The Epidemiology of cerebral palsy in Ontario, Canada: patterns of occurrence and effects of two maternal exposures
Notice bibliographique
Résumé
Background: Cerebral palsy (CP) is a lifelong disorder of posture and movement and is the most common cause of physical disability in children. Since there is no cure available for CP, understanding causal mechanisms is essential to develop effective primary prevention strategies. Whereas plenty of research has examined temporal trends in overall CP rates, changes in CP rates by key sociodemographic characteristics over time have been scarcely described. Furthermore, limited evidence exists in the literature on the role of preconception and antenatal modifiable factors that lie early in the causal pathways that lead to CP. Objectives: This doctoral research aimed to (1) estimate the prevalence and temporal trends of CP in Ontario, Canada among children born in 2002–2017 both overall and by population characteristics; (2) examine the effect of maternal pre-gestational (PGDM) and gestational diabetes (GDM) on the risk of CP; and (3) assess the effect of exposure to maternal unintentional injury during pregnancy on the risk of CP.Methods: We created a longitudinal retrospective cohort of over 2 million in-hospital births with maternal and their own health records by linking several individual- and area-level provincial administrative health datasets in Ontario, Canada. All children were followed from birth until the end of follow-up in 2018 to ascertain the study outcome. PGDM was identified from an administrative data-derived registry for diabetes in the province. GDM and unintentional injuries were defined based on inpatient, outpatient, or emergency department (for injuries) diagnoses during the index pregnancy. We used a non-linear Poisson model to examine temporal trends in CP rates—overall and stratified by characteristics—in young children (0–4 years) born in the same year (referred as birth ‘cohort’) between 2002–2013 (n=1,587,087 live births) to allow for an equal follow-up time (4 years and 364 days) for all children. For the effects of exposure to maternal diabetes and injuries during pregnancy, we estimated crude and adjusted risk ratios using the Cox proportional hazards models to account for the unequal follow-up time in children.Results: CP prevalence increased gradually from 2002, peaked among children born in 2007, then steadily decreased in more recent years. CP rates were consistently higher in boys, children born early, small, or with birth defects, and children of young (<20 years), old (>40 years), primiparous, and grand multiparous (>4 previous live births) mothers and those with inadequate prenatal care; however, gaps by these characteristics have narrowed over time. Socioeconomic inequalities in CP persisted and remained stable over the study period.Children of mothers with PGDM showed an increased risk of CP (hazard ratio (HR) of 1.84 (95% confidence interval (CI): 1.59, 2.14) in the model adjusted for maternal sociodemographic and pre-existing factors). However, no similar associations were found for GDM (adjusted HR 0.91 (95% CI: 0.77, 1.06)). Maternal unintentional injury was also associated with a slightly higher risk of CP (HR 1.33 (95% CI: 1.18, 150) after adjusting for maternal sociodemographic and lifestyle factors).Conclusion: In this large population-based cohort of over 2 million live births, CP rates over time are on decreasing trend in more recent years. However, changes in CP rates differed by child, maternal, and sociodemographic characteristics. The increased risk of CP in children exposed to maternal PGDM and unintentional injuries observed in this research fills important gaps in knowledge about the role of maternal exposures on children’s neurodevelopment. Results of this thesis research not only lead to a better appreciation of the magnitude of the CP burden in Canada but also offer insights on potential causal mechanisms of CP. Ultimately, this work will contribute to developing preventative strategies to reduce the risk of this disabling disorder in Canada and elsewhere
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».