Moreover, models incorporating age, frequency and years of tobacco smoking with marijuana accounted for 25-44% of variance in adolescent nicotine dependence. Interestingly, CPD was only minimally associated with the frequency of marijuana use and made minimal contribution to the model since associations with the mFTQ were similar after removing the question about CPD.The finding that with the exception of drive and priority, the other subscales of the NDSS were not significantly associated with marijuana frequency was not surprising since most of these adolescent smokers were light and intermittent tobacco users and dimensions of dependence such as stereotypy and tolerance become more prominent as teens develop more regular and established patterns of smoking . However, despite relatively light tobacco use, the drive subscale, which measures the compulsion to smoke, and the priority subscale, which measures the preference of smoking over other reinforcers, were associated with marijuana use. It is possible that since both marijuana and tobacco share common pathways of use, smoking cues for one substance may trigger craving for the other, and thus reinforce patterns of use. As such, tobacco and marijuana may serve as reciprocal reinforcers. Some limitations of this brief include the relatively small sample size and the lack of detailed information on the timing of the initiation of marijuana use with regard to cigarette smoking. Future studies will need to examine how the proximity of marijuana use to cigarette smoking affects the degree of nicotine addiction. For example, pipp horticulture racks cost examining whether concomitant use impacts the level of nicotine addiction more than smoking marijuana separately from tobacco. The sample largely consisted of light smokers, which reflects adolescent smoking in the US.
That we found such a strong association between marijuana use and nicotine addiction in this group of relatively light tobacco smokers is notable, and reinforces the relevance of the association.Data for this study came from the Tobacco Perceptions Study, an 8-wave prospective cohort study designed to measure tobacco and marijuana perceptions, intentions, actual use, social norms, and marketing among California high school students. The 10 high schools were chosen using convenience sampling; the original sampling frame included all students in the 9th and 12th grades from these schools. Ninth graders were chosen since the average age of first trying a cigarette in the U.S. was 14.5 years, thus providing for a prospective examination of the impact of perceptions on tobacco use. Twelfth graders were chosen as following them into young adulthood would afford a broader sample of young adults then obtained by simply sampling college students or those who joined the workforce. Our cohort study was designed to examine changes in use and perceptions of tobacco products over time instead of making population-level estimates, which was suitable to test the validity, reliability, and predictive strength of the measurement items used. Independent variables came from Wave 1 through Wave 3 and dependent variables from Waves 5–7. Details of the study design and procedures are provided elsewhere . Variables tested are described in detail below.We initially examined 26 Short-Term Risks, Benefits, and Long-Term Risks perceptions items and then discarded those with limited variation or too few participant responses. We formed candidate scales from the earliest of Waves 1 and 3 for which all measurement items for a given product were available. Measurements were taken from distinct samples, as indicated in tables; all significance tests were two-sided.
Analyses were adjusted for clustering by school. Scales were correlated with measures from the same wave for convergent and discriminant validity analysis. When correlating scales with future behavior, comparable independent variables from the same wave were used where possible . Analysis was then carried out in three stages for each product using the final 19 Short-Term Risks and Benefits items. First, to determine how many factors to extract for rotation, minimum average partials was used for three reasons: accuracy , tendency to under-factor when inaccurate , and factors with too small loadings were not retained . As some data were missing, the expectation-maximization algorithm was used to estimate the variance/covariance matrix for each product and then that matrix was factor analyzed. This approach used all available data rather than excluding cases with a missing item. For ease of interpretation, rotated factor loading cutoffs of >0.40 were examined . The possible influence of missing data was examined by repeating the process using only records without missing data. The final factor loadings were virtually identical. Items with weak or virtually no loadings were investigated as follows: first, descriptive statistics were computed, then correlation matrices including the problematic item and other items thought to be measuring the same construct were analyzed. Based on the strength of correlation and face validity of the item, a decision was made to either retain or remove the item. The seven Long-Term Risk items were not factored because they were highly correlated and with only seven items, we examined the correlation matrix manually and created a single scale by combining the items. Second, once candidate scales were identified in the first step, Cronbach’s α was used to check internal consistency of the newly-created scales and to identify items that could be removed either because removal improved α or did not degrade α .
Cronbach’s α was recomputed for any changed scales. Thereafter, we checked the face validity of the scales and formed the final scales for all products. Scales were then correlated with each other and related independent variables to check for convergent and discriminant validity. Third, we constructed correlation matrices to explore whether the newly-developed scales correlated with future behavior and compared their predictive ability with related measures and theoretical constructs shown to be predictive of behavior . All measures above were used as independent variables in bivariate correlation analysis and as comparators in predictive testing of initiation and escalation of tobacco and marijuana use. Age, a known correlate of perceptions and behavior, was adjusted for . We estimated Kendall’s tau-b correlation coefficients which are robust to non-linearity and extreme observations .Correlation analysis among the factors indicated that scales representing risks were highly interrelated and were not related to benefits. The long-term risks scale correlated positively with general harm for all products and with perceived prevalence for cigarettes, hookah, vaped marijuana, and blunts; long-term risks correlated negatively with behavioral intention for all products except cigars and e-cigarettes, with ever-use for all except hookah and smokeless, and with age for hookah, smoked marijuana and vaped marijuana, and blunts. The short-term risks scale correlated positively with general harm for all products and with perceived prevalence for cigarettes, vaped marijuana, blunts, and smokeless tobacco. The short-term risks scale correlated negatively with ever-use for all products except smokeless, with age for all except cigarettes, and with behavioral intention for all except cigars and e-cigarettes. The benefits scale correlated positively with perceived prevalence for all products, with ever-use for all products except smokeless, and with behavioral intention for all except cigars. Benefits correlated negatively with general harm for e-cigarettes, hookah, blunts, and smoked marijuana, it positively correlated); no correlation with age was found. The social risks scale correlated positively with general harm and negatively with age; the addiction risks scale correlated positively with general harm and perceived prevalence .The variable or construct that most strongly predicted initiation for all products was willingness, followed by behavioral intention . The long-term risks scale and benefits scale were found to be third-most strongly predictive of initiation for different products. The long-term-risks scale was third for smokeless, e-cigarettes, blunts, and vaped marijuana and smoked marijuana and benefit came in third as predictors of initiation for all products except for e-cigarettes and smokeless. Fourth was the short-term risks scale . General harm predicted initiation for just three products , although the strength of the correlation was strong for both marijuana products. Perceived prevalence proved weakly predictive and only for initiation of blunts and smoked marijuana . Escalation of use for all products correlated strongly with willingness to use; other than the social risks scale that weakly predicted against escalation, willingness was the sole strong predictor for escalation of smokeless tobacco use. For hookah, blunts, and smoked marijuana and vaped marijuana, though, commercial grow racks behavioral intention outperformed willingness as the strongest predictor of escalation.
Our short-term risks scale was the third-best performer against escalation of hookah use, general harm was third for predicting against escalation of smoked marijuana. General harm was the next best performer against escalation of vaped-marijuana, followed by short-term risks . Perceived prevalence weakly predicted escalation of hookah, blunts, and smoked marijuana and vaped marijuana use, whereas general harm strongly predicted against escalation although only for smoked marijuana and vaped marijuana .In this study, we created and validated new scales that measure perceptions of specific risks and benefits associated with AYA use of cigarettes, cigars, smokeless tobacco, hookah, blunts, smoked marijuana, and vaped marijuana, and compared their predictive ability with measures of perceptions of general harm, social norms , willingness, and behavioral intentions. We report the following three key findings in answer to our research questions. First, measures of perceptions of specific social and health risks and benefits were less strongly associated than measures of perceptions of global outcomes with willingness, social norms, and intentions to use tobacco and marijuana; these differences varied by product. Scales measuring perceptions of specific risks were highly interrelated and not related to the benefits scales, which could indicate that AYA’s perceived risks and benefits are isolated or unrelated concepts. Second, although we demonstrated the reliability and validity of using the specific risk and benefit perceptions measurement scales to identify factors underlying motivation for initiation and escalation of tobacco and marijuana use, measures of perceived general harm outperformed specific measures in predicting uptake and continued use. Third, there were clear differences between the strength of correlation between specific measures of short-term and long-term risks and measures of willingness, social norms, and behavioral intentions, which likely indicates differences in utility and saliency of the scales for predicting behavior. Study implications include support for the use of brief measures of perceived general harm when seeking to determine initiation and escalation of tobacco and marijuana use among AYA, resulting in shorter surveys. Shorter surveys reduce the risk and deleterious effects of participant fatigue from lengthy surveys containing a range of products and that enumerate specific risks and benefits, thereby garnering more accurate and useful data. In addition, the extent to which our scales of perceived health and social risks and benefits correlated differently across products suggests that these measures may capture motivational determinants that are product-focused. Further, such motivations may lie earlier in the causal chain and mediate actual use, which suggests that targeting these motivations may bring about behavior change . For example, the perceived harmfulness of cigarettes appears to have permeated our sample, which could be due to tobacco control messaging, resulting in decreased reporting of use and of intentions to use. However, perceived short-term and long-term risks of using e-cigarettes, blunts, and smoked and vaped marijuana were not correlated with behavior or behavioral intentions, suggesting a need to address specific health and social outcomes perceptions. Some of our findings relate to particular under-studied products and are therefore worth mentioning. For example, there are few longitudinal studies examining smokeless tobacco and AYA initiation and escalation in rural areas where such use is most widespread, and there is limited evidence of how perceptions of long-term risk influence smokeless tobacco use over time . It is plausible that the correlation we identified in this study between initiation of smokeless tobacco and the long-term risk scale could be due to long-standing social stigma related to smokeless tobacco, in addition to the scale’s specific delineated risks . Stigma is a likely factor in AYA decisions to use smokeless tobacco, especially when one considers that long-term health consequences of smokeless tobacco use are similar to long-term health consequences of using other tobacco products. Additionally, our finding that initiation of blunts most strongly correlated with the measure of perceived prevalence among peers comports with other studies reporting that initiation of blunts is most likely due to a perception that many peers are using them . Ethnographic studies among adolescents and young adults also suggest that blunt use is more often perceived to have social benefits . These findings can guide development of future, in-depth studies that focus on the often-overlooked use of smokeless tobacco and burgeoning use of blunts among AYA.