This originally appeared at https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1898878
October 2014
Marcus A. Bachhuber, MD1,2,3; Brendan Saloner, PhD3,4; Chinazo O. Cunningham, MD, MS5; et alColleen L. Barry, PhD, MPP3,6
JAMA Intern Med. 2014;174(10):1668-1673. doi:10.1001/jamainternmed.2014.4005
Abstract
Importance Opioid analgesic overdose mortality continues to rise in the United States, driven by increases in prescribing for chronic pain. Because chronic pain is a major indication for medical cannabis, laws that establish access to medical cannabis may change overdose mortality related to opioid analgesics in states that have enacted them.
Objective To determine the association between the presence of state medical cannabis laws and opioid analgesic overdose mortality.
Design, Setting, and Participants A time-series analysis was conducted of medical cannabis laws and state-level death certificate data in the United States from 1999 to 2010; all 50 states were included.
Exposures Presence of a law establishing a medical cannabis program in the state.
Main Outcomes and Measures Age-adjusted opioid analgesic overdose death rate per 100 000 population in each state. Regression models were developed including state and year fixed effects, the presence of 3 different policies regarding opioid analgesics, and the state-specific unemployment rate.
Results Three states (California, Oregon, and Washington) had medical cannabis laws effective prior to 1999. Ten states (Alaska, Colorado, Hawaii, Maine, Michigan, Montana, Nevada, New Mexico, Rhode Island, and Vermont) enacted medical cannabis laws between 1999 and 2010. States with medical cannabis laws had a 24.8% lower mean annual opioid overdose mortality rate (95% CI, −37.5% to −9.5%; P = .003) compared with states without medical cannabis laws. Examination of the association between medical cannabis laws and opioid analgesic overdose mortality in each year after implementation of the law showed that such laws were associated with a lower rate of overdose mortality that generally strengthened over time: year 1 (−19.9%; 95% CI, −30.6% to −7.7%; P = .002), year 2 (−25.2%; 95% CI, −40.6% to −5.9%; P = .01), year 3 (−23.6%; 95% CI, −41.1% to −1.0%; P = .04), year 4 (−20.2%; 95% CI, −33.6% to −4.0%; P = .02), year 5 (−33.7%; 95% CI, −50.9% to −10.4%; P = .008), and year 6 (−33.3%; 95% CI, −44.7% to −19.6%; P < .001). In secondary analyses, the findings remained similar.
Conclusions and Relevance Medical cannabis laws are associated with significantly lower state-level opioid overdose mortality rates. Further investigation is required to determine how medical cannabis laws may interact with policies aimed at preventing opioid analgesic overdose.
Introduction
Chronic noncancer pain is common in the United States,1 and the proportion of patients with noncancer pain who receive prescriptions for opioids has almost doubled over the past decade.2 In parallel to this increase in prescriptions, rates of opioid use disorders and overdose deaths have risen dramatically.3,4 Policies such as prescription drug monitoring programs, increased scrutiny of patients and providers, and enhanced access to substance abuse treatment have been advocated to reduce the risk of opioid analgesics5; however, relatively less attention has focused on how the availability of alternative nonopioid treatments may affect overdose rates.
As of July 2014, a total of 23 states have enacted laws establishing medical cannabis programs6 and chronic or severe pain is the primary indication in most states.7–10 Medical cannabis laws are associated with increased cannabis use among adults.11 This increased access to medical cannabis may reduce opioid analgesic use by patients with chronic pain, and therefore reduce opioid analgesic overdoses. Alternatively, if cannabis adversely alters the pharmacokinetics of opioids or serves as a “gateway” or “stepping stone” leading to further substance use,12–14 medical cannabis laws may increase opioid analgesic overdoses. Given these potential effects, we examined the relationship between implementation of state medical cannabis laws and opioid analgesic overdose deaths in the United States between 1999 and 2010.
Methods
The opioid analgesic overdose mortality rate in each state from 1999 to 2010 was abstracted using the Wide-ranging Online Data for Epidemiologic Research interface to multiple cause-of-death data from the Centers for Disease Control and Prevention.15 We defined opioid analgesic overdose deaths as fatal drug overdoses of any intent (International Statistical Classification of Diseases,10th revision [ICD-10], codes X40-X44, X60-X64, and Y10-Y14) where an opioid analgesic was also coded (T40.2-T40.4). This captures all overdose deaths where an opioid analgesic was involved including those involving polypharmacy or illicit drug use (eg, heroin). Analysis of publicly available secondary data is considered exempt by the University of Pennsylvania Institutional Review Board.
Three states (California, Oregon, and Washington) had medical cannabis laws effective prior to 1999.6 Ten states (Alaska, Colorado, Hawaii, Maine, Michigan, Montana, Nevada, New Mexico, Rhode Island, and Vermont) implemented medical cannabis laws between 1999 and 2010. Nine states (Arizona, Connecticut, Delaware, Illinois, Maryland, Massachusetts, Minnesota, New Hampshire, and New York) had medical cannabis laws effective after 2010, which is beyond the study period. New Jersey’s medical cannabis law went into effect in the last quarter of 2010 and was counted as effective after the study period. In each year, we first plotted the mean age-adjusted opioid analgesic overdose mortality rate in states that had a medical cannabis law vs states that did not.
Next, we determined the association between medical cannabis laws and opioid analgesic–related deaths using linear time-series regression models. For the dependent variable, we used the logarithm of the year- and state-specific age-adjusted opioid analgesic overdose mortality rate. Our main independent variable of interest was the presence of medical cannabis laws, which we modeled in 2 ways.
In our first regression model, we included an indicator for the presence of a medical cannabis law in the state and year. All years prior to a medical cannabis law were coded as 0 and all years after the year of passage were coded as 1. Because laws could be implemented at various points in the year, we coded the law as a fraction for years of implementation (eg, 0.5 for a law that was implemented on July 1). The coefficient on this variable therefore represents the mean difference, expressed as a percentage, in the annual opioid analgesic overdose mortality rate associated with the implementation of medical cannabis laws. To estimate the absolute difference in mortality associated with medical cannabis laws in 2010, we calculated the expected number of opioid analgesic overdose deaths in medical cannabis states had laws not been present and subtracted the actual number of overdose deaths recorded.
In our second model, we allowed the effect of medical cannabis laws to vary depending on the time elapsed since enactment, because states may have experienced delays in patient registration, distribution of identification cards, and establishment of dispensaries, if applicable. Accordingly, we coded years with no law present as 0, but included separate coefficients to measure each year since implementation of the medical cannabis law for states that adopted such laws. States that implemented medical cannabis laws before the study period were coded similarly (eg, in 1999, California was coded as 3 because the law was implemented in 1996). This model provides separate estimates for 1 year after implementation, 2 years after implementation, and so forth.
Each model adjusted for state and year (fixed effects). We also included 4 time-varying state-level factors: (1) the presence of a state-level prescription drug monitoring program (a state-level registry containing information on controlled substances prescribed in a state),16 (2) the presence of a law requiring or allowing a pharmacist to request patient identification before dispensing medications,17 (3) the presence of regulations establishing increased state oversight of pain management clinics,18 and (4) state- and year-specific unemployment rates to adjust for the economic climate.19 Colinearity among independent variables was assessed by examining variance inflation factors; no evidence of colinearity was found. For all models, robust standard errors were calculated using procedures to account for correlation within states over time.
To assess the robustness of our results, we performed several further analyses. First, we excluded intentional opioid analgesic overdose deaths from the age-adjusted overdose mortality rate to focus exclusively on nonsuicide deaths. Second, because heroin and prescription opioid use are interrelated for some individuals,20–23 we included overdose deaths related to heroin, even if no opioid analgesic was coded. Third, we assessed the robustness of our findings to the inclusion of state-specific linear time trends that can be used to adjust for differential factors that changed linearly over the study period (eg, hard-to-measure attitudes or cultural changes). Fourth, we tested whether trends in opioid analgesic overdose mortality predated the implementation of medical cannabis laws by including indicator variables in a separate regression model for the 2 years before the passage of the law.24 Finally, to test the specificity of any association found between medical cannabis laws and opioid analgesic overdose mortality, we examined the association between state medical cannabis laws and age-adjusted death rates of other medical conditions without strong links to cannabis use: heart disease (ICD-10 codes I00-I09, I11, I13, and I20-I51)25 and septicemia (A40-A41). All analyses were performed using SAS, version 9.3 (SAS Institute Inc).
Results
The mean age-adjusted opioid analgesic overdose mortality rate increased in states with and without medical cannabis laws during the study period (Figure 1). Throughout the study period, states with medical cannabis laws had a higher opioid analgesic overdose mortality rate and the rates rose for both groups; however, between 2009 and 2010 the rate in states with medical cannabis laws appeared to plateau.
In the adjusted model, medical cannabis laws were associated with a mean 24.8% lower annual rate of opioid analgesic overdose deaths (95% CI, −37.5% to −9.5%; P = .003) (Table), compared with states without laws. In 2010, this translated to an estimated 1729 (95% CI, 549 to 3151) fewer deaths than expected. Medical cannabis laws were associated with lower rates of opioid analgesic overdose mortality, which generally strengthened in the years after passage (Figure 2): year 1 (−19.9%; 95% CI, −30.6% to −7.7%; P = .002), year 2 (−25.2%; 95% CI, −40.6% to −5.9%; P = .01), year 3 (−23.6%; 95% CI, −41.1% to −1.0%; P = .04), year 4 (−20.2%; 95% CI, −33.6% to −4.0%; P = .02), year 5 (−33.7%; 95% CI, −50.9% to −10.4%; P = .008), and year 6 (−33.3%; 95% CI, −44.7% to −19.6%; P < .001). The other opioid analgesic policies, as well as state unemployment rates, were not significantly associated with opioid analgesic mortality rates.
In additional analyses, the association between medical cannabis laws and opioid analgesic mortality rates was similar after excluding intentional deaths (ie, suicide) and when including all heroin overdose deaths, even if an opioid analgesic was not involved (Table). Including state-specific linear time trends in the model resulted in a borderline significant association between laws and opioid analgesic overdose mortality (−17.9%; 95% CI, −32.7% to 0.3%; P = .054). When examining the years prior to law implementation, we did not find an association between medical cannabis laws and opioid analgesic overdose mortality 2 years prior to law implementation (−13.1%; 95% CI, −45.5% to 38.6%; P = .56) or 1 year prior (1.2%; 95% CI, −41.2% to 74.0%; P = .97). Finally, we did not find significant associations between medical cannabis laws and mortality associated with heart disease (1.4%; 95% CI, −0.2% to 2.9%; P = .09) or septicemia (−1.8%; 95% CI, −7.6% to 4.3%; P = .55).
Discussion
In an analysis of death certificate data from 1999 to 2010, we found that states with medical cannabis laws had lower mean opioid analgesic overdose mortality rates compared with states without such laws. This finding persisted when excluding intentional overdose deaths (ie, suicide), suggesting that medical cannabis laws are associated with lower opioid analgesic overdose mortality among individuals using opioid analgesics for medical indications. Similarly, the association between medical cannabis laws and lower opioid analgesic overdose mortality rates persisted when including all deaths related to heroin, even if no opioid analgesic was present, indicating that lower rates of opioid analgesic overdose mortality were not offset by higher rates of heroin overdose mortality. Although the exact mechanism is unclear, our results suggest a link between medical cannabis laws and lower opioid analgesic overdose mortality.
Approximately 60% of all opioid analgesic overdoses occur among patients who have legitimate prescriptions from a single provider.26 This group may be sensitive to medical cannabis laws; patients with chronic noncancer pain who would have otherwise initiated opioid analgesics may choose medical cannabis instead. Although evidence for the analgesic properties of cannabis is limited, it may provide analgesia for some individuals.27,28 In addition, patients already receiving opioid analgesics who start medical cannabis treatment may experience improved analgesia and decrease their opioid dose,29,30 thus potentially decreasing their dose-dependent risk of overdose.31,32 Finally, if medical cannabis laws lead to decreases in polypharmacy—particularly with benzodiazepines—in people taking opioid analgesics, overdose risk would be decreased. Further analyses examining the association between medical cannabis laws and patterns of opioid analgesic use and polypharmacy in the population as a whole and across different groups are needed.
A connection between medical cannabis laws and opioid analgesic overdose mortality among individuals who misuse or abuse opioids is less clear. Previous laboratory work has shown that cannabinoids act at least in part through an opioid receptor mechanism33,34 and that they increase dopamine concentrations in the nucleus accumbens in a fashion similar to that of heroin and several other drugs with abuse potential.33,35 Clinically, cannabis use is associated with modest reductions in opioid withdrawal symptoms for some people,36,37 and therefore may reduce opioid use. In contrast, cannabis use has been linked with increased use of other drugs, including opioids14,38–40; however, a causal relationship has not been established.14,41 Increased access to cannabis through medical cannabis laws could influence opioid misuse in either direction, and further study is required.
Although the mean annual opioid analgesic overdose mortality rate was lower in states with medical cannabis laws compared with states without such laws, the findings of our secondary analyses deserve further consideration. State-specific characteristics, such as trends in attitudes or health behaviors, may explain variation in medical cannabis laws and opioid analgesic overdose mortality, and we found some evidence that differences in these characteristics contributed to our findings. When including state-specific linear time trends in regression models, which are used to adjust for hard-to-measure confounders that change over time, the association between laws and opioid analgesic overdose mortality weakened. In contrast, we did not find evidence that states that passed medical cannabis laws had different overdose mortality rates in years prior to law passage, providing a temporal link between laws and changes in opioid analgesic overdose mortality. In addition, we did not find evidence that laws were associated with differences in mortality rates for unrelated conditions (heart disease and septicemia), suggesting that differences in opioid analgesic overdose mortality cannot be explained by broader changes in health. In summary, although we found a lower mean annual rate of opioid analgesic mortality in states with medical cannabis laws, a direct causal link cannot be established.
This study has several limitations. First, this analysis is ecologic and cannot adjust for characteristics of individuals within the states, such as socioeconomic status, race/ethnicity, or medical and psychiatric diagnoses. Although we found that the association between medical cannabis laws and lower opioid overdose mortality strengthened in the years after implementation, this could represent heterogeneity between states that passed laws earlier in the study period vs those that passed the laws later. Second, death certificate data may not correctly classify cases of opioid analgesic overdose deaths, and reporting of opioid analgesics on death certificates may differ among states; misclassification could bias our results in either direction. Third, although fixed-effects models can adjust for time-invariant characteristics of each state and state-invariant time effects, there may be important time- and state-varying confounders not included in our models. Finally, our findings apply to states that passed medical cannabis laws during the study period and the association between future laws and opioid analgesic overdose mortality may differ.
Conclusions
Although the present study provides evidence that medical cannabis laws are associated with reductions in opioid analgesic overdose mortality on a population level, proposed mechanisms for this association are speculative and rely on indirect evidence. Further rigorous evaluation of medical cannabis policies, including provisions that vary among states,14,42 is required before their wide adoption can be recommended. If the relationship between medical cannabis laws and opioid analgesic overdose mortality is substantiated in further work, enactment of laws to allow for use of medical cannabis may be advocated as part of a comprehensive package of policies to reduce the population risk of opioid analgesics.
Article Information
Accepted for Publication: May 2, 2014.
Corresponding Author: Marcus A. Bachhuber, MD, Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, 423 Guardian Dr, 1303-A Blockley Hall, Philadelphia, PA 19104 ([email protected]).
Published Online: August 25, 2014. doi:10.1001/jamainternmed.2014.4005.
Author Contributions: Dr Bachhuber had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Bachhuber, Saloner, Barry.
Acquisition, analysis, or interpretation of data: Bachhuber, Cunningham, Barry.
Drafting of the manuscript: Bachhuber, Saloner.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Bachhuber, Saloner, Barry.
Study supervision: Cunningham, Barry.
Conflict of Interest Disclosures: Dr Cunningham’s husband was recently employed by Pfizer Pharmaceuticals and is currently employed by Quest Diagnostics. No other disclosures are reported.
Funding/Support: This work was funded by National Institutes of Health (NIH) grants R01DA032110 and R25DA023021 and the Center for AIDS Research at the Albert Einstein College of Medicine andMontefiore Medical Center grant NIH AI-51519. Dr Saloner received funding support from the Robert Wood Johnson Foundation Health and Society Scholars Program. Dr Bachhuber received funding support from the Philadelphia Veterans Affairs Medical Center and the Robert Wood Johnson Foundation Clinical Scholars Program.
Role of the Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The findings and conclusions of this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.
Corrections: This article was corrected on August 27, 2014, to fix a typographical error in Figure 1 and on September 10, 2014, to fix an incorrect term in the Discussion.
References
Gaskin DJ, Richard P. The economic costs of pain in the United States. J Pain. 2012;13(8):715-724.PubMedGoogle ScholarCrossref
Daubresse M, Chang HY, Yu Y, et al. Ambulatory diagnosis and treatment of nonmalignant pain in the United States, 2000-2010. Med Care. 2013;51(10):870-878.PubMedGoogle ScholarCrossref
Centers for Disease Control and Prevention. Vital signs: overdoses of prescription opioid pain relievers—United States, 1999-2008. MMWR Morb Mortal Wkly Rep. 2011;60(43):1487-1492.PubMedGoogle Scholar
Jones CM, Mack KA, Paulozzi LJ. Pharmaceutical overdose deaths, United States, 2010. JAMA. 2013;309(7):657-659.
ArticlePubMedGoogle ScholarCrossref
National Center for Injury Prevention and Control Division of Unintentional Injury Prevention. Policy impact: prescription painkiller overdoses. http://www.cdc.gov/homeandrecreationalsafety/pdf/policyimpact-prescriptionpainkillerod.pdf. Published November 2011. Accessed January 27, 2014.
National Conference of State Legislatures. State medical marijuana laws. http://www.ncsl.org/research/health/state-medical-marijuana-laws.aspx. Published July 7, 2014. Accessed July 14, 2014.
Nevada Division of Public and Behavioral Health. Medical Marijuana Program Monthly Report. http://health.nv.gov/MedicalMarijuana_Reports.htm. January 2, 2014. Accessed January 27, 2014.
Licensing and Regulatory Affairs Bureau of Health Care Services. Michigan Medical Marihuana Act Statistical Report for Fiscal Year 2013. http://www.michigan.gov/documents/lara/BHCS_MMMP_MCL_333.26426i12345_Report_FINAL_12-4-13_441658_7.pdf. Published December 4, 2013. Accessed January 31, 2014.
Oregon Public Health Authority. Oregon Medical Marijuana Program statistics.https://public.health.oregon.gov/diseasesconditions/chronicdisease/medicalmarijuanaprogram/pages/data.aspx. Published January 1, 2014. Accessed January 31, 2014.
Montana Department of Public Health and Human Services. Montana Marijuana Program Registry information. http://www.dphhs.mt.gov/marijuanaprogram/mmpregistryinformation.pdf. Published January 1, 2014. Accessed January 31, 2014.
Cerdá M, Wall M, Keyes KM, Galea S, Hasin D. Medical marijuana laws in 50 states: investigating the relationship between state legalization of medical marijuana and marijuana use, abuse and dependence. Drug Alcohol Depend. 2012;120(1-3):22-27.PubMedGoogle ScholarCrossref
Fiellin LE, Tetrault JM, Becker WC, Fiellin DA, Hoff RA. Previous use of alcohol, cigarettes, and marijuana and subsequent abuse of prescription opioids in young adults. J Adolesc Health. 2013;52(2):158-163.PubMedGoogle ScholarCrossref
Marijuana and Medicine: Assessing the Science Base. Washington, DC: National Academies Press; 1999.
Volkow ND, Baler RD, Compton WM, Weiss SR. Adverse health effects of marijuana use. N Engl J Med. 2014;370(23):2219-2227.PubMedGoogle ScholarCrossref
Centers for Disease Control and Prevention. CDC WONDER. National Vital Statistics System: 2010: Multiple Cause of Death Data. http://wonder.cdc.gov/mcd.html. Accessed May 30, 2014.
National Alliance for Model State Drug Laws. Prescription Drug Monitoring Program dates of operation. March 2014.http://www.namsdl.org/library/1E4C142E-1372-636C-DD7711E627D4B892/. Accessed April 2, 2014.
Office for State Tribal Local and Territorial Support Centers for Disease Control and Prevention. Menu of state prescription drug identification laws. http://www.cdc.gov/phlp/docs/menu-pdil.pdf. Published June 30, 2013. Accessed January 27, 2014.
Office for State Tribal Local and Territorial Support Centers for Disease Control and Prevention. Menu of pain management clinic regulation. http://www.cdc.gov/phlp/docs/menu-pmcr.pdf. Published September 28, 2012. Accessed January 27, 2014.
US Bureau of Labor Statistics. Annual state unemployment. http://www.bls.gov/lau/home.htm. Accessed February 5, 2014.
Unick GJ, Rosenblum D, Mars S, Ciccarone D. Intertwined epidemics: national demographic trends in hospitalizations for heroin- and opioid-related overdoses, 1993-2009. PLoS One. 2013;8(2):e54496. doi:10.1371/journal.pone.0054496.PubMedGoogle ScholarCrossref
Khosla N, Juon HS, Kirk GD, Astemborski J, Mehta SH. Correlates of non-medical prescription drug use among a cohort of injection drug users in Baltimore City. Addict Behav. 2011;36(12):1282-1287.PubMedGoogle ScholarCrossref
Cicero TJ, Ellis MS, Surratt HL. Effect of abuse-deterrent formulation of OxyContin. N Engl J Med. 2012;367(2):187-189.PubMedGoogle ScholarCrossref
Peavy KM, Banta-Green CJ, Kingston S, Hanrahan M, Merrill JO, Coffin PO. “Hooked on” prescription-type opiates prior to using heroin: results from a survey of syringe exchange clients. J Psychoactive Drugs. 2012;44(3):259-265.PubMedGoogle ScholarCrossref
Angrist J, Pischke J. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press; 2009.
Sidney S, Beck JE, Tekawa IS, Quesenberry CP, Friedman GD. Marijuana use and mortality. Am J Public Health. 1997;87(4):585-590.PubMedGoogle ScholarCrossref
Centers for Disease Control and Prevention. CDC grand rounds: prescription drug overdoses—a US epidemic. MMWR Morb Mortal Wkly Rep. 2012;61(1):10-13.PubMedGoogle Scholar
Lynch ME, Campbell F. Cannabinoids for treatment of chronic non-cancer pain; a systematic review of randomized trials. Br J Clin Pharmacol. 2011;72(5):735-744.PubMedGoogle ScholarCrossref
Kumar RN, Chambers WA, Pertwee RG. Pharmacological actions and therapeutic uses of cannabis and cannabinoids. Anaesthesia. 2001;56(11):1059-1068.PubMedGoogle ScholarCrossref
Abrams DI, Couey P, Shade SB, Kelly ME, Benowitz NL. Cannabinoid-opioid interaction in chronic pain. Clin Pharmacol Ther. 2011;90(6):844-851.PubMedGoogle ScholarCrossref
Lynch ME, Clark AJ. Cannabis reduces opioid dose in the treatment of chronic non-cancer pain. J Pain Symptom Manage. 2003;25(6):496-498.PubMedGoogle ScholarCrossref
Dunn KM, Saunders KW, Rutter CM, et al. Opioid prescriptions for chronic pain and overdose: a cohort study. Ann Intern Med. 2010;152(2):85-92.PubMedGoogle ScholarCrossref
Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305(13):1315-1321.
ArticlePubMedGoogle ScholarCrossref
Tanda G, Pontieri FE, Di Chiara G. Cannabinoid and heroin activation of mesolimbic dopamine transmission by a common μ1 opioid receptor mechanism. Science. 1997;276(5321):2048-2050.PubMedGoogle ScholarCrossref
Scavone JL, Sterling RC, Van Bockstaele EJ. Cannabinoid and opioid interactions: implications for opiate dependence and withdrawal. Neuroscience. 2013;248:637-654.PubMedGoogle ScholarCrossref
Ashton CH. Pharmacology and effects of cannabis: a brief review. Br J Psychiatry. 2001;178:101-106.PubMedGoogle ScholarCrossref
Hermann D, Klages E, Welzel H, Mann K, Croissant B. Low efficacy of non-opioid drugs in opioid withdrawal symptoms. Addict Biol. 2005;10(2):165-169.PubMedGoogle ScholarCrossref
Scavone JL, Sterling RC, Weinstein SP, Van Bockstaele EJ. Impact of cannabis use during stabilization on methadone maintenance treatment. Am J Addict. 2013;22(4):344-351.PubMedGoogle ScholarCrossref
Lynskey MT, Heath AC, Bucholz KK, et al. Escalation of drug use in early-onset cannabis users vs co-twin controls. JAMA. 2003;289(4):427-433.
ArticlePubMedGoogle ScholarCrossref
Yamaguchi K, Kandel DB. Patterns of drug use from adolescence to young adulthood, III: predictors of progression. Am J Public Health. 1984;74(7):673-681.PubMedGoogle ScholarCrossref
Fergusson DM, Horwood LJ. Early onset cannabis use and psychosocial adjustment in young adults. Addiction. 1997;92(3):279-296.PubMedGoogle ScholarCrossref
Kandel DB. Does marijuana use cause the use of other drugs? JAMA. 2003;289(4):482-483.
ArticlePubMedGoogle ScholarCrossref
Pacula RL, Sevigny EL. Marijuana liberalization policies: why we can’t learn much from policy still in motion. J Policy Anal Manage. 2014;33(1):212-221.PubMedGoogle ScholarCrossref
