Such real-time reports and ecological momentary assessments are becoming increasingly feasible as mobile and web technologies grow and advance . Obtaining EMA data via cellular phones has become increasingly popular and has already been used to collect information about alcohol and drug use as well as ART adherence among PLWHA. EMA for both alcohol/drug use and medication adherence may be highly useful for clinicians and health care providers in order to ensure patients are receiving and optimally adhering to treatments. Data from the current study additionally do not address other related factors that may be influencing or driving the association between at-risk drinking and ART non-adherence. For example, impulsivity measures have been found to have associations with both alcohol use and medication adherence , potentially suggesting impulsivity as a mechanism for the relationship between alcohol use and medication adherence. Alternatively, the relationship between alcohol use and ART adherence may be explained by beliefs about mixing alcohol and ART doses . Lastly, heavy drinking has been shown to be associated with cognitive deficits, especially among PLWHA . Such deficits are commonly associated with ART non-adherence . Although the current study measured and controlled for global cognitive function as a way to address these relationships, this measure of cognition was not associated with at-risk drinking or ART adherence,vertical farming supplies and does not capture any acute deficits that occur during and acutely after alcohol consumption.
Such induced acute deficits may manifest as memory lapses or “blurring” of dose timing, contributing to ART non-adherence . These are factors that were not measured in the current study but certainly warrant further examination in future research. Overall, our findings suggest that screening PLWHA for at-risk drinking, years of education, and plasma viral load may provide clinicians with a better indication of patients at the greatest risk for ART non-adherence compared to self-reported ART adherence alone. Our findings also support the use of the NIAAA guidelines for at-risk drinking as a marker of ART non-adherence risk among PLWHA. There is a clear recommendation for future research to continue clarifying the association between level of alcohol use and ART adherence. Future directions include examining the temporal relationship between alcohol use and ART adherence among PLWHA, similar to the Braithwaite et al. study. Such a future study may benefit from taking advantage of EMA via mobile technologies to ensure more accurate self-reporting of alcohol use and ART adherence. Additionally, while much research is being done on treatment for opioid dependence and injection drug use among PLWHA, the literature is scant for treating AUDs in this population. Future studies among PLWHA may explore the efficacy of evidenced based interventions known to reduce alcohol use in HIV-uninfected populations, including pharmacotherapy and psychotherapy. For example, pharmacotherapy for problematic alcohol use can involve the prescription of FDA-approved medications such as a camprosate or naltrexone, which have shown to be equally effective in reducing alcohol consumption when paired with psychotherapy co-interventions . Psychotherapy for problematic alcohol use often includes one or more of the following treatment modalities : self-help groups , individual therapy , group therapy , or couple therapy .
The current study and supporting literature suggests that simply decreasing alcohol consumption may be a step in the right direction for many PLWHA. Considering the elevated prevalence of AUDs and heavy drinking patterns as well as the stronger association between alcohol use and mortality among PLWHA compared to uninfected adults , it may be beneficial to place a greater emphasis on educating health care providers on this topic as well as treating AUDs and decreasing alcohol consumption in this patient population. Explanations for adolescent and young adult tobacco and marijuana use reside in theories of health behavior, which argue that behavior is influenced by risk and benefit perceptions, social norms , and willingness and intentions to engage in such behavior . Cross-sectional and longitudinal studies have established a relationship between risk and benefit perceptions, behavioral intentions, and actual use of tobacco and marijuana products among AYAs . Despite the important theoretical and empirical relationship between perceptions of risks and benefits and actual tobacco and marijuana use, the measurement of such perceptions varies greatly across studies, including differences in how the questions are asked and outcomes being assessed . One common difference in perceptions measures used in AYA tobacco-related or marijuana-related research is whether participants are asked about perceptions of general harm related to tobacco or marijuana or about perceptions of specific health and social outcomes or perceptions of specific benefits . Studies have independently used measures assessing perceptions of both general and specific risks related to tobacco and marijuana use to predict actual use.
Absent in the literature is an assessment of the psychometric properties of measures assessing perceptions of specific risks and benefits. Further, studies have not examined whether and how perceptions measures that ask about a more general outcome such as “harm” versus specific outcomes related to risks or benefits compare with AYA’s willingness to use if one of your best friends were to offer it and intentions to use in the next year ; and if they have differential ability to predict actual use of tobacco and marijuana. To improve measurement utility and support measurement uniformity in the field of risk and benefit perceptions, and to provide clear direction for the development of more parsimonious perception surveys, we refined and validated perceptions measurement scales composed of items asking about short-term social and health risks, long-term health risks, and benefits. We then set out to answer the following three questions regarding AYA use of tobacco , blunts , smoked marijuana , and vaped marijuana: Whether and how measures of perceptions of specific social and health risks and benefits and measures of perceived general harm are differentially associated with measures of willingness, social norms, and intentions to use? Are specific versus general measures differentially associated with and predictive of behavior? Are specific perceptions measures differentially predictive of behavior compared to measures of willingness, social norms, and behavioral intentions? Findings from this study will identify measures of perceived risks and benefits most strongly linked with intentions, willingness, and actual behavior. Ultimately, the findings will support uniformity across studies and improve study comparability, thus increasing generalizability of findings and enabling a cohesive evidence-base to understand and accurately predict AYA tobacco and marijuana use behaviors.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,cannabis indoor greenhouse 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. We initially examined 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.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. Oblique rotation was employed as it allows derived factors to correlate . 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.