Group differences were tested using Pearson chi-square or t-tests, as appropriate. Differences of in-home smoking prevalence among the four tobacco-use and cannabis-use groups were assessed using predicted probabilities from mixed effects logistic regression models. All models were adjusted for age and sex with country of origin as a random effect to account for variation in sample sizes of each country and to address the possibility of country-specific influence on associations of in-home smoking by cannabis and tobacco use. Odds ratios comparing cannabis-only users, tobacco-only users, and dual users to non-users were computed separately for in-home cannabis smoking and in-home tobacco smoking. The ORs for in-home cannabis smoking and in-home tobacco smoking were visually compared by observing non-overlapping 95% confidence intervals. Statistical differences in the ORs were assessed using generalized estimating equations by implementing the methods outlined by Horton & Fitzmaurice . To address possible confounding by participant-level characteristics and country level characteristics , ORs were computed from models that additionally adjusted for clubbing frequency, drinking frequency, past year drug use, ploy-drug use, area of residence, and country as a fixed and random effect. Logistic regression models were then repeated for each included country to illustrate country-specific differences of in-home smoking among the four mutually exclusive cannabis- and tobacco-use groups. All statistical tests were two-tailed with an alpha of 0.05. Data analysis was performed using R . Global trends in the decriminalization and legalization of cannabis use should prompt increased research that seeks to identify and constrain harm and improve public health.
In this large convenience sample of people from 17 countries who used at least one psychoactive drug in the past year, past-year in-home cannabis smoking was slightly more prevalent than past-year in-home tobacco smoking in the overall sample. Approximately 80% of current cannabis-only users reported that cannabis grow set up was smoked in their home while in-home tobacco smoking took place in the residences of 68% of current tobacco-only users. Overall past-year rates of in-home smoking were high among current users of both cannabis and tobacco, and in these groups, in-home cannabis smoke was more prevalent than in-home tobacco smoke. Taken together, these results support our hypothesis that in-home cannabis smoking would be higher than in-home tobacco smoking. Since this is the first study we are aware of that measured the behaviors of in-home cannabis and tobacco smoking, there are no studies to which we can directly compare our results. From studies that focused on household rules, evidence from a convenience sample of US Facebook users reported that of the 54% of respondents who allowed cannabis use on their property, 71% allowed cannabis smoking inside their home; unfortunately, rates of in home tobacco smoking were not available . Another study, among US university students, reported that in-home cannabis use was allowed by 36% of tobacco smokers and 59% of cannabis smokers while in-home tobacco use was allowed by 25% of tobacco smokers and 36% of cannabis smokers . In our study, the generally higher rates among US respondents could be attributed to our assessment of home smoking behavior rather than home smoking policy. They could also be attributed to selection bias from studying self-selected sentinel drug users, though a recent study showed that the age and sex distributions of GDS respondents who use cannabis were similar to probability-based samples in the three countries studied—Australia, Switzerland, and the US . The high rates might also be partly the result of our yes/no inquiry about past-year in-home smoking, which could include rare instances of the behavior that are not representative of usual behavioral patterns.In a recent cross sectional study in Australia, Canada, England, and the United States, 73.6%, 78.3%, 65.5%, and 60.8% of respondents, respectively, endorsed cannabis smoking as less harmful than cigarette smoking .
Decades of tobacco control campaigns and policy likely increased the perceived harm of tobacco smoke, contributing to the lower rates of in-home tobacco use. If true, results from this study suggest that it may be time to develop similar campaigns and policy to correct perceptions of harm of cannabis smoke. This study underscores the importance of studying in-home cannabis smoking, which occurs at a higher rate than in-home tobacco smoking; however, more research is needed. Cannabis users comprise a sizeable population, both in absolute and relative terms, and with cannabis laws becoming more liberal, the number of users is rising. A major public health goal for future studies is to identify how often in-home cannabis smoking occurs in the general public and in high-use populations. Knowing the rates in the overall population will reveal the full scope of the behavior while understanding rates in high-use groups will quantify the behavior among those who have the most to gain from intervention. Obtaining these prevalence data will inform interventions to eliminate in-home smoking and will encourage further research, including the identification of public health messages designed to prevent in-home smoking and research on the health consequences of firsthand, secondhand, and third hand cannabis smoke exposure. As with all studies, results of the present analysis should be viewed considering the strengths and limitations of the methods used. While we included data from 107,274 men and women from 17 countries, the data were collected using anonymized web-based surveys specifically targeted toward those who use drugs through advertisements on social media and drug-scene-related media outlets. While non-probability based sampling and self-selection bias prevent generalizable estimates of prevalence , the inferences that were presented comparing in-home tobacco and cannabis smoking by use-status are expected to have significantly less bias since it is unlikely that the primary prerequisite for selection bias was present. In the Introduction, we referenced online information on tobacco and cannabis control policies that highlight significant variance in the type and implementation of policies across countries. To our knowledge, no study has attempted to quantify the impact of such regulatory differences on the in-home use of cannabis and tobacco, an investigation that was beyond the scope of the present study. However, the presence and diversity of public policies toward both substances should be considered when interpreting our findings.
To reduce bias related to confounding by participant-level characteristics and by country-level characteristics such as regulatory policies, our models statistically controlled for several potential confounders, and included country as a fixed and random effect. Future investigations of how tobacco and cannabis control policies impact in-home smoking are warranted, especially studies that explore how legalized cannabis use, with and without legalized outdoor use, impacts in-home use and subsequent SHS and THS exposure. The self-report origin of our cannabis and tobacco use status and in-home smoking status is also a limitation, although the GDS is a well-respected entity among the drug-using community and is known for preserving anonymity, making it more likely to elicit accurate responses. We were unable to study usual in-home smoking patterns including the frequency of in-home smoking or household composition because we did not collect usable data on these important factors—future studies should capture more detailed data. As a result, our estimates likely overestimate problematic in-home smoking, although this is not expected to greatly bias results since the overestimation is likely non-differential—i.e., similar for both in-home cannabis and in-home tobacco smoking. Transmission of severe acute respiratory syndrome coronavirus 2 , the virus responsible for causing coronavirus disease 2019 , has led to unprecedented morbidity and mortality across the U.S. . Risk factors for COVID-19-relatedsevere illness resulting in possible hospitalization include: active or former smoking status and/or having pre-existing comorbidities or an immunocompromised status . Combustible and non-combustible tobacco users are vulnerable to clinical morbidities, including impaired pulmonary function and respiratory illnesses . Research suggests combustible smoking or vaping cannabis is associated with respiratory-related symptoms and disease . Additionally, vaping nicotine, flavorings, and/or tetrahydrocannabinol products may place individuals at increased risk for COVID-19- related symptomatology and illness due to impairment of normal pulmonary defenses to inhaled viral pathogens . Smoking, and possibly e-cigarette use, can upregulate the angiotensin-converting enzyme-2 receptor, which is the receptor for SARS-CoV-2 . However, human research studies are lacking on concurrent e-cigarette and cannabis use and COVID-19-related health outcomes. Research has linked respiratory symptoms or disease with adult current e-cigarette use , current cannabis combustible smoking and vaping , and lifetime e-cigarette and cannabis use . College student ecigarette use and outdoor cannabis grow smoking and vaping reached historical highs between 2017 and 2019 . Currently, 22% and 14% of students report past 30-day nicotine and cannabis vaping, respectively . Over one-in-four students report current cannabis use including other routes of administration , with 1-in-17 reporting daily cannabis use. While current dual use of e-cigarettes and combustible cigarettes has been associated with increased risk of COVID-19 symptoms and diagnosis among 13–24-year-olds , less is known about COVID-19-related risks associated with concurrent e-cigarette and cannabis use.
Given the high prevalence of e-cigarette and cannabis use among college students , research is needed to assess the associations between concurrent use and COVID-19-related outcomes. This investigation assessed whether current e-cigarette and cannabis use was associated with COVID-19 symptomatology, testing, and diagnosis among college student current e-cigarette users. We hypothesized concurrent users of e-cigarettes and cannabis would be at increased odds of experiencing COVID-19 symptoms and having a prior positive COVID- 19 diagnosis compared with exclusive e-cigarette users. Additionally, we assessed whether frequency of e-cigarette and cannabis use was associated with COVID-19 symptoms, testing, and diagnosis. We hypothesized that when compared to infrequent exclusive e-cigarette users, intermediate or daily exclusive e-cigarette users as well as infrequent, intermediate, and frequent concurrent e-cigarette and cannabis users would be at increased odds of reporting COVID-19 symptoms and diagnosis. Based on COVID-19 random selection testing policies at each university during the study period, we posited there would be no difference in COVID-19 testing between the exclusive e-cigarette and concurrent use groups. Data are from a cross-sectional, online survey conducted October December 2020. Participants were college students ages 18–26 years from four geographically diverse, large U.S. public universities who reported current ecigarette use. Institutional review boards at each university independently vetted and approved all study procedures by November 2020; data collection occurred after respective IRB approval. Students at each university had the option to complete their coursework online, in person, or a hybrid model. Students residing in university housing/ residences were allowed to remain on each of the respective campuses during the data collection period. COVID-19 testing programs at each of the four respective campuses were similar and required randomly selected students to undergo testing. Eligible participants were recruited by disseminating emails via campus-wide listservs and undergraduate and graduate course listservs. Participant recruitment strategically took place at least over one month into the fall semester due to the study’s aim of capturing past 30-day behavior during the academic year. Solicitations sought students between the ages of 18–26 who “vape or use e-cigarettes” and were currently on campus. The recruitment email included a website link to a survey hosted on Qualtrics , and stated the estimated completion time was 10 minutes. Potential participants were provided with a research information sheet which they needed to acknowledge prior to proceeding to the survey.
The information sheet reinforced the recruitment email’s information . Response rates were not available due to recruitment strategies employed. However, sample size calculations using a 95% confidence interval , 100,000-population size, and a conservative 50–50 split considering the population is relatively varied , assert a minimum of 383 completed surveys were needed to have sufficient power for statistical analysis. To assess cannabis use, students were asked, “During the past 30 days, how many times did you use marijuana?” Response options were: 0, 1–2, 3–9, 10–19, 20–39, and 40 or more times. We classified the sample of current e-cigarette users based on their current cannabis use response as: exclusive e-cigarette users and concurrent ecigarette and cannabis users . To assess frequency of use patterns, we combined responses from the item on how many times students used cannabis with the item on how many days students used e-cigarettes in the past 30 days .