Assessment of demand for methamphetamine and cigarettes among individuals with methamphetamine use disorder.

Abstract

Methamphetamine (MA) is a highly addictive stimulant with recent upward trends in prevalence and associated public health problems. Drug demand, as assessed by hypothetical purchasing tasks, has been useful in addictions research and may help our understanding of the factors influencing MA use. However, no studies have assessed MA demand using current models of demand. The purpose of the current study was to assess demand for MA using a hypothetical drug purchasing task. Given high rates of cigarette smoking among MA users, it was of interest also to assess and compare demand for MA relative to cigarettes. Participants consisted of non-treatment-seeking volunteers with MA use disorder (N ϭ 18), of whom 17 reported daily smoking. Results showed the exponentiated demand model provided a good fit to consumption data. Results from Bayesian generalized linear modeling demonstrated multiple positive relationships (posterior probability Ն75%) between self-reported drug use (days MA used in the past 30 days, cigarettes smoked per day) and indices of demand for each drug (Q o , O max , P max , and break point). Comparing MA to cigarettes, results from Bayesian generalized linear mixed modeling revealed greater abuse liability for MA compared to cigarettes (posterior probability Ն99%) based on ␣ and essential value. Overall, the findings of the current study support the feasibility and validity of the exponentiated demand model for assessing demand for drugs among individuals with MA use disorder. Public Health SignificanceMethamphetamine is a significant public health concern with increasing negative impact. Behavioral economic measures, such as drug demand, may provide key insight into the decision making processes involved in methamphetamine use.

The Blinded-Dose Purchase Task: assessing hypothetical demand based on cocaine, methamphetamine, and alcohol administration

“…Significant positive associations were also observed between weekly stimulant use and P max for the 20 and 40 mg doses along with O max and BP 1 for the 20 mg dose. Although hypothetical methamphetamine purchase tasks have rarely been investigated (Chalmers et al, 2010;Yoon et al, 2021), results of the present study are generally consistent with previous research, although somewhat divergent. Yoon et al (2021) assessed simulated methamphetamine purchasing among individuals with methamphetamine use disorder and found significant positive correlations between intensity for methamphetamine and self-reported methamphetamine used in the past month, but no other demand metrics utilizing standard correlations as in the present study.…”

“…Although hypothetical methamphetamine purchase tasks have rarely been investigated (Chalmers et al, 2010;Yoon et al, 2021), results of the present study are generally consistent with previous research, although somewhat divergent. Yoon et al (2021) assessed simulated methamphetamine purchasing among individuals with methamphetamine use disorder and found significant positive correlations between intensity for methamphetamine and self-reported methamphetamine used in the past month, but no other demand metrics utilizing standard correlations as in the present study. These differences may be due to varied methods used in the demand task (drug had to be consumed that day in Yoon et al, versus 1 month in the present study) or population (individuals with methamphetamine use disorder versus recreational stimulant users).…”

Assessing cocaine motivational value: Comparison of brain reactivity bias toward cocaine cues and cocaine demand.

“…These results demonstrate the feasibility of a methodology that incorporates behavioral science and brain imaging for assessing cocaine motivational value in the context of a clinical trial. Regarding cocaine demand, the present findings add to the growing number of studies demonstrating the relevance of hypothetical purchasing tasks for assessing demand for psychomotor stimulants such as cocaine (Bruner & Johnson, 2014; Koffarnus et al, 2015; Strickland, Lile, et al, 2016; Yoon, de Dios, Suchting, Vincent, et al, 2021; Yoon et al, 2020; Yoon, Suchting, de Dios, et al, 2021) and methamphetamine (Yoon, Suchting, Cassidy, et al, 2021). Orderliness of the data was notable, with only a single data point from a single participant exhibiting nonsystematicity.…”

“…We took a subset of these values (95%) as the most credible interval and quantified the posterior probability (PP) that the effect exists. A PP ≥ 75% was chosen to indicate a minimum level of support in favor of the existence of predictor effects, consistent with guidelines and with our recent work (Schmitz et al, 2017;Suchting et al, 2020;Yoon, Suchting, Cassidy, et al, 2021). Models used weakly informative priors (b = ∼Normal [μ = 0, σ 2 = 100]) to maximize the influence of the present data on PP.…”

No difference in COVID-19 treatment outcomes among current methamphetamine, cannabis and alcohol users

“…Concomitant tobacco use was highly prevalent in our patients; 78.1%, 80.4%, and 75.0% of METH, cannabis, and alcohol users respectively reported current or past tobacco use. Other studies have found similarly high correlation between tobacco smoking and cannabis (Benowitz et al 2019;Schauer et al 2017) and also METH use (Sung et al 2015;Yoon et al 2021). Tobacco smoking has well known adverse pulmonary effects, and therefore can contribute to negative respiratory outcome of COVID-19 in these patient populations (Carrico et al 2018).…”