Statistically derived patterns of behavioral economic risk among heavy-drinking college students: A latent profile analysis.

Abstract

High levels of 3 behavioral economic indices (delay discounting, alcohol demand, and proportionate substance-related reinforcement) are consistently associated with greater alcohol misuse and alcoholrelated problems. However, it is unclear whether and how these variables jointly increase the risk for alcohol-related outcomes among college students who engage in heavy episodic drinking (HED; 4/5ϩ drinks for women/men, respectively). The current study used a person-centered approach to identify similar patterns of behavioral economic domains among heavy-drinking college students and investigate the relationship between these empirically derived classes and alcohol-related outcomes. A sample of 393 college students (60.8% female, 78.9% White/Caucasian) reporting at least 2 heavy drinking episodes in the previous month completed measures of alcohol use and problems, demographics, delay discounting, and alcohol reward value (alcohol demand and proportionate substance-related reinforcement). Latent profile analyses revealed that a 3-class solution provided the best fit to the data: a low reward value, high discounting (LRHD) class (n ϭ 53), a moderate reward value, low discounting (MRLD) class (n ϭ 214), and a high reward value, high discounting (HRHD) class (n ϭ 126). Members of the HRHD class reported significantly greater alcohol consumption, past-month HED episodes, alcohol-related problems, and symptoms of alcohol use disorder than those in the MRLD and LRHD classes. The results suggest that there are 3 constellations of behavioral economic processes and that, consistent with the reinforcer pathology model, students who overvalue alcohol-related reward and discount the future more steeply are at the greatest risk for alcohol misuse and alcohol-related problems.

Associations between alcohol demand, delayed reward discounting, and high-intensity drinking in a diverse emerging adult sample.

“…A higher k -value indicates a higher discounting of larger future rewards (i.e., stronger preference for an immediate reward and steep discounting of bigger, temporally delayed reward). DRD is associated with heavy drinking in emerging young adults (Campbell et al, 2021; Minhas et al, 2020; Tucker et al, 2021).…”

“…Although several studies have found an association between DRD and alcohol-related risk among emerging adults (Minhas et al, 2020; Vuchinich & Simpson, 1998), the literature examining DRD and alcohol use and associated problems in nonclinical emerging adult samples is mixed, with some studies finding no association between discounting rates and drinking risk (Dennhardt & Murphy; 2011; McCarthy et al, 2012; Teeters & Murphy; 2015). Campbell et al (2021) used latent class analyses to examine specific patterns of behavioral economic risk in a sample of heavy-drinking college students and found evidence for a high reward, high discounting class (i.e., high intensity, high O max , lower elasticity, and steep discounting) that was significantly associated with greater alcohol consumption, HED, alcohol-related consequences, and alcohol use disorder symptoms. These behavioral economic indices may also be useful for identifying individuals who are at risk for HID, but, to date, that hypothesis has not been tested.…”

Value-based decision-making predicts alcohol use and related problems in young men

“…Chronic alcohol use may also be linked to low loss aversion ( Bernhardt et al, 2017 ), possibly independent of strong delay discounting ( Thrailkill et al, 2022 ). Conflicting reports on delay discounting and loss aversion have appeared, especially concerning non-dependent drinking ( Campbell et al, 2021 ; Herman and Duka, 2020 ; Mayhew et al, 2020 ; Poulton et al, 2022 ; Stancato et al, 2020 ; Tucker et al, 2021 ; Zorick et al, 2022 ). Delay discounting cannot reliably distinguish between different levels, patterns, or problems of non-dependent drinking ( Campbell et al, 2021 ; Herman and Duka, 2020 ; Mayhew et al, 2020 ; Poulton et al, 2022 ; Stancato et al, 2020 ; Tucker et al, 2021 ).…”

Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research

“…This is key in viewing the distinguishing between LCMM and K-means; that is, latent features are extracted from the results of a model and the results probabilistically determine which class best characterizes the individual’s responses. In a relevant example of this approach, Campbell et al (2021) applied a derivative of LCA – Latent Profile Analysis (LPA) – to evaluate various continuous outcomes (e.g., discounting rate, indicators of demand). Using a latent approach with continuous indicators, the authors found three distinct classes of college students who engage in heavy drinking: low reward value, high discounting (LRHD); moderate reward value, low discounting (MRLD); high reward value, high discounting (HRHD).…”

“…Although the Campbell et al (2021) study provides an excellent exemplar of methods derived from LCA to indicators of demand and decision-making across various tasks, the goal of the current work is more general and specific to responding within a decision-making task. That is, the sample of decision-makers in a discounting task is likely to include classes of responders that demonstrated monotonically decreasing choices (i.e., systematic) and those who varied from that expected trend (i.e., non-systematic).…”