Demand curve analysis of marijuana use among persons living with HIV

Greenwald, Mark K.; Sarvepalli, Siri S; Cohn, Jonathan; Lundahl, Leslie H.

doi:10.1016/j.drugalcdep.2021.108524

Cited by 5 publications

(16 citation statements)

References 68 publications

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“…While both paradigms are important pieces of this pathology, they are conducted as separate measures with separate equations, parameters, and assumed underlying processes. However, recent work with human subjects has provided some evidence that time can function as a restraint on demand, and that resulting demand curves are functionally similar to those using monetary costs (Greenwald et al, 2021;Gunawan, 2021;Schwartz & Hursh, in press), confirming earlier work with laboratory animals (Bauman, 1991, using rodents;Tsunematsu, 2001, using pigeons). In the recent human studies, participants were asked to determine the probability of purchasing a good at a fixed price if the good were delivered after varying time delays (Schwartz & Hursh, in press).…”

supporting

confidence: 55%

“…The first step in this simulation was to create demand curve functions based on temporal cost. Greenwald et al (2021) have shown that demand metrics from time-cost curves are functionally similar to those from money-cost curves. The own-price demand equation was described in Hursh and Silberberg (2008) and has been referenced by over 650 articles and has been widely confirmed across species and commodities.…”

Section: The Conceptual Framework Of the Simulationmentioning

confidence: 98%

“…The extensions here assume that time can act as a constraint and would generate demand curves that are similar in form to those driven by other real or hypothetical constraints. Several studies have provided evidence that time costs can drive demand that is similar to money constraints (Bauman, 1991;Gunawan, 2021;Schwartz & Hursh, in press), but to our knowledge, the study by Greenwald et al (2021) is the only example of time cost used in a cross-price application and that is a limitation in need of empirical verification. However, conceptually, the general model offered here is not dependent on the use of temporal constraint; in the same way that Rachlin (2006) proposed that discounting is not limited to temporal discounting, the two-commodity demand model could be applied to discounting under any form of constraint.…”

Section: Limitationsmentioning

confidence: 99%

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A general model of demand and discounting.

Hursh

Schwartz

2023

Psychology of Addictive Behaviors

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Objective: A simulation using a two-commodity demand model, with time as the constraint, replicated the primary findings from delay discounting experiments and introduces explicit terms for time elasticity and cross-price substitution into the delay discounting paradigm. Method: A two-commodity temporal demand equation based on Hursh and Silberberg (2008) and Hursh (2014) was used to emulate delay discounting experiments. The own-price and cross-price demand curves intersected and plotting those indifference points emulated the usual hyperbolic discount function for substitutes. Simulations examined delay discounting in relation to (a) time elasticity of demand, (b) substitution between the delayed and immediate alternatives, and (c) amplitude of demand for the delayed alternative. Results: The simulated discount functions with substitutes were hyperbolic. The discount rate was a direct function of increasing time elasticity and substitutability of delayed alternative demand, shifting the function toward an exponential model. Amplitude of demand for the delayed alternative was inversely proportional to discount rate and supported a hyperboloid model with a power function of time (Killeen, 2015; Rachlin, 2006). The emulation of cross-commodity discounting involving drugs points to amplitude and persistence of time-dependent demand and cross-commodity substitution as primary factors. Conclusions: This report describes the first general model of time-dependent demand and delay discounting. The model implicates cross-commodity substitution as a potential factor in delay discounting. In the context of substance use disorder, the model underscores the importance of defining the properties of multicommodity demand (time elasticity, substitution, and amplitude) specific to the commodities and context.

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supporting

confidence: 55%

Section: The Conceptual Framework Of the Simulationmentioning

confidence: 98%

Section: Limitationsmentioning

confidence: 99%

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A general model of demand and discounting.

Hursh

Schwartz

2023

Psychology of Addictive Behaviors

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“…Gunawan ( 2021 ) found that probability hypothetical purchase tasks for cigarettes using time costs resulted in demand curves similar to those with monetary costs and with adequate fits by demand models. Likewise, Greenwald et al ( 2021 ) found that demand parameters estimated using time costs were functionally similar to those created using monetary costs.…”

mentioning

confidence: 92%

Time Cost and Demand: Implications for Public Policy

Schwartz

Hursh

2022

Perspect Behav Sci

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“…Of the 59 total participants, 46 reported using heroin (22 snorted, 23 injected, 1 smoked), 1 snorted fentanyl, 10 took oral hydrocodone, and 2 took oral oxycodone. The purchasing task is modeled after extant purchasing tasks for various substances [e.g., (69)(70)(71)], but personalizing the task for specific opioids/routes is novel. Participants are asked to imagine a typical day, with no access to other opioids unless they buy the preferred opioid at the listed prices.…”

Section: Experimental Assessments 231 Hypothetical Opioid and Benzodi...mentioning

confidence: 99%

Anhedonia modulates benzodiazepine and opioid demand among persons in treatment for opioid use disorder

et al. 2023

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BackgroundBenzodiazepine (BZD) misuse is a significant public health problem, particularly in conjunction with opioid use, due to increased risks of overdose and death. One putative mechanism underlying BZD misuse is affective dysregulation, via exaggerated negative affect (e.g., anxiety, depression, stress-reactivity) and/or impaired positive affect (anhedonia). Similar to other misused substances, BZD consumption is sensitive to price and individual differences. Although purchase tasks and demand curve analysis can shed light on determinants of substance use, few studies have examined BZD demand, nor factors related to demand.MethodsThis ongoing study is examining simulated economic demand for alprazolam (among BZD lifetime misusers based on self-report and DSM-5 diagnosis; n = 23 total; 14 male, 9 female) and each participant’s preferred-opioid/route using hypothetical purchase tasks among patients with opioid use disorder (n = 59 total; 38 male, 21 female) who are not clinically stable, i.e., defined as being early in treatment or in treatment longer but with recent substance use. Aims are to determine whether: (1) BZD misusers differ from never-misusers on preferred-opioid economic demand, affective dysregulation (using questionnaire and performance measures), insomnia/behavioral alertness, psychiatric diagnoses or medications, or urinalysis results; and (2) alprazolam demand among BZD misusers is related to affective dysregulation or other measures.ResultsLifetime BZD misuse is significantly (p < 0.05) related to current major depressive disorder diagnosis, opioid-negative and methadone-negative urinalysis, higher trait anxiety, greater self-reported affective dysregulation, and younger age, but not preferred-opioid demand or insomnia/behavioral alertness. Alprazolam and opioid demand are each significantly positively related to higher anhedonia and, to a lesser extent, depression symptoms but no other measures of negative-affective dysregulation, psychiatric conditions or medications (including opioid agonist therapy or inpatient/outpatient treatment modality), or sleep-related problems.ConclusionAnhedonia (positive-affective deficit) robustly predicted increased BZD and opioid demand; these factors could modulate treatment response. Routine assessment and effective treatment of anhedonia in populations with concurrent opioid and sedative use disorder may improve treatment outcomes.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT03696017, identifier NCT03696017.

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Demand curve analysis of marijuana use among persons living with HIV

Cited by 5 publications

References 68 publications

A general model of demand and discounting.

A general model of demand and discounting.

Time Cost and Demand: Implications for Public Policy

Anhedonia modulates benzodiazepine and opioid demand among persons in treatment for opioid use disorder

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