Modeling childhood wheezing in small areas in Manitoba
Notice bibliographique
Résumé
Introduction: Asthma has a significant impact on the Manitoba healthcare system. Asthma related health expenditures in Canada are around $2 billion annually and are the leading causes of emergency treatment for the younger demographic. Asthma is, however, challenging to diagnose at an earlier age and routine checks are not possible within the younger age groups. Wheezing however is one of the symptoms of asthma but is not exclusive to asthma. Ideally a predictive model for asthma development to have the most clinical impact is needed before children reach the age of two, which current models fail to provide. Objectives: The objectives were to: (1) determine at what extent does location affect the severity of wheezing in Manitoba, and (2) determine how wheezing severity changes throughout childhood. Methods: This project used data from the Canadian Healthy Infant Longitudinal Development (CHILD) which is a prospective longitudinal pregnancy cohort. The study population comprised of 1,055 participants from Manitoba for which recruitment of pregnant mothers was conducted from 2009-2012 within a radius of Winnipeg and Morden-Winkler. A logistic longitudinal model was developed which used wheezing severity as a response and incorporated area and individual effects into the model. The study used the 96 regional health authority districts (RHADs) as small areas in Manitoba. A zero-inflated Poisson (ZIP) model with random effects was also developed to model the number of wheezing episodes for children within the CHILD study. The two types of models were used to determine how wheezing frequency and severity changed throughout thus covering two definitions of wheezing severity. Area-level logistic and ZIP models were used to answer the first objective of this project by mapping the predicted proportions and rates for each small area in Manitoba. The second objective was also assessed by using the longitudinal logistic and ZIP models. Results: The unit-level binary logistic model showed an increased odds of wheezing in the case of previous maternal asthma (OR: 3.31, 95% CI: (1.87, 5.81)) and smoking (OR: 2.85, 95% CI: 0.98, 7.46)). Living near a farm (OR: 0.70, 95% CI: (0.23, 1.82)) decreased the odds of wheezing while the ZIP model showed that among those already experiencing wheezing, living near a farm (RR: 2.45, 95% CI: (1.11, 5.93)) increased the average rate of wheezing. The areas with the highest predicted proportions (and average rates) of wheezing were Gimli, Hanover, Spruce Woods, and St Pierre. Conclusion: Our study demonstrated that wheezing is heavily affected by maternal health history. Gimli, Spruce Woods, and St Pierre were found to have the highest proportions of wheezing and persistent wheezing episodes, but further recruitment in Manitoba is needed to verify these results.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».