Season and preterm birth in Norway: A cautionary tale
Bibliographic record
Abstract
BACKGROUND: Preterm birth is a common, costly and dangerous pregnancy complication. Seasonality of risk would suggest modifiable causes. METHODS: We examine seasonal effects on preterm birth, using data from the Medical Birth Registry of Norway (2,321,652 births), and show that results based on births are misleading and a fetuses-at-risk approach is essential. In our harmonic-regression Cox proportional hazards model we consider fetal risk of birth between 22 and 37 completed weeks of gestation. We examine effects of both day of year of conception (for early effects) and day of ongoing gestation (for seasonal effects on labour onset) as modifiers of gestational-age-based risk. RESULTS: Naïve analysis of preterm rates across days of birth shows compelling evidence for seasonality (P < 10(-152)). However, the reconstructed numbers of conceptions also vary with season (P < 10(-307)), confounding results by inducing seasonal variation in the age distribution of the fetal population at risk. When we instead properly treat fetuses as the individuals at risk, restrict analysis to pregnancies with relatively accurate ultrasound-based assessment of gestational age (available since 1998) and adjust for socio-demographic factors and maternal smoking, we find modest effects of both time of year of conception and time of year at risk, with peaks for early preterm near early January and early July. CONCLUSIONS: Analyses of seasonal effects on preterm birth are demonstrably vulnerable to confounding by seasonality of conception, measurement error in conception dating, and socio-demographic factors. The seasonal variation based on fetuses reveals two peaks for early preterm, coinciding with New Year's Day and the early July beginning of Norway's summer break, and may simply reflect a holiday-related pattern of unintended conception.
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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 | 0.044 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".