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A probabilistic model of relapse in drug addiction. | LitMetric

A probabilistic model of relapse in drug addiction.

Math Biosci

Department of Computational Medicine, UCLA, Los Angeles, 90095-1766, CA, USA; Department of Mathematics, California State University at Northridge, Los Angeles, 91330, CA, USA. Electronic address:

Published: June 2024

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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Source
http://dx.doi.org/10.1016/j.mbs.2024.109184DOI Listing

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