Loading...
The URL can be used to link to this page
Your browser does not support the video tag.
Public Comment - Agenda Item # 3.1 - Ron Kirkish
In 1975 Autism Started a Rapid Climb so Did THC Content Graph of THC Strength Rise and Autism Rate Rise -- A Coincidence? 1 in 5000 thildr 5.31 1970 1975 1980 1985 1990 1995 2000 2005 Year 2010 2015 MARIJUANA THC LEVELS AUTISM RATE Graph was created from data obtained from the following source(s): Centers for Disease Control and Prevention. Prevalence of Autism Spectrum Disorders — Autism and Developmental Disabilities Monitoring Network, United States, 2006 . Surveillance Summaries. December 18, 2009. MMWR 2009;58(No. SS-10). t European Archives of Psychiatry and Clinical Neuroscience https://doi.org/10.1007/s00406-022-01446-0 ORIGINAL PAPER Impact of converging sociocultural and substance -related trends on US autism rates: combined geospatiotemporal and causal inferential analysis Albert Stuart Reecel,2 • Gary Kenneth Hulsela Received: 9 November 2021 / Accepted: 7 June 2022 ©The Author(s) 2022 1) Check for updates Abstract Whilst cannabis is known to be toxic to brain development, it is unknown if it is driving rising US autism rates (ASMR). A longitudinal epidemiological study was conducted using national autism census data from the US Department of Education Individuals with Disabilities Act (IDEA) 1991-2011 and nationally representative drug exposure (cigarettes, alcohol, anal- gesic, and cocaine abuse, and cannabis use monthly, daily, and in pregnancy) datasets from National Survey of Drug Use and Health and US Census (income and ethnicity) and CDC Wonder population and birth data. Analysis was conducted in R. 266,950 were autistic of a population of 40,119,464 8-year-olds in 1994-2011. At national level after adjustment, daily cannabis use was significantly related to ASMR (/3 estimate=4.37 (95%C.I. 4.06, 4.68), P <2.2 x 10-16) as was first pregnancy trimester cannabis exposure (/3 estimate = 0.12 (0.08, 0.16), P = 1.7 x 10-12). At state level following adjustment for cannabis, cannabigerol (from /3 estimate =-13.77 (-19.41, 8.13), P = 1.8 x 10-6) and A9-tetrahydrocannabinol (from /3 estimate =1.96 (0.88-3.04), P = 4 x 10-4) were significant. Geospatial state -level modelling showed exponential relationship between ASMR and A9-tetrahydrocannabinol and cannabigerol exposure. Exponential coefficients for the relationship between modelled ASMR and A9-tetrahydrocannabinol and cannabigerol exposure were 7.053 (6.39-7.71) and 185.334 (167.88-202.79; both P < 2.0 x 10-7). E-values are an instrument related to the evidence for causality in observational studies. High E-values were noted. Dichotomized legal status was linked with elevated ASMR. Data show cannabis use is associated with ASMR, is powerful enough to affect overall trends, and persists after controlling for other major covariates. Cannabinoids are expo- nentially associated with ASMR. The cannabis —autism relationship satisfies criteria of causal inference. Keywords Cannabis • Cannabinoid A9-tetrahydrocannabinol • Cannabigerol • Pathways and mechanisms Introduction It is well known that the incidence of autistic spectrum dis- order is increasing in the USA, with current annual rates as high as 1.68% being reported nationwide by Centers for Disease Control, Atlanta, Georgia (CDC) [1 ]. Indeed up to 4.5% of 8-year-old boys in New Jersey have been diagnosed with this disorder [1]. For reasons which are unclear, the syndrome is more common in boys than girls perhaps related ® Albert Stuart Reece stuart.reece@bigpond.com 2 Division of Psychiatry, University of Western Australia, Crawley, WA 6009, Australia School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia to the many extra neurological genes on the X-chromosome, which is randomly inactivated in females thereby providing a wider range of spare alleles from which to support neuro- logical development [2]. Whilst the literature identifies several causes which con- tribute to the incidence of autism, including obesity, mater- nal diabetes, advanced parental age, twin linkage, bleeding, having another autistic sibling, higher income, and exposure to some drugs including cannabinoids [3-7], the primary drivers of the present surge have remained largely elusive. Of concern, all three longitudinal studies of brain devel- opment following prenatal cannabis exposure (PCE) have identified adverse neurological outcomes mimicking atten- tion deficit hyperactivity disorder (ADHD) and autistic spec- trum features [8]. At a time of major commercialization of the cannabis industry, such findings must be of particular concern. Published online: 02 July 2022 Springer European Archives of Psychiatry and Clinical Neuroscience It is of interest that a recent population -wide study of all births in Ontario 2007-2012 using coarsened exact matching and controlling for a wide variety of socioeconomic, medi- cal, maternal age, maternal psychiatric, other substance use, and obstetric covariates found a 51 % higher adjusted rate of autistic spectrum disorders (adjusted hazard ratio = 1.51 (95% CI 0.17-1.96)) following cannabis -exposed pregnan- cies which was invariant across all socioeconomic strata [9]. Because these syndromes are not usually identified prior to the age of 8 years, there is inevitably a lengthy delay in reporting the current state of the epidemic. At the time of conducting our analysis, we were aware that drug exposure was highly correlated to ethnocultural factors and that PCE was known to be rising across USA. It was felt to be important to take such considerations into account in conducting our analysis. Our primary hypothesis was that increasing substance and/or cannabinoid exposure might constitute a primary underlying driver of US autism rate (ASMR) across time. This hypothesis was formulated prior to data analysis. We wished to explore the effects and relative contribution of external demographic and socioeconomic covariates in a formal geotemporospatial framework. Methods Data sources State autism rates were derived from the US Department of Education Individuals with Disabilities (IDEA) database [10]. State population data from the US Census Bureau were used to calculate national rates. State population, ethnicity and median household income data was sourced from US Census via the tidycensus package in "R" from Comprehensive "R" Archive Network (CRAN). Data on national age of child-bearing was sourced from the births registries of the CDC Wonder website [ 1 1 ]. Drug use data in various demographic subgroups and in pregnancy was taken from the nationally representative National Survey of Drugs and Health (NSDUH) conducted each year by the Substance Abuse and Mental Health Services Administra- tion (SAMHSA) and particularly from the online interactive Substance Abuse and Mental Health Data Archive (SAM- HDA [12]). Data on national cannabinoid concentrations was from Drug Enforcement Agency [13, 14]. Missing data were casewise deleted in linear (1m) and panel (plm) regres- sion except where otherwise described. State cannabinoid exposure estimates were derived by multiplying the monthly cannabis use rate by state by the concentration of the various cannabinoids obtained in Fed- eral seizures. Data on A9-tetrahydrocannabinol (A9THC), cannabinol (CBN), cannabidiol (CBD), cannabigerol (CBG), cannabichromene (CBC) and tetrahydrocannabidivarin (THCV) were available [13, 14]. Ethnicity was defined by SAMHSA and US Census. These official definitions of ethnicity were used in analysis. Statistics This study was conducted in 2019. Data was processed using "R Studio" version 1.2.5042 based on "R" version 4.0.0 [15]. All graphs were prepared in ggplot2 package [16] from the tidyverse [17] and 3-D graphs were drawn in NCSS software [18]. All graphs and tables are original and have not been previously published elsewhere. Variables were log trans- formed as guided by the Shapiro test. Details of R-packages used are provided in the online statistical methods. Mixed effects models were performed using R package nlme using State as a grouping variable weighted by inverse probability weights as described below [19]. Two-step panel regression was conducted for space—time panel data using package plm [20, 21]. For panel regression the pooling model was used, effect was over both space and time, random method was that of Swarmy and the instrumental method was that of Amemiya. These settings are required by the software or were found on preliminary analyses to give optimal out- put precision. Geospatial links were constructed canoni- cally using the poly2nb function from spdep [22]. Spatial links were edited with Alaska and Hawaii elided (moved) conceptually to Oregon and Washington and to California, respectively, both to reflect sociocultural relationships and to prevent areal zones with no spatial relationships which complicates geospatial analysis. Generalized two-step geo- spatial regression was performed using the spreml function from package splm [23, 24], including both spatial autocor- relation errors and spatial lags and random effects using the error structure of Kapoor, Kelejian and Prucha and with the method of Baltagi, Pfaffermayr, Jong and Song with initial values of zeros (sem2srre) [25]. Model specification was checked with Lagrange multiplier tests and models were compared by their log -likelihood (logLik) ratios at model optimization using the spatial Hausman test (sphtest). Model reduction was by the classical technique with sequential deletion of the least significant term. Two-step regression is a powerful well -established tech- nique which utilizes instrumental variables that are thought to more accurately reflect the real situation underlying the listed covariates. It has been used in panel and geospatial models in this report due to overwhelming evidence (pre- sented below) of very different cannabis use patterns by ethnicity to more accurately explore the underlying drug exposure relationships. Predicted fitted values from final models were calculated by matrix multiplication inserting appropriate values along- side matching model terms. Springer European Archives of Psychiatry and Clinical Neuroscience Causal inference Inverse probability weighting (IPW) was conducted using the R package ipw [26]. IPW values were calcu- lated using the last month cannabis use as the exposure of interest in a time -dependent manner. The numerator was a series of additive terms including four drug variables excluding cannabis exposure, four ethnicities, median household income and five ethnic cannabis exposure terms. The denominator included this list together with monthly cannabis exposure. Interactive models included a four-way interaction between tobacco, alcohol, canna- bis and analgesic consumption. Weight truncation was not required. All mixed effects and robust models were inverse probability weighted. Robust generalized linear regression was performed in the survey package (using svyglm) with State as the grouping variable utilizing the IPW weights [27]. EValue determination was performed using the R pack- age EValue [28-30]. As eValue estimation of regression coefficients requires a model standardized deviation, this could not be performed on svyglm models; it was per- formed instead on mixed effects models structured and weighted similarly to the svyglm models. P < 0.05 was considered significant. Results Input data The national rate of autistic spectrum disorder was derived from the IDEA database combined with state population data obtained from US Census and used to compute national rates of autism. It was combined with other data as shown in eTable 1 and graphed in eFigure 1. The IDEA dataset for the 50 US states was almost complete for the 18 years 1994-2011. Only five data points were missing for this period: New Hampshire in 1994, Montana 2006, Vermont in 2007 and 2008, and Wyoming 2010 and these were filled by temporal kriging (mean substitution). This dataset com- prehended 266,950 autistic children of a total US population of 40,119,464 8-year-olds, a mean rate of 66.5/10,000, for the period 1994-2011. Since the IDEA database began in 1991 and terminated in 2011, it was extended through to 2018 using conservative published national projections [31] which are actually below the most recent CDC estimate (1.31% in 2014 v. 1.68% in [1]). Data on cannabis use by ethnic group, daily cannabis smoking and cannabis use in pregnancy was only available from SAMHSA at the national level, which indicated that these variables needed to be analysed at the national level. Authoritative and nationally representative surveys have shown repeatedly that rates of cannabis use in pregnancy closely parallel those in the general community [32-38]. Figure 1 presents a sequential map series showing the progress of autism across USA 1992-2011. Figure 2 presents a bivariate map series of the autism rate together with the cannabis use rate and one notes that both are elevated in the northeast and northwest of the country (pink and purple areas). Figure 3 presents a similar bivariate map of USA show- ing autism and cigarette use plotted together. As cigarette use declines, this map appears to be "turning bluer" than the previous map. The United Nations 2019 World Drug Report clearly demonstrates that recent American use of cannabis relates primarily to increased daily use [39]. SAMHSA provide data that stratify the monthly frequency of cannabis use into groups as non -user, 1-2 days, 3-5 days, 6-19 days and 20-30 days shown in eFigure 2. The confidence intervals are taken directly from SAMHDA. Again, one notes that Asian - Americans smoke less cannabis 20-30 days per month and more are non -users. Using the midpoint of these daily inter- vals as a multiplicand, it is possible to calculate the mean daily use of each ethnic group over time with the results shown in Fig. 4 and eFigures 2 and 3. Clear differences in mean daily cannabis use by ethnicity are evident. As disclosed by United Nations Office of Drugs and Crime (UNODP), the pattern of cannabis use matters. SAM- HDA data show that in 2017 about 92.6% of Americans smoked cannabis to a trivial extent (<3 days/month) and 7.35% smoked> 3 days/month (eTable 2). These data allow the calculation of an Ethnic Cannabis Exposure Score which can be plotted against a State —Time index and against time (eFigure 4A and 4B). These data show that without exception in each state, the Ethnic Can- nabis Exposure Score rose across time. The red line in the centre of Panel B shows the median trajectory as a loess curve of best fit. Regression results Linear regression was used to investigate the association between daily cannabis use and ethnicity. The covariates were time and ethnicity. eTable 3 shows the results in a model quadratic in time and confirms highly significant dif- ferences in cannabis use by ethnicity (from /3 estimate = 1.67 (95% CI 1.45-1.89), P < 2.2 x 10-16; quadratic superior to linear model, ANOVA F=2.147, df=13, P=0.019). eFigure 5 shows that high intensity cannabis use is falling amongst teenagers, but rising in older age groups. eFigure 6 confirms these age -dependent trends in the first trimester of pregnancy which shows more cannabis use than later tri- mesters. eFigure 7 has been drawn from CDC birth data Springer European Archives of Psychiatry and Clinical Neuroscience U) a) d a N rn E L N 0 cn In 0 L 0 ca L 0 1- L 0 0 E L a N E 0 Data: IDEA Dataset, 1992-2011 O O 0 tn 0 CO 0 0 0 0 Map sequence of autism rates across USA selected years 1992-2011. Data from IDEA Dataset in reference 4 Springer European Archives of Psychiatry and Clinical Neuroscience a m a O g= 0 o r co U = v co ❑ — c n z > -o m m o N N_ ❑ co w C ❑ c m i m ❑ E N 8 r ciO O / IyauoI 'asn s!geuueo % CO N 0°W 110°W 100°W 90°W 80°W °W 110°W 100°W 90°W 80°W FFTTF Z 2 Z Z Z Z a M C09 N N a v CN9 0 N N Fig. 2 Bivariate choropleth maps of the relationship between autism and cannabis use over time 4Z Springer European Archives of Psychiatry and Clinical Neuroscience O Z z Z Z Z Z o a n M N N N 0 o ANWOIN esn auaie6ij % Z Z Z Z n o a o m o N j Z Z 0 3 N Z Z z Z Z Z u o R ▪ ▪ oV th [� N N Fig. 3 Bivariate choropleth maps of the relationship between autism and cigarette use over time Springer European Archives of Psychiatry and Clinical Neuroscience Trimester 1 0.050%- 0.040%- y y 0.030%- co m as T 8 0.020%- 0 U •• • • d 0.010%- • • 0.000% - • • Quantitative Cannabis Exposure in Each Pregnancy Trimester Over Time USA, NSDUH, SAMHSA Trimester 2 Trimester 3 • • 2002 2006 2010 2014 2018 2002 • 2006 • 2010 2014 Year • 2018 2002 2006 2010 2014 2018 Fig. 4 Plots of cannabis use in each pregnancy trimester over time. Data from SAMHDA from SAMHSA and confirms the trend of childbirth to be occurring at older maternal ages. In the light of the findings of eFigure 5, this implies that these women are moving up into a higher can- nabis use age bracket. Figure 5 presents the mean data for cannabis use by preg- nancy trimester for all age groups and confirms that first trimester cannabis use is rising with time, a trend not seen at later trimesters. The SAMHSA data for 2015 is incomplete, so this point has been filled by mean substitution (0.027). The correlation between time and the rising use of cannabis in pregnancy is R = 0.6115 (P = 0.001). The slope of the first trimester regression line is significantly different to that in the third trimester (/3 estimate = — 4.97 x 10-8 (-8.44E-08 to —1.5E-08), P = 0.007, model Adj. R2 = 0.174, F = 4.31, df = 3.44, P = 0.009). These data invite exploration by regression analysis. Panel regression was utilized as time is an implicit variable rather than an explicit one (important in small data tables), and one can easily include both temporal lags and instru- mental variables in the R package plm. Only a limited num- ber of variables can be included because of the small number of observations. The Ethnic Cannabis Exposure Score was multiplied by the THC Potency to capture the effect of rising THC concentrations. The variable was called the "Ethnic Cannabis Score THC Potency". Cigarettes, the cannabis index, analgesics, three races and median household income have been included as covariates for 1994-2018. When the regression is performed for the national autism rate in this manner the results indicated in Table 1 are obtained. A very high level of statistical significance of all the variables is noted (all P < 2.2 x 10-16) Panel regression may also be used to model the rela- tionship between ASMR and first trimester cannabis use. The covariates in this model were first trimester cannabis use, THC potency, median household income, cocaine and analgesic use, and the three most common races (Caucasian -American, African -American and Hispanic - American). This model has one interaction between first trimester cannabis use and THC potency and 2 years of lag. The instrumental variables along with the highly sig- nificant results are listed in Table 1. Robustness analysis A robustness analysis on these data using published high and low estimates of the national autism rate for 1994-2018 derived from projections from states where cannabis was illegal and those where it was legal, respectively [31], con- firmed these conclusions (eTable 4). Geospatial regression Naturally, we were interested to explore if these relationships extended to an analysis at state level. eFigure 8 sets out the geospatial links and weights used. Geospatial regression was performed in 2002-2011 with results shown in eTable 5 using five drugs —cigarettes, alcohol abuse, monthly cannabis, misuse of analgesics, Springer European Archives of Psychiatry and Clinical Neuroscience 6- 0- Mean Monthly Days of Cannabis Use by Ethnicity by C.I. Width Data - NSDUH, SAMHSA, 2002-2017, with Narrow and Wide Confidence Intervals Narrow Confidence Intervals 2006 i J. 2010 2014 Year Broader Confidence Intervals 2006 2010 Fig. 5 Mean cannabis use by ethnicity. Data from SAMHDA from SAMHSA Table 1 National panel regression model results Instrumental±lagged variables General population model 2 lags, 1 interaction Lag (Cannabis_Monthly), 0:2 Lag (09THC_Exposure), 0:2 Lag (Cannabigerol_Exposure), 0:2 Cocaine_Annual First trimester pregnancy exposure 2 lags, 1 interaction Lag(First_Trimester_Cannabis_Exposure), 0:2 Lag(THC_Potency), 0:2 Lag(White_Ethnicity), 0:2 Lag(Hispanic_Ethnicity), 0:2 Parameter Cigarettes_Monthly African-American_Ethnicity Ethnic_Cannabis_Score_THC_Potency Hispanic_Ethnicity Medi an_Household_Income Non-Medical_Use_of_Analgesics Caucasian-American_Ethnicity Cigarettes_Monthly: Ethnic_Cannabis_Score_ THC_Potency 2014 First_Trimester_Cannabis_Exposure: THC_Potency Caucasian-Americ an_Eth ni c ity First_Trimester_Cannabis_Exposure Cocaine_Annual Race / Ethnicity u American Indian / AN $I Asian Hispanic 1+1 Mixed • NH Black Native Hawaiian / PI NH White Parameter estimate CI 31.83 (29.79-33.87) 11.15 (10.6-11.7) 4.37 (4.06-4.68) 0.83 (0.77-0.89) 1.5E-05 (1.4E-05-1.6E-05) -2.98 (-3.3-2.7) - 14.79 (-15.3-14.3) - 18.65 (-19.9-17.4) - 0.06 (-0.08-0.04) - 6.19 (-7.07-5.31) 0.12 (0.08-0.16) 0.25 (0.15-0.35) 0:2 represents 0-2 years temporal lag, THC tetrahydrocannabinol, d9THC A9- tetrahydrocannabinol, CI 95% confidence interval cocaine —and the five races —Caucasian -American, Afri- can -American, Hispanic -American, Asian -American and American Indians and Alaskan Natives —and median house- hold income were considered as covariates, and instrumen- tal variables were used for monthly cannabis use, A9THC P value <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 < 2.2e-16 1.7E-12 3.9E-08 and cannabigerol and the annual Ethnic Cannabis Expo- sure Score was used to control for cannabis exposure aris- ing in relation to ethnic origin. A three-way interaction term included cigarettes, cannabis and opioids. As shown Springer European Archives of Psychiatry and Clinical Neuroscience in eTable 5, significant results for cannabis were obtained (from /3 estimate,- 8.41 (3.08-13.74), P = 0.002) at 2 years lag. Clearly in such a study, one is concerned that ethnocul- tural factors relating to increased drug exposure in certain communities might be acting in addition to ethnophar- macogenomic factors relating to different responses to, or processing of, addictive drugs. To control at least in part for this effect, we performed a further regression not with the states' racial composition, but with the Ethnic Cannabis Exposure Score described above. The instrumental variable list was similar to that described above. These results are shown in eTable 6, where terms including cannabis are noted to be significant (from /3 estimate = 10.88 (5.97-15.79), P =1.4 x 10-5) at 2 years lag, cannabis is independently sig- nificant alone (/3 estimate=0.63 (0.13-1.13), P=0.014) and the Ethnic Cannabis Exposure Score is highly significant at all lags (from /3 estimate = 0.17 (0.09-0.26), P = 4.6 x 10-5). Finally, we were interested to learn if the inclusion of spe- cific cannabinoids in the model would be significant when race and median household income were included. Geospa- tial links were derived from the R spdep package and edited as shown in eFigure 8A to achieve the final spatial links shown in eFigure 8B. The regression results from spatial two -stage and lagged models are shown in Table 2 with full model details provided in eTable 7. Instrumental variables included individual terms for ethnic cannabis exposure and are indicated in the table. Terms including cannabinoids are significant in an unlagged model (from /3 estimate =-13.77 (-19.41 to - 8.13), P =1.8 x 10-6) and across all models 09THC and cannabigerol are independently significant (from /3 estimate =1.96 (0.88-3.04), P = 4 x 10-4 and 13 esti- mate = 0.81(0.34-1.28), P = 9 x 10-4). Spatial Hausman tests confirm that the unlagged model is superior to models lagged to 2 and 4 years (ChiSq. = 66.879, df = 9, P = 3.21 x 10-11 and ChiSq. = 626.46, df = 9, P= 8.744 x 10-129). It was also of interest to consider the outcome if ethnic cannabis exposure terms were included as covariates in the model and no instrumental variables were used at all. This interesting and highly significant model is shown in the final panel of Table 2. 09THC exposure and the 09THC: can- nabigerol interaction are both significant as are five ethnic cannabis exposure terms. Effect size The availability of a final (unlagged) geospatial model allows modelling of cannabinoid effects and potentially the calculation of an effect size. When minimal and maximal values for THC and cannabigerol exposure are inserted into this model, autism rates of 0.37 and 38.42, respectively, are predicted, a variation of 102.72-fold. Similarly, ASMR at each decile of cannabinoid exposure may be calculated as shown in eTable 8 and Fig. 6. Steep rises with rising can- nabinoid concentration are shown (top panels) which are linear on log plots, thus implying exponential relationships (middle panels) and to which tight -fitting regression lines may be fitted for deciles 2-9 (lower panels). The exponential regression coefficients for the relationship between ASMR and THC and cannabigerol exposure for deciles 2-9 are 7.053 (6.39-7.71) and 185.334 (167.88-202.79) with both P < 2.0 x 10-7 (eTable 9) and both Pearson correlation coef- ficients R > 0.992, P < 2.0 x 10-7. As one doubles the THC exposure from 0.4 to 0.8 and to 1.6%% (compound units), the predicted ASMR rises from 0.022 to 0.382 to 107.83/10,000 children or 4,736.81-fold. As the cannabigerol exposure rises from 0.02 to 0.04 to 0.08%%, the modelled ASMR rises from 0.059 to 2.43 to 4029.65/10,000 children, or 67,511.42-fold which reflects the exponential relationship. The THC-cannabigerol-autism rate relationship is illustrated from different perspectives in the 3D plots of eFigure 9. Causal inference In addition to geospatiotemporal modelling, this dataset lends itself also to the techniques of formal causal inference to investigate further the nature of the association between cannabis exposure and autism. Inverse probability weighting was conducted considering the monthly use of cannabis as the key exposure of interest. Although this was an observational ecological study, weight- ing the key exposure variable in this manner allows one to achieve a quasi -randomized design. Robust regression was conducted in the R package survey. When a full list of the five drug variables, four ethnici- ties, median household income and five ethnic cannabis exposures was included in the robust regression model, the results are as shown in Table 3. In the additive model only a single ethnicity, non -Hispanic Asian is significant. The other five significant terms all include cannabis. Cannabis exposure alone is significant (/3 estimate =1.08 (0.63-1.54), P = 2.90 x 10-5) and terms involving ethnic cannabis expo- sure are significant (from /3 estimate = 3.63 (2.94-4.34), P=5.9x 10-13) In a model including a four-way interaction term between substance exposure terms tobacco -alcohol -cannabis -anal- gesics, 13 of 22 terms remaining in the final model included cannabis. In five cases, this related to ethnic cannabis expo- sure. In eight cases cannabis exposure itself was significant in interactive terms. Cannabis exposure alone was also sig- nificant (/3 estimate = 803.00 (326.72-1279.28), P = 0.0024). When a similar exercise is conducted using mixed effects models, qualitatively similar results were obtained (eTable 10). Springer European Archives of Psychiatry and Clinical Neuroscience Table 2 Geospatial state -based regression of autism rate by individual cannabinoids, race and income General Instumental±lagged variables 0 lags Cannabis, monthly A9THC Cannabigerol NHWhite_Score NHB1ack_Score Hispanic_Score NHAsian_Score NHAIAN_Score 2 lags cannabis, monthly, 0:2 A9THC, 0:2 Cannabigerol, 0:2 NHWhite_Score, 0:2 NHB1ack_Score, 0:2 Hispanic_Score, 0:2 NHAsian_Score, 0:2 NHAIAN_Score, 0:2 4 lags cannabis, monthly, 0:4 A9THC, 0:4 Cannabigerol, 0:4 NHWhite_Score, 0:4 NHB1ack_Score, 0:4 Hispanic_Score, 0:4 NHAsian_Score, 0:4 NHAIAN_Score, 0:4 0 lags, 0 instrumental variables Parameters Parameter NHAsian Ethnicity NHWhite Ethnicity Cannabigerol: Alcohol_Abuse Alcohol_Abuse Cannabigerol NHAIAN Ethnicity cigmon: Cannabigerol: Alcohol_Abuse A9THC Cigarettes: A9THC A9THC: Cannabigerol Cigarettes: A9THC: Cannabigerol NHAsian Ethnicity NHWhite Ethnicity Alcohol_Abuse NHAIAN Ethnicity Cannabigerol: Alcohol_Abuse A9THC Cannabigerol A9THC: Cannabigerol NHAfrican-American Ethnicity NHAIAN Ethnicity NHAsian Ethnicity NHWhite Ethnicity Cannabigerol: Alcohol_Abuse A9THC Alcohol_Abuse Cigarettes: Cannabigerol: Alcohol_Abuse Cigarettes: A9THC Cigarettes: Alcohol_Abuse NHAIAN Alcohol_Abuse CBG: Alcohol_Abuse Asian.Am.Cannabis Cauc.Am.Cannabis Hispanic.Am.Cannabis NHAsian AIAN.Am.Cannabis A9THC Afric.Am.Cannabis NHWhite A9THC:Cannabigerol Estimate 95% CI P value 0.43 (0.33-0.53) <2.2e-16 2.01 (1.42-2.6) 1.5E-11 - 13.77 (-19.41 to-8.13) 1.8E-06 - 44.35 (-65.89 to-22.81) 5.5E-05 0.81 (0.34-1.28) 9.0E-04 - 0.04 (-0.06 to-0.02) 0.002 8.91 (2.79-15.03) 0.004 4.59 (1.41-7.77) 0.005 - 16.23 (-28.64 to-3.82) 0.010 0.94 (0.21-1.67) 0.011 - 3.39 (-6.21 to-0.57) 0.018 0.42 (0.3-0.54) 3.1E-12 1.95 (1.22-2.68) 1.2E-07 - 43.92 (-69.97 to-17.87) 0.001 - 0.06 (-0.1 to-0.02) 0.001 -11.24 (-18.12 to-4.36) 0.001 1.14 (0.36-1.92) 0.005 0.81 (0.22-1.4) 0.007 0.25 (0.03-0.47) 0.023 0.08 (0-0.16) 0.046 - 0.11 (-0.13 to-0.09) 9.0E-15 0.37 (0.23-0.51) 1.9E-07 1.52 (0.74-2.3) 1.0E-04 -22.68 (-34.89 to-10.47) 3.0E-04 1.96 (0.88-3.04) 4.0E-04 - 72.45 (-114.28 to-30.62) 7.0E-04 71.65 (25.41-117.89) 0.002 - 6.44 (-10.63 to-2.25) 0.003 214.56 (56.98-372.14) 0.008 - 0.14 (-0.17 to-0.1) 2.9E-14 - 53.52 (-68.57 to-38.47) 3.2E-12 - 13.87 (-17.85 to-9.89) 8.5E-12 2.60 (1.79-3.42) 4.3E-10 - 3.23 (-4.27 to-2.19) 1.1E-09 2.96 (1.99-3.93) 2.2E-09 0.34 (0.22-0.45) 5.6E-09 0.48 (0.32-0.65) 7.1E-09 2.08 (1.23-2.92) 1.4E-06 0.30 (0.15-0.45) 8.8E-05 1.25 (0.55-1.94) 0.0004 0.24 (0.06-0.41) 0.0098 NH non -Hispanic, Am American, NHAIAN non -Hispanic -American Indian/Alaskan-Native, 0:2 0-2 years temporal lag, d9THC A9-tetrahydrocannabinol, CI 95% confidence interval, 0:4 0-4 years temporal lag i Springer European Archives of Psychiatry and Clinical Neuroscience Fig. 6 Modelled autism rate by exposure to A9THC and cannabigerol. A Linear. B Loga- rithmic and C regression plots for A9THC and cannabigerol, respectively 25- C 20- 0- 0.3 0:3 Modelled Autism Rate by ..9THC Exposure Decile 0.6 0.9 1.2 ..9THC Exposure Level Modelled Log(Autism Rate) by ..9THC Exposure Decile 0.6 0.9 1.2 ..9THC Exposure Level Modelled Log(Autism Rate) by ..9THC Exposure Deciles 2 to 9 0.4 0.6 0.8 1.0 1.2 ..9THC Exposure Level The above findings using IPW show that cannabis appears to be causally related to the autism rate. However, it is theo- retically possible that some unidentified and unmeasured confounding factor, which is correlated with both the expo- sure of interest and the outcome, might be confounding these results in the background. The magnitude required of this Modelled Autism Rate by Cannabigerol Exposure Decile 25- C 22 20- 0 0 15- 0 0 Log (Autism Rate) 5- 2- o- 0.02 0.03 0.04 Cannabigerol Exposure Level 0.05 Modelled Log(Autism Rate) by Cannabigerol Exposure Decile 0.02 0.03 0.04 0.05 Cannabigerol Exposure Level Modelled Log(Autism Rate) by Cannabigerol Exposure Deciles 2 to 9 o.02 o.03 0.04 Cannabigerol Exposure Level 0.05 unknown dual correlation effect to obviate the present results can be quantified using the eValue. Table 4 lists a set of eValues calculated from some of the main results of this study listed above. One notes that many of these eValues are very high, especially those deriving from spatial models. This implies that a significant degree of unmeasured confounding is unlikely. This fits with the Springer European Archives of Psychiatry and Clinical Neuroscience Table 3 Multivariable robust regression models of autism rate Parameter Additive Hispanic.Cannabis NHAIAN.Cannabis NHAsian.Cannabis Cannabis NHAsian NHWhite.Cannabis Interactive NHAsian Cannabis Cannabis: Analgesics Analgesics Cigarettes: Alcohol: Cannabis Cigarettes: Alcohol: Cannabis: Analgesics Cigarettes: Alcohol Cigarettes: Alcohol: Analgesics NHWhite.Cannabis Alcohol: Analgesics Cigarettes: Analgesics Alcohol Cigarettes Alcohol: Cannabis: Analgesics Cigarettes: Cannabis: Analgesics Alcohol: Cannabis Cigarettes: Cannabis Estimate CI P Value 3.63 (2.94-4.34) 5.9E-13 1.94 (1.34-2.55) 1.3E-07 1.27 (0.81-1.73) 2.3E-06 1.08 (0.63-1.54) 2.9E-05 0.25 (0.13-0.37) 2.0E-04 -11.70 (-16.61 to-6.81) 2.8E-05 0.31 (0.15-0.47) 0.0008 803.00 (326.72-1279.28) 0.0024 265.20 (105.46-424.54) 0.0026 791.40 (302.96-1279.04) 0.0032 32,850.00 (8008-57,792) 0.0145 10,730.00 (2428.8-18,971.2) 0.0160 97,700.00 (22,240-173,160) 0.0163 31,900.00 (6812-56,988) 0.0184 -3.98 (-7.12 to-0.84) 0.0185 - 9688.00 (-16393.2 to-2986.8) 0.0080 - 2634.00 (-4454.76 to-805.24) 0.0080 -29570.00 (-49,788 to-9412) 0.0071 - 7994.00 (-13419.2 to-2560.8) 0.0070 - 3244.00 (-5435.2to-1044.8) 0.0069 -886.60 (-1484.8 to-289.2) 0.0066 -9894.00 (-16534.4 to-3245.6) 0.0063 - 2689.00 (-4477.52 to-902.48) 0.0059 CI 95% confidence interval, NH non -Hispanic, NHAIAN non -Hispanic -American Indian/Alaskan-Native highly significant findings obtained in many of the earlier results, and particularly with the close geotemporospatial relationships demonstrated earlier. These 29 E-value estimates and lower bounds may be listed consecutively as shown in Table 5. Since both E Value lists are shown in descending order, this presentation dis- rupts the pairing structure shown in Table 4. From this table it is observed that of the E-value estimates, 4 are infinite and 25/30 (83.3%) exceed 9 and so are in the high range [40] and 26/30 (86.7%) are greater than 1.25 and thus exceed the threshold of causality [29]. Similarly for the minimum E-values, 1 is infinite, 22/30 (73.3%) exceed 9 and thus are in the high range and 25/30 exceed 1.25 (83.3%) and therefore cross the threshold for causal effects. Consider- ing the descriptive statistics for these two data pairs, the E-value estimates have a median of 5.97 x 108 (interquar- tile range (IQR) 17.97, 2.40 x 1065) and the lower bound of the E-values has a median value of 1.07 x 104 (IQR 5.54, 6.51 x 1024). These are very high and very dramatic results and effectively exclude a significant role for hypothetical confounder covariates. Finally, it has previously been shown that liberal legis- lative paradigms for cannabis are associated with elevated rates of autism [41]; however, this has not been confirmed in the geospatial context. eFigure 11 shows the (log) autism rate against time by legal status dichotomized as illegal status v. liberal status. Table 6 sets out the result of geospatial regression of the (log) autism rate against the dichotomized legal status and confirms a highly significant finding. This regression coefficient is associated with a relative risk of 2.05 (95% CI 1.20, 3.49) and eValues of 3.51 and 1.70, which are clearly relatively high. These E-values have been included in Table 5. Discussion The principal question addressed by the present study was to explore the mystery of the remarkable rise in US autism rate which has remained hitherto largely unexplained. This study is an epidemiological investigation which uses national panel and state -level geospatial regression to ana- lyse ecological covariates of childhood autism across a diverse range of domains including socioeconomic, eth- nicity and drug exposure. A particular focus of this study is on environmental exposure to cannabis and selected Springer European Archives of Psychiatry and Clinical Neuroscience Table 4 eValues for major results from foregoing analyses Parameter Decile Estimates THC_Decile CBG_Decile Mixed Effects Models Additive African.Am.Cannabis Cannabis NHAIAN.Cannabis Interactive Cigarettes: Cannabis: Cocaine Cigarettes: Cannabis NHWhite.Cannabis Cigarettes: Cannabis: Analgesics: Cocaine Cigarettes: Cannabis: Analgesics Alcohol: Cannabis: Cocaine Alcohol: Cannabis Alcohol: Cannabis: Analgesics: Cocaine Alcohol: Cannabis: Analgesics Spatial Spreml Models 0lags THC Cannabigerol THC*Cannabigerol Cigarettes: Cannabigerol: Alcohol 2 lags THC Cannabigerol THC*Cannabigerol 4 lags THC THC*Cannabigerol 0 Lags, Zero Instrumental Variables THC Afrc.Am.Cannabis Hispanic.Am.Cannabis Asian.Am.Cannabis AIAN.Am.Cannabis THC: Cannabigerol Table /i estimate (C.I.) eTable 9 7.0526 (6.37, 7.71) 185.33 (167.87, 202.79) eTable 10 0.509099 (0.39-0.63) 0.393926 (0.30-0.49) 0.258642 (0.10-0.41) eTable 10 3753.1 (1451.28-6054.92) 15,065.8 (5585.85-24,545.75) 2 (0.73-3.27) 1167.2 (409.64-1924.76) Table 2 Table 2 4717 (1593.82-7840.18) 9890.4 (2955.51-16,825.29) 38,348.6 (9877.05-66,820.15) 2955.3 (673.59-5237.01) 11,491.4 (2112.41-20,870.39) 4.58 (1.41, 7.76) 0.81 (0.33, 1.29) 0.94 (0.21, 1.67) 8.91 (2.80, 15.02) 1.14 (0.35, 4.31) 0.81 (0.23, 1.39) 0.25 (0.023, 0.46) Table 2 1.95 (0.87, 3.04) 71.65 (25.41, 117.88) Table 2 2.07 (1.23, 2.91) 0.29 (0.14, 0.44) 2.95 (1.99, 3.93) 2.6 (1.78, 3.42) 0.48 (0.32, 0.65) 0.24 (0.06, 0.41) RR (95% CI) eValues 1.10E+27 (3.26E+24, 3.71E+29) 2.19E+27, 6.51E+24 Infinity (Infinity, Infinity) Infinity, Infinity 1.011 (1.008, 1.014) 1.102 (1.064, 1.011) 1.006 (1.002, 1.009) 6.1E+51 (1.22E+20, 3.04E+83) 7.6E+207 (2.2E+77, Infinity) 1.06 (1.02, 1.11) 1.3E+ 16 (4.7E + 04, 3.4E+ 26) 1.2E+65 (2.0E+22, 1.2E+108) 2.9E+136 (9.4E+40, 9.3E+2321) Infinity (1.2E+ 137, Infinity) 6.0E+40 (2.3E+09, 1.5E+72) 3.6E+ 158 (2.6E+29, 5.2E+287) 1.92E+15 (5.34E+04, 6.93E+25) 495.54 (12.81, 1.92E+04) 1.38E+03(5.30, 3.61E+04) 4.82E+29 (2.38E+07, 9.75E+49) 6.03E+03 (14.51, 2.51E+06) 480.0 (5.65, 4.07E+04) 6.48 (1.29, 32.42) 349.01 (13.73, 8.87E+04) 1.19E+93 (1.36E+33, 1.05E+153) 5.71E+06 (10.5E+04, 3.11E+09) 9.24 (3.04, 28.02) 4.28E+09 (3.04E+06, 6.04E+ 12) 2.98E+08 (6.60E+05, 1.34E+11) 37.14 (10.95, 125.96) 5.82 (1.53, 22.12) 1.12, 1.10 1.10, 1.08 1.08, 1.05 1.22E+52, 4.5E+20 Infinity, 4.37E+77 1.33, 1.18 2.5E+16, 9.4E+05 2.4E+65, 2.4E+22 5.9E+136, 1.88E+41 Infinity, 2.4E+ 137 1.2E+41, 4.5E+09 Infinity, 5.1E+ 129 3.85E+15, 1.07E+04 990.59, 25.11 2.77E+03, 10.07 9.65E+29, 4.77E+09 1.21E+04, 28.51 959.59, 10.78 12.44 1.91 697.51, 26.95 2.39E+93, 2.71E+33 1.14E+08, 2.10E+04 17.97, 5.54 8.56E+10, 6.07E+06 5.96E+08, 1.32E+06 73.77, 21.39 11.12, 2.43 THC A9 tetrahydrocannabinol, CBG cannabigerol, Am American, NH non -Hispanic, RR relative rate, CI 95% confidence interval cannabinoids which have been noted to be neurotoxic with effects on foetal brain development including microceph- aly, anencephaly and impaired child neurological devel- opment [8, 42-45]. Given historically very different and well -established rates of cannabis use by ethnic groups, two -stage panel and geospatial regression techniques have been utilized to carefully adjust for these effects. Spatiotemporal regression studies implicate both ethnic and drug exposure variables as being significantly associ- ated with autism incidence with three ethnicities, Cauca- sian -American, Asian -American and American -Indian and Alaskan -Native Americans, three drugs, tobacco, alcohol Springer European Archives of Psychiatry and Clinical Neuroscience Table 5 List of E-Values No E Value Estimates 1 Infinity 2 Infinity 3 Infinity 4 Infinity 5 5.90E+136 6 2.39E+93 7 2.40E + 65 8 1.22E+52 9 1.20E+41 10 9.65E+29 11 2.19E+27 12 2.50E+16 13 3.85E+15 14 8.56E+10 15 5.96E+08 16 1.14E+08 17 1.21E+04 18 2.77E+03 19 990.59 20 959.59 21 697.51 22 73.77 23 17.97 24 12.44 25 11.12 26 1.33 27 1.12 28 1.10 29 1.08 Lower Bound E Values Infinity 2.40E+ 137 5.10E+ 129 4.37E+77 1.88E+41 2.71E+33 6.51E+24 2.40E + 22 4.50E + 20 4.77E + 09 4.50E + 09 6.07E + 06 1.32E+06 9.40E+05 2.10E+04 1.07E+04 28.51 26.95 25.11 21.39 10.78 10.07 5.54 2.43 1.91 1.18 1.10 1.08 1.05 abuse or dependence, and two cannabinoids, A9THC and cannabigerol, remaining in final models with high levels of statistical significance when ethnic cannabis use is included as instrumental variables. When ethnic cannabis use is included as covariates, all five of them remain significant in final models. Table 6 Geospatial Regression of Dichotomized Legal Status Parameters Parameter Application of the techniques of causal inference to this dataset indicate that the cannabis -autism association satis- fies the criteria for causality. Geospatial analysis confirmed the previously demon- strated increased rate of autism in states where cannabis is legal. Of importance, effect size studies demonstrated that the relationship between both A9THC and cannabigerol and autism is exponential and powerful enough to induce the seismic paradigm shift which has been observed epidemiologically. One notes also that autism is rising whilst the use of the classical intoxicants tobacco and alcohol is falling. Since opioid and cocaine use only impact a small segment of the community, this naturally impugns cannabis use which alone is rising dramatically. Whilst the rise in autism rates has been said to be due to changes in its rate of diagnosis, careful studies in the USA have shown that the rise is indeed real beyond simply an increase in diagnostic suspicion or nosology [9]. Modelling studies based on the final models across both space and time provide robust epidemiological evidence of a strong upward exponential association between both A9THC and cannabigerol and the autism rate. Combined with concordant trends in tobacco, alcohol and cannabis use (mentioned above) and multiple biological pathways (mentioned below), and satisfaction of causal criteria, these strong and consistent findings across both space and time strongly implicate rising cannabis exposure in the commu- nity and in pregnancy as a primary underlying driver of the wave of autism and epidemiologically support our opening hypotheses. Whilst cannabis was only used more than 3 days per month by 7.35% of the population in 2017, high intensity cannabis use has grown dramatically across the USA in the past decade with overall daily or near daily use doubling nationwide [39] and having increased from 0.38 to 1.5% in the > 35 years cohort 2002-2017 (Fig. 3 [12]). As part of increased use, the rate of cannabis exposure during the first trimester of pregnancy is growing steeply as cannabis use in the wider population increases. Furthermore, women are having their children later and in so doing are moving Model Estimate 95% CI P Value LogLik Parameters Value P value Spatial spreml Model Liberal Legal Status 0.0938 (0.02-0.16) 0.0085 191.68 phi 9.8E-06 NA psi 0.9508 <2.2e-16 rho -0.8141 <2.2e-16 lambda 0.0938 <2.2e-16 C195% Confidence Interval, LogLik Log likelihood ratio at model optimization i Springer European Archives of Psychiatry and Clinical Neuroscience into older cohorts with cannabis users having a longitudinal history of greater cumulative cannabis exposure. It is noted due to the long half-time of cannabis retention and excre- tion from body fat stores in regular cannabis smokers that first trimester exposure will occur almost inevitably even if the mother stops cannabis consumption upon receiving a diagnosis of pregnancy [46, 47]. In this sense, therefore, the present rapid increase in numbers presenting with child autism is occurring against a background of sociodemographic trends in the wider com- munity where high intensity cannabis use is becoming more common. Mechanisms That cannabis potency and use is increasing, is retained in tissue for significant periods, and has been shown to have a number of severely neurotoxic activities particularly on the developing brain is pertinent. Several reports from CDC have linked cannabis exposure with anencephalus [43, 44] with separate data linking it to spina bifida in Canada [42], microcephaly in Hawaii [45] and adverse child neurologi- cal outcomes in Pittsburgh, Toronto and the Netherlands [8]. A generalized inhibitory effect on cell growth has been reported [48-51], as have interference with synapse forma- tion by inhibition of neuroligin and neurexin, key partners in synapse formation and determination [7, 52, 53]; an uncou- pling of neuronal mitochondrial oxidative phosphorylation [54, 55] and of grey —white matter connections [56], and increase in astrogliosis [47], neuroinflammation [57] and thus brain aging [58], an inhibition of brain neurogenesis and thus plasticity [59, 60] and adverse effect on the slit:robo ratio which is one of the key determinants of the formation of the exuberant cortex characterizing human beings [61, 62] along with numerous other genetic and epigenetic dis- ruptions [63-66]. Epigenetic mechanisms Recently, profoundly insightful and deeply meaningful results from an epigenome-wide association study (EWAS) of cannabis dependence and withdrawal have been published [67]. The authors examined the DNA methylation status of 20 cannabis -dependent patients both before and after an 11-week period of documented abstinence and compared these results with those from a comparable group of can- nabis -naive control patients who were sampled at similar time points. The results were of profound importance as relates to perturbation of normal brain development. Significant hits were found for the brain, cerebrum, cerebral cortex, head development, brain size, brain formation, forebrain pat- terning, proliferation of neural cells, brain neurogenesis, neuronal morphology, central nervous system development (139 hits), neuronal outgrowth and brain cell movement. When major brain receptors were considered, there were 132 hits for the AMPA receptor (GRIA), 165 hits for the kainate receptor (GRIK), 26 hits for the NMDA receptor (GRIN), 11 hits for the delta glutamate receptor (GRID), 122 hits for the metabotropic glutamate receptor (GRM), 125 hits for the GABA-A receptor (GABRA), 22 hits for the GABA-B receptor (GABRB), 85 hits for the serotonin recep- tor (HTR), 17 hits for the dopamine receptor (DRD1), 52 hits for the dopamine transporter (DAT, SLC6A3), 7 hits for the oxytocin receptor (OXTR), 5 hits for the µ-opioid recep- tor (ORPM1) and 5 hits for the S-opioid receptor (ORPD1). 14 and 8 hits were noted for Down syndrome cell adhe- sion molecule (DSCAM) and discs large homolog associated protein 2 (DLGAP2) which have both been previously linked with autism [68-70]. As noted above, the exuberant outgrowth of the human cortex has been causally attributed to the slit—robo system. There were 351 hits for slits and 40 hits for robo. Addition- ally, there were 8 hits for a slit—robo Rho activating GTPase activating protein 2 (SRGAP2). It has also been shown that the exuberant frontal out- growth of the human cortex can be attributed to a steep gra- dient of the key human morphogen retinoic acid [71, 72]. A high concentration of this key transcription factor at the frontal pole fell to low levels at the premotor cortex. Indeed, forced expression of this gradient in the mouse reproduced the high number of cells seen in the human neocortex in the murine model [71]. The high frontal concentration of reti- noic acid was maintained by an isoform of alcohol dehydro- genase (ALDH1), the lower premotor cortical level was con- trolled by metabolism by enzymes of the cytochrome system (CYP26B 1) and the retinoic acid signal was transduced by the key retinoid receptors RXRA and RARB. There were 13 hits in the Schrott dataset for the enzymes of the ALDH1 system (including cadherin 8 and protocadherin 17), 10 hits for the cytochromes of the CYP2 series, and 9 hits for the retinoid receptor group. While these very impressive and stunning results do not formally prove the salience of epigenomic results in the aetiology of cannabis -associated congenital brain damage, they do strongly imply that such data is highly pertinent and likely to at least partly contribute to meaningful and detailed explanatory and causal mechanisms which manifest clini- cally as the autistic spectrum of disorders. Causal inference Some comments on the use of the techniques of causal infer- ence in this study are in order. As mentioned in "Methods", all mixed effects and robust regressions were performed 1 Springer European Archives of Psychiatry and Clinical Neuroscience with inverse probability weighting. This is the technique of choice in causal modelling, which has the effect of making an observational group broadly comparable across its sub- groups, an effect which greatly increases the power of the study from being purely observational in nature to a pseudo - randomized study which has been shown to produce ana- lytical results similar to those found in formal randomized controlled trials [73]. Hence the use of such inverse prob- ability controlled modelling, especially using several regres- sion techniques (here mixed effects and robust), allows us to be confident that the results reported are indeed of a causal nature and not simply associational as may otherwise be mistakenly assumed. Secondly, we used the technique of E-values widely throughout the linear, mixed effects and spatial models which were reported. E-values quantitate the degree of asso- ciation required of some hypothetical confounder covari- ate with both the exposure of interest and the outcome of concern to explain away an apparently causal relationship. The scale of the extraordinarily high E-values reported in this study is unprecedented in the autism literature to our knowledge. As noted in "Results", we found that the median E-value estimate was very high 5.96 x 108 and of the lower bound of the E-values was 1.07 x 104. Five E-value esti- mates were infinite and one minimum E-value was infinite. E-values of this extremely high magnitude clearly discount the realistic possibility that the reported results may be due to some extraneous and unidentified confounder covariate [29, 30, 74-76]. It may be that the very high magnitude of the E-values reported in the present study reflect the very large sample size. Combining inverse probability weighting, E-values, vari- ous forms of regression techniques along with the study of the association in its native space—time context provides several strong lines of analytical epidemiological evidence that the relationship reported is real in nature, powerful in its effect, and amply satisfies the quantitative criteria for epidemiological causality. Strengths and limitations The present study has a number of strengths and limitations. Its strengths include the use of several nationally representa- tive databases, the application of geospatial and causal infer- ence analytical techniques to these questions for the first time to our knowledge, the timeliness of the information presented, the cultural and community -wide implications at a time when cannabis use is expanding rapidly the use of multiple forms of regression including space—time stud- ies and the use of the formal and quantitative techniques of causal inferential modelling. The limitations of the present study relate mainly to its ecological design which include the lack of individual participant -level data. In the present study, community cannabis use was used as a surrogate marker for parental cannabis use, as there is no direct database of which we are aware which links these covariates directly, and as the cannabis use of pregnant women has been shown to follow community cannabis use in several studies [35, 36, 38, 77-79]. The findings of this exploration of these wide-ranging studies are, however, provocative and indicate further research in this area. Generalizability Given that the data we have employed come from the USA, which by many metrics is reflective of other Western coun- tries, the study findings are likely to be generalizable to other nations. Whilst there are to our knowledge no other similar wide-ranging analyses of autism, adverse reports of neu- rological function following widespread cannabis use have issued from other countries such as Egypt, China, India and Morocco [39]. Conclusions Our results implicate both 09THC and cannabigerol in these studies, which suggest that merely lowering the 19THC con- tent of widely available cannabinoid preparations would not constitute a sufficient public health response. These data including geotemporospatial analysis and pseudo -randomi- zation of an observational population confirm our open- ing hypothesis that increased cannabis use and its related socioethnodemographic trends is one of the principal causes and primary drivers of escalating US autism rates. The issue of the exponential relationship between exposure to the can- nabinoids A9-tetrahydrocannabinol and cannabigerol is of particular concern and necessarily implies a non -linear, and in a public health sense, apparently abrupt relationship between exposure and downstream consequences, which would be consistent with multiple mechanistic pathways. In view of the present aggressive growth phase of the emerging cannabis industry, further research on the factors identified in this ecological study, including higher definition spati- otemporal epidemiological studies, are indicated. Supplementary Information The online version contains supplemen- tary materiaravailable at https://doi.org/ 10.1007/s00406-022-01446-0. Author contributions ASR assembled the data, designed and con- ducted the analyses, and wrote the first manuscript draft. GKH pro- vided technical and logistic support, co -wrote the paper, assisted with gaining ethical approval and provided advice on manuscript preparation and general guidance to study conduct. Funding Open Access funding enabled and organized by CAUL and its Member Institutions. No funding was provided for this study. No funding organization played any role in the design and conduct of the 2i Springer European Archives of Psychiatry and Clinical Neuroscience study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Data has been made publicly available on the Men- deley Database Repository and can be accessed from this URL http:// dx.doi.org/10.17632/p7myt3fbzs.1 Declarations Competing interests The authors declare that they have no competing interests. Ethical approval All information used was de -identified and publicly available group data. The Human Research Ethics Committee of the University of Western Australia provided ethical approval for the study to be undertaken, 7th June 2019 (No. RA/4/20/4724). Consent for publication Not applicable. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References 1. Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels 7, War- ren Z, Kurzius-Spencer M, Zahorodny W, Robinson Rosenberg C, White T et al (2018) Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabili- ties monitoring network, 11 sites, United States, 2014. MMWR Surveill Summ 67(6):1-23 2. Migeon B (2014) Females are mosaics. X-inactivation and sex dif- ferences in disease, vol 1, 1 edn. Oxford University Press, Auck- land, Capetown. 3. Durkin MS, Maenner MJ, Newschaffer CJ, Lee LC, Cunniff CM, Daniels JL, Kirby RS, Leavitt L, Miller L, Zahorodny W et al (2008) Advanced parental age and the risk of autism spectrum disorder. Am J Epidemiol 168(11):1268-1276 4. Osokine I, Erlebacher A (2017) Inflammation and autism: from maternal gut to fetal brain. Trends Mol Med 23(12):1070-1071 5. Wu S, Wu F, Ding Y, Hou J, Bi J, Zhang Z (2017) Advanced parental age and autism risk in children: a systematic review and meta -analysis. Acta Psychiatr Scand 135(1):29-41 6. Jung KM, Sepers M, Henstridge CM, Lassalle 0, Neuhofer D, Martin H, Ginger M, Frick A, DiPatrizio NV, Mackie K et al (2012) Uncoupling of the endocannabinoid signalling complex in a mouse model of fragile X syndrome. Nat Commun 3:1080 7. Foldy C, Malenka RC, Sudhof TC (2013) Autism -associated neuroligin-3 mutations commonly disrupt tonic endocannabi- noid signaling. Neuron 78(3):498-509 8. Brents L (2017) Correlates and consequences of prenatal can- nabis exposure (PCE): identifying and characterizing vulner- able maternal populations and determining outcomes in exposed offspring. In: Preedy VR (ed) Handbook of cannabis and related pathologies: biology, pharmacology, diagnosis and treatment, vol 1. Edn. Academic Press, London, pp 160-170 9. Corsi D, Donelle J, Sucha E, Hawken S, Hsu H, El-Chaar D, Fell D, Wen SW, Walker M (2020) Maternal cannabis use in pregnancy and child neurodevelopmental outcomes. Nat Med 26(10):1536-1540 10. Nevison C, Blaxill M, Zahorodny W (2018) California autism prevalence trends from 1931 to 2014 and comparison to national ASD data from IDEA and ADDM. J Autism Dev Disord 48(12):4103-4117 11. CDC wonder, natality information, live births [https://wonder. cdc.gov/natal ity.html] 12. Substance abuse and mental health data archive [https://www. datafiles.samhsa.gov/] 13. ElSohly MA, Mehmedic Z, Foster S, Gon C, Chandra S, Church JC (2016) Changes in cannabis potency over the last 2 decades (1995-2014): analysis of current data in the United States. Biol Psychiatry 79(7):613-619 14. ElSohly MA, Ross SA, Mehmedic Z, Arafat R, Yi B, Banahan BF 3rd (2000) Potency trends of delta9-THC and other cannabi- noids in confiscated marijuana from 1980-1997. J Forensic Sci 45(1):24-30 15. R-3.6.1 for windows (32/64 bit) [https://cran.r-project.org/hin/ windows/base/] 16. Wickham H (2016) ggplot2: elegant graphics for data analysis, vol 1. Springer, NY 17. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, Francios R, Groelmund G, Hayes A, Henry L, Hester J et al (2019) Welcome to the tidy verse. J Open Source Softw 4(43):1686-1691 18. NCSS statistical software [https://www.ness.com/] 19. Pinheiro J, Bates D, DebRoy S, Sarkar D, Team RC (2020) nlme: linear and nonlinear mixed effects models, vol. 1: R: comprehen- sive R archive network. 20. Package'plm' [baps://cran.r-project.org/weh/packages/plm/plm. pol] 21. Croissant Y, Millo G (2019) Panel data econometrics with R, vol 1. Wiley, Oxford, UK 22. Bivand R., Anselin L, Berke 0, Bernat A, Carvalho M, Chun Y, Dormann C, Dray S, Halbersma R, Lewis -Koh N et al (2007) The spdep package. In: CRAN. p 1-143. 23. Millo G, Piras G (2012) splm: Spatial Panel Data Models in R. J of Stat Softw 47(1):1-38 24. Millo G, Piras G (2018) Package 'splm'. In: vol hops://cran.r-proje ct.org/weh/packages/splm/splm.pdf edn. Trieste, Italy: CRAN (Central R-Archive Network). pp 1-27. 25. Kapoor M, Kelejian HH, Prucha IR (2007) Panel data models with spatially correlated error components. J Econom 140(1):97-130 26. Van der Wal WM, Geskus RB (2011) ipw: an R package for inverse probability weighting. J Stat Softw 43(13):1-23 27. Lumley T (2010) Complex surveys: a guide to analysis using R, vol 1. Wiley, Hoboken. 28. Package 'E Value' 29. VanderWeele TJ, Ding P, Mathur M (2019) Technical considera- tions in the use of the E-value. J Causal Inference 7(2):1-11 30. VanderWeele TJ, Ding P (2017) Sensitivity analysis in obser- vational research: introducing the E-Value. Ann Intern Med 167(4):268-274 1 Springer European Archives of Psychiatry and Clinical Neuroscience 31. Reece AS, Hulse GK (2019) Effect of cannabis legalization on us autism incidence and medium term projections. Clin Pediatr: Open Access 4(2):1-17 32. McCantz-Katz E (2018) 2017 annual report snippets, NSDUH, SAMHSA, USA DHHS selected streamlined trends. Substance abuse and mental health services administration US department of health and human services, vol 1. SAMHSA, DHHS, USA, Rockville, Maryland, pp 1-78 33. McCantz-Katz E (2019) Urgent and emerging issues in pre- vention: marijuana, kratom and e-cigarettes. Substance abuse and mental health services administration USDoHaHS, vol 1. SAMHSA, DHHS, USA, Rockville, Maryland, pp 1-43 34. McCantz-Katz E (2020) The national survey of drug use and health: 2019. Substance abuse and mental health services administration, US department of health and human services, vol 1. SAMHSA, US DHHS, Silver Springs, Maryland, pp 1-63 35. Volkow ND, Compton WM, Wargo EM (2017) The risks of marijuana use during pregnancy. JAMA 317(2):129-130 36. Volkow ND, Han B, Compton WM, Blanco C (2017) Marijuana use during stages of pregnancy in the United States. Ann Intern Med 166(10):763-764 37. Corsi DJ (2020) The potential association between prena- tal cannabis use and congenital anomalies. J Addict Med 14(6):451-453 38. Corsi DJ, Donelle J, Sucha E, Hawken S, Hsu H, El-Chaar D, Bisnaire L, Fell D, Wen SW, Walker M (2020) Maternal cannabis use in pregnancy and child neurodevelopmental outcomes. Nat Med 26(10):1536-1540 39. United National Office of Drugs and Crime (2019) World drug report 2019. In: World Health Organization office of drugs and crime, vol 1-5. United National World Health Organization, Geneva, Switzerland. https://wdr.unodc.org/wdr2019/index.html. 40. Pearl J, Mackaenzie D (2019) The book of why: the new science of cause and effect, vol 1. Basic Books, NY 2019. 41. Reece AS, Hulse GK (2019) Epidemiological associations of vari- ous substances and multiple cannabinoids with autism in USA. Clin Pediatr: Open Access 4(2):1-20 42. Reece AS, Hulse GK (2019) Cannabis consumption patterns par- allel the east -west gradient in Canadian neural tube defect inci- dence: an ecological study. Glob Pediatr Health 6:1-12 43. Van Gelder MMHJ, Donders ART, Devine 0, Roeleveld N, Reefhuis J (2014) Using Bayesian models to assess the effects of under -reporting of cannabis use on the association with birth defects, national birth defects prevention study, 1997-2005. Pae- diatr Perinat Epidemiol 28(5):424-433 44. Van Gelder MMHJ, Reefhuis J, Caton AR, Werler MM, Druschel CM, Roeleveld N (2009) Maternal periconceptional illicit drug use and the risk of congenital malformations. Epidemiology 20(1):60-66 45. Forrester MB, Merz RD (2007) Risk of selected birth defects with prenatal illicit drug use, Hawaii, 1986-2002. J Toxicol Environ Health 70(1):7-18 46. Volkow ND, Baler RD, Compton WM, Weiss SR (2014) Adverse health effects of marijuana use. N Engl J Med 370(23):2219-2227 47. Reece AS (2009) Chronic toxicology of cannabis. Clinical toxicol- ogy (In Press) (Accepted 28/05/09). 48. McClean DK, Zimmerman AM (1976) Action of delta 9-tetrahy- drocannabinol on cell division and macromolecular synthesis in division -synchronized protozoa. Pharmacology 14(4):307-321 49. Parker SJ, Zuckerman BS, Zimmermann AS (1999) The effects of maternal marijuana use during pregnancy on fetal growth. In: Nahas GG, Sutin KM, Harvey DJ, Agurell S (eds) Marijuana in medicine, vol 1. Edn. Humana Press, Totowa, NY, pp 461-468 50. Tahir SK, Zimmerman AM (1991) Influence of marihuana on cellular structures and biochemical activities. Pharmacol Biochem Behav 40(3):617-623 51. Zimmerman AM, Raj AY (1980) Influence of cannabinoids on somatic cells in vivo. Pharmacology 21(4):277-287 52. Radyushkin K, Hammerschmidt K, Boretius S, Varoqueaux F, El-Kordi A, Ronnenberg A, Winter D, Frahm J, Fischer J, Brose N et al (2009) Neuroligin-3-deficient mice: model of a monogenic heritable form of autism with an olfactory deficit. Genes Brain Behav 8(4):416-425 53. Wang J, Yuan W, Li MD (2011) Genes and pathways co -asso- ciated with the exposure to multiple drugs of abuse, including alcohol, amphetamine/methamphetamine, cocaine, marijuana, morphine, and/or nicotine: a review of proteomics analyses. Mol Neurobiol 44(3):269-286 54. Hebert-Chatelain E, Desprez T, Serrat R, Bellocchio L, Soria - Gomez E, Busquets-Garcia A, Pagano Zottola AC, Delamarre A, Cannich A, Vincent P et al (2016) A cannabinoid link between mitochondria and memory. Nature 539(7630):555-559 55. Hebert-Chatelain E, Reguero L, Puente N, Lutz B, Chaouloff F, Rossignol R, Piazza PV, Benard G, Grandes P, Marsicano G (2014) Cannabinoid control of brain bioenergetics: explor- ing the subcellular localization of the CBI receptor. Mol Metab 3(4):495-504 56. Zalesky A, Solowij N, Yucel M, Lubman DI, Takagi M, Hard- ing IH, Lorenzetti V, Wang R, Searle K, Pantelis C et al (2012) Effect of long-term cannabis use on axonal fiber connectivity. Brain 135(Pt 7):2245-2255 57. Cutando L, Maldonado R, Ozaita A (2017) Microglial activation and cannabis exposure. In: Preedy V (ed) Handbook of cannabis and related pathologies: biology, pharmacology, diagnosis and treatment, vol 1. edn. Academic Press, NY, pp 401-412 58. Ising C, Venegas C, Zhang S, Scheiblich H, Schmidt SV, Vieira- Saecker A, Schwartz S, Albasset S, McManus RM, Tejera D et al (2019) NLRP3 inflammasome activation drives tau pathology. Nature 575(7784):669-673 59. Eisch AJ (2002) Adult neurogenesis: implications for psychiatry. Prog Brain Res 138:315-342 60. Eisch AJ, Barrot M, Schad CA, Self DW, Nestler EJ (2000) Opi- ates inhibit neurogenesis in the adult rat hippocampus. Proc Natl Acad Sci U S A 97(13):7579-7584 61. Alpar A, Tortoriello G, Calvigioni D, Niphakis MJ, Milenkovic I, Bakker J, Cameron GA, Hanics J, Morris CV, Fuzik J et al (2014) Endocannabinoids modulate cortical development by configuring Slit2/Robol signaling. Nat Commun 5:4421 62. Cardenas A, Villalba A, de Juan RC, Pico E, Kyrousi C, Tzika AC, Tessier -Lavigne M, Ma L, Drukker M, Cappello S et al (2018) Evolution of cortical neurogenesis in amniotes controlled by robo signaling levels. Cell 174(3 ):590.e521-606.e521 63. Murphy SK, Itchon-Ramos N, Visco Z, Huang Z, Grenier C, Schrott R, Acharya K, Boudreau MH, Price TM, Raburn DJ et al (2018) Cannabinoid exposure and altered DNA methylation in rat and human sperm. Epigenetics 13(12):1208-1221 64. Colizzi M, Iyegbe C, Powell J, Ursini G, Porcelli A, Bonvino A, Taurisano P, Romano R, Masellis R, Blasi G et al (2015) Interac- tion between functional genetic variation of DRD2 and cannabis use on risk of psychosis. Schizophr Bull 41(5):1171-1182 65. Spronk DB, Van der Schaaf ME, Cools R, De Bruijn ER, Franke B, van Wel JH, Ramaekers JG, Verkes RI (2016) Acute effects of cocaine and cannabis on reversal learning as a function of COMT and DRD2 genotype. Psychopharmacology 233(2):199-211 66. Watson CT, Szutorisz H, Garg P, Martin Q, Landry JA, Sharp AJ, Hurd YL (2015) Genome-wide DNA methylation profiling reveals epigenetic changes in the rat nucleus accumbens associ- ated with cross -generational effects of adolescent THC exposure. Neuropsychopharmacology 40(13):2993-3005 67. Schrott R, Murphy SK, Modliszewski JL, King DE, Hill B, Itchon- Ramos N, Raburn D, Price T, Levin ED, Vandrey R et al (2021) Springer European Archives of Psychiatry and Clinical Neuroscience Refraining from use diminishes cannabis -associated epigenetic changes in human sperm. Environ Epigenetics 7(1):1-10 68. Schrott R, Acharya K, Itchon-Ramos N, Hawkey AB, Pippen E, Mitchell JT, Kollins SH, Levin ED, Murphy SK (2019) Cannabis use is associated with potentially heritable widespread changes in autism candidate gene DLGAP2 DNA methylation in sperm. Epigenetics 15(1-2):161-173 69. GeneCards: Down syndrome cell adhesion molecule [https://www. genecards.org/cgi-binkarddisp.pl?gene=DSCAM] 70. Grossman TR, Gamliel A, Wessells RJ, Taghli-Lamallem 0, Jepsen K, Ocorr K, Korenberg JR, Peterson KL, Rosenfeld MG, Bodmer R et al (2011) Over -expression of DSCAM and COL6A2 cooperatively generates congenital heart defects. PLoS Genet 7(11):e1002344 71. Shibata M, Pattabiraman K, Lorente-Galdos B, Andrijevic D, Kim SK, Kaur N, Muchnik SK, Xing X, Santpere G, Sousa AMM et al (2021) Regulation of prefrontal patterning and connectivity by retinoic acid. Nature 598(7881):483-488 72. Carlson BM (2019) Human embryology and developmental biol- ogy, vol 1, 6 edn. Elsevier, Philadelphia. 73. Raad H, Cornelius V, Chan S, Williamson E, Cro S (2020) An evaluation of inverse probability weighting using the propensity score for baseline covariate adjustment in smaller population ran- domised controlled trials with a continuous outcome. BMC Med Res Methodol 20(1):70 74. VanderWeele TJ, Martin JN, Mathur MB (2020) E-values and incidence density sampling. Epidemiology 31(6):e51—e52 75. VanderWeele TJ, Mathur MB (2020) Commentary: developing best -practice guidelines for the reporting of E-values. Int J Epi- demiol 49(5 ):1495-1497 76. VanverWeele TJ, Mathur M, Chen Y (2020) Outcome -wide longi- tudinal designs for causal inference: a new template for empirical studies. Stat Sci 35:437-466 77. Corsi DJ, Walsh L, Weiss D, Hsu H, El-Chaar D, Hawken S, Fell DB, Walker M (2019) Association between self -reported prena- tal cannabis use and maternal perinatal, and neonatal outcomes. JAMA 322(2):145 78. McCance-Katz EF (2018) The key role of prevention in addressing the current landscape of substance use in America: a perspective. In: Services HaH (ed) SAMSHA's 14th annual prevention day. US Department of Health and Human Services, Washington DC. 79. Volkow ND, Han B, Compton WM, McCance-Katz EF (2019) Self -reported medical and nonmedical cannabis use among preg- nant women in the United States. JAMA 322(2):167-169 Springer