Rates of prenatal screening across health care regions in Ontario, Canada: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: It is recommended that all pregnant women be offered screening for Down syndrome and open neural tube defects, but emerging prenatal tests that are not publicly insured may compromise access. We evaluated screening rates for publicly insured screening tests across health care regions in the province of Ontario and determined whether maternal, provider or regional characteristics are associated with screening uptake. METHODS: We conducted a population-based retrospective cohort study involving pregnant women in Ontario who were at or beyond 16 weeks' gestation in 2007-2009. We ascertained prenatal screening rates using linked health administrative and prenatal screening datasets. We examined maternal, provider and regional characteristics associated with screening uptake. Rate ratios (RRs) were estimated. RESULTS: Of the 264 737 women included in the study, 62.2% received prenatal screening; uptake varied considerably by region (range 27.8%-80.3%). A greater proportion of women initiated screening in the first rather than the second trimester (50.0% v. 12.2%). Factors associated with lower screening rates included living in a rural area versus an urban area (adjusted rate ratio 0.64, 95% confidence interval [CI] 0.63-0.66), receiving first-trimester care from a family physician or midwife versus an obstetrician (adjusted rate ratio 0.91, 95% CI 0.90-0.92, and 0.40, 95% CI 0.38-0.43, respectively) and being in a lower income quintile (adjusted RR for lowest v. highest 0.95, 95% CI 0.94-0.96). Being an immigrant or a refugee was associated with higher screening rates. INTERPRETATION: There were significant maternal, provider and regional differences in the uptake of prenatal screening across the province. With discrepancies expected to increase with the emergence of noninvasive prenatal tests paid for out of pocket by many women, policy efforts to reduce barriers to prenatal screening and optimize its availability are warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".