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. 2020 Aug 4;33(10):1350–1357. doi: 10.1080/09540121.2020.1799922

Daily and near-daily cannabis use is associated with HIV viral load suppression in people living with HIV who use cocaine

Deepika E Slawek a,b,CONTACT, Julia Arnsten a,b, Nancy Sohler c, Chenshu Zhang b, Robert Grossberg a,b, Melissa Stein a,b, Chinazo O Cunningham a,b
PMCID: PMC7858684  NIHMSID: NIHMS1617674  PMID: 32748649

ABSTRACT

Disparities remain in HIV viral load (VL) suppression between people living with HIV (PLWH) who use cocaine and those who do not. It is not known how cannabis use affects VL suppression in PLWH who use cocaine. We evaluated the relationship between cannabis use and VL suppression among PLWH who use cocaine. We conducted a secondary data analysis of 119 baseline interviews from a randomized controlled trial in the Bronx, NY (6/2012 to 1/2017). Participants were adult PLWH prescribed antiretrovirals for ≥16 weeks, who endorsed imperfect antiretroviral adherence and used cocaine in the past 30-days. In bivariate and multivariable regression analyses, we examined how cannabis use, is associated with VL suppression among PLWH who use cocaine. Participants were a mean age of 50 years; most were male (64%) and non-Hispanic black (55%). Participants with VL suppression used cocaine less frequently than those with no VL suppression (p < 0.01); cannabis use was not significantly different. In regression analysis, compared with no use, daily/near-daily cannabis use was associated with VL suppression (aOR = 4.2, 95% CI: 1.1–16.6, p < 0.05). Less-frequent cannabis use was not associated with VL suppression. Further investigation is needed to understand how cannabis use impacts HIV outcomes among PLWH who use cocaine.

KEYWORDS: HIV, substance use, cocaine, cannabis, viral suppression

1. Introduction

Antiretroviral therapy (ART) has significantly improved HIV outcomes, including HIV viral load (VL) suppression, among people living with HIV (PLWH). Despite advances in diagnosis of HIV and linkage to care, almost half of PLWH have yet to achieve VL suppression (Centers for Disease Control, 2017). PLWH who use drugs have disproportionately poorer VL suppression than PLWH who do not use drugs (Samji et al., 2013). These disparities are particularly emphasized among PLWH who use cocaine (Arnsten et al., 2002; Hinkin et al., 2007; Malta et al., 2010). Crack cocaine use is associated with entry into care at late stages of HIV disease (Celentano et al., 2001; Wang et al., 2004), poor ART adherence (Arnsten et al., 2002; Lucas et al., 2002, 2007; Sharpe et al., 2004), progression of HIV disease (Ingersoll, 2004), and poor treatment outcomes (Zolopa, 2010). When compared with other substance use, cocaine use is associated with an increase in HIV risk behaviors (Edlin et al., 1994) and faster development of AIDS-defining illnesses such as tuberculosis (Webber et al., 1999). The mechanism by which cocaine use leads to poor HIV outcomes has been explored but is not completely understood (Dash et al., 2015). While poor ART adherence contributes to progression of HIV, in pre-clinical studies, cocaine promotes HIV viral replication and cell entry, and immunologic decline (Dash et al., 2015). Despite these negative health consequences, there are few effective therapies for the management of cocaine use disorder. No known pharmacotherapies exist and psychosocial interventions have not been shown to have long-term benefits (Fischer et al., 2015).

In light of animal studies showing a link between the endocannabinoid system and substance use disorders (Parker et al., 2004; Prud'homme et al., 2015), a growing body of literature describes the use of cannabis as a substitute for other substances (Lau et al., 2015; Reddon et al., 2018; Socias et al., 2017). Cannabis use is common among PLWH, with national estimates of cannabis use among PLWH ranging from 25 to 38% (Mimiaga et al., 2013). Evaluation of VL suppression among PLWH who use cannabis has resulted in inconsistent findings, including an association between cannabis use and improved, worsened, and no change in VL suppression (Furler et al., 2004; Lake et al., 2017; Milloy et al., 2015). Similar patterns are reported with the association between cannabis use and ART adherence, with some studies showing an association between cannabis use and worse ART adherence and others showing no association with ART adherence (Abrams et al., 2003; Bonn-Miller et al., 2014; Bredt et al., 2002; D'Souza et al., 2012; de Jong et al., 2005; Furler et al., 2004; Harris et al., 2014; Slawson et al., 2015; Tucker et al., 2003).

Though cannabis use with the intent of reducing the use of cocaine use has been evaluated in some small studies (Lau et al., 2015; Socias et al., 2017), the effect of cannabis use on VL suppression among PLWH who use cocaine has not been explored.

We conducted a secondary data analysis of baseline interviews from a randomized controlled trial to explore the relationship between cannabis use and VL suppression among PLWH who use cocaine in the Bronx, NY.

2. Methods

Baseline data collected as part of Project FIRST, a randomized controlled trial, was used to conduct this secondary data analysis. Project FIRST tested the efficacy of abstinence-reinforcing contingency management (financial incentives for abstinence from opioids and cocaine), on VL suppression among PLWH who were using opioids or cocaine, were prescribed ART, and had suboptimal VL suppression (Cunningham, 2017).

2.1. Setting

The study took place at the Albert Einstein College of Medicine/Montefiore Medical Center’s (Montefiore) Clinical Research Center or drug treatment centers in the Bronx, NY. Montefiore is a large urban medical center consisting of four hospitals, four emergency departments, six drug treatment centers, a hospital-based HIV clinic, and over 20 community clinics. Collectively, among the ambulatory treatment sites, over 4200 HIV-infected patients receive primary care and over 4400 patients receive drug treatment. Of patients receiving HIV or drug treatment at Montefiore, the vast majority is black or Hispanic and has Medicaid (Glenn et al., 2016).

2.2. Participants

Participants were recruited from June 15, 2012 to January 23, 2017 from Montefiore and the general public. Recruitment strategies included: (1) referrals from Montefiore providers, (2) flyers and brochures in Montefiore sites and surrounding community-based organizations and (3) advertisements in newspapers.

Inclusion criteria for these exploratory analyses were: (1) at least 18 years old, (2) English or Spanish fluency, (3) HIV-infected, (4) currently taking ART for at least 16 weeks, (5) imperfect (<100%) adherence to ART in the previous four weeks, (6) (a) opioid use disorder and currently receiving medication-assisted treatment (methadone or buprenorphine) or (b) cocaine use disorder and (7) self-reported cocaine use in the past month. Eligibility criteria 3, 4, and 6 were confirmed by medical records.

Exclusion criteria included: (1) inability to give informed consent, (2) inability to follow the research protocol (e.g., Visits twice weekly), (3) current chronic pain syndrome that requires prescription opioid analgesics for one or more months, (4) unstable health, defined as hospitalized three or more times in the previous six months.

All participants provided written informed consent.

2.3. Data sources and collection

Blood tests (VL and CD4 count): At baseline visits, blood samples were collected for HIV VL measurements initially via VERSANT® HIV-1 RNA 3.0 Assay (bDNA), then via Montefiore’s central lab. CD4 count was measured at baseline initially by the FACS® MulitSET™ System (Becton Dickinson and Co.), then via Montefiore’s central lab.

Questionnaires: At the baseline visit, research staff administered questionnaires using Audio Computer-Assisted Self-Interview (ACASI) technology. The ACASI system displayed each question on a computer monitor while playing an audio recording of the question. Participants entered responses directly on the computer.

Medical records: Research staff reviewed medical records to confirm eligibility prior to enrollment in the study. With oversight from a physician, a trained research assistant conducted data extraction from medical records.

2.4. Outcome and exposure definitions

Our primary outcome was VL suppression, analyzed as a dichotomous measure (suppressed or <50 copies/mL versus unsuppressed or ≥50 copies/mL). According to national guidelines, the primary treatment goal for PLWH is maximally and durably suppressed plasma VL, defined as undetectable VL for at least 6 months (Panel on Antiretroviral Guidelines for Adults and Adolescents, 2019). Therefore, VL suppression was defined as all VL measures suppressed in the 6-month period before enrollment, including at the enrollment visit (study lab test).

Our primary exposure variable was frequency of cannabis use in the past 30-days. The distribution of data supported categorizing frequency of cannabis use as “no use” (0 days), “less-frequent use” (1–19 days), and “daily/near-daily use” (≥20 days). These cut-points have also been established in prior literature (Campbell et al., 2018) including the National Survey on Drug Use and Health (Compton et al., 2016).

2.5. Other measures

Other measures were sociodemographic characteristics, including age, gender (male, female, transgender), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic other). Additionally, self-reported clinical characteristics included: alcohol use (AUDIT) (Saunders et al., 1993), current substance use (number of days of non-prescribed opioid or heroin use, cocaine, amphetamines, hallucinogens, inhalants, and polysubstance use in the past 30 days), depressive symptoms (Center for Epidemiological Studies Depression Scale, CESD) (Radloff, 1977), and psychiatric symptoms in the past 30 days (Addiction Severity Index, ASI) (McLellan et al., 1992).

2.6. . Analyses

We first reviewed data using descriptive summaries and graphical analyses to ensure that values were within appropriate ranges, to check for the presence of outliers and abnormal values, and to verify that the distributions of measures met the assumptions of statistical tests described below. We examined whether the three categories of cannabis use differed on potential confounding variables identified a priori based on previous literature and clinical relevance (age, gender, race/ethnicity, alcohol use, non-prescribed opioid or heroin use, depressive symptoms, and psychiatric symptoms) using ANOVA or Kruskal–Wallis for continuous variables when appropriate and chi-squared or Fischer’s exact test for categorical variables. Covariates that were associated at an alpha of 0.10 or less were included in multivariable analyses. Age, gender, and race/ethnicity were included in the model regardless of their statistical significance due to clinical relevance.

Multiple logistic regression analysis examined the association between frequency of cannabis use and VL suppression. All tests were two tailed and considered statistically significant if p < 0.05. We examined the relationship of days of cannabis use in the past 30-days and days of other substance use in the past 30-days, including cocaine, opioids, and alcohol, using Spearman correlation.

Adjusted odds ratios are reported. All analyses were completed in STATA 15.0 (StataCorp, College Station, TX, USA).

3. Results

Of 171 participants who completed the baseline interview, 119 met eligibility criteria for inclusion in these analyses (Figure 1). Participants’ mean age was 50.2 years (SD ±8), and most were male (64%) and non-Hispanic black (53%). Of the 119 participants, 37 (31%) had VL suppression. No cannabis use was reported among 65 (55%) participants, 39 (33%) reported less-frequent cannabis use, and 15 (13%) reported daily/near-daily cannabis use. Compared to participants without VL suppression, those with VL suppression were more likely to be older (mean years ± SD: 49 ± 8 vs. 53 + 7 p < 0.01) and non-Hispanic black (45% vs. 76%, p < 0.01), have a CD4 count greater than 500 (26% vs. 70%, p < 0.01), and use cocaine less frequently (median number of days used in past 30 days [IQR] 11 [3–15] vs. 6 [2–5], p < 0.01). Frequency of cannabis use was not significantly different between those with and without VL suppression. There were no significant differences in any other sociodemographic or clinical characteristics between participants with and without VL suppression, including gender, annual family income, depressive symptoms, psychiatric symptoms, non-prescribed opioid or heroin use, and hazardous alcohol use (see Table 1).

Figure 1.

Figure 1.

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Flow chart showing screening and enrollment of participants in presented analyses.

Table 1. Characteristics of participants by HIV viral load (VL) suppressiona (n = 119).

  Total n = 119 (%) No VL Suppression n = 82 (69%) VL Suppression
n = 37 (31%)		 p
Frequency of cannabis useb       0.11
No use 65 (55) 45 (55) 20 (54) –
Less-frequent use 39 (33) 30 (37) 9 (24) –
Daily/near-daily use 15 (13) 7 (9) 8 (22) –
Sociodemographic characteristics
Age, years, mean (SD) 50 (8) 49 (8) 53 (7) 0.01
Gender       0.44
Male 76 (64) 54 (66) 22 (60) –
Female 41 (34) 26 (32) 15 (41) –
Transgender 2 (2) 2 (2) 0 –
Race/Ethnicity       0.01
Hispanic 26 (22) 23 (28) 3 (8) –
Non-Hispanic Black 65 (55) 37 (45) 28 (76) –
Non-Hispanic White 6 (5) 4 (5) 2 (5) –
Non-Hispanic Otherc 22 (18) 18 (22) 4 (11) –
Annual Family Income, US dollarsd, median (IQR) 12.1 (1.2, 17.0) 12.1 (1.2, 16.5) 10.3 (9.9, 12.0) 0.26
Clinical characteristics
CD4>500e 47 (40) 21 (26) 26 (70) <0.01
Depressive symptomsf 71 (60) 52 (63) 19 (51) 0.16
Psychiatric symptoms, median (IQR)g 0.09 (0, 0.27) 0.09 (0, 0.27) 0.02 (0, 0.20) 0.27
Substance use
Non-prescribed opioid or heroin useh 45 (38) 34 (41) 11 (30) 0.22
Hazardous alcohol usei 40 (34) 28 (34) 12 (32) 0.86
Cocaine use, days, median (IQR)j 9 (3, 15) 11 (3, 15) 6 (2, 5) <0.01
Drug use severity, mean (SD)l 0.33 (0.14) 0.34 (0.14) 0.29 (0.13) 0.06
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adichotomous measure suppressed if three consecutive undetectable HIV viral loads at baseline and prior to enrollment; bno use = 0 days, moderate use = 1–15 days, frequent use >15 days; cResponded Native American, Asian, or ‘some other race’; dIn $1000’s of dollars; eAt time of enrollment; fCenter for Epidemiologic Studies Depression Scale37 >16 indicating symptoms concerning for depression; gAddiction Severity Index psychiatric status subscale38; hself-reported use in the past 30-days; iAlcohol use disorder identification test >836; jself-reported days of cocaine use in the past 30; kself-reported >15 days of cocaine use out of 30 days; lAddiction Severity Index drug use subscale38; Abbreviations: VL – viral load, SD – standard deviation.

Participants with VL suppression used cocaine on fewer days than those with no VL suppression (p < 0.01), and fewer participants in the VL suppression group used cocaine on more than 15 days out of the past 30 then those in the no VL suppression group (p < 0.01). This difference was not observed in nonprescribed opioid or heroin use or hazardous alcohol use (Table 1).

In multiple logistic regression analysis, compared with no cannabis use, daily or near-daily cannabis use was significantly associated with VL suppression (aOR = 4.2, 95% CI: 1.1–16.6, p < 0.05). When compared to no cannabis use, less-frequent cannabis use was not significantly associated with VL suppression (aOR = 0.8, 95% CI: 0.3–2.2, p = 0.6) (see Table 2). While we found no association between days of cannabis use and days of cocaine use (correlation coefficient = −0.02, p = 0.81) or opioid use (correlation coefficient = 0.12, p = 0.19), we found an association between days of cannabis use and alcohol use (correlation coefficient = 0.19, p = 0.04).

Table 2. Odds of HIV viral load suppression by frequency of cannabis use among people who use cocaine (n = 119).

Variable Adjusted odds ratio 95% Confidence interval
Less-frequent cannabis use (1–19 days) 0.8 0.3–2.2
Near-daily/daily cannabis use (>20 days) 4.2 1.1–16.6*
Age, years 1.1 1.0–1.2*
Male gendera 1.9 0.7–4.9
Non-Hispanic blackb 0.2 0.1–0.6*
Number of days of cocaine use in past 30 days 0.9 0.8–0.9*
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aversus female gender and transgender.

bversus all other race-ethnicity.

*p < 0.05.

4. Discussion

In this secondary data analysis of PLWH who use cocaine enrolled in a randomized controlled trial in the Bronx, NY, daily/near-daily cannabis use was significantly associated with VL suppression when compared with no cannabis use. This association was not found among PLWH with less-frequent cannabis use.

Few studies have examined the relationship between cannabis use and HIV VL suppression specifically in cocaine users. In a cohort study of PLWH presenting for HIV care, cocaine use was independently associated with lack of HIV VL suppression, but cannabis use was not (Rasbach et al., 2013). In another cohort study of PLWH who use drugs, including opioids, cocaine, alcohol, and stimulants, daily cannabis use was not associated with HIV outcomes, including VL suppression (Lake et al., 2017).

There are several explanations for why daily/near-daily cannabis use was associated with VL suppression when compared with no cannabis use. One hypothesis is that those who use cocaine and cannabis have more financial means that those who use cocaine and not cannabis. These financial means could translate to more resources that can be applied to health, such as health insurance and medications. However, we found that participants with VL suppression and without VL suppression reported similar family income.

Another possibility is that cannabis use may reduce the frequency of other substance use. Several studies have found an association between cannabis use and reduced frequency of other substance use, including opioids, alcohol, and cocaine (Lau et al., 2015; Lucas et al., 2013, 2016; Sohler et al., 2018). In qualitative studies, cannabis has been identified as a substitute for cocaine use (Socias et al., 2017). It is possible that a reduction in non-prescribed opioid, cocaine, or alcohol use could lead to improvement in engagement in HIV care and subsequent improved VL suppression. While other studies have found that cannabis use is associated with reductions in cocaine, opioids, and alcohol, our study did not find this. Our logistic regression model adjusted for frequency of cocaine use to account for this possibility. When adjusting for frequency of cocaine use, the association between frequent cannabis use and VL suppression remained statistically significant. There was also no association between days of cannabis use and days of cocaine use. Our findings highlight the need for a dedicated study examining how cannabis use affects HIV outcomes in people who use cocaine to further elucidate this complex relationship.

Our study has important clinical implications on the impact of cannabis use on PLWH in the setting of increasing legalization of medical and recreational cannabis. With increasing access to cannabis, clinicians will need to counsel patients on use of cannabis, including in PLWH with a history of or active cocaine use. Our findings suggest that cannabis use in this population may not be associated with worse HIV outcomes, and in fact may have influences on chronic health conditions such as HIV. Cannabis use among people who use cocaine may have a positive impact on other chronic health conditions such as diabetes and hepatitis C virus, that require adherence to medications for successful treatment.

When caring for PLWH who use cocaine, deciding how to counsel patients on cannabis use remains unclear. Abstinence from all substance use has been encouraged in current HIV guidelines (Panel on Antiretroviral Guidelines for Adults and Adolescents, 2019), though our findings suggest that this may not be necessary in PLWH who use cocaine. Cannabis use still has potential harms, including intoxication and subsequent accidents (Monte et al., 2019), THC-associated exacerbation of psychosis (Volkow et al., 2014) and acute psychiatric symptoms (Volkow et al., 2016), and interactions between cannabis use and medications (Damkier et al., 2019). Chronic use of cannabis can lead to cannabis hyperemesis syndrome (Monte et al., 2019), neuropsychological impairment (Volkow et al., 2016), and cannabis use disorder (Volkow et al., 2016). In PLWH who use cocaine and endorse cannabis use or are found to have cannabis in routine urine toxicology, a discussion should be had with patients of the potential risks of and benefits of cannabis use. Our findings suggest that at the very least, patients should not be penalized for cannabis use. Medications and continued follow up should not be withheld from patients. It could be reasonable to refrain from testing urine for THC entirely.

Our study has limitations. First, this is an exploratory secondary analysis of data from a parent study that was not designed to test this research question. A larger study population, particularly of participants who use cannabis frequently, could illuminate the relationship more clearly between cannabis use and VL suppression in PLWH who use cocaine. Second, the study population was very narrowly defined. Recruitment efforts were targeted at finding PLWH who had imperfect ART adherence and who were actively using opioids or cocaine. Those participants who met eligibility criteria for our analysis may not be representative of all PLWH who use cocaine and cannabis, and therefore our findings may not be generalizable. Third, our analysis was performed on a cross-sectional sample of baseline interviews from the parent study. The cross-sectional design of our study limits our ability to determine whether these findings are sustained longitudinally. Our findings only allow us to report associations, but we cannot report causality.

4.1. Conclusion

In summary, daily/near-daily cannabis use was associated with VL suppression among PLWH who use cocaine. With rapidly growing availability of cannabis in the setting of medical and recreational cannabis legalization, it is important to understand how cannabis use impacts HIV and other health outcomes among PLWH who are especially vulnerable to negative health outcomes, such as PLWH who use cocaine. More work is needed to fully understand this relationship.

Funding Statement

This work was supported by National Institute on Drug Abuse R01DA032110, K24DA036955, and National Center for Advancing Translational Sciences UL1TR001073.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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