Literature DB >> 35966825

European epidemiological patterns of cannabis- and substance-related congenital cardiovascular anomalies: geospatiotemporal and causal inferential study.

Albert Stuart Reece1, Gary Kenneth Hulse1.   

Abstract

As prenatal and community cannabis exposures have recently been linked with congenital heart disease (CHD), it was of interest to explore these associations in Europe in a causal framework and space-time context. Congenital anomaly data from Eurocat, drug-use data from the European Monitoring Centre for Drugs and Drug Addiction, and income from the World Bank. Countries with rising daily cannabis use had in general higher congenital anomaly rates over time than those without (time: status interaction: β-Est. = 0.0267, P = 0.0059). At inverse probability-weighted panel regression, cannabis terms were positive and significant for CHD, severe CHD, atrial septal defect, ventricular septal defect, atrioventricular septal defect, patent ductus arteriosus, tetralogy of Fallot, vascular disruptions, double outlet right ventricle, transposition of the great vessels, hypoplastic right heart, and mitral valve anomalies from 1.75 × 10-19, 4.20 × 10-11, <2.2 × 10-16, <2.2 × 10-16, 1.58 × 10-12, 4.30 × 10-9, 4.36 × 10-16, 3.50 × 10-8, 5.35 × 10-12, <2.2 × 10-16, 5.65 × 10-5 and 6.06 × 10-10. At spatial regression, terms including cannabis were positive and significant for this same list of anomalies from 0.0038, 1.05 × 10-10, 0.0215, 8.94 × 10-6, 1.23 × 10-5, 2.05 × 10-5, 1.07 × 10-6, 8.77 × 10-5, 9.11 × 10-6, 0.0001, 3.10 × 10-7 and 2.17 × 10-7. 92.6% and 75.2% of 149 E-value estimates and minimum E-values were in high zone >9; 100.0% and 98.7% >1.25. Data show many congenital cardiac anomalies exhibit strong bivariate relationships with metrics of cannabis exposure. Causal inferential modelling for the twelve anomalies selected demonstrated convincing evidence of robust relationships to cannabis which survived adjustment and fulfilled epidemiological criteria for causal relationships. Space-time regression was similarly confirmatory. Epigenomic pathways constitute viable potential mechanisms. Given exponential genotoxic dose-response effects, careful and astute control of cannabinoid penetration is indicated.
© The Author(s) 2022. Published by Oxford University Press.

Entities:  

Keywords:  alcohol; cancer; cancerogenesis; cannabinoid; cannabis; epigenotoxicity; genotoxicity; mutagenesis; oncogenesis; tobacco; transgenerational inheritance

Year:  2022        PMID： 35966825      PMCID： PMC9364688          DOI： 10.1093/eep/dvac015

Source DB:  PubMed          Journal:  Environ Epigenet        ISSN： 2058-5888


Introduction

Cardiovascular anomalies form the commonest group of congenital anomalies (CAs) [1-3]. Following on from recent reports in HI, CO, Canada, Australia and the USA [4-8], as well as preliminary reports from Europe [9], it was of interest to study how the European pattern of cardiac congenital anomalies (CCAs) would behave when investigated in a formal causal inferential analytical framework and in the space-time context from which the native data were drawn. The earliest report of this association from HI, USA identified hypoplastic left heart syndrome, tetralogy of Fallot, pulmonary valve stenosis or atresia, ventricular septal defect (VSD) and atrial septal defect (ASD) as being significantly related to prenatal cannabis exposure [4]. In Canada, the total cardiovascular defects were linked with community cannabis consumption [7]. In CO, USA, pulmonary artery anomalies, ASD, patent ductus arteriosus (PDA) and VSD were significantly cannabis related [5]. In Australia, ASD, VSD, PDA and tetralogy of Fallot were more common in a high cannabis using area, while the transposition of the great vessels was of borderline significance [6]. In the report from the USA, interrupted aortic arch, hypoplastic left heart syndrome, aortic valve stenosis, VSD, pulmonary valve atresia, total anomalous pulmonary return, tetralogy of Fallot, coarctation of the aorta, ASD and single ventricle were all related to Δ9-tetrahydrocannabinol (THC) exposure, and the transposition of the great vessels was related to cannabidiol exposure [8, 10]. Morphogenesis of the heart and great vessels is very complex. The heart at first forms from the fusion of the two dorsal aortae which become pulsatile. This fused dorsal heart tube then folds, twists and turns to provide the somewhat twisted shape of the adult heart. The heart forms from cells in six cardiogenic fields, the primary, secondary and lateral heart fields, the proepicardium, the neural crest and parts of the great vessels are formed from the somites of the pharyngeal arches [11]. Genes that are important to heart development and are known as the core regulatory network are MEF2, NKX2, GATA, Tbx and Hand-1 and Hand-2 [11]. Impairment of Hand-1 expression leads to left ventricular anomalies. Impairments of Hand-2 lead to right ventricular anomalies [11]. Genes that are important in arteriogenesis include sonic hedgehog/vascular endothelial growth factor/notch/Eph-4/Ephrin-B2 [11] Genes that are important in venogenesis include COUP-TFII/notch/Eph-4. Moreover, the recent availability of detailed epigenomic resources describing lists of genes altered both in cannabis dependence and in cannabis withdrawal [12] indicates that it should be possible to gain a fair degree of mechanistic insight into the pathways which may be acting to generate the observed profile of teratological disturbances. Cannabis is used widely by young people in the reproductive age group. Indeed, one recent study reported that 24% of pregnant Californian teenagers recently tested positive for cannabis [13], a situation that appears to have deteriorated since the COVID-19 pandemic commenced [14]. It was recently reported that an estimated 169 000 American women used cannabis while pregnant [15-18]. Moreover, it was recently reported that 69% of Coloradan cannabis dispensaries actively recommended cannabis preparations for many of the side effects of pregnancy, such as insomnia, tiredness, anxiety and nausea [19], which interestingly is the identical group of indications for which thalidomide was also recommended in about 1958 [20]. The European Network of Population-Based Registries for the Epidemiological Surveillance of Congenital Anomalies (EUROCAT) [21] database tracks 95 birth defects across time which provides unusual detail and depth to analyses of body systems. It also provides a category for system-wide anomalies such as total congenital heart disease (CHD). Importantly, the total CA rate (CAR) includes live births, stillbirths and cases where early termination for anomaly was practised, all combined together so that it represents a total overall picture across all classes of births. The European Monitoring Centre for Drugs and Drug Addiction has a very detailed dataset of various metrics of cannabis use, which combined with a recent epidemiological analysis of cannabis use patterns across Europe [22] means that in-depth and detailed analyses can be conducted in this population. One of our particular concerns going into this analysis was that many cannabinoids have shown a very clear exponential genotoxic dose–response relationship relating to diverse mutagenic and DNA-damaging activities [23-33], as well as the basic metabolic reactions on which genomic and epigenomic stability depends [34-39]. Moreover, recent epidemiological studies have confirmed that at the highest environmental doses of cannabis, a sharp jump is observed in the rates of many CAs [10]. Since a number of European countries have recently experienced a major increase in all of cannabis use prevalence, daily cannabis use intensity and cannabinoid potency [22, 40] it would appear that community cannabinoid exposure has increased rapidly there in recent years. Furthermore, there is some indication that cannabinoids are entering the food chain in countries such as France based on large crops of cannabis grown in parts of that nation and accompanying teratological outbreaks of major limb deformities in both bovine and human babies [41-43]. For these reasons, we are concerned that the sharp increase in cannabinoid penetration into the community colliding with the well-defined genotoxic dose-response curve [25–29, 33, 38] may well lead to further major genotoxic outcomes reflected in patterns of disease in whole populations. The present study set out to study the following hypotheses and study questions: (i) Is there a bivariate relationship between exposure to various metrics of cannabis exposure and some cardiac CAs? (ii) Are these relationships robust to multivariable adjustment? (iii) Do these relationships fulfill quantitative criteria for causality? (iv) Was this confirmed at space-time analysis? (v) To what extent do epigenomic mechanisms potentially explain these observations? These hypotheses were formulated prior to the study analysis being performed.

Methods

Data

Data on all available CARs were downloaded by each individual year for each of 14 nations from the EUROCAT website [21] and analysed. The total CAR includes anomaly rates among live births, stillbirths and cases where early termination for anomaly was practised, all combined together so that it represents a total overall picture across all classes of births. The nations selected were chosen on the basis of the availability of their CA data for most of the years 2010–2019. National tobacco (percent daily tobacco use prevalence) and alcohol (litres of pure alcohol consumed per capita annually) use data were downloaded from the World Health Organization [44]. Drug use data for cannabis, amphetamines and cocaine were taken from the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) [45]. Data relate to both sexes considered together. Last month cannabis use data were also supplemented by data on the THC content of cannabis herb and resin provided in recently published reports [22]. Data on daily cannabis use were also available from the EMCDDA and were collated in recent reports [22]. Median household income data (in $USD) were taken from the World Bank [46].

National Assignment

Nations were categorized as being either high and rising daily cannabis use or low and/or falling daily cannabis use based on a recent European epidemiological study (see Supplementary Fig. S4) [22]. Thus, Belgium, Croatia, France, Germany, Italy, Netherlands, Norway, Portugal and Spain were categorized as nations experiencing increasing daily use, while Bulgaria, Finland, Hungary, Poland and Sweden were nations which were experiencing low or falling levels of daily cannabis use.

Derived Data

The availability of several metrics of cannabis use, exposure and consumption made it possible to calculate various derived metrics. Hence, last month cannabis use prevalence data were multiplied by the THC content of cannabis herb and resin to derive compound metrics. These metrics were also multiplied by imputed daily cannabis use prevalence rates to derive further compound metrics for both cannabis herb and resin.

Data Imputation

Missing data were completed by linear interpolation. This was particularly the case for daily cannabis use. Fifty-nine data points on daily cannabis use from the EMCDDA were available for these 14 nations across this period. Linear interpolation expanded this dataset to 129 data points (further details are provided in the Results section). Data on cannabis resin THC concentration were not available for Sweden. However, it was noted that the resin to herb THC concentration was almost constant in nearby Norway at 17.7, so this ratio was applied to the Swedish cannabis herb THC concentration data to derive estimates of Swedish cannabis resin THC concentration. Similarly, data for the cannabis resin THC concentration in Poland were not available. The resin to herb THC concentration ratio of nearby Germany was used to estimate the resin THC content in Poland from the known Polish herb THC concentrations. Since geospatial analytical techniques do not tolerate missing data, the dataset was completed by the last observation carried forwards or backwards for Croatia in 2018 and 2019 and Netherlands in 2010. It was not appropriate to use multiple imputation methods for this dataset as multiple imputations cannot be applied in panel or spatial multivariable regression techniques.

Statistics

Data were processed in R Studio version 1.4.1717 based on R version 4.1.1 from the Comprehensive R Archive Network and the R Foundation for Statistical Computing [47]. The analysis was conducted in December 2021. Data were manipulated using dplyr from the tidyverse [48]. Data were log-transformed where appropriate to improve compliance with normality assumptions based on the results of the Shapiro–Wilks test. Graphs were drawn in ggplot2 from tidyverse. Maps were drawn using ggplot2, sf (simple features) [49] and both custom colour palettes and palettes taken from the viridis and viridisLite packages [50]. Bivariate maps were drawn with package colorplaner [51]. All illustrations are original and have not been published previously. Linear regression was conducted in Base R. Mixed-effects regression was performed using package nlme [52]. In all multivariable models, model reduction was by the classical technique of serial deletion of the least significant term to yield a final reduced model which is the model presented. Multiple linear models were processed in a single pass using combined techniques from R packages purrr and broom [48, 53, 54]. The overall effect of covariates in multivariable models may be quantified as the marginal effect. In this case, the overall marginal effect was calculated using the R package margins [55].

Covariate Selection

The presence of multiple different metrics for cannabis consumption and exposure created a problem for analysis as it was not clear which was the most appropriate metric to employ for any particular model. Indiscriminate use of excessive covariates in a multivariable model would unnecessarily consume degrees of freedom and thereby restrict the ability to assess interactions. This issue was formally addressed by the use of random forest regression using the R package ranger [56] with variable importance being formally assessed via the R package vip (variable importance plot) [57]. The most predictive covariates from this process were entered into the regression modelling equations. The tables from this analysis are presented in the Results section.

Panel and Geospatial Analysis

Panel analysis was conducted using R package plm [58] across both space and time simultaneously using the ‘twoways’ effect. The spatial weights matrix was calculated using the edge and corner ‘queen’ relationships using R package spdep (spatial dependency) [59]. Geospatial modelling was conducted using the spatial panel random effects maximum likelihood (spreml) function from the package spml which allows detailed modelling and correction of model error structures [60, 61]. Such models may produce four model coefficients of interest which are useful in determining the most appropriate error structure for the model. These coefficients are phi, the random error effect; psi, the serial correlation effect; rho, the spatial coefficient, and theta, the spatial autocorrelation coefficient. In each case, the most appropriate error structure was chosen for each spatial model generally taking care to preserve the model error specification across related models. The appropriate error structure was determined by the backward methods from the full general model to the most specific model, as has been described [62]. Both panel and geospatial models were temporally lagged as indicated by 1–2 years.

Causal Inference

The formal tools of causal inference were used in this analysis. Inverse probability weighting (ipw) is the technique of choice to convert a purely observational study into a pseudo-randomized study from which it is appropriate to make causal inferences [63]. All multivariable panel models presented herein were inverse probability weighted. ipw was performed using the R package ipw. Similarly, E-values (expected values) quantify the correlation required for some hypothetical unmeasured confounder covariate with both the exposure of concern and the outcome of interest in order to explain away some apparently causal relationship [64-66]. It therefore provides a quantitative measure of the robustness of the model to extraneous covariates which have not been accounted for within the measured parameters. E-values have a confidence interval associated with them and the 95% lower bound of this confidence interval is reported herein. E-value estimates >1.25 are said to indicate causality [67] with E-values >9 being described as high [68]. E-values were calculated from the R package E-value [69]. Both ipw and E-values are foundational and pivotal techniques used in formal causal inferential methods in order to allow causal relationships to be assessed from real-world observational studies.

Data Availability

Raw datasets, including 3800 lines of computation code in R, have been made freely available through the Mendeley data repository at the following URLs: 10.17632/tysn37t426.1 and 10.17632/nm3tgcvvzd.1. This study was not pre-registered in the Open Science Framework.

Ethics

Ethical approval for this study was provided by the Human Research Ethics Committee of the University of Western Australia number RA/4/20/4724 on 24 September 2021.

Results

Supplementary Table ST1 provides an overview of study data. As shown, total CAR data were derived from 14 nations for 24 cardiovascular anomalies for the years 2010–2019. The total CAR includes stillborn and early termination of pregnancy for anomaly rates in addition to babies born normally alive and is a critical metric required to properly assess trends in CARs. The total sample size of the whole Eurocat database for these nations was 77 410 rates. Drug and cannabis use and exposure rates are also shown in this table including various compound metrics. Median household income is also shown. Daily cannabis use data across the various jurisdictions of Europe were largely incomplete as indicated in Supplementary Table ST2 where only 59 raw data points are shown. These data were completed by linear interpolation, as shown in Supplementary Table ST3, so that 129 points were finally available for analysis. Figures 1–3 show the rates of the various CAs against the use of the different substances. Eight CAs are shown in each figure which has been split into three parts to accommodate all 24 CAs. From these figures, it is apparent that tobacco exposure is for the most part negatively related to CAR. Regression line slopes for alcohol are mostly flat or negative but generally weak. Regression line slopes for amphetamine are mostly flat or somewhat positive. The lines of best fit for cocaine are generally positive, and some, such as CHD, double outlet right ventricle and mitral valve anomalies, are quite strongly so. The cannabis exposure metric chosen in this graph is daily cannabis use interpolated. For most anomalies, the regression line is flat or weakly positive. However, for some anomalies such as CHD, double outlet right ventricle and mitral valve anomalies, the regression line indicates strong and robust obviously positive associations. Panelled scatterplot of log (rates of selected CCAs) against rates of substance exposure—1 Panelled scatterplot of log (rates of selected CCAs) against rates of substance exposure—2 Panelled scatterplot of log (rates of selected CCAs) against rates of substance exposure—3 Figures 4–6 chart the bivariate relationships of the different cannabis metrics with the spectrum of CARs. Some of the steepest positively sloped lines are for atrioventricular septal defect (AVSD), severe CHD, double outlet right ventricle, pulmonary valve atresia, transposition of the great vessels and hypoplasia of the right heart which are all seen as a function of cannabis resin THC concentration. Panelled scatterplot of log (rates of selected CCAs) against rates of cannabis metric exposure—1 Panelled scatterplot of log (rates of selected CCAs) against rates of cannabis metric exposure—2 Panelled scatterplot of log (rates of selected CCAs) against rates of cannabis metric exposure—3 Figure 7 shows a graphical map of the CHD rate across Europe during this decade. Many European nations are darkly shaded, indicating relatively high rates of CHD. Figure 8 depicts the rates of severe CHD which appears to have a more varied pattern. Figures 9–12 illustrate the rates for ASD, VSD, PDA and Ebstein’s anomaly, respectively.
Figure 7:

Sequential map-graphs of log (rates of CHD) across selected European countries over time, 2010–2019

Figure 8:

Sequential map-graphs of log (rates of severe CHD) across selected European countries over time, 2010–2019

Sequential map-graphs of log (rates of CHD) across selected European countries over time, 2010–2019 Sequential map-graphs of log (rates of severe CHD) across selected European countries over time, 2010–2019 Sequential map-graphs of log (rates of ASD) across selected European countries over time, 2010–2019 Sequential map-graphs of log (rates of VSD) across selected European countries over time, 2010–2019 Sequential map-graphs of log (rates of PDA) across selected European countries over time, 2010–2019 Sequential map-graphs of log (rates of Ebsteins anomaly) across selected European countries over time, 2010–2019 Patterns of daily cannabis use are illustrated in Fig. 13 which indicates increases in Spain, Netherlands, Belgium, France and Norway, reductions in Poland and little change in Bulgaria.
Figure 13:

Sequential map-graphs of rates of daily cannabis use interpolated across selected European countries over time, 2010–2019

Sequential map-graphs of rates of daily cannabis use interpolated across selected European countries over time, 2010–2019 Figure 14 is a bivariate colorplane plot showing the bivariate relationship between CHD and the compound cannabis metric last month cannabis use × resin THC concentration × daily use interpolated. The areas covered by Spain and France are seen to obviously turn from tan and brown in 2010 to purple and crimson in 2019, indicating that they have moved up into the high cannabis metric—high CHD zone of the colorplane (shown in the key at the right hand side of the graph).
Figure 14:

Bivariate colorplane sequential map-graphs of log (rates of CHD) by last month cannabis use: cannabis resin THC concentration: daily cannabis use interpolated across selected European countries over time, 2010–2019

Bivariate colorplane sequential map-graphs of log (rates of CHD) by last month cannabis use: cannabis resin THC concentration: daily cannabis use interpolated across selected European countries over time, 2010–2019 A similar change occurs across France and Spain for severe CHD as indicated in Fig. 15.
Figure 15:

Bivariate colorplane sequential map-graphs of log (rates of severe CHD) by last month cannabis use: cannabis resin THC concentration: daily cannabis use interpolated across selected European countries over time, 2010–2019

Bivariate colorplane sequential map-graphs of log (rates of severe CHD) by last month cannabis use: cannabis resin THC concentration: daily cannabis use interpolated across selected European countries over time, 2010–2019 For the relationship between cannabis herb and CHD, the map is noted to have turned increasingly purple with time as nations more into the higher zone on both covariates (Fig. 16). Similar comments apply to the relationship between severe CHD and cannabis herb THC concentration, as shown in Fig. 17.
Figure 16:

Bivariate colorplane sequential map-graphs of log (rates of CHD) by cannabis herb THC concentration across selected European countries over time, 2010–2019

Figure 17:

Bivariate colorplane sequential map-graphs of log (rates of severe CHD) by cannabis herb THC concentration across selected European countries over time, 2010–2019

Bivariate colorplane sequential map-graphs of log (rates of CHD) by cannabis herb THC concentration across selected European countries over time, 2010–2019 Figure 18 shows the bivariate relationship between severe CHD and cannabis resin THC concentration. Here, the map is noted to turn pink and purple across most countries across the decade.
Figure 18:

Bivariate colorplane sequential map-graphs of log (rates of severe CHD) by cannabis resin THC concentration across selected European countries over time, 2010–2019

Bivariate colorplane sequential map-graphs of log (rates of severe CHD) by cannabis herb THC concentration across selected European countries over time, 2010–2019 Bivariate colorplane sequential map-graphs of log (rates of severe CHD) by cannabis resin THC concentration across selected European countries over time, 2010–2019 Figure 19 shows the relationship between AVSD and cannabis resin THC concentration. The area for France changes shades of purple across the period. The areas covered by Germany and Norway appear to change in their blue-purple hues across this period.
Figure 19:

Bivariate colorplane sequential map-graphs of log (rates of AVSD) by cannabis resin THC concentration across selected European countries over time, 2010–2019

Bivariate colorplane sequential map-graphs of log (rates of AVSD) by cannabis resin THC concentration across selected European countries over time, 2010–2019 When the relationship between transposition of the great vessels and cannabis resin THC concentration is considered, the map is also noted to have become more purple generally (Fig. 20).
Figure 20:

Bivariate colorplane sequential map-graphs of log (rates of transposition of the great vessels) by cannabis resin THC concentration across selected European countries over time, 2010–2019

Bivariate colorplane sequential map-graphs of log (rates of transposition of the great vessels) by cannabis resin THC concentration across selected European countries over time, 2010–2019 Based on recent findings reported in major epidemiological reviews [22], it is possible to categorize European nations into increasing and high daily use and low or decreasing daily use groups. A panelled series of these defects across the decade are shown in Fig. 21. For most anomalies, there is a significant overlap between the areas covered by both sets of nations. However, for some anomalies, such as CHD, double outlet right ventricle, hypoplastic left heart syndrome, mitral valve anomalies and tricuspid valve stenosis or atresia, the rate in the nations with increasing daily use is higher than those which do not have this feature. Mixed-effects regression with anomaly as the random effect confirms that the time: daily use rate interaction is significantly higher in the countries with increasing cannabis use (β-Est. = 0.267, t = 2.756, P = 0.059; model AIC = 5317.51, LogLik. = −2652.75).
Figure 21:

Log rates of CCAs by daily cannabis use status by CA. See Methods section for categorization of nations

Log rates of CCAs by daily cannabis use status by CA. See Methods section for categorization of nations The slopes of 275 of these regression lines for each anomaly and each substance are provided in Supplementary Table ST4 using a purrr-broom simultaneous multigroup analytical workflow. From this list, the 69 models with positive and significant regression coefficients were selected out and are shown in Table 1. The table is ordered in descending order of minimum E-values (mEV). It is of interest therefore that the first 30 terms in this table all relate to cannabis exposure metrics. Of the 69 terms, 15 relate to cocaine, 5 to amphetamines, 1 to alcohol and 48 (69.6%) to various cannabis metrics. It is noted that the E-value estimates descend from 1.35 × 1019, and the mEVs decline from 4.78 × 1010.
Table 1:

Significant positive slopes from bivariate regression

AnomalySubstanceMean Anomaly RateEstimateStd. ErrorSigma t-statistic P-Value E-Value Estimate E-Value Lower Bound
Mitral valve anomaliesDaily.Interpol.1.500333.81287.75880.70974.35803.07E-051.35E + 194.78E + 10
Congenital heartDaily.Interpol.79.906218.61274.53130.41454.10767.94E-051.11968E + 183.96E + 09
Congenital heartHerb79.90627.25191.14760.38016.31926.48E-096.95E + 073.22E + 05
Congenital heartLMCannabis_Herb79.90627.42711.56500.40524.74596.58E-063.51E + 073.63E + 04
PV stenosisDaily.Interpol.4.036024.23718.37710.78632.89330.00463.04046E + 121.77E + 04
Double outlet RVHerb1.10797.18711.65280.54744.34853.18E-053.09E + 051.43E + 03
Mitral valve anomaliesLMCannabis_Herb1.50039.39522.83400.73373.31510.00132.30E + 05236.98
Severe CHDHerb21.66094.22061.09390.42533.85821.85E-041.67E + 04171.06
Mitral valve anomaliesHerb1.50037.84482.19920.72833.56715.45E-043.61E + 04166.84
Congenital heartLM_Cannabis79.90625.90881.99910.42902.95570.00395.55E + 05138.14
PDADaily.Interpol.2.674323.928610.12800.95062.36260.019917 732 087 319102.52
Double outlet RVDaily.Interpol.1.107914.57096.34350.58022.29700.023616 820 336 69358.96
Double outlet RVLMCannabis_Herb1.10796.35642.21130.57252.87450.00494.88E + 0449.96
Transpos Grt VessHerb3.28535.10771.53610.59733.32510.00124.79E + 0348.76
AVSDResin4.01673.73610.73430.66015.08781.59E-06344.4947.12
Severe CHDResin21.66092.14760.42640.38335.03611.98E-06326.9244.70
AVSDHerb4.01675.37121.75490.68243.06070.00272.58E + 0326.03
Mitral valve anomaliesLM_Cannabis1.50038.60353.49400.74982.46240.01546.85E + 0416.60
Mitral valve anomaliesLM.Cannabis_x_Herb.THC_x_Daily.Interpol.1.50033.16480.86210.72603.67103.82E-04105.1412.24
PDAHerb2.67436.56302.39690.93202.73820.00711.21E + 0311.95
Congenital heartLM.Cannabis_x_Herb.THC_x_Daily.Interpol.79.90621.70860.50330.42393.39479.71E-0477.878.94
Hypoplastic right heartResin0.67571.78230.52560.47253.39089.83E-0461.408.01
Vascular disruptionsHerb6.66754.03691.67510.55482.41000.01771.50E + 036.42
Aortic atresia ∼Daily.Interpol.0.506710.35384.98960.45642.07510.04041 849 078 3955.99
Transpos Grt VessLMCannabis_Herb3.28534.92622.12750.61072.31550.02233.08E + 035.70
VSDResin41.64642.01140.70530.63412.85170.005235.364.39
PDALM.Cannabis_x_Herb.THC_x_Daily.Interpol.2.67433.02561.10460.94302.73900.007236.564.04
PV stenosisLM.Cannabis_x_Herb.THC_x_Daily.Interpol.4.03602.49870.92510.78982.70090.008035.083.85
Severe CHDLMCannabis_Resin21.66090.97130.34390.41182.82440.005716.593.28
Transpos Grt VessResin3.28531.81580.69630.62592.60770.010427.513.28
Congenital heartCocaine79.90620.35360.04890.36497.22788.21E-114.263.21
AVSDLMCannabis_Resin4.01671.60050.59540.71292.68830.008414.912.88
Tetralogy of FallotLMCannabis_Herb3.05324.54962.14490.61572.12110.03601.66E + 032.76
Vascular disruptionsResin6.66751.59290.65300.56222.43930.016625.842.72
VSDHerb41.64643.71831.71680.66752.16580.0323317.462.65
PV stenosisLM.Cannabis_x_Resin.THC_x_Daily.Interpol.4.03601.16970.40950.76642.85620.00527.492.47
Double outlet RVCocaine1.10790.35910.07160.53415.01472.17E-063.092.26
Double outlet RVResin1.10791.56740.69000.59412.27150.025421.562.14
Mitral valve anomaliesCocaine1.50030.43500.09430.70324.61371.12E-052.912.11
PV atresiaResin1.09931.47850.66170.59482.23430.027618.691.98
Transpos Grt VessCocaine3.28530.27720.07350.59023.77142.53E-042.441.76
Severe CHDCocaine21.66090.19700.05320.42723.70433.22E-042.411.74
VSDLMCannabis_Resin41.64641.18630.53730.64342.20780.029410.181.72
Transpos Grt VessLMCannabis_Resin3.28531.15830.52740.63162.19610.030310.091.69
ASDHerb21.06953.76141.85270.72042.03020.0445230.981.66
Hypoplastic left heartCocaine2.30720.27250.08330.66893.27130.00142.251.59
PDACocaine2.67430.37090.11470.92143.23290.00162.241.58
Hypoplastic right heartLMCannabis_Resin0.67570.87250.40680.48712.14460.03439.681.58
PV atresiaCocaine1.09930.20210.07100.57022.84650.00522.111.45
Vascular disruptionsLog(Amphetamine)6.66750.19690.07110.55022.76870.00672.121.43
Congenital heartLM.Cannabis_x_Resin.THC_x_Daily.Interpol.79.90620.51140.23900.44492.13970.03495.141.41
PV stenosisCocaine4.03600.26360.09690.77792.72130.00752.061.40
Tetralogy of FallotCocaine3.05320.20610.07580.60872.71900.00752.061.40
Aortic atresia ∼Cocaine0.50670.16070.06040.45072.65830.00912.111.40
VSDLM.Cannabis_x_Resin.THC_x_Daily.Interpol.41.64640.74130.34990.65472.11880.03665.051.38
ASDDaily.Interpol.21.06950.23630.08930.71222.64540.00932.041.38
Arterial truncusLog(Amphetamine)0.94270.19640.07530.60092.60650.01032.031.37
Severe CHDLog(Amphetamine)21.66090.14340.05500.43872.60600.01032.031.36
Aortic valve S/ALog(Amphetamine)1.49070.18650.07510.59872.48420.01441.991.32
Coarctation aortaCocaine3.61300.22960.09260.74332.48040.01451.981.32
AVSDCocaine4.01670.21000.08610.69162.43830.01621.971.30
Vascular disruptionsLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.6.66750.17760.07440.55502.38620.01882.011.29
Congenital heartLog(Amphetamine)79.90620.13140.05630.43542.33410.02151.961.26
Mitral valve anomaliesAnnual_Alcohol1.50030.11000.03830.74252.87440.00491.551.26
TV S/ACocaine0.59070.12730.05530.44382.30280.02301.921.24
VSDCocaine41.64640.18330.08310.66712.20710.02921.891.20
Tetralogy of FallotLM.Cannabis_x_Resin.THC_x_Daily.Interpol.3.05320.70430.35480.66391.98500.04994.691.13
AVSDLog(Amphetamine)4.01670.18100.08730.69612.07320.04031.851.13
Ebstein’s anomalyCocaine0.43270.09240.04600.36912.01010.04671.821.08
Table 1 lists both P-values and E-values. By analogy with the genomic and epigenomic literature, it is therefore possible to present these data for graphical consideration as a ‘volcano plot’ which plots the negative logarithm of the P-value against the fold change in the anomaly rate quantified by the E-value. Figure 22 presents such a plot for the E-value estimate itself, and Fig. 23 presents a similar plot for the mEV. In these figures, strong signals are indicated for CHD and mitral valve anomalies and many other CAs as indicated.
Figure 22:

Volcano plot of negative log of P-values against log of E-value estimates for bivariate regressions

Figure 23:

Volcano plot of negative log of P-values against log of mEV for bivariate regressions

Significant positive slopes from bivariate regression Volcano plot of negative log of P-values against log of E-value estimates for bivariate regressions Volcano plot of negative log of P-values against log of mEV for bivariate regressions Given these impressive results at bivariate analysis, it is of interest to investigate how all of these variables compare in multivariable regression techniques. Given that there are so many primary and compound cannabis metrics, it is not entirely clear which covariates are most suitable to use, given that the regression equations to be employed have a very finite number of degrees of freedom. The issue of covariate selection was addressed formally using random Forrest regression in the ranger package in tandem with the variable importance package vip to create tables of relative variable importance. These tables are presented as Supplementary Tables S5–S16 for the 12 CAs in which we were most interested. As the data are well suited to panel regression, this format was chosen to perform multiple regression analyses on these data. All panel regression models were inverse probability weighted which is an important step that transfers the analysis from merely an observational study into a pseudo-randomized analytical and interpretive context. Supplementary Table S17 shows the result of panel regression of the CHD dataset against the group of covariates indicated in the table. Additive, interactive and models that lagged temporally to 1 and 2 years are presented. In each case, several terms including cannabis have positive regression coefficients and are highly statistically significant. This same pattern of results is continued across all of the selected CAs, namely severe CHD, ASD, VSD, AVSDs, patient ductus arteriosus, tetralogy of Fallot, vascular disruption, double outlet right ventricle, transposition of the great arteries, hypoplastic right heart syndrome and mitral valve anomalies, as shown in Supplementary Tables S18–S28. In all cases, all multivariable panel models are inverse probability weighted to allow causal relationships to be formally assessed. Having made the important above observations, the next issue was whether these relationships would persist when formal space-time modelling was undertaken in a fashion which allows random effects, serial correlation, spatial correlation and spatial autocorrelation in the error structure to be formally considered. Geospatial links were derived, edited and finalized, as indicated in Supplementary Fig. S1, and these formed the basis of the sparse spatial weights matrix which was then employed in the R package spml to conduct formal spatiotemporal regression. Tables 2–13 present the results of final reduced models for each of the 12 anomalies cited in the preceding paragraphs immediately above. In virtually all models terms including cannabis metrics persist in the final models, have positive regression coefficients and are statistically significant usually at high levels. Final multivariate geospatial regression models for CHD Final multivariate geospatial regression models for severe CHD Final multivariate geospatial regression models for ASD Final multivariate geospatial regression models for VSD Final multivariate geospatial regression models for AVSD Final multivariate geospatial regression models for PDA Final multivariate geospatial regression models for tetralogy of Fallot Final multivariate geospatial regression models for vascular disruptions Final multivariate geospatial regression models for double outlet right ventricle Final multivariate geospatial regression models for transposition of the great vessels Final multivariate geospatial regression models for hypoplastic right heart Final multivariate geospatial regression models for mitral valve anomalies Tables 14 and 15 present the E-values to emerge from these positive and significant cannabis-related terms in the panel and spatial regression model sets, respectively. E-values from panel models E-values from geospatial models These lists are then combined into the lists of 149 E-values derived from both sets of models, as shown in Table 16. It was found that 138/149 (92.6%) of the E-value estimates were >9 and so in the high zone, and all 149 exceeded the cut-off for causality at 1.25 [67]. 112/149 (75.2%) mEVs exceeded 9 and so fell in the high zone [68], and 147/149 mEVs (98.7%) exceeded the threshold for causality at 1.25 [67].
Table 16:

List of all E-values

No. E-Value EstimateLower Bound E-Value
1Infinity4.28E + 135
21.54E + 1392.06E + 49
31.17E + 953.41E + 44
44.37E + 593.15E + 34
53.41E + 571.07E + 21
65.07E + 497.81E + 20
77.77E + 391.04E + 17
85.36E + 357.82E + 16
95.17E + 334.97E + 16
102.08E + 323.88E + 16
113.71E + 284.76E + 15
121.63E + 271.86E + 15
131.04E + 253.72E + 13
145.10E + 242.54E + 13
151.67E + 248.06E + 12
164.45E + 235.81E + 12
176.92E + 211.44E + 12
181.60E + 214.28E + 11
195.56E + 202.95E + 10
208.06E + 191.77E + 10
213.79E + 191.30E + 10
222.31E + 181.26E + 10
234.00E + 174.81E + 09
241.16E + 172.81E + 09
251.37E + 161.61E + 09
269.01E + 151.34E + 09
278.51E + 143.82E + 08
283.95E + 142.03E + 08
299.24E + 136.85E + 07
305.25E + 136.31E + 07
315.19E + 131.18E + 07
324.84E + 137.61E + 05
332.21E + 135.89E + 05
341.19E + 135.74E + 05
351.11E + 134.91E + 05
363.38E + 122.94E + 05
372.54E + 122.58E + 05
387.34E + 112.02E + 05
392.74E + 111.96E + 05
401.20E + 111.96E + 05
419.50E + 091.86E + 05
427.28E + 091.74E + 05
437.25E + 097.77E + 04
444.74E + 094.19E + 04
456.66E + 082.73E + 04
464.17E + 082.14E + 04
473.52E + 081.76E + 04
482.93E + 081.60E + 04
492.51E + 081.08E + 04
509.44E + 079.39E + 03
518.11E + 077.03E + 03
526.18E + 074.93E + 03
535.02E + 073.72E + 03
544.41E + 073.67E + 03
553.89E + 072.43E + 03
562.95E + 072.10E + 03
577.57E + 061.68E + 03
585.09E + 061.52E + 03
593.11E + 061.23E + 03
603.04E + 061.18E + 03
612.38E + 061.08E + 03
622.38E + 061.03E + 03
631.80E + 061.03E + 03
641.58E + 06868.53
659.91E + 05850.24
666.44E + 05740.60
674.08E + 05686.08
683.64E + 05576.84
693.59E + 05503.90
703.30E + 05344.71
713.21E + 05255.77
722.22E + 05212.19
732.21E + 05155.66
742.18E + 05121.42
751.34E + 05118.27
761.20E + 05111.26
771.20E + 0591.92
781.19E + 0590.06
791.09E + 0581.68
807.77E + 0470.68
817.76E + 0470.68
826.79E + 0467.27
835.06E + 0461.38
844.53E + 0459.42
853.97E + 0455.25
862.33E + 0454.91
871.79E + 0450.46
881.24E + 0449.19
898.04E + 0344.81
906.74E + 0343.32
916.71E + 0343.32
925.88E + 0342.72
935.88E + 0332.41
944.95E + 0329.73
954.79E + 0325.77
964.73E + 0325.13
973.92E + 0325.13
983.81E + 0323.91
993.78E + 0323.91
1003.46E + 0322.14
1012.80E + 0320.47
1021.61E + 0319.41
1031.48E + 0319.02
1041.48E + 0318.81
1051.41E + 0317.74
1061.31E + 0317.69
1071.11E + 0315.34
108960.3514.31
109901.9512.31
110897.511.38
111801.0911.26
112616.4910.85
113474.568.52
114293.638.12
115293.638.07
116281.877.86
117263.777.55
118243.57.38
119144.66.31
120108.115.99
12166.555.91
12262.875.44
12362.365.19
12454.684.83
12552.934.51
12644.483.89
12736.963.67
12833.673.62
12933.653.48
13026.763.06
13125.732.95
13225.492.76
13324.972.67
13415.342.56
13513.112.28
13612.512.10
1379.272.03
1389.241.99
1396.951.99
1405.641.98
1415.561.81
1424.921.71
1434.911.59
1444.061.58
1453.721.53
1462.491.44
1472.371.26
1481.541.24
1491.481.07
List of all E-values Table 17 lists these E-values by CA. Table 18 summarizes these data by CA listed in descending order of median mEV. This table is of interest for several reasons and is considered further in the Discussion section below.
Table 17:

E-values by anomaly

No.RegressionAnomalyModel_TypeTermGroup P-Value E-Value EstimateLower Bound E-Value
1SpatialASD2 LagsDaily.Interpol.Daily0.02151.54E + 1391.07E + 21
2PanelASDInteractiveHerbHerb2.32E-123.59E + 051.76E + 04
3PanelASDAdditiveHerbHerb0.00131.79E + 0481.68
4PanelASD2 LagsLM.Cannabis_x_Herb.THCHerb<2.2E-169.01E + 152.54E + 13
5PanelASD1 LagLM.Cannabis_x_Herb.THCHerb8.72E-157.28E + 096.31E + 07
6PanelASD4 LagsLM.Cannabis_x_Herb.THCHerb0.01682.51E + 0890.06
7PanelASDAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.01334.911.81
8SpatialASD1 LagTobacco: Daily.Interpol.Daily0.02104.41E + 0725.77
9SpatialAVSDAdditiveHerbHerb0.00145.88E + 0343.32
10PanelAVSDInteractiveLM.Cannabis_x_Resin.THCResin8.05E-071.67E + 241.86E + 15
11PanelAVSD1 LagLM.Cannabis_x_Resin.THCResin0.00424.17E + 081.18E + 03
12PanelAVSDAdditiveResinResin4.05E-10897.5155.66
13SpatialAVSDAdditiveResinResin0.029633.651.99
14SpatialAVSDInteractiveTobacco: ResinResin1.23E-054.062.56
15SpatialAVSD2 LagsTobacco: ResinResin0.02892.371.26
16PanelAVSD1 LagTobacco: ResinResin1.11E-075.643.62
17PanelAVSDInteractiveTobacco: ResinResin4.26E-075.563.48
18PanelAVSD2 LagsTobacco: ResinResin1.58E-121.541.44
19PanelAVSDInteractiveTobacco: Resin: LM.Cannabis_x_Resin.THCResin0.0026263.7711.38
20PanelCong. heart disease1 LagHerb.THCHerb1.75E-198.06E + 193.88E + 16
21PanelCong. heart diseaseInteractiveHerb.THCHerb9.29E-103.95E + 142.95E + 10
22PanelCong. heart diseaseAdditiveHerb.THCHerb1.93E-163.38E + 121.26E + 10
23PanelCong. heart disease2 LagsHerb.THCHerb1.15E-056.66E + 081.86E + 05
24SpatialCong. heart diseaseAdditiveHerb.THCHerb0.00381.20E + 0570.68
25SpatialCong. heart diseaseInteractiveHerb.THCHerb0.00381.20E + 0570.68
26SpatialCong. heart disease2 LagsHerb.THCHerb0.0238144.62.95
27SpatialCong. heart disease1 LagHerb.THCHerb0.0401801.091.98
28PanelCong. heart disease2 LagsHerb.THC: LM.Cannabis_x_Herb.THCHerb4.14E-114.37E + 593.41E + 44
29PanelCong. heart diseaseInteractiveLM.Cannabis_x_Herb.THCHerb6.23E-035.19E + 132.14E + 04
30PanelCong. heart diseaseInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.:LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily1.91E-025.56E + 209.39E + 03
31PanelDouble outlet right ventricleInteractiveHerbHerb6.33E-051.34E + 05740.60
32PanelDouble outlet right ventricle2 LagsHerb: ResinHerb0.01385.10E + 245.89E + 05
33SpatialDouble outlet right ventricle2 LagsLM.Cannabis_x_Herb.THCHerb9.11E-062.54E + 121.18E + 07
34PanelDouble outlet right ventricle2 LagsLM.Cannabis_x_Herb.THCHerb0.00309.24E + 131.74E + 05
35SpatialDouble outlet right ventricleInteractiveLM.Cannabis_x_Herb.THCHerb0.02081.63E + 272.73E + 04
36SpatialDouble outlet right ventricleAdditiveLM.Cannabis_x_Herb.THCHerb0.00997.76E + 0425.13
37PanelDouble outlet right ventricleInteractiveLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THCDaily9.88E-052.08E + 327.82E + 16
38PanelDouble outlet right ventricleInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00053.52E + 081.08E + 04
39PanelDouble outlet right ventricle2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00523.92E + 0322.14
40PanelDouble outlet right ventricleInteractiveTobacco: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily5.35E-124.53E + 043.72E + 03
41SpatialHypoplastic right heart2 LagsDaily.Interpol.Daily5.10E-052.22E + 052.10E + 03
42SpatialHypoplastic right heart1 LagDaily.Interpol.Daily0.0079901.9532.41
43SpatialHypoplastic right heart2 LagsLM.Cannabis_x_Herb.THCHerb0.00351.17E + 952.06E + 49
44SpatialHypoplastic right heart1 LagLM.Cannabis_x_Herb.THCHerb0.03335.17E + 331.34E + 09
45PanelHypoplastic right heartAdditiveLM.Cannabis_x_Herb.THCHerb0.02655.02E + 0717.69
46PanelHypoplastic right heartInteractiveLM.Cannabis_x_Resin.THCResin0.00529.242.67
47SpatialHypoplastic right heart2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin1.09E-062.21E + 134.19E + 04
48SpatialHypoplastic right heartInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin3.10E-071.48E + 03118.27
49SpatialHypoplastic right heartAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin5.22E-06960.3567.27
50SpatialHypoplastic right heart1 LagLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00036.18E + 077.55
51PanelHypoplastic right heartAdditiveResinResin0.00501.11E + 0314.31
52PanelHypoplastic right heart1 LagResinResin5.65E-0525.737.38
53PanelHypoplastic right heart2 LagsResinResin0.000925.495.44
54PanelMitral valve anomaliesAdditiveDaily.Interpol.Daily6.06E-106.92E + 214.76E + 15
55PanelMitral valve anomaliesInteractiveDaily.Interpol.Daily7.46E-092.31E + 185.81E + 12
56PanelMitral valve anomalies2 LagsHerbHerb0.01558.04E + 0310.85
57PanelMitral valve anomaliesInteractiveLM.Cannabis_x_Herb.THCHerb0.01896.44E + 0519.02
58SpatialMitral valve anomalies1 LagLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily0.00383.71E + 282.81E + 09
59SpatialMitral valve anomalies2 LagsLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily0.01155.36E + 352.03E + 08
60SpatialMitral valve anomaliesInteractiveTobacco: Daily.Interpol.Daily2.17E-079.91E + 057.03E + 03
61SpatialMitral valve anomalies1 LagTobacco: Daily.Interpol.Daily4.14E-061.58E + 064.93E + 03
62SpatialMitral valve anomalies2 LagsTobacco: Daily.Interpol.Daily0.00383.97E + 0449.19
63PanelMitral valve anomalies1 LagTobacco: Daily.Interpol.Daily1.27E-139.276.31
64PanelPDAAdditiveDaily.Interpol.Daily4.3E-095.07E + 493.15E + 34
65PanelPDA1 LagLM.Cannabis_x_Herb.THCHerb7.29E-148.51E + 144.28E + 11
66PanelPDA2 LagsLM.Cannabis_x_Herb.THCHerb2.33E-077.34E + 116.85E + 07
67PanelPDAInteractiveLM.Cannabis_x_Herb.THCHerb3.27E-053.64E + 051.52E + 03
68SpatialPDA2 LagsLM.Cannabis_x_Herb.THCHerb0.010766.558.12
69PanelPDAAdditiveLM.Cannabis_x_Herb.THCHerb0.01582.80E + 038.07
70SpatialPDA1 LagLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily0.00261.37E + 163.82E + 08
71SpatialPDAInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily2.50E-053.21E + 051.23E + 03
72SpatialPDAAdditiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily0.00114.73E + 0344.81
73PanelPDAInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily0.04491.481.07
74SpatialPDA1 LagTobacco: Daily.Interpol.Daily4.13E-051.11E + 135.74E + 05
75SpatialPDAInteractiveTobacco: Daily.Interpol.Daily0.00155.09E + 06576.84
76SpatialPDA2 LagsTobacco: Daily.Interpol.Daily0.00087.25E + 09344.71
77PanelPDA1 LagTobacco: Daily.Interpol.Daily1.39E-082.93E + 087.61E + 05
78PanelPDA2 LagsTobacco: Daily.Interpol.Daily0.00423.30E + 05111.26
79PanelPDAInteractiveTobacco: Daily.Interpol.Daily8.61E-10243.559.42
80SpatialSevere cong. heart diseaseInteractiveHerbHerb1.05E-101.19E + 131.61E + 09
81SpatialSevere Cong. heart diseaseAdditiveHerbHerb8.22E-078.11E + 077.77E + 04
82SpatialSevere cong. heart disease1 LagHerbHerb0.03911.20E + 113.67E + 03
83SpatialSevere cong. heart disease2 LagsHerbHerb0.02216.71E + 035.99
84PanelSevere cong. heart diseaseInteractiveHerb.THCHerb1.31E-053.89E + 072.94E + 05
85PanelSevere cong. heart disease1 LagHerb.THCHerb0.00786.74E + 0318.81
86PanelSevere cong. heart disease2 LagsHerb.THCHerb4.41E-023.46E + 032.10
87SpatialSevere cong. heart diseaseInteractiveLM.Cannabis_x_Resin.THCResin2.35E-052.21E + 051.03E + 03
88SpatialSevere cong. heart diseaseAdditiveLM.Cannabis_x_Resin.THCResin0.0002281.8720.47
89SpatialSevere cong. heart disease1 LagLM.Cannabis_x_Resin.THC: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.Resin0.00502.18E + 053.06
90SpatialSevere cong. heart disease2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00262.74E + 111.60E + 04
91PanelSevere cong. heart diseaseAdditiveLM.Herb.Daily.IntpltdDaily4.74E-025.25E + 133.67
92PanelSevere cong. heart diseaseAdditiveLM.Herb.THCHerb1.48E-037.57E + 06850.24
93PanelSevere cong. heart diseaseAdditiveLM.Herb.THC.Daily.InterpolHerb4.43E-024.92E + 001.24
94PanelSevere cong. heart diseaseInteractiveLM.ResinResin9.62E-072.33E + 04686.08
95PanelSevere cong. heart disease2 LagsLM.ResinResin7.23E-061.31E + 0391.92
96PanelSevere cong. heart disease1 LagLM.ResinResin0.0003616.4929.73
97PanelSevere cong. heart diseaseInteractiveLM.Resin.Daily.InterpolDaily4.20E-114.84E + 131.30E + 10
98PanelSevere cong. heart diseaseInteractiveLM.Resin.Daily.Interpol: LM.Herb.THC.Daily.InterpolDaily1.22E-13108.1142.72
99PanelSevere cong. heart disease2 LagsLM.Resin.Daily.Interpol: LM.ResinDaily4.42E-024.08E + 052.76
100PanelSevere cong. heart disease1 LagLM.Resin: LM.Herb.THC.Daily.InterpolDaily0.00051.80E + 061.03E + 03
101PanelSevere cong. heart diseaseInteractiveTobacco: LM.Herb.THC.Daily.InterpolDaily3.85E-082.492.03
102PanelTetralogy of FallotInteractiveDaily.Interpol.Daily2.1E-091.10E + 3074.28E + 135
103PanelTetralogy of FallotAdditiveDaily.Interpol.Daily5.96E-093.79E + 193.72E + 13
104SpatialTetralogy of FallotAdditiveHerbHerb0.00332.95E + 07503.90
105PanelTetralogy of Fallot2 LagsLM.Cannabis_x_Herb.THCHerb3.86E-081.04E + 251.04E + 17
106PanelTetralogy of FallotInteractiveLM.Cannabis_x_Herb.THCHerb0.01591.19E + 0517.74
107PanelTetralogy of Fallot1 LagLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily2.59E-101.24E + 041.08E + 03
108PanelTetralogy of FallotInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily4.68E-0736.9612.31
109PanelTetralogy of Fallot2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin4.36E-163.78E + 03868.53
110PanelTetralogy of Fallot2 LagsResinResin0.0460474.561.71
111SpatialTetralogy of Fallot1 LagTobacco: HerbHerb5.26E-0513.114.83
112SpatialTetralogy of Fallot2 LagsTobacco: LM.Cannabis_x_Herb.THCHerb1.07E-0662.8715.34
113SpatialTetralogy of FallotInteractiveTobacco: ResinResin1.44E-0526.767.86
114PanelTransposition great vessels1 LagHerbHerb<2.2E-163.11E + 062.02E + 05
115PanelTransposition great vessels2 LagsHerbHerb0.00564.74E + 091.68E + 03
116PanelTransposition great vesselsInteractiveHerbHerb0.00135.06E + 04121.42
117SpatialTransposition great vesselsInteractiveHerbHerb0.00054.95E + 0361.38
118PanelTransposition great vesselsAdditiveHerbHerb0.00039.44E + 078.52
119SpatialTransposition great vesselsAdditiveLM.Cannabis_x_Herb.THCHerb0.00997.77E + 0425.13
120PanelTransposition great vesselsAdditiveLM.Cannabis_x_Resin.THCResin0.00021.61E + 0350.46
121PanelTransposition great vessels2 LagsLM.Cannabis_x_Resin.THCResin0.00341.41E + 0319.41
122SpatialTransposition great vessels2 LagsLM.Cannabis_x_Resin.THCResin0.000112.514.51
123PanelTransposition great vesselsInteractiveLM.Cannabis_x_Resin.THC: ResinResin8.46E-057.77E + 397.81E + 20
124PanelTransposition great vessels2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.043754.681.59
125SpatialTransposition great vessels1 LagResinResin0.037015.341.53
126PanelTransposition great vesselsInteractiveTobacco: ResinResin0.00023.722.28
127PanelVascular disruptions1 LagLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily6.72E-041.09E + 05255.77
128PanelVascular disruptions2 LagsLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily1.76E-041.48E + 0355.25
129PanelVascular disruptionsInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: ResinDaily1.27E-054.00E + 171.77E + 10
130PanelVascular disruptions1 LagLM.Cannabis_x_Resin.THCResin9.61E-044.79E + 0354.91
131PanelVascular disruptionsInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin1.56E-063.81E + 03212.19
132SpatialVascular disruptionsAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin8.77E-05293.6323.91
133SpatialVascular disruptionsInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin8.77E-05293.6323.91
134PanelVascular disruptionsAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin4.85E-0552.9311.26
135SpatialVascular disruptions2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.000824.975.19
136SpatialVascular disruptions1 LagLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.02846.951.58
137PanelVascular disruptions2 LagsResinResin3.50E-086.79E + 042.43E + 03
138PanelVascular disruptionsInteractiveResinResin0.003862.365.91
139PanelVascular disruptionsAdditiveResinResin9.68E-0344.483.89
140SpatialVSD2 LagsDaily.Interpol.Daily0.01373.41E + 571.44E + 12
141SpatialVSDAdditiveHerbHerb0.001433.671.99
142PanelVSD2 LagsLM.Cannabis_x_Herb.THCHerb2.7E-141.60E + 214.97E + 16
143SpatialVSD2 LagsLM.Cannabis_x_Herb.THCHerb2.15E-054.45E + 238.06E + 12
144SpatialVSDInteractiveLM.Cannabis_x_Herb.THCHerb8.94E-061.16E + 174.81E + 09
145SpatialVSD1 LagLM.Cannabis_x_Herb.THCHerb0.00039.50E + 094.91E + 05
146PanelVSD1 LagLM.Cannabis_x_Herb.THCHerb<2.2E-163.04E + 062.58E + 05
147PanelVSDAdditiveLM.Cannabis_x_Herb.THCHerb<2.2E-162.38E + 061.96E + 05
148PanelVSDInteractiveLM.Cannabis_x_Herb.THCHerb<2.2E-162.38E + 061.96E + 05
149SpatialVSDAdditiveResinResin0.02965.88E + 0343.32
Table 18:

Summary of E-values by anomaly

AnomalyNumberMean mEVMedian mEVMinimum mEVMaximum mEVMean E-Value EstimateMedian E-Value EstimatemEV EstimateMaximum E-Value Estimate
VSD104.97E + 15374 500.001.994.97E + 163.41E + 564.75E + 0933.673.41E + 57
Cong. heart disease113.10E + 4321 400.001.983.41E + 443.97E + 583.38E + 12144.64.37E + 59
Double outlet right ventricle107.82E + 1519 050.0022.147.82E + 162.08E + 311.27E + 1239202.08E + 32
ASD81.34E + 208845.031.811.07E + 211.93E + 1381.48E + 084.911.54E + 139
Mitral valve anomalies104.77E + 145980.006.314.76E + 155.36E + 341 285 5009.275.36E + 35
PDA161.97E + 33903.421.073.15E + 343.17E + 482 727 0001.485.07E + 49
Tetralogy of Fallot123.57E + 134260.821.714.28E + 1359.17E + 305809013.111.10E + 307
Severe cong. heart disease226.64E + 0867.321.241.30E + 105.15E + 12219 5002.495.25E + 13
Hypoplastic right heart131.58E + 4832.412.672.06E + 499.00E + 9314809.241.17E + 95
Transposition great vessels136.01E + 1925.131.537.81E + 205.98E + 3849503.727.77E + 39
Vascular disruptions131.36E + 0923.911.581.77E + 103.08E + 16293.636.954.00E + 17
AVSD111.69E + 143.621.261.86E + 151.52E + 2333.651.541.67E + 24
E-values by anomaly Summary of E-values by anomaly These multivariable data are also similarly suited to illustration by Volcano plots as was done earlier for the bivariate results. The multivariable data for the E-value estimates are shown in Supplementary Fig. S2. In this plot, the presence of the tetralogy of Fallot far to the right forces all the points to the left side of the graph and makes the remainder of the graph difficult to interpret clearly. A similar problem occurs when the mEVs are considered, as shown in Supplementary Fig. S3.

Discussion

Main Results

Study data indicate that 17 of the 23 congenital cardiac anomalies demonstrated strong bivariate relationships with different metrics of cannabis exposure. Twelve of these anomalies were selected for detailed study in inverse probability weight panel regression models and in spatiotemporal models. In all twelve cases cannabis metrics persisted after adjustment in multivariable panel and spatial models. The use of ipw in panel models moved the analysis from a merely observational paradigm into a pseudorandomized paradigm from which it is both proper and appropriate to draw causal conclusions. When models were assessed for uncontrolled confounding E-values were noted to be very high throughout effectively excluding alternative explanations on quantitative grounds. The present results for ASD, VSD, AVSD, PDA and transposition of the great vessels are consistent with those previously reported in other series [4–9, 70]. Interestingly a companion paper to this one has shown that the VACTERL syndrome (Vertebral, anorectal, cardiac, tracheo-esophageal fistulae/esophageal atresia, renal and limb anomalies) was strongly and causally linked with European cannabinoid exposure which is itself consistent with cannabis-induced inhibition of the sonic hedgehog pathway as has previously been demonstrated [32, 71]. Since cardiac abnormalities are part of the VACTERL syndrome this finding confirms current study findings. The commonest cardiac anomalies seen in VACTERL syndrome are VSD and tetralogy of Fallot, two of the anomalies described herein [71-73]. In this respect the present results closely paralleled similar studies recently published from Canada, Australia, HI, CO, and the USA [4–9, 70]. They are also consistent with the wide spectrum of genotoxic activities which have been demonstrated experimentally for cannabis and cannabinoids. The seventeen anomalies which showed strong bivariate relationships to cannabis exposure metrics were: (i) congenital heart defects, Severe CHD, Double outlet right ventricle, Transposition of great vessels, Single ventricle, VSD, ASD, AVSD, Tetralogy of Fallot, Ebstein’s anomaly, Pulmonary valve stenosis, Pulmonary valve atresia, Mitral valve anomalies, Hypoplastic right heart, Aortic atresia/interrupted aortic arch, PDA as only CHD in term infants (≥37 weeks) and Vascular disruption anomalies. The six anomalies which did not show a bivariate relationship to cannabis exposure were: (ii) arterial truncus, tricuspid valve stenosis and atresia, aortic valve stenosis or atresia, coarctation of the aorta, hypoplastic left heart, total anomalous pulmonary venous return. The twelve anomalies for which extended inverse probability weighted panel regression and space-time modelling was undertaken were: (iii) congenital heart defects, Severe CHD, Double outlet right ventricle, Transposition of great vessels, VSD, ASD, AVSD, Tetralogy of Fallot, Mitral valve anomalies, Hypoplastic right heart, PDA as only CHD in term infants (≥37 weeks) and Vascular disruption anomalies. For these reasons, these figures have been re-plotted at higher magnification as Figs 24 and 25 which address the E-value estimates and the mEVs, respectively. As compared to the earlier bivariate analysis, it is now apparent that ASD, VSD, tetralogy of Fallot, severe CHD, transposition of the great vessels and mitral valve anomalies accompany CHD in the upper reaches of these plots. Magnified volcano plot of negative log of P-values against log of E-value estimates for multivariate regressions Magnified volcano plot of negative log of P-values against log of mEV for multivariate regressions The E-value list may also be listed in order of the environmental exposure to intoxicant, as shown in Table 19. When the exposures are categorized into three primary exposures as daily cannabis exposure interpolated and herb and resin THC concentrations, the outcomes summarized in Table 20 are seen. These values are again listed in descending order of median mEV. The value for daily use interpolated is noted to be much greater than that for the herb and cannabis resin THC concentrations.
Table 19:

E-values by the major covariate group

No.RegressionAnomalyModel_TypeTermGroup P-Value E-Value EstimateLower Bound E-Value
1SpatialASD2 LagsDaily.Interpol.Daily0.02151.54E + 1391.07E + 21
2PanelASDInteractiveHerbHerb2.32E-123.59E + 051.76E + 04
3PanelASDAdditiveHerbHerb0.00131.79E + 0481.68
4PanelASD2 LagsLM.Cannabis_x_Herb.THCHerb<2.2E-169.01E + 152.54E + 13
5PanelASD1 LagLM.Cannabis_x_Herb.THCHerb8.72E-157.28E + 096.31E + 07
6PanelASD4 LagsLM.Cannabis_x_Herb.THCHerb0.01682.51E + 0890.06
7PanelASDAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.01334.911.81
8SpatialASD1 LagTobacco: Daily.Interpol.Daily0.02104.41E + 0725.77
9SpatialAVSDAdditiveHerbHerb0.00145.88E + 0343.32
10PanelAVSDInteractiveLM.Cannabis_x_Resin.THCResin8.05E-071.67E + 241.86E + 15
11PanelAVSD1 LagLM.Cannabis_x_Resin.THCResin0.00424.17E + 081.18E + 03
12PanelAVSDAdditiveResinResin4.05E-10897.5155.66
13SpatialAVSDAdditiveResinResin0.029633.651.99
14SpatialAVSDInteractiveTobacco: ResinResin1.23E-054.062.56
15SpatialAVSD2 LagsTobacco: ResinResin0.02892.371.26
16PanelAVSD1 LagTobacco: ResinResin1.11E-075.643.62
17PanelAVSDInteractiveTobacco: ResinResin4.26E-075.563.48
18PanelAVSD2 LagsTobacco: ResinResin1.58E-121.541.44
19PanelAVSDInteractiveTobacco: Resin: LM.Cannabis_x_Resin.THCResin0.0026263.7711.38
20PanelCong. heart disease1 LagHerb.THCHerb1.75E-198.06E + 193.88E + 16
21PanelCong. heart diseaseInteractiveHerb.THCHerb9.29E-103.95E + 142.95E + 10
22Panelcong. heart diseaseAdditiveHerb.THCHerb1.93E-163.38E + 121.26E + 10
23PanelCong. heart disease2 LagsHerb.THCHerb1.15E-056.66E + 081.86E + 05
24SpatialCong. heart diseaseAdditiveHerb.THCHerb0.00381.20E + 0570.68
25SpatialCong. heart diseaseInteractiveHerb.THCHerb0.00381.20E + 0570.68
26SpatialCong. heart disease2 LagsHerb.THCHerb0.0238144.62.95
27SpatialCong. heart disease1 LagHerb.THCHerb0.0401801.091.98
28PanelCong. heart disease2 LagsHerb.THC: LM.Cannabis_x_Herb.THCHerb4.14E-114.37E + 593.41E + 44
29PanelCong. heart diseaseInteractiveLM.Cannabis_x_Herb.THCHerb6.23E-035.19E + 132.14E + 04
30PanelCong. heart diseaseInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily1.91E-025.56E + 209.39E + 03
31PanelDouble outlet right ventricleInteractiveHerbHerb6.33E-051.34E + 05740.60
32PanelDouble outlet right ventricle2 LagsHerb: ResinHerb0.01385.10E + 245.89E + 05
33SpatialDouble outlet right ventricle2 LagsLM.Cannabis_x_Herb.THCHerb9.11E-062.54E + 121.18E + 07
34PanelDouble outlet right ventricle2 LagsLM.Cannabis_x_Herb.THCHerb0.00309.24E + 131.74E + 05
35SpatialDouble outlet right ventricleInteractiveLM.Cannabis_x_Herb.THCHerb0.02081.63E + 272.73E + 04
36SpatialDouble outlet right ventricleAdditiveLM.Cannabis_x_Herb.THCHerb0.00997.76E + 0425.13
37PanelDouble outlet right ventricleInteractiveLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THCDaily9.88E-052.08E + 327.82E + 16
38PanelDouble outlet right ventricleInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00053.52E + 081.08E + 04
39PanelDouble outlet right ventricle2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00523.92E + 0322.14
40PanelDouble outlet right ventricleInteractiveTobacco: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily5.35E-124.53E + 043.72E + 03
41SpatialHypoplastic right heart2 LagsDaily.Interpol.Daily5.10E-052.22E + 052.10E + 03
42SpatialHypoplastic right heart1 LagDaily.Interpol.Daily0.0079901.9532.41
43SpatialHypoplastic right heart2 LagsLM.Cannabis_x_Herb.THCHerb0.00351.17E + 952.06E + 49
44SpatialHypoplastic right heart1 LagLM.Cannabis_x_Herb.THCHerb0.03335.17E + 331.34E + 09
45PanelHypoplastic right heartAdditiveLM.Cannabis_x_Herb.THCHerb0.02655.02E + 0717.69
46PanelHypoplastic right heartInteractiveLM.Cannabis_x_Resin.THCResin0.00529.242.67
47SpatialHypoplastic right heart2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin1.09E-062.21E + 134.19E + 04
48SpatialHypoplastic right heartInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin3.10E-071.48E + 03118.27
49SpatialHypoplastic right heartAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin5.22E-06960.3567.27
50SpatialHypoplastic right heart1 LagLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00036.18E + 077.55
51PanelHypoplastic right heartAdditiveResinResin0.00501.11E + 0314.31
52PanelHypoplastic right heart1 LagResinResin5.65E-0525.737.38
53PanelHypoplastic right heart2 LagsResinResin0.000925.495.44
54PanelMitral valve anomaliesAdditiveDaily.Interpol.Daily6.06E-106.92E + 214.76E + 15
55PanelMitral valve anomaliesInteractiveDaily.Interpol.Daily7.46E-092.31E + 185.81E + 12
56PanelMitral valve anomalies2 LagsHerbHerb0.01558.04E + 0310.85
57PanelMitral valve anomaliesInteractiveLM.Cannabis_x_Herb.THCHerb0.01896.44E + 0519.02
58SpatialMitral valve anomalies1 LagLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily0.00383.71E + 282.81E + 09
59SpatialMitral valve anomalies2 LagsLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily0.01155.36E + 352.03E + 08
60SpatialMitral valve anomaliesInteractiveTobacco: Daily.Interpol.Daily2.17E-079.91E + 057.03E + 03
61SpatialMitral valve anomalies1 LagTobacco: Daily.Interpol.Daily4.14E-061.58E + 064.93E + 03
62SpatialMitral valve anomalies2 LagsTobacco: Daily.Interpol.Daily0.00383.97E + 0449.19
63PanelMitral valve anomalies1 LagTobacco: Daily.Interpol.Daily1.27E-139.276.31
64PanelPDAAdditiveDaily.Interpol.Daily4.3E-095.07E + 493.15E + 34
65PanelPDA1 LagLM.Cannabis_x_Herb.THCHerb7.29E-148.51E + 144.28E + 11
66PanelPDA2 LagsLM.Cannabis_x_Herb.THCHerb2.33E-077.34E + 116.85E + 07
67PanelPDAInteractiveLM.Cannabis_x_Herb.THCHerb3.27E-053.64E + 051.52E + 03
68SpatialPDA2 LagsLM.Cannabis_x_Herb.THCHerb0.010766.558.12
69PanelPDAAdditiveLM.Cannabis_x_Herb.THCHerb0.01582.80E + 038.07
70SpatialPDA1 LagLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily0.00261.37E + 163.82E + 08
71SpatialPDAInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily2.50E-053.21E + 051.23E + 03
72SpatialPDAAdditiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily0.00114.73E + 0344.81
73PanelPDAInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily0.04491.481.07
74SpatialPDA1 LagTobacco: Daily.Interpol.Daily4.13E-051.11E + 135.74E + 05
75SpatialPDAInteractiveTobacco: Daily.Interpol.Daily0.00155.09E + 06576.84
76SpatialPDA2 LagsTobacco: Daily.Interpol.Daily0.00087.25E + 09344.71
77PanelPDA1 LagTobacco: Daily.Interpol.Daily1.39E-082.93E + 087.61E + 05
78PanelPDA2 LagsTobacco: Daily.Interpol.Daily0.00423.30E + 05111.26
79PanelPDAInteractiveTobacco: Daily.Interpol.Daily8.61E-10243.559.42
80SpatialSevere cong. heart diseaseInteractiveHerbHerb1.05E-101.19E + 131.61E + 09
81SpatialSevere cong. heart diseaseAdditiveHerbHerb8.22E-078.11E + 077.77E + 04
82SpatialSevere cong. heart disease1 LagHerbHerb0.03911.20E + 113.67E + 03
83SpatialSevere cong. heart disease2 LagsHerbHerb0.02216.71E + 035.99
84PanelSevere cong. heart diseaseInteractiveHerb.THCHerb1.31E-053.89E + 072.94E + 05
85PanelSevere cong. heart disease1 LagHerb.THCHerb0.00786.74E + 0318.81
86PanelSevere cong. heart disease2 LagsHerb.THCHerb4.41E-023.46E + 032.10
87SpatialSevere cong. heart diseaseInteractiveLM.Cannabis_x_Resin.THCResin2.35E-052.21E + 051.03E + 03
88SpatialSevere cong. heart diseaseAdditiveLM.Cannabis_x_Resin.THCResin0.0002281.8720.47
89SpatialSevere cong. heart disease1 LagLM.Cannabis_x_Resin.THC: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.Resin0.00502.18E + 053.06
90SpatialSevere cong. heart disease2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.00262.74E + 111.60E + 04
91PanelSevere cong. heart diseaseAdditiveLM.Herb.Daily.IntpltdDaily4.74E-025.25E + 133.67
92PanelSevere cong. heart diseaseAdditiveLM.Herb.THCHerb1.48E-037.57E + 06850.24
93PanelSevere cong. heart diseaseAdditiveLM.Herb.THC.Daily.InterpolHerb4.43E-024.92E + 001.24
94PanelSevere cong. heart diseaseInteractiveLM.ResinResin9.62E-072.33E + 04686.08
95PanelSevere cong. heart disease2 LagsLM.ResinResin7.23E-061.31E + 0391.92
96PanelSevere cong. heart disease1 LagLM.ResinResin0.0003616.4929.73
97PanelSevere cong. heart diseaseInteractiveLM.Resin.Daily.InterpolDaily4.20E-114.84E + 131.30E + 10
98PanelSevere cong. heart diseaseInteractiveLM.Resin.Daily.Interpol: LM.Herb.THC.Daily.InterpolDaily1.22E-13108.1142.72
99PanelSevere cong. heart disease2 LagsLM.Resin.Daily.Interpol: LM.ResinDaily4.42E-024.08E + 052.76
100PanelSevere cong. heart disease1 LagLM.Resin: LM.Herb.THC.Daily.InterpolDaily0.00051.80E + 061.03E + 03
101PanelSevere cong. heart diseaseInteractiveTobacco: LM.Herb.THC.Daily.InterpolDaily3.85E-082.492.03
102PanelTetralogy of FallotInteractiveDaily.Interpol.Daily2.1E-091.10E + 3074.28E + 135
103PanelTetralogy of FallotAdditiveDaily.Interpol.Daily5.96E-093.79E + 193.72E + 13
104SpatialTetralogy of FallotAdditiveHerbHerb0.00332.95E + 07503.90
105PanelTetralogy of Fallot2 LagsLM.Cannabis_x_Herb.THCHerb3.86E-081.04E + 251.04E + 17
106PanelTetralogy of FallotInteractiveLM.Cannabis_x_Herb.THCHerb0.01591.19E + 0517.74
107PanelTetralogy of Fallot1 LagLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.Daily2.59E-101.24E + 041.08E + 03
108PanelTetralogy of FallotInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily4.68E-0736.9612.31
109PanelTetralogy of Fallot2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin4.36E-163.78E + 03868.53
110PanelTetralogy of Fallot2 LagsResinResin0.0460474.561.71
111SpatialTetralogy of Fallot1 LagTobacco: HerbHerb5.26E-0513.114.83
112SpatialTetralogy of Fallot2 LagsTobacco: LM.Cannabis_x_Herb.THCHerb1.07E-0662.8715.34
113SpatialTetralogy of FallotInteractiveTobacco: ResinResin1.44E-0526.767.86
114PanelTransposition great vessels1 LagHerbHerb<2.2E-163.11E + 062.02E + 05
115PanelTransposition great vessels2 LagsHerbHerb0.00564.74E + 091.68E + 03
116PanelTransposition great vesselsInteractiveHerbHerb0.00135.06E + 04121.42
117SpatialTransposition great vesselsInteractiveHerbHerb0.00054.95E + 0361.38
118PanelTransposition great vesselsAdditiveHerbHerb0.00039.44E + 078.52
119SpatialTransposition great vesselsAdditiveLM.Cannabis_x_Herb.THCHerb0.00997.77E + 0425.13
120PanelTransposition great vesselsAdditiveLM.Cannabis_x_Resin.THCResin0.00021.61E + 0350.46
121PanelTransposition great vessels2 LagsLM.Cannabis_x_Resin.THCResin0.00341.41E + 0319.41
122SpatialTransposition great vessels2 LagsLM.Cannabis_x_Resin.THCResin0.000112.514.51
123PanelTransposition Great VesselsInteractiveLM.Cannabis_x_Resin.THC: ResinResin8.46E-057.77E + 397.81E + 20
124PanelTransposition great vessels2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.043754.681.59
125SpatialTransposition great vessels1 LagResinResin0.037015.341.53
126PanelTransposition great vesselsInteractiveTobacco: ResinResin0.00023.722.28
127PanelVascular disruptions1 LagLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily6.72E-041.09E + 05255.77
128PanelVascular disruptions2 LagsLM.Cannabis_x_Herb.THC_x_Daily.Interpol.Daily1.76E-041.48E + 0355.25
129PanelVascular disruptionsInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: ResinDaily1.27E-054.00E + 171.77E + 10
130PanelVascular disruptions1 LagLM.Cannabis_x_Resin.THCResin9.61E-044.79E + 0354.91
131PanelVascular disruptionsInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin1.56E-063.81E + 03212.19
132SpatialVascular disruptionsAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin8.77E-05293.6323.91
133SpatialVascular disruptionsInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin8.77E-05293.6323.91
134PanelVascular disruptionsAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin4.85E-0552.9311.26
135SpatialVascular disruptions2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.000824.975.19
136SpatialVascular disruptions1 LagLM.Cannabis_x_Resin.THC_x_Daily.Interpol.Resin0.02846.951.58
137PanelVascular disruptions2 LagsResinResin3.50E-086.79E + 042.43E + 03
138PanelVascular disruptionsInteractiveResinResin0.003862.365.91
139PanelVascular disruptionsAdditiveResinResin9.68E-0344.483.89
140SpatialVSD2 LagsDaily.Interpol.Daily0.01373.41E + 571.44E + 12
141SpatialVSDAdditiveHerbHerb0.001433.671.99
142PanelVSD2 LagsLM.Cannabis_x_Herb.THCHerb2.7E-141.60E + 214.97E + 16
143SpatialVSD2 LagsLM.Cannabis_x_Herb.THCHerb2.15E-054.45E + 238.06E + 12
144SpatialVSDInteractiveLM.Cannabis_x_Herb.THCHerb8.94E-061.16E + 174.81E + 09
145SpatialVSD1 LagLM.Cannabis_x_Herb.THCHerb0.00039.50E + 094.91E + 05
146PanelVSD1 LagLM.Cannabis_x_Herb.THCHerb<2.2E-163.04E + 062.58E + 05
147PanelVSDAdditiveLM.Cannabis_x_Herb.THCHerb<2.2E-162.38E + 061.96E + 05
148PanelVSDInteractiveLM.Cannabis_x_Herb.THCHerb<2.2E-162.38E + 061.96E + 05
149SpatialVSDAdditiveResinResin0.02965.88E + 0343.32
Table 20:

Summary of E-values by the major covariate group

GroupNumberMean mEVMedian mEVMinimum mEVMaximum mEVMean E-value estimateMedian E-value estimatemEV estimateMaximum E-value estimate
Daily202.14E + 13459806.314.28E + 1355.50E + 3051.69E + 089.271.10E + 307
Herb752.75E + 4715201.072.06E + 491.56E + 937.57E + 061.481.17E + 95
Resin541.45E + 1919.941.267.81E + 201.44E + 38756.9951.547.77E + 39
E-values by the major covariate group Summary of E-values by the major covariate group These comparisons are formalized in Table 21 where the various groups are compared for the E-value estimates and mEVs using the Wilcoxon test. In this case for both the E-value estimate and the mEV, the daily to resin and herb to resin comparisons are significant, but the comparisons between daily use interpolated and herb THC concentration are not significant (Table 21).
Table 21:

Wilcoxon tests for inter-group comparisons for the major covariate group

Comparison W-StatisticAlternative P-Value
Lower E-Value, Daily_v_Herb917two.sided0.1285
Lower E-Value, Daily_v_Resin888two.sided2.34E-05
Lower E-Value, Herb_v_Resin2924two.sided1.79E-05
E-Value Estimate, Daily_v_Herb872two.sided0.2674
E-Value Estimate, Daily_v_Resin886two.sided2.61E-05
E-Value Estimate, Herb_v_Resin3167two.sided5.05E-08
Wilcoxon tests for inter-group comparisons for the major covariate group

Main Results in Detail

At bivariate analysis the effects of tobacco and alcohol were weak or negative across all CCA’s, while daily cannabis use interpolated was much stronger and more positive (Figs 1–3). While daily cannabis use was the most significant covariate on bivariate analyses (Table 1), resin and herb THC concentrations were also strongly related to CCA’s (Figs 4–6).
Figure 1:

Panelled scatterplot of log (rates of selected CCAs) against rates of substance exposure—1

On bivariate map plots it was clear that CHD and severe CHD plotted against cannabis herb concentrations was rising simultaneously in countries with increasing cannabis use, such as Spain, France, Belgium, Netherlands, Croatia, Norway, Italy, Germany & Bulgaria indicated by all turning pink/purple (Figs 16–17). Trends for AVSD plotted against resin THC concentration were also moving together in Germany, France, Bulgaria, Norway, Netherlands all turned (Fig. 19). Trends for transposition of the great vessels plotted against cannabis resin THC concentration were moving together in Norway, Belgium, Italy, Spain, France, Bulgaria indicated by all turning pink/purple (Fig. 20). Countries with rising daily cannabis use had in general higher CAR’s over time than those without (time: status interaction: β-Est. = 0.0267, t = 2.7563, P = 0.0059; Fig. 21). At inverse probability weighted panel regression cannabis terms were positive and significant for CHD, severe CHD, ASD, VSD, AVSD, PDA, tetralogy of Fallot, vascular disruptions, double outlet right ventricle, transposition of the great vessels, hypoplastic right heart and mitral valve anomalies from: 1.75x10−19, 4.20x10−11, <2.2x10−16, <2.2x10−16, 1.58x10−12, 4.30x10−9, 4.36x10−16, 3.50x10−8, 5.35x10−12, <2.2x10−16, 5.65x10−5, 6.06 × 10−10 (Supplementary Tables S17–S28). At spatial regression terms including cannabis were positive and significant for this same list of anomalies from: 0.0038, 1.05x10−10, 0.0215, 8.94x10−6, 1.23x10−5, 2.05x10−5, 1.07x10−6, 8.77x10−5, 9.11x10−6, 0.0001, 3.10x10−7, 2.17 × 10−7 (Tables 2–13).
Table 2:

Final multivariate geospatial regression models for CHD

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Herb + LM.Cannabis_x_Herb.THC + Amphetamines + Cocaine + Income
Herb.THC2.09 (0.67, 3.5)0.0038psi0.84099<2.2E-16
Cocaine0.17 (0.05, 0.29)0.0045rho−0.611263.03E-08
lambda0.620614.97E-10
Interactive
Rate ∼ Tobacco * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * Herb + LM.Cannabis_x_Herb.THC + Alcohol + Amphetamines + Cocaine + Income
Herb.THC2.09 (0.67, 3.5)0.0038psi0.84099<2.2E-16
Cocaine0.17 (0.05, 0.29)0.0045rho−0.611263.03E-08
lambda0.62614.97E-10
1 Lag
Rate ∼ Tobacco,1) * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC_x_Daily.Interpol.,1) + Herb + LM.Cannabis_x_Herb.THC,1) + Alcohol,1) + Amphetamines,1) + Cocaine,1) + Income,1)
Herb.THC2.17 (0.1, 4.23)0.0401psi0.85902<2.2E-16
rho0.629291.20E-07
lambda−0.47480.0018
2 Lags
Rate ∼ Tobacco,2) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * LM.Cannabis_x_Herb.THC_x_Daily.Interpol.,2) + Herb + LM.Cannabis_x_Herb.THC,2) +Alcohol,2) + Amphetamines,2) + Cocaine,2) + Income,2)
Herb.THC1.91 (0.25, 3.56)0.0238psi0.88721<2.2E-16
Cocaine−0.16 (−0.29, −0.03)0.0158rho−0.56332.83E-05
lambda0.64362.11E-09
92.6% and 75.2% of 149 E-value estimates and mEVs exceeded nine and 100.0% and 98.7% exceeded 1.25 (Table 16). The order of relationship to cannabis judged by median mEV’s was VSD > CHD > double outlet right ventricle > ASD > mitral valve abnormalities > PDA > tetralogy of Fallot > severe CHD > hypoplastic right heart syndrome > transposition great vessels > vascular disruptions > AVSD (Table 18). The order of covariates judged by the median mEV was daily cannabis use interpolated > herb THC concentration > resin THC concentration (Table 20). A detailed categorical quintile analysis of this data was presented previously [9]. The reader who is interested in precise results from such analyses including risk ratios, attributable fractions in the exposed and population attributable fractions is cordially invited to peruse this important resource. We note that in the present study were tobacco and alcohol use were unrelated or were negatively related to CAR’s. This findings is at variance with other results such as those previously reported from North America [8, 74]. Such differing results will require further research.

Mechanisms

Genotoxicity

Cannabinoids have been note to induce genotoxicity my many routes including severe morphological damage to sperm [75, 76], inducing high rates of failed oocyte division [77], induction of single- and double- stranded DNA breaks [78-80], formation of end-to-end chromosomal fusions and translocations [76, 81, 82], involvement in testicular cancer where whole genome doubling or quadrupling are known oncogenic events [81], disruption of telomerase [12, 83], interference with key epigenotoxic machinery (KMT2A [84]), induction of DNA bridges [77, 85, 86], alterations of DNA methylation which are heritable for the following generations [12, 84, 87–94], failure of histone-protamine substitution in sperm [95], and also alterations of histone proteins including acetylated- and phosphorylated- histones [96-99] which have also been shown to be heritable [99].

Epigenomics

Interpretation of current CCA cannabis related findings is assisted by comparing the list of genes in the core cardiogenic complex to those identified in the recently published whole genome epigenomic screen of cannabis dependence and withdrawal [12]. When this is done there are four hits identified for MEF2, 249 hits for NKX2, 127 hits for GATA and 15 hits for Tbx. No hits could be identified for Hand −1 or −2. In considering the list of genes identified as crucial in vasculogenesis there were five hits for VEGF, 6 for notch and 427 for Eph (and none for the Ephrins or COUP-TFII). It was curious that there were initially no hits for the sonic hedgehog gene. However it is noted that for four of the key genes involved in the sonic hedgehog pathway or for its key interacting partners some hits were identified. Hence for GLI3 (Gli family zinc finger 3) there were 183 hits; for MEGF8 (multiple EGF-like domains 8) there were 105 hits, for TMEM107 (transmembrane protein 107) there were 22 hits and for BMP4 (bone morphogenetic protein 4) there were 166 hits. Since sonic hedgehog is a key morphogen controlling not only heart and arterial morphogenesis but the formation of many organs and structures this finding is potentially very important indeed. Importantly there were 151 hits for KMT (histone lysine methyltransferase) in the epigenome-wide cannabinoid effects screen described [12]. This is important as this enzyme forms a key component of the epigenomic machinery.

Epigenomic Implication of Cardiovascular-Relevant Pathways

The report from Schrott and colleagues described several pathways that were prominently identified amongst the epigenome-wide screen conducted in rats and humans of cannabis dependence and withdrawal [12]. These included cardiogenesis, agenesis of the organism and growth of the organism. During cannabis withdrawal these workers reported that the area of the blood vessel component and organismal death were of concern [12]. There were nine hits on the cardiovascular system (CVS) including morphology of the CVS (39 genes, page 321; 35 genes, page 324), quantity of cardiomyocytes (4 genes, page 321) and hypoplasia of the heart chamber (7 genes, page 299). Under heart there were three hits. Hypoplasia of the heart (10 genes, page 288; 7 genes, page 299; 6 genes, page 306), and hypoplasia of the trabeculae carnae (4 genes page 321). There were 26 genes noted on page 318 related to cardiogenesis. These findings achieve particular significance in terms of our positive identification of hypoplastic right heart as a cannabis related anomaly (present report). Atrial development occurred in several places including morphogenesis of the atrium (4 genes, page 314), morphogenesis of the atrial septum (3 genes page 318), atrial hypoplasia (2 genes page 319) and abnormal atrial morphology (6 genes, page 322). In relation to the atrioventricular valves which are derived from the endocardial cushions abnormal morphology of the atrioventricular cushions (6 genes, page 296), lack of atrioventricular canal cushions (4 genes, page 300) and morphogenesis of the atrioventricular valve (3 genes, page 321). Concerning the ventricle it was noted that formation of the ventricular septum (7 genes, page 315), VSDs type 3 (3 genes, page 301), hypoplasia of the cardiac ventricle (6 genes, page 306) and formation of the ventricular septum (7 genes, page 315). In relation to blood vessel formation there angiogenesis was noted (54 genes, page 289), vascular development (56 genes, page 294), vasculogenesis (42 genes, page 302), sprouting angiogenesis (7 genes, page 324), movement of vascular endothelial cells (13 genes page 316), migration of endothelial cells (19 genes, page 317), migration of vascular endothelial cells (12 genes, page 317), angiogenesis of pulmonary vein (1 gene, page 357) and breakdown of blood vessel (1 gene, page 357). These results are detailed in Table 22. Overall it is noted that this spectrum of epigenomic perturbations accounts for the epidemiologically observed pattern of teratological defects very well indeed.
Table 22:

EWAS annotations in epigenomic data of Schrott et.al [15]

Nearest Gene NamePageDependence StatusNumber Genes Identified P-Value
Cardiovasculature
 Abnormal CVS324Withdrawal350.002740
 CVS Development321Withdrawal390.001920
Heart
 Hypoplasia of heart288Withdrawal108.83E-08
 Cardiogenesis318Withdrawal260.001640
 Hypoplasia trabeculae carnae321Withdrawal40.001960
 Quantity cardiomyocytes321Withdrawal40.001960
 Hypoplasia heart chamber299Withdrawal70.000021
Atrium
 Atrial morphogenesis314Withdrawal40.000855
 Atrial septal morphogenesis318Withdrawal30.001650
 Atrial hypoplasia319Withdrawal20.001770
 Abnormal atrial morphology322Withdrawal60.002100
Atrioventricular valves
 Abnormal morphology AV cushions296Withdrawal69.04E-06
 Lack of AV canal cushions300Withdrawal40.000040
 AV valve morphogenesis321Withdrawal30.002040
Ventricle
 VSD type 3301Withdrawal30.000051
 Hypoplasia heart ventricle306Withdrawal60.000157
 Formation ventricular septum315Withdrawal70.001060
Vessels
 Angiogenesis289Withdrawal541.73E-06
 CVS development294Withdrawal567.32E-06
 Vasculogenesis302Withdrawal426.65E-05
 Sprouting angiogenesis324Withdrawal70.002690
 Vascular endothelial cell movement316Withdrawal130.001210
 Endothelial cell migration317Withdrawal190.001450
 Vascular endothelial cell migration317Withdrawal120.001530
 Early onset hypertension357Withdrawal10.007010
 Blood vessel breakdown357Withdrawal10.007010
 Pulmonary vein angiogenesis357Withdrawal10.007010
EWAS annotations in epigenomic data of Schrott et.al [15]

Qualitative Causal Inference

n 1965 A.B. Hill, a famous epidemiologist investigating the link between tobacco and lung cancer, set out his now well-known criteria to determine if a reported association might be causal [100]. His criteria included strength of association, consistency amongst different studies, specificity, temporality, coherence with known data, biological plausibility, dose-response curve, analogy with similar situations elsewhere and experimental confirmation. All of these criteria are fulfilled for congenital heart disorders as described above.

Quantitative Causal Inference

One of the main limitations of observational studies is the non-comparability of study groups, so-called ‘comparing apples with oranges’. This issue is addressed in the present multivariable panel regression modelling by the use of ipw which is the technique of choice in causal inference to circumvent this issue. One of the main limitations of observational studies is the non-comparability of study groups, so-called ‘comparing apples with oranges’. This issue is addressed in the present multivariable panel regression modelling by the use of ipw which is the technique of choice in causal inference to circumvent this issue. The other major issue with observational studies is the so-called concern of uncontrolled confounding. The E-values (or expected value) quantifies and constrains the degree of association demanded of some external unknown covariate with both the exposure of concern and the outcome of interest in order to explain away some apparently causal relationship. E-values >9 are said to be unusually high and assign causality [68]; however, an E-value greater than only 1.25 is generally required to assign causality [67]. E-values also have a 95% confidence interval. The lower bound is the one quoted in this report which identifies that 97.5% of the estimates will lie above that position. As noted the very high lower E-values in this report give the reader confidence in the outcomes described.

Generalizability

This is the largest dataset of CAs in the world and the present results necessarily carry great weight based on that alone. Results are further strengthened by their clear concordance with many datasets external to the present study [4–9, 70]. The results seen in bivariate analysis were confirmed in multiple regression modelling with two different regression techniques. Importantly the major techniques for causal inference have been employed namely ipw and E-values which transforms the analysis from a purely observational dataset into a pseudorandomized paradigm from which causal inferences may properly be drawn. In that our work demonstrates causality in this context, given the sophisticated form of modelling and the large size of the database we are happy that our results are widely generalizable.

Strengths and Limitations

The strengths of our study are that it is based on one of the largest datasets in the world and uses sophisticated forms of analysis in multivariable adjustment. We have also widely deployed the key tools of causal inference to allow pseudorandomization of our analysis and causal inferential modelling. The E-values which have been calculated are generally in the higher range which further adds robustness to the results. The present results are also consistent with results from several other recent reports. Moreover univariate and bivariate maps have been liberally supplied to allow the reader to actually visualize the datasets being described. Ranger regression was used for formal variable selection. Limitations of the study would include that, in common with many other epidemiological studies, individual cannabis exposure data was not available to the present investigators. Also some of the data had to be interpolated as in some datasets the degree of missing data was significant. This needs to be born in mind particularly when interpreting the data for daily cannabis use. Moreover the strong background of a genetic and biologically plausible explanatory frameworks, assisted greatly by the epigenetic work of Schrott and colleagues, makes findings mechanistically reasonable, further strengthening our confidence in both findings and generalizability. It is also observed that men and women who use cannabis may also differ in important respects from those who do not, albeit it can be argued that differences in long term relationship stability, rates of employment, and income are caused by and deeply confounded with cannabis use in that there is an evident two-way relationship between common sociodemographic covariates and drug use. In this sense it is possible that multivariable studies could over-control for such variables. While multivariable modelling can adjust to some extent for some of these covariates further large detailed subgroup investigations are indicated.

Conclusion

In conclusion we note that 17 congenital cardiac anomalies were positively related to metrics of cannabis exposure at bivariate analysis. When 12 of these CCA’s were studied in detail by inverse probability weighted panel regression and geospatial regression robust relationships to cannabis were confirmed which persisted after adjustment and fulfilled quantitative epidemiological criteria for causal relationships. Pathophysiologically the inhibition by cannabis through epigenomic pathways with the cardiac core regulatory gene complex and particularly the sonic hedgehog pathway by epigenomic mechanisms via heritable changes in DNA methylation is impressive and worthy of further study. The findings that 12–17 CCA’s could be related to metrics of cannabis exposure and most powerfully daily cannabis use in the largest CA dataset in the world is very concerning indeed particularly in the context of rapidly rising prevalence and daily intensity of cannabis use and cannabinoid potency and most especially in the context of the exponential genotoxic dose-response curve which has been demonstrated for cannabis genotoxicity both in the laboratory and epidemiologically. From this impressive dataset it can only be concluded that rational drug policy would require careful control of the penetration of powerful genotoxic compounds such as cannabinoids into the community in order to protect and preserve the genome and epigenome of the coming several generations. Click here for additional data file.
Table 3:

Final multivariate geospatial regression models for severe CHD

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Herb + LM.Cannabis_x_Resin.THC + Amphetamines + Cocaine + Income
Herb4.22 (2.54, 5.9)8.22E-07psi0.39421.34E-04
Income0 (0, 0)5.29E-05rho−0.51691.10E-04
LM.Cannabis_x_Resin.THC1.19 (0.56, 1.82)0.0002lambda0.31810.0240
Tobacco0.03 (0.02, 0.05)0.0004
Cocaine0.17 (0.06, 0.29)0.0040
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−2.7 (−3.77, −1.63)8.39E-07
Interactive
Rate ∼ Tobacco * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Herb * LM.Cannabis_x_Resin.THC + Alcohol + Herb + Amphetamines + Cocaine + Income
Herb7.47 (5.2, 9.74)1.05E-10psi0.30910.0042
Income0 (0, 0)1.42E-05rho−0.30240.0058
LM.Cannabis_x_Resin.THC2.95 (1.58, 4.32)2.35E-05
Tobacco0.04 (0.02, 0.06)3.87E-05
Cocaine0.21 (0.09, 0.32)0.0005
Herb: LM.Cannabis_x_Resin.THC−18.6 (−33.38, −3.82)0.0138
Tobacco: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−0.1 (−0.15, −0.05)0.0003
1 Lag
Rate ∼ Tobacco,1) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * LM.Cannabis_x_Resin.THC,1) * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Herb,1) + Alcohol,1) + Amphetamines,1) + Cocaine,1) + Income,1)
Income0 (0, 0)9.59E-05psi0.7862<2.2E-16
LM.Cannabis_x_Resin.THC: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.5.2 (1.57, 8.83)0.0050rho0.67081.73E-15
Alcohol0.09 (0.02, 0.15)0.0091lambda−0.56792.68E-08
Herb2.43 (0.12, 4.74)0.0391
LM.Cannabis_x_Resin.THC−1.45 (−2.42, −0.48)0.0035
2 Lags
Rate ∼ Tobacco,2) * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC,2) + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Herb,2) + Alcohol,2) + Amphetamines,2) + Cocaine,2) + Income,2)
Tobacco0.06 (0.04, 0.09)1.08E-05psi0.43170.0004
Income0 (0, 0)7.82E-08
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.9.27 (3.23, 15.31)0.0026
Herb2.93 (0.42, 5.44)0.0221
Tobacco: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−0.28 (−0.51, −0.06)0.0143
LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−4.14 (−6.9, −1.38)0.0032
Table 4:

Final multivariate geospatial regression models for ASD

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + Daily.Interpol. + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Herb + Amphetamines + Cocaine + Income
Herb3.88 (−0.56, 8.31)0.0865psi0.66684<2.2E-16
Interactive
Rate ∼ Tobacco + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Daily.Interpol. + Herb + Alcohol + Amphetamines + Cocaine + Income
No significant parameters remaining in the final model
1 Lag
Rate ∼ Tobacco + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Daily.Interpol. + Herb + Alcohol + Amphetamines + Cocaine + Income
Tobacco: Daily.Interpol.8.56 (1.29, 15.83)0.0210psi0.80977<2.2E-16
Daily.Interpol.−209.01 (−381.79, −36.23)0.0177
2 Lags
Rate ∼ Tobacco * Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Tobacco0.13 (0.04, 0.21)0.0028psi0.78396<2.2E-16
Income0 (0, 0)0.0053
Daily.Interpol.210 (31.44, 388.56)0.0215
Tobacco: Daily.Interpol.−7.99 (−15.32, −0.66)0.0324
Cocaine−0.38 (−0.67, −0.08)0.0135
Table 5:

Final multivariate geospatial regression models for VSD

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + Resin + Herb + Amphetamines + Cocaine + Income
Herb4.95 (1.91, 8)0.0014psi0.49097.62E-06
Cocaine0.31 (0.12, 0.5)0.0016rho−0.61337.46E-07
Resin1.76 (0.17, 3.35)0.0296lambda0.56341.06E-06
Amphetamines−0.2 (−0.37, −0.03)0.0209
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−0.98 (−1.71, −0.26)0.0079
Interactive
Rate ∼ Tobacco * Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
LM.Cannabis_x_Herb.THC17.3 (9.66, 24.94)8.94E-06psi0.47356.35E-05
Income0 (0, 0)0.0058rho−0.58674.97E-07
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−4.63 (−7.33, −1.93)0.0008lambda0.54041.94E-06
1 Lag
Rate ∼ Tobacco,1) * Daily.Interpol.,1) + LM.Cannabis_x_Herb.THC,1) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol.,1) + Alcohol,1) + LM.Cannabis_x_Herb.THC_x_Daily.Interpol., 1) + Amphetamines,1) + Cocaine,1) + Income,1)
LM.Cannabis_x_Herb.THC10.81 (4.89, 16.73)0.0003psi0.54641.31E-10
rho0.55623.30E-05
lambda0.33060.0462
2 Lags
Rate ∼ Tobacco,2) + Daily.Interpol.,2) + LM.Cannabis_x_Herb.THC,2) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol.,2) + Alcohol,2) + LM.Cannabis_x_Herb.THC_x_Daily.Interpol., 2) + Amphetamines,2) + Cocaine,2) + Income,2)
Tobacco0.04 (0, 0.07)0.0352psi0.43330.0003
Daily.Interpol.45.5 (9.24, 81.76)0.0137rho−0.53020.0004
LM.Cannabis_x_Herb.THC18.6 (10.03, 27.17)2.15E-05lambda0.56826.71E-06
Income0 (0, 0)0.0012
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−7.13 (−12.05, −2.21)0.0046
Cocaine−0.42 (−0.7, −0.13)0.0043
Alcohol−0.1 (−0.17, −0.03)0.0032
Table 6:

Final multivariate geospatial regression models for AVSD

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + Resin + Herb + Amphetamines + Cocaine + Income)
Herb4.95 (1.91, 8)0.0014psi0.49097.62E-06
Cocaine0.31 (0.12, 0.5)0.0016rho−0.61337.46E-07
Resin1.76 (0.17, 3.35)0.0296lambda0.56341.06E-06
Amphetamines−0.2 (−0.37, −0.03)0.0209
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−0.98 (−1.71, −0.26)0.0079
Interactive
Rate ∼ Tobacco * Resin * LM.Cannabis_x_Resin.THC + Alcohol + Amphetamines + Cocaine + Income
Tobacco: Resin0.52 (0.29, 0.75)1.23E-05psi0.4011.49E-05
Income0 (0, 0)0.0015
Cocaine0.37 (0.08, 0.67)0.0133
Resin−6.16 (−11.98, −0.34)0.0378
Tobacco: LM.Cannabis_x_Resin.THC−0.15 (−0.24, −0.06)0.0013
2 Lags
Rate ∼ Tobacco,2) * Resin,2) + LM.Cannabis_x_Resin.THC,2) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol., 2) + Herb,2) + Daily.Interpol.,2) + Alcohol,2) + Amphetamines,2) + Cocaine,2) + Income,2)
Income0 (0, 0)0.0020psi0.41910.0006
Tobacco: Resin0.25 (0.03, 0.47)0.0289rho0.54250.0004
lambda−0.59808.42E-05
Table 7:

Final multivariate geospatial regression models for PDA

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis + Amphetamines + Cocaine + Income
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.5.83 (2.33, 9.32)0.0011psi0.591711.43E-12
Cocaine0.48 (0.17, 0.79)0.0026rho−0.617887.19E-08
LM.Cannabis−30.65 (−45.35, −15.94)4.41E-05lambda0.60492.06E-09
Interactive
Rate ∼ Tobacco * Daily.Interpol. + LM.Cannabis + Alcohol + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Amphetamines + Cocaine + Income
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.6.45 (3.45, 9.46)2.50E-05psi0.368140.000342
Tobacco: Daily.Interpol.7.94 (3.04, 12.85)0.0015rho−0.688355.12E-12
Daily.Interpol.−168.25 (−292.36, −44.13)0.0079lambda0.669962.47E-15
Alcohol−0.16 (−0.25, −0.08)0.0001
Tobacco−0.07 (−0.11, −0.04)9.56E-05
LM.Cannabis−29.84 (−42.83, −16.84)6.76E-06
1 Lag
Rate ∼ Tobacco,1) * Daily.Interpol.,1) + LM.Cannabis_x_Herb.THC,1) + LM.Cannabis,1) + LM.Cannabis_x_Herb.THC_x_Daily.Interpol.,1) + Alcohol,1) + Daily.Interpol.,1) + Amphetamines,1) + Cocaine,1) + Income,1)
Tobacco: Daily.Interpol.9.38 (4.9, 13.86)4.13E-05psi0.39640.000433
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.7.55 (2.63, 12.47)0.0026rho0.72302<2.2E-16
LM.Cannabis_x_Herb.THC−17.48 (−29.69, −5.28)0.0050lambda−0.630949.92E-11
Daily.Interpol.−220.55 (−334.75, −106.35)0.0002
Tobacco−0.13 (−0.18, −0.09)1.02E-09
2 Lags
Rate ∼ Tobacco,2) * Daily.Interpol.,2) + LM.Cannabis_x_Herb.THC,2) + LM.Cannabis,2) + LM.Cannabis_x_Herb.THC_x_Daily.Interpol.,2) + Alcohol,2) + Daily.Interpol.,2) + Amphetamines,2) + Cocaine,2) + Income,2)
Tobacco: Daily.Interpol.2.52 (1.05, 3.99)0.0008psi0.453432.48E-05
LM.Cannabis_x_Herb.THC15.77 (3.67, 27.88)0.0107rho0.71484<2.2E-16
LM.Cannabis−32.01 (−49.41, −14.61)0.0003lambda−0.60135.76E-09
Alcohol−0.22 (−0.3, −0.14)9.40E-08
Table 8:

Final multivariate geospatial regression models for tetralogy of Fallot

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Herb + Daily.Interpol. + Herb + Amphetamines + Cocaine + Income
Cocaine0.25 (0.13, 0.37)6.77E-05rho−0.59276.13E-09
Herb3.18 (1.06, 5.31)0.0033lambda0.54143.29E-07
Tobacco−0.02 (−0.04, −0.01)0.0023
Amphetamines−0.22 (−0.32, −0.12)1.24E-05
Interactive
Rate ∼ Tobacco * Resin + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Tobacco: Resin0.43 (0.23, 0.62)1.44E-05rho−0.68195.55E-14
Income0 (0, 0)7.88E-05lambda0.612086.66E-11
Alcohol−0.05 (−0.09, −0.01)0.0232
Amphetamines−0.18 (−0.29, −0.08)0.0004
Tobacco−0.08 (−0.12, −0.04)0.0003
Resin−10.3 (−15.49, −5.11)0.0001
1 Lag
Rate ∼ Tobacco,1) * Herb,1) + Resin,1) + Alcohol,1) + Amphetamines,1) + Cocaine,1) + Income,1)
Cocaine0.27 (0.15, 0.39)7.90E-06psi−0.247150.0132
Tobacco: Herb0.96 (0.49, 1.42)5.26E-05
Amphetamines−0.16 (−0.27, −0.04)0.0066
Herb−17.03 (−28.64, −5.42)0.0040
Tobacco−0.12 (−0.17, −0.07)1.80E-06
2 Lags
Rate ∼ Tobacco,2) * LM.Cannabis_x_Herb.THC,2) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol., 2) * LM.Cannabis_x_Herb.THC_x_Daily.Interpol., 2) + Alcohol,2) + Amphetamines,2) + Cocaine,2) + Income,2)
Tobacco: LM.Cannabis_x_Herb.THC0.59 (0.35, 0.82)1.07E-06rho−0.63671.58E-08
Cocaine0.29 (0.11, 0.46)0.00115lambda0.52542.40E-05
Amphetamines−0.24 (−0.36, −0.13)5.82E-05
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−5.14 (−7.5, −2.78)2.00E-05
Tobacco−0.04 (−0.05, −0.02)1.93E-06
Table 9:

Final multivariate geospatial regression models for vascular disruptions

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. +Resin + Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Alcohol0.12 (0.08, 0.16)1.23E-08rho0.7048<2.2E-16
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.2.1 (1.05, 3.15)8.77E-05lambda−0.49026.17E-06
Income0 (0, 0)0.0001
Cocaine0.24 (0.09, 0.39)0.0021
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−4.63 (−7.45, −1.81)0.0013
Interactive
Rate ∼ Tobacco + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. * Resin + Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Alcohol0.12 (0.08, 0.16)1.23E-08rho0.7048<2.2E-16
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.2.1 (1.05, 3.15)8.77E-05lambda−0.49026.17E-06
Income0 (0, 0)0.0001
Cocaine0.24 (0.09, 0.39)0.0021
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−4.63 (−7.45, −1.81)0.0013
1 Lag
Rate ∼ Tobacco + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Resin + Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Income0 (0, 0)1.04E-05rho0.666691.83E-12
Alcohol0.1 (0.05, 0.14)3.48E-05lambda−0.53367.94E-06
Amphetamines0.13 (0.03, 0.23)0.0139
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.59 (0.06, 1.11)0.0284
2 Lags
Rate ∼ Tobacco + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Resin + Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Alcohol0.11 (0.06, 0.16)2.03E-05rho0.693612.72E-15
Income0 (0, 0)0.0001lambda−0.53377.84E-06
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.1.13 (0.47, 1.79)0.0008
Table 10:

Final multivariate geospatial regression models for double outlet right ventricle

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Herb + Resin + LM.Cannabis_x_Resin.THC + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Amphetamines + Cocaine + Income
Income0 (0, 0)5.64E-13psi0.196080.0393
LM.Cannabis_x_Herb.THC5.13 (1.23, 9.03)0.0099
Interactive
Rate ∼ Tobacco * LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Income0 (0, 0)6.71E-08psi0.17310.0712
LM.Cannabis_x_Herb.THC29.6 (4.51, 54.69)0.0208
Tobacco0.05 (0, 0.1)0.0497
Tobacco: LM.Cannabis_x_Herb.THC−1.01 (−2.02, 0)0.0499
2 Lags
Rate ∼ Tobacco * LM.Cannabis_x_Herb.THC + Daily.Interpol. * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Income0 (0, 0)<2.2E-16psi−0.10720.331
LM.Cannabis_x_Herb.THC12.8 (7.14, 18.46)9.11E-06
Alcohol0.06 (0.01, 0.11)0.0207
Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−40.5 (−73.04, −7.96)0.0146
Table 11:

Final multivariate geospatial regression models for transposition of the great vessels

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + Daily.Interpol. + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + Herb + Amphetamines + Cocaine + Income
Income0 (0, 0)5.64E-13psi0.19610.0393
LM.Cannabis_x_Herb.THC5.13 (1.23, 9.03)0.0099
Interactive
Rate ∼ Tobacco * LM.Cannabis_x_Resin.THC + Resin * Herb + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Herb3.41 (1.49, 5.33)0.0005rho−0.7757<2.2E-16
Cocaine0.21 (0.09, 0.33)0.0005lambda0.64251.99E-13
Income0 (0, 0)0.0235
Tobacco0.02 (0, 0.05)0.0455
Alcohol−0.07 (−0.11, −0.03)0.0004
1 Lag
Rate ∼ Tobacco * LM.Cannabis_x_Resin.THC + Resin * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Income0 (0, 0)0.0020rho−0.7509<2.2E-16
Tobacco0.03 (0, 0.06)0.0308lambda0.67066.45E-14
Cocaine0.15 (0.01, 0.28)0.0343
Resin0.94 (0.06, 1.82)0.0370
Alcohol−0.07 (−0.12, −0.03)0.0011
2 Lags
Rate ∼ Tobacco * LM.Cannabis_x_Resin.THC + Resin * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
LM.Cannabis_x_Resin.THC1.16 (0.58, 1.74)0.0001rho−0.75796.87E-16
Alcohol−0.08 (−0.12, −0.04)3.85E-05lambda0.67524.76E-15
Income0 (0, 0)9.61E-07
Table 12:

Final multivariate geospatial regression models for hypoplastic right heart

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValue
Additive
Rate ∼ Tobacco + Alcohol + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + Amphetamines + Cocaine + Income
Alcohol0.08 (0.04, 0.13)0.0005Least squares
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.2.68 (1.53, 3.83)5.22E-06SD0.3951
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−7.03 (−9.93, −4.13)2.10E-06
Cocaine0.22 (0.06, 0.39)0.0066
Income0 (0, 0)2.28E-05
Interactive
Rate ∼ Tobacco,1) * LM.Cannabis_x_Herb.THC_x_Daily.Interpol., 1) + LM.Cannabis_x_Herb.THC,1) + LM.Cannabis_x_Resin.THC_x_Daily.Interpol., 1) + Alcohol,1) + Daily.Interpol.,1) + Amphetamines,1) + Cocaine,1) + Income,1)
Income0 (0, 0)3.32E-08Least squares
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.2.84 (1.75, 3.93)3.10E-07SD0.3914
Alcohol0.06 (0.02, 0.1)0.0064
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Herb.THC−57.4 (−79.74, −35.06)5.02E-07
1 Lag
Rate ∼ Tobacco * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Daily.Interpol. + Amphetamines + Cocaine + Income
Income0 (0, 0)1.37E-06Least squares
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.2.79 (1.28, 4.31)0.0003SD0.4160
Alcohol0.07 (0.02, 0.12)0.0053
Daily.Interpol.35.17 (9.24, 61.1)0.0079
LM.Cannabis_x_Herb.THC7.88 (0.62, 15.14)0.0333
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−11.66 (−17.57, −5.75)0.0001
2 Lag
Rate ∼ Tobacco * LM.Cannabis_x_Herb.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Daily.Interpol. + Amphetamines + Cocaine + Income
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.5.24 (3.12, 7.36)1.09E-06Least squares
Income0 (0, 0)4.19E-05SD0.4106
Daily.Interpol.98.5 (50.87, 146.13)5.10E-05
LM.Cannabis_x_Herb.THC13.6 (4.53, 22.67)0.0035
Cocaine−0.34 (−0.65, −0.03)0.0295
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.−22 (−30.55, −13.45)4.22E-07
Table 13:

Final multivariate geospatial regression models for mitral valve anomalies

Parameter ValuesModel Parameters
ParameterEstimate (CI) P-ValueParameterValueSignificance
Additive
Rate ∼ Tobacco + Alcohol + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + LM.Cannabis_x_Herb.THC + Daily.Interpol. + Amphetamines + Cocaine + Income
Income0 (0, 0)0.0005psi0.45692.28E-07
Alcohol0.12 (0.05, 0.2)0.0015rho0.60403.16E-06
lambda−0.70521.56E-10
Interactive
Rate ∼ Tobacco * Daily.Interpol. + LM.Cannabis_x_Herb.THC + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Alcohol0.25 (0.19, 0.31)4.69E-16rho0.61929.08E-11
Tobacco: Daily.Interpol.6.77 (4.21, 9.33)2.17E-07lambda−0.7536<2.2E-16
Amphetamines0.14 (0.02, 0.26)0.0185
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−0.73 (−1.37, −0.09)0.0260
Daily.Interpol.−130.64 (−193.93, −67.35)5.22E-05
Tobacco−0.15 (−0.19, −0.11)<2.2E-16
1 Lag
Rate ∼ Tobacco * Daily.Interpol. + LM.Cannabis_x_Herb.THC * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Alcohol0.25 (0.19, 0.31)1.41E-15rho0.68126.99E-16
Tobacco: Daily.Interpol.6.27 (3.6, 8.94)4.14E-06lambda−0.7776<2.2E-16
Amphetamines0.2 (0.08, 0.33)0.0011
LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.30.04 (9.68, 50.4)0.0038
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−3.04 (−4.78, −1.3)0.0006
Daily.Interpol.−127.56 (−192.42, −62.7)0.0001
Tobacco−0.15 (−0.19, −0.11)1.41E-15
2 Lags
Rate ∼ Tobacco * Daily.Interpol. + LM.Cannabis_x_Herb.THC * LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Alcohol + Amphetamines + Cocaine + Income
Alcohol0.25 (0.18, 0.32)1.01E-12rho0.64042.70E-11
Tobacco: Daily.Interpol.4.88 (1.58, 8.18)0.0038lambda−0.7388<2.2E-16
Amphetamines0.21 (0.07, 0.36)0.0044
LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.40.2 (9.02, 71.37)0.0115
Daily.Interpol.−90.59 (−171.28, −9.9)0.0278
LM.Cannabis_x_Resin.THC_x_Daily.Interpol.−3.95 (−6.36, −1.53)0.0014
Tobacco−0.14 (−0.18, −0.1)2.04E-11
Table 14:

E-values from panel models

No.Cardiovascular AnomalyModel TypeTerm P-Value E-Value EstimateLower Bound E-Value
1Cong. heart diseaseAdditiveHerb.THC1.93E-163.38E + 121.26E + 10
2Cong. heart diseaseInteractiveHerb.THC9.29E-103.95E + 142.95E + 10
3Cong. heart diseaseInteractiveLM.Cannabis_x_Herb.THC6.23E-035.19E + 132.14E + 04
4Cong. heart diseaseInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.1.91E-025.56E + 209.39E + 03
5Cong. heart disease1 LagHerb.THC1.75E-198.06E + 193.88E + 16
6Cong. heart disease2 LagsHerb.THC1.15E-056.66E + 081.86E + 05
7Cong. heart disease2 LagsHerb.THC: LM.Cannabis_x_Herb.THC4.14E-114.37E + 593.41E + 44
8Severe cong. heart diseaseAdditiveLM.Herb.THC.Daily.Interpol4.43E-024.92E + 001.24
9Severe cong. heart diseaseAdditiveLM.Herb.Daily.Intpltd4.74E-025.25E + 133.67
10Severe Cong. Heart DiseaseAdditiveLM.Herb.THC1.48E-037.57E + 06850.24
11Severe cong. heart diseaseInteractiveLM.Resin.Daily.Interpol4.20E-114.84E + 131.30E + 10
12Severe cong. heart diseaseInteractiveHerb.THC1.31E-053.89E + 072.94E + 05
13Severe cong. heart diseaseInteractiveLM.Resin9.62E-072.33E + 04686.08
14Severe cong. heart diseaseInteractiveTobacco: LM.Herb.THC.Daily.Interpol3.85E-082.492.03
15Severe cong. heart diseaseInteractiveLM.Resin.Daily.Interpol: LM.Herb.THC.Daily.Interpol1.22E-13108.1142.72
16Severe cong. heart disease1 LagLM.Resin0.0003616.4929.73
17Severe cong. heart disease1 LagHerb.THC0.00786.74E + 0318.81
18Severe cong. heart disease1 LagLM.Resin: LM.Herb.THC.Daily.Interpol0.00051.80E + 061.03E + 03
19Severe cong. heart disease2 LagsLM.Resin7.23E-061.31E + 0391.92
20Severe cong. heart disease2 LagsHerb.THC4.41E-023.46E + 032.10
21Severe cong. heart disease2 LagsLM.Resin.Daily.Interpol: LM.Resin4.42E-024.08E + 052.76
22ASDAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.01334.911.81
23ASDAdditiveHerb0.00131.79E + 0481.68
24ASDInteractiveHerb2.32E-123.59E + 051.76E + 04
25ASD1 LagLM.Cannabis_x_Herb.THC8.72E-157.28E + 096.31E + 07
26ASD2 LagsLM.Cannabis_x_Herb.THC<2.2E-169.01E + 152.54E + 13
27ASD4 LagsLM.Cannabis_x_Herb.THC0.01682.51E + 0890.06
28VSDAdditiveLM.Cannabis_x_Herb.THC<2.2E-162.38E + 061.96E + 05
29VSDInteractiveLM.Cannabis_x_Herb.THC<2.2E-162.38E + 061.96E + 05
30VSD1 LagLM.Cannabis_x_Herb.THC<2.2E-163.04E + 062.58E + 05
31VSD2 LagsLM.Cannabis_x_Herb.THC2.7E-141.60E + 214.97E + 16
32AVSDAdditiveResin4.05E-10897.5155.66
33AVSDInteractiveLM.Cannabis_x_Resin.THC8.05E-071.67E + 241.86E + 15
34AVSDInteractiveTobacco: Resin4.26E-075.563.48
35AVSDInteractiveTobacco: Resin: LM.Cannabis_x_Resin.THC0.0026263.7711.38
36AVSD1 LagLM.Cannabis_x_Resin.THC0.00424.17E + 081.18E + 03
37AVSD1 LagTobacco: Resin1.11E-075.643.62
38AVSD2 LagsTobacco: Resin1.58E-121.541.44
39PDAAdditiveDaily.Interpol.4.3E-095.07E + 493.15E + 34
40PDAAdditiveLM.Cannabis_x_Herb.THC0.01582.80E + 038.07
41PDAInteractiveLM.Cannabis_x_Herb.THC3.27E-053.64E + 051.52E + 03
42PDAInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.0.04491.481.07
43PDAInteractiveTobacco: Daily.Interpol.8.61E-10243.559.42
44PDA1 LagLM.Cannabis_x_Herb.THC7.29E-148.51E + 144.28E + 11
45PDA1 LagTobacco: Daily.Interpol.1.39E-082.93E + 087.61E + 05
46PDA2 LagsLM.Cannabis_x_Herb.THC2.33E-077.34E + 116.85E + 07
47PDA2 LagsTobacco: Daily.Interpol.0.00423.30E + 05111.26
48Tetralogy of FallotAdditiveDaily.Interpol.5.96E-093.79E + 193.72E + 13
49Tetralogy of FallotInteractiveDaily.Interpol.2.1E-09Infinty4.28E + 135
50Tetralogy of FallotInteractiveLM.Cannabis_x_Herb.THC0.01591.19E + 0517.74
51Tetralogy of FallotInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.4.68E-0736.9612.31
52Tetralogy of Fallot1 LagLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.2.59E-101.24E + 041.08E + 03
53Tetralogy of Fallot2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.4.36E-163.78E + 03868.53
54Tetralogy of Fallot2 LagsResin0.0460474.561.71
55Tetralogy of Fallot2 LagsLM.Cannabis_x_Herb.THC3.86E-081.04E + 251.04E + 17
56Vascular disruptionsAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.4.85E-0552.9311.26
57Vascular disruptionsAdditiveResin9.68E-0344.483.89
58Vascular disruptionsInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.1.56E-063.81E + 03212.19
59Vascular disruptionsInteractiveResin0.003862.365.91
60Vascular disruptionsInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: Resin1.27E-054.00E + 171.77E + 10
61Vascular disruptions1 LagLM.Cannabis_x_Herb.THC_x_Daily.Interpol.6.72E-041.09E + 05255.77
62Vascular disruptions1 LagLM.Cannabis_x_Resin.THC9.61E-044.79E + 0354.91
63Vascular disruptions2 LagsLM.Cannabis_x_Herb.THC_x_Daily.Interpol.1.76E-041.48E + 0355.25
64Vascular disruptions2 LagsResin3.50E-086.79E + 042.43E + 03
65Double outlet right ventricleInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.00053.52E + 081.08E + 04
66Double outlet right ventricleInteractiveHerb6.33E-051.34E + 05740.60
67Double outlet right ventricleInteractiveLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC9.88E-052.08E + 327.82E + 16
68Double outlet right ventricleInteractiveTobacco: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.5.35E-124.53E + 043.72E + 03
69Double outlet right ventricle2 LagsLM.Cannabis_x_Herb.THC0.00309.24E + 131.74E + 05
70Double outlet right ventricle2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.00523.92E + 0322.14
71Double outlet right ventricle2 LagsHerb: Resin0.01385.10E + 245.89E + 05
72Transposition great vesselsAdditiveHerb0.00039.44E + 078.52
73Transposition great vesselsAdditiveLM.Cannabis_x_Resin.THC0.00021.61E + 0350.46
74Transposition great vesselsInteractiveHerb0.00135.06E + 04121.42
75Transposition great vesselsInteractiveTobacco: Resin0.00023.722.28
76Transposition great vesselsInteractiveLM.Cannabis_x_Resin.THC: Resin8.46E-057.77E + 397.81E + 20
77Transposition great vessels1 LagHerb<2.2E-163.11E + 062.02E + 05
78Transposition great vessels2 LagsLM.Cannabis_x_Resin.THC0.00341.41E + 0319.41
79Transposition great vessels2 LagsHerb0.00564.74E + 091.68E + 03
80Transposition great vessels2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.043754.681.59
81Hypoplastic right heartAdditiveResin0.00501.11E + 0314.31
82Hypoplastic right heartAdditiveLM.Cannabis_x_Herb.THC0.02655.02E + 0717.69
83Hypoplastic right heartInteractiveLM.Cannabis_x_Resin.THC0.00529.242.67
84Hypoplastic right heart1 LagResin5.65E-0525.737.38
85Hypoplastic right heart2 LagsResin0.000925.495.44
86Mitral valve anomaliesAdditiveDaily.Interpol.6.06E-106.92E + 214.76E + 15
87Mitral valve anomaliesInteractiveDaily.Interpol.7.46E-092.31E + 185.81E + 12
88Mitral valve anomaliesInteractiveLM.Cannabis_x_Herb.THC0.01896.44E + 0519.02
89Mitral valve anomalies1 LagTobacco: Daily.Interpol.1.27E-139.276.31
90Mitral valve anomalies2 LagsHerb0.01558.04E + 0310.85
Table 15:

E-values from geospatial models

No.RegressionAnomalyModel TypeTerm P-Value E-Value EstimateLower Bound E-Value
1SpatialCong. heart diseaseAdditiveHerb.THC0.00381.20E + 0570.68
2SpatialCong. heart diseaseInteractiveHerb.THC0.00381.20E + 0570.68
3SpatialCong. heart disease1 LagHerb.THC0.0401801.091.98
4SpatialCong. heart disease2 LagsHerb.THC0.0238144.62.95
5SpatialSevere cong. heart diseaseAdditiveHerb8.22E-078.11E + 077.77E + 04
6SpatialSevere cong. heart diseaseAdditiveLM.Cannabis_x_Resin.THC0.0002281.8720.47
7SpatialSevere cong. heart diseaseInteractiveHerb1.05E-101.19E + 131.61E + 09
8SpatialSevere cong. heart diseaseInteractiveLM.Cannabis_x_Resin.THC2.35E-052.21E + 051.03E + 03
9SpatialSevere cong. heart disease1 LagLM.Cannabis_x_Resin.THC: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.0.00502.18E + 053.06
10SpatialSevere cong. heart disease1 LagHerb0.03911.20E + 113.67E + 03
11SpatialSevere cong. heart disease2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.00262.74E + 111.60E + 04
12SpatialSevere cong. heart disease2 LagsHerb0.02216.71E + 035.99
13SpatialASD1 LagTobacco: Daily.Interpol.0.02104.41E + 0725.77
14SpatialASD2 LagsDaily.Interpol.0.02151.54E + 1391.07E + 21
15SpatialVSDAdditiveHerb0.001433.671.99
16SpatialVSDAdditiveResin0.02965.88E + 0343.32
17SpatialVSDInteractiveLM.Cannabis_x_Herb.THC8.94E-061.16E + 174.81E + 09
18SpatialVSD1 LagLM.Cannabis_x_Herb.THC0.00039.50E + 094.91E + 05
19SpatialVSD2 LagsDaily.Interpol.0.01373.41E + 571.44E + 12
20SpatialVSD2 LagsLM.Cannabis_x_Herb.THC2.15E-054.45E + 238.06E + 12
21SpatialAVSDAdditiveHerb0.00145.88E + 0343.32
22SpatialAVSDAdditiveResin0.029633.651.99
23SpatialAVSDInteractiveTobacco: Resin1.23E-054.062.56
24SpatialAVSD2 LagsTobacco: Resin0.02892.371.26
25SpatialPDAAdditiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.0.00114.73E + 0344.81
26SpatialPDAInteractiveLM.Cannabis_x_Herb.THC_x_Daily.Interpol.2.50E-053.21E + 051.23E + 03
27SpatialPDAInteractiveTobacco: Daily.Interpol.0.00155.09E + 06576.84
28SpatialPDA1 LagTobacco: Daily.Interpol.4.13E-051.11E + 135.74E + 05
29SpatialPDA1 LagLM.Cannabis_x_Herb.THC_x_Daily.Interpol.0.00261.37E + 163.82E + 08
30SpatialPDA2 LagsTobacco: Daily.Interpol.0.00087.25E + 09344.71
31SpatialPDA2 LagsLM.Cannabis_x_Herb.THC0.010766.558.12
32SpatialTetralogy of FallotAdditiveHerb0.00332.95E + 07503.90
33SpatialTetralogy of FallotInteractiveTobacco: Resin1.44E-0526.767.86
34SpatialTetralogy of Fallot1 LagTobacco: Herb5.26E-0513.114.83
35SpatialTetralogy of Fallot2 LagsTobacco: LM.Cannabis_x_Herb.THC1.07E-0662.8715.34
36SpatialVascular disruptionsAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.8.77E-05293.6323.91
37SpatialVascular disruptionsInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.8.77E-05293.6323.91
38SpatialVascular disruptions1 LagLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.02846.951.58
39SpatialVascular disruptions2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.000824.975.19
40SpatialDouble outlet right ventricleAdditiveLM.Cannabis_x_Herb.THC0.00997.76E + 0425.13
41SpatialDouble outlet right ventricleInteractiveLM.Cannabis_x_Herb.THC0.02081.63E + 272.73E + 04
42SpatialDouble outlet right ventricle2 LagsLM.Cannabis_x_Herb.THC9.11E-062.54E + 121.18E + 07
43SpatialTransposition great vesselsAdditiveLM.Cannabis_x_Herb.THC0.00997.77E + 0425.13
44SpatialTransposition great vesselsInteractiveHerb0.00054.95E + 0361.38
45SpatialTransposition great vessels1 LagResin0.037015.341.53
46SpatialTransposition great vessels2 LagsLM.Cannabis_x_Resin.THC0.000112.514.51
47SpatialHypoplastic right heartAdditiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.5.22E-06960.3567.27
48SpatialHypoplastic right heartInteractiveLM.Cannabis_x_Resin.THC_x_Daily.Interpol.3.10E-071.48E + 03118.27
49SpatialHypoplastic right heart1 LagLM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.00036.18E + 077.55
50SpatialHypoplastic right heart1 LagDaily.Interpol.0.0079901.9532.41
51SpatialHypoplastic right heart1 LagLM.Cannabis_x_Herb.THC0.03335.17E + 331.34E + 09
52SpatialHypoplastic right heart2 LagsLM.Cannabis_x_Resin.THC_x_Daily.Interpol.1.09E-062.21E + 134.19E + 04
53SpatialHypoplastic right heart2 LagsDaily.Interpol.5.10E-052.22E + 052.10E + 03
54SpatialHypoplastic right heart2 LagsLM.Cannabis_x_Herb.THC0.00351.17E + 952.06E + 49
55SpatialMitral valve anomaliesInteractiveTobacco: Daily.Interpol.2.17E-079.91E + 057.03E + 03
56SpatialMitral valve anomalies1 LagTobacco: Daily.Interpol.4.14E-061.58E + 064.93E + 03
57SpatialMitral valve anomalies1 LagLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.00383.71E + 282.81E + 09
58SpatialMitral valve anomalies2 LagsTobacco: Daily.Interpol.0.00383.97E + 0449.19
59SpatialMitral valve anomalies2 LagsLM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.0.01155.36E + 352.03E + 08
  62 in total

1.  Cannabinoid-Induced Changes in the Activity of Electron Transport Chain Complexes of Brain Mitochondria.

Authors:  Namrata Singh; Jana Hroudová; Zdeněk Fišar
Journal:  J Mol Neurosci       Date:  2015-03-29       Impact factor: 3.444

2.  Hypoploid metaphases in cultured lymphocytes of marihuana smokers.

Authors:  A Morishima; R T Henrich; J Jayaraman; G G Nahas
Journal:  Adv Biosci       Date:  1978 Jul 22-23

3.  Influence of delta9-tetrahydrocannabinol on cell proliferation and macromolecular biosynthesis in human cells.

Authors:  M J Mon; R L Jansing; S Doggett; J L Stein; G S Stein
Journal:  Biochem Pharmacol       Date:  1978       Impact factor: 5.858

4.  Morphological and cytochemical effects of marijuana cigarette smoke on epithelioid cells of lung explants from mice.

Authors:  C Leuchtenberger; R Leuchtenberger
Journal:  Nature       Date:  1971-11-26       Impact factor: 49.962

5.  In vitro induction of segregational errors of chromosomes by natural cannabinoids in normal human lymphocytes.

Authors:  R T Henrich; T Nogawa; A Morishima
Journal:  Environ Mutagen       Date:  1980

6.  Recommendations From Cannabis Dispensaries About First-Trimester Cannabis Use.

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Journal:  Obstet Gynecol       Date:  2018-06       Impact factor: 7.661

7.  Genome-Wide DNA Methylation Profiling Reveals Epigenetic Changes in the Rat Nucleus Accumbens Associated With Cross-Generational Effects of Adolescent THC Exposure.

Authors:  Corey T Watson; Henrietta Szutorisz; Paras Garg; Qammarah Martin; Joseph A Landry; Andrew J Sharp; Yasmin L Hurd
Journal:  Neuropsychopharmacology       Date:  2015-06-05       Impact factor: 7.853

8.  Cannabinoids Exacerbate Alcohol Teratogenesis by a CB1-Hedgehog Interaction.

Authors:  Eric W Fish; Laura B Murdaugh; Chengjin Zhang; Karen E Boschen; Oswald Boa-Amponsem; Haley N Mendoza-Romero; Michael Tarpley; Lhoucine Chdid; Somnath Mukhopadhyay; Gregory J Cole; Kevin P Williams; Scott E Parnell
Journal:  Sci Rep       Date:  2019-11-05       Impact factor: 4.379

9.  Low doses of widely consumed cannabinoids (cannabidiol and cannabidivarin) cause DNA damage and chromosomal aberrations in human-derived cells.

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