Evidence map›Paper›PMID 28704769›Full record

ArticleDrug and alcohol dependence2017

Characterizing marijuana concentrate users: A web-based survey.

Raminta Daniulaityte, Francois R Lamy, Monica Barratt, Ramzi W Nahhas, Silvia S Martins, Edward W Boyer, Amit Sheth, Robert G Carlson

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Trends and Socio-Demographic Differences of Cannabis Vaping in the USA and Canada.International journal of environmental research and public health · 2022
    Article
  12. Article
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  15. Review
  16. Persistent Exposure to ΔThe Journal of pharmacology and experimental therapeutics · 2021
    Article
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  19. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Raminta DaniulaityteCenter for Interventions, Treatment, and Addictions Research (CITAR), Department of Population and Public Health Sciences, Wright State University Boonshoft School of Medicine, United States; Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of Computer Science and Engineering, Wright State University, United States. Electronic address: raminta.daniulaityte@wright.edu.
Francois R LamyCenter for Interventions, Treatment, and Addictions Research (CITAR), Department of Population and Public Health Sciences, Wright State University Boonshoft School of Medicine, United States; Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of Computer Science and Engineering, Wright State University, United States.
Monica BarrattDrug Policy Modelling Program, National Drug and Alcohol Research Centre, UNSW, Australia; National Drug Research Institute, Faculty of Public Health, Curtin University, Australia; Centre for Population Health; Burnet Institute, Australia.
Ramzi W NahhasDivision of Epidemiology and Biostatistics, Department of Population and Public Health Sciences, Wright State University Boonshoft School of Medicine, United States; Department of Psychiatry, Wright State University Boonshoft School of Medicine, United States.
Silvia S MartinsDepartment of Epidemiology; Columbia University Mailman School of Public Health, United States.
Edward W BoyerDepartment of Emergency Medicine, Brigham and Women's Hospital, United States.
Amit ShethOhio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of Computer Science and Engineering, Wright State University, United States.
Robert G CarlsonCenter for Interventions, Treatment, and Addictions Research (CITAR), Department of Population and Public Health Sciences, Wright State University Boonshoft School of Medicine, United States; Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of Computer Science and Engineering, Wright State University, United States.

Funding

Modeling Social Behavior for Healthcare Utilization in DepressionR01MH105384 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI PATHAK, JYOTISHMAN, SHETH, AMIT P · 2015 to 2018
$2.1M
Trending: Social media analysis to monitor cannabis and synthetic cannabinoid useR01DA039454 · NIDA · WRIGHT STATE UNIVERSITY · PI DANIULAITYTE, RAMINTA, SHETH, AMIT P · 2014 to 2016
$1.4M
NIDA National Early Warning System Network (iN3): An Innovative ApproachR56DA038366 · NIDA · WRIGHT STATE UNIVERSITY · PI BOYER, EDWARD W, CARLSON, ROBERT GEOFFREY · 2014 to 2014
$300k
NIDA NIH HHS R01 DA039454NIDA NIH HHS R56 DA038366NIMH NIH HHS R01 MH105384
6 · The paper itself

Abstract

aimsThe study seeks to characterize marijuana concentrate users, describe reasons and patterns of use, perceived risk, and identify predictors of daily/near daily use.

methodsAn anonymous web-based survey was conducted (April-June 2016) with 673 US-based cannabis users recruited via the Bluelight.org web-forum and included questions about marijuana concentrate use, other drugs, and socio-demographics. Multivariable logistic regression analyses were conducted to identify characteristics associated with greater odds of lifetime and daily use of marijuana concentrates.

resultsAbout 66% of respondents reported marijuana concentrate use. The sample was 76% male, and 87% white. Marijuana concentrate use was viewed as riskier than flower cannabis. Greater odds of marijuana concentrate use was associated with living in states with "recreational" (AOR=4.91; p=0.001) or "medical, less restrictive" marijuana policies (AOR=1.87; p=0.014), being male (AOR=2.21, p=0.002), younger (AOR=0.95, p<0.001), number of other drugs used (AOR=1.23, p<0.001), daily herbal cannabis use (AOR=4.28, p<0.001), and lower perceived risk of cannabis use (AOR=0.96, p=0.043). About 13% of marijuana concentrate users reported daily/near daily use. Greater odds of daily concentrate use was associated with being male (AOR=9.29, p=0.033), using concentrates for therapeutic purposes (AOR=7.61, p=0.001), using vape pens for marijuana concentrate administration (AOR=4.58, p=0.007), and lower perceived risk of marijuana concentrate use (AOR=0.92, p=0.017).

conclusionsMarijuana concentrate use was more common among male, younger and more experienced users, and those living in states with more liberal marijuana policies. Characteristics of daily users, in particular patterns of therapeutic use and utilization of different vaporization devices, warrant further research with community-recruited samples.

Indexed as

CannabisHumansInternetMarijuana SmokingMedical MarijuanaRiskSurveys and QuestionnairesVapingMedical MarijuanaCannabisMarijuana concentratesWeb survey

Identifiers

PMID28704769
PMCPMC5567791

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.