The Alberta Congenital Anomalies Surveillance System: a 40-year review with prevalence and trends for selected congenital anomalies, 1997–2019
Bibliographic record
Abstract
INTRODUCTION: Current published long-term provincial or territorial congenital anomaly data are lacking for Canada. We report on prevalence (per 1000 total births) and trends in 1997-2019, in Alberta, Canada, for selected congenital anomalies. Associated risk factors are also discussed. METHODS: We used data from the Alberta Congenital Anomalies Surveillance System (ACASS) to calculate the prevalence and perform chi-square linear trend analyses. RESULTS: From 1997 to 2019, the overall prevalence of neural tube defects was stable, at 0.74 per 1000 total births. The same was true for spina bifida (0.38), orofacial clefts (1.99), more severe CHDs (transposition of the great arteries, 0.38; tetralogy of Fallot, 0.33; and hypoplastic left heart syndrome, 0.32); and gastroschisis (0.38). Anencephaly, cleft palate and anorectal malformation significantly decreased with a prevalence of 0.23, 0.75 and 0.54 per 1000 total births, respectively. Significantly increasing trends were reported for anotia/microtia (0.24), limb reduction anomalies (0.73), omphalocele (0.36) and Down syndrome (2.21) and for hypospadias and undescended testes (4.68 and 5.29, respectively, per 1000 male births). CONCLUSION: Congenital anomalies are an important public health concern with significant social and societal costs. Surveillance data gathered by ACASS for over 40 years can be used for planning and policy decisions and the evaluation of prevention strategies. Contributing genetic and environmental factors are discussed as is the need for continued surveillance and research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".