marijuana in the united states: the developmental pathways
TRANSCRIPT
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Marijuana in the United States: The developmental pathways leading to and from initiation
April 13, 2016
Scott P. Novak, Ph.D.Senior Research Scientist
tPresented to: Bennett Pierce Prevention Research Center at the Pennsylvania State UniversityApril 13, 2016
Today’s Talk: Theoretical Overview
Review the political and scientific climate in the United States toward Marijuana legalization
Describe the patterns of consumption involving the developmental progression from initiation to dependence and the different marijuana products (e.g., smoked, edibles)
Discuss methodological challenges and novel approaches to data collection for marijuana users
Today’s Talk: Methodological Overview
Collection of bio-specimens in the study of marijuana
Using innovative methods like Internet Panels to capture user groups
Studying the unique patterns of consumption using intensive longitudinal data
Understanding socio-cultural norms using Social Media monitoring via Twitter
Career Pathway Transdiscplinary/Multidiscplinary (UWis, Kentucky)
– Master’s/PhD (Medical Behavioral Science) Sociology/Social Psychology (Quantitative) Psychometrics Health behavioral change Developmental epidemiology
– RWJ Faculty Health Scholar (Harvard School of Public Health) Biological mechanisms of disease and health Biostatistics Psychiatric Epidemiology
– National Cancer Institute Faculty Fellow (R25) (Brown University) Biological mechanisms of disease and health Genetics and Neuroscience
Mean Tweets
I wonder what company paid Dr. Novak's way to "PainWeek". This paper is going right into my "BS" file. K. B.
Anyway, I find your little article to be nothing but rhetoric and propaganda. Janis A.
I'd say this particular paper, were it available in printed format, would serve as kindling for my next bonfire “Dr.” Matthew Klein
Source: http://www.medscape.com/viewarticle/851037#vp_2
Climate of Medical Marijuana: 23 States plus DC and Guam Marijuana is Currently a DEA Schedule I Substance: No medical benefit
Will PA be next!
HB142 and SB3
No smoked forms
MD/DO prescribers
State approved list
Issue Divides Academics and Politics
American Medical Association: Changed position from against marijuana to “evidence unknown”
Drug Enforcement Agency: Expected ruling on marijuana late 2016/early 2017
Academic Groups: Against marijuana, using tools of tobacco “advocacy” against view that marijuana is a dangerous drug, using tools of anti-tobacco blending advocacy and science
Significant public health “threat” or strategic importance (Dr. Nora Volkov, NIDA, 2015 CPPD)
What is the difference between Medical and Recreational
Difference in “street” marijuana versus federally regulated marijuana are pronounced
RTI (B. Thomas PI of NIDA’sMarijuana Supply Program, S. Novak, NIDA Co-I)
“Street” marijuana more potent (THC), higher proportion of illicit constituents (laced with hallucinogens)
Difference between medical and recreational: variation by state in what can be sold in each type of dispensary
How do you access medical marijuana? Medical Prescriber who is licensed to practice medicine applies to
state for prescriber authorization.
Patients must visit prescriber and receive a letter from doctor and then apply to state for medical marijuana card. In most states, doctors can only write letter if patient has state-approved listed medical condition.
Most common conditions: Migraine and Anxiety, followed by physical conditions, such as fibromyalgia, lower back pain, and cancer. State lists under common contention
Patient can visit state-licensed dispensary. No monitoring so person can get up to dispensary limit each day.
Laws in states as to where dispensaries can “set up”, mostly in commercial zones, away from schools and recreation; and also limit combustible and “visual packaging”
What are the Effects of Legalization Over Time?
Trends in Past-Year Marijuana Over time
Dramatic increases in:
*Number of usershighest rate: youthincreases: 55+, women
*Number of days used highest rate: youthincreases: ages 35-59increases: whites increases: males
*Perceived Harm (users and non-users alike)
*Moderate increases in DSM-IV/V disordered use, cannabis use withdrawal, dramatic increase in delirium; added to ICD-10
Data from National Survey on Drug Use and Health (SAMHSA, 2014)
Effects of 48 hour induced abstinence on daily medical use
18
11
7.8
6.5
4.9
2.1 1.9
0
2
4
6
8
10
12
14
16
18
20
Anxiety Depressive Paranoia Intrusivethoughts
Poor motivation Memory loss Appetite
Respondents
Source: Novak, S.P., and Peiper, N, (2016).
New Public Debate: Role of Medical Marijuana in Pain Relief
Trends in Marijuana and Prescription Opioid Abuse
Marijuana is on the rise, while Prescription Opioid Abuse is on the decline
Source: Novak, S.P., Peiper, N, and Zarkin, G.A. (2016).
Marijuana and Poly Use of Opioids and Alcohol
10.8
4.9
65.3
2.4
8.3
2.5
55.1
0.6
10.212.6
4.2
66.2
2.0
10.4
2.2
54.6
1.0
11.6
0
10
20
30
40
50
60
70
Cannabis NMPR Alcohol NMPROnly
CannabisOnly
BothNMPR andCannabis
AlcoholOnly
CannabisOnly
BothAlcohol
andCannabis
2003 2013
Panel 1: Past-Year Prevalence1 Panel 2: Past-Year Poly-Use Prevalence: NMPR2 Panel 3: Past-Year Poly-Use Prevalence: Alcohol3
Take home: Illicit Rx Opioid Use and Marijuana on Decline, increase with alcohol
Peak Exposure for Number of Opioid Days by Marijuana
15
4138
47
33
51
74
66
0
10
20
30
40
50
60
70
80
MJ Quartile 1 MJ Quartile 2 MJ Quartile 3 MJ Quartile 4
2003 2013
Take Home: While poly opioid/marijuana use going down, significant increases in number of days using opioids across all levels of cannabis
Peak Exposure based on Number of Days Alcohol by Marijuana
95
106
120126
97103
125119
0
20
40
60
80
100
120
140
MJ Quartile 1 MJ Quartile 2 MJ Quartile 3 MJ Quartile 4
2003 2013
Take Home: The effects for marijuana and illicit opioid use not present for less frequent marijuana users, but more pronounced for higher levels of use
Source: Bachhuber, 2014
Medical Marijuana and Opioid Use
States with a medical cannabis law have higher rates of overdose? (reason: Selection Effect as States with higher rates of overdose more likely to add a medical marijuana law)
Post-Implementation Difference in Mortality Rate
States with higher overdose rates implemented MML at higher rates than non MML states.
Above figure shows trend toward reduction of opioid deaths after implementation of MML
Source: Bachhuber, 2014
Theoretical Issues: Marijuana as a gateway drug? Gateway drug: Kandel (1982)---illicit use opens up to cultural groups
and physiological experiences toward priming of illicit drugs
Other Socio-Cultural Theories: – Delinquent youth at higher risk of initiation (peer association) – Sensation seeking youth higher risk of initiation – Mad, Sad, and Glad risk factors for initiation – General repertoire (Problem Behavior Theory, Jessor and Jessor)
Is it still a gateway drug? – Now viewed as more normative, especially with medical marijuana
Is marijuana better explained by Health Behavior Theories or Delinquency ?
– Theory of Reasoned Action (I. Ajzen and M. Fishbein) – Theory of Planned Behavior (I. Ajzen) – Triadic Theory of Influence (B. Flay)
Types of Marijuana
Bud
Vape
Edible
Waxy
Alcohol: little attention to product variability Tobacco: eCigarattesMarijuana: New attention to product as 40% of marijuana sold in Colorado was edible.
Marijuana edibles: Packaging leads to unintentional exposures
Look like regular food, and packaging may be mistaken by youth and elderly
Takes about 45 minutes to take effect for peak exposure
Similar in packaging to tobacco (candied cigarettes) and alcohol (caffeine beverages)
Marijuana Edibles: New Prevention Initiatives
High rates of marijuana overdoses in Colorado required immediate public health interventions. In Colorado, smoke-free laws prevent use in public spaces, hotels bans smoked forms, so edibles very popular with tourists; also very palatable to non-smokers
Medical Marijuana Study in San Francisco
NIDA study of Medical Marijuana Users in SF
Goal: Identify patterns of consumptions and predictors of use
Survey of 500 medical marijuana users (ages 18+ in San Fran
Part of NIDA’s Medicinal Cannabis Program (RTI)
Medical Cannabis: What’s In it?
Significant Misperceptions in Perceptions of THC and CBD and Actual Levels; Over 80% couldn’t name what’s in each product, and 40% product mislabeling
Intensive Longitudinal Data: Daily Paper Diary
Issued a Daily Diary for Each Patient
Named Motivation for Use, Number of times used, and Product Used
Key Findings
– Over 90% of medical cannabis users reported daily use
– Average number of times used per day was 3 times
– Payday effect was observed, such that use was higher in the 3 days following the 1st and 15th of the month
– Use was higher over the weekends than weekdays
– Less than 20% reported use of edibles, but edibles more likely to be used on weekday than weekend
– Edibles were easier to use during work-hours as was vapes
Key Findings – Combustible use was lower among persons with COPD and
HIV/AIDS compared to those with anxiety and general back pain
– No race/gender/age effects were associated with usage patterns
– Persons with a cannabis card over 5 years had less variability in use compared to recently minted medical cannabis users
– No real concern about labels---users were more interested in the “budtenders” and their role in helping them choose appropriate medicines
– Most of the use was for euphoria and only 20% of events were used for pure therapeutic motivations
– Medical deliveries accounted for 20% of purchases, compared to 80% who reported purchasing onsite
Methodological Issues in Data Collection for Marijuana Use
Higher rates of non-response compared to non-drug users
More likely to have stable residence compared to illicit drug users, but similarly likely to withhold consent for research studies
Research using web-panels as a new form of recruitment—– Opt-In (e.g., Neilson) – Sampling Frame (GfK)
Opt in Panels: Cheaper and easier to recruit large numbers of users, but generalizability questionable; Common trick: use targets to ensure sample matches population-level characteristics
Sampling Frame: Generalizability better, but sample is limited and high a high likelihood of respondent burden. Often presume that drug abusers are well-represented in sample, but bias is very high and “hidden”.
Tracking Marijuana Users is Very Challenging
Web-Panels
• Utility for Research Studies – Pre-screened – Time-saving – Cost-saving
• Strengths: – Pre-screened – Time-saving – Low cost – Monitoring and Measurement
• Limitations: – Selection bias: Internet access – Selection bias: Opt-in – Representativeness?
Why Do We Care about Generalizability?
Some stakeholders unaware of importance: – Sampling often less of a concern in RCTs– Stratify groups for precision medicine – Selection probability unknown
Clinics and Patients Single Stage Sample
Multi Stage Sample
Web Panels Use Quota Sampling
Non-probabilistic—no master sampling frame. Similar to target sampling, save that it is more systematic Two types
– Proportionate to Size (PS)– Purposive
Similarities to target sampling: (a) choosing the relevant stratification and dividing the
population accordingly (proportion or purposive) (b) calculating a quota for each stratum(c) continuing to invite cases until the quota for each
stratum is met.
Stratification/Target
Proportionate To Size – Racial targets and Drug Use Targets
Race/Ethnicity % of Population Drug Use/Yes Sample Target (n=1000)
Proportion
White, (non-Hisp)
82% 8% 66 820
Hispanic 12% 5% 6 120
Black, (non-Hisp)
6% 12% 7 60
Purposive Sample
Select respondents based on informative characteristics – Want to ensure you have enough drug cases to analyze
– Can aim for a 50:50 split to identify simple comparisons
– Can use case-control to identify relation between exposure and outcome, but goal is not surveillance
– Can also mix…..
Stratification/Target
Need to select sufficient numbers of cases
Race/Ethnicity % of Population Drug Use/Yes Sample Target (n=1000)
Proportion
White, (non-Hisp) 82% 8% 66 820
Hispanic 12% 5% 6 120
Black, (non-Hisp) 6% 12% 7 60
Stratification/Target
Purposive (mixture of quota and Purposive) – Can add more cases in selected cells and then multiply by inflation factor
(4) – Create weights—everyone in White, non-Hispanic gets a weight of 1.
Everyone in Hispanic of Black gets a weight of .25. Sum of weights will add to 1000 (population total!) Can then analyze sample, have power for studying outcomes, and preserve population distribution
Race/Ethnicity % of Population Drug Use/Yes Sample Target (n=1000)
Proportion
White 82% 8% 66 820Hispanic 12% 5% 24 (IF=4) 120Black 6% 12% 28 (IF=4) 60
A Small Tweak
What if don’t know population prevalence of outcome of interest? How do you select quotas?
Find related variable that you do know the population prevalence and then identify correlation to variable of interest
Example: Prescription Stimulant Abuse and Cannabis a) Unknown: Rx Abusers b) Known: Cannabis Abusers c) Studies have shown that about 90% of prescription stimulant
misusers have also used cannabis
Modifying the weights
The transitive theorem: If you weight using a highly-related known variable (Cannabis) than you in fact increasing likelihood that you are reducing the bias of the population estimate of the target variable of interest
Does this work?
Rim Weighting
Also known as incomplete post-stratification and raking ratio estimation
Allows control for more than one set of post-strata
Iterative method– Apply post-stratification to each set of post-strata in turn, until all
have been aligned once– Repeat last step until all are within allowable tolerances
Both post-stratification and rim weighting can be applied to data with existing weights, such as inverse probability weights
Some Comparisons: Lifetime Drug Use
Quota Study National Data Cannabis* 35.4% 36%Heroin** 1.8% 2% Cocaine** 9.5% 8% Rx Stimulant 6.1% N/A
*Used in weighting **Sensitivity Analysis
Financial Advantages to Hybrid Methods
For about 1.5 million dollars total: – Hire market research firms in the US to locate and
survey youth/high-risk persons who use marijuana or fit population characteristics
– Use web-panel survey – 1-800 misdial services (route calls to telephone
screening surveys
NIH Reviews Mixed!
Applications to Twitter
Using Twitter for Theory Generation and Rapid ReponseTheory important to social science
Not well-supported by NIH
Focus on ‘quick hitters’ publications
Theory and Paradigm often confused
Our next goal: To use emergent data to develop new bio-behavioral models of marijuana use typologies
Bridge animal and human models of self-administration
Human Consumption ==Animal Model-Human Typology
Twitter-great source of unfiltered, cheap data
Discussion on Marijuana: Tweets
Explored 8,868 posts from 1/1/15 to 6/30/15 related to marijuana and the workplace
Emerging themes:
(1) Avoiding or “beating” drug testing
Crimson Hexagon Tweets
Emerging themes:
(2) Avoiding detection while intoxicated at work
Crimson Hexagon Tweets
Emerging themes:
(3) Relieving work-related stress
Crimson Hexagon Tweets
Emerging themes:
(4) Anger/disdain towards marijuana users
1. Developed text analysis tool, “DataFunnel”
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2. Developed classification tool, “Classiphy”
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3. Developed website, “CannabisConvo”
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4. Developed search terms
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Initial pull using 19 marijuana terms
Weed Marijuana Bong Cannabis Mmj MedicalMarijuana LegalizeMarijuana Cannabinoids Mmot LegalizeCannabis DaggaDebate …
Filter results to topics of interest
Medical – Mmj, Medical, Health, Doctor, … Motivation for use (11)
– Pain – Pain, Ache, Hurt, Opioid, Arthritis, …– Anxiety – Anxiety, Anxious, Panic, …– Sleep – Sleep, Sleeping, Slept, Insomnia, …– …
Modes of use (25)– Joint – Joint, Jay, Spliff, Doobie, …– Vapor – Vapor, Vaping, Electronic, Venturi, …– Edibles – Edibles, Brownies, Gummies, …– …
States (50 + D.C.)– Alaska – Alaska, Anchorage, Juneau, Murkowski, …
…
5. Created catalogue for marijuana legal status by state
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Colorado
6. Developed codebook
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Primary categories include…– States mentioned in the tweet– Motivations for use– Modes of use (image to the right)– Side effects– Economics and marketing– Social climate– Regulations/legal issues
7. Coded tweets and developed classification algorithm
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1. Developed coding guidelines(image to the right) which included 15 illustrative examples
2. Coded 913 tweets in Classiphy (Slide 7)
3. Trained and tested a classification algorithm
8. Experimented with various data summaries and analyses
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– Phrase Extraction (below)– Topic Modeling– Trending phrases (below)– Social network graph (right)– Trend lines (below-right)– Legal vs. non-legal states (below)– Geographic analysis
Findings
There are a lot of data – 140k tweets/day from API– Of those, 40K tweets/day (28%) related to medical marijuana and 2.5K
tweets/day (1.7%) are geotagged– 115k tweets/day from GNIP
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There is a lot of unwanted “noise”…– Spam– Retweet farms– False positives – Tweets that are simply uninteresting
…but there are interesting results quantitatively and qualitatively
– Interesting tweets related to every imaginable topic and subtopic
– Apparent differences between legalized and non-legalized states
– Trends and spikes (e.g., 4/20)
Limitations and lessons learned
Limited inferential ability using API data, so GNIP may be a better option
– Sample of unknown size with API– Changes are often made to the API– Sparse geographic data with both sources
Need to develop an evolving, systematic process to refine search terms
Complications around public display of tweets
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Findings from classification exercise
Manual coding is laborious and difficult. – For example, are the following tweets about a motivation for using marijuana? “Not smoking weed x insomnia = me moaning on Twitter at 1AM” “I took sleeping medicine and my little brother goes, ‘Are you on drugs? Have you
taken the marijuana?’ Innocence at its finest” “I honestly haven't smoked that much weed yet; I'm destressing from two nights
of insomnia and the general stress of life by spewing thoughts”
Additional complications arise when the topic of interest is not highly prevalent.
Despite such difficulties and despite our small sample size, the initial classification model performed pretty well.
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Summary Marijuana is a drug that is currently being re-framed from an illegal to legal drug, and there are many challenges to its study.
Theoretical: Is it a gateway drug? How is it used with other drugs?
Methodological: How can we efficiently access population of users?
Epistemological: Need to develop novel theories using rapid data available
