Investigating socio‐economic disparities in preterm birth: evidence for selective study participation and selection bias
Bibliographic record
Abstract
Selective study participation can theoretically lead to selection bias. We explored this issue in the context of a multicentre cohort study of socio-economic disparities in preterm birth. Women with singleton pregnancies were recruited from four large Montreal maternity hospitals and invited to return for an interview, vaginal examination and venepuncture at 24-26 weeks of gestation. We compared the observed preterm birth rate (ultrasound confirmed) among the 5146 cohort women to that expected based on all 108 724 Montreal Census Metropolitan Area (CMA) singleton births for 1998-2000. The observed preterm birth rate in the study cohort was 5.1%, compared with 6.3% in the CMA (P < 0.001) (unadjusted morbidity ratio [95% CI] = 0.80 [0.71, 0.90]). Within each stratum of maternal education and neighbourhood income (the latter based on postal code matched links to the 2001 Canadian census), cohort women had substantially lower rates of preterm birth than women from the CMA. No significant association between socio-economic status (SES) and preterm birth was observed in the study cohort, except among 'indicated' (non-spontaneous) cases. The association between neighbourhood income and preterm birth was biased to the null in the study cohort, with adjusted odds ratios in the poorest vs. richest quintiles of 1.01 [0.63, 1.64] in the cohort vs. 1.28 [1.18, 1.39] in the CMA, although no such bias was observed for the association with maternal education assessed at the individual level. We speculate that the lower-than-expected preterm birth rate and attenuated association between neighbourhood income and preterm birth may be related to selective participation by women more psychologically invested in their pregnancies. Investigators should consider the potential for biased associations in pregnancy/birth cohort studies, especially associations based on SES or race/ethnicity, and carry out sensitivity analyses to gauge their effects.
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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 | 0.322 | 0.454 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".