Summary
Background.
Through increasing cannabis availability, state cannabis legalization has been posited as one potential solution to the opioid crisis by providing an alternative to long-term opioid use for treating chronic pain. Forty U.S. states have legalized cannabis for medical use and 25 for recreational use. We systematically reviewed the literature to investigate the relationship of state medical and recreational cannabis legalization (MCL and RCL, respectively) to rates of opioid prescriptions, nonmedical use, opioid use disorder (OUD), opioid-related hospitalizations and emergency department visits, and overdose deaths.
Methods.
We searched EconLit, EMBASE, MEDLINE, and Web of Science Core Collection for English-language studies published through 10/21/25 that estimated MCL or RCL enactment effects on opioid outcomes compared to states that did not enact such laws. This study is registered with PROSPERO, CRD42023416119.
Findings.
Of 43 eligible studies, 18 investigated MCL, 11 investigated RCL, and 14 investigated both. Findings on MCL, RCL and opioid prescribing were mixed. No study found decreases in non-medical opioid use after MCL enactment; only one study found decreased non-medical use after RCL enactment. No studies found that MCL or RCL decreased OUD. MCL enactment was associated with lower opioid overdose mortality rates in earlier but not more recent studies; studies of RCL and opioid overdose fatalities had inconsistent results.
Interpretations.
The mixed study findings suggest that MCL and RCL are just two factors in a complex set of influences on opioid outcomes that require further study. Meanwhile, cannabis legalization should not be considered an effective policy for curbing the opioid epidemic.
1. Introduction
In 2022, 5.8 million United States (U.S.) adults had an opioid use disorder (OUD), 8.5 million reported nonmedical use of opioid pain relievers, and >1.0 million used heroin in the past year (Center for Behavioral Health Statistics and Quality, 2023). Since 1999, more than 1,000,000 opioid overdose deaths have occurred in the U.S., with overdoses remaining a leading cause of injury deaths (Spencer et al., 2024). To address the crisis, many interventions have been proposed, including policies shown to reduce opioid prescribing and increase access to naloxone, non-opioid pain treatments, and OUD treatment. Legalizing cannabis has also been proposed as an intervention to lower opioid-related harm (Hammaker et al., 2023; Hudak and Stenglein, 2020).
While cannabis remains illegal at the federal level, 40 U.S. states have enacted medical cannabis laws (MCL), and 25 states have enacted recreational cannabis laws (RCL). MCLs permit cannabis use for approved medical conditions after authorization from a healthcare provider. MCLs have commonalities, although their operational features (e.g., medical conditions permitted) vary within and between states over time. RCLs allow adults to possess and use cannabis. Substantial interest exists in the effect of cannabis legalization on the U.S. opioid epidemic (Hammaker et al., 2023), based on the assumption that wider access to cannabis should reduce opioid demand by providing an alternative intervention for pain, which is a common reason for medical cannabis use (Boehnke et al., 2019). An influential 2017 National Academies report (The National Academies of Sciences, 2017) suggested therapeutic benefits for chronic pain. While more recent reviews show weaker evidence (De Vita et al., 2018; Wang et al., 2021), reductions in opioid use following medical cannabis initiation have been found in observational studies of self-report data (Nielsen et al., 2022; Piper et al., 2017). Thus, in MCL or RCL states where cannabis is more accessible for legal use, it could be used as a substitute for pain medications, reducing opioid prescriptions. Moreover, despite some mixed findings (De Aquino et al., 2022; Gorfinkel et al., 2021), preliminary evidence also suggests that cannabinoids may alleviate OUD symptoms by reducing withdrawal symptoms (Lofwall et al., 2016) and craving (Hurd et al., 2019), although cannabinoids may also increase some opioid-related harms (Olfson et al., 2018; Wilson et al., 2022). Thus, overall, by offering an alternative to prescribed opioids for pain and an intervention for OUD, cannabis legalization could plausibly affect opioid prescribing, nonmedical use, OUD, and therefore the most serious downstream effect, opioid-related hospitalizations and overdose deaths.
Previous reviews have evaluated cannabis legalization’s effects on single outcomes such as opioid prescribing (Athanassiou et al., 2023), nonmedical use of prescription opioids (Anderson and Rees, 2023), or OUD (Wendelboe et al., 2019), but did not synthesize the literature on legalization across its effects on multiple opioid outcomes. Other reviews were more general and non-systematic, focused on methodological considerations or development of clinical framework (Hammaker et al., 2023; Tormohlen et al., 2021; Victor et al., 2022). In 2019, a systematic review examined multiple opioid outcomes (Chihuri and Li, 2019), but many new studies have since emerged. Moreover, cannabis use (Hasin et al., 2015; Hasin et al., 2022), along with the potency and variety of legal products (Freeman et al., 2020; Hasin et al., 2021), has surged since 2019. Given ongoing interest in cannabis legalization as a tool against the opioid crisis (Hammaker et al., 2023; Hudak and Stenglein, 2020) and the need for an unbiased evaluation, we conducted an updated systematic review to investigate the relationship of MCL and RCL enactment to changes in the following opioid outcomes: prescriptions for opioid medications, nonmedical opioid use, OUD, opioid-related hospitalizations and emergency department visits, and overdoses. We also evaluated whether legalizing dispensaries (which potentially increases availability) was differentially associated with the opioid outcomes.
2. Methods
2.1. Data Sources and Searches
We followed a protocol published in PROSPERO in May 2023 (CRD42023416119) and structured the review according to PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines.(Moher et al., 2009) In July 2023 and updated in October 2025, we searched 4 online databases (EconLit, EMBASE, MEDLINE, Web of Science Core Collection) for titles and abstracts of U.S. studies that examined a longitudinal association between MCL or RCL enactment with opioid outcomes, including: prescriptions for opioid pain relievers, nonmedical use of opioids, OUD, opioid-related hospitalizations and emergency department visits, and opioid overdoses. We also examined references from selected materials to identify additional articles. We focused on U.S. studies for two reasons: (1) The U.S. has experienced not only a disproportionate burden of opioid morbidity and mortality but also an earlier and more extreme rise and fall in opioid prescribing and use (Humphreys, 2017; The Lancet Regional Health –, 2023); and (2) the staggered adoption of state MCLs and RCLs over the past 25 years provides useful controls to measure the effect of these laws relative to states without cannabis legalization. All identified studies were exported to DistillerSR, a web interface developed to systematize the review process (Evidence Partners, 2019), and duplicates were removed using the same tool. Online Supplement 1 details the search terms and algorithm used in the search.
2.2. Study Selection
All titles and abstracts were independently screened for eligibility by 2 of 4 investigators (D.S.F., L.G., S.G., D.B.); those considered relevant by both investigators advanced to full text review. Disagreements between investigators were reconciled by the first author (D.S.F.). In each stage of the selection process, the order of studies was randomized to prevent bias due to reviewer fatigue. We included observational studies if they estimated before-and-after change in levels of an opioid outcome after enactment of an MCL or RCL within a single U.S. state or in a set of states relative to other states that did not enact a cannabis law. Cross-sectional studies without before-and-after change were excluded. No restrictions were placed on sample size or population age. Online Supplement 2 showed the study selection form.
2.3. Data Extraction and Quality Assessment
Three researchers (L.G., S.G., D.B.) independently read selected articles and extracted study details using a customized DistillerSR data extraction form (Online Supplement 3) that captured data on the specific policy studied, outcome data source, study design, and results, including point estimates and confidence intervals (CIs) or p-values. The first author (D.S.F.) reviewed the extracted data from each article for accuracy. Finally, 2 investigators (D.S.F., L.G.) independently assessed risk of bias (ROB) for each study using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool (Sterne et al., 2016). This widely-used tool is applied to evaluate the risk of bias in non-randomized observational studies from confounding, selection bias, and information bias. Risk of bias was assessed within six domains (bias in selection of participants into the study, bias due to missing data, bias in data collection methods, bias due to confounding, bias in analyses, and overall bias), grading each domain as low, moderate, or serious. Explanation of criteria used to determine ROB grade are included in Online Table 1. A study is determined to be low risk of bias if the study is comparable to a well-performed randomized trial and is determined to be at low risk of bias for all domains; a study is judged to be moderate risk of bias if it provides sound evidence for a non-randomized study, i.e., low to moderate risk of bias for all domains. Studies with serious risk of bias have one or more important problems, evidenced by a study having a serious risk of bias in at least one domain.
2.4. Data Synthesis and Analysis
Due to heterogeneity in methods and analyses used in the included studies, we did not conduct a meta-analysis. Instead, we performed a qualitative assessment and synthesis using methods outlined by the Agency for Healthcare Research and Quality (Berkman et al., 2008). We categorized studies into 2 groups: those examining MCL enactment only and those evaluating RCL enactment following MCL enactment (all RCL states had previously adopted MCLs). MCL and RCL adoption dates are most commonly operationalized in the literature as the date on which medical or recreational cannabis possession or use becomes legally protected in a state, whether defined as the date of formal enactment or the date on which legal protections take effect (RAND-USC Schaeffer Opioid Policy Tools and Information Center, 2025). Because these dates often coincide or are used interchangeably across studies, we refer to both collectively as policy adoption dates. In contrast, legal access to cannabis through medical or recreational dispensaries is frequently delayed by regulatory development, licensing processes, and business establishment. Accordingly, studies examining the effects of MCL or RCL dispensaries typically define implementation as the date on which the first medical or recreational dispensary becomes legally protected and open for business within the state. Studies that independently investigated both the effects of MCL and RCL enactment contributed to both groups, while studies that did not distinguish between MCL and RCL enactment dates were excluded.
For each MCL and RCL, we examined 5 outcomes: opioid prescriptions, opioid use, OUD, opioid-related hospitalizations and emergency department visits, and opioid overdoses. Studies examining opioid prescriptions focused on quantity dispensed, days prescribed, and drug schedule (Online Supplement 4). Studies investigating use measured the percentage of individuals reporting past-month, past-year, or lifetime nonmedical use of prescription opioids or heroin. Studies on OUD examined past-year diagnoses and those on overdoses examined poisoning deaths from prescription opioids, heroin, or synthetic opioids (e.g., fentanyl).
2.5. Role of Funding Source
The National Institute on Drug Abuse had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The first author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
3. Results
Figure 1 depicts the literature search and selection process. Forty-three articles met the inclusion criteria: 18 investigated MCL enactment, 11 investigated RCL enactment, and 14 separately investigated MCL and RCL enactment. Tables 1 through 4 show the policy and outcome configurations evaluated. Of the 32 studies of MCL, 28 examined enactment in general (Ali et al., 2023; Ali et al., 2021; Averett and Smith, 2019; Bachhuber et al., 2014; Bradford et al., 2018; Castillo-Carniglia et al., 2023; Cerdá et al., 2018; Cerda et al., 2025; Chan et al., 2020; Dunn, Tyler J. et al., 2025; Dunn, T. J. et al., 2025; Flexon et al., 2019; Flexon et al., 2024; Jayawardhana and Fernandez, 2021; Kim et al., 2022; Liang et al., 2018; Martins et al., 2025; Mathur and Ruhm, 2023; McMichael et al., 2020; Powell et al., 2018; Samples et al., 2025; Segura et al., 2019; Shah et al., 2019; Shi, 2017; Shover et al., 2019; Wen and Hockenberry, 2018; Wen et al., 2015; Wong and Lin, 2019) and 13 examined dispensaries (Ali et al., 2021; Averett and Smith, 2019; Bradford et al., 2018; Castillo-Carniglia et al., 2023; Chan et al., 2020; Liang et al., 2018; Mannes et al., 2025; Mathur and Ruhm, 2023; McGinty et al., 2023; Nguyen et al., 2024; Powell et al., 2018; Shi, 2017; Smith, 2020). Of the 25 studies of RCL, 20 examined enactment (Ali et al., 2023; Alley et al., 2020; Castillo-Carniglia et al., 2023; Cerda et al., 2025; Chan et al., 2020; Denkyirah, E. K. et al., 2025; Denkyirah, Elisha Kwaku et al., 2025; Drake et al., 2021; Fleming et al., 2022; Flexon et al., 2019; Kropp Lopez et al., 2020; Livingston et al., 2017; Martins et al., 2025; Mathur and Ruhm, 2023; McMichael et al., 2020; Mennis et al., 2021; Raman et al., 2023; Sabia et al., 2024; Shi et al., 2019; Wen and Hockenberry, 2018) and 7 examined dispensaries (Alcocer, 2020; Castillo-Carniglia et al., 2023; Chan et al., 2020; Livingston et al., 2017; Mannes et al., 2025; Mathur and Ruhm, 2023; Nguyen et al., 2024). Online Figure 1 displays the study periods alongside the dates of MCL/RCL enactments and dispensary openings. The figure shows a steady rise in MCL states from 1996 to 2014, followed by an accelerated increase in MCL enactment after 2014. Although California enacted legislation to protect MCL dispensaries in 2004, most states established dispensaries between 2010 and 2018 (RAND-USC Schaeffer Opioid Policy Tools and Information Center, 2025). MCL studies examined an average of 12.6 years of data (range=2-25 years), spanning 1991 to 2022, with substantial temporal overlap between MCL enactment and dispensary operations across the included study periods. In contrast, RCL enactment began in 2012, with the first recreational dispensaries opening in 2014. By 2020, 11 states had enacted RCLs, 10 of which had operational dispensaries. RCL studies analyzed an average of 12.8 years of data (range=5-22 years); however, fewer than half (40%; n=15) included data beyond 2018. Consequently, relatively few studies captured the most recent RCL enactments or dispensary openings, as the majority of RCL policy implementation occurred towards the later years of the study period. Regarding risk of bias (ROB; Online Table 1), 12 of 31 studies had low ROB (Ali et al., 2023; Ali et al., 2021; Castillo-Carniglia et al., 2023; Cerda et al., 2025; Chan et al., 2020; Mannes et al., 2025; Mathur and Ruhm, 2023; Nguyen et al., 2024; Raman et al., 2023; Segura et al., 2019; Shah et al., 2019; Smith, 2020), 21 had moderate ROB (Alcocer, 2020; Averett and Smith, 2019; Bachhuber et al., 2014; Bradford et al., 2018; Cerdá et al., 2018; Denkyirah, E. K. et al., 2025; Denkyirah, Elisha Kwaku et al., 2025; Drake et al., 2021; Jayawardhana and Fernandez, 2021; Kim et al., 2022; Liang et al., 2018; Martins et al., 2025; McGinty et al., 2023; McMichael et al., 2020; Powell et al., 2018; Samples et al., 2025; Shi, 2017; Shi et al., 2019; Shover et al., 2019; Wen and Hockenberry, 2018; Wen et al., 2015), and 10 had serious ROB (Alley et al., 2020; Dunn, Tyler J. et al., 2025; Dunn, T. J. et al., 2025; Fleming et al., 2022; Flexon et al., 2019; Flexon et al., 2024; Kropp Lopez et al., 2020; Livingston et al., 2017; Sabia et al., 2024; Wong and Lin, 2019). The most common sources of bias were participant selection (e.g., few states included), confounding (e.g., inadequate adjustment for time-varying differences between MCL/RCL states and no-CL states), and analysis (i.e., improper model specification).
Figure 1.
Flowchart of identification, screening, eligibility review, and selection of studies included in the systematic review of the association between state cannabis laws and opioid outcomes.
Table 1.
Summary of studies examining the association between cannabis legalization and opioid prescriptions.
| Ref | Study years | States included | Population | Data source (level of aggregation) | Outcome | Policy | Empirical strategy | Results | ROB |
|---|---|---|---|---|---|---|---|---|---|
| Low or Moderate Risk of Bias | |||||||||
| Bradford et al. (2018) | 2010-2015 | 50 U.S. states and D.C. | Medicare Part D enrollees | Center for Medicare and Medicaid Services, Medicare Part D prescription Drug Event (state level data) | Number of daily doses of opioid prescriptions dispensed (excluding buprenorphine products) | MCL enactment, MCL dispensaries, MCL home cultivation | Difference-in-differences model adjusted for state-level conditions (RCL, operational PDMP, herfindahl index of physician market competition, % of population below federal poverty line, % of population enrolled in Medicare, % of Medicare in Medicare Advantage plans, total state population), time trend, and state fixed effects. | MCLs were associated with a non-significant 2.21 (95%CI=−4.57, 0.15) million daily dose decrease in filled prescriptions. MCL dispensaries and MCL with home cultivation allowed were associated with a 3.74 (95%CI=−6.29, −1.19) and 1.79 (95%CI=−3.53, −0.05) million daily dose decrease in filled opioid prescriptions. | Moderate |
| Cerda et al., (2025) | 2013-2019 | 50 states and D.C. (1,920 counties) | US population | IQVIA Xponent Database (county level data) | Patients with any opioid prescriptions ≥30 days (long duration) and any prescription of ≥90 MME per day (high-dose) | MCL enactment RCL enactment |
Difference-in-differences (DiD) regression at the county level. Besag-York Mollie conditional autoregressive models were used to account for spatial correlation of contiguous counties. Random nonlinear effects for time and random county-time interaction terms. State-level fixed effects. Adjustment for percent male, Hispanic, non-Hispanic Black, non-Hispanic White, other race(s), unemployed, households below the federal poverty standard, median household income, population density, overall county mortality rates, opioid policies (access to naloxone, Good Samaritan laws, opioid prescribing limits for acute pain, and pain clinic laws), and state ratios of fentanyl to total opioids among drug seized. | MCL enactment was associated with an increase of 194.8 (95% CrI, 145.0-242.5) and 34.5 (95% CrI, 15.5-53.8) patients per 100,000 person-years filling high-dose and long-duration opioid prescriptions. RCL enactment was associated with −115.6 (95% CrI, −152.9, −76.2) fewer patients per 100,000 person-years filling long-duration opioid prescriptions relative to MCL alone. RCL supply was not associated with patients filling high-dose opioid prescriptions. |
Low |
| Liang et al. (2018) | 1993-2014 | 50 U.S. states and D.C. | Medicaid enrollees who enrolled in fee-for-service programs | Medicaid State Drug Utilization data, Centers for Medicare and Medicaid Services (CMS) (state level data) |
Number of filled opioid prescriptions, dosage of filled prescriptions (in oral MME) | MCL enactment, MCL dispensaries RCL dispensaries |
Linear time-series regressions controlling for cannabis decriminalization, recreational cannabis legalization, state PDMP, Medicaid expansion under the Affordable Care Act, median household income, number of active physicians per 1000 population, percentage of residence with household income below the federal poverty level, unemployment rate, state fixed effects, year and quarter indicators, state specific linear trends (separate state-specific time trends before and after 2006, with clustered random errors. | Schedule II opioids: No association found between MCL and number of prescriptions or dosage of Schedule II opioids. No associations found between MCL dispensaries with number of prescriptions or dosage of Schedule II opioids. Schedule III opioids: MCL was associated with a 29.6% (95%CI=2.4, 56.7%; P=0.03) reduction in number of Schedule III prescriptions and 29.9% (95%CI=4.8, 55.0%; P=0.02) reduction in dosage of Schedule III opioids. MCL dispensaries were not associated with Schedule III opioid prescriptions. RCL dispensaries were not associated with Schedule III opioid prescriptions. |
Moderate |
| McGinty et al., 2023 | 2010-2022 | 29 U.S. states | Adults, aged ≥18, continuously enrolled in commercial or Medicare Advantage insurance plans, diagnosed with conditions that commonly lead to chronic noncancer pain | OptumLabs Data Warehouse, medical and pharmacy claims data covered by commercial and Medicare Advantage insurance plans for 30 million adults (state level data) | Any opioid prescription, mean number of opioid prescriptions per patient prescribed opioids, mean days’ supply, mean MME per day per patient, and high-risk opioid prescriptions (i.e., ≥7-day supply or greater than 50 MME per day) | MCL dispensaries | Synthetic control model comprised of a weighted average of non-MCL or RCL states, adjusted for state-specific fixed effects and time-varying state-level covariates (% female, % with a mental illness or substance use disorder diagnosis, mean patient age, and mean patient Elixhauser Comorbidity Index) and opioid policies (dose and/or duration limits on opioid prescriptions). | MCL dispensaries were not associated with any of the examined prescription opioid outcomes (i.e., opioid prescriptions, mean number of opioid prescriptions per patient, mean days’ supply, mean MME per day, ≥7 day supply, and ≥50 MME per day). | Moderate |
| McMichael et al. (2020) | 2011-2018 | 50 U.S. states and D.C. | US population | Symphony Health’s Integrated Dataverse dataset, includes both private and public payers (individual level data) | Total number of MMEs prescribed by a provider, total days’ supply prescribed by a provider, number of unique patients to whom each provider prescribed opioids, percentage of a provider’s patients receiving any opioid, and whether a provider prescribed any opioids | MCL enactment RCL enactment |
Difference-in-differences model adjusted for state policies (Medicaid expansion under the Affordable Care Act, pain clinic law, must-access PDMP), individual-provider fixed effects, year fixed effects, and state fixed effects with clustered standard errors. | MCL enactment was associated with a 2.0% (SE=0.003) reduction in MME and RCL enactment reduced MMEs by an additional 9.5% (SE=0.004). MCL enactment was associated with a 4.3% (SE=0.003) reduction in total day’s supply of opioids and RCL enactment reduced total day’s supply by an additional 8.2% (SE=0.003). MCL enactment was associated with a 1.6% (SE=0.002) reduction in the number of patients receiving opioids and RCL enactment reduced number of patients receiving opioids by an additional 4.5% (SE=0.002). MCL enactment was associated with a 0.1% (SE=0.00001) increase in the percentage of patients receiving opioids with RCL enactment reducing the percentage of patients receiving opioids by 0.5% (SE=0.0002). MCL enactment was associated with 0.9% (SE=0.001) fewer providers prescribing any opioids with RCL enactment reducing the percentage by another 1.2% (SE=0.001). MCLs and RCLs were found to have stronger effects among commercially insured and among Medicare population than across all individuals. Among Medicaid beneficiaries, MCLs have a negative effect on prescribing, while RCLs are associated with an increase on 4 of the 5 outcomes. |
Moderate |
| Nguyen et al., 2024 | 2006-2020 | 50 U.S. states and D.C. | US population | IQVIA Xponent database (data includes more ≥90% of all retail prescriptions) (state level data) | Opioid prescriptions per 100 persons | MCL dispensary RCL dispensary |
Generalized difference-in-differences model adjusted for state-level economic indicators (state poverty rates, real gross domestic product), opioid policies (must-access PDMP, Good Samaritan laws, naloxone access laws), and state and year fixed effects. | Neither MCL nor RCL dispensaries were associated with changes in opioid prescriptions. | Low |
| Powell et al. (2018) | 2000-2013 | 50 U.S. States and D.C. | US population | DEA Automation of Reports and Consolidated Orders System (ARCOS) (state level data) | Per capita MME | MCL enactment, MCL dispensaries | Difference-in-differences and event study analysis. Models included MCL and legally protected dispensaries, sociodemographics (% male, age distribution, % White, unemployment rate), state policies (must access PDMP, pill mill law, alcohol tax rate), state and year fixed effects, and standard errors clustered at the state-level. | No significant association between MCL enactment (1.03; 95%CI= 0.98, 1.08) nor MCL with dispensaries (0.98; 95%CI= 0.93, 1.03) and per capita MME dispensed. | Moderate |
| Raman et al. (2023) | 2010-2019 | 50 U.S. states and D.C. | US Adults, ages ≥21 | DEA Automation of Reports and Consolidated Orders System (ARCOS) (state level data) | Grams of opioid distributed per 100,000 adults. Five outcome variables: any opioid, codeine, hydrocodone, morphine, and oxycodone | RCL enactment | Difference-in-differences at the state level, adjusted for state characteristics (MCL, Good Samaritan law, Naloxone Access law, Opioid Prescribing Limits law, buprenorphine prescribers, Medicaid expansion under the Affordable Care Act, % male, % Black) with state and year fixed-effects. | RCL was associated with an 25.7% reduction (95%CI=−46.2, −5.2) in pharmacy-based distribution of codeine, increasing in magnitude over time, from 17.5% reduction 1 year after RCL implementation to 37.3% reduction 4 years after implementation. RCLs were not associated with changes in opioid distributions overall or among hydrocodone, morphine, or oxycodone. |
Low |
| Shah et al. (2019) | 2006-2014 | 50 U.S. states and D.C. | Commercially insured adults | IMS Lifelink+ database, nationally representative database of commercially insured patients, 10% random sample (individual level data) | Opioid use, ≥1 opioid prescription in a year; Chronic opioid use, ≥90 days of opioid use, within a 180-day period in a year; High-risk opioid use, opioid use with (a) ≥1 day of overlap between opioid and benzodiazepine prescription (b) A maximum daily dose ≥ 120 MME, or (c) A substance use disorder diagnosis in the same year as an opioid prescription. | MCL enactment | Logistic regression controlling for individual-level variables (age, sex, payer-type, state of residence, chronic non-cancer pain, non-chronic pain, cancer, childbirth, dental visit, trauma, surgery, inpatient admissions for conditions other than those mentioned earlier, emergency room visit for conditions other than those mentioned earlier) and state-level variables (unemployment, PDMP, pain clinic laws, and physicians per 1000 residents). | MCLs were associated with a small reduction in the odds of past-year prescription opioid use (aOR=0.95; 95%CI=0.94, 0.96), past-year chronic prescription opioid use (aOR=0.93; 95%CI=0.91, 0.95), and high-risk opioid use (aOR=0.96; 95%CI=0.94, 0.98). | Low |
| Shi et al. (2019) | 2010-2017 | 14 US states and D.C. in the | Medicaid enrollees | State Drug Utilization data from the Centers for Medicare and Medicaid Services (state level data) | Number of opioid prescriptions, total doses of opioid prescriptions in MME | RCL enactment | Difference-in-differences, controlling for state-level, median income, unemployment rate, statewide MCL in effect, PDMP, Medicaid expansion under the Affordable Care Act, and poverty rate. RCL in different years were analyzed separately. Separate models were further run for Schedule II and III opioids. | Schedule II opioids: RCLs in 2012, 2015 and 2016/17 were not associated with changes in number of nor total MME of schedule II prescribed. Schedule III opioids: RCL in 2015 was associated with a decrease in the number of prescribed (DiD=−0.32; 95%CI=−0.49, −0.15) and total MME prescribed (DiD=−0.30; 95%CI=−0.55, −0.044). All other analyses of Schedule III opioids alone showed no association with RCL. |
Moderate |
| Wen & Hockenberry (2018) | 2011-2016 | 50 U.S. States and D.C. | Medicaid enrollees | State Drug Utilization data from the Centers for Medicare and Medicaid Services (state level data) | Number of opioid prescriptions covered by Medicaid on a quarter per 1,000 Medicaid enrollees | MCL enactment RCL enactment |
Difference-in-differences model adjusted for state physician supply, buprenorphine-waivered physician supply, economic conditions (unemployment rate, poverty rate, median household income), current state policies (must access PDMP, pain clinic regulations, Medicaid expansion), state and quarter fixed effects, and standard errors clustered by state. | MCL implementation was associated with a 5.88% (95%CI=−11.55%, −0.21%) lower rate of Medicaid-covered prescriptions. RCL implementation was associated with an additional 6.38% (95%CI=−12.20%, −0.56%) lower rate of Medicaid-covered prescriptions. Lower rate of prescribing associated with RCL was mainly concerned with Schedule II opioids (−7.79%; 95%CI=−14.73%, −0.85%), whereas the lower prescribing rate associated with MCL was more pronounced in Schedule III-V opioids (−10.40%; 95%CI=−19.05%, −1.74%). |
Moderate |
| Serious Risk of Bias | |||||||||
| Dunn et al., 2025a | 2012-2015 | 47 U.S. states and D.C. (excluding Connecticut, Massachusetts, New Hampshire) | Medicare beneficiaries, aged ≥65 | Medicare administrative claims data, 5% national sample continuously enrolled in Medicare Parts A, B, and D during study period (Part C beneficiaries excluded) (individual level data) | Doses of prescription opioids filled per beneficiary per month (measured MME) | MCL enactment | Difference-in-difference model with pre/post by MCL status, adjusting for age, sex, race and ethnicity (White African American, Asian, Hispanic, and Other), and comorbidities (Charlson Comorbidity Index). | MCL enactment in 2014 was associated with a decrease of 99.8 MME per member per month compared to non-MCL states (P<0.001). Compared to states with existing MCLs, MCL enactment in 2014 was associated with a decrease of 32.7 MME per member per month (P=0.02). | Serious |
| Dunn et al., 2025b | 2012-2015 | 47 U.S. states and D.C. (excluding Connecticut, Massachusetts, New Hampshire) | Medicare beneficiaries, aged ≥65 | Medicare administrative claims data, 5% national sample continuously enrolled in Medicare Parts A, B, and D during study period (Part C beneficiaries excluded) (individual level data) | Doses of prescription opioids filled per beneficiary per month (measured MME) | MCL enactment | Difference-in-difference model with pre/post by MCL status adjusting for age, sex, and comorobidities (Charlson Comorbidity Index). MCL status by race (White versus non-White). | No significant differences between White and non-White Medicare beneficiaries was found in changes in average opioid dosage between states implementing MCL in 2014 and states that did not (P=0.07) and states that had implemented MCL before 2014 (P=0.26). | Serious |
| Kropp Lopez et al. (2020) | 2007-2017 | Colorado, Utah, Maryland | State population | DEA Automation of Reports and Consolidated Orders System (ARCOS) (state level data) | Per capita MME | RCL enactment | T-tests compared pre-RCL (2007-2012) and post-RCL (2013-2017) for the nine pain medications and two OUD medications. | Relative to 2012, opioids for pain decreased by 31.5% in RCL enacting Colorado, compared to 14.3% and 23.5% in non-RCL enacting Utah and Maryland, respectively. | Serious |
Notes: MCL enactment is when a medical cannabis law becomes effective (i.e., patients have legal protection to possess cannabis for medical purposes). RCL enactment is the date when recreational cannabis is legally protected in the state for adult use. MCL dispensary is the date when the first medical cannabis dispensary is legally protected and open for business in a state. RCL dispensary is the date when the first recreational dispensary is legally protected and open for business in a state.
MCL=medical cannabis law; RCL=recreational cannabis law; CI=confidence interval; CrL=creditability limits; aOR=Adjusted odds ratio; ARCOS=Automation of Reports and Consolidated Orders System; D.C.=District of Columbia; DEA=Drug Enforcement Administration; DiD=Difference-in-difference; MME=Morphine milligram equivalents; OUD=Opioid use disorder; P=p-value; PDMP=Prescription Drug Monitoring Programs; ROB=Risk of Bias; RR=Risk Ratio; SE=Standard error; U.S.=United States
3.1. MCL, RCL enactment and opioid prescriptions
Table 1 shows the 14 papers have examined the relationship between MCL/RCL and rates of opioid prescriptions; 6 examined MCL (1 low ROB (Shah et al., 2019); 3 moderate ROB (Bradford et al., 2018; McGinty et al., 2023; Powell et al., 2018; Shah et al., 2019); 2 serious ROB (Dunn, Tyler J. et al., 2025; Dunn, T. J. et al., 2025)), 3 examined RCL (1 low ROB (Raman et al., 2023); 1 moderate ROB (Shi et al., 2019); 1 serious ROB (Kropp Lopez et al., 2020)), and 5 examined MCL and RCL (2 low ROB (Cerda et al., 2025; Nguyen et al., 2024); 3 moderate ROB (Liang et al., 2018; McMichael et al., 2020; Wen and Hockenberry, 2018)). Five studies examined the effect of MCL dispensaries (Bradford et al., 2018; Liang et al., 2018; McGinty et al., 2023; Nguyen et al., 2024; Powell et al., 2018). Data sources included Medicaid (Dunn, Tyler J. et al., 2025; Dunn, T. J. et al., 2025; Liang et al., 2018; Raman et al., 2023; Shi et al., 2019; Wen and Hockenberry, 2018), Medicare (Bradford et al., 2018), DEA ARCOS (Kropp Lopez et al., 2020; Powell et al., 2018), Colorado state PDMP (Kropp Lopez et al., 2020), and commercial (IQVIA Xponent (Cerda et al., 2025; Nguyen et al., 2024), OptumLabs Data Warehouse (McGinty et al., 2023), IMS Lifelink+ database (Shah et al., 2019), Symphony Health’s IDV dataset (McMichael et al., 2020)). Opioid prescriptions were operationalized as number of filled opioid prescriptions within a given population (Liang et al., 2018; Nguyen et al., 2024; Shi et al., 2019; Wen and Hockenberry, 2018), patients with ≥1 opioid prescription in a year (McMichael et al., 2020; Shah et al., 2019), patients with ≥90 day opioid prescription (Cerda et al., 2025; Shah et al., 2019), morphine milligram equivalent doses (Dunn, Tyler J. et al., 2025; Dunn, T. J. et al., 2025; Kropp Lopez et al., 2020; McMichael et al., 2020; Powell et al., 2018; Raman et al., 2023; Shi et al., 2019), and daily doses of opioid prescriptions (Bradford et al., 2018; McGinty et al., 2023; McMichael et al., 2020). All but two (McMichael et al., 2020; Shah et al., 2019) of the low-to-moderate ROB studies were ecologic, adjusting for state-level sociodemographics and opioid-related state polities (e.g., prescription drug monitoring programs, pill mill laws).
In low-to-moderate ROB studies of MCL enactment (Bradford et al., 2018; Cerda et al., 2025; Liang et al., 2018; McMichael et al., 2020; Powell et al., 2018; Shah et al., 2019; Wen and Hockenberry, 2018), 3 found reduced opioid prescription rates (McMichael et al., 2020; Shah et al., 2019; Wen and Hockenberry, 2018), 1 found increased opioid prescription rates (Cerda et al., 2025), 1 found no association (Powell et al., 2018), and 2 had mixed results (Bradford et al., 2018; Liang et al., 2018). Two studies found greater reductions in the number of Schedule III opioid prescriptions dispensed (i.e., codeine and buprenorphine) than Schedule II opioid prescriptions (e.g., hydrocodone, oxycodone, morphine) (Liang et al., 2018; Wen and Hockenberry, 2018). Of the 5 MCL studies examining enactment of dispensary provisions (Bradford et al., 2018; Liang et al., 2018; McGinty et al., 2023; Nguyen et al., 2024; Powell et al., 2018), 1 found a decrease in the number of daily doses dispensed (Bradford et al., 2018) and 4 found no association (Liang et al., 2018; McGinty et al., 2023; Nguyen et al., 2024; Powell et al., 2018). The studies finding associations of MCL dispensaries and reduced daily doses examined shorter time periods (2010-2015) (Bradford et al., 2018), while the studies finding no such association examined longer periods, ranging from 13 (McGinty et al., 2023) to 22 years (Liang et al., 2018).
In 6 studies of RCL enactment of low-to-moderate ROB, results were mixed: 2 studies found reduced opioid prescriptions (Cerda et al., 2025; McMichael et al., 2020), 1 found no association (Liang et al., 2018), and in 3, results varied by the opioid schedule (Table 1) (Raman et al., 2023; Shi et al., 2019; Wen and Hockenberry, 2018). One study of 2011-2016 data from Medicaid enrollees (Wen and Hockenberry, 2018) found RCL associated with reduced Schedule II opioid prescriptions (in contrast to this study’s finding on MCL and Schedule III opioids): RCLs were associated with a 6.38% (95%CI=−12.20%, −0.56%) reduction in Medicaid-covered prescriptions, with lower prescribing rates mainly among Schedule II opioids (−7.79%; 95%CI=−14.73%, −0.85%). Another study using 2010-2017 data from Medicaid enrollees (Shi et al., 2019) found no association between RCL and prescribing of Schedule II opioids but did find decreases in both the number of patients prescribed Schedule III opioids and the total MME prescribed. A third study using 2010-2019 DEA ARCOS data (Raman et al., 2023) found no association between RCL and grams of opioids distributed overall or grams of Schedule II opioids, but did find that RCL enactment was associated with a 25.7% (95% CI=−46.2%, −5.2%) reduction in pharmacy-based codeine distribution, which is often in a Schedule III formulation. One low-to-moderate ROB study examining RCL dispensaries found no association with number of opioid prescriptions (Nguyen et al., 2024).
3.2. MCL, RCL enactment, non-medical opioid use and OUD
Table 2 shows the 15 studies that have examined the relationship between cannabis legalization, non-medical opioid use and OUD: 8 examined MCL enactment (2 low ROB (Ali et al., 2021; Segura et al., 2019); 4 moderate ROB (Cerdá et al., 2018; Powell et al., 2018; Samples et al., 2025; Wen et al., 2015); 2 serious ROB (Flexon et al., 2024; Wong and Lin, 2019)), 3 examined RCL enactment (3 serious ROB (Alley et al., 2020; Fleming et al., 2022; Sabia et al., 2024)), and 4 independently examined both MCL and RCL enactment (2 low ROB (Ali et al., 2023; Mannes et al., 2025); 1 moderate ROB (Martins et al., 2025); 1 serious ROB (Flexon et al., 2019),). The vast majority of studies (73%) used data from the National Survey on Drug Use and Health (NSDUH) (Ali et al., 2023; Ali et al., 2021; Flexon et al., 2019; Flexon et al., 2024; Martins et al., 2025; Powell et al., 2018; Sabia et al., 2024; Samples et al., 2025; Segura et al., 2019; Wen et al., 2015; Wong and Lin, 2019); other data sources included Monitoring the Future (Cerdá et al., 2018), Young Adult Health Study (Fleming et al., 2022), national College Health Assessment (Alley et al., 2020), and Veterans Health Administration (Mannes et al., 2025). Of these studies, nonmedical prescription opioid use was most frequently studied (Ali et al., 2023; Ali et al., 2021; Alley et al., 2020; Cerdá et al., 2018; Fleming et al., 2022; Flexon et al., 2019; Flexon et al., 2024; Martins et al., 2025; Powell et al., 2018; Sabia et al., 2024; Samples et al., 2025; Segura et al., 2019; Wen et al., 2015; Wong and Lin, 2019), followed by heroin use (Ali et al., 2023; Wen et al., 2015; Wong and Lin, 2019) and OUD (Ali et al., 2023; Ali et al., 2021; Mannes et al., 2025; Martins et al., 2025; Segura et al., 2019). All low-to-moderate ROB studies except 1 (Sabia et al., 2024), were conducted at the individual level, adjusting for individual sociodemographics and state factors (e.g., alcohol tax, prescription drug monitoring programs), while one study (Powell et al., 2018) adjusted only for state-level variables.
Table 2.
Summary of studies examining the association between cannabis legalization and nonmedical opioid use and opioid use disorder.
| Ref | Study years | States included | Population | Data source (level of aggregation) | Outcome | Policy | Empirical strategy | Results | ROB |
|---|---|---|---|---|---|---|---|---|---|
| Low or Moderate Risk of Bias | |||||||||
| Ali et al. (2021) | 2002–2014 | 50 U.S. states and D.C. | US women, aged ≥18 years | NSDUH (individual-level) | Self-reported past-month or past-year nonmedical prescription opioid use, past-year initiation, and past-year prescription opioid use disorder and heroin use disorder | MCL enactment, MCL dispensaries | Difference-in-differences model adjusted for individual characteristics (age, educational attainment, race and ethnicity, marital status, income [measured as % of the federal poverty threshold], insurance coverage status) and state characteristics (unemployment rate, PDMP), state fixed-effects, month-by-year- fixed effects, clustered standard errors. | MCL enactment was associated with a statistically significant 0.003 reduction (SE=0.001) in the probability of past-month nonmedical prescription opioid use; not associated with past-year nonmedical use, past-year initiation, or past-year OUD. MCL dispensaries were not associated with past-month or past-year nonmedical opioid use, past-year initiation, or past-year opioid use disorder. MCL enactment was associated with an increase in frequency of past-year opioid use among pregnant women (38.2 more days), but associated with a decrease in frequency of past-year use among women with infants (26.7 fewer days) and among women with children (16.3 fewer days). MCL dispensaries were associated with a further reduction in frequency of past-year opioid use among women with infants (39.5 fewer days) and women with children (14.1 fewer days). |
Low |
| Ali et al. (2023) | 2004–2020 | 50 U.S. states and D.C | US adults, aged ≥19 | NSDUH (individual-level) | Self-reported past-month or past-year nonmedical opioid use or past-year prescription opioid use disorder | MCL enactment, MCL dispensaries RCL enactment |
Difference-in-differences model adjusted for individual characteristics (age, gender, educational attainment, race, ethnicity, marital status, income [measured as % of the federal poverty threshold], insurance coverage status) and state characteristics (unemployment rate, mandatory-use of PDMP, naloxone law), state fixed-effects, month-by-year- fixed effects, clustered standard errors. | From 2004–2014: Neither MCL enactment nor MCL dispensaries were associated with opioid-related outcomes. RCL enactment was associated with a 0.003 reduction (SE=0.001) in the probability of using opioids weekly during the past year and a 0.001 (SE=0.0004) reduction in the probability of a past-year OUD. RCL dispensaries were associated with a 0.002 reduction (SE=0.001) in the probability of using opioids weekly during the past year and a 0.001 (SE=0.001) reduction in the probability of a past-year OUD. From 2015–2018: Neither MCL nor RCL enactment were associated with any opioid-related outcomes. RCL dispensaries were associated with a 0.001 (SE=0.001) increase in the probability of past-year OUD. From 2004–2020: RCL enactment was associated with a 0.001 reduction (SE=0.0003) in the probability of having a heroin use disorder and a 68% increase (SE=15.01) in the frequency of heroin use during the past year. MCL enactment was not associated with any heroin-related outcomes. MCL dispensaries were associated with a 45.94% reduction (SE=16.9) in the frequency of heroin use during the past year and a 0.001 reduction (SE=0.0003) in the probability of using heroin weekly during the past year. |
Low |
| Cerda et al. (2018) | 1991–2015 | 48 contiguous U.S. states | US public and private attending students in 8th, 10th, and 12th grade | Monitoring the Future, (individual-level) | Self-reported nonmedical use of prescription opioid without a prescription or doctor’s permission in the last 30 days. | MCL enactment | Difference-in-differences model adjusted for individual-level covariates (grade, age, sex, race/ethnicity, highest parental education), school-level covariates (number of students per grade, public vs. private school, whether school was in a metropolitan statistical area), state-level covariates (% of population male, White, aged 10–24 years, and ≥25 years without a high school degree), and state policies (excise tax on beer, per-pack tax on cigarettes, yearly state-specific purity-adjusted heroin prices), and adjusted for state fixed effects. | Among 8th graders, the prevalence of nonmedical opioid use decreased after MCL enactment from 2.1% to 0.9% (aOR=0.43; 95%CI=0.36, 0.52; P<0.0001). Among 10th graders, MCL enactment was associated with a nonsignificant decrease in nonmedical prescription opioid use, from 2.8% to 2.5% (aOR=0.89; 95%CI=0.75, 1.05; P=0.17). Among 12th graders, the prevalence of nonmedical prescription opioid use increased after MCL enactment from 2.2% to 3.1% (aOR=1.42; 95%CI=1.21, 1.66; P<0.0001). |
Moderate |
| Mannes et al., 2025 | 2005–2022 | 50 U.S. states and D.C. | Veteran Health Administration patients, aged 18–75 years | Veteran Health Administration patients(individual-level) | Annual prevalence of OUD diagnosis | MCL enactment,MCL dispensaries RCL enactment,RCL dispensaries |
Staggered adoption DiD model adjusted for sex, race/ethnicity, age, substance use disorder, receipt of prescription opioids, must-access PDMP, and state-level sociodemographics (% male, % race/ethnicity, % ≥18 years, unemployment, income less than federal poverty level, median household income). Models included fixed state and year effects. | Relative to no cannabis legalization, MCL enactment and RCL enactment were associated with a 0.06% (95%CI=0.05–0.06%) and 0.07% (95%CI=0.06–0.08%) increase in OUD prevalence, respectively. Stratified by age, MCL and RCL enactment was associated with increases in both patients 35–64 years and 65–75 years, but not 18–34 years. Patients with chronic pain had larger increases in OUD prevalence following MCL and RCL enactment than those without chronic pain. MCL and RCL dispensaries were associated with a 0.07% (95%CI=0.06–0.08%) and 0.05% (95%CI=0.04–0.06%) increase in OUD prevalence compared to states without. |
Low |
| Martins et al., 2024 | 2015–2019 | 50 U.S. states and D.C. | US Adults, aged ≥18 | NSDUH (individual-level) | Opioid misuse (including either nonmedical use of prescription opioids and/or heroin use), past month and past-year. DSM-IV opioid use disorder (i.e., abuse and dependence of either nonmedical prescription opioids and heroin use), past-year |
MCL enactment RCL enactment |
Logistic regression model with year and state fixed effects, individual-level factors (age, education, household income, insurance status, race and ethnicity, sex) and opioid-related state policies (limits on initial opioid prescriptions, pain clinic regulations, must access PDMP). | Neither MCL nor RCL enactment was associated with changes in past-month or past-year opioid misuse. Among respondents reporting past-year cannabis use, there were decreased odds of past year opioid misuse (aOR=0.57; 95%CI=0.38, 0.85) among those in states with MCL compared to those in states without cannabis laws. RCL was not associated with changes in the odds of any opioid outcome beyond MCL adoption. Neither MCL nor RCL adoption was associated with changes in past-year OUD overall nor stratified by past-year cannabis use. |
Moderate |
| Powell et al. (2018) | 1999–2013 | 50 U.S. States and D.C. | US population aged ≥12 | NSDUH (state-level) | Self-reported nonmedical use of pain relievers | MCL enactment,MCL dispensaries | Difference-in-differences and event study analysis. Models included MCL and legally protected dispensaries, sociodemographics (% male, age distribution, % White, unemployment rate), state policies (must access PDMP, pill mill law, alcohol tax rate), state and year fixed effects, and standard errors clustered at the state-level. | No significant association was found between MCL enactment (1.03; 95%CI= 0.91, 1.15) nor MCL with dispensaries (0.99; 95%CI= 0.88, 1.12) and past-year nonmedical opioid pain reliever use. | Moderate |
| Samples et al., 2025 | 2004–2014 | 50 U.S. states and D.C. | US adults, aged ≥21 | NSDUH (individual-level) | Past-year nonmedical prescription opioid use. Frequency of use: occasional use (1–12 days per year), regular use (13–52 days), and frequent use (≥53 days). | MCL enactment | Multi-level mixed effects generalized linear model adjusting for survey year, Individual-level factors (sex, age, race/ethnicity, education, income, employment, marital status, health insurance status, past-year substance use, cannabis or alcohol use disorder), state-level conditions (total population, % 10–24 years, % male, % White, % <high school education, unemployment rate, median household income), and state PDMP | MCL enactment was associated with a 2.1% increase (95%CI=0.5, 3.8) in occasional nonmedical prescription opioid use, a 0.6% decrease (95%CI=-0.1, -1.1) in regular use, and a 1.5% decrease (95%CI=-0.4, -2.7) in frequent use. Among those with cannabis use disorder, MCL enactment was associated with a 5.0% increase (95%CI=1.0, 9.1) in occasional use and a 4.5% decrease (95%CI=-1.3, -7.6) in frequent nonmedical prescription opioid use compared to those without cannabis use disorder. |
Moderate |
| Segura et al. (2019) | 2004–2014 | 50 U.S. states and D.C. | US population aged ≥12 | NSDUH (individual-level) | Self-reported nonmedical prescription opioid use and prescription opioid use disorder during the past year | MCL enactment,MCL dispensaries | Multilevel logistic regression adjusted for individual characteristics (sex, age, racial/ethnic group, survey year, any health coverage, family income, and population density on individual’s household location, state of residence) and state conditions (% of population White, male, aged 10–24 years, ≥25 years with at least a high school education, unemployment rate, median household income, PDMP). | Prevalence of nonmedical prescription opioid use increased from 4.29% to 4.74% following state MCL enactment without dispensaries (aOR=1.11; 95%CI=1.03, 1.20) and from 4.29% to 4.83% following state MCL enactment with dispensaries (aOR=1.13; 95%CI=1.06, 1.22). Prevalence of prescription opioid use disorder among nonmedical prescription opioid users increased non significantly from 15.46% to 16.36% following MCL enactment without dispensaries (aOR=1.07; 95%CI=0.89, 1.29) and decreased non significantly from 15.46% to 13.72% following MCL enactment with dispensaries (aOR=0.87; 95%CI=0.72, 1.05). |
Low |
| Wen et al. (2015) | 2004–2012 | 50 U.S. states and D.C. | US population aged 12–20 or ≥21 | NSDUH (individual-level) | Self-reported past-year nonmedical use of prescription pain medication and past-year heroin use | MCL enactment | Difference-in-differences model adjusted for individual-level (age, sex, race/ethnicity, self-reported health, cigarette smoking, health insurance, family income, urbanicity, marital status, education, employment) and state factors (unemployment, average personal and household income, beer tax rate) with state and year fixed effects and clustered standard errors. | MCL enactment was not associated with any immediate nor delayed effects on non-medical use of prescription pain medication nor heroin use among persons aged 12–20 nor ≥21. | Moderate |
| Serious Risk of Bias | |||||||||
| Alley et al. (2020) | 2008–2018 | 48 U.S. states | College and university students, aged 18–26 | National College Health Assessment-II(individual-level) | Self-reported past-year nonmedical use of prescription opioids. | RCL enactment | Mixed model with logistic regression (nested by state, institution, then individual), controlling for individual-level covariates (sex, race/ethnicity, sexuality, relationship status, residence type, legal age of drinking, first year in school, international student status, and membership in a fraternity/sorority) and institution-level variables (institution, institution enrollment size, private or public, and survey administration season). | RCL were associated with no significant change in past-year nonmedical use of prescription opioids (aOR=1.015; 95%CI=0.949, 1.085). | Serious |
| Fleming et al. (2022) | 2014–2019 | Washington state | US adults in Washington state, aged 18–25 | Young Adult Health Study (individual-level) | Self-reported past-year pain reliever misuse | RCL enactment (July 2014) | Logistic regression models to estimate the yearly change in prevalence, stratified by age (18–20, 21–25). Models adjusted for sex, race/ethnicity, age, employment, and college status. Poststratification weights were used to approximate the statewide population. | The odds of nonmedical opioid pain reliever use decreased by 0.82 (95%CI=0.76, 0.88) among respondents 18–20 years and decreased by 0.87 (95%CI=0.82, 0.93) among respondents 21–25 years. | Serious |
| Flexon et al. (2019) | 2002–2017 | 50 U.S. States and D.C. | US population aged ≥18, non-institutionalized | NSDUH (state-level) | Self-reported past year nonmedical prescription pain reliever use | MCL enactment RCL enactment |
Fixed effects panel regression of MCL or RCL enactment on state-level proportions. No adjustment variables were included. | Respondents in MCL (b=0.002, P>0.05) and RCL (b=0.001, P>0.05) states reported similar levels of nonmedical opioid pain reliever use as respondents in states without legal cannabis. | Serious |
| Flexon et al., 2024 | 2017–2019 | 50 U.S. states and D.C. | US Adults, aged ≥18 | NSDUH (individual-level) | Past-year prescription opioid use and past-year nonmedical prescription opioid use | MCL enacted | Logistic regression model adjusting for year and individual-level age, sex, race/ethnicity, self-reported health, cigarette smoking, health insurance, family income, county metro status, marital status, education, employment, and past-year cannabis consumption level. | Individuals who reside in states that have passed an MCL had 11% lower odds (aOR=0.89; 95%CI=0.87, 0.92) of prescription opioid use and 10% lower odds of nonmedical prescription opioid use (aOR=0.90; 95%CI= 0.85, 0.95) than those living in states that have not passed MCL. | Serious |
| Sabia et al., 2024 | 2002–2019 | 50 U.S. states and D.C. | US adults, aged ≥18 | NSDUH (state-level) | Past-year nonmedical prescription opioid use | RCL enactment | Difference-in-differences analysis, state-level political and economic conditions (unemployment rate, per capita gross domestic product, governor political party, state minimum wage, racial composition), state policies (MCL, cannabis decriminalization, real beer tax per gallon, must-access PDMP, naloxone access law, Good Samaritan law, state earned income tax credit refundable rate, maximum Supplemental Nutrition Assistance Program benefit, Medicaid expansion), state- and year-fixed effects | RCL enactment was not associated with nonmedical prescription opioid use. | Serious |
| Wong et al. (2019) | 2013–2014 | 50 U.S. states and D.C. | US population, aged 12–18 | NSDUH (individual-level) | Self-reported past-year use of heroin and nonmedical use of prescription opioids. | MCL enactment | Created a matched sample of adolescents in MCL vs non-MCL states using propensity-score matching (PSM) along with the nearest-neighbor matching method. Logistic regression controlling for individual-level age, sex, race/ethnicity, income, metropolitan residence, and use of cigarette, alcohol and/or marijuana. | MCL were associated with increased odds of heroin use (aOR=2.61; 95%CI=1.10, 6.16) and no change in the odds of nonmedical prescription opioid use (aOR=1.06; 95%CI=0.90, 1.23). | Serious |
In the low-to-moderate ROB studies of MCL and nonmedical opioid use in adults, 5 found no significant change (Ali et al., 2023; Flexon et al., 2019; Martins et al., 2025; Powell et al., 2018; Wen et al., 2015; Wong and Lin, 2019), 2 found increased non-medical opioid use (Ali et al., 2021; Segura et al., 2019), and 2 found mixed results, with 1 study finding results varied by age (Cerdá et al., 2018) and another by frequency of use (Samples et al., 2025). In a study of students across the U.S., MCL enactment was associated with decreased use in 8th graders, no change in 10th graders, and increased use in 12th graders (Cerdá et al., 2018). One study examining frequency of nonmedical opioid use found MCL enactment was associated with increased odds of occasional nonmedical use (1-12 days per year) but decreased odds of both regular (13-52 days per year) and frequent use (≥53 days per year) (Samples et al., 2025). In respondents reporting past-year cannabis use, Martins et al. (2025) found MCL enactment was associated with decreased odds of past year nonmedical opioid use.
Among the 3 low-to-moderate ROB studies examining RCL enactment and nonmedical opioid use, 1 found no significant change in nonmedical opioid use (Martins et al., 2025) and 1 found RCL enactment was associated with an increase only in heroin use (Ali et al., 2023).
Five low-to-moderate ROB studies examined MCL and OUD, 1 found increased OUD diagnoses (Mannes et al., 2025) and 4 found no significant change in prescription opioid use disorders (Ali et al., 2023; Ali et al., 2021; Martins et al., 2025; Segura et al., 2019). In a study of Veterans Health Administration patients, MCL and RCL enactment was associated with increases in OUD among patients 35-64 years and 65-75 years, but not ages 18-34. Moreover, veteran patients with chronic pain diagnoses had larger increase in OUD prevalence following both MCL and RCL than those without chronic pain (Mannes et al., 2025). In the only other study of RCL and substance use disorders (low ROB), prescription opioid use disorder remained unchanged, while heroin use disorder decreased (Ali et al., 2023).
3.3. MCL, RCL enactment and opioid-related hospitalizations and emergency department visits
Table 3 shows the 3 papers published between 2017-2021 studied the relationship between cannabis legalization and opioid-related hospitalizations and emergency department (ED) visits, all of which were ecologic and adjusted for state-level sociodemographics and opioid-related policies. All three studies used the Healthcare Cost and Utilization Project (HCUP) dataset to identify opioid-related hospitalizations and ED visits. Of the 2 studies of MCLs (2 moderate ROB (Jayawardhana and Fernandez, 2021; Shi, 2017)), both found MCL enactment associated with a reduction in opioid-related hospitalizations. One of these studies found that MCL implementation was not associated with opioid-related ED visits, while MCL dispensaries were associated with an increase in opioid-related ED visits. Of the 2 studies examining RCLs (both moderate ROB (Drake et al., 2021; Jayawardhana and Fernandez, 2021)), one found RCL enactment was associated with quarterly increases in opioid-related hospitalizations but not ED visits (Jayawardhana and Fernandez, 2021), whereas the other found RCLs associated with reduced opioid-related emergency department visits by 7.8% for two quarters after implementation (6 months), with the association becoming null in the third and fourth quarters post-implementation (Drake et al., 2021).
Table 3.
Summary of studies examining the association between cannabis legalization and hospitalizations and emergency department visits
| Ref | Study years | States included | Population | Data source (level of aggregation) | Outcome | Policy | Empirical strategy | Results | ROB |
|---|---|---|---|---|---|---|---|---|---|
| Low or Moderate Risk of Bias | |||||||||
| Shi et al. (2017) | 1997-2014 | 26 U.S. states | US population, non-federal, non-rehabilitation hospitals | Healthcare Cost and Utilization Project (HCUP), State Inpatient Database (SID)(Individual-level data) | Annual rate of hospitalizations involving opioid dependence or abuse (ICD-9-CM: 304.0, 305.5, 304.7) and those involving opioid overdose (ICD-9-CM: 965.00, 965.02, 965.09) | MCL enactment,MCL dispensaries | Difference-in-differences model with state and year fixed effects and adjusted for state covariates (population size, unemployment, median household income, beer tax rate per gallon, uninsured rate) and state policies (cannabis decriminalization, PDMP, pain clinic laws) with standard errors clustered at the state level. | MCL enactment was associated with a 23% reduction (95%CI=−0.41, −0.07; P=0.008) in hospitalizations related to opioid dependence or abuse and a 13% reduction (95%CI=−0.25, −0.02; P=0.025) in hospitalizations related to opioid pain reliever overdose. After accounting for MCL implementation, MCL dispensaries did not have an independent association with opioid hospitalizations or opioid overdose. Test of lagged effects. MCL enactment was associated with a significant reduction in opioid hospitalizations only in Year 1 of the policy (−0.09; 95%CI=−0.18, −0.01) and only in year 3 for opioid overdose (−0.12; 95%CI=−0.20, −0.04). |
Moderate |
| Drake et al., (2021) | 2011-2017 | 29 U.S. states | US population, non-federal, non-rehabilitation hospitals | Healthcare Cost and Utilization Project (HCUP), State Emergency Department Databases (SEDD)(Individual-level data) | Opioid-related emergency department (ED) visits per 100,000 population. Opioid-related ED visits include poisonings from opioids and disorders related to misuse of opioids. | RCL enactment | Event study model adjusted for state policies (MCL, mandatory PDMP, Medicaid expansion, good Samaritan law, Naloxone access law), state-level socioeconomic variable (poverty rate, median household income, unemployment, community hospital beds per 1,000 population), and state and quarter fixed effects. | RCLs are associated with 7.8% (95%CI=−13.6, −2.0) and 7.7% (95%CI=−13.8, −1.5) decrease in opioid-related ED visit rates in the first two quarters following implementation. The effects dissipated in the third (3.0%; 95%CI=−9.8, 3.7) and fourth (2.4%; 95%CI=−9.5, 4.8) quarters following implementation. Stratified analysis shows reductions in opioid-related ED for men (11.5%; 95%CI=−17.9, −5.0) and adults aged 25–44 (−10.6; 95%CI=−17.3, −4.0). |
Moderate |
| Jayawardhana & Fernandez (2021) | 2005-2016 | 35-47 U.S. states and D.C. | US population, non-federal, non-rehabilitation hospitals | Healthcare Cost and Utilization Project (HCUP), State Inpatient Database (SID) and State Emergency Department Databases (SEDD) (Individual-level data) |
Opioid-related hospitalizations and opioid-related ED visits, including opioid abuse, dependence, and poisonings. | MCL enactment, MCL dispensaries RCL enactment |
Difference-in-differences model that included an indicator variable for MCL implementation, MCL dispensary, sociodemographic variables (population size, median household income, % uninsured, annual unemployment, annual per capita ethanol consumption), state-level policy variables (RCL, MCL dispensary, mandatory access PDMP, pill mill legislation, good Samaritan laws, naloxone availability, Medicaid expansion), both state and quarter fixed effects, with standard errors clustered at the state-level. | MCL implementation was associated with a reduction of 30.66 (SE=14.43) opioid-related hospitalizations per 100,000 population per quarter. Among MCL states, states that allow dispensary experience 15.11 (SE=7.66) additional opioid-related hospitalizations per 100,000 per quarter than states with no MCL dispensaries but only the initial MCL. MCL implementation was not associated (b=2.05; SE=17.93) with opioid-related ED visits per 100,000 population. However, states that allow dispensary (and home cultivation) experience an additional 102.8 (SE=32.02) opioid ED visits per 100,000 per quarter compared to states without MCL. States that enacted RCL experienced 41.53 (SE=19.31) additional opioid-related hospitalizations per 100,000 per quarter than states with no cannabis laws. RCL implementation was not associated (b=1.95; SE=9.98) opioid-related ED visits. |
Moderate |
Notes: MCL enactment is when a medical cannabis law becomes effective (i.e., patients have legal protection to possess cannabis for medical purposes). RCL enactment is the date when recreational cannabis is legally protected in the state for adult use. MCL dispensary is the date when the first medical cannabis dispensary is legally protected and open for business in a state. RCL dispensary is the date when the first recreational dispensary is legally protected and open for business in a state.
MCL=medical cannabis law; RCL=recreational cannabis law; CI=confidence interval; aOR=Adjusted odds ratio; D.C.=District of Columbia; DiD=Difference-in-difference; ED=emergency department; ICD-9-CM=International Classification of Diseases, Ninth Revision, Clinical Modification; P=p-value; PDMP=Prescription Drug Monitoring Programs; ROB=Risk of Bias; RR=Risk Ratio; SE=Standard error; U.S.=United States
3.4. MCL, RCL enactment and fatal overdose
Between 2014-2023, 16 papers examined the relationship of cannabis legalization to drug overdose deaths. Of these, 5 examined MCL (1 low ROB (Smith, 2020); 4 moderate ROB (Averett and Smith, 2019; Bachhuber et al., 2014; Kim et al., 2022; Powell et al., 2018)), 5 examined RCL (2 low ROB (Denkyirah, E. K. et al., 2025; Denkyirah, Elisha Kwaku et al., 2025); 1 moderate ROB (Alcocer, 2020); 2 serious ROB (Livingston et al., 2017; Sabia et al., 2024)), and 6 examined both MCL and RCL (5 low ROB (Castillo-Carniglia et al., 2023; Cerda et al., 2025; Chan et al., 2020; Mathur and Ruhm, 2023; Nguyen et al., 2024); 1 moderate ROB (Shover et al., 2019)). Of these, 7 MCL studies (Averett and Smith, 2019; Castillo-Carniglia et al., 2023; Chan et al., 2020; Mathur and Ruhm, 2023; Nguyen et al., 2024; Powell et al., 2018; Smith, 2020) and 5 RCL studies (Alcocer, 2020; Castillo-Carniglia et al., 2023; Chan et al., 2020; Mathur and Ruhm, 2023; Nguyen et al., 2024) examined dispensaries. The most common source of overdose death data was the National Vital Statistics System via Centers for Disease Control Wide-ranging Online Data for Epidemiologic Research (WONDER). All studies but one (Kim et al., 2022) were ecologic and adjusted for state-level sociodemographics and opioid-related policies, while 3 additionally adjusted for county-level effects (Castillo-Carniglia et al., 2023; Cerda et al., 2025; Smith, 2020).
Based on 11 low-to-moderate ROB studies, results for the association between MCL enactment and overdose deaths were mixed: 2 studies found reductions (Bachhuber et al., 2014; Smith, 2020), 2 found no association (Cerda et al., 2025; Kim et al., 2022), 1 found increases (Mathur and Ruhm, 2023), and 6 found mixed results (Averett and Smith, 2019; Castillo-Carniglia et al., 2023; Cerda et al., 2025; Chan et al., 2020; Powell et al., 2018; Shover et al., 2019). Importantly, the association of MCL enactment with overdose deaths changed over time. Specifically, studies finding associations of MCL with reductions in drug overdose deaths used data from earlier years, e.g., 1999-2010 (Bachhuber et al., 2014), while studies of more recent data produced null results. For example, a non-significant reduction in opioid overdose deaths following MCL enactment for years 1999-2010 became clearly null for years 1999-2013 (Powell et al., 2018). Another significant decrease in prescription opioid and heroin deaths from 1999-2010 became nonsignificant when three more recent years were added (1999-2013) (Powell et al., 2018). A second replication (Shover et al., 2019) also found that including more recent years of data turned a reduction in overdose deaths associated with MCL to a null finding. In fact, studies utilizing the same analytical methods as those used earlier (Powell et al., 2018; Shover et al., 2019) but incorporating the most recent data (2002-2020 (Castillo-Carniglia et al., 2023) and 1999-2019 (Mathur and Ruhm, 2023)) found MCL enactment was associated with increases in drug overdose deaths. An additional notable finding is that even if MCL was associated with a null finding, several studies showed that dispensaries were associated with reductions in overdose deaths, including studies of the later years when MCL enactment was associated with increased overdose deaths (Averett and Smith, 2019; Castillo-Carniglia et al., 2023; Cerda et al., 2025; Powell et al., 2018; Smith, 2020). Only 1 study found MCL dispensaries were associated with increases in opioid overdose deaths (Mathur and Ruhm, 2023). Of note, the one study that adjusted for individual-level sociodemographic factors found no association between MCL and overdose from 2000-2010 (Kim et al., 2022).
Fewer studies of low-to-moderate ROB addressed RCL enactment and opioid overdose deaths: 2 studies found RCL enactment was associated with reductions in deaths (Castillo-Carniglia et al., 2023; Denkyirah, Elisha Kwaku et al., 2025), 3 found no association (Alcocer, 2020; Cerda et al., 2025; Shover et al., 2019), and 3 were mixed (Cerda et al., 2025; Chan et al., 2020; Mathur and Ruhm, 2023). Overall, with fewer studies available, the relationship is less clear. For example, while a nonsignificant increase in opioid overdose deaths was associated with RCL enactment in one study, a reduction in synthetic opioid deaths was associated with RCL dispensaries in the same study (Chan et al., 2020). In another study, reduced opioid overdose fatalities were associated with both RCL enactment and RCL with dispensaries (Castillo-Carniglia et al., 2023). In yet another study (Mathur and Ruhm, 2023), RCL enactment was associated with a nonsignificant increase in overdoses and RCL dispensaries were associated with a larger significant increase in opioid overdose deaths. Results do not point to years of data analyzed nor dispensaries as explaining the mixed results.
4. Discussion
This systematic review examined the current scientific literature on the relationship between state cannabis legalization and opioid-related outcomes, including prescriptions, nonmedical use, opioid use disorders, opioid-related hospitalizations and ED visits, and fatal overdoses. By differentiating between medical cannabis laws (MCL) and recreational cannabis laws (RCL), the distinct impact of each policy on various opioid-related outcomes could be examined. Nearly twice as many studies explored the effects of MCLs as RCLs, although the growing body of research on both types of legalization highlights the continuing importance of considering both types of policy effects. Among the 43 studies included, 14 examined opioid prescriptions, 15 focused on non-medical opioid use and opioid use disorder, 3 examined opioid-related hospitalizations and ED visits, and 16 analyzed overdose deaths. Overall, these studies produced mixed results, both within and across the opioid-related outcomes.
Approximately half of the studies focused on opioid prescribing reported reduced prescriptions after MCL/RCL enactment (Kropp Lopez et al., 2020; McMichael et al., 2020; Raman et al., 2023; Shah et al., 2019; Shi et al., 2019; Wen and Hockenberry, 2018), and none found increases. On the whole, studies showed reductions of the less restrictive classes of opioids (i.e., Schedule III, including codeine and buprenorphine) than of Schedule II opioids (e.g., hydromorphone, oxycodone, morphine). These findings suggest that cannabis legalization may reduce the overprescribing opioids. The reason for this could be the analgesic properties commonly ascribed to cannabis (Bicket et al., 2023; Hayaki et al., 2024; Karmali et al., 2023). While overall, evidence is mixed on the efficacy of cannabis to treat pain (Hsu et al., 2025), numerous studies have shown that cannabis can provide small to moderate improvement in chronic pain symptoms (De Vita et al., 2018; Stockings et al., 2018; Wang et al., 2021; Yanes et al., 2019). Studies further suggest that initiation of medical cannabis is frequently associated with reductions in opioid use (Piper et al., 2017), and that cannabinoids may function as an opioid-sparing adjunct, potentially enabling lower opioid doses for analgesia (Boehnke et al., 2016; Nielsen et al., 2022). The finding that medical and recreational cannabis laws are primarily associated with reductions in prescribing of less-restricted Schedule III opioids (Liang et al., 2018; Shi et al., 2019; Wen and Hockenberry, 2018) is consistent with an effect concentrated among patients with less severe pain, for whom non-opioid alternatives may be more readily substituted. In addition, the changing results across studies from earlier versus more recent time periods underscore the need for caution in use of older evidence when formulating current policies.
The relationship between cannabis legalization and nonmedical opioid use is less clear. While most studies reported no change or increases in opioid use following MCL enactment, including misuse of prescription opioids and heroin, results varied by grade level (Cerdá et al., 2018) and age groups (Mannes et al., 2025). However, with only two studies identifying age-dependent effects (Cerdá et al., 2018; Mannes et al., 2025) and two finding no age-group differences (Segura et al., 2019; Wen et al., 2015), whether age-dependent effects are due to age or cohort effects remain unclear, and would require additional years of data to disentangle. Future studies to investigate age, period, and cohort differences could help identify high-risk groups. With only three studies examining the relationship of MCL and RCL to OUD, additional studies on these serious conditions are needed.
Studies of the relationship between MCL enactment and drug overdose deaths also suggested shifts in MCL effects over time. Early studies suggested reductions in overdose deaths following MCL enactment, while analyses using more recent data showed null or even positive associations of MCLs with overdose deaths (Castillo-Carniglia et al., 2023; Mathur and Ruhm, 2023). Despite this, with only one exception (Mathur and Ruhm, 2023), several studies found that MCL dispensaries were associated with reductions in overdose deaths, even in more recent years (Averett and Smith, 2019; Castillo-Carniglia et al., 2023; Powell et al., 2018; Smith, 2020). These evolving findings suggest the need for continuous monitoring and evaluation of cannabis policies, especially given the temporal dynamics in overdose-related outcomes. Similar mixed results were found among the more limited number of studies of RCL enactment and overdose deaths. However, results for RCL enactment and opioid overdose are not explained by recency or number of years of data used, or by dispensary provisions in the laws. With only three studies examining cannabis legalization and opioid-related hospitalizations and ED visits, this relationship will remain unclear until more research becomes available.
The differing effects of MCL and RCL on opioid outcomes across studies raise questions about the reasons for the inconsistencies. While discrepancies between studies can potentially be examined to indicate potential mechanisms of action, such work is made complex by the use of different sources of data and/or policy information, varying definitions of outcomes, and other methodological issues. However, our one consistent finding, i.e., replication of the association between MCL dispensaries and overdose deaths across years, shows that applying similar methods to data across years revealed an important changing association over time. Replication studies are essential for disentangling the heterogeneity observed in the literature. As the body of work on cannabis-legalization effects on opioid outcomes grow, future research should explicitly test whether discrepancies across extant studies stem from methodological differences or from genuine changes in the law-outcome relationship. For example, although most studies evaluating the effects of cannabis legalization on nonmedical opioid use and OUD have relied on the NSDUH, no replication studies have been published in this area nor have studies systematically examined the effects of methodological choices on results, e.g. the use of state-level public-use data versus restricted-use individual-level data. Moreover, recent concerns about the validity of general population studies for assessing rare and stigmatized drug-related (Keyes et al., 2022; Reuter et al., 2021), necessitate the development and application of novel methodological approaches in this area. Until such investigations are undertaken, explanations for cross-study heterogeneity will remain speculative and warrant systematic exploration.
Additional issues that may contribute to inconsistent findings include the following: (1) use of staggered-adoption difference-in-difference analyses, whose results may vary depending on the period studied due to variation in the control group definitions (i.e., the states that did not enact MCL or RCL) (Sant’Anna and Zhao, 2020); (2) low statistical power (Roth et al., 2023); (3) secular changes in MCL and RCL effects as more and more states enact these laws and access to high potency cannabis is increased (Hasin et al., 2021); (4) substantial changes in the illicit drug market over time, including the rise and spread of fentanyl (Bruzelius et al., 2024; Zoorob, 2019); (5) state opioid policies, e.g., must-access prescription drug monitoring programs, naloxone distribution to reverse opioid overdose, and medications for OUD; and (6) the challenges of modeling complex interactions among individual-level characteristics (e.g., sociodemographic factors, health behaviors), healthcare system dynamics (e.g., opioid prescribing norms, access to nonpharmacological pain treatments), and state and federal policy environments. Prior research has applied systems science approaches to elucidate the interconnections among these multilevel drivers of the opioid epidemic, enabling examination of both the intended and unintended policy effects, particularly over the longer term timeframe (Rao et al., 2021; Wakeland et al., 2015). Incorporating cannabis policies and cannabis use into these models may help clarify the inconsistent findings observed in this review and improve predictions of the long-term effects of these laws across a broad range of opioid-related outcomes. Finally, heterogeneous effects of legalization across high-risk populations may be obscured in larger population-based studies. As such, future studies and systematic reviews are warranted on the effects of cannabis legalization on opioid-outcomes in high-risk (e.g., adolescents, people in recovery) and specific treatment populations (e.g., OUD, chronic pain, long-term opioid therapy patients). Overall, an association between cannabis legalization and reductions in opioid prescribing may exist, but the relationship between legalization and more serious outcomes, e.g., OUD and overdose, remains less certain.
Nearly all studies included in this review were ecologic analyses, with adjustment variables at the state rather than individual level. Therefore, individual socioeconomic factors were often not adjusted. The studies that did examine individual-level data, primarily those examining non-medical opioid use or OUD, found no associations with MCL or RCL, and the one study of MCL and overdose deaths that adjusted for individual-level sociodemographics found no association (Kim et al., 2022). The two studies that examined MCL and opioid prescriptions adjusting for individual sociodemographic and health variables found decreases of very small effect sizes (McMichael et al., 2020; Shah et al., 2019). Note that while cannabis legalization is associated with increases in nonmedical cannabis use, perceived cannabis availability, and cannabis use disorder, the MCL/RCL effects are relatively weak (Cerdá et al., 2020; Martins et al., 2016). Given that cannabis legalization is hypothesized to affect opioid outcomes through its effect on cannabis use, these weak effects on cannabis use, the often proposed mechanism of effect on opioid outcomes, may help explain the small and inconsistent associations observed between MCL, RCL and the more downstream opioid outcomes. As such, any effect that cannabis legalization may have on opioid outcomes is likely to be relatively minor compared to other influences, such as the prescription opioid supply, illicit drug market, or socioeconomic conditions (Fink et al., 2024).
Limitations
Study limitations are noted. First, all studies were observational, and many had serious risk of bias. However, the direction and magnitude of study results did not vary by study quality. Additionally, studies used many different modeling strategies to account for confounding and often examined different years of data and different outcomes. Many did not report effect estimates with confidence intervals or provide sufficient information to calculate standardized effect estimates, precluding a formal meta-analysis. Whether statistically insignificant findings were due to the absence of association or to insufficient power was often unclear. Although we followed a rigorous, pre-registered, search protocol, we may have missed studies. Generalizability of these results outside the US may be limited as data from countries outside the US were excluded. A limitation of studies conducted using county- or state-level data is the risk of ecological fallacy, as associations observed at these aggregated levels may not reflect individual-level relationships. Finally, we required studies to be published in English because of limited translation resources. However, our review of references from the identified studies did not identify any non-English publications that met our eligibility criteria.
Conclusions
More than 25 years into the U.S. opioid epidemic, individuals and communities continue to be deeply burdened by opioid-related morbidity and mortality. To change the course of the epidemic, policy makers require evidence-based interventions to reduce opioid-related morbidity and mortality. As the cannabis policy landscape continues to evolve, interest continues in understanding the potential effects of cannabis legalization on the opioid outcomes. However, evidence to date does not provide a clear picture of change in opioid outcomes associated with cannabis legalization. The mixed and generally weak results should be considered in relation to the minimal effects of legalization on access to cannabis and its use, a purported mediator of the relationship between legalization and opioid outcomes. Inconclusive evidence on the benefits of cannabis legalization in reducing opioid-outcomes, particularly in light of the known harms of cannabis use (e.g., psychiatric and substance use disorders, traffic safety), suggest policy makers should consider instead interventions to reduce opioid-related morbidity and mortality, such as expanded access to low-barrier OUD care, that have strong evidence of public health benefits and low risk of unintended consequences.
Supplementary Material
Table 4.
Summary of studies examining the association between cannabis legalization and opioid overdose deaths
| Ref | Study years | States included | Population | Data source (level of aggregation) | Outcome | Policy | Empirical strategy | Results | ROB |
|---|---|---|---|---|---|---|---|---|---|
| Low or Moderate Risk of Bias | |||||||||
| Alcocer et al. (2020) | 1999-2017 | Colorado compared to synthetic control group created from 31 states with MCL laws | Colorado residents | National Vital Statistics System (state-level) |
Opioid overdose mortality rate per 100,000 in population (ICD-10 underlying cause codes X40–X44, X60–X64, and Y10–Y14 and contributing cause codes T40.0–40.4) | RCL dispensary | Difference in differences with a “Constructed Control State” as the comparison group (constructed using weighted average of all potential ‘control states’ that closely resemble Colorado’s overdose rates [all of which have a general form of MCL]). Models controlled for state-level median income, unemployment rate, percent age, percent with education, poverty rate and opioid prescription rates. |
RCL dispensaries were not associated with a significant change in the rate of opioid overdose deaths per 100,000 (DiD=−5%, confidence intervals and significance tests not reported, non-statistically significant in follow up testing). | Moderate |
| Averett & Smith (2019) | 1999-2015 | 50 U.S. states and D.C. | US population | National Vital Statistics System (state-level) |
Prescription opioid overdose death (ICD-10 UCD codes: X40-X44, X60-X64, X85, Y10-Y14) where an opioid analgesic was also coded (MCOD: T40.2-T40.4) | MCL enactment, MCL dispensary | Difference-in-difference model adjusted for state sociodemographics (% population white, male, unemployment rate), state policies (PDMP, alcohol tax, political party of governor), state and year fixed effects. | MCL enactment was not associated with opioid overdose deaths. After accounting for MCL enactment, MCL dispensaries were associated with reductions in opioid overdose deaths (−0.29; SE=0.10; P<.01). |
Moderate |
| Bachhuber et al. (2014) | 1999-2010 | 50 U.S. states and D.C. | US population | National Vital Statistics System (state-level) | Opioid overdose (UCD: X40-44, X60-64, Y10-Y14) involving: Prescription opioid related deaths (MCOD: T40.2-T40.4) | MCL enactment | Difference-in-differences models adjusted for state and year fixed effects, state-level factors (operational PDMP, pharmacist access to PDMP, pain clinic law, unemployment rate, with robust standard errors). | MCL enactment was associated with a 24.8% lower (95%CI=−37.5%, −9.5%, P=0.003) annual rate of opioid analgesic overdose deaths compared with states without laws. The association between MCL enactment and reductions in opioid analgesic overdose deaths strengthened in the years after passage: year 1 (−19.9%; 95%CI=−30.6%, −7.7%; P=.002), year 2 (−25.2%; 95%CI=−40.6%, −5.9%; P=0.01), year 3 (−23.6%; 95%CI=−41.1%, −1.0%; P=0.04), year 4 (−20.2%; 95%CI=−33.6%, −4.0%; P=.02), year 5 (−33.7%; 95%CI=−50.9%, −10.4%; P=0.008), year 6 (−33.3%; 95%CI=−44.7%, −19.6%; P<.001) |
Moderate |
| Castillo-Carniglia et al. (2023) | 2002-2020 | 49 U.S. states (AK and D.C. were excluded due to changes in county boundaries) | US population | National Vital Statistics System (county-level) |
Overdose death (UCD: X40-44, X60-64, X85, Y10-Y14) involving any opioid (MCOD: T40.1–40.4). | MCL enactment, MCL dispensary RCL enactment, RCL dispensary |
Difference-in-differences (DiD) regression at the county-level. Models include county-level (% male, % age, race/ethnicity composition, % below federal poverty line, % unemployed, median household income, population density) and state-level (proportion of fentanyl seizures, Good Samaritan laws, naloxone access laws, PDMP, Medicaid expansion) covariates, state-fixed effects, conditional autoregressive random effect, county random intercept with structured time effect to allow for counties to have differential dynamic growths in overdose over time. | MCL enactment was associated with an increase in the rates of overdose deaths related to any opioids (RR=1.14; 95%CI=1.11, 1.18). MCL dispensaries are associated with fewer opioid overdose deaths (RR=0.88; 95%CI=0.85, 0.91). RCL enactment (RR=0.81; 95%CI=0.75, 0.88) and RCL dispensaries (RR=0.83; 95%CI=0.76, 0.91) were associated with lower rates of opioid overdose. |
Low |
| Cerda et al., (2025) | 2013-2020 | 50 states and D.C. (3133 counties, aggregating the southeastern Health Service Region in Alaska due to boundary change) 15+ years | US population | National Vital Statistics System (county-level) |
Overdose death (UCD: X40-44, X60-64, X85, Y10-Y14) involving: Any opioid (MCOD: T40.0-40.4, T40.6), Natural or semisynthetic opioids (T40.2), heroin (T40.1), and synthetics other than methadone (T40.4) | MCL enactment RCL enactment |
Difference-in-differences (DiD) regression at the county level. Besag-York Mollie conditional autoregressive models were used to account for spatial correlation of contiguous counties. Models included random nonlinear effects for time and random county-time interaction terms. State-level fixed effects. Adjustment for percent male, Hispanic, non-Hispanic Black, non-Hispanic White, other race(s), unemployed, households below the federal poverty standard, median household income, population density, overall county mortality rates, opioid policies (access to naloxone, Good Samaritan overdose laws, opioid prescribing limits for acute pain, and pain clinic laws), and state ratios of fentanyl to total opioids among drug seized. | No significant association between MCL enactment and any opioid overdose. MCL was associated with increase of 0.3 (95%CrI= 0.2-0.4) natural/semisynthetic and 0.4 (95%CrI=0.1-0.7) synthetic opioid-involved overdose deaths per 100,000 person-years. MCL enactment was associated with an increase of 0.9 (95% CrI=0.4, 1.5) opioid-involved overdose deaths for 45-64 years and 0.9 (95%CrI=0.3, 1.4) opioid-involved overdose deaths for 65+ years per 100,000 person-years compared to 15-44 years. No significant association between RCL enactment and any opioid overdose. RCL was associated with 0.3 (95%CrI=0.1-0.4) additional natural/semi-synthetic deaths and 0.3 (95%CrI=−0.5, −0.1) fewer heroin-involved deaths per 100,000 person-years relative to MCL alone. No differences in opioid-involved overdose deaths by age were found. |
Low |
| Chan et al. (2019) | 1999-2017 | 50 U.S. states and D.C. | US population | National Vital Statistics System (state-level) |
Opioid-related deaths (ICD-10 UCD codes: X40-X44, X60-X64, X85, Y10-Y14) where any opioid was also coded (MCOD: T40.1-T40.4), where prescription opioids were coded (T40.2-T40.4), or where synthetic opioids were coded (T40.4) | MCL enactment, MCL dispensary RCL enactment, RCL dispensary |
Difference-in-differences model adjusted for state sociodemographics (age, sex, race/ethnicity, population, income, unemployment rate) and state policies (pill mill law, must access PDMP, beer tax), state and year fixed effects. Model included MCL enactment, MCL dispensary, RCL enactment, and RCL dispensary in a single model. | Neither MCL nor RCL enactment were associated with deaths from any opioid, prescription opioids, or synthetic opioids. MCL dispensaries were associated with a 34% (95%CI=−0.16, −0.52) reduction in deaths from any opioid, 30% (95%CI=−0.14, −0.46) reduction in deaths from prescription opioids, and 33% (95%CI=−0.08, −0.58) reduction in deaths from synthetic opioids. RCL dispensaries were associated with a 40% (95%CI=−0.16, −0.64) reduction in deaths from any synthetic opioids; RCL dispensaries was not associated with deaths from any opioid nor prescription opioids. |
Low |
| Denkyirah et al., (2025)a | 2003-2018 | Colorado, Oregon, Washington | Colorado, Oregon, and Washington residents | Kaiser Family Foundation’s State Health Facts Database via National Vital Statistics System (state-level) |
Overdose death (UCD: X40-44, X60-64, X85, Y10-Y14) involving any opioid (MCOD: T40.0-40.4, T40.6) | RCL dispensary | Synthetic control design, a weighted average of variables from similar states that did not implement RCL. Variables used to construct synthetics include opioid prescriptions per 100 persons, substance use treatment centers per capita, health insurance coverage rate, hospital beds per 1000, logarithm of the population, marriage rate, median age, labor participation rate, unemployment rate, gallons of ethanol consumed, number of sworn police officers per 1000. | RCL enactment with dispensary was not associated with opioid deaths in Colorado, Oregon, or Washington. | Low |
| Denkyirah et al., (2025)b | 2003-2019 2003-2022 |
46 U.S. states and D.C. (excluding Alaska, North Dakota, South Dakota, Wyoming) | US population | Kaiser Family Foundation’s State Health Facts Database via National Vital Statistics System (state-level) |
Overdose death (UCD: X40-44, X60-64, X85, Y10-Y14) involving any opioid (MCOD: T40.0-40.4, T40.6) | RCL enactment | DiD with multiple time-periods using stabilized inverse-probability weights and two-way fixed effects DiD with state-level population density, logarithm total population, marriage rate, median age, % male, substance use treatment facility per 100,000 persons, unemployment rate, labor participation rate, % attended college, police officers per 1000 persons, % Black, % Hispanic. Year and state fixed effects and state-specific linear time trends. Robust standard errors clustered at the state level. | 2003-2019: RCL enactment was associated with a decrease of 3.51 (95%CI= −1.65, −5.37) opioid overdose deaths per 100,000. Greater decreases in opioid overdose death rates were found for early adopter states compared to later adopter states. Greatest reductions were found 1 year after enactment. 2003-2022: When including pandemic years, RCL enactment was associated with a decrease of 2.73 (95%CI= −0.48, −4.97) opioid overdose deaths per 100,000. |
Low |
| Kim et al. (2022) | 2000-2011 | 50 U.S. states, ≥18 years at death, completed the National Health Interview Survey, surveyed between 1986-1999 | US adults, ≥18 years | National Vital Statistics System (individual-level) |
Prescription opioid overdose death (ICD-10 UCD codes: X40-X44, X60-X64, Y10-Y14) where an opioid analgesic was also coded (MCOD: T40.2-T40.4) | MCL enactment | Multivariable logistic regression model adjusted for individual-level (age, gender, race/ethnicity, martial status, educational attainment, poverty status) and state-level factors (US region, PDMP) and stratified by sampling time frame (2000-2005 vs. 2006-2011). | Between 2000-2005: Opioid overdose deaths were not associated with MCL between 2000-2005 nor between 2006-2010. | Moderate |
| Mathur et al. (2023) | 1999-2019 | 50 states and D.C. (excluded 2009 data from D.C., NJ, WV due to problems with death classification for those states in 2009) | US population | National Vital Statistics System (state-level) |
Overdose death (MCOD T-codes T40.0-40.4 and T40.6) | MCL enactment, MCL dispensary RCL enactment, RCL dispensary |
Difference-in-differences (DiD) regression at the state-level. Models include state-level covariates median income, unemployment rate, % age, % sex, % race/ethnicity, beer tax, must access PDMP laws, pill mill laws, Good Samaritan, and Naloxone laws. MCL model adjusts for RCL. | MCLs were associated with a 17.7% (95%CI= 1.08, 1.28) increase in the rates of opioid overdose compared to states with no CL. MCL with dispensary allowance were associated with a 28.1% (95%CI= 1.16, 1.42) increase in the rate of opioid overdose compared to states with no CL. RCL enactment was not associated with opioid overdose deaths compared to states with no CL. RCL with dispensary allowance were associated with a 25.1% (95%CI= 1.00, 1.56) increase in the rate of opioid overdose compared to states with no CL. |
Low |
| Nguyen et al., 2024 | 2006-2020 | 50 U.S. states and D.C. | US population | National Vital Statistics System (state-level) |
Overdose death (UCD: X40-44, X60-64, X85, Y10-Y14) involving any opioid (MCOD: T40.1-40.4) | MCL dispensary RCL dispensary |
Generalized difference-in-differences model adjusted for state-level economic indicators (state poverty rates, real gross domestic product), opioid policies (must-access PDMP, Good Samaritan laws, naloxone access laws), and state and year fixed effects. | Neither MCL nor RCL dispensaries were associated with changes in any opioid overdose deaths. When examining opioid overdose by type, RCL dispensaries were associated with 4.9 fewer (95%CI=−9.49, −0.30) in deaths due to synthetic opioid. |
Low |
| Powell et al. (2018) | 1999-2013 | 50 U.S. States and D.C. | US population | National Vital Statistics System (state-level) |
Opioid-related deaths (ICD-10 UCD codes: X40-X44, X60-X64, X85, Y10-Y14) where an opioid analgesic was also coded (MCOD: T40.2-T40.4) with or without heroin (MCOD: T40.1) | MCL enactment, MCL dispensary | Difference-in-differences and event study analysis. Models included MCL and legally protected dispensaries, sociodemographics (% male, age distribution, % White, unemployment rate), state policies (must access PDMP, pill mill law, alcohol tax rate), state and year fixed effects, and standard errors clustered at the state-level. | No significant association was found between MCL enactment and overdose deaths involving prescription opioids only nor deaths involving prescription opioids and heroin.. After MCL enactment, MCL dispensaries are associated with a 23.8% (0.76; 95%CI= 0.65, 0.87) reduction in deaths involving only prescription opioids and 23.0% (0.77; 95%CI= 0.66, 0.93) reduction in deaths involving prescription opioids and heroin. |
Moderate |
| Shover et al. (2019) | 1999-2010, 1999-2017 | 50 U.S. states and D.C. | US population | National Vital Statistics System (state-level) |
Opioid overdose (UCD: X40-44, X60-64, Y10-Y14) involving: Prescription opioid related deaths (MCOD: T40.2-T40.4) | MCL enactment RCL enactment |
Difference-in-differences models adjusted for state and year fixed effects, state-level factors (operational PDMP, pharmacist access to PDMP, pain clinic law, unemployment rate, with robust standard errors. | From 1999-2010: MCL enactment was associated with a 21.1% reduction (95%CI=−35.7, −3.0) in opioid overdose deaths per 100,000 population. From 1999-2017: MCL enactment was associated with a 22.7% increase (95%CI=2.0, 47.6) in opioid overdose deaths per 100,000 population. RCL was not associated with opioid overdose deaths. |
Moderate |
| Smith (2020) | 1999-2014 | 49 U.S. states were included (California was excluded from this analysis because it had dispensaries open prior to 1999). | US population | National Vital Statistics System (county-level) |
Opioid overdose (UCD: X40-44, X60-64, 85, Y10-Y14) involving: Prescription opioid related deaths (MCOD: T40.2) | MCL dispensary | Difference-in-differences model adjusted for county level variables (unemployment rate, annual average weekly wages, % male, % non-Hispanic White, age distribution), state level variables (per gallon beer tax rate, operational PDMP), county and year fixed effects Sensitivity analysis using synthetic control model |
Deaths involving prescription opioids decline by 9% during the first year of dispensary operations in a county, growing to 11% the second year and attenuating in the third year. | Low |
| Serious risk of bias | |||||||||
| Livingston et al. (2017) | 2000-2015 | Colorado, Nevada, and Utah | Residents of Colorado | National Vital Statistics System (state-level) | Opioid overdose mortality (ICD-10 underlying cause codes not included) | RCL dispensary | Interrupted time-series regression using Autoregressive Integrated Moving Averages Model (ARIMA) controlling for pre-legalization trend, number of opioid-related deaths in the comparison state (Nevada and Utah) in each month, and PDMP. | Opioid-related deaths decreased by 0.7 deaths per month relative to the baseline period (b=−0.68; 95%CI=−1.34, −0.03). Compared to the model-smoothed opioid-related deaths per month just prior to legalization, the modeled number at the end of follow-up resulted in a 6.5% reduction in opioid-related deaths. |
Serious |
| Sabia et al., 2024 | 2000-2019 | 50 U.S. states and D.C. | US population | National Vital Statistics System (state-level) |
Overdose death (UCD: X40-44, X60-64, X85, Y10-Y14) involving heroin (T40.1), fentanyl (T40.4), other non-heroin, non-fentanyl related opioids (T40.0, T40.2, T40.3, T40.6) | RCL enactment | Difference-in-differences analysis, state-level political and economic conditions (unemployment rate, per capita gross domestic product, governor political party, state minimum wage, racial composition), state policies (MCL, cannabis decriminalization, real beer tax per gallon, must-access PDMP, naloxone access law, Good Samaritan law, state earned income tax credit refundable rate, maximum Supplemental Nutrition Assistance Program benefit, Medicaid expansion), state- and year-fixed effects | RCL enactment was associated with a 0.25 per 100,000 person reduction in heroin (95%CI=−0.50, −0.01), 0.44 per 100,000 person reduction in fentanyl (95%CI=−0.77, −0.12), and 0.34 per 100,000 person reduction in other opioids (95%CI=−0.64, −0.04). | Serious |
Notes: MCL enactment is when a medical cannabis law becomes effective (i.e., patients have legal protection to possess cannabis for medical purposes). RCL enactment is the date when recreational cannabis is legally protected in the state for adult use. MCL dispensary is the date when the first medical cannabis dispensary is legally protected and open for business in a state. RCL dispensary is the date when the first recreational dispensary is legally protected and open for business in a state.
AK=Alaska; aOR=Adjusted odds ratio; CI=Confidence interval; CrL=credibility interval; D.C.=District of Columbia; DiD=Difference-in-difference; ICD-10=International Classification of Diseases, 10th Revision; ICD-9-CM=International Classification of Diseases, Ninth Revision, Clinical Modification; MCL=Medical cannabis law; MCOD=Multiple causes of death; NJ=New Jersey; P=p-value; PDMP=Prescription Drug Monitoring Programs; RCL=Recreational cannabis law; RR=Risk Ratio; SE=Standard error; UCD=Underlying cause of death; U.S.=United States; WV=West Virginia
ICD-9-CM Codes
304.0=Opioid type dependence
304.7= Combinations of opioid type drug with any other
305.5=Opioid abuse
965.00=Poisoning by opium (alkaloids), unspecified
965.02=Poisoning by methadone
965.09=Poisoning by other opiates and related narcotics
ICD-10 Codes
X40=Accidental poisoning by and exposure to nonopioid analgesics, antipyretics and antirheumatics
X41=Accidental poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified
X42=Accidental poisoning by and exposure to narcotics and psychodysleptics [hallucinogens], not elsewhere classified
X43=Accidental poisoning by and exposure to other drugs acting on the autonomic nervous system
X44=Accidental poisoning by and exposure to other and unspecified drugs, medicaments and biological substances
X60=Intentional self-poisoning by and exposure to nonopioid analgesics, antipyretics and antirheumatics
X61=Intentional self-poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified
X62=Intentional self-poisoning by and exposure to narcotics and psychodysleptics [hallucinogens], not elsewhere classified
X63=Intentional self-poisoning by and exposure to other drugs acting on the autonomic nervous system
X64=Intentional self-poisoning by and exposure to other and unspecified drugs, medicaments and biological substances
X85=Assault by drugs, medicaments and biological substances
Y10=Poisoning by and exposure to nonopioid analgesics, antipyretics and antirheumatics, undetermined intent
Y11=Poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified, undetermined intent
Y12=Poisoning by and exposure to narcotics and psychodysleptics [hallucinogens], not elsewhere classified, undetermined intent
Y13=Poisoning by and exposure to other drugs acting on the autonomic nervous system, undetermined intent
Y14=Poisoning by and exposure to other and unspecified drugs, medicaments and biological substances, undetermined intent
T40.0=Poisoning by narcotics and psychodysleptics [hallucinogens], Opium
T40.1=Poisoning by narcotics and psychodysleptics [hallucinogens], Heroin
T40.2=Poisoning by narcotics and psychodysleptics [hallucinogens], Other opioids
T40.3=Poisoning by narcotics and psychodysleptics [hallucinogens], Methadone
T40.4=Poisoning by narcotics and psychodysleptics [hallucinogens], Other synthetic narcotics
T40.6=Poisoning by narcotics and psychodysleptics [hallucinogens], Other and unspecified narcotics
T42.4=Poisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugs, Benzodiazepines
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