Polysubstance use: Delay discounting in relationship to remission
Quddos, F.; Tomlinson, D.; Fontes, R.; Tegge, A.; Bickel, W.
Show abstract
BackgroundRemission from substance use disorders (SUDs) is typically conceptualized as an all-or-none phenomenon. Here, we present a novel construct: proportion of remission (PrR; i.e., the proportion of substances an individual is in remission from relative to lifetime SUD history), a continuous construct that may better capture progress towards recovery in polysubstance use. MethodsIndividuals (n= 2,417) in recovery from SUDs were recruited from International Quit and Recovery Registry (IQRR). Individuals completed a $1000 adjusting amount delay discounting (DD) task, and questions about current and past substance use over the past 12 months and lifetime. We estimated a series of single-level binomial regressions models using PrR as the independent variable and DD, maximum time in recovery, and maximum quit time as dependent variables. In addition, we performed a moderated mediation analysis to understand the relationship between PrR and recovery variables. ResultsWe report that DD, maximum time in recovery, and maximum quit time significantly predicted PrR in individuals with a history of polysubstance use. Further, we found that the relationship between maximum time in recovery and PrR was mediated by the maximum quit time across substances and differed by varying levels of DD. ConclusionResults suggest that longer quit time of any substance is related to improved recovery outcomes, particularly for individuals with low discounting rates. Together, interventions that focus on harm reduction and/or those that modulate DD may lead to improved clinical outcomes (including quit time and PrR) in individuals with a history of polysubstance use.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Agreement between DSM-IV and DSM-5 measures of substance use disorders in a sample of adult substance users 94%
- How does methamphetamine affect the brain? A systematic review of magnetic resonance imaging studies 93%
- Testing the association between tobacco and cannabis use and cognitive functioning: Findings from an observational and Mendelian randomization study 93%
Similar papers in this journal
- Polygenic Scores Predict the Development of Alcohol and Nicotine Use Problems from Adolescence through Young Adulthood 94%
- Alcohol use and cognitive functioning in young adults: improving causal inference 93%
- Diagnostic Validity of Drinking Behaviour for Identifying Alcohol Use Disorder: Findings from a Nationally Representative Sample of Community Adults and an Inpatient Clinical Sample 92%
Similar papers in this journal
- Risk Factors for Mild, Moderate, and Severe Alcohol Use Disorder (AUD) in a sample of adult substance users: Implications for DSM-5 AUD Classification 97%
- A Cascade of Care for Alcohol Use Disorder: Using 2015-2018 National Survey on Drug Use and Health Data to Identify Gaps in Care 95%
- Social interaction with an alcohol-intoxicated or cocaine-injected peer selectively alters social behaviors and drinking in adolescent male and female rats. 92%
Similar papers in this journal
- A Multivariate Approach to Understanding the Genetic Overlap between Externalizing Phenotypes and Substance Use Disorders 93%
- Identifying risk factors involved in the common versus specific liabilities to substance abuse: A genetically informed approach 92%
- Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls 92%
Similar papers in this journal
- Diagnosis and treatment of opioid-related disorders in a South African private sector medical insurance scheme: a cohort study 89%
- A qualitative study exploring the impact of the COVID-19 pandemic on People Who Inject Drugs (PWID) and drug service provision in the UK: PWID and service provider perspectives 89%
- Characterizing Declines in US Overdose Deaths Compared to Exponential Predictions 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.