Neonatal Adverse Outcomes among Hospital Livebirths in Canada: A National Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: In Canada, newborn morbidity far surpasses mortality. The neonatal adverse outcome indicator (NAOI) summarizes neonatal morbidity, but Canadian trend data are lacking. METHODS: This Canada-wide retrospective cross-sectional study included hospital livebirths between 24 and 42 weeks' gestation, from 2013 to 2022. Data were obtained from the Canadian Institute of Health Information's Discharge Abstract Database, excluding Quebec. The NAOI included 15 newborn complications (e.g., birth trauma, intraventricular hemorrhage, or respiratory failure) and seven interventions (e.g., resuscitation by intubation and/or chest compressions), adapted from Australia's NAOI. Rates of NAOI were calculated by gestational age. Unadjusted rate ratios (RR) and 95% confidence interval (CI) were calculated for neonatal mortality, neonatal intensive care unit (NICU) admission, and extended hospital stay, each in relation to the number of NAOI components present (0, 1, 2, 3, 4, or ≥5). RESULTS: Among 2,821,671 newborns, the NAOI rate was 7.6%. NAOI increased from 7.3% in 2013 to 8.0% in 2022 (p < 0.01). NAOI prevalence was highest in the most preterm infants. Compared to no NAOI, RRs (95% CI) for mortality were 8.5 (7.6-9.5) with 1, 118.1 (108.4-128.4) with 3, and 395.3 (367.2-425.0) with ≥5 NAOI components. Respective RRs for NICU admission were 6.7 (6.6-6.7), 11.2 (10.9-11.3), and 11.9 (11.6-12.2), and RR for extended hospital stay were 6.6 (6.4-6.7), 12.2 (11.7-12.7), and 26.4 (25.2-27.5). International comparison suggested that Canada had a higher prevalence of NAOI. CONCLUSION: The Canadian NAOI captures neonatal morbidity using hospitalization data and is associated with neonatal mortality, NICU admission, and extended hospital stay. Newborn morbidity may be on the rise in recent years. INTRODUCTION: In Canada, newborn morbidity far surpasses mortality. The neonatal adverse outcome indicator (NAOI) summarizes neonatal morbidity, but Canadian trend data are lacking. METHODS: This Canada-wide retrospective cross-sectional study included hospital livebirths between 24 and 42 weeks' gestation, from 2013 to 2022. Data were obtained from the Canadian Institute of Health Information's Discharge Abstract Database, excluding Quebec. The NAOI included 15 newborn complications (e.g., birth trauma, intraventricular hemorrhage, or respiratory failure) and seven interventions (e.g., resuscitation by intubation and/or chest compressions), adapted from Australia's NAOI. Rates of NAOI were calculated by gestational age. Unadjusted rate ratios (RR) and 95% confidence interval (CI) were calculated for neonatal mortality, neonatal intensive care unit (NICU) admission, and extended hospital stay, each in relation to the number of NAOI components present (0, 1, 2, 3, 4, or ≥5). RESULTS: Among 2,821,671 newborns, the NAOI rate was 7.6%. NAOI increased from 7.3% in 2013 to 8.0% in 2022 (p < 0.01). NAOI prevalence was highest in the most preterm infants. Compared to no NAOI, RRs (95% CI) for mortality were 8.5 (7.6-9.5) with 1, 118.1 (108.4-128.4) with 3, and 395.3 (367.2-425.0) with ≥5 NAOI components. Respective RRs for NICU admission were 6.7 (6.6-6.7), 11.2 (10.9-11.3), and 11.9 (11.6-12.2), and RR for extended hospital stay were 6.6 (6.4-6.7), 12.2 (11.7-12.7), and 26.4 (25.2-27.5). International comparison suggested that Canada had a higher prevalence of NAOI. CONCLUSION: The Canadian NAOI captures neonatal morbidity using hospitalization data and is associated with neonatal mortality, NICU admission, and extended hospital stay. Newborn morbidity may be on the rise in recent years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".