Anemia Near Delivery Is Prevalent, Pernicious, and Associated With Lower Neighbourhood Income: An Analysis of Over 50 000 Pregnancies
Bibliographic record
Abstract
OBJECTIVES: Anemia in pregnancy has negative impacts on maternal and neonatal morbidity and mortality and has been described as an issue of health equity. The primary aim of our study was to describe the rates of anemia near delivery and assess whether this correlates with neighbourhood-level income status. METHODS: We conducted a retrospective cohort study of pregnant persons delivering from January 2012 through December 2022 at 2 large academic centres. We used log-binomial regression to estimate the association between neighbourhood-level income quintile and anemia near delivery, defined as a hemoglobin <110 g/L within 30 days of delivery, controlling for maternal age, parity, thalassemia trait, number of fetuses, blood group, and service provider type. Secondary maternal and fetal outcomes were analyzed descriptively. RESULTS: A total of 51 782 deliveries were included; the majority were singleton (97%) pregnancies delivered vaginally (61%). Although 77% of patients had a complete blood count done within 30 days of delivery, only 13% had a ferritin value checked within 9 months of delivery. Approximately 30% of all patients were anemic near delivery, with higher rates of anemia in lower income quintiles; patients in the lowest income quintile were 18% more likely to be anemic than those in the highest income quintile (relative risk 1.18; 95% CI 1.12-1.25). CONCLUSIONS: Even within a high-resource academic setting, anemia in pregnancy is common. Given the high rates of anemia in our study, particularly, amongst patients in lower income quintiles, widespread targeted educational and system interventions are required to ensure equitable patient care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".