"Retweet to Pass the Blunt": Analyzing Geographic and Content Features of Cannabis-Related Tweeting Across the United States

Exploring Twitter chatter to assess the type and availability of cannabis-related products in Thailand

Cannabis-related tweets were collected between January and April 2022 to estimate the availability and characteristics of cannabis products advertised on Twitter amid the legalization of recreational cannabis in Thailand. The Twitter API was called using the tweepy Python library to collect cannabis-related tweets in the Thai language. A total of 185,558 unique tweets were collected over the duration of the data collection period based on 83 search terms. Twenty thousand random tweets were manually coded by four Thai native speakers to assess the volume and characteristics of tweets proposing cannabis. 72.6% of collected tweets from the 20,000 random samples were coded as relevant to the study. 54.6% of relevant tweets were advertising cannabis products, 29.8% were personal communications, and 15.6% were related to news or media content. Among the tweets that advertised cannabis products, 94.4% proposed cannabis flower, 2.4% cannabis edibles and 1.8% cannabis concentrates. Consumption of potent forms of cannabis such as cannabis edibles and concentrates increase the risk of harmful side-effects, especially in a population with limited knowledge about these products. Our findings call for additional monitoring efforts and for increasing the public awareness on potent cannabis products emerging in Thailand.

What is the hype on #MedicinalCannabis in the United States? A content analysis of medicinal cannabis tweets

INTRODUCTION

Medicinal cannabis is now legal in 44 US jurisdictions. Between 2020 and 2021 alone, four US jurisdictions legalised medicinal cannabis. The aim of this study is to identify themes in medicinal cannabis tweets from US jurisdictions with different legal statuses of cannabis from January to June 2021.

METHODS

A total of 25,099 historical tweets from 51 US jurisdictions were collected using Python. Content analysis was performed on a random sample of tweets accounting for the population size of each US jurisdictions (n = 750). Results were presented separately by tweets posted from jurisdictions where all cannabis use (non-medicinal and medicinal) is 'fully legalised', 'illegal' and legal for 'medical-only' use.

RESULTS

Four themes were identified: 'Policy', 'Therapeutic value', 'Sales and industry opportunities' and 'Adverse effects'. Most of the tweets were posted by the public. The most common theme was related to 'Policy' (32.5%-61.5% of the tweets). Tweets on 'Therapeutic value' were prevalent in all jurisdictions and accounted for 23.8%-32.1% of the tweets. Sales and promotional activities were prominent even in illegal jurisdictions (12.1%-26.5% of the tweets). Fewer than 10% of tweets were about intoxication and withdrawal symptoms.

DISCUSSION AND CONCLUSION

This study has explored if content themes of medicinal cannabis tweets differed by cannabis legal status. Most tweets were pro-cannabis and they were related to policy, therapeutic value, and sales and industry opportunities. Tweets on unsubstantiated health claims, adverse effects and crime warrants continued surveillance as these conversations could allow us to estimate cannabis-related harms to inform health surveillance.

Health, safety, and socioeconomic impacts of cannabis liberalization laws: An evidence and gap map

Background

Globally, cannabis laws and regulations are rapidly changing. Countries are increasingly permitting access to cannabis under various decriminalization, medicalization, and legalization laws. With strong economic, public health, and social justice incentives driving these domestic cannabis policy reforms, liberalization trends are bound to continue. However, despite a large and growing body of interdisciplinary research addressing the policy-relevant health, safety, and socioeconomic consequences of cannabis liberalization, there is a lack of robust primary and systematic research that comprehensively investigates the consequences of these reforms.

Objectives

This evidence and gap map (EGM) summarizes the empirical evidence on cannabis liberalization policies. Primary objectives were to develop a conceptual framework linking cannabis liberalization policies to relevant outcomes, descriptively summarize the empirical evidence, and identify areas of evidence concentration and gaps.

Search Methods

We comprehensively searched for eligible English-language empirical studies published across 23 academic databases and 11 gray literature sources through August 2020. Additions to the pool of potentially eligible studies from supplemental sources were made through November 2020.

Selection Criteria

The conceptual framework for this EGM draws upon a legal epidemiological perspective highlighting the causal effects of law and policy on population-level outcomes. Eligible interventions include policies that create or expand access to a legal or decriminalized supply of cannabis: comprehensive medical cannabis laws (MCLs), limited medical cannabidiol laws (CBDLs), recreational cannabis laws (RCLs), industrial hemp laws (IHLs), and decriminalization of cultivations laws (DCLs). Eligible outcomes include intermediate responses (i.e., attitudes/behaviors and markets/environments) and longer-term consequences (health, safety, and socioeconomic outcomes) of these laws.

Data Collection and Analysis

Both dual screening and dual data extraction were performed with third person deconfliction. Primary studies were appraised using the Maryland Scientific Methods Scale and systematic reviews were assessed using AMSTAR 2.

Main Results

The EGM includes 447 studies, comprising 438 primary studies and nine systematic reviews. Most research derives from the United States, with little research from other countries. By far, most cannabis liberalization research focuses on the effects of MCLs and RCLs. Studies targeting other laws-including CBDLs, IHLs, and DCLs-are relatively rare. Of the 113 distinct outcomes we documented, cannabis use was the single most frequently investigated. More than half these outcomes were addressed by three or fewer studies, highlighting substantial evidence gaps in the literature. The systematic evidence base is relatively small, comprising just seven completed reviews on cannabis use (3), opioid-related harms (3), and alcohol-related outcomes (1). Moreover, we have limited confidence in the reviews, as five were appraised as minimal quality and two as low quality.

Authors’ Conclusions

More primary and systematic research is needed to better understand the effects of cannabis liberalization laws on longer-term-and arguably more salient-health, safety, and socioeconomic outcomes. Since most research concerns MCLs and RCLs, there is a critical need for research on the societal impacts of industrial hemp production, medical CBD products, and decriminalized cannabis cultivation. Future research should also prioritize understanding the heterogeneous effects of these laws given differences in specific provisions and implementation across jurisdictions.

Twitter activity surrounding the Finnish green party's cannabis legalisation proposal: A mixed-methods analysis

Background: In September 2021, a Finnish political party, the Greens, voted to include cannabis policy reform in their party programme, which would legalise the use, possession, manufacture and sale of cannabis. A rapid public discussion has emerged on different social media platforms, including Twitter. Methods: We downloaded 10 days of Twitter data and prepared it for further text analysis, including sentiment, topic modelling and thematic content analysis. Results: Before the proposal, the average daily number of tweets was approximately 140. However, during the week of the proposal, there was a significant increase in tweet volume, reaching a peak of 6,600 tweets on a single day, with a daily average of over 2,700 tweets. Sentiment analysis showed that during the public discussion, the sentiment scores of the tweets were more likely to be positive. Through topic modelling analysis, we obtained the weight of the topic for each tweet, which enabled us to identify the most representative tweets in our corpus. To narrow the sample size for content analysis, we selected tweets that had a topic percentage distribution of over 0.95 (N=188) for closer thematic content analysis. Several positive and negative themes emerged, which were then categorised under broader topics. Similar themes were identified in the most retweeted, liked and commented tweets, which came mainly from known public figures, including politicians, health experts and NGO leaders. Conclusion: Our results show that the discussion was not limited to cannabis legalisation, but instead covered a variety of topics related to drug policy.

Sentiment and Topic Modeling Analysis on Twitter Reveals Concerns over Cannabis-Containing Food after Cannabis Legalization in Thailand

OBJECTIVES

Twitter has been used to express a diverse range of public opinions about cannabis legalization in Thailand. The purpose of this study was to observe changes in sentiments after cannabis legalization and to investigate health-related topics discussed on Twitter.

METHODS

Tweets in Thai and English related to cannabis were scraped from Twitter between May 1 and June 13, 2022, during cannabis legalization in Thailand. Sentiment and topic-modeling analyses were used to compare the content of tweets before and after legalization. Health-related topics were manually grouped into categories by their content and rated according to the number of corresponding tweets.

RESULTS

We collected 21,242 and 6,493 tweets, respectively, for Thai and English search terms. A sharp increase in the number of tweets related to cannabis legalization was detected at the time of its public announcement. Sentiment analysis in the Thai search group showed a significant change (p < 0.0001) in sentiment distribution after legalization, with increased negative and decreased positive sentiments. A significant change was not found in the English search group (p = 0.4437). Regarding cannabis-containing food as a leading issue, topic-modeling analysis revealed public concerns after legalization in the Thai search group, but not the English one. Topics related to cannabis tourism surfaced only in the English search group.

CONCLUSIONS

Since cannabis legalization, the primary health-related concern has been cannabis-containing food. Education and clear regulations on cannabis use are required to strengthen oversight of cannabis in the Thai population, as well as among medical tourists.

Trends in cannabis use intention around the period of cannabis legalisation in Australia: An age-period-cohort model

INTRODUCTION

This study examines age, time period and birth cohort trends in cannabis use intention and weekly use in Australia over a period in which medicinal cannabis was legalised.

METHODS

Hierarchical age-period-cohort models were used to analyse the National Drug Strategy Household Survey between 2001 and 2019, including 158,395 participants aged 18-79 years.

RESULTS

The hierarchical age-period-cohort model demonstrated a decrease in likelihood of intending to try cannabis as age increases. Similar age effects were found in intending to use cannabis as often or less often. There was broad-based shift in attitudes for people wanting to try cannabis (2007: b = -0.51 [-0.82, -0.21]; 2019: b = 0.68 [0.38, 0.98]) or use cannabis more often (2007: b = -0.15 [-0.50, 0.20]; 2019: b = 0.83 [0.49, 1.18]). The population trend of weekly cannabis use decreased in the earlier periods but increased since 2013 (b = -0.13 [-0.25, -0.02] vs 2019: b = 0.06 [-0.09, 0.20]). This suggests that legalisation would increase uptake of cannabis and consumption among current consumers. There were distinctive inter-generation variations: people born between 1950s and 1960s had more liberal views towards cannabis use than people born before or after (p < 0.05). There were indications that young people born in the 1990 s are catching up with the baby boomers in using cannabis more often if it was legal.

DISCUSSION AND CONCLUSIONS

There has been a population-based shift in Australia in favourable attitudes towards cannabis use, more so among those born in the 1950s to 1960s than other generations. Liberal attitudes and more frequent cannabis use may put certain cohorts at higher risks of cannabis dependence and related harms.

#TurntTrending: a systematic review of substance use portrayals on social media platforms

AIMS

There is a growing body of literature exploring the types of substance-related content and their portrayals on various social media platforms. We aimed to summarize how content related to substances is portrayed on various social media platforms.

METHODS

This systematic review was pre-registered on PROSPERO (ref: CRD42021291853). A comprehensive search was conducted in the databases of PubMed, Scopus, PsycINFO and Web of Science in April 2021. Original qualitative studies published post-2004 that included thematic and sentiment analyses of social media content on tobacco, alcohol, psychostimulant, e-cigarette, cannabis, opiate, stimulant/amphetamine, inhalant and novel psychoactive substance were included. Social media platforms were defined as online web- or application-based platforms that allowed users to generate content and interact via 'liking', comment or messaging features. Only studies that included summative and/or thematic content analyses of substance-related social media content were included.

RESULTS

A total of 73 studies, which covered 15 905 182 substance-related posts on Twitter, YouTube, Instagram, Pinterest, TikTok and Weibo, were identified. A total of 76.3% of all substance-related content was positive in its depiction of substance use, with 20.2% of content depicting use negatively. Sentiment regarding opiate use however was commonly negative (55.5%). Most studies identified themes relating to Health, Safety and Harms (65.0%) of substance use. Themes relating to Promotions/Advertisements (63.3%), Informative content (55.0%) and Use behaviours (43.3%) were also frequently identified.

CONCLUSIONS

Substance-related content that promotes engagement with substance use or actively depicts use appears to be widely available on social media. The large public presence of this content may have concerning influences on attitudes, behaviours and risk perceptions relating to substance use, particularly among the most vulnerable and heaviest users of social media-adolescents and young adults.

Sharing pro-marijuana messaging on social media: The moderating role of legislation

ObjectivesThis study investigates whether recreational marijuana legislation and perceived social norms (descriptive and injunctive) affect college students' propensity to share pro-marijuana messages. We examine which referent group (close friends, typical student, parents) most influence those norms. Participants: A sample of 343 college students participated in the study. Of these students, 214 were from Washington State, where recreational marijuana is legal, and 129 were from Wyoming, where recreational marijuana is illegal. Method: Data, from an online survey, were analyzed through PROCESS analyses. Results: College students in Washington State who believed a typical peer would want them to share pro-marijuana messaging were marginally more likely to share pro-marijuana messages than their counterparts in Wyoming. However, among students who thought a typical peer would not approve of them sharing pro-marijuana messaging, the opposite pattern emerged. Conclusion: Restrictive recreational marijuana legislation does not uniformly abate related message sharing on social media.

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology) to jointly extract entities and their relationship using gated fusion sharing mechanism. Our model is further tailored to provide more focus to the entities mention in the sentence through entity-position aware attention layer, where ontology is used to locate the target entities position. Experimental results show that inclusion of the knowledge-aware attentive representation in association with BERT can extract the cannabis-depression relationship with better coverage in comparison to the state-of-the-art relation extractor.

Monitoring the sentiment of cannabis-related tweets in the lead up to New Zealand's cannabis referendum

INTRODUCTION AND AIMS

In October 2020, New Zealanders will vote on whether cannabis should be legalised for recreational use. With this in mind, the aim of the present study is to gauge the views and opinions of the New Zealand population on cannabis via tweets. To achieve this, we conducted a sentiment analysis of all historic cannabis-related tweets and referendum-specific tweets written in New Zealand.

DESIGN AND METHODS

We used a Twitter-sponsored commercial platform to access all historic cannabis-related tweets written in New Zealand and used search terms to remove non-cannabis-related terms. Next, we used the platform's machine learning function to code the sentiment of tweets (i.e. positive/pro-cannabis, negative/anti-cannabis or neutral).

RESULTS

Between July 2009 and August 2020, 304 760 cannabis-related tweets were written in New Zealand. Overall, the tweets were predominantly positive (62.0%) and there was a higher proportion of positive tweets written in 2020 (65.3%) compared to negative or neutral tweets. Similarly, for referendum-specific tweets, the 2020 data reveal a generally positive view of cannabis (53.5%).

DISCUSSION AND CONCLUSIONS

Both cannabis-related, and referendum-specific tweets, suggest that Twitter users in New Zealand have a generally positive view of cannabis. Given the nature of Twitter, the current method will allow us to study whether views toward cannabis change as the referendum nears and capture any late swings in pro- or anti-cannabis sentiment (abcd-lab.shinyapps.io/cannabis_sentiment/).

Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study

BACKGROUND

Infodemiology is an emerging field of research that utilizes user-generated health-related content, such as that found in social media, to help improve public health. Twitter has become an important venue for studying emerging patterns in health issues such as substance use because it can reflect trends in real-time and display messages generated directly by users, giving a uniquely personal voice to analyses. Over the past year, several states in the United States have passed legislation to legalize adult recreational use of cannabis and the federal government in Canada has done the same. There are few studies that examine the sentiment and content of tweets about cannabis since the recent legislative changes regarding cannabis have occurred in North America.

OBJECTIVE

To examine differences in the sentiment and content of cannabis-related tweets by state cannabis laws, and to examine differences in sentiment between the United States and Canada between 2017 and 2019.

METHODS

In total, 1,200,127 cannabis-related tweets were collected from January 1, 2017, to June 17, 2019, using the Twitter application programming interface. Tweets then were grouped geographically based on cannabis legal status (legal for adult recreational use, legal for medical use, and no legal use) in the locations from which the tweets came. Sentiment scoring for the tweets was done with VADER (Valence Aware Dictionary and sEntiment Reasoner), and differences in sentiment for states with different cannabis laws were tested using Tukey adjusted two-sided pairwise comparisons. Topic analysis to determine the content of tweets was done using latent Dirichlet allocation in Python, using a Java implementation, LdaMallet, with Gensim wrapper.

RESULTS

Significant differences were seen in tweet sentiment between US states with different cannabis laws (P=.001 for negative sentiment tweets in fully illegal compared to legal for adult recreational use states), as well as between the United States and Canada (P=.003 for positive sentiment and P=.001 for negative sentiment). In both cases, restrictive state policy environments (eg, those where cannabis use is fully illegal, or legal for medical use only) were associated with more negative tweet sentiment than less restrictive policy environments (eg, where cannabis is legal for adult recreational use). Six key topics were found in recent US tweet contents: fun and recreation (keywords, eg, love, life, high); daily life (today, start, live); transactions (buy, sell, money); places of use (room, car, house); medical use and cannabis industry (business, industry, company); and legalization (legalize, police, tax). The keywords representing content of tweets also differed between the United States and Canada.

CONCLUSIONS

Knowledge about how cannabis is being discussed online, and geographic differences that exist in these conversations may help to inform public health planning and prevention efforts. Public health education about how to use cannabis in ways that promote safety and minimize harms may be especially important in places where cannabis is legal for adult recreational and medical use.

Listed for sale: Analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket

BACKGROUND

The United States is facing a "triple wave" epidemic fueled by novel synthetic opioids. Cryptomarkets, anonymous marketplaces located on the deep web, play an increasingly important role in the distribution of illicit substances. This article presents the data collected and processed by the eDarkTrends platform concerning the availability trends of novel synthetic opioids listed on one cryptomarket.

METHODS

Listings from the DreamMarket cryptomarket "Opioids" and "Research Chemicals" sections were collected between March 2018 and January 2019. Collected data were processed using eDarkTrends Named Entity Recognition algorithm to identify opioid drugs, and to analyze their availability trends in terms of frequency of listings, available average weights, average prices, and geographic indicators of shipment origin and destination information.

RESULTS

95,011 opioid-related listings were collected through 26 crawling sessions. 33 novel synthetic opioids were identified in 3.3 % of the collected listings. 44.7 % of these listings advertised fentanyl (pharmaceutical and non-pharmaceutical) or fentanyl analogs for an average of 2.8 kgs per crawl. "Synthetic heroin" accounted for 33.2 % of novel synthetic opioid listings for an average 1.1 kgs per crawl with 97.7 % of listings advertised as shipped from Canada. Other novel synthetic opioids (e.g., U-47,700, AP-237) represented 22 % of these listings for an average of 6.1 kgs per crawl with 97.2 % of listings advertised as shipped from China.

CONCLUSIONS

Our data indicate consistent availability of a wide variety of novel synthetic opioids both in retail and wholesale-level amounts. Identification of new substances highlights the value of cryptomarket data for early warning systems of emerging substance use trends.

Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison

BACKGROUND

The coronavirus disease (COVID-19) pandemic presents one of the most challenging global crises at the dawn of a new decade. Public health authorities (PHAs) are increasingly adopting the use of social media such as Facebook to rapidly communicate and disseminate pandemic response measures to the public. Understanding of communication strategies across different PHAs and examining the public response on the social media landscapes can help improve practices for disseminating information to the public.

OBJECTIVE

This study aims to examine COVID-19-related outreach efforts of PHAs in Singapore, the United States, and England, and the corresponding public response to these outreach efforts on Facebook.

METHODS

Posts and comments from the Facebook pages of the Ministry of Health (MOH) in Singapore, the Centers for Disease Control and Prevention (CDC) in the United States, and Public Health England (PHE) in England were extracted from January 1, 2019, to March 18, 2020. Posts published before January 1, 2020, were categorized as pre-COVID-19, while the remaining posts were categorized as peri-COVID-19 posts. COVID-19-related posts were identified and classified into themes. Metrics used for measuring outreach and engagement were frequency, mean posts per day (PPD), mean reactions per post, mean shares per post, and mean comments per post. Responses to the COVID-19 posts were measured using frequency, mean sentiment polarity, positive to negative sentiments ratio (PNSR), and positive to negative emotions ratio (PNER). Toxicity in comments were identified and analyzed using frequency, mean likes per toxic comment, and mean replies per toxic comment. Trend analysis was performed to examine how the metrics varied with key events such as when COVID-19 was declared a pandemic.

RESULTS

The MOH published more COVID-19 posts (n=271; mean PPD 5.0) compared to the CDC (n=94; mean PPD 2.2) and PHE (n=45; mean PPD 1.4). The mean number of comments per COVID-19 post was highest for the CDC (mean CPP 255.3) compared to the MOH (mean CPP 15.6) and PHE (mean CPP 12.5). Six major themes were identified, with posts about prevention and safety measures and situation updates being prevalent across the three PHAs. The themes of the MOH's posts were diverse, while the CDC and PHE posts focused on a few themes. Overall, response sentiments for the MOH posts (PNSR 0.94) were more favorable compared to response sentiments for the CDC (PNSR 0.57) and PHE (PNSR 0.55) posts. Toxic comments were rare (0.01%) across all PHAs.

CONCLUSIONS

PHAs' extent of Facebook use for outreach purposes during the COVID-19 pandemic varied among the three PHAs, highlighting the strategies and approaches that other PHAs can potentially adopt. Our study showed that social media analysis was capable of providing insights about the communication strategies of PHAs during disease outbreaks.

Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio

As America's opioid crisis has become an "epidemic of epidemics," Ohio has been identified as one of the high burden states regarding fentanyl-related overdose mortality. This study aims to examine changes in the availability of fentanyl, fentanyl analogs, and other non-pharmaceutical opioids on cryptomarkets and assess relationship with the trends in unintentional overdoses in Ohio to provide timely information for epidemiologic surveillance. Cryptomarket data were collected at two distinct periods of time: (1) Agora data covered June 2014-September 2015 and were obtained from Grams archive; (2) Dream Market data from March-April 2018 were extracted using a dedicated crawler. A Named Entity Recognition algorithm was developed to identify and categorize the type of fentanyl and other synthetic opioids advertised on cryptomarkets. Time-lagged correlations were used to assess the relationship between the fentanyl, fentanyl analog and other synthetic opioid-related ads from cryptomarkets and overdose data from the Cincinnati Fire Department Emergency Responses and Montgomery County Coroner's Office. Analysis from the cryptomarket data reveals increases in fentanyl-like drugs and changes in the types of fentanyl analogues and other synthetic opioids advertised in 2015 and 2018 with potent substances like carfentanil available during the second period. The time-lagged correlation was the largest when comparing Agora data to Cincinnati Emergency Responses 1 month later 0.84 (95% CI 0.45, 0.96). The time-lagged correlation between Agora data and Montgomery County drug overdoses was the largest when comparing synthetic opioid-related Agora ads to Montgomery County overdose deaths 7 months later 0.78 (95% CI 0.47, 0.92). Further investigations are required to establish the relationship between cryptomarket availability and unintentional overdose trends related to specific fentanyl analogs and/or other illicit synthetic opioids.

A Twitter-based survey on marijuana concentrate use

AIMS

The purpose of this paper is to analyze characteristics of marijuana concentrate users, describe patterns and reasons of use, and identify factors associated with daily use of concentrates among U.S.-based cannabis users recruited via a Twitter-based online survey.

METHODS

An anonymous Web-based survey was conducted in June 2017 with 687 U.S.-based cannabis users recruited via Twitter-based ads. The survey included questions about state of residence, socio-demographic characteristics, and cannabis use including marijuana concentrates. Multiple logistic regression analyses were conducted to identify characteristics associated with lifetime and daily use of marijuana concentrates.

RESULTS

Almost 60% of respondents were male, 86% were white, and the mean age was 43.0 years. About 48% reported marijuana concentrate use. After adjusting for multiple testing, significant predictors of concentrate use included: living in "recreational" (AOR = 2.04; adj. p = .042) or "medical, less restrictive" (AOR = 1.74; adj. p = .030) states, being younger (AOR = 0.97, adj. p = .008), and daily herbal cannabis use (AOR = 2.57, adj. p = .008). Out of 329 marijuana concentrate users, about 13% (n = 44) reported daily/near daily use. Significant predictors of daily concentrate use included: living in recreational states (AOR = 3.59, adj. p = .020) and using concentrates for therapeutic purposes (AOR = 4.34, adj. p = .020).

CONCLUSIONS

Living in states with more liberal marijuana policies is associated with greater likelihood of marijuana concentrate use and with more frequent use. Characteristics of daily users, in particular, patterns of therapeutic use warrant further research with community-recruited samples.