The 13-items included: social, mellow, creative,top of the world, increased sex drive, energetic, dizzy, nauseous, drowsy, lazy, unable to concentrate, out of control, and guilty. Participants were asked “in the period shortly after you used did it make you feel…”? Responses were scored as present or absent and the item scores summed to make the positive and negative scales.To examine the dimensionality of the 13 subjective experiences examined for each substance, we conducted Mokken Scale Analysis using the statistical software STATA. Mokken scaling analysis extends traditional factor analysis by systematically hierarchically ordering items that are highly correlated. Mokken analysis provides a nonparametric, iterative scale-building technique that identifies the smallest set of internally consistent scales from a given item pool. This model assumes the presence of one or more latent traits that can be measured by subject responses to a set of items. MSA is probabilistic and hierarchical, meaning that the items can be ordered by a degree of “difficulty”;individuals who agree with a more difficultitem will tend to agree with less difficult items. Scales from MSA are formed by taking pairs of items with the highest correlation and including other items until there is no further improvement. Loevinger’s H coefficients, which indicate the fit of an item to the scale, are computed for each item within a scale and for the scale as a whole. H coefficients ranging between 0.3 and 0.4 indicate a weak scale, 0.4–0.5 a medium scale, and 0.5–0.9 a strong scale. In MSA, an item can remain “unscaled” because it could not be added to one of the alternative scales without weakening the scale’s homogeneity. Based on our previous analyses , we used the scaling derived from the CADD sample. Positive and negative scales were standardized by age, sex, vertical outdoor farming and clinical status with two groups. Pairwise correlations between the resulting two scales were then determined for alcohol, tobacco, and marijuana.

Two consistent subjective experience scales were revealed using MSA. The item guilty did not fit into either scale for any substance. The item out of control was dropped due to extremely low endorsement for tobacco and because this item fluctuated between the positive scale and the negative scale. For all three drugs the positive scale included: relaxed, sociable, creative, euphoric, energetic, and increased sex drive. Items included in the negative scale for all three drugs were: lazy, drowsy, unable to concentrate, dizzy, and nauseous.Table 2 provides the H-coefficients for the positive and negative scales for each of the three drugs with the items shown in their hierarchical ordering. For the positive subjective experiences H-coefficients for alcohol,tobacco, and marijuana were 0.45, 0.52, and 0.43, respectively. For the negative scale, the H-coefficients were 0.42, 0.52, and 0.54, respectively. Means and standard deviations for each subjective experience scale across all three drugs are shown in Table 3. Mean scores differed as a function of age, sex, and clinical status. For all but marijuana, younger subjects had significantly lower mean scores on both positive and negative scales. Males scored significantly higher for the positive scales across all drugs than females. Males also had a higher mean score for the alcohol negative scale and a lower mean score for the marijuana negative scale. Mean scores on the positive experiences to tobacco and negative experiences to marijuana scales were higher in our community sample than in our clinical sample. Mean scores were lower in our community sample than in our clinical sample for positive experiences to marijuana and negative experiences to alcohol scales.In the current report, we examined subjective experiences to three commonly used drugs of abuse among young adults from the general community and an area treatment program. In these data, we obtained results that supported previous observations indicating positive and negative subjective experiences for a particular drug were predictive of problem use of that same drug. We then extended this relationship in two ways. First, we obtained results that supported the notion that positive and negative experiences to one drug are similar to those experienced for another drug and second, that subjective experiences to a drug are predictive of the risk for problem use of other drugs.

We interpret these findings to suggest that subjective experiences may be a useful indicator of a common liability towards use and problem use of multiple substances. Following on our previous work on marijuana and subjective experiences , we used Mokken scaling to simultaneously examine whether subjective experiences to three drugs are associated with drug use outcomes. From these analyses we observed that the subjective experience scales for each of the three drugs were comparable to those found in previous studies despite using a different methodology. We observed differences in item means and hierarchical ordering of the items by substance suggesting that subjects are reporting drug specific subjective experiences. This interpretation is consistent with findings from laboratory based studies which have shown that subjects can differentiate between a placebo and a drug or between different drugs based on subjective experiences. As different combinations of alcohol, tobacco, and marijuana use are commonly reported in epidemiological studies, we investigated the relationship between subjective experiences to different drugs in poly-substance users. We observed that subjective experiences to one drug were significantly correlated with experiences to another drug, though the strength of the relationship varied for different drug combinations. The strongest relationships were between alcohol and marijuana, replicating two previous studies , and between alcohol and tobacco. These particular drug combinations target similar neuronal receptor systems and are reported to enhance the overall drug experience when taken together. Further, as subjective experiences are thought to reflect the underlying physiology of a drug’s actions , these cross-substance relationships may provide a closer approximation of a common risk factor suitable for molecular genetic investigation. In this sample of community and clinical subjects, subjective experiences for one drug were associated with outcomes related to a different drug.

Though our results replicate findings that relate positive experiences with greater use of other drugs, we also identified that negative experiences were predictive of abuse and dependence status of a different drug. In particular, negative effects of alcohol and marijuana were associated with misuse of these same drugs as well as tobacco. Although this may appear counter-intuitive, a possible explanation could be that subjects who needed greater amounts of a drug in order to feel its effects drove the observed association. Findings from laboratory-based drug discrimination studies suggest that some subjects are unable to differentiate between drug and placebo at a standard training dose. Differences between the two conditions could, however, be reported as non-discriminators were exposed to greater doses of a drug. Interestingly, those who were able to discriminate between non-exposure and exposure to a drug reported stronger positive and negative subjective experiences, often simultaneously , at greater doses. This underscores the importance of dose in determining individuals’ drug sensitivity as assessed by subjective experiences. The relationship between drug dose, the resulting subjective experiences, and problem drug use has also been examined using self-ratings to the effects of alcohol [SRE; 28]. The SRE primarily assesses negative experiences to alcohol such as dizziness and passing out as related to the dosing levels needed to feel the sedative effects of alcohol. Among adolescent and adult samples of both sexes and family-history positive studies of alcoholics , low levels of response, as measured by the SRE have been implicated as a risk factor for alcohol use disorders. This notion that some drinkers need to ingest greater amounts of alcohol to feel its sedative effects and that this effect is related to greater drinking quantities has been recently supported and extended to include the observation that this relationship is also relevant to those reporting lower levels of stimulant effects during the first five drinks. Our finding that negative alcohol experiences were predictive of problem alcohol use is consistent with this research, despite using a different questionnaire, and extends it to include the potential prediction of other drug use problem behaviors.

Findings from the current study should be considered in light of a number of limitations. First, subjective experiences were collected from participants who ranged between 11 and 30 years of age. Though scaling of the different experiences was consistent between younger and older subjects, the older subjects have typically had a longer use history of alcohol, tobacco, and marijuana. Second, subjective experiences were only collected from those who reported using alcohol and marijuana six or more times and daily use of tobacco for a month. Thus, we were not able to include experiences from those who had used only a few times. Third, we were not able to measure differences in dosage, quality of a drug, depth of inhalation, peer use and drug use setting some of which impact self-reported subjective experiences. Fourth, we were not able to establish the reference point from which people were making their ratings. This is due to the unclear phrasing of the stem questions that asks about experiences “shortly after using.” We cannot know whether subjects were reporting their initial experiences, experiences in the minutes or hours immediately following recent drug intake or the conglomeration of their drug experience, making causal inferences regarding the observed associations not possible. Lastly, the lower endorsement rates for the experiences of tobacco may be due to the dichotomous fashion of responses that limit the sensitivity to detect the milder effects. Further, there more stringent requirements for collecting subjective experiences to tobacco may have made it more difficult to detect significant relationships with subjective experiences to alcohol, tobacco,rolling grow table and their more problematic use behaviors.Despite the high co-morbidity between marijuana and nicotine use, only few studies have directly addressed the mechanisms that lead to their concurrent use. A recent review by Agrawal describes multiple etiologies that influence their comorbidity. This includes route of administration , cross drug adaptation, response to treatments, environmental effects and genetic factors. Others have also alluded to the “gateway drug” hypothesis whereby the use of one drug may potentiate the effects of the other. For example, in a longitudinal study in 14–15 year olds, marijuana use increased the likelihood of initiating nicotine use up to 8 times and developing nicotine dependence up to 3 times suggesting marijuana’s role as a gateway drug. This was further supported by findings showing that women who used marijuana were at 4.4 odds of later developing nicotine use and dependence. The same group also reported in 43,093 adults that nicotine smoking increased the risk for marijuana use and dependence up to 3 times. This latter finding suggests a bi-directional potentiating effect and indicates that more complex factors may drive combined use.

Although the animal literature has characterized the neural mechanisms that may underlie these potentiating effects, it is also possible that personality factors contribute to this phenomenon. Combined marijuana and nicotine use has been associated with differential effects on clinical diagnoses, cognitive and psychosocial problems, and outcomes. For example, Bonn-Miller and colleagues examined associations between negative emotions that discriminate marijuana-only users from co-morbid marijuana and nicotine users. They found that, in general, nicotine-only using individuals had significantly greater negative emotionality than marijuana users, co-morbid marijuana and nicotine users, and non-using controls. Earlier work by Degenhardt showed that while nicotine and marijuana use were both individually associated with increased rates of negative emotion, this relationship appeared to be driven by neuroticism in marijuana users. Taken together, these studies argue for different patterns of co-morbidity in nicotine and marijuana using populations. To date, however, distinctions in trait markers, such as personality factors, have not yet been addressed in this ubiquitous group of co-morbid users. These differences suggest the need for fine-tuning the ability to discriminate risk-profiles between these groups as they also relate to clinical treatment outcomes. Factors that contribute to risk profiles include personality traits that have been examined as putative markers for treatment outcomes. For example, in a prospective four-year study in 112 adults with chronic alcoholism, Krampe et al. determined that the presence of any personality disorder was associated with a decrease in four-year abstinence probability. Similarly, using the NEO Personality Inventory-Revised Betkowska-Korpala found that following treatment, abstinent patients have higher levels of agreeableness and conscientiousness than patients who relapsed within a year following the therapy. This suggests that personality profiles have high predictive values for SUD outcomes and should be considered during treatment programs.