A probabilistic model of relapse in drug addiction

IF 1.9 4区 数学 Q2 BIOLOGY Mathematical Biosciences Pub Date : 2024-04-04 DOI:10.1016/j.mbs.2024.109184
Sayun Mao , Tom Chou , Maria R. D’Orsogna
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Abstract

More than 60% of individuals recovering from substance use disorder relapse within one year. Some will resume drug consumption even after decades of abstinence. The cognitive and psychological mechanisms that lead to relapse are not completely understood, but stressful life experiences and external stimuli that are associated with past drug-taking are known to play a primary role. Stressors and cues elicit memories of drug-induced euphoria and the expectation of relief from current anxiety, igniting an intense craving to use again; positive experiences and supportive environments may mitigate relapse. We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the “positive activation; negative activation” paradigm and the “peak-end” rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues, and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of contentment, rather than large, episodic jolts of happiness.

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毒瘾复发的概率模型
超过 60% 的药物使用障碍康复者会在一年内复发。有些人甚至在戒毒数十年后又开始吸毒。导致复吸的认知和心理机制尚不完全清楚,但众所周知,与过去吸食毒品有关的紧张生活经历和外部刺激起着主要作用。压力和暗示会引起对毒品导致的欣快感的回忆以及对缓解当前焦虑的期望,从而点燃再次吸毒的强烈渴望;积极的经历和支持性环境可能会减轻复吸。我们利用已知的精神病学概念,如 "正激活;负激活 "范式和 "峰终 "规则,构建了一个依赖于外部因素(生活事件的强度和时间)和个体特质(对这些事件的心理反应)的复吸率数学模型。我们分析了哪些压力源、线索和积极事件的组合和排序会导致最大的复发概率,并提出了最大限度降低复发可能性的干预措施。我们发现,最好的保护因素是接触温和但持续的满足感来源,而不是大量的、偶发性的快乐冲击。
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来源期刊
Mathematical Biosciences
Mathematical Biosciences 生物-生物学
CiteScore
7.50
自引率
2.30%
发文量
67
审稿时长
18 days
期刊介绍: Mathematical Biosciences publishes work providing new concepts or new understanding of biological systems using mathematical models, or methodological articles likely to find application to multiple biological systems. Papers are expected to present a major research finding of broad significance for the biological sciences, or mathematical biology. Mathematical Biosciences welcomes original research articles, letters, reviews and perspectives.
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