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An in-depth analysis of the relationship between cannabis use and illicit drug use, discussing key findings from various studies and the implications of these findings for the gateway hypothesis. The document also explores the influence of demographics, drug use history, and other factors on the relationship between cannabis use and illicit drug use.
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U.S. Department of Justice Office of Justice Programs National Institute of Justice
U.S. Department of Justice Office of Justice Programs
810 Seventh St. N.W. Washington, DC 20531
David B. Muhlhausen, Ph.D.
Director, National Institute of Justice
This and other publications and products of the National Institute of Justice can be found at:
National Institute of Justice
Strengthen Science • Advance Justice
nij.ojp.gov
Office of Justice Programs
Building Solutions • Supporting Communities • Advancing Justice
OJP.gov
The National Institute of Justice is the research, development, and evaluation agency of the U.S. Department of Justice. NIJ’s mission is to advance scientific research, development, and evaluation to enhance the administration of justice and public safety.
The National Institute of Justice is a component of the Office of Justice Programs, which also includes the Bureau of Justice Assistance; the Bureau of Justice Statistics; the Office for Victims of Crime; the Office of Juvenile Justice and Delinquency Prevention; and the Office of Sex Offender Sentencing, Monitoring, Apprehending, Registering, and Tracking.
Opinions or conclusions expressed in this paper are those of the authors and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Gateway Hypothesis COMMITMENT TO UNBIASED RESEARCH
This report was conducted by the Federal Research Division (FRD) within the Library of Congress.
FRD provides customized research and analysis on domestic and international topics to agencies
of the U.S. government, the government of the District of Columbia, and authorized federal
contractors on a cost-recovery basis. This report represents an independent analysis by FRD and
the authors, who have sought to adhere to accepted standards of scholarly objectivity. It should
not be construed as an expression of an official U.S. government position, policy, or decision.
Significant measures were taken to ensure that the topic was researched and written with a
commitment to nonpartisan scientific integrity, and with as little bias as possible:
Each researcher was selected and vetted by FRD. Potential contributors were screened and eliminated for factors that might introduce bias to the research. FRD interviewed approximately 25 PhD statisticians to identity the most qualified, and least biased, researchers.
Each researcher was selected for having not previously researched/written on the topic.
The identity of the client was never disclosed to the researchers. Each researcher signed a nondisclosure agreement, committing to not discuss the project outside of the project team. FRD’s chief and business manager were the only individuals aware of the client’s identity and acted as the communication channel between the client and research staff.
FRD solicited feedback from outside experts and that feedback has been incorporated. None of the experts have written specifically on the topic of cannabis and do not have a vested interest in a particular outcome.
As a nonpartisan cost-recovery government organization, FRD is free of the business and political drivers that could otherwise impact the integrity of its research.
The funding agency owns the intellectual property developed by FRD while working under the agreement. FRD holds no rights to sell, share, or promote intellectual property.
The project team thanks the following experts for their contributions to this report:
Dr. Mary Amanda Dew, professor of psychiatry, psychology, epidemiology, and biostatistics at the University of Pittsburgh, and
Dr. Tomoko Steen, a research specialist in the Library’s Science, Technology, and Business Division and an adjunct professor of microbiology and immunology at Georgetown University.
Gateway Hypothesis HOW TO READ THIS REPORT
The gateway hypothesis contends that using cannabis causes an individual to progress to using
harder illicit drugs such as cocaine or heroin. FRD reviewed the existing corpus of literature on this hypothesis to evaluate the integrity of the scientific and statistical evidence used to
conclude whether cannabis does indeed serve as a gateway drug.
Given the complexity of both substance abuse and statistics, as well as the specific terminology
involved, a short primer precedes the main text of the report. This section outlines the overall
structure of the report and defines the key statistical concepts that were used to examine the relationship between one’s use of cannabis and other illicit drugs.
This report provides an analysis of 23 peer-reviewed research studies, including both human-
and animal-based studies, on cannabis use and its association with the subsequent use of other
to evaluate the statistical reliability of their findings.
The structure of the report includes the following sections:
A high-level summary of FRD’s findings Sec. 2 (pp. 11–12) A short history of the gateway hypothesis and U.S. drug laws Sec. 3 (pp. 13–19) An explanation/evaluation of association and causation Sec. 4 (pp. 20–26) FRD’s key findings from the literature review Sec. 5 (pp. 27–33) Profiles for 16 human-based studies Sec. 9 (pp. 42–72) Profiles for 7 animal-based studies Sec. 10 (pp. 73–84)
The main text provides readers with a high-level summary of FRD’s analysis, as well as general
background information on the history of the gateway hypothesis and addictive drug laws in the
United States. There is also an overview on association and causation, the two key measures that
were considered for this report. This section provides real-world examples of the terms, as well
as guidance on evaluating them in statistical research. After the key findings and conclusion,
appendices detail the process through which FRD identified the relevant studies, along with how that relevancy was determined.
Gateway Hypothesis HOW TO READ THIS REPORT
Correlation : A type of association that measures the strength of a straight-line or “linear” relationship between two variables. Stated differently, if the variables are graphed and the points on the graph collectively assume the shape of a straight line sloping upward or downward, then there is evidence of a linear relationship between those variables. Correlations can be either positive or negative.
Covariate : A variable that is related to a result (the result is often referred to as the “response variable”). Because covariates are associated with a result, that association should be examined; if the research does not examine this association, then it may make incorrect assessments about the relationship of other variables on the result. For example, if research on short-term memory loss among athletes only examined the influence of age and did not include covariates such as head trauma experience and gender, then the results could suggest that age has a stronger influence on memory loss than it actually does.
Hazard Ratio : A hazard ratio is the ratio of two hazard functions, or the estimated probability of some “event” occurring over time for one group compared with another. Hazard functions measure the probability that an individual will experience a particular event given some personal characteristic (e.g., age or gender) and given that they have not yet experienced that event. For example, a hazard ratio could measure the estimated probability that individuals who receive a kidney transplant from a close, living family member will survive over time (e.g., day 1, day 2... day 365) compared to individuals who receive a kidney transplant from a deceased, unrelated donor.
Model Types : In statistics, models are equations or graphs that are used to describe or represent certain phenomena, such as the association between variables. To illustrate that association, regression models (a common type of statistical model) use mathematical equations for straight lines or curves, while path diagrams use graphs containing variable names and arrows.
Odds Ratio : An odds ratio measures the magnitude of an association between two variables. In statistics, odds are the number of times an event occurs (“success”) divided by the number of times it does not occur (“failure”), which is equivalent to the probability of the event occurring. For example, if medication A is given to 20 liver cancer patients, of whom 14 survive and 6 do not, then medication A has 2.33 times greater odds of success than of failure. If medication B is given to a separate but comparable group of liver cancer patients and has 1.4 times greater odds of success than of failure, then the odds ratio is 2.33 divided by 1.4, which equals 1.66. Thus, medication A has 1.66 greater odds of success than medication B.
Gateway Hypothesis HOW TO READ THIS REPORT
P-Value : A p-value is the estimated probability that a variable’s value or other statistical result could have been produced by chance or random error. P-values can be interpreted as measures of the strength of evidence in support of a hypothesis that certain variables
statistical result (e.g., mean or median) has a less than 5 percent probability of resulting from chance or random error. The lower the p-value, the lower the probability that the result is due to chance or random error.
Significance : An assessment of the meaningfulness or importance of any statistical findings; essentially, whether or not a variable’s value or some other statistical result is significantly smaller or larger than would be expected by chance alone. Statistical
less than a pre-defined threshold (often 0.05). However, statistical significance does not necessarily mean that the result is practically or substantively significant. For example, if the odds of an event occurring increase by 1 percent, the finding may have little practical significance even though it is statistically significant.
Slope : In regression models, slope is an estimation of how much change in one variable is associated with change in another. More specifically, it is an estimate of the average change in a response variable that is associated with a one-unit change in a predictor variable. For example, in a regression model where height is the predictor variable and weight is the response variable, a slope of 2.2 would indicate that a one-unit (i.e., one- inch) increase in height is associated with an average change in weight of 2.2 pounds.
Standard Error : An estimate of the difference between a statistic result derived from a sample population and the true value for the entire population. For example, if a survey of 1,000 Americans found their average weight was 150 pounds and that around 67 percent of the sample was within 12.5 pounds of that average, the standard error would be 12.5 divided by the square root of the sample size (i.e., 12.5/31.6=0.4). Thus, the true weight of the entire American population would be 0.4 pounds more or less than the sample average of 150 pounds.
Validity : The truth of statements about statistical results. In statistics, there a several types of validity, including internal and external validity.
Variable : A measurable characteristic of a population (e.g., age, gender, and weight) that varies in value among other components of that population. Measureable characteristics of population components that do not vary in value are called “constants.”
Gateway Hypothesis TABLES OF CONTENT
Gateway Hypothesis 1. SUMMARY OF RESEARCH METHODOLOGY
Maryland Scientific Methods Scale : To develop a standardized evaluation of the human- and animal-based studies’ internal validity, FRD adopted the Maryland Scientific Methods Scale (SMS). The scale runs from 1 to 5 and rates the studies’ use of certain research designs; higher numbers indicate the use of research methods most likely to yield internally valid findings. Studies that randomly selected research subjects from a larger population and randomly assigned those subjects to control and experimental groups were rated higher than those that did not incorporate these methods into their research designs.^3
Additional Analysis by FRD : Since the SMS focuses on some aspects of internal validity, FRD also rated other features of the statistical methods used. In particular, the project team asked:
The study’s SMS was then downgraded by one point for each problem that was identified in the additional analysis, resulting in a final score. For example, a twin study with serious flaws in the statistical analysis would receive a level rating of 3 rather than 4.
Gateway Hypothesis 2. KEY FINDINGS: REPORT
1. The existing statistical research and analysis show mixed results and do not clearly demonstrate scientific support for cannabis use leading to harder illicit drug use. As a result, FRD has determined that no causal link between cannabis use and the use of other illicit drugs can be claimed at this time.
The inability to conclusively claim cannabis as a gateway drug is due to data- gathering limitations, failures to eliminate confounding variables, and questions about the applicability of findings from animal-based studies to human behavior.
Some studies found statistically significant associations between confounding variables, such as individual and peer drug use, mental health issues, and socioeconomic status, and the use of illicit drugs.
2. The current state of research on this topic is very limited and existing studies suffer from difficulties in gathering information and applying the findings to a larger population.
FRD found only one review of the current research on the gateway hypothesis in the existing literature—chapter 14 of a National Academies of Sciences, Engineering, and Medicine report. Published in 2017, it highlights several articles studying the associations among cannabis use and the use of other illegal substances, changes in the rates/use patterns of these drugs, and the development of substance dependence or abuse disorders. While the chapter does indicate that there is some evidence of a statistical association between cannabis use and the use of other illicit drugs, it also notes that there is not enough information, at the moment, to claim a causal link.^4
Many studies of human cannabis use are based on self-reported data from longitudinal or retrospective studies. This collection method is known to be inaccurate and biased, as subjects often cannot recall the details requested by the researchers or the study participants attempt to provide answers they believe reflect most favorably upon themselves.
Some of the studies derived biased data by sampling from heroin users, street youth, and other at-risk populations. As such, the results can only be applied to narrow sections of the overall population.
Animal-based studies offer the ability to examine hypotheses using research techniques that cannot be legally used on humans. However, there are significant limitations to applying the results of this research to human behavior.
Gateway Hypothesis 3. INTRODUCTION
Cannabis use and its perceived ability to drive individuals to the subsequent use of cocaine,
heroin, or other illicit drugs has been studied and debated for decades. While the term “gateway
drug” has no formal legal or medical definition, it suggests that cannabis users go on to use and
become dependent on other illegal substances. Yet existing statistical research and analysis show mixed results and do not clearly demonstrate scientific support for this theory. As a result,
FRD has determined that no causal link between cannabis use and the use of other illicit
drugs can be claimed at this time.
Prior to the passage of the Controlled Substances Act (CSA), more formally known as Title II of the Comprehensive Drug Abuse Prevention and Control Act of 1970, the United States had over
200 laws concerning addictive drugs and other “public health and consumer protections.”^6 The
CSA, signed by President Richard Nixon, consolidated these federal regulations into a single
statute, expanded their scope, and changed the government’s management of controlled
substances by dividing them into five different class schedules. Based on the drugs’ potential for
abuse, the categories descend in severity:
Schedule 1 : Drugs, substances, and chemicals with “no currently accepted medical use and a high potential for abuse.” Examples include cannabis, heroin, LSD, and ecstasy.
Schedule 2 : Drugs, substances, and chemicals with “a high potential for abuse, with use potentially leading to severe psychological or physical dependence.” Examples include cocaine, methamphetamine, oxycodone, fentanyl, and methadone.
Schedule 3 : Drugs, substances, and chemicals with “a moderate to low potential for physical and psychological dependence.” Examples include anabolic steroids and ketamine.
Schedule 4 : Drugs, substances, and chemicals with “a low potential for abuse and low risk of dependence.” Examples include Ambien, Valium, and Xanax.
Schedule 5 : Drugs, substances, and chemicals with a “lower potential for abuse than Schedule IV and consist of preparations containing limited quantities of certain narcotics.” These drugs are generally used for antidiarrheal, antitussive, and analgesic purposes; examples include Lyrica and Robitussin AC.^7
The CSA also established the National Commission on Marihuana and Drug Abuse, which examined the inclusion of cannabis as a Schedule 1 substance. Though the commissioners
recommended using “a social control policy” instead of criminalization to discourage the use of
cannabis, it is still categorized as an illicit substance by the federal government.^8 To date, ten
states have legalized the recreational use of cannabis, while 30 states have legalized its use as a
medical treatment.^9
Gateway Hypothesis 3. INTRODUCTION
While the concern that cannabis use leads one to use other illicit drugs has existed for decades,
the idea has drawn more interest in the past few years given the current opioid epidemic in the United States. According to the U.S. Centers for Disease Control and Prevention (CDC), in 2016,
115 Americans died every day due to an overdose.^10 Additionally, it is estimated that between
21 percent and 29 percent of prescription opioid users misuse the drugs, with addiction rates
ranging between 8 percent and 12 percent.^11 Moreover, it has been suggested that this misuse
of prescription opioids may lead to later heroin use, as one study in 2016 found that 7.5 percent of nondependent illicit pharmaceutical opioid users transitioned to heroin.^12
When including overdose deaths from other drugs, the CDC estimated that there were over
72,000 incidents in 2017, with the trends increasing rapidly. Of these incidents, synthetic, semi-
synthetic, and natural opioids were responsible for the majority of deaths (44,364), followed by
heroin (15,958), cocaine (14,556), and methamphetamine (10,721; see figure 1).^13
Source: National Institute on Drug Abuse, “Overdose Death Rates,” revised August 2018, https://www.drugabuse.gov/ related-topics/trends-statistics/overdose-death-rates; based on data from the CDC’s WONDER database.
Gateway Hypothesis 3. INTRODUCTION
Though Kandel’s hypothesis has been considered the standard drug use pattern for the past four decades—especially when considering the relationship between cannabis use and the use
of other illicit drugs—recent evidence indicates that while a substantial number of Americans
As noted in section 2, according to statistics published in 2018 by SAMHSA, an estimated
118.2 million Americans aged 12 and older have used cannabis at least once; nearly all of them (99 percent, 117.1 million) have also used alcohol or tobacco. Of those who have used cannabis,
32 percent have used cocaine, 12 percent have used methamphetamines, and 4 percent have
are much lower: 0.3 percent of those Americans who have used cannabis have abused heroin,
0.2 percent have abused cocaine, and 0.1 percent have abused methamphetamines (see fig. 4).^17
Gateway Hypothesis 3. INTRODUCTION
Number of People (in millions) Who Have Ever Used:
Alcohol Alcohol/Tobacco, Cannabis & Cocaine (32%)
215.7 (^) Alcohol/Tobacco 37. & Cannabis
Alcohol/Tobacco, Cannabis & Meth 117.1 (12%) 14.
Tobacco^ Alcohol/Tobacco, Cannabis & Heroin (4%) 4.
