Abstract
Limited research has examined psychosocial factors that differ among cigarette users, marijuana users, and co-users and influence their cessation efforts. We examined: 1) sociodemographic, mental health, and other substance use in relation to user category; and 2) associations among these factors in relation to recent quit attempts and readiness to quit among single product versus co-users. We used a cross-sectional design to study college students aged 18–25 from seven Georgia campuses, focusing on the 721 reporting cigarette and/or marijuana use in the past 4 months (238 cigarette-only; 331 marijuana-only; 152 co-users). Multinomial logistic regression showed that correlates (p’s<.05) of cigarette-only versus co-use included attending public or technical colleges (vs. private) and not using little cigars/cigarillos (LCCs), e-cigarettes, and alcohol. Correlates of marijuana-only versus co-use included being Black or Hispanic (vs. White), not attending technical school, and not using LCCs and e-cigarettes. Importance was rated higher for quitting cigarettes versus marijuana, but confidence was rated lower for quitting cigarettes versus marijuana (p’s<.001). Co-users were more likely to report readiness to quit and quit attempts of cigarettes versus marijuana (p’s<.001). While 23.26% of marijuana-only and 15.13% of cigarette-only users reported readiness to quit, 41.18% of cigarette-only and 21.75% of marijuana-only users reported recent quit attempts (p’s<.001). Binary logistic regressions indicated distinct correlates of readiness to quit and quit attempts of cigarettes and marijuana. Cessation efforts of the respective products must attend to co-use with the other product to better understand relative perceptions of importance and confidence in quitting and actual cessation efforts.
Keywords: Young adults, Risk factors, Tobacco use, Marijuana use, Polysubstance use
1. INTRODUCTION
Tobacco use is the leading cause of preventable death in the US [1, 2]. While progress has been made in reducing smoking prevalence [3], cigarette smoking remains prevalent among college students [4]. Additionally, marijuana is the most commonly used federally illicit drug in the US [5]. As many states and municipalities have begun to legalize or decriminalize marijuana use [6], the health effects of marijuana use are still not well understood, particularly among youth and young adults [7–9].
In this context, the rates of marijuana and tobacco co-use have been increasing in the US [10–12]. One study showed that roughly 5% of the 2012 NSDUH sample reported past 30-day cigarette and marijuana co-use [13]. In this study, approximately 26% of cigarette smokers also reported past-month marijuana use, and over 70% of marijuana smokers also smoked cigarettes [13]. Tobacco and marijuana have a complementary and synergistic relationship, wherein the effects of one may reinforce and/or enhance the effects of the other, potentially through psychological and/or physiological mechanisms [14, 15]. Co-use and cessation within this context is especially important to study given that dual and concurrent use may increase the frequency of tobacco use [16], as well as risks of nicotine dependence [17] and marijuana dependence [17, 18].
While many studies of tobacco and marijuana use exist, there are several gaps in the literature. For example, there is limited research examining the sociodemographic, mental health, and other substance use profiles distinguishing cigarette users, marijuana users, or co-users of both products. One study of 3,021 adolescents and young adults found an association between early cigarette use initiation and later abuse of alcohol and tobacco, although early marijuana adoption did not show the same association [19]. Other studies have shown similar connections between cigarette use and future marijuana and drug use, lending plausibility to certain drug use pathways [20]. Not only have studies shown that there may be substance use pathways and connections between tobacco, marijuana, and alcohol, but also that the rates of co-use appear to be increasing in youth [11, 21].
Additionally, there have been a number of studies that have shown a positive relationship between increased depressive symptoms and both tobacco and marijuana prevalence and intensity [22]. Studies on tobacco use and depression show a strong and consistent connection [23, 24]. In addition, multiple studies have shown a link between marijuana use and psychotic or affective mental outcomes [25], particularly among younger adolescents. A 2003 study using results from the Child and Adolescent Survey of Mental Health found that marijuana and tobacco co-use was related to greater likelihood of depression diagnosis and psychiatric diagnosis in childhood [22]. In a recent study of college students, marijuana use was identified as a significant correlate of depression score among those that also smoked cigarettes [26].
A risk behavior associated with co-use of marijuana and cigarettes is the use of other substances, particularly alternative tobacco products (ATPs). The prevalence of ATP use has risen in recent years, and the perceptions of the perceived harm, addictiveness, and social unacceptability of such products has changed [27]. Additionally, many people utilize ATPs, particularly e-cigarettes, as a tool for quitting the use of cigarettes [28, 29]. However, some of these products, particularly little cigars/cigarillos (LCCs) and hookah, may be used in conjunction with marijuana [30–35].
Understanding cessation of tobacco and marijuana is essential. Some studies have shown that tobacco cessation is heavily influenced by the use of ATPs, marijuana, and alcohol. A 13-year follow up study of adults in Baltimore found that those who smoked marijuana at baseline were almost twice as likely to still be using tobacco at follow-up, even after adjusting for race, educational attainment, and marital status [36]. There have been a few studies that have indicated that, relative to marijuana-only users, co-users of marijuana and tobacco products have increased likelihood of cannabis use disorders, psychosocial problems, and poorer cannabis cessation outcomes [37]. However, limited research has examined factors related to marijuana cessation efforts or cessation of either substance in the context of co-users of both substances.
Additionally, a range of sociodemographic factors may predict use of tobacco and/or marijuana, as well as cessation-related behaviors. For example, men have higher substance use prevalence compared to women [38]. Additionally, while Blacks have lower cigarette smoking prevalence than Whites, [39] marijuana use prevalence is roughly equal or higher among Blacks than Whites [40, 41]. Moreover, substance use prevalence has been found to be higher among sexual minorities [42–44]. Furthermore, smoking prevalence has been shown to be higher among young adult college students attending technical colleges compared to those attending public or private colleges [45, 46]. These factors may also impact cessation efforts and should be considered in examining use, co-use, and cessation-related attitudes and behaviors.
Given the aforementioned research and the gaps in the literature, this study examined: 1) sociodemographic, mental health, and other substance use that may distinguish user categories (cigarette-only, marijuana-only, and co-use) among young adults; and 2) associations of these factors in relation to recent (past 4-month) quit attempts and readiness to quit in the next 30 days among single product users versus co-users.
2. MATERIALS AND METHODS
2.1. Participants and Procedures
Project DECOY (Documenting Experiences with Cigarettes and Other Tobacco in Young Adults) is a sequential mixed-methods longitudinal cohort study of 3,418 college students attending seven colleges and universities in Georgia [47]. Our overall study and reporting approaches were guided by the CHERRIES guidelines [48]. This study was approved by the Emory University and ICF International Institutional Review Boards as well as those of the participating colleges.
The study was initiated in 2014. Data were collected from seven Georgia colleges, including two public schools, two private schools, two community/technical colleges, and one historically black university. Detailed descriptions of the recruitment, sampling, and retention procedures can be found elsewhere [49], but described briefly here.
Eligible participants were between 18 and 25 years of age who were able to read English. A list of students was obtained from each institution’s office of the registrar. One public and two private universities had 3,000 students randomly selected from those that were eligible; the remaining colleges and universities had eligible student bodies that were smaller than 3,000, and therefore all eligible students were contacted for enrollment. The invitation emails described the study (longitudinal study with six assessments over two years) and the incentives for participating. If potential participants were interested, they clicked on a link embedded in the email, which launched them to the consent form. After reading the consent form, they had the option to consent by clicking a link which then launched the baseline (wave 1) survey. Recruitment at each school was closed after recruitment goals at each school were reached. Response rates ranged from 12.0% to 59.4%, with an overall response rate of 22.9% (N=3,574/15,607) observed within 72 hours at each school, meeting recruitment targets. A week after completion of the baseline survey, participants were asked to confirm their participation in the study via an emailed link and were provided their first gift card ($30). The response rate after confirmation was 95.6% (N=3,418/3,574). The baseline sample was largely representative of each school’s demographic profile, although respondents were disproportionately female.
Every four months, a survey link was sent to participants who received incentives of increasing value ($30 for the first two assessments, $40 for the second two, $50 for the final two). The analysis in this paper is based upon responses to Wave 4 (N=2,923), as this was the first wave of data collection in which we assessed readiness to quit using marijuana and marijuana cessation attempts. For inclusion in this analysis, students who reported either marijuana use or cigarette use in the last four months were selected from the Wave 4 sample. This resulted in a sample of 721 students.
2.2. Measures
2.2.1. Sociodemographics
We assessed age, sex, sexual orientation, race/ethnicity, and type of school attended.
2.2.2. Mental Health
Level of depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) questionnaire, with a range of 0–27, with higher scores being associated with more depressive symptoms. The scale has been validated in multiple studies [50], and alpha in the current study was 0.85.
2.2.3. Cigarette & Marijuana Use
To assess cigarette use, participants were asked, “During the past 4 months, on how many days did you smoke cigarettes?” Similarly, to assess marijuana use, participants were asked, “In the past 4 months, on how many days did you use marijuana?” Participants selected a value from a drop-down menu between zero (no use) and 120 (use every day). For marijuana use, participants had the option to “refuse.”
2.2.4. Other Substance Use
Survey items for use of little cigars/cigarillos (LCCs), smokeless tobacco (chew, snus, dip, etc.), e-cigarette, and hookah were described in binary form based upon any use reported in the previous four months. Additionally, past 30-day alcohol use and past 30-day binge drinking were reported as yes/no variables. Binge drinking was defined as drinking 5 or more drinks in a session for a male or 4 or more drinks for a female.
2.2.5. Importance and Confidence in Quitting
All respondents that smoked at least one cigarette in the past four months were asked, “On a scale of 0 to 10, how important is it that you quit using cigarettes (or not smoke if you don’t currently), with 0 being not at all important and 10 being absolutely important?” and “On a scale from 0 to 10, how confident are you that you could quit using cigarettes if you wanted to (or not smoke if you don’t currently), with 0 being not at all confident and 10 being absolutely confident?” Adaptations of these questions and this same approach was used for those reporting use of marijuana in the past 4 months.
2.2.6. Readiness to Quit
All participants that smoked cigarettes during the previous four months were asked, “Are you seriously thinking about quitting the use of cigarettes? Those that responded “Yes, within the next 30 days” were coded as being ready to quit. Those that were serious about quitting in less than six months, more than six months, or who were not considering quitting were coded as not ready to quit. An adaptation of this question and this same approach was used for those reporting use of marijuana in the past 4 months.
2.2.7. Quit Attempts
All respondents that used cigarettes in the past four months are asked, “How many times did you quit smoking for one day or longer because you were trying to quit cigarettes for good?” All those that tried to quit in the past four months were coded as having a quit attempt. The four-month variables were used to ensure that all respondents that had attempted to quit since the last wave was administered would be included in the analysis. An adaptation of this question and this same approach was used for those reporting use of marijuana in the past 4 months.
2.3. Data Analysis
All data cleaning and analysis were completed using SAS 9.4. For statistical tests, alpha was set at .05. Bivariate analyses were conducted to compare cigarette-only users to co-users and marijuana-only users to co-users, using Chi-squared tests for categorical variables and Student’s t-test for continuous variables. We then conducted a multinomial logistic regression were conducted to compare cigarette-only and marijuana-only users to co-users (as the referent). Then, among all cigarette users (including co-users), we conducted bivariate analyses and binary logistic regressions to examine correlates of readiness to quit and recent quit attempts, respectively. We conducted parallel analyses of these outcomes among all marijuana users. (Note that that we conducted sensitivity analyses to test if a) the number of predictors had impact on power and b) if collapsing of racial categories would have impact on estimates. Neither set of analyses yielded different findings.)
3. RESULTS
3.1. Participant Characteristics
Of the 721 students included in this analysis (Table 1), 54.09% (n=390) had smoked cigarettes and 66.99% (n=483) had smoked marijuana in the previous four months. Of note, 33.01% used only cigarettes in the past four months, 45.91% used only marijuana in the past four months, and 21.08% used both marijuana and cigarettes during the past four months.
Table 1.
Participant Characteristics and Bivariate Analyses Regarding Use Group Membership, N=721
| Total N=721 |
Co-use N=152 |
Cigarette- only use N=238 |
Co-use vs. cigarette-only use, p-value | Marijuana- only use N=331 |
Co-use vs. marijuana-only use, p-value | |
|---|---|---|---|---|---|---|
| Sociodemographics | ||||||
| Age (SD) | 21.60 (1.96) | 21.43 (1.94) | 21.94 (2.05) | 0.015 | 21.43 (1.88) | 0.965 |
| Sex (%) | ||||||
| Female | 409 (56.73) | 81 (53.29) | 124 (52.10) | 0.819 | 204 (61.63) | 0.083 |
| Male | 312 (43.27) | 71 (46.71) | 114 (47.90) | 127 (38.37) | ||
| Sexual Orientation (%) | ||||||
| Heterosexual | 629 (87.24) | 127 (83.55) | 208 (87.39) | 0.288 | 294 (88.82) | 0.108 |
| Other | 91 (12.76) | 25 (16.45) | 30 (12.61) | 37 (11.18) | ||
| Race/Ethnicity (%) | ||||||
| White | 407 (56.45) | 97 (63.82) | 152 (63.87) | 0.723 | 158 (47.73) | 0.003 |
| Black | 174 (24.13) | 27 (17.76) | 36 (15.13) | 111 (33.53) | ||
| Hispanic | 29 (4.02) | 4 (2.63) | 8 (3.36) | 17 (5.14) | ||
| Asian | 48 (6.66) | 9 (5.92) | 22 (9.24) | 17 (5.14) | ||
| Other | 63 (8.74) | 15 (9.87) | 20 (8.40) | 28 (8.46) | ||
| Type of School (%) | ||||||
| Private | 283 (39.25) | 68 (44.74) | 72 (30.25) | 0.001 | 143 (43.20) | 0.011 |
| Public | 197 (27.32) | 35 (23.03) | 69 (28.99) | 93 (28.10) | ||
| Technical college | 144 (19.97) | 30 (19.74) | 81 (34.03) | 33 (9.97) | ||
| HBCU | 97 (13.45) | 19 (12.50) | 16 (6.72) | 62 (18.73) | ||
| Depressive Symptoms (SD) | 6.44 (5.89) | 7.03 (6.02) | 6.50 (6.12) | 0.405 | 6.12 (5.66) | 0.115 |
| Frequency of Use (SD) | ||||||
| Days used cigarettes, past 4 mos | 32.46 (43.19) | 35.71 (45.33) | 0.489 | -- | -- | |
| Days used marijuana, past 4 mos | 40.77 (43.90) | -- | -- | 21.88 (36.02) | <0.001 | |
| Other Substance Use (%) | ||||||
| Little cigars/cigarillos | 185 (25.66) | 56 (36.84) | 50 (21.02) | 0.001 | 79 (23.87) | 0.003 |
| Smokeless tobacco | 47 (6.52) | 14 (9.21) | 22 (9.24) | 0.991 | 11 (3.32) | 0.007 |
| E-cigarettes | 83 (11.51) | 38 (25.00) | 28 (11.76) | 0.001 | 17 (5.14) | <0.001 |
| Hookah | 130 (18.03) | 42 (27.63) | 31 (13.03) | <0.001 | 57 (17.22) | 0.009 |
| Past 30-day alcohol use | 620 (86.11) | 142 (94.04) | 187 (78.57) | <0.001 | 291 (87.92) | 0.039 |
| Past 30-day binge drinking | 449 (62.27) | 111 (73.03) | 139 (58.40) | 0.003 | 199 (60.12) | 0.006 |
| Quit-Related Factors | ||||||
| Cigarettes | ||||||
| Importance of quitting (SD) | -- | 6.10 (3.84) | 6.53 (3.90) | 0.281 | -- | -- |
| Confidence in quitting (SD) | -- | 8.24 (2.76) | 8.28 (2.79) | 0.895 | -- | -- |
| Readiness to quit, next 30 days (%) | -- | 29 (19.08) | 36 (15.13) | 0.307 | -- | -- |
| Quit attempts, past 4 mos (%) | -- | 57 (37.50) | 98 (41.18) | 0.469 | -- | -- |
| Marijuana | ||||||
| Importance of quitting (SD) | -- | 3.30 (3.24) | -- | -- | 4.19 (3.64) | 0.01 |
| Confidence in quitting (SD) | -- | 9.13 (2.93) | -- | -- | 9.94 (2.37) | 0.003 |
| Readiness to quit, next 30 days (%) | -- | 15 (9.87) | -- | -- | 77 (23.26) | 0.001 |
| Quit attempts, past 4 mos (%) | -- | 33 (21.71) | -- | -- | 72 (21.75) | 0.992 |
Note: Comparisons between cigarette-only vs. co-use and between marijuana-only vs. co-use were conducted using Chi-squared tests for categorical variables and Student’s t-test for continuous variables.
Across user subgroups, importance of quitting cigarettes was rated as higher than of quitting marijuana on average, but confidence in quitting was rated lower on average for cigarettes versus marijuana (p’s<.001). Co-users were more likely to report readiness to quit and recent quit attempts of cigarettes versus marijuana (p’s<.001). While 23.26% of marijuana-only users and 15.13% of cigarette-only users reported readiness to quit, respectively (p<.001), 41.18% of cigarette-only users and 21.75% of marijuana-only users reported recent quit attempts (p<.001).
3.2. Use Group Membership
Bivariate analyses showed that correlates of cigarette-only use versus co-use included: being older (p=.015), attending a technical school (p=.001), and not using alternative tobacco products (p’s<.01) excluding smokeless tobacco. Correlates of marijuana-only use versus co-use included: being Black (p=.003), attending a public school or HBCU (p=.011), fewer days of marijuana use (p<.001), not using alternative tobacco products (p’s<.05), and reporting higher importance (p=.01) and confidence in quitting marijuana (p=.003) and greater likelihood of reporting being ready to quit marijuana (p=.001).
Multinomial logistic regression of group membership (not shown in tables) showed that correlates of cigarette-only use versus co-use included: attending a public (OR=2.09, 95% CI [1.17, 3.74]) or technical college (OR=2.85, CI [1.50, 5.40]) (vs. private school), and lower likelihood of using LCCs (OR=0.48, CI [0.26, 0.78]), e-cigarettes (OR=0.47, CI [0.24, 0.89]), and alcohol (OR=0.25, CI [0.10, 0.62]). Correlates of marijuana-only use versus co-use included: being Black (OR=4.65, CI [2.10, 10.26]) or Hispanic (OR=4.40, CI [1.13, 17.19]) (vs. White), not attending a technical school (OR=0.44, CI [0.22, 0.86]), and not using LCCs (OR=0.38, CI [0.22, 0.66]) and e-cigarettes (OR=0.20, CI [0.10, 0.40]).
3.3. Readiness to Quit Among Cigarette Smokers
In bivariate analyses, correlates of readiness to quit using cigarettes included: being older (p<.001), fewer days of cigarette use (p=.012), using LCCs (p=.025); and higher reported importance of quitting (p<.001; not shown in tables). In multivariate logistic regression analyses (Table 2), correlates of readiness to quit included: being older (p=.001), using LCCs (p=.019) and e-cigarettes (p=.029), and lower likelihood of use of hookah (p=.006).
Table 2.
Binary Logistic Regression Regarding Readiness to Quit and Quit Attempts Among Cigarette Smokers, N=390
| Variable | Readiness to Quit
|
Quit Attempts
|
||||||
|---|---|---|---|---|---|---|---|---|
| OR | 95% Lower | 95% Upper | p | OR | 95% Lower | 95% Upper | p | |
| Age | 1.306 | 1.119 | 1.523 | 0.001 | 1.026 | 0.916 | 1.150 | 0.656 |
| Male (ref Female) | 0.650 | 0.330 | 1.282 | 0.214 | 0.729 | 0.444 | 1.195 | 0.210 |
| Race (ref White) | ||||||||
| Black | 2.410 | 0.895 | 6.490 | 0.082 | 1.290 | 0.581 | 2.860 | 0.532 |
| Hispanic | 0.567 | 0.063 | 5.074 | 0.612 | 0.972 | 0.260 | 3.624 | 0.966 |
| Asian | 0.757 | 0.154 | 3.729 | 0.732 | 2.095 | 0.851 | 5.161 | 0.108 |
| Other | 1.548 | 0.574 | 4.177 | 0.388 | 1.580 | 0.751 | 3.326 | 0.228 |
| Sexual Orientation (ref Heterosexual) | ||||||||
| Sexual minority | 0.504 | 0.199 | 1.275 | 0.148 | 1.461 | 0.779 | 2.738 | 0.237 |
| School Type (ref Private) | ||||||||
| Public | 0.847 | 0.368 | 1.949 | 0.696 | 1.577 | 0.884 | 2.813 | 0.123 |
| Technical | 0.829 | 0.355 | 1.939 | 0.666 | 1.346 | 0.719 | 2.521 | 0.353 |
| HBCU | 0.563 | 0.145 | 2.185 | 0.407 | 0.811 | 0.274 | 2.397 | 0.705 |
| Depressive Symptoms | 0.988 | 0.938 | 1.041 | 0.653 | 1.005 | 0.969 | 1.042 | 0.783 |
| Substance Use (ref No use) | ||||||||
| Little cigars/cigarillos (past 4 mos) | 2.379 | 1.151 | 4.917 | 0.019 | 1.520 | 0.874 | 2.646 | 0.138 |
| Smokeless tobacco (past 4 mos) | 1.088 | 0.362 | 3.268 | 0.880 | 1.046 | 0.466 | 2.349 | 0.914 |
| E-cigarettes (past 4 mos) | 2.448 | 1.094 | 5.480 | 0.029 | 2.053 | 1.098 | 3.841 | 0.024 |
| Hookah (past 4 mos) | 0.245 | 0.091 | 0.662 | 0.006 | 0.839 | 0.446 | 1.579 | 0.587 |
| Alcohol in the past 30 days | 0.870 | 0.316 | 2.396 | 0.787 | 1.181 | 0.554 | 2.518 | 0.667 |
| Binge drinking in the past 30 days | 1.063 | 0.501 | 2.252 | 0.874 | 1.108 | 0.630 | 1.950 | 0.721 |
| Cigarette Use Characteristics | ||||||||
| Cigarette-only (ref Co-use) | 0.700 | 0.366 | 1.340 | 0.282 | 1.343 | 0.826 | 2.181 | 0.234 |
| Days of cigarette use | 1.006 | 0.999 | 1.013 | 0.083 | 1.000 | 0.995 | 1.005 | 0.955 |
3.4. Quit Attempts Among Cigarette Smokers
Bivariate analyses indicated that correlates of having made a recent cigarette quite attempt included: recent use of e-cigarettes (p=.016) and higher reported importance of quitting (p<.001; not shown in tables). Multivariable analyses (Table 2) indicated that the only correlate of recent quit attempts was reporting use of e-cigarettes (p=.024).
3.5. Readiness to Quit Among Marijuana Users
In bivariate analyses, correlates of readiness to quit using marijuana included: being Black (p=.035), attending a technical college or HBCU (p=.005), higher level of depressive symptoms (p=.004), not co-using with cigarettes (p=.001), and reporting higher importance in quitting (p<.001). In multivariate logistic regression analyses (Table 3), correlates of readiness to quit marijuana included: attending a technical college (p=.028) or HBCU (p=.030) (vs. private), higher levels of depressive symptoms (p<.001), and being a marijuana-only user versus a co-user (p<.001).
Table 3.
Binary Logistic Regression Regarding Readiness to Quit and Quit Attempts Among Marijuana Users, N=483
| Variable | Readiness to Quit
|
Quit Attempts
|
||||||
|---|---|---|---|---|---|---|---|---|
| OR | 95% Lower | 95% Upper | p | OR | 95% Lower | 95% Upper | p | |
| Age | 0.938 | 0.814 | 1.081 | 0.378 | 1.038 | 0.906 | 1.190 | 0.588 |
| Male (ref Female) | 1.493 | 0.824 | 2.707 | 0.187 | 1.281 | 0.717 | 2.289 | 0.403 |
| Race (ref White) | ||||||||
| Black | 1.375 | 0.641 | 2.947 | 0.413 | 3.081 | 1.527 | 6.219 | 0.002 |
| Hispanic | 1.350 | 0.392 | 4.648 | 0.635 | 2.113 | 0.641 | 6.959 | 0.219 |
| Asian | 0.549 | 0.113 | 2.674 | 0.458 | 0.397 | 0.048 | 3.255 | 0.389 |
| Other | 1.848 | 0.776 | 4.402 | 0.166 | 2.568 | 1.112 | 5.930 | 0.027 |
| Sexual Orientation (ref Heterosexual) | ||||||||
| Sexual minority | 0.553 | 0.231 | 1.321 | 0.182 | 1.177 | 0.574 | 2.415 | 0.656 |
| School Type (ref Private) | ||||||||
| Public | 1.396 | 0.725 | 2.686 | 0.318 | 1.976 | 1.035 | 3.772 | 0.039 |
| Technical | 2.614 | 1.110 | 6.157 | 0.028 | 3.156 | 1.420 | 7.017 | 0.005 |
| HBCU | 2.778 | 1.104 | 6.987 | 0.030 | 1.419 | 0.595 | 3.384 | 0.429 |
| Depressive Symptoms | 1.084 | 1.040 | 1.131 | <0.001 | 1.093 | 1.049 | 1.138 | <0.001 |
| Substance Use (ref No use) | ||||||||
| Little cigars/cigarillos (past 4 mos) | 0.909 | 0.475 | 1.741 | 0.775 | 1.951 | 1.091 | 3.489 | 0.024 |
| Smokeless tobacco (past 4 mos) | 2.654 | 0.836 | 8.423 | 0.098 | 1.952 | 0.626 | 6.081 | 0.249 |
| E-cigarettes (past 4 mos) | 0.486 | 0.155 | 1.530 | 0.218 | 1.130 | 0.466 | 2.741 | 0.787 |
| Hookah (past 4 mos) | 1.105 | 0.546 | 2.238 | 0.781 | 1.359 | 0.721 | 2.559 | 0.343 |
| Alcohol in the past 30 days | 0.816 | 0.330 | 2.016 | 0.660 | 1.278 | 0.529 | 3.092 | 0.586 |
| Binge drinking in the past 30 days | 1.261 | 0.688 | 2.313 | 0.454 | 0.737 | 0.420 | 1.293 | 0.288 |
| Marijuana Use Characteristics | ||||||||
| Marijuana-only (ref Co-use) | 3.107 | 1.519 | 6.356 | 0.002 | 1.136 | 0.625 | 2.065 | 0.675 |
| Days of marijuana use | 0.996 | 0.988 | 1.003 | 0.255 | 0.995 | 0.988 | 1.001 | 0.125 |
3.6. Quit Attempts Among Marijuana Users
Bivariate analyses indicated that correlates of having made a recent marijuana quite attempt included: being Black (p<.001), attending a public college, technical college, or HBCU (p<.001), higher levels of depressive symptoms (p<.001), using LCCs (p<.001) or hookah (p=.041), and reporting higher importance of quitting (p<.001) but lower confidence in quitting marijuana (p=.002). Multivariable analyses (Table 3) indicated that correlates of recent marijuana quit attempts included: being Black (p=.002) or other race (p=.027) versus White, attending a public (p=.039) or technical college (p=.005), higher levels of depressive symptoms (p<.001), and using LCCs (p=.024).
3.7. Correlation Analysis of Depression and Frequency of Use
Due to the findings in this study regarding the association between increased depression score and both readiness to quit marijuana and marijuana quit attempts, correlations were conducted to assess the associations between cigarette and marijuana use frequency and level of depressive symptoms. Days of cigarette smoking in the past four months was correlated with depression score [Pearson’s r=0.07, p=0.05], but days of marijuana use in the past four months was not correlated with depression score [Pearson’s r= 0.01, p=0.81].
4. DISCUSSION
The current study documented several novel findings. First, it is important to note the high rates of co-use in this sample of young adults (5.2% of our overall sample of 2,923), with more than a fifth of cigarette or marijuana users using both (38.97% of cigarette users; 31.47% of marijuana users). Our statistics largely parallel national findings that used a shorter time-window (30-day instead of 4-month); that is, 5% of the 2012 NSDUH sample reported past 30-day cigarette and marijuana co-use [13]. While our estimates of marijuana use among cigarette users aligns well with these national data, our prevalence of co-use among marijuana users is roughly half that of these national estimates [13]. This may be due to the young adult nature of our sample, which may reflect the increasingly positive perceptions of marijuana among young people [51, 52].
Regarding correlates of user group, co-users were more likely to use LCCs and e-cigarettes than both cigarette-only and marijuana-only users, highlighting the consistent trend in the literature regarding polysubstance use [30, 53–55]. Marijuana-only users versus co-users were more likely to be Black or Hispanic. This might reflect the overall lower cigarette use rates among these racial/ethnic subgroups compared to Whites [39] in the context of roughly equal or greater marijuana use rates, particularly among Blacks [40, 41]. This is particularly relevant given the increased social acceptability and increasing popularity of blunts (and hence LCCs) among Blacks [56, 57]. Those attending technical colleges were more likely to be cigarette-only users than marijuana-only users or co-users, which aligns with the literature [30, 45, 46].
In terms of quitting-related attitudes, importance of quitting cigarettes was rated as higher than quitting marijuana across user subgroups; moreover, the subgroups simultaneously reported lower confidence in their ability to quit smoking cigarettes. In addition, co-users were more likely to report readiness to quit and recent quit attempts of cigarettes versus marijuana. This might reflect the accumulating literature indicating that marijuana is perceived to be less risky in terms of health consequences and addiction and more socially acceptable [31, 51, 58], thus warranting less efforts toward cessation. Interestingly, almost a fourth of marijuana-only users reported readiness to quit marijuana and recent marijuana quit attempts, while among cigarette-only users, about 15% reported readiness to quit cigarettes and about 40% reported recent cigarette quit attempts. These differences in readiness to quit versus recent quit attempts among cigarette users is interesting to note. This may reflect some variability in terms of self-perception as a smoker, particularly among light or nondaily smokers, and thus the need to quit and differences in perceiving quit attempts [59, 60].
Regarding cigarette cessation-related factors, those ready to quit were older, more likely to use LCCs and e-cigarettes, and less likely to use hookah. The finding regarding age has been documented previously [61]. Additionally, the only correlate of recent quit attempts was use of e-cigarettes. Collectively, the findings regarding e-cigarette use may reflect the use of e-cigarettes to quit [62–64]. However, the findings regarding differential associations of LCC versus hookah use to readiness to quit are more difficult to interpret and require additional research.
In terms of marijuana cessation-related factors, those ready to quit marijuana were more likely to attend a technical college or HBCU, have higher levels of depressive symptoms, and use marijuana only. Those more likely to have made a recent marijuana quit attempt were more likely to be Black or other race versus White, attend a public or technical college, report higher levels of depressive symptoms, and use LCCs. The connection between higher depressive symptoms and readiness to quit marijuana is unusual, as many studies show an association between marijuana use and increased depressive symptoms [23, 26] and other psychotic or affective mental health outcomes [25]. Some studies have shown that adults with a recent major depressive episode had greater odds of quit attempts but a lower quit ratio [65]. The connection between higher depression score and both readiness to quit marijuana and quit attempts is unusual considering research that shows amotivational behavior among marijuana users [66]. The findings regarding race and type of school attended have not previously been documented to our knowledge but may suggest that certain cultures or environments may promote cessation of marijuana use. These findings warrant further research.
The current findings have implications for research and practice. In terms of research, further examination of cessation behaviors among co-users must be compared to single product users to determine how intervention efforts for co-users may need to be distinct. In addition, some of the cultural or environmental factors (e.g., race, college context) that might influence use and cessation should be further examined to identify opportunities for intervention Regarding practice, practitioners addressing quitting smoking cigarettes or marijuana should assess use of both substances and relative motivation and confidence in ability to quit in order to better intervene. Additionally, surveillance efforts should attempt to better assess co-use versus single product use in order to better understand trends in use and cessation over time.
Limitations to this study include limited generalizability due to its recruitment from colleges and universities in Georgia. An additional limitation is the relatively low response rate (22.9%) through the email recruitment method, as well as attrition over the course of the study. Possible selection and/or response bias may have affected results. However, the intent of this rapid recruitment method was to target participants more engaged with email and more likely to be retained throughout the duration of the longitudinal study.
5. CONCLUSIONS
In this study, importance of quitting cigarettes was rated as higher than of quitting marijuana, but participants reported lower confidence in quitting cigarette use. In addition, co-users were more likely to report readiness to quit and recent quit attempts of cigarettes versus marijuana. Interestingly, almost a fourth of marijuana-only users reported readiness to quit and recent quit attempts, while among cigarette-only users, about 15% reported readiness to quit and about 40% reported recent quit attempts. Moreover, psychosocial predictors of quit-related factors were found among cigarette users versus marijuana users, suggesting that different intervention targets might be needed to address cessation of cigarette use versus marijuana use and may need to be different depending on co-use status.
HIGHLIGHTS.
Importance of quitting cigarettes was higher than of quitting marijuana.
However, participants reported lower confidence in quitting cigarette use.
Co-users were more likely to be ready and attempt to quit cigarettes vs. marijuana.
Predictors of quit-related factors were found among cigarette vs. marijuana users.
Different cessation intervention targets may be needed in the context of co-use.
Acknowledgments
1. ROLE OF FUNDING SOURCES
This research was supported by the National Cancer Institute (1R01CA179422-01; PI: Berg). The funders had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
We would like to thank our Campus Advisory Board members across the state of Georgia in developing and assisting in administering this survey. We also would like to thank ICF Macro for their scientific input and technical support in conducting this research.
Footnotes
2. CONTRIBUTORS
MNM lead the overall analyses and writing of the manuscript. RH, MW, and CJB helped to conceptualize the paper, advised on analyses, and assisted with the final writing of the manuscript.
3. CONFLICTS OF INTERESTS
The authors declare no conflicts of interest.
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