Bivariate correlations were used to examine the relationship among behavioral economic indices within each substance and between pairing indices . An exploratory factor analysis was conducted using principle component analysis estimation method with an oblique rotation to allow for a multi-factorial solution with correlated factors. The PCA approach is consistent with previous research examining the latent structure of demand , with the rationale that characterizing total variance among indices was preferable due to the high levels of variability among associations between demand indices . Factor structure was determined by an Eigenvalue >1 and by further examination of the scree plot. When interpreting the rotated factor pattern, factor loading of 0.40 on the pattern matrix was the criteria used to determine if an item significantly loaded onto a given factor .A series of hierarchical multiple regressions with PROC REG were used to test our second and third aims in relation to same-substance and cross-substance associations between use severity/past 30-day use and latent factors of demand and demand indices. The primary outcomes were the latent factors of demand derived from the PCA. Due to the lack of existing research on the traditional five behavioral economic indices for alcohol and cigarettes in a sample of heavy drinking smokers, we ran parallel models including the five indices of demand as opposed to the latent factors of demand. These results are presented in the Supplementary Materials. In the lowest block were demographic characteristics . Due to the possible influence of income on choice behavior, income was included in this block of analyses.
The second block included same-substance predictors ,heavy duty propagation trays while the third block included cross-substance predictors . The same pattern of analyses was replicated for cigarette smoking indices, such that the second block included same-substance indicators of cigarette smoking and the third block included cross-substance indicators of alcohol use. To control alpha inflation an omnibus approach was used in the hierarchical regression, such that if the change in R2 was not significant, the block of coefficients was not considered further. This approach reduces alpha inflation by reducing the total number of tests. No correction for Type I error was implemented based on the rationale that Type I error needs to be considered at the level of families of hypotheses separately and not for the number of variables in the whole set of analyses reported . In the present analyses, the primary outcomes of the two factors of demand represent two families of hypotheses suggesting correction for Type I error may not be necessary. Results from full models including the three aforementioned blocks and reduced models, excluding non-significant blocks, are reported. Analyses were conducted in SAS University Edition version 9.4 .Power analyses for the final study sample of n=322 for the APT and n=334 for the CPT were conducted in G*Power 3.1 . We conducted a sensitivity analysis to determine the minimum effect size that could be reliably detected in the planned hierarchical multiple regressions with three sets of predictors in an F test for a fixed multiple regression with an R 2 increase setting the alpha level at p<.05 and power = .80. Across the APT and CPT, the results revealed the sample size afforded an 80% power to detect an effect size of f2 = .03 which is slightly above the small effect cut-off .In a large sample of heavy drinking smokers, this study examined the association between latent factors of demand for nicotine and alcohol, in terms of same-substance associations cross-substance associations between use severity/past 30 day use for each substance in relation to demand for the other substance .
In examining same-substance relationships reflected in the second block of the hierarchical regression models for alcohol, a relatively consistent pattern emerged such that greater alcohol use severity, as indexed by ADS and past 30-day use, was associated with greater derived Persistence and Amplitude values. For demand for cigarettes, there was a similar pattern of consistency in cigarette use variables, represented by FTND and past 30-day cigarette use, as these variables were associated with both Persistence and Amplitude. These findings were consistent with the literature and support the notion that use and dependence of a substance is related to demand for a substance that can be captured through hypothetical purchase tasks . Results from our final aim of testing cross-substance associations revealed an interesting pattern whereby cigarette dependence and use predicted Persistence values for alcohol, however not Amplitude values for alcohol. For Persistence, these effects were seen in the expected direction whereby greater FTND predicted greater Persistence values. The same pattern was not seen in predicting cigarette demand such that alcohol dependence and use did not significantly predict Persistence values over and above alcohol use factors. However, alcohol use and dependence significantly predicted Amplitude values. Notably, this alcohol variables in the final block reached statistical significance with change in R2 , but when examining the coefficients of this block, neither drinks per drinking day nor ADS scores were significant. Further, the additional amount of variance this cross-substance use block was able to predict in Amplitude was rather small in comparison to the significant cross-substance use block predicting Persistence values . Results of our principle component analysis aligned with previous literature supporting that Persistence reflects four main dimensions of the demand curve, maximum expenditure , price corresponding to maximum expenditure , first price suppressing consumption to zero , and the overall slope of the demand curve . This factor Persistence represents interrelated measures of sensitivity to escalating price that has been hypothesized to reflect how far, in terms of price, an individual is willing to spend on alcohol . The second factor Amplitude consisted of only one demand indices, Intensity, thus reflecting how much in consumption an individual is willing to consume . These findings suggest that the Persistence factor is operative in these findings for alcohol, suggesting that greater tobacco involvement is associated with insensitivity to the escalating response cost for alcohol.
This pattern did not transition to cigarette outcomes, where results indicate the alcohol involvement is associated with greater overall consumption when free as represented by the Amplitude factor. These results align in part with previous work examining alcohol demand in a sample of heavy drinking smokers. We found greater nicotine dependence to relatively consistently predict greater willingness to spend on alcohol reflected in the Persistence factor which is consistent with Amlung and colleagues findings of smokers experiencing greater alcohol Omax and Breakpoint than non-smokers. Additionally, our results support Yurasek and colleagues finding that Pmax for alcohol was also elevated among smokers. When examining additional comorbidities, a recent study found greater alcohol demand among those who co-use alcohol and cannabis . Furthermore, an early study of commodity specificity revealed that tobacco demand is fundamentally independent of food demand suggesting that purchase tasks are not simply capturing a generic reward sensitivity . In line with what has previously been suggested , there is the possibility of a general hypersensitivity to all rewards that individuals who co-use both alcohol and cigarettes may experience. If there were a generalized hypersensitivity to reward,vertical cannabis we would expect to see a consistent pattern across cigarettes and alcohol such that alcohol use would predict cigarette demand and vice versa. In our sample, we found nicotine dependence and use to be relatively consistent in predicting greater insensitivity to the escalating response cost for alcohol via Persistence factor while alcohol dependence and use only predicted Amplitude reflecting intensity of demand for cigarettes. These results imply from a behavioral economics framework, there may be a stronger effect of nicotine dependence on demand for alcohol than the other way around . There are various possibilities by which tobacco involvement would predict greater reinforcing value of alcohol. One possibility is a methodological issue such that is plausible that this sample had a more stable smoking pattern with more variability in alcohol use, which in turn may explain these effects. In samples that use cigarettes more sporadically, alcohol may have a stronger effect driving demand for cigarettes. Another possibility is asymmetric pharmacological interactions with alcohol potentiating nicotine’s effects but the opposite not being true to the same extent. From a behavioral economics standpoint, this is turn could mean there are asymmetical behavioral interactions, such that smoking is more of a complement than alcohol with smoking making drinking better to a larger extent than drinking makes smoking better. A final possibility is that smoking involvement is a proxy for other items, such as comorbid psychiatric issues or other risk factors, such as adverse childhood events. From this perspective, alcohol becomes more valuable because smokers tend to be more disadvantaged and otherwise vulnerable.
Results indicated significant correlations within demand indices for cigarettes among all demand indices, with the exception of Intensity and Pmax. When examining demand for alcohol, nearly all demand indices were highly correlated apart from intensity which did not correlate with Pmax, Breakpoint, and Elasticity. While each of these demand indices are functionally related all having been derived from the same demand curve, the construct of relative reinforcing efficacy value is proposed to be heterogenous in nature . Thus, the consistent patterns of correlations may serve as a reflection of the demand curve, and deviations in correlations within a substance may reflect a unique aspect of our co-use population where by demand indices for one substance, namely cigarettes, are more strongly inter-related than demand indices for alcohol. The higher correlations may also have been a result of the differences in pricing structure. The present study should be interpreted in light of its strengths including a large sample size and use of all five behavioral economic indices to examine the effects alcohol could have on all aspects of the demand curve for cigarettes and vice versa. Limitations include the use of ADS and FTND as self-report measures of use severity, as opposed to a formal AUD or TUD diagnoses. In addition, the APT used an early price structure that was modelled on a progressiveratio operant schedule, with a doubling of response requirements that leads to the inclusion of non-market prices. This approach includes large intervals between prices that can inflate variance and may be responsible, for example, in the within-task differences in correlations for the APT and CPT, which used a narrower range of market-compatible prices. In summary, our results show that latent factors of demand derived from behavioral economic indices may be sensitive to cross-substance relationships and specifically that such relationships are asymmetrically stronger for smoking variables affecting alcohol demand, not the other way around. Whether this is a function of differential pharmacological interactions between alcohol and nicotine or whether it is because smoking severity is a proxy for other factors that lead to higher alcohol reinforcing value cannot be inferred in the current study, but warrants subsequent examination. More broadly, understanding cross-commodity demand relationships has the potential to illuminate both overlapping and non-overlapping aspects of substance misuse. Various sex differences exist in relation to cigarette use. Prior evidence has suggested that female smokers are at increased risks for negative health consequences when compared to males , with women experiencing greater nicotine withdrawal symptoms, craving, and negative affect , poorer smoking cessation outcomes , and increased risk of mortality . These smoking differences may be related to changes in hormone levels because of menstrual cycle phase. Two hormones of interest that have been shown to vary across the menstrual cycle are progesterone and estradiol . The menstrual cycle can be divided into two phases: the follicular phase and the luteal phase . The follicular phase begins the first day of menses and extends until ovulation. At the beginning of the follicular phase, P4 and E2 levels are low. P4 levels reach their peak during the luteal phase. E2 begins to increase during the follicular phase, reaching a peak at the end of this phase which is signaled by ovulation occurring approximately day 14 of a typical 28 – 30- day menstrual cycle. During the luteal phase, E2 decreases reaching an intermediate secondary peak. By the late luteal phase, both P4 and E2 levels decrease . Changes in sex hormones, specifically P4 and E2, regulate numerous neurotransmitter systems, thus influencing a variety of behaviors, including those related to problematic substance use.