DATA LINKAGE FOR EVALUATING MATERNAL INFLUENCES ON INFANT MORTALITY AND MALTREATMENT IN CANADA
Notice bibliographique
Résumé
Abstract BACKGROUND A number of social risk factors are reported to increase infant mortality rates and child maltreatment. Public health programs attempt to mitigate risk factors and improve outcomes for infants. This study aimed to explore the association of exposure factors in mothers with infant mortality and maltreatment in Ontario. OBJECTIVES Objectives for this study included: 1. Describe prevalence of infant mortality and maltreatment in Ontario. 2. Explore how maternal risk factors influence infant mortality and maltreatment. DESIGN/METHODS This was a population-based study of 845, 567 infants born between April 1, 2005 and March 31, 2015 using administrative and healthcare databases available at the Institute of Clinical Evaluative Sciences (ICES). Maternal risk factors were selected based on public health home visiting referral criteria. These exposures included, maternal adversity (substance abuse, intimate partner violence, homelessness), newcomer status (new to Canada in past 3 years) and young maternal age (less than 22 years of age). The primary outcome measure was all-cause mortality of infants less than 12 months age. The secondary outcome measures were combined fatal and non-fatal child maltreatment outcomes and were defined using International Classification of Diseases for maltreatment diagnoses. Baseline characteristics and outcomes were described. The association between maternal risk factors and infant mortality and maltreatment was analysed using multivariable logistic modelling, including analysis by type of maternal risk factors and number of risk factors. RESULTS All-cause deaths were present in 0.14% and combined fatal and non-fatal maltreatment outcomes were present in 0.05% of the study population. Young maternal age increased the risk of all-cause mortality 2.4 times (n 171, OR 2.4, 95% CI 2.0–3.0) and maltreatment 6.3 times (n 292, OR 6.3, 95% CI 5.0–7.8). Mental health diagnosis increased the odds of maltreatment by 90% (n 209, OR 1.9, 95% CI 1.5–2.4). Adversity increased the odds of maltreatment by 63% (n 40, OR 1.63, 95% CI 1.0–2.6). The risk of maltreatment also increased as the number of risk factors increased with an OR of 3.5 (95% CI 2.9–4.4) with one risk factor, an OR of 8.2 (95% CI 5.9–11.4) with two risk factors, and an OR of 10.9 (95% CI 5.7 20.7) with three or more risk factors. Newcomer status was not associated with increased risk of maltreatment and mortality. Gestational age showed increasing ORs as prematurity increased. Material deprivation was included as a covariate and was associated with increased risk of maltreatment with increased level of deprivation. CONCLUSION Young maternal age carried the greatest risk of death and maltreatment in infants. There was also an increasing risk of infant mortality and maltreatment with increasing number of risk factors. These findings are important for ensuring public health interventions are targeting the most vulnerable populations with the aim of preventing maltreatment.
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 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 | 0,072 | 0,226 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,016 | 0,039 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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 ».