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www.rti.orgRTI International is a registered trademark and a trade name of Research Triangle Institute.
The Association Between Motivational Subtypes and Medical Cannabis Consumption: A 30-Day Diary Study
The Cannabis Science and Policy Summit
Scott P. Novak, Ph.D.
Senior Research Scientist
tAmericas Regional Meeting of the International Society for the Study of Drug Policy on Cannabis Policy
New York, New York
April 18, 2016
Conflicts of Interest
Dr. Novak has no Financial or Personal Conflicts of Interest; he
supports medicalization of cannabis, but not full-scale legalization
The opinions and interpretation of study findings do not
necessarily reflect those of the funding agencies or RTI
International
Acknowledgements
Funding:
National Institute on Drug Abuse (P.I. Dr. Novak, DA030427)
RTI International Strategic Investment Fund (SIF)
RTI Co-authors:
Gary A. Zarkin, Ph.D. (Economics)
Nick Peiper, Ph.D., MPH (Public Health)
Camille Gourdet, J.D. (Law)
Mark Edlund, M.D., Ph.D. (Medicine/Pain)
Jenny Wiley, Ph.D. (Behavioral Pharmacology)
Olivia Taylor, MS (Communication Science)
Georgiy Bobashev, Ph.D. (Computational Science)
Elizabeth Ball, BA (Social Psychology)
Consultant:
Diana Fishbein, Ph.D. (Neuroscience, Penn State University)
Today’s Talk: Overview
Identify the patterns of consumption involving commonly used
cannabis products (e.g., smoked, edibles) in 30 day window
among medical cannabis patients
Today’s Talk: Overview
Identify the patterns of consumption involving commonly used
cannabis products (e.g., smoked, edibles) in 30 day window
among medical cannabis patients
Examine the association between consumption patterns and
individual differences in delayed discounting, discretionary
income, diversion, and disease status
Today’s Talk: Overview
Identify the patterns of consumption involving commonly used
cannabis products (e.g., smoked, edibles) in 30 day window
among medical cannabis patients
Examine the association between consumption patterns and
individual differences in delayed discounting, discretionary
income, peer influence, novelty seeking, and medical need
Discuss the policy-implications of product regulation for
promoting public health outcomes in the United States
Translational Scientific Lens: Bridging Science and Policy
Population
Science
Laboratory
Science
Public
Clinical
Policy
Step 1: Identify number of different
subtypes (classes) of consumption
Practices
Step 2: Estimate number of
persons in each type (classes)
Step 3: Link consumption practices
with adverse mental and physical
health outcomes using animal
models of self-administration
Step 4: Connect consumption
practices with physiological and
psychological outcomes
Step 5: Using computer simulation
modeling, project the health
outcomes based on known
population parameters (validation)
Step 6: Extrapolate the impact of
different policies to expand or
restrict different
products/consumption practices
Translational Scientific Lens: Bridging Science and Policy
Population
Science
Laboratory
Science
Public
Clinical
Policy
Step 1: Identify number of different
subtypes (classes) of consumption
Practices
Step 2: Estimate number of
persons in each type (classes)
Step 3: Link consumption practices
with adverse mental and physical
health outcomes using animal
models of self-administration
Step 4: Connect consumption
practices with physiological and
psychological outcomes
Step 5: Using computer simulation
modeling, project the health
outcomes based on known
population parameters (validation)
Step 6: Extrapolate the impact of
different policies to expand or
restrict different
products/consumption practices
The Present Study
Medical Cannabis Users recruited in San Francisco, CA (n=50)
The Present Study
Medical Cannabis Users recruited in San Francisco, CA (n=50)
Recruitment flyers placed at local dispensaries in San Francisco
and advertisements placed on Craigslist, Online Cannabis
websites
The Present Study
Medical Cannabis Users recruited in San Francisco, CA (n=50)
Recruitment flyers placed at local dispensaries in San Francisco
and advertisements placed on Craigslist, Online Cannabis
websites
Subjects screened over the phone for age (age 18+) and medical
cannabis condition
The Present Study
Medical Cannabis Users recruited in San Francisco, CA (n=50)
Recruitment flyers placed at local dispensaries in San Francisco
and advertisements placed on Craigslist, Online Cannabis
websites
Subjects screened over the phone for age (age 18+) and medical
cannabis condition
Data collection occurred at RTI office (Sansone/Market), presented
valid driver’s license/state ID and medical ID card for cannabis
The Present Study
Medical Cannabis Users recruited in San Francisco, CA (n=50)
Recruitment flyers placed at local dispensaries in San Francisco
and advertisements placed on Craigslist, Online Cannabis
websites
Subjects screened over the phone for age (age 18+) and medical
cannabis condition
Data collection occurred at RTI office (Sansone/Market), presented
valid driver’s license/state ID and medical ID card for cannabis
Data Collection: May to July of 2015
All procedures and survey items approved by RTI IRB
Intensive Longitudinal Data: Daily Paper Diary
Issued a Daily Diary for
Each Patient
Named Motivation for
Use, Number of times
used, and Product Used
Goal was to identify
peak exposure, average
exposure, minimum
exposure and variability
Medical Cannabis: What’s In it?
Used “off-the-shelf” cannalytics testing kit: Identify 20 or so active
pharmacological agents, including Cannabidiol (CBD) and
Tetrahydocannabidiol (THC)
Sample Descriptive Statistics
Characteristic Percent
Males
Females
72%
28%
White
Black
Hispanic
Asian
Other
50%
15%
18%
12%
5%
18-24
25-39
40-55
55 or older
25%
33%
35%
07%
Employed (F/PT) 85%
Initiated use prior to
marijuana card
96%
Age of First Use <18 90%
Diversion 68%
Public Aid 35%
Common Medical Conditions among Marijuana Patients
15%
20%
5%
4%
15%
17%
22%
2%
41%
Migraine Lower Back HIV/AIDS Cancer G.I. Sleeping Anxiety PTSD
Mental Disorder/Sleep/Anxiety most common conditions, followed by
non-specific pain (lower back)
Identify the patterns of consumption involving commonly used cannabis products
Statistical Methodology
Latent Class Analysis: observed indicators are caused by an
unobserved, or latent variable of interest
Covariation among the observed indicators is expected
Study the patterns of interrelationships among the observed
indicators to understand and characterize the underlying latent
variable
Goal: To group individuals into categories, each one of which
contains individuals who are similar to each other and different from
individuals in other categories
Identify the number of categories and assign each person 1 and only
1 category that best represents their observed data
Use class assignment as predictor or outcome in explanatory
modeling
20
The LCA Model
Observed Continuous (y’s)
or Categorical Items (u’s)
Categorical Latent Class
Variable (c)
Continuous or Categorical
Covariates (x)
C
Y1 Y2 Y3 Yp
X
. . .
21
How is this modeling process conducted?
Run through models imposing different numbers of classes
Estimation via the EM algorithm
– Start with random split of people into classes.
– Reclassify based on a improvement criterion
– Reclassify until the best classification of people is found.
Types of Marijuana
Bud
Vape
Edible
Waxy
Alcohol: little attention to product variability
Tobacco: eCigarattes
Marijuana: New attention to product as 40% of
marijuana sold in Colorado was edible.
Observed variables in each classification
Motivation Product
Therapeutic (Pure) Flower or bud
Euphoric (Pure) Hash
Therapeutic and Euphoric (Mixed) Wax
Oil
Edibles
Topical Cream
Tinctures
Capsules
Latent Classes of Motivations by Product Type
Pure Therapy10%
Pure Euphoria10%
Mixed 55%
Smoked Euphoria25%
Results of Initial LCA
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Pure Therapy Pure Euphoria Mixed Smoke-Euphoria
Results of Initial LCA: Pure Therapy (10%)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Pure Therapy
Edible/Mix
Cream/Med
Pil/Med
Edible/Med
Results of Initial LCA: Pure Euphoria (10%)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Pure Euphoria
Edible/Euphoria Smoked/Euphoria
Dab/Euphoria
Results of Initial LCA: Mixed (55%)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Mixed
SMK/Mixed
DAB/MixedEdible/Mixed
Results of Initial LCA: Smoked Euphoria (25%)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Smoke-Euphoria
Edible/Euphoria
Smoked/Euphoria
Dab/Mixed
Who is in which class?
Characteristics of Latent Classes
Legend: O.R.=Odds Ratio (relative to other 3 classes); Note: adjusted for age and sex; NS=Non-significant at P<.05; All listed coefficients
significant at P<.05
Pure Therapy Pure
Euphoria
Mixed Smoked
Euphoria
Males (vs.
Females)
O.R.=0.79 O.R.=1.6 NS O.R.=1.91
Migraine O.R.=1.59 O.R.=.65 NS NS
Anxiety O.R.=0.65 O.R.=1.43 NS O.R.=1.54
Cancer/COPD NS NS NS O.R.=0.25
Diversion O.R.=0.76 O.R.=1.25 NS O.R.=1.36
Delayed
Discounting
O.R.=1.09 O.R.=0.97 NS O.R.=0.98
Public Aid O.R.=1.5 O.R.=0.75 NS O.R.=0.68
Probability of Consumption by Day of Week
0.6
8
0.4
6
0.5
5
0.4
5
0.6
5
0.7
2
0.8
7
0.6
7
0.5
8
0.5
8
0.4
8
0.6
0.7
8
0.8
8
0.6
7
0.4
5
0.5
9
0.5
0.6
1
0.7
1
0.8
7
0.6
8
0.4
3
0.4
9
0.5
5
0.6
2
0.7
0.8
7
SUN MON TUES W ED THUR FRIDAY SAT
Week 1 Week 2 Week 3 Week 4
Probability of Consumption and Modal Consumption Events
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
% Use Events/Day
Probability of Consumption Based on Date
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
% Use Events/Day
Payday
Payday
Payday
The “Payday” effect for Probability of Use each Date
0.75
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
% Use
Payday
Payday
Payday
Key Findings
Very few persons consume medical marijuana for purely medicinal-
type reasons; euphoria/relaxation key part. Possible indirect pain
relief pathways (secondary)
Significant proportion of sample appears to consume via smoked,
despite the adverse health effects.
Edibles comprised about 40% of the user groups, but often mixed in
with other user groups.
Males gravitate more toward euphoric use.
Experienced user groups: nearly all user groups reported initiation
prior to receiving medical card, and onset prior to age 18.
Use linked to weekend, and “Payday Effect”.
Limitations and Offsetting Strengths
LIMITATIONS:
Limited Number of Patients: Limited power for subgroup analysis
Study conducted only in CA: Concerns about generalizability
Self-Report Data: No Urine Drug Screen (UDS) to ensure validity
STRENGTHS:
Intensive Measures of Use: Capture diverse range of usage patterns
Diverse number of products: Ability to identify different consumption practices
Next Steps in Scientific Program
Population
Science
Laboratory
Science
Public
Clinical
Policy
Step 1: Identify number of different
subtypes (classes) of consumption
Practices
Step 2: Estimate number of
persons in each type (classes)
Step 3: Link consumption practices
with adverse mental and physical
health outcomes using animal
models of self-administration
Step 4: Connect consumption
practices with physiological and
psychological outcomes
Step 5: Using computer simulation
modeling, project the health
outcomes based on known
population parameters (validation)
Step 6: Extrapolate the impact of
different policies to expand or
restrict different
products/consumption practices
What are the policy implications?
Policy Implications
REMS: For medications with abuse liability, FDA requires Risk
Evaluation and Mitigation Strategy (REMS) by manufacturers. But,
medical users are every experienced.
TOBACCO Policy: Concerns of how to regulate, as tobacco, alcohol,
gambling, or prescription medicine. Significant number of users that
smoke signifies that tobacco regulatory framework holds merit, but
universal adoption should be cautioned.
RESHEDULING: Potential Scheduling of Cannabis by DEA in 2016?
