Sharing of prepared cannabis and cannabis-related paraphernalia may increase the risk of COVID-19 given its mode of transmission.This observed difference may have several explanations.First, the COVID- 19 pandemic exacerbated economic and social challenges among SM populations which has increased mental and physical health issues and decreased general well-being.However, social connectedness and social support among SM groups has been shown to reduce anxiety, depression, and other mental health challenges before and during the COVID-19 pandemic.Moreover, gay, bisexual, and other men who have sex with men have reported substance use as a way of community connection.Thus, sharing of prepared cannabis and cannabis-related paraphernalia may provide an avenue for SM individuals to connect with one another especially during the pandemic.Second, other substance use and in particular methamphetamine use during the pandemic was greater for SM compared to non-SM individuals and among SM men compared to non-SM men.Methamphetamine use has been shown to be higher among SM men compared to the general population with use reported in various settings including public , private parties, work environments, and sexual interactions.Moreover, cannabis co-use with methamphetamine has been reported to help with coming off methamphetamine highs.Thus, the social use of methamphetamine with others and the co-use of cannabis and methamphetamine may partially explain the differences in sharing of cannabis between SM and non-SM groups, especially among men.Finally, sexual experiences with causal or new partners among SM men may have also increased opportunities for sharing cannabis dry rack.Multiple studies have shown that the number of sexual partners and new sex partners among SM has decreased especially during the first COVID-19 wave.Yet, the prevalence of more than 1 partner during this time frame was still high.Additionally, these studies highlighted that decreases in number of partners and sex with new partners started to level off in the latter half of the first pandemic wave which is the time frame of “during the pandemic period” for this study.
However, we are unable to conclude who sharing was occurring with and are unable to make any conclusions about COVID-19 risk.There are other additional limitations to consider while interpreting the results.First, this study used a convenience sample comprised of primarily non-Hispanic White individuals, who were highly educated, with a high prevalence of substance use.Findings from this study may have limited generalizability to those who use cannabis in the US at large.Second, our study examined one period during the pandemic and cannot make any conclusions about lasting differences in frequency or sharing of cannabis.Third, there may have been potential mis-classification of outcome measures and with reporting of sexual identity, but we expect it have non-differential impacts to this study.Moreover, our survey was anonymous and likely minimized the social desirability bias.Last, we were unable to conduct sub analyses for frequency and sharing of cannabis during the pandemic for women because of small sample size.At the end of December 2019, the novel SARS-CoV-2 virus, the virus that causes coronavirus disease 2019 , emerged.1-3 SARS-CoV-2 is a highly transmissible single-stranded RNA virus that leads to upper and lower respiratory infections, is spread primarily through droplets and airborne transmission, and causes symptoms of fever, cough, and dyspnea.Community transmission in the United States was estimated to have begun in February 2020 and by mid-March 2020, all 50 states and territories reported cases of COVID- 19.To slow the spread of COVID-19, non-pharmaceutical interventions were implemented across the US.However, it is unclear what effect, both intended and unintended, these policy implications had on populations and individual level behaviors.Non-pharmaceutical interventions were developed in response to the 2009 H1N1 pandemic and included a protocol to slow the spread of future novel respiratory influenza A virus pandemics.NPIs are strategies for disease control when no pharmaceutical alternative exists and include actions at the personal, environmental, and community level.Specifically, NPIs implemented during the COVID-19 pandemic included travel restrictions, limitations on mass gatherings and recommendations for transition to virtual events, social distancing measures, stay-at-home orders, closure of non-essential work spaces and schools, and cloth face covering guidance.Such NPI mitigation strategies aimed at decreasing or limiting person to person contact thus reducing the probability of infection.NPIs were recommended by the US Centers for Disease Control and Prevention and governing authorities but policies were ultimately made by state and local officials based on conditions relevant to that jurisdiction.For instance, geographical differences in the US contributed to differences in COVID-19 incidence because of varying distributions of epidemiological and population level factors.These factors include timing of COVID-19 introduction, population density, age distribution, prevalence of underlying medical conditions, timing and extent of community mitigation measures, diagnostic testing capacity, and public health reporting practices.Therefore, policies between states across the pandemic have varied in intensity, timing, and duration.
A study from the CDC examined differences among stay-at-home orders across states from March 1st to May 31st, 2020, on population movement.Stay-at-home orders were associated with decreased population movement; however, movement increased significantly as states began lifting restrictions.Kaufman et al.reported the initial effect of state variation in social distancing policies and non-essential business closures on COVID-19 rates.Social distancing and closure of non-essential businesses and public schools were shown to reduce daily COVID- 19 cases by 15.4% with effects varying across states.Finally, Pan et al.showed that there was heterogeneity in NPI domains across the US census region and concluded that states with the most aggressive policies had the highest mitigation of COVID-19 infection.While heterogeneity in intensity and duration of state policies on COVID-19 mitigation were demonstrated, all such studies have been restricted to the initial wave of the pandemic and have only assessed the associations of policies on COVID-19 infection spread at the population level.To date, there have been little to no studies that assessed the associations of policies on individual behaviors.Thus, the goal of this study is to understand how varying COVID-19 state policy influenced individual behaviors.Cannabis use in the US continues to grow with 46% of the population reporting past year cannabis use in 2019.12 Cannabis is predominantly a dried flower with smoked cannabis being the most popular mode in the US.Cannabis can be smoked as cigarettes or with pipes, water pipes , cannabis vaporizers , e-cigarettes for cannabis extracts, and rigs for cannabis extracts.Moreover, cannabis social practices before the COVID-19 pandemic have previously involved sharing prepared cannabis such as cannabis cigarettes, joints, and blunt with others.15 In this paper, we define sharing of cannabis as sharing of prepared cannabis and cannabis-related paraphernalia.Sharing of paraphernalia for cannabis, tobacco, and crack cocaine use has previously been demonstrated as a risk factor for other respiratory infections.Thus, sharing of prepared cannabis and cannabis-related paraphernalia, or rather non-sharing, is proposed as a proxy for COVID-19 risk mitigation behaviors.This study aimed to do the following: 1) Describe variations in US state COVID-19 policy; and 2) Quantify the magnitude of association that state’s COVID-19 policy has on individual level sharing of prepared cannabis and cannabis related paraphernalia.This study uses a semi-individual design where the exposure of interest is at the population level and the outcome is at the individual level as seen in air pollution studies and studies on motorcycle helmet policy.For example, motorcycle helmet laws differ across states and were grouped together based on type of helmet legislation.Following this categorization, injury patterns from individual health records were evaluated by controlling for both individual and population level variables.Thus, a semi-individual study design is more efficient than an ecological study designs as one may control for individual and group level confounding, it has less measurement error, and is inherently based on individuals.Data for this study were collected from an anonymous US-based web survey on cannabis and cannabidiol related behaviors from August 2020 – September 2020.Detailed methods on this survey have been previously specified in detail.Briefly, survey respondents were comprised of a non-random convenience sample, 18 years of age or older, who reported non-medical cannabis, cannabis for medical use, and/or CBD use in the last 12 months, and resided in the United States.
Respondents were recruited through Reddit, Bluelight, Craigslist, and Twitter, received 5 USD for their participation, and were prevented from “ballot stuffing” by limiting to a unique internet protocol address.Respondents were included in this study if they reported non-medical cannabis use and self-reported using cannabis in the following ways: smoking ; vaporizing plant; and/or vaping oil/concentrates.Individual level data for this study was draw from the National Cannabis Study described above.The COVID-19 Cannabis Survey was supported by US National Institute on Drug Abuse and Semel Charitable Foundation.In this single survey, participants were asked to recall their non-medical cannabis use behaviors at two 3-month time points: before the COVID-19 pandemic and during the COVID-19 pandemic.Data from this survey include non-medical cannabis frequency of use, mode of use, sharing of prepared cannabis and cannabis-related paraphernalia, planting racks and demographics.We then drew population/ecological level data from three different sources.The first data source was from the was the Kaiser Family Foundation’s State COVID-19 Data and Policy Actions accessed through GitHub repositories with policy data by US states starting June 4, 2020 through November 19, 2021.Specifically, we used information from 3 time points that overlapped with our study period during the pandemic.The data in June 2020 included the following policies: Stay-at-home order; non-essential business closures; larger gathering ban; and restaurant limits.The data from July and August 2020 included the following policies: those from June plus bar closures and face covering requirements.The second data source was from Johns Hopkins University and Medicine COVID-19 Dashboard by the Center for Systems Science and Engineering with data stored in a GitHub repository from April 4, 2020 until January 12, 2022.COVID-19 data included confirmed infections, deaths, recovered infections, active infections, testing, and hospitalizations by state.For this study, we used COVID-19 confirmed infections from May 24, 2020.The final data source was from the US Census, which included state population size in 2020 and state percent urbanicity in 2010.At the time of the analysis, the Census did not have state percent urbanicity beyond the year 2010.The outcome of interest was respondent’s self-reported sharing of prepared cannabis and cannabis-related paraphernalia during the COVID-19 pandemic.Respondents used a Likert-scale to agree with the following question, “I shared joints, blunts, bongs, pipes, vaporizers, or vape pens used for cannabis ,” with answer choices being never, sometimes, about half the time, most of the time, and always.We dichotomized sharing of cannabis paraphernalia to no sharing and any sharing.The exposure of interest was state’s COVID-19 policy actions.We scored policies by strength using a standardized coding method ranging from 0 to 5 as suggested by Lane et al., and the CDC on stay-at-home orders.
In short, a policy had a score of 5 if the mandate was very high and 0 for no recommendations or rules implemented for that policy.The KFF State COVID-19 Data and Policy Actions data source had policy information on six policies for each US state.These policies included stay-at-home orders, non-essential business closures, large gather bans, restaurant limits, bar closures, and face covering requirements.For instance, stay-at-home order included statewide orders, new stay-at-home order, high-risk groups, rolled back to high-risk groups, lifted, and no state order.We coded statewide and new stay-at-home order as 5, high-risk groups and rolled back to high-risk groups as 4, and lifted or no state order as 0.Policies for non-essential business closures included: some non-essential businesses permitted to reopen, some non-essential businesses permitted to reopen with reduced capacity, new business closures or limits, all non-essential business to permitted to reopen with reduced capacity, all non-essential businesses permitted to reopen, and no state order.Policies for large gathering bans included all gatherings prohibited, >10 people prohibited, new limit on large gatherings in place, expanded to new limit below 25, expanded to new limit of 25, expanded to new limit of 25 or fewer, expanded to new limit above 25, lifted, other, and no state order.Policies for restaurant limits included closed except for takeout/delivery, newly closed to dine-in services, new capacity limits, reopened to dine in service with limited capacity, limited dine-in service, reopened to dine-in service, and no state order.Policies for bar closures included closed, newly closed, new service limits, and reopened.And finally, policies for face covering requirement included required for general public, required for certain employees; allows local officials to require for general public, required for certain employees, allows local officials to require for general public, and no state order.Detailed coding of state policies can be found in Appendix 4.A.1.For each month, we summed the values for each of the four specified policies for June , the six specified policies for July , and the six specified policies for August.