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

Albert Stuart Reece; Gary Kenneth Hulse

doi:10.1093/eep/dvac015

. 2022 Jul 5;8(1):dvac015. doi: 10.1093/eep/dvac015

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

Albert Stuart Reece ^1,^2,^*, Gary Kenneth Hulse ^3,⁴

PMCID: PMC9364688 PMID: 35966825

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.

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

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.

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

Figure 2: — Panelled scatterplot of log (rates of selected CCAs) against rates of substance exposure—2

Figure 3: — 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.

Figure 4: — Panelled scatterplot of log (rates of selected CCAs) against rates of cannabis metric exposure—1

Figure 5: — Panelled scatterplot of log (rates of selected CCAs) against rates of cannabis metric exposure—2

Figure 6: — 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 8: — Sequential map-graphs of log (rates of severe CHD) across selected European countries over time, 2010–2019

Figure 9: — Sequential map-graphs of log (rates of ASD) across selected European countries over time, 2010–2019

Figure 10: — Sequential map-graphs of log (rates of VSD) across selected European countries over time, 2010–2019

Figure 11: — Sequential map-graphs of log (rates of PDA) across selected European countries over time, 2010–2019

Figure 12: — 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

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).

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

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

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 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.

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

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

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

Anomaly	Substance	Mean Anomaly Rate	Estimate	Std. Error	Sigma	t-statistic	P-Value	E-Value Estimate	E-Value Lower Bound
Mitral valve anomalies	Daily.Interpol.	1.5003	33.8128	7.7588	0.7097	4.3580	3.07E-05	1.35E + 19	4.78E + 10
Congenital heart	Daily.Interpol.	79.9062	18.6127	4.5313	0.4145	4.1076	7.94E-05	1.11968E + 18	3.96E + 09
Congenital heart	Herb	79.9062	7.2519	1.1476	0.3801	6.3192	6.48E-09	6.95E + 07	3.22E + 05
Congenital heart	LMCannabis_Herb	79.9062	7.4271	1.5650	0.4052	4.7459	6.58E-06	3.51E + 07	3.63E + 04
PV stenosis	Daily.Interpol.	4.0360	24.2371	8.3771	0.7863	2.8933	0.0046	3.04046E + 12	1.77E + 04
Double outlet RV	Herb	1.1079	7.1871	1.6528	0.5474	4.3485	3.18E-05	3.09E + 05	1.43E + 03
Mitral valve anomalies	LMCannabis_Herb	1.5003	9.3952	2.8340	0.7337	3.3151	0.0013	2.30E + 05	236.98
Severe CHD	Herb	21.6609	4.2206	1.0939	0.4253	3.8582	1.85E-04	1.67E + 04	171.06
Mitral valve anomalies	Herb	1.5003	7.8448	2.1992	0.7283	3.5671	5.45E-04	3.61E + 04	166.84
Congenital heart	LM_Cannabis	79.9062	5.9088	1.9991	0.4290	2.9557	0.0039	5.55E + 05	138.14
PDA	Daily.Interpol.	2.6743	23.9286	10.1280	0.9506	2.3626	0.0199	17 732 087 319	102.52
Double outlet RV	Daily.Interpol.	1.1079	14.5709	6.3435	0.5802	2.2970	0.0236	16 820 336 693	58.96
Double outlet RV	LMCannabis_Herb	1.1079	6.3564	2.2113	0.5725	2.8745	0.0049	4.88E + 04	49.96
Transpos Grt Vess	Herb	3.2853	5.1077	1.5361	0.5973	3.3251	0.0012	4.79E + 03	48.76
AVSD	Resin	4.0167	3.7361	0.7343	0.6601	5.0878	1.59E-06	344.49	47.12
Severe CHD	Resin	21.6609	2.1476	0.4264	0.3833	5.0361	1.98E-06	326.92	44.70
AVSD	Herb	4.0167	5.3712	1.7549	0.6824	3.0607	0.0027	2.58E + 03	26.03
Mitral valve anomalies	LM_Cannabis	1.5003	8.6035	3.4940	0.7498	2.4624	0.0154	6.85E + 04	16.60
Mitral valve anomalies	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	1.5003	3.1648	0.8621	0.7260	3.6710	3.82E-04	105.14	12.24
PDA	Herb	2.6743	6.5630	2.3969	0.9320	2.7382	0.0071	1.21E + 03	11.95
Congenital heart	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	79.9062	1.7086	0.5033	0.4239	3.3947	9.71E-04	77.87	8.94
Hypoplastic right heart	Resin	0.6757	1.7823	0.5256	0.4725	3.3908	9.83E-04	61.40	8.01
Vascular disruptions	Herb	6.6675	4.0369	1.6751	0.5548	2.4100	0.0177	1.50E + 03	6.42
Aortic atresia ∼	Daily.Interpol.	0.5067	10.3538	4.9896	0.4564	2.0751	0.0404	1 849 078 395	5.99
Transpos Grt Vess	LMCannabis_Herb	3.2853	4.9262	2.1275	0.6107	2.3155	0.0223	3.08E + 03	5.70
VSD	Resin	41.6464	2.0114	0.7053	0.6341	2.8517	0.0052	35.36	4.39
PDA	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	2.6743	3.0256	1.1046	0.9430	2.7390	0.0072	36.56	4.04
PV stenosis	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	4.0360	2.4987	0.9251	0.7898	2.7009	0.0080	35.08	3.85
Severe CHD	LMCannabis_Resin	21.6609	0.9713	0.3439	0.4118	2.8244	0.0057	16.59	3.28
Transpos Grt Vess	Resin	3.2853	1.8158	0.6963	0.6259	2.6077	0.0104	27.51	3.28
Congenital heart	Cocaine	79.9062	0.3536	0.0489	0.3649	7.2278	8.21E-11	4.26	3.21
AVSD	LMCannabis_Resin	4.0167	1.6005	0.5954	0.7129	2.6883	0.0084	14.91	2.88
Tetralogy of Fallot	LMCannabis_Herb	3.0532	4.5496	2.1449	0.6157	2.1211	0.0360	1.66E + 03	2.76
Vascular disruptions	Resin	6.6675	1.5929	0.6530	0.5622	2.4393	0.0166	25.84	2.72
VSD	Herb	41.6464	3.7183	1.7168	0.6675	2.1658	0.0323	317.46	2.65
PV stenosis	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	4.0360	1.1697	0.4095	0.7664	2.8562	0.0052	7.49	2.47
Double outlet RV	Cocaine	1.1079	0.3591	0.0716	0.5341	5.0147	2.17E-06	3.09	2.26
Double outlet RV	Resin	1.1079	1.5674	0.6900	0.5941	2.2715	0.0254	21.56	2.14
Mitral valve anomalies	Cocaine	1.5003	0.4350	0.0943	0.7032	4.6137	1.12E-05	2.91	2.11
PV atresia	Resin	1.0993	1.4785	0.6617	0.5948	2.2343	0.0276	18.69	1.98
Transpos Grt Vess	Cocaine	3.2853	0.2772	0.0735	0.5902	3.7714	2.53E-04	2.44	1.76
Severe CHD	Cocaine	21.6609	0.1970	0.0532	0.4272	3.7043	3.22E-04	2.41	1.74
VSD	LMCannabis_Resin	41.6464	1.1863	0.5373	0.6434	2.2078	0.0294	10.18	1.72
Transpos Grt Vess	LMCannabis_Resin	3.2853	1.1583	0.5274	0.6316	2.1961	0.0303	10.09	1.69
ASD	Herb	21.0695	3.7614	1.8527	0.7204	2.0302	0.0445	230.98	1.66
Hypoplastic left heart	Cocaine	2.3072	0.2725	0.0833	0.6689	3.2713	0.0014	2.25	1.59
PDA	Cocaine	2.6743	0.3709	0.1147	0.9214	3.2329	0.0016	2.24	1.58
Hypoplastic right heart	LMCannabis_Resin	0.6757	0.8725	0.4068	0.4871	2.1446	0.0343	9.68	1.58
PV atresia	Cocaine	1.0993	0.2021	0.0710	0.5702	2.8465	0.0052	2.11	1.45
Vascular disruptions	Log(Amphetamine)	6.6675	0.1969	0.0711	0.5502	2.7687	0.0067	2.12	1.43
Congenital heart	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	79.9062	0.5114	0.2390	0.4449	2.1397	0.0349	5.14	1.41
PV stenosis	Cocaine	4.0360	0.2636	0.0969	0.7779	2.7213	0.0075	2.06	1.40
Tetralogy of Fallot	Cocaine	3.0532	0.2061	0.0758	0.6087	2.7190	0.0075	2.06	1.40
Aortic atresia ∼	Cocaine	0.5067	0.1607	0.0604	0.4507	2.6583	0.0091	2.11	1.40
VSD	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	41.6464	0.7413	0.3499	0.6547	2.1188	0.0366	5.05	1.38
ASD	Daily.Interpol.	21.0695	0.2363	0.0893	0.7122	2.6454	0.0093	2.04	1.38
Arterial truncus	Log(Amphetamine)	0.9427	0.1964	0.0753	0.6009	2.6065	0.0103	2.03	1.37
Severe CHD	Log(Amphetamine)	21.6609	0.1434	0.0550	0.4387	2.6060	0.0103	2.03	1.36
Aortic valve S/A	Log(Amphetamine)	1.4907	0.1865	0.0751	0.5987	2.4842	0.0144	1.99	1.32
Coarctation aorta	Cocaine	3.6130	0.2296	0.0926	0.7433	2.4804	0.0145	1.98	1.32
AVSD	Cocaine	4.0167	0.2100	0.0861	0.6916	2.4383	0.0162	1.97	1.30
Vascular disruptions	LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	6.6675	0.1776	0.0744	0.5550	2.3862	0.0188	2.01	1.29
Congenital heart	Log(Amphetamine)	79.9062	0.1314	0.0563	0.4354	2.3341	0.0215	1.96	1.26
Mitral valve anomalies	Annual_Alcohol	1.5003	0.1100	0.0383	0.7425	2.8744	0.0049	1.55	1.26
TV S/A	Cocaine	0.5907	0.1273	0.0553	0.4438	2.3028	0.0230	1.92	1.24
VSD	Cocaine	41.6464	0.1833	0.0831	0.6671	2.2071	0.0292	1.89	1.20
Tetralogy of Fallot	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	3.0532	0.7043	0.3548	0.6639	1.9850	0.0499	4.69	1.13
AVSD	Log(Amphetamine)	4.0167	0.1810	0.0873	0.6961	2.0732	0.0403	1.85	1.13
Ebstein’s anomaly	Cocaine	0.4327	0.0924	0.0460	0.3691	2.0101	0.0467	1.82	1.08

Parameter Values			Model Parameters
Parameter	Estimate (CI)	P-Value	Parameter	Value	Significance
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.THC	2.09 (0.67, 3.5)	0.0038	psi	0.84099	<2.2E-16
Cocaine	0.17 (0.05, 0.29)	0.0045	rho	−0.61126	3.03E-08
			lambda	0.62061	4.97E-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.THC	2.09 (0.67, 3.5)	0.0038	psi	0.84099	<2.2E-16
Cocaine	0.17 (0.05, 0.29)	0.0045	rho	−0.61126	3.03E-08
			lambda	0.6261	4.97E-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.THC	2.17 (0.1, 4.23)	0.0401	psi	0.85902	<2.2E-16
			rho	0.62929	1.20E-07
			lambda	−0.4748	0.0018
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.THC	1.91 (0.25, 3.56)	0.0238	psi	0.88721	<2.2E-16
Cocaine	−0.16 (−0.29, −0.03)	0.0158	rho	−0.5633	2.83E-05
			lambda	0.6436	2.11E-09

No.	Cardiovascular Anomaly	Model Type	Term	P-Value	E-Value Estimate	Lower Bound E-Value
1	Cong. heart disease	Additive	Herb.THC	1.93E-16	3.38E + 12	1.26E + 10
2	Cong. heart disease	Interactive	Herb.THC	9.29E-10	3.95E + 14	2.95E + 10
3	Cong. heart disease	Interactive	LM.Cannabis_x_Herb.THC	6.23E-03	5.19E + 13	2.14E + 04
4	Cong. heart disease	Interactive	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	1.91E-02	5.56E + 20	9.39E + 03
5	Cong. heart disease	1 Lag	Herb.THC	1.75E-19	8.06E + 19	3.88E + 16
6	Cong. heart disease	2 Lags	Herb.THC	1.15E-05	6.66E + 08	1.86E + 05
7	Cong. heart disease	2 Lags	Herb.THC: LM.Cannabis_x_Herb.THC	4.14E-11	4.37E + 59	3.41E + 44
8	Severe cong. heart disease	Additive	LM.Herb.THC.Daily.Interpol	4.43E-02	4.92E + 00	1.24
9	Severe cong. heart disease	Additive	LM.Herb.Daily.Intpltd	4.74E-02	5.25E + 13	3.67
10	Severe Cong. Heart Disease	Additive	LM.Herb.THC	1.48E-03	7.57E + 06	850.24
11	Severe cong. heart disease	Interactive	LM.Resin.Daily.Interpol	4.20E-11	4.84E + 13	1.30E + 10
12	Severe cong. heart disease	Interactive	Herb.THC	1.31E-05	3.89E + 07	2.94E + 05
13	Severe cong. heart disease	Interactive	LM.Resin	9.62E-07	2.33E + 04	686.08
14	Severe cong. heart disease	Interactive	Tobacco: LM.Herb.THC.Daily.Interpol	3.85E-08	2.49	2.03
15	Severe cong. heart disease	Interactive	LM.Resin.Daily.Interpol: LM.Herb.THC.Daily.Interpol	1.22E-13	108.11	42.72
16	Severe cong. heart disease	1 Lag	LM.Resin	0.0003	616.49	29.73
17	Severe cong. heart disease	1 Lag	Herb.THC	0.0078	6.74E + 03	18.81
18	Severe cong. heart disease	1 Lag	LM.Resin: LM.Herb.THC.Daily.Interpol	0.0005	1.80E + 06	1.03E + 03
19	Severe cong. heart disease	2 Lags	LM.Resin	7.23E-06	1.31E + 03	91.92
20	Severe cong. heart disease	2 Lags	Herb.THC	4.41E-02	3.46E + 03	2.10
21	Severe cong. heart disease	2 Lags	LM.Resin.Daily.Interpol: LM.Resin	4.42E-02	4.08E + 05	2.76
22	ASD	Additive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0133	4.91	1.81
23	ASD	Additive	Herb	0.0013	1.79E + 04	81.68
24	ASD	Interactive	Herb	2.32E-12	3.59E + 05	1.76E + 04
25	ASD	1 Lag	LM.Cannabis_x_Herb.THC	8.72E-15	7.28E + 09	6.31E + 07
26	ASD	2 Lags	LM.Cannabis_x_Herb.THC	<2.2E-16	9.01E + 15	2.54E + 13
27	ASD	4 Lags	LM.Cannabis_x_Herb.THC	0.0168	2.51E + 08	90.06
28	VSD	Additive	LM.Cannabis_x_Herb.THC	<2.2E-16	2.38E + 06	1.96E + 05
29	VSD	Interactive	LM.Cannabis_x_Herb.THC	<2.2E-16	2.38E + 06	1.96E + 05
30	VSD	1 Lag	LM.Cannabis_x_Herb.THC	<2.2E-16	3.04E + 06	2.58E + 05
31	VSD	2 Lags	LM.Cannabis_x_Herb.THC	2.7E-14	1.60E + 21	4.97E + 16
32	AVSD	Additive	Resin	4.05E-10	897.5	155.66
33	AVSD	Interactive	LM.Cannabis_x_Resin.THC	8.05E-07	1.67E + 24	1.86E + 15
34	AVSD	Interactive	Tobacco: Resin	4.26E-07	5.56	3.48
35	AVSD	Interactive	Tobacco: Resin: LM.Cannabis_x_Resin.THC	0.0026	263.77	11.38
36	AVSD	1 Lag	LM.Cannabis_x_Resin.THC	0.0042	4.17E + 08	1.18E + 03
37	AVSD	1 Lag	Tobacco: Resin	1.11E-07	5.64	3.62
38	AVSD	2 Lags	Tobacco: Resin	1.58E-12	1.54	1.44
39	PDA	Additive	Daily.Interpol.	4.3E-09	5.07E + 49	3.15E + 34
40	PDA	Additive	LM.Cannabis_x_Herb.THC	0.0158	2.80E + 03	8.07
41	PDA	Interactive	LM.Cannabis_x_Herb.THC	3.27E-05	3.64E + 05	1.52E + 03
42	PDA	Interactive	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	0.0449	1.48	1.07
43	PDA	Interactive	Tobacco: Daily.Interpol.	8.61E-10	243.5	59.42
44	PDA	1 Lag	LM.Cannabis_x_Herb.THC	7.29E-14	8.51E + 14	4.28E + 11
45	PDA	1 Lag	Tobacco: Daily.Interpol.	1.39E-08	2.93E + 08	7.61E + 05
46	PDA	2 Lags	LM.Cannabis_x_Herb.THC	2.33E-07	7.34E + 11	6.85E + 07
47	PDA	2 Lags	Tobacco: Daily.Interpol.	0.0042	3.30E + 05	111.26
48	Tetralogy of Fallot	Additive	Daily.Interpol.	5.96E-09	3.79E + 19	3.72E + 13
49	Tetralogy of Fallot	Interactive	Daily.Interpol.	2.1E-09	Infinty	4.28E + 135
50	Tetralogy of Fallot	Interactive	LM.Cannabis_x_Herb.THC	0.0159	1.19E + 05	17.74
51	Tetralogy of Fallot	Interactive	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	4.68E-07	36.96	12.31
52	Tetralogy of Fallot	1 Lag	LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	2.59E-10	1.24E + 04	1.08E + 03
53	Tetralogy of Fallot	2 Lags	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	4.36E-16	3.78E + 03	868.53
54	Tetralogy of Fallot	2 Lags	Resin	0.0460	474.56	1.71
55	Tetralogy of Fallot	2 Lags	LM.Cannabis_x_Herb.THC	3.86E-08	1.04E + 25	1.04E + 17
56	Vascular disruptions	Additive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	4.85E-05	52.93	11.26
57	Vascular disruptions	Additive	Resin	9.68E-03	44.48	3.89
58	Vascular disruptions	Interactive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	1.56E-06	3.81E + 03	212.19
59	Vascular disruptions	Interactive	Resin	0.0038	62.36	5.91
60	Vascular disruptions	Interactive	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: Resin	1.27E-05	4.00E + 17	1.77E + 10
61	Vascular disruptions	1 Lag	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	6.72E-04	1.09E + 05	255.77
62	Vascular disruptions	1 Lag	LM.Cannabis_x_Resin.THC	9.61E-04	4.79E + 03	54.91
63	Vascular disruptions	2 Lags	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	1.76E-04	1.48E + 03	55.25
64	Vascular disruptions	2 Lags	Resin	3.50E-08	6.79E + 04	2.43E + 03
65	Double outlet right ventricle	Interactive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0005	3.52E + 08	1.08E + 04
66	Double outlet right ventricle	Interactive	Herb	6.33E-05	1.34E + 05	740.60
67	Double outlet right ventricle	Interactive	LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC	9.88E-05	2.08E + 32	7.82E + 16
68	Double outlet right ventricle	Interactive	Tobacco: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	5.35E-12	4.53E + 04	3.72E + 03
69	Double outlet right ventricle	2 Lags	LM.Cannabis_x_Herb.THC	0.0030	9.24E + 13	1.74E + 05
70	Double outlet right ventricle	2 Lags	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0052	3.92E + 03	22.14
71	Double outlet right ventricle	2 Lags	Herb: Resin	0.0138	5.10E + 24	5.89E + 05
72	Transposition great vessels	Additive	Herb	0.0003	9.44E + 07	8.52
73	Transposition great vessels	Additive	LM.Cannabis_x_Resin.THC	0.0002	1.61E + 03	50.46
74	Transposition great vessels	Interactive	Herb	0.0013	5.06E + 04	121.42
75	Transposition great vessels	Interactive	Tobacco: Resin	0.0002	3.72	2.28
76	Transposition great vessels	Interactive	LM.Cannabis_x_Resin.THC: Resin	8.46E-05	7.77E + 39	7.81E + 20
77	Transposition great vessels	1 Lag	Herb	<2.2E-16	3.11E + 06	2.02E + 05
78	Transposition great vessels	2 Lags	LM.Cannabis_x_Resin.THC	0.0034	1.41E + 03	19.41
79	Transposition great vessels	2 Lags	Herb	0.0056	4.74E + 09	1.68E + 03
80	Transposition great vessels	2 Lags	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0437	54.68	1.59
81	Hypoplastic right heart	Additive	Resin	0.0050	1.11E + 03	14.31
82	Hypoplastic right heart	Additive	LM.Cannabis_x_Herb.THC	0.0265	5.02E + 07	17.69
83	Hypoplastic right heart	Interactive	LM.Cannabis_x_Resin.THC	0.0052	9.24	2.67
84	Hypoplastic right heart	1 Lag	Resin	5.65E-05	25.73	7.38
85	Hypoplastic right heart	2 Lags	Resin	0.0009	25.49	5.44
86	Mitral valve anomalies	Additive	Daily.Interpol.	6.06E-10	6.92E + 21	4.76E + 15
87	Mitral valve anomalies	Interactive	Daily.Interpol.	7.46E-09	2.31E + 18	5.81E + 12
88	Mitral valve anomalies	Interactive	LM.Cannabis_x_Herb.THC	0.0189	6.44E + 05	19.02
89	Mitral valve anomalies	1 Lag	Tobacco: Daily.Interpol.	1.27E-13	9.27	6.31
90	Mitral valve anomalies	2 Lags	Herb	0.0155	8.04E + 03	10.85

No.	Regression	Anomaly	Model Type	Term	P-Value	E-Value Estimate	Lower Bound E-Value
1	Spatial	Cong. heart disease	Additive	Herb.THC	0.0038	1.20E + 05	70.68
2	Spatial	Cong. heart disease	Interactive	Herb.THC	0.0038	1.20E + 05	70.68
3	Spatial	Cong. heart disease	1 Lag	Herb.THC	0.0401	801.09	1.98
4	Spatial	Cong. heart disease	2 Lags	Herb.THC	0.0238	144.6	2.95
5	Spatial	Severe cong. heart disease	Additive	Herb	8.22E-07	8.11E + 07	7.77E + 04
6	Spatial	Severe cong. heart disease	Additive	LM.Cannabis_x_Resin.THC	0.0002	281.87	20.47
7	Spatial	Severe cong. heart disease	Interactive	Herb	1.05E-10	1.19E + 13	1.61E + 09
8	Spatial	Severe cong. heart disease	Interactive	LM.Cannabis_x_Resin.THC	2.35E-05	2.21E + 05	1.03E + 03
9	Spatial	Severe cong. heart disease	1 Lag	LM.Cannabis_x_Resin.THC: LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	0.0050	2.18E + 05	3.06
10	Spatial	Severe cong. heart disease	1 Lag	Herb	0.0391	1.20E + 11	3.67E + 03
11	Spatial	Severe cong. heart disease	2 Lags	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0026	2.74E + 11	1.60E + 04
12	Spatial	Severe cong. heart disease	2 Lags	Herb	0.0221	6.71E + 03	5.99
13	Spatial	ASD	1 Lag	Tobacco: Daily.Interpol.	0.0210	4.41E + 07	25.77
14	Spatial	ASD	2 Lags	Daily.Interpol.	0.0215	1.54E + 139	1.07E + 21
15	Spatial	VSD	Additive	Herb	0.0014	33.67	1.99
16	Spatial	VSD	Additive	Resin	0.0296	5.88E + 03	43.32
17	Spatial	VSD	Interactive	LM.Cannabis_x_Herb.THC	8.94E-06	1.16E + 17	4.81E + 09
18	Spatial	VSD	1 Lag	LM.Cannabis_x_Herb.THC	0.0003	9.50E + 09	4.91E + 05
19	Spatial	VSD	2 Lags	Daily.Interpol.	0.0137	3.41E + 57	1.44E + 12
20	Spatial	VSD	2 Lags	LM.Cannabis_x_Herb.THC	2.15E-05	4.45E + 23	8.06E + 12
21	Spatial	AVSD	Additive	Herb	0.0014	5.88E + 03	43.32
22	Spatial	AVSD	Additive	Resin	0.0296	33.65	1.99
23	Spatial	AVSD	Interactive	Tobacco: Resin	1.23E-05	4.06	2.56
24	Spatial	AVSD	2 Lags	Tobacco: Resin	0.0289	2.37	1.26
25	Spatial	PDA	Additive	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	0.0011	4.73E + 03	44.81
26	Spatial	PDA	Interactive	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	2.50E-05	3.21E + 05	1.23E + 03
27	Spatial	PDA	Interactive	Tobacco: Daily.Interpol.	0.0015	5.09E + 06	576.84
28	Spatial	PDA	1 Lag	Tobacco: Daily.Interpol.	4.13E-05	1.11E + 13	5.74E + 05
29	Spatial	PDA	1 Lag	LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	0.0026	1.37E + 16	3.82E + 08
30	Spatial	PDA	2 Lags	Tobacco: Daily.Interpol.	0.0008	7.25E + 09	344.71
31	Spatial	PDA	2 Lags	LM.Cannabis_x_Herb.THC	0.0107	66.55	8.12
32	Spatial	Tetralogy of Fallot	Additive	Herb	0.0033	2.95E + 07	503.90
33	Spatial	Tetralogy of Fallot	Interactive	Tobacco: Resin	1.44E-05	26.76	7.86
34	Spatial	Tetralogy of Fallot	1 Lag	Tobacco: Herb	5.26E-05	13.11	4.83
35	Spatial	Tetralogy of Fallot	2 Lags	Tobacco: LM.Cannabis_x_Herb.THC	1.07E-06	62.87	15.34
36	Spatial	Vascular disruptions	Additive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	8.77E-05	293.63	23.91
37	Spatial	Vascular disruptions	Interactive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	8.77E-05	293.63	23.91
38	Spatial	Vascular disruptions	1 Lag	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0284	6.95	1.58
39	Spatial	Vascular disruptions	2 Lags	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0008	24.97	5.19
40	Spatial	Double outlet right ventricle	Additive	LM.Cannabis_x_Herb.THC	0.0099	7.76E + 04	25.13
41	Spatial	Double outlet right ventricle	Interactive	LM.Cannabis_x_Herb.THC	0.0208	1.63E + 27	2.73E + 04
42	Spatial	Double outlet right ventricle	2 Lags	LM.Cannabis_x_Herb.THC	9.11E-06	2.54E + 12	1.18E + 07
43	Spatial	Transposition great vessels	Additive	LM.Cannabis_x_Herb.THC	0.0099	7.77E + 04	25.13
44	Spatial	Transposition great vessels	Interactive	Herb	0.0005	4.95E + 03	61.38
45	Spatial	Transposition great vessels	1 Lag	Resin	0.0370	15.34	1.53
46	Spatial	Transposition great vessels	2 Lags	LM.Cannabis_x_Resin.THC	0.0001	12.51	4.51
47	Spatial	Hypoplastic right heart	Additive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	5.22E-06	960.35	67.27
48	Spatial	Hypoplastic right heart	Interactive	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	3.10E-07	1.48E + 03	118.27
49	Spatial	Hypoplastic right heart	1 Lag	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0003	6.18E + 07	7.55
50	Spatial	Hypoplastic right heart	1 Lag	Daily.Interpol.	0.0079	901.95	32.41
51	Spatial	Hypoplastic right heart	1 Lag	LM.Cannabis_x_Herb.THC	0.0333	5.17E + 33	1.34E + 09
52	Spatial	Hypoplastic right heart	2 Lags	LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	1.09E-06	2.21E + 13	4.19E + 04
53	Spatial	Hypoplastic right heart	2 Lags	Daily.Interpol.	5.10E-05	2.22E + 05	2.10E + 03
54	Spatial	Hypoplastic right heart	2 Lags	LM.Cannabis_x_Herb.THC	0.0035	1.17E + 95	2.06E + 49
55	Spatial	Mitral valve anomalies	Interactive	Tobacco: Daily.Interpol.	2.17E-07	9.91E + 05	7.03E + 03
56	Spatial	Mitral valve anomalies	1 Lag	Tobacco: Daily.Interpol.	4.14E-06	1.58E + 06	4.93E + 03
57	Spatial	Mitral valve anomalies	1 Lag	LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0038	3.71E + 28	2.81E + 09
58	Spatial	Mitral valve anomalies	2 Lags	Tobacco: Daily.Interpol.	0.0038	3.97E + 04	49.19
59	Spatial	Mitral valve anomalies	2 Lags	LM.Cannabis_x_Herb.THC: LM.Cannabis_x_Resin.THC_x_Daily.Interpol.	0.0115	5.36E + 35	2.03E + 08

No.	E-Value Estimate	Lower Bound E-Value
1	Infinity	4.28E + 135
2	1.54E + 139	2.06E + 49
3	1.17E + 95	3.41E + 44
4	4.37E + 59	3.15E + 34
5	3.41E + 57	1.07E + 21
6	5.07E + 49	7.81E + 20
7	7.77E + 39	1.04E + 17
8	5.36E + 35	7.82E + 16
9	5.17E + 33	4.97E + 16
10	2.08E + 32	3.88E + 16
11	3.71E + 28	4.76E + 15
12	1.63E + 27	1.86E + 15
13	1.04E + 25	3.72E + 13
14	5.10E + 24	2.54E + 13
15	1.67E + 24	8.06E + 12
16	4.45E + 23	5.81E + 12
17	6.92E + 21	1.44E + 12
18	1.60E + 21	4.28E + 11
19	5.56E + 20	2.95E + 10
20	8.06E + 19	1.77E + 10
21	3.79E + 19	1.30E + 10
22	2.31E + 18	1.26E + 10
23	4.00E + 17	4.81E + 09
24	1.16E + 17	2.81E + 09
25	1.37E + 16	1.61E + 09
26	9.01E + 15	1.34E + 09
27	8.51E + 14	3.82E + 08
28	3.95E + 14	2.03E + 08
29	9.24E + 13	6.85E + 07
30	5.25E + 13	6.31E + 07
31	5.19E + 13	1.18E + 07
32	4.84E + 13	7.61E + 05
33	2.21E + 13	5.89E + 05
34	1.19E + 13	5.74E + 05
35	1.11E + 13	4.91E + 05
36	3.38E + 12	2.94E + 05
37	2.54E + 12	2.58E + 05
38	7.34E + 11	2.02E + 05
39	2.74E + 11	1.96E + 05
40	1.20E + 11	1.96E + 05
41	9.50E + 09	1.86E + 05
42	7.28E + 09	1.74E + 05
43	7.25E + 09	7.77E + 04
44	4.74E + 09	4.19E + 04
45	6.66E + 08	2.73E + 04
46	4.17E + 08	2.14E + 04
47	3.52E + 08	1.76E + 04
48	2.93E + 08	1.60E + 04
49	2.51E + 08	1.08E + 04
50	9.44E + 07	9.39E + 03
51	8.11E + 07	7.03E + 03
52	6.18E + 07	4.93E + 03
53	5.02E + 07	3.72E + 03
54	4.41E + 07	3.67E + 03
55	3.89E + 07	2.43E + 03
56	2.95E + 07	2.10E + 03
57	7.57E + 06	1.68E + 03
58	5.09E + 06	1.52E + 03
59	3.11E + 06	1.23E + 03
60	3.04E + 06	1.18E + 03
61	2.38E + 06	1.08E + 03
62	2.38E + 06	1.03E + 03
63	1.80E + 06	1.03E + 03
64	1.58E + 06	868.53
65	9.91E + 05	850.24
66	6.44E + 05	740.60
67	4.08E + 05	686.08
68	3.64E + 05	576.84
69	3.59E + 05	503.90
70	3.30E + 05	344.71
71	3.21E + 05	255.77
72	2.22E + 05	212.19
73	2.21E + 05	155.66
74	2.18E + 05	121.42
75	1.34E + 05	118.27
76	1.20E + 05	111.26
77	1.20E + 05	91.92
78	1.19E + 05	90.06
79	1.09E + 05	81.68
80	7.77E + 04	70.68
81	7.76E + 04	70.68
82	6.79E + 04	67.27
83	5.06E + 04	61.38
84	4.53E + 04	59.42
85	3.97E + 04	55.25
86	2.33E + 04	54.91
87	1.79E + 04	50.46
88	1.24E + 04	49.19
89	8.04E + 03	44.81
90	6.74E + 03	43.32
91	6.71E + 03	43.32
92	5.88E + 03	42.72
93	5.88E + 03	32.41
94	4.95E + 03	29.73
95	4.79E + 03	25.77
96	4.73E + 03	25.13
97	3.92E + 03	25.13
98	3.81E + 03	23.91
99	3.78E + 03	23.91
100	3.46E + 03	22.14
101	2.80E + 03	20.47
102	1.61E + 03	19.41
103	1.48E + 03	19.02
104	1.48E + 03	18.81
105	1.41E + 03	17.74
106	1.31E + 03	17.69
107	1.11E + 03	15.34
108	960.35	14.31
109	901.95	12.31
110	897.5	11.38
111	801.09	11.26
112	616.49	10.85
113	474.56	8.52
114	293.63	8.12
115	293.63	8.07
116	281.87	7.86
117	263.77	7.55
118	243.5	7.38
119	144.6	6.31
120	108.11	5.99
121	66.55	5.91
122	62.87	5.44
123	62.36	5.19
124	54.68	4.83
125	52.93	4.51
126	44.48	3.89
127	36.96	3.67
128	33.67	3.62
129	33.65	3.48
130	26.76	3.06
131	25.73	2.95
132	25.49	2.76
133	24.97	2.67
134	15.34	2.56
135	13.11	2.28
136	12.51	2.10
137	9.27	2.03
138	9.24	1.99
139	6.95	1.99
140	5.64	1.98
141	5.56	1.81
142	4.92	1.71
143	4.91	1.59
144	4.06	1.58
145	3.72	1.53
146	2.49	1.44
147	2.37	1.26
148	1.54	1.24
149	1.48	1.07

Anomaly	Number	Mean mEV	Median mEV	Minimum mEV	Maximum mEV	Mean E-Value Estimate	Median E-Value Estimate	mEV Estimate	Maximum E-Value Estimate
VSD	10	4.97E + 15	374 500.00	1.99	4.97E + 16	3.41E + 56	4.75E + 09	33.67	3.41E + 57
Cong. heart disease	11	3.10E + 43	21 400.00	1.98	3.41E + 44	3.97E + 58	3.38E + 12	144.6	4.37E + 59
Double outlet right ventricle	10	7.82E + 15	19 050.00	22.14	7.82E + 16	2.08E + 31	1.27E + 12	3920	2.08E + 32
ASD	8	1.34E + 20	8845.03	1.81	1.07E + 21	1.93E + 138	1.48E + 08	4.91	1.54E + 139
Mitral valve anomalies	10	4.77E + 14	5980.00	6.31	4.76E + 15	5.36E + 34	1 285 500	9.27	5.36E + 35
PDA	16	1.97E + 33	903.42	1.07	3.15E + 34	3.17E + 48	2 727 000	1.48	5.07E + 49
Tetralogy of Fallot	12	3.57E + 134	260.82	1.71	4.28E + 135	9.17E + 305	8090	13.11	1.10E + 307
Severe cong. heart disease	22	6.64E + 08	67.32	1.24	1.30E + 10	5.15E + 12	219 500	2.49	5.25E + 13
Hypoplastic right heart	13	1.58E + 48	32.41	2.67	2.06E + 49	9.00E + 93	1480	9.24	1.17E + 95
Transposition great vessels	13	6.01E + 19	25.13	1.53	7.81E + 20	5.98E + 38	4950	3.72	7.77E + 39
Vascular disruptions	13	1.36E + 09	23.91	1.58	1.77E + 10	3.08E + 16	293.63	6.95	4.00E + 17
AVSD	11	1.69E + 14	3.62	1.26	1.86E + 15	1.52E + 23	33.65	1.54	1.67E + 24

Group	Number	Mean mEV	Median mEV	Minimum mEV	Maximum mEV	Mean E-value estimate	Median E-value estimate	mEV estimate	Maximum E-value estimate
Daily	20	2.14E + 134	5980	6.31	4.28E + 135	5.50E + 305	1.69E + 08	9.27	1.10E + 307
Herb	75	2.75E + 47	1520	1.07	2.06E + 49	1.56E + 93	7.57E + 06	1.48	1.17E + 95
Resin	54	1.45E + 19	19.94	1.26	7.81E + 20	1.44E + 38	756.995	1.54	7.77E + 39

Comparison	W-Statistic	Alternative	P-Value
Lower E-Value, Daily_v_Herb	917	two.sided	0.1285
Lower E-Value, Daily_v_Resin	888	two.sided	2.34E-05
Lower E-Value, Herb_v_Resin	2924	two.sided	1.79E-05
E-Value Estimate, Daily_v_Herb	872	two.sided	0.2674
E-Value Estimate, Daily_v_Resin	886	two.sided	2.61E-05
E-Value Estimate, Herb_v_Resin	3167	two.sided	5.05E-08

Nearest Gene Name	Page	Dependence Status	Number Genes Identified	P-Value
Cardiovasculature
Abnormal CVS	324	Withdrawal	35	0.002740
CVS Development	321	Withdrawal	39	0.001920
Heart
Hypoplasia of heart	288	Withdrawal	10	8.83E-08
Cardiogenesis	318	Withdrawal	26	0.001640
Hypoplasia trabeculae carnae	321	Withdrawal	4	0.001960
Quantity cardiomyocytes	321	Withdrawal	4	0.001960
Hypoplasia heart chamber	299	Withdrawal	7	0.000021
Atrium
Atrial morphogenesis	314	Withdrawal	4	0.000855
Atrial septal morphogenesis	318	Withdrawal	3	0.001650
Atrial hypoplasia	319	Withdrawal	2	0.001770
Abnormal atrial morphology	322	Withdrawal	6	0.002100
Atrioventricular valves
Abnormal morphology AV cushions	296	Withdrawal	6	9.04E-06
Lack of AV canal cushions	300	Withdrawal	4	0.000040
AV valve morphogenesis	321	Withdrawal	3	0.002040
Ventricle
VSD type 3	301	Withdrawal	3	0.000051
Hypoplasia heart ventricle	306	Withdrawal	6	0.000157
Formation ventricular septum	315	Withdrawal	7	0.001060
Vessels
Angiogenesis	289	Withdrawal	54	1.73E-06
CVS development	294	Withdrawal	56	7.32E-06
Vasculogenesis	302	Withdrawal	42	6.65E-05
Sprouting angiogenesis	324	Withdrawal	7	0.002690
Vascular endothelial cell movement	316	Withdrawal	13	0.001210
Endothelial cell migration	317	Withdrawal	19	0.001450
Vascular endothelial cell migration	317	Withdrawal	12	0.001530
Early onset hypertension	357	Withdrawal	1	0.007010
Blood vessel breakdown	357	Withdrawal	1	0.007010
Pulmonary vein angiogenesis	357	Withdrawal	1	0.007010

Parameter Values			Model Parameters
Parameter	Estimate (CI)	P-Value	Parameter	Value	Significance
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)	0.0011	psi	0.59171	1.43E-12
Cocaine	0.48 (0.17, 0.79)	0.0026	rho	−0.61788	7.19E-08
LM.Cannabis	−30.65 (−45.35, −15.94)	4.41E-05	lambda	0.6049	2.06E-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-05	psi	0.36814	0.000342
Tobacco: Daily.Interpol.	7.94 (3.04, 12.85)	0.0015	rho	−0.68835	5.12E-12
Daily.Interpol.	−168.25 (−292.36, −44.13)	0.0079	lambda	0.66996	2.47E-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-05	psi	0.3964	0.000433
LM.Cannabis_x_Herb.THC_x_Daily.Interpol.	7.55 (2.63, 12.47)	0.0026	rho	0.72302	<2.2E-16
LM.Cannabis_x_Herb.THC	−17.48 (−29.69, −5.28)	0.0050	lambda	−0.63094	9.92E-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)	0.0008	psi	0.45343	2.48E-05
LM.Cannabis_x_Herb.THC	15.77 (3.67, 27.88)	0.0107	rho	0.71484	<2.2E-16
LM.Cannabis	−32.01 (−49.41, −14.61)	0.0003	lambda	−0.6013	5.76E-09
Alcohol	−0.22 (−0.3, −0.14)	9.40E-08

Parameter Values			Model Parameters
Parameter	Estimate (CI)	P-Value	Parameter	Value	Significance
Additive
Rate ∼ Tobacco + Alcohol + LM.Cannabis_x_Resin.THC_x_Daily.Interpol. + Herb + Daily.Interpol. + Herb + Amphetamines + Cocaine + Income
Cocaine	0.25 (0.13, 0.37)	6.77E-05	rho	−0.5927	6.13E-09
Herb	3.18 (1.06, 5.31)	0.0033	lambda	0.5414	3.29E-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: Resin	0.43 (0.23, 0.62)	1.44E-05	rho	−0.6819	5.55E-14
Income	0 (0, 0)	7.88E-05	lambda	0.61208	6.66E-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)*
Cocaine	0.27 (0.15, 0.39)	7.90E-06	psi	−0.24715	0.0132
Tobacco: Herb	0.96 (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.THC	0.59 (0.35, 0.82)	1.07E-06	rho	−0.6367	1.58E-08
Cocaine	0.29 (0.11, 0.46)	0.00115	lambda	0.5254	2.40E-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

PERMALINK

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

Albert Stuart Reece

Gary Kenneth Hulse

Abstract

Introduction

Methods

Data

National Assignment

Derived Data

Data Imputation

Statistics

Covariate Selection

Panel and Geospatial Analysis

Causal Inference

Data Availability

Ethics

Results

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Figure 11:

Figure 12:

Figure 13:

Figure 14:

Figure 15:

Figure 16:

Figure 17:

Figure 18:

Figure 19:

Figure 20:

Figure 21:

Table 1:

Figure 22:

Figure 23:

Table 2:

Table 3:

Table 4:

Table 5:

Table 6:

Table 7:

Table 8:

Table 9:

Table 10:

Table 11:

Table 12:

Table 13:

Table 14:

Table 15:

Table 16:

Table 17:

Table 18:

Discussion

Main Results

Figure 24:

Figure 25:

Table 19:

Table 20:

Table 21:

Main Results in Detail

Mechanisms

Genotoxicity

Epigenomics

Epigenomic Implication of Cardiovascular-Relevant Pathways

Table 22:

Qualitative Causal Inference

Quantitative Causal Inference

Generalizability

Strengths and Limitations

Conclusion

Supplementary Material

Acknowledgements

Contributor Information