up data retention rate was 86.7% (2093), the 6-month retention rate was 88.4% (2136), and the 12-month retention rate was 87.2% (2107). For the primary outcome of 30-day PPA at the 12-month follow-up, iCanQuit participants had 1.49 times higher odds of quitting smoking compared with QuitGuide participants (28.2% [293 of 1040] vs 21.1% [225 of 1067]; odds ratio [OR], 1.49; 95% CI, 1.22-1.83; P < .001). Effect sizes were very similar and statistically significant for 7-day PPA at the 12-month follow-up (OR, 1.35; 95% CI, 1.12-1.63; P = .002), prolonged abstinence at the 12-month follow-up (OR, 2.00; 95% CI, 1.45-2.76; P < .001), abstinence from all tobacco products (including e-cigarettes) at the 12-month follow-up (OR, 1.60; 95% CI, 1.28-1.99; P < .001), 30-day PPA at 3-month follow-up (OR, 2.20; 95% CI, 1.68-2.89; P < .001), 30-day PPA at 6-month follow-up (OR, 2.03; 95% CI, 1.63-2.54; P < .001), 7-day PPA at 3-month follow-up (OR, 2.04; 95% CI, 1.64-2.54; P < .001), and 7-day PPA at 6-month follow-up (OR, 1.73; 95% CI, 1.42-2.10; P < .001).

CONCLUSIONS AND RELEVANCE

This trial provides evidence that, compared with a USCPG-based smartphone application, an ACT-based smartphone application was more efficacious for quitting cigarette smoking and thus can be an impactful treatment option.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02724462.

Weight concerns as a predictor of smoking cessation according to nicotine dependence: A population-based study

Background:

Nicotine-dependent smokers find it difficult to quit smoking. Additionally, smoking-specific weight concerns may affect smoking cessation although the evidence is controversial. We investigated whether smoking-specific weight concerns predict the probability of cessation and, if so, whether the effect varies according to the level of nicotine dependence.

Methods:

The study was conducted with a population-based sample of 355 adult daily smokers who participated in the baseline examination in 2007 and in the 2014 follow-up. Baseline nicotine dependence was classified as low or high (Fagerström Test for Nicotine Dependence; 0–3 vs. 4–10 points). Within these groups, we examined whether baseline weight concerns predict smoking status (daily, occasional, ex-smoker) at follow-up by using multinomial logistic regression with adjustment for multiple covariates.

Results:

Among low-dependent participants at baseline, 28.5% had quit smoking, while among highly dependent participants 26.1% had quit smoking. The interaction between weight concerns and nicotine dependence on follow-up smoking status was significant. Among participants with low nicotine dependence per the fully adjusted model, greater weight concerns predicted a lower likelihood of both smoking cessation (relative risk ratio 0.93 [95% CI 0.87–1.00]) and smoking reduction to occasional occurrence (0.89 [95% CI 0.81–0.98]). Weight concerns were not associated with follow-up smoking status among participants with high nicotine dependence.

Conclusions:

Weight concerns are associated with a smaller likelihood of quitting among smokers with low nicotine dependence. Weight concerns should be addressed in smoking cessation interventions, especially with smokers who have low nicotine dependence.

Repeated measures latent class analysis of daily smoking in three smoking cessation studies

BACKGROUND

Person-centered approaches to the study of behavior change, such as repeated measures latent class analysis (RMLCA), can be used to identify patterns of change and link these to later behavior change outcomes.

METHODS

Daily smoking status data from three smoking cessation studies (N=287, N=334, and N=403) were submitted to RMLCA to identify latent classes of smokers based on patterns of abstinence across the first 27days of a quit attempt. Three-month biochemically verified abstinence rates were compared among latent classes with particular patterns of smoking across days. Pharmacotherapy variables and baseline individual differences were added as covariates of latent class membership.

RESULTS

Results of separate and pooled analyses supported a five-class solution that replicated across studies. Latent classes included a large class that achieved immediate stable abstinence, a smaller class of cessation failures, and three classes with partial abstinence that increased, decreased, or remained stable over time. Three-month point-prevalence abstinence rates varied among the latent classes, with 38-55% abstinent among early quitters, 3-20% abstinent among those who smoked intermittently throughout the first 27days, and fewer than 5% abstinent in the classes marked by little or delayed change in smoking. High-dose nicotine patch and bupropion promoted membership in abstinent classes. Demographics, nicotine dependence, and craving were related to latent class in multiple studies and pooled analyses.

CONCLUSIONS

We identified five patterns of smoking behavior in the first weeks of a smoking cessation attempt. These patterns are robust across multiple studies and are related to later point-prevalence abstinence rates.

Behavioral informatics: Dynamical models for measuring and assessing behaviors for precision interventions

Poor health-related behaviors represent a major challenge to healthcare due to their significant impact on chronic and acute diseases and their effect on the quality of life. Recent advances in technology have enabled an unprecedented opportunity to assess objectively, unobtrusively and continuously human behavior and have opened the possibility of optimizing individual-tailored, precision interventions within the framework of behavioral informatics. A key prerequisite for this optimization is the ability to assess and predict effects of interventions. This is potentially achievable with computational models of behavior and behavior change. In this paper we describe various approaches to computational modeling and describe a new hybrid model based on a dual process theoretical framework for behavior change. The model leverages cognitive learning theories and is shown to be consistent with mobile intervention data. We also illustrate how system-theoretic approaches can be used to assess the effect of coaching and participants' health behaviors.

Translating research into prevention of high-risk behaviors in the presence of complex systems: definitions and systems frameworks

To impact population health, it is critical to collaborate across disciplinary and practice-based silos and integrate resources, experiences, and knowledge to exert positive change. Complex systems shape both the prevention outcomes researchers, practitioners, and policymakers seek to impact and how research is translated and can either impede or support movement from basic scientific discovery to impactful and scaled-up prevention practice. Systems science methods can be used to facilitate designing translation support that is grounded in a richer understanding of the many interacting forces affecting prevention outcomes across contexts. In this paper, we illustrate how one systems science method, system dynamics, could be used to advance research, practice, and policy initiatives in each stage of translation from discovery to translation of innovation into global communities (T0-T5), with tobacco prevention as an example. System dynamics can be applied to each translational stage to integrate disciplinary knowledge and document testable hypotheses to inform translation research and practice.

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care

Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.

The four causes of ADHD: a framework

In addition to the symptoms singled out by the diagnostic criteria for Attention-Deficit Hyperactivity Disorder (ADHD), a comprehensive definition should inform us of the events that trigger ADHD in both its acute and chronic manifestations; the neurobiology that underlies it; and the evolutionary forces that have kept it in the germ line of our species. These factors are organized in terms of Aristotle's four kinds of "causes," or explanations: formal, efficient, material, and final. This framework systematizes the nosology, biology, psychology, and evolutionary pressures that cause ADHD.

Continuous-Time System Identification of a Smoking Cessation Intervention

Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behavior change. System identification problems that draw from two modeling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modeling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.

Control Systems Engineering for Understanding and Optimizing Smoking Cessation Interventions

Cigarette smoking remains a major public health issue. Despite a variety of treatment options, existing intervention protocols intended to support attempts to quit smoking have low success rates. An emerging treatment framework, referred to as adaptive interventions in behavioral health, addresses the chronic, relapsing nature of behavioral health disorders by tailoring the composition and dosage of intervention components to an individual's changing needs over time. An important component of a rapid and effective adaptive smoking intervention is an understanding of the behavior change relationships that govern smoking behavior and an understanding of intervention components' dynamic effects on these behavioral relationships. As traditional behavior models are static in nature, they cannot act as an effective basis for adaptive intervention design. In this article, behavioral data collected daily in a smoking cessation clinical trial is used in development of a dynamical systems model that describes smoking behavior change during cessation as a self-regulatory process. Drawing from control engineering principles, empirical models of smoking behavior are constructed to reflect this behavioral mechanism and help elucidate the case for a control-oriented approach to smoking intervention design.

Online social networks and smoking cessation: a scientific research agenda

BACKGROUND

Smoking remains one of the most pressing public health problems in the United States and internationally. The concurrent evolution of the Internet, social network science, and online communities offers a potential target for high-yield interventions capable of shifting population-level smoking rates and substantially improving public health.

OBJECTIVE

Our objective was to convene leading practitioners in relevant disciplines to develop the core of a strategic research agenda on online social networks and their use for smoking cessation, with implications for other health behaviors.

METHODS

We conducted a 100-person, 2-day, multidisciplinary workshop in Washington, DC, USA. Participants worked in small groups to formulate research questions that could move the field forward. Discussions and resulting questions were synthesized by the workshop planning committee.

RESULTS

We considered 34 questions in four categories (advancing theory, understanding fundamental mechanisms, intervention approaches, and evaluation) to be the most pressing.

CONCLUSIONS

Online social networks might facilitate smoking cessation in several ways. Identifying new theories, translating these into functional interventions, and evaluating the results will require a concerted transdisciplinary effort. This report presents a series of research questions to assist researchers, developers, and funders in the process of efficiently moving this field forward.