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. Author manuscript; available in PMC: 2025 Aug 8.
Published in final edited form as: Int J Ment Health Addict. 2024 Mar 25;23(3):2496–2512. doi: 10.1007/s11469-024-01271-7

Gender differences in cannabis outcomes after recreational legalization: a United States repeated cross-sectional study, 2008–2017

Luis E Segura 1, Natalie S Levy 1, Christine M Mauro 2, Pia M Mauro 1, Sarah Gutkind 1, Morgan M Philbin 3, Deborah S Hasin 4,5, Silvia S Martins 1
PMCID: PMC12333662  NIHMSID: NIHMS2028605  PMID: 40786531

Abstract

Cannabis use has been more prevalent among men than women and prior work has found differing impact of recreational cannabis laws (RCL) by age. We examined changes in the prevalence of past-year and past-month cannabis use, past-month daily cannabis use, and DSM-5-proxy cannabis use disorder (CUD) in the past-year before and after RCL enactment by gender alone and stratified by age using 2008–2017 repeated cross-sectional samples of the US National Survey on Drug Use and Health. Changes in cannabis outcomes were estimated using adjusted multi-level logistic regression with state random intercepts and two-way and three-way interactions between RCL, gender, and age group. Enactment of RCL was associated with higher increases in past-year (+3.2%; aOR= 1.30 [95%CI = 1.19 to 1.41]) and past-month (+2.3; 1.37 [1.24 to 1.51]) cannabis use in women than men (+2.1%; 1.15 [1.06 to 1.25] and +1.7%; 1.19 [1.08 to 1.30]). No increases in past-month daily cannabis use and past-year DSM-5 CUD among those using cannabis were observed after RCL enactment. There were no increases in any cannabis outcomes after RCL enactment among those 12–20 years old. RCL enactment may contribute to narrowing of the cannabis gender gap. Ongoing surveillance is essential to ensure that the social justice aims of legalization are achieved without negative public health consequences.

Keywords: recreational cannabis laws, cannabis use, cannabis use disorder, gender differences, cannabis legalization

Introduction

In the United States (US), the prevalence of cannabis use and cannabis use disorder (CUD) are consistently higher among men than women (Carliner et al., 2017; Hasin, Saha, et al., 2015; Hasin, Shmulewitz, & Sarvet, 2019; Johnson et al., 2015; Kerr, Lui, & Ye, 2018). In the 2019 National Survey on Drug Use and Health (NSDUH), 20.4% of men aged 12 and older reported past-year cannabis use compared with 14.8% of women (Center for Behavioral Health Statistics and Quality, 2020). While cannabis use has increased among all US adults since 2007, the prevalence of past-year and daily cannabis use has increased more rapidly among men than women, widening the gender gap in cannabis use (Carliner et al., 2017). Researchers have proposed that increases in cannabis use and differences by gender may be explained by the changing landscape of cannabis legalization in the US (Carliner et al., 2017). Recently, like the US in 2012 (National Conference of State Legislatures, 2023), other countries in North and South America, including Uruguay in 2013 (República de Uruguay, 2014), and Canada in 2018 (Cannabis Act, 2018), have legalized cannabis consumption for recreational purposes. In Europe, Malta also legalized recreational cannabis use in 2021 (Farrelly et al., 2023). In addition, more than 40 countries worldwide have legalized cannabis for medical purposes and several others have decriminalized cannabis use (Mollner, 2022). As of November 2023, 38 states and Washington, DC have legalized cannabis for medical purposes; of these, 24 states and Washington, DC have legalized recreational use (National Conference of State Legislatures, 2023). While a growing body of research has examined cannabis outcomes following enactment of medical cannabis laws (MCL) in the US (C. M. Mauro et al., 2019), research on the effects of recreational cannabis laws (RCL) is nascent. Additionally, no study to date has examined the effects of RCL by gender.

Available research suggests the overall prevalence of past-month cannabis use increased among adults after enactment of both MCL and RCL (Hasin et al., 2017; S. S. Martins et al., 2016; C. M. Mauro et al., 2019). Conversely, no increases have been found in adolescent (ages 12–17) or young adults’ (ages 18–25) past-year, past-month or daily cannabis use attributable to MCL (C. M. Mauro et al., 2019; Sarvet et al., 2018) or RCL (Coley et al., 2020; Dills, Goffard, Miron, & Partin, 2021; Johnson & Guttmannova, 2019). A recent nationally-representative US study observed increases (from 22·8% to 27·2%) in past-year CUD after RCL enactment among adolescents aged 12–17 who reported past-year cannabis use (Cerda et al., 2019); however, the authors cautioned that this finding might reasonably be attributed to random error or unmeasured confounding (Cerda et al., 2019). This same study identified increases in past-month cannabis use (from 5·65% to 7·10%) and CUD among all adults 26+ following RCL enactment, but no changes in frequent use or CUD among those reporting cannabis use (Cerda et al., 2019). Additionally, increases in past-month cannabis use after MCL enactment were reported for adult men (from 7·0% to 8·7%) and women (from 3·1% to 4·3%) (C. M. Mauro et al., 2019), but age group differences by gender remain unexamined. Together, these findings suggest that the effects of RCL may vary by subgroup. Recent studies suggest that the gender gap in cannabis use also differs by age. For example, differences in the prevalence of cannabis use have increased by gender among adults (Carliner et al., 2017) and decreased among adolescents (Hasin et al., 2019; Johnson et al., 2015). However, whether these trends by gender and age will persist and the potential impact of RCL on these differences remains unclear. Given pre-existing differences in patterns of cannabis use, documenting differential effects of legalization by gender is important for evaluating potential unintended consequences of legalization and informing public health responses.

In this study, we used 2008–2017 NSDUH data to compare the impact of RCL on cannabis outcomes in the US for men and women overall and by age group. Specifically, we assessed the association between RCL enactment and past-year and past-month cannabis use and past-month daily cannabis use and past-year DSM-5 CUD among people reporting cannabis use in the past year, by gender alone and by age group. We hypothesized that like MCL, RCL would be associated with increases in past-year and past-month cannabis use for adult men and women, but not for adolescent boys and girls ages 12–20. We also hypothesized that past-month daily cannabis use and CUD among those who used cannabis would remain unchanged after RCL enactment for all groups.

Methods

Sample

We used restricted-access data from 838,600 individuals aged 12+ who participated in the 2008–2017 NSDUH, which allowed us to identify respondents’ state of residence and RCL exposure status. The 2017 survey was the most recently available at the time of analysis (September 2019 to March 2020). The NSDUH is an annual cross-sectional household survey of the US non-institutionalized population aged 12+ that uses a multistage probability design to generate nationally-generalizable prevalence estimates of substance use-related behaviors and mental health. Trained interviewers administered the survey using computer-assisted personal interviewing and audio computer-assisted self-interviewing to guarantee privacy and accurate reporting of sensitive information. Participants gave informed consent prior to being interviewed. The NSDUH includes survey weights to generate nationally-representative estimates and to adjust for the probability of selection at each sampling stage, nonresponse, coverage, and extreme weights. Screening and interview response rates over the study period varied from 73% to 89% and 67% to 76%, respectively (Center for Behavioral Health Statistics and Quality, 2017).

Measures

Exposure – State-level cannabis laws

Our primary exposure was residing in a state with enacted RCL. RCL and MCL enactment dates were obtained from the Marijuana Policy Project (Marijuana Policy Project, 2016) and ProCon.org (ProCon.org; ProCon.org) and were based on the specific language of the statute, accounting for any necessary conditions for the law to go into effect. These dates have been used in previous work (Silvia S. Martins et al., 2021) and are summarized in Appendix Table 1.

For descriptive purposes, we used a state-level cannabis laws that was categorized as a fixed three-level variable (Never MCL/RCL, MCL only/No RCL, and Ever RCL). For regression models, we used a time-varying indicator of state MCL and RCL status that compared the date on which a participant was interviewed to the MCL/RCL enactment date in their state of residence. For example, if the interview date occurred after the RCL enactment date, then participants were classified as exposed to RCL. Participants interviewed before the RCL enactment date were classified as unexposed. This resulted in six possible categories (Figure 1): Never MCL/RCL; Before MCL/Never RCL; After MCL/Never RCL; Before MCL/Before RCL; After MCL/Before RCL; and After MCL/After RCL. Example code for creating this time-varying indicator is available in the Appendix and previous publications (Silvia S. Martins et al., 2021).

Figure 1.

Figure 1.

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States’ cannabis law effective status between 2008–17

Abbreviations. MCL: Medical Cannabis Laws; RCL: Recreational Cannabis Laws.

All nine states that enacted RCL during the study period had previously enacted MCL. For regression analyses, we focused on the period after MCL enactment to isolate the effects of RCL by comparing cannabis outcomes in the period before RCL enactment (After MCL/Before RCL) with the period after RCL enactment (After MCL/After RCL).

Outcomes – Cannabis use recency, daily cannabis use, and cannabis use disorder

The outcomes investigated in this study included cannabis use recency (past-year and past-month cannabis use), daily cannabis use (cannabis use on 20 or more days in the past month (P. M. Mauro et al., 2018)) and CUD. Similar to previously published work (Compton, Han, Jones, & Blanco, 2019), we created a proxy measure of past-year DSM-5 CUD using the individual DSM-IV items asked in the NSDUH to approximate the 10 criteria used for DSM-5 diagnosis (Appendix Table 2). We excluded from this proxy DSM-5 CUD measure the criteria for cravings and withdrawal, because they were not collected in the NSDUH. Participants who endorsed two or more proxy DSM-5 CUD criteria were coded as having a DSM-5 CUD diagnosis (Compton et al., 2019; Levy, Mauro, Mauro, Segura, & Martins, 2021; Silvia S. Martins et al., 2021). Sensitivity analyses using past-year DSM-IV CUD are included in the Appendix (Appendix Tables 3 and 4).

Effect modifiers – Gender and age

Analyses were stratified by gender and age. Gender (men/women) was recorded by the interviewer. Age at interview was categorized as: 12–20, 21–30, 31–40, and ≥41 years old.

Confounders – Individual and state-level indicators

We included individual- and state-level variables as potential confounders. Individual-level covariates included racial/ethnic group (non-Hispanic white, non-Hispanic Black, Hispanic, Other [Native American, Pacific Islander, Asian and More than one race]); survey year (2008–2017); nativity (US-born, foreign-born); total family income (<$20,000; $20,000–49,999; $50,000–74,999; $75,000+); and urbanicity (large metro, small metro, nonmetro). State-level predictors were based on 2010 US Census data on the proportion of each state’s population that was white, male, aged 10–24 years, aged 25+ with at least a high school education, state unemployment rates, and median household income.

Statistical Analysis

Descriptive analysis

We calculated the weighted annual prevalence of cannabis use recency, frequency, and disorder by cannabis law enactment status (i.e., Never MCL/RCL, MCL only/No RCL, and Ever RCL) overall, by gender, and by gender-age group by combining weighted counts and population totals for states belonging to each category of enactment status in each survey year. Variance estimates were calculated using Taylor linearization, which incorporates variables related to the survey design to account for clustering (Lohr, 2009).

Main Analysis – Testing the association between RCL enactment and cannabis outcomes

We then tested the association between RCL enactment and cannabis outcomes by comparing the odds of cannabis recency, frequency, and disorder during the period After MCL/Before RCL to the period After MCL/After RCL by gender alone and in combination with age group. We used separate multilevel logistic regressions for each outcome and included state-level random intercepts, individual and state covariates, and two- and three-way interactions between the time-varying RCL exposure, gender, and age group. We included data from states without enacted MCL and/or RCL to control for underlying time trends in each outcome from 2008–2017 and from states after MCL but before RCL enactment to control for preexisting effects from medical cannabis legalization. Survey weights were not used because we included all individual-level indicators related to sampling design (Little, 2004). Covariate adjusted, model-based prevalences were obtained from the fitted multi-level logistic models using marginal predictions and shown in Table 2 and Table 3.

Table 2.

Cannabis recency, frequency, and disorder after versus before legalization of recreational cannabis by gender among US individuals aged 12 and older. NSDUH 2008–2017.

Past-Year Cannabis Use Past-Month Cannabis Use
% Before RCL; After MCL % After RCL; After MCL aOR (95%CI) after vs before % Before RCL; After MCL % After RCL; After MCL aOR (95%CI) after vs before
Women 12.8 16.0 1.30 (1.19; 1.41)a 7.1 9.4 1.37 (1.24; 1.51)a
Men 17.4 19.5 1.15 (1.06; 1.25)b 10.9 12.6 1.19 (1.08; 1.30)b
Past-Month Daily Cannabis Use* Past-Year DSM 5 Cannabis Use Disorder†
% Before RCL; After MCL % After RCL; After MCL aOR (95%CI) after vs before % Before RCL; After MCL % After RCL; After MCL aOR (95%CI) after vs before
Women 40.7 43.1 1.10 (0.94; 1.30) 29.7 29.7 1.00 (0.84; 1.19)
Men 30.1 30.0 0.99 (0.83; 1.20) 37.1 38.2 1.05 (0.90; 1.23)
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% proportion of use.

RCL: Recreational Cannabis Law enactment.

MCL: Medical Cannabis Law enactment.

aOR: adjusted Odds Ratio before vs after RCL enactment.

DSM: Diagnostic Statistical Manual of Mental Disorders.

Individual-level predictors: time as a continuous variable using a piecewise spline function of year with a knot in 2011, age group, nativity, urbanicity, and total family income.

State-level predictors: proportion of the state that was white, male, ages 10–24, >25 years with at least a high school education, unemployment, and state’s median household income.

Models adjusted for RCL passage + gender + interaction between RCL and racial/ethnic group + gender + individual-level predictors + state-level predictors.

*

Daily Cannabis Use is the use of cannabis almost daily (20 or more days) in the past month among people who used cannabis in the past-month.

†

Past-Year Cannabis Use Disorder was calculated among people who used cannabis in the past-year.

For point estimates with corresponding lower limit 95% confidence interval (LL95%CI) greater than 1, we estimated e-values to quantify the minimum strength of the relationship between an unmeasured/uncontrolled confounder and both our exposure (RCL) and outcome (cannabis outcome) needed to reduce the aOR and the lower limit of the 95% confidence interval (LL95%CI) to the null. For past-year cannabis use these were:

a

e-value for aOR = 1.54 & LL95%CI = 1.41;

b

e-value for aOR = 1.35 & LL95%CI = 1.20.

For past-month cannabis use these were:

a

e-value for aOR = 2.08 & LL95%CI = 1.79;

b

e-value for aOR = 1.41 & LL95%CI = 1.24.

Table 3.

Cannabis recency, frequency, and disorder after versus before legalization of recreational cannabis by gender and age group. NSDUH 2008–2017.

Past-Year Cannabis Use Past-Month Cannabis Use Past-Month Daily Cannabis Use* Past-year DSM-5 Cannabis Use Disorder†
% Before RCL, After RCL % After RCL, After RCL aOR (95%CI) after vs before % Before RCL, After RCL % After RCL, After RCL aOR (95%CI) after vs before % Before RCL, After RCL % After RCL, After RCL aOR (95%CI) after vs before % Before RCL, After RCL % After RCL, After RCL aOR (95%CI) after vs before
12–20 yo
Women 21.9 22.9 1.06 (0.93; 1.21) 11.3 12.6 1.13 (0.97; 1.33) 25.7 25.4 0.98 (0.72; 1.35) 38.4 39.4 1.04 (0.80; 1.35)
 Men 24.6 21.9 0.86 (0.75; 0.98)a 14.2 12.5 0.87 (0.74; 1.01) 38.1 36.9 0.95 (0.71; 1.26) 45.4 45.8 1.02 (0.79; 1.31)
21–30 yo
Women 15.8 21.1 1.43 (1.25; 1.63)b 8.6 11.9 1.44 (1.23; 1.68)a 32.6 33.3 1.03 (0.78; 1.36) 28.9 28.4 0.98 (0.74; 1.29)
 Men 23.2 28.0 1.29 (1.12; 1.48)c 14.5 17.9 1.29 (1.11; 1.50)b 43.9 47.6 1.16 (0.91; 1.48) 37.5 40.5 1.13 (0.91; 1.42)
31–40 yo
Women 9.0 12.2 1.41 (1.15; 1.72)d 5.2 7.6 1.49 (1.17; 1.90)c 33.2 31.9 0.94 (0.59; 1.51) 18.3 15.0 0.79 (0.43; 1.45)
 Men 15.1 18.5 1.27 (1.05; 1.55)e 9.8 12.7 1.34 (1.07; 1.67)d 41.6 47.2 1.26 (0.85; 1.87) 24.0 21.1 0.85 (0.54; 1.35)
41+ yo
Women 4.4 7.0 1.64 (1.36; 1.97)f 2.8 4.7 1.71 (1.36; 2.14)e 33.8 29.6 0.83 (0.53; 1.29) 13.1 16.8 1.34 (0.74; 2.43)
 Men 7.8 10.2 1.34 (1.13; 1.59)g 5.4 7.2 1.37 (1.12; 1.66)f 35.0 38.9 1.18 (0.81; 1.71) 17.4 17.3 1.00 (0.62; 1.60)
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% proportion of use.

RCL: Recreational Cannabis Law passage.

aOR: adjusted Odds Ratio before vs after RCL passage.

DSM: Diagnostic Statistical Manual of Mental Disorders.

Individual-level predictors: time as a continuous variable using a piecewise spline function of year with a knot in 2011, gender, nativity, urbanicity, and total family income.

State-level predictors: proportion of the state that was white, male, ages 10–24, >25 years with at least a high school education, unemployment, and state’s median household income.

Models adjusted for RCL passage + gender + age group + two-way and three-way interactions between RCL, racial/ethnic group, and age group + individual-level predictors + state-level predictors.

*

Daily Cannabis Use is the use of cannabis almost daily (20 or more days) in the past month among people who used cannabis in the past month.

†

Past-Year Cannabis Use Disorder was calculated among people who used cannabis.

For point estimates with corresponding lower limit 95% confidence interval (LL95%CI) greater than 1, we estimated e-values to quantify the minimum strength of the relationship between an unmeasured/uncontrolled confounder and both our exposure (RCL) and outcome (cannabis outcome) needed to reduce the aOR and the lower limit of the 95% confidence interval (LL95%CI) to the null.

For past-year cannabis these were:

a

among men aged 12–20 e-value for aOR = 1.36 & LL95%CI = 1.11;

b

among women aged 21–30 e-value for aOR = 1.68 & LL95%CI = 1.48;

c

among men aged 21–30 e-value for aOR = 1. 52 & LL95%CI = 1.31;

d

among women aged 31–40 e-value for aOR = 1.65 & LL95%CI = 1.35;

e

among men aged 31–40 e-value for aOR = 1.51 & LL95%CI = 1.18;

f

among women aged 41+ e-value for aOR = 2.67 & LL95%CI = 2.06;

g

among men aged 41+ e-value for aOR = 2.01 & LL95%CI = 1.51.

For past-month cannabis use these were:

a

among women aged 21–30 e-value for aOR = 1.69 & LL95%CI = 1.46;

b

among men aged 21–30 e-value for aOR = 1.53 & LL95%CI = 1.29;

c

among women aged 31–40 e-value for aOR = 2.34 & LL95%CI = 1.62;

d

among men aged 31–40 e-value for aOR = 1.59 & LL95%CI = 1.22;

e

among women aged 41+ e-value for aOR = 2.81 & LL95%CI = 2.06;

f

among men aged 41+ e-value for aOR = 2.09 & LL95%CI = 1.49.

For our main contrast of interest, we computed adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) comparing the model-based prevalence of cannabis outcomes after versus before RCL enactment (After RCL/After MCL vs. After MCL/Before RCL). This allowed us to estimate the change from before to after RCL enactment controlling for differences by state and over time, approximating a difference-in-difference approach (Cerda et al., 2019). As done in previous research examining post-RCL effects, we included year as a continuous variable and used a piecewise spline with a knot at 2011 to capture underlying trends in cannabis use over time (Cerda et al., 2019).

Sensitivity analysis

We conducted sensitivity analyses with e-values to evaluate the potential impact of time-varying unmeasured confounding (Haneuse, VanderWeele, & Arterburn, 2019; Segura et al., 2019; VanderWeele & Ding, 2017) on our results. Small e-values (values closer to 1.0) suggest that unmeasured confounding may account for observed associations; larger e-values indicate results are increasingly robust to unmeasured confounding. E-values were obtained for the estimated aOR and lower level of the 95%CI (LL95%CI) using the EValue package in R software (Haneuse et al., 2019; Segura et al., 2019; VanderWeele & Ding, 2017).

All analyses were conducted at the US Census Bureau’s New York Regional Data Center using Linux-based R statistical software v3.5.2. R packages used for data analysis and figures were survey (Lumley, 2004), lme4 (Bates, Mächler, Bolker, & Walker, 2015), emmeans (Lenth, Singmann, Love, Buerkner, & Herve, 2018) and ggplot2 (Wickham, 2011). Output was reviewed by Substance Abuse and Mental Health Services Administration staff and sample sizes were rounded to the nearest hundred to ensure confidentiality. This manuscript was prepared according to STROBE guidelines for cross-sectional studies (Von Elm et al., 2007) and approved by the Columbia University Institutional Review Board (approval number AAAS4624).

Role of the funding source

This study was funded by grants from the US National Institutes of Health and National Institute on Drug Abuse, which had no role in study design, data collection, data analysis, data interpretation, or writing of this manuscript.

Results

Slightly more than half of participants were women, regardless of RCL enactment status (Table 1). The prevalence of past-year, past-month, and daily cannabis use, and DSM-5 CUD among all NSDUH participants was higher in states with any RCL (16.1%, 10.6%, 4.6%, and 1.8%, respectively) followed by states with MCL only (13.5%, 8.5%, 3.6%, and 1.6%, respectively) and no cannabis laws (10.3%, 6.1%, 2.5%, and 1.3%, respectively).

Table 1.

NSDUH sample distribution of demographic characteristics, cannabis outcomes, and state-level covariates by states’ cannabis law status. NSDUH 2008–17

No Cannabis Laws (n = 191,600) Medical Cannabis Laws only (n = 490,400) Recreational Cannabis Laws (n = 156,600)
N Weighted % N Weighted % N Weighted %
Individual-level predictors
Gender
 Women 99,900 51.7 254,300 51.5 80,700 51.0
 Men 91,700 48.7 236,100 48.6 76,000 48.9
Age, mean age (min, max) 43.0 (12, 102) 43.8 (12, 105) 43.06 (12, 100)
 12–20 78,100 15.0 201,900 14.4 64,300 14.8
 21–30 48,600 16.5 123,400 16.1 39,800 16.6
 31–40 22,400 15.6 55,800 15.2 18,100 15.8
 41–50 19,600 16.2 50,900 16.1 16,200 16.2
 51–64 13,400 21.1 34,100 21.5 10,900 21.1
 65+ 9,500 15.6 24,300 16.8 7,400 15.5
Race/Ethnicity
 White 122,100 66.3 299,200 64.6 90,600 58.7
 Black 28,900 15.6 55,500 10.2 14,600 6.5
 Hispanic 12,000 4.6 48,900 8.7 17,500 12.2
 Other 28,600 13.5 86,700 16.4 33,900 22.6
Nativity
 US-born 175,400 89.5 429,500 82.6 135,200 78.2
 Foreign-born 16,100 10.5 60,900 17.4 21,500 21.8
Education
 Less than HS 21,100 16.0 48,100 13.3 15,400 13.6
 HS graduate 41,000 29.3 103,400 27.9 31,800 25.4
 Some college 42,600 27.8 105,700 27.7 33,800 28.5
 College graduate 30,000 26.9 87,000 31.1 29,000 32.5
Income
 <$20,000 44,100 19.3 103,800 17.3 33,500 16.8
 $20,000-$49,999 64,400 33.4 154,300 30.8 49,400 30.2
 $50,000-$74,999 31,400 17.4 78,800 16.6 24,600 16.2
 $75,000+ 51,600 29.9 153,500 35.4 49,200 36.8
Urbanicity
 Large Metro 60,600 42.5 247,600 61.0 80,600 64.8
 Small Metro 77,100 35.5 163,300 27.9 52,400 27.1
 Nonmetro 53,900 21.9 79,400 11.1 23,600 8.2
Insurance
 Private 116,900 65.2 302,800 67.2 95,000 66.3
 Medicaid/SCHIP 33,500 8.6 104,400 12.3 35,800 13.4
 Medicare 12,500 20.3 31,600 20.6 9,600 18.8
 Military 8,400 6.1 15,900 4.4 5,100 4.1
 Other 4,200 1.8 12,700 2.1 4,100 2.2
 None 31,700 16.5 63,100 12.4 19,800 12.9
 Any Health
Insurance		 159,900 83.5 427,300 87.6 136,900 87.1
Substance Use
 Past Year Cannabis
Use		 28,900 10.3 94,000 13.5 34,900 16.1
 Past Month
Cannabis Use		 16,500 6.1 57,400 8.5 22,100 10.6
 Past Month Daily
Cannabis Use* 		6,400 2.5 22,500 3.6 9,000 4.6
 DSM-5 Cannabis
Use Disorder† 		4,800 1.3 15,100 1.6 5,400 1.8
State-level Predictors
 % state Male, mean
(min, max)		 49.2 (48.30, 51.00) 49.1 (47.10, 52.00) 49.55 (47.10, 52.00)
 % state white, mean
(min, max)		 73.8 (59.10, 93.90) 72.5 (24.30, 96.90) 68.85 (30.80, 96.90)
 % state 10–24 yo,
mean (min, max)		 21.3 (20.10, 23.90) 20.7 (18.60, 28.40) 21.24 (18.60, 23.40)
 % state ≥25 yo with
at least HS, mean
 (min, max)		 16.7 (7.70, 27.10) 14.9 (8.20, 25.20) 15.87 (8.90, 23.20)
 Unemployment rate,
mean (min, max)		 5.9 (2.50, 8.00) 6.5 (2.70, 9.40) 7.21 (2.90, 9.40)
 Median Household
Income, mean (min,
 max)		 $45,313 ($31,330,
$60,674)		 $50,858 ($29,696,
$68,854)		 $53,424 ($37,240,
$64,576)
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Abbreviations. HS: Highschool, PY: Past-Year, PM: Past-Month.

State-level predictors came from the 2010 American Community Survey.

*

Daily Cannabis Use is the use of cannabis almost daily (20 or more days) in the past month among cannabis users.

†

Past-Year Cannabis Use Disorder was calculated among people who used cannabis.

All RCL states had previously enacted MCL.

DSM: Diagnostic Statistical Manual of Mental Disorders.

Changes in cannabis recency, frequency, and disorder in men and women aged 12+ reporting cannabis use after RCLs enactment (Table 2)

Among women aged 12+, past-year cannabis use increased after RCL enactment, compared with the period before RCL enactment, from 12.8% to 16.0% (aOR= 1.30 [95%CI = 1.19 to 1.41]) as did past-month use (7.1% to 9.4%) (aOR=1.37 [1.24 to 1.51]). Similarly, for men aged 12+, past-year cannabis use increased from 17.4% to 19.5% (1.15 [1.06 to 1.25]) after RCL enactment and past-month use increased from 10.9% to 12.6% (1.19 [1.08 to 1.30]). Among those reporting cannabis use, neither past-month daily cannabis use nor past-year DSM-5 CUD changed after RCL enactment. Moderate e-values for the aOR ranged from 1.35 to 2.08, and for the LL95%CI between 1.20 and 1.79. Increases in past-month cannabis use among women were the most robust to potential unmeasured confounding (e-values 2.08 for the aOR and 1.79 for the LL95%CI).

Past-year cannabis use in men and women by age group (Table 3)

Past-year cannabis use increased after RCL enactment in women aged 21+, with higher increases among those aged 21– 30 (+5.3%; aOR=1.43 [95%CI=1.25 to 1.63]), Table 3). Boys and men ages 12–20 experienced a decrease in past-year cannabis use from 24.6% to 21.9% (aOR=0.86 [0.75 to 0.98]) after RCL enactment, while no significant changes were observed among girls and women 12–20. Among men, past-year cannabis use also increased after RCL enactment in those aged 21+, with higher increases among those aged 21–30 (+4.8%; 1.29[1.12 to 1.48]), Table 3). E-values to assess the amount of unmeasured confounding needed to reduce the aOR and the LL95%CI to the null ranged from 1.36 to 2.67 and 1.11 to 2.06, respectively. Increases after RCL were more robust for women and men aged 41+—aOR e-values 2.67 and 2.01 and LL95%CI e-values 2.06 and 1.51, respectively.

Past-month cannabis use in men and women by age group (Table 3)

Past-month cannabis use increased among women aged 21+ after RCLs were enacted, with higher increases among those ages 21–30 (+3.3%; aOR=1.44[1.23 to 1.68]), Table 3). Men also showed increases in past-month cannabis use among those aged 21+ after enacting RCLs, with higher increases in those 21–30 (+3.4%; aOR=1.29[1.11 to 1.50]), Table 3). No changes in past-month cannabis use were observed for men or women aged 12–20. Increases in past-month cannabis use were more robust to unmeasured confounding for women aged 31–40 (e-values for aOR= 2.34 and LL95%CI= 1.62) and aged 41+ (e-values for aOR= 2.81 and LL95%CI= 2.06) than for women of other age groups and men.

Past-month daily cannabis use and past-year DSM-5 CUD among men and women who use cannabis by age group (Table 3)

Past-month daily cannabis use and past-year DSM-5 CUD among people that use cannabis did not increase after RCL legalization for women or men in any age group.

Discussion

This study is the first to evaluate the effect of RCLs between 2008 and 2017 on cannabis use recency, frequency, and disorder and its subgroup variation by gender overall and by age group. Past-year and past-month cannabis use increased for both men and women after RCL enactment and increases were larger for women than men, which were driven by increases among adults. Specifically, past-year cannabis use decreased among men aged 12–20 after RCL, while past-year and past-month cannabis use increased among both men and women aged 21+. No significant increases were found in past-month daily cannabis use or in past-year DSM-5 CUD among those who used cannabis.

Our study contributes to the nascent literature on the effects of recreational cannabis legalization on cannabis outcomes. Similar to previous research (Cerda et al., 2019), we found increases in past-month and past-year cannabis use among adult men and women after legalization that were robust to unmeasured confounding, particularly among men and women aged 41+. These increases might be explained by more permissive attitudes towards cannabis use, increased availability, and lower risk of legal problems after RCL (Carliner et al., 2017; Grucza, Agrawal, Krauss, Cavazos-Rehg, & Bierut, 2016; Pacek, Mauro, & Martins, 2015). These should all be investigated further in future studies.

While past research identified a widening cannabis gender gap over time (Carliner et al., 2017), we found that within states that enacted RCL, the gender gap may be narrowing due to higher post-RCL increases in women’s cannabis use than men’s. Research has demonstrated how gender norms affect cannabis use patterns, perceptions, and the effects of policies (Greaves & Hemsing, 2020; Hemsing & Greaves, 2020). For example, individuals who use cannabis are likely to perceive cannabis as lower risk and more available than those who do not (Salloum, Krauss, Agrawal, Bierut, & Grucza), and at the same time cannabis perceptions differ by gender. A recent study found that the proportion of women perceiving cannabis as both low risk and available increased between 2002 and 2018 (Levy et al., 2021). These changes in cannabis perceptions among women may partially explain the narrowing cannabis gender gap. Like previous research, we found that women report less cannabis use than men, which may in part be attributed to gender norms and expectations resulting from higher religiosity, maternal status, and less liberal attitudes towards cannabis (Elder & Greene, 2019; Greaves & Hemsing, 2020). While support for cannabis legalization is at an all-time high (Brenan, 2020; Daniller, 2019), women tend to be less supportive of cannabis legalization than men despite women’s generally higher support for liberal policies (e.g., gun control policies and government regulation) (Elder & Greene, 2019). Cannabis policies are thought to advance social equity outcomes (Kilmer, 2019) and may also contribute to changing gender norms leading to increased cannabis uptake among women (Greaves & Hemsing, 2020). As such, while gendered cannabis use patterns may result in less use and less support for legalization among women generally, women living in states with enacted RCL may adopt a more liberal view of cannabis use, leading to larger increases in cannabis use after RCL compared with their male counterparts. Should this trend be replicated as additional states and countries adopt RCL, we may well observe an overall narrowing of the previously documented gender gap in cannabis use.

We found post-RCL decreases in past-year and past-month cannabis use in males aged 12–20 and no prevalence changes among younger females, similar to previous research (Dills et al., 2021). There were no increases in past-month daily cannabis use among youth who used cannabis. These results may be expected in part because recreational cannabis is only legalized for adults ages 21+ (Staff, 2015). Our findings are consistent with prior studies that found no increases in adolescent cannabis outcomes after MCL enactment (Hasin, Wall, et al., 2015; Keyes et al., 2016; S. S. Martins et al., 2016; Sarvet et al., 2018). In addition, our findings build upon those from a recent study that showed no increases in cannabis use nor frequent cannabis use in adolescents ages 12–17 after RCL enactment (Cerda et al., 2019).

We did not find any increases in daily cannabis use or past-year DSM-5 CUD among men and women who used cannabis in any age group. This contrasts with findings from a recent study reporting a small increase in past-year DSM-IV CUD after RCL in adolescents who used cannabis, which the authors cautioned could reasonably be due to unmeasured confounding or random error (Cerda et al., 2019). While increases in the overall population prevalence of frequent cannabis use and CUD after recreational legalization occurred among those aged 26+, no post-RCL changes in these cannabis outcomes were observed among adults 26+ who reported using cannabis (Cerda et al., 2019). The evidence thus far suggests that post-RCL population-level increases in cannabis outcomes like CUD (Cerda et al., 2019) may be driven by positive trends in cannabis use in the entire population which preceded legalization (Dills et al., 2021) and put additional individuals at risk, rather than higher frequencies of cannabis consumption (S. S. Martins et al., 2016) or problematic use among those using cannabis post-RCL.

Limitations are noted. First, this study relied on self-reported measures of cannabis use and CUD. While audio computer-assisted self-interviews reduces social desirability concerns because respondents provide confidential self-reports without interacting with an interviewer (Administration, 2015) measurement error may remain. Second, we created a proxy measure of DSM-5 CUD based on previous research (Compton et al., 2019); this proxy did not include cravings or cannabis withdrawal because these criteria were not measured in the NSDUH, which may underestimate CUD prevalence but would not affect our estimated associations with RCL. Third, the NSDUH sampling excludes individuals who are homeless, unstably housed, and in institutions or correctional settings, which could underestimate prevalences. Fourth, our study examines concurrent changes in the prevalence and odds of cannabis outcomes by age and gender. As more states adopt RCLs, future studies should examine changes in these outcomes over a longer period of time, as well as longitudinally explore modes of cannabis use and Tetrahydrocannabinol (THC) potency overall and by gender. This study also has several strengths including its large, nationally representative sample across multiple years, data reported by gender and age, and a survey design that provides accurate US state-level estimates.

This study contributes to the understanding of gender differences in cannabis recency, frequency, disorder, and the cannabis gender gap after enactment of laws permitting recreational cannabis use in the US. Cannabis legalization may be contributing to changes in gendered patterns of cannabis use and narrowing the cannabis gender gap in the US (Hemsing & Greaves, 2020). Our findings offer valuable insights into the gendered patterns of cannabis use and the associated gender gap, facilitating the promotion of safe cannabis consumption, particularly important in a swiftly evolving policy landscape. Additionally, delving into gender dynamics in policy impact analysis can contribute to the development of more customized and precise regulations, standards, and public policies about cannabis.

Moreover, considering the possible negative consequences of drug use, low drug use disorder screening, and low drug use disclosure to healthcare providers in the US (P. M. Mauro, Samples, Klein, & Martins, 2020), cannabis legalization could help decrease drug use stigma and gender and age biases, enabling discussion of patients’ drug use with their healthcare providers and prompting early drug use treatment when needed. While we, along with others (Cerda et al., 2019; Dills et al., 2021; S. S. Martins et al., 2016), have not found changes in heavier cannabis use or CUD among those reporting cannabis use following RCLs, ongoing surveillance overall, by gender, and by gender and age group is essential to ensure that the social justice aims of legalization are achieved without negative public health consequences.

Longer-term studies are needed across gender and age groups to monitor whether the prevalence of daily cannabis use and CUD among those reporting cannabis use remains unchanged in the future. Because CUD may only develop several years after regular cannabis use, increases in the odds of DSM-5 CUD among people who use cannabis may lag legalization (Copeland & Swift, 2009; Swift, Coffey, Carlin, Degenhardt, & Patton, 2008; Wen, Hockenberry, & Cummings, 2015). Future research should also explore whether variations in policy provisions (e.g., number of legal dispensaries, cultivation, and consumption restrictions) of cannabis laws have different effects on the cannabis gender gap, on men and women of different age groups, and on adverse consequences of drug use.

Supplementary Material

Supplementary

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