Meta-analysis of maternal and neonatal outcomes of cannabis use in pregnancy current to March 2024
grid.530734.6Dartmouth Health Children’s, Department of Neonatology, Lebanon, NH USA
Marchand Institute for Minimally Invasive Surgery, Mesa, AZ USA
Abstract
Importance
Following expansive legalization of cannabis in many parts of the United States, cannabis use in pregnancy has increased several fold. There is a pressing need to understand the maternal and neonatal outcomes associated with this exposure.
Objective
To quantify the maternal and neonatal outcomes of mothers using cannabis during pregnancy.
Data sources
We searched five databases for all relevant observational studies, from each database’s inception until March 1st 2024.
Study selection
Two reviewers separately screened the studies in duplicate. Our initial search yielded 5184 studies, of which 51 (0.98%) were included in our qualitative synthesis.
Data extraction and synthesis
Our study adhered to PRISMA guidelines and independent extraction by two researchers was utilized. We used a 95% confidence interval and the random effects model, as there was significant heterogeneity between studies.
Results
The 51 included studies yielded a total population of 7,920,383 pregnant women. Cannabis consumption was associated with increased risks of low birth weight (RR = 1.69,95% CI = (1.34,2.14),P < 0.0001), small for gestational age (RR = 1.79,95% CI = (1.52, 2.1),P < 0.00001), major anomalies (RR = 1.81,95% CI = (1.48, 2.23),P < 0.00001), decreased head circumference (MD = -0.34,95% CI = (-0.57,-0.11),P = 0.004), birth weight (MD = -177.81,95% CI = (-224.72,-130.91),P < 0.00001), birth length (MD = -0.87,95% CI = (-1.15,-0.59),P < 0.00001), gestational age (MD = -0.21,95% CI = (-0.35,-0.08),P = 0.002), NICU admission (RR = 1.55,95% CI = (1.36,1.78),P < 0.00001), perinatal mortality (RR = 1.72,95% CI = (1.09,2.71),P = 0.02), and preterm delivery (RR = 1.39,95% CI = (1.23,1.56),P < 0.00001). Cannabis use was also associated with a decreased risk of gestational diabetes in pregnancy (RR = 0.64,95% CI = (0.55,0.75),P < 0.00001).
Conclusions
Inclusion of the latest published data continues to show worse maternal and neonatal outcomes for mothers using cannabis in pregnancy.
Introduction
The daily consumption of cannabis is increasing in the United States from 3% in 2002 to 7% in 2017 to 11% today [1]. Rates are even higher in reproductive age adults with teens at 22% and young adults at 19% [2]. The best estimates of consumption during pregnancy reach approximately 4.5% [3], making cannabis the most common illegal substance used during pregnancy [4]. Over half of women using cannabis prior to pregnancy choose to continue use during pregnancy, especially during the first trimester which includes fetal organogenesis [5, 6].
One possible cause for this increase may be the legalization of medical and recreational cannabis in many regions of the United States [7]. This has the potential to increase the perception among pregnant women that cannabis use may be safe or that it could represent a lower risk alternative to other medications during pregnancy [8, 9]. This comes despite most major obstetrical organizations continuing to encourage discontinuation in women who are or plan to become pregnant [10, 11].
Fetal effects of cannabis are theorized to occur secondary to delta-9-tetrahydrocannabinol (THC), which crosses the placenta and binds to receptors present on fetal cells [12]. THC binding to the cannabinoid receptors may result in disruption of cannabinoid signaling, which may then result in alterations of levels of dopamine, GABA, serotonin, adrenalin, and glutamate; potentially interfering with placental and/or fetal development [13, 14].
Despite recommendations, the harmful effects of cannabis during pregnancy are still controversial, and recent meta-analyses are not in complete agreement. A link for even the most commonly associated outcome, low birth weight [15, 16], has not been found in all meta-analyses [17]. Other outcomes, such as increased maternal hypertension [16, 18], increased rates of preterm delivery [18], increased neonatal invasive care unit (NICU) admission [15, 16], increased infant death rates [19], and maternal psychological disorders [20, 21], are inconsistently found to be associated with cannabis in different meta-analyses.
In an attempt to solve this controversy, we aimed to conduct the largest systematic review and meta-analysis performed thus far, including all possible observational studies in order to obtain the largest sample size.
Methods
Our systematic review and meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) [22].
Searching databases
We performed our search through all major databases, including Web of Science, PubMed, Cochrane Library, ClinicalTrials.Gov and SCOPUS. We used the following search strategy ("Pregnancy"OR"Pregnant Women"OR pregnant OR pregnancy OR Gestation) AND ("Cannabis"OR Ganjas OR Hemps OR Hashish OR Hashishs OR Bhang OR Bhangs OR cannabis OR Cannabis OR marihuana OR ganja OR Hemp OR weed OR hash OR"Mary Jane") for all relevant articles from each database’s inception until March 1 st 2024.
Inclusion and exclusion criteria
The inclusion criteria used were (1) population of pregnant females; (2) exposure of cannabis use of any frequency or method of reporting; (3) comparison was cannabis non-users; (4) outcomes were maternal and neonatal outcomes; and (5) study design included any double armed observational studies (such as prospective cohort studies, retrospective cohort studies, cross-sectional studies, or case–control studies.)
The exclusion criteria were non-pregnant women, single-arm studies, case reports, case series, studies published in languages rather than English, reviews, conference abstracts, editorial letters or notes, and animal studies.
Screening and study selection
The resulting records from searching databases were exported into EndNote X8.0.1 [23] which were then exported to Excel software after removing duplicates to start screening which was done independently by screening title and abstracts according to the inclusion criteria. Then, the full texts of the resulting records were screened also to determine the final included studies. Any conflict about the inclusion of any article was solved by consensus between the authors.
Data extraction
First, we extracted general demographics from the included studies. This included the study name, country, design, study dates, the number of participants in each group, the method of determining cannabis use, maternal age in each group, alcohol use in each group, number of smokers in each group, and number of women older than 35 years. Next, we extracted the maternal and neonatal outcomes in each group, which included the maternal outcomes (gestational diabetes mellitus, preeclampsia, cesarean section, and gestational hypertension) and the neonatal outcomes (low birth weight (defined as less than 2500 g), small for gestational age (defined as less than the 10 th percentile), preterm delivery before 37 weeks, NICU admission, birth weight in grams, the perinatal mortality rate (defined as the percentage of fetal deaths in pregnancies of seven or more months plus number of deaths of live-born children in the first 6 days following birth), gestational age, birth length in centimeters, head circumference in centimeters, major and minor congenital anomalies, major anomalies, and gender.)
Quality assessment
The quality assessment was performed using the Newcastle Ottawa Scale. This is a star-based method composed of three main items: selection of each group, group comparability, and exposure ascertainment [24]. Each study was assessed and a total score was given to determine the final judgment of whether the study was of poor (0–3 stars), fair (4–6 stars), or good quality (7–9 stars) [24].
Statistical analysis
We performed this analysis with Review Manager Software using a risk ratio (RR) with a 95% confidence interval (CI) for the qualitative variables and mean difference (MD) with a 95% CI for the quantitative variables. The heterogeneity between studies in each outcome was assessed using the I2 statistical test and Cochrane Q test. The outcomes were considered heterogeneous when the I2 was > 50% and the P value was < 0.1 [25]. The random effects model was chosen due to the presented heterogeneity between the included studies. We tried to solve the presented heterogeneity by the “leave-one-out"method, to exclude the study responsible for causing heterogeneity [25]. Results were considered significant when the determined P values were below 0.05. Given the potential influence of confounding variables like smoking, we relied on the random-effects model to incorporate between-study differences, including variations in adjustment for confounders. While smoking status data were extracted where available (Table 2), we did not perform subgroup analyses based on adjustment for smoking due to inconsistent reporting across studies and the lack of uniform covariate adjustment data, which would limit the reliability of such stratification.
Results
Literature search results
The literature search resulted in 5184 studies after removing duplicates, all of which entered the title and abstract screening phase. From there, only 136 were eligible for the next phase, which was full-text screening. This ultimately resulted in 51 studies being eligible to be included in the meta-analysis. Figure 1 shows the PRISMA flow diagram explaining the full details of screening results and the study selection process.
General demographic data of the included results
We included 51 observational studies with a total population of 7,920,383 women 111,939 were cannabis users and 7,808,444 were non-users [21, 26–75]. Twenty-seven studies were retrospective cohort studies [21, 27, 29–31, 37, 42, 45, 48, 49, 51, 52, 55–57, 60, 62–65, 67–71, 73, 75], 22 studies were prospective cohort studies [26, 28, 32–36, 38–41, 43, 44, 46, 47, 50, 54, 58, 61, 66, 72, 74], one study was cross-sectional [59], and one was case–control study [53]. Tables 1 and 2 show the full details of the general demographic data of the included studies.Author Country Study Design Study Dates Marijuana user group (number) Non-Marijuana users group (number) Method of determining Marijuana Use Avalos et al., 2023 [37] United States retrospective cohort Between January 1, 2011, and July 31, 2020 22,624 342,300 Self-reported and urine toxicology screening Dodge et al., 2023 [29] United States retrospective cohort Between 2016 and 2020 109 171 Self-reported urine toxicology screening or cord toxicology screening Dunn et al., 2023 [31] Australia retrospective cohort Between January 1, 2019 and December 31, 2019 50 3054 Self-reported Prewitt et al., 2023 [55] United States retrospective cohort Between 2007 and 2011 9,144 2,371,302 Self-reported Jones et al., 2022 [68] Canada retrospective cohort Between January 1, 2017 and June 20, 2019 483 1057 Meconium toxicology screening Koto et al., 2022 [49] Canada retrospective cohort Between January 1, 2004 and June 30, 2004 3144 103 138 Self-reported Metz et al., 2022 [67] United States retrospective cohort Not reported 47 980 Urine toxicology Brik et al., 2022 [70] Spain retrospective cohort Between January 2013 and December 2020 60 198 Urine toxicology Bruno et al., 2022 [72] United States prospective cohort Between October 2010 and September 2013 136 9027 Self-reported and urine toxicology screening Luke et al., 2022 [52] Canada retrospective cohort Between April 1, 2012 and March 31, 2019 20410 1031360 Self-reported Klebanoff et al., 2020 [46] United States prospective cohort Between 2010 and 2016 117 244 Urine toxicology Gabrhelik et al., 2021 [36] Norway prospective cohort Between 1999 and 2008 272 10101 Self-reported Bandoli et al., 2021 [48] United States retrospective cohort Between 2011 and 2017 29112 3037957 Diagnostic code Sasso et al., 2021 [57] United States retrospective cohort Between 2014 and 2018 151 192 Self-reported Straub et al., 2021 [69] United States retrospective cohort Between March 11, 2011 and March 31, 2016 1268 4075 Urine toxicology Bailey et al., 2020 [71] United States retrospective cohort Not reported 531 531 Urine toxicology Grzeskowiak et al., 2020 [40] New Zealand, United Kingdom, Australia and Ireland prospective cohort Between November 2004 and February 2011 217 5393 Self-reported Kharbanda et al., 2020 [45] United States retrospective cohort Between July 1, 2015, and December 1, 2017 283 3152 Urine toxicology Klebanoff et al., 2021 [47] United States prospective cohort Between 2010 and 2015 119 244 Self-reported and urine toxicology screening Nawa et al., 2020 [53] United States case-control Between 1998 and 2018 328 5933 Self-reported Corsi et al., 2019 [27] Canada retrospective cohort Between April 1, 2012, and December 31, 2017 9427 652190 Self-reported Luke et al., 2019 [51] Canada retrospective cohort Between April 1, 2008 and March 31, 2016 5801 237339 Self-reported Rodriguez et al., 2019 [56] United States retrospective cohort Between September 2011 and May 2017 211 995 Self-reported and urine toxicology screening Ko et al., 2018 [63] United States retrospective cohort Between 2012 and 2015 463 8549 Self-reported Coleman-Cowger et al., 2018 [74] United States prospective cohort Between January and December 2017 60 354 Self-reported and urine toxicology screening Serino et al., 2018 [58] United States prospective cohort Between 2004 and 2010 38 49 Self-reported Dotters-Katz et al., 2017 [30] United States retrospective cohort Between 1997 and 2004 135 1732 Self-reported and urine toxicology screening Metz et al., 2017 [21] United States retrospective cohort Between March 2006 and September 2008 48 1562 Self-reported and THC-COOH (11-Nor-9-carboxy-THC) detection in umbilical cord homogenate Leemaqz et al., 2016 [50] New Zealand, United Kingdom, Australia and Ireland prospective cohort Between November 2004 and February 2011 315 95 Self-reported Mark et al., 2016 [64] United States retrospective cohort Between July 1, 2009 and June 30, 2010 116 280 Self-reported and urine toxicology screening Warshak et al., 2015 [62] United States retrospective cohort Between January 2008 and January 2011 361 6107 Self-reported and urine toxicology screening Conner et al., 2016 [15] United States retrospective cohort Between 2004 and 2008 680 7458 Self-reported and urine toxicology screening Alhusen et al., 2013 [26] United States prospective cohort Between February 2009 and February 2010 64 102 Self-reported Hayatbakhsh et al., 2012 [42] Australia retrospective cohort Between 2000 and 2006 647 24227 Self-reported Gray et al., 2010 [39] United States prospective cohort Not reported 38 48 Self-reported, meconium toxicology screening and oral fluid toxicology screening El Marroun et al., 2010 [33] Netherlands prospective cohort Between April 2002 and January 2006 23 85 Self-reported El Marroun et al., 2009 [32] Netherlands prospective cohort Between April 2002 and January 2006 214 5785 Self-reported Burns et al., 2006 [73] Australia retrospective cohort Between 1998 and 2002 2172 412 731 Diagnostic code Barros et al., 2006 [59] Brazil cross-sectional Not reported 26 534 Maternal hair and neonatal meconium Hurd et al., 2005 [44] United States prospective cohort Between January 2000 and December 2002 44 95 Self-reported, urine toxicology screening and neonatal meconium screening Fergusson et al., 2002 [34] England prospective cohort Between April 1, 1991 and December 31, 1992 250 11890 Self-reported Sherwood et al., 1999 [60] United Kingdom retrospective cohort Between November 1994 and May 1995 75 213 Urine toxicology Parker et al., 1999 [54] United States prospective cohort Between July, 1984 through June, 1987 202 1024 Urine toxicology Day et al., 1991 [28] United States prospective cohort Not reported 174 210 Self-reported Witter et al., 1990 [65] United States retrospective cohort Between 1983 and 1985 417 7933 Self-reported Zuckerman et al., 1989 [66] United States prospective cohort Between July 1984 and June 1987 202 895 Self-reported and urine toxicology screening Hayes et al., 1988 [43] Jamaica prospective cohort Not reported 30 26 Self-reported Hatch et al., 1987 [41] United States prospective cohort Between May 12, 1980, and March 12, 1982 367 3490 Self-reported Tennes et al., 1985 [61] United States prospective cohort Between November 1981 and November 1982 258 498 Self-reported Fried et al., 1984 [35] Canada prospective cohort Not reported 84 499 Self-reported Gibson et al., 1983 [38] Australia prospective cohort Not reported 392 6909 Self-reported Study name Maternal age Alcohol abuse Smoking Maternal age ≥ 35 MJ users MJ non-users MJ users MJ non-users MJ users MJ non-users MJ users MJ non-users Author mean SD total mean SD total event total event total event total event total event total event total Avalos et al., 2023 [37] - - - - - - 4335 22624 28524 342300 5566 22624 12255 342300 - - - - Dodge et al., 2023 [29] 22.7 3.6 109 25.4 4.8 171 - - - - - - - - - - - - Dunn et al., 2023 [31] - - - - - - 8 50 103 3054 33 50 222 3054 3 50 735 3054 Prewitt et al., 2023 [55] - - - - - - 412 9144 2075 2371302 2382 9144 71206 2371302 636 9144 411808 2371302 Jones et al., 2022 [68] 26.5 5.1 483 27.6 5.7 1057 12 483 23 1057 214 483 398 1057 - - - - Koto et al., 2022 [49] 25.7 5.39 3144 29.8 5.5 103138 226 3144 206 103138 1886 3144 16502 103138 - - - - Metz et al., 2022 [67] - - - - - - - - - - 24 47 137 980 1 47 137 980 Brik et al., 2022 [70] 28.5 5.21 60 30.7 4.2 198 0 60 0 198 0 60 0 198 - - - - Bruno et al., 2022 [72] 22.9 4.4 136 26.5 5.82 9027 - - - - 71 136 550 9027 - - - - Luke et al., 2022 [52] - - - - - - 2768 20410 17417 1031360 11232 20410 80379 1031360 1688 20410 231860 1031360 Klebanoff et al., 2020 [46] 25.8 5.1 116 26.7 5.4 243 34 117 45 244 79 117 78 244 - - - - Gabrhelik et al., 2021 [36] - - - - - - 212 265 6921 9918 108 204 1516 7831 29 271 1356 10045 Bandoli et al., 2021 [48] - - - - - - 1499 29112 4732 3037957 10721 29112 82645 3037957 - - - - Sasso et al., 2021 [57] 27.62 6.19 151 30.2 7.12 192 64 151 10 192 - - - - Straub et al., 2021 [69] 25.85 5.28 1268 27.04 5.72 4075 356 1268 1089 4075 1025 1268 2126 4075 - - - - Bailey et al., 2020 [71] 24.4 5.3 531 24.4 5.1 531 11 531 11 531 353 531 353 531 - - - - Grzeskowiak et al., 2020 [40] 23.8 5.7 217 28.86 5.42 5393 30 217 540 5393 111 217 486 5393 - - - - Kharbanda et al., 2020 [45] 25.4 5.3 283 29.9 5 3152 - - - - 118 283 186 3152 - - - - Klebanoff et al., 2021 [47] - - - - - - 30 119 44 244 49 119 77 244 - - - - Nawa et al., 2020 [53] - - - - - - - - - - - - - - - - - - Corsi et al., 2019 [27] - - - - - - 1787 9427 13185 652190 5554 9427 48260 652190 435 9427 110208 652190 Luke et al., 2019 [51] - - - - - - 700 5801 2168 237339 4038 5801 39370 237 339 451 5801 55517 237339 Rodriguez et al., 2019 [56] 18.8 1.5 211 18.8 1.8 995 0 211 4 995 45 211 47 995 - - - - Ko et al., 2018 [63] - - - - - - 79 463 667 8549 199 463 1060 8549 45 463 1334 8549 Coleman-Cowger et al., 2018 [74] 27.3 4.9 60 28.2 5.4 354 17 60 61 354 0 60 0 354 - - - - Serino et al., 2018 [58] 23.9 4.9 38 25.9 5.6 49 - - - - - - - - - - - - Dotters-Katz et al., 2017 [30] 26.33 6.74 135 25.67 6.68 1732 - - - - 104 135 431 1732 - - - - Metz et al., 2017 [21] - - - - - - - - - - 28 48 183 1562 2 48 217 1562 Leemaqz et al., 2016 [50] - - - - - - - - - - - - - - - - - - Mark et al., 2016 [64] 22.9 5 116 23 5.9 280 8 116 6 280 50 116 53 280 - - - - Warshak et al., 2015 [62] 24 5.2 361 25.3 5.9 6107 - - - - 208 361 1214 6107 - - - - Conner et al., 2016 [15] 24 5.3 680 25 6.1 7458 52 680 60 7458 395 680 1066 7458 - - - - Alhusen et al., 2013 [26] - - - - - - - - - - - - - - - - - - Hayatbakhsh et al., 2012 [42] - - - - - - - - - - - - - - - - - - Gray et al., 2010 [39] 24.4 5.1 38 24.3 5.2 48 - - - - 33 38 22 48 - - - - El Marroun et al., 2010 [33] 29.35 3.86 23 31.8 3.7 85 13 23 40 85 19 23 0 85 - - - - El Marroun et al., 2009 [32] 26.76 5.76 214 29.99 5.16 5785 41 214 902 5785 116 214 77 5785 - - - - Burns et al., 2006 [73] - - - - - - 88 2172 0 412731 1679 2172 67487 412731 - - - - Barros et al., 2006 [59] 16.5 1.5 26 16.9 1.5 534 - - - - - - - - - - - - Hurd et al., 2005 [44] 22.4 0.6 44 23.4 0.7 95 24 44 22 95 17 44 17 95 - - - - Fergusson et al., 2002 [34] 25.5 250 27.8 11890 86.5 250 2306 11890 172 250 2794 11890 - - - - Sherwood et al., 1999 [60] - - - - - - - - - - - - - - - - - - Parker et al., 1999 [54] - - - - - - - - - - 162 202 307 1024 - - - - Day et al., 1991 [28] 22.91 - 174 22.9 - 210 130 174 109 210 122 174 86 210 - - - - Witter et al., 1990 [65] - - - - - - 222 417 2499 7933 327 417 2975 7933 - - - - Zuckerman et al., 1989 [66] 24 4.9 202 24.1 5.7 895 - - - - - - - - - - - - Hayes et al., 1988 [43] - - - - - - - - - - - - - - - - - - Hatch et al., 1987 [41] - - - - - - 303 367 2364 3490 211 367 978 3490 3 367 201 3490 Tennes et al., 1985 [61] 21.8 - 258 23 - 498 181 258 149 498 103 258 149 498 - - - - Fried et al., 1984 [35] 26.1 - 84 29.3 - 499 5 84 15 499 15 84 25 499 - - - - Gibson et al., 1983 [38] - - - - - - - - - - - - - - - - - -
Results of the quality assessment
According to the Newcastle Ottawa scale, the majority of the included cohorts were judged to be of fair quality [76]. They showed a low risk of bias in the outcome assessment and comparability domains. However, in some studies the method of determining exposure was based on self-reports, the analysis was not controlled for confounders, and several studies did not specifically report the outcomes of interest. Notably, Hayes et al., Hayatbakhsh et al., Alhusen et al., and Leemaqz et al. were judged to be of poor quality because of these factors [26, 42, 43, 50]. Likewise, Zuckerman et al., Sherwood et al., and Dodge et al. were also judged to be of poor quality, despite using more scientific methods to determine cannabis exposure [29, 60, 66]. Witter et al., Hurd et al., Burns et al., Conner et al., Mark et al., Metz et al., Jones et al., and Avalos et al. were judged to be of good quality since they showed a low risk of bias in selection, comparability, and outcome assessment domains [21, 37, 44, 64, 65, 68, 73, 75]. Nawa et al. is the only included case–control study and it was judged to be of poor quality since their analysis was not controlled for confounders. Moreover, their exposure determination was also based on self-reporting [53].
Barros et al. was the only included cross-sectional study. It was judged as good quality since there was no risk of bias in the three domains of the Newcastle Ottawa scale [59]. Table 3 shows the full details of the quality assessment results. Selection Comparability Outcome Quality
JudgmentAuthor year Representativeness of the exposed cohort Selection of the non exposed cohort Ascertainment of exposure Demonstration that outcome of interest was not present at start of study Comparability of cohorts on the basis of the design or analysis Assessment of outcome Was follow-up long enough for outcomes to occur Adequacy of follow up of cohorts Avalos et al. 2023 * * * ** * * * good Dodge et al. 2023 * * * * * * poor Dunn et al. 2023 * * ** * * * fair Prewitt et al. 2023 * * ** * * * fair Jones et al. 2022 * * * * ** * * * good Koto et al. 2022 * * ** * * * fair Metz et al. 2022 * * * * * * * fair Brik et al. 2022 * * * ** * * * good Bruno et al. 2022 * * * * * * * * good Luke et al. 2022 * * ** * * * fair Klebanoff et al. 2020 * * * ** * * * good Gabrhelik et al. 2021 * * ** * * * fair Bandoli et al. 2021 * * * ** * * * good Sasso et al. 2021 * * * * * * fair Straub et al. 2021 * * ** * * * fair Bailey et al. 2020 * * ** * * * fair Grzeskowiak et al. 2020 * * ** * * * fair Kharbanda et al. 2020 * * * * * * * fair Klebanoff et al. 2021 * * * ** * * * good Corsi et al. 2019 * * ** * * * fair Luke et al. 2019 * * ** * * * fair Rodriguez et al. 2019 * * * * ** * * * good Ko et al. 2018 * * ** * * * fair Coleman-Cowger et al. 2018 * * * * ** * * * good Serino et al. 2018 * * ** * * * fair Dotters-Katz et al. 2017 * * * * * * * fair Metz et al. 2017 * * * * * * * * good Leemaqz et al. 2016 * * * * * poor Mark et al. 2016 * * * ** * * * good Warshak et al. 2015 * * * * * * * fair Conner et al. 2016 * * * ** * * * good Alhusen et al. 2013 * * * * * poor Hayatbakhsh et al. 2012 * * * * * poor Gray et al. 2010 * * * * * * * fair El Marroun et al. 2010 * * ** * * * fair El Marroun et al. 2009 * * ** * * * fair Burns et al. 2006 * * * ** * * * good Hurd et al. 2005 * * * ** * * * good Fergusson et al. 2002 * * ** * * * fair Sherwood et al. 1999 * * * * * * poor Parker et al. 1999 * * * * * * * fair Day et al. 1991 * * ** * * * fair Witter et al. 1990 * * * * ** * * * good Zuckerman et al. 1989 * * * * * * poor Hayes et al. 1988 * * * * * poor Hatch et al. 1987 * * ** * * * fair Tennes et al. 1985 * * ** * * * fair Fried et al. 1984 * * ** * * * fair Gibson et al. 1983 * * ** * * * fair
Maternal outcomes
We compared the following maternal outcomes between both groups: cesarean section, gestational diabetes, gestational hypertension, and preeclampsia; however, all these outcomes showed no significant differences between the groups except for gestational diabetes which was significantly decreased in cannabis users compared to non-users (RR = 0.64, 95% CI = (0.55, 0.75), P < 0.00001). However, this outcome was heterogeneous (like most other maternal outcomes) and we could not solve heterogeneity by leave-one-out method (P < 0.00001, I2 = 91%), Fig. 2 shows the analysis of maternal outcomes.
Neonatal outcomes
Regarding neonatal weight outcomes including the birth weight, the incidence of low birth weight, and the diagnosis of small for gestational age, all of these showed results that favored the non-user group as there was decreased birth weights in cannabis users (MD = −177.81, 95% CI = (−224.72, −130.91), P < 0.00001), an increased number of low birth weight infants (RR = 1.69, 95% CI = (1.34, 2.14), P < 0.0001), and an increased number of infants diagnosed as small for gestational age (RR = 1.79, 95% CI = (1.52, 2.1), P < 0.00001). However, all these outcomes were again heterogeneous and we could not solve the heterogeneity using any method. Figure 3 shows the full details.
Regarding other neonatal characteristics, head circumference, gestational age, and birth length were also significantly decreased in cannabis users compared to non-users (MD = −0.34, 95% CI = (−0.57, −0.11), P = 0.004), (MD = −0.21, 95% CI = (−0.35, −0.08), P = 0.002), and (MD = −0.87, 95% CI = (−1.15, −0.59), P < 0.00001), respectively. Again, all these outcomes were heterogeneous and we could not solve the heterogeneity. Figure 4 shows the full details.
Regarding anomalies, the combination of major and minor anomalies showed no significant difference between the two groups, but was also heterogeneous. In order to solve the heterogeneity, we excluded Zuckerman 1989 et al. [66] from the analysis, however the outcome still did not reach statistical significance (RR = 1.08, 95% CI = (0.96, 1.22), P = 0.19) and (P = 0.49, I2 = 0%), as seen in Fig. 5.
There was an increased risk of only major anomalies in cannabis users compared to non-users; however, the outcome was heterogeneous. This was solved by excluding Bandoli 2021 et al. [48] and the results remained significant (RR = 1.81, 95% CI = (1.48, 2.23), P < 0.00001) and (P = 0.11, I2 = 55%), as seen in Fig. 5.
Also, complications like NICU admission, perinatal mortality, and preterm delivery were significantly decreased among cannabis non-users compared to users (RR = 1.55, 95% CI = (1.36, 1.78), P < 0.00001), (RR = 1.72, 95% CI = (1.09, 2.71), P = 0.02), and (RR = 1.39, 95% CI = (1.23, 1.56), P < 0.00001), respectively. However, all these outcomes were heterogeneous and none could be solved by recognized methods, as seen in Fig. 6.
As expected, cannabis use had no effect on infant gender (RR = 1, 95, 95% CI = (0.99, 1.01), P = 0.89), as seen in Fig. 7.
Discussion
Our systematic review included 7,920,383 women and found that cannabis consumption was associated with increased risks of low birth weight, small for gestational age, major anomalies, decreased head circumference, decreased neonatal weight, decreased birth length, decreased gestational age, NICU admission, perinatal mortality, and preterm delivery; however, it was associated with decreased risk of gestational diabetes. This constitutes the largest meta-analysis on this subject to date, and hopefully will add strong evidence to the argument that cannabis use in pregnancy is associated with poor neonatal outcomes. As stated below, however, many questions still remain unanswered as far as if these findings apply to all methods of ingesting cannabis, and if these results remain relevant when controlling for tobacco smoking, environmental exposures, and alcohol use in pregnancy. As for the unexpected finding of an association between cannabis use and reduced gestational diabetes risk, our researchers speculate that this may be the result of the common practice of using cannabis to alleviate chronic joint pain from morbid obesity. Many of these individuals likely have already been diagnosed with Type II diabetes prior to pregnancy, thus making it impossible for them to receive a diagnosis of gestational diabetes, and giving the misleading impression that cannabis may protect against the same. This hypothesis requires further investigation due to limited data on pregestational diabetes prevalence.
We acknowledge the significant heterogeneity observed across most outcomes, which is not unexpected given the inclusion of 51 studies spanning diverse populations, methodologies, and exposure definitions. Potential sources of this heterogeneity include variations in the frequency, quantity, and recency of cannabis use, which our binary classification (users vs. non-users) may not fully capture. For instance, heavy or frequent use might amplify adverse outcomes compared to occasional use, while recency, such as use concentrated in the first trimester versus throughout pregnancy, could influence fetal development differently due to critical windows of organogenesis. Additionally, the method of assessing cannabis exposure varied across studies, with some relying on self-reports and others using biological validation (e.g., urine toxicology or meconium screening), as detailed in Table 1. These differences could contribute to heterogeneity by affecting the accuracy and consistency of exposure classification. For example, self-reports may underestimate use due to social desirability bias, whereas biological measures might detect use that participants did not disclose.
Many systematic reviews and meta-analyses have supported the effect of cannabis consumption in increasing risks of neonatal adverse effects, especially preterm delivery, NICU admission, low birth weight, and smaller head circumference, as was seen in our findings [16, 18, 77].
Our meta-analysis, encompassing 51 studies and 7,920,383 women, aligns with and extends findings from prior meta-analyses by Conner 2016 et al. [15], Gunn 2016 et al. [16], Lo 2023 et al. [17], and Marchand 2022 et al. [18]. Like Gunn 2016 and Marchand 2022, we found significant associations between prenatal cannabis use and increased risks of low birth weight (RR = 1.69, 95% CI = 1.34–2.14 vs. Gunn’s OR = 1.77 and Marchand’s OR = 1.87), preterm delivery (RR = 1.39, 95% CI = 1.23–1.56 vs. Gunn’s OR = 1.43, Lo’s elevated risk, and Marchand’s OR = 1.42), SGA (RR = 1.79, 95% CI = 1.52–2.1, consistent with Lo and Marchand), and NICU admission (RR = 1.55, 95% CI = 1.36–1.78, echoing Gunn’s OR = 2.02 and Marchand’s findings). However, our results diverge from Conner 2016, which reported no independent cannabis effect after adjusting for tobacco (OR = 1.43 for low birth weight reduced post-adjustment), suggesting our broader, unadjusted associations may partly reflect confounding. Unlike Gunn’s unique finding of maternal anemia (OR = 1.36), we found no significant maternal outcomes except a decreased gestational diabetes risk (RR = 0.64, 95% CI = 0.55–0.75), potentially a spurious signal. Compared to Lo 2023, which found no clear cannabis-only mortality link, our increased perinatal mortality (RR = 1.72, 95% CI = 1.09–2.71) suggests newer studies may amplify this signal, though with borderline significance. Our inclusion of 35 additional studies beyond Marchand 2022’s 16 reinforces these associations, adding novel outcomes like major anomalies (RR = 1.81, 95% CI = 1.48–2.23) and decreased head circumference (MD = −0.34, 95% CI = −0.57 to −0.11), not emphasized in earlier works. This expanded scope, current to March 2024, suggests a consistent pattern of neonatal risk, though confounding remains a challenge, aligning with all four prior reviews’ cautions.
Associated smoking with cannabis consumption could be an important confounding factor that can be responsible for this association as found in Conner 2016 et al. who found that there was no significant difference between cannabis users and non-users regarding neonatal outcomes after controlling confounders like tobacco smoking [15] which was supported also by English 1997 et al. [78] who included only studies which adjusted the tobacco use. This effect results from the larger percentage of cannabis smokers also smoking cigarettes during pregnancy than non-users [79]. While our large sample size (over 7 million women) suggests robustness, uncontrolled tobacco use remains a potential confounder, as noted in prior studies [15, 78]. Further evidence for this has been presented in the 2017 cross-sectional analysis by Haight et al. [80], which found high frequency cannabis use was related to lower birth weights regardless of cigarette use. To further explore this, we reviewed the 51 included studies and found that approximately 20 (39%) explicitly reported adjusting for smoking status in their statistical analyses (e.g., Conner et al., 2015; Metz et al., 2017; Avalos et al., 2023), as noted in their respective methodologies or results sections [15, 21, 37]. The remaining studies either did not adjust for smoking or did not clearly report such adjustments, often due to reliance on self-reported data or lack of detailed covariate control. This variability likely contributes to the observed heterogeneity across outcomes. While we considered stratifying our analysis by adjustment status, the inconsistent reporting of adjustment methods and the lack of standardized data on smoking adjustment across studies precluded a meaningful meta-analytic separation. Instead, we relied on the random-effects model to account for this variability, ensuring our pooled estimates reflect the real-world diversity of study designs and confounder handling.
Another potential source of heterogeneity could be the timing of cannabis exposure during pregnancy, which our study did not stratify due to limited data granularity in the included studies. Early exposure during the first trimester, a period of rapid fetal organogenesis, might pose different risks compared to use later in gestation, potentially affecting outcomes like congenital anomalies or preterm delivery differently. While some studies in our review (e.g., Dodge et al., 2023; El Marroun et al., 2009) explored timing-specific effects, the majority provided only aggregate exposure data, precluding a meta-analytic stratification by trimester [29, 32]. This limitation is inherent to the retrospective nature of our source material, but it highlights an important avenue for future research.
Almost all recent systematic reviews have agreed with cannabis increasing the risk of poor neonatal outcomes, especially weight outcomes [18, 77, 81], preterm delivery [18, 77, 81], and NICU admission [18, 81]. However, secondary to the large number of included studies, this analysis was able to include many other neonatal outcomes that have not been thoroughly addressed in previous analyses. These outcomes included fetal anomalies, neonatal mortality, birth length, head circumference, and decreased gestational age. This is considered a strength of our review. Moreover, we found maternal cannabis use was associated with an increased risk of infant death during the first year of life, with an adjusted risk ratio of 1.72 compared to non-users. This findings is consistent with a 2023 retrospective study, Bandoli et al. [82], that specifically analyzed this outcome and further found that the specifically increased causes of mortality were sudden unexpected death and death attributable to perinatal conditions.
Many recent studies have also supported the association of cannabis consumption with anomalies affecting many systems like gastrointestinal, neuronal, nephrological, cardiovascular and musculoskeletal, although there is no consensus as to what the mechanism of this damage truly is [83–86]. Some authors have hypothesized that this may be secondary to cannabis’s role in the methylation of fetal DNA, which may increase the risk of birth defects and other anomalies [87]. Others have postulated that it could be cannabis’s role in glucose and insulin regulation that affects fetal growth and may explain its teratogenicity [32]. As the endocannabinoid system is important in the early stages of cell survival and formation of the neuronal system [88], other authors have suspected that disruption of this system may be the cause of birth defects and other adverse neonatal outcomes associated with cannabis [89]. Lastly, other authors have speculated that cannabis damages placental endocrine function by enhancing ESR1 and CYP19 AI transcription, which may increase estradiol production, causing disruption [90].
Besides neonatal outcomes, the association between cannabis use and maternal complications is also controversial. Many studies have found pregnant cannabis users were found to have higher risks of less studied outcomes not included in this study, including alcohol consumption, anemia, depression, and anxiety [16, 50, 91]. However, when focusing on the most commonly studied outcomes, such as placental abruption, antepartum or postpartum bleeding, and gestational hypertension, most [50, 62, 91], but not all [92] studies showed no significant association with cannabis use. Lastly, we found an unexpected result compared to the previous literature on the decreased risk of gestational diabetes mellitus in cannabis users compared to non-users. Most previous studies have found no association [50, 62, 91], and one study, Porr et al. [93], actually found that cannabis use was associated with increased HbA1c in diabetes mellitus. Another study, Ayonrinde et al. [94], also found that cannabis use increased the caloric intake, weight, and percentage of fatty liver during pregnancy, which in turn increased insulin resistance. Pan et al. [95] in 2023 found that preconceptional cannabis use was associated with increased gestational diabetes risk in pregnant women who never used tobacco; however, among those on current or previous using tobacco, no significant results were observed. Consistent with these studies and as stated above, we believe the protective association we have seen against gestational diabetes is most likely not a true signal, and is secondary to the likely higher percentage of pregestational diabetics in the cannabis use group, making it impossible for these women to receive a diagnosis of gestational diabetes during pregnancy. Unfortunately we do not have the specific data as to the percentages of pre-existing diabetics in both groups that would be necessary to test this hypothesis.
Strengths and limitations
Our primary strength lies in the inclusion of 7,920,383 women, making this the largest meta-analysis to date on cannabis use in pregnancy, and our examination of a broad range of maternal and neonatal outcomes, many of which were underexplored in prior reviews. However, we recognize several limitations, notably the significant heterogeneity across studies, which is inevitable given the scale and diversity of our 51 included studies. Key sources of this heterogeneity include concomitant tobacco smoking, variations in exposure timing, cannabis consumption methods (e.g., smoking vs. ingestion), and concurrent use of other substances like alcohol. Specifically, while Table 2 provides raw numbers of smokers in each study, only about 39% of studies (20/51) explicitly adjusted for smoking in their analyses, as reviewed from their methodologies (e.g., [15, 21, 37]). This inconsistency in confounder adjustment, particularly for smoking—a known risk factor for adverse neonatal outcomes—may influence our pooled estimates. Additionally, our binary classification of cannabis use (users vs. non-users) may obscure nuances in frequency, quantity, and recency of use, while varied exposure ascertainment methods (Table 1) add further complexity. However, re-analyzing the data to separate studies by smoking adjustment status was not feasible due to incomplete or unclear reporting of adjustment methods in many studies, which would compromise the validity of such subgroup analyses. Our use of the random-effects model mitigates this by accounting for such variability, and reliance on observational data inherently increases bias risk, including from self-reported cannabis use. We recommend future studies standardize confounder reporting, particularly for smoking, to enable more precise analyses, but believe our current approach maximizes inclusivity and generalizability without necessitating additional stratification.
Conclusion
Cannabis use is associated with adverse neonatal outcomes including low birth weight, small for gestational age, major anomalies, decreased head circumference, decreased neonatal weight, decreased birth length, decreased gestational age at time of delivery, higher rates of NICU admissions, higher rates of perinatal mortality, and a higher rate of preterm delivery. We also found that cannabis use was associated with decreased risk of gestational diabetes, although we are cautious about overinterpreting this finding and believe it may be related to cannabis users having a higher rate of pregestational diabetes. We believe that the size of this study can help bring consensus to the debate of cannabis’s associate with adverse neonatal outcomes, and would very much like to see more prospective observational studies, especially those classifying patients according to the concomitant use of tobacco products and by the different different delivery methods of cannabis products. While variability in smoking adjustment across studies limits our ability to isolate its confounding effects fully, the large sample size and consistent associations strengthen the clinical implications of these findings. Future research with uniform adjustment for confounders like smoking could refine these estimates, but our current results robustly support counseling against cannabis use in pregnancy.
Acknowledgements
Acknowledgements: The Marchand Institute for Minimally Invasive Surgery would like to acknowledge the efforts of all the students, researchers, residents, and fellows at the institute who put their time and effort into these projects without compensation, only for the betterment of women’s health. We firmly assure them that the future of medicine belongs to them.
Commitment to diversity
The Marchand Institute remains committed to diversity and tolerance in its research and actively maintains a workplace free of racism and sexism. Greater than half of the authors for this study are female, and many represent diverse backgrounds and under-represented ethnic groups.
Funding
No authors received any payment for this work; all work was volunteer.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
This Manuscript has been reviewed by the institutional IRB board at Marchand Institute and was found to be exempt from IRB review. (January 2024). Data used was exempt from consent to participate or publish secondary to the nature of the study being a systematic review, retrospectively looking at previously published data.
Consent for publication
Data used was exempt from consent to participate or publish secondary to the nature of the study being a systematic review, retrospectively looking at previously published data.
Competing interests
The authors declare no competing interests.