How valid are the rates of Down syndrome internationally? Findings from the International Clearinghouse for Birth Defects Surveillance and Research
Bibliographic record
Abstract
Rates of Down syndrome (DS) show considerable international variation, but a systematic assessment of this variation is lacking. The goal of this study was to develop and test a method to assess the validity of DS rates in surveillance programs, as an indicator of quality of ascertainment. The proposed method compares the observed number of cases with DS (livebirths plus elective pregnancy terminations, adjusted for spontaneous fetal losses that would have occurred if the pregnancy had been allowed to continue) in each single year of maternal age, with the expected number of cases based on the best-published data on rates by year of maternal age. To test this method we used data from birth years 2000 to 2005 from 32 surveillance programs of the International Clearinghouse for Birth Defects Surveillance and Research. We computed the adjusted observed versus expected ratio (aOE) of DS birth prevalence among women 25-44 years old. The aOE ratio was close to unity in 13 programs (the 95% confidence interval included 1), above 1 in 2 programs and below 1 in 18 programs (P < 0.05). These findings suggest that DS rates internationally can be evaluated simply and systematically, and underscores how adjusting for spontaneous fetal loss is crucial and feasible. The aOE ratio can help better interpret and compare the reported rates, measure the degree of under- or over-registration, and promote quality improvement in surveillance programs that will ultimately provide better data for research, service planning, and public health programs.
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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 | 0.060 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".