Socioeconomic, Ethnocultural, Substance- and Cannabinoid-Related Epidemiology of Down Syndrome USA 1986–2016: Combined Geotemporospatial and Causal Inference Investigation
Bibliographic record
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 × 107) and 95% lower bounds ranged to 1.1 × 1071 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".