Markov model of smoking cessation

Abstract

Survival functions from smoking cessation interventions are described by a three-state Markov model. On quitting, smokers transit through a state of withdrawal characterized by a high rate of relapse, and then into a more secure state of long-term abstinence. The Markov model embodies the dynamic nature of the cessation/ relapse process; it permits stronger inference to long-term abstinence rates, provides measures of treatment efficacy, describes the outcomes of new quit attempts, and suggests mechanisms for the survival process.quitting tobacco | survival model | nicotine | monoamine oxidase S cientists construct and validate maps between events or processes and their descriptions with words, graphs, or equations. Those maps are most secure when close to the object of study, such as mechanics is to the movement of massive bodies and chemistry is to its constituents. However, sometimes there is an advantage to working at more molar or molecular levels-statistical mechanics are one level up, and quantum chemistry is one level down. The most promising level of attack is typically where the greatest order is found, which can serve as a stable fulcrum for leverage above or below. In this paper, an epidemiological model is applied to tobacco addiction, which allows inferences both to behavioral processes and underlying physiological dysfunction.Tobacco causes ∼440,000 premature deaths and $157 billion in health-related economic losses annually in the United States (1). The habit is tenacious, with around 90% of unassisted quitters smoking again within 1 y and the best interventions seldom decreasing that by more than 15% (2). Most attempts fail within a few weeks, leaving most trials underpowered for evaluating interventions beyond 3 mo. Characterization of the relapse process would permit information from the early epochs to improve inferences concerning long-term abstinence. Noting that many survival functions decrease at a similar rate in the right tail, Piasecki et al. (3) called for a dynamic model of abstinence that would explain such uniformity. The present paper answers that call with a simple Markov model of the transitions between stages of the quitting process.

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

“…2) and its association with overall prevalence of tobacco use. At an individual level, a psychiatrist would describe how NRT increases neurochemical rewards, leading to slow reduction of craving and habitual use [58]. A psychologist could suggest that, without consideration of the potential influence of environmental cues and social norms, the impact of NRT may be limited long-term.…”

Modelaje matemático del modelo neurobiológico de las adicciones

“…Las Cadenas de Markov o el proceso de Markov representa un método cuantitativo dinámico que toma en consideración la probabilidad de que un paciente cambie de un estado de salud a otro en un determinado período de tiempo (Ahn et al, 2014;Behrens, Caulkins, Tragler, Haunshmied, & Feichtinger, 1999;Borack, 1998;Caulkins, Behrens, Knoll, Tragler, & Zuba, 2004;Everingham & Rydell, 1994;Graham, Collins, Wugalter, Chung, & Hansen, 1991;Killeen, 2011;Maruotti & Rocci, 2012;Shumway, Chouljian, & Hargreaves, 1994).…”

Continuous-time system identification of a smoking cessation intervention

“…Approximately 10 million annual global deaths are expected to result from smoking by 2020 (Fish and Bartholomew 2007), and the global smoking population is expected to surpass 1.7 billion by 2025 (Erhardt 2009). In the U.S., cigarette smoking is the leading cause of premature death and $157B in economic loss is attributed to tobacco use annually (Centers for Disease Control and Prevention 2010, Killeen 2011). 40 years of decreases in smoking rates have recently stalled in the U.S., where one in five adults is an active smoker (Centers for Disease Control and Prevention 2012).…”