Comparison of black–white disparities in preterm birth between Canada and the United States
Bibliographic record
Abstract
BACKGROUND: A higher risk of preterm birth among black women than among white women is well established in the United States. We compared differences in preterm birth between non-Hispanic black and white women in Canada and the US, hypothesizing that disparities would be less extreme in Canada given the different historical experiences of black populations and Canada's universal health care system. METHODS: Using data on singleton live births in Canada and the US for 2004-2006, we estimated crude and adjusted risk ratios and risk differences in preterm birth (< 37 wk) and very preterm birth (< 32 wk) among non-Hispanic black versus non-Hispanic white women in each country. Adjusted models for the US were standardized to the covariate distribution of the Canadian cohort. RESULTS: In Canada, 8.9% and 5.9% of infants born to black and white mothers, respectively, were preterm; the corresponding figures in the US were 12.7% and 8.0%. Crude risk ratios for preterm birth among black women relative to white women were 1.49 (95% confidence interval [CI] 1.32 to 1.66) in Canada and 1.57 (95% CI 1.56 to 1.58) in the US (p value for heterogeneity [pH] = 0.3). The crude risk differences for preterm birth were 2.94 (95% CI 1.91 to 3.96) in Canada and 4.63 (95% CI 4.56 to 4.70) in the US (pH = 0.003). Adjusted risk ratios for preterm birth (pH = 0.1) were slightly higher in Canada than in the US, whereas adjusted risk differences were similar in both countries. Similar patterns were observed for racial disparities in very preterm birth. INTERPRETATION: Relative disparities in preterm birth and very preterm birth between non-Hispanic black and white women were similar in magnitude in Canada and the US. Absolute disparities were smaller in Canada, which reflects a lower overall risk of preterm birth in Canada than in the US in both black and white populations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".