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Socioeconomic, Ethnocultural, Substance- and Cannabinoid-Related Epidemiology of Down Syndrome USA 1986–2016: Combined Geotemporospatial and Causal Inference Investigation

Author

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  • Albert Stuart Reece

    (Division of Psychiatry, University of Western Australia, Crawley, WA 6009, Australia
    School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia)

  • Gary Kenneth Hulse

    (Division of Psychiatry, University of Western Australia, Crawley, WA 6009, Australia
    School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia)

Abstract

Background: Down syndrome (DS) is the commonest of the congenital genetic defects whose incidence has been rising in recent years for unknown reasons. This study aims to assess the impact of substance and cannabinoid use on the DS Rate (DSR) and assess their possible causal involvement. Methods: An observational population-based epidemiological study 1986–2016 was performed utilizing geotemporospatial and causal inferential analysis. Participants included all patients diagnosed with DS and reported to state based registries with data obtained from National Birth Defects Prevention Network of Centers for Disease Control. Drug exposure data was from the National Survey of Drug Use and Health (NSDUH) a nationally representative sample interviewing 67,000 participants annually. Drug exposures assessed were: cigarette consumption, alcohol abuse, analgesic/opioid abuse, cocaine use and last month cannabis use. Covariates included ethnicity and median household income from US Census Bureau; maternal age of childbearing from CDC births registries; and cannabinoid concentrations from Drug Enforcement Agency. Results: NSDUH reports 74.1% response rate. Other data was population-wide. DSR was noted to rise over time and with cannabis use and cannabis-use quintile. In the optimal geospatial model lagged to four years terms including Δ9-tetrahydrocannabinol and cannabigerol were significant (from β-est. = 4189.96 (95%C.I. 1924.74, 6455.17), p = 2.9 × 10 −4 ). Ethnicity, income, and maternal age covariates were not significant. DSR in states where cannabis was not illegal was higher than elsewhere (β-est. = 2.160 (1.5, 2.82), R.R. = 1.81 (1.51, 2.16), p = 4.7 × 10 −10 ). In inverse probability-weighted mixed models terms including cannabinoids were significant (from β-estimate = 18.82 (16.82, 20.82), p < 0.0001). 62 E-value estimates ranged to infinity with median values of 303.98 (IQR 2.50, 2.75 × 10 7 ) and 95% lower bounds ranged to 1.1 × 10 71 with median values of 10.92 (IQR 1.82, 7990). Conclusions. Data show that the association between DSR and substance- and cannabinoid- exposure is robust to multivariable geotemporospatial adjustment, implicate particularly cannabigerol and Δ9-tetrahydrocannabinol, and fulfil quantitative epidemiological criteria for causality. Nevertheless, detailed experimental studies would be required to formally demonstrate causality. Cannabis legalization was associated with elevated DSR’s at both bivariate and multivariable analysis. Findings are consistent with those from Hawaii, Colorado, Canada, Australia and Europe and concordant with several cellular mechanisms. Given that the cannabis industry is presently in a rapid growth-commercialization phase the present findings linking cannabis use with megabase scale genotoxicity suggest unrecognized DS risk factors, are of public health importance and suggest that re-focussing the cannabis debate on multigenerational health concerns is prudent.

Suggested Citation

  • Albert Stuart Reece & Gary Kenneth Hulse, 2022. "Socioeconomic, Ethnocultural, Substance- and Cannabinoid-Related Epidemiology of Down Syndrome USA 1986–2016: Combined Geotemporospatial and Causal Inference Investigation," IJERPH, MDPI, vol. 19(20), pages 1-37, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13340-:d:943716
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    References listed on IDEAS

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    1. van der Wal, Willem M. & Geskus, Ronald B., 2011. "ipw: An R Package for Inverse Probability Weighting," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i13).
    2. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    3. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    4. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
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    1. Albert Stuart Reece & Gary Kenneth Hulse, 2023. "Clinical Epigenomic Explanation of the Epidemiology of Cannabinoid Genotoxicity Manifesting as Transgenerational Teratogenesis, Cancerogenesis and Aging Acceleration," IJERPH, MDPI, vol. 20(4), pages 1-24, February.

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