IMPORTANCE Delay discounting is a behavioral economic index of impulsive preferences for smaller-immediate or larger-delayed rewards that is argued to be a transdiagnostic process across health conditions. Studies suggest some psychiatric disorders are associated with differences in discounting compared with controls, but null findings have also been reported. OBJECTIVE To conduct a meta-analysis of the published literature on delay discounting in people with psychiatric disorders. DATA SOURCES PubMed, MEDLINE, PsycInfo, Embase, and Web of Science databases were searched through December 10, 2018. The psychiatric keywords used were based on DSM-IV or DSM-5 diagnostic categories. Collected data were analyzed from December 10, 2018, through June 1, 2019. STUDY SELECTION Following a preregistered Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, 2 independent raters reviewed titles, abstracts, and full-text articles. English-language articles comparing monetary delay discounting between participants with psychiatric disorders and controls were included. DATA EXTRACTION AND SYNTHESIS Hedges g effect sizes were computed and random-effects models were used for all analyses. Heterogeneity statistics, one-study-removed analyses, and publication bias indices were also examined. MAIN OUTCOMES AND MEASURES Categorical comparisons of delay discounting between a psychiatric group and a control group. RESULTS The sample included 57 effect sizes from 43 studies across 8 diagnostic categories. Significantly steeper discounting for individuals with a psychiatric disorder compared with controls was observed for major depressive disorder (Hedges g = 0.37; P = .002; k = 7), schizophrenia (Hedges g = 0.46; P = .004; k = 12), borderline personality disorder (Hedges g = 0.60; P < .001; k = 8), bipolar disorder (Hedges g = 0.68; P < .001; k = 4), bulimia nervosa (Hedges g = 0.41; P = .001; k = 4), and binge-eating disorder (Hedges g = 0.34; P = .001; k = 7). In contrast, anorexia nervosa exhibited statistically significantly shallower discounting (Hedges g =-0.30; P < .001; k = 10). Modest evidence of publication bias was indicated by a statistically significant Egger test for schizophrenia and at the aggregate level across studies. CONCLUSIONS AND RELEVANCE Results of this study appear to provide empirical support for delay discounting as a transdiagnostic process across most of the psychiatric disorders examined; the literature search also revealed limited studies in some disorders, notably posttraumatic stress disorder, which is a priority area for research.

Cannabis users treat legal cannabis as a superior commodity compared with illegal cannabis and exhibit asymmetric substitutability favoring legal product. Cannabis price policies that include somewhat higher consumer costs for legal cannabis relative to contraband (but not excessively higher costs) would not be expected to incentivize and expand the illegal market.

Online crowdsourcing websites such as Amazon’s Mechanical Turk (MTurk) are increasingly being used in addictions research. However, there is a relative paucity of such research examining the validity of administering behavioral economic alcohol-related measures, via an online crowdsourcing platform. This study sought to validate an alcohol purchase task (APT) for assessing demand and a questionnaire measure of proportionate alcohol reinforcement, using an online sample of participants recruited via MTurk. Participants (N = 865, 59% female) were recruited via MTurk to complete the APT, proportionate alcohol reinforcement questionnaire, Alcohol Use Disorders Identification Test (AUDIT), and demographics. Responses on the APT were highly systematic (<3% non-systematic data) and conformed to prototypical demand curves. Correlation analyses revealed significant associations among AUDIT total scores with a majority of the alcohol demand indices (rs .08–53, ps < .05) as well as proportionate alcohol reinforcement (r = .43, p < .001). Regression analyses controlling for relevant covariates indicated that intensity, BP, Omax, elasticity, and reinforcement ratio predicted significant variance in AUDIT scores. This study further supports the use of online crowdsourcing websites for investigating behavioral economic determinants of alcohol misuse.