Reconstructing a Pregnancy Cohort to Examine Potential Selection Bias in Studies on Racial Disparities in Preterm Delivery
Bibliographic record
Abstract
BACKGROUND: Epidemiologic studies examining preconception risk factors on perinatal outcomes are typically restricted to livebirths. By including only non-terminated pregnancies, estimates for the underlying pregnancy cohort may be subject to selection bias. We examined if potential selection bias due to induced termination by maternal race may result in different estimates of the non-Hispanic black - non-Hispanic white risk ratio (RR) for preterm delivery (PTD) among a reconstructed pregnancy cohort ('pseudo-pregnancy cohort'). METHODS: Using New York City registries of 1.6 million livebirths, spontaneous terminations, and induced terminations among non-Hispanic black and non-Hispanic white women (2000-12), we multiply imputed PTD (<37 weeks) and early PTD (<32 weeks) outcomes for induced terminations based on maternal race, age, parity, marital status, nativity, and medical care payer to construct the pseudo-pregnancy cohort. RESULTS: Among non-Hispanic black and non-Hispanic white women, 55% and 19% of pregnancies ended in induced termination and 13% and 8% resulted in PTD, respectively. Although several factors were associated with both PTD and induced termination, PTD RRs in the birth (RR 1.64, 95% confidence interval (CI) 1.62, 1.66) and pseudo-pregnancy (RR 1.63, 95% CI 1.56, 1.71) cohorts were similar. However, early PTD RR was somewhat larger in the birth (RR 2.80, 95% CI 2.71, 2.89) than pseudo-pregnancy (RR 2.47, 95% CI 2.23, 2.73) cohort. CONCLUSIONS: Using birth certificate data - thereby excluding induced terminations - to estimate the PTD racial disparity did not produce biased estimates. Our data suggest observed PTD disparities likely are not artefacts of selection bias due to induced termination.
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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 | 0.054 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".