Neonatal Adverse Outcomes among Hospital Livebirths in Canada: A National Retrospective Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».