Introduction
Cannabis is one of the most widely used drugs, with approximately 2.5% of people worldwide currently using cannabis in some form (World Drug Report 2019; World Health Organization, n.d.). As of 2019 in the United States (US), about 18% of Americans aged 12 and older reported having ever used cannabis in 2019 (Centers for Disease Control and Prevention, 2021), and in 2020, there were 2.8 million new cannabis users in the US, of which about 1 million were adolescents aged 12–17 (Substance Abuse and Mental Health Services Administration, 2020). Laws and practices surrounding cannabis have evolved significantly over the years, varying both on the state and country levels. In the US, cannabis was initially classified as a Schedule I drug under the Controlled Substances Act of 1970, indicating it had “a high potential for abuse” and “no currently accepted medical use” (Eddy, Reference Eddy2010). However, in the following decades, individual states enacted policies regarding the criminalization of cannabis for both personal and medical use. Recently, there has been a growing push for the decriminalization of medical and recreational cannabis (Grucza et al., Reference Grucza, Vuolo, Krauss, Plunk, Agrawal, Chaloupka and Bierut2018; Mahabir et al, Reference Mahabir, Merchant, Smith and Garibaldi2020). Public support for cannabis decriminalization has increased sharply, and as of 2022 only 10% of adults in the US believe all cannabis should be illegal (Van Green, Reference Van Green2022). Nonetheless, there remain significant gaps in our understanding of the potential consequences and benefits of this drug.
One research gap is in understanding the relationship between cannabis use and mental health, specifically whether cannabis use leads to common psychological disorders such as depression. In 2014, Lev-Ran et al. published a meta-analysis investigating the development of depression among cannabis users who were not depressed at baseline (Lev-Ran et al., Reference Lev-Ran, Roerecke, Foll, George, McKenzie and Rehm2014). Their systematic review of the literature concluded in December 2012 and was not limited to any country, language, or age group. They included 14 studies that either assessed the clinical diagnosis or symptoms of major depressive disorder (MDD) or dysthymia (mild chronic depression) (Patel & Rose, Reference Patel and Rose2021). They found that cannabis use was associated with higher odds of developing depression among adults (Odds Ratio [OR]: 1.17, 95% CI: 1.05, 1.30). Lev-Ran et al. also conducted meta-regressions on the age of cannabis onset (aged 18 years or younger compared to greater than 18 years) and on heavy cannabis use but found that both were not significant (ps > 0.05) and underpowered due to the small number of studies included. Sensitivity analyses showed that the OR for developing depression among heavy cannabis users compared to non-users was also not significant (OR: 1.34, 95% CI: 0.96–1.87). Overall, this meta-analysis indicated a positive relationship between cannabis use and the development of depression over time. No differences were found by age of onset or heaviness of cannabis use, although the latter analyses were underpowered.
Since Lev-Ran et al. (Reference Lev-Ran, Roerecke, Foll, George, McKenzie and Rehm2014), there have been other systematic reviews and meta-analyses that explored the relationship between cannabis use and mental health. In 2018, Mammen and co-authors published a systematic analysis that looked at mental health outcomes among people with anxiety or other mood disorders at baseline. They found that recent cannabis use (within the prior 6 months) was associated with increased levels of mental health symptoms (Mammen et al., Reference Mammen, Rueda, Roerecke, Bonato, Lev-Ran and Rehm2018). This offers evidence of the relationship between cannabis and poor mental health outcomes, although all participants in the Mammen et al. article had a mood disorder at baseline. Another meta-analysis published in 2019 explored cannabis use in adolescence and mental health outcomes, including depression, in young adulthood and found a pooled OR of 1.37 (95% CI: 1.16, 1.62) (Gobbi et al., Reference Gobbi, Atkin, Zytynski, Wang, Askari, Boruff and Mayo2019). This is further evidence that there is a relationship between cannabis and depression, even though the authors limited their review to only include studies with adolescent cannabis users.
Several explanations for the cannabis-depression link have been proposed. One mechanism focuses on the role of cannabis use, especially during formative periods, in changing brain structure and regulations, (Langlois et al, Reference Langlois, Potvin, Khullar and Tourjman2021) with some evidence from animal models (Bambico, Nguyen, Katz, & Gobbi, Reference Bambico, Nguyen, Katz and Gobbi2010). Another implicates shared genetic factors that might be responsible for both depression and cannabis use (Hodgson et al., Reference Hodgson, Almasy, Knowles, Kent, Curran, Dyer and Glahn2017). Others focused on shared social or environmental factors (Degenhardt et al, Reference Degenhardt, Hall and Lynskey2003). Evidence supporting these mechanisms indicates that multiple pathways might be in play in explaining the relationship between cannabis and depression.
Considering the changing landscape of cannabis in the US and globally, an update to the Lev-Ran et al. meta-analysis is warranted. Besides growing social and legal acceptance of cannabis, over the years, the potency of cannabis products has been increasing (ElSohly et al, Reference ElSohly, Chandra, Radwan, Majumdar and Church2021; ElSohly et al., Reference ElSohly, Mehmedic, Foster, Gon, Chandra and Church2016). Modes of administration are also changing, with vaping cannabis becoming more popular, as well as using concentrated cannabis products (e.g., shatter, wax), all of which differ in terms of potency and delivery of the psychoactive substance (Prince & Conner, Reference Prince and Conner2019). Furthermore, it is particularly important to address the issue of mental health in the US. Studies have shown that rates of depression in the US have increased in recent years, especially since the start of the COVID-19 pandemic (Niedzwiedz et al., Reference Niedzwiedz, Green, Benzeval, Campbell, Craig, Demou and Katikireddi2021; Zheng et al., Reference Zheng, Morstead, Sin, Klaiber, Umberson, Kamble and DeLongis2021). The purpose of this article was to update the Lev-Ran et al. meta-analysis by including additional studies published since 2013 and to further explore the relationship between cannabis use and depression. In addition, we conducted meta-analyses looking at the nuanced relationships between early-onset cannabis use (i.e., initiating use in adolescence) and depression, as well as heavy cannabis use and depression. We hypothesized that those who start using cannabis at a younger age would be more likely to develop depression, compared to those who initiate use as adults, which may be due to the psychosocial consequences of early cannabis use that have been demonstrated in the literature (Meier, Reference Meier2021; Pacheco-Colón et al., Reference Pacheco-Colón, Ramirez and Gonzalez2019). We also expected that heavy cannabis users would be more likely to develop depression, compared to light/occasional users due to the potential for a dose–response relationship between cannabis and depression.
Methods
This review follows the PRISMA 2020 reporting guidelines (Page et al., Reference Page, McKenzie, Bossuyt, Boutron, Hoffmann, Mulrow and Moher2021). Although this review closely adhered to systematic review methodologies, the timing of the project did not allow for registration in the International Prospective Register of Systematic Reviews (PROSPERO).
To guide this review, we applied the PICO framework: Population (non-institutionalized youth and adults worldwide); Intervention (cannabis use); Comparison (non-cannabis users); and Outcome (depression).
Eligibility criteria
The literature search included literature from inception through March 31, 2023. Initial eligibility criteria were established a priori, but additional criteria were added after the literature search began. Articles could originate from any country as long as they were available in English. The inclusion criteria established prior to the literature search were: (1) studies had to report on cannabis use separate from other drug use; (2) studies had to report on depression, separate from any other mental health outcomes; and (3) studies had to be longitudinal, with cannabis use measured prior to the outcome of depression or controlled for at baseline. Exclusion criteria were: (1) cross-sectional studies and (2) other systematic review and meta-analysis articles. After the preliminary search, we decided to exclude studies that only focused on special populations that were likely to have a higher risk of both cannabis use and depression, to reduce confounders. For example, LGBTQ-only studies were excluded (Dyar et al., Reference Dyar, Bradley, Morgan, Sullivan and Mustanski2020) as it has been shown that gender and sexual minority populations have higher rates of depression than cisgender and heterosexual populations (Ferlatte et al., Reference Ferlatte, Salway, Rice, Oliffe, Knight and Ogrodniczuk2020) as well as higher rates of problematic cannabis use (Dyar et al., Reference Dyar, Feinstein, Newcomb and Whitton2021). In addition, studies that focused on war Veteran-only populations were excluded (Gunn et al., Reference Gunn, Stevens, Micalizzi, Jackson, Borsari and Metrik2020) because this population also has unique experiences that may lead to differing rates of cannabis use (Davis et al., Reference Davis, Lin, Ilgen and Bohnert2018) and depression (Liu et al., Reference Liu, Collins, Wang, Xie and Bie2019) compared to the general population. We excluded studies that only included participants who were seeking treatment for a mental health issue at baseline (Bahorik et al., Reference Bahorik, Sterling, Campbell, Weisner, Ramo and Satre2018).
Search strategies
Systematic literature searches were conducted by VC and CC. The initial search started with articles that cited the Lev-Ran et al. meta-analysis. Searches were then conducted in PubMed, Ovid Medline, and Google Scholar. Gray literature was searched using ProQuest for unpublished theses and dissertations. An ancestor search of the Lev-Ran articles was conducted by reviewing all the “Cited By” articles in PubMed. The full search strategy, including keywords and limits for each database, can be found in Supplemental Materials I.
Screening
The screening was conducted in three stages (initial screening of titles; title and abstract screening; and full-text screening) on Rayyan (Ouzzani et al., Reference Ouzzani, Hammady, Fedorowicz and Elmagarmid2016) using the eligibility criteria specified above. VC conducted the original screening of potential studies published through March 31, 2022, with no specified beginning date. Both VC and CC conducted the screening of potential studies published between April 1, 2022, and March 31, 2023. Disagreements between VC and CC were discussed and TP acted as the tie-breaking vote for seven articles when agreement could not be reached. Detailed screening results, including citations of excluded articles and reasons for exclusion, can be found in Supplement II.
Extraction
Study information was independently extracted by VC, with TP serving to assist with extracting data when clarity was needed. The coding sheet can be found in Supplement III. The final codes are detailed below.
Cannabis Use Measure. Cannabis use in any form was the exposure of interest. Codes specific to cannabis included: how cannabis use was measured (for example, self-reported frequency, or a validated measure such as the Composite International Diagnostic Interview [CIDI]). Specific definitions of exposure were collected. These ranged from ever use to cannabis use disorder. Most of the articles included analyses for different levels of exposure, so these were extracted as separate effect sizes. For example, Pedersen (Reference Pedersen2008) reported on participants who had used cannabis 1–10 times in the past 12 months, as well as those who used cannabis 11 or more times in the past 12 months.
Although we intended to code for the method of cannabis use (i.e., edibles, inhaled), this was not reported consistently across eligible studies. However, we were able to code for early onset/adolescent use which we defined as cannabis use prior to age 18, and heavy cannabis use. While heavy use was not universally defined, we determined that studies reporting outcomes for “cannabis dependence,” “cannabis use disorder,” “diagnosis of cannabis abuse,” “chronic cannabis use,” “persistent use” and “daily use” met the criteria for heavy use.
Depression Measure. The outcome was any type of clinical or self-reported depression, including major depressive disorder (MDD), major depressive episode (MDE), and general depressive symptoms. We coded for the specific measure that was used (e.g., Clinical Interview Schedule [CIS]-Revised). We also coded if the depression measure was obtained via a self-report survey, structured interview, or indeterminate. Other mental health issues, such as anxiety (Feingold, et al., Reference Feingold, Weiser, Rehm and Lev-Ran2015; Kedzior & Laeber, Reference Kedzior and Laeber2014) and schizophrenia, were not coded due to the complexity of additional codes while considering the limited resources of the project.
Results from Individual Studies. Adjusted odds ratios (aORs) for the depression outcomes were extracted from all studies that controlled for depression at baseline. If studies reported multiple outcomes for different exposure categories, we extracted those into separate effect sizes. For example, Feingold et al. (Reference Feingold, Weiser, Rehm and Lev-Ran2015) reported aORs for those who used cannabis less than weekly, weekly to less than daily, and daily, resulting in three effects for this meta-analysis (Feingold et al., Reference Feingold, Weiser, Rehm and Lev-Ran2015). Overall, we extracted 1–5 effects from all the articles (median: 2 effects per study). When studies reported multiple aORs for a single effect, we chose the one that was adjusted for the most variables, to maintain consistency across studies. Control variables for each of the studies can be found in Table 3. We converted aOR to log-odds ratios using the R program metafor based on extant literature (Colditz et al., Reference Colditz, Brewer, Berkey, Wilson, Burdick, Fineberg and Mosteller1994).
Other Coded Data. For each study, we collected data on setting (years that the study took place, country [US vs non-US], and US state of participants), age of participants at baseline (under 18, over 18, or mix), length of follow-up period, and any adjustments made in the analyses. We also attempted to code for the percentages of gender identities in the sample, but that variable was inconsistently reported in the primary studies included in this meta-analysis.
Risk of Bias. The risk of bias was assessed using an adapted version of the “Tool to Assess Risk of Bias in Cohort Studies” available from Cochrane (Supplement IV) (Sterne et al., Reference Sterne, Hernan, McAleenan, Reeves and Higgins2022). We utilized 7 out of the 8 items that applied to our systematic review, including “Can we be confident in the assessment of exposure?” We did not use the item that assessed co-interventions, since it was not appropriate for this systematic review (Supplement V). As suggested by the literature, multiple tests were used to triangulate evidence of publication bias (Vevea et al., Reference Vevea, Coburn and Sutton2019). The risk of bias was assessed by testing funnel plot asymmetry (Pustejovsky & Tipton, Reference Pustejovsky and Tipton2022) and conducting an Egger’s regression test adapted for dependent effect sizes (Duval & Tweedie, Reference Duval and Tweedie2000; Egger et al., Reference Egger, Davey Smith, Schneider and Minder1997).
Meta-analytic Approach. Rstudio version 1.2 was used to conduct all analyses (RStudio Team, 2018). We used a random effects model to synthesize the results given the likely variation across studies in population and context. The random effects models were estimated using REML. Many studies reported multiple effect sizes due to subgroup analyses (e.g., different levels of cannabis use, different ages of onset of use); thus, given the presence of dependent effect sizes, we used robust variance estimation for the analysis using the package clubSandwich (Pustejovsky & Tipton, Reference Pustejovsky and Tipton2022). We estimated effect size models using a hierarchical and correlated effects model assuming a correlation of 0.6 among dependent effect sizes. This allowed us to make more conservative assumptions about statistical significance. For the meta-analyses of early onset, heavy use, and country, we examined effect size moderators using multilevel meta-regression and RVE adjustment using metafor and clubSandwich. As suggested by Williams et al. (2021), these models are exploratory in nature (Williams et al, Reference Williams, Citkowicz, Miller, Lindsay and Walters2022).
Results
Search results
Overall, 1,599 titles were initially screened across PubMed (1372), MedLine (222), and ProQuest (5). From the ancestor search of the Lev-Ran articles, we screened an additional 969 articles. After duplicates were removed (n = 249) there were 2,319 articles that were title screened. We excluded 2,123 articles based on title screening, leaving 94 articles for abstract/full-text screen. The most common reasons for exclusion were review articles, included only special populations (i.e., Veterans (Gunn et al., Reference Gunn, Stevens, Micalizzi, Jackson, Borsari and Metrik2020), LGBT (London-Nadeau et al., Reference London-Nadeau, Rioux, Parent, Vitaro, Côté, Boivin and Castellanos-Ryan2021)), study design (i.e., cross-sectional (Hawke et al, Reference Hawke, Wilkins and Henderson2020)), or did not look at depression separately from other mental health outcomes (Spineli & Pandis, Reference Spineli and Pandis2020). In the end, 22 studies were included in the meta-analysis, which included the 14 original articles from the Lev-Ran meta-analysis. Summaries of the included studies can be found in Tables 1 and 2. Study selection is visualized in the PRISMA diagram (Figure 1).
Table 1. Summary of studies included in the meta-analysis
Notes: *indicates article was included in the Lev-Ran, Reference Lev-Ran, Roerecke, Foll, George, McKenzie and Rehm2014 meta-analysis.
Table 2. Measurements used in included studies
Notes: * Indicates article was included in the Lev-Ran, Reference Lev-Ran, Roerecke, Foll, George, McKenzie and Rehm2014 meta-analysis.
Figure 1. PRISMA diagram.
Study characteristics
Eleven of the studies took place in the United States, three of which were national surveys. The others were based in Sweden (n = 2), Australia (n = 2); New Zealand (n = 2), The Netherlands (n = 1), United Kingdom (n = 1), Canada (n = 1), Switzerland (n = 1), and Norway (n = 1). The majority (n = 12) enrolled participants who were under 18 at the first time point. The median follow-up time was 7 years (interquartile range: 3–16 years).
Meta-analysis
Extracted effects can be found in Table 3.
Table 3. Results from the systematic review
Notes: *included in the initial Lev-Ran et al. article.
a adjusted for variables in column 4, unless none specified.
b sub-analysis for early onset.
c sub-analysis for heavy/problematic use.
Mean Effect Size and Heterogeneity. We estimated the mean effect size using a correlated and hierarchical effects model (CHE) with robust variance estimation using clubSandwich. The estimated mean log-odds of depression was 0.25 (SE = 0.06, df = 18, p < .001). This corresponds to an OR of 1.29, with a 95% CI [1.13, 1.46]. This would suggest a higher risk of developing depression among people who used cannabis at baseline.
We utilized the model to create a 95% prediction interval of 0.75 and 2.20. This is the likely range of effect size values where we would expect a randomly selected new study for the meta-analysis to fall. This is a wide interval, suggesting a high degree of uncertainty around future predictions.
Meta-regression
To explore the heterogeneity across studies, we used the CHE model with robust variance estimation to fit a meta-regression using Country (US versus non-US), Cannabis Use (Non-heavy vs Heavy), and Onset (Late vs Early) as moderators. Table 4 presents the results of the meta-regression model with all three moderators and the associated Satterthwaite degrees of freedom. The meta-regression results test the difference between the levels of each moderator, controlling for the other moderators in the model. Only Cannabis Use was statistically significant at the p < 0.10 level, indicating that controlling for Country and Onset, heavy cannabis users had higher log odds of depression than non-heavy users. Table 4 also presents the adjusted means for each level of the three moderators, adjusted for the other moderators in the model. With the exception of US studies, the adjusted sub-group mean odds ratios were all significantly different from 1. The US studies had an average odds ratio that did not differ from 1.
Table 4. Moderator results from mixed-effects meta-regression model
Note: *significant at p < 0.10, ** significant at p < .01; ^ indicates reference group;
The first column reports the conditional means. The conditional means are the predicted values (OR) from a multivariable meta-regression model that simultaneously controlled for all the listed moderators (e.g., the odds ratio for U.S. studies when all other moderators are fixed at their observed mean). The standard errors (SE) were adjusted for effect size dependencies using robust variance estimation. The p values assess whether the levels of a moderator are statistically significantly different from one another, controlling for all other moderators in the model.
Risk of Bias. Results from this risk of bias tool can be found in Table 5. Overall, there was a medium risk of bias across all studies with a mean of 2.17 (sd = 0.57) on a scale from 1 (low risk of bias) to 5 (high risk of bias). All studies were assigned a “1” for “Was the selection of exposed and non-exposed cohorts drawn from the same population?” as they all selected cannabis users and non-users from the same population. The next lowest score was for whether there was adequate control for prognostic variables related to the outcome of depression (mean = 2.14), but this one also had the highest variability (sd = 1.03). The category that had the highest risk of bias was in the measure of the exposure, with a mean of 2.82 and the lowest variability (sd = 0.45). All of the studies relied on self-report from the participants to classify cannabis use. The question that assessed the measurement of the outcome had a moderate level of bias (mean = 2.34, sd = 0.45). Of note, only one study looked at clinical records to make a determination for depression) (Haroz et al., Reference Haroz, Ritchey, Bass, Kohrt, Augustinavicius, Michalopoulos and Bolton2017).
Table 5. Risk of Bias assessment, average scores ranging from 1 (low risk of bias) to 5 (high risk of bias)
Notes: *indicates article was included in the Lev-Ran, Reference Lev-Ran, Roerecke, Foll, George, McKenzie and Rehm2014 meta-analysis. Scores were based on the following scale: 1 = “Definitely yes (low risk of bias)”; 2 = “Probably yes”; 3 = “Probably no”; 4 = “Definitely no (high risk of bias).” Half-points were given if the study was deemed to be between two levels in the risk of bias scale. aWas selection of exposed and non-exposed cohorts drawn from the same population? bCan we be confident in the assessment of the exposure? cCan we be confident that the outcome of interest was not present at start of the study? If depression at the start of the study was controlled for, we assigned a score of 1.75. dDid the study match exposed and unexposed for all variables that are associated with the outcome of interest or did the statistical analysis adjust for these prognostic variables? eCan we be confident in the assessment of the presence or abssence of prognostic factors? fCan we be confident in the assessment of outcome? gWas the follow up of cohorts adequate? hAssessed using the modified “Tool to Assess Risk of Bias in Cohort Studies” see Supplement III.
A meta-regression using items on the Risk of Bias assessment tool as predictors was conducted. We recorded each study on the Cochrane Tool metric as either high or low risk of bias, where a score of above 2.5 was considered high. We focused on two of the questions: (3) Can we be confident that the outcome of interest was not present at the start of the study?; and (4) Can we be confident in the assessment of the presence or absence of prognostic factors? These were run in separate meta-regression models as a single predictor for developing depression. Neither item was related to effect size heterogeneity.
Selection Bias/Publication Bias. We also produced a funnel plot (Figure 2) and conducted Egger’s sandwich regression test to check for the risk of publication bias in the presence of dependent effect sizes. The Egger’s test produced a score of 0.07, which was not significant (p = .89). This, in addition to the non-symmetric funnel plot, does not provide evidence of bias; however, it is not possible to completely rule out any risk of either selection or publication bias.
Figure 2. Funnel plot for publication bias.
Discussion
This systematic review and meta-analysis was designed to explore the longitudinal relationship between cannabis use and depression, building upon a prior meta-analysis by Lev-Ran et al. (2013). In updating this work, identified eight additional studies (Arseneault, Reference Arseneault2002; Blanco et al., Reference Blanco, Hasin, Wall, Flórez-Salamanca, Hoertel, Wang and Olfson2016; Copeland et al., Reference Copeland, Hill and Shanahan2022; Danielsson et al., Reference Danielsson, Lundin, Agardh, Allebeck and Forsell2016; Feingold et al., Reference Feingold, Weiser, Rehm and Lev-Ran2015; Gage et al., Reference Gage, Hickman, Heron, Munafò, Lewis, Macleod and Zammit2015; Hengartner et al., Reference Hengartner, Angst, Ajdacic-Gross and Rössler2020; Schaefer et al., Reference Schaefer, Hamdi, Malone, Vrieze, Wilson, McGue and Iacono2021) that we added to the original studies from the Lev-Ran et al. analysis (Bovasso, Reference Bovasso2001; Brook et al., Reference Brook, Brook, Zhang, Cohen and Whiteman2002; Brook et al., Reference Brook, Lee, Brown, Finch and Brook2011; Degenhardt et al., Reference Degenhardt, Coffey, Romaniuk, Swift, Carlin, Hall and Patton2013; Fergusson & Horwood, Reference Fergusson and Horwood1997; Georgiades & Boyle, Reference Georgiades and Boyle2007; Harder et al., Reference Harder, Stuart and Anthony2008; Manrique-Garcia et al., Reference Manrique-Garcia, Zammit, Dalman, Hemmingsson and Allebeck2012; Marmorstein & Iacono, Reference Marmorstein and Iacono2011; Paton et al., Reference Paton, Kessler and Kandel1977; Patton et al., Reference Patton, Coffey, Carlin, Degenhardt, Lynskey and Hall2002; Pedersen, Reference Pedersen2008; van Laar et al, Reference van Laar, van Dorsselaer, Monshouwer and de Graaf2007).
Our findings indicated an odds ratio of 1.29 (95% CI: 1.13, 1.46) compared to non-users, consistent with Lev-Ran et al.’s 1.17 (95% CI: 1.05–1.30) but with a high degree of heterogeneity (95% PI: 0.75, 2.20). Similar to Lev-Ran et al., we found that studies with heavy cannabis users reported a higher odds ratio than those on non-heavy users and that the effect estimate did not significantly differ between early- and late-onset cannabis users. This lack of statistically significant difference may reflect the high heterogeneity in exposure and outcome measurements across studies, making it difficult to infer robust conclusions.
There was no significant difference in odds of depression after cannabis use when US and non-US countries were compared directly and holding the heaviness of cannabis use and onset constant. However, our interpretation of this insignificant finding remains cautious as dichotomizing US vs non-US results may not be inappropriate for the outcome of depression. For instance, a systematic review of qualitative literature exploring the definition of depression globally found that the Diagnostic and Statistical Manual of Mental Disorders (DSM) commonly used in Western countries does not adequately reflect how depression is expressed by individuals in other countries (Haroz et al., Reference Haroz, Ritchey, Bass, Kohrt, Augustinavicius, Michalopoulos and Bolton2017; Kessler & Bromet, Reference Kessler and Bromet2013).
Limitations
A few key limitations should be noted. First, this review was not registered in advance with PROSPERO or a similar systematic review registry, which could have enhanced transparency and methodological rigor by establishing a priori protocols for study selection and data extraction. As such, some risk of selection bias exists, given the absence of a public pre-registration outlining the study design.
Second, we did not search all possible databases and instead relied heavily on PubMed as our primary data source. While this was in line with the previous methodology of Lev-Ran et al., it may have limited the comprehensiveness of our search and increased the risk of missing relevant studies published in other databases. Cannabis use and mental health studies, in particular, are multidisciplinary and may be published in journals indexed in databases like PsycINFO or Scopus, potentially omitting relevant literature.
Furthermore, we excluded studies focusing on specific subpopulations, such as LGBTQ+ communities, or individuals with specific psychiatric comorbidities. While this decision was made to maintain a more generalizable sample, it may have inadvertently reduced the applicability of our findings to these groups.
In summary, these limitations underscore the need for a cautious interpretation of our results, as they may not fully reflect the broader body of literature on cannabis use and depression. Future research should prioritize protocol registration, utilize a wider range of databases, and include diverse populations to provide a more comprehensive understanding of this complex relationship.
Suggestions for future research
Throughout the process, we identified several opportunities for future research to more clearly determine the relationship between cannabis use and depression.
The definition of who is a cannabis user is not consistent across studies. For example, some studies dichotomize people into cannabis “users” and “non-users.” However, even this is not consistent across studies. The article by van Laar et al. (Reference van Laar, van Dorsselaer, Monshouwer and de Graaf2007) defined a cannabis user as anyone who used it more than 5 times in their lifetime, whereas Manrique-Garcia et al. (Reference Manrique-Garcia, Zammit, Dalman, Hemmingsson and Allebeck2012) defined a cannabis user as anyone who used it at least once. Manrique-Garcia et al. had tried to categorize into more groups, but were unable to due to “the small number of cases.” Attempts to label cannabis users as “heavy,” “moderate,” and “light” were also not consistent. Furthermore, some studies looked at the past year to determine the heaviness of use, while others looked at lifetime use.
Accordingly, another limitation of the studies is that many of them were not able to consider changes in cannabis use over time. Measuring cannabis use at a single time point may not account for the waxing and waning of use across the lifecycle of an individual. While many cannabis users start in adolescence or young adulthood, there is evidence that some may discontinue the behavior as they “mature out” (Arora et al., Reference Arora, Qualls, Bobitt, Milavetz and Kaskie2021; Kosty et al., Reference Kosty, Seeley, Farmer, Stevens and Lewinsohn2017). It may be beneficial to explore whether quitting cannabis as an adult reduces the risk of depression to the levels of non-users or if youth cannabis exposure is enough to elevate the risk of depression despite subsequent non-use. This could have major implications for interventions that aim to delay the onset of cannabis use. Furthermore, due to the nature of the articles included in the meta-analysis, determining an average length of follow-up is not plausible. This limits our interpretation of the role of time in the relationship between cannabis use and depression.
Similar to cannabis use, depression was inconsistently measured across studies. While “major depressive disorder” and “major depressive episode” were commonly cited, other definitions such as “depressive disorder” and “depressive symptoms” were used. Additionally, there were several diagnostic tools and scales used to measure depression across the studies; the most predominant was the DSM, with different versions (III, III-R, IV, and V) being used across studies. It is not guaranteed that an individual who met the criteria for depression on one measure would meet it on another; therefore, the heterogeneity of the outcome measure is an area of concern that future research needs to consider in order to gain a better understanding of the relationship between cannabis and depression.
Finally, we were unable to analyze differences in the relationship between cannabis use and depression across genders, racial/ethnic groups, and age groups due to insufficient reporting in the studies reviewed. This limitation highlights an important gap, as understanding how these relationships vary by gender, race/ethnicity, and age is crucial to comprehensively addressing the potential disproportionate burden of cannabis on specific demographics. Future research should prioritize collecting and reporting these demographic details to allow for more nuanced analysis and to support targeted interventions.
Conclusions
There is evidence that cannabis use is associated with the onset of depression over time. While we updated a previous meta-analysis from 2012 and found similar results, gaps in our understanding of how cannabis affects mental health remain. The current state of the literature has not kept up with the changing landscape of cannabis use in the US and the world. Although this is partly due to the nature of longitudinal research, we recommend that researchers focus on the following: 1) consider the mode of cannabis use (e.g., edibles, concentrates); 2) find ways to better define cannabis use, specifically definitions of heavy versus light use; and 3) conduct analyses to investigate how cannabis use changes over time relates to the risk of depression.
Lastly, although our findings indicate an association, it is likely that confounding factors, which were not fully captured in our analysis, contribute to this relationship. Therefore, while our study explores a temporal, longitudinal connection, it cannot definitively establish causation.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S0033291724003143.
Funding statement
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interest
There are no conflicts of interest to declare.
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