The Association Between Measles Cases and Migration/Settlement Patterns in Ontario
Notice bibliographique
Résumé
Abstract Background Measles is a serious infectious disease that contributes significantly to the burden of disease in many developing countries. In most developed nations, such as Canada, endemic transmission of measles has been declared eliminated thanks to rigorous vaccination programs, but isolated outbreaks of the disease continue to happen. Therefore, a thorough understanding of the factors contributing to these outbreaks is necessary. Objectives There were two main objectives of this thesis. The first objective was to assess the geospatial distribution of reported measles cases in Ontario with a goal of identifying clusters of reported measles. For this objective, the main hypothesis was that measles cases would not be randomly distributed across Ontario and instead would cluster in certain regions. The second objective was to explore some of the factors that may be associated with measles clusters. For this objective, the main hypothesis was that the proportion of immigrants, population density, low-income prevalence and education level would be associated with measles clusters. Methods The first objective was achieved through a thorough geospatial analysis using SaTScan and R. Individual forward sortation areas were used as the spatial unit of analysis. The analysis leveraged data from multiple sources: 2016 Census data, Ontario measles cases data from iPHIS from 2008 to 2019, a shapefile of all forward sortation areas in Canada from Statistics Canada and centroid coordinates of forward sortation areas that were obtained using web scrapping techniques on the geolocation service of Natural Resources Canada. The maximal window size of the geospatial analysis was chosen using the maximum clustering heterogeneous set-proportion technique. The geospatial analysis was run with 99,999 Monte Carlo repetitions under a Poisson distribution using the purely spatial analysis. The Ontario population from the 2016 Census was used as the population at risk. Any cluster with a p ≤ 0.05 was deemed statistically significant. The second objective was achieved through a case-control study: Forward sortation areas that were within statistically significant measles clusters were considered as cases and the rest of the forward sortation areas were considered as controls. Demographic data necessary to assess the factors of interest were extracted from the 2016 Census. A univariable logistic regression model was run to compute the odds ratio and test the association between the factors of interest and measles clusters. 95% confidence intervals were computed for each odds ratio. Data-curation techniques and data analysis were performed in R 4.0.4. Results From 2008 through 2019, 178 measles cases were identified. 82% of cases lacked necessary vaccination or vaccination records against measles, 35% of cases were linked to traveling outside of Ontario, 20% of cases reported being in contact with a known case, and 72% of cases were less than 5 years old or older than 21. Ten measles clusters were identified of which six were deemed statistically significant. These six significant clusters represented 7% of the population at risk but contained nearly 40% of all reported measles cases between 2008 and 2019. Measles clusters had a strong association with the proportion of immigrants living within them, population density and prevalence of low-income. No association was found between education level and measles clusters. Conclusion The results indicate that most measles cases in Ontario are unvaccinated or lack proof of vaccination; arise through secondary transmission within the province; arise from undetected transmission; and are adults or infants. Additionally, it is possible to see that the risk of reported measles cases is not randomly distributed across the province, but instead measles cases tend to cluster in certain regions. Such clusters tend to be characterized by specific population-level factors that may be contributing to the risk of reported measles. Targeted and equitable interventions are needed as we continue on the path to eradication.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».