Different vulnerabilities may interact to influence experiences of the pandemic. All participants were enrolled in a clinical trial, were not experiencing severe medical problems from their substance use, owned smartphones, and were proficient in English. Hence, findings may not generalize to more impoverished, medically complicated, or diverse groups. Future research into pandemic-related stressors and substance use should aim to recruit a more diverse sample. Smoking and alcohol misuse often co-occur. In the United States, the prevalence of nicotine dependence among individuals with alcohol dependence is 45.4%, while the prevalence of any alcohol use disorder among adults with nicotine dependence is 22.8% . These co-dependent individuals have more difficulty quitting smoking . An outstanding problem among those with substance use disorders is their disproportionate valuation of the drug and their disproportionate allocation of resources to obtaining the drug compared to participating in other daily activities . This imbalance between drug-related vs. regular activities reflects reinforced drug consumption patterns , and the differences in how drugs and nondrug reinforcers exhibit differential reinforcement strengths can be operationalized using a concept known as Relative Reinforcing Efficacy . One validated laboratory approach to measuring the RRE of drugs is hypothetical purchasing tasks, which assess changes in drug purchase and consumption as a function of increasing drug price . The consumption pattern can yield the demand curve modeled by Q = Q0∗10k , an exponentiated version of the classic equation by Hursh and Silberberg . Q represents consumption at price C; Q0 represents consumption at or near price zero,grow rack α represents the rate of change in demand elasticity, and k is the span of consumption values in log units. Other demand indices derived from the demand curve include: break point , Omax , and Pmax .
Pmax also indicates the price at which the slope of the demand curve becomes <-1, indicating a shift from relatively inelastic demand where changes in consumption is resistant to increases in price to relatively elastic demand. Research using the alcohol purchase task has found alcohol demand to be associated with alcohol use. For example, college students with recent heavy drinking exhibited greater intensity, Omax, and break point than recent lighter drinkers , and the APT’s reliability and validity was further confirmed among college students . Importantly, heavy drinking smokers exhibited greater Omax, Pmax, and break point for alcohol compared to heavy drinking nonsmokers , suggesting that smoking may increase the demand for alcohol. Research using the cigarette purchase task has suggested that cigarette demand indices are associated with smoking behaviors. Nicotine dependence severity was positively associated with the break point, intensity, Pmax, and Omax among young light smokers and among moderately heavy smokers .For instance, it was shown that smokers with schizophrenia reported higher intensity, consumption, and expenditure than smokers without schizophrenia . Researchers have further studied the latent structure of the demand indices to identify higher-level factors in the RRE domain that potentially better explain drug use behaviors. Two latent factors, labeled Persistence and Amplitude, have been identified for different drugs, including marijuana , alcohol , and cigarettes . The Persistence factor was found to consist of break point, Omax, Pmax, and elasticity. Higher levels of break point, Omax, and Pmax, and lower elasticity values were associated with higher Persistence scores, reflecting more persistent demand for the studied drug. However, the Amplitude factor appears to be more heterogeneous. The demand index that loads to this factor is the intensity, and thus it may reflect the maximum possible amount acquired and consumed by users, but other demand indices, such as Omax and elasticity , were found to load on the Amplitude factor.
While many studies have evaluated the RRE of alcohol and cigarettes separately, most were conducted in nonclinical samples, particularly among younger college students. Smokers with alcohol use disorder represent a special population known to be more treatment resistant because of their dual dependency . Recently, there have been several attempts studying the demand for alcohol and cigarettes among populations with concurrent use of alcohol and cigarettes. For instance, it was found that smokers showed greater demand for alcohol than nonsmokers among a college student sample . Extending these results from university settings to communities, Amlung et al.provided further evidence of increased demand for alcohol among smokers compared to nonsmokers. Recently, in a larger community sample of non-treatment seeking heavy drinking smokers, Green et al.found that alcohol and cigarette demand indices were positively correlated and more importantly, they found that compared to alcohol-related dependence measures, smoking-related measures accounted for more variance in alcohol demand’s Persistence factor, suggesting that smoking may play a reinforcing role in increasing alcohol demand among non-treatment seeking heavy drinking sample. These three studies have provided important insights for the interrelationships between the demand for alcohol and cigarettes, shedding light on developing interventions for alcohol and tobacco co-dependence. To complement these findings, we evaluated the demand for alcohol and cigarettes among treatment-seeking smokers with AUD, a clinical population that has not been examined previously. Specifically, the current study used the APT and CPT to examine the baseline demand for alcohol and cigarettes among smokers with AUD enrolled in a clinical trial for the concurrent treatment of AUD and smoking. We aimed to compare the alcohol and cigarette demand indices and their latent factor structures and examine each drug’s demand metrics’ relationship with the dependence severity of alcohol and nicotine. Our finding that participants had higher Omax and elasticity in the APT than in the CPT suggests that they were willing to allocate more economic resources toward alcohol than cigarettes and were less sensitive to the price escalation of the alcohol than that of cigarettes. These results suggest that alcohol had relatively greater RRE than cigarettes among smokers with alcohol use disorder.
Our results were consistent with an earlier study among alcohol-dependent individuals . They used a multiple-choice questionnaire to assess the crossover point between drug and monetary values and found that the crossover point for the monetary option was higher for a drink than for a cigarette, suggesting that alcohol had greater RRE than cigarettes did among a similar population. The greater values of Omax and lower elasticity scores in the APT than those in the CPT suggested that smokers with AUD had greater demand for alcohol than cigarettes. Consistent with difference in elasticity between alcohol and cigarette demand, our findings support the notion that smokers with AUD were more resistant to the price elevation in terms of reducing their alcohol consumption compared with their cigarette consumption. Notably, greater and more sustained demand for alcohol may be related to one’s smoking status per se, as previous research showed that heavy drinking smokers reported greater alcohol demand than heavy drinking nonsmokers . Although our participants reported lower intensity of alcohol than that of cigarettes,microgreens shelving this difference in intensity may reflect the inherent difference in characteristics between alcohol and cigarettes, such as packaging and consumption patterns specific to the products. The relative difference in intensity between alcohol and cigarettes demand, as well as their relative difference in baseline consumption patterns is consistent with previous research using a similar sample—heavy drinking smokers . Our PCA suggested a robust two-factor latent structure for the APT that accounted for 80.65% of the variance. This finding is consistent with previous research that identified a two-factor solution for marijuana , alcohol , and cigarettes . Moreover, consistent with these studies, the first factor includes break point, Omax, Pmax, and elasticity for both alcohol and cigarette demands. These four indices reflect the sensitivity to the increasing prices of alcohol and cigarettes. Thus, this factor indicates the persistence of alcohol and cigarette use behaviors among this population. The second factor has been commonly referred to as Amplitude , which reflects individuals’ consumption levels when the cost was minimum. This factor was mainly attributable to the intensity index. However, previous research identified differential contributions from a second demand index. Three studies found extra loading from Omax , one study found elasticity , and one found no extra indices . Unlike these studies, we found that the Amplitude factor had extra loading from the break point and Pmax, although three studies found similar non-significant negative loadings from Pmax . These results highlight the heterogeneity of the second factor, despite the consistent loading from intensity. For the cigarette demand’s PCA, we replicated a two-factor . Overall, the loadings to the first factor were similar to our findings with the APT’s PCA. However, the Persistence factor accounted for 52.55% of the variance in alcohol demand vs. 46.67% of the variance in cigarette demand, which suggests that smokers with AUD are characterized by higher persistence use of alcohol than cigarettes, consistent with the differences of Omax and elasticity between APT and CPT.
Perhaps the most interesting finding with the cigarette demand’s PCA was the second factor. This factor pattern is unique because it has been partially reported. For example, Bidwell et al.and O’Connor et al.reported Omax, while González-Roz et al.reported elasticity to load to the second factor. Except for the same factor loading to the second factor, the loading from the other four demand indices have a complementary pattern . These differential loading patterns highlight the heterogeneity of the Amplitude factor, and distinct latent factors may contribute to the observed differential demand for alcohol and cigarettes. We found that cigarette demand indices were significantly correlated with FTND scores, baseline smoking rate, and smoking withdrawal . These positive correlations have been reported in several studies , and suggest that smokers who were more dependent on nicotine have more demand for cigarettes. Notably, the correlations between cigarette demand indices and WSWS negative affect scores have not been previously reported. Although our participants were relatively satiated with smoking when they completed the CPT, these findings suggest that smokers’ withdrawal experience was positively associated with their demand for cigarettes. In contrast, we did not find alcohol demand indices and latent factors were correlated with alcohol-dependence measures except for the SAWS scores. Several studies have reported positive correlations, such as drinks per week , monthly binge drinking days , and AUDIT scores . Although the exact reasons for this discrepancy are unclear, we speculate that two factors may be relevant. The first is that the APT used in our study is different from other studies in terms of its instruction about framing the hypothetical drinking context, which will be discussed more in the study limitations later. Briefly, our generic description of the drinking situations may be insufficient to allow participants to imagine their typical drinking scenarios thus that they could not accurately report their alcohol demand. The other possible reason might be differences between study populations. Unlike previous studies , our participants were treatment seeking, and thus their motivation of quitting/reducing drinking and smoking may have changed how they responded in these purchase tasks. Besides the difference of motivations, our participants were more dependent on alcohol than the undergraduate samples tested previously —the average AUDIT score in our sample was almost twice that of theirs. Similarly, all of our participants had a diagnosis of AUD, while only about 50% who were dependent or abusing alcohol in the study by Amlung et al. . Additionally, our participants were also heavy smokers and importantly, several studies found that smoking resulted in higher demand for alcohol than nonsmoking . Thus, smoking may have resulted in a higher and more uniform alcohol use demand, masking a possible linear relationship between dependence and demand. Consistent with this possibility, we did not find any relationships between alcohol demand and alcohol misuse diagnoses. Although this possibility exists, future studies evaluating this population will help address whether heavy smoking can indeed mask the relationship between alcohol dependence measures and alcohol demand indices. The positive correlations between alcohol and cigarette demand indices suggest that those who had higher demand for alcohol tended to have higher demand for cigarettes too. This co-demand pattern is consistent with a recent study which revealed the same positive correlations among a similar sample of heavy drinking smokers . Moreover, by conducting hierarchical multiple regression analyses, their study found that smoking had a positive impact on the alcohol demand, but not the other way around .