Abstract
Objective:
Blunt smoking presents unique public health concerns relative to other methods of marijuana use, including greater exposure to toxins and carcinogens as well as increased risk for cannabis use disorder. This study examines correlates of self-reported daily blunt use among a nationally representative sample of adult blunt users in the United States.
Methods:
We pooled and analyzed five years of cross-sectional data from n=10,826 adult blunt smokers in the United States using the National Survey on Drug Use and Health (2014 to 2018). Multiple logistic regression analysis examined correlates of daily blunt use among non-Hispanic White, non-Hispanic African American, and Hispanic/Latino adult blunt users in the United States. Next, multiple logistic regression analysis stratified by race/ethnicity were conducted. This study examined: (1) socio-demographic (age, sex, income); (2) behavioral (alcohol, tobacco, and illicit drug use); (3) intrapersonal (depression); and (4) regulatory (marijuana laws) factors.
Results:
African Americans had the greatest prevalence of daily blunt use (24.2%), relative to Whites (9.1%) and Hispanic/Latinos (13.9%) (p<0.001). African Americans aged 26–34 years old (Adjusted Odds Ratio [Adj OR]: 1.37) and living in medical marijuana states (Adj OR: 1.28) were more likely to be daily blunt users; these factors were not associated with daily blunt use in the full sample or in stratified models of Whites or Hispanic/Latinos. Alcohol use was negatively associated with daily blunt use among Whites and Hispanic/Latinos but not African Americans.
Conclusions:
Socio-demographic, behavioral, and regulatory factors appear differently associated with daily blunt use across racial/ethnic groups.
Keywords: Marijuana, race, ethnicity, medical marijuana, Blunt Use
INTRODUCTION
National data reveals marijuana use among adults has more than tripled from 2001 (4.1%) (Hasin et al., 2015) to 2014 (12.9%) in the United States (Compton, Han, Hughes, Jones, & Blanco, 2017). Blunts (i.e. hollowed out cigars filled with marijuana) are among the most common methods to use marijuana (Schauer, Rosenberry, & Peters, 2017) and presents an elevated health risk relative to other combustible (e.g. pipes) and non-combustible (e.g. edibles) methods (Cooper & Haney, 2009; Fairman, 2015; Penetar et al., 2005; Peters, Budney, & Carroll, 2012; Timberlake, 2009, 2013). Specifically, blunt use has been linked to greater exposure to carcinogens and carbon monoxide (Cooper & Haney, 2009), increased risk for both nicotine and cannabis use disorder (Peters et al., 2012; Schauer et al., 2017), and other negative health outcomes (e.g., respiratory disease; heart disease) (National Academies of Sciences & Medicine, 2017; Schauer et al., 2017). Gaining a more in-depth understanding of adult blunt use is a public health imperative given the prevalence and long-term epidemiological concerns of this behavior.
While several studies provide a general understanding of adult blunt use behaviors (Cohn, Johnson, Ehlke, & Villanti, 2016; Montgomery & Mantey, 2017; Schauer et al., 2017), an under-researched topic is daily blunt use. This is a critical gap in the literature given that the proportion of daily/near-daily adult marijuana users increased from 18.0% in 2002 to 27.2% in 2017 (Compton, Han, Jones, & Blanco, 2019). Additionally, increased frequency of blunt use is indictive of dependence (Compton et al., 2019; Hasin et al., 2015; Peters, Schwartz, Wang, O’Grady, & Blanco, 2014), thus adults who report daily blunt use are more likely to need cessation treatment. Furthermore, the dose-response relationship between toxicant exposure and subsequent health complications (Blanco et al., 2016; Fried, James, & Watkinson, 2001; National Academies of Sciences & Medicine, 2017; Tetrault et al., 2007) suggests daily blunt users have an increased risk of negative health outcomes relative to non-daily blunt users. As such, research on daily blunt use may inform health interventions aimed at reducing marijuana use, particularly among those exhibiting symptoms of dependence.
Overall, the prevalence of marijuana use (via any method) does not statistically vary across race/ethnicity in the United States (Steigerwald et al., 2018). However, several studies have found racial/ethnic differences in method of marijuana use with blunt use being highest among African Americans (Cohn et al., 2016; Montgomery & Mantey, 2017) and increasing among Hispanic/Latinos (Schauer et al., 2017). Per the Social Ecological Model (SEM), behaviors are influences by a reciprocal relationship between multiple levels (i.e. individual, social, community, and environmental) of factors and determinants (Control & Prevention, 2019). Examples of individual-level factors linked to blunt use variations include age, income, and sex (Corey et al., 2018; Fairman, 2015; Montgomery & Mantey, 2017; Schauer et al., 2017). Furthermore, research has found that the relationship between these individual-level factors and blunt use differ by racial/ethnic group (Montgomery & Mantey, 2018; Montgomery & Mantey, 2017; Schauer et al., 2017).
While socially the perceived harms of marijuana use have declined substantially in the United States (Compton et al., 2017), the regulatory environment (e.g. increased legalization of medical and recreational marijuana) continues to evolve on the state-level (Compton et al., 2019). The combination of regulatory environment and social factors has likely impacted blunt use behaviors in the United States (Schauer, Berg, Kegler, Donovan, & Windle, 2015; Wang, Ramo, Lisha, & Cataldo, 2016). To date, limited research has explored factors associated with daily/near-daily blunt use across levels identified by the SEM (Timberlake, 2009, 2013). Therefore, understanding the individual, social, and environmental differences among daily blunt users as it relates to race/ethnicity may help to inform tailored and culturally appropriate interventions strategies aimed at reducing adult blunt use. Additionally, given the differences in the prevalence of blunt use across race/ethnic groups, extensive research is needed to determine if, and the extent of blunt use variance across these groups.
Study Aims & Hypotheses
Guided by the SEM, this study has two complementary aims. First, this study aims to examine socio-demographic, behavioral, and regulatory correlates of daily blunt use among a nationally representative sample of adult blunt users from 2014 to 2018. Second, this study aims to examine socio-demographic, behavioral, and regulatory correlates of daily blunt use across racial/ethnic groups. Specifically, this study will examine correlates of daily blunt use among: (1) non-Hispanic, Whites; (2) Hispanic/Latinos; and (3) non-Hispanic, African Americans. For the purposes of this study, these groups are mutually exclusive.
METHODS
Study Sample
This study pools five years (2014 to 2018) of cross-sectional data collected from the National Survey on Drug Use and Health (NSDUH). The NSDUH is an annual survey that utilizes multi-state area probability sampling methods to select a nationally representative sample of individuals aged 12 and older in the United States.
Inclusion criteria for this study were: 18 years of age or older, self-reporting any blunt use in the past 30-days (i.e. current use), and having complete data on all study variables. Given that other studies have demonstrated sociodemographic differences and the public health impact of blunt use overall relative to non-use of blunts(Cohn et al., 2016; Montgomery & Mantey, 2017; Montgomery, Mantey, Peters, Herrmann, & Winhusen, 2020), this study was designed to extend this research by examining differences in the frequency of blunt use among blunt smokers. To achieve the aim of assessing differences among daily versus non-daily blunt users, non-blunt marijuana users were excluded.
Additionally, this study only examined individuals who identified as non-Hispanic White, non-Hispanic African American, and Hispanic/Latino (regardless of identified race). This study elected to exclude non-Hispanic/Latino individuals who identified as any other race/ethnicity. Given the documented prevalence of blunt use among non-Hispanic White, non-Hispanic African-American and Hispanic/Latino adults (Cohn et al., 2016; Schauer et al., 2015; Schauer et al., 2017), this study assessed the impact of daily blunt use among these racial/ethnic groups to provide a deeper understanding of blunt use in these subgroups.
These criteria resulted in a final sample of n=10,826 and weight sample of N=38,879,645 adult blunt smokers in the United States from 2014 – 2018. Stratified samples resulted in [n=5,781; N=20,302,169] non-Hispanic White, [n=2,053; N=6,795,163] Hispanic/Latino, and [n=2,992; N=11,782,313] non-Hispanic African American adult blunt smokers in the United States from 2014–2018.
We elected to omit other race/ethnic groups from this study for two, complementary reasons. First, the sample size for the excluded race/ethnic groups were too small to generate stable and robust estimates. Second, the diversity and heterogeneity within and across these groups prohibits collapsing individuals within these groups into a single binary “other” category (Burlew et al., 2011).
Measures
Blunt Use:
This research focuses on daily blunt use among adults who self-reported smoking marijuana blunts in the past 30-day (i.e. current blunt users). Current blunt users were asked to self-report the number of days they smoked a blunt in the past 30-days. Participants who self-reported smoking blunts on all 30 days were considered “daily blunt users” while those who reported 1 to 29 days of blunt use were considered “non-daily blunt users.” Participants were advised on the definition of ‘blunt’ using the following phrasing “Sometimes people take some tobacco out of a cigar and replace it with marijuana. This is sometimes called a ‘blunt.’ Socio-Demographic Variables. This study explores several socio-demographic variables. First, race/ethnicity which was categorized into these mutually exclusive groups: non-Hispanic Whites (referent group), Hispanic/Latinos, and non-Hispanic, African Americans. Race/ethnicity was also used in the stratified analysis for the second hypothesis in this study.
This study also examined age, annual income, sex, and educational attainment. Age was categorized into the following groups: 18 to 25 years old (referent group); 26 to 34 years old; 35 to 49 years old; and 50+ years old. Annual income was categorized into the following groups: $75,000 or more (referent); $50,000 – $74,999; $20,000 – $49,999; and less than $20,000 per year. Sex is a binary variable; males were coded as the referent group. Educational attainment was categorized into the following groups: college graduate or higher (referent); some college/vocational training; high school (or equivalent) graduate; less than high school.
Behavioral Variables.
This study examined a number of behavioral factors associated with marijuana blunt use. First, we explored alcohol use behaviors. Self-reported past 30-day alcohol use for each participant was categorizing into four mutually exclusive groups: no alcohol use (referent); light alcohol use; binge alcohol use; and heavy alcohol use. For this study, binge alcohol use was defined as drinking 5 or more drinks on the same occasion on one to four days, heavy alcohol use was defined as drinking 5 or more drinks on the same occasion on five or more days, and light alcohol use was defined as any alcohol use in the past 30-days that did not meet the threshold for binge or heavy alcohol use.
This study also examined past 30-day cigarette smoking; non-smokers were coded as the referent group. Similarly, past 30-day use of illicit drugs other than marijuana was included in this study. Participants were asked to self-report any use of hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, and/or sedatives in the past 30-days; non-users were coded as the referent group.
This study also examined self-reported history of a major depressive episode (MDE) in the past 12-months. Participants were classed as having a MDE in the past 12-months if they: self-reported feeling depressed and/or loss of interest or pleasure in daily activities for 2 weeks or longer, while also reporting any other symptoms of MDE (i.e., changes in appetite or weight, sleep problems, other notices that respondent was restless or lethargic, feeling tired or having low energy nearly every day, feeling worthless nearly every day, inability to concentrate or make decisions, or any thoughts or plans of suicide) .This guideline for coding MDE was based on reported symptoms of at least 5 out of the 9 Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria (Castillo et al., 2007; Kessler et al., 2004).
Regulatory Variables.
The status of state-level medical marijuana laws for each participant was included in this study. This variable was imputed by the NSDUH and corresponds to the respondent’s date of interview, the state of residency and an external source containing the most recent list of states with medical marijuana laws along with the exact date in which the law or ballot initiative was approved by voters. The entire sample was classified into one of three categories, including “in state where medical marijuana law passed before interview”, “not in state where medical marijuana law passed” or “in state where medical marijuana law passed after interview.” Participants who were categorized as living in a state where medical marijuana laws passed after the interview were reclassified as “not in state where medical marijuana law passed” for the purposes of this study.
Data Analysis.
First, bivariate analyses (i.e., chi-square tests) were conducted to examine differences in daily blunt use prevalence across each study variable. Second, we conducted a weighted, multiple logistic regression model on the full sample of adult marijuana users. This model included all study variables. Lastly, we conducted weighted, multiple logistic regression models stratified by race/ethnicity i.e. weighted, multiple logistic regression models were conducted for non-Hispanic, Whites; (2) Hispanic/Latinos; and (3) non-Hispanic, African Americans. Year of survey was also included in each statistical model in order to control for the random intercept of this variable. This technique results in a multi-level model that accounts for the variability within each study variables between each survey year (e.g., population level changes in prevalence of daily blunt use). Data were weighted to be representative of adult blunt users who identified as non-Hispanic White, non-Hispanic African American, or Hispanic/Latino from 2014 to 2018. All analyses were conducted using STATA 14.2 (College Station, TX).
RESULTS
Descriptive Statistics
Overall, 15.3% of adult blunt users reported smoking blunt daily from 2014 to 2018. Non-Hispanic African American blunt users had the highest prevalence of daily use (25.2%), followed by Hispanic/Latinos (15.0%) and non-Hispanic Whites (9.7%) (p<0.001). There was no statistical change in daily blunt use among adult blunt users over time (p=0.394). Additional descriptive statistics are available for the full sample in Table 1 and by race/ethnicity in Table 2.
Table 1:
Descriptive Statistics of Adult Blunt Smokers in the United States from 2014–2018 (n=10,826; N=38,879,645)
| Full Sample Percent (95% CI) | Not Daily Blunt Usere Percent (95% CI) | Daily Blunt Userf Percent (95% CI) | |
|---|---|---|---|
| Percent of Sample | 100% | 84.7% (83.7 – 85.6) | 15.3% (14.4 – 16.3) |
| Race/Ethnicity | p <0.001 | ||
| Non-Hispanic, White | 52.2% (51.0 – 53.5) | 9.7% (8.7 – 10.7) | |
| Hispanic/Latino | 30.3% (29.0 – 31.6) | 15.0% (12.9 – 17.4) | |
| Non-Hispanic, African American | 17.5% (16.6 – 18.4) | 25.2% (23.2 – 27.3) | |
| Sex | p = 0.408 | ||
| Male | 62.8% (61.3 – 64.2) | 15.6% (14.4 – 16.8) | |
| Female | 37.2% (35.8 – 38.7) | 14.8% (13.5 – 16.3) | |
| Age Level | p = 0.002 | ||
| 18–25 years old | 49.4% (48.0 – 50.7) | 13.5% (12.4 – 14.7) | |
| 26 – 34 years old | 27.4% (26.3 – 28.6) | 18.3% (16.4 – 20.3) | |
| 35 – 49 years old | 17.3% (16.4 – 18.3) | 16.8% (14.6 – 19.2) | |
| 50+ years old | 5.9% (5.0 – 6.9) | 12.5% (8.3 – 18.5) | |
| Annual Income | p <0.001 | ||
| Less than $20,000 | 30.1% (28.9 – 31.3) | 17.8% (16.2 – 19.5) | |
| $20,000 – 49,999 | 35.6% (34.5 – 36.7) | 16.5% (15.0 – 18.0) | |
| $50,000 – 74,999 | 13.8% (12.9 – 14.7) | 12.5% (10.7 – 14.7) | |
| $75,000+ | 20.5% (19.5 – 21.7) | 11.5% (9.6 – 13.7) | |
| Education Level | p <0.001 | ||
| Less than HS | 16.8% (16.0 – 17.7) | 21.4% (18.8 – 24.3) | |
| HS Graduate | 33.0% (31.9 – 34.2) | 18.4% (16.7 – 20.2) | |
| Some College | 39.6% (38.4 – 40.8) | 12.1% (11.0 – 13.4) | |
| College Graduate | 10.5% (9.6 – 11.6) | 7.8% (5.8 – 10.5) | |
| Legal Medical Mariiuana a | p = 0.006 | ||
| No | 55.9% (54.5 – 57.2) | 13.9% (12.5 – 15.4) | |
| Yes | 44.1% (42.8 – 45.5) | 17.1% (15.7 – 18.5) | |
| Depression (MDE) | p = 0.030 | ||
| No | 92.2% (91.6 – 92.8) | 15.7% (14.7 – 16.7) | |
| Yes | 7.8% (7.2 – 8.4) | 11.1% (8.2 – 14.9) | |
| Cigarette Smoker b | p <0.001 | ||
| No | 41.4% (40.0 – 42.9) | 13.1% (11.8 – 14.5) | |
| Yes | 58.6% (57.1 – 60.0) | 16.9% (15.6 – 18.3) | |
| Alcohol Use c | p <0.001 | ||
| None | 19.0 (17.9 – 20.2) | 20.2% (18.2 – 22.5) | |
| Light Drinking | 20.5% (19.3 – 21.8) | 17.4% (15.5 – 19.5) | |
| Binge Drinking | 39.1% (37.8 – 40.4) | 13.5% (12.5 – 14.7) | |
| Heavy Drinking | 21.4% (20.4 – 22.3) | 12.1% (10.3 – 14.2) | |
| Illicit Drug Use d | p = 0.062 | ||
| No | 75.8% (74.6 – 76.9) | 15.8% (14.8 – 16.8) | |
| Yes | 24.2% (23.1 – 25.4) | 13.9% (12.2 – 15.8) | |
| Year | p = 0.394 | ||
| 2014 | 19.0% (17.9 – 20.1) | 16.0% (14.2 – 18.0) | |
| 2015 | 18.5% (17.5 – 19.5) | 15.7% (14.0 – 17.5)) | |
| 2016 | 19.9% (19.0 – 20.9) | 15.7% (13.4 – 18.3) | |
| 2017 | 20.3% (19.2 – 21.6) | 13.7% (12.3 – 15.2) | |
| 2018 | 22.3% (20.9 – 23.8) | 15.5% (13.8 – 17.4) | |
Lives in a state where medical marijuana is legally recognized.
Self-reported cigarette smoking in the past 30-days
“Light Drinking” is defined as using alcohol but not binge drinking in the past 30 days; “Binge Drinking” is defined as drinking 5 or more drinks on the same occasion on at least 1 but not more than 4 days in the past 30 days; and “Heavy Drinking” is binge drinking more than 5 times in the past 30 days
Illicit Drug Use” indicates ever use of hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives.
Self-reported blunt smoking on 1 to 29 of the past 30-days
Self-reported blunt smoking on all 30 of the past 30-days
Table 2:
Descriptive Statistics of Subsample Daily Blunt Use by Race/Ethnicity (n=10,826; N=38,879,645)
| Non-Hispanic, White (n=5,781; N=20,302,169) | Hispanic/Latino (n=2,053; N=6,795,163) | Non-Hispanic, African American (n=2,992; N=11,782,313) | |
|---|---|---|---|
| Daily Blunt Use | 9.7% (8.7 – 10.7) | 25.2% (23.2 – 27.3) | 25.2% (23.2 – 27.3) |
| Sex | p = 0.926 | p = 0.292 | p = 0.579 |
| Male | 9.6% (8.3 – 11.1) | 16.0% (13.3 – 19.1) | 25.7% (22.9 – 28.7) |
| Female | 9.7% (8.4 – 11.2) | 13.3% (9.9 – 17.7) | 24.5% (21.7 – 27.5) |
| Age Level | p = 0.722 | p = 0.056 | p = 0.002 |
| 18–25 years old | 9.2% (8.2 – 10.2) | 12.9% (10.6 – 15.6) | 24.6% (21.7 – 27.7) |
| 26 – 34 years old | 9.7% (7.6 – 12.3) | 18.7% (14.5 – 23.8) | 31.4% (27.6 – 35.4) |
| 35 – 49 years old | 11.2% (8.4 – 14.8) | 18.8% (10.8 – 30.7) | 22.6% (18.6 – 27.1) |
| 50+ years old | 10.1% (4.3 – 22.0) | 5.0% (1.3 – 17.2) | 15.7% (9.4 – 25.2) |
| Annual Income | p = 0.394 | p = 0.031 | p = 0.539 |
| Less than $20,000 | 11.0% (8.8 – 13.6) | 17.0% (12.8 – 22.2) | 26.7% (24.2 – 29.2) |
| $20,000 – 49,999 | 10.5% (8.7 – 12.5) | 17.5% (14.0 – 21.7) | 25.3% (22.1 – 28.8) |
| $50,000 – 74,999 | 8.0% (6.0 – 10.5) | 10.8% (7.6 – 15.1) | 23.8% (18.8 – 29.7) |
| $75,000+ | 8.1% (6.2 – 10.5) | 10.1% (6.2 – 15.9) | 22.5% (16.7 – 29.7) |
| Education Level | p <0.001 | p = 0.019 | p = 0.002 |
| Less than HS | 15.6% (12.8 – 19.0) | 21.2% (15.4 – 28.5) | 29.7% (24.5 – 35.5) |
| HS Graduate | 13.0% (11.1 – 15.2) | 14.8% (11.2 – 19.1) | 28.5% (25.0 – 32.2) |
| Some College | 6.7% (5.4 – 8.3) | 13.3% (10.8 – 16.3) | 21.7% (18.9 – 24.8) |
| College Graduate | 3.8% (2.1 – 7.1) | 8.6% (4.4 – 16.2) | 17.3% (12.1 – 24.2) |
| Legal Medical Mariiuana a | p = 0.305 | p = 0.915 | p = 0.014 |
| No | 9.1% (7.7 – 10.7) | 14.9% (12.3 – 18.0) | 22.7% (19.6 – 26.0) |
| Yes | 10.4% (8.7 – 12.3) | 15.2% (11.2 – 20.3) | 27.5% (25.2 – 30.0) |
| Depression (MDE) | p = 0.301 | p = 0.344 | p = 0.309 |
| No | 9.9% (8.8 – 11.1) | 15.2% (12.9 – 17.9) | 25.5% (23.3 – 27.8) |
| Yes | 7.4% (4.2 – 12.6) | 12.0% (7.3 – 19.0) | 21.3% (15.1 – 29.1) |
| Cigarette Smoker b | p <0.001 | p = 0.007 | p <0.001 |
| No | 7.4% (6.2 – 8.8) | 12.5% (9.6 – 16.1) | 21.1% (18.3 – 24.3) |
| Yes | 10.9% (9.7 – 12.3) | 17.5% (15.4 – 20.0) | 28.7% (25.9 – 31.7) |
| Alcohol Use c | p <0.001 | p = 0.027 | p = 0.167 |
| None | 15.9% (13.4 – 18.7) | 21.7% (16.1 – 28.5) | 27.0% (22.0 – 32.8) |
| Light Drinking | 8.7% (6.6 – 11.4) | 14.8% (11.2 – 19.3) | 27.8% (23.8 – 32.1) |
| Binge Drinking | 8.2% (6.7 – 10.0) | 13.4% (10.8 – 16.5) | 22.5% (20.0 – 25.1) |
| Heavy Drinking | 7.9% (6.4 – 9.7) | 12.7% (8.4 – 18.7) | 25.4% (20.4 – 31.2) |
| Illicit Drug Use d | p = 0.604 | p = 0.962 | p = 0.310 |
| No | 9.5% (8.4 – 10.8) | 15.0% (12.6 – 17.9) | 24.7% (22.5 – 27.1) |
| Yes | 10.0% (8.4 – 11.9) | 14.9% (11.9 –18.6) | 28.3% (22.3 – 35.2) |
| Year | p = 0.394 | p = 0.205 | p = 0.699 |
| 2014 | 8.6% (6.4 – 11.5) | 19.2% (13.7 – 26.3) | 27.1% (22.6 – 32.1) |
| 2015 | 9.2% (7.2 – 11.7) | 14.4% (10.2 – 19.9) | 26.9% (22.3 – 32.1) |
| 2016 | 10.3% (7.4 – 14.2) | 17.0% (12.4 – 23.0) | 24.2% (20.8 – 27.9) |
| 2017 | 8.6% (7.2 – 10.2) | 12.7% (9.9 – 16.2) | 23.6% (19.8 – 28.0) |
| 2018 | 11.4% (9.1 – 14.1) | 12.7% (9.2 – 17.3) | 24.5% (20.2 – 29.4) |
Lives in a state where medical marijuana is legally recognized.
Self-reported cigarette smoking in the past 30-days
“Light Drinking” is defined as using alcohol but not binge drinking in the past 30 days; “Binge Drinking” is defined as drinking 5 or more drinks on the same occasion on at least 1 but not more than 4 days in the past 30 days; and “Heavy Drinking” is binge drinking more than 5 times in the past 30 days
Illicit Drug Use” indicates ever use of hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives.
Self-reported blunt smoking on 1 to 29 of the past 30-days
Self-reported blunt smoking on all 30 of the past 30-days
Full Study Sample
Among the full sample of adult blunt users (Table 3), non-Hispanic African Americans (Adj OR: 3.09; 95% CI: 2.55 – 3.74) and Hispanic/Latinos (Adj OR: 1.70; 95% CI: 1.37 – 2.10) were more likely to be daily blunt users, relative to non-Hispanic Whites. Similarly, individuals aged 26–34 had significantly greater rates of daily blunt use, relative to individuals aged 18–25 (Adj OR: 1.29; 95% CI: 1.11 – 1.50). Education was inversely associated with daily blunt use as individuals with some college (Adj OR: 1.48; 95% CI: 1.09 – 2.00), high school diploma or GED (Adj OR: 2.17; 95% CI: 1.53 – 3.08), and less than high school diploma (Adj OR: 2.54; 95% CI: 1.75 – 3.68) had greater odds of daily blunt use than individuals with a college degree or higher. MDE in the past 12-months (Adj OR: 1.65; 95% CI: 1.30 – 2.09) and past 30-day cigarette smoking (Adj OR: 1.40; 95% CI: 1.18 – 1.66) were also associated with greater odds of daily blunt use. Conversely, binge drinking (Adj OR: 0.65; 95% CI: 0.55 – 0.77) and heavy drinking (Adj OR: 0.65; 95% CI: 0.52 – 0.80) were associated with lower odds of daily blunt use.
Table 3:
Correlates of Daily Blunt Use Among Adult Blunt Users in the United States
| Full Sample (n=10,826; N=38,879,645) | |
|---|---|
| Adj OR 95% Confidence Intervals | |
| Race/Ethnicity | |
| Non-Hispanic, White | 1.00 (Referent) |
| Hispanic/Latino | 1.70 *** (1.37 – 2.11) |
| Non-Hispanic, African American | 3.13 *** (2.59 – 3.78) |
| Sex | |
| Male | 1.00 (Referent) |
| Female | 1.01 (0.97 – 1.17) |
| Age Level | |
| 18–25 years old | 1.00 (Referent) |
| 26 – 34 years old | 1.31 ** (1.12 – 1.52) |
| 35 – 49 years old | 1.02 (0.83 – 1.25) |
| 50+ years old | 0.61 (0.36 – 1.03) |
| Annual Income | |
| Less than $20,000 | 1.00 (Referent) |
| $20,000 – 49,999 | 0.94 (0.69 – 1.29) |
| $50,000 – 74,999 | 1.10 (0.88 – 1.36) |
| $75,000+ | 1.10 (0.88 – 1.17) |
| Education Level | |
| Less than HS | 1.00 (Referent |
| HS Graduate | 1.46 * (1.07 – 1.98) |
| Some College | 2.19 *** (1.54 – 3.10) |
| College Graduate | 2.53 *** (1.74 – 3.67) |
| Legal Medical Marijuana a | |
| No | 1.00 (Referent |
| Yes | 1.16 (0.97 – 1.39) |
| Depression (MDE) | |
| No | 1.00 (Referent |
| Yes | 0.78 (0.54 – 1.15) |
| Cigarette Smoker b | |
| No | 1.00 (Referent |
| Yes | 1.39 *** (1.18 – 1.64) |
| Alcohol Use c | |
| None | 1.00 (Referent |
| Light Drinking | 0.84 (0.71 – 1.01) |
| Binge Drinking | 0.66 *** (0.56 – 0.78) |
| Heavy Drinking | 0.65 *** (0.53 – 0.81) |
| Illicit Drug Use d | |
| No | 1.00 (Referent |
| Yes | 1.11 (0.84 – 1.31) |
| Year | |
| 2014 | 1.00 (Referent |
| 2015 | 0.98 (0.81 – 1.17) |
| 2016 | 1.03 (0.83 – 1.26) |
| 2017 | 0.91 (0.74 – 1.13) |
| 2018 | 1.06 (0.85 – 1.31) |
p < .05;
p < .01;
p < .0001
Lives in a state where medical marijuana is legally recognized.
Self-reported cigarette smoking in the past 30-days
“Light Drinking” is defined as using alcohol but not binge drinking in the past 30 days; “Binge Drinking” is defined as drinking 5 or more drinks on the same occasion on at least 1 but not more than 4 days in the past 30 days; and “Heavy Drinking” is binge drinking more than 5 times in the past 30 days
Illicit Drug Use” indicates ever use of hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives.
Self-reported blunt smoking on 1 to 29 of the past 30-days
Self-reported blunt smoking on all 30 of the past 30-days
Stratified Analyses
Among non-Hispanic Whites, individuals with high school diploma or GED (Adj OR: 3.19; 95% CI: 1.58 – 6.43), and less than high school diploma (Adj OR: 3.71; 95% CI: 1.82 – 7.58) had greater odds of daily blunt use than individuals with a college degree or higher. Similarly, experiencing a MDE in the past 12-months (Adj OR: 1.75; 95% CI: 1.18 – 2.58) and past 30-day cigarette smoking (Adj OR: 1.30; 95% CI: 1.04 – 1.62) were also associated with greater odds of daily blunt use. Conversely, alcohol use was inversely associated with daily blunt use: light drinkers (Adj OR: 0.62; 95% CI: 0.42 – 0.91), binge drinkers (Adj OR: 0.59; 95% CI: 0.42 – 0.83), and heavy drinkers (Adj OR: 0.62; 95% CI: 0.43 – 0.80) had lower odds of daily blunt use, relative to non-drinkers.
Among non-Hispanic African Americans, odds of daily blunt use were greater among individuals aged 26 to 34 (Adj OR: 1.37; 95% CI: 1.07 – 1.75) and lower among individuals aged 50 or older (Adj OR: 0.48; 95% CI: 0.26 – 0.89), relative to individuals aged 18 to 25. Individuals with a high school diploma or GED (Adj OR: 1.84; 95% CI: 1.17 – 2.89), and less than high school diploma (Adj OR: 1.96; 95% CI: 1.10 – 3.51) had greater odds of daily blunt use than individuals with a college degree or higher. Experiencing a MDE (Adj OR: 1.55; 95% CI: 1.06 – 2.29) and past 30-day cigarette smoking (Adj OR: 1.43; 95% CI: 1.15 – 1.79) had greater odds of daily blunt use. Additionally, living in a medical marijuana state was associated with greater odds of daily blunt use (Adj OR: 1.28; 95% CI: 1.05 – 1.56) among non-Hispanic African Americans.
Among Hispanic/Latinos, individuals with less than a high school diploma (Adj OR: 2.17 – 1.13) and past 30-day cigarette smokers (Adj OR: 1.46 – 95% CI: 1.06 – 2.00) had greater odds of daily blunt use. Conversely, binge drinkers (Adj OR: 0.59; 95% CI: 0.39 – 0.90) and heavy drinkers (Adj OR: 0.52; 95% CI: 0.28 – 0.97) had lower odds of daily blunt use, relative to non-drinkers. Details of analyses stratified by race/ethnicity are available in Table 4.
Table 4:
Correlates of Daily Blunt Use Among Adult Blunt Users in the United States, Stratified by Race/Ethnicity
| Non-Hispanic White (n=5,781; N=20,302,169) | Hispanic/Latino (n=2,053; N=6,795,163) | Non-Hispanic, African American (n=2,992; N=11,782,313) | |
|---|---|---|---|
| Adj OR 95% Confidence Interval | Adj OR 95% Confidence Interval | Adj OR 95% Confidence Interval | |
| Sex | |||
| Male | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| Female | 1.07 (0.83 – 1.37) | 0.90 (0.61 – 1.33) | 1.00 (0.80 – 1.24) |
| Age Level | |||
| 18–25 years old | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| 26 – 34 years old | 1.07 (0.81 – 1.41) | 1.46 (0.98 – 2.18) | 1.38 * (1.08 – 1.77) |
| 35 – 49 years old | 1.06 (0.78 – 1.78) | 1.60 (0.81 – 3.14) | 0.84 (0.62 – 1.16) |
| 50+ years old | 0.94 (0.36 – 2.44) | 0.28 (0.06 – 1.41) | 0.50 * (0.27 – 0.92) |
| Annual Income | |||
| Less than $20,000 | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| $20,000 – 49,999 | 0.92 (0.55 – 1.51) | 0.98 (0.46 – 2.06) | 1.00 (0.59 – 1.70) |
| $50,000 – 74,999 | 1.07 (0.76 – 1.51) | 1.67 (0.92 – 3.03) | 0.95 (0.65 – 1.40) |
| $75,000+ | 1.16 (0.79 – 1.70) | 1.41 (0.75 – 2.64) | 1.00 (0.68 – 1.47) |
| Education Level | |||
| Less than HS | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| HS Graduate | 1.65 (0.89 – 3.08) | 1.37 (0.69 – 2.70) | 1.28 (0.80 – 2.05) |
| Some College | 3.20 ** (1.59 – 6.46) | 1.51 (0.70 – 3.26) | 1.84 * (1.17 – 2.90) |
| College Graduate | 3.66 ** (1.77 – 7.55) | 2.24 * (1.16 – 4.30) | 1.95 * (1.10 – 3.48) |
| Legal Medical Marijuana a | |||
| No | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| Yes | 1.11 (0.84 – 1.47) | 0.98 (0.63 – 1.54) | 1.28 * (1.05 – 1.57) |
| Depression (MDE) | |||
| No | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| Yes | 0.72 (0.39 – 1.34) | 0.75 (0.40 – 1.43) | 0.85 (0.53 – 1.36) |
| Cigarette Smoker b | |||
| No | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| Yes | 1.29 * (1.03 – 1.62) | 1.45 * (1.06 – 1.98) | 1.43 ** (1.15 – 1.79) |
| Alcohol Use c | |||
| None | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| Light Drinking | 0.60 ** (0.41 – 0.89) | 0.71 (0.43 – 1.16) | 1.11 (0.78 – 1.58) |
| Binge Drinking | 0.59 ** (0.42 – 0.84) | 0.59 * (0.38 – 0.91) | 0.78 (0.60 – 1.03) |
| Heavy Drinking | 0.87 ** (0.42 – 0.79) | 0.53 (0.27 – 1.01) | 0.80 (0.60 – 1.28) |
| Illicit Drug Use d | |||
| No | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| Yes | 1.08 (0.84 – 1.40) | 1.01 (0.74 – 1.39) | 1.22 (0.87 – 1.70) |
| Year | |||
| 2014 | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) |
| 2015 | 1.15 (0.84 – 1.57) | 0.71 (0.42 – 1.19) | 0.99 (0.71 – 1.39) |
| 2016 | 1.35 (0.87 – 2.08) | 0.82 (0.47 – 1.44) | 0.92 (0.67 – 1.27) |
| 2017 | 1.14 (0.79 – 1.65) | 0.62 (0.37 – 1.05) | 0.93 (0.69 – 1.26) |
| 2018 | 1.53 * (1.03 – 2.29) | 0.60 (0.35 – 1.04) | 0.97 (0.67 – 1.41) |
p < .05;
p < .01;
p < .0001
Lives in a state where medical marijuana is legally recognized.
Self-reported cigarette smoking in the past 30-days
“Light Drinking” is defined as using alcohol but not binge drinking in the past 30 days; “Binge Drinking” is defined as drinking 5 or more drinks on the same occasion on at least 1 but not more than 4 days in the past 30 days; and “Heavy Drinking” is binge drinking more than 5 times in the past 30 days
Illicit Drug Use” indicates ever use of hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants, or sedatives.
Self-reported blunt smoking on 1 to 29 of the past 30-days
Self-reported blunt smoking on all 30 of the past 30-days
DISCUSSION
This study examined several risk factors associated with daily blunt use among a nationally representative sample of adult blunt users in the United States from 2014 to 2018. Findings revealed significant differences in daily blunt use by race/ethnicity with non-Hispanic African Americans and Hispanic/Latinos each having significantly greater odds of daily blunt use than non-Hispanic Whites. Additionally, non-Hispanic African Americans had greater odds of daily blunt use than Hispanic/Latinos as evident by the magnitude of the relationship observed in this analysis (i.e. 95% CI did not overlap) (Corraini, Olsen, Pedersen, Dekkers, & Vandenbroucke, 2017). This findings corresponds to existing literature that found blunt use highly prevalent among African American adults, particularly taking into account the myriad of precipitating factors (socioeconomic, psychosocial) that exists among racial minorities communities (Montgomery & Oluwoye, 2016). As such, findings from this study reveal unique risk profiles for daily blunt use across racial/ethnic groups.
Several significant descriptive figures were observed that advance our understanding of daily and non-daily blunt use among adults. Most notably, this study found that more than 1 in 7 adult blunt users (15.3%) reported daily use. This finding mirrors a previous study that found similar rates of daily marijuana use among adults, independent of use method (Davenport, 2018).
This study also explored racial/ethnic differences in factors associated with greater odds for daily blunt use (relative to non-daily use) for non-Hispanic Whites, non-Hispanic African Americans, and Hispanic/Latinos. While lower levels of education and past month cigarette use were found to be strong predictors of daily blunt use across all racial/ethnic groups, other factors associated with daily blunt use were varied by race/ethnicity. Notably, age (i.e., 26–34 years) was associated with higher odds of daily blunt use among non-Hispanic African Americans but not among non-Hispanic Whites or Hispanic/Latinos. Regarding age, the increased odds of daily blunt use among non-Hispanic African American adults between the ages of 26–34 is not surprising, as the overall past month use of any marijuana rates significantly increased from 8.7% in 2015 to 10.4% in 2018 among African Americans aged 26 or older in the United States (Abuse, 2019). The increase in past month marijuana use among non-Hispanic African Americans might be partially explained by the increase in daily blunt use found among African Americans between the ages of 26–34. Moreover, African Americans represented only 15% of a national sample of marijuana users, but they comprised 41% of current blunts users (i.e., 21–30 days in the past month) (Fairman, 2015). Given the positive association between frequency of blunt use and problematic marijuana use experiences (i.e. marijuana use disorders) (Fairman, 2015; Hasin et al., 2015), additional research is needed to prevent and treat blunt use overall, and especially among young non-Hispanic African American adults.
Surprisingly, living in a state where medical marijuana is legalized was associated with daily blunt use among non-Hispanic African American adults, but not among non-Hispanic White or Hispanic/Latino adults. Although unclear, it is possible that the disproportionate location of medical marijuana dispensaries in minority communities contributes to a decreased perception of harm of marijuana among racial/ethnic minorities (Cooke, Freisthler, & Mulholland, 2018; Thomas & Freisthler, 2017). For instance, Los Angeles census tracts with more medical marijuana dispensaries had a higher proportion of African American residents (Thomas & Freisthler, 2017). Moreover, a marketing study in Long Beach found that medical marijuana dispensaries identify and target recreational users, particularly young African American males, who either live near a dispensary or visit areas in close proximity to the dispensary for other purposes (Cooke et al., 2018). Although restricted to California, these studies suggest that the spatial distribution and marketing practices of medical marijuana dispensaries might disproportionately impact blunt use behaviors and health outcomes in African American communities. More studies are needed to assess if and how medical marijuana legislation impacts socioeconomic and health outcomes among racial minorities, such as African Americans, who have been historically and disproportionately targeted by drug and alcohol laws and policies (Robinson et al., 2018; Theall et al., 2011).
Interestingly, drinking habits (light, binge and heavy drinking) was inversely related to daily blunt use. Specifically: (1) light, binge and heavy drinking was associated with lower odds of daily blunt use among non-Hispanic Whites; (2) binge drinking was associated with daily blunt use among Hispanic/Latinos; but (3) no significance was found between drinking and blunt use among non-Hispanic African American adults. Ethnographic studies have found that young adults and minorities often participate in group activities that promote dual use of alcohol and blunts (Dunlap, Johnson, Benoit, & Sifaneck, 2006; Johnson, Bardhi, Sifaneck, & Dunlap, 2006), which might partially explain while drinking is not associated with a lower odds of daily blunt use among African Americans. It is important to note that the dual use of alcohol and blunts (relative to alcohol and non-blunt marijuana use) among African American adolescents and young adults is associated with an increased odds of alcohol dependence compared to those who use alcohol only (Montgomery, Zapolski, Banks, & Floyd, 2019). These findings from previous studies underscore the complex relationship between alcohol and blunt use while highlighting the need for further exploration.
It should be noted that the estimates (i.e. 95% confidence interval) for these factors did not differ across race/ethnic groups; though examining confidence interval ranges is not a direct test for effect modification (Knol, Pestman, & Grobbee, 2011). However, given the number of indicators (i.e. variables) and stratified analyses, tests for effect modification for all variables were not deemed analytically appropriate. However, this exploratory study does provide a foundation for future research to directly explore unique correlates and risk factors for daily blunt use (e.g., age, alcohol use, regulatory environment) across race/ethnic groups.
Although the current study used pooled data from a nationally representative sample of non-Hispanic African American, Hispanic/Latino and non-Hispanic White adults to examine daily blunt use, a few limitations should be noted. First, all data were self-reported and thus subject to response and recall bias. Second, the dataset survey did not explore blunt use patterns, such as the average amount of marijuana consumed in each blunt. Moreover, survey items did not assess the extent to which participants were aware of the medical marijuana laws in their state. Similarly, data were not available on the legalization of recreational marijuana/cannabis in each status. Future studies should include survey items that will allow for an in-depth assessment of recreational and medical marijuana among diverse samples of adult users. Third, the publicly available version of the dataset used for this study (NSDUH) does not allow for examination of race and ethnicity as separate variables. As such, this study was unable to account for varying racial identities among individuals who identified as Hispanic/Latino. And fourth, by examining blunt use level dichotomously, our model assumes that daily blunt users are categorically different from non-daily blunt users. Future research tailored to identifying variance in frequency of use is needed to identify nuances in blunt use behaviors, particularly as it relates to the sociodemographic variables of interest in this study.
To our knowledge, this is the first study using nationally representative data to identify diverse potential risk factors for daily blunt use among adult blunt users. Several implications can be drawn from this study. First, daily blunt use is prevalent among marijuana users. Given that daily or near daily blunt smoking is associated with more severe health outcomes such as marijuana use disorder (Fairman, 2015; Hasin et al., 2015)and concurrent substance use (alcohol use, smoking and illicit drug use), more research is needed to understand, prevent and treat daily blunt use among groups with higher prevalence and increased odds of adverse health outcomes, such as African American young adults. Second, lower socioeconomic status such as levels of educational attainment are significant correlates of daily blunt use across racial/ethnic groups. Additional studies are needed to further understand how education and other socioeconomic determinants contribute to daily blunt use. For example, a stronger understanding of how contextual determinants of health, such as substance availability and norms, impact the relationship between education and illicit drug use might provide additional insight for effective interventions and policies (Galea et al., 2007). Finally, sociodemographic, behavioral and regulatory factors are differentially associated with daily blunt use by race/ethnicity, suggesting the need for culturally appropriate tailored research and prevention strategies to mitigate initiation and herald the management of blunt use behaviors.
Public Health Significance:
This study is among the first to examine daily blunt use among adults using a nationally representative sample. Findings highlight racial/ethnic differences in correlates of daily blunt use. This research provides sociocultural context in our understanding of daily blunt use and thus assists in the efforts to improve health equity.
Funding Statement:
Research reported in this presentation was supported National Institute on Drug Abuse K23DA042130 (PI: Montgomery) and by the University of Texas Health Science Center at Houston School of Public Health Cancer Education and Career Development Program - National Cancer Institute/NIH Grant - National Cancer Institute/NIH Grant T32/CA057712. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
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