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. Author manuscript; available in PMC: 2020 May 1.
Published in final edited form as: Psychol Addict Behav. 2019 Mar 7;33(3):190–196. doi: 10.1037/adb0000452

A Meta-Analytic Investigation of the Associations between Cannabis Use and Cannabis-Related Negative Consequences

Matthew R Pearson 1
PMCID: PMC6892582  NIHMSID: NIHMS1008261  PMID: 30843713

Abstract

In the current climate on policy change regarding cannabis (i.e., decriminalization, medicalization, and legalization), various stakeholders have strong interest in determining the associations between cannabis use and important outcomes. The present study sought to quantify the association between indicators of cannabis use and the experience of negative cannabis-related consequences. We found 19 unique studies that examined the associations between cannabis use and negative consequences as measured by one of four measures: Marijuana Problems Scale (MPS), Rutgers Marijuana Problem Index (RMPI), Cannabis Problems Questionnaire (CPQ), or the Marijuana Consequences Questionnaire (MACQ). We used random effects meta-analytic techniques to estimate the average strength of association between cannabis use and negative consequences, determine the level of heterogeneity in effect sizes, and examine possible moderators of these associations (measure of consequences, gender/sex distribution).We found that cannabis use had a medium-sized association with consequences, rw = .367 with high levels of heterogeneity that depended to some extent on the specific consequence measure used. Similar to a meta-analytic integration of the alcohol use-consequences association, we found that most of the variance in cannabis-related negative consequences was not explained by any single indicator of cannabis use, pointing to the fact that additional factors need to be examined to explain the experience of negative consequences from cannabis use and that additional indicators of cannabis use may be needed.

Keywords: cannabis use, negative consequences, meta-analysis, Marijuana Consequences Questionnaire, Rutgers Marijuana Problem Index, Cannabis Problems Questionnaire, Marijuana Problems Scale

In the context of increased medicalization and legalization of cannabis, there is rising demand to quantify the full range of risks and benefits of cannabis use. On the one hand, the therapeutic potential of cannabis is being examined for a wide range of conditions including posttraumatic stress disorder (Greer, Grob, & Halberstadt, 2014; Roitman, Mechoulam, Cooper-Kazaz, & Shalev, 2014) and chronic pain (Martín-Sánchez, Furukawa, Taylor, & Martin, 2009), among many others. On the other hand, the risks of cannabis are being examined on a wide range of negative outcomes including psychosis (Marconi, Di Forti, Lewis, Murray, & Vassos, 2016), cannabis hyperemesis syndrome (Dezieck et al., 2017), impaired driving (Asbridge, Hayden, & Cartwright, 2012), and sleep disturbances (Wong, Craun, Bravo, Pearson, & Protective Strategies Study Team, 2018), among many others. Given the mixed evidence for the associations between cannabis use and most outcomes, it is likely that the effects of cannabis are modest, nuanced, and/or variable across individuals.

Rather than examining a single possible consequence of cannabis use, researchers often use broad-spectrum measures of negative consequences to consolidate across specific outcomes and gain a sense of the overall effects of cannabis. There are several of these broad-spectrum measures of negative consequences, four of which have been used in several cannabis studies to date: Marijuana Problems Scale (MPS; Stephens, Roffman, & Curtin, 2000), Rutgers Marijuana Problem Index (RMPI; White, Labouvie, & Papadartsakis, 2005), Cannabis Problems Questionnaire (CPQ; Copeland, Gilmour, Gates, & Swift, 2005), and the Marijuana Consequences Questionnaire (MACQ; Simons, Dvorak, Merrill, & Read, 2012). Because these broad-spectrum measures aggregate across a wide range of possible consequences, it is likely that the associations between cannabis use and these negative consequences measures represent the upper-bound of relationships between cannabis use and outcomes.

Precise characterization of the strength of these associations has a number of advantages. First, knowing the strength of the association between cannabis use and cannabis-related negative consequences can inform the amount of harm reduction to be expected following an intervention based on the effect size on cannabis use (i.e., an X-reduction in cannabis use would be expected to result in a Y-reduction in cannabis-related negative consequences). Second, this information can be used to make a priori sample size considerations based on statistical power (i.e., prevent conducting underpowered studies). Third, determining moderators of these associations could help researchers select cannabis use indicators or negative consequences measures to be used in a study for a specific population.

The present study uses meta-analytic techniques to quantify the strength of associations between indicators of cannabis use and negative consequences. First, we determine the overall strength of association and examine the degree of heterogeneity in this association. Second, we examine possible moderators of the use-consequences associations including the type of consequences measure (MPS, RMPI, CPQ, and MACQ), the type of cannabis use indicator (frequency, quantity, other), population (adolescents, adults), and gender/sex distribution of the sample (i.e., is the use-consequences relationship weaker or stronger among males vs. females?).

Method

Identification of Studies

To examine the effect size (i.e., variance explained) between cannabis use indicators and cannabis-related negative consequences, we used a forward-searching strategy to identify all studies citing one of the four broad-spectrum measures of cannabis-related negative consequences using PsycInfo, PubMed, and Google Scholar: MPS (Stephens et al., 2000), MRPI (White et al., 2005), CPQ (Copeland et al., 2005), and MACQ (Simons et al., 2012). Then, these studies were examined to determine if they contained a correlation between any indicator of cannabis use and the negative consequences measure. When it was clear that an article contained the information necessary to calculate an association between cannabis use and negative consequences but did not report it in a manuscript, we reached out to the corresponding/senior author of the manuscript to request the information needed for this meta-analysis (the size of the correlation, the sample size, the cannabis use indicator, the negative consequences measure, and the % of females in the sample). We also invited leading cannabis researchers in the field to provide this information for any unpublished studies. One unpublished study was included in the meta-analysis and four effect sizes were provided by authors for studies that the necessary information was not reported in the manuscript.

It is important to note this study is not a census-based meta-analysis in which we attempt to obtain every available dataset to estimate effect sizes; rather, this reflects a sampling-based meta-analysis such that we attempted to obtain a reasonable number of effect size estimates for each measure so that we can obtain an accurate estimate of these effect sizes and plausible moderators of these effects. Altogether, we obtained effect size estimates from 19 unique studies (see Table 1).

Table 1.

Summary of Studies Included in Meta-Analysis

Study Name Population Sampling Region Measure r 95% CI N % Female M age
(Blevins et al., 2018) Emerging adults, community Convenience, Community advertising Northeast, USA MPS .210 .107, .309 345 46.1 21
(Bravo, Prince, Pearson, & MOST, 2017) College students Convenience, Psychology Participant Pools 11 sites, all major regions, USA MACQ .350 .312, .387 2129 66.9 19.95
(Buckner, Ecker, & Cohen, 2010) College students Convenience, email Southeast, USA MPS .315 .235, .390 487 54 20.01
(Buckner et al., 2018) College students Convenience, psychology courses Southeast/ Midwest, USA MPS .120 −.039, .273 154 58.4 20.44
(Copeland et al., 2005) Adults Convenience, stratified by low, medium, and high cannabis use Sydney, Australia CPQ .570 .421, .689 100 41 27.5
(Davis et al., 2018) Young adults Convenience, online Unknown RMPI .150 .065, .233 524 12 24
(Dvorak & Day, 2014) College students Convenience, online Midwest, USA MACQ .620 .576, .661 817 64.5 20.14
(Ecker & Buckner, 2014) College students Convenience, Psychology Participant Pool Southeast, USA MPS .350 .231, .459 230 63 19.68
(Ecker et al., 2017) College students Convenience, online Northeast, USA MPS .330 .146, .492 103 78.6 21.2
(Ecker & Buckner, 2018) College students Convenience, Psychology Participant Pool Southeast, USA MPS .280 .160, .392 244 76.2 20.32
(Elliott & Carey, 2013) College students Convenience, Psychology Participant Pool Northeast, USA RMPI .470 .276, .627 78 57 20.04
(Elliott, Carey, & Vanable, 2014) College students Convenience, Psychology Participant Pool Northeast, USA RMPI .530 .404, .637 149 49 19.34
(Lee, Neighbors, Hendershot, & Grossbard, 2009) College students Convenience, online West, USA RMPI .380 .286, .467 346 55.2 18.03
(Martin et al., 2006) Adolescents Convenience, stratified by low, medium, and high cannabis use Sydney, Australia CPQ .590 .445, .705 100 46 16.1
(Phillips, Phillips, Lalonde, & Tormohlen, 2015) College students Convenience West, USA RMPI .262 .002, .489 57 63 20.05
(Phillips, Lalonde, Phillips, & Schneider, 2017) College students Convenience, Psychology classes West, USA RMPI .174 −.004, .341 122 43 19.68
(Phillips, 2018) College students Convenience West, USA RMPI .388 .126, .520 79 59.5 20.35
(Schmits et al., 2016) Adolescents Convenience, classrooms Belgium CPQ .470 .381, .551 325 49.6 15.7
(Simons et al., 2012) College students Convenience, Psychology Participant Pool Midwest/ Northeast, USA MACQ .310 .207, .407 315 51.1 20.52
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Note. MPS = Marijuana Problems Scale, RMPI = Rutgers Marijuana Problem Index, CPQ = Cannabis Problems Questionnaire, MACQ = Marijuana Consequences Questionnaire.

Negative Consequences Measures

MPS.

The MPS is a 19-item measure originally administered on a three-point response scale ranging from 0 = no problem, 1 = minor problem, and 2 = serious problem (Stephens et al., 2000). Participants respond to the stem “Has marijuana use caused you...” and includes items like “problems in your family,” “to lose a job,” “memory loss”, and “legal problems.” Often the MPS is scored by scoring “no problem” as 0 and “minor problem” or “serious problem” as 1 so that a summed score reflects the total number of unique negative consequences from cannabis use.

RMPI.

The RMPI is an 18-item measure originally administered on a five-point response scale: 0 = 0 times, 1 = 1–2 times, 2 = 3–5 times, 3 = 6–10 times, 4 = >10 times (White et al., 2005). The items are identical to the alcohol problems measured by the Rutgers Alcohol Problem Index (RAPI) (White & Labouvie, 1989) with “alcohol” replaced with “marijuana” for some items. Example items include “Neglected your responsibilities,” “Missed a day (or part of a day) of school or work,” “Felt physically or psychologically dependent on marijuana,” and “Felt that you needed more marijuana than you used to in order to get the same effect.”

CPQ.

The CPQ is a 22-item measure originally administered on a binary response scale: 0=no, 1=yes. The items were modeled after the Alcohol Problems Questionnaire (APQ) (Williams & Drummond, 1994). Factor analyses suggested a three-factor structure of the CPQ including physical consequences (9 items; “Have you had pains in your chest or lungs after a smoking session?”), psychological consequences (7 items; “Have you felt so depressed that you felt like doing away with yourself?”), and social consequences (6 items; “Have you spent more time with smoking friends than other kinds of friends?”). A 27-item version of the CPQ has been adapted for adolescents (Martin, Copeland, Gilmour, Gates, & Swift, 2006), which for analysis purposes, we collapse into a single measure given the very high degree of overlap between these measures.

MACQ.

The MACQ is a 50-item measure originally administered on a binary response scale: 0=no, 1=yes (Simons et al., 2012). The items were modeled after the 48-item Young Adult Alcohol Consequences Questionnaire, which has 8 factors (YAACQ) (Read, Kahler, Strong, & Colder, 2006). Most items were simply changed to reference “marijuana” rather than “alcohol”, but items focused on withdrawal and hangover were modified to better reflect the effects of cannabis, one item was added to assess deficits in motivation, and one item was added to assess paranoia. Confirmatory factor analyses supported this 8-factor solution: social-interpersonal consequences (6 items; “My marijuana use has created problems between myself and my boyfriend/girlfriend/spouse/parents, or other near relatives”), impaired control (“I often have found it difficult to limit how much marijuana I use”), self-perception (“I have been unhappy because of my marijuana use”), self-care (“I have not had as much time to pursue activities or recreation because of my marijuana use”), risk behaviors (“I have taken foolish risks when I have been high”), academic/occupational consequences (“I have neglected obligations to family, work, or school because of my marijuana use”), physical dependence (“I have felt anxious, irritable, lost my appetite or had stomach pains after stopping or cutting down on marijuana use”), and blackout use (“I have not been able to remember large stretches of time while using marijuana”). Using item response theory, the developers also created a 21-item brief version of the measure.

Cannabis Use Indicators

Although we coded cannabis use indicators given our expectation that we could examine frequency vs. quantity measures of cannabis use, we only had a quantity measure in one published study. Thus, we decided to use a shifting unit of analysis approach instead, such that if there were multiple cannabis use indicators in a particular study, we would average the correlations of these distinct indicators with negative consequences so that every study only provided one effect size estimate (Cooper, 2015) so as not to overestimate the informational value of these associations.

Analysis Plan

Across all studies, we used cross-sectional correlation coefficients to estimate the strength of associations between cannabis use indicators and cannabis-related negative consequences. We used random-effects aggregate data meta-analytic techniques in Comprehensive Meta-Analysis V2 (Borenstein, Hedges, Higgins, & Rothstein, 2010) to account for the distribution of true effects. This method entails transforming each correlation using the Fisher’s z transformation, weighting these Fisher’s z scores based on sample size (i.e., precision), averaging and calculating confidence intervals around these Fisher’s z scores, and then converting these Fisher’s z scores back into correlations to produce weighted correlation coefficients and confidence intervals around these point estimates. We used the Q statistic to determine if there was statistically significant heterogeneity in effect sizes; we used I2 as a measure of how much variation across studies was due to heterogeneity rather than chance (Higgins, Thompson, Deeks, & Altman, 2003). To determine if gender/sex breakdown moderated the strength of the use-consequences association, we used meta-regression with the percentage of females/women in the sample as a predictor (Borenstein, Hedges, Higgins, & Rothstein, 2011). We conducted these analyses in the total sample of studies.

Results

All effect sizes are summarized in Table 2. See supplemental figures for funnel plots. Across all negative consequences measures (19 studies), we found a weighted correlation coefficient of .367 indicating that 13.5% of the variance in negative consequences was accounted for by cannabis use. Further, there was significant heterogeneity across samples with over 90% of the total variation across studies due to heterogeneity (i.e., not sampling variability). As a sensitivity analysis, we conducted a “one study removed” analysis such that the weighted correlation coefficient was re-estimated removing each study, and we found that no single study changed the overall effect size estimate by more than |.023|.

Table 2.

Weighted correlation coefficients of use-consequences associations overall and by consequences measures.

rw 95% CI k N Q P I2
All Studies .367 .292, .437 19 6704 182.95 <.001 90.16
MPS .272 .208, .333 6 1563 8.50 .131 41.14
RMPI .333 .201, .450 7 1355 32.67 <.001 81.64
CPQ .525 .440, .601 3 525 2.86 .239 30.08
MACQ .440 .213, .621 3 3261 81.99 <.001 97.56
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Note. MPS = Marijuana Problems Scale, RMPI = Rutgers Marijuana Problem Index, CPQ = Cannabis Problems Questionnaire, MACQ = Marijuana Consequences Questionnaire.

The MPS demonstrated the weakest use-consequences association (r = .272, 6 studies) which did not demonstrate significant heterogeneity across samples. The RMPI demonstrated a somewhat stronger association (r = .333, 7 studies) with significant heterogeneity across samples, amounting to over 80% of the total variation across studies due to heterogeneity. The MACQ demonstrated an even stronger association (r = .440, 3 studies) with significant heterogeneity across samples, amounting to over 97% of the total variation across studies due to heterogeneity. The CPQ demonstrated the strongest use-consequences association (r = .525, 3 studies) which did not demonstrate significant heterogeneity across samples.

Our meta-regression including all studies found that gender/sex breakdown of the sample was a significant predictor of use-consequences effect size such that samples with a higher percentage of females/women demonstrated a stronger use-consequences association, b = .38, p < .001. However, when we removed a single study from the meta-regression (Davis, Arterberry, Bonar, Bohnert, & Walton, 2018) that was a univariate outlier (i.e., only 12% females/women), gender/sex breakdown was no longer a significant predictor of effect size (b = −.00, p = .999).

To demonstrate one practical implication of these findings, we calculated the required sample sizes to achieve 80% (moderate) and 95% (strong) statistical power to detect the association between cannabis use and cannabis-related negative consequences (see Table 3). We used effect size estimates from the best point estimate (i.e., average) or a conservative estimate based on the lower-bound point of the confidence interval. We used the full sample of studies as well as effect size estimates for each measure. Required sample sizes ranged from 53 subjects to have 80% power to detect the average effect size observed across all studies to 311 subjects to have 95% power to detect the lower-bound effect size observed for the RMPI.

Table 3.

Required sample size to achieve 80 and 95% power to detect correlations of different effect sizes derived from the meta-analysis

Source of Effect Size Estimate Effect Size
(ρ)		 % Power
(1–β)		 Type I Error Rate
(α)		 Required Sample Size
(n*)
All Studies – Average .367 .80 .05 53
All Studies – Average .367 .95 .05 86
All Studies – Lower Bound .292 .80 .05 87
All Studies – Lower Bound .292 .95 .05 142
MPS – Average .272 .80 .05 101
MPS – Average .272 .95 .05 165
MPS – Lower Bound .208 .80 .05 176
MPS – Lower Bound .208 .95 .05 290
RMPI – Average .333 .80 .05 65
RMPI – Average .333 .95 .05 107
RMPI – Lower Bound .201 .80 .05 189
RMPI – Lower Bound .201 .95 .05 311
CPQ – Average .525 .80 .05 23
CPQ – Average .525 .95 .05 37
CPQ – Lower Bound .440 .80 .05 35
CPQ – Lower Bound .440 .95 .05 57
MACQ – Average .440 .80 .05 35
MACQ – Average .440 .95 .05 57
MACQ – Lower Bound .213 .80 .05 168
MACQ – Lower Bound .213 .95 .05 276
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Note. MPS = Marijuana Problems Scale, RMPI = Rutgers Marijuana Problem Index, CPQ = Cannabis Problems Questionnaire, MACQ = Marijuana Consequences Questionnaire. Power analyses were conducted using G

*

Power 3 (Faul, Erdfelder, Lang, & Buchner, 2007) for a correlation: point biserial model.

Discussion

The present study quantified the association between cannabis use and broad-spectrum measures of cannabis-related negative consequences. We focused our analyses on four consequences measures that have received a fair amount of research attention. Similar to a recent meta-analytic examination of the associations between alcohol use and alcohol-related negative consequences (Prince, Pearson, Bravo, & Montes, 2018), we found that any particular indicator of cannabis use fails to explain the majority of the variance in cannabis-related negative consequences. This finding highlights the importance of identifying other factors that may account for experiencing negative consequences beyond indicators of cannabis use levels (e.g., impulsivity, use of protective behavioral strategies; Wilson et al., 2018).

Interestingly, the most widely used measures of consequences, the Marijuana Problems Scale (MPS; Stephens et al., 2000) and the Rutgers Marijuana Problem Index (RMPI; White et al., 2005), also demonstrated the weakest associations with cannabis use. Thus, one could argue that these measures may be less sensitive to the effects of cannabis compared to some other measures like the Marijuana Consequences Questionnaire (MACQ; Simons et al., 2012) and Cannabis Problems Questionnaire (CPQ; Copeland et al., 2005), which demonstrated stronger associations between cannabis use and cannabis-related negative consequences. Relatedly, the range of required sample sizes to detect use-consequences associations varied widely across these measures, which is another important consideration for researchers. Although we provided the required sample sizes for detecting these specific associations with moderate (80%) and strong (95%) statistical power (see Table 3), researchers examining any outcome of cannabis use should consider the range of required sample sizes given that cannabis-related negative consequences likely exhibit some of the strongest associations with cannabis use. For example, meta-analyses demonstrate that the therapeutic effects of cannabinoids on various outcomes are more modest (e.g., pain, r = .094, converted from odds ratio; Whiting et al., 2015) as are the effects of cannabis on cognitive functioning (r = .123, converted from d, Scott et al., 2018) and unfavorable traffic events (r = .184, converted from odds ratio, Hostiuc et al., 2018). Thus, if detecting the association between cannabis use and cannabis-related negative consequences would require upwards of 311 participants to have strong statistical power, it is likely that detecting the associations between cannabis use and other outcomes will require substantially more participants to reach acceptable levels of statistical power.

When removing a single outlier, we did not observe a significant effect of gender/sex breakdown on the use-consequences association, which provides at least some preliminary support that these associations are largely similar for men and women. However, it remains important to examine gender/sex invariance of these measures of cannabis-related negative consequences to determine if men and women tend to interpret these consequences similarly and whether experiencing specific consequences are reflective of the same level of cannabis-related problems across men and women.

There are important caveats to consider when interpreting these results. For example, although the CPQ has exhibited the strongest use-consequences associations, it has been used (almost) exclusively in Australia by a single research team. Thus, these stronger associations and lower amount of heterogeneity in effect sizes may be due to greater population homogeneity in these studies. Further, for two out of three of these studies (Copeland et al., 2005; Martin et al., 2006), the researchers obtained a stratified sample of cannabis users (i.e., low, medium, and high amount of use), which may strengthen the associations between cannabis use and negative consequences. The single study that did not use a stratified sample with the CPQ did report a somewhat weaker correlation coefficient (.47 vs. .57/.59; Schmits et al., 2016).

Related to these concerns, other factors need to be considered when selecting a measure of cannabis-related negative consequences. First, these measures differ to some extent on content coverage so one measure may better tap into specific consequences of interest to a research team. For example, in the development of the CPQ (Copeland et al., 2005), items that were endorsed by fewer than 10% of the sample were dropped, leading to some higher severity consequences to be omitted from the final measure. In contrast, the MACQ includes 17 items that were endorsed by fewer than 10% of the development sample (i.e., college student marijuana users), thus capturing consequences with a wider range of severity (Simons et al., 2012). Other concerns include whether the measure has demonstrated measurement invariance for the populations of interest. To our knowledge, the brief version of the MACQ is the only one of these measures that have demonstrated measurement invariance across distinct countries (Bravo, Pearson, Pilatti, Mezquita, & Cross-cultural Addictions Study Team, 2018). Thus, additional psychometric testing of each of these measures among distinct populations of interest is still needed.

There are several limitations to the present meta-analytic integration. Although the present study is the first of its kind to systematically quantify the association between cannabis use and cannabis-related negative consequences with a high level of precision, we employed a sampling-based meta-analysis rather than a census-based meta-analysis. Stated differently, we did not attempt to obtain every available dataset to estimate these effect sizes, rather we relied on what is reported in published studies and what was volunteered to us by cannabis researchers. A census-based meta-analysis would require significantly more time and resources, though would be better-equipped to examine a wider range of possible moderators of these associations. We anticipated that we would be able to examine population of interest (i.e., adolescents, adults, treatment-seekers, community samples, etc.) and indicators of cannabis use (i.e., cannabis use frequency, cannabis use quantity, etc.) as possible moderators of the use-consequences association; however, we found very little variability on these putative moderators so we were unable to examine their effects. Only two samples used adolescents (Martin et al., 2006; Schmits, Mathys, & Quertemont, 2016), and only one study reported the association between marijuana use quantity and cannabis-related negative consequences (Buckner, Walukevich, & Henslee, 2018). Although there are serious challenges to assessing cannabis use quantity (Prince, Conner, & Pearson, 2018), it will be important to distinguish the relative contribution of frequency, quantity, and other features of cannabis use in determining the experience of negative consequences as improved measures of cannabis use are used by researchers (Cuttler & Spradlin, 2017; Mariani, Brooks, Haney, & Levin, 2011).

Despite these limitations, the present study offers the most precise estimates of the associations between cannabis use and cannabis-related negative consequences obtained to date. Although cannabis use is moderately associated with cannabis-related negative consequences, more variation in the experience of consequences is not explained by any single indicator of use, indicating that other factors need to be examined to explain why some individuals experience few consequences from their cannabis use while other experience more consequences. Use-consequences estimates were provided stratified by specific consequences measure and the required sample sizes to detect effect sizes observed across studies, which is one source of information that researchers can use to determine which measure will best meet their research needs.

Supplementary Material

Figures

Acknowledgments

Matthew R. Pearson is supported by a career development grant (K01AA023233) from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The funding agency had no role in the study design, data collection, analysis, or interpretation. The content is solely the responsibility of the author and does not necessarily represent the official views of the NIAAA or the National Institutes of Health.

A previous version of this meta-analysis was presented as a poster at the 2018 Annual Scientific Meeting of the Research Society on Marijuana.

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