To the best of our knowledge, there has not been a published report that has followed HIV+ individuals and MSM longitudinally over an extended period to characterize the natural history of their marijuana use. Past studies on substance use patterns in these populations have often focused on alcohol or heavy episodic drinking, cigarette smoking or stimulant use. Therefore, the objectives of the current study are to characterize the longitudinal trajectories of marijuana use in a sample of HIV-seropositive and HIVseronegative MSM over a period of 29 years, and to identify factors associated with unique trajectories of marijuana use, as well as those that can change over time that may modify the course of the trajectory. The MACS study protocols were approved by the institutional review boards at the respective recruitment centers and their community affiliates and informed consent was obtained from all participants. MACS participants return every 6 months for a physical examination, collection of blood specimens and completion of a detailed interview and questionnaires. The interview and questionnaires include demographic, psychosocial, behavioral and medical history data. The questions about their recreational drug use, including marijuana, alcohol, poppers, cocaine, crack, heroin, methamphetamine, ecstasy, injection drug use as well as smoking history since their last visit were collected using Audio Computer Assisted Self-Interviewing , an approach previously demonstrated to provide more accurate assessments of ‘sensitive behaviors’ than interview-administered questionnaires among MSM. This analysis included data collected from standardized cannabis grow supplies use questions from semiannual study visits 1 through visit 59 . The study sample included 3658 participants who had data about marijuana use for at least 25 % of their possible study visits during the follow-up period. Specifically, the men enrolled in 1984–1985 and 1987–1991 had 15 and 13 visits or more respectively, whereas, the men enrolled in 2001–2003 had 6 or more visits.
The median years of follow-up was 11.5 years. Alcohol Use—Using data regarding frequency of drinking and average number of alcoholic drinks, alcohol consumption at baseline and at each study visit was categorized as hazardous drinking, low or moderate use or no alcohol use. Tobacco Use—Participants were classified as never, former and current smokers of cigarettes at each study visit. Participants were asked two questions including: whether they ever smoked cigarettes and whether they smoke cigarettes now. Participants were considered to be current smokers if they responded ‘Yes’ to both questions. Participants were categorized as former smokers if they answered ‘Yes’ to ever smoking cigarettes and ‘No’ to the smoking cigarettes now. Never smokers included participants who answered ‘No’ to both questions. In addition, among current smokers, pack-years of smoking at initial visit and at each subsequent visit was calculated using participants’ responses to questions about the number of packs of cigarettes smoked per day. Stimulant/Recreational Drug Use—Participants were considered to be users of stimulant drugs if they reported the use of any of the following drugs at baseline and at each study visit: crack cocaine, other forms of cocaine, methamphetamines , other recreational drugs such as “ecstasy” or MDMA . Clinical Factors—HIV serostatus was assessed using an enzyme-linked immunosorbent assay with confirmatory Western blot tests on all MACS participants at each participant’s initial visit and at each study visit for participants who were initially HIV−. Standardized flow cytometry was used to quantify CD4 + T-lymphocyte subset levels by each MACS site and categorized as ≤200/mm3 , 201–500/mm3 , and >500/mm3 . Levels of plasma HIV RNA were measured using either the standard reverse transcription-polymerase chain reaction assay or with the Roche ultrasensitive assay were used to create a dichotomous variable to denote detectable versus undetectable viral load. Participants were classified at each MACS study visit as HCV positive if they were found to be in the process of seroconversion, acute infection, chronic infection, clearing , or previously HCV positive, but now clear of HCV RNA.
In addition to the aforementioned covariates, we considered that the trajectories of marijuana use over time among HIV+ participants may be influenced by factors specific to HIV-infection. ART use was dichotomized as use of any ART since the last study visit versus no therapy used. Attrition—Two binary variables were constructed and used as covariates to adjust for the effect of attrition: one for participants who dropped out or were lost to follow up and the other for those who died within the follow-up period .We used participant’s self-reported frequency of marijuana use across the follow-up period to identify trajectories using a semi-parametric group-based mixture model: PROC TRAJ SAS procedure. This approach sorts each participant’s frequency of marijuana use over their follow-up period into ‘clusters’ and estimates a single model—consisting of distinct trajectories. The procedure calculates the probability of each participant belonging to each trajectory group and assigns individuals into trajectories based on their highest probability of trajectory membership. Participants were followed from the time of enrollment until either the time of death, lost to follow up or until the end of the study period . We began by fitting a series of models with two to five trajectories by assuming linear, quadratic and cubic shape of the trajectory group curves. Several factors were considered in determining model fit and the optimal number of trajectory groups that best represented the heterogeneity of groups within the data: including, a priori knowledge from previous research on trajectories of marijuana use, model fit statistics including Bayesian information criterion, Akaike Information Criterion, average posterior probability of group membership, significance of the shape of the trajectory group curves , and size of the group membership . Model fitting was an iterative process, starting with a quadratic specification for the shape of the trajectory group curves and assessing whether an additional group resulted in a better model fit based on the aforementioned criteria.
We then estimated higher order shapes of the trajectory group curves and subsequently dropped non-significant terms. Models used a zeroinflated Poisson distribution to account for the large number of participants who reported not using marijuana. After the optimal number of trajectory groups and shape of trajectory change were selected, we included covariates of interest to the trajectory models. For this analysis, two types of covariates were considered: time-fixed/risk factors of trajectory group membership and time varying covariates. These time-fixed/risk factors comprise characteristics established before or at the time of the initial period of trajectories that may serve to predict membership in a given trajectory. Time-varying covariates measured during the course of the trajectory provide trajectory group-specific estimates of whether these covariates alter the course of the trajectory. One advantage of the PROC TRAJ software is that it allows for joint estimation of the parameters that describe the shape of the trajectory group curves,cannabis grow facility adjusted odds ratio and the coefficient estimates . We estimated models for all participants as well as by HIV serostatus. The analysis of all participants was adjusted for sociodemographic characteristics, depressive symptoms, substance use variables, hepatitis C infection status, attrition variables, and HIV serostatus. To account for potential differences in marijuana use by geographic region/site and MACS enrollment cohort, all models included variables for MACS center and enrollment cohort. In the analysis restricted to HIV+ participants, we included other clinical factors relevant to HIV+ status such as ART use, CD4 counts, and viral load detectability. All analysis was performed in SAS 9.4 . The 3658 participants in this study contributed a total of 105,595 person-visits; the median number of visits was 23 representing approximately 11 years . Among those who were HIV+ , the mean age at baseline visit was 35 years [standard deviation = 7.7], median number of visits was 23 , the majority were non-Hispanic whites and 24 % were non-Hispanic blacks . At baseline, among the HIV+ participants, marijuana use was high , 90 % used alcohol , 67 % reported stimulant/recreational drug use, 44 % were current smokers and 29 % were classified as having clinically significant depressive symptoms . At baseline, the HIV− participants in this study reported lower marijuana use , stimulant/recreational drug use , rates of current smoking and depressive symptoms than the HIV+ participants. Both groups were similar with regard to alcohol use . Marijuana Trajectories—Using data for the entire sample, participants’ self-reported frequency of marijuana use across the follow-up period identified four groups with distinct trajectories of marijuana use. We chose a four-group solution based on model parsimony, interpretability of trajectories, BIC and AIC values, significance of the polynomial growth terms, average posterior probabilities and trajectory group size membership . Model fit information and average posterior probabilities of all models are displayed in supplemental Tables 2 to 5. Figure 1 displays the trajectories of marijuana use of these four groups, which we labelled as: “Abstainer/Infrequent”, “Decreasers”, “Increasers” and “Chronic high” trajectory groups.
The abstainer or infrequent use group was characterized by a group of men who abstained from or infrequently used marijuana during the follow-up period. The decreaser group consisted of a group of men who reduced their marijuana use from nearly weekly use to infrequent use over the follow-up period. The increaser group comprised a group of men who initially decreased their marijuana use during the first 10 years of follow-up, after which they began to increase their use over time. The chronic high group represents a group of men who persistently used marijuana nearly daily over the follow-up period. Figure 2 displays trajectories of marijuana use among HIV+ participants: 61 % were in the abstainer/ infrequent use group, 14 % were in the decreaser group, 14 % in the increaser group, and 11 % in the chronic high group. Table 2 displays the baseline characteristics of the entire sample by the four identified trajectory groups. The median number of visits was lower among those in the increaser trajectory group. Participants in the abstainer/infrequent use group were older at baseline compared to the other groups. Frequency of marijuana use at baseline varied across the marijuana trajectory groups: as the proportion of daily users were < 1 % in the abstainer/ infrequent, 3 % in the decreasers, 10 % in the increasers, and 54 % in the chronic high groups. Racial status , detectable HIV viral load and CD4 counts were similar across the marijuana trajectory groups. This study utilized data from the MACS cohort to assess different patterns of marijuana use and to examine both risk factors and time-varying correlates associated with the different trajectories of marijuana use. Our analysis revealed that MSM in the MACS exhibited four distinct trajectories of marijuana use over time, including: abstainer/infrequent, decreasers, increasers and chronic high groups. Most of the men in this cohort displayed a pattern of abstaining or infrequent use over time whereas approximately 10 % who used daily or near daily at their index visit continued this pattern of use over their follow-up visits. About a quarter of the men changed their pattern of use over time, either decreasing or increasing use . Overall, our analysis suggested that these patterns of marijuana use over time were similar for both HIV+ and HIV− participants. In the analysis among all men, HIV+ status was associated with membership across all three trajectory groups reporting any marijuana use. Among HIV+ participants, having a detectable HIV RNA over time was associated with increasing marijuana use only among the men who increased their marijuana use during the follow-up period. Self-reported ART use over time in HIV+ men was associated with reducing marijuana use in the abstainer/ infrequent and increaser groups. Overall, alcohol consumption, cigarette, stimulant/ recreational drug use and IDU over time were associated with increasing marijuana use in nearly all trajectory groups. To the best of our knowledge, we are not aware of any previous study that has examined trajectories of marijuana use among HIV+ and HIV− MSM over a long period of follow-up. Prior studies that have assessed trajectories of marijuana use have focused on adolescents transitioning into young adulthood or racial/ethnic minorities, with a few studies reporting trajectories of use covering adulthood. Direct comparisons of the results from our study with prior research may not be straightforward due to the different populations studied and age periods covered. However, nearly all studies on trajectories of marijuana use have identified a group that abstained or used infrequently, with some identifying a chronic high user group and a few identifying groups that increased and decreased their use.