Accounting for the Distribution of Adverse Birth Outcomes in Ontario: A Hierarchical Analysis of Provincial and Local Outcomes
Notice bibliographique
Résumé
Background: Adverse birth outcomes present a difficult and chronic challenge in Ontario, in Canada and in developed countries in general. Increasing proportions of preterm births, significant regional disparities and the high cost of treating all adverse birth outcomes have focused attention on explaining them and developing effective treatments. Methods: Birth outcomes and maternal characteristics for approximately 626,000 births, about 90% of births in 2005–2009, were linked to small geographic areas throughout Ontario. For each of four adverse outcomes: late preterm, moderate to very preterm, small for gestation age and still births, proportions of total births were calculated for the full province and for each small geographic area. Geographic hotspots of elevated rates were identified for each of the different adverse birth outcomes using the local Moran’s I statistic. Data for nine known ecologic and individual risk factors were then linked to the areas. Hierarchical regression analysis was used to model each of the outcomes for the full province and for dispersed local areas. The resulting models for the different outcomes were contrasted. Results: Significant geographic hotspots exist for each of the four outcomes. Hotspots for the different outcomes were found to be largely spatially exclusive. For like outcomes, predictive models differed markedly between local areas (i.e. local groups of hotspots) as well as between full-province and local areas. Ecologic level variables played a strong role in all models; the influence of individual level risk factors was consistently modified by ecologic risk factors except for small for gestational births. Conclusions: The finding of significant hotspots for different adverse birth outcomes indicates that certain geographic areas have aetiologies or patterns of predictors sufficient to create significantly elevated levels of particular outcomes. The finding that hotspots for the different adverse outcomes are largely exclusive implies that the aetiologies are specific; i.e., those that are sufficient to create significantly higher levels for one outcome do not also create significantly higher levels of others. The consistently strong role of ecologic level risk factors in modifying individual level risk factors implies that contextual characteristics are an important part of the aetiology of adverse birth outcomes. Differences in local area models suggest the existence of location-specific (rather than universal) aetiologies. The findings support the need for more careful attention to local context when explaining birth outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».